Add files using upload-large-folder tool

third_party/GraspGen/sam3/sam3/agent/__init__.py

@@ -0,0 +1,3 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe

third_party/GraspGen/sam3/sam3/agent/agent_core.py

@@ -0,0 +1,565 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import copy
+import json
+import os
+
+import cv2
+from PIL import Image
+
+from .client_llm import send_generate_request
+from .client_sam3 import call_sam_service
+from .viz import visualize
+
+
+def save_debug_messages(messages_list, debug, debug_folder_path, debug_jsonl_path):
+    """Save messages to debug jsonl file if debug is enabled"""
+    if debug and debug_jsonl_path:
+        # Ensure the debug directory exists before writing
+        os.makedirs(debug_folder_path, exist_ok=True)
+        with open(debug_jsonl_path, "w") as f:
+            for msg in messages_list:
+                f.write(json.dumps(msg, indent=4) + "\n")
+
+
+def cleanup_debug_files(debug, debug_folder_path, debug_jsonl_path):
+    """Clean up debug files when function successfully returns"""
+    if debug and debug_folder_path:
+        try:
+            if os.path.exists(debug_jsonl_path):
+                os.remove(debug_jsonl_path)
+            if os.path.exists(debug_folder_path):
+                os.rmdir(debug_folder_path)
+        except Exception as e:
+            print(f"Warning: Could not clean up debug files: {e}")
+
+
+def count_images(messages):
+    """Count the total number of images present in the messages history."""
+    total = 0
+    for message in messages:
+        # Check if message has content (should be a list)
+        if "content" in message and isinstance(message["content"], list):
+            # Iterate through each content item
+            for content_item in message["content"]:
+                # Check if content item is a dict with type "image"
+                if (
+                    isinstance(content_item, dict)
+                    and content_item.get("type") == "image"
+                ):
+                    total += 1
+    return total
+
+
+def _prune_messages_for_next_round(
+    messages_list,
+    used_text_prompts,
+    latest_sam3_text_prompt,
+    img_path,
+    initial_text_prompt,
+):
+    """Return a new messages list that contains only:
+    1) messages[:2] (with optional warning text added to the second message's content)
+    2) the latest assistant message (and everything after it) that contains a segment_phrase tool call
+    """
+    # There should not be more than 10 messages in the conversation history
+    assert len(messages_list) < 10
+
+    # Part 1: always keep the first two message JSONs
+    part1 = copy.deepcopy(messages_list[:2])
+
+    # Part 2: search backwards for the latest assistant message containing a segment_phrase tool call
+    part2_start_idx = None
+    for idx in range(len(messages_list) - 1, 1, -1):
+        msg = messages_list[idx]
+        # We only consider assistant messages with a "content" list
+        if msg.get("role") != "assistant" or "content" not in msg:
+            continue
+        # Look for any content element that is a text containing the segment_phrase tool call
+        for content in msg["content"]:
+            if (
+                isinstance(content, dict)
+                and content.get("type") == "text"
+                and "<tool>" in content.get("text", "")
+                and "segment_phrase" in content.get("text", "")
+            ):
+                part2_start_idx = idx
+                break
+        if part2_start_idx is not None:
+            break
+
+    part2 = messages_list[part2_start_idx:] if part2_start_idx is not None else []
+
+    # Part 3: decide whether to add warning text to the second message in part1
+    previously_used = (
+        [p for p in used_text_prompts if p != latest_sam3_text_prompt]
+        if latest_sam3_text_prompt
+        else list(used_text_prompts)
+    )
+    if part2 and len(previously_used) > 0:
+        warning_text = f'Note that we have previously called the segment_phrase tool with each "text_prompt" in this list: {list(previously_used)}, but none of the generated results were satisfactory. So make sure that you do not use any of these phrases as the "text_prompt" to call the segment_phrase tool again.'
+        # Replace the second message entirely to keep exactly 2 content items
+        part1[1] = {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": img_path},
+                {
+                    "type": "text",
+                    "text": f"The above image is the raw input image. The initial user input query is: '{initial_text_prompt}'."
+                    + " "
+                    + warning_text,
+                },
+            ],
+        }
+        assert len(part1[1]["content"]) == 2
+
+    # Build the new messages list: part1 (with optional warning), then part2
+    new_messages = list(part1)
+    new_messages.extend(part2)
+    return new_messages
+
+
+def agent_inference(
+    img_path: str,
+    initial_text_prompt: str,
+    debug: bool = False,
+    send_generate_request=send_generate_request,
+    call_sam_service=call_sam_service,
+    max_generations: int = 100,
+    output_dir="../../sam3_agent_out",
+):
+    """
+    Given a text prompt and an image, this tool will perform all aspects of agentic problem solving,
+    while saving sam3 and MLLM outputs to their respective directories.
+
+    Args:
+        img_path: Path to the input image
+        initial_text_prompt: Initial text prompt from the user
+        debug: Whether to enable debug mode
+        max_generations: Maximum number of send_generate_request calls allowed (default: 100)
+    """
+    # setup dir
+    sam_output_dir = os.path.join(output_dir, "sam_out")
+    error_save_dir = os.path.join(output_dir, "none_out")
+    debug_save_dir = os.path.join(output_dir, "agent_debug_out")
+    os.makedirs(sam_output_dir, exist_ok=True)
+    os.makedirs(error_save_dir, exist_ok=True)
+    os.makedirs(debug_save_dir, exist_ok=True)
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    MLLM_SYSTEM_PROMPT_PATH = os.path.join(
+        current_dir, "system_prompts/system_prompt.txt"
+    )
+    ITERATIVE_CHECKING_SYSTEM_PROMPT_PATH = os.path.join(
+        current_dir, "system_prompts/system_prompt_iterative_checking.txt"
+    )
+    # init variables
+    PATH_TO_LATEST_OUTPUT_JSON = ""
+    LATEST_SAM3_TEXT_PROMPT = ""
+    USED_TEXT_PROMPTS = (
+        set()
+    )  # Track all previously used text prompts for segment_phrase
+    generation_count = 0  # Counter for number of send_generate_request calls
+
+    # debug setup
+    debug_folder_path = None
+    debug_jsonl_path = None
+    if debug:
+        debug_folder_path = os.path.join(
+            debug_save_dir, f"{img_path.rsplit('/', 1)[-1].rsplit('.', 1)[0]}"
+        )
+        debug_jsonl_path = os.path.join(debug_folder_path, "debug_history.json")
+        os.makedirs(debug_folder_path, exist_ok=True)
+
+    # The helper functions are now defined outside the agent_inference function
+    with open(MLLM_SYSTEM_PROMPT_PATH, "r") as f:
+        system_prompt = f.read().strip()
+    with open(ITERATIVE_CHECKING_SYSTEM_PROMPT_PATH, "r") as f:
+        iterative_checking_system_prompt = f.read().strip()
+
+    # Construct the initial message list
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": img_path},
+                {
+                    "type": "text",
+                    "text": f"The above image is the raw input image. The initial user input query is: '{initial_text_prompt}'.",
+                },
+            ],
+        },
+    ]
+    print(f"> Text prompt: {initial_text_prompt}")
+    print(f"> Image path: {img_path}")
+
+    print("\n\n")
+    print("-" * 30 + f" Round {str(generation_count + 1)}" + "-" * 30)
+    print("\n\n")
+    generated_text = send_generate_request(messages)
+    print(f"\n>>> MLLM Response [start]\n{generated_text}\n<<< MLLM Response [end]\n")
+    while generated_text is not None:
+        save_debug_messages(messages, debug, debug_folder_path, debug_jsonl_path)
+        assert (
+            "<tool>" in generated_text,
+            f"Generated text does not contain <tool> tag: {generated_text}",
+        )
+        generated_text = generated_text.split("</tool>", 1)[0] + "</tool>"
+        tool_call_json_str = (
+            generated_text.split("<tool>")[-1]
+            .split("</tool>")[0]
+            .strip()
+            .replace(r"}}}", r"}}")  # remove extra } if any
+        )
+        try:
+            tool_call = json.loads(tool_call_json_str)
+        except json.JSONDecodeError:
+            raise ValueError(f"Invalid JSON in tool call: {tool_call_json_str}")
+
+        if PATH_TO_LATEST_OUTPUT_JSON == "":
+            # The first tool call must be segment_phrase or report_no_mask
+            assert (
+                tool_call["name"] == "segment_phrase"
+                or tool_call["name"] == "report_no_mask"
+            )
+
+        if tool_call["name"] == "segment_phrase":
+            print("🔍 Calling segment_phrase tool...")
+            assert list(tool_call["parameters"].keys()) == ["text_prompt"]
+
+            # Check if this text_prompt has been used before
+            current_text_prompt = tool_call["parameters"]["text_prompt"]
+            if current_text_prompt in USED_TEXT_PROMPTS:
+                print(
+                    f"❌ Text prompt '{current_text_prompt}' has been used before. Requesting a different prompt."
+                )
+                duplicate_prompt_message = f"You have previously used '{current_text_prompt}' as your text_prompt to call the segment_phrase tool. You may not use it again. Please call the segment_phrase tool again with a different, perhaps more general, or more creative simple noun phrase prompt, while adhering to all the rules stated in the system prompt. You must also never use any of the following text_prompt(s): {str(list(USED_TEXT_PROMPTS))}."
+                messages.append(
+                    {
+                        "role": "assistant",
+                        "content": [{"type": "text", "text": generated_text}],
+                    }
+                )
+                messages.append(
+                    {
+                        "role": "user",
+                        "content": [{"type": "text", "text": duplicate_prompt_message}],
+                    }
+                )
+            else:
+                # Add the text_prompt to the set of used prompts
+                USED_TEXT_PROMPTS.add(current_text_prompt)
+                LATEST_SAM3_TEXT_PROMPT = current_text_prompt
+                PATH_TO_LATEST_OUTPUT_JSON = call_sam_service(
+                    image_path=img_path,
+                    text_prompt=current_text_prompt,
+                    output_folder_path=sam_output_dir,
+                )
+                sam3_outputs = json.load(open(PATH_TO_LATEST_OUTPUT_JSON, "r"))
+                sam3_output_image_path = sam3_outputs["output_image_path"]
+                num_masks = len(sam3_outputs["pred_boxes"])
+
+                messages.append(
+                    {
+                        "role": "assistant",
+                        "content": [{"type": "text", "text": generated_text}],
+                    }
+                )
+                if num_masks == 0:
+                    print("❌ No masks generated by SAM3, reporting no mask to Qwen.")
+                    sam3_output_text_message = f"The segment_phrase tool did not generate any masks for the text_prompt '{current_text_prompt}'. Now, please call the segment_phrase tool again with a different, perhaps more general, or more creative simple noun phrase text_prompt, while adhering to all the rules stated in the system prompt. Please be reminded that the original user query was '{initial_text_prompt}'."
+                    messages.append(
+                        {
+                            "role": "user",
+                            "content": [
+                                {"type": "text", "text": sam3_output_text_message}
+                            ],
+                        }
+                    )
+                else:
+                    sam3_output_text_message = rf"The segment_phrase tool generated {num_masks} available masks. All {num_masks} available masks are rendered in this image below, now you must analyze the {num_masks} available mask(s) carefully, compare them against the raw input image and the original user query, and determine your next action. Please be reminded that the original user query was '{initial_text_prompt}'."
+                    messages.append(
+                        {
+                            "role": "user",
+                            "content": [
+                                {"type": "text", "text": sam3_output_text_message},
+                                {"type": "image", "image": sam3_output_image_path},
+                            ],
+                        }
+                    )
+                print("\n\n>>> sam3_output_text_message:\n", sam3_output_text_message)
+
+        elif tool_call["name"] == "examine_each_mask":
+            print("🔍 Calling examine_each_mask tool...")
+            assert LATEST_SAM3_TEXT_PROMPT != ""
+
+            # Make sure that the last message is a image
+            assert messages[-1]["content"][1]["type"] == "image", (
+                "Second content element should be an image"
+            )
+            messages.pop()  # Remove the last user message
+            # Add simplified replacement message
+            simplified_message = {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "The segment_phrase tool generated several masks. Now you must analyze the mask(s) carefully, compare them against the raw input image and the original user query, and determine your next action.",
+                    }
+                ],
+            }
+            messages.append(simplified_message)
+
+            current_outputs = json.load(open(PATH_TO_LATEST_OUTPUT_JSON, "r"))
+            num_masks = len(current_outputs["pred_masks"])
+            masks_to_keep = []
+
+            # MLLM check the mask one by one
+            for i in range(num_masks):
+                print(f"🔍 Checking mask {i + 1}/{num_masks}...")
+                image_w_mask_i, image_w_zoomed_in_mask_i = visualize(current_outputs, i)
+
+                image_w_zoomed_in_mask_i_path = os.path.join(
+                    sam_output_dir, rf"{LATEST_SAM3_TEXT_PROMPT}.png".replace("/", "_")
+                ).replace(".png", f"_zoom_in_mask_{i + 1}.png")
+                image_w_mask_i_path = os.path.join(
+                    sam_output_dir, rf"{LATEST_SAM3_TEXT_PROMPT}.png".replace("/", "_")
+                ).replace(".png", f"_selected_mask_{i + 1}.png")
+                image_w_zoomed_in_mask_i.save(image_w_zoomed_in_mask_i_path)
+                image_w_mask_i.save(image_w_mask_i_path)
+
+                iterative_checking_messages = [
+                    {"role": "system", "content": iterative_checking_system_prompt},
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": f"The raw input image: "},
+                            {"type": "image", "image": img_path},
+                            {
+                                "type": "text",
+                                "text": f"The initial user input query is: '{initial_text_prompt}'",
+                            },
+                            {
+                                "type": "text",
+                                "text": f"Image with the predicted segmentation mask rendered on it: ",
+                            },
+                            {"type": "image", "image": image_w_mask_i_path},
+                            {
+                                "type": "text",
+                                "text": f"Image with the zoomed-in mask: ",
+                            },
+                            {"type": "image", "image": image_w_zoomed_in_mask_i_path},
+                        ],
+                    },
+                ]
+                checking_generated_text = send_generate_request(
+                    iterative_checking_messages
+                )
+
+                # Process the generated text to determine if the mask should be kept or rejected
+                if checking_generated_text is None:
+                    raise ValueError(
+                        "Generated text is None, which is unexpected. Please check the Qwen server and the input parameters."
+                    )
+                print(f"Generated text for mask {i + 1}: {checking_generated_text}")
+                verdict = (
+                    checking_generated_text.split("<verdict>")[-1]
+                    .split("</verdict>")[0]
+                    .strip()
+                )
+                if "Accept" in verdict:
+                    assert not "Reject" in verdict
+                    print(f"Mask {i + 1} accepted, keeping it in the outputs.")
+                    masks_to_keep.append(i)
+                elif "Reject" in verdict:
+                    assert not "Accept" in verdict
+                    print(f"Mask {i + 1} rejected, removing it from the outputs.")
+                else:
+                    raise ValueError(
+                        f"Unexpected verdict in generated text: {checking_generated_text}. Expected 'Accept' or 'Reject'."
+                    )
+
+            updated_outputs = {
+                "original_image_path": current_outputs["original_image_path"],
+                "orig_img_h": current_outputs["orig_img_h"],
+                "orig_img_w": current_outputs["orig_img_w"],
+                "pred_boxes": [current_outputs["pred_boxes"][i] for i in masks_to_keep],
+                "pred_scores": [
+                    current_outputs["pred_scores"][i] for i in masks_to_keep
+                ],
+                "pred_masks": [current_outputs["pred_masks"][i] for i in masks_to_keep],
+            }
+
+            image_w_check_masks = visualize(updated_outputs)
+            image_w_check_masks_path = os.path.join(
+                sam_output_dir, rf"{LATEST_SAM3_TEXT_PROMPT}.png"
+            ).replace(
+                ".png",
+                f"_selected_masks_{'-'.join(map(str, [i + 1 for i in masks_to_keep]))}.png".replace(
+                    "/", "_"
+                ),
+            )
+            image_w_check_masks.save(image_w_check_masks_path)
+            # save the updated json outputs and append to message history
+            messages.append(
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": generated_text}],
+                }
+            )
+            if len(masks_to_keep) == 0:
+                messages.append(
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": f"The original user query was: '{initial_text_prompt}'. The examine_each_mask tool examined and rejected all of the masks generated by the segment_phrase tool. Now, please call the segment_phrase tool again with a different, perhaps more general, or more creative simple noun phrase text_prompt, while adhering to all the rules stated in the system prompt.",
+                            }
+                        ],
+                    }
+                )
+            else:
+                messages.append(
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": f"The original user query was: '{initial_text_prompt}'. After calling the examine_each_mask tool on the available masks, the number of available masks is now {len(masks_to_keep)}. All {len(masks_to_keep)} available masks are rendered in this image below, now you must analyze the {len(masks_to_keep)} available mask(s) carefully, compare them against the raw input image and the original user query, and determine your next action.",
+                            },
+                            {"type": "image", "image": image_w_check_masks_path},
+                        ],
+                    }
+                )
+
+            # Create a new filename based on the original path to avoid filename length issues
+            base_path = PATH_TO_LATEST_OUTPUT_JSON
+            # Remove any existing "masks_" suffix to avoid duplication
+            if "masks_" in base_path:
+                base_path = base_path.split("masks_")[0] + ".json"
+            # Create new filename with current masks; use a clearer suffix when empty
+            if len(masks_to_keep) == 0:
+                PATH_TO_LATEST_OUTPUT_JSON = base_path.replace(
+                    ".json", "masks_none.json"
+                )
+            else:
+                PATH_TO_LATEST_OUTPUT_JSON = base_path.replace(
+                    ".json", f"masks_{'_'.join(map(str, masks_to_keep))}.json"
+                )
+            json.dump(updated_outputs, open(PATH_TO_LATEST_OUTPUT_JSON, "w"), indent=4)
+
+        elif tool_call["name"] == "select_masks_and_return":
+            print("🔍 Calling select_masks_and_return tool...")
+            current_outputs = json.load(open(PATH_TO_LATEST_OUTPUT_JSON, "r"))
+
+            assert list(tool_call["parameters"].keys()) == ["final_answer_masks"]
+            masks_to_keep = tool_call["parameters"]["final_answer_masks"]
+
+            # Keep only valid mask indices, remove duplicates, and preserve deterministic ascending order
+            available_masks = set(range(1, len(current_outputs["pred_masks"]) + 1))
+            masks_to_keep = sorted({i for i in masks_to_keep if i in available_masks})
+            # Change this to a update message telling the model to try again along with information about errors made.
+
+            final_outputs = {
+                "original_image_path": current_outputs["original_image_path"],
+                "orig_img_h": current_outputs["orig_img_h"],
+                "orig_img_w": current_outputs["orig_img_w"],
+                "pred_boxes": [
+                    current_outputs["pred_boxes"][i - 1] for i in masks_to_keep
+                ],
+                "pred_scores": [
+                    current_outputs["pred_scores"][i - 1] for i in masks_to_keep
+                ],
+                "pred_masks": [
+                    current_outputs["pred_masks"][i - 1] for i in masks_to_keep
+                ],
+            }
+
+            rendered_final_output = visualize(final_outputs)
+            messages.append(
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": generated_text}],
+                }
+            )
+
+            # Clean up debug files before successful return
+            cleanup_debug_files(debug, debug_folder_path, debug_jsonl_path)
+            return messages, final_outputs, rendered_final_output
+
+        elif tool_call["name"] == "report_no_mask":
+            print("🔍 Calling report_no_mask tool...")
+            height, width = cv2.imread(img_path).shape[:2]
+            final_outputs = {
+                "original_image_path": img_path,
+                "orig_img_h": height,
+                "orig_img_w": width,
+                "pred_boxes": [],
+                "pred_scores": [],
+                "pred_masks": [],
+            }
+            rendered_final_output = Image.open(img_path)
+            messages.append(
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "text": generated_text}],
+                }
+            )
+            return messages, final_outputs, rendered_final_output
+
+        else:
+            raise ValueError(f"Unknown tool call: {tool_call['name']}")
+
+        # sometimes the MLLM don't know when to stop, and generates multiple tool calls in one round, so we need to split the generated text by </tool> and only keep the first one
+
+        for message in messages:
+            if message["role"] == "assistant" and "content" in message:
+                for content in message["content"]:
+                    if (
+                        isinstance(content, dict)
+                        and content.get("type") == "text"
+                        and "text" in content
+                    ):
+                        content["text"] = (
+                            content["text"].split("</tool>", 1)[0] + "</tool>\n\n"
+                        )
+        # Prune the messages history before the next MLLM generation round according to the 3-part rules.
+        # This keeps history compact and ensures the model sees only the allowed parts.
+        messages = _prune_messages_for_next_round(
+            messages,
+            USED_TEXT_PROMPTS,
+            LATEST_SAM3_TEXT_PROMPT,
+            img_path,
+            initial_text_prompt,
+        )
+        # make sure there can never be more than 2 images in the context
+        assert count_images(messages) <= 2
+        generation_count += 1
+        if generation_count > max_generations:
+            raise ValueError(
+                f"Exceeded maximum number of allowed generation requests ({max_generations})"
+            )
+
+        print("\n\n")
+        print("-" * 30 + f" Round {str(generation_count + 1)}" + "-" * 30)
+        print("\n\n")
+        generated_text = send_generate_request(messages)
+        print(
+            f"\n>>> MLLM Response [start]\n{generated_text}\n<<< MLLM Response [end]\n"
+        )
+
+    print("\n\n>>> SAM 3 Agent execution ended.\n\n")
+
+    error_save_path = os.path.join(
+        error_save_dir,
+        f"{img_path.rsplit('/', 1)[-1].rsplit('.', 1)[0]}_error_history.json",
+    )
+    with open(error_save_path, "w") as f:
+        json.dump(messages, f, indent=4)
+    print("Saved messages history that caused error to:", error_save_path)
+    raise ValueError(
+        rf"Generated text is None, which is unexpected. Please check the Qwen server and the input parameters for image path: {img_path} and initial text prompt: {initial_text_prompt}."
+    )

third_party/GraspGen/sam3/sam3/agent/client_llm.py

@@ -0,0 +1,207 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import base64
+import os
+from typing import Any, Optional
+
+from openai import OpenAI
+
+
+def get_image_base64_and_mime(image_path):
+    """Convert image file to base64 string and get MIME type"""
+    try:
+        # Get MIME type based on file extension
+        ext = os.path.splitext(image_path)[1].lower()
+        mime_types = {
+            ".jpg": "image/jpeg",
+            ".jpeg": "image/jpeg",
+            ".png": "image/png",
+            ".gif": "image/gif",
+            ".webp": "image/webp",
+            ".bmp": "image/bmp",
+        }
+        mime_type = mime_types.get(ext, "image/jpeg")  # Default to JPEG
+
+        # Convert image to base64
+        with open(image_path, "rb") as image_file:
+            base64_data = base64.b64encode(image_file.read()).decode("utf-8")
+            return base64_data, mime_type
+    except Exception as e:
+        print(f"Error converting image to base64: {e}")
+        return None, None
+
+
+def send_generate_request(
+    messages,
+    server_url=None,
+    model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+    api_key=None,
+    max_tokens=4096,
+):
+    """
+    Sends a request to the OpenAI-compatible API endpoint using the OpenAI client library.
+
+    Args:
+        server_url (str): The base URL of the server, e.g. "http://127.0.0.1:8000"
+        messages (list): A list of message dicts, each containing role and content.
+        model (str): The model to use for generation (default: "llama-4")
+        max_tokens (int): Maximum number of tokens to generate (default: 4096)
+
+    Returns:
+        str: The generated response text from the server.
+    """
+    # Process messages to convert image paths to base64
+    processed_messages = []
+    for message in messages:
+        processed_message = message.copy()
+        if message["role"] == "user" and "content" in message:
+            processed_content = []
+            for c in message["content"]:
+                if isinstance(c, dict) and c.get("type") == "image":
+                    # Convert image path to base64 format
+                    image_path = c["image"]
+
+                    print("image_path", image_path)
+                    new_image_path = image_path.replace(
+                        "?", "%3F"
+                    )  # Escape ? in the path
+
+                    # Read the image file and convert to base64
+                    try:
+                        base64_image, mime_type = get_image_base64_and_mime(
+                            new_image_path
+                        )
+                        if base64_image is None:
+                            print(
+                                f"Warning: Could not convert image to base64: {new_image_path}"
+                            )
+                            continue
+
+                        # Create the proper image_url structure with base64 data
+                        processed_content.append(
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:{mime_type};base64,{base64_image}",
+                                    "detail": "high",
+                                },
+                            }
+                        )
+
+                    except FileNotFoundError:
+                        print(f"Warning: Image file not found: {new_image_path}")
+                        continue
+                    except Exception as e:
+                        print(f"Warning: Error processing image {new_image_path}: {e}")
+                        continue
+                else:
+                    processed_content.append(c)
+
+            processed_message["content"] = processed_content
+        processed_messages.append(processed_message)
+
+    # Create OpenAI client with custom base URL
+    client = OpenAI(api_key=api_key, base_url=server_url)
+
+    try:
+        print(f"🔍 Calling model {model}...")
+        response = client.chat.completions.create(
+            model=model,
+            messages=processed_messages,
+            max_completion_tokens=max_tokens,
+            n=1,
+        )
+        # print(f"Received response: {response.choices[0].message}")
+
+        # Extract the response content
+        if response.choices and len(response.choices) > 0:
+            return response.choices[0].message.content
+        else:
+            print(f"Unexpected response format: {response}")
+            return None
+
+    except Exception as e:
+        print(f"Request failed: {e}")
+        return None
+
+
+def send_direct_request(
+    llm: Any,
+    messages: list[dict[str, Any]],
+    sampling_params: Any,
+) -> Optional[str]:
+    """
+    Run inference on a vLLM model instance directly without using a server.
+
+    Args:
+        llm: Initialized vLLM LLM instance (passed from external initialization)
+        messages: List of message dicts with role and content (OpenAI format)
+        sampling_params: vLLM SamplingParams instance (initialized externally)
+
+    Returns:
+        str: Generated response text, or None if inference fails
+    """
+    try:
+        # Process messages to handle images (convert to base64 if needed)
+        processed_messages = []
+        for message in messages:
+            processed_message = message.copy()
+            if message["role"] == "user" and "content" in message:
+                processed_content = []
+                for c in message["content"]:
+                    if isinstance(c, dict) and c.get("type") == "image":
+                        # Convert image path to base64 format
+                        image_path = c["image"]
+                        new_image_path = image_path.replace("?", "%3F")
+
+                        try:
+                            base64_image, mime_type = get_image_base64_and_mime(
+                                new_image_path
+                            )
+                            if base64_image is None:
+                                print(
+                                    f"Warning: Could not convert image: {new_image_path}"
+                                )
+                                continue
+
+                            # vLLM expects image_url format
+                            processed_content.append(
+                                {
+                                    "type": "image_url",
+                                    "image_url": {
+                                        "url": f"data:{mime_type};base64,{base64_image}"
+                                    },
+                                }
+                            )
+                        except Exception as e:
+                            print(
+                                f"Warning: Error processing image {new_image_path}: {e}"
+                            )
+                            continue
+                    else:
+                        processed_content.append(c)
+
+                processed_message["content"] = processed_content
+            processed_messages.append(processed_message)
+
+        print("🔍 Running direct inference with vLLM...")
+
+        # Run inference using vLLM's chat interface
+        outputs = llm.chat(
+            messages=processed_messages,
+            sampling_params=sampling_params,
+        )
+
+        # Extract the generated text from the first output
+        if outputs and len(outputs) > 0:
+            generated_text = outputs[0].outputs[0].text
+            return generated_text
+        else:
+            print(f"Unexpected output format: {outputs}")
+            return None
+
+    except Exception as e:
+        print(f"Direct inference failed: {e}")
+        return None

third_party/GraspGen/sam3/sam3/agent/client_sam3.py

@@ -0,0 +1,139 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import json
+import os
+
+import torch
+from PIL import Image
+from sam3.model.box_ops import box_xyxy_to_xywh
+from sam3.train.masks_ops import rle_encode
+
+from .helpers.mask_overlap_removal import remove_overlapping_masks
+from .viz import visualize
+
+
+def sam3_inference(processor, image_path, text_prompt):
+    """Run SAM 3 image inference with text prompts and format the outputs"""
+    image = Image.open(image_path)
+    orig_img_w, orig_img_h = image.size
+
+    # model inference
+    inference_state = processor.set_image(image)
+    inference_state = processor.set_text_prompt(
+        state=inference_state, prompt=text_prompt
+    )
+
+    # format and assemble outputs
+    pred_boxes_xyxy = torch.stack(
+        [
+            inference_state["boxes"][:, 0] / orig_img_w,
+            inference_state["boxes"][:, 1] / orig_img_h,
+            inference_state["boxes"][:, 2] / orig_img_w,
+            inference_state["boxes"][:, 3] / orig_img_h,
+        ],
+        dim=-1,
+    )  # normalized in range [0, 1]
+    pred_boxes_xywh = box_xyxy_to_xywh(pred_boxes_xyxy).tolist()
+    pred_masks = rle_encode(inference_state["masks"].squeeze(1))
+    pred_masks = [m["counts"] for m in pred_masks]
+    outputs = {
+        "orig_img_h": orig_img_h,
+        "orig_img_w": orig_img_w,
+        "pred_boxes": pred_boxes_xywh,
+        "pred_masks": pred_masks,
+        "pred_scores": inference_state["scores"].tolist(),
+    }
+    return outputs
+
+
+def call_sam_service(
+    sam3_processor,
+    image_path: str,
+    text_prompt: str,
+    output_folder_path: str = "sam3_output",
+):
+    """
+    Loads an image, sends it with a text prompt to the service,
+    saves the results, and renders the visualization.
+    """
+    print(f"📞 Loading image '{image_path}' and sending with prompt '{text_prompt}'...")
+
+    text_prompt_for_save_path = (
+        text_prompt.replace("/", "_") if "/" in text_prompt else text_prompt
+    )
+
+    os.makedirs(
+        os.path.join(output_folder_path, image_path.replace("/", "-")), exist_ok=True
+    )
+    output_json_path = os.path.join(
+        output_folder_path,
+        image_path.replace("/", "-"),
+        rf"{text_prompt_for_save_path}.json",
+    )
+    output_image_path = os.path.join(
+        output_folder_path,
+        image_path.replace("/", "-"),
+        rf"{text_prompt_for_save_path}.png",
+    )
+
+    try:
+        # Send the image and text prompt as a multipart/form-data request
+        serialized_response = sam3_inference(sam3_processor, image_path, text_prompt)
+
+        # 1. Prepare the response dictionary
+        serialized_response = remove_overlapping_masks(serialized_response)
+        serialized_response = {
+            "original_image_path": image_path,
+            "output_image_path": output_image_path,
+            **serialized_response,
+        }
+
+        # 2. Reorder predictions by scores (highest to lowest) if scores are available
+        if "pred_scores" in serialized_response and serialized_response["pred_scores"]:
+            # Create indices sorted by scores in descending order
+            score_indices = sorted(
+                range(len(serialized_response["pred_scores"])),
+                key=lambda i: serialized_response["pred_scores"][i],
+                reverse=True,
+            )
+
+            # Reorder all three lists based on the sorted indices
+            serialized_response["pred_scores"] = [
+                serialized_response["pred_scores"][i] for i in score_indices
+            ]
+            serialized_response["pred_boxes"] = [
+                serialized_response["pred_boxes"][i] for i in score_indices
+            ]
+            serialized_response["pred_masks"] = [
+                serialized_response["pred_masks"][i] for i in score_indices
+            ]
+
+        # 3. Remove any invalid RLE masks that is too short (shorter than 5 characters)
+        valid_masks = []
+        valid_boxes = []
+        valid_scores = []
+        for i, rle in enumerate(serialized_response["pred_masks"]):
+            if len(rle) > 4:
+                valid_masks.append(rle)
+                valid_boxes.append(serialized_response["pred_boxes"][i])
+                valid_scores.append(serialized_response["pred_scores"][i])
+        serialized_response["pred_masks"] = valid_masks
+        serialized_response["pred_boxes"] = valid_boxes
+        serialized_response["pred_scores"] = valid_scores
+
+        with open(output_json_path, "w") as f:
+            json.dump(serialized_response, f, indent=4)
+        print(f"✅ Raw JSON response saved to '{output_json_path}'")
+
+        # 4. Render and save visualizations on the image and save it in the SAM3 output folder
+        print("🔍 Rendering visualizations on the image ...")
+        viz_image = visualize(serialized_response)
+        os.makedirs(os.path.dirname(output_image_path), exist_ok=True)
+        viz_image.save(output_image_path)
+        print("✅ Saved visualization at:", output_image_path)
+    except Exception as e:
+        print(f"❌ Error calling service: {e}")
+
+    return output_json_path

third_party/GraspGen/sam3/sam3/agent/inference.py

@@ -0,0 +1,67 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import json
+import os
+
+from sam3.agent.agent_core import agent_inference
+
+
+def run_single_image_inference(
+    image_path,
+    text_prompt,
+    llm_config,
+    send_generate_request,
+    call_sam_service,
+    output_dir="agent_output",
+    debug=False,
+):
+    """Run inference on a single image with provided prompt"""
+
+    llm_name = llm_config["name"]
+
+    if not os.path.exists(image_path):
+        raise FileNotFoundError(f"Image file not found: {image_path}")
+
+    # Create output directory
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Generate output file names
+    image_basename = os.path.splitext(os.path.basename(image_path))[0]
+    prompt_for_filename = text_prompt.replace("/", "_").replace(" ", "_")
+
+    base_filename = f"{image_basename}_{prompt_for_filename}_agent_{llm_name}"
+    output_json_path = os.path.join(output_dir, f"{base_filename}_pred.json")
+    output_image_path = os.path.join(output_dir, f"{base_filename}_pred.png")
+    agent_history_path = os.path.join(output_dir, f"{base_filename}_history.json")
+
+    # Check if output already exists and skip
+    if os.path.exists(output_json_path):
+        print(f"Output JSON {output_json_path} already exists. Skipping.")
+        return
+
+    print(f"{'-' * 30} Starting SAM 3 Agent Session... {'-' * 30} ")
+    agent_history, final_output_dict, rendered_final_output = agent_inference(
+        image_path,
+        text_prompt,
+        send_generate_request=send_generate_request,
+        call_sam_service=call_sam_service,
+        output_dir=output_dir,
+        debug=debug,
+    )
+    print(f"{'-' * 30} End of SAM 3 Agent Session... {'-' * 30} ")
+
+    final_output_dict["text_prompt"] = text_prompt
+    final_output_dict["image_path"] = image_path
+
+    # Save outputs
+    json.dump(final_output_dict, open(output_json_path, "w"), indent=4)
+    json.dump(agent_history, open(agent_history_path, "w"), indent=4)
+    rendered_final_output.save(output_image_path)
+
+    print(f"\n✅ Successfully processed single image!")
+    print(f"Output JSON: {output_json_path}")
+    print(f"Output Image: {output_image_path}")
+    print(f"Agent History: {agent_history_path}")
+    return output_image_path

third_party/GraspGen/sam3/sam3/agent/viz.py

@@ -0,0 +1,118 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import cv2
+import numpy as np
+import pycocotools.mask as mask_utils
+from PIL import Image
+
+from .helpers.visualizer import Visualizer
+from .helpers.zoom_in import render_zoom_in
+
+
+def visualize(
+    input_json: dict,
+    zoom_in_index: int | None = None,
+    mask_alpha: float = 0.15,
+    label_mode: str = "1",
+    font_size_multiplier: float = 1.2,
+    boarder_width_multiplier: float = 0,
+):
+    """
+    Unified visualization function.
+
+    If zoom_in_index is None:
+        - Render all masks in input_json (equivalent to visualize_masks_from_result_json).
+        - Returns: PIL.Image
+
+    If zoom_in_index is provided:
+        - Returns two PIL.Images:
+            1) Output identical to zoom_in_and_visualize(input_json, index).
+            2) The same instance rendered via the general overlay using the color
+               returned by (1), equivalent to calling visualize_masks_from_result_json
+               on a single-mask json_i with color=color_hex.
+    """
+    # Common fields
+    orig_h = int(input_json["orig_img_h"])
+    orig_w = int(input_json["orig_img_w"])
+    img_path = input_json["original_image_path"]
+
+    # ---------- Mode A: Full-scene render ----------
+    if zoom_in_index is None:
+        boxes = np.array(input_json["pred_boxes"])
+        rle_masks = [
+            {"size": (orig_h, orig_w), "counts": rle}
+            for rle in input_json["pred_masks"]
+        ]
+        binary_masks = [mask_utils.decode(rle) for rle in rle_masks]
+
+        img_bgr = cv2.imread(img_path)
+        if img_bgr is None:
+            raise FileNotFoundError(f"Could not read image: {img_path}")
+        img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+
+        viz = Visualizer(
+            img_rgb,
+            font_size_multiplier=font_size_multiplier,
+            boarder_width_multiplier=boarder_width_multiplier,
+        )
+        viz.overlay_instances(
+            boxes=boxes,
+            masks=rle_masks,
+            binary_masks=binary_masks,
+            assigned_colors=None,
+            alpha=mask_alpha,
+            label_mode=label_mode,
+        )
+        pil_all_masks = Image.fromarray(viz.output.get_image())
+        return pil_all_masks
+
+    # ---------- Mode B: Zoom-in pair ----------
+    else:
+        idx = int(zoom_in_index)
+        num_masks = len(input_json.get("pred_masks", []))
+        if idx < 0 or idx >= num_masks:
+            raise ValueError(
+                f"zoom_in_index {idx} is out of range (0..{num_masks - 1})."
+            )
+
+        # (1) Replicate zoom_in_and_visualize
+        object_data = {
+            "labels": [{"noun_phrase": f"mask_{idx}"}],
+            "segmentation": {
+                "counts": input_json["pred_masks"][idx],
+                "size": [orig_h, orig_w],
+            },
+        }
+        pil_img = Image.open(img_path)
+        pil_mask_i_zoomed, color_hex = render_zoom_in(
+            object_data, pil_img, mask_alpha=mask_alpha
+        )
+
+        # (2) Single-instance render with the same color
+        boxes_i = np.array([input_json["pred_boxes"][idx]])
+        rle_i = {"size": (orig_h, orig_w), "counts": input_json["pred_masks"][idx]}
+        bin_i = mask_utils.decode(rle_i)
+
+        img_bgr_i = cv2.imread(img_path)
+        if img_bgr_i is None:
+            raise FileNotFoundError(f"Could not read image: {img_path}")
+        img_rgb_i = cv2.cvtColor(img_bgr_i, cv2.COLOR_BGR2RGB)
+
+        viz_i = Visualizer(
+            img_rgb_i,
+            font_size_multiplier=font_size_multiplier,
+            boarder_width_multiplier=boarder_width_multiplier,
+        )
+        viz_i.overlay_instances(
+            boxes=boxes_i,
+            masks=[rle_i],
+            binary_masks=[bin_i],
+            assigned_colors=[color_hex],
+            alpha=mask_alpha,
+            label_mode=label_mode,
+        )
+        pil_mask_i = Image.fromarray(viz_i.output.get_image())
+
+        return pil_mask_i, pil_mask_i_zoomed

third_party/GraspGen/sam3/sam3/eval/coco_reindex.py

@@ -0,0 +1,232 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+"""
+Self-contained COCO JSON re-indexing function that creates temporary files.
+"""
+
+import json
+import os
+import tempfile
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+
+def reindex_coco_to_temp(input_json_path: str) -> Optional[str]:
+    """
+    Convert 0-indexed COCO JSON file to 1-indexed and save to temporary location.
+
+    Args:
+        input_json_path: Path to the input COCO JSON file
+
+    Returns:
+        Path to the new 1-indexed JSON file in temporary directory, or None if no conversion needed
+
+    Raises:
+        FileNotFoundError: If input file doesn't exist
+        json.JSONDecodeError: If input file is not valid JSON
+        ValueError: If input file is not a valid COCO format
+    """
+
+    def is_coco_json(data: Dict[str, Any]) -> bool:
+        """Check if data appears to be a COCO format file."""
+        if not isinstance(data, dict):
+            return False
+        # A COCO file should have at least one of these keys
+        coco_keys = {"images", "annotations", "categories"}
+        return any(key in data for key in coco_keys)
+
+    def check_zero_indexed(data: Dict[str, Any]) -> Tuple[bool, bool, bool]:
+        """
+        Check if annotations, images, or categories start from index 0.
+
+        Returns:
+            Tuple of (annotations_zero_indexed, images_zero_indexed, categories_zero_indexed)
+        """
+        annotations_zero = False
+        images_zero = False
+        categories_zero = False
+
+        # Check annotations
+        annotations = data.get("annotations", [])
+        if annotations and any(ann.get("id", -1) == 0 for ann in annotations):
+            annotations_zero = True
+
+        # Check images
+        images = data.get("images", [])
+        if images and any(img.get("id", -1) == 0 for img in images):
+            images_zero = True
+
+        # Check categories
+        categories = data.get("categories", [])
+        if categories and any(cat.get("id", -1) == 0 for cat in categories):
+            categories_zero = True
+
+        return annotations_zero, images_zero, categories_zero
+
+    def reindex_coco_data(data: Dict[str, Any]) -> Dict[str, Any]:
+        """Convert 0-indexed COCO data to 1-indexed."""
+        modified_data = data.copy()
+
+        annotations_zero, images_zero, categories_zero = check_zero_indexed(data)
+
+        # Create ID mapping for consistency
+        image_id_mapping = {}
+        category_id_mapping = {}
+
+        # Process images first (since annotations reference image IDs)
+        if images_zero and "images" in modified_data:
+            for img in modified_data["images"]:
+                old_id = img["id"]
+                new_id = old_id + 1
+                image_id_mapping[old_id] = new_id
+                img["id"] = new_id
+
+        # Process categories (since annotations reference category IDs)
+        if categories_zero and "categories" in modified_data:
+            for cat in modified_data["categories"]:
+                old_id = cat["id"]
+                new_id = old_id + 1
+                category_id_mapping[old_id] = new_id
+                cat["id"] = new_id
+
+        # Process annotations
+        if "annotations" in modified_data:
+            for ann in modified_data["annotations"]:
+                # Update annotation ID if needed
+                if annotations_zero:
+                    ann["id"] = ann["id"] + 1
+
+                # Update image_id reference if images were reindexed
+                if images_zero and ann.get("image_id") is not None:
+                    old_image_id = ann["image_id"]
+                    if old_image_id in image_id_mapping:
+                        ann["image_id"] = image_id_mapping[old_image_id]
+
+                # Update category_id reference if categories were reindexed
+                if categories_zero and ann.get("category_id") is not None:
+                    old_category_id = ann["category_id"]
+                    if old_category_id in category_id_mapping:
+                        ann["category_id"] = category_id_mapping[old_category_id]
+
+        return modified_data
+
+    # Validate input path
+    if not os.path.exists(input_json_path):
+        raise FileNotFoundError(f"Input file not found: {input_json_path}")
+
+    # Load and validate JSON data
+    try:
+        with open(input_json_path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+    except json.JSONDecodeError as e:
+        raise json.JSONDecodeError(f"Invalid JSON in {input_json_path}: {e}")
+
+    # Validate COCO format
+    if not is_coco_json(data):
+        raise ValueError(
+            f"File does not appear to be in COCO format: {input_json_path}"
+        )
+
+    # Check if reindexing is needed
+    annotations_zero, images_zero, categories_zero = check_zero_indexed(data)
+
+    if not (annotations_zero or images_zero or categories_zero):
+        # No conversion needed - just copy to temp location
+        input_path = Path(input_json_path)
+        temp_dir = tempfile.mkdtemp()
+        temp_filename = f"{input_path.stem}_1_indexed{input_path.suffix}"
+        temp_path = os.path.join(temp_dir, temp_filename)
+
+        with open(temp_path, "w", encoding="utf-8") as f:
+            json.dump(data, f, indent=2, ensure_ascii=False)
+
+        return temp_path
+
+    # Perform reindexing
+    modified_data = reindex_coco_data(data)
+
+    # Create temporary file
+    input_path = Path(input_json_path)
+    temp_dir = tempfile.mkdtemp()
+    temp_filename = f"{input_path.stem}_1_indexed{input_path.suffix}"
+    temp_path = os.path.join(temp_dir, temp_filename)
+
+    # Write modified data to temporary file
+    with open(temp_path, "w", encoding="utf-8") as f:
+        json.dump(modified_data, f, indent=2, ensure_ascii=False)
+
+    return temp_path
+
+
+# Example usage and test function
+def test_reindex_function():
+    """Test the reindex function with a sample COCO file."""
+
+    # Create a test COCO file
+    test_data = {
+        "info": {"description": "Test COCO dataset", "version": "1.0", "year": 2023},
+        "images": [
+            {"id": 0, "width": 640, "height": 480, "file_name": "test1.jpg"},
+            {"id": 1, "width": 640, "height": 480, "file_name": "test2.jpg"},
+        ],
+        "categories": [
+            {"id": 0, "name": "person", "supercategory": "person"},
+            {"id": 1, "name": "car", "supercategory": "vehicle"},
+        ],
+        "annotations": [
+            {
+                "id": 0,
+                "image_id": 0,
+                "category_id": 0,
+                "bbox": [100, 100, 50, 75],
+                "area": 3750,
+                "iscrowd": 0,
+            },
+            {
+                "id": 1,
+                "image_id": 1,
+                "category_id": 1,
+                "bbox": [200, 150, 120, 80],
+                "area": 9600,
+                "iscrowd": 0,
+            },
+        ],
+    }
+
+    # Create temporary test file
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
+        json.dump(test_data, f, indent=2)
+        test_file_path = f.name
+
+    try:
+        # Test the function
+        result_path = reindex_coco_to_temp(test_file_path)
+        print(f"Original file: {test_file_path}")
+        print(f"Converted file: {result_path}")
+
+        # Load and display the result
+        with open(result_path, "r") as f:
+            result_data = json.load(f)
+
+        print("\nConverted data sample:")
+        print(f"First image ID: {result_data['images'][0]['id']}")
+        print(f"First category ID: {result_data['categories'][0]['id']}")
+        print(f"First annotation ID: {result_data['annotations'][0]['id']}")
+        print(f"First annotation image_id: {result_data['annotations'][0]['image_id']}")
+        print(
+            f"First annotation category_id: {result_data['annotations'][0]['category_id']}"
+        )
+
+        # Clean up
+        os.unlink(result_path)
+        os.rmdir(os.path.dirname(result_path))
+
+    finally:
+        # Clean up test file
+        os.unlink(test_file_path)
+
+
+if __name__ == "__main__":
+    test_reindex_function()

third_party/GraspGen/sam3/sam3/eval/coco_writer.py

@@ -0,0 +1,354 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+"""
+COCO prediction dumper for distributed training.
+
+Handles collection and dumping of COCO-format predictions from models.
+Supports distributed processing with multiple GPUs/processes.
+"""
+
+import copy
+import gc
+import heapq
+import json
+import logging
+import os
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Optional
+
+import pycocotools.mask as mask_utils
+import torch
+from iopath.common.file_io import g_pathmgr
+from sam3.eval.coco_eval_offline import convert_to_xywh
+from sam3.train.masks_ops import rle_encode
+from sam3.train.utils.distributed import (
+    all_gather,
+    gather_to_rank_0_via_filesys,
+    get_rank,
+    is_main_process,
+)
+
+
+### Helper functions and classes
+
+
+class HeapElement:
+    """Utility class to make a heap with a custom comparator based on score."""
+
+    def __init__(self, val):
+        self.val = val
+
+    def __lt__(self, other):
+        return self.val["score"] < other.val["score"]
+
+
+class PredictionDumper:
+    """
+    Handles collection and dumping of COCO-format predictions from a model.
+
+    This class processes model outputs through a postprocessor, converts them to COCO format,
+    and saves them to disk. It supports distributed processing with multiple GPUs/processes.
+    """
+
+    def __init__(
+        self,
+        dump_dir: str,
+        postprocessor,
+        maxdets: int,
+        iou_type: str,
+        gather_pred_via_filesys: bool = False,
+        merge_predictions: bool = False,
+        pred_file_evaluators: Optional[Any] = None,
+    ):
+        """
+        Initialize the PredictionDumper.
+
+        Args:
+            dump_dir: Directory to dump predictions.
+            postprocessor: Module to convert the model's output into COCO format.
+            maxdets: Maximum number of detections per image.
+            iou_type: IoU type to evaluate. Can include "bbox", "segm"
+            gather_pred_via_filesys: If True, use the filesystem for collective gathers across
+                processes (requires a shared filesystem). Otherwise, use torch collective ops.
+            merge_predictions: If True, merge predictions from all processes and dump to a single file.
+        """
+        self.iou_type = iou_type
+        self.maxdets = maxdets
+        self.dump_dir = dump_dir
+        self.postprocessor = postprocessor
+        self.gather_pred_via_filesys = gather_pred_via_filesys
+        self.merge_predictions = merge_predictions
+        self.pred_file_evaluators = pred_file_evaluators
+        if self.pred_file_evaluators is not None:
+            assert merge_predictions, (
+                "merge_predictions must be True if pred_file_evaluators are provided"
+            )
+        assert self.dump_dir is not None, "dump_dir must be provided"
+
+        if is_main_process():
+            os.makedirs(self.dump_dir, exist_ok=True)
+            logging.info(f"Created prediction dump directory: {self.dump_dir}")
+
+        # Initialize state
+        self.reset()
+
+    def update(self, *args, **kwargs):
+        """
+        Process and accumulate predictions from model outputs.
+
+        Args:
+            *args, **kwargs: Arguments passed to postprocessor.process_results()
+        """
+        predictions = self.postprocessor.process_results(*args, **kwargs)
+        results = self.prepare(predictions, self.iou_type)
+        self._dump(results)
+
+    def _dump(self, results):
+        """
+        Add results to the dump list with precision rounding.
+
+        Args:
+            results: List of prediction dictionaries in COCO format.
+        """
+        dumped_results = copy.deepcopy(results)
+        for r in dumped_results:
+            if "bbox" in r:
+                r["bbox"] = [round(coord, 5) for coord in r["bbox"]]
+            r["score"] = round(r["score"], 5)
+        self.dump.extend(dumped_results)
+
+    def synchronize_between_processes(self):
+        """
+        Synchronize predictions across all processes and save to disk.
+
+        If gather_pred_via_filesys is True, uses filesystem for gathering.
+        Otherwise, uses torch distributed collective operations.
+        Saves per-rank predictions to separate JSON files.
+        """
+        logging.info("Prediction Dumper: Synchronizing between processes")
+
+        if not self.merge_predictions:
+            dumped_file = (
+                Path(self.dump_dir)
+                / f"coco_predictions_{self.iou_type}_{get_rank()}.json"
+            )
+            logging.info(
+                f"Prediction Dumper: Dumping local predictions to {dumped_file}"
+            )
+            with g_pathmgr.open(str(dumped_file), "w") as f:
+                json.dump(self.dump, f)
+        else:
+            self.dump = self.gather_and_merge_predictions()
+            dumped_file = Path(self.dump_dir) / f"coco_predictions_{self.iou_type}.json"
+            if is_main_process():
+                logging.info(
+                    f"Prediction Dumper: Dumping merged predictions to {dumped_file}"
+                )
+                with g_pathmgr.open(str(dumped_file), "w") as f:
+                    json.dump(self.dump, f)
+
+        self.reset()
+        return dumped_file
+
+    def gather_and_merge_predictions(self):
+        """
+        Gather predictions from all processes and merge them, keeping top predictions per image.
+
+        This method collects predictions from all processes, then keeps only the top maxdets
+        predictions per image based on score. It also deduplicates predictions by (image_id, category_id).
+
+        Returns:
+            List of merged prediction dictionaries.
+        """
+        logging.info("Prediction Dumper: Gathering predictions from all processes")
+        gc.collect()
+
+        if self.gather_pred_via_filesys:
+            dump = gather_to_rank_0_via_filesys(self.dump)
+        else:
+            dump = all_gather(self.dump, force_cpu=True)
+
+        # Combine predictions, keeping only top maxdets per image
+        preds_by_image = defaultdict(list)
+        seen_img_cat = set()
+
+        for cur_dump in dump:
+            cur_seen_img_cat = set()
+            for p in cur_dump:
+                image_id = p["image_id"]
+                cat_id = p["category_id"]
+
+                # Skip if we've already seen this image/category pair in a previous dump
+                if (image_id, cat_id) in seen_img_cat:
+                    continue
+
+                cur_seen_img_cat.add((image_id, cat_id))
+
+                # Use a min-heap to keep top predictions
+                if len(preds_by_image[image_id]) < self.maxdets:
+                    heapq.heappush(preds_by_image[image_id], HeapElement(p))
+                else:
+                    heapq.heappushpop(preds_by_image[image_id], HeapElement(p))
+
+            seen_img_cat.update(cur_seen_img_cat)
+
+        # Flatten the heap elements back to a list
+        merged_dump = sum(
+            [[h.val for h in cur_preds] for cur_preds in preds_by_image.values()], []
+        )
+
+        return merged_dump
+
+    def compute_synced(self):
+        """
+        Synchronize predictions across processes and compute summary.
+
+        Returns:
+            Summary dictionary from summarize().
+        """
+        dumped_file = self.synchronize_between_processes()
+        if not is_main_process():
+            return {"": 0.0}
+
+        meters = {}
+        if self.pred_file_evaluators is not None:
+            for evaluator in self.pred_file_evaluators:
+                results = evaluator.evaluate(dumped_file)
+                meters.update(results)
+
+        if len(meters) == 0:
+            meters = {"": 0.0}
+        return meters
+
+    def compute(self):
+        """
+        Compute without synchronization.
+
+        Returns:
+            Empty metric dictionary.
+        """
+        return {"": 0.0}
+
+    def reset(self):
+        """Reset internal state for a new evaluation round."""
+        self.dump = []
+
+    def prepare(self, predictions, iou_type):
+        """
+        Route predictions to the appropriate preparation method based on iou_type.
+
+        Args:
+            predictions: Dictionary mapping image IDs to prediction dictionaries.
+            iou_type: Type of evaluation ("bbox", "segm").
+
+        Returns:
+            List of COCO-format prediction dictionaries.
+        """
+        if iou_type == "bbox":
+            return self.prepare_for_coco_detection(predictions)
+        elif iou_type == "segm":
+            return self.prepare_for_coco_segmentation(predictions)
+        else:
+            raise ValueError(f"Unknown iou type: {iou_type}")
+
+    def prepare_for_coco_detection(self, predictions):
+        """
+        Convert predictions to COCO detection format.
+
+        Args:
+            predictions: Dictionary mapping image IDs to prediction dictionaries
+                containing "boxes", "scores", and "labels".
+
+        Returns:
+            List of COCO-format detection dictionaries.
+        """
+        coco_results = []
+        for original_id, prediction in predictions.items():
+            if len(prediction) == 0:
+                continue
+
+            boxes = prediction["boxes"]
+            boxes = convert_to_xywh(boxes).tolist()
+            scores = prediction["scores"].tolist()
+            labels = prediction["labels"].tolist()
+
+            coco_results.extend(
+                [
+                    {
+                        "image_id": original_id,
+                        "category_id": labels[k],
+                        "bbox": box,
+                        "score": scores[k],
+                    }
+                    for k, box in enumerate(boxes)
+                ]
+            )
+        return coco_results
+
+    @torch.no_grad()
+    def prepare_for_coco_segmentation(self, predictions):
+        """
+        Convert predictions to COCO segmentation format.
+
+        Args:
+            predictions: Dictionary mapping image IDs to prediction dictionaries
+                containing "masks" or "masks_rle", "scores", and "labels".
+                Optionally includes "boundaries" and "dilated_boundaries".
+
+        Returns:
+            List of COCO-format segmentation dictionaries with RLE-encoded masks.
+        """
+        coco_results = []
+        for original_id, prediction in predictions.items():
+            if len(prediction) == 0:
+                continue
+
+            scores = prediction["scores"].tolist()
+            labels = prediction["labels"].tolist()
+
+            boxes = None
+            if "boxes" in prediction:
+                boxes = prediction["boxes"]
+                boxes = convert_to_xywh(boxes).tolist()
+                assert len(boxes) == len(scores)
+
+            if "masks_rle" in prediction:
+                rles = prediction["masks_rle"]
+                areas = []
+                for rle in rles:
+                    cur_area = mask_utils.area(rle)
+                    h, w = rle["size"]
+                    areas.append(cur_area / (h * w))
+            else:
+                masks = prediction["masks"]
+                masks = masks > 0.5
+                h, w = masks.shape[-2:]
+
+                areas = masks.flatten(1).sum(1) / (h * w)
+                areas = areas.tolist()
+
+                rles = rle_encode(masks.squeeze(1))
+
+                # Memory cleanup
+                del masks
+                del prediction["masks"]
+
+            assert len(areas) == len(rles) == len(scores)
+
+            for k, rle in enumerate(rles):
+                payload = {
+                    "image_id": original_id,
+                    "category_id": labels[k],
+                    "segmentation": rle,
+                    "score": scores[k],
+                    "area": areas[k],
+                }
+                if boxes is not None:
+                    payload["bbox"] = boxes[k]
+
+                coco_results.append(payload)
+
+        return coco_results

third_party/GraspGen/sam3/sam3/eval/hota_eval_toolkit/__init__.py

@@ -0,0 +1,3 @@
+# flake8: noqa
+
+# pyre-unsafe

third_party/GraspGen/sam3/sam3/eval/hota_eval_toolkit/run_ytvis_eval.py

@@ -0,0 +1,116 @@
+# flake8: noqa
+
+# pyre-unsafe
+
+"""run_youtube_vis.py
+Run example:
+run_youtube_vis.py --USE_PARALLEL False --METRICS HOTA --TRACKERS_TO_EVAL STEm_Seg
+Command Line Arguments: Defaults, # Comments
+    Eval arguments:
+            'USE_PARALLEL': False,
+            'NUM_PARALLEL_CORES': 8,
+            'BREAK_ON_ERROR': True,  # Raises exception and exits with error
+            'RETURN_ON_ERROR': False,  # if not BREAK_ON_ERROR, then returns from function on error
+            'LOG_ON_ERROR': os.path.join(code_path, 'error_log.txt'),  # if not None, save any errors into a log file.
+            'PRINT_RESULTS': True,
+            'PRINT_ONLY_COMBINED': False,
+            'PRINT_CONFIG': True,
+            'TIME_PROGRESS': True,
+            'DISPLAY_LESS_PROGRESS': True,
+            'OUTPUT_SUMMARY': True,
+            'OUTPUT_EMPTY_CLASSES': True,  # If False, summary files are not output for classes with no detections
+            'OUTPUT_DETAILED': True,
+            'PLOT_CURVES': True,
+    Dataset arguments:
+        'GT_FOLDER': os.path.join(code_path, 'data/gt/youtube_vis/youtube_vis_training'),  # Location of GT data
+        'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/youtube_vis/youtube_vis_training'),
+        # Trackers location
+        'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
+        'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)
+        'CLASSES_TO_EVAL': None,  # Classes to eval (if None, all classes)
+        'SPLIT_TO_EVAL': 'training',  # Valid: 'training', 'val'
+        'PRINT_CONFIG': True,  # Whether to print current config
+        'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
+        'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
+        'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
+    Metric arguments:
+        'METRICS': ['TrackMAP', 'HOTA', 'CLEAR', 'Identity']
+"""
+
+import argparse
+import os
+import sys
+from multiprocessing import freeze_support
+
+from . import trackeval
+
+
+def run_ytvis_eval(args=None, gt_json=None, dt_json=None):
+    # Command line interface:
+    default_eval_config = trackeval.Evaluator.get_default_eval_config()
+    # print only combined since TrackMAP is undefined for per sequence breakdowns
+    default_eval_config["PRINT_ONLY_COMBINED"] = True
+    default_dataset_config = trackeval.datasets.YouTubeVIS.get_default_dataset_config()
+    default_metrics_config = {"METRICS": ["HOTA"]}
+    config = {
+        **default_eval_config,
+        **default_dataset_config,
+        **default_metrics_config,
+    }  # Merge default configs
+    parser = argparse.ArgumentParser()
+    for setting in config.keys():
+        if type(config[setting]) == list or type(config[setting]) == type(None):
+            parser.add_argument("--" + setting, nargs="+")
+        else:
+            parser.add_argument("--" + setting)
+    args = parser.parse_args(args).__dict__
+    for setting in args.keys():
+        if args[setting] is not None:
+            if type(config[setting]) == type(True):
+                if args[setting] == "True":
+                    x = True
+                elif args[setting] == "False":
+                    x = False
+                else:
+                    raise Exception(
+                        "Command line parameter " + setting + "must be True or False"
+                    )
+            elif type(config[setting]) == type(1):
+                x = int(args[setting])
+            elif type(args[setting]) == type(None):
+                x = None
+            else:
+                x = args[setting]
+            config[setting] = x
+    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}
+    dataset_config = {
+        k: v for k, v in config.items() if k in default_dataset_config.keys()
+    }
+    metrics_config = {
+        k: v for k, v in config.items() if k in default_metrics_config.keys()
+    }
+
+    # Run code
+    evaluator = trackeval.Evaluator(eval_config)
+    # allow directly specifying the GT JSON data and Tracker (result)
+    # JSON data as Python objects, without reading from files.
+    dataset_config["GT_JSON_OBJECT"] = gt_json
+    dataset_config["TRACKER_JSON_OBJECT"] = dt_json
+    dataset_list = [trackeval.datasets.YouTubeVIS(dataset_config)]
+    metrics_list = []
+    # for metric in [trackeval.metrics.TrackMAP, trackeval.metrics.HOTA, trackeval.metrics.CLEAR,
+    #                trackeval.metrics.Identity]:
+    for metric in [trackeval.metrics.HOTA]:
+        if metric.get_name() in metrics_config["METRICS"]:
+            metrics_list.append(metric())
+    if len(metrics_list) == 0:
+        raise Exception("No metrics selected for evaluation")
+    output_res, output_msg = evaluator.evaluate(dataset_list, metrics_list)
+    return output_res, output_msg
+
+
+if __name__ == "__main__":
+    import sys
+
+    freeze_support()
+    run_ytvis_eval(sys.argv[1:])

third_party/GraspGen/sam3/sam3/eval/hota_eval_toolkit/trackeval/__init__.py

@@ -0,0 +1,6 @@
+# flake8: noqa
+
+# pyre-unsafe
+
+from . import datasets, metrics, utils
+from .eval import Evaluator

third_party/GraspGen/sam3/sam3/eval/hota_eval_toolkit/trackeval/_timing.py

@@ -0,0 +1,70 @@
+# flake8: noqa
+
+# pyre-unsafe
+
+import inspect
+from functools import wraps
+from time import perf_counter
+
+DO_TIMING = False
+DISPLAY_LESS_PROGRESS = False
+timer_dict = {}
+counter = 0
+
+
+def time(f):
+    @wraps(f)
+    def wrap(*args, **kw):
+        if DO_TIMING:
+            # Run function with timing
+            ts = perf_counter()
+            result = f(*args, **kw)
+            te = perf_counter()
+            tt = te - ts
+
+            # Get function name
+            arg_names = inspect.getfullargspec(f)[0]
+            if arg_names[0] == "self" and DISPLAY_LESS_PROGRESS:
+                return result
+            elif arg_names[0] == "self":
+                method_name = type(args[0]).__name__ + "." + f.__name__
+            else:
+                method_name = f.__name__
+
+            # Record accumulative time in each function for analysis
+            if method_name in timer_dict.keys():
+                timer_dict[method_name] += tt
+            else:
+                timer_dict[method_name] = tt
+
+            # If code is finished, display timing summary
+            if method_name == "Evaluator.evaluate":
+                print("")
+                print("Timing analysis:")
+                for key, value in timer_dict.items():
+                    print("%-70s %2.4f sec" % (key, value))
+            else:
+                # Get function argument values for printing special arguments of interest
+                arg_titles = ["tracker", "seq", "cls"]
+                arg_vals = []
+                for i, a in enumerate(arg_names):
+                    if a in arg_titles:
+                        arg_vals.append(args[i])
+                arg_text = "(" + ", ".join(arg_vals) + ")"
+
+                # Display methods and functions with different indentation.
+                if arg_names[0] == "self":
+                    print("%-74s %2.4f sec" % (" " * 4 + method_name + arg_text, tt))
+                elif arg_names[0] == "test":
+                    pass
+                else:
+                    global counter
+                    counter += 1
+                    print("%i %-70s %2.4f sec" % (counter, method_name + arg_text, tt))
+
+            return result
+        else:
+            # If config["TIME_PROGRESS"] is false, or config["USE_PARALLEL"] is true, run functions normally without timing.
+            return f(*args, **kw)
+
+    return wrap

third_party/GraspGen/sam3/sam3/eval/hota_eval_toolkit/trackeval/eval.py

@@ -0,0 +1,398 @@
+# flake8: noqa
+
+# pyre-unsafe
+
+import os
+import time
+import traceback
+from functools import partial
+from multiprocessing.pool import Pool
+
+import numpy as np
+
+from . import _timing, utils
+from .metrics import Count
+from .utils import TrackEvalException
+
+try:
+    import tqdm
+
+    TQDM_IMPORTED = True
+except ImportError as _:
+    TQDM_IMPORTED = False
+
+
+class Evaluator:
+    """Evaluator class for evaluating different metrics for different datasets"""
+
+    @staticmethod
+    def get_default_eval_config():
+        """Returns the default config values for evaluation"""
+        code_path = utils.get_code_path()
+        default_config = {
+            "USE_PARALLEL": False,
+            "NUM_PARALLEL_CORES": 8,
+            "BREAK_ON_ERROR": True,  # Raises exception and exits with error
+            "RETURN_ON_ERROR": False,  # if not BREAK_ON_ERROR, then returns from function on error
+            "LOG_ON_ERROR": os.path.join(
+                code_path, "error_log.txt"
+            ),  # if not None, save any errors into a log file.
+            "PRINT_RESULTS": True,
+            "PRINT_ONLY_COMBINED": False,
+            "PRINT_CONFIG": True,
+            "TIME_PROGRESS": True,
+            "DISPLAY_LESS_PROGRESS": True,
+            "OUTPUT_SUMMARY": True,
+            "OUTPUT_EMPTY_CLASSES": True,  # If False, summary files are not output for classes with no detections
+            "OUTPUT_DETAILED": True,
+            "PLOT_CURVES": True,
+        }
+        return default_config
+
+    def __init__(self, config=None):
+        """Initialise the evaluator with a config file"""
+        self.config = utils.init_config(config, self.get_default_eval_config(), "Eval")
+        # Only run timing analysis if not run in parallel.
+        if self.config["TIME_PROGRESS"] and not self.config["USE_PARALLEL"]:
+            _timing.DO_TIMING = True
+            if self.config["DISPLAY_LESS_PROGRESS"]:
+                _timing.DISPLAY_LESS_PROGRESS = True
+
+    def _combine_results(
+        self,
+        res,
+        metrics_list,
+        metric_names,
+        dataset,
+        res_field="COMBINED_SEQ",
+        target_tag=None,
+    ):
+        assert res_field.startswith("COMBINED_SEQ")
+        # collecting combined cls keys (cls averaged, det averaged, super classes)
+        tracker_list, seq_list, class_list = dataset.get_eval_info()
+        combined_cls_keys = []
+        res[res_field] = {}
+
+        # narrow the target for evaluation
+        if target_tag is not None:
+            target_video_ids = [
+                annot["video_id"]
+                for annot in dataset.gt_data["annotations"]
+                if target_tag in annot["tags"]
+            ]
+            vid2name = {
+                video["id"]: video["file_names"][0].split("/")[0]
+                for video in dataset.gt_data["videos"]
+            }
+            target_video_ids = set(target_video_ids)
+            target_video = [vid2name[video_id] for video_id in target_video_ids]
+
+            if len(target_video) == 0:
+                raise TrackEvalException(
+                    "No sequences found with the tag %s" % target_tag
+                )
+
+            target_annotations = [
+                annot
+                for annot in dataset.gt_data["annotations"]
+                if annot["video_id"] in target_video_ids
+            ]
+            assert all(target_tag in annot["tags"] for annot in target_annotations), (
+                f"Not all annotations in the target sequences have the target tag {target_tag}. "
+                "We currently only support a target tag at the sequence level, not at the annotation level."
+            )
+        else:
+            target_video = seq_list
+
+        # combine sequences for each class
+        for c_cls in class_list:
+            res[res_field][c_cls] = {}
+            for metric, metric_name in zip(metrics_list, metric_names):
+                curr_res = {
+                    seq_key: seq_value[c_cls][metric_name]
+                    for seq_key, seq_value in res.items()
+                    if not seq_key.startswith("COMBINED_SEQ")
+                    and seq_key in target_video
+                }
+                res[res_field][c_cls][metric_name] = metric.combine_sequences(curr_res)
+        # combine classes
+        if dataset.should_classes_combine:
+            combined_cls_keys += [
+                "cls_comb_cls_av",
+                "cls_comb_det_av",
+                "all",
+            ]
+            res[res_field]["cls_comb_cls_av"] = {}
+            res[res_field]["cls_comb_det_av"] = {}
+            for metric, metric_name in zip(metrics_list, metric_names):
+                cls_res = {
+                    cls_key: cls_value[metric_name]
+                    for cls_key, cls_value in res[res_field].items()
+                    if cls_key not in combined_cls_keys
+                }
+                res[res_field]["cls_comb_cls_av"][metric_name] = (
+                    metric.combine_classes_class_averaged(cls_res)
+                )
+                res[res_field]["cls_comb_det_av"][metric_name] = (
+                    metric.combine_classes_det_averaged(cls_res)
+                )
+        # combine classes to super classes
+        if dataset.use_super_categories:
+            for cat, sub_cats in dataset.super_categories.items():
+                combined_cls_keys.append(cat)
+                res[res_field][cat] = {}
+                for metric, metric_name in zip(metrics_list, metric_names):
+                    cat_res = {
+                        cls_key: cls_value[metric_name]
+                        for cls_key, cls_value in res[res_field].items()
+                        if cls_key in sub_cats
+                    }
+                    res[res_field][cat][metric_name] = (
+                        metric.combine_classes_det_averaged(cat_res)
+                    )
+        return res, combined_cls_keys
+
+    def _summarize_results(
+        self,
+        res,
+        tracker,
+        metrics_list,
+        metric_names,
+        dataset,
+        res_field,
+        combined_cls_keys,
+    ):
+        config = self.config
+        output_fol = dataset.get_output_fol(tracker)
+        tracker_display_name = dataset.get_display_name(tracker)
+        for c_cls in res[
+            res_field
+        ].keys():  # class_list + combined classes if calculated
+            summaries = []
+            details = []
+            num_dets = res[res_field][c_cls]["Count"]["Dets"]
+            if config["OUTPUT_EMPTY_CLASSES"] or num_dets > 0:
+                for metric, metric_name in zip(metrics_list, metric_names):
+                    # for combined classes there is no per sequence evaluation
+                    if c_cls in combined_cls_keys:
+                        table_res = {res_field: res[res_field][c_cls][metric_name]}
+                    else:
+                        table_res = {
+                            seq_key: seq_value[c_cls][metric_name]
+                            for seq_key, seq_value in res.items()
+                        }
+
+                    if config["PRINT_RESULTS"] and config["PRINT_ONLY_COMBINED"]:
+                        dont_print = (
+                            dataset.should_classes_combine
+                            and c_cls not in combined_cls_keys
+                        )
+                        if not dont_print:
+                            metric.print_table(
+                                {res_field: table_res[res_field]},
+                                tracker_display_name,
+                                c_cls,
+                                res_field,
+                                res_field,
+                            )
+                    elif config["PRINT_RESULTS"]:
+                        metric.print_table(
+                            table_res, tracker_display_name, c_cls, res_field, res_field
+                        )
+                    if config["OUTPUT_SUMMARY"]:
+                        summaries.append(metric.summary_results(table_res))
+                    if config["OUTPUT_DETAILED"]:
+                        details.append(metric.detailed_results(table_res))
+                    if config["PLOT_CURVES"]:
+                        metric.plot_single_tracker_results(
+                            table_res,
+                            tracker_display_name,
+                            c_cls,
+                            output_fol,
+                        )
+                if config["OUTPUT_SUMMARY"]:
+                    utils.write_summary_results(summaries, c_cls, output_fol)
+                if config["OUTPUT_DETAILED"]:
+                    utils.write_detailed_results(details, c_cls, output_fol)
+
+    @_timing.time
+    def evaluate(self, dataset_list, metrics_list, show_progressbar=False):
+        """Evaluate a set of metrics on a set of datasets"""
+        config = self.config
+        metrics_list = metrics_list + [Count()]  # Count metrics are always run
+        metric_names = utils.validate_metrics_list(metrics_list)
+        dataset_names = [dataset.get_name() for dataset in dataset_list]
+        output_res = {}
+        output_msg = {}
+
+        for dataset, dataset_name in zip(dataset_list, dataset_names):
+            # Get dataset info about what to evaluate
+            output_res[dataset_name] = {}
+            output_msg[dataset_name] = {}
+            tracker_list, seq_list, class_list = dataset.get_eval_info()
+            print(
+                "\nEvaluating %i tracker(s) on %i sequence(s) for %i class(es) on %s dataset using the following "
+                "metrics: %s\n"
+                % (
+                    len(tracker_list),
+                    len(seq_list),
+                    len(class_list),
+                    dataset_name,
+                    ", ".join(metric_names),
+                )
+            )
+
+            # Evaluate each tracker
+            for tracker in tracker_list:
+                # if not config['BREAK_ON_ERROR'] then go to next tracker without breaking
+                try:
+                    # Evaluate each sequence in parallel or in series.
+                    # returns a nested dict (res), indexed like: res[seq][class][metric_name][sub_metric field]
+                    # e.g. res[seq_0001][pedestrian][hota][DetA]
+                    print("\nEvaluating %s\n" % tracker)
+                    time_start = time.time()
+                    if config["USE_PARALLEL"]:
+                        if show_progressbar and TQDM_IMPORTED:
+                            seq_list_sorted = sorted(seq_list)
+
+                            with (
+                                Pool(config["NUM_PARALLEL_CORES"]) as pool,
+                                tqdm.tqdm(total=len(seq_list)) as pbar,
+                            ):
+                                _eval_sequence = partial(
+                                    eval_sequence,
+                                    dataset=dataset,
+                                    tracker=tracker,
+                                    class_list=class_list,
+                                    metrics_list=metrics_list,
+                                    metric_names=metric_names,
+                                )
+                                results = []
+                                for r in pool.imap(
+                                    _eval_sequence, seq_list_sorted, chunksize=20
+                                ):
+                                    results.append(r)
+                                    pbar.update()
+                                res = dict(zip(seq_list_sorted, results))
+
+                        else:
+                            with Pool(config["NUM_PARALLEL_CORES"]) as pool:
+                                _eval_sequence = partial(
+                                    eval_sequence,
+                                    dataset=dataset,
+                                    tracker=tracker,
+                                    class_list=class_list,
+                                    metrics_list=metrics_list,
+                                    metric_names=metric_names,
+                                )
+                                results = pool.map(_eval_sequence, seq_list)
+                                res = dict(zip(seq_list, results))
+                    else:
+                        res = {}
+                        if show_progressbar and TQDM_IMPORTED:
+                            seq_list_sorted = sorted(seq_list)
+                            for curr_seq in tqdm.tqdm(seq_list_sorted):
+                                res[curr_seq] = eval_sequence(
+                                    curr_seq,
+                                    dataset,
+                                    tracker,
+                                    class_list,
+                                    metrics_list,
+                                    metric_names,
+                                )
+                        else:
+                            for curr_seq in sorted(seq_list):
+                                res[curr_seq] = eval_sequence(
+                                    curr_seq,
+                                    dataset,
+                                    tracker,
+                                    class_list,
+                                    metrics_list,
+                                    metric_names,
+                                )
+
+                    # Combine results over all sequences and then over all classes
+                    res, combined_cls_keys = self._combine_results(
+                        res, metrics_list, metric_names, dataset, "COMBINED_SEQ"
+                    )
+
+                    if np.all(
+                        ["tags" in annot for annot in dataset.gt_data["annotations"]]
+                    ):
+                        # Combine results over the challenging sequences and then over all classes
+                        # currently only support "tracking_challenging_pair"
+                        res, _ = self._combine_results(
+                            res,
+                            metrics_list,
+                            metric_names,
+                            dataset,
+                            "COMBINED_SEQ_CHALLENGING",
+                            "tracking_challenging_pair",
+                        )
+
+                    # Print and output results in various formats
+                    if config["TIME_PROGRESS"]:
+                        print(
+                            "\nAll sequences for %s finished in %.2f seconds"
+                            % (tracker, time.time() - time_start)
+                        )
+
+                    self._summarize_results(
+                        res,
+                        tracker,
+                        metrics_list,
+                        metric_names,
+                        dataset,
+                        "COMBINED_SEQ",
+                        combined_cls_keys,
+                    )
+                    if "COMBINED_SEQ_CHALLENGING" in res:
+                        self._summarize_results(
+                            res,
+                            tracker,
+                            metrics_list,
+                            metric_names,
+                            dataset,
+                            "COMBINED_SEQ_CHALLENGING",
+                            combined_cls_keys,
+                        )
+
+                    # Output for returning from function
+                    output_res[dataset_name][tracker] = res
+                    output_msg[dataset_name][tracker] = "Success"
+
+                except Exception as err:
+                    output_res[dataset_name][tracker] = None
+                    if type(err) == TrackEvalException:
+                        output_msg[dataset_name][tracker] = str(err)
+                    else:
+                        output_msg[dataset_name][tracker] = "Unknown error occurred."
+                    print("Tracker %s was unable to be evaluated." % tracker)
+                    print(err)
+                    traceback.print_exc()
+                    if config["LOG_ON_ERROR"] is not None:
+                        with open(config["LOG_ON_ERROR"], "a") as f:
+                            print(dataset_name, file=f)
+                            print(tracker, file=f)
+                            print(traceback.format_exc(), file=f)
+                            print("\n\n\n", file=f)
+                    if config["BREAK_ON_ERROR"]:
+                        raise err
+                    elif config["RETURN_ON_ERROR"]:
+                        return output_res, output_msg
+
+        return output_res, output_msg
+
+
+@_timing.time
+def eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):
+    """Function for evaluating a single sequence"""
+
+    raw_data = dataset.get_raw_seq_data(tracker, seq)
+    seq_res = {}
+    for cls in class_list:
+        seq_res[cls] = {}
+        data = dataset.get_preprocessed_seq_data(raw_data, cls)
+        for metric, met_name in zip(metrics_list, metric_names):
+            seq_res[cls][met_name] = metric.eval_sequence(data)
+    return seq_res

third_party/GraspGen/sam3/sam3/eval/hota_eval_toolkit/trackeval/utils.py

@@ -0,0 +1,197 @@
+# flake8: noqa
+
+# pyre-unsafe
+
+import argparse
+import csv
+import os
+from collections import OrderedDict
+
+
+def init_config(config, default_config, name=None):
+    """Initialise non-given config values with defaults"""
+    if config is None:
+        config = default_config
+    else:
+        for k in default_config.keys():
+            if k not in config.keys():
+                config[k] = default_config[k]
+    if name and config["PRINT_CONFIG"]:
+        print("\n%s Config:" % name)
+        for c in config.keys():
+            print("%-20s : %-30s" % (c, config[c]))
+    return config
+
+
+def update_config(config):
+    """
+    Parse the arguments of a script and updates the config values for a given value if specified in the arguments.
+    :param config: the config to update
+    :return: the updated config
+    """
+    parser = argparse.ArgumentParser()
+    for setting in config.keys():
+        if type(config[setting]) == list or type(config[setting]) == type(None):
+            parser.add_argument("--" + setting, nargs="+")
+        else:
+            parser.add_argument("--" + setting)
+    args = parser.parse_args().__dict__
+    for setting in args.keys():
+        if args[setting] is not None:
+            if type(config[setting]) == type(True):
+                if args[setting] == "True":
+                    x = True
+                elif args[setting] == "False":
+                    x = False
+                else:
+                    raise Exception(
+                        "Command line parameter " + setting + "must be True or False"
+                    )
+            elif type(config[setting]) == type(1):
+                x = int(args[setting])
+            elif type(args[setting]) == type(None):
+                x = None
+            else:
+                x = args[setting]
+            config[setting] = x
+    return config
+
+
+def get_code_path():
+    """Get base path where code is"""
+    return os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+
+
+def validate_metrics_list(metrics_list):
+    """Get names of metric class and ensures they are unique, further checks that the fields within each metric class
+    do not have overlapping names.
+    """
+    metric_names = [metric.get_name() for metric in metrics_list]
+    # check metric names are unique
+    if len(metric_names) != len(set(metric_names)):
+        raise TrackEvalException(
+            "Code being run with multiple metrics of the same name"
+        )
+    fields = []
+    for m in metrics_list:
+        fields += m.fields
+    # check metric fields are unique
+    if len(fields) != len(set(fields)):
+        raise TrackEvalException(
+            "Code being run with multiple metrics with fields of the same name"
+        )
+    return metric_names
+
+
+def write_summary_results(summaries, cls, output_folder):
+    """Write summary results to file"""
+
+    fields = sum([list(s.keys()) for s in summaries], [])
+    values = sum([list(s.values()) for s in summaries], [])
+
+    # In order to remain consistent upon new fields being adding, for each of the following fields if they are present
+    # they will be output in the summary first in the order below. Any further fields will be output in the order each
+    # metric family is called, and within each family either in the order they were added to the dict (python >= 3.6) or
+    # randomly (python < 3.6).
+    default_order = [
+        "HOTA",
+        "DetA",
+        "AssA",
+        "DetRe",
+        "DetPr",
+        "AssRe",
+        "AssPr",
+        "LocA",
+        "OWTA",
+        "HOTA(0)",
+        "LocA(0)",
+        "HOTALocA(0)",
+        "MOTA",
+        "MOTP",
+        "MODA",
+        "CLR_Re",
+        "CLR_Pr",
+        "MTR",
+        "PTR",
+        "MLR",
+        "CLR_TP",
+        "CLR_FN",
+        "CLR_FP",
+        "IDSW",
+        "MT",
+        "PT",
+        "ML",
+        "Frag",
+        "sMOTA",
+        "IDF1",
+        "IDR",
+        "IDP",
+        "IDTP",
+        "IDFN",
+        "IDFP",
+        "Dets",
+        "GT_Dets",
+        "IDs",
+        "GT_IDs",
+    ]
+    default_ordered_dict = OrderedDict(
+        zip(default_order, [None for _ in default_order])
+    )
+    for f, v in zip(fields, values):
+        default_ordered_dict[f] = v
+    for df in default_order:
+        if default_ordered_dict[df] is None:
+            del default_ordered_dict[df]
+    fields = list(default_ordered_dict.keys())
+    values = list(default_ordered_dict.values())
+
+    out_file = os.path.join(output_folder, cls + "_summary.txt")
+    os.makedirs(os.path.dirname(out_file), exist_ok=True)
+    with open(out_file, "w", newline="") as f:
+        writer = csv.writer(f, delimiter=" ")
+        writer.writerow(fields)
+        writer.writerow(values)
+
+
+def write_detailed_results(details, cls, output_folder):
+    """Write detailed results to file"""
+    sequences = details[0].keys()
+    fields = ["seq"] + sum([list(s["COMBINED_SEQ"].keys()) for s in details], [])
+    out_file = os.path.join(output_folder, cls + "_detailed.csv")
+    os.makedirs(os.path.dirname(out_file), exist_ok=True)
+    with open(out_file, "w", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow(fields)
+        for seq in sorted(sequences):
+            if seq == "COMBINED_SEQ":
+                continue
+            writer.writerow([seq] + sum([list(s[seq].values()) for s in details], []))
+        writer.writerow(
+            ["COMBINED"] + sum([list(s["COMBINED_SEQ"].values()) for s in details], [])
+        )
+
+
+def load_detail(file):
+    """Loads detailed data for a tracker."""
+    data = {}
+    with open(file) as f:
+        for i, row_text in enumerate(f):
+            row = row_text.replace("\r", "").replace("\n", "").split(",")
+            if i == 0:
+                keys = row[1:]
+                continue
+            current_values = row[1:]
+            seq = row[0]
+            if seq == "COMBINED":
+                seq = "COMBINED_SEQ"
+            if (len(current_values) == len(keys)) and seq != "":
+                data[seq] = {}
+                for key, value in zip(keys, current_values):
+                    data[seq][key] = float(value)
+    return data
+
+
+class TrackEvalException(Exception):
+    """Custom exception for catching expected errors."""
+
+    ...

third_party/GraspGen/sam3/sam3/eval/postprocessors.py

@@ -0,0 +1,652 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+"""Postprocessors class to transform MDETR output according to the downstream task"""
+
+import dataclasses
+import logging
+from collections import defaultdict
+from typing import Dict, List, Optional
+
+import numpy as np
+import torch
+from sam3.model import box_ops
+from sam3.model.data_misc import BatchedInferenceMetadata, interpolate
+from sam3.train.masks_ops import rle_encode, robust_rle_encode
+from torch import nn
+
+
+class PostProcessNullOp(nn.Module):
+    def __init__(self, **kwargs):
+        super(PostProcessNullOp).__init__()
+        pass
+
+    def forward(self, input):
+        pass
+
+    def process_results(self, **kwargs):
+        return kwargs["find_stages"]
+
+
+class PostProcessImage(nn.Module):
+    """This module converts the model's output into the format expected by the coco api"""
+
+    def __init__(
+        self,
+        max_dets_per_img: int,
+        iou_type="bbox",
+        to_cpu: bool = True,
+        use_original_ids: bool = False,
+        use_original_sizes_box: bool = False,
+        use_original_sizes_mask: bool = False,
+        convert_mask_to_rle: bool = False,
+        always_interpolate_masks_on_gpu: bool = True,
+        use_presence: bool = True,
+        detection_threshold: float = -1.0,
+    ) -> None:
+        super().__init__()
+        self.max_dets_per_img = max_dets_per_img
+        self.iou_type = iou_type
+        self.to_cpu = to_cpu
+        self.convert_mask_to_rle = convert_mask_to_rle
+        self.always_interpolate_masks_on_gpu = always_interpolate_masks_on_gpu
+
+        self.use_presence = use_presence
+        self.detection_threshold = detection_threshold
+        self.use_original_ids = use_original_ids
+        self.use_original_sizes_box = use_original_sizes_box
+        self.use_original_sizes_mask = use_original_sizes_mask
+
+    @torch.no_grad()
+    def forward(
+        self,
+        outputs,
+        target_sizes_boxes,
+        target_sizes_masks,
+        forced_labels=None,
+        consistent=False,
+        ret_tensordict: bool = False,  # This is experimental
+    ):
+        """Perform the computation
+        Parameters:
+            outputs: raw outputs of the model
+            target_sizes_boxes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
+                          For evaluation, this must be the original image size (before any data augmentation)
+                          For visualization, this should be the image size after data augment, but before padding
+            target_sizes_masks: same but used to resize masks
+            forced_labels: tensor of dimension [batch_size] containing the label to force for each image of the batch
+                           This is useful when evaluating the model using standard metrics (eg on COCO, LVIS). In that case,
+                           we query the model with every possible class label, so we when we pass the predictions to the evaluator,
+                           we want to make sure that the predicted "class" matches the one that was queried.
+            consistent: whether all target sizes are equal
+            ret_tensordict: Experimental argument. If true, return a tensordict.TensorDict instead of a list of dictionaries for easier manipulation.
+        """
+        if ret_tensordict:
+            assert consistent is True, (
+                "We don't support returning TensorDict if the outputs have different shapes"
+            )  # NOTE: It's possible but we don't support it.
+            assert self.detection_threshold <= 0.0, "TODO: implement?"
+            try:
+                from tensordict import TensorDict
+            except ImportError:
+                logging.info(
+                    "tensordict is not installed. Install by running `pip install tensordict --no-deps`. Falling back by setting `ret_tensordict=False`"
+                )
+                ret_tensordict = False
+
+        out_bbox = outputs["pred_boxes"] if "pred_boxes" in outputs else None
+        out_logits = outputs["pred_logits"]
+        pred_masks = outputs["pred_masks"] if self.iou_type == "segm" else None
+        out_probs = out_logits.sigmoid()
+        if self.use_presence:
+            presence_score = outputs["presence_logit_dec"].sigmoid().unsqueeze(1)
+            out_probs = out_probs * presence_score
+
+        assert target_sizes_boxes.shape[1] == 2
+        assert target_sizes_masks.shape[1] == 2
+        batch_size = target_sizes_boxes.shape[0]
+
+        boxes, scores, labels, keep = self._process_boxes_and_labels(
+            target_sizes_boxes, forced_labels, out_bbox, out_probs
+        )
+        assert boxes is None or len(boxes) == batch_size
+        out_masks = self._process_masks(
+            target_sizes_masks, pred_masks, consistent=consistent, keep=keep
+        )
+        del pred_masks
+
+        if boxes is None:
+            assert out_masks is not None
+            assert not ret_tensordict, (
+                "We don't support returning TensorDict if the output does not contain boxes"
+            )
+            B = len(out_masks)
+            boxes = [None] * B
+            scores = [None] * B
+            labels = [None] * B
+
+        results = {
+            "scores": scores,
+            "labels": labels,
+            "boxes": boxes,
+        }
+        if out_masks is not None:
+            if self.convert_mask_to_rle:
+                results.update(masks_rle=out_masks)
+            else:
+                results.update(masks=out_masks)
+
+        if ret_tensordict:
+            results = TensorDict(results).auto_batch_size_()
+            if self.to_cpu:
+                results = results.cpu()
+        else:
+            # Convert a dictonary of lists/tensors to list of dictionaries
+            results = [
+                dict(zip(results.keys(), res_tuple))
+                for res_tuple in zip(*results.values())
+            ]
+
+        return results
+
+    def _process_masks(self, target_sizes, pred_masks, consistent=True, keep=None):
+        if pred_masks is None:
+            return None
+        if self.always_interpolate_masks_on_gpu:
+            gpu_device = target_sizes.device
+            assert gpu_device.type == "cuda"
+            pred_masks = pred_masks.to(device=gpu_device)
+        if consistent:
+            assert keep is None, "TODO: implement?"
+            # All masks should have the same shape, expected when processing a batch of size 1
+            target_size = target_sizes.unique(dim=0)
+            assert target_size.size(0) == 1, "Expecting all target sizes to be equal"
+            out_masks = (
+                interpolate(
+                    pred_masks,
+                    target_size.squeeze().tolist(),
+                    mode="bilinear",
+                    align_corners=False,
+                ).sigmoid()
+                > 0.5
+            )
+            if self.convert_mask_to_rle:
+                raise RuntimeError("TODO: implement?")
+            if self.to_cpu:
+                out_masks = out_masks.cpu()
+        else:
+            out_masks = [[]] * len(pred_masks)
+
+            assert keep is None or len(keep) == len(pred_masks)
+            for i, mask in enumerate(pred_masks):
+                h, w = target_sizes[i]
+                if keep is not None:
+                    mask = mask[keep[i]]
+                # Uses the gpu version fist, moves masks to cpu if it fails"""
+                try:
+                    interpolated = (
+                        interpolate(
+                            mask.unsqueeze(1),
+                            (h, w),
+                            mode="bilinear",
+                            align_corners=False,
+                        ).sigmoid()
+                        > 0.5
+                    )
+                except Exception as e:
+                    logging.info("Issue found, reverting to CPU mode!")
+                    mask_device = mask.device
+                    mask = mask.cpu()
+                    interpolated = (
+                        interpolate(
+                            mask.unsqueeze(1),
+                            (h, w),
+                            mode="bilinear",
+                            align_corners=False,
+                        ).sigmoid()
+                        > 0.5
+                    )
+                    interpolated = interpolated.to(mask_device)
+
+                if self.convert_mask_to_rle:
+                    out_masks[i] = robust_rle_encode(interpolated.squeeze(1))
+                else:
+                    out_masks[i] = interpolated
+                    if self.to_cpu:
+                        out_masks[i] = out_masks[i].cpu()
+
+        return out_masks
+
+    def _process_boxes_and_labels(
+        self, target_sizes, forced_labels, out_bbox, out_probs
+    ):
+        if out_bbox is None:
+            return None, None, None, None
+        assert len(out_probs) == len(target_sizes)
+        if self.to_cpu:
+            out_probs = out_probs.cpu()
+        scores, labels = out_probs.max(-1)
+        if forced_labels is None:
+            labels = torch.ones_like(labels)
+        else:
+            labels = forced_labels[:, None].expand_as(labels)
+
+        # convert to [x0, y0, x1, y1] format
+        boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)
+
+        img_h, img_w = target_sizes.unbind(1)
+        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
+        boxes = boxes * scale_fct[:, None, :]
+
+        if self.to_cpu:
+            boxes = boxes.cpu()
+
+        keep = None
+        if self.detection_threshold > 0:
+            # Filter out the boxes with scores below the detection threshold
+            keep = scores > self.detection_threshold
+            assert len(keep) == len(boxes) == len(scores) == len(labels)
+
+            boxes = [b[k.to(b.device)] for b, k in zip(boxes, keep)]
+            scores = [s[k.to(s.device)] for s, k in zip(scores, keep)]
+            labels = [l[k.to(l.device)] for l, k in zip(labels, keep)]
+
+        return boxes, scores, labels, keep
+
+    def process_results(
+        self, find_stages, find_metadatas: List[BatchedInferenceMetadata], **kwargs
+    ):
+        if find_stages.loss_stages is not None:
+            find_metadatas = [find_metadatas[i] for i in find_stages.loss_stages]
+        assert len(find_stages) == len(find_metadatas)
+        results = {}
+        for outputs, meta in zip(find_stages, find_metadatas):
+            img_size_for_boxes = (
+                meta.original_size
+                if self.use_original_sizes_box
+                else torch.ones_like(meta.original_size)
+            )
+            img_size_for_masks = (
+                meta.original_size
+                if self.use_original_sizes_mask
+                else torch.ones_like(meta.original_size)
+            )
+            detection_results = self(
+                outputs,
+                img_size_for_boxes,
+                img_size_for_masks,
+                forced_labels=(
+                    meta.original_category_id if self.use_original_ids else None
+                ),
+            )
+            ids = (
+                meta.original_image_id if self.use_original_ids else meta.coco_image_id
+            )
+            assert len(detection_results) == len(ids)
+            for img_id, result in zip(ids, detection_results):
+                if img_id.item() not in results:
+                    results[img_id.item()] = result
+                else:
+                    assert set(results[img_id.item()].keys()) == set(result.keys())
+                    for k in result.keys():
+                        if isinstance(result[k], torch.Tensor):
+                            results[img_id.item()][k] = torch.cat(
+                                [results[img_id.item()][k], result[k]], dim=0
+                            )
+                        elif isinstance(result[k], list):
+                            results[img_id.item()][k] += result[k]
+                        else:
+                            raise NotImplementedError(
+                                f"Unexpected type {type(result[k])} in result."
+                            )
+        # Prune the results to the max number of detections per image.
+        for img_id, result in results.items():
+            if (
+                self.max_dets_per_img > 0
+                and len(result["scores"]) > self.max_dets_per_img
+            ):
+                _, topk_indexes = torch.topk(
+                    result["scores"], self.max_dets_per_img, dim=0
+                )
+                if self.to_cpu:
+                    topk_indexes = topk_indexes.cpu()
+                for k in result.keys():
+                    if isinstance(results[img_id][k], list):
+                        results[img_id][k] = [
+                            results[img_id][k][i] for i in topk_indexes.tolist()
+                        ]
+                    else:
+                        results[img_id][k] = results[img_id][k].to(topk_indexes.device)[
+                            topk_indexes
+                        ]
+
+        return results
+
+
+class PostProcessAPIVideo(PostProcessImage):
+    """This module converts the video model's output into the format expected by the YT-VIS api"""
+
+    def __init__(
+        self,
+        *args,
+        to_cpu: bool = True,
+        convert_mask_to_rle: bool = False,
+        always_interpolate_masks_on_gpu: bool = True,
+        prob_thresh: float = 0.5,
+        use_presence: bool = False,
+        **kwargs,
+    ):
+        super().__init__(
+            *args,
+            # Here we always set `convert_mask_to_rle=False` in the base `PostProcessAPI` class
+            # (so that its `_process_masks` won't return a list of RLEs). If we want to return
+            # RLEs for video masklets, we handle it in this `PostProcessAPIVideo` class instead.
+            convert_mask_to_rle=False,
+            # Here we always set `to_cpu=False` in the base `PostProcessAPI` class (so that
+            # the interpolated masks won't be automatically moved back to CPU). We will handle
+            # it in this `PostProcessAPIVideo` class instead.
+            always_interpolate_masks_on_gpu=always_interpolate_masks_on_gpu,
+            use_presence=use_presence,
+            **kwargs,
+        )
+        # Expected keys in the output dict to postprocess
+        self.EXPECTED_KEYS = [
+            "pred_logits",
+            "pred_boxes",
+            "pred_masks",
+        ]
+        # Whether to post-process video masklets (under packed representation) into RLE format
+        self.convert_mask_to_rle_for_video = convert_mask_to_rle
+        self.to_cpu_for_video = to_cpu
+        self.prob_thresh = prob_thresh
+
+    def process_results(
+        self, find_stages, find_metadatas: List[BatchedInferenceMetadata], **kwargs
+    ):
+        """
+        Tracking Postprocessor for SAM 3 video model.
+        This function takes in the output of the SAM 3 video model and processes it to extract all the tracklet predictions.
+        Args:
+            find_stages: A list of tensors representing the output of the SAM 3 video model.
+            find_metadatas: A list of BatchedInferenceMetadata objects containing metadata about each frame.
+            **kwargs: Additional keyword arguments.
+        Returns:
+            A dictionary of predcitions with video_id as key.
+        """
+
+        # Import tensordict here to avoid global dependency.
+        try:
+            from tensordict import TensorDict
+        except ImportError as e:
+            logging.error(
+                "tensordict is not installed, please install by running `pip install tensordict --no-deps`"
+            )
+            raise e
+        # Notes and assumptions:
+        # 1- This postprocessor assumes results only for a single video.
+        # 2- There are N stage outputs corresponding to N video frames
+        # 3- Each stage outputs contains PxQ preds, where P is number of prompts and Q is number of object queries. The output should also contain the tracking object ids corresponding to each object query.
+        # 4- The tracking object id has a default value of -1, indicating that the object query is not tracking any object in the frame, and hence its predictions can be ingored for a given frame.
+        # 5- Some objects may be tracked in a subset of frames only. So, we first extract the predictions in a packed representation (for efficient postprocessing -- specially memory)
+        # and then we convert the packed representation into a padded one, where we zero pad boxes/masks for objects that are not tracked in some frames.
+        # 6- We refer to objects by an object id, which is a tuple (prompt_idx, obj_id)
+
+        assert len(find_stages) > 0, "There is nothing to postprocess?"
+        PROMPT_AXIS, OBJ_QUERY_AXIS = (0, 1)
+        NO_OBJ_ID = -1
+        # Maps object ID -> [indices in packed tensor]
+        tracked_objects_packed_idx = defaultdict(list)
+        # Maps object ID -> [indices in padded tensor (abs frame index)]
+        tracked_objects_frame_idx = defaultdict(list)
+        total_num_preds = 0
+        # This will hold the packed representation of predictions.
+        vid_preds_packed: List[TensorDict] = []
+        vid_masklets_rle_packed: List[Optional[Dict]] = []
+        video_id = -1  # We assume single video postprocessing, this ID should be unique in the datapoint.
+
+        for frame_idx, (frame_outs, meta) in enumerate(
+            zip(find_stages, find_metadatas)
+        ):
+            # only store keys we need to extract the results
+            frame_outs_td = TensorDict(
+                {k: frame_outs[k] for k in self.EXPECTED_KEYS}
+            ).auto_batch_size_()  # Shape is [P,Q,...]
+            meta_td = TensorDict(
+                dataclasses.asdict(meta)
+            ).auto_batch_size_()  # Shape is [P,...]
+            unique_vid_id = meta.original_image_id.unique()
+            assert unique_vid_id.size(0) == 1
+            if video_id == -1:
+                video_id = unique_vid_id.item()
+            else:
+                assert video_id == unique_vid_id.item(), (
+                    "We can only postprocess one video per datapoint"
+                )
+            # keeping track of which objects appear in the current frame
+            obj_ids_per_frame = frame_outs["pred_object_ids"]
+            assert obj_ids_per_frame.size(-1) == frame_outs["pred_logits"].size(-2)
+            if self.prob_thresh is not None:
+                # only keep the predictions on this frame with probability above the threshold
+                # (remove those predictions during the keep-alive period of a tracking query,
+                # where its "pred_object_ids" is still the tracked object ID rather than -1)
+                pred_probs = frame_outs["pred_logits"].sigmoid().squeeze(-1)
+                obj_ids_per_frame = torch.where(
+                    pred_probs >= self.prob_thresh, obj_ids_per_frame, NO_OBJ_ID
+                )
+            tracked_obj_ids_idx = torch.where(obj_ids_per_frame != NO_OBJ_ID)
+            # Object id is a tuple of (prompt_idx, obj_id). This is because the model can assign same obj_id for two different prompts.
+            tracked_obj_ids = [
+                (p_id.item(), obj_ids_per_frame[p_id, q_id].item())
+                for p_id, q_id in zip(
+                    tracked_obj_ids_idx[PROMPT_AXIS],
+                    tracked_obj_ids_idx[OBJ_QUERY_AXIS],
+                )
+            ]
+            if len(tracked_obj_ids) == 0:
+                continue
+            # For each object, we keep track of the packed and padded (frame index) indices
+            for oid in tracked_obj_ids:
+                tracked_objects_packed_idx[oid].append(total_num_preds)
+                tracked_objects_frame_idx[oid].append(frame_idx)
+                total_num_preds += 1
+
+            # Since we have P*Q masks per frame, mask interpolation is the GPU memory bottleneck or time bottleneck in case of cpu processing.
+            # Instead, we first extract results only for tracked objects, reducing the number of masks to K = sum_i(tracked_objs_per_ith_prompt), hopefully <<< P*Q
+            tracked_objs_outs_td = frame_outs_td[
+                tracked_obj_ids_idx
+            ]  # [P,Q,...] --> [K,...]
+            meta_td = meta_td[tracked_obj_ids_idx[PROMPT_AXIS].cpu()]
+            if self.always_interpolate_masks_on_gpu:
+                gpu_device = meta_td["original_size"].device
+                assert gpu_device.type == "cuda"
+                tracked_objs_outs_td = tracked_objs_outs_td.to(device=gpu_device)
+            frame_results_td = self(
+                tracked_objs_outs_td.unsqueeze(1),
+                (
+                    meta_td["original_size"]
+                    if self.use_original_sizes
+                    else torch.ones_like(meta_td["original_size"])
+                ),
+                forced_labels=(
+                    meta_td["original_category_id"] if self.use_original_ids else None
+                ),
+                consistent=True,
+                ret_tensordict=True,
+            ).squeeze(1)
+            del tracked_objs_outs_td
+
+            # Optionally, remove "masks" from output tensor dict and directly encode them
+            # to RLE format under packed representations
+            if self.convert_mask_to_rle_for_video:
+                interpolated_binary_masks = frame_results_td.pop("masks")
+                rle_list = rle_encode(interpolated_binary_masks, return_areas=True)
+                vid_masklets_rle_packed.extend(rle_list)
+            # Optionally, move output TensorDict to CPU (do this after RLE encoding step above)
+            if self.to_cpu_for_video:
+                frame_results_td = frame_results_td.cpu()
+            vid_preds_packed.append(frame_results_td)
+
+        if len(vid_preds_packed) == 0:
+            logging.debug(f"Video {video_id} has no predictions")
+            return {video_id: []}
+
+        vid_preds_packed = torch.cat(vid_preds_packed, dim=0)
+        ############### Construct a padded representation of the predictions ###############
+        num_preds = len(tracked_objects_packed_idx)
+        num_frames = len(find_stages)
+        # We zero pad any missing prediction
+        # NOTE: here, we also have padded tensors for "scores" and "labels", but we overwrite them later.
+        padded_frames_results = TensorDict(
+            {
+                k: torch.zeros(
+                    num_preds, num_frames, *v.shape[1:], device=v.device, dtype=v.dtype
+                )
+                for k, v in vid_preds_packed.items()
+            },
+            batch_size=[
+                num_preds,
+                num_frames,
+            ],
+        )
+        padded_frames_results["scores"][...] = -1e8  # a very low score for empty object
+        # Track scores and labels of each pred tracklet, only for frames where the model was able to track that object
+        tracklet_scores = []
+        tracklet_labels = []
+        # Optionally, fill the list of RLEs for masklets
+        # note: only frames with actual predicted masks (in packed format) will be
+        # filled with RLEs; the rest will remains None in results["masks_rle"]
+        if self.convert_mask_to_rle_for_video:
+            vid_masklets_rle_padded = [[None] * num_frames for _ in range(num_preds)]
+        for o_idx, oid in enumerate(tracked_objects_packed_idx):
+            oid2packed_idx = tracked_objects_packed_idx[oid]
+            oid2padded_idx = tracked_objects_frame_idx[oid]
+            obj_packed_results = vid_preds_packed[oid2packed_idx]
+            padded_frames_results[o_idx][oid2padded_idx] = obj_packed_results
+            if self.convert_mask_to_rle_for_video:
+                for packed_idx, padded_idx in zip(oid2packed_idx, oid2padded_idx):
+                    vid_masklets_rle_padded[o_idx][padded_idx] = (
+                        vid_masklets_rle_packed[packed_idx]
+                    )
+            # NOTE: We need a single confidence score per tracklet for the mAP metric.
+            # We use the average confidence score across time. (How does this impact AP?)
+            tracklet_scores.append(obj_packed_results["scores"].mean())
+            # We also need to have a unique category Id per tracklet.
+            # This is not a problem for phrase AP, however, for mAP we do majority voting across time.
+            tracklet_labels.append(obj_packed_results["labels"].mode()[0])
+
+        results = padded_frames_results.to_dict()
+        results["scores"] = torch.stack(tracklet_scores, dim=0)
+        results["labels"] = torch.stack(tracklet_labels, dim=0)
+        if self.convert_mask_to_rle_for_video:
+            results["masks_rle"] = vid_masklets_rle_padded
+        # we keep the frame-level scores since it's needed by some evaluation scripts
+        results["per_frame_scores"] = padded_frames_results["scores"]
+
+        return {video_id: results}
+
+
+class PostProcessTracking(PostProcessImage):
+    """This module converts the model's output into the format expected by the coco api"""
+
+    def __init__(
+        self,
+        max_dets_per_img: int,
+        iou_type="bbox",
+        force_single_mask: bool = False,
+        **kwargs,
+    ) -> None:
+        super().__init__(max_dets_per_img=max_dets_per_img, iou_type=iou_type, **kwargs)
+        self.force_single_mask = force_single_mask
+
+    def process_results(
+        self, find_stages, find_metadatas: BatchedInferenceMetadata, **kwargs
+    ):
+        assert len(find_stages) == len(find_metadatas)
+        results = {}
+        for outputs, meta in zip(find_stages, find_metadatas):
+            if self.force_single_mask:
+                scores, labels = outputs["pred_logits"].max(-1)
+                m = []
+                for i in range(len(outputs["pred_masks"])):
+                    score, idx = scores[i].max(0)
+                    m.append(outputs["pred_masks"][i][idx])
+                outputs["pred_masks"] = torch.stack(m, 0).unsqueeze(1)
+            detection_results = self(outputs, meta.original_size, consistent=False)
+            assert len(detection_results) == len(meta.coco_image_id)
+            results.update(
+                {
+                    (media_id.item(), object_id.item(), frame_index.item()): result
+                    for media_id, object_id, frame_index, result in zip(
+                        meta.original_image_id,
+                        meta.object_id,
+                        meta.frame_index,
+                        detection_results,
+                    )
+                }
+            )
+        return results
+
+
+class PostProcessCounting(nn.Module):
+    """This module converts the model's output to be evaluated for counting tasks"""
+
+    def __init__(
+        self,
+        use_original_ids: bool = False,
+        threshold: float = 0.5,
+        use_presence: bool = False,
+    ) -> None:
+        """
+        Args:
+            use_original_ids: whether to use the original image ids or the coco ids
+            threshold: threshold for counting (values above this are counted)
+        """
+        super().__init__()
+        self.use_original_ids = use_original_ids
+        self.threshold = threshold
+        self.use_presence = use_presence
+
+    def forward(self, outputs, target_sizes):
+        """Perform the computation
+        Parameters:
+            outputs: raw outputs of the model
+            target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
+        """
+        # Extract scores from model outputs and apply sigmoid
+        scores = torch.sigmoid(outputs["pred_logits"]).squeeze(-1)  # [B, N]
+        if self.use_presence:
+            presence_score = outputs["presence_logit_dec"].sigmoid()
+            if presence_score.ndim == 1:
+                presence_score = presence_score.unsqueeze(1)  # [B, 1]
+            scores = scores * presence_score  # [B, N]
+
+        # Calculate counts by summing values above threshold
+        counts = (scores > self.threshold).float().sum(dim=1)
+
+        assert len(counts) == len(target_sizes)
+        results = []
+        for count in counts:
+            results.append({"count": count.item()})
+
+        return results
+
+    @torch.no_grad()
+    def process_results(
+        self, find_stages, find_metadatas: List[BatchedInferenceMetadata], **kwargs
+    ):
+        assert len(find_stages) == len(find_metadatas)
+        results = {}
+        for outputs, meta in zip(find_stages, find_metadatas):
+            detection_results = self(
+                outputs,
+                meta.original_size,
+            )
+            ids = (
+                meta.original_image_id if self.use_original_ids else meta.coco_image_id
+            )
+            assert len(detection_results) == len(ids)
+            for img_id, result in zip(ids, detection_results):
+                results[img_id.item()] = result
+
+        return results

third_party/GraspGen/sam3/sam3/eval/saco_veval_evaluators.py

@@ -0,0 +1,840 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+import json
+import os
+import tempfile
+from collections import defaultdict
+from typing import Dict, Optional, Sequence, Tuple
+
+import numpy as np
+import pycocotools.mask
+from sam3.eval.cgf1_eval import CGF1_METRICS
+from sam3.eval.conversion_util import (
+    convert_ytbvis_to_cocovid_gt,
+    convert_ytbvis_to_cocovid_pred,
+)
+from sam3.eval.hota_eval_toolkit.run_ytvis_eval import run_ytvis_eval
+from sam3.eval.teta_eval_toolkit import config, Evaluator, metrics
+from sam3.eval.teta_eval_toolkit.datasets import COCO, TAO
+from sam3.eval.ytvis_coco_wrapper import YTVIS
+from sam3.eval.ytvis_eval import VideoDemoF1Eval, YTVISeval
+from sam3.train.nms_helper import process_frame_level_nms, process_track_level_nms
+
+
+def _get_metric_index(metric_name: str, iou_threshold: Optional[float] = None) -> int:
+    """
+    Find the index of a metric in CGF1_METRICS by name and IoU threshold.
+
+    Args:
+        metric_name: Name of the metric (e.g., "cgF1", "precision", "recall")
+        iou_threshold: IoU threshold (None for average over 0.5:0.95, or specific value like 0.5, 0.75)
+
+    Returns:
+        Index of the metric in CGF1_METRICS
+
+    Raises:
+        ValueError: If metric not found
+    """
+    for idx, metric in enumerate(CGF1_METRICS):
+        if metric.name == metric_name and metric.iou_threshold == iou_threshold:
+            return idx
+    raise ValueError(
+        f"Metric '{metric_name}' with IoU threshold {iou_threshold} not found in CGF1_METRICS"
+    )
+
+
+class BasePredFileEvaluator:
+    """A base class for evaluating a prediction file."""
+
+    pass
+
+
+class YTVISPredFileEvaluator(BasePredFileEvaluator):
+    """Evaluate class mAP for YT-VIS prediction files."""
+
+    def __init__(
+        self,
+        gt_ann_file: str,
+        dataset_name: str = "video",
+        iou_types: Optional[Sequence[str]] = None,
+    ):
+        self.gt_ann_file = gt_ann_file
+        self.dataset_name = dataset_name
+        self.iou_types = list(iou_types) if iou_types is not None else ["bbox", "segm"]
+        assert all(iou_type in ["bbox", "segm"] for iou_type in self.iou_types)
+
+    def evaluate(self, pred_file: str) -> Dict[str, float]:
+        # use our internal video evaluation toolkit for YT-VIS pred file
+        # (i.e. the same one we're using for video phrase AP)
+        results = {}
+        use_cats = True  # YT-VIS mAP evaluation uses categories
+        ytvisGT = YTVIS(self.gt_ann_file, ignore_gt_cats=not use_cats)
+        # the original YT-VIS GT annotations have uncompressed RLEs ("counts" is an integer list)
+        # rather than compressed RLEs ("counts" is a string), so we first convert them here.
+        if "segm" in self.iou_types:
+            for ann in ytvisGT.dataset["annotations"]:
+                ann["segmentations"] = [
+                    _compress_rle(rle) for rle in ann["segmentations"]
+                ]
+
+        with open(pred_file) as f:
+            dt = json.load(f)
+        # Our prediction file saves "video_id" and absolute (unnormalized) boxes.
+        # Note that we should use the official (original) YT-VIS annotations (i.e. the one
+        # saved via "scripts/datasets/training/ytvis_split.py", instead of the one saved
+        # via "scripts/api_db_to_ytvis_json.py") in this evaluator, which contain absolute
+        # boxes coordinates in its GT annotations.
+        for d in dt:
+            d["image_id"] = d["video_id"]
+        ytvisDT = ytvisGT.loadRes(dt)
+
+        for iou_type in self.iou_types:
+            ytvisEval = YTVISeval(ytvisGT, ytvisDT, iou_type)
+
+            # set the area ranges for small, medium, and large objects (using
+            # absolute pixel areas) as in the official YT-VIS evaluation toolkit:
+            # https://github.com/achalddave/ytvosapi/blob/eca601117c9f86bad084cb91f1d918e9ab665a75/PythonAPI/ytvostools/ytvoseval.py#L538
+            ytvisEval.params.areaRng = [
+                [0**2, 1e5**2],
+                [0**2, 128**2],
+                [128**2, 256**2],
+                [256**2, 1e5**2],
+            ]
+            ytvisEval.params.areaRngLbl = ["all", "small", "medium", "large"]
+            ytvisEval.params.useCats = use_cats
+
+            ytvisEval.evaluate()
+            ytvisEval.accumulate()
+            ytvisEval.summarize()
+            result_key = f"{self.dataset_name}_{'mask' if iou_type == 'segm' else 'bbox'}_mAP_50_95"
+            results[result_key] = ytvisEval.stats[0]
+
+        # video-NP level results not supported for `YTVISPredFileEvaluator` yet
+        video_np_level_results = {}
+        return results, video_np_level_results
+
+
+class VideoPhraseApEvaluator(BasePredFileEvaluator):
+    """Evaluate Video Phrase AP with YT-VIS format prediction and GT files."""
+
+    def __init__(
+        self,
+        gt_ann_file: str,
+        dataset_name: str = "video",
+        iou_types: Optional[Sequence[str]] = None,
+    ):
+        self.gt_ann_file = gt_ann_file
+        self.dataset_name = dataset_name
+        self.iou_types = list(iou_types) if iou_types is not None else ["bbox", "segm"]
+        assert all(iou_type in ["bbox", "segm"] for iou_type in self.iou_types)
+
+    def evaluate(self, pred_file: str) -> Dict[str, float]:
+        with open(self.gt_ann_file) as f:
+            gt = json.load(f)
+        with open(pred_file) as f:
+            dt = json.load(f)
+        # For phrase AP and demo F1 evaluation, we need to remap each pair of (video_id, category_id) to
+        # a new unique video_id, so that we don't mix detections from different categories under `useCat=False`
+        gt, dt = remap_video_category_pairs_to_unique_video_ids(gt, dt)
+        if "segm" in self.iou_types:
+            for ann in gt["annotations"]:
+                ann["segmentations"] = [
+                    _compress_rle(rle) for rle in ann["segmentations"]
+                ]
+        for d in dt:
+            d["image_id"] = d["video_id"]
+
+        results = {}
+        use_cats = False  # Phrase AP evaluation does not use categories
+        ytvisGT = YTVIS(annotation_file=None, ignore_gt_cats=not use_cats)
+        ytvisGT.dataset = gt
+        ytvisGT.createIndex()
+        ytvisDT = ytvisGT.loadRes(dt)
+
+        for iou_type in self.iou_types:
+            phraseApEval = YTVISeval(ytvisGT, ytvisDT, iou_type)
+
+            # set the area ranges for small, medium, and large objects (using
+            # absolute pixel areas) as in the official YT-VIS evaluation toolkit:
+            # https://github.com/achalddave/ytvosapi/blob/eca601117c9f86bad084cb91f1d918e9ab665a75/PythonAPI/ytvostools/ytvoseval.py#L538
+            phraseApEval.params.areaRng = [
+                [0**2, 1e5**2],
+                [0**2, 128**2],
+                [128**2, 256**2],
+                [256**2, 1e5**2],
+            ]
+            phraseApEval.params.areaRngLbl = ["all", "small", "medium", "large"]
+            phraseApEval.params.useCats = use_cats
+
+            phraseApEval.evaluate()
+            phraseApEval.accumulate()
+            phraseApEval.summarize()
+            result_prefix = f"{self.dataset_name}"
+            result_prefix += f"_{'mask' if iou_type == 'segm' else 'bbox'}_phrase_ap"
+            # fetch Phrase AP results from the corresponding indices in `phraseApEval.stats`
+            # (see `_summarizeDets` in https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py)
+            results[result_prefix + "_50_95"] = phraseApEval.stats[0]  # IoU=0.5:0.95
+            results[result_prefix + "_50"] = phraseApEval.stats[1]  # IoU=0.5
+            results[result_prefix + "_75"] = phraseApEval.stats[2]  # IoU=0.75
+
+        # video-NP level results not supported for `VideoPhraseApEvaluator` yet
+        video_np_level_results = {}
+        return results, video_np_level_results
+
+
+class VideoCGF1Evaluator(BasePredFileEvaluator):
+    """Evaluate Video Demo F1 with YT-VIS format prediction and GT files."""
+
+    def __init__(
+        self,
+        gt_ann_file: str,
+        dataset_name: str = "video",
+        prob_thresh: float = 0.5,
+        iou_types: Optional[Sequence[str]] = None,
+    ):
+        self.gt_ann_file = gt_ann_file
+        self.dataset_name = dataset_name
+        self.prob_thresh = prob_thresh
+        self.iou_types = list(iou_types) if iou_types is not None else ["bbox", "segm"]
+        assert all(iou_type in ["bbox", "segm"] for iou_type in self.iou_types)
+
+    def evaluate(self, pred_file: str) -> Dict[str, float]:
+        with open(self.gt_ann_file) as f:
+            gt = json.load(f)
+        with open(pred_file) as f:
+            dt = json.load(f)
+        # compute IL_MCC and CG-F1 can only be computed if we have "video_np_pairs" keys in the GT JSON
+        compute_ilmcc_and_cgf1 = "video_np_pairs" in gt
+        if not compute_ilmcc_and_cgf1:
+            print(
+                f"Warning: IL_MCC and CG-F1 are not computed for {pred_file=} as it does not have 'video_np_pairs' keys in the GT JSON"
+            )
+        # For phrase AP and demo F1 evaluation, we need to remap each pair of (video_id, category_id) to
+        # a new unique video_id, so that we don't mix detections from different categories under `useCat=False`
+        gt, dt = remap_video_category_pairs_to_unique_video_ids(
+            gt, dt, add_negative_np_pairs=compute_ilmcc_and_cgf1
+        )
+        if "segm" in self.iou_types:
+            for ann in gt["annotations"]:
+                ann["segmentations"] = [
+                    _compress_rle(rle) for rle in ann["segmentations"]
+                ]
+        for d in dt:
+            d["image_id"] = d["video_id"]
+
+        results = {}
+        use_cats = False  # Demo F1 evaluation does not use categories
+        ytvisGT = YTVIS(annotation_file=None, ignore_gt_cats=not use_cats)
+        ytvisGT.dataset = gt
+        ytvisGT.createIndex()
+        ytvisDT = ytvisGT.loadRes(dt)
+
+        video_np_level_results = {}
+        for iou_type in self.iou_types:
+            demoF1Eval = VideoDemoF1Eval(ytvisGT, ytvisDT, iou_type, self.prob_thresh)
+
+            demoF1Eval.params.useCats = use_cats
+            demoF1Eval.params.areaRng = [[0**2, 1e5**2]]
+            demoF1Eval.params.areaRngLbl = ["all"]
+            demoF1Eval.params.maxDets = [100000]
+
+            demoF1Eval.evaluate()
+            demoF1Eval.accumulate()
+            demoF1Eval.summarize()
+            result_prefix = f"{self.dataset_name}"
+            result_prefix += f"_{'mask' if iou_type == 'segm' else 'bbox'}_demo"
+
+            stats = demoF1Eval.stats
+
+            if compute_ilmcc_and_cgf1:
+                # Average IoU threshold (0.5:0.95)
+                cgf1_micro_avg_idx = _get_metric_index("cgF1", None)
+                positive_micro_f1_avg_idx = _get_metric_index("positive_micro_F1", None)
+                ilmcc_avg_idx = _get_metric_index("IL_MCC", None)
+                results[result_prefix + "_cgf1_micro_50_95"] = stats[cgf1_micro_avg_idx]
+                results[result_prefix + "_ilmcc_50_95"] = stats[ilmcc_avg_idx]
+                results[result_prefix + "_positive_micro_f1_50_95"] = stats[
+                    positive_micro_f1_avg_idx
+                ]
+
+                # IoU = 0.5
+                cgf1_micro_50_idx = _get_metric_index("cgF1", 0.5)
+                positive_micro_f1_50_idx = _get_metric_index("positive_micro_F1", 0.5)
+                results[result_prefix + "_cgf1_micro_50"] = stats[cgf1_micro_50_idx]
+                results[result_prefix + "_ilmcc_50"] = float(
+                    np.array(stats[cgf1_micro_50_idx])
+                    / np.array(stats[positive_micro_f1_50_idx])
+                )
+                results[result_prefix + "_positive_micro_f1_50"] = stats[
+                    positive_micro_f1_50_idx
+                ]
+
+                # IoU = 0.75
+                cgf1_micro_75_idx = _get_metric_index("cgF1", 0.75)
+                positive_micro_f1_75_idx = _get_metric_index("positive_micro_F1", 0.75)
+                results[result_prefix + "_cgf1_micro_75"] = stats[cgf1_micro_75_idx]
+                results[result_prefix + "_ilmcc_75"] = float(
+                    np.array(stats[cgf1_micro_75_idx])
+                    / np.array(stats[positive_micro_f1_75_idx])
+                )
+                results[result_prefix + "_positive_micro_f1_75"] = stats[
+                    positive_micro_f1_75_idx
+                ]
+
+            self.extract_video_np_level_results(demoF1Eval, video_np_level_results)
+
+        return results, video_np_level_results
+
+    def extract_video_np_level_results(self, demoF1Eval, video_np_level_results):
+        """Aggregate statistics for video-level metrics."""
+        num_iou_thrs = len(demoF1Eval.params.iouThrs)
+        iou_50_index = int(np.where(demoF1Eval.params.iouThrs == 0.5)[0])
+        iou_75_index = int(np.where(demoF1Eval.params.iouThrs == 0.75)[0])
+
+        result_prefix = "mask" if demoF1Eval.params.iouType == "segm" else "bbox"
+
+        assert len(demoF1Eval.evalImgs) == len(demoF1Eval.cocoGt.dataset["images"])
+        for i, video in enumerate(demoF1Eval.cocoGt.dataset["images"]):
+            # the original video id and category id before remapping
+            video_id = video["orig_video_id"]
+            category_id = video["orig_category_id"]
+            eval_img_dict = demoF1Eval.evalImgs[i]
+
+            TPs = eval_img_dict.get("TPs", np.zeros(num_iou_thrs, dtype=np.int64))
+            FPs = eval_img_dict.get("FPs", np.zeros(num_iou_thrs, dtype=np.int64))
+            FNs = eval_img_dict.get("FNs", np.zeros(num_iou_thrs, dtype=np.int64))
+            assert len(TPs) == len(FPs) == len(FNs) == num_iou_thrs
+            # F1 = 2*TP / (2*TP + FP + FN), and we set F1 to 1.0 if denominator is 0
+            denominator = 2 * TPs + FPs + FNs
+            F1s = np.where(denominator > 0, 2 * TPs / np.maximum(denominator, 1), 1.0)
+            local_results = {
+                f"{result_prefix}_TP_50_95": float(TPs.mean()),
+                f"{result_prefix}_FP_50_95": float(FPs.mean()),
+                f"{result_prefix}_FN_50_95": float(FNs.mean()),
+                f"{result_prefix}_F1_50_95": float(F1s.mean()),
+                f"{result_prefix}_TP_50": float(TPs[iou_50_index]),
+                f"{result_prefix}_FP_50": float(FPs[iou_50_index]),
+                f"{result_prefix}_FN_50": float(FNs[iou_50_index]),
+                f"{result_prefix}_F1_50": float(F1s[iou_50_index]),
+                f"{result_prefix}_TP_75": float(TPs[iou_75_index]),
+                f"{result_prefix}_FP_75": float(FPs[iou_75_index]),
+                f"{result_prefix}_FN_75": float(FNs[iou_75_index]),
+                f"{result_prefix}_F1_75": float(F1s[iou_75_index]),
+            }
+            if (video_id, category_id) not in video_np_level_results:
+                video_np_level_results[(video_id, category_id)] = {}
+            video_np_level_results[(video_id, category_id)].update(local_results)
+
+
+class VideoTetaEvaluator(BasePredFileEvaluator):
+    """Evaluate TETA metric using YouTubeVIS format prediction and GT files."""
+
+    def __init__(
+        self,
+        gt_ann_file: str,
+        dataset_name: str = "video",
+        tracker_name: str = "Sam3",
+        nms_threshold: float = 0.5,
+        nms_strategy: str = "none",  # "track", "frame", or "none"
+        prob_thresh: float = 0.5,
+        is_exhaustive: bool = False,
+        use_mask: bool = False,
+        num_parallel_cores: int = 8,
+    ):
+        self.gt_ann_file = gt_ann_file
+        self.dataset_name = dataset_name
+        self.tracker_name = tracker_name
+        self.nms_threshold = nms_threshold
+        self.nms_strategy = nms_strategy.lower()  # Convert to lowercase for consistency
+        self.prob_thresh = prob_thresh
+        self.metric_prefix = "TETA"
+        self.is_exhaustive = is_exhaustive
+        self.use_mask = use_mask
+        self.num_parallel_cores = num_parallel_cores
+
+        # Verify NMS strategy is valid
+        valid_strategies = ["track", "frame", "none"]
+        print("current nms_strategy:", self.nms_strategy)
+        if self.nms_strategy not in valid_strategies:
+            raise ValueError(
+                f"Invalid NMS strategy: {self.nms_strategy}. Must be one of {valid_strategies}"
+            )
+
+        print(f"Initialized VideoTetaEvaluator with NMS strategy: {self.nms_strategy}")
+        print(f"Probability threshold set to: {self.prob_thresh}")
+        print(f"Dataset exhaustivity set to: {self.is_exhaustive}")
+        print(f"Tracker name set to: {self.tracker_name}")
+        print(f"Dataset name set to: {self.dataset_name}")
+        print(f"Use mask set to: {self.use_mask}")
+
+    def process_predictions(self, pred_file: str, tmp_dir: str) -> str:
+        """Process predictions with selected NMS strategy"""
+        with open(pred_file, "r") as f:
+            raw_preds = json.load(f)
+        print(f"Processing predictions with {self.nms_strategy} NMS strategy")
+
+        # Filter by score threshold
+        if self.prob_thresh > 0:
+            raw_preds = [d for d in raw_preds if d["score"] >= self.prob_thresh]
+            print(
+                f"Filtered to {len(raw_preds)} predictions with score >= {self.prob_thresh}"
+            )
+        # Group predictions by video_id
+        video_groups = defaultdict(list)
+        for pred in raw_preds:
+            video_groups[pred["video_id"]].append(pred)
+        # Process based on NMS strategy
+        if self.nms_strategy == "track":
+            process_track_level_nms(video_groups, nms_threshold=self.nms_threshold)
+        elif self.nms_strategy == "frame":
+            process_frame_level_nms(video_groups, nms_threshold=self.nms_threshold)
+        elif self.nms_strategy == "none":
+            print("Skipping NMS processing as strategy is set to 'none'")
+            # No processing needed for "none" strategy
+        # Save processed predictions
+        processed_preds = [
+            track for tracks in video_groups.values() for track in tracks
+        ]
+        processed_path = os.path.join(tmp_dir, "processed_preds.json")
+        with open(processed_path, "w") as f:
+            json.dump(processed_preds, f)
+
+        print(f"Saved processed predictions to {processed_path}")
+        return processed_path
+
+    def evaluate(self, pred_file: str) -> Tuple[Dict[str, float], Dict]:
+        """Main evaluation method"""
+
+        print(f"Evaluating TETA Metric with {self.nms_strategy.upper()} NMS strategy")
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            # Process predictions first
+            processed_pred_file = self.process_predictions(pred_file, tmp_dir)
+
+            # Convert GT to COCO-vid format
+            gt_dir = os.path.join(tmp_dir, "gt")
+            os.makedirs(gt_dir, exist_ok=True)
+            gt_coco_path = os.path.join(gt_dir, "annotations.json")
+            convert_ytbvis_to_cocovid_gt(self.gt_ann_file, gt_coco_path)
+
+            # Convert processed predictions to COCO-vid format
+            pred_dir = os.path.join(tmp_dir, "predictions")
+            tracker_dir = os.path.join(pred_dir, self.tracker_name)
+            os.makedirs(tracker_dir, exist_ok=True)
+            pred_coco_path = os.path.join(tracker_dir, "track_results_cocofmt.json")
+            convert_ytbvis_to_cocovid_pred(
+                youtubevis_pred_path=processed_pred_file,
+                converted_dataset_path=gt_coco_path,
+                output_path=pred_coco_path,
+            )
+            # Configure TETA evaluator
+            default_eval_config = config.get_default_eval_config()
+            default_eval_config["PRINT_ONLY_COMBINED"] = True
+            default_eval_config["DISPLAY_LESS_PROGRESS"] = True
+            default_eval_config["OUTPUT_TEMP_RAW_DATA"] = True
+            default_eval_config["NUM_PARALLEL_CORES"] = self.num_parallel_cores
+            default_dataset_config = config.get_default_dataset_config()
+            default_dataset_config["TRACKERS_TO_EVAL"] = [self.tracker_name]
+            default_dataset_config["GT_FOLDER"] = gt_dir
+            default_dataset_config["OUTPUT_FOLDER"] = pred_dir
+            default_dataset_config["TRACKER_SUB_FOLDER"] = tracker_dir
+            default_dataset_config["USE_MASK"] = self.use_mask
+
+            evaluator = Evaluator(default_eval_config)
+            if self.is_exhaustive:
+                dataset_list = [COCO(default_dataset_config)]
+                dataset_parsing_key = "COCO"
+            else:
+                dataset_list = [TAO(default_dataset_config)]
+                dataset_parsing_key = "TAO"
+
+            # Run evaluation
+            eval_results, _ = evaluator.evaluate(
+                dataset_list, [metrics.TETA(exhaustive=self.is_exhaustive)]
+            )
+
+            # Extract and format results
+            results = {
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_teta": float(
+                    eval_results[dataset_parsing_key]["TETA"][0]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_loc_a": float(
+                    eval_results[dataset_parsing_key]["TETA"][1]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_assoc_a": float(
+                    eval_results[dataset_parsing_key]["TETA"][2]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_cls_a": float(
+                    eval_results[dataset_parsing_key]["TETA"][3]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_loc_re": float(
+                    eval_results[dataset_parsing_key]["TETA"][4]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_loc_pr": float(
+                    eval_results[dataset_parsing_key]["TETA"][5]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_assoc_re": float(
+                    eval_results[dataset_parsing_key]["TETA"][6]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_assoc_pr": float(
+                    eval_results[dataset_parsing_key]["TETA"][7]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_cls_re": float(
+                    eval_results[dataset_parsing_key]["TETA"][8]
+                ),
+                f"{self.dataset_name}_{'mask' if self.use_mask else 'bbox'}_cls_pr": float(
+                    eval_results[dataset_parsing_key]["TETA"][9]
+                ),
+            }
+
+        # video-NP level results not supported for `VideoTetaEvaluator` yet
+        video_np_level_results = {}
+        return results, video_np_level_results
+
+
+class VideoPhraseHotaEvaluator(BasePredFileEvaluator):
+    """Evaluate Video Phrase HOTA with YT-VIS format prediction and GT files."""
+
+    def __init__(
+        self,
+        gt_ann_file: str,
+        dataset_name: str = "video",
+        prob_thresh: float = 0.5,
+        iou_types: Optional[Sequence[str]] = None,
+        compute_video_mot_hota: bool = False,
+    ):
+        self.gt_ann_file = gt_ann_file
+        self.dataset_name = dataset_name
+        self.prob_thresh = prob_thresh
+        self.metric_prefix = "phrase"
+        # the list of metrics to collect from the HOTA evaluation results
+        self.metric_to_collect = [
+            "HOTA",
+            "DetA",
+            "AssA",
+            "DetRe",
+            "DetPr",
+            "AssRe",
+            "AssPr",
+            "LocA",
+            "OWTA",
+        ]
+        self.iou_types = list(iou_types) if iou_types is not None else ["bbox", "segm"]
+        assert all(iou_type in ["bbox", "segm"] for iou_type in self.iou_types)
+
+        # If True, compute video MOT HOTA, aggregating predictions/GT from all categories.
+        self.compute_video_mot_hota = compute_video_mot_hota
+
+    def evaluate(self, pred_file: str) -> Dict[str, float]:
+        # use the YT-VIS evaluation toolkit in TrackEval
+
+        with open(self.gt_ann_file) as f:
+            gt = json.load(f)
+        with open(pred_file) as f:
+            dt = json.load(f)
+        # keep only predictions with score above the probability threshold
+        dt = [d for d in dt if d["score"] > self.prob_thresh]
+        for d in dt:
+            assert len(d["areas"]) == len(d["bboxes"])
+            assert len(d["areas"]) == len(d["segmentations"])
+            # remove empty boxes (otherwise they will count as false positives for during
+            # per-frame detection accuracy in HOTA evaluation)
+            for t in range(len(d["bboxes"])):
+                bbox = d["bboxes"][t]
+                if d["areas"][t] == 0 or bbox is None or all(x == 0 for x in bbox):
+                    d["segmentations"][t] = None
+                    d["bboxes"][t] = None
+                    d["areas"][t] = None
+            # check that box occurence and mask occurence are consistent
+            for bbox, mask, area in zip(d["bboxes"], d["segmentations"], d["areas"]):
+                assert (area is None) == (bbox is None)
+                assert (area is None) == (mask is None)
+            # set all scores to 1.0 for HOTA evaluation (just like Demo F1, the exact score
+            # value is not used in HOTA metrics; it will be treated as a detection prediction
+            # as long as its score is above the threshold)
+            d["score"] = 1.0
+
+        # remap the GT and DT annotations for phrase HOTA evaluation
+        gt = _fill_in_ann_height_width(gt)
+        if not self.compute_video_mot_hota:
+            # remap the GT and DT annotations for phrase HOTA evaluation
+            gt, dt = self._remap_gt_dt(gt, dt)
+        else:
+            # Compute video-level MOT HOTA
+            # Apply track-level NMS
+            video_groups = defaultdict(list)
+            for pred in dt:
+                video_groups[pred["video_id"]].append(pred)
+            process_track_level_nms(video_groups, nms_threshold=0.5)
+            dt = [track for tracks in video_groups.values() for track in tracks]
+
+            # Remap GT track ids for class-agnostic HOTA
+            gt, dt = remap_gt_dt_class_agnostic(gt, dt)
+
+        # run the HOTA evaluation using TrackEval on the remapped (video_id, category_id) pairs
+        out_dict = {}
+        video_np_level_results = {}
+        for iou_type in self.iou_types:
+            output_res, _ = run_ytvis_eval(
+                args=[
+                    "--METRICS",
+                    "HOTA",
+                    "--IOU_TYPE",
+                    iou_type,
+                    "--DATASET_NAME",
+                    self.dataset_name,
+                    "--USE_PARALLEL",
+                    "True",
+                    "--NUM_PARALLEL_CORES",
+                    "8",
+                    "--PLOT_CURVES",
+                    "False",
+                    "--LOG_ON_ERROR",
+                    "None",
+                    "--PRINT_ONLY_COMBINED",
+                    "True",
+                    "--OUTPUT_SUMMARY",
+                    "False",
+                    "--OUTPUT_DETAILED",
+                    "False",
+                    "--TIME_PROGRESS",
+                    "False",
+                    "--PRINT_CONFIG",
+                    "False",
+                ],
+                gt_json=gt,
+                dt_json=dt,
+            )
+            self.extract_video_np_level_results(
+                iou_type=iou_type,
+                remapped_gt=gt,
+                raw_results=output_res[self.dataset_name]["tracker"],
+                video_np_level_results=video_np_level_results,
+            )
+
+            def _summarize_results(output_res, iou_type, field, suffix):
+                eval_res = output_res[self.dataset_name]["tracker"][field]
+                result_prefix = f"{self.dataset_name}_{'mask' if iou_type == 'segm' else 'bbox'}_{suffix}"
+                for metric_name in self.metric_to_collect:
+                    eval_res_hota = eval_res["cls_comb_cls_av"]["HOTA"]
+                    result_key = f"{result_prefix}_{self.metric_prefix}_{metric_name}"
+                    result_value = float(np.mean(eval_res_hota[metric_name]))
+                    out_dict[result_key] = result_value
+
+            _summarize_results(output_res, iou_type, "COMBINED_SEQ", "all")
+            if "COMBINED_SEQ_CHALLENGING" in output_res[self.dataset_name]["tracker"]:
+                _summarize_results(
+                    output_res, iou_type, "COMBINED_SEQ_CHALLENGING", "challenging"
+                )
+
+        # video-NP level results not supported for `VideoPhraseHotaEvaluator` yet
+        return out_dict, video_np_level_results
+
+    def _remap_gt_dt(self, gt, dt):
+        # For phrase HOTA evaluation, we need to remap each pair of (video_id, category_id) to
+        # a new unique video_id, so that we don't mix detections from different categories
+        gt, dt = remap_video_category_pairs_to_unique_video_ids(gt, dt)
+        # We further map all the categories to category_id=1 in HOTA evaluation toolkit
+        # for phrase HOTA (similar to "useCat=False" for video phrase AP)
+        remapped_category_id = 1
+        gt["categories"] = [
+            {
+                "supercategory": "object",
+                "id": remapped_category_id,
+                "name": "_REMAPPED_FOR_PHRASE_METRICS_",
+            }
+        ]
+        for ann in gt["annotations"]:
+            ann["category_id"] = remapped_category_id
+        for d in dt:
+            d["category_id"] = remapped_category_id
+        # To be compatible with the TrackEval YT-VIS evaluation toolkit, we need to give
+        # unique filenames to each remapped video, so we add remapped video_id as prefix.
+        for video in gt["videos"]:
+            new_video_id = video["id"]
+            video["file_names"] = [
+                f"remapped_vid_{new_video_id:012d}/{name}"
+                for name in video["file_names"]
+            ]
+        return gt, dt
+
+    def extract_video_np_level_results(
+        self, iou_type, remapped_gt, raw_results, video_np_level_results
+    ):
+        """Aggregate statistics for video-level metrics."""
+        result_prefix = "mask" if iou_type == "segm" else "bbox"
+        for video in remapped_gt["videos"]:
+            # the original video id and category id before remapping
+            video_id = video["orig_video_id"]
+            category_id = video["orig_category_id"]
+            video_key = f"remapped_vid_{video['id']:012d}"
+            results = raw_results[video_key]["_REMAPPED_FOR_PHRASE_METRICS_"]["HOTA"]
+
+            local_results = {}
+            for metric_name in self.metric_to_collect:
+                result_key = f"{result_prefix}_{metric_name}"
+                local_results[result_key] = float(results[metric_name].mean())
+            if (video_id, category_id) not in video_np_level_results:
+                video_np_level_results[(video_id, category_id)] = {}
+            video_np_level_results[(video_id, category_id)].update(local_results)
+
+
+class VideoClassBasedHotaEvaluator(VideoPhraseHotaEvaluator):
+    def __init__(
+        self,
+        gt_ann_file: str,
+        dataset_name: str = "video",
+        prob_thresh: float = 0.5,
+    ):
+        super().__init__(gt_ann_file, dataset_name, prob_thresh)
+        self.metric_prefix = "class"
+
+    def _remap_gt_dt(self, gt, dt):
+        return gt, dt  # no remapping needed for class-based HOTA evaluation
+
+    def extract_video_np_level_results(self, *args, **kwargs):
+        pass  # no video-NP level results for class-based HOTA evaluation
+
+
+def _compress_rle(rle):
+    """Convert RLEs from uncompressed (integer list) to compressed (string) format."""
+    if rle is None:
+        return None
+    if isinstance(rle["counts"], list):
+        rle = pycocotools.mask.frPyObjects(rle, rle["size"][0], rle["size"][1])
+        rle["counts"] = rle["counts"].decode()
+    return rle
+
+
+def remap_video_category_pairs_to_unique_video_ids(
+    gt_json, dt_json, add_negative_np_pairs=False
+):
+    """
+    Remap each pair of (video_id, category_id) to a new unique video_id. This is useful
+    for phrase AP and demo F1 evaluation on videos, where we have `useCat=False` and
+    rely on separating different NPs (from the same video) into different new video ids,
+    so that we don't mix detections from different categories in computeIoU under `useCat=False`.
+
+    This is consistent with how do we phrase AP and demo F1 evaluation on images, where we
+    use a remapped unique coco_image_id for each image-NP pair (based in its query["id"] in
+    CustomCocoDetectionAPI.load_queries in modulated_detection_api.py)
+    """
+    # collect the unique video_id-category_id pairs
+    video_id_to_video = {v["id"]: v for v in gt_json["videos"]}
+    video_id_category_id_pairs = set()
+    for pred in dt_json:
+        video_id_category_id_pairs.add((pred["video_id"], pred["category_id"]))
+    for ann in gt_json["annotations"]:
+        video_id_category_id_pairs.add((ann["video_id"], ann["category_id"]))
+
+    # assign the video_id-category_id pairs to unique video ids
+    video_id_category_id_pairs = sorted(video_id_category_id_pairs)
+    video_id_category_id_to_new_video_id = {
+        pair: (i + 1) for i, pair in enumerate(video_id_category_id_pairs)
+    }
+    # also map the negative NP pairs -- this is needed for IL_MCC and CG-F1 evaluation
+    if add_negative_np_pairs:
+        for vnp in gt_json["video_np_pairs"]:
+            pair = (vnp["video_id"], vnp["category_id"])
+            if pair not in video_id_category_id_to_new_video_id:
+                video_id_category_id_to_new_video_id[pair] = (
+                    len(video_id_category_id_to_new_video_id) + 1
+                )
+
+    # map the "video_id" in predictions
+    for pred in dt_json:
+        pred["video_id"] = video_id_category_id_to_new_video_id[
+            (pred["video_id"], pred["category_id"])
+        ]
+    # map the "video_id" in gt_json["annotations"]
+    for ann in gt_json["annotations"]:
+        ann["video_id"] = video_id_category_id_to_new_video_id[
+            (ann["video_id"], ann["category_id"])
+        ]
+    # map and duplicate gt_json["videos"]
+    new_videos = []
+    for (
+        video_id,
+        category_id,
+    ), new_video_id in video_id_category_id_to_new_video_id.items():
+        video = video_id_to_video[video_id].copy()
+        video["id"] = new_video_id
+        # preserve the original video_id and category_id of each remapped video entry,
+        # so that we can associate sample-level eval metrics with the original video-NP pairs
+        video["orig_video_id"] = video_id
+        video["orig_category_id"] = category_id
+        new_videos.append(video)
+    gt_json["videos"] = new_videos
+
+    return gt_json, dt_json
+
+
+def remap_gt_dt_class_agnostic(gt, dt):
+    """
+    For class-agnostic HOTA, merge all GT tracks for each video (across NPs),
+    ensure unique track_ids, and set all category_id to 1.
+    Also, add orig_video_id and orig_category_id for compatibility.
+    """
+    # 1. Remap all GT track_ids to be unique per video
+    gt_anns_by_video = defaultdict(list)
+    for ann in gt["annotations"]:
+        gt_anns_by_video[ann["video_id"]].append(ann)
+
+    # Ensure unique track ids across tracks of all videos
+    next_tid = 1
+    for _, anns in gt_anns_by_video.items():
+        # Map old track_ids to new unique ones
+        old_to_new_tid = {}
+        for ann in anns:
+            old_tid = ann["id"]
+            if old_tid not in old_to_new_tid:
+                old_to_new_tid[old_tid] = next_tid
+                next_tid += 1
+            ann["id"] = old_to_new_tid[old_tid]
+            # Set category_id to 1 for class-agnostic
+            ann["category_id"] = 1
+
+    # Set all GT categories to a single category
+    gt["categories"] = [
+        {
+            "supercategory": "object",
+            "id": 1,
+            "name": "_REMAPPED_FOR_PHRASE_METRICS_",
+        }
+    ]
+
+    # Add orig_video_id and orig_category_id to each video for compatibility
+    anns_by_video = defaultdict(list)
+    for ann in gt["annotations"]:
+        anns_by_video[ann["video_id"]].append(ann)
+    for video in gt["videos"]:
+        video["orig_video_id"] = video["id"]
+        # Use the first annotation's original category_id if available, else None
+        orig_cat = (
+            anns_by_video[video["id"]][0]["category_id"]
+            if anns_by_video[video["id"]]
+            else None
+        )
+        video["orig_category_id"] = orig_cat
+        video["file_names"] = [
+            f"remapped_vid_{video['id']:012d}/{name}" for name in video["file_names"]
+        ]
+
+    # Set all DT category_id to 1
+    for d in dt:
+        d["category_id"] = 1
+    return gt, dt
+
+
+def _fill_in_ann_height_width(gt_json):
+    """Fill in missing height/width in GT annotations from its video info."""
+    video_id_to_video = {v["id"]: v for v in gt_json["videos"]}
+    for ann in gt_json["annotations"]:
+        if "height" not in ann or "width" not in ann:
+            video = video_id_to_video[ann["video_id"]]
+            if "height" not in ann:
+                ann["height"] = video["height"]
+            if "width" not in ann:
+                ann["width"] = video["width"]
+
+    return gt_json

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/__init__.py

@@ -0,0 +1,7 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+from . import config, datasets, metrics, utils
+from .eval import Evaluator

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/_timing.py

@@ -0,0 +1,71 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+import inspect
+from functools import wraps
+from time import perf_counter
+
+DO_TIMING = False
+DISPLAY_LESS_PROGRESS = False
+timer_dict = {}
+counter = 0
+
+
+def time(f):
+    @wraps(f)
+    def wrap(*args, **kw):
+        if DO_TIMING:
+            # Run function with timing
+            ts = perf_counter()
+            result = f(*args, **kw)
+            te = perf_counter()
+            tt = te - ts
+
+            # Get function name
+            arg_names = inspect.getfullargspec(f)[0]
+            if arg_names[0] == "self" and DISPLAY_LESS_PROGRESS:
+                return result
+            elif arg_names[0] == "self":
+                method_name = type(args[0]).__name__ + "." + f.__name__
+            else:
+                method_name = f.__name__
+
+            # Record accumulative time in each function for analysis
+            if method_name in timer_dict.keys():
+                timer_dict[method_name] += tt
+            else:
+                timer_dict[method_name] = tt
+
+            # If code is finished, display timing summary
+            if method_name == "Evaluator.evaluate":
+                print("")
+                print("Timing analysis:")
+                for key, value in timer_dict.items():
+                    print("%-70s %2.4f sec" % (key, value))
+            else:
+                # Get function argument values for printing special arguments of interest
+                arg_titles = ["tracker", "seq", "cls"]
+                arg_vals = []
+                for i, a in enumerate(arg_names):
+                    if a in arg_titles:
+                        arg_vals.append(args[i])
+                arg_text = "(" + ", ".join(arg_vals) + ")"
+
+                # Display methods and functions with different indentation.
+                if arg_names[0] == "self":
+                    print("%-74s %2.4f sec" % (" " * 4 + method_name + arg_text, tt))
+                elif arg_names[0] == "test":
+                    pass
+                else:
+                    global counter
+                    counter += 1
+                    print("%i %-70s %2.4f sec" % (counter, method_name + arg_text, tt))
+
+            return result
+        else:
+            # If config["TIME_PROGRESS"] is false, or config["USE_PARALLEL"] is true, run functions normally without timing.
+            return f(*args, **kw)
+
+    return wrap

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/config.py

@@ -0,0 +1,155 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+"""Config."""
+import argparse
+import os
+
+
+def parse_configs():
+    """Parse command line."""
+    default_eval_config = get_default_eval_config()
+    default_eval_config["DISPLAY_LESS_PROGRESS"] = True
+    default_dataset_config = get_default_dataset_config()
+    default_metrics_config = {"METRICS": ["TETA"]}
+    config = {
+        **default_eval_config,
+        **default_dataset_config,
+        **default_metrics_config,
+    }
+    parser = argparse.ArgumentParser()
+    for setting in config.keys():
+        if type(config[setting]) == list or type(config[setting]) == type(None):
+            parser.add_argument("--" + setting, nargs="+")
+        else:
+            parser.add_argument("--" + setting)
+    args = parser.parse_args().__dict__
+    for setting in args.keys():
+        if args[setting] is not None:
+            if type(config[setting]) == type(True):
+                if args[setting] == "True":
+                    x = True
+                elif args[setting] == "False":
+                    x = False
+                else:
+                    raise Exception(
+                        f"Command line parameter {setting} must be True/False"
+                    )
+            elif type(config[setting]) == type(1):
+                x = int(args[setting])
+            elif type(args[setting]) == type(None):
+                x = None
+            else:
+                x = args[setting]
+            config[setting] = x
+    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}
+    dataset_config = {
+        k: v for k, v in config.items() if k in default_dataset_config.keys()
+    }
+    metrics_config = {
+        k: v for k, v in config.items() if k in default_metrics_config.keys()
+    }
+
+    return eval_config, dataset_config, metrics_config
+
+
+def get_default_eval_config():
+    """Returns the default config values for evaluation."""
+    code_path = get_code_path()
+    default_config = {
+        "USE_PARALLEL": True,
+        "NUM_PARALLEL_CORES": 8,
+        "BREAK_ON_ERROR": True,
+        "RETURN_ON_ERROR": False,
+        "LOG_ON_ERROR": os.path.join(code_path, "error_log.txt"),
+        "PRINT_RESULTS": True,
+        "PRINT_ONLY_COMBINED": True,
+        "PRINT_CONFIG": True,
+        "TIME_PROGRESS": True,
+        "DISPLAY_LESS_PROGRESS": True,
+        "OUTPUT_SUMMARY": True,
+        "OUTPUT_EMPTY_CLASSES": True,
+        "OUTPUT_TEM_RAW_DATA": True,
+        "OUTPUT_PER_SEQ_RES": True,
+    }
+    return default_config
+
+
+def get_default_dataset_config():
+    """Default class config values"""
+    code_path = get_code_path()
+    default_config = {
+        "GT_FOLDER": os.path.join(
+            code_path, "data/gt/tao/tao_training"
+        ),  # Location of GT data
+        "TRACKERS_FOLDER": os.path.join(
+            code_path, "data/trackers/tao/tao_training"
+        ),  # Trackers location
+        "OUTPUT_FOLDER": None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
+        "TRACKERS_TO_EVAL": ['TETer'],  # Filenames of trackers to eval (if None, all in folder)
+        "CLASSES_TO_EVAL": None,  # Classes to eval (if None, all classes)
+        "SPLIT_TO_EVAL": "training",  # Valid: 'training', 'val'
+        "PRINT_CONFIG": True,  # Whether to print current config
+        "TRACKER_SUB_FOLDER": "data",  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
+        "OUTPUT_SUB_FOLDER": "",  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
+        "TRACKER_DISPLAY_NAMES": None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
+        "MAX_DETECTIONS": 0,  # Number of maximal allowed detections per image (0 for unlimited)
+        "USE_MASK": False,  # Whether to use mask data for evaluation
+    }
+    return default_config
+
+
+def init_config(config, default_config, name=None):
+    """Initialize non-given config values with defaults."""
+    if config is None:
+        config = default_config
+    else:
+        for k in default_config.keys():
+            if k not in config.keys():
+                config[k] = default_config[k]
+    if name and config["PRINT_CONFIG"]:
+        print("\n%s Config:" % name)
+        for c in config.keys():
+            print("%-20s : %-30s" % (c, config[c]))
+    return config
+
+
+def update_config(config):
+    """
+    Parse the arguments of a script and updates the config values for a given value if specified in the arguments.
+    :param config: the config to update
+    :return: the updated config
+    """
+    parser = argparse.ArgumentParser()
+    for setting in config.keys():
+        if type(config[setting]) == list or type(config[setting]) == type(None):
+            parser.add_argument("--" + setting, nargs="+")
+        else:
+            parser.add_argument("--" + setting)
+    args = parser.parse_args().__dict__
+    for setting in args.keys():
+        if args[setting] is not None:
+            if type(config[setting]) == type(True):
+                if args[setting] == "True":
+                    x = True
+                elif args[setting] == "False":
+                    x = False
+                else:
+                    raise Exception(
+                        "Command line parameter " + setting + "must be True or False"
+                    )
+            elif type(config[setting]) == type(1):
+                x = int(args[setting])
+            elif type(args[setting]) == type(None):
+                x = None
+            else:
+                x = args[setting]
+            config[setting] = x
+    return config
+
+
+def get_code_path():
+    """Get base path where code is"""
+    return os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/datasets/__init__.py

@@ -0,0 +1,7 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+"""Datasets."""
+from .coco import COCO
+from .tao import TAO

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/datasets/_base_dataset.py

@@ -0,0 +1,381 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+import csv
+import io
+import os
+import traceback
+import zipfile
+from abc import ABC, abstractmethod
+from copy import deepcopy
+
+import numpy as np
+
+from .. import _timing
+from ..utils import TrackEvalException
+
+
+class _BaseDataset(ABC):
+    @abstractmethod
+    def __init__(self):
+        self.tracker_list = None
+        self.seq_list = None
+        self.class_list = None
+        self.output_fol = None
+        self.output_sub_fol = None
+        self.should_classes_combine = True
+        self.use_super_categories = False
+
+    # Functions to implement:
+
+    @abstractmethod
+    def _load_raw_file(self, tracker, seq, is_gt):
+        ...
+
+    @_timing.time
+    @abstractmethod
+    def get_preprocessed_seq_data(self, raw_data, cls):
+        ...
+
+    @abstractmethod
+    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
+        ...
+
+    # Helper functions for all datasets:
+
+    @classmethod
+    def get_class_name(cls):
+        return cls.__name__
+
+    def get_name(self):
+        return self.get_class_name()
+
+    def get_output_fol(self, tracker):
+        return os.path.join(self.output_fol, tracker, self.output_sub_fol)
+
+    def get_display_name(self, tracker):
+        """Can be overwritten if the trackers name (in files) is different to how it should be displayed.
+        By default this method just returns the trackers name as is.
+        """
+        return tracker
+
+    def get_eval_info(self):
+        """Return info about the dataset needed for the Evaluator"""
+        return self.tracker_list, self.seq_list, self.class_list
+
+    @_timing.time
+    def get_raw_seq_data(self, tracker, seq):
+        """Loads raw data (tracker and ground-truth) for a single tracker on a single sequence.
+        Raw data includes all of the information needed for both preprocessing and evaluation, for all classes.
+        A later function (get_processed_seq_data) will perform such preprocessing and extract relevant information for
+        the evaluation of each class.
+
+        This returns a dict which contains the fields:
+        [num_timesteps]: integer
+        [gt_ids, tracker_ids, gt_classes, tracker_classes, tracker_confidences]:
+                                                                list (for each timestep) of 1D NDArrays (for each det).
+        [gt_dets, tracker_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.
+        [similarity_scores]: list (for each timestep) of 2D NDArrays.
+        [gt_extras]: dict (for each extra) of lists (for each timestep) of 1D NDArrays (for each det).
+
+        gt_extras contains dataset specific information used for preprocessing such as occlusion and truncation levels.
+
+        Note that similarities are extracted as part of the dataset and not the metric, because almost all metrics are
+        independent of the exact method of calculating the similarity. However datasets are not (e.g. segmentation
+        masks vs 2D boxes vs 3D boxes).
+        We calculate the similarity before preprocessing because often both preprocessing and evaluation require it and
+        we don't wish to calculate this twice.
+        We calculate similarity between all gt and tracker classes (not just each class individually) to allow for
+        calculation of metrics such as class confusion matrices. Typically the impact of this on performance is low.
+        """
+        # Load raw data.
+        raw_gt_data = self._load_raw_file(tracker, seq, is_gt=True)
+        raw_tracker_data = self._load_raw_file(tracker, seq, is_gt=False)
+        raw_data = {**raw_tracker_data, **raw_gt_data}  # Merges dictionaries
+
+        # Calculate similarities for each timestep.
+        similarity_scores = []
+        for _, (gt_dets_t, tracker_dets_t) in enumerate(
+            zip(raw_data["gt_dets"], raw_data["tk_dets"])
+        ):
+            ious = self._calculate_similarities(gt_dets_t, tracker_dets_t)
+            similarity_scores.append(ious)
+        raw_data["similarity_scores"] = similarity_scores
+        return raw_data
+
+    @staticmethod
+    def _load_simple_text_file(
+        file,
+        time_col=0,
+        id_col=None,
+        remove_negative_ids=False,
+        valid_filter=None,
+        crowd_ignore_filter=None,
+        convert_filter=None,
+        is_zipped=False,
+        zip_file=None,
+        force_delimiters=None,
+    ):
+        """Function that loads data which is in a commonly used text file format.
+        Assumes each det is given by one row of a text file.
+        There is no limit to the number or meaning of each column,
+        however one column needs to give the timestep of each det (time_col) which is default col 0.
+
+        The file dialect (deliminator, num cols, etc) is determined automatically.
+        This function automatically separates dets by timestep,
+        and is much faster than alternatives such as np.loadtext or pandas.
+
+        If remove_negative_ids is True and id_col is not None, dets with negative values in id_col are excluded.
+        These are not excluded from ignore data.
+
+        valid_filter can be used to only include certain classes.
+        It is a dict with ints as keys, and lists as values,
+        such that a row is included if "row[key].lower() is in value" for all key/value pairs in the dict.
+        If None, all classes are included.
+
+        crowd_ignore_filter can be used to read crowd_ignore regions separately. It has the same format as valid filter.
+
+        convert_filter can be used to convert value read to another format.
+        This is used most commonly to convert classes given as string to a class id.
+        This is a dict such that the key is the column to convert, and the value is another dict giving the mapping.
+
+        Optionally, input files could be a zip of multiple text files for storage efficiency.
+
+        Returns read_data and ignore_data.
+        Each is a dict (with keys as timesteps as strings) of lists (over dets) of lists (over column values).
+        Note that all data is returned as strings, and must be converted to float/int later if needed.
+        Note that timesteps will not be present in the returned dict keys if there are no dets for them
+        """
+
+        if remove_negative_ids and id_col is None:
+            raise TrackEvalException(
+                "remove_negative_ids is True, but id_col is not given."
+            )
+        if crowd_ignore_filter is None:
+            crowd_ignore_filter = {}
+        if convert_filter is None:
+            convert_filter = {}
+        try:
+            if is_zipped:  # Either open file directly or within a zip.
+                if zip_file is None:
+                    raise TrackEvalException(
+                        "is_zipped set to True, but no zip_file is given."
+                    )
+                archive = zipfile.ZipFile(os.path.join(zip_file), "r")
+                fp = io.TextIOWrapper(archive.open(file, "r"))
+            else:
+                fp = open(file)
+            read_data = {}
+            crowd_ignore_data = {}
+            fp.seek(0, os.SEEK_END)
+            # check if file is empty
+            if fp.tell():
+                fp.seek(0)
+                dialect = csv.Sniffer().sniff(
+                    fp.readline(), delimiters=force_delimiters
+                )  # Auto determine structure.
+                dialect.skipinitialspace = (
+                    True  # Deal with extra spaces between columns
+                )
+                fp.seek(0)
+                reader = csv.reader(fp, dialect)
+                for row in reader:
+                    try:
+                        # Deal with extra trailing spaces at the end of rows
+                        if row[-1] in "":
+                            row = row[:-1]
+                        timestep = str(int(float(row[time_col])))
+                        # Read ignore regions separately.
+                        is_ignored = False
+                        for ignore_key, ignore_value in crowd_ignore_filter.items():
+                            if row[ignore_key].lower() in ignore_value:
+                                # Convert values in one column (e.g. string to id)
+                                for (
+                                    convert_key,
+                                    convert_value,
+                                ) in convert_filter.items():
+                                    row[convert_key] = convert_value[
+                                        row[convert_key].lower()
+                                    ]
+                                # Save data separated by timestep.
+                                if timestep in crowd_ignore_data.keys():
+                                    crowd_ignore_data[timestep].append(row)
+                                else:
+                                    crowd_ignore_data[timestep] = [row]
+                                is_ignored = True
+                        if (
+                            is_ignored
+                        ):  # if det is an ignore region, it cannot be a normal det.
+                            continue
+                        # Exclude some dets if not valid.
+                        if valid_filter is not None:
+                            for key, value in valid_filter.items():
+                                if row[key].lower() not in value:
+                                    continue
+                        if remove_negative_ids:
+                            if int(float(row[id_col])) < 0:
+                                continue
+                        # Convert values in one column (e.g. string to id)
+                        for convert_key, convert_value in convert_filter.items():
+                            row[convert_key] = convert_value[row[convert_key].lower()]
+                        # Save data separated by timestep.
+                        if timestep in read_data.keys():
+                            read_data[timestep].append(row)
+                        else:
+                            read_data[timestep] = [row]
+                    except Exception:
+                        exc_str_init = (
+                            "In file %s the following line cannot be read correctly: \n"
+                            % os.path.basename(file)
+                        )
+                        exc_str = " ".join([exc_str_init] + row)
+                        raise TrackEvalException(exc_str)
+            fp.close()
+        except Exception:
+            print("Error loading file: %s, printing traceback." % file)
+            traceback.print_exc()
+            raise TrackEvalException(
+                "File %s cannot be read because it is either not present or invalidly formatted"
+                % os.path.basename(file)
+            )
+        return read_data, crowd_ignore_data
+
+    @staticmethod
+    def _calculate_mask_ious(masks1, masks2, is_encoded=False, do_ioa=False):
+        """Calculates the IOU (intersection over union) between two arrays of segmentation masks.
+        If is_encoded a run length encoding with pycocotools is assumed as input format, otherwise an input of numpy
+        arrays of the shape (num_masks, height, width) is assumed and the encoding is performed.
+        If do_ioa (intersection over area) , then calculates the intersection over the area of masks1 - this is commonly
+        used to determine if detections are within crowd ignore region.
+        :param masks1:  first set of masks (numpy array of shape (num_masks, height, width) if not encoded,
+                        else pycocotools rle encoded format)
+        :param masks2:  second set of masks (numpy array of shape (num_masks, height, width) if not encoded,
+                        else pycocotools rle encoded format)
+        :param is_encoded: whether the input is in pycocotools rle encoded format
+        :param do_ioa: whether to perform IoA computation
+        :return: the IoU/IoA scores
+        """
+
+        # Only loaded when run to reduce minimum requirements
+        from pycocotools import mask as mask_utils
+
+        # use pycocotools for run length encoding of masks
+        if not is_encoded:
+            masks1 = mask_utils.encode(
+                np.array(np.transpose(masks1, (1, 2, 0)), order="F")
+            )
+            masks2 = mask_utils.encode(
+                np.array(np.transpose(masks2, (1, 2, 0)), order="F")
+            )
+
+        # use pycocotools for iou computation of rle encoded masks
+        ious = mask_utils.iou(masks1, masks2, [do_ioa] * len(masks2))
+        if len(masks1) == 0 or len(masks2) == 0:
+            ious = np.asarray(ious).reshape(len(masks1), len(masks2))
+        assert (ious >= 0 - np.finfo("float").eps).all()
+        assert (ious <= 1 + np.finfo("float").eps).all()
+
+        return ious
+
+    @staticmethod
+    def _calculate_box_ious(bboxes1, bboxes2, box_format="xywh", do_ioa=False):
+        """Calculates the IOU (intersection over union) between two arrays of boxes.
+        Allows variable box formats ('xywh' and 'x0y0x1y1').
+        If do_ioa (intersection over area) , then calculates the intersection over the area of boxes1 - this is commonly
+        used to determine if detections are within crowd ignore region.
+        """
+        if box_format in "xywh":
+            # layout: (x0, y0, w, h)
+            bboxes1 = deepcopy(bboxes1)
+            bboxes2 = deepcopy(bboxes2)
+
+            bboxes1[:, 2] = bboxes1[:, 0] + bboxes1[:, 2]
+            bboxes1[:, 3] = bboxes1[:, 1] + bboxes1[:, 3]
+            bboxes2[:, 2] = bboxes2[:, 0] + bboxes2[:, 2]
+            bboxes2[:, 3] = bboxes2[:, 1] + bboxes2[:, 3]
+        elif box_format not in "x0y0x1y1":
+            raise (TrackEvalException("box_format %s is not implemented" % box_format))
+
+        # layout: (x0, y0, x1, y1)
+        min_ = np.minimum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])
+        max_ = np.maximum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :])
+        intersection = np.maximum(min_[..., 2] - max_[..., 0], 0) * np.maximum(
+            min_[..., 3] - max_[..., 1], 0
+        )
+        area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (
+            bboxes1[..., 3] - bboxes1[..., 1]
+        )
+
+        if do_ioa:
+            ioas = np.zeros_like(intersection)
+            valid_mask = area1 > 0 + np.finfo("float").eps
+            ioas[valid_mask, :] = (
+                intersection[valid_mask, :] / area1[valid_mask][:, np.newaxis]
+            )
+
+            return ioas
+        else:
+            area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (
+                bboxes2[..., 3] - bboxes2[..., 1]
+            )
+            union = area1[:, np.newaxis] + area2[np.newaxis, :] - intersection
+            intersection[area1 <= 0 + np.finfo("float").eps, :] = 0
+            intersection[:, area2 <= 0 + np.finfo("float").eps] = 0
+            intersection[union <= 0 + np.finfo("float").eps] = 0
+            union[union <= 0 + np.finfo("float").eps] = 1
+            ious = intersection / union
+            return ious
+
+    @staticmethod
+    def _calculate_euclidean_similarity(dets1, dets2, zero_distance=2.0):
+        """Calculates the euclidean distance between two sets of detections, and then converts this into a similarity
+        measure with values between 0 and 1 using the following formula: sim = max(0, 1 - dist/zero_distance).
+        The default zero_distance of 2.0, corresponds to the default used in MOT15_3D, such that a 0.5 similarity
+        threshold corresponds to a 1m distance threshold for TPs.
+        """
+        dist = np.linalg.norm(dets1[:, np.newaxis] - dets2[np.newaxis, :], axis=2)
+        sim = np.maximum(0, 1 - dist / zero_distance)
+        return sim
+
+    @staticmethod
+    def _check_unique_ids(data, after_preproc=False):
+        """Check the requirement that the tracker_ids and gt_ids are unique per timestep"""
+        gt_ids = data["gt_ids"]
+        tracker_ids = data["tk_ids"]
+        for t, (gt_ids_t, tracker_ids_t) in enumerate(zip(gt_ids, tracker_ids)):
+            if len(tracker_ids_t) > 0:
+                unique_ids, counts = np.unique(tracker_ids_t, return_counts=True)
+                if np.max(counts) != 1:
+                    duplicate_ids = unique_ids[counts > 1]
+                    exc_str_init = (
+                        "Tracker predicts the same ID more than once in a single timestep "
+                        "(seq: %s, frame: %i, ids:" % (data["seq"], t + 1)
+                    )
+                    exc_str = (
+                        " ".join([exc_str_init] + [str(d) for d in duplicate_ids]) + ")"
+                    )
+                    if after_preproc:
+                        exc_str_init += (
+                            "\n Note that this error occurred after preprocessing (but not before), "
+                            "so ids may not be as in file, and something seems wrong with preproc."
+                        )
+                    raise TrackEvalException(exc_str)
+            if len(gt_ids_t) > 0:
+                unique_ids, counts = np.unique(gt_ids_t, return_counts=True)
+                if np.max(counts) != 1:
+                    duplicate_ids = unique_ids[counts > 1]
+                    exc_str_init = (
+                        "Ground-truth has the same ID more than once in a single timestep "
+                        "(seq: %s, frame: %i, ids:" % (data["seq"], t + 1)
+                    )
+                    exc_str = (
+                        " ".join([exc_str_init] + [str(d) for d in duplicate_ids]) + ")"
+                    )
+                    if after_preproc:
+                        exc_str_init += (
+                            "\n Note that this error occurred after preprocessing (but not before), "
+                            "so ids may not be as in file, and something seems wrong with preproc."
+                        )
+                    raise TrackEvalException(exc_str)

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/datasets/coco.py

@@ -0,0 +1,639 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+"""COCO Dataset."""
+import copy
+import itertools
+import json
+import os
+from collections import defaultdict
+
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+
+from .. import _timing, utils
+from ..config import get_default_dataset_config, init_config
+from ..utils import TrackEvalException
+from ._base_dataset import _BaseDataset
+
+
+class COCO(_BaseDataset):
+    """Tracking datasets in COCO format."""
+
+    def __init__(self, config=None):
+        """Initialize dataset, checking that all required files are present."""
+        super().__init__()
+        # Fill non-given config values with defaults
+        self.config = init_config(config, get_default_dataset_config(), self.get_name())
+        self.gt_fol = self.config["GT_FOLDER"]
+        self.tracker_fol = self.config["TRACKERS_FOLDER"]
+        self.should_classes_combine = True
+        self.use_super_categories = False
+        self.use_mask = self.config["USE_MASK"]
+
+        self.tracker_sub_fol = self.config["TRACKER_SUB_FOLDER"]
+        self.output_fol = self.config["OUTPUT_FOLDER"]
+        if self.output_fol is None:
+            self.output_fol = self.tracker_fol
+        self.output_sub_fol = self.config["OUTPUT_SUB_FOLDER"]
+
+        if self.gt_fol.endswith(".json"):
+            self.gt_data = json.load(open(self.gt_fol, "r"))
+        else:
+            gt_dir_files = [
+                file for file in os.listdir(self.gt_fol) if file.endswith(".json")
+            ]
+            if len(gt_dir_files) != 1:
+                raise TrackEvalException(
+                    f"{self.gt_fol} does not contain exactly one json file."
+                )
+
+            with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:
+                self.gt_data = json.load(f)
+
+        # fill missing video ids
+        self._fill_video_ids_inplace(self.gt_data["annotations"])
+
+        # get sequences to eval and sequence information
+        self.seq_list = [
+            vid["name"].replace("/", "-") for vid in self.gt_data["videos"]
+        ]
+        self.seq_name2seqid = {
+            vid["name"].replace("/", "-"): vid["id"] for vid in self.gt_data["videos"]
+        }
+        # compute mappings from videos to annotation data
+        self.video2gt_track, self.video2gt_image = self._compute_vid_mappings(
+            self.gt_data["annotations"]
+        )
+        # compute sequence lengths
+        self.seq_lengths = {vid["id"]: 0 for vid in self.gt_data["videos"]}
+        for img in self.gt_data["images"]:
+            self.seq_lengths[img["video_id"]] += 1
+        self.seq2images2timestep = self._compute_image_to_timestep_mappings()
+        self.seq2cls = {
+            vid["id"]: {
+                "pos_cat_ids": list(
+                    {track["category_id"] for track in self.video2gt_track[vid["id"]]}
+                ),
+            }
+            for vid in self.gt_data["videos"]
+        }
+
+        # Get classes to eval
+        considered_vid_ids = [self.seq_name2seqid[vid] for vid in self.seq_list]
+        seen_cats = set(
+            [
+                cat_id
+                for vid_id in considered_vid_ids
+                for cat_id in self.seq2cls[vid_id]["pos_cat_ids"]
+            ]
+        )
+        # only classes with ground truth are evaluated in TAO
+        self.valid_classes = [
+            cls["name"] for cls in self.gt_data["categories"] if cls["id"] in seen_cats
+        ]
+        cls_name2clsid_map = {
+            cls["name"]: cls["id"] for cls in self.gt_data["categories"]
+        }
+
+        if self.config["CLASSES_TO_EVAL"]:
+            self.class_list = [
+                cls.lower() if cls.lower() in self.valid_classes else None
+                for cls in self.config["CLASSES_TO_EVAL"]
+            ]
+            if not all(self.class_list):
+                valid_cls = ", ".join(self.valid_classes)
+                raise TrackEvalException(
+                    "Attempted to evaluate an invalid class. Only classes "
+                    f"{valid_cls} are valid (classes present in ground truth"
+                    " data)."
+                )
+        else:
+            self.class_list = [cls for cls in self.valid_classes]
+        self.cls_name2clsid = {
+            k: v for k, v in cls_name2clsid_map.items() if k in self.class_list
+        }
+        self.clsid2cls_name = {
+            v: k for k, v in cls_name2clsid_map.items() if k in self.class_list
+        }
+        # get trackers to eval
+        if self.config["TRACKERS_TO_EVAL"] is None:
+            self.tracker_list = os.listdir(self.tracker_fol)
+        else:
+            self.tracker_list = self.config["TRACKERS_TO_EVAL"]
+
+        if self.config["TRACKER_DISPLAY_NAMES"] is None:
+            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
+        elif (self.config["TRACKERS_TO_EVAL"] is not None) and (
+            len(self.config["TK_DISPLAY_NAMES"]) == len(self.tracker_list)
+        ):
+            self.tracker_to_disp = dict(
+                zip(self.tracker_list, self.config["TK_DISPLAY_NAMES"])
+            )
+        else:
+            raise TrackEvalException(
+                "List of tracker files and tracker display names do not match."
+            )
+
+        self.tracker_data = {tracker: dict() for tracker in self.tracker_list}
+
+        for tracker in self.tracker_list:
+            if self.tracker_sub_fol.endswith(".json"):
+                with open(os.path.join(self.tracker_sub_fol)) as f:
+                    curr_data = json.load(f)
+            else:
+                tr_dir = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)
+                tr_dir_files = [
+                    file for file in os.listdir(tr_dir) if file.endswith(".json")
+                ]
+                if len(tr_dir_files) != 1:
+                    raise TrackEvalException(
+                        f"{tr_dir} does not contain exactly one json file."
+                    )
+                with open(os.path.join(tr_dir, tr_dir_files[0])) as f:
+                    curr_data = json.load(f)
+
+            # limit detections if MAX_DETECTIONS > 0
+            if self.config["MAX_DETECTIONS"]:
+                curr_data = self._limit_dets_per_image(curr_data)
+
+            # fill missing video ids
+            self._fill_video_ids_inplace(curr_data)
+
+            # make track ids unique over whole evaluation set
+            self._make_tk_ids_unique(curr_data)
+
+            # get tracker sequence information
+            curr_vids2tracks, curr_vids2images = self._compute_vid_mappings(curr_data)
+            self.tracker_data[tracker]["vids_to_tracks"] = curr_vids2tracks
+            self.tracker_data[tracker]["vids_to_images"] = curr_vids2images
+
+    def get_display_name(self, tracker):
+        return self.tracker_to_disp[tracker]
+
+    def _load_raw_file(self, tracker, seq, is_gt):
+        """Load a file (gt or tracker) in the TAO format
+
+        If is_gt, this returns a dict which contains the fields:
+        [gt_ids, gt_classes]:
+            list (for each timestep) of 1D NDArrays (for each det).
+        [gt_dets]: list (for each timestep) of lists of detections.
+
+        if not is_gt, this returns a dict which contains the fields:
+        [tk_ids, tk_classes]:
+            list (for each timestep) of 1D NDArrays (for each det).
+        [tk_dets]: list (for each timestep) of lists of detections.
+        """
+        seq_id = self.seq_name2seqid[seq]
+        # file location
+        if is_gt:
+            imgs = self.video2gt_image[seq_id]
+        else:
+            imgs = self.tracker_data[tracker]["vids_to_images"][seq_id]
+
+        # convert data to required format
+        num_timesteps = self.seq_lengths[seq_id]
+        img_to_timestep = self.seq2images2timestep[seq_id]
+        data_keys = ["ids", "classes", "dets"]
+        # if not is_gt:
+        #     data_keys += ["tk_confidences"]
+        raw_data = {key: [None] * num_timesteps for key in data_keys}
+        for img in imgs:
+            # some tracker data contains images without any ground truth info,
+            # these are ignored
+            if img["id"] not in img_to_timestep:
+                continue
+            t = img_to_timestep[img["id"]]
+            anns = img["annotations"]
+            tk_str = utils.get_track_id_str(anns[0])
+
+            if self.use_mask:
+                # When using mask, extract segmentation data
+                raw_data["dets"][t] = [ann.get("segmentation") for ann in anns]
+            else:
+                # When using bbox, extract bbox data
+                raw_data["dets"][t] = np.atleast_2d([ann["bbox"] for ann in anns]).astype(
+                    float
+                )
+            raw_data["ids"][t] = np.atleast_1d([ann[tk_str] for ann in anns]).astype(
+                int
+            )
+            raw_data["classes"][t] = np.atleast_1d(
+                [ann["category_id"] for ann in anns]
+            ).astype(int)
+            # if not is_gt:
+            #     raw_data["tk_confidences"][t] = np.atleast_1d(
+            #         [ann["score"] for ann in anns]
+            #     ).astype(float)
+
+        for t, d in enumerate(raw_data["dets"]):
+            if d is None:
+                raw_data["dets"][t] = np.empty((0, 4)).astype(float)
+                raw_data["ids"][t] = np.empty(0).astype(int)
+                raw_data["classes"][t] = np.empty(0).astype(int)
+                # if not is_gt:
+                #     raw_data["tk_confidences"][t] = np.empty(0)
+
+        if is_gt:
+            key_map = {"ids": "gt_ids", "classes": "gt_classes", "dets": "gt_dets"}
+        else:
+            key_map = {"ids": "tk_ids", "classes": "tk_classes", "dets": "tk_dets"}
+        for k, v in key_map.items():
+            raw_data[v] = raw_data.pop(k)
+
+        raw_data["num_timesteps"] = num_timesteps
+        raw_data["seq"] = seq
+        return raw_data
+
+    def get_preprocessed_seq_data_thr(self, raw_data, cls, assignment=None):
+        """Preprocess data for a single sequence for a single class.
+
+        Inputs:
+            raw_data: dict containing the data for the sequence already
+                read in by get_raw_seq_data().
+            cls: class to be evaluated.
+        Outputs:
+            gt_ids:
+                list (for each timestep) of ids of GT tracks
+            tk_ids:
+                list (for each timestep) of ids of predicted tracks (all for TP
+                matching (Det + AssocA))
+            tk_overlap_ids:
+                list (for each timestep) of ids of predicted tracks that overlap
+                with GTs
+            tk_dets:
+                list (for each timestep) of lists of detections that
+                corresponding to the tk_ids
+            tk_classes:
+                list (for each timestep) of lists of classes that corresponding
+                to the tk_ids
+            tk_confidences:
+                list (for each timestep) of lists of classes that corresponding
+                to the tk_ids
+            sim_scores:
+                similarity score between gt_ids and tk_ids.
+        """
+        if cls != "all":
+            cls_id = self.cls_name2clsid[cls]
+
+        data_keys = [
+            "gt_ids",
+            "tk_ids",
+            "gt_id_map",
+            "tk_id_map",
+            "gt_dets",
+            "gt_classes",
+            "gt_class_name",
+            "tk_overlap_classes",
+            "tk_overlap_ids",
+            "tk_class_eval_tk_ids",
+            "tk_dets",
+            "tk_classes",
+            # "tk_confidences",
+            "tk_exh_ids",
+            "sim_scores",
+        ]
+        data = {key: [None] * raw_data["num_timesteps"] for key in data_keys}
+        unique_gt_ids = []
+        unique_tk_ids = []
+        num_gt_dets = 0
+        num_tk_cls_dets = 0
+        num_tk_overlap_dets = 0
+        overlap_ious_thr = 0.5
+        loc_and_asso_tk_ids = []
+        exh_class_tk_ids = []
+
+        for t in range(raw_data["num_timesteps"]):
+            # only extract relevant dets for this class for preproc and eval
+            if cls == "all":
+                gt_class_mask = np.ones_like(raw_data["gt_classes"][t]).astype(bool)
+            else:
+                gt_class_mask = np.atleast_1d(
+                    raw_data["gt_classes"][t] == cls_id
+                ).astype(bool)
+
+            # select GT that is not in the evaluating classes
+            if assignment is not None and assignment:
+                all_gt_ids = list(assignment[t].keys())
+                gt_ids_in = raw_data["gt_ids"][t][gt_class_mask]
+                gt_ids_out = set(all_gt_ids) - set(gt_ids_in)
+                tk_ids_out = set([assignment[t][key] for key in list(gt_ids_out)])
+
+            # compute overlapped tracks and add their ids to overlap_tk_ids
+            sim_scores = raw_data["similarity_scores"]
+            overlap_ids_masks = (sim_scores[t][gt_class_mask] >= overlap_ious_thr).any(
+                axis=0
+            )
+            overlap_tk_ids_t = raw_data["tk_ids"][t][overlap_ids_masks]
+            if assignment is not None and assignment:
+                data["tk_overlap_ids"][t] = list(set(overlap_tk_ids_t) - tk_ids_out)
+            else:
+                data["tk_overlap_ids"][t] = list(set(overlap_tk_ids_t))
+
+            loc_and_asso_tk_ids += data["tk_overlap_ids"][t]
+
+            data["tk_exh_ids"][t] = []
+            if cls == "all":
+                continue
+
+            # add the track ids of exclusive annotated class to exh_class_tk_ids
+            tk_exh_mask = np.atleast_1d(raw_data["tk_classes"][t] == cls_id)
+            tk_exh_mask = tk_exh_mask.astype(bool)
+            exh_class_tk_ids_t = raw_data["tk_ids"][t][tk_exh_mask]
+            exh_class_tk_ids.append(exh_class_tk_ids_t)
+            data["tk_exh_ids"][t] = exh_class_tk_ids_t
+
+        # remove tk_ids that has been assigned to GT belongs to other classes.
+        loc_and_asso_tk_ids = list(set(loc_and_asso_tk_ids))
+
+        # remove all unwanted unmatched tracker detections
+        for t in range(raw_data["num_timesteps"]):
+            # add gt to the data
+            if cls == "all":
+                gt_class_mask = np.ones_like(raw_data["gt_classes"][t]).astype(bool)
+            else:
+                gt_class_mask = np.atleast_1d(
+                    raw_data["gt_classes"][t] == cls_id
+                ).astype(bool)
+                data["gt_classes"][t] = cls_id
+                data["gt_class_name"][t] = cls
+
+            gt_ids = raw_data["gt_ids"][t][gt_class_mask]
+            if self.use_mask:
+                gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]
+            else:
+                gt_dets = raw_data["gt_dets"][t][gt_class_mask]
+            data["gt_ids"][t] = gt_ids
+            data["gt_dets"][t] = gt_dets
+
+            # filter pred and only keep those that highly overlap with GTs
+            tk_mask = np.isin(
+                raw_data["tk_ids"][t], np.array(loc_and_asso_tk_ids), assume_unique=True
+            )
+            tk_overlap_mask = np.isin(
+                raw_data["tk_ids"][t],
+                np.array(data["tk_overlap_ids"][t]),
+                assume_unique=True,
+            )
+
+            tk_ids = raw_data["tk_ids"][t][tk_mask]
+            if self.use_mask:
+                tk_dets = [raw_data['tk_dets'][t][ind] for ind in range(len(tk_mask)) if
+                            tk_mask[ind]]
+            else:
+                tk_dets = raw_data["tk_dets"][t][tk_mask]
+
+            tracker_classes = raw_data["tk_classes"][t][tk_mask]
+
+            # add overlap classes for computing the FP for Cls term
+            tracker_overlap_classes = raw_data["tk_classes"][t][tk_overlap_mask]
+            # tracker_confidences = raw_data["tk_confidences"][t][tk_mask]
+            sim_scores_masked = sim_scores[t][gt_class_mask, :][:, tk_mask]
+
+            # add filtered prediction to the data
+            data["tk_classes"][t] = tracker_classes
+            data["tk_overlap_classes"][t] = tracker_overlap_classes
+            data["tk_ids"][t] = tk_ids
+            data["tk_dets"][t] = tk_dets
+            # data["tk_confidences"][t] = tracker_confidences
+            data["sim_scores"][t] = sim_scores_masked
+            data["tk_class_eval_tk_ids"][t] = set(
+                list(data["tk_overlap_ids"][t]) + list(data["tk_exh_ids"][t])
+            )
+
+            # count total number of detections
+            unique_gt_ids += list(np.unique(data["gt_ids"][t]))
+            # the unique track ids are for association.
+            unique_tk_ids += list(np.unique(data["tk_ids"][t]))
+
+            num_tk_overlap_dets += len(data["tk_overlap_ids"][t])
+            num_tk_cls_dets += len(data["tk_class_eval_tk_ids"][t])
+            num_gt_dets += len(data["gt_ids"][t])
+
+        # re-label IDs such that there are no empty IDs
+        if len(unique_gt_ids) > 0:
+            unique_gt_ids = np.unique(unique_gt_ids)
+            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
+            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
+            data["gt_id_map"] = {}
+            for gt_id in unique_gt_ids:
+                new_gt_id = gt_id_map[gt_id].astype(int)
+                data["gt_id_map"][new_gt_id] = gt_id
+
+            for t in range(raw_data["num_timesteps"]):
+                if len(data["gt_ids"][t]) > 0:
+                    data["gt_ids"][t] = gt_id_map[data["gt_ids"][t]].astype(int)
+
+        if len(unique_tk_ids) > 0:
+            unique_tk_ids = np.unique(unique_tk_ids)
+            tk_id_map = np.nan * np.ones((np.max(unique_tk_ids) + 1))
+            tk_id_map[unique_tk_ids] = np.arange(len(unique_tk_ids))
+
+            data["tk_id_map"] = {}
+            for track_id in unique_tk_ids:
+                new_track_id = tk_id_map[track_id].astype(int)
+                data["tk_id_map"][new_track_id] = track_id
+
+            for t in range(raw_data["num_timesteps"]):
+                if len(data["tk_ids"][t]) > 0:
+                    data["tk_ids"][t] = tk_id_map[data["tk_ids"][t]].astype(int)
+                if len(data["tk_overlap_ids"][t]) > 0:
+                    data["tk_overlap_ids"][t] = tk_id_map[
+                        data["tk_overlap_ids"][t]
+                    ].astype(int)
+
+        # record overview statistics.
+        data["num_tk_cls_dets"] = num_tk_cls_dets
+        data["num_tk_overlap_dets"] = num_tk_overlap_dets
+        data["num_gt_dets"] = num_gt_dets
+        data["num_tk_ids"] = len(unique_tk_ids)
+        data["num_gt_ids"] = len(unique_gt_ids)
+        data["num_timesteps"] = raw_data["num_timesteps"]
+        data["seq"] = raw_data["seq"]
+
+        self._check_unique_ids(data)
+
+        return data
+
+    @_timing.time
+    def get_preprocessed_seq_data(
+        self, raw_data, cls, assignment=None, thresholds=[50, 75]
+    ):
+        """Preprocess data for a single sequence for a single class."""
+        data = {}
+        if thresholds is None:
+            thresholds = [50, 75]
+        elif isinstance(thresholds, int):
+            thresholds = [thresholds]
+
+        for thr in thresholds:
+            assignment_thr = None
+            if assignment is not None:
+                assignment_thr = assignment[thr]
+            data[thr] = self.get_preprocessed_seq_data_thr(
+                raw_data, cls, assignment_thr
+            )
+
+        return data
+
+    def _calculate_similarities(self, gt_dets_t, tk_dets_t):
+        """Compute similarity scores."""
+        if self.use_mask:
+            similarity_scores = self._calculate_mask_ious(gt_dets_t, tk_dets_t, is_encoded=True, do_ioa=False)
+        else:
+            similarity_scores = self._calculate_box_ious(gt_dets_t, tk_dets_t)
+        return similarity_scores
+
+    def _compute_vid_mappings(self, annotations):
+        """Computes mappings from videos to corresponding tracks and images."""
+        vids_to_tracks = {}
+        vids_to_imgs = {}
+        vid_ids = [vid["id"] for vid in self.gt_data["videos"]]
+
+        # compute an mapping from image IDs to images
+        images = {}
+        for image in self.gt_data["images"]:
+            images[image["id"]] = image
+
+        tk_str = utils.get_track_id_str(annotations[0])
+        for ann in annotations:
+            ann["area"] = ann["bbox"][2] * ann["bbox"][3]
+
+            vid = ann["video_id"]
+            if ann["video_id"] not in vids_to_tracks.keys():
+                vids_to_tracks[ann["video_id"]] = list()
+            if ann["video_id"] not in vids_to_imgs.keys():
+                vids_to_imgs[ann["video_id"]] = list()
+
+            # fill in vids_to_tracks
+            tid = ann[tk_str]
+            exist_tids = [track["id"] for track in vids_to_tracks[vid]]
+            try:
+                index1 = exist_tids.index(tid)
+            except ValueError:
+                index1 = -1
+            if tid not in exist_tids:
+                curr_track = {
+                    "id": tid,
+                    "category_id": ann["category_id"],
+                    "video_id": vid,
+                    "annotations": [ann],
+                }
+                vids_to_tracks[vid].append(curr_track)
+            else:
+                vids_to_tracks[vid][index1]["annotations"].append(ann)
+
+            # fill in vids_to_imgs
+            img_id = ann["image_id"]
+            exist_img_ids = [img["id"] for img in vids_to_imgs[vid]]
+            try:
+                index2 = exist_img_ids.index(img_id)
+            except ValueError:
+                index2 = -1
+            if index2 == -1:
+                curr_img = {"id": img_id, "annotations": [ann]}
+                vids_to_imgs[vid].append(curr_img)
+            else:
+                vids_to_imgs[vid][index2]["annotations"].append(ann)
+
+        # sort annotations by frame index and compute track area
+        for vid, tracks in vids_to_tracks.items():
+            for track in tracks:
+                track["annotations"] = sorted(
+                    track["annotations"],
+                    key=lambda x: images[x["image_id"]]["frame_id"],
+                )
+                # compute average area
+                track["area"] = sum(x["area"] for x in track["annotations"]) / len(
+                    track["annotations"]
+                )
+
+        # ensure all videos are present
+        for vid_id in vid_ids:
+            if vid_id not in vids_to_tracks.keys():
+                vids_to_tracks[vid_id] = []
+            if vid_id not in vids_to_imgs.keys():
+                vids_to_imgs[vid_id] = []
+
+        return vids_to_tracks, vids_to_imgs
+
+    def _compute_image_to_timestep_mappings(self):
+        """Computes a mapping from images to timestep in sequence."""
+        images = {}
+        for image in self.gt_data["images"]:
+            images[image["id"]] = image
+
+        seq_to_imgs_to_timestep = {vid["id"]: dict() for vid in self.gt_data["videos"]}
+        for vid in seq_to_imgs_to_timestep:
+            curr_imgs = [img["id"] for img in self.video2gt_image[vid]]
+            curr_imgs = sorted(curr_imgs, key=lambda x: images[x]["frame_id"])
+            seq_to_imgs_to_timestep[vid] = {
+                curr_imgs[i]: i for i in range(len(curr_imgs))
+            }
+
+        return seq_to_imgs_to_timestep
+
+    def _limit_dets_per_image(self, annotations):
+        """Limits the number of detections for each image.
+
+        Adapted from https://github.com/TAO-Dataset/.
+        """
+        max_dets = self.config["MAX_DETECTIONS"]
+        img_ann = defaultdict(list)
+        for ann in annotations:
+            img_ann[ann["image_id"]].append(ann)
+
+        for img_id, _anns in img_ann.items():
+            if len(_anns) <= max_dets:
+                continue
+            _anns = sorted(_anns, key=lambda x: x["score"], reverse=True)
+            img_ann[img_id] = _anns[:max_dets]
+
+        return [ann for anns in img_ann.values() for ann in anns]
+
+    def _fill_video_ids_inplace(self, annotations):
+        """Fills in missing video IDs inplace.
+
+        Adapted from https://github.com/TAO-Dataset/.
+        """
+        missing_video_id = [x for x in annotations if "video_id" not in x]
+        if missing_video_id:
+            image_id_to_video_id = {
+                x["id"]: x["video_id"] for x in self.gt_data["images"]
+            }
+            for x in missing_video_id:
+                x["video_id"] = image_id_to_video_id[x["image_id"]]
+
+    @staticmethod
+    def _make_tk_ids_unique(annotations):
+        """Makes track IDs unqiue over the whole annotation set.
+
+        Adapted from https://github.com/TAO-Dataset/.
+        """
+        track_id_videos = {}
+        track_ids_to_update = set()
+        max_track_id = 0
+
+        tk_str = utils.get_track_id_str(annotations[0])
+        for ann in annotations:
+            t = int(ann[tk_str])
+            if t not in track_id_videos:
+                track_id_videos[t] = ann["video_id"]
+
+            if ann["video_id"] != track_id_videos[t]:
+                # track id is assigned to multiple videos
+                track_ids_to_update.add(t)
+            max_track_id = max(max_track_id, t)
+
+        if track_ids_to_update:
+            print("true")
+            next_id = itertools.count(max_track_id + 1)
+            new_tk_ids = defaultdict(lambda: next(next_id))
+            for ann in annotations:
+                t = ann[tk_str]
+                v = ann["video_id"]
+                if t in track_ids_to_update:
+                    ann[tk_str] = new_tk_ids[t, v]
+        return len(track_ids_to_update)

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/datasets/tao.py

@@ -0,0 +1,661 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+"""TAO Dataset."""
+import copy
+import itertools
+import json
+import os
+from collections import defaultdict
+
+import numpy as np
+
+from .. import _timing
+from ..config import get_default_dataset_config, init_config
+from ..utils import TrackEvalException
+from ._base_dataset import _BaseDataset
+
+
+class TAO(_BaseDataset):
+    """Dataset class for TAO tracking"""
+
+    def __init__(self, config=None):
+        """Initialize dataset, checking that all required files are present."""
+        super().__init__()
+        # Fill non-given config values with defaults
+        self.config = init_config(config, get_default_dataset_config(), self.get_name())
+        self.gt_fol = self.config["GT_FOLDER"]
+        self.tracker_fol = self.config["TRACKERS_FOLDER"]
+        self.should_classes_combine = True
+        self.use_super_categories = False
+        self.use_mask = self.config["USE_MASK"]
+
+
+        self.tracker_sub_fol = self.config["TRACKER_SUB_FOLDER"]
+        self.output_fol = self.config["OUTPUT_FOLDER"]
+        if self.output_fol is None:
+            self.output_fol = self.tracker_fol
+        self.output_sub_fol = self.config["OUTPUT_SUB_FOLDER"]
+
+        if self.gt_fol.endswith(".json"):
+            self.gt_data = json.load(open(self.gt_fol, "r"))
+        else:
+            gt_dir_files = [
+                file for file in os.listdir(self.gt_fol) if file.endswith(".json")
+            ]
+            if len(gt_dir_files) != 1:
+                raise TrackEvalException(
+                    f"{self.gt_fol} does not contain exactly one json file."
+                )
+
+            with open(os.path.join(self.gt_fol, gt_dir_files[0])) as f:
+                self.gt_data = json.load(f)
+
+        # merge categories marked with a merged tag in TAO dataset
+        self._merge_categories(self.gt_data["annotations"] + self.gt_data["tracks"])
+
+        # get sequences to eval and sequence information
+        self.seq_list = [
+            vid["name"].replace("/", "-") for vid in self.gt_data["videos"]
+        ]
+        self.seq_name2seqid = {
+            vid["name"].replace("/", "-"): vid["id"] for vid in self.gt_data["videos"]
+        }
+        # compute mappings from videos to annotation data
+        self.video2gt_track, self.video2gt_image = self._compute_vid_mappings(
+            self.gt_data["annotations"]
+        )
+        # compute sequence lengths
+        self.seq_lengths = {vid["id"]: 0 for vid in self.gt_data["videos"]}
+        for img in self.gt_data["images"]:
+            self.seq_lengths[img["video_id"]] += 1
+        self.seq2images2timestep = self._compute_image_to_timestep_mappings()
+        self.seq2cls = {
+            vid["id"]: {
+                "pos_cat_ids": list(
+                    {track["category_id"] for track in self.video2gt_track[vid["id"]]}
+                ),
+                "neg_cat_ids": vid["neg_category_ids"],
+                "not_exh_labeled_cat_ids": vid["not_exhaustive_category_ids"],
+            }
+            for vid in self.gt_data["videos"]
+        }
+
+        # Get classes to eval
+        considered_vid_ids = [self.seq_name2seqid[vid] for vid in self.seq_list]
+        seen_cats = set(
+            [
+                cat_id
+                for vid_id in considered_vid_ids
+                for cat_id in self.seq2cls[vid_id]["pos_cat_ids"]
+            ]
+        )
+        # only classes with ground truth are evaluated in TAO
+        self.valid_classes = [
+            cls["name"] for cls in self.gt_data["categories"] if cls["id"] in seen_cats
+        ]
+        cls_name2clsid_map = {
+            cls["name"]: cls["id"] for cls in self.gt_data["categories"]
+        }
+
+        if self.config["CLASSES_TO_EVAL"]:
+            self.class_list = [
+                cls.lower() if cls.lower() in self.valid_classes else None
+                for cls in self.config["CLASSES_TO_EVAL"]
+            ]
+            if not all(self.class_list):
+                valid_cls = ", ".join(self.valid_classes)
+                raise TrackEvalException(
+                    "Attempted to evaluate an invalid class. Only classes "
+                    f"{valid_cls} are valid (classes present in ground truth"
+                    " data)."
+                )
+        else:
+            self.class_list = [cls for cls in self.valid_classes]
+        self.cls_name2clsid = {
+            k: v for k, v in cls_name2clsid_map.items() if k in self.class_list
+        }
+        self.clsid2cls_name = {
+            v: k for k, v in cls_name2clsid_map.items() if k in self.class_list
+        }
+        # get trackers to eval
+        print(self.config["TRACKERS_TO_EVAL"] )
+        if self.config["TRACKERS_TO_EVAL"] is None:
+            self.tracker_list = os.listdir(self.tracker_fol)
+        else:
+            self.tracker_list = self.config["TRACKERS_TO_EVAL"]
+
+        if self.config["TRACKER_DISPLAY_NAMES"] is None:
+            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
+        elif (self.config["TRACKERS_TO_EVAL"] is not None) and (
+            len(self.config["TK_DISPLAY_NAMES"]) == len(self.tracker_list)
+        ):
+            self.tracker_to_disp = dict(
+                zip(self.tracker_list, self.config["TK_DISPLAY_NAMES"])
+            )
+        else:
+            raise TrackEvalException(
+                "List of tracker files and tracker display names do not match."
+            )
+
+        self.tracker_data = {tracker: dict() for tracker in self.tracker_list}
+
+        for tracker in self.tracker_list:
+            if self.tracker_sub_fol.endswith(".json"):
+                with open(os.path.join(self.tracker_sub_fol)) as f:
+                    curr_data = json.load(f)
+            else:
+                tr_dir = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol)
+                tr_dir_files = [
+                    file for file in os.listdir(tr_dir) if file.endswith(".json")
+                ]
+                if len(tr_dir_files) != 1:
+                    raise TrackEvalException(
+                        f"{tr_dir} does not contain exactly one json file."
+                    )
+                with open(os.path.join(tr_dir, tr_dir_files[0])) as f:
+                    curr_data = json.load(f)
+
+            # limit detections if MAX_DETECTIONS > 0
+            if self.config["MAX_DETECTIONS"]:
+                curr_data = self._limit_dets_per_image(curr_data)
+
+            # fill missing video ids
+            self._fill_video_ids_inplace(curr_data)
+
+            # make track ids unique over whole evaluation set
+            self._make_tk_ids_unique(curr_data)
+
+            # merge categories marked with a merged tag in TAO dataset
+            self._merge_categories(curr_data)
+
+            # get tracker sequence information
+            curr_vids2tracks, curr_vids2images = self._compute_vid_mappings(curr_data)
+            self.tracker_data[tracker]["vids_to_tracks"] = curr_vids2tracks
+            self.tracker_data[tracker]["vids_to_images"] = curr_vids2images
+
+    def get_display_name(self, tracker):
+        return self.tracker_to_disp[tracker]
+
+    def _load_raw_file(self, tracker, seq, is_gt):
+        """Load a file (gt or tracker) in the TAO format
+
+        If is_gt, this returns a dict which contains the fields:
+        [gt_ids, gt_classes]:
+            list (for each timestep) of 1D NDArrays (for each det).
+        [gt_dets]: list (for each timestep) of lists of detections.
+
+        if not is_gt, this returns a dict which contains the fields:
+        [tk_ids, tk_classes, tk_confidences]:
+            list (for each timestep) of 1D NDArrays (for each det).
+        [tk_dets]: list (for each timestep) of lists of detections.
+        """
+        seq_id = self.seq_name2seqid[seq]
+        # file location
+        if is_gt:
+            imgs = self.video2gt_image[seq_id]
+        else:
+            imgs = self.tracker_data[tracker]["vids_to_images"][seq_id]
+
+        # convert data to required format
+        num_timesteps = self.seq_lengths[seq_id]
+        img_to_timestep = self.seq2images2timestep[seq_id]
+        data_keys = ["ids", "classes", "dets"]
+        if not is_gt:
+            data_keys += ["tk_confidences"]
+        raw_data = {key: [None] * num_timesteps for key in data_keys}
+        for img in imgs:
+            # some tracker data contains images without any ground truth info,
+            # these are ignored
+            if img["id"] not in img_to_timestep:
+                continue
+            t = img_to_timestep[img["id"]]
+            anns = img["annotations"]
+            if self.use_mask:
+                # When using mask, extract segmentation data
+                raw_data["dets"][t] = [ann.get("segmentation") for ann in anns]
+            else:
+                # When using bbox, extract bbox data
+                raw_data["dets"][t] = np.atleast_2d([ann["bbox"] for ann in anns]).astype(
+                    float
+                )
+            raw_data["ids"][t] = np.atleast_1d(
+                [ann["track_id"] for ann in anns]
+            ).astype(int)
+            raw_data["classes"][t] = np.atleast_1d(
+                [ann["category_id"] for ann in anns]
+            ).astype(int)
+            if not is_gt:
+                raw_data["tk_confidences"][t] = np.atleast_1d(
+                    [ann["score"] for ann in anns]
+                ).astype(float)
+
+        for t, d in enumerate(raw_data["dets"]):
+            if d is None:
+                raw_data["dets"][t] = np.empty((0, 4)).astype(float)
+                raw_data["ids"][t] = np.empty(0).astype(int)
+                raw_data["classes"][t] = np.empty(0).astype(int)
+                if not is_gt:
+                    raw_data["tk_confidences"][t] = np.empty(0)
+
+        if is_gt:
+            key_map = {"ids": "gt_ids", "classes": "gt_classes", "dets": "gt_dets"}
+        else:
+            key_map = {"ids": "tk_ids", "classes": "tk_classes", "dets": "tk_dets"}
+        for k, v in key_map.items():
+            raw_data[v] = raw_data.pop(k)
+
+        raw_data["num_timesteps"] = num_timesteps
+        raw_data["neg_cat_ids"] = self.seq2cls[seq_id]["neg_cat_ids"]
+        raw_data["not_exh_labeled_cls"] = self.seq2cls[seq_id][
+            "not_exh_labeled_cat_ids"
+        ]
+        raw_data["seq"] = seq
+        return raw_data
+
+    def get_preprocessed_seq_data_thr(self, raw_data, cls, assignment=None):
+        """Preprocess data for a single sequence for a single class.
+
+        Inputs:
+            raw_data: dict containing the data for the sequence already
+                read in by get_raw_seq_data().
+            cls: class to be evaluated.
+        Outputs:
+            gt_ids:
+                list (for each timestep) of ids of GT tracks
+            tk_ids:
+                list (for each timestep) of ids of predicted tracks (all for TP
+                matching (Det + AssocA))
+            tk_overlap_ids:
+                list (for each timestep) of ids of predicted tracks that overlap
+                with GTs
+            tk_neg_ids:
+                list (for each timestep) of ids of predicted tracks that with
+                the class id on the negative list for the current sequence.
+            tk_exh_ids:
+                list (for each timestep) of ids of predicted tracks that do not
+                overlap with existing GTs but have the class id on the
+                exhaustive annotated class list for the current sequence.
+            tk_dets:
+                list (for each timestep) of lists of detections that
+                corresponding to the tk_ids
+            tk_classes:
+                list (for each timestep) of lists of classes that corresponding
+                to the tk_ids
+            tk_confidences:
+                list (for each timestep) of lists of classes that corresponding
+                to the tk_ids
+            sim_scores:
+                similarity score between gt_ids and tk_ids.
+        """
+        if cls != "all":
+            cls_id = self.cls_name2clsid[cls]
+
+        data_keys = [
+            "gt_ids",
+            "tk_ids",
+            "gt_id_map",
+            "tk_id_map",
+            "gt_dets",
+            "gt_classes",
+            "gt_class_name",
+            "tk_overlap_classes",
+            "tk_overlap_ids",
+            "tk_neg_ids",
+            "tk_exh_ids",
+            "tk_class_eval_tk_ids",
+            "tk_dets",
+            "tk_classes",
+            "tk_confidences",
+            "sim_scores",
+        ]
+        data = {key: [None] * raw_data["num_timesteps"] for key in data_keys}
+        unique_gt_ids = []
+        unique_tk_ids = []
+        num_gt_dets = 0
+        num_tk_cls_dets = 0
+        num_tk_overlap_dets = 0
+        overlap_ious_thr = 0.5
+        loc_and_asso_tk_ids = []
+
+        for t in range(raw_data["num_timesteps"]):
+            # only extract relevant dets for this class for preproc and eval
+            if cls == "all":
+                gt_class_mask = np.ones_like(raw_data["gt_classes"][t]).astype(bool)
+            else:
+                gt_class_mask = np.atleast_1d(
+                    raw_data["gt_classes"][t] == cls_id
+                ).astype(bool)
+
+            # select GT that is not in the evaluating classes
+            if assignment is not None and assignment:
+                all_gt_ids = list(assignment[t].keys())
+                gt_ids_in = raw_data["gt_ids"][t][gt_class_mask]
+                gt_ids_out = set(all_gt_ids) - set(gt_ids_in)
+                tk_ids_out = set([assignment[t][key] for key in list(gt_ids_out)])
+
+            # compute overlapped tracks and add their ids to overlap_tk_ids
+            sim_scores = raw_data["similarity_scores"]
+            overlap_ids_masks = (sim_scores[t][gt_class_mask] >= overlap_ious_thr).any(
+                axis=0
+            )
+            overlap_tk_ids_t = raw_data["tk_ids"][t][overlap_ids_masks]
+            if assignment is not None and assignment:
+                data["tk_overlap_ids"][t] = list(set(overlap_tk_ids_t) - tk_ids_out)
+            else:
+                data["tk_overlap_ids"][t] = list(set(overlap_tk_ids_t))
+
+            loc_and_asso_tk_ids += data["tk_overlap_ids"][t]
+
+            data["tk_exh_ids"][t] = []
+            data["tk_neg_ids"][t] = []
+
+            if cls == "all":
+                continue
+
+        # remove tk_ids that has been assigned to GT belongs to other classes.
+        loc_and_asso_tk_ids = list(set(loc_and_asso_tk_ids))
+
+        # remove all unwanted unmatched tracker detections
+        for t in range(raw_data["num_timesteps"]):
+            # add gt to the data
+            if cls == "all":
+                gt_class_mask = np.ones_like(raw_data["gt_classes"][t]).astype(bool)
+            else:
+                gt_class_mask = np.atleast_1d(
+                    raw_data["gt_classes"][t] == cls_id
+                ).astype(bool)
+                data["gt_classes"][t] = cls_id
+                data["gt_class_name"][t] = cls
+
+            gt_ids = raw_data["gt_ids"][t][gt_class_mask]
+            if self.use_mask:
+                gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]
+            else:
+                gt_dets = raw_data["gt_dets"][t][gt_class_mask]
+            data["gt_ids"][t] = gt_ids
+            data["gt_dets"][t] = gt_dets
+
+            # filter pred and only keep those that highly overlap with GTs
+            tk_mask = np.isin(
+                raw_data["tk_ids"][t], np.array(loc_and_asso_tk_ids), assume_unique=True
+            )
+            tk_overlap_mask = np.isin(
+                raw_data["tk_ids"][t],
+                np.array(data["tk_overlap_ids"][t]),
+                assume_unique=True,
+            )
+
+            tk_ids = raw_data["tk_ids"][t][tk_mask]
+            if self.use_mask:
+                tk_dets = [raw_data['tk_dets'][t][ind] for ind in range(len(tk_mask)) if
+                            tk_mask[ind]]
+            else:
+                tk_dets = raw_data["tk_dets"][t][tk_mask]
+            tracker_classes = raw_data["tk_classes"][t][tk_mask]
+
+            # add overlap classes for computing the FP for Cls term
+            tracker_overlap_classes = raw_data["tk_classes"][t][tk_overlap_mask]
+            tracker_confidences = raw_data["tk_confidences"][t][tk_mask]
+            sim_scores_masked = sim_scores[t][gt_class_mask, :][:, tk_mask]
+
+            # add filtered prediction to the data
+            data["tk_classes"][t] = tracker_classes
+            data["tk_overlap_classes"][t] = tracker_overlap_classes
+            data["tk_ids"][t] = tk_ids
+            data["tk_dets"][t] = tk_dets
+            data["tk_confidences"][t] = tracker_confidences
+            data["sim_scores"][t] = sim_scores_masked
+            data["tk_class_eval_tk_ids"][t] = set(
+                list(data["tk_overlap_ids"][t])
+                + list(data["tk_neg_ids"][t])
+                + list(data["tk_exh_ids"][t])
+            )
+
+            # count total number of detections
+            unique_gt_ids += list(np.unique(data["gt_ids"][t]))
+            # the unique track ids are for association.
+            unique_tk_ids += list(np.unique(data["tk_ids"][t]))
+
+            num_tk_overlap_dets += len(data["tk_overlap_ids"][t])
+            num_tk_cls_dets += len(data["tk_class_eval_tk_ids"][t])
+            num_gt_dets += len(data["gt_ids"][t])
+
+        # re-label IDs such that there are no empty IDs
+        if len(unique_gt_ids) > 0:
+            unique_gt_ids = np.unique(unique_gt_ids)
+            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
+            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
+            data["gt_id_map"] = {}
+            for gt_id in unique_gt_ids:
+                new_gt_id = gt_id_map[gt_id].astype(int)
+                data["gt_id_map"][new_gt_id] = gt_id
+
+            for t in range(raw_data["num_timesteps"]):
+                if len(data["gt_ids"][t]) > 0:
+                    data["gt_ids"][t] = gt_id_map[data["gt_ids"][t]].astype(int)
+
+        if len(unique_tk_ids) > 0:
+            unique_tk_ids = np.unique(unique_tk_ids)
+            tk_id_map = np.nan * np.ones((np.max(unique_tk_ids) + 1))
+            tk_id_map[unique_tk_ids] = np.arange(len(unique_tk_ids))
+
+            data["tk_id_map"] = {}
+            for track_id in unique_tk_ids:
+                new_track_id = tk_id_map[track_id].astype(int)
+                data["tk_id_map"][new_track_id] = track_id
+
+            for t in range(raw_data["num_timesteps"]):
+                if len(data["tk_ids"][t]) > 0:
+                    data["tk_ids"][t] = tk_id_map[data["tk_ids"][t]].astype(int)
+                if len(data["tk_overlap_ids"][t]) > 0:
+                    data["tk_overlap_ids"][t] = tk_id_map[
+                        data["tk_overlap_ids"][t]
+                    ].astype(int)
+
+        # record overview statistics.
+        data["num_tk_cls_dets"] = num_tk_cls_dets
+        data["num_tk_overlap_dets"] = num_tk_overlap_dets
+        data["num_gt_dets"] = num_gt_dets
+        data["num_tk_ids"] = len(unique_tk_ids)
+        data["num_gt_ids"] = len(unique_gt_ids)
+        data["num_timesteps"] = raw_data["num_timesteps"]
+        data["seq"] = raw_data["seq"]
+
+        self._check_unique_ids(data)
+
+        return data
+
+    @_timing.time
+    def get_preprocessed_seq_data(
+        self, raw_data, cls, assignment=None, thresholds=[50, 75]
+    ):
+        """Preprocess data for a single sequence for a single class."""
+        data = {}
+        if thresholds is None:
+            thresholds = [50]
+        elif isinstance(thresholds, int):
+            thresholds = [thresholds]
+
+        for thr in thresholds:
+            assignment_thr = None
+            if assignment is not None:
+                assignment_thr = assignment[thr]
+            data[thr] = self.get_preprocessed_seq_data_thr(
+                raw_data, cls, assignment_thr
+            )
+
+        return data
+
+    def _calculate_similarities(self, gt_dets_t, tk_dets_t):
+        """Compute similarity scores."""
+        if self.use_mask:
+            similarity_scores = self._calculate_mask_ious(gt_dets_t, tk_dets_t, is_encoded=True, do_ioa=False)
+        else:
+            similarity_scores = self._calculate_box_ious(gt_dets_t, tk_dets_t)
+        return similarity_scores
+
+    def _merge_categories(self, annotations):
+        """Merges categories with a merged tag.
+
+        Adapted from https://github.com/TAO-Dataset.
+        """
+        merge_map = {}
+        for category in self.gt_data["categories"]:
+            if "merged" in category:
+                for to_merge in category["merged"]:
+                    merge_map[to_merge["id"]] = category["id"]
+
+        for ann in annotations:
+            ann["category_id"] = merge_map.get(ann["category_id"], ann["category_id"])
+
+    def _compute_vid_mappings(self, annotations):
+        """Computes mappings from videos to corresponding tracks and images."""
+        vids_to_tracks = {}
+        vids_to_imgs = {}
+        vid_ids = [vid["id"] for vid in self.gt_data["videos"]]
+
+        # compute an mapping from image IDs to images
+        images = {}
+        for image in self.gt_data["images"]:
+            images[image["id"]] = image
+
+        for ann in annotations:
+            ann["area"] = ann["bbox"][2] * ann["bbox"][3]
+
+            vid = ann["video_id"]
+            if ann["video_id"] not in vids_to_tracks.keys():
+                vids_to_tracks[ann["video_id"]] = list()
+            if ann["video_id"] not in vids_to_imgs.keys():
+                vids_to_imgs[ann["video_id"]] = list()
+
+            # fill in vids_to_tracks
+            tid = ann["track_id"]
+            exist_tids = [track["id"] for track in vids_to_tracks[vid]]
+            try:
+                index1 = exist_tids.index(tid)
+            except ValueError:
+                index1 = -1
+            if tid not in exist_tids:
+                curr_track = {
+                    "id": tid,
+                    "category_id": ann["category_id"],
+                    "video_id": vid,
+                    "annotations": [ann],
+                }
+                vids_to_tracks[vid].append(curr_track)
+            else:
+                vids_to_tracks[vid][index1]["annotations"].append(ann)
+
+            # fill in vids_to_imgs
+            img_id = ann["image_id"]
+            exist_img_ids = [img["id"] for img in vids_to_imgs[vid]]
+            try:
+                index2 = exist_img_ids.index(img_id)
+            except ValueError:
+                index2 = -1
+            if index2 == -1:
+                curr_img = {"id": img_id, "annotations": [ann]}
+                vids_to_imgs[vid].append(curr_img)
+            else:
+                vids_to_imgs[vid][index2]["annotations"].append(ann)
+
+        # sort annotations by frame index and compute track area
+        for vid, tracks in vids_to_tracks.items():
+            for track in tracks:
+                track["annotations"] = sorted(
+                    track["annotations"],
+                    key=lambda x: images[x["image_id"]]["frame_index"],
+                )
+                # compute average area
+                track["area"] = sum(x["area"] for x in track["annotations"]) / len(
+                    track["annotations"]
+                )
+
+        # ensure all videos are present
+        for vid_id in vid_ids:
+            if vid_id not in vids_to_tracks.keys():
+                vids_to_tracks[vid_id] = []
+            if vid_id not in vids_to_imgs.keys():
+                vids_to_imgs[vid_id] = []
+
+        return vids_to_tracks, vids_to_imgs
+
+    def _compute_image_to_timestep_mappings(self):
+        """Computes a mapping from images to timestep in sequence."""
+        images = {}
+        for image in self.gt_data["images"]:
+            images[image["id"]] = image
+
+        seq_to_imgs_to_timestep = {vid["id"]: dict() for vid in self.gt_data["videos"]}
+        for vid in seq_to_imgs_to_timestep:
+            curr_imgs = [img["id"] for img in self.video2gt_image[vid]]
+            curr_imgs = sorted(curr_imgs, key=lambda x: images[x]["frame_index"])
+            seq_to_imgs_to_timestep[vid] = {
+                curr_imgs[i]: i for i in range(len(curr_imgs))
+            }
+
+        return seq_to_imgs_to_timestep
+
+    def _limit_dets_per_image(self, annotations):
+        """Limits the number of detections for each image.
+
+        Adapted from https://github.com/TAO-Dataset/.
+        """
+        max_dets = self.config["MAX_DETECTIONS"]
+        img_ann = defaultdict(list)
+        for ann in annotations:
+            img_ann[ann["image_id"]].append(ann)
+
+        for img_id, _anns in img_ann.items():
+            if len(_anns) <= max_dets:
+                continue
+            _anns = sorted(_anns, key=lambda x: x["score"], reverse=True)
+            img_ann[img_id] = _anns[:max_dets]
+
+        return [ann for anns in img_ann.values() for ann in anns]
+
+    def _fill_video_ids_inplace(self, annotations):
+        """Fills in missing video IDs inplace.
+
+        Adapted from https://github.com/TAO-Dataset/.
+        """
+        missing_video_id = [x for x in annotations if "video_id" not in x]
+        if missing_video_id:
+            image_id_to_video_id = {
+                x["id"]: x["video_id"] for x in self.gt_data["images"]
+            }
+            for x in missing_video_id:
+                x["video_id"] = image_id_to_video_id[x["image_id"]]
+
+    @staticmethod
+    def _make_tk_ids_unique(annotations):
+        """Makes track IDs unqiue over the whole annotation set.
+
+        Adapted from https://github.com/TAO-Dataset/.
+        """
+        track_id_videos = {}
+        track_ids_to_update = set()
+        max_track_id = 0
+        for ann in annotations:
+            t = ann["track_id"]
+            if t not in track_id_videos:
+                track_id_videos[t] = ann["video_id"]
+
+            if ann["video_id"] != track_id_videos[t]:
+                # track id is assigned to multiple videos
+                track_ids_to_update.add(t)
+            max_track_id = max(max_track_id, t)
+
+        if track_ids_to_update:
+            print("true")
+            next_id = itertools.count(max_track_id + 1)
+            new_tk_ids = defaultdict(lambda: next(next_id))
+            for ann in annotations:
+                t = ann["track_id"]
+                v = ann["video_id"]
+                if t in track_ids_to_update:
+                    ann["track_id"] = new_tk_ids[t, v]
+        return len(track_ids_to_update)

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/eval.py

@@ -0,0 +1,277 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+import copy
+import os
+import pickle
+import time
+import traceback
+from functools import partial
+from multiprocessing.pool import Pool
+
+import numpy as np
+
+from . import _timing, utils
+from .config import get_default_eval_config, init_config
+from .utils import TrackEvalException
+
+
+class Evaluator:
+    """Evaluator class for evaluating different metrics for each datasets."""
+
+    def __init__(self, config=None):
+        """Initialize the evaluator with a config file."""
+        self.config = init_config(config, get_default_eval_config(), "Eval")
+        # Only run timing analysis if not run in parallel.
+        if self.config["TIME_PROGRESS"] and not self.config["USE_PARALLEL"]:
+            _timing.DO_TIMING = True
+            if self.config["DISPLAY_LESS_PROGRESS"]:
+                _timing.DISPLAY_LESS_PROGRESS = True
+
+    @_timing.time
+    def evaluate(self, dataset_list, metrics_list):
+        """Evaluate a set of metrics on a set of datasets."""
+        config = self.config
+        metrics_list = metrics_list
+        metric_names = utils.validate_metrics_list(metrics_list)
+        dataset_names = [dataset.get_name() for dataset in dataset_list]
+        output_res = {}
+        output_msg = {}
+
+        for dataset, dname in zip(dataset_list, dataset_names):
+            # Get dataset info about what to evaluate
+            output_res[dname] = {}
+            output_msg[dname] = {}
+            tracker_list, seq_list, class_list = dataset.get_eval_info()
+            print(
+                f"\nEvaluating {len(tracker_list)} tracker(s) on "
+                f"{len(seq_list)} sequence(s) for {len(class_list)} class(es)"
+                f" on {dname} dataset using the following "
+                f'metrics: {", ".join(metric_names)}\n'
+            )
+
+            # Evaluate each tracker
+            for tracker in tracker_list:
+                try:
+                    output_res, output_msg = self.evaluate_tracker(
+                        tracker,
+                        dataset,
+                        dname,
+                        class_list,
+                        metrics_list,
+                        metric_names,
+                        seq_list,
+                        output_res,
+                        output_msg,
+                    )
+                except Exception as err:
+                    output_res[dname][tracker] = None
+                    if type(err) == TrackEvalException:
+                        output_msg[dname][tracker] = str(err)
+                    else:
+                        output_msg[dname][tracker] = "Unknown error occurred."
+                    print("Tracker %s was unable to be evaluated." % tracker)
+                    print(err)
+                    traceback.print_exc()
+                    if config["LOG_ON_ERROR"] is not None:
+                        with open(config["LOG_ON_ERROR"], "a") as f:
+                            print(dname, file=f)
+                            print(tracker, file=f)
+                            print(traceback.format_exc(), file=f)
+                            print("\n\n\n", file=f)
+                    if config["BREAK_ON_ERROR"]:
+                        raise err
+                    elif config["RETURN_ON_ERROR"]:
+                        return output_res, output_msg
+
+        return output_res, output_msg
+
+    def evaluate_tracker(
+        self,
+        tracker,
+        dataset,
+        dname,
+        class_list,
+        metrics_list,
+        metric_names,
+        seq_list,
+        output_res,
+        output_msg,
+    ):
+        """Evaluate each sequence in parallel or in series."""
+        print("\nEvaluating %s\n" % tracker)
+        time_start = time.time()
+        config = self.config
+        if config["USE_PARALLEL"]:
+            with Pool(config["NUM_PARALLEL_CORES"]) as pool:
+                _eval_sequence = partial(
+                    eval_sequence,
+                    dataset=dataset,
+                    tracker=tracker,
+                    class_list=class_list,
+                    metrics_list=metrics_list,
+                    metric_names=metric_names,
+                )
+                results = pool.map(_eval_sequence, seq_list)
+                res = dict(zip(seq_list, results))
+        else:
+            res = {}
+            for curr_seq in sorted(seq_list):
+                res[curr_seq] = eval_sequence(
+                    curr_seq, dataset, tracker, class_list, metrics_list, metric_names
+                )
+
+
+        # collecting combined cls keys (cls averaged, det averaged, super classes)
+        cls_keys = []
+        res["COMBINED_SEQ"] = {}
+        # combine sequences for each class
+        for c_cls in class_list:
+            res["COMBINED_SEQ"][c_cls] = {}
+            for metric, mname in zip(metrics_list, metric_names):
+                curr_res = {
+                    seq_key: seq_value[c_cls][mname]
+                    for seq_key, seq_value in res.items()
+                    if seq_key != "COMBINED_SEQ"
+                }
+                # combine results over all sequences and then over all classes
+                res["COMBINED_SEQ"][c_cls][mname] = metric.combine_sequences(curr_res)
+
+        # combine classes
+        if dataset.should_classes_combine:
+            if config["OUTPUT_PER_SEQ_RES"]:
+                video_keys = res.keys()
+            else:
+                video_keys = ["COMBINED_SEQ"]
+            for v_key in video_keys:
+                cls_keys += ["average"]
+                res[v_key]["average"] = {}
+                for metric, mname in zip(metrics_list, metric_names):
+                    cls_res = {
+                        cls_key: cls_value[mname]
+                        for cls_key, cls_value in res[v_key].items()
+                        if cls_key not in cls_keys
+                    }
+                    res[v_key]["average"][
+                        mname
+                    ] = metric.combine_classes_class_averaged(
+                        cls_res, ignore_empty=True
+                    )
+
+        # combine classes to super classes
+        if dataset.use_super_categories:
+            for cat, sub_cats in dataset.super_categories.items():
+                cls_keys.append(cat)
+                res["COMBINED_SEQ"][cat] = {}
+                for metric, mname in zip(metrics_list, metric_names):
+                    cat_res = {
+                        cls_key: cls_value[mname]
+                        for cls_key, cls_value in res["COMBINED_SEQ"].items()
+                        if cls_key in sub_cats
+                    }
+                    res["COMBINED_SEQ"][cat][
+                        mname
+                    ] = metric.combine_classes_det_averaged(cat_res)
+        # Print and output results in various formats
+        if config["TIME_PROGRESS"]:
+            print(
+                f"\nAll sequences for {tracker} finished in"
+                f" {time.time() - time_start} seconds"
+            )
+        output_fol = dataset.get_output_fol(tracker)
+        os.makedirs(output_fol, exist_ok=True)
+
+        # take a mean of each field of each thr
+        if config["OUTPUT_PER_SEQ_RES"]:
+            all_res = copy.deepcopy(res)
+            summary_keys = res.keys()
+        else:
+            all_res = copy.deepcopy(res["COMBINED_SEQ"])
+            summary_keys = ["COMBINED_SEQ"]
+        thr_key_list = [50]
+        for s_key in summary_keys:
+            for metric, mname in zip(metrics_list, metric_names):
+                if mname != "TETA":
+                    if s_key == "COMBINED_SEQ":
+                        metric.print_table(
+                            {"COMBINED_SEQ": res["COMBINED_SEQ"][cls_keys[0]][mname]},
+                            tracker,
+                            cls_keys[0],
+                        )
+                    continue
+
+                for c_cls in res[s_key].keys():
+                    for thr in thr_key_list:
+                        all_res[s_key][c_cls][mname][thr] = metric._summary_row(
+                            res[s_key][c_cls][mname][thr]
+                        )
+                    x = (
+                        np.array(list(all_res[s_key][c_cls]["TETA"].values()))
+                        .astype("float")
+                        .mean(axis=0)
+                    )
+                    all_res_summary = list(x.round(decimals=2).astype("str"))
+                    all_res[s_key][c_cls][mname]["ALL"] = all_res_summary
+                if config["OUTPUT_SUMMARY"] and s_key == "COMBINED_SEQ":
+                    for t in thr_key_list:
+                        metric.print_summary_table(
+                            all_res[s_key][cls_keys[0]][mname][t],
+                            t,
+                            tracker,
+                            cls_keys[0],
+                        )
+
+        if config["OUTPUT_TEM_RAW_DATA"]:
+            out_file = os.path.join(output_fol, "teta_summary_results.pth")
+            pickle.dump(all_res, open(out_file, "wb"))
+            print("Saved the TETA summary results.")
+
+        # output
+        output_res[dname][mname] = all_res[s_key][cls_keys[0]][mname][t]
+        output_msg[dname][tracker] = "Success"
+
+        return output_res, output_msg
+
+
+@_timing.time
+def eval_sequence(seq, dataset, tracker, class_list, metrics_list, metric_names):
+    """Function for evaluating a single sequence."""
+    raw_data = dataset.get_raw_seq_data(tracker, seq)
+    seq_res = {}
+
+    if "TETA" in metric_names:
+        thresholds = [50]
+        data_all_class = dataset.get_preprocessed_seq_data(
+            raw_data, "all", thresholds=thresholds
+        )
+        teta = metrics_list[metric_names.index("TETA")]
+        assignment = teta.compute_global_assignment(data_all_class)
+
+        # create a dict to save Cls_FP for each class in different thr.
+        cls_fp = {
+            key: {
+                cls: np.zeros((len(np.arange(0.5, 0.99, 0.05)))) for cls in class_list
+            }
+            for key in thresholds
+        }
+
+    for cls in class_list:
+        seq_res[cls] = {}
+        data = dataset.get_preprocessed_seq_data(raw_data, cls, assignment, thresholds)
+
+        for metric, mname in zip(metrics_list, metric_names):
+            if mname == "TETA":
+                seq_res[cls][mname], cls_fp, _ = metric.eval_sequence(
+                    data, cls, dataset.clsid2cls_name, cls_fp
+                )
+            else:
+                seq_res[cls][mname] = metric.eval_sequence(data)
+
+    if "TETA" in metric_names:
+        for thr in thresholds:
+            for cls in class_list:
+                seq_res[cls]["TETA"][thr]["Cls_FP"] += cls_fp[thr][cls]
+
+    return seq_res

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/metrics/__init__.py

@@ -0,0 +1,6 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+from .teta import TETA

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/metrics/_base_metric.py

@@ -0,0 +1,150 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+from abc import ABC, abstractmethod
+
+import numpy as np
+
+from .. import _timing
+from ..utils import TrackEvalException
+
+
+class _BaseMetric(ABC):
+    @abstractmethod
+    def __init__(self):
+        self.plottable = False
+        self.integer_fields = []
+        self.float_fields = []
+        self.array_labels = []
+        self.integer_array_fields = []
+        self.float_array_fields = []
+        self.fields = []
+        self.summary_fields = []
+        self.registered = False
+
+    #####################################################################
+    # Abstract functions for subclasses to implement
+
+    @_timing.time
+    @abstractmethod
+    def eval_sequence(self, data):
+        ...
+
+    @abstractmethod
+    def combine_sequences(self, all_res):
+        ...
+
+    @abstractmethod
+    def combine_classes_class_averaged(self, all_res, ignore_empty=False):
+        ...
+
+    @abstractmethod
+    def combine_classes_det_averaged(self, all_res):
+        ...
+
+    def plot_single_tracker_results(self, all_res, tracker, output_folder, cls):
+        """Plot results, only valid for metrics with self.plottable."""
+        if self.plottable:
+            raise NotImplementedError(
+                f"plot_results is not implemented for metric {self.get_name()}"
+            )
+        else:
+            pass
+
+    #####################################################################
+    # Helper functions which are useful for all metrics:
+
+    @classmethod
+    def get_name(cls):
+        return cls.__name__
+
+    @staticmethod
+    def _combine_sum(all_res, field):
+        """Combine sequence results via sum"""
+        return sum([all_res[k][field] for k in all_res.keys()])
+
+    @staticmethod
+    def _combine_weighted_av(all_res, field, comb_res, weight_field):
+        """Combine sequence results via weighted average."""
+        return sum(
+            [all_res[k][field] * all_res[k][weight_field] for k in all_res.keys()]
+        ) / np.maximum(1.0, comb_res[weight_field])
+
+    def print_table(self, table_res, tracker, cls):
+        """Print table of results for all sequences."""
+        print("")
+        metric_name = self.get_name()
+        self._row_print(
+            [metric_name + ": " + tracker + "-" + cls] + self.summary_fields
+        )
+        for seq, results in sorted(table_res.items()):
+            if seq == "COMBINED_SEQ":
+                continue
+            summary_res = self._summary_row(results)
+            self._row_print([seq] + summary_res)
+        summary_res = self._summary_row(table_res["COMBINED_SEQ"])
+        self._row_print(["COMBINED"] + summary_res)
+
+    def _summary_row(self, results_):
+        vals = []
+        for h in self.summary_fields:
+            if h in self.float_array_fields:
+                vals.append("{0:1.5g}".format(100 * np.mean(results_[h])))
+            elif h in self.float_fields:
+                vals.append("{0:1.5g}".format(100 * float(results_[h])))
+            elif h in self.integer_fields:
+                vals.append("{0:d}".format(int(results_[h])))
+            else:
+                raise NotImplementedError(
+                    "Summary function not implemented for this field type."
+                )
+        return vals
+
+    @staticmethod
+    def _row_print(*argv):
+        """Print results in evenly spaced rows, with more space in first row."""
+        if len(argv) == 1:
+            argv = argv[0]
+        to_print = "%-35s" % argv[0]
+        for v in argv[1:]:
+            to_print += "%-10s" % str(v)
+        print(to_print)
+
+    def summary_results(self, table_res):
+        """Return a simple summary of final results for a tracker."""
+        return dict(
+            zip(self.summary_fields, self._summary_row(table_res["COMBINED_SEQ"]),)
+        )
+
+    def detailed_results(self, table_res):
+        """Return detailed final results for a tracker."""
+        # Get detailed field information
+        detailed_fields = self.float_fields + self.integer_fields
+        for h in self.float_array_fields + self.integer_array_fields:
+            for alpha in [int(100 * x) for x in self.array_labels]:
+                detailed_fields.append(h + "___" + str(alpha))
+            detailed_fields.append(h + "___AUC")
+
+        # Get detailed results
+        detailed_results = {}
+        for seq, res in table_res.items():
+            detailed_row = self._detailed_row(res)
+            if len(detailed_row) != len(detailed_fields):
+                raise TrackEvalException(
+                    f"Field names and data have different sizes "
+                    f"({len(detailed_row)} and {len(detailed_fields)})"
+                )
+            detailed_results[seq] = dict(zip(detailed_fields, detailed_row))
+        return detailed_results
+
+    def _detailed_row(self, res):
+        detailed_row = []
+        for h in self.float_fields + self.integer_fields:
+            detailed_row.append(res[h])
+        for h in self.float_array_fields + self.integer_array_fields:
+            for i, _ in enumerate([int(100 * x) for x in self.array_labels]):
+                detailed_row.append(res[h][i])
+            detailed_row.append(np.mean(res[h]))
+        return detailed_row

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/metrics/teta.py

@@ -0,0 +1,401 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+"""Track Every Thing Accuracy metric."""
+
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+
+from .. import _timing
+from ._base_metric import _BaseMetric
+
+EPS = np.finfo("float").eps  # epsilon
+
+
+class TETA(_BaseMetric):
+    """TETA metric."""
+
+    def __init__(self, exhaustive=False, config=None):
+        """Initialize metric."""
+        super().__init__()
+        self.plottable = True
+        self.array_labels = np.arange(0.0, 0.99, 0.05)
+        self.cls_array_labels = np.arange(0.5, 0.99, 0.05)
+
+        self.integer_array_fields = [
+            "Loc_TP",
+            "Loc_FN",
+            "Loc_FP",
+            "Cls_TP",
+            "Cls_FN",
+            "Cls_FP",
+        ]
+        self.float_array_fields = (
+            ["TETA", "LocA", "AssocA", "ClsA"]
+            + ["LocRe", "LocPr"]
+            + ["AssocRe", "AssocPr"]
+            + ["ClsRe", "ClsPr"]
+        )
+        self.fields = self.float_array_fields + self.integer_array_fields
+        self.summary_fields = self.float_array_fields
+        self.exhaustive = exhaustive
+
+    def compute_global_assignment(self, data_thr, alpha=0.5):
+        """Compute global assignment of TP."""
+        res = {
+            thr: {t: {} for t in range(data_thr[thr]["num_timesteps"])}
+            for thr in data_thr
+        }
+
+        for thr in data_thr:
+            data = data_thr[thr]
+            # return empty result if tracker or gt sequence is empty
+            if data["num_tk_overlap_dets"] == 0 or data["num_gt_dets"] == 0:
+                return res
+
+            # global alignment score
+            ga_score, _, _ = self.compute_global_alignment_score(data)
+
+            # calculate scores for each timestep
+            for t, (gt_ids_t, tk_ids_t) in enumerate(
+                zip(data["gt_ids"], data["tk_ids"])
+            ):
+                # get matches optimizing for TETA
+                amatch_rows, amatch_cols = self.compute_matches(
+                    data, t, ga_score, gt_ids_t, tk_ids_t, alpha=alpha
+                )
+                gt_ids = [data["gt_id_map"][tid] for tid in gt_ids_t[amatch_rows[0]]]
+                matched_ids = [
+                    data["tk_id_map"][tid] for tid in tk_ids_t[amatch_cols[0]]
+                ]
+                res[thr][t] = dict(zip(gt_ids, matched_ids))
+
+        return res
+
+    def eval_sequence_single_thr(self, data, cls, cid2clsname, cls_fp_thr, thr):
+        """Computes TETA metric for one threshold for one sequence."""
+        res = {}
+        class_info_list = []
+        for field in self.float_array_fields + self.integer_array_fields:
+            if field.startswith("Cls"):
+                res[field] = np.zeros(len(self.cls_array_labels), dtype=float)
+            else:
+                res[field] = np.zeros((len(self.array_labels)), dtype=float)
+
+        # return empty result if tracker or gt sequence is empty
+        if data["num_tk_overlap_dets"] == 0:
+            res["Loc_FN"] = data["num_gt_dets"] * np.ones(
+                (len(self.array_labels)), dtype=float
+            )
+            if self.exhaustive:
+                cls_fp_thr[cls] = data["num_tk_cls_dets"] * np.ones(
+                    (len(self.cls_array_labels)), dtype=float
+                )
+            res = self._compute_final_fields(res)
+            return res, cls_fp_thr, class_info_list
+
+        if data["num_gt_dets"] == 0:
+            if self.exhaustive:
+                cls_fp_thr[cls] = data["num_tk_cls_dets"] * np.ones(
+                    (len(self.cls_array_labels)), dtype=float
+                )
+            res = self._compute_final_fields(res)
+            return res, cls_fp_thr, class_info_list
+
+        # global alignment score
+        ga_score, gt_id_count, tk_id_count = self.compute_global_alignment_score(data)
+        matches_counts = [np.zeros_like(ga_score) for _ in self.array_labels]
+
+        # calculate scores for each timestep
+        for t, (gt_ids_t, tk_ids_t, tk_overlap_ids_t, tk_cls_ids_t) in enumerate(
+            zip(
+                data["gt_ids"],
+                data["tk_ids"],
+                data["tk_overlap_ids"],
+                data["tk_class_eval_tk_ids"],
+            )
+        ):
+            # deal with the case that there are no gt_det/tk_det in a timestep
+            if len(gt_ids_t) == 0:
+                if self.exhaustive:
+                    cls_fp_thr[cls] += len(tk_cls_ids_t)
+                continue
+
+            # get matches optimizing for TETA
+            amatch_rows, amatch_cols = self.compute_matches(
+                data, t, ga_score, gt_ids_t, tk_ids_t, list(self.array_labels)
+            )
+
+            # map overlap_ids to original ids.
+            if len(tk_overlap_ids_t) != 0:
+                sorter = np.argsort(tk_ids_t)
+                indexes = sorter[
+                    np.searchsorted(tk_ids_t, tk_overlap_ids_t, sorter=sorter)
+                ]
+                sim_t = data["sim_scores"][t][:, indexes]
+                fpl_candidates = tk_overlap_ids_t[(sim_t >= (thr / 100)).any(axis=0)]
+                fpl_candidates_ori_ids_t = np.array(
+                    [data["tk_id_map"][tid] for tid in fpl_candidates]
+                )
+            else:
+                fpl_candidates_ori_ids_t = []
+
+            if self.exhaustive:
+                cls_fp_thr[cls] += len(tk_cls_ids_t) - len(tk_overlap_ids_t)
+
+            # calculate and accumulate basic statistics
+            for a, alpha in enumerate(self.array_labels):
+                match_row, match_col = amatch_rows[a], amatch_cols[a]
+                num_matches = len(match_row)
+                matched_ori_ids = set(
+                    [data["tk_id_map"][tid] for tid in tk_ids_t[match_col]]
+                )
+                match_tk_cls = data["tk_classes"][t][match_col]
+                wrong_tk_cls = match_tk_cls[match_tk_cls != data["gt_classes"][t]]
+
+                num_class_and_det_matches = np.sum(
+                    match_tk_cls == data["gt_classes"][t]
+                )
+
+                if alpha >= 0.5:
+                    for cid in wrong_tk_cls:
+                        if cid in cid2clsname:
+                            cname = cid2clsname[cid]
+                            cls_fp_thr[cname][a - 10] += 1
+                    res["Cls_TP"][a - 10] += num_class_and_det_matches
+                    res["Cls_FN"][a - 10] += num_matches - num_class_and_det_matches
+
+                res["Loc_TP"][a] += num_matches
+                res["Loc_FN"][a] += len(gt_ids_t) - num_matches
+                res["Loc_FP"][a] += len(set(fpl_candidates_ori_ids_t) - matched_ori_ids)
+
+                if num_matches > 0:
+                    matches_counts[a][gt_ids_t[match_row], tk_ids_t[match_col]] += 1
+
+        # calculate AssocA, AssocRe, AssocPr
+        self.compute_association_scores(res, matches_counts, gt_id_count, tk_id_count)
+
+        # calculate final scores
+        res = self._compute_final_fields(res)
+        return res, cls_fp_thr, class_info_list
+
+    def compute_global_alignment_score(self, data):
+        """Computes global alignment score."""
+        num_matches = np.zeros((data["num_gt_ids"], data["num_tk_ids"]))
+        gt_id_count = np.zeros((data["num_gt_ids"], 1))
+        tk_id_count = np.zeros((1, data["num_tk_ids"]))
+
+        # loop through each timestep and accumulate global track info.
+        for t, (gt_ids_t, tk_ids_t) in enumerate(zip(data["gt_ids"], data["tk_ids"])):
+            # count potential matches between ids in each time step
+            # these are normalized, weighted by match similarity
+            sim = data["sim_scores"][t]
+            sim_iou_denom = sim.sum(0, keepdims=True) + sim.sum(1, keepdims=True) - sim
+            sim_iou = np.zeros_like(sim)
+            mask = sim_iou_denom > (0 + EPS)
+            sim_iou[mask] = sim[mask] / sim_iou_denom[mask]
+            num_matches[gt_ids_t[:, None], tk_ids_t[None, :]] += sim_iou
+
+            # calculate total number of dets for each gt_id and tk_id.
+            gt_id_count[gt_ids_t] += 1
+            tk_id_count[0, tk_ids_t] += 1
+
+        # Calculate overall Jaccard alignment score between IDs
+        ga_score = num_matches / (gt_id_count + tk_id_count - num_matches)
+        return ga_score, gt_id_count, tk_id_count
+
+    def compute_matches(self, data, t, ga_score, gt_ids, tk_ids, alpha):
+        """Compute matches based on alignment score."""
+        sim = data["sim_scores"][t]
+        score_mat = ga_score[gt_ids[:, None], tk_ids[None, :]] * sim
+        # Hungarian algorithm to find best matches
+        match_rows, match_cols = linear_sum_assignment(-score_mat)
+
+        if not isinstance(alpha, list):
+            alpha = [alpha]
+        alpha_match_rows, alpha_match_cols = [], []
+        for a in alpha:
+            matched_mask = sim[match_rows, match_cols] >= a - EPS
+            alpha_match_rows.append(match_rows[matched_mask])
+            alpha_match_cols.append(match_cols[matched_mask])
+        return alpha_match_rows, alpha_match_cols
+
+    def compute_association_scores(self, res, matches_counts, gt_id_count, tk_id_count):
+        """Calculate association scores for each alpha.
+
+        First calculate scores per gt_id/tk_id combo,
+        and then average over the number of detections.
+        """
+        for a, _ in enumerate(self.array_labels):
+            matches_count = matches_counts[a]
+            ass_a = matches_count / np.maximum(
+                1, gt_id_count + tk_id_count - matches_count
+            )
+            res["AssocA"][a] = np.sum(matches_count * ass_a) / np.maximum(
+                1, res["Loc_TP"][a]
+            )
+            ass_re = matches_count / np.maximum(1, gt_id_count)
+            res["AssocRe"][a] = np.sum(matches_count * ass_re) / np.maximum(
+                1, res["Loc_TP"][a]
+            )
+            ass_pr = matches_count / np.maximum(1, tk_id_count)
+            res["AssocPr"][a] = np.sum(matches_count * ass_pr) / np.maximum(
+                1, res["Loc_TP"][a]
+            )
+
+    @_timing.time
+    def eval_sequence(self, data, cls, cls_id_name_mapping, cls_fp):
+        """Evaluate a single sequence across all thresholds."""
+        res = {}
+        class_info_dict = {}
+
+        for thr in data:
+            res[thr], cls_fp[thr], cls_info = self.eval_sequence_single_thr(
+                data[thr], cls, cls_id_name_mapping, cls_fp[thr], thr
+            )
+            class_info_dict[thr] = cls_info
+
+        return res, cls_fp, class_info_dict
+
+    def combine_sequences(self, all_res):
+        """Combines metrics across all sequences."""
+        data = {}
+        res = {}
+
+        if all_res:
+            thresholds = list(list(all_res.values())[0].keys())
+        else:
+            thresholds = [50]
+        for thr in thresholds:
+            data[thr] = {}
+            for seq_key in all_res:
+                data[thr][seq_key] = all_res[seq_key][thr]
+        for thr in thresholds:
+            res[thr] = self._combine_sequences_thr(data[thr])
+
+        return res
+
+    def _combine_sequences_thr(self, all_res):
+        """Combines sequences over each threshold."""
+        res = {}
+        for field in self.integer_array_fields:
+            res[field] = self._combine_sum(all_res, field)
+        for field in ["AssocRe", "AssocPr", "AssocA"]:
+            res[field] = self._combine_weighted_av(
+                all_res, field, res, weight_field="Loc_TP"
+            )
+        res = self._compute_final_fields(res)
+        return res
+
+    def combine_classes_class_averaged(self, all_res, ignore_empty=False):
+        """Combines metrics across all classes by averaging over classes.
+
+        If 'ignore_empty' is True, then it only sums over classes
+        with at least one gt or predicted detection.
+        """
+        data = {}
+        res = {}
+        if all_res:
+            thresholds = list(list(all_res.values())[0].keys())
+        else:
+            thresholds = [50]
+        for thr in thresholds:
+            data[thr] = {}
+            for cls_key in all_res:
+                data[thr][cls_key] = all_res[cls_key][thr]
+        for thr in data:
+            res[thr] = self._combine_classes_class_averaged_thr(
+                data[thr], ignore_empty=ignore_empty
+            )
+        return res
+
+    def _combine_classes_class_averaged_thr(self, all_res, ignore_empty=False):
+        """Combines classes over each threshold."""
+        res = {}
+
+        def check_empty(val):
+            """Returns True if empty."""
+            return not (val["Loc_TP"] + val["Loc_FN"] + val["Loc_FP"] > 0 + EPS).any()
+
+        for field in self.integer_array_fields:
+            if ignore_empty:
+                res_field = {k: v for k, v in all_res.items() if not check_empty(v)}
+            else:
+                res_field = {k: v for k, v in all_res.items()}
+            res[field] = self._combine_sum(res_field, field)
+
+        for field in self.float_array_fields:
+            if ignore_empty:
+                res_field = [v[field] for v in all_res.values() if not check_empty(v)]
+            else:
+                res_field = [v[field] for v in all_res.values()]
+            res[field] = np.mean(res_field, axis=0)
+        return res
+
+    def combine_classes_det_averaged(self, all_res):
+        """Combines metrics across all classes by averaging over detections."""
+        data = {}
+        res = {}
+        if all_res:
+            thresholds = list(list(all_res.values())[0].keys())
+        else:
+            thresholds = [50]
+        for thr in thresholds:
+            data[thr] = {}
+            for cls_key in all_res:
+                data[thr][cls_key] = all_res[cls_key][thr]
+        for thr in data:
+            res[thr] = self._combine_classes_det_averaged_thr(data[thr])
+        return res
+
+    def _combine_classes_det_averaged_thr(self, all_res):
+        """Combines detections over each threshold."""
+        res = {}
+        for field in self.integer_array_fields:
+            res[field] = self._combine_sum(all_res, field)
+        for field in ["AssocRe", "AssocPr", "AssocA"]:
+            res[field] = self._combine_weighted_av(
+                all_res, field, res, weight_field="Loc_TP"
+            )
+        res = self._compute_final_fields(res)
+        return res
+
+    @staticmethod
+    def _compute_final_fields(res):
+        """Calculate final metric values.
+
+        This function is used both for both per-sequence calculation,
+        and in combining values across sequences.
+        """
+        # LocA
+        res["LocRe"] = res["Loc_TP"] / np.maximum(1, res["Loc_TP"] + res["Loc_FN"])
+        res["LocPr"] = res["Loc_TP"] / np.maximum(1, res["Loc_TP"] + res["Loc_FP"])
+        res["LocA"] = res["Loc_TP"] / np.maximum(
+            1, res["Loc_TP"] + res["Loc_FN"] + res["Loc_FP"]
+        )
+
+        # ClsA
+        res["ClsRe"] = res["Cls_TP"] / np.maximum(1, res["Cls_TP"] + res["Cls_FN"])
+        res["ClsPr"] = res["Cls_TP"] / np.maximum(1, res["Cls_TP"] + res["Cls_FP"])
+        res["ClsA"] = res["Cls_TP"] / np.maximum(
+            1, res["Cls_TP"] + res["Cls_FN"] + res["Cls_FP"]
+        )
+
+        res["ClsRe"] = np.mean(res["ClsRe"])
+        res["ClsPr"] = np.mean(res["ClsPr"])
+        res["ClsA"] = np.mean(res["ClsA"])
+
+        res["TETA"] = (res["LocA"] + res["AssocA"] + res["ClsA"]) / 3
+
+        return res
+
+    def print_summary_table(self, thr_res, thr, tracker, cls):
+        """Prints summary table of results."""
+        print("")
+        metric_name = self.get_name()
+        self._row_print(
+            [f"{metric_name}{str(thr)}: {tracker}-{cls}"] + self.summary_fields
+        )
+        self._row_print(["COMBINED"] + thr_res)

third_party/GraspGen/sam3/sam3/eval/teta_eval_toolkit/utils.py

@@ -0,0 +1,48 @@
+# fmt: off
+# flake8: noqa
+
+# pyre-unsafe
+
+import csv
+import os
+from collections import OrderedDict
+
+
+def validate_metrics_list(metrics_list):
+    """Get names of metric class and ensures they are unique, further checks that the fields within each metric class
+    do not have overlapping names.
+    """
+    metric_names = [metric.get_name() for metric in metrics_list]
+    # check metric names are unique
+    if len(metric_names) != len(set(metric_names)):
+        raise TrackEvalException(
+            "Code being run with multiple metrics of the same name"
+        )
+    fields = []
+    for m in metrics_list:
+        fields += m.fields
+    # check metric fields are unique
+    if len(fields) != len(set(fields)):
+        raise TrackEvalException(
+            "Code being run with multiple metrics with fields of the same name"
+        )
+    return metric_names
+
+
+def get_track_id_str(ann):
+    """Get name of track ID in annotation."""
+    if "track_id" in ann:
+        tk_str = "track_id"
+    elif "instance_id" in ann:
+        tk_str = "instance_id"
+    elif "scalabel_id" in ann:
+        tk_str = "scalabel_id"
+    else:
+        assert False, "No track/instance ID."
+    return tk_str
+
+
+class TrackEvalException(Exception):
+    """Custom exception for catching expected errors."""
+
+    ...

third_party/GraspGen/sam3/sam3/eval/ytvis_coco_wrapper.py

@@ -0,0 +1,148 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+# pyre-unsafe
+
+import copy
+import json
+import logging
+
+import numpy as np
+import pycocotools.mask as mask_util
+from pycocotools.coco import COCO
+from typing_extensions import override
+
+
+class YTVIS(COCO):
+    """
+    Helper class for reading YT-VIS annotations
+    """
+
+    @override
+    def __init__(self, annotation_file: str = None, ignore_gt_cats: bool = True):
+        """
+        Args:
+            annotation_file: Path to the annotation file
+            ignore_gt_cats: If True, we ignore the ground truth categories and replace them with a dummy "object" category. This is useful for Phrase AP evaluation.
+        """
+        self.ignore_gt_cats = ignore_gt_cats
+        super().__init__(annotation_file=annotation_file)
+
+    @override
+    def createIndex(self):
+        # We rename some keys to match the COCO format before creating the index.
+        if "annotations" in self.dataset:
+            for ann in self.dataset["annotations"]:
+                if "video_id" in ann:
+                    ann["image_id"] = int(ann.pop("video_id"))
+                if self.ignore_gt_cats:
+                    ann["category_id"] = -1
+                else:
+                    ann["category_id"] = int(ann["category_id"])
+                if "bboxes" in ann:
+                    # note that in some datasets we load under this YTVIS class,
+                    # some "bboxes" could be None for when the GT object is invisible,
+                    # so we replace them with [0, 0, 0, 0]
+                    ann["bboxes"] = [
+                        bbox if bbox is not None else [0, 0, 0, 0]
+                        for bbox in ann["bboxes"]
+                    ]
+                if "areas" in ann:
+                    # similar to "bboxes", some areas could be None for when the GT
+                    # object is invisible, so we replace them with 0
+                    areas = [a if a is not None else 0 for a in ann["areas"]]
+                    # Compute average area of tracklet
+                    ann["area"] = np.mean(areas)
+        if "videos" in self.dataset:
+            for vid in self.dataset["videos"]:
+                vid["id"] = int(vid["id"])
+            self.dataset["images"] = self.dataset.pop("videos")
+
+        if self.ignore_gt_cats:
+            self.dataset["categories"] = [
+                {"supercategory": "object", "id": -1, "name": "object"}
+            ]
+        else:
+            for cat in self.dataset["categories"]:
+                cat["id"] = int(cat["id"])
+        super().createIndex()
+
+    @override
+    def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
+        if len(areaRng) > 0:
+            logging.warning(
+                "Note that we filter out objects based on their *average* area across the video, not per frame area"
+            )
+
+        return super().getAnnIds(imgIds=imgIds, catIds=catIds, iscrowd=iscrowd)
+
+    @override
+    def showAnns(self, anns, draw_bbox=False):
+        raise NotImplementedError("Showing annotations is not supported")
+
+    @override
+    def loadRes(self, resFile):
+        # Adapted from COCO.loadRes to support tracklets/masklets
+        res = YTVIS(ignore_gt_cats=self.ignore_gt_cats)
+        res.dataset["images"] = [img for img in self.dataset["images"]]
+
+        if type(resFile) == str:
+            with open(resFile) as f:
+                anns = json.load(f)
+        elif type(resFile) == np.ndarray:
+            anns = self.loadNumpyAnnotations(resFile)
+        else:
+            anns = resFile
+        assert type(anns) == list, "results is not an array of objects"
+        annsImgIds = [ann["image_id"] for ann in anns]
+        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), (
+            "Results do not correspond to current coco set"
+        )
+        if "bboxes" in anns[0] and not anns[0]["bboxes"] == []:
+            res.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
+            for id, ann in enumerate(anns):
+                bbs = [(bb if bb is not None else [0, 0, 0, 0]) for bb in ann["bboxes"]]
+                xxyy = [[bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]] for bb in bbs]
+                if not "segmentations" in ann:
+                    ann["segmentations"] = [
+                        [[x1, y1, x1, y2, x2, y2, x2, y1]] for (x1, x2, y1, y2) in xxyy
+                    ]
+                ann["areas"] = [bb[2] * bb[3] for bb in bbs]
+                # NOTE: We also compute average area of a tracklet across video, allowing us to compute area based mAP.
+                ann["area"] = np.mean(ann["areas"])
+                ann["id"] = id + 1
+                ann["iscrowd"] = 0
+        elif "segmentations" in anns[0]:
+            res.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
+            for id, ann in enumerate(anns):
+                ann["bboxes"] = [
+                    mask_util.toBbox(segm) for segm in ann["segmentations"]
+                ]
+                if "areas" not in ann:
+                    ann["areas"] = [
+                        mask_util.area(segm) for segm in ann["segmentations"]
+                    ]
+                # NOTE: We also compute average area of a tracklet across video, allowing us to compute area based mAP.
+                ann["area"] = np.mean(ann["areas"])
+                ann["id"] = id + 1
+                ann["iscrowd"] = 0
+
+        res.dataset["annotations"] = anns
+        res.createIndex()
+        return res
+
+    @override
+    def download(self, tarDir=None, imgIds=[]):
+        raise NotImplementedError
+
+    @override
+    def loadNumpyAnnotations(self, data):
+        raise NotImplementedError("We don't support numpy annotations for now")
+
+    @override
+    def annToRLE(self, ann):
+        raise NotImplementedError("We expect masks to be already in RLE format")
+
+    @override
+    def annToMask(self, ann):
+        raise NotImplementedError("We expect masks to be already in RLE format")

third_party/GraspGen/sam3/sam3/model/__init__.py

@@ -0,0 +1,3 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe

third_party/GraspGen/sam3/sam3/model/act_ckpt_utils.py

@@ -0,0 +1,116 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import inspect
+from functools import wraps
+from typing import Callable, TypeVar, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+from torch.utils._pytree import tree_map_only
+
+# Type variables for better type hinting
+T = TypeVar("T")
+Module = TypeVar("Module", bound=nn.Module)
+
+
+def activation_ckpt_wrapper(module: Union[nn.Module, Callable]) -> Callable:
+    """
+    Wraps a given module to enable or disable activation checkpointing.
+
+    Activation checkpointing (gradient checkpointing) trades compute for memory by
+    recomputing intermediate activations during the backward pass instead of storing
+    them in memory during the forward pass.
+
+    When activation checkpointing is enabled, the wrapper expects only keyword arguments,
+    and it maps these to positional arguments based on the module's signature.
+
+    Args:
+        module: The module or function to wrap with activation checkpointing
+
+    Returns:
+        A wrapped callable that supports activation checkpointing
+
+    Usage:
+        The returned wrapper function can be called with the same arguments as the
+        original module, with an additional `act_ckpt_enable` keyword argument to control
+        activation checkpointing and optional `use_reentrant` parameter.
+
+    Example:
+        ```python
+        wrapped_module = activation_ckpt_wrapper(my_module)
+        output = wrapped_module(x=input_tensor, y=another_tensor, act_ckpt_enable=True)
+        ```
+    """
+
+    @wraps(module)
+    def act_ckpt_wrapper(
+        *args, act_ckpt_enable: bool = True, use_reentrant: bool = False, **kwargs
+    ):
+        if act_ckpt_enable:
+            if len(args) > 0:
+                raise ValueError(
+                    "This wrapper expects keyword arguments only when `act_ckpt_enable=True`"
+                )
+            # Get the signature of the target function/module
+            callable_fn = module.forward if isinstance(module, nn.Module) else module
+            sig = inspect.signature(callable_fn)
+            # Create a mapping of parameter names to their default values
+            param_defaults = {
+                name: param.default for name, param in sig.parameters.items()
+            }
+            args = []
+            for p_name in param_defaults.keys():
+                if p_name in kwargs:
+                    args.append(kwargs.pop(p_name))
+                elif param_defaults[p_name] is not inspect.Parameter.empty:
+                    # Set arg to default value if it's not in kwargs. Useful for primitive types or args that default to None
+                    args.append(param_defaults[p_name])
+                elif (
+                    sig.parameters[p_name].kind is not inspect.Parameter.VAR_KEYWORD
+                ):  # Skip **kwargs parameter
+                    raise ValueError(f"Missing positional argument: {p_name}")
+
+            # Scan remaining kwargs for torch.Tensor
+            remaining_keys = list(kwargs.keys())
+            for key in remaining_keys:
+                if isinstance(kwargs[key], torch.Tensor):
+                    # Remove the tensor from kwargs, assuming it's not required by the module.
+                    # If it is required, the module's signature should be modified to accept it as a positional or keyword argument.
+                    kwargs[key] = "_REMOVED_BY_ACT_CKPT_WRAPPER_"
+
+            ret = checkpoint.checkpoint(
+                module, *args, use_reentrant=use_reentrant, **kwargs
+            )
+        else:
+            ret = module(*args, **kwargs)
+
+        return ret
+
+    return act_ckpt_wrapper
+
+
+def clone_output_wrapper(f: Callable[..., T]) -> Callable[..., T]:
+    """
+    Clone the CUDA output tensors of a function to avoid in-place operations.
+
+    This wrapper is useful when working with torch.compile to prevent errors
+    related to in-place operations on tensors.
+
+    Args:
+        f: The function whose CUDA tensor outputs should be cloned
+
+    Returns:
+        A wrapped function that clones any CUDA tensor outputs
+    """
+
+    @wraps(f)
+    def wrapped(*args, **kwargs):
+        outputs = f(*args, **kwargs)
+        return tree_map_only(
+            torch.Tensor, lambda t: t.clone() if t.is_cuda else t, outputs
+        )
+
+    return wrapped

third_party/GraspGen/sam3/sam3/model/box_ops.py

@@ -0,0 +1,219 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+"""
+Utilities for bounding box manipulation and GIoU.
+"""
+
+from typing import Tuple
+
+import torch
+
+
+def box_cxcywh_to_xyxy(x):
+    x_c, y_c, w, h = x.unbind(-1)
+    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
+    return torch.stack(b, dim=-1)
+
+
+def box_cxcywh_to_xywh(x):
+    x_c, y_c, w, h = x.unbind(-1)
+    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (w), (h)]
+    return torch.stack(b, dim=-1)
+
+
+def box_xywh_to_xyxy(x):
+    x, y, w, h = x.unbind(-1)
+    b = [(x), (y), (x + w), (y + h)]
+    return torch.stack(b, dim=-1)
+
+
+def box_xywh_to_cxcywh(x):
+    x, y, w, h = x.unbind(-1)
+    b = [(x + 0.5 * w), (y + 0.5 * h), (w), (h)]
+    return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_xywh(x):
+    x, y, X, Y = x.unbind(-1)
+    b = [(x), (y), (X - x), (Y - y)]
+    return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_cxcywh(x):
+    x0, y0, x1, y1 = x.unbind(-1)
+    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
+    return torch.stack(b, dim=-1)
+
+
+def box_area(boxes):
+    """
+    Batched version of box area. Boxes should be in [x0, y0, x1, y1] format.
+
+    Inputs:
+    - boxes: Tensor of shape (..., 4)
+
+    Returns:
+    - areas: Tensor of shape (...,)
+    """
+    x0, y0, x1, y1 = boxes.unbind(-1)
+    return (x1 - x0) * (y1 - y0)
+
+
+def masks_to_boxes(masks):
+    """Compute the bounding boxes around the provided masks
+
+    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
+
+    Returns a [N, 4] tensors, with the boxes in xyxy format
+    """
+    if masks.numel() == 0:
+        return torch.zeros((0, 4), device=masks.device)
+
+    h, w = masks.shape[-2:]
+
+    y = torch.arange(0, h, dtype=torch.float, device=masks.device)
+    x = torch.arange(0, w, dtype=torch.float, device=masks.device)
+    y, x = torch.meshgrid(y, x)
+
+    x_mask = masks * x.unsqueeze(0)
+    x_max = x_mask.flatten(1).max(-1)[0] + 1
+    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    y_mask = masks * y.unsqueeze(0)
+    y_max = y_mask.flatten(1).max(-1)[0] + 1
+    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    boxes = torch.stack([x_min, y_min, x_max, y_max], 1)
+    # Invalidate boxes corresponding to empty masks.
+    boxes = boxes * masks.flatten(-2).any(-1)
+    return boxes
+
+
+def box_iou(boxes1, boxes2):
+    """
+    Batched version of box_iou. Boxes should be in [x0, y0, x1, y1] format.
+
+    Inputs:
+    - boxes1: Tensor of shape (..., N, 4)
+    - boxes2: Tensor of shape (..., M, 4)
+
+    Returns:
+    - iou, union: Tensors of shape (..., N, M)
+    """
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+
+    # boxes1: (..., N, 4) -> (..., N, 1, 2)
+    # boxes2: (..., M, 4) -> (..., 1, M, 2)
+    lt = torch.max(boxes1[..., :, None, :2], boxes2[..., None, :, :2])
+    rb = torch.min(boxes1[..., :, None, 2:], boxes2[..., None, :, 2:])
+
+    wh = (rb - lt).clamp(min=0)  # (..., N, M, 2)
+    inter = wh[..., 0] * wh[..., 1]  # (..., N, M)
+
+    union = area1[..., None] + area2[..., None, :] - inter
+
+    iou = inter / union
+    return iou, union
+
+
+def generalized_box_iou(boxes1, boxes2):
+    """
+    Batched version of Generalized IoU from https://giou.stanford.edu/
+
+    Boxes should be in [x0, y0, x1, y1] format
+
+    Inputs:
+    - boxes1: Tensor of shape (..., N, 4)
+    - boxes2: Tensor of shape (..., M, 4)
+
+    Returns:
+    - giou: Tensor of shape (..., N, M)
+    """
+    iou, union = box_iou(boxes1, boxes2)
+
+    # boxes1: (..., N, 4) -> (..., N, 1, 2)
+    # boxes2: (..., M, 4) -> (..., 1, M, 2)
+    lt = torch.min(boxes1[..., :, None, :2], boxes2[..., None, :, :2])
+    rb = torch.max(boxes1[..., :, None, 2:], boxes2[..., None, :, 2:])
+
+    wh = (rb - lt).clamp(min=0)  # (..., N, M, 2)
+    area = wh[..., 0] * wh[..., 1]  # (..., N, M)
+
+    return iou - (area - union) / area
+
+
+@torch.jit.script
+def fast_diag_generalized_box_iou(boxes1, boxes2):
+    assert len(boxes1) == len(boxes2)
+    box1_xy = boxes1[:, 2:]
+    box1_XY = boxes1[:, :2]
+    box2_xy = boxes2[:, 2:]
+    box2_XY = boxes2[:, :2]
+    # assert (box1_xy >= box1_XY).all()
+    # assert (box2_xy >= box2_XY).all()
+    area1 = (box1_xy - box1_XY).prod(-1)
+    area2 = (box2_xy - box2_XY).prod(-1)
+
+    lt = torch.max(box1_XY, box2_XY)  # [N,2]
+    lt2 = torch.min(box1_XY, box2_XY)
+    rb = torch.min(box1_xy, box2_xy)  # [N,2]
+    rb2 = torch.max(box1_xy, box2_xy)
+
+    inter = (rb - lt).clamp(min=0).prod(-1)
+    tot_area = (rb2 - lt2).clamp(min=0).prod(-1)
+
+    union = area1 + area2 - inter
+
+    iou = inter / union
+
+    return iou - (tot_area - union) / tot_area
+
+
+@torch.jit.script
+def fast_diag_box_iou(boxes1, boxes2):
+    assert len(boxes1) == len(boxes2)
+    box1_xy = boxes1[:, 2:]
+    box1_XY = boxes1[:, :2]
+    box2_xy = boxes2[:, 2:]
+    box2_XY = boxes2[:, :2]
+    # assert (box1_xy >= box1_XY).all()
+    # assert (box2_xy >= box2_XY).all()
+    area1 = (box1_xy - box1_XY).prod(-1)
+    area2 = (box2_xy - box2_XY).prod(-1)
+
+    lt = torch.max(box1_XY, box2_XY)  # [N,2]
+    rb = torch.min(box1_xy, box2_xy)  # [N,2]
+
+    inter = (rb - lt).clamp(min=0).prod(-1)
+
+    union = area1 + area2 - inter
+
+    iou = inter / union
+
+    return iou
+
+
+def box_xywh_inter_union(
+    boxes1: torch.Tensor, boxes2: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    # Asuumes boxes in xywh format
+    assert boxes1.size(-1) == 4 and boxes2.size(-1) == 4
+    boxes1 = box_xywh_to_xyxy(boxes1)
+    boxes2 = box_xywh_to_xyxy(boxes2)
+    box1_tl_xy = boxes1[..., :2]
+    box1_br_xy = boxes1[..., 2:]
+    box2_tl_xy = boxes2[..., :2]
+    box2_br_xy = boxes2[..., 2:]
+    area1 = (box1_br_xy - box1_tl_xy).prod(-1)
+    area2 = (box2_br_xy - box2_tl_xy).prod(-1)
+
+    assert (area1 >= 0).all() and (area2 >= 0).all()
+    tl = torch.max(box1_tl_xy, box2_tl_xy)
+    br = torch.min(box1_br_xy, box2_br_xy)
+
+    inter = (br - tl).clamp(min=0).prod(-1)
+    union = area1 + area2 - inter
+
+    return inter, union

third_party/GraspGen/sam3/sam3/model/data_misc.py

@@ -0,0 +1,210 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+"""
+Misc functions, including distributed helpers.
+"""
+
+import collections
+import re
+from dataclasses import dataclass, field as field_ptr_behaviour, fields, is_dataclass
+from typing import Any, get_args, get_origin, List, Mapping, Optional, Sequence, Union
+
+import torch
+
+
+MyTensor = Union[torch.Tensor, List[Any]]
+
+
+def interpolate(
+    input, size=None, scale_factor=None, mode="nearest", align_corners=None
+):
+    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
+    """
+    Equivalent to nn.functional.interpolate, but with support for empty channel sizes.
+    """
+    if input.numel() > 0:
+        return torch.nn.functional.interpolate(
+            input, size, scale_factor, mode, align_corners
+        )
+
+    assert input.shape[0] != 0 or input.shape[1] != 0, (
+        "At least one of the two first dimensions must be non zero"
+    )
+
+    if input.shape[1] == 0:
+        # Pytorch doesn't support null dimension on the channel dimension, so we transpose to fake a null batch dim
+        return torch.nn.functional.interpolate(
+            input.transpose(0, 1), size, scale_factor, mode, align_corners
+        ).transpose(0, 1)
+
+    # empty batch dimension is now supported in pytorch
+    return torch.nn.functional.interpolate(
+        input, size, scale_factor, mode, align_corners
+    )
+
+
+@dataclass
+class BatchedPointer:
+    stage_ids: MyTensor
+    stage_ids__type = torch.long
+    query_ids: MyTensor
+    query_ids__type = torch.long
+    object_ids: MyTensor
+    object_ids__type = torch.long
+    ptr_mask: MyTensor
+    ptr_mask__type = torch.bool
+    ptr_types: MyTensor
+    ptr_types__type = torch.long
+
+
+@dataclass
+class FindStage:
+    img_ids: MyTensor
+    img_ids__type = torch.long
+    text_ids: MyTensor
+    text_ids__type = torch.long
+
+    input_boxes: MyTensor
+    input_boxes__type = torch.float
+    input_boxes_mask: MyTensor
+    input_boxes_mask__type = torch.bool
+    input_boxes_label: MyTensor
+    input_boxes_label__type = torch.long
+
+    input_points: MyTensor
+    input_points__type = torch.float
+    input_points_mask: MyTensor
+    input_points_mask__type = torch.bool
+
+    # We track the object ids referred to by this query.
+    # This is beneficial for tracking in videos without the need for pointers.
+    object_ids: Optional[List[List]] = None  # List of objects per query
+
+
+@dataclass
+class BatchedFindTarget:
+    # The number of boxes in each find query
+    num_boxes: MyTensor
+    num_boxes__type = torch.long
+
+    # Target boxes in normalized CxCywh format
+    boxes: MyTensor
+    boxes__type = torch.float
+    # Target boxes in normalized CxCywh format but in padded representation
+    # as used in BinaryHungarianMatcherV2 (unlike the packed ones in `boxes`)
+    boxes_padded: MyTensor
+    boxes_padded__type = torch.float
+
+    # For hybrid matching, we repeat the boxes
+    repeated_boxes: MyTensor
+    repeated_boxes__type = torch.float
+
+    # Target Segmentation masks
+    segments: Optional[MyTensor]
+    segments__type = torch.bool
+
+    # Target Semantic Segmentation masks
+    semantic_segments: Optional[MyTensor]
+    semantic_segments__type = torch.bool
+
+    is_valid_segment: Optional[MyTensor]
+    is_valid_segment__type = torch.bool
+
+    # Whether annotations are exhaustive for each query
+    is_exhaustive: MyTensor
+    is_exhaustive__type = torch.bool
+
+    # The object id for each ground-truth box, in both packed and padded representations
+    object_ids: MyTensor
+    object_ids__type = torch.long
+    object_ids_padded: MyTensor
+    object_ids_padded__type = torch.long
+
+
+@dataclass
+class BatchedInferenceMetadata:
+    """All metadata required to post-process a find stage"""
+
+    # Coco id that corresponds to the "image" for evaluation by the coco evaluator
+    coco_image_id: MyTensor
+    coco_image_id__type = torch.long
+
+    # id in the original dataset, such that we can use the original evaluator
+    original_image_id: MyTensor
+    original_image_id__type = torch.long
+
+    # Original category id (if we want to use the original evaluator)
+    original_category_id: MyTensor
+    original_category_id__type = torch.int
+
+    # Size of the raw image (height, width)
+    original_size: MyTensor
+    original_size__type = torch.long
+
+    # id of the object in the media (track_id for a video)
+    object_id: MyTensor
+    object_id__type = torch.long
+
+    # index of the frame in the media (0 in the case of a single-frame media)
+    frame_index: MyTensor
+    frame_index__type = torch.long
+
+    # Adding for relations inference
+    # get_text_input: List[Optional[str]]
+
+    # Adding for TA conditional inference
+    is_conditioning_only: List[Optional[bool]]
+
+
+@dataclass
+class BatchedDatapoint:
+    img_batch: torch.Tensor
+    find_text_batch: List[str]
+    find_inputs: List[FindStage]
+    find_targets: List[BatchedFindTarget]
+    find_metadatas: List[BatchedInferenceMetadata]
+    raw_images: Optional[List[Any]] = None
+
+
+def convert_my_tensors(obj):
+    def is_optional_field(field) -> bool:
+        return get_origin(field) is Union and type(None) in get_args(field)
+
+    for field in fields(obj):
+        if is_dataclass(getattr(obj, field.name)):
+            convert_my_tensors(getattr(obj, field.name))
+            continue
+
+        field_type = field.type
+        if is_optional_field(field.type):
+            field_type = Union[get_args(field.type)[:-1]]  # Get the Optional field type
+
+        if field_type != MyTensor or getattr(obj, field.name) is None:
+            continue
+
+        elif len(getattr(obj, field.name)) and isinstance(
+            getattr(obj, field.name)[0], torch.Tensor
+        ):
+            stack_dim = 0
+            if field.name in [
+                "input_boxes",
+                "input_boxes_label",
+            ]:
+                stack_dim = 1
+            setattr(
+                obj,
+                field.name,
+                torch.stack(getattr(obj, field.name), dim=stack_dim).to(
+                    getattr(obj, field.name + "__type")
+                ),
+            )
+        else:
+            setattr(
+                obj,
+                field.name,
+                torch.as_tensor(
+                    getattr(obj, field.name), dtype=getattr(obj, field.name + "__type")
+                ),
+            )
+    return obj

third_party/GraspGen/sam3/sam3/model/decoder.py

@@ -0,0 +1,953 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+"""
+Transformer decoder.
+Inspired from Pytorch's version, adds the pre-norm variant
+"""
+
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+import torch
+from sam3.sam.transformer import RoPEAttention
+from torch import nn, Tensor
+from torchvision.ops.roi_align import RoIAlign
+
+from .act_ckpt_utils import activation_ckpt_wrapper
+from .box_ops import box_cxcywh_to_xyxy
+from .model_misc import (
+    gen_sineembed_for_position,
+    get_activation_fn,
+    get_clones,
+    inverse_sigmoid,
+    MLP,
+)
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        activation: str,
+        d_model: int,
+        dim_feedforward: int,
+        dropout: float,
+        cross_attention: nn.Module,
+        n_heads: int,
+        use_text_cross_attention: bool = False,
+    ):
+        super().__init__()
+
+        # cross attention
+        self.cross_attn = cross_attention
+        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # cross attention text
+        self.use_text_cross_attention = use_text_cross_attention
+        if use_text_cross_attention:
+            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+            self.catext_norm = nn.LayerNorm(d_model)
+
+        # self attention
+        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm2 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.activation = get_activation_fn(activation)
+        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm3 = nn.LayerNorm(d_model)
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, tgt):
+        with torch.amp.autocast(device_type="cuda", enabled=False):
+            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout4(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward(
+        self,
+        # for tgt
+        tgt: Optional[Tensor],  # nq, bs, d_model
+        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))
+        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4
+        memory_text: Optional[Tensor] = None,  # num_token, bs, d_model
+        text_attention_mask: Optional[Tensor] = None,  # bs, num_token
+        # for memory
+        memory: Optional[Tensor] = None,  # hw, bs, d_model
+        memory_key_padding_mask: Optional[Tensor] = None,
+        memory_level_start_index: Optional[Tensor] = None,  # num_levels
+        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
+        memory_pos: Optional[Tensor] = None,  # pos for memory
+        # sa
+        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention
+        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention
+        # dac
+        dac=False,
+        dac_use_selfatt_ln=True,
+        presence_token=None,
+        # skip inside deformable attn
+        identity=0.0,
+        **kwargs,  # additional kwargs for compatibility
+    ):
+        """
+        Input:
+            - tgt/tgt_query_pos: nq, bs, d_model
+            -
+        """
+        # self attention
+        if self.self_attn is not None:
+            if dac:
+                # we only apply self attention to the first half of the queries
+                assert tgt.shape[0] % 2 == 0
+                num_o2o_queries = tgt.shape[0] // 2
+                tgt_o2o = tgt[:num_o2o_queries]
+                tgt_query_pos_o2o = tgt_query_pos[:num_o2o_queries]
+                tgt_o2m = tgt[num_o2o_queries:]
+            else:
+                tgt_o2o = tgt
+                tgt_query_pos_o2o = tgt_query_pos
+
+            if presence_token is not None:
+                tgt_o2o = torch.cat([presence_token, tgt_o2o], dim=0)
+                tgt_query_pos_o2o = torch.cat(
+                    [torch.zeros_like(presence_token), tgt_query_pos_o2o], dim=0
+                )
+                tgt_query_pos = torch.cat(
+                    [torch.zeros_like(presence_token), tgt_query_pos], dim=0
+                )
+
+            q = k = self.with_pos_embed(tgt_o2o, tgt_query_pos_o2o)
+            tgt2 = self.self_attn(q, k, tgt_o2o, attn_mask=self_attn_mask)[0]
+            tgt_o2o = tgt_o2o + self.dropout2(tgt2)
+            if dac:
+                if not dac_use_selfatt_ln:
+                    tgt_o2o = self.norm2(tgt_o2o)
+                tgt = torch.cat((tgt_o2o, tgt_o2m), dim=0)  # Recombine
+                if dac_use_selfatt_ln:
+                    tgt = self.norm2(tgt)
+            else:
+                tgt = tgt_o2o
+                tgt = self.norm2(tgt)
+
+        if self.use_text_cross_attention:
+            tgt2 = self.ca_text(
+                self.with_pos_embed(tgt, tgt_query_pos),
+                memory_text,
+                memory_text,
+                key_padding_mask=text_attention_mask,
+            )[0]
+            tgt = tgt + self.catext_dropout(tgt2)
+            tgt = self.catext_norm(tgt)
+
+        if presence_token is not None:
+            presence_token_mask = torch.zeros_like(cross_attn_mask[:, :1, :])
+            cross_attn_mask = torch.cat(
+                [presence_token_mask, cross_attn_mask], dim=1
+            )  # (bs*nheads, 1+nq, hw)
+
+        # Cross attention to image
+        tgt2 = self.cross_attn(
+            query=self.with_pos_embed(tgt, tgt_query_pos),
+            key=self.with_pos_embed(memory, memory_pos),
+            value=memory,
+            attn_mask=cross_attn_mask,
+            key_padding_mask=(
+                memory_key_padding_mask.transpose(0, 1)
+                if memory_key_padding_mask is not None
+                else None
+            ),
+        )[0]
+
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # ffn
+        tgt = self.forward_ffn(tgt)
+
+        presence_token_out = None
+        if presence_token is not None:
+            presence_token_out = tgt[:1]
+            tgt = tgt[1:]
+
+        return tgt, presence_token_out
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        frozen: bool,
+        interaction_layer,
+        layer,
+        num_layers: int,
+        num_queries: int,
+        return_intermediate: bool,
+        box_refine: bool = False,
+        num_o2m_queries: int = 0,
+        dac: bool = False,
+        boxRPB: str = "none",
+        # Experimental: An object query for SAM 2 tasks
+        instance_query: bool = False,
+        # Defines the number of additional instance queries,
+        # 1 or 4 are the most likely for single vs multi mask support
+        num_instances: int = 1,  # Irrelevant if instance_query is False
+        dac_use_selfatt_ln: bool = True,
+        use_act_checkpoint: bool = False,
+        compile_mode=None,
+        presence_token: bool = False,
+        clamp_presence_logits: bool = True,
+        clamp_presence_logit_max_val: float = 10.0,
+        use_normed_output_consistently: bool = True,
+        separate_box_head_instance: bool = False,
+        separate_norm_instance: bool = False,
+        resolution: Optional[int] = None,
+        stride: Optional[int] = None,
+    ):
+        super().__init__()
+        self.d_model = d_model
+        self.layers = get_clones(layer, num_layers)
+        self.fine_layers = (
+            get_clones(interaction_layer, num_layers)
+            if interaction_layer is not None
+            else [None] * num_layers
+        )
+        self.num_layers = num_layers
+        self.num_queries = num_queries
+        self.dac = dac
+        if dac:
+            self.num_o2m_queries = num_queries
+            tot_num_queries = num_queries
+        else:
+            self.num_o2m_queries = num_o2m_queries
+            tot_num_queries = num_queries + num_o2m_queries
+        self.norm = nn.LayerNorm(d_model)
+        self.return_intermediate = return_intermediate
+        self.bbox_embed = MLP(d_model, d_model, 4, 3)
+        self.query_embed = nn.Embedding(tot_num_queries, d_model)
+        self.instance_query_embed = None
+        self.instance_query_reference_points = None
+        self.use_instance_query = instance_query
+        self.num_instances = num_instances
+        self.use_normed_output_consistently = use_normed_output_consistently
+
+        self.instance_norm = nn.LayerNorm(d_model) if separate_norm_instance else None
+        self.instance_bbox_embed = None
+        if separate_box_head_instance:
+            self.instance_bbox_embed = MLP(d_model, d_model, 4, 3)
+        if instance_query:
+            self.instance_query_embed = nn.Embedding(num_instances, d_model)
+        self.box_refine = box_refine
+        if box_refine:
+            nn.init.constant_(self.bbox_embed.layers[-1].weight.data, 0)
+            nn.init.constant_(self.bbox_embed.layers[-1].bias.data, 0)
+
+            self.reference_points = nn.Embedding(num_queries, 4)
+            if instance_query:
+                self.instance_reference_points = nn.Embedding(num_instances, 4)
+
+        assert boxRPB in ["none", "log", "linear", "both"]
+        self.boxRPB = boxRPB
+        if boxRPB != "none":
+            try:
+                nheads = self.layers[0].cross_attn_image.num_heads
+            except AttributeError:
+                nheads = self.layers[0].cross_attn.num_heads
+
+            n_input = 4 if boxRPB == "both" else 2
+            self.boxRPB_embed_x = MLP(n_input, d_model, nheads, 2)
+            self.boxRPB_embed_y = MLP(n_input, d_model, nheads, 2)
+            self.compilable_cord_cache = None
+            self.compilable_stored_size = None
+            self.coord_cache = {}
+
+            if resolution is not None and stride is not None:
+                feat_size = resolution // stride
+                coords_h, coords_w = self._get_coords(
+                    feat_size, feat_size, device="cuda"
+                )
+                self.compilable_cord_cache = (coords_h, coords_w)
+                self.compilable_stored_size = (feat_size, feat_size)
+
+        self.roi_pooler = (
+            RoIAlign(output_size=7, spatial_scale=1, sampling_ratio=-1, aligned=True)
+            if interaction_layer is not None
+            else None
+        )
+        if frozen:
+            for p in self.parameters():
+                p.requires_grad_(False)
+
+        self.presence_token = None
+        self.clamp_presence_logits = clamp_presence_logits
+        self.clamp_presence_logit_max_val = clamp_presence_logit_max_val
+        if presence_token:
+            self.presence_token = nn.Embedding(1, d_model)
+            self.presence_token_head = MLP(d_model, d_model, 1, 3)
+            self.presence_token_out_norm = nn.LayerNorm(d_model)
+
+        self.ref_point_head = MLP(2 * self.d_model, self.d_model, self.d_model, 2)
+        self.dac_use_selfatt_ln = dac_use_selfatt_ln
+        self.use_act_checkpoint = use_act_checkpoint
+
+        nn.init.normal_(self.query_embed.weight.data)
+        if self.instance_query_embed is not None:
+            nn.init.normal_(self.instance_query_embed.weight.data)
+
+        assert self.roi_pooler is None
+        assert self.return_intermediate, "support return_intermediate only"
+        assert self.box_refine, "support box refine only"
+
+        self.compile_mode = compile_mode
+        self.compiled = False
+        # We defer compilation till after the first forward, to first warm-up the boxRPB cache
+
+        # assign layer index to each layer so that some layers can decide what to do
+        # based on which layer index they are (e.g. cross attention to memory bank only
+        # in selected layers)
+        for layer_idx, layer in enumerate(self.layers):
+            layer.layer_idx = layer_idx
+
+    @staticmethod
+    def _get_coords(H, W, device):
+        coords_h = torch.arange(0, H, device=device, dtype=torch.float32) / H
+        coords_w = torch.arange(0, W, device=device, dtype=torch.float32) / W
+        return coords_h, coords_w
+
+    def _get_rpb_matrix(self, reference_boxes, feat_size):
+        H, W = feat_size
+        boxes_xyxy = box_cxcywh_to_xyxy(reference_boxes).transpose(0, 1)
+        bs, num_queries, _ = boxes_xyxy.shape
+        if self.compilable_cord_cache is None:
+            self.compilable_cord_cache = self._get_coords(H, W, reference_boxes.device)
+            self.compilable_stored_size = (H, W)
+
+        if torch.compiler.is_dynamo_compiling() or self.compilable_stored_size == (
+            H,
+            W,
+        ):
+            # good, hitting the cache, will be compilable
+            coords_h, coords_w = self.compilable_cord_cache
+        else:
+            # cache miss, will create compilation issue
+            # In case we're not compiling, we'll still rely on the dict-based cache
+            if feat_size not in self.coord_cache:
+                self.coord_cache[feat_size] = self._get_coords(
+                    H, W, reference_boxes.device
+                )
+            coords_h, coords_w = self.coord_cache[feat_size]
+
+            assert coords_h.shape == (H,)
+            assert coords_w.shape == (W,)
+
+        deltas_y = coords_h.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 1:4:2]
+        deltas_y = deltas_y.view(bs, num_queries, -1, 2)
+        deltas_x = coords_w.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 0:3:2]
+        deltas_x = deltas_x.view(bs, num_queries, -1, 2)
+
+        if self.boxRPB in ["log", "both"]:
+            deltas_x_log = deltas_x * 8  # normalize to -8, 8
+            deltas_x_log = (
+                torch.sign(deltas_x_log)
+                * torch.log2(torch.abs(deltas_x_log) + 1.0)
+                / np.log2(8)
+            )
+
+            deltas_y_log = deltas_y * 8  # normalize to -8, 8
+            deltas_y_log = (
+                torch.sign(deltas_y_log)
+                * torch.log2(torch.abs(deltas_y_log) + 1.0)
+                / np.log2(8)
+            )
+            if self.boxRPB == "log":
+                deltas_x = deltas_x_log
+                deltas_y = deltas_y_log
+            else:
+                deltas_x = torch.cat([deltas_x, deltas_x_log], dim=-1)
+                deltas_y = torch.cat([deltas_y, deltas_y_log], dim=-1)
+
+        if self.training:
+            assert self.use_act_checkpoint, "activation ckpt not enabled in decoder"
+        deltas_x = activation_ckpt_wrapper(self.boxRPB_embed_x)(
+            x=deltas_x,
+            act_ckpt_enable=self.training and self.use_act_checkpoint,
+        )  # bs, num_queries, W, n_heads
+        deltas_y = activation_ckpt_wrapper(self.boxRPB_embed_y)(
+            x=deltas_y,
+            act_ckpt_enable=self.training and self.use_act_checkpoint,
+        )  # bs, num_queries, H, n_heads
+
+        if not torch.compiler.is_dynamo_compiling():
+            assert deltas_x.shape[:3] == (bs, num_queries, W)
+            assert deltas_y.shape[:3] == (bs, num_queries, H)
+
+        B = deltas_y.unsqueeze(3) + deltas_x.unsqueeze(
+            2
+        )  # bs, num_queries, H, W, n_heads
+        if not torch.compiler.is_dynamo_compiling():
+            assert B.shape[:4] == (bs, num_queries, H, W)
+        B = B.flatten(2, 3)  # bs, num_queries, H*W, n_heads
+        B = B.permute(0, 3, 1, 2)  # bs, n_heads, num_queries, H*W
+        B = B.contiguous()  # memeff attn likes ordered strides
+        if not torch.compiler.is_dynamo_compiling():
+            assert B.shape[2:] == (num_queries, H * W)
+        return B
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        reference_boxes: Optional[Tensor] = None,  # num_queries, bs, 4
+        # for memory
+        level_start_index: Optional[Tensor] = None,  # num_levels
+        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
+        valid_ratios: Optional[Tensor] = None,
+        # for text
+        memory_text: Optional[Tensor] = None,
+        text_attention_mask: Optional[Tensor] = None,
+        # if `apply_dac` is None, it will default to `self.dac`
+        apply_dac: Optional[bool] = None,
+        is_instance_prompt=False,
+        decoder_extra_kwargs: Optional[Dict] = None,
+        # ROI memory bank
+        obj_roi_memory_feat=None,
+        obj_roi_memory_mask=None,
+        box_head_trk=None,
+    ):
+        """
+        Input:
+            - tgt: nq, bs, d_model
+            - memory: \\sum{hw}, bs, d_model
+            - pos: \\sum{hw}, bs, d_model
+            - reference_boxes: nq, bs, 4 (after sigmoid)
+            - valid_ratios/spatial_shapes: bs, nlevel, 2
+        """
+        if memory_mask is not None:
+            assert self.boxRPB == "none", (
+                "inputting a memory_mask in the presence of boxRPB is unexpected/not implemented"
+            )
+
+        apply_dac = apply_dac if apply_dac is not None else self.dac
+        if apply_dac:
+            assert (tgt.shape[0] == self.num_queries) or (
+                self.use_instance_query
+                and (tgt.shape[0] == self.instance_query_embed.num_embeddings)
+            )
+
+            tgt = tgt.repeat(2, 1, 1)
+            # note that we don't tile tgt_mask, since DAC doesn't
+            # use self-attention in o2m queries
+            if reference_boxes is not None:
+                assert (reference_boxes.shape[0] == self.num_queries) or (
+                    self.use_instance_query
+                    and (
+                        reference_boxes.shape[0]
+                        == self.instance_query_embed.num_embeddings
+                    )
+                )
+                reference_boxes = reference_boxes.repeat(2, 1, 1)
+
+        bs = tgt.shape[1]
+        intermediate = []
+        intermediate_presence_logits = []
+        presence_feats = None
+
+        if self.box_refine:
+            if reference_boxes is None:
+                # In this case, we're in a one-stage model, so we generate the reference boxes
+                reference_boxes = self.reference_points.weight.unsqueeze(1)
+                reference_boxes = (
+                    reference_boxes.repeat(2, bs, 1)
+                    if apply_dac
+                    else reference_boxes.repeat(1, bs, 1)
+                )
+                reference_boxes = reference_boxes.sigmoid()
+            intermediate_ref_boxes = [reference_boxes]
+        else:
+            reference_boxes = None
+            intermediate_ref_boxes = None
+
+        output = tgt
+        presence_out = None
+        if self.presence_token is not None and is_instance_prompt is False:
+            # expand to batch dim
+            presence_out = self.presence_token.weight[None].expand(1, bs, -1)
+
+        box_head = self.bbox_embed
+        if is_instance_prompt and self.instance_bbox_embed is not None:
+            box_head = self.instance_bbox_embed
+
+        out_norm = self.norm
+        if is_instance_prompt and self.instance_norm is not None:
+            out_norm = self.instance_norm
+
+        for layer_idx, layer in enumerate(self.layers):
+            reference_points_input = (
+                reference_boxes[:, :, None]
+                * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
+            )  # nq, bs, nlevel, 4
+
+            query_sine_embed = gen_sineembed_for_position(
+                reference_points_input[:, :, 0, :], self.d_model
+            )  # nq, bs, d_model*2
+
+            # conditional query
+            query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, d_model
+
+            if self.boxRPB != "none" and reference_boxes is not None:
+                assert spatial_shapes.shape[0] == 1, (
+                    "only single scale support implemented"
+                )
+                memory_mask = self._get_rpb_matrix(
+                    reference_boxes,
+                    (spatial_shapes[0, 0], spatial_shapes[0, 1]),
+                )
+                memory_mask = memory_mask.flatten(0, 1)  # (bs*n_heads, nq, H*W)
+            if self.training:
+                assert self.use_act_checkpoint, (
+                    "Activation checkpointing not enabled in the decoder"
+                )
+            output, presence_out = activation_ckpt_wrapper(layer)(
+                tgt=output,
+                tgt_query_pos=query_pos,
+                tgt_query_sine_embed=query_sine_embed,
+                tgt_key_padding_mask=tgt_key_padding_mask,
+                tgt_reference_points=reference_points_input,
+                memory_text=memory_text,
+                text_attention_mask=text_attention_mask,
+                memory=memory,
+                memory_key_padding_mask=memory_key_padding_mask,
+                memory_level_start_index=level_start_index,
+                memory_spatial_shapes=spatial_shapes,
+                memory_pos=pos,
+                self_attn_mask=tgt_mask,
+                cross_attn_mask=memory_mask,
+                dac=apply_dac,
+                dac_use_selfatt_ln=self.dac_use_selfatt_ln,
+                presence_token=presence_out,
+                **(decoder_extra_kwargs or {}),
+                act_ckpt_enable=self.training and self.use_act_checkpoint,
+                # ROI memory bank
+                obj_roi_memory_feat=obj_roi_memory_feat,
+                obj_roi_memory_mask=obj_roi_memory_mask,
+            )
+
+            # iter update
+            if self.box_refine:
+                reference_before_sigmoid = inverse_sigmoid(reference_boxes)
+                if box_head_trk is None:
+                    # delta_unsig = self.bbox_embed(output)
+                    if not self.use_normed_output_consistently:
+                        delta_unsig = box_head(output)
+                    else:
+                        delta_unsig = box_head(out_norm(output))
+                else:
+                    # box_head_trk use a separate box head for tracking queries
+                    Q_det = decoder_extra_kwargs["Q_det"]
+                    assert output.size(0) >= Q_det
+                    delta_unsig_det = self.bbox_embed(output[:Q_det])
+                    delta_unsig_trk = box_head_trk(output[Q_det:])
+                    delta_unsig = torch.cat([delta_unsig_det, delta_unsig_trk], dim=0)
+                outputs_unsig = delta_unsig + reference_before_sigmoid
+                new_reference_points = outputs_unsig.sigmoid()
+
+                reference_boxes = new_reference_points.detach()
+                if layer_idx != self.num_layers - 1:
+                    intermediate_ref_boxes.append(new_reference_points)
+            else:
+                raise NotImplementedError("not implemented yet")
+
+            intermediate.append(out_norm(output))
+            if self.presence_token is not None and is_instance_prompt is False:
+                # norm, mlp head
+                intermediate_layer_presence_logits = self.presence_token_head(
+                    self.presence_token_out_norm(presence_out)
+                ).squeeze(-1)
+
+                # clamp to mitigate numerical issues
+                if self.clamp_presence_logits:
+                    intermediate_layer_presence_logits.clamp(
+                        min=-self.clamp_presence_logit_max_val,
+                        max=self.clamp_presence_logit_max_val,
+                    )
+
+                intermediate_presence_logits.append(intermediate_layer_presence_logits)
+                presence_feats = presence_out.clone()
+
+        if not self.compiled and self.compile_mode is not None:
+            self.forward = torch.compile(
+                self.forward, mode=self.compile_mode, fullgraph=True
+            )
+            self.compiled = True
+
+        return (
+            torch.stack(intermediate),
+            torch.stack(intermediate_ref_boxes),
+            (
+                torch.stack(intermediate_presence_logits)
+                if self.presence_token is not None and is_instance_prompt is False
+                else None
+            ),
+            presence_feats,
+        )
+
+
+class TransformerEncoderCrossAttention(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        frozen: bool,
+        pos_enc_at_input: bool,
+        layer,
+        num_layers: int,
+        use_act_checkpoint: bool = False,
+        batch_first: bool = False,  # Do layers expect batch first input?
+        # which layers to exclude cross attention? default: None, means all
+        # layers use cross attention
+        remove_cross_attention_layers: Optional[list] = None,
+    ):
+        super().__init__()
+        self.d_model = d_model
+        self.layers = get_clones(layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = nn.LayerNorm(d_model)
+        self.pos_enc_at_input = pos_enc_at_input
+        self.use_act_checkpoint = use_act_checkpoint
+
+        if frozen:
+            for p in self.parameters():
+                p.requires_grad_(False)
+
+        self.batch_first = batch_first
+
+        # remove cross attention layers if specified
+        self.remove_cross_attention_layers = [False] * self.num_layers
+        if remove_cross_attention_layers is not None:
+            for i in remove_cross_attention_layers:
+                self.remove_cross_attention_layers[i] = True
+        assert len(self.remove_cross_attention_layers) == len(self.layers)
+
+        for i, remove_cross_attention in enumerate(self.remove_cross_attention_layers):
+            if remove_cross_attention:
+                self.layers[i].cross_attn_image = None
+                self.layers[i].norm2 = None
+                self.layers[i].dropout2 = None
+
+    def forward(
+        self,
+        src,  # self-attention inputs
+        prompt,  # cross-attention inputs
+        src_mask: Optional[Tensor] = None,  # att.mask for self-attention inputs
+        prompt_mask: Optional[Tensor] = None,  # att.mask for cross-attention inputs
+        src_key_padding_mask: Optional[Tensor] = None,
+        prompt_key_padding_mask: Optional[Tensor] = None,
+        src_pos: Optional[Tensor] = None,  # pos_enc for self-attention inputs
+        prompt_pos: Optional[Tensor] = None,  # pos_enc for cross-attention inputs
+        feat_sizes: Optional[list] = None,
+        num_obj_ptr_tokens: int = 0,  # number of object pointer *tokens*
+    ):
+        if isinstance(src, list):
+            assert isinstance(src_key_padding_mask, list) and isinstance(src_pos, list)
+            assert len(src) == len(src_key_padding_mask) == len(src_pos) == 1
+            src, src_key_padding_mask, src_pos = (
+                src[0],
+                src_key_padding_mask[0],
+                src_pos[0],
+            )
+
+        assert src.shape[1] == prompt.shape[1], (
+            "Batch size must be the same for src and prompt"
+        )
+
+        output = src
+
+        if self.pos_enc_at_input and src_pos is not None:
+            output = output + 0.1 * src_pos
+
+        if self.batch_first:
+            # Convert to batch first
+            output = output.transpose(0, 1)
+            src_pos = src_pos.transpose(0, 1)
+            prompt = prompt.transpose(0, 1)
+            prompt_pos = prompt_pos.transpose(0, 1)
+
+        for layer in self.layers:
+            kwds = {}
+            if isinstance(layer.cross_attn_image, RoPEAttention):
+                kwds = {"num_k_exclude_rope": num_obj_ptr_tokens}
+
+            output = activation_ckpt_wrapper(layer)(
+                tgt=output,
+                memory=prompt,
+                tgt_mask=src_mask,
+                memory_mask=prompt_mask,
+                tgt_key_padding_mask=src_key_padding_mask,
+                memory_key_padding_mask=prompt_key_padding_mask,
+                pos=prompt_pos,
+                query_pos=src_pos,
+                dac=False,
+                attn_bias=None,
+                act_ckpt_enable=self.training and self.use_act_checkpoint,
+                **kwds,
+            )
+            normed_output = self.norm(output)
+
+        if self.batch_first:
+            # Convert back to seq first
+            normed_output = normed_output.transpose(0, 1)
+            src_pos = src_pos.transpose(0, 1)
+
+        return {
+            "memory": normed_output,
+            "pos_embed": src_pos,
+            "padding_mask": src_key_padding_mask,
+        }
+
+
+class TransformerDecoderLayerv1(nn.Module):
+    def __init__(
+        self,
+        activation: str,
+        cross_attention: nn.Module,
+        d_model: int,
+        dim_feedforward: int,
+        dropout: float,
+        pos_enc_at_attn: bool,
+        pos_enc_at_cross_attn_keys: bool,
+        pos_enc_at_cross_attn_queries: bool,
+        pre_norm: bool,
+        self_attention: nn.Module,
+    ):
+        super().__init__()
+        self.d_model = d_model
+        self.dim_feedforward = dim_feedforward
+        self.dropout_value = dropout
+        self.self_attn = self_attention
+        self.cross_attn_image = cross_attention
+
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation_str = activation
+        self.activation = get_activation_fn(activation)
+        self.pre_norm = pre_norm
+
+        self.pos_enc_at_attn = pos_enc_at_attn
+        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
+        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys
+
+    def forward_post(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        **kwargs,
+    ):
+        q = k = tgt + query_pos if self.pos_enc_at_attn else tgt
+
+        # Self attention
+        tgt2 = self.self_attn(
+            q,
+            k,
+            value=tgt,
+            attn_mask=tgt_mask,
+            key_padding_mask=tgt_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # Cross attention to image
+        tgt2 = self.cross_attn_image(
+            query=tgt + query_pos if self.pos_enc_at_cross_attn_queries else tgt,
+            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+
+        # FFN
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward_pre(
+        self,
+        tgt,
+        memory,
+        dac: bool = False,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        attn_bias: Optional[Tensor] = None,
+        **kwargs,
+    ):
+        if dac:
+            # we only apply self attention to the first half of the queries
+            assert tgt.shape[0] % 2 == 0
+            other_tgt = tgt[tgt.shape[0] // 2 :]
+            tgt = tgt[: tgt.shape[0] // 2]
+        tgt2 = self.norm1(tgt)
+        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
+        tgt2 = self.self_attn(
+            q,
+            k,
+            value=tgt2,
+            attn_mask=tgt_mask,
+            key_padding_mask=tgt_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        if dac:
+            # Recombine
+            tgt = torch.cat((tgt, other_tgt), dim=0)
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.cross_attn_image(
+            query=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
+            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+            attn_bias=attn_bias,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        dac: bool = False,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        attn_bias: Optional[Tensor] = None,
+        **kwds: Any,
+    ) -> torch.Tensor:
+        fwd_fn = self.forward_pre if self.pre_norm else self.forward_post
+        return fwd_fn(
+            tgt,
+            memory,
+            dac=dac,
+            tgt_mask=tgt_mask,
+            memory_mask=memory_mask,
+            tgt_key_padding_mask=tgt_key_padding_mask,
+            memory_key_padding_mask=memory_key_padding_mask,
+            pos=pos,
+            query_pos=query_pos,
+            attn_bias=attn_bias,
+            **kwds,
+        )
+
+
+class TransformerDecoderLayerv2(TransformerDecoderLayerv1):
+    def __init__(self, cross_attention_first=False, *args: Any, **kwds: Any):
+        super().__init__(*args, **kwds)
+        self.cross_attention_first = cross_attention_first
+
+    def _forward_sa(self, tgt, query_pos):
+        # Self-Attention
+        tgt2 = self.norm1(tgt)
+        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
+        tgt2 = self.self_attn(q, k, v=tgt2)
+        tgt = tgt + self.dropout1(tgt2)
+        return tgt
+
+    def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):
+        if self.cross_attn_image is None:
+            return tgt
+
+        kwds = {}
+        if num_k_exclude_rope > 0:
+            assert isinstance(self.cross_attn_image, RoPEAttention)
+            kwds = {"num_k_exclude_rope": num_k_exclude_rope}
+
+        # Cross-Attention
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.cross_attn_image(
+            q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
+            k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            v=memory,
+            **kwds,
+        )
+        tgt = tgt + self.dropout2(tgt2)
+        return tgt
+
+    def forward_pre(
+        self,
+        tgt,
+        memory,
+        dac: bool,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        attn_bias: Optional[Tensor] = None,
+        num_k_exclude_rope: int = 0,
+    ):
+        assert dac is False
+        assert tgt_mask is None
+        assert memory_mask is None
+        assert tgt_key_padding_mask is None
+        assert memory_key_padding_mask is None
+        assert attn_bias is None
+
+        if self.cross_attention_first:
+            tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)
+            tgt = self._forward_sa(tgt, query_pos)
+        else:
+            tgt = self._forward_sa(tgt, query_pos)
+            tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)
+
+        # MLP
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+    def forward(self, *args: Any, **kwds: Any) -> torch.Tensor:
+        if self.pre_norm:
+            return self.forward_pre(*args, **kwds)
+        raise NotImplementedError

third_party/GraspGen/sam3/sam3/model/edt.py

@@ -0,0 +1,175 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+"""Triton kernel for euclidean distance transform (EDT)"""
+
+import torch
+import triton
+import triton.language as tl
+
+"""
+Disclaimer: This implementation is not meant to be extremely efficient. A CUDA kernel would likely be more efficient.
+Even in Triton, there may be more suitable algorithms.
+
+The goal of this kernel is to mimic cv2.distanceTransform(input, cv2.DIST_L2, 0).
+Recall that the euclidean distance transform (EDT) calculates the L2 distance to the closest zero pixel for each pixel of the source image.
+
+For images of size NxN, the naive algorithm would be to compute pairwise distances between every pair of points, leading to a O(N^4) algorithm, which is obviously impractical.
+One can do better using the following approach:
+- First, compute the distance to the closest point in the same row. We can write it as Row_EDT[i,j] = min_k (sqrt((k-j)^2) if input[i,k]==0 else +infinity). With a naive implementation, this step has a O(N^3) complexity
+- Then, because of triangular inequality, we notice that the EDT for a given location [i,j] is the min of the row EDTs in the same column. EDT[i,j] = min_k Row_EDT[k, j]. This is also O(N^3)
+
+Overall, this algorithm is quite amenable to parallelization, and has a complexity O(N^3). Can we do better?
+
+It turns out that we can leverage the structure of the L2 distance (nice and convex) to find the minimum in a more efficient way.
+We follow the algorithm from "Distance Transforms of Sampled Functions" (https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf), which is also what's implemented in opencv
+
+For a single dimension EDT, we can compute the EDT of an arbitrary function F, that we discretize over the grid. Note that for the binary EDT that we're interested in, we can set F(i,j) = 0 if input[i,j]==0 else +infinity
+For now, we'll compute the EDT squared, and will take the sqrt only at the very end.
+The basic idea is that each point at location i spawns a parabola around itself, with a bias equal to F(i). So specifically, we're looking at the parabola (x - i)^2 + F(i)
+When we're looking for the row EDT at location j, we're effectively looking for min_i (x-i)^2 + F(i). In other word we want to find the lowest parabola at location j.
+
+To do this efficiently, we need to maintain the lower envelope of the union of parabolas. This can be constructed on the fly using a sort of stack approach:
+ - every time we want to add a new parabola, we check if it may be covering the current right-most parabola. If so, then that parabola was useless, so we can pop it from the stack
+ - repeat until we can't find any more parabola to pop. Then push the new one.
+
+This algorithm runs in O(N) for a single row, so overall O(N^2) when applied to all rows
+Similarly as before, we notice that we can decompose the algorithm for rows and columns, leading to an overall run-time of O(N^2)
+
+This algorithm is less suited for to GPUs, since the one-dimensional EDT computation is quite sequential in nature. However, we can parallelize over batch and row dimensions.
+In Triton, things are particularly bad at the moment, since there is no support for reading/writing to the local memory at a specific index (a local gather is coming soon, see https://github.com/triton-lang/triton/issues/974, but no mention of writing, ie scatter)
+One could emulate these operations with masking, but in initial tests, it proved to be worst than naively reading and writing to the global memory. My guess is that the cache is compensating somewhat for the repeated single-point accesses.
+
+
+The timing obtained on a H100 for a random batch of masks of dimension 256 x 1024 x 1024 are as follows:
+- OpenCV: 1780ms (including round-trip to cpu, but discounting the fact that it introduces a synchronization point)
+- triton, O(N^3) algo: 627ms
+- triton, O(N^2) algo: 322ms
+
+Overall, despite being quite naive, this implementation is roughly 5.5x faster than the openCV cpu implem
+
+"""
+
+
+@triton.jit
+def edt_kernel(inputs_ptr, outputs_ptr, v, z, height, width, horizontal: tl.constexpr):
+    # This is a somewhat verbatim implementation of the efficient 1D EDT algorithm described above
+    # It can be applied horizontally or vertically depending if we're doing the first or second stage.
+    # It's parallelized across batch+row (or batch+col if horizontal=False)
+    # TODO: perhaps the implementation can be revisited if/when local gather/scatter become available in triton
+    batch_id = tl.program_id(axis=0)
+    if horizontal:
+        row_id = tl.program_id(axis=1)
+        block_start = (batch_id * height * width) + row_id * width
+        length = width
+        stride = 1
+    else:
+        col_id = tl.program_id(axis=1)
+        block_start = (batch_id * height * width) + col_id
+        length = height
+        stride = width
+
+    # This will be the index of the right most parabola in the envelope ("the top of the stack")
+    k = 0
+    for q in range(1, length):
+        # Read the function value at the current location. Note that we're doing a singular read, not very efficient
+        cur_input = tl.load(inputs_ptr + block_start + (q * stride))
+        # location of the parabola on top of the stack
+        r = tl.load(v + block_start + (k * stride))
+        # associated boundary
+        z_k = tl.load(z + block_start + (k * stride))
+        # value of the function at the parabola location
+        previous_input = tl.load(inputs_ptr + block_start + (r * stride))
+        # intersection between the two parabolas
+        s = (cur_input - previous_input + q * q - r * r) / (q - r) / 2
+
+        # we'll pop as many parabolas as required
+        while s <= z_k and k - 1 >= 0:
+            k = k - 1
+            r = tl.load(v + block_start + (k * stride))
+            z_k = tl.load(z + block_start + (k * stride))
+            previous_input = tl.load(inputs_ptr + block_start + (r * stride))
+            s = (cur_input - previous_input + q * q - r * r) / (q - r) / 2
+
+        # Store the new one
+        k = k + 1
+        tl.store(v + block_start + (k * stride), q)
+        tl.store(z + block_start + (k * stride), s)
+        if k + 1 < length:
+            tl.store(z + block_start + ((k + 1) * stride), 1e9)
+
+    # Last step, we read the envelope to find the min in every location
+    k = 0
+    for q in range(length):
+        while (
+            k + 1 < length
+            and tl.load(
+                z + block_start + ((k + 1) * stride), mask=(k + 1) < length, other=q
+            )
+            < q
+        ):
+            k += 1
+        r = tl.load(v + block_start + (k * stride))
+        d = q - r
+        old_value = tl.load(inputs_ptr + block_start + (r * stride))
+        tl.store(outputs_ptr + block_start + (q * stride), old_value + d * d)
+
+
+def edt_triton(data: torch.Tensor):
+    """
+    Computes the Euclidean Distance Transform (EDT) of a batch of binary images.
+
+    Args:
+        data: A tensor of shape (B, H, W) representing a batch of binary images.
+
+    Returns:
+        A tensor of the same shape as data containing the EDT.
+        It should be equivalent to a batched version of cv2.distanceTransform(input, cv2.DIST_L2, 0)
+    """
+    assert data.dim() == 3
+    assert data.is_cuda
+    B, H, W = data.shape
+    data = data.contiguous()
+
+    # Allocate the "function" tensor. Implicitly the function is 0 if data[i,j]==0 else +infinity
+    output = torch.where(data, 1e18, 0.0)
+    assert output.is_contiguous()
+
+    # Scratch tensors for the parabola stacks
+    parabola_loc = torch.zeros(B, H, W, dtype=torch.uint32, device=data.device)
+    parabola_inter = torch.empty(B, H, W, dtype=torch.float, device=data.device)
+    parabola_inter[:, :, 0] = -1e18
+    parabola_inter[:, :, 1] = 1e18
+
+    # Grid size (number of blocks)
+    grid = (B, H)
+
+    # Launch initialization kernel
+    edt_kernel[grid](
+        output.clone(),
+        output,
+        parabola_loc,
+        parabola_inter,
+        H,
+        W,
+        horizontal=True,
+    )
+
+    # reset the parabola stacks
+    parabola_loc.zero_()
+    parabola_inter[:, :, 0] = -1e18
+    parabola_inter[:, :, 1] = 1e18
+
+    grid = (B, W)
+    edt_kernel[grid](
+        output.clone(),
+        output,
+        parabola_loc,
+        parabola_inter,
+        H,
+        W,
+        horizontal=False,
+    )
+    # don't forget to take sqrt at the end
+    return output.sqrt()

third_party/GraspGen/sam3/sam3/model/encoder.py

@@ -0,0 +1,596 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+# Based on https://github.com/IDEA-Research/GroundingDINO
+
+# pyre-unsafe
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn, Tensor
+
+from .act_ckpt_utils import activation_ckpt_wrapper
+from .model_misc import get_activation_fn, get_clones, get_valid_ratio
+
+
+class TransformerEncoderLayer(nn.Module):
+    """
+    Transformer encoder layer that performs self-attention followed by cross-attention.
+
+    This layer was previously called TransformerDecoderLayer but was renamed to better
+    reflect its role in the architecture. It processes input sequences through self-attention
+    and then cross-attention with another input (typically image features).
+
+    The layer supports both pre-norm and post-norm configurations, as well as
+    positional encoding at different stages of the attention mechanism.
+    """
+
+    def __init__(
+        self,
+        activation: str,
+        cross_attention: nn.Module,
+        d_model: int,
+        dim_feedforward: int,
+        dropout: float,
+        pos_enc_at_attn: bool,
+        pos_enc_at_cross_attn_keys: bool,
+        pos_enc_at_cross_attn_queries: bool,
+        pre_norm: bool,
+        self_attention: nn.Module,
+    ):
+        """
+        Initialize a transformer encoder layer.
+
+        Args:
+            activation: Activation function to use in the feedforward network
+            cross_attention: Cross-attention module for attending to image features
+            d_model: Model dimension/hidden size
+            dim_feedforward: Dimension of the feedforward network
+            dropout: Dropout probability
+            pos_enc_at_attn: Whether to add positional encodings at self-attention
+            pos_enc_at_cross_attn_keys: Whether to add positional encodings to keys in cross-attention
+            pos_enc_at_cross_attn_queries: Whether to add positional encodings to queries in cross-attention
+            pre_norm: Whether to use pre-norm (True) or post-norm (False) architecture
+            self_attention: Self-attention module
+        """
+        super().__init__()
+        self.d_model = d_model
+        self.dim_feedforward = dim_feedforward
+        self.dropout_value = dropout
+        self.self_attn = self_attention
+        self.cross_attn_image = cross_attention
+
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation_str = activation
+        self.activation = get_activation_fn(activation)
+        self.pre_norm = pre_norm
+
+        self.pos_enc_at_attn = pos_enc_at_attn
+        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
+        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys
+
+        self.layer_idx = None
+
+    def forward_post(
+        self,
+        tgt: Tensor,
+        memory: Tensor,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        **kwargs,
+    ) -> Tensor:
+        """
+        Forward pass for post-norm architecture.
+
+        In post-norm architecture, normalization is applied after attention and feedforward operations.
+
+        Args:
+            tgt: Input tensor to be processed
+            memory: Memory tensor for cross-attention
+            tgt_mask: Mask for self-attention
+            memory_mask: Mask for cross-attention
+            tgt_key_padding_mask: Key padding mask for self-attention
+            memory_key_padding_mask: Key padding mask for cross-attention
+            pos: Positional encoding for memory
+            query_pos: Positional encoding for query
+            **kwargs: Additional keyword arguments
+
+        Returns:
+            Processed tensor
+        """
+        q = k = tgt + query_pos if self.pos_enc_at_attn else tgt
+
+        # Self attention
+        tgt2 = self.self_attn(
+            q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # Cross attention to image
+        tgt2 = self.cross_attn_image(
+            query=tgt + query_pos if self.pos_enc_at_cross_attn_queries else tgt,
+            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+
+        # FFN
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward_pre(
+        self,
+        tgt: Tensor,
+        memory: Tensor,
+        dac: bool = False,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        # attn_bias: Optional[Tensor] = None,
+        # **kwargs,
+    ) -> Tensor:
+        """
+        Forward pass for pre-norm architecture.
+
+        In pre-norm architecture, normalization is applied before attention and feedforward operations.
+
+        Args:
+            tgt: Input tensor to be processed
+            memory: Memory tensor for cross-attention
+            dac: Whether to use Divide-and-Conquer attention
+            tgt_mask: Mask for self-attention
+            memory_mask: Mask for cross-attention
+            tgt_key_padding_mask: Key padding mask for self-attention
+            memory_key_padding_mask: Key padding mask for cross-attention
+            pos: Positional encoding for memory
+            query_pos: Positional encoding for query
+            attn_bias: Optional attention bias tensor
+            **kwargs: Additional keyword arguments
+
+        Returns:
+            Processed tensor
+        """
+        if dac:
+            # we only apply self attention to the first half of the queries
+            assert tgt.shape[0] % 2 == 0
+            other_tgt = tgt[tgt.shape[0] // 2 :]
+            tgt = tgt[: tgt.shape[0] // 2]
+        tgt2 = self.norm1(tgt)
+        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
+        tgt2 = self.self_attn(
+            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        if dac:
+            # Recombine
+            tgt = torch.cat((tgt, other_tgt), dim=0)
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.cross_attn_image(
+            query=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
+            key=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+            # attn_bias=attn_bias,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+    def forward(
+        self,
+        tgt: Tensor,
+        memory: Tensor,
+        dac: bool = False,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        # attn_bias: Optional[Tensor] = None,
+        # **kwds: Any,
+    ) -> torch.Tensor:
+        """
+        Forward pass for the transformer encoder layer.
+
+        Args:
+            tgt: Input tensor to be processed
+            memory: Memory tensor (e.g., image features) for cross-attention
+            dac: Whether to use Divide-and-Conquer attention (only apply self-attention to first half)
+            tgt_mask: Mask for self-attention
+            memory_mask: Mask for cross-attention
+            tgt_key_padding_mask: Key padding mask for self-attention
+            memory_key_padding_mask: Key padding mask for cross-attention
+            pos: Positional encoding for memory
+            query_pos: Positional encoding for query
+            attn_bias: Optional attention bias tensor
+            **kwds: Additional keyword arguments
+
+        Returns:
+            Processed tensor after self-attention, cross-attention, and feedforward network
+        """
+        fwd_fn = self.forward_pre if self.pre_norm else self.forward_post
+        return fwd_fn(
+            tgt,
+            memory,
+            dac=dac,
+            tgt_mask=tgt_mask,
+            memory_mask=memory_mask,
+            tgt_key_padding_mask=tgt_key_padding_mask,
+            memory_key_padding_mask=memory_key_padding_mask,
+            pos=pos,
+            query_pos=query_pos,
+            # attn_bias=attn_bias,
+            # **kwds,
+        )
+
+
+class TransformerEncoder(nn.Module):
+    """
+    Transformer encoder that processes multi-level features.
+
+    This encoder takes multi-level features (e.g., from a backbone network) and processes
+    them through a stack of transformer encoder layers. It supports features from multiple
+    levels (e.g., different resolutions) and can apply activation checkpointing for memory
+    efficiency during training.
+
+    Args:
+        layer: The encoder layer to be stacked multiple times
+        num_layers: Number of encoder layers to stack
+        d_model: Model dimension/hidden size
+        num_feature_levels: Number of feature levels to process
+        frozen: Whether to freeze the parameters of this module
+        use_act_checkpoint: Whether to use activation checkpointing during training
+    """
+
+    def __init__(
+        self,
+        layer: nn.Module,
+        num_layers: int,
+        d_model: int,
+        num_feature_levels: int,
+        frozen: bool = False,
+        use_act_checkpoint: bool = False,
+    ):
+        super().__init__()
+        self.layers = get_clones(layer, num_layers)
+        self.num_layers = num_layers
+
+        self.num_feature_levels = num_feature_levels
+        self.level_embed = None
+        if num_feature_levels > 1:
+            self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+
+        if frozen:
+            for p in self.parameters():
+                p.requires_grad_(False)
+
+        self.use_act_checkpoint = use_act_checkpoint
+
+        # assign layer index to each layer so that some layers can decide what to do
+        # based on which layer index they are (e.g. cross attention to memory bank only
+        # in selected layers)
+        for layer_idx, layer in enumerate(self.layers):
+            layer.layer_idx = layer_idx
+
+    @staticmethod
+    def get_reference_points(spatial_shapes, valid_ratios, device):
+        with torch.no_grad():
+            reference_points_list = []
+            for lvl, (H_, W_) in enumerate(spatial_shapes):
+                ref_y, ref_x = torch.meshgrid(
+                    torch.linspace(
+                        0.5, H_ - 0.5, H_, dtype=torch.float32, device=device
+                    ),
+                    torch.linspace(
+                        0.5, W_ - 0.5, W_, dtype=torch.float32, device=device
+                    ),
+                )
+                ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
+                ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
+                ref = torch.stack((ref_x, ref_y), -1)
+                reference_points_list.append(ref)
+            reference_points = torch.cat(reference_points_list, 1)
+            reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+
+        return reference_points
+
+    def _prepare_multilevel_features(self, srcs, masks, pos_embeds):
+        assert len(srcs) == self.num_feature_levels, (
+            "mismatch between expected and received # of feature levels"
+        )
+
+        src_flatten = []
+        mask_flatten = []
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        has_mask = masks is not None and masks[0] is not None
+        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
+            bs, c, h, w = src.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+
+            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
+            if has_mask:
+                mask = mask.flatten(1)
+            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
+            if self.level_embed is not None:
+                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
+            else:
+                lvl_pos_embed = pos_embed
+            lvl_pos_embed_flatten.append(lvl_pos_embed)
+            src_flatten.append(src)
+            if has_mask:
+                mask_flatten.append(mask)
+        src_flatten = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
+        mask_flatten = torch.cat(mask_flatten, 1) if has_mask else None  # bs, \sum{hxw}
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
+        spatial_shapes = torch.tensor(
+            spatial_shapes, dtype=torch.long, device=src_flatten.device
+        )
+        level_start_index = torch.cat(
+            (
+                spatial_shapes.new_zeros((1,)),
+                spatial_shapes.prod(1).cumsum(0)[:-1],
+            )
+        )
+        if has_mask:
+            valid_ratios = torch.stack([get_valid_ratio(m) for m in masks], 1)
+        else:
+            valid_ratios = torch.ones(
+                (src_flatten.shape[0], self.num_feature_levels, 2),
+                device=src_flatten.device,
+            )
+
+        return (
+            src_flatten,
+            mask_flatten,
+            lvl_pos_embed_flatten,
+            level_start_index,
+            valid_ratios,
+            spatial_shapes,
+        )
+
+    def forward(
+        self,
+        src: List[Tensor],
+        src_key_padding_masks: Optional[List[Tensor]] = None,
+        pos: Optional[List[Tensor]] = None,
+        prompt: Optional[Tensor] = None,
+        prompt_key_padding_mask: Optional[Tensor] = None,
+        encoder_extra_kwargs: Optional[Dict] = None,
+    ) -> Tuple[Tensor, Optional[Tensor], Tensor, Tensor, Tensor, Tensor]:
+        """
+        Process multi-level features through the transformer encoder.
+
+        Args:
+            src: List of multi-level features, each with shape (batch_size, channels, height, width)
+            src_key_padding_masks: List of padding masks for each feature level, each with shape (batch_size, height, width)
+            pos: List of positional embeddings for each feature level, each with shape (batch_size, channels, height, width)
+            prompt: Optional text/prompt features to attend to, with shape (seq_len, batch_size, d_model)
+            prompt_key_padding_mask: Optional padding mask for prompt, with shape (batch_size, seq_len)
+            encoder_extra_kwargs: Optional additional arguments to pass to each encoder layer
+
+        Returns:
+            A tuple containing:
+            - output: Processed features with shape (seq_len, batch_size, d_model)
+            - key_padding_masks_flatten: Flattened padding masks
+            - lvl_pos_embed_flatten: Flattened positional embeddings
+            - level_start_index: Starting indices for each feature level
+            - spatial_shapes: Spatial dimensions of each feature level
+            - valid_ratios: Valid ratios for each feature level
+        """
+        assert len(src) == self.num_feature_levels, (
+            "must be equal to num_feature_levels"
+        )
+        if src_key_padding_masks is not None:
+            assert len(src_key_padding_masks) == self.num_feature_levels
+        if pos is not None:
+            assert len(pos) == self.num_feature_levels
+        # Flatten multilevel feats and add level pos embeds
+        (
+            src_flatten,
+            key_padding_masks_flatten,
+            lvl_pos_embed_flatten,
+            level_start_index,
+            valid_ratios,
+            spatial_shapes,
+        ) = self._prepare_multilevel_features(src, src_key_padding_masks, pos)
+
+        reference_points = self.get_reference_points(
+            spatial_shapes, valid_ratios, device=src_flatten.device
+        )
+
+        output = src_flatten
+        for layer in self.layers:
+            layer_kwargs = {}
+
+            assert isinstance(layer, TransformerEncoderLayer)
+            layer_kwargs["memory"] = prompt
+            layer_kwargs["memory_key_padding_mask"] = prompt_key_padding_mask
+            layer_kwargs["query_pos"] = lvl_pos_embed_flatten
+            layer_kwargs["tgt"] = output
+            layer_kwargs["tgt_key_padding_mask"] = key_padding_masks_flatten
+
+            if self.training:
+                assert self.use_act_checkpoint, "activation ckpt not enabled in encoder"
+            if encoder_extra_kwargs is not None:
+                layer_kwargs.update(encoder_extra_kwargs)
+            output = activation_ckpt_wrapper(layer)(
+                **layer_kwargs,
+                act_ckpt_enable=self.training and self.use_act_checkpoint,
+            )
+        # return as seq first
+        return (
+            output.transpose(0, 1),
+            (
+                key_padding_masks_flatten.transpose(0, 1)
+                if key_padding_masks_flatten is not None
+                else None
+            ),
+            lvl_pos_embed_flatten.transpose(0, 1),
+            level_start_index,
+            spatial_shapes,
+            valid_ratios,
+        )
+
+
+class TransformerEncoderFusion(TransformerEncoder):
+    """
+    Transformer encoder that fuses text and image features.
+
+    This encoder extends TransformerEncoder to handle both text and image features,
+    with the ability to add pooled text features to image features for better
+    cross-modal fusion. It supports torch.compile for performance optimization.
+
+    Args:
+        layer: The encoder layer to be stacked multiple times
+        num_layers: Number of encoder layers to stack
+        d_model: Model dimension/hidden size
+        num_feature_levels: Number of feature levels to process
+        add_pooled_text_to_img_feat: Whether to add pooled text features to image features
+        pool_text_with_mask: Whether to use the mask when pooling text features
+        compile_mode: Mode for torch.compile, or None to disable compilation
+        **kwargs: Additional arguments to pass to the parent class
+    """
+
+    def __init__(
+        self,
+        layer: nn.Module,
+        num_layers: int,
+        d_model: int,
+        num_feature_levels: int,
+        add_pooled_text_to_img_feat: bool = True,
+        pool_text_with_mask: bool = False,
+        compile_mode: Optional[str] = None,
+        **kwargs,
+    ):
+        super().__init__(
+            layer,
+            num_layers,
+            d_model,
+            num_feature_levels,
+            **kwargs,
+        )
+        self.add_pooled_text_to_img_feat = add_pooled_text_to_img_feat
+        if self.add_pooled_text_to_img_feat:
+            self.text_pooling_proj = nn.Linear(d_model, d_model)
+        self.pool_text_with_mask = pool_text_with_mask
+        if compile_mode is not None:
+            self.forward = torch.compile(
+                self.forward, mode=compile_mode, fullgraph=True
+            )
+
+    @staticmethod
+    def get_reference_points(spatial_shapes, valid_ratios, device):
+        # Not needed here
+        return None
+
+    def forward(
+        self,
+        src: List[Tensor],
+        prompt: Tensor,
+        src_key_padding_mask: Optional[List[Tensor]] = None,
+        src_pos: Optional[List[Tensor]] = None,
+        prompt_key_padding_mask: Optional[Tensor] = None,
+        prompt_pos: Optional[Tensor] = None,
+        feat_sizes: Optional[List[int]] = None,
+        encoder_extra_kwargs: Optional[Dict] = None,
+    ):
+        # Restore spatial shapes of vision
+        bs = src[0].shape[1]  # seq first
+        if feat_sizes is not None:
+            assert len(feat_sizes) == len(src)
+            if src_key_padding_mask is None:
+                src_key_padding_mask = [None] * len(src)
+            for i, (h, w) in enumerate(feat_sizes):
+                src[i] = src[i].reshape(h, w, bs, -1).permute(2, 3, 0, 1)
+                src_pos[i] = src_pos[i].reshape(h, w, bs, -1).permute(2, 3, 0, 1)
+                src_key_padding_mask[i] = (
+                    src_key_padding_mask[i].reshape(h, w, bs).permute(2, 0, 1)
+                    if src_key_padding_mask[i] is not None
+                    else None
+                )
+        else:
+            assert all(x.dim == 4 for x in src), (
+                "expected list of (bs, c, h, w) tensors"
+            )
+
+        if self.add_pooled_text_to_img_feat:
+            # Fusion: Add mean pooled text to image features
+            pooled_text = pool_text_feat(
+                prompt, prompt_key_padding_mask, self.pool_text_with_mask
+            )
+            pooled_text = self.text_pooling_proj(pooled_text)[
+                ..., None, None
+            ]  # prompt is seq first
+            src = [x.add_(pooled_text) for x in src]
+
+        (
+            out,
+            key_padding_masks_flatten,
+            lvl_pos_embed_flatten,
+            level_start_index,
+            spatial_shapes,
+            valid_ratios,
+        ) = super().forward(
+            src,
+            src_key_padding_masks=src_key_padding_mask,
+            pos=src_pos,
+            prompt=prompt.transpose(0, 1),
+            prompt_key_padding_mask=prompt_key_padding_mask,
+            encoder_extra_kwargs=encoder_extra_kwargs,
+        )
+
+        return {
+            "memory": out,
+            "padding_mask": key_padding_masks_flatten,
+            "pos_embed": lvl_pos_embed_flatten,
+            "memory_text": prompt,
+            "level_start_index": level_start_index,
+            "spatial_shapes": spatial_shapes,
+            "valid_ratios": valid_ratios,
+        }
+
+
+def pool_text_feat(prompt, prompt_mask, pool_with_mask):
+    # prompt has shape (seq, bs, dim)
+    if not pool_with_mask:
+        return prompt.mean(dim=0)
+
+    # prompt_mask has shape (bs, seq), where False is valid and True is padding
+    assert prompt_mask.dim() == 2
+    # is_valid has shape (seq, bs, 1), where 1 is valid and 0 is padding
+    is_valid = (~prompt_mask).float().permute(1, 0)[..., None]
+    # num_valid has shape (bs, 1)
+    num_valid = torch.clamp(torch.sum(is_valid, dim=0), min=1.0)
+
+    # mean pool over all the valid tokens
+    pooled_text = (prompt * is_valid).sum(dim=0) / num_valid
+    return pooled_text

third_party/GraspGen/sam3/sam3/model/geometry_encoders.py

@@ -0,0 +1,838 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torchvision
+from typing_extensions import override
+
+from .act_ckpt_utils import activation_ckpt_wrapper
+from .box_ops import box_cxcywh_to_xyxy
+from .model_misc import get_clones
+
+
+def is_right_padded(mask):
+    """Given a padding mask (following pytorch convention, 1s for padded values),
+    returns whether the padding is on the right or not."""
+    return (mask.long() == torch.sort(mask.long(), dim=-1)[0]).all()
+
+
+def concat_padded_sequences(seq1, mask1, seq2, mask2, return_index: bool = False):
+    """
+    Concatenates two right-padded sequences, such that the resulting sequence
+    is contiguous and also right-padded.
+
+    Following pytorch's convention, tensors are sequence first, and the mask are
+    batch first, with 1s for padded values.
+
+    :param seq1: A tensor of shape (seq1_length, batch_size, hidden_size).
+    :param mask1: A tensor of shape (batch_size, seq1_length).
+    :param seq2: A tensor of shape (seq2_length, batch_size,  hidden_size).
+    :param mask2: A tensor of shape (batch_size, seq2_length).
+    :param return_index: If True, also returns the index of the ids of the element of seq2
+        in the concatenated sequence. This can be used to retrieve the elements of seq2
+    :return: A tuple (concatenated_sequence, concatenated_mask) if return_index is False,
+        otherwise (concatenated_sequence, concatenated_mask, index).
+    """
+    seq1_length, batch_size, hidden_size = seq1.shape
+    seq2_length, batch_size, hidden_size = seq2.shape
+
+    assert batch_size == seq1.size(1) == seq2.size(1) == mask1.size(0) == mask2.size(0)
+    assert hidden_size == seq1.size(2) == seq2.size(2)
+    assert seq1_length == mask1.size(1)
+    assert seq2_length == mask2.size(1)
+
+    torch._assert_async(is_right_padded(mask1))
+    torch._assert_async(is_right_padded(mask2))
+
+    actual_seq1_lengths = (~mask1).sum(dim=-1)
+    actual_seq2_lengths = (~mask2).sum(dim=-1)
+
+    final_lengths = actual_seq1_lengths + actual_seq2_lengths
+    max_length = seq1_length + seq2_length
+    concatenated_mask = (
+        torch.arange(max_length, device=seq2.device)[None].repeat(batch_size, 1)
+        >= final_lengths[:, None]
+    )
+
+    # (max_len, batch_size, hidden_size)
+    concatenated_sequence = torch.zeros(
+        (max_length, batch_size, hidden_size), device=seq2.device, dtype=seq2.dtype
+    )
+    concatenated_sequence[:seq1_length, :, :] = seq1
+
+    # At this point, the element of seq1 are in the right place
+    # We just need to shift the elements of seq2
+
+    index = torch.arange(seq2_length, device=seq2.device)[:, None].repeat(1, batch_size)
+    index = index + actual_seq1_lengths[None]
+
+    concatenated_sequence = concatenated_sequence.scatter(
+        0, index[:, :, None].expand(-1, -1, hidden_size), seq2
+    )
+
+    if return_index:
+        return concatenated_sequence, concatenated_mask, index
+
+    return concatenated_sequence, concatenated_mask
+
+
+class Prompt:
+    """Utility class to manipulate geometric prompts.
+
+    We expect the sequences in pytorch convention, that is sequence first, batch second
+    The dimensions are expected as follows:
+    box_embeddings shape: N_boxes x B x C_box
+    box_mask shape: B x N_boxes. Can be None if nothing is masked out
+    point_embeddings shape: N_points x B x C_point
+    point_mask shape: B x N_points. Can be None if nothing is masked out
+    mask_embeddings shape: N_masks x B x 1 x H_mask x W_mask
+    mask_mask shape: B x N_masks. Can be None if nothing is masked out
+
+    We also store positive/negative labels. These tensors are also stored batch-first
+    If they are None, we'll assume positive labels everywhere
+    box_labels: long tensor of shape N_boxes x B
+    point_labels: long tensor of shape N_points x B
+    mask_labels: long tensor of shape N_masks x B
+    """
+
+    def __init__(
+        self,
+        box_embeddings=None,
+        box_mask=None,
+        point_embeddings=None,
+        point_mask=None,
+        box_labels=None,
+        point_labels=None,
+        mask_embeddings=None,
+        mask_mask=None,  # Attention mask for mask prompt
+        mask_labels=None,
+    ):
+        # Check for null prompt
+        if (
+            box_embeddings is None
+            and point_embeddings is None
+            and mask_embeddings is None
+        ):
+            self.box_embeddings = None
+            self.box_labels = None
+            self.box_mask = None
+            self.point_embeddings = None
+            self.point_labels = None
+            self.point_mask = None
+            self.mask_embeddings = None
+            self.mask_mask = None
+            # Masks are assumed positive only for now.
+            self.mask_labels = None
+            return
+        # Get sequence lengths and device
+        box_seq_len, point_seq_len, mask_seq_len, bs, device = (
+            self._init_seq_len_and_device(
+                box_embeddings, point_embeddings, mask_embeddings
+            )
+        )
+
+        # Initialize embeds, labels, attention masks.
+        box_embeddings, box_labels, box_mask = self._init_box(
+            box_embeddings, box_labels, box_mask, box_seq_len, bs, device
+        )
+        point_embeddings, point_labels, point_mask = self._init_point(
+            point_embeddings, point_labels, point_mask, point_seq_len, bs, device
+        )
+        mask_embeddings, mask_labels, mask_mask = self._init_mask(
+            mask_embeddings, mask_labels, mask_mask, mask_seq_len, bs, device
+        )
+
+        # Dimension checks
+        assert box_embeddings is not None and list(box_embeddings.shape[:2]) == [
+            box_seq_len,
+            bs,
+        ], (
+            f"Wrong dimension for box embeddings. Expected [{box_seq_len}, {bs}, *] got {box_embeddings.shape}"
+        )
+        assert box_mask is not None and list(box_mask.shape) == [
+            bs,
+            box_seq_len,
+        ], (
+            f"Wrong dimension for box mask. Expected [{bs}, {box_seq_len}] got {box_mask.shape}"
+        )
+        assert point_embeddings is not None and list(point_embeddings.shape[:2]) == [
+            point_seq_len,
+            bs,
+        ], (
+            f"Wrong dimension for point embeddings. Expected [{point_seq_len}, {bs}, *] got {point_embeddings.shape}"
+        )
+        assert point_mask is not None and list(point_mask.shape) == [
+            bs,
+            point_seq_len,
+        ], (
+            f"Wrong dimension for point mask. Expected [{bs}, {point_seq_len}] got {point_mask.shape}"
+        )
+        assert box_labels is not None and list(box_labels.shape) == [
+            box_seq_len,
+            bs,
+        ], (
+            f"Wrong dimension for box labels. Expected [{box_seq_len}, {bs}] got {box_labels.shape}"
+        )
+        assert point_labels is not None and list(point_labels.shape) == [
+            point_seq_len,
+            bs,
+        ], (
+            f"Wrong dimension for point labels. Expected [{point_seq_len}, {bs}] got {point_labels.shape}"
+        )
+        assert (
+            # Allowed to be None, we leave it to the encoder to check for validity before encoding.
+            mask_embeddings is None
+            or list(mask_embeddings.shape[:2])
+            == [
+                mask_seq_len,
+                bs,
+            ]
+        ), (
+            f"Wrong dimension for mask embeddings. Expected [{mask_seq_len}, {bs}, *] got {mask_embeddings.shape}"
+        )
+        assert mask_mask is None or list(mask_mask.shape) == [
+            bs,
+            mask_seq_len,
+        ], (
+            f"Wrong dimension for mask attn. mask. Expected [{bs}, {mask_seq_len}] got {mask_mask.shape}"
+        )
+
+        # Device checks
+        assert box_embeddings is not None and box_embeddings.device == device, (
+            f"Expected box embeddings to be on device {device}, got {box_embeddings.device}"
+        )
+        assert box_mask is not None and box_mask.device == device, (
+            f"Expected box mask to be on device {device}, got {box_mask.device}"
+        )
+        assert box_labels is not None and box_labels.device == device, (
+            f"Expected box labels to be on device {device}, got {box_labels.device}"
+        )
+        assert point_embeddings is not None and point_embeddings.device == device, (
+            f"Expected point embeddings to be on device {device}, got {point_embeddings.device}"
+        )
+        assert point_mask is not None and point_mask.device == device, (
+            f"Expected point mask to be on device {device}, got {point_mask.device}"
+        )
+        assert point_labels is not None and point_labels.device == device, (
+            f"Expected point labels to be on device {device}, got {point_labels.device}"
+        )
+        assert mask_embeddings is None or mask_embeddings.device == device, (
+            f"Expected mask embeddings to be on device {device}, got {mask_embeddings.device}"
+        )
+        assert mask_mask is None or mask_mask.device == device, (
+            f"Expected mask attn. mask to be on device {device}, got {mask_mask.device}"
+        )
+
+        self.box_embeddings = box_embeddings
+        self.point_embeddings = point_embeddings
+        self.box_mask = box_mask
+        self.point_mask = point_mask
+        self.box_labels = box_labels
+        self.point_labels = point_labels
+        self.mask_embeddings = mask_embeddings
+        self.mask_labels = mask_labels
+        self.mask_mask = mask_mask
+
+    def _init_seq_len_and_device(
+        self, box_embeddings, point_embeddings, mask_embeddings
+    ):
+        box_seq_len = point_seq_len = mask_seq_len = 0
+        bs = None
+        device = None
+        if box_embeddings is not None:
+            bs = box_embeddings.shape[1]
+            box_seq_len = box_embeddings.shape[0]
+            device = box_embeddings.device
+
+        if point_embeddings is not None:
+            point_seq_len = point_embeddings.shape[0]
+            if bs is not None:
+                assert bs == point_embeddings.shape[1], (
+                    f"Batch size mismatch between box and point embeddings. Got {bs} and {point_embeddings.shape[1]}."
+                )
+            else:
+                bs = point_embeddings.shape[1]
+            if device is not None:
+                assert device == point_embeddings.device, (
+                    "Device mismatch between box and point embeddings"
+                )
+            else:
+                device = point_embeddings.device
+
+        if mask_embeddings is not None:
+            mask_seq_len = mask_embeddings.shape[0]
+            if bs is not None:
+                assert bs == mask_embeddings.shape[1], (
+                    f"Batch size mismatch between box/point and mask embedding. Got {bs} and {mask_embeddings.shape[1]}"
+                )
+            else:
+                bs = mask_embeddings.shape[1]
+            if device is not None:
+                assert device == mask_embeddings.device, (
+                    "Device mismatch between box/point and mask embeddings."
+                )
+            else:
+                device = mask_embeddings.device
+
+        return box_seq_len, point_seq_len, mask_seq_len, bs, device
+
+    def _init_box(self, box_embeddings, box_labels, box_mask, box_seq_len, bs, device):
+        if box_embeddings is None:
+            box_embeddings = torch.zeros(box_seq_len, bs, 4, device=device)
+        if box_labels is None:
+            box_labels = torch.ones(box_seq_len, bs, device=device, dtype=torch.long)
+        if box_mask is None:
+            box_mask = torch.zeros(bs, box_seq_len, device=device, dtype=torch.bool)
+        return box_embeddings, box_labels, box_mask
+
+    def _init_point(
+        self, point_embeddings, point_labels, point_mask, point_seq_len, bs, device
+    ):
+        """
+        Identical to _init_box. Except that C=2 for points (vs. 4 for boxes).
+        """
+        if point_embeddings is None:
+            point_embeddings = torch.zeros(point_seq_len, bs, 2, device=device)
+        if point_labels is None:
+            point_labels = torch.ones(
+                point_seq_len, bs, device=device, dtype=torch.long
+            )
+        if point_mask is None:
+            point_mask = torch.zeros(bs, point_seq_len, device=device, dtype=torch.bool)
+        return point_embeddings, point_labels, point_mask
+
+    def _init_mask(
+        self, mask_embeddings, mask_labels, mask_mask, mask_seq_len, bs, device
+    ):
+        # NOTE: Mask embeddings can be of arbitrary resolution, so we don't initialize it here.
+        # In case we append new mask, we check that its resolution matches exisiting ones (if any).
+        # In case mask_embeddings is None, we should never encode it.
+        if mask_labels is None:
+            mask_labels = torch.ones(mask_seq_len, bs, device=device, dtype=torch.long)
+        if mask_mask is None:
+            mask_mask = torch.zeros(bs, mask_seq_len, device=device, dtype=torch.bool)
+        return mask_embeddings, mask_labels, mask_mask
+
+    def append_boxes(self, boxes, labels, mask=None):
+        if self.box_embeddings is None:
+            self.box_embeddings = boxes
+            self.box_labels = labels
+            self.box_mask = mask
+            return
+
+        bs = self.box_embeddings.shape[1]
+        assert boxes.shape[1] == labels.shape[1] == bs
+        assert list(boxes.shape[:2]) == list(labels.shape[:2])
+        if mask is None:
+            mask = torch.zeros(
+                bs, boxes.shape[0], dtype=torch.bool, device=boxes.device
+            )
+
+        self.box_labels, _ = concat_padded_sequences(
+            self.box_labels.unsqueeze(-1), self.box_mask, labels.unsqueeze(-1), mask
+        )
+        self.box_labels = self.box_labels.squeeze(-1)
+        self.box_embeddings, self.box_mask = concat_padded_sequences(
+            self.box_embeddings, self.box_mask, boxes, mask
+        )
+
+    def append_points(self, points, labels, mask=None):
+        if self.point_embeddings is None:
+            self.point_embeddings = points
+            self.point_labels = labels
+            self.point_mask = mask
+            return
+
+        bs = self.point_embeddings.shape[1]
+        assert points.shape[1] == labels.shape[1] == bs
+        assert list(points.shape[:2]) == list(labels.shape[:2])
+        if mask is None:
+            mask = torch.zeros(
+                bs, points.shape[0], dtype=torch.bool, device=points.device
+            )
+
+        self.point_labels, _ = concat_padded_sequences(
+            self.point_labels.unsqueeze(-1), self.point_mask, labels.unsqueeze(-1), mask
+        )
+        self.point_labels = self.point_labels.squeeze(-1)
+        self.point_embeddings, self.point_mask = concat_padded_sequences(
+            self.point_embeddings, self.point_mask, points, mask
+        )
+
+    def append_masks(self, masks, labels=None, attn_mask=None):
+        if labels is not None:
+            assert list(masks.shape[:2]) == list(labels.shape[:2])
+        if self.mask_embeddings is None:
+            self.mask_embeddings = masks
+            mask_seq_len, bs = masks.shape[:2]
+            if labels is None:
+                self.mask_labels = torch.ones(
+                    mask_seq_len, bs, device=masks.device, dtype=torch.long
+                )
+            else:
+                self.mask_labels = labels
+            if attn_mask is None:
+                self.mask_mask = torch.zeros(
+                    bs, mask_seq_len, device=masks.device, dtype=torch.bool
+                )
+            else:
+                self.mask_mask = attn_mask
+        else:
+            raise NotImplementedError("Only one mask per prompt is supported.")
+
+    def clone(self):
+        return Prompt(
+            box_embeddings=(
+                None if self.box_embeddings is None else self.box_embeddings.clone()
+            ),
+            box_mask=None if self.box_mask is None else self.box_mask.clone(),
+            point_embeddings=(
+                None if self.point_embeddings is None else self.point_embeddings.clone()
+            ),
+            point_mask=None if self.point_mask is None else self.point_mask.clone(),
+            box_labels=None if self.box_labels is None else self.box_labels.clone(),
+            point_labels=(
+                None if self.point_labels is None else self.point_labels.clone()
+            ),
+        )
+
+
+class MaskEncoder(nn.Module):
+    """
+    Base class for mask encoders.
+    """
+
+    def __init__(
+        self,
+        mask_downsampler: nn.Module,
+        position_encoding: nn.Module,
+    ):
+        super().__init__()
+        self.mask_downsampler = mask_downsampler
+        self.position_encoding = position_encoding
+
+    def forward(self, masks, *args, **kwargs) -> Tuple[torch.Tensor, torch.Tensor]:
+        masks = self.mask_downsampler(masks)
+        masks_pos = self.position_encoding(masks).to(masks.dtype)
+
+        return masks, masks_pos
+
+
+class FusedMaskEncoder(MaskEncoder):
+    """
+    Identical to memory.SimpleMaskEncoder but follows the interface of geometry_encoders.MaskEncoder.
+    We also remove the `skip_mask_sigmoid` option (to be handled outside the MaskEncoder).
+    Fuses backbone image features with mask features.
+    """
+
+    def __init__(
+        self,
+        mask_downsampler: nn.Module,
+        position_encoding: nn.Module,
+        fuser: nn.Module,
+        in_dim: int = 256,
+        out_dim: int = 256,
+    ):
+        super().__init__(mask_downsampler, position_encoding)
+        self.fuser = fuser
+        self.out_proj = nn.Identity()
+        if out_dim != in_dim:
+            self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)
+        self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)
+
+    @override
+    def forward(
+        self,
+        masks: torch.Tensor,
+        pix_feat: torch.Tensor,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        masks = self.mask_downsampler(masks)
+
+        ## Fuse pix_feats and downsampled masks
+        # in case the visual features are on CPU, cast them to CUDA
+        pix_feat = pix_feat.to(masks.device)
+
+        x = self.pix_feat_proj(pix_feat)
+        x = x + masks
+        x = self.fuser(x)
+        x = self.out_proj(x)
+
+        pos = self.position_encoding(x).to(x.dtype)
+
+        return x, pos
+
+
+class SequenceGeometryEncoder(nn.Module):
+    """
+    This a fully fledged encoder for geometric prompts.
+    It assumes boxes are passed in the "normalized CxCyWH" format, and points in normalized xy
+    This allows flexibility in how to encode the features (eg do pooling)
+
+    Points and boxes can be encoded with any of the three possibilities:
+     - direct projection: we just compute a linear from coordinate space to d_model
+     - pooling: pool features from the backbone in the requested location.
+                For boxes, it's a roi align
+                For points it's a grid sample
+     - pos encoder: Take the position encoding of the point or box center
+
+    These three options are mutually compatible. If several are selected, we'll take a simple addition
+
+    As an alternative, we offer the possibility to encode points only.
+    In that case, the boxes are converted to two points for the top left and bottom right corners (with appropriate labels)
+
+    On top of these encodings, we offer the possibility to further encode the prompt sequence with a transformer.
+    """
+
+    def __init__(
+        self,
+        encode_boxes_as_points: bool,
+        points_direct_project: bool,
+        points_pool: bool,
+        points_pos_enc: bool,
+        boxes_direct_project: bool,
+        boxes_pool: bool,
+        boxes_pos_enc: bool,
+        d_model: int,
+        pos_enc,
+        num_layers: int,
+        layer: nn.Module,
+        roi_size: int = 7,  # for boxes pool
+        add_cls: bool = True,
+        add_post_encode_proj: bool = True,
+        mask_encoder: MaskEncoder = None,
+        add_mask_label: bool = False,
+        use_act_ckpt: bool = False,
+    ):
+        super().__init__()
+
+        self.d_model = d_model
+        self.pos_enc = pos_enc
+        self.encode_boxes_as_points = encode_boxes_as_points
+        self.roi_size = roi_size
+        # There usually are two labels: positive and negatives.
+        # If we encode boxes as points, we have 3 types of points: regular, top left, bottom right
+        # These 3 types can be positives or negatives, hence 2*3 = 6 labels
+        num_labels = 6 if self.encode_boxes_as_points else 2
+        self.label_embed = torch.nn.Embedding(num_labels, self.d_model)
+
+        # This is a cls token, can be used for pooling if need be.
+        # It also ensures that the encoded sequences are always non-empty
+        self.cls_embed = None
+        if add_cls:
+            self.cls_embed = torch.nn.Embedding(1, self.d_model)
+
+        assert points_direct_project or points_pos_enc or points_pool, (
+            "Error: need at least one way to encode points"
+        )
+        assert (
+            encode_boxes_as_points
+            or boxes_direct_project
+            or boxes_pos_enc
+            or boxes_pool
+        ), "Error: need at least one way to encode boxes"
+
+        self.points_direct_project = None
+        if points_direct_project:
+            self.points_direct_project = nn.Linear(2, self.d_model)
+        self.points_pool_project = None
+        if points_pool:
+            self.points_pool_project = nn.Linear(self.d_model, self.d_model)
+        self.points_pos_enc_project = None
+        if points_pos_enc:
+            self.points_pos_enc_project = nn.Linear(self.d_model, self.d_model)
+
+        self.boxes_direct_project = None
+        self.boxes_pool_project = None
+        self.boxes_pos_enc_project = None
+        if not encode_boxes_as_points:
+            if boxes_direct_project:
+                self.boxes_direct_project = nn.Linear(4, self.d_model)
+            if boxes_pool:
+                self.boxes_pool_project = nn.Conv2d(
+                    self.d_model, self.d_model, self.roi_size
+                )
+            if boxes_pos_enc:
+                self.boxes_pos_enc_project = nn.Linear(self.d_model + 2, self.d_model)
+
+        self.final_proj = None
+        if add_post_encode_proj:
+            self.final_proj = nn.Linear(self.d_model, self.d_model)
+            self.norm = nn.LayerNorm(self.d_model)
+
+        self.img_pre_norm = nn.Identity()
+        if self.points_pool_project is not None or self.boxes_pool_project is not None:
+            self.img_pre_norm = nn.LayerNorm(self.d_model)
+
+        self.encode = None
+        if num_layers > 0:
+            assert add_cls, (
+                "It's currently highly recommended to add a CLS when using a transformer"
+            )
+            self.encode = get_clones(layer, num_layers)
+            self.encode_norm = nn.LayerNorm(self.d_model)
+
+        if mask_encoder is not None:
+            assert isinstance(mask_encoder, MaskEncoder), (
+                f"Expected mask_encoder of type MaskEncoder. Got {type(mask_encoder)}."
+            )
+            if add_mask_label:
+                self.mask_label_embed = torch.nn.Embedding(2, self.d_model)
+        self.add_mask_label = add_mask_label
+        self.mask_encoder = mask_encoder
+        self.use_act_ckpt = use_act_ckpt
+
+    def _encode_points(self, points, points_mask, points_labels, img_feats):
+        points_embed = None
+        n_points, bs = points.shape[:2]
+
+        if self.points_direct_project is not None:
+            proj = self.points_direct_project(points)
+            assert points_embed is None
+            points_embed = proj
+
+        if self.points_pool_project is not None:
+            # points are [Num_points, bs, 2], normalized in [0, 1]
+            # the grid needs to be [Bs, H_out, W_out, 2] normalized in [-1,1]
+            # Will take H_out = num_points, w_out = 1
+            grid = points.transpose(0, 1).unsqueeze(2)
+            # re normalize to [-1, 1]
+            grid = (grid * 2) - 1
+            sampled = torch.nn.functional.grid_sample(
+                img_feats, grid, align_corners=False
+            )
+            assert list(sampled.shape) == [bs, self.d_model, n_points, 1]
+            sampled = sampled.squeeze(-1).permute(2, 0, 1)
+            proj = self.points_pool_project(sampled)
+            if points_embed is None:
+                points_embed = proj
+            else:
+                points_embed = points_embed + proj
+
+        if self.points_pos_enc_project is not None:
+            x, y = points.unbind(-1)
+            enc_x, enc_y = self.pos_enc._encode_xy(x.flatten(), y.flatten())
+            enc_x = enc_x.view(n_points, bs, enc_x.shape[-1])
+            enc_y = enc_y.view(n_points, bs, enc_y.shape[-1])
+            enc = torch.cat([enc_x, enc_y], -1)
+
+            proj = self.points_pos_enc_project(enc)
+            if points_embed is None:
+                points_embed = proj
+            else:
+                points_embed = points_embed + proj
+
+        type_embed = self.label_embed(points_labels.long())
+        return type_embed + points_embed, points_mask
+
+    def _encode_boxes(self, boxes, boxes_mask, boxes_labels, img_feats):
+        boxes_embed = None
+        n_boxes, bs = boxes.shape[:2]
+
+        if self.boxes_direct_project is not None:
+            proj = self.boxes_direct_project(boxes)
+            assert boxes_embed is None
+            boxes_embed = proj
+
+        if self.boxes_pool_project is not None:
+            H, W = img_feats.shape[-2:]
+
+            # boxes are [Num_boxes, bs, 4], normalized in [0, 1]
+            # We need to denormalize, and convert to [x, y, x, y]
+            boxes_xyxy = box_cxcywh_to_xyxy(boxes)
+            scale = torch.tensor([W, H, W, H], dtype=boxes_xyxy.dtype)
+            scale = scale.pin_memory().to(device=boxes_xyxy.device, non_blocking=True)
+            scale = scale.view(1, 1, 4)
+            boxes_xyxy = boxes_xyxy * scale
+            sampled = torchvision.ops.roi_align(
+                img_feats, boxes_xyxy.float().transpose(0, 1).unbind(0), self.roi_size
+            )
+            assert list(sampled.shape) == [
+                bs * n_boxes,
+                self.d_model,
+                self.roi_size,
+                self.roi_size,
+            ]
+            proj = self.boxes_pool_project(sampled)
+            proj = proj.view(bs, n_boxes, self.d_model).transpose(0, 1)
+            if boxes_embed is None:
+                boxes_embed = proj
+            else:
+                boxes_embed = boxes_embed + proj
+
+        if self.boxes_pos_enc_project is not None:
+            cx, cy, w, h = boxes.unbind(-1)
+            enc = self.pos_enc.encode_boxes(
+                cx.flatten(), cy.flatten(), w.flatten(), h.flatten()
+            )
+            enc = enc.view(boxes.shape[0], boxes.shape[1], enc.shape[-1])
+
+            proj = self.boxes_pos_enc_project(enc)
+            if boxes_embed is None:
+                boxes_embed = proj
+            else:
+                boxes_embed = boxes_embed + proj
+
+        type_embed = self.label_embed(boxes_labels.long())
+        return type_embed + boxes_embed, boxes_mask
+
+    def _encode_masks(
+        self,
+        masks: torch.Tensor,
+        attn_mask: torch.Tensor,
+        mask_labels: torch.Tensor,
+        img_feats: torch.Tensor = None,
+    ):
+        n_masks, bs = masks.shape[:2]
+        assert n_masks == 1, (
+            "We assume one mask per prompt for now. Code should still be functional if this assertion is removed."
+        )
+        assert list(attn_mask.shape) == [
+            bs,
+            n_masks,
+        ], (
+            f"Expected attn_mask to be of shape {bs}x{n_masks}. Got {list(attn_mask.shape)}."
+        )
+        masks, pos = self.mask_encoder(
+            masks=masks.flatten(0, 1).float(),
+            pix_feat=img_feats,
+        )
+        H, W = masks.shape[-2:]
+        n_tokens_per_mask = H * W
+        # NOTE: We directly add pos enc here as we usually don't keep track of pos encoding for the concatenated prompt (text, other geometric prompts). Might need to do some refactoring for more flexibility.
+        masks = masks + pos
+        masks = masks.view(n_masks, bs, *masks.shape[1:]).flatten(
+            -2
+        )  # n_masks x bs x C x H*W
+        masks = masks.permute(0, 3, 1, 2).flatten(0, 1)  # n_masks * H*W x bs x C
+        attn_mask = attn_mask.repeat_interleave(n_tokens_per_mask, dim=1)
+        if self.add_mask_label:
+            masks = masks + self.mask_label_embed(mask_labels.long())
+        return masks, attn_mask
+
+    def forward(self, geo_prompt: Prompt, img_feats, img_sizes, img_pos_embeds=None):
+        points = geo_prompt.point_embeddings
+        points_mask = geo_prompt.point_mask
+        points_labels = geo_prompt.point_labels
+        boxes = geo_prompt.box_embeddings
+        boxes_mask = geo_prompt.box_mask
+        boxes_labels = geo_prompt.box_labels
+        masks = geo_prompt.mask_embeddings
+        masks_mask = geo_prompt.mask_mask
+        masks_labels = geo_prompt.mask_labels
+        seq_first_img_feats = img_feats[-1]  # [H*W, B, C]
+        seq_first_img_pos_embeds = (
+            img_pos_embeds[-1]
+            if img_pos_embeds is not None
+            else torch.zeros_like(seq_first_img_feats)
+        )
+
+        if self.points_pool_project or self.boxes_pool_project:
+            assert len(img_feats) == len(img_sizes)
+            cur_img_feat = img_feats[-1]
+            cur_img_feat = self.img_pre_norm(cur_img_feat)
+            H, W = img_sizes[-1]
+            assert cur_img_feat.shape[0] == H * W
+            N, C = cur_img_feat.shape[-2:]
+            # Put back in NxCxHxW
+            cur_img_feat = cur_img_feat.permute(1, 2, 0)
+            cur_img_feat = cur_img_feat.view(N, C, H, W)
+            img_feats = cur_img_feat
+
+        if self.encode_boxes_as_points:
+            assert boxes is not None
+            assert geo_prompt.box_mask is not None
+            assert geo_prompt.box_labels is not None
+            assert boxes.shape[-1] == 4
+
+            boxes_xyxy = box_cxcywh_to_xyxy(boxes)
+            top_left, bottom_right = boxes_xyxy.split(split_size=2, dim=-1)
+
+            labels_tl = geo_prompt.box_labels + 2
+            labels_br = geo_prompt.box_labels + 4
+
+            # Append to the existing points
+            points, _ = concat_padded_sequences(
+                points, points_mask, top_left, boxes_mask
+            )
+            points_labels, points_mask = concat_padded_sequences(
+                points_labels.unsqueeze(-1),
+                points_mask,
+                labels_tl.unsqueeze(-1),
+                boxes_mask,
+            )
+            points_labels = points_labels.squeeze(-1)
+
+            points, _ = concat_padded_sequences(
+                points, points_mask, bottom_right, boxes_mask
+            )
+            points_labels, points_mask = concat_padded_sequences(
+                points_labels.unsqueeze(-1),
+                points_mask,
+                labels_br.unsqueeze(-1),
+                boxes_mask,
+            )
+            points_labels = points_labels.squeeze(-1)
+
+        final_embeds, final_mask = self._encode_points(
+            points=points,
+            points_mask=points_mask,
+            points_labels=points_labels,
+            img_feats=img_feats,
+        )
+
+        if not self.encode_boxes_as_points:
+            boxes_embeds, boxes_mask = self._encode_boxes(
+                boxes=boxes,
+                boxes_mask=boxes_mask,
+                boxes_labels=boxes_labels,
+                img_feats=img_feats,
+            )
+
+            final_embeds, final_mask = concat_padded_sequences(
+                final_embeds, final_mask, boxes_embeds, boxes_mask
+            )
+
+        if masks is not None and self.mask_encoder is not None:
+            masks_embed, masks_mask = self._encode_masks(
+                masks=masks,
+                attn_mask=masks_mask,
+                mask_labels=masks_labels,
+                img_feats=img_feats,
+            )
+            if points.size(0) == boxes.size(0) == 0:
+                return masks_embed, masks_mask
+        bs = final_embeds.shape[1]
+        assert final_mask.shape[0] == bs
+        if self.cls_embed is not None:
+            cls = self.cls_embed.weight.view(1, 1, self.d_model).repeat(1, bs, 1)
+            cls_mask = torch.zeros(
+                bs, 1, dtype=final_mask.dtype, device=final_mask.device
+            )
+            final_embeds, final_mask = concat_padded_sequences(
+                final_embeds, final_mask, cls, cls_mask
+            )
+
+        if self.final_proj is not None:
+            final_embeds = self.norm(self.final_proj(final_embeds))
+
+        if self.encode is not None:
+            for lay in self.encode:
+                final_embeds = activation_ckpt_wrapper(lay)(
+                    tgt=final_embeds,
+                    memory=seq_first_img_feats,
+                    tgt_key_padding_mask=final_mask,
+                    pos=seq_first_img_pos_embeds,
+                    act_ckpt_enable=self.training and self.use_act_ckpt,
+                )
+            final_embeds = self.encode_norm(final_embeds)
+        # Finally, concat mask embeddings if any
+        if masks is not None and self.mask_encoder is not None:
+            final_embeds, final_mask = concat_padded_sequences(
+                final_embeds, final_mask, masks_embed, masks_mask
+            )
+        return final_embeds, final_mask

third_party/GraspGen/sam3/sam3/model/io_utils.py

@@ -0,0 +1,715 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import contextlib
+import os
+import queue
+import re
+import time
+from threading import Condition, get_ident, Lock, Thread
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchvision.transforms.functional as TF
+from PIL import Image
+from sam3.logger import get_logger
+from tqdm import tqdm
+
+logger = get_logger(__name__)
+
+IS_MAIN_PROCESS = os.getenv("IS_MAIN_PROCESS", "1") == "1"
+RANK = int(os.getenv("RANK", "0"))
+
+IMAGE_EXTS = [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"]
+VIDEO_EXTS = [".mp4", ".mov", ".avi", ".mkv", ".webm"]
+
+
+def load_resource_as_video_frames(
+    resource_path,
+    image_size,
+    offload_video_to_cpu,
+    img_mean=(0.5, 0.5, 0.5),
+    img_std=(0.5, 0.5, 0.5),
+    async_loading_frames=False,
+    video_loader_type="cv2",
+):
+    """
+    Load video frames from either a video or an image (as a single-frame video).
+    Alternatively, if input is a list of PIL images, convert its format
+    """
+    if isinstance(resource_path, list):
+        img_mean = torch.tensor(img_mean, dtype=torch.float16)[:, None, None]
+        img_std = torch.tensor(img_std, dtype=torch.float16)[:, None, None]
+        assert all(isinstance(img_pil, Image.Image) for img_pil in resource_path)
+        assert len(resource_path) is not None
+        orig_height, orig_width = resource_path[0].size
+        orig_height, orig_width = (
+            orig_width,
+            orig_height,
+        )  # For some reason, this method returns these swapped
+        images = []
+        for img_pil in resource_path:
+            img_np = np.array(img_pil.convert("RGB").resize((image_size, image_size)))
+            assert img_np.dtype == np.uint8, "np.uint8 is expected for JPEG images"
+            img_np = img_np / 255.0
+            img = torch.from_numpy(img_np).permute(2, 0, 1)
+            # float16 precision should be sufficient for image tensor storage
+            img = img.to(dtype=torch.float16)
+            # normalize by mean and std
+            img -= img_mean
+            img /= img_std
+            images.append(img)
+        images = torch.stack(images)
+        if not offload_video_to_cpu:
+            images = images.cuda()
+        return images, orig_height, orig_width
+
+    is_image = (
+        isinstance(resource_path, str)
+        and os.path.splitext(resource_path)[-1].lower() in IMAGE_EXTS
+    )
+    if is_image:
+        return load_image_as_single_frame_video(
+            image_path=resource_path,
+            image_size=image_size,
+            offload_video_to_cpu=offload_video_to_cpu,
+            img_mean=img_mean,
+            img_std=img_std,
+        )
+    else:
+        return load_video_frames(
+            video_path=resource_path,
+            image_size=image_size,
+            offload_video_to_cpu=offload_video_to_cpu,
+            img_mean=img_mean,
+            img_std=img_std,
+            async_loading_frames=async_loading_frames,
+            video_loader_type=video_loader_type,
+        )
+
+
+def load_image_as_single_frame_video(
+    image_path,
+    image_size,
+    offload_video_to_cpu,
+    img_mean=(0.5, 0.5, 0.5),
+    img_std=(0.5, 0.5, 0.5),
+):
+    """Load an image as a single-frame video."""
+    images, image_height, image_width = _load_img_as_tensor(image_path, image_size)
+    images = images.unsqueeze(0).half()
+
+    img_mean = torch.tensor(img_mean, dtype=torch.float16)[:, None, None]
+    img_std = torch.tensor(img_std, dtype=torch.float16)[:, None, None]
+    if not offload_video_to_cpu:
+        images = images.cuda()
+        img_mean = img_mean.cuda()
+        img_std = img_std.cuda()
+    # normalize by mean and std
+    images -= img_mean
+    images /= img_std
+    return images, image_height, image_width
+
+
+def load_video_frames(
+    video_path,
+    image_size,
+    offload_video_to_cpu,
+    img_mean=(0.5, 0.5, 0.5),
+    img_std=(0.5, 0.5, 0.5),
+    async_loading_frames=False,
+    video_loader_type="cv2",
+):
+    """
+    Load the video frames from video_path. The frames are resized to image_size as in
+    the model and are loaded to GPU if offload_video_to_cpu=False. This is used by the demo.
+    """
+    assert isinstance(video_path, str)
+    if video_path.startswith("<load-dummy-video"):
+        # Check for pattern <load-dummy-video-N> where N is an integer
+        match = re.match(r"<load-dummy-video-(\d+)>", video_path)
+        num_frames = int(match.group(1)) if match else 60
+        return load_dummy_video(image_size, offload_video_to_cpu, num_frames=num_frames)
+    elif os.path.isdir(video_path):
+        return load_video_frames_from_image_folder(
+            image_folder=video_path,
+            image_size=image_size,
+            offload_video_to_cpu=offload_video_to_cpu,
+            img_mean=img_mean,
+            img_std=img_std,
+            async_loading_frames=async_loading_frames,
+        )
+    elif os.path.splitext(video_path)[-1].lower() in VIDEO_EXTS:
+        return load_video_frames_from_video_file(
+            video_path=video_path,
+            image_size=image_size,
+            offload_video_to_cpu=offload_video_to_cpu,
+            img_mean=img_mean,
+            img_std=img_std,
+            async_loading_frames=async_loading_frames,
+            video_loader_type=video_loader_type,
+        )
+    else:
+        raise NotImplementedError("Only video files and image folders are supported")
+
+
+def load_video_frames_from_image_folder(
+    image_folder,
+    image_size,
+    offload_video_to_cpu,
+    img_mean,
+    img_std,
+    async_loading_frames,
+):
+    """
+    Load the video frames from a directory of image files ("<frame_index>.<img_ext>" format)
+    """
+    frame_names = [
+        p
+        for p in os.listdir(image_folder)
+        if os.path.splitext(p)[-1].lower() in IMAGE_EXTS
+    ]
+    try:
+        frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
+    except ValueError:
+        # fallback to lexicographic sort if the format is not "<frame_index>.<img_ext>"
+        logger.warning(
+            f'frame names are not in "<frame_index>.<img_ext>" format: {frame_names[:5]=}, '
+            f"falling back to lexicographic sort."
+        )
+        frame_names.sort()
+    num_frames = len(frame_names)
+    if num_frames == 0:
+        raise RuntimeError(f"no images found in {image_folder}")
+    img_paths = [os.path.join(image_folder, frame_name) for frame_name in frame_names]
+    img_mean = torch.tensor(img_mean, dtype=torch.float16)[:, None, None]
+    img_std = torch.tensor(img_std, dtype=torch.float16)[:, None, None]
+
+    if async_loading_frames:
+        lazy_images = AsyncImageFrameLoader(
+            img_paths, image_size, offload_video_to_cpu, img_mean, img_std
+        )
+        return lazy_images, lazy_images.video_height, lazy_images.video_width
+
+    # float16 precision should be sufficient for image tensor storage
+    images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float16)
+    video_height, video_width = None, None
+    for n, img_path in enumerate(
+        tqdm(img_paths, desc=f"frame loading (image folder) [rank={RANK}]")
+    ):
+        images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size)
+    if not offload_video_to_cpu:
+        images = images.cuda()
+        img_mean = img_mean.cuda()
+        img_std = img_std.cuda()
+    # normalize by mean and std
+    images -= img_mean
+    images /= img_std
+    return images, video_height, video_width
+
+
+def load_video_frames_from_video_file(
+    video_path,
+    image_size,
+    offload_video_to_cpu,
+    img_mean,
+    img_std,
+    async_loading_frames,
+    gpu_acceleration=False,
+    gpu_device=None,
+    video_loader_type="cv2",
+):
+    """Load the video frames from a video file."""
+    if video_loader_type == "cv2":
+        return load_video_frames_from_video_file_using_cv2(
+            video_path=video_path,
+            image_size=image_size,
+            img_mean=img_mean,
+            img_std=img_std,
+            offload_video_to_cpu=offload_video_to_cpu,
+        )
+    elif video_loader_type == "torchcodec":
+        logger.info("Using torchcodec to load video file")
+        lazy_images = AsyncVideoFileLoaderWithTorchCodec(
+            video_path=video_path,
+            image_size=image_size,
+            offload_video_to_cpu=offload_video_to_cpu,
+            img_mean=img_mean,
+            img_std=img_std,
+            gpu_acceleration=gpu_acceleration,
+            gpu_device=gpu_device,
+        )
+        # The `AsyncVideoFileLoaderWithTorchCodec` class always loads the videos asynchronously,
+        # so we just wait for its loading thread to finish if async_loading_frames=False.
+        if not async_loading_frames:
+            async_thread = lazy_images.thread
+            if async_thread is not None:
+                async_thread.join()
+        return lazy_images, lazy_images.video_height, lazy_images.video_width
+    else:
+        raise RuntimeError("video_loader_type must be either 'cv2' or 'torchcodec'")
+
+
+def load_video_frames_from_video_file_using_cv2(
+    video_path: str,
+    image_size: int,
+    img_mean: tuple = (0.5, 0.5, 0.5),
+    img_std: tuple = (0.5, 0.5, 0.5),
+    offload_video_to_cpu: bool = False,
+) -> torch.Tensor:
+    """
+    Load video from path, convert to normalized tensor with specified preprocessing
+
+    Args:
+        video_path: Path to video file
+        image_size: Target size for square frames (height and width)
+        img_mean: Normalization mean (RGB)
+        img_std: Normalization standard deviation (RGB)
+
+    Returns:
+        torch.Tensor: Preprocessed video tensor in shape (T, C, H, W) with float16 dtype
+    """
+    import cv2  # delay OpenCV import to avoid unnecessary dependency
+
+    # Initialize video capture
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise ValueError(f"Could not open video: {video_path}")
+
+    original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    num_frames = num_frames if num_frames > 0 else None
+
+    frames = []
+    pbar = tqdm(desc=f"frame loading (OpenCV) [rank={RANK}]", total=num_frames)
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Convert BGR to RGB and resize
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame_resized = cv2.resize(
+            frame_rgb, (image_size, image_size), interpolation=cv2.INTER_CUBIC
+        )
+        frames.append(frame_resized)
+        pbar.update(1)
+    cap.release()
+    pbar.close()
+
+    if len(frames) == 0:
+        raise RuntimeError(
+            f"No frames could be decoded from video: {video_path}. "
+            f"The file may be corrupted, empty, or encoded with an unsupported codec."
+        )
+
+    # Convert to tensor
+    frames_np = np.stack(frames, axis=0).astype(np.float32)  # (T, H, W, C)
+    video_tensor = torch.from_numpy(frames_np).permute(0, 3, 1, 2)  # (T, C, H, W)
+
+    img_mean = torch.tensor(img_mean, dtype=torch.float16).view(1, 3, 1, 1)
+    img_std = torch.tensor(img_std, dtype=torch.float16).view(1, 3, 1, 1)
+    if not offload_video_to_cpu:
+        video_tensor = video_tensor.cuda()
+        img_mean = img_mean.cuda()
+        img_std = img_std.cuda()
+    # normalize by mean and std
+    video_tensor -= img_mean
+    video_tensor /= img_std
+    return video_tensor, original_height, original_width
+
+
+def load_dummy_video(image_size, offload_video_to_cpu, num_frames=60):
+    """
+    Load a dummy video with random frames for testing and compilation warmup purposes.
+    """
+    video_height, video_width = 480, 640  # dummy original video sizes
+    images = torch.randn(num_frames, 3, image_size, image_size, dtype=torch.float16)
+    if not offload_video_to_cpu:
+        images = images.cuda()
+    return images, video_height, video_width
+
+
+def _load_img_as_tensor(img_path, image_size):
+    """Load and resize an image and convert it into a PyTorch tensor."""
+    img = Image.open(img_path).convert("RGB")
+    orig_width, orig_height = img.width, img.height
+    img = TF.resize(img, size=(image_size, image_size))
+    img = TF.to_tensor(img)
+    return img, orig_height, orig_width
+
+
+class AsyncImageFrameLoader:
+    """
+    A list of video frames to be load asynchronously without blocking session start.
+    """
+
+    def __init__(self, img_paths, image_size, offload_video_to_cpu, img_mean, img_std):
+        self.img_paths = img_paths
+        self.image_size = image_size
+        self.offload_video_to_cpu = offload_video_to_cpu
+        self.img_mean = img_mean
+        self.img_std = img_std
+        # items in `self._images` will be loaded asynchronously
+        self.images = [None] * len(img_paths)
+        # catch and raise any exceptions in the async loading thread
+        self.exception = None
+        # video_height and video_width be filled when loading the first image
+        self.video_height = None
+        self.video_width = None
+
+        # load the first frame to fill video_height and video_width and also
+        # to cache it (since it's most likely where the user will click)
+        self.__getitem__(0)
+
+        # load the rest of frames asynchronously without blocking the session start
+        def _load_frames():
+            try:
+                for n in tqdm(
+                    range(len(self.images)),
+                    desc=f"frame loading (image folder) [rank={RANK}]",
+                ):
+                    self.__getitem__(n)
+            except Exception as e:
+                self.exception = e
+
+        self.thread = Thread(target=_load_frames, daemon=True)
+        self.thread.start()
+
+    def __getitem__(self, index):
+        if self.exception is not None:
+            raise RuntimeError("Failure in frame loading thread") from self.exception
+
+        img = self.images[index]
+        if img is not None:
+            return img
+
+        img, video_height, video_width = _load_img_as_tensor(
+            self.img_paths[index], self.image_size
+        )
+        self.video_height = video_height
+        self.video_width = video_width
+        # float16 precision should be sufficient for image tensor storage
+        img = img.to(dtype=torch.float16)
+        # normalize by mean and std
+        img -= self.img_mean
+        img /= self.img_std
+        if not self.offload_video_to_cpu:
+            img = img.cuda()
+        self.images[index] = img
+        return img
+
+    def __len__(self):
+        return len(self.images)
+
+
+class TorchCodecDecoder:
+    """
+    A wrapper to support GPU device and num_threads in TorchCodec decoder,
+    which are not supported by `torchcodec.decoders.SimpleVideoDecoder` yet.
+    """
+
+    def __init__(self, source, dimension_order="NCHW", device="cpu", num_threads=1):
+        from torchcodec import _core as core
+
+        self._source = source  # hold a reference to the source to prevent it from GC
+        if isinstance(source, str):
+            self._decoder = core.create_from_file(source, "exact")
+        elif isinstance(source, bytes):
+            self._decoder = core.create_from_bytes(source, "exact")
+        else:
+            raise TypeError(f"Unknown source type: {type(source)}.")
+        assert dimension_order in ("NCHW", "NHWC")
+
+        device_string = str(device)
+        core.scan_all_streams_to_update_metadata(self._decoder)
+        core.add_video_stream(
+            self._decoder,
+            dimension_order=dimension_order,
+            device=device_string,
+            num_threads=(1 if "cuda" in device_string else num_threads),
+        )
+        video_metadata = core.get_container_metadata(self._decoder)
+        best_stream_index = video_metadata.best_video_stream_index
+        assert best_stream_index is not None
+        self.metadata = video_metadata.streams[best_stream_index]
+        assert self.metadata.num_frames_from_content is not None
+        self._num_frames = self.metadata.num_frames_from_content
+
+    def __len__(self) -> int:
+        return self._num_frames
+
+    def __getitem__(self, key: int):
+        from torchcodec import _core as core
+
+        if key < 0:
+            key += self._num_frames
+        if key >= self._num_frames or key < 0:
+            raise IndexError(
+                f"Index {key} is out of bounds; length is {self._num_frames}"
+            )
+        frame_data, *_ = core.get_frame_at_index(
+            self._decoder,
+            frame_index=key,
+        )
+        return frame_data
+
+
+class FIFOLock:
+    """A lock that ensures FIFO ordering of lock acquisitions."""
+
+    def __init__(self):
+        self._lock = Lock()
+        self._waiters = queue.Queue()
+        self._condition = Condition()
+
+    def acquire(self):
+        ident = get_ident()
+        with self._condition:
+            self._waiters.put(ident)
+            while self._waiters.queue[0] != ident or not self._lock.acquire(
+                blocking=False
+            ):
+                self._condition.wait()
+                # got the lock and it's our turn
+
+    def release(self):
+        with self._condition:
+            self._lock.release()
+            self._waiters.get()
+            self._condition.notify_all()
+
+    def __enter__(self):
+        self.acquire()
+
+    def __exit__(self, t, v, tb):
+        self.release()
+
+
+class AsyncVideoFileLoaderWithTorchCodec:
+    """
+    Loading frames from video files asynchronously without blocking session start.
+
+    Unlike `AsyncVideoFileLoader`, this class uses PyTorch's offical TorchCodec library
+    for video decoding, which is more efficient and supports more video formats.
+    """
+
+    def __init__(
+        self,
+        video_path,
+        image_size,
+        offload_video_to_cpu,
+        img_mean,
+        img_std,
+        gpu_acceleration=True,
+        gpu_device=None,
+        use_rand_seek_in_loading=False,
+    ):
+        # Check and possibly infer the output device (and also get its GPU id when applicable)
+        assert gpu_device is None or gpu_device.type == "cuda"
+        gpu_id = (
+            gpu_device.index
+            if gpu_device is not None and gpu_device.index is not None
+            else torch.cuda.current_device()
+        )
+        if offload_video_to_cpu:
+            out_device = torch.device("cpu")
+        else:
+            out_device = torch.device("cuda") if gpu_device is None else gpu_device
+        self.out_device = out_device
+        self.gpu_acceleration = gpu_acceleration
+        self.gpu_id = gpu_id
+        self.image_size = image_size
+        self.offload_video_to_cpu = offload_video_to_cpu
+        if not isinstance(img_mean, torch.Tensor):
+            img_mean = torch.tensor(img_mean, dtype=torch.float16)[:, None, None]
+        self.img_mean = img_mean
+        if not isinstance(img_std, torch.Tensor):
+            img_std = torch.tensor(img_std, dtype=torch.float16)[:, None, None]
+        self.img_std = img_std
+
+        if gpu_acceleration:
+            self.img_mean = self.img_mean.to(f"cuda:{self.gpu_id}")
+            self.img_std = self.img_std.to(f"cuda:{self.gpu_id}")
+            decoder_option = {"device": f"cuda:{self.gpu_id}"}
+        else:
+            self.img_mean = self.img_mean.cpu()
+            self.img_std = self.img_std.cpu()
+            decoder_option = {"num_threads": 1}  # use a single thread to save memory
+
+        self.rank = int(os.environ.get("RANK", "0"))
+        self.world_size = int(os.environ.get("WORLD_SIZE", "1"))
+        self.async_reader = TorchCodecDecoder(video_path, **decoder_option)
+
+        # `num_frames_from_content` is the true number of frames in the video content
+        # from the scan operation (rather than from the metadata, which could be wrong)
+        self.num_frames = self.async_reader.metadata.num_frames_from_content
+        self.video_height = self.async_reader.metadata.height
+        self.video_width = self.async_reader.metadata.width
+
+        # items in `self._images` will be loaded asynchronously
+        self.images_loaded = [False] * self.num_frames
+        self.images = torch.zeros(
+            self.num_frames,
+            3,
+            self.image_size,
+            self.image_size,
+            dtype=torch.float16,
+            device=self.out_device,
+        )
+        # catch and raise any exceptions in the async loading thread
+        self.exception = None
+        self.use_rand_seek_in_loading = use_rand_seek_in_loading
+        self.rand_seek_idx_queue = queue.Queue()
+        # use a lock to avoid race condition between concurrent access to torchcodec
+        # libs (which are not thread-safe); the lock is replaced with a nullcontext
+        # when the video is fully loaded
+        self.torchcodec_access_lock = FIFOLock()
+        self._start_video_loading()
+
+    def _load_one_frame(self, idx):
+        frame_resized = self._transform_frame(self.async_reader[idx])
+        return frame_resized
+
+    @torch.inference_mode()
+    def _start_video_loading(self):
+        desc = f"frame loading (TorchCodec w/ {'GPU' if self.gpu_acceleration else 'CPU'}) [rank={RANK}]"
+        pbar = tqdm(desc=desc, total=self.num_frames)
+        self.num_loaded_frames = 0
+        # load the first frame synchronously to cache it before the session is opened
+        idx = self.num_loaded_frames
+        self.images[idx] = self._load_one_frame(idx)
+        self.images_loaded[idx] = True
+        self.num_loaded_frames += 1
+        pbar.update(n=1)
+        self.all_frames_loaded = self.num_loaded_frames == self.num_frames
+
+        # load the frames asynchronously without blocking the session start
+        def _load_frames():
+            finished = self.all_frames_loaded
+            chunk_size = 16
+            while not finished:
+                # asynchronously load `chunk_size` frames each time we acquire the lock
+                with self.torchcodec_access_lock, torch.inference_mode():
+                    for _ in range(chunk_size):
+                        try:
+                            idx = self.num_loaded_frames
+                            self.images[idx] = self._load_one_frame(idx)
+                            self.images_loaded[idx] = True
+                            self.num_loaded_frames += 1
+                            pbar.update(n=1)
+                            if self.num_loaded_frames >= self.num_frames:
+                                finished = True
+                                break
+                        except Exception as e:
+                            self.exception = e
+                            raise
+
+                    # also read the frame that is being randomly seeked to
+                    while True:
+                        try:
+                            idx = self.rand_seek_idx_queue.get_nowait()
+                            if not self.images_loaded[idx]:
+                                self.images[idx] = self._load_one_frame(idx)
+                                self.images_loaded[idx] = True
+                        except queue.Empty:
+                            break
+                        except Exception as e:
+                            self.exception = e
+                            raise
+
+            # finished -- check whether we have loaded the total number of frames
+            if self.num_loaded_frames != self.num_frames:
+                raise RuntimeError(
+                    f"There are {self.num_frames} frames in the video, but only "
+                    f"{self.num_loaded_frames} frames can be loaded successfully."
+                )
+            else:
+                self.all_frames_loaded = True
+                pbar.close()
+                with self.torchcodec_access_lock:
+                    import gc
+
+                    # all frames have been loaded, so we can release the readers and free their memory
+                    # also remove pbar and thread (which shouldn't be a part of session saving)
+                    reader = self.async_reader
+                    if reader is not None:
+                        reader._source = None
+                    self.async_reader = None
+                    self.pbar = None
+                    self.thread = None
+                    self.rand_seek_idx_queue = None
+                    gc.collect()
+                # remove the lock (replace it with nullcontext) when the video is fully loaded
+                self.torchcodec_access_lock = contextlib.nullcontext()
+
+        self.thread = Thread(target=_load_frames, daemon=True)
+        self.thread.start()
+
+    def _transform_frame(self, frame):
+        frame = frame.clone()  # make a copy to avoid modifying the original frame bytes
+        frame = frame.float()  # convert to float32 before interpolation
+        frame_resized = F.interpolate(
+            frame[None, :],
+            size=(self.image_size, self.image_size),
+            mode="bicubic",
+            align_corners=False,
+        )[0]
+        # float16 precision should be sufficient for image tensor storage
+        frame_resized = frame_resized.half()  # uint8 -> float16
+        frame_resized /= 255
+        frame_resized -= self.img_mean
+        frame_resized /= self.img_std
+        if self.offload_video_to_cpu:
+            frame_resized = frame_resized.cpu()
+        elif frame_resized.device != self.out_device:
+            frame_resized = frame_resized.to(device=self.out_device, non_blocking=True)
+        return frame_resized
+
+    def __getitem__(self, index):
+        if self.exception is not None:
+            raise RuntimeError("Failure in frame loading thread") from self.exception
+
+        max_tries = 1200
+        for _ in range(max_tries):
+            # use a lock to avoid race condition between concurrent access to torchcodec
+            # libs (which are not thread-safe); the lock is replaced with a nullcontext
+            # when the video is fully loaded
+            with self.torchcodec_access_lock:
+                if self.images_loaded[index]:
+                    return self.images[index]
+
+                if self.use_rand_seek_in_loading:
+                    # async loading hasn't reached this frame yet, so we load this frame individually
+                    # (it will be loaded by in _load_frames thread and added to self.images[index])
+                    self.rand_seek_idx_queue.put(index)
+
+            time.sleep(0.1)
+
+        raise RuntimeError(f"Failed to load frame {index} after {max_tries} tries")
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getstate__(self):
+        """
+        Remove a few attributes during pickling, so that this async video loader can be
+        saved and loaded as a part of the model session.
+        """
+        # wait for async video loading to finish before pickling
+        async_thread = self.thread
+        if async_thread is not None:
+            async_thread.join()
+        # release a few objects that cannot be pickled
+        reader = self.async_reader
+        if reader is not None:
+            reader._source = None
+        self.async_reader = None
+        self.pbar = None
+        self.thread = None
+        self.rand_seek_idx_queue = None
+        self.torchcodec_access_lock = contextlib.nullcontext()
+        return self.__dict__.copy()

third_party/GraspGen/sam3/sam3/model/maskformer_segmentation.py

@@ -0,0 +1,327 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import math
+from typing import Dict, List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+
+from .model_misc import MLP
+
+
+class LinearPresenceHead(nn.Sequential):
+    def __init__(self, d_model):
+        # a hack to make `LinearPresenceHead` compatible with old checkpoints
+        super().__init__(nn.Identity(), nn.Identity(), nn.Linear(d_model, 1))
+
+    def forward(self, hs, prompt, prompt_mask):
+        return super().forward(hs)
+
+
+class MaskPredictor(nn.Module):
+    def __init__(self, hidden_dim, mask_dim):
+        super().__init__()
+        self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
+
+    def forward(self, obj_queries, pixel_embed):
+        if len(obj_queries.shape) == 3:
+            if pixel_embed.ndim == 3:
+                # batch size was omitted
+                mask_preds = torch.einsum(
+                    "bqc,chw->bqhw", self.mask_embed(obj_queries), pixel_embed
+                )
+            else:
+                mask_preds = torch.einsum(
+                    "bqc,bchw->bqhw", self.mask_embed(obj_queries), pixel_embed
+                )
+        else:
+            # Assumed to have aux masks
+            if pixel_embed.ndim == 3:
+                # batch size was omitted
+                mask_preds = torch.einsum(
+                    "lbqc,chw->lbqhw", self.mask_embed(obj_queries), pixel_embed
+                )
+            else:
+                mask_preds = torch.einsum(
+                    "lbqc,bchw->lbqhw", self.mask_embed(obj_queries), pixel_embed
+                )
+
+        return mask_preds
+
+
+class SegmentationHead(nn.Module):
+    def __init__(
+        self,
+        hidden_dim,
+        upsampling_stages,
+        use_encoder_inputs=False,
+        aux_masks=False,
+        no_dec=False,
+        pixel_decoder=None,
+        act_ckpt=False,
+        shared_conv=False,
+        compile_mode_pixel_decoder=None,
+    ):
+        super().__init__()
+        self.use_encoder_inputs = use_encoder_inputs
+        self.aux_masks = aux_masks
+        if pixel_decoder is not None:
+            self.pixel_decoder = pixel_decoder
+        else:
+            self.pixel_decoder = PixelDecoder(
+                hidden_dim,
+                upsampling_stages,
+                shared_conv=shared_conv,
+                compile_mode=compile_mode_pixel_decoder,
+            )
+        self.no_dec = no_dec
+        if no_dec:
+            self.mask_predictor = nn.Conv2d(
+                hidden_dim, 1, kernel_size=3, stride=1, padding=1
+            )
+        else:
+            self.mask_predictor = MaskPredictor(hidden_dim, mask_dim=hidden_dim)
+
+        self.act_ckpt = act_ckpt
+
+        # used to update the output dictionary
+        self.instance_keys = ["pred_masks"]
+
+    @property
+    def device(self):
+        self._device = getattr(self, "_device", None) or next(self.parameters()).device
+        return self._device
+
+    def to(self, *args, **kwargs):
+        # clear cached _device in case the model is moved to a different device
+        self._device = None
+        return super().to(*args, **kwargs)
+
+    def _embed_pixels(
+        self,
+        backbone_feats: List[torch.Tensor],
+        image_ids,
+        encoder_hidden_states,
+    ) -> torch.Tensor:
+        feature_device = backbone_feats[0].device  # features could be on CPU
+        model_device = self.device
+        image_ids_ = image_ids.to(feature_device)
+        if self.use_encoder_inputs:
+            if backbone_feats[0].shape[0] > 1:
+                # For bs > 1, we construct the per query backbone features
+                backbone_visual_feats = []
+                for feat in backbone_feats:
+                    # Copy the img features per query (pixel decoder won't share img feats)
+                    backbone_visual_feats.append(feat[image_ids_, ...].to(model_device))
+            else:
+                # Bs=1, we rely on broadcasting for query-based processing
+                backbone_visual_feats = [bb_feat.clone() for bb_feat in backbone_feats]
+            # Extract visual embeddings
+            encoder_hidden_states = encoder_hidden_states.permute(1, 2, 0)
+            spatial_dim = math.prod(backbone_feats[-1].shape[-2:])
+            encoder_visual_embed = encoder_hidden_states[..., :spatial_dim].reshape(
+                -1, *backbone_feats[-1].shape[1:]
+            )
+
+            backbone_visual_feats[-1] = encoder_visual_embed
+            if self.act_ckpt:
+                pixel_embed = checkpoint.checkpoint(
+                    self.pixel_decoder, backbone_visual_feats, use_reentrant=False
+                )
+            else:
+                pixel_embed = self.pixel_decoder(backbone_visual_feats)
+        else:
+            backbone_feats = [x.to(model_device) for x in backbone_feats]
+            pixel_embed = self.pixel_decoder(backbone_feats)
+            if pixel_embed.shape[0] == 1:
+                # For batch_size=1 training, we can avoid the indexing to save memory
+                pixel_embed = pixel_embed.squeeze(0)
+            else:
+                pixel_embed = pixel_embed[image_ids, ...]
+        return pixel_embed
+
+    def forward(
+        self,
+        backbone_feats: List[torch.Tensor],
+        obj_queries: torch.Tensor,
+        image_ids,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Dict[str, torch.Tensor]:
+        if self.use_encoder_inputs:
+            assert encoder_hidden_states is not None
+
+        pixel_embed = self._embed_pixels(
+            backbone_feats=backbone_feats,
+            image_ids=image_ids,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+
+        if self.no_dec:
+            mask_pred = self.mask_predictor(pixel_embed)
+        elif self.aux_masks:
+            mask_pred = self.mask_predictor(obj_queries, pixel_embed)
+        else:
+            mask_pred = self.mask_predictor(obj_queries[-1], pixel_embed)
+
+        return {"pred_masks": mask_pred}
+
+
+class PixelDecoder(nn.Module):
+    def __init__(
+        self,
+        hidden_dim,
+        num_upsampling_stages,
+        interpolation_mode="nearest",
+        shared_conv=False,
+        compile_mode=None,
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_dim
+        self.num_upsampling_stages = num_upsampling_stages
+        self.interpolation_mode = interpolation_mode
+        conv_layers = []
+        norms = []
+        num_convs = 1 if shared_conv else num_upsampling_stages
+        for _ in range(num_convs):
+            conv_layers.append(nn.Conv2d(self.hidden_dim, self.hidden_dim, 3, 1, 1))
+            norms.append(nn.GroupNorm(8, self.hidden_dim))
+
+        self.conv_layers = nn.ModuleList(conv_layers)
+        self.norms = nn.ModuleList(norms)
+        self.shared_conv = shared_conv
+        self.out_dim = self.conv_layers[-1].out_channels
+        if compile_mode is not None:
+            self.forward = torch.compile(
+                self.forward, mode=compile_mode, dynamic=True, fullgraph=True
+            )
+            # Needed to make checkpointing happy. But we don't know if the module is checkpointed, so we disable it by default.
+            torch._dynamo.config.optimize_ddp = False
+
+    def forward(self, backbone_feats: List[torch.Tensor]):
+        # Assumes backbone features are already projected (C == hidden dim)
+
+        prev_fpn = backbone_feats[-1]
+        fpn_feats = backbone_feats[:-1]
+        for layer_idx, bb_feat in enumerate(fpn_feats[::-1]):
+            curr_fpn = bb_feat
+            prev_fpn = curr_fpn + F.interpolate(
+                prev_fpn, size=curr_fpn.shape[-2:], mode=self.interpolation_mode
+            )
+            if self.shared_conv:
+                # only one conv layer
+                layer_idx = 0
+            prev_fpn = self.conv_layers[layer_idx](prev_fpn)
+            prev_fpn = F.relu(self.norms[layer_idx](prev_fpn))
+
+        return prev_fpn
+
+
+class UniversalSegmentationHead(SegmentationHead):
+    """This module handles semantic+instance segmentation"""
+
+    def __init__(
+        self,
+        hidden_dim,
+        upsampling_stages,
+        pixel_decoder,
+        aux_masks=False,
+        no_dec=False,
+        act_ckpt=False,
+        presence_head: bool = False,
+        dot_product_scorer=None,
+        cross_attend_prompt=None,
+    ):
+        super().__init__(
+            hidden_dim=hidden_dim,
+            upsampling_stages=upsampling_stages,
+            use_encoder_inputs=True,
+            aux_masks=aux_masks,
+            no_dec=no_dec,
+            pixel_decoder=pixel_decoder,
+            act_ckpt=act_ckpt,
+        )
+        self.d_model = hidden_dim
+
+        if dot_product_scorer is not None:
+            assert presence_head, (
+                "Specifying a dot product scorer without a presence head is likely a mistake"
+            )
+
+        self.presence_head = None
+        if presence_head:
+            self.presence_head = (
+                dot_product_scorer
+                if dot_product_scorer is not None
+                else LinearPresenceHead(self.d_model)
+            )
+
+        self.cross_attend_prompt = cross_attend_prompt
+        if self.cross_attend_prompt is not None:
+            self.cross_attn_norm = nn.LayerNorm(self.d_model)
+
+        self.semantic_seg_head = nn.Conv2d(self.pixel_decoder.out_dim, 1, kernel_size=1)
+        self.instance_seg_head = nn.Conv2d(
+            self.pixel_decoder.out_dim, self.d_model, kernel_size=1
+        )
+
+    def forward(
+        self,
+        backbone_feats: List[torch.Tensor],
+        obj_queries: torch.Tensor,
+        image_ids,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        prompt: Optional[torch.Tensor] = None,
+        prompt_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        assert encoder_hidden_states is not None
+        bs = encoder_hidden_states.shape[1]
+
+        if self.cross_attend_prompt is not None:
+            tgt2 = self.cross_attn_norm(encoder_hidden_states)
+            tgt2 = self.cross_attend_prompt(
+                query=tgt2,
+                key=prompt,
+                value=prompt,
+                key_padding_mask=prompt_mask,
+            )[0]
+            encoder_hidden_states = tgt2 + encoder_hidden_states
+
+        presence_logit = None
+        if self.presence_head is not None:
+            pooled_enc = encoder_hidden_states.mean(0)
+            presence_logit = (
+                self.presence_head(
+                    pooled_enc.view(1, bs, 1, self.d_model),
+                    prompt=prompt,
+                    prompt_mask=prompt_mask,
+                )
+                .squeeze(0)
+                .squeeze(1)
+            )
+
+        pixel_embed = self._embed_pixels(
+            backbone_feats=backbone_feats,
+            image_ids=image_ids,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+
+        instance_embeds = self.instance_seg_head(pixel_embed)
+
+        if self.no_dec:
+            mask_pred = self.mask_predictor(instance_embeds)
+        elif self.aux_masks:
+            mask_pred = self.mask_predictor(obj_queries, instance_embeds)
+        else:
+            mask_pred = self.mask_predictor(obj_queries[-1], instance_embeds)
+
+        return {
+            "pred_masks": mask_pred,
+            "semantic_seg": self.semantic_seg_head(pixel_embed),
+            "presence_logit": presence_logit,
+        }

third_party/GraspGen/sam3/sam3/model/memory.py

@@ -0,0 +1,203 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import math
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    from timm.layers import DropPath
+except ModuleNotFoundError:
+    # compatibility for older timm versions
+    from timm.models.layers import DropPath
+
+from .model_misc import get_clones, LayerNorm2d
+
+
+class SimpleMaskDownSampler(nn.Module):
+    """
+    Progressively downsample a mask by total_stride, each time by stride.
+    Note that LayerNorm is applied per *token*, like in ViT.
+
+    With each downsample (by a factor stride**2), channel capacity increases by the same factor.
+    In the end, we linearly project to embed_dim channels.
+    """
+
+    def __init__(
+        self,
+        embed_dim=256,
+        kernel_size=4,
+        stride=4,
+        padding=0,
+        total_stride=16,
+        activation=nn.GELU,
+        # Option to interpolate the input mask first before downsampling using convs. In that case, the total_stride is assumed to be after interpolation.
+        # If set to input resolution or None, we don't interpolate. We default to None to be safe (for older configs or if not explicitly set)
+        interpol_size=None,
+    ):
+        super().__init__()
+        num_layers = int(math.log2(total_stride) // math.log2(stride))
+        assert stride**num_layers == total_stride
+        self.encoder = nn.Sequential()
+        mask_in_chans, mask_out_chans = 1, 1
+        for _ in range(num_layers):
+            mask_out_chans = mask_in_chans * (stride**2)
+            self.encoder.append(
+                nn.Conv2d(
+                    mask_in_chans,
+                    mask_out_chans,
+                    kernel_size=kernel_size,
+                    stride=stride,
+                    padding=padding,
+                )
+            )
+            self.encoder.append(LayerNorm2d(mask_out_chans))
+            self.encoder.append(activation())
+            mask_in_chans = mask_out_chans
+
+        self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1))
+        self.interpol_size = interpol_size
+        if self.interpol_size is not None:
+            assert isinstance(self.interpol_size, (list, tuple)), (
+                f"Unsupported type {type(self.interpol_size)}. Should be a list or tuple."
+            )
+            self.interpol_size = list(interpol_size)
+            assert len(self.interpol_size) == 2
+
+    def forward(self, x: torch.Tensor):
+        if self.interpol_size is not None and self.interpol_size != list(x.shape[-2:]):
+            x = F.interpolate(
+                x.float(),
+                size=self.interpol_size,
+                align_corners=False,
+                mode="bilinear",
+                antialias=True,
+            )
+        return self.encoder(x)
+
+
+# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt)
+class CXBlock(nn.Module):
+    r"""ConvNeXt Block. There are two equivalent implementations:
+    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
+    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
+    We use (2) as we find it slightly faster in PyTorch
+
+    Args:
+        dim (int): Number of input channels.
+        drop_path (float): Stochastic depth rate. Default: 0.0
+        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
+    """
+
+    def __init__(
+        self,
+        dim,
+        kernel_size=7,
+        padding=3,
+        drop_path=0.0,
+        layer_scale_init_value=1e-6,
+        use_dwconv=True,
+    ):
+        super().__init__()
+        self.dwconv = nn.Conv2d(
+            dim,
+            dim,
+            kernel_size=kernel_size,
+            padding=padding,
+            groups=dim if use_dwconv else 1,
+        )  # depthwise conv
+        self.norm = LayerNorm2d(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(
+            dim, 4 * dim
+        )  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(4 * dim, dim)
+        self.gamma = (
+            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
+            if layer_scale_init_value > 0
+            else None
+        )
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x):
+        input = x
+        x = self.dwconv(x)
+        x = self.norm(x)
+        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+
+        x = input + self.drop_path(x)
+        return x
+
+
+class SimpleFuser(nn.Module):
+    def __init__(self, layer, num_layers, dim=None, input_projection=False):
+        super().__init__()
+        self.proj = nn.Identity()
+        self.layers = get_clones(layer, num_layers)
+
+        if input_projection:
+            assert dim is not None
+            self.proj = nn.Conv2d(dim, dim, kernel_size=1)
+
+    def forward(self, x):
+        # normally x: (N, C, H, W)
+        x = self.proj(x)
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+
+class SimpleMaskEncoder(nn.Module):
+    def __init__(
+        self,
+        out_dim,
+        mask_downsampler,
+        fuser,
+        position_encoding,
+        in_dim=256,  # in_dim of pix_feats
+    ):
+        super().__init__()
+
+        self.mask_downsampler = mask_downsampler
+
+        self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)
+        self.fuser = fuser
+        self.position_encoding = position_encoding
+        self.out_proj = nn.Identity()
+        if out_dim != in_dim:
+            self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)
+
+    def forward(
+        self,
+        pix_feat: torch.Tensor,
+        masks: torch.Tensor,
+        skip_mask_sigmoid: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        ## Process masks
+        # sigmoid, so that less domain shift from gt masks which are bool
+        if not skip_mask_sigmoid:
+            masks = F.sigmoid(masks)
+        masks = self.mask_downsampler(masks)
+
+        ## Fuse pix_feats and downsampled masks
+        # in case the visual features are on CPU, cast them to CUDA
+        pix_feat = pix_feat.to(masks.device)
+
+        x = self.pix_feat_proj(pix_feat)
+        x = x + masks
+        x = self.fuser(x)
+        x = self.out_proj(x)
+
+        pos = self.position_encoding(x).to(x.dtype)
+
+        return {"vision_features": x, "vision_pos_enc": [pos]}

third_party/GraspGen/sam3/sam3/model/model_misc.py

@@ -0,0 +1,430 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+"""Various utility models"""
+
+import copy
+import math
+import weakref
+from collections.abc import Iterator
+from contextlib import AbstractContextManager
+from enum import auto, Enum
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn, Tensor
+from typing_extensions import override
+
+
+def inverse_sigmoid(x, eps=1e-3):
+    """
+    The inverse function for sigmoid activation function.
+    Note: It might face numberical issues with fp16 small eps.
+    """
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+class MultiheadAttentionWrapper(nn.MultiheadAttention):
+    def forward(self, *args, **kwargs):
+        kwargs["need_weights"] = False
+        return super().forward(*args, **kwargs)
+
+
+class DotProductScoring(torch.nn.Module):
+    def __init__(
+        self,
+        d_model,
+        d_proj,
+        prompt_mlp=None,
+        clamp_logits=True,
+        clamp_max_val=12.0,
+    ):
+        super().__init__()
+        self.d_proj = d_proj
+        assert isinstance(prompt_mlp, torch.nn.Module) or prompt_mlp is None
+        self.prompt_mlp = prompt_mlp  # an optional MLP projection for prompt
+        self.prompt_proj = torch.nn.Linear(d_model, d_proj)
+        self.hs_proj = torch.nn.Linear(d_model, d_proj)
+        self.scale = float(1.0 / np.sqrt(d_proj))
+        self.clamp_logits = clamp_logits
+        if self.clamp_logits:
+            self.clamp_max_val = clamp_max_val
+
+    def mean_pool_text(self, prompt, prompt_mask):
+        # is_valid has shape (seq, bs, 1), where 1 is valid and 0 is padding
+        is_valid = (~prompt_mask).float().permute(1, 0)[..., None]
+        # num_valid has shape (bs, 1)
+        num_valid = torch.clamp(torch.sum(is_valid, dim=0), min=1.0)
+        # mean pool over all the valid tokens -- pooled_prompt has shape (bs, proj_dim)
+        pooled_prompt = (prompt * is_valid).sum(dim=0) / num_valid
+        return pooled_prompt
+
+    def forward(self, hs, prompt, prompt_mask):
+        # hs has shape (num_layer, bs, num_query, d_model)
+        # prompt has shape (seq, bs, d_model)
+        # prompt_mask has shape (bs, seq), where 1 is valid and 0 is padding
+        assert hs.dim() == 4 and prompt.dim() == 3 and prompt_mask.dim() == 2
+
+        # apply MLP on prompt if specified
+        if self.prompt_mlp is not None:
+            prompt = self.prompt_mlp(prompt)
+
+        # first, get the mean-pooled version of the prompt
+        pooled_prompt = self.mean_pool_text(prompt, prompt_mask)
+
+        # then, project pooled_prompt and hs to d_proj dimensions
+        proj_pooled_prompt = self.prompt_proj(pooled_prompt)  # (bs, d_proj)
+        proj_hs = self.hs_proj(hs)  # (num_layer, bs, num_query, d_proj)
+
+        # finally, get dot-product scores of shape (num_layer, bs, num_query, 1)
+        scores = torch.matmul(proj_hs, proj_pooled_prompt.unsqueeze(-1))
+        scores *= self.scale
+
+        # clamp scores to a max value to avoid numerical issues in loss or matcher
+        if self.clamp_logits:
+            scores.clamp_(min=-self.clamp_max_val, max=self.clamp_max_val)
+
+        return scores
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: Union[float, Tensor] = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
+class TransformerWrapper(nn.Module):
+    def __init__(
+        self,
+        encoder,
+        decoder,
+        d_model: int,
+        two_stage_type="none",  # ["none"] only for now
+        pos_enc_at_input_dec=True,
+    ):
+        super().__init__()
+
+        self.encoder = encoder
+        self.decoder = decoder
+        self.num_queries = decoder.num_queries if decoder is not None else None
+        self.pos_enc_at_input_dec = pos_enc_at_input_dec
+
+        # for two stage
+        assert two_stage_type in ["none"], "unknown param {} of two_stage_type".format(
+            two_stage_type
+        )
+        self.two_stage_type = two_stage_type
+
+        self._reset_parameters()
+        self.d_model = d_model
+
+    def _reset_parameters(self):
+        for n, p in self.named_parameters():
+            if p.dim() > 1:
+                if (
+                    "box_embed" not in n
+                    and "query_embed" not in n
+                    and "reference_points" not in n
+                ):
+                    nn.init.xavier_uniform_(p)
+
+
+class MLP(nn.Module):
+    """Very simple multi-layer perceptron (also called FFN)"""
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        num_layers: int,
+        dropout: float = 0.0,
+        residual: bool = False,
+        out_norm: Optional[nn.Module] = None,
+    ):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+        self.drop = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        # whether to add the output as a residual connection to the input
+        if residual and input_dim != output_dim:
+            raise ValueError("residual is only supported if input_dim == output_dim")
+        self.residual = residual
+        # whether to apply a normalization layer to the output
+        assert isinstance(out_norm, nn.Module) or out_norm is None
+        self.out_norm = out_norm or nn.Identity()
+
+    def forward(self, x):
+        orig_x = x
+        for i, layer in enumerate(self.layers):
+            x = self.drop(F.relu(layer(x))) if i < self.num_layers - 1 else layer(x)
+        if self.residual:
+            x = x + orig_x
+        x = self.out_norm(x)
+        return x
+
+
+def get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def get_clones_seq(module, N):
+    return nn.Sequential(*[copy.deepcopy(module) for i in range(N)])
+
+
+def get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+
+def get_activation_module(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return nn.ReLU
+    if activation == "gelu":
+        return nn.GELU
+    if activation == "glu":
+        return nn.GLU
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+
+def get_valid_ratio(mask):
+    _, H, W = mask.shape
+    valid_H = torch.sum(~mask[:, :, 0], 1)
+    valid_W = torch.sum(~mask[:, 0, :], 1)
+    valid_ratio_h = valid_H.float() / H
+    valid_ratio_w = valid_W.float() / W
+    valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+    return valid_ratio
+
+
+def gen_sineembed_for_position(pos_tensor, num_feats=256):
+    assert num_feats % 2 == 0
+    num_feats = num_feats // 2
+    # n_query, bs, _ = pos_tensor.size()
+    # sineembed_tensor = torch.zeros(n_query, bs, 256)
+    scale = 2 * math.pi
+    dim_t = torch.arange(num_feats, dtype=torch.float32, device=pos_tensor.device)
+    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode="floor")) / num_feats)
+    x_embed = pos_tensor[:, :, 0] * scale
+    y_embed = pos_tensor[:, :, 1] * scale
+    pos_x = x_embed[:, :, None] / dim_t
+    pos_y = y_embed[:, :, None] / dim_t
+    pos_x = torch.stack(
+        (pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3
+    ).flatten(2)
+    pos_y = torch.stack(
+        (pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3
+    ).flatten(2)
+    if pos_tensor.size(-1) == 2:
+        pos = torch.cat((pos_y, pos_x), dim=2)
+    elif pos_tensor.size(-1) == 4:
+        w_embed = pos_tensor[:, :, 2] * scale
+        pos_w = w_embed[:, :, None] / dim_t
+        pos_w = torch.stack(
+            (pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3
+        ).flatten(2)
+
+        h_embed = pos_tensor[:, :, 3] * scale
+        pos_h = h_embed[:, :, None] / dim_t
+        pos_h = torch.stack(
+            (pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3
+        ).flatten(2)
+
+        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
+    else:
+        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
+    return pos
+
+
+class SAM3Output(list):
+    """
+    A class representing the output of a SAM3 model.
+    It provides an iterable interface that supports different iteration modes, including iterating over all steps per stage,
+    last step per stage, and flattened output.
+    Attributes:
+        output: The output of the SAM3 model, represented as a list of lists.
+        iter_mode: The current iteration mode.
+    Example:
+        >>> output = [[1, 2], [3, 4], [5, 6]]
+        >>> sam3_output = SAM3Output(output)
+        >>> for step in sam3_output:
+        ...     print(step)
+        [1, 2]
+        [3, 4]
+        [5, 6]
+        >>> with SAM3Output.iteration_mode(SAM3Output.IterMode.LAST_STEP_PER_STAGE) as sam3_last_step_out:
+        ...     for step in sam3_last_step_out:
+        ...         print(step)
+        [2]
+        [4]
+        [6]
+        >>> with SAM3Output.iteration_mode(SAM3Output.IterMode.FLATTENED) as sam3_flattened_out:
+        ...     for step in sam3_flattened_out:
+        ...         print(step)
+        1
+        2
+        3
+        4
+        5
+        6
+    """
+
+    class IterMode(Enum):
+        # Defines the type of iterator over ouptuts.
+        ALL_STEPS_PER_STAGE = auto()
+        LAST_STEP_PER_STAGE = auto()
+        FLATTENED = auto()  # Returns each interactivity step as if it is a separate stage (this is used in SAM3Image model)
+
+    def __init__(
+        self,
+        output: List[List[Dict]] = None,
+        iter_mode: IterMode = IterMode.ALL_STEPS_PER_STAGE,
+        loss_stages: Optional[List[int]] = None,
+    ):
+        if output is not None:
+            assert (
+                isinstance(output, list)
+                and len(output) > 0
+                and isinstance(output[0], list)
+            ), "Expected output to be a list of lists"
+            self.output = output
+        else:
+            self.output = []
+        assert isinstance(iter_mode, SAM3Output.IterMode), (
+            f"iter_mode shoulf be of enum type 'SAM3Output.IterMode'. Got {type(iter_mode)}"
+        )
+
+        self.iter_mode = iter_mode
+        # We create a weak reference to self to be used in the lambda functions.
+        # This is to avoid cyclic references and let SAM3Output be garabge collected.
+        self_ref = weakref.ref(self)
+        self._mode2iter = {
+            SAM3Output.IterMode.ALL_STEPS_PER_STAGE: lambda: iter(self_ref().output),
+            SAM3Output.IterMode.LAST_STEP_PER_STAGE: lambda: (
+                inner_list[-1] for inner_list in self_ref().output
+            ),
+            SAM3Output.IterMode.FLATTENED: lambda: (
+                element for inner_list in self_ref().output for element in inner_list
+            ),
+        }
+        self.loss_stages = loss_stages
+
+    @override
+    def __iter__(self) -> Iterator:
+        return self._mode2iter[self.iter_mode]()
+
+    def __getitem__(self, index):
+        """
+        Returns the item at the specified index.
+        Args:
+            index (int): The index of the item to return.
+        Returns:
+            list or element: The item at the specified index.
+        """
+        assert isinstance(index, int), f"index should be an integer. Got {type(index)}"
+        if self.iter_mode == SAM3Output.IterMode.ALL_STEPS_PER_STAGE:
+            return self.output[index]
+        elif self.iter_mode == SAM3Output.IterMode.LAST_STEP_PER_STAGE:
+            return self.output[index][-1]
+        elif self.iter_mode == SAM3Output.IterMode.FLATTENED:
+            if index == -1:
+                return self.self.output[-1][-1]
+            else:
+                flattened_output = sum(self.output, [])
+                return flattened_output[index]
+
+    class _IterationMode(AbstractContextManager):
+        """
+        A context manager that temporarily changes the iteration mode of a SAM3Output object.
+        This class is used internally by the SAM3Output.iteration_mode method.
+        """
+
+        def __init__(
+            self, model_output: "SAM3Output", iter_mode: "SAM3Output.IterMode"
+        ):
+            self._model_output = model_output
+            self._orig_iter_mode = model_output.iter_mode
+            self._new_iter_mode = iter_mode
+
+        @override
+        def __enter__(self) -> "SAM3Output":
+            self._model_output.iter_mode = self._new_iter_mode
+            return self._model_output
+
+        @override
+        def __exit__(self, exc_type, exc_value, traceback):
+            self._model_output.iter_mode = self._orig_iter_mode
+            return super().__exit__(exc_type, exc_value, traceback)
+
+    @staticmethod
+    def iteration_mode(
+        model_output: "SAM3Output", iter_mode: IterMode
+    ) -> _IterationMode:
+        """
+        Returns a context manager that allows you to temporarily change the iteration mode of the SAM3Output object.
+        Args:
+            model_output: The SAM3Output object.
+            iter_mode: The new iteration mode.
+        Returns:
+            SAM3Output._IterationMode: A context manager that changes the iteration mode of the SAM3Output object.
+        """
+        return SAM3Output._IterationMode(model_output=model_output, iter_mode=iter_mode)
+
+    def append(self, item: list):
+        assert isinstance(item, list), (
+            f"Only list items are supported. Got {type(item)}"
+        )
+        self.output.append(item)
+
+    def __repr__(self):
+        return self.output.__repr__()
+
+    def __len__(self):
+        if self.iter_mode in [
+            SAM3Output.IterMode.ALL_STEPS_PER_STAGE,
+            SAM3Output.IterMode.LAST_STEP_PER_STAGE,
+        ]:
+            return len(self.output)
+        elif self.iter_mode == SAM3Output.IterMode.FLATTENED:
+            flattened_output = sum(self.output, [])
+            return len(flattened_output)

third_party/GraspGen/sam3/sam3/model/necks.py

@@ -0,0 +1,126 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+"""Necks are the interface between a vision backbone and the rest of the detection model"""
+
+from copy import deepcopy
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+
+
+class Sam3DualViTDetNeck(nn.Module):
+    def __init__(
+        self,
+        trunk: nn.Module,
+        position_encoding: nn.Module,
+        d_model: int,
+        scale_factors=(4.0, 2.0, 1.0, 0.5),
+        add_sam2_neck: bool = False,
+    ):
+        """
+        SimpleFPN neck a la ViTDet
+        (From detectron2, very lightly adapted)
+        It supports a "dual neck" setting, where we have two identical necks (for SAM3 and SAM2), with different weights
+
+        :param trunk: the backbone
+        :param position_encoding: the positional encoding to use
+        :param d_model: the dimension of the model
+        """
+        super().__init__()
+        self.trunk = trunk
+        self.position_encoding = position_encoding
+        self.convs = nn.ModuleList()
+
+        self.scale_factors = scale_factors
+        use_bias = True
+        dim: int = self.trunk.channel_list[-1]
+
+        for _, scale in enumerate(scale_factors):
+            current = nn.Sequential()
+
+            if scale == 4.0:
+                current.add_module(
+                    "dconv_2x2_0",
+                    nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2),
+                )
+                current.add_module(
+                    "gelu",
+                    nn.GELU(),
+                )
+                current.add_module(
+                    "dconv_2x2_1",
+                    nn.ConvTranspose2d(dim // 2, dim // 4, kernel_size=2, stride=2),
+                )
+                out_dim = dim // 4
+            elif scale == 2.0:
+                current.add_module(
+                    "dconv_2x2",
+                    nn.ConvTranspose2d(dim, dim // 2, kernel_size=2, stride=2),
+                )
+                out_dim = dim // 2
+            elif scale == 1.0:
+                out_dim = dim
+            elif scale == 0.5:
+                current.add_module(
+                    "maxpool_2x2",
+                    nn.MaxPool2d(kernel_size=2, stride=2),
+                )
+                out_dim = dim
+            else:
+                raise NotImplementedError(f"scale_factor={scale} is not supported yet.")
+
+            current.add_module(
+                "conv_1x1",
+                nn.Conv2d(
+                    in_channels=out_dim,
+                    out_channels=d_model,
+                    kernel_size=1,
+                    bias=use_bias,
+                ),
+            )
+            current.add_module(
+                "conv_3x3",
+                nn.Conv2d(
+                    in_channels=d_model,
+                    out_channels=d_model,
+                    kernel_size=3,
+                    padding=1,
+                    bias=use_bias,
+                ),
+            )
+            self.convs.append(current)
+
+        self.sam2_convs = None
+        if add_sam2_neck:
+            # Assumes sam2 neck is just a clone of the original neck
+            self.sam2_convs = deepcopy(self.convs)
+
+    def forward(
+        self, tensor_list: List[torch.Tensor]
+    ) -> Tuple[
+        List[torch.Tensor],
+        List[torch.Tensor],
+        Optional[List[torch.Tensor]],
+        Optional[List[torch.Tensor]],
+    ]:
+        xs = self.trunk(tensor_list)
+        sam3_out, sam3_pos = [], []
+        sam2_out, sam2_pos = None, None
+        if self.sam2_convs is not None:
+            sam2_out, sam2_pos = [], []
+        x = xs[-1]  # simpleFPN
+        for i in range(len(self.convs)):
+            sam3_x_out = self.convs[i](x)
+            sam3_pos_out = self.position_encoding(sam3_x_out).to(sam3_x_out.dtype)
+            sam3_out.append(sam3_x_out)
+            sam3_pos.append(sam3_pos_out)
+
+            if self.sam2_convs is not None:
+                sam2_x_out = self.sam2_convs[i](x)
+                sam2_pos_out = self.position_encoding(sam2_x_out).to(sam2_x_out.dtype)
+                sam2_out.append(sam2_x_out)
+                sam2_pos.append(sam2_pos_out)
+        return sam3_out, sam3_pos, sam2_out, sam2_pos

third_party/GraspGen/sam3/sam3/model/position_encoding.py

@@ -0,0 +1,126 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import math
+from typing import Optional
+
+import torch
+from torch import nn
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(
+        self,
+        num_pos_feats,
+        temperature: int = 10000,
+        normalize: bool = True,
+        scale: Optional[float] = None,
+        precompute_resolution: Optional[int] = None,
+    ):
+        super().__init__()
+        assert num_pos_feats % 2 == 0, "Expecting even model width"
+        self.num_pos_feats = num_pos_feats // 2
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+        self.cache = {}
+        # Precompute positional encodings under `precompute_resolution` to fill the cache
+        # and avoid symbolic shape tracing errors in torch.compile in PyTorch 2.4 nightly.
+        if precompute_resolution is not None:
+            # We precompute pos enc for stride 4, 8, 16 and 32 to fill `self.cache`.
+            precompute_sizes = [
+                (precompute_resolution // 4, precompute_resolution // 4),
+                (precompute_resolution // 8, precompute_resolution // 8),
+                (precompute_resolution // 16, precompute_resolution // 16),
+                (precompute_resolution // 32, precompute_resolution // 32),
+            ]
+            for size in precompute_sizes:
+                tensors = torch.zeros((1, 1) + size, device="cuda")
+                self.forward(tensors)
+                # further clone and detach it in the cache (just to be safe)
+                self.cache[size] = self.cache[size].clone().detach()
+
+    def _encode_xy(self, x, y):
+        # The positions are expected to be normalized
+        assert len(x) == len(y) and x.ndim == y.ndim == 1
+        x_embed = x * self.scale
+        y_embed = y * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, None] / dim_t
+        pos_y = y_embed[:, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2
+        ).flatten(1)
+        pos_y = torch.stack(
+            (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2
+        ).flatten(1)
+        return pos_x, pos_y
+
+    @torch.no_grad()
+    def encode_boxes(self, x, y, w, h):
+        pos_x, pos_y = self._encode_xy(x, y)
+        pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
+        return pos
+
+    encode = encode_boxes  # Backwards compatibility
+
+    @torch.no_grad()
+    def encode_points(self, x, y, labels):
+        (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape
+        assert bx == by and nx == ny and bx == bl and nx == nl
+        pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten())
+        pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1)
+        pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)
+        return pos
+
+    @torch.no_grad()
+    def forward(self, x):
+        cache_key = None
+        cache_key = (x.shape[-2], x.shape[-1])
+        if cache_key in self.cache:
+            return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1)
+        y_embed = (
+            torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device)
+            .view(1, -1, 1)
+            .repeat(x.shape[0], 1, x.shape[-1])
+        )
+        x_embed = (
+            torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device)
+            .view(1, 1, -1)
+            .repeat(x.shape[0], x.shape[-2], 1)
+        )
+
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        if cache_key is not None:
+            self.cache[cache_key] = pos[0]
+        return pos

third_party/GraspGen/sam3/sam3/model/sam1_task_predictor.py

@@ -0,0 +1,457 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+# All rights reserved.
+
+# pyre-unsafe
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from PIL.Image import Image
+from sam3.model.sam3_tracker_base import Sam3TrackerBase
+from sam3.model.utils.sam1_utils import SAM2Transforms
+
+
+# Adapted from https://github.com/facebookresearch/sam2/blob/main/sam2/sam2_image_predictor.py
+class SAM3InteractiveImagePredictor(nn.Module):
+    def __init__(
+        self,
+        sam_model: Sam3TrackerBase,
+        mask_threshold=0.0,
+        max_hole_area=256.0,
+        max_sprinkle_area=0.0,
+        **kwargs,
+    ) -> None:
+        """
+        Uses SAM-3 to calculate the image embedding for an image, and then
+        allow repeated, efficient mask prediction given prompts.
+
+        Arguments:
+          sam_model : The model to use for mask prediction.
+          mask_threshold (float): The threshold to use when converting mask logits
+            to binary masks. Masks are thresholded at 0 by default.
+          max_hole_area (int): If max_hole_area > 0, we fill small holes in up to
+            the maximum area of max_hole_area in low_res_masks.
+          max_sprinkle_area (int): If max_sprinkle_area > 0, we remove small sprinkles up to
+            the maximum area of max_sprinkle_area in low_res_masks.
+        """
+        super().__init__()
+        self.model = sam_model
+        self._transforms = SAM2Transforms(
+            resolution=self.model.image_size,
+            mask_threshold=mask_threshold,
+            max_hole_area=max_hole_area,
+            max_sprinkle_area=max_sprinkle_area,
+        )
+
+        # Predictor state
+        self._is_image_set = False
+        self._features = None
+        self._orig_hw = None
+        # Whether the predictor is set for single image or a batch of images
+        self._is_batch = False
+
+        # Predictor config
+        self.mask_threshold = mask_threshold
+
+        # Spatial dim for backbone feature maps
+        self._bb_feat_sizes = [
+            (288, 288),
+            (144, 144),
+            (72, 72),
+        ]
+
+    @torch.no_grad()
+    def set_image(
+        self,
+        image: Union[np.ndarray, Image],
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method.
+
+        Arguments:
+          image (np.ndarray or PIL Image): The input image to embed in RGB format. The image should be in HWC format if np.ndarray, or WHC format if PIL Image
+          with pixel values in [0, 255].
+          image_format (str): The color format of the image, in ['RGB', 'BGR'].
+        """
+        self.reset_predictor()
+        # Transform the image to the form expected by the model
+        if isinstance(image, np.ndarray):
+            logging.info("For numpy array image, we assume (HxWxC) format")
+            self._orig_hw = [image.shape[:2]]
+        elif isinstance(image, Image):
+            w, h = image.size
+            self._orig_hw = [(h, w)]
+        else:
+            raise NotImplementedError("Image format not supported")
+
+        input_image = self._transforms(image)
+        input_image = input_image[None, ...].to(self.device)
+
+        assert len(input_image.shape) == 4 and input_image.shape[1] == 3, (
+            f"input_image must be of size 1x3xHxW, got {input_image.shape}"
+        )
+        logging.info("Computing image embeddings for the provided image...")
+        backbone_out = self.model.forward_image(input_image)
+        (
+            _,
+            vision_feats,
+            _,
+            _,
+        ) = self.model._prepare_backbone_features(backbone_out)
+        # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos
+        vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
+
+        feats = [
+            feat.permute(1, 2, 0).view(1, -1, *feat_size)
+            for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])
+        ][::-1]
+        self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]}
+        self._is_image_set = True
+        logging.info("Image embeddings computed.")
+
+    @torch.no_grad()
+    def set_image_batch(
+        self,
+        image_list: List[Union[np.ndarray]],
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image batch, allowing
+        masks to be predicted with the 'predict_batch' method.
+
+        Arguments:
+          image_list (List[np.ndarray]): The input images to embed in RGB format. The image should be in HWC format if np.ndarray
+          with pixel values in [0, 255].
+        """
+        self.reset_predictor()
+        assert isinstance(image_list, list)
+        self._orig_hw = []
+        for image in image_list:
+            assert isinstance(image, np.ndarray), (
+                "Images are expected to be an np.ndarray in RGB format, and of shape  HWC"
+            )
+            self._orig_hw.append(image.shape[:2])
+        # Transform the image to the form expected by the model
+        img_batch = self._transforms.forward_batch(image_list)
+        img_batch = img_batch.to(self.device)
+        batch_size = img_batch.shape[0]
+        assert len(img_batch.shape) == 4 and img_batch.shape[1] == 3, (
+            f"img_batch must be of size Bx3xHxW, got {img_batch.shape}"
+        )
+        logging.info("Computing image embeddings for the provided images...")
+        backbone_out = self.model.forward_image(img_batch)
+        (
+            _,
+            vision_feats,
+            _,
+            _,
+        ) = self.model._prepare_backbone_features(backbone_out)
+        # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos
+        vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
+
+        feats = [
+            feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)
+            for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])
+        ][::-1]
+        self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]}
+        self._is_image_set = True
+        self._is_batch = True
+        logging.info("Image embeddings computed.")
+
+    def predict_batch(
+        self,
+        point_coords_batch: List[np.ndarray] = None,
+        point_labels_batch: List[np.ndarray] = None,
+        box_batch: List[np.ndarray] = None,
+        mask_input_batch: List[np.ndarray] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+        normalize_coords=True,
+    ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
+        """This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple images.
+        It returns a tuple of lists of masks, ious, and low_res_masks_logits.
+        """
+        assert self._is_batch, "This function should only be used when in batched mode"
+        if not self._is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image_batch(...) before mask prediction."
+            )
+        num_images = len(self._features["image_embed"])
+        all_masks = []
+        all_ious = []
+        all_low_res_masks = []
+        for img_idx in range(num_images):
+            # Transform input prompts
+            point_coords = (
+                point_coords_batch[img_idx] if point_coords_batch is not None else None
+            )
+            point_labels = (
+                point_labels_batch[img_idx] if point_labels_batch is not None else None
+            )
+            box = box_batch[img_idx] if box_batch is not None else None
+            mask_input = (
+                mask_input_batch[img_idx] if mask_input_batch is not None else None
+            )
+            mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(
+                point_coords,
+                point_labels,
+                box,
+                mask_input,
+                normalize_coords,
+                img_idx=img_idx,
+            )
+            masks, iou_predictions, low_res_masks = self._predict(
+                unnorm_coords,
+                labels,
+                unnorm_box,
+                mask_input,
+                multimask_output,
+                return_logits=return_logits,
+                img_idx=img_idx,
+            )
+            masks_np = masks.squeeze(0).float().detach().cpu().numpy()
+            iou_predictions_np = (
+                iou_predictions.squeeze(0).float().detach().cpu().numpy()
+            )
+            low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()
+            all_masks.append(masks_np)
+            all_ious.append(iou_predictions_np)
+            all_low_res_masks.append(low_res_masks_np)
+
+        return all_masks, all_ious, all_low_res_masks
+
+    def predict(
+        self,
+        point_coords: Optional[np.ndarray] = None,
+        point_labels: Optional[np.ndarray] = None,
+        box: Optional[np.ndarray] = None,
+        mask_input: Optional[np.ndarray] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+        normalize_coords=True,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+
+        Arguments:
+          point_coords (np.ndarray or None): A Nx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (np.ndarray or None): A length N array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A length 4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form 1xHxW, where
+            for SAM, H=W=256.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+          normalize_coords (bool): If true, the point coordinates will be normalized to the range [0,1] and point_coords is expected to be wrt. image dimensions.
+
+        Returns:
+          (np.ndarray): The output masks in CxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (np.ndarray): An array of length C containing the model's
+            predictions for the quality of each mask.
+          (np.ndarray): An array of shape CxHxW, where C is the number
+            of masks and H=W=256. These low resolution logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self._is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) before mask prediction."
+            )
+
+        # Transform input prompts
+
+        mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(
+            point_coords, point_labels, box, mask_input, normalize_coords
+        )
+
+        masks, iou_predictions, low_res_masks = self._predict(
+            unnorm_coords,
+            labels,
+            unnorm_box,
+            mask_input,
+            multimask_output,
+            return_logits=return_logits,
+        )
+
+        masks_np = masks.squeeze(0).float().detach().cpu().numpy()
+        iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy()
+        low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()
+        return masks_np, iou_predictions_np, low_res_masks_np
+
+    def _prep_prompts(
+        self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1
+    ):
+        unnorm_coords, labels, unnorm_box, mask_input = None, None, None, None
+        if point_coords is not None:
+            assert point_labels is not None, (
+                "point_labels must be supplied if point_coords is supplied."
+            )
+            point_coords = torch.as_tensor(
+                point_coords, dtype=torch.float, device=self.device
+            )
+            unnorm_coords = self._transforms.transform_coords(
+                point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]
+            )
+            labels = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
+            if len(unnorm_coords.shape) == 2:
+                unnorm_coords, labels = unnorm_coords[None, ...], labels[None, ...]
+        if box is not None:
+            box = torch.as_tensor(box, dtype=torch.float, device=self.device)
+            unnorm_box = self._transforms.transform_boxes(
+                box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]
+            )  # Bx2x2
+        if mask_logits is not None:
+            mask_input = torch.as_tensor(
+                mask_logits, dtype=torch.float, device=self.device
+            )
+            if len(mask_input.shape) == 3:
+                mask_input = mask_input[None, :, :, :]
+        return mask_input, unnorm_coords, labels, unnorm_box
+
+    @torch.no_grad()
+    def _predict(
+        self,
+        point_coords: Optional[torch.Tensor],
+        point_labels: Optional[torch.Tensor],
+        boxes: Optional[torch.Tensor] = None,
+        mask_input: Optional[torch.Tensor] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+        img_idx: int = -1,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+        Input prompts are batched torch tensors and are expected to already be
+        transformed to the input frame using SAM2Transforms.
+
+        Arguments:
+          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (torch.Tensor or None): A BxN array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          boxes (np.ndarray or None): A Bx4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form Bx1xHxW, where
+            for SAM, H=W=256. Masks returned by a previous iteration of the
+            predict method do not need further transformation.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (torch.Tensor): The output masks in BxCxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (torch.Tensor): An array of shape BxC containing the model's
+            predictions for the quality of each mask.
+          (torch.Tensor): An array of shape BxCxHxW, where C is the number
+            of masks and H=W=256. These low res logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self._is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) before mask prediction."
+            )
+
+        if point_coords is not None:
+            concat_points = (point_coords, point_labels)
+        else:
+            concat_points = None
+
+        # Embed prompts
+        if boxes is not None:
+            box_coords = boxes.reshape(-1, 2, 2)
+            box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device)
+            box_labels = box_labels.repeat(boxes.size(0), 1)
+            # we merge "boxes" and "points" into a single "concat_points" input (where
+            # boxes are added at the beginning) to sam_prompt_encoder
+            if concat_points is not None:
+                concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)
+                concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)
+                concat_points = (concat_coords, concat_labels)
+            else:
+                concat_points = (box_coords, box_labels)
+
+        sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(
+            points=concat_points,
+            boxes=None,
+            masks=mask_input,
+        )
+
+        # Predict masks
+        batched_mode = (
+            concat_points is not None and concat_points[0].shape[0] > 1
+        )  # multi object prediction
+        high_res_features = [
+            feat_level[img_idx].unsqueeze(0)
+            for feat_level in self._features["high_res_feats"]
+        ]
+        low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder(
+            image_embeddings=self._features["image_embed"][img_idx].unsqueeze(0),
+            image_pe=self.model.sam_prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embeddings,
+            dense_prompt_embeddings=dense_embeddings,
+            multimask_output=multimask_output,
+            repeat_image=batched_mode,
+            high_res_features=high_res_features,
+        )
+
+        # Upscale the masks to the original image resolution
+        masks = self._transforms.postprocess_masks(
+            low_res_masks, self._orig_hw[img_idx]
+        )
+        low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0)
+        if not return_logits:
+            masks = masks > self.mask_threshold
+
+        return masks, iou_predictions, low_res_masks
+
+    def get_image_embedding(self) -> torch.Tensor:
+        """
+        Returns the image embeddings for the currently set image, with
+        shape 1xCxHxW, where C is the embedding dimension and (H,W) are
+        the embedding spatial dimension of SAM (typically C=256, H=W=64).
+        """
+        if not self._is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) to generate an embedding."
+            )
+        assert self._features is not None, (
+            "Features must exist if an image has been set."
+        )
+        return self._features["image_embed"]
+
+    @property
+    def device(self) -> torch.device:
+        return self.model.device
+
+    def reset_predictor(self) -> None:
+        """
+        Resets the image embeddings and other state variables.
+        """
+        self._is_image_set = False
+        self._features = None
+        self._orig_hw = None
+        self._is_batch = False

third_party/GraspGen/sam3/sam3/model/sam3_image.py

@@ -0,0 +1,880 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import os
+from copy import deepcopy
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import torch
+from sam3.model.model_misc import SAM3Output
+from sam3.model.sam1_task_predictor import SAM3InteractiveImagePredictor
+from sam3.model.vl_combiner import SAM3VLBackbone
+from sam3.perflib.nms import nms_masks
+from sam3.train.data.collator import BatchedDatapoint
+
+from .act_ckpt_utils import activation_ckpt_wrapper
+from .box_ops import box_cxcywh_to_xyxy
+from .geometry_encoders import Prompt
+from .model_misc import inverse_sigmoid
+
+
+def _update_out(out, out_name, out_value, auxiliary=True, update_aux=True):
+    out[out_name] = out_value[-1] if auxiliary else out_value
+    if auxiliary and update_aux:
+        if "aux_outputs" not in out:
+            out["aux_outputs"] = [{} for _ in range(len(out_value) - 1)]
+        assert len(out["aux_outputs"]) == len(out_value) - 1
+        for aux_output, aux_value in zip(out["aux_outputs"], out_value[:-1]):
+            aux_output[out_name] = aux_value
+
+
+class Sam3Image(torch.nn.Module):
+    TEXT_ID_FOR_TEXT = 0
+    TEXT_ID_FOR_VISUAL = 1
+    TEXT_ID_FOR_GEOMETRIC = 2
+
+    def __init__(
+        self,
+        backbone: SAM3VLBackbone,
+        transformer,
+        input_geometry_encoder,
+        segmentation_head=None,
+        num_feature_levels=1,
+        o2m_mask_predict=True,
+        dot_prod_scoring=None,
+        use_instance_query: bool = True,
+        multimask_output: bool = True,
+        use_act_checkpoint_seg_head: bool = True,
+        interactivity_in_encoder: bool = True,
+        matcher=None,
+        use_dot_prod_scoring=True,
+        supervise_joint_box_scores: bool = False,  # only relevant if using presence token/score
+        detach_presence_in_joint_score: bool = False,  # only relevant if using presence token/score
+        separate_scorer_for_instance: bool = False,
+        num_interactive_steps_val: int = 0,
+        inst_interactive_predictor: SAM3InteractiveImagePredictor = None,
+        **kwargs,
+    ):
+        super().__init__()
+        self.backbone = backbone
+        self.geometry_encoder = input_geometry_encoder
+        self.transformer = transformer
+        self.hidden_dim = transformer.d_model
+        self.num_feature_levels = num_feature_levels
+        self.segmentation_head = segmentation_head
+
+        self.o2m_mask_predict = o2m_mask_predict
+
+        self.dot_prod_scoring = dot_prod_scoring
+        self.use_act_checkpoint_seg_head = use_act_checkpoint_seg_head
+        self.interactivity_in_encoder = interactivity_in_encoder
+        self.matcher = matcher
+
+        self.num_interactive_steps_val = num_interactive_steps_val
+        self.use_dot_prod_scoring = use_dot_prod_scoring
+
+        if self.use_dot_prod_scoring:
+            assert dot_prod_scoring is not None
+            self.dot_prod_scoring = dot_prod_scoring
+            self.instance_dot_prod_scoring = None
+            if separate_scorer_for_instance:
+                self.instance_dot_prod_scoring = deepcopy(dot_prod_scoring)
+        else:
+            self.class_embed = torch.nn.Linear(self.hidden_dim, 1)
+            self.instance_class_embed = None
+            if separate_scorer_for_instance:
+                self.instance_class_embed = deepcopy(self.class_embed)
+
+        self.supervise_joint_box_scores = supervise_joint_box_scores
+        self.detach_presence_in_joint_score = detach_presence_in_joint_score
+
+        # verify the number of queries for O2O and O2M
+        num_o2o_static = self.transformer.decoder.num_queries
+        num_o2m_static = self.transformer.decoder.num_o2m_queries
+        assert num_o2m_static == (num_o2o_static if self.transformer.decoder.dac else 0)
+        self.dac = self.transformer.decoder.dac
+
+        self.use_instance_query = use_instance_query
+        self.multimask_output = multimask_output
+
+        self.inst_interactive_predictor = inst_interactive_predictor
+
+    @property
+    def device(self):
+        self._device = getattr(self, "_device", None) or next(self.parameters()).device
+        return self._device
+
+    def to(self, *args, **kwargs):
+        # clear cached _device in case the model is moved to a different device
+        self._device = None
+        return super().to(*args, **kwargs)
+
+    def _get_img_feats(self, backbone_out, img_ids):
+        """Retrieve correct image features from backbone output."""
+        if "backbone_fpn" in backbone_out:
+            if "id_mapping" in backbone_out and backbone_out["id_mapping"] is not None:
+                img_ids = backbone_out["id_mapping"][img_ids]
+                # If this assert fails, it likely means we're requesting different img_ids (perhaps a different frame?)
+                # We currently don't expect this to happen. We could technically trigger a recompute here,
+                # but likely at the cost of a cpu<->gpu sync point, which would deteriorate perf
+                torch._assert_async((img_ids >= 0).all())
+
+            vis_feats = backbone_out["backbone_fpn"][-self.num_feature_levels :]
+            vis_pos_enc = backbone_out["vision_pos_enc"][-self.num_feature_levels :]
+            vis_feat_sizes = [x.shape[-2:] for x in vis_pos_enc]  # (H, W) shapes
+            # index and flatten visual features NxCxHxW => HWxNxC (batch-first => seq-first)
+            img_feats = [x[img_ids].flatten(2).permute(2, 0, 1) for x in vis_feats]
+            img_pos_embeds = [
+                x[img_ids].flatten(2).permute(2, 0, 1) for x in vis_pos_enc
+            ]
+            return backbone_out, img_feats, img_pos_embeds, vis_feat_sizes
+
+        # Image features not available in backbone output, so we compute them on the fly
+        # This case likely occurs for video. In that case, we want to forward only the current frame
+        img_batch = backbone_out["img_batch_all_stages"]
+        if img_ids.numel() > 1:
+            # Only forward backbone on unique image ids to avoid repetitive computation
+            unique_ids, _ = torch.unique(img_ids, return_inverse=True)
+        else:
+            unique_ids, _ = img_ids, slice(None)
+        # Compute the image features on those unique image ids
+        # note: we allow using a list (or other indexable types) of tensors as img_batch
+        # (e.g. for async frame loading in demo). In this case we index img_batch.tensors directly
+        if isinstance(img_batch, torch.Tensor):
+            image = img_batch[unique_ids]
+        elif unique_ids.numel() == 1:
+            image = img_batch[unique_ids.item()].unsqueeze(0)
+        else:
+            image = torch.stack([img_batch[i] for i in unique_ids.tolist()])
+        # `img_batch` might be fp16 and offloaded to CPU
+        image = image.to(dtype=torch.float32, device=self.device)
+        # Next time we call this function, we want to remember which indices we computed
+        id_mapping = torch.full(
+            (len(img_batch),), -1, dtype=torch.long, device=self.device
+        )
+        id_mapping[unique_ids] = torch.arange(len(unique_ids), device=self.device)
+        backbone_out = {
+            **backbone_out,
+            **self.backbone.forward_image(image),
+            "id_mapping": id_mapping,
+        }
+        assert "backbone_fpn" in backbone_out
+        return self._get_img_feats(backbone_out, img_ids=img_ids)
+
+    def _encode_prompt(
+        self,
+        backbone_out,
+        find_input,
+        geometric_prompt,
+        visual_prompt_embed=None,
+        visual_prompt_mask=None,
+        encode_text=True,
+        prev_mask_pred=None,
+    ):
+        # index text features (note that regardless of early or late fusion, the batch size of
+        # `txt_feats` is always the number of *prompts* in the encoder)
+        txt_ids = find_input.text_ids
+        txt_feats = backbone_out["language_features"][:, txt_ids]
+        txt_masks = backbone_out["language_mask"][txt_ids]
+
+        feat_tuple = self._get_img_feats(backbone_out, find_input.img_ids)
+        backbone_out, img_feats, img_pos_embeds, vis_feat_sizes = feat_tuple
+
+        if prev_mask_pred is not None:
+            img_feats = [img_feats[-1] + prev_mask_pred]
+        # Encode geometry
+        geo_feats, geo_masks = self.geometry_encoder(
+            geo_prompt=geometric_prompt,
+            img_feats=img_feats,
+            img_sizes=vis_feat_sizes,
+            img_pos_embeds=img_pos_embeds,
+        )
+        if visual_prompt_embed is None:
+            visual_prompt_embed = torch.zeros(
+                (0, *geo_feats.shape[1:]), device=geo_feats.device
+            )
+            visual_prompt_mask = torch.zeros(
+                (*geo_masks.shape[:-1], 0),
+                device=geo_masks.device,
+                dtype=geo_masks.dtype,
+            )
+        if encode_text:
+            prompt = torch.cat([txt_feats, geo_feats, visual_prompt_embed], dim=0)
+            prompt_mask = torch.cat([txt_masks, geo_masks, visual_prompt_mask], dim=1)
+        else:
+            prompt = torch.cat([geo_feats, visual_prompt_embed], dim=0)
+            prompt_mask = torch.cat([geo_masks, visual_prompt_mask], dim=1)
+        return prompt, prompt_mask, backbone_out
+
+    def _run_encoder(
+        self,
+        backbone_out,
+        find_input,
+        prompt,
+        prompt_mask,
+        encoder_extra_kwargs: Optional[Dict] = None,
+    ):
+        feat_tuple = self._get_img_feats(backbone_out, find_input.img_ids)
+        backbone_out, img_feats, img_pos_embeds, vis_feat_sizes = feat_tuple
+
+        # Run the encoder
+        prompt_pos_embed = torch.zeros_like(prompt)
+        # make a copy of the image feature lists since the encoder may modify these lists in-place
+        memory = self.transformer.encoder(
+            src=img_feats.copy(),
+            src_key_padding_mask=None,
+            src_pos=img_pos_embeds.copy(),
+            prompt=prompt,
+            prompt_pos=prompt_pos_embed,
+            prompt_key_padding_mask=prompt_mask,
+            feat_sizes=vis_feat_sizes,
+            encoder_extra_kwargs=encoder_extra_kwargs,
+        )
+        encoder_out = {
+            # encoded image features
+            "encoder_hidden_states": memory["memory"],
+            "pos_embed": memory["pos_embed"],
+            "padding_mask": memory["padding_mask"],
+            "level_start_index": memory["level_start_index"],
+            "spatial_shapes": memory["spatial_shapes"],
+            "valid_ratios": memory["valid_ratios"],
+            "vis_feat_sizes": vis_feat_sizes,
+            # encoded text features (or other prompts)
+            "prompt_before_enc": prompt,
+            "prompt_after_enc": memory.get("memory_text", prompt),
+            "prompt_mask": prompt_mask,
+        }
+        return backbone_out, encoder_out, feat_tuple
+
+    def _run_decoder(
+        self,
+        pos_embed,
+        memory,
+        src_mask,
+        out,
+        prompt,
+        prompt_mask,
+        encoder_out,
+    ):
+        bs = memory.shape[1]
+        query_embed = self.transformer.decoder.query_embed.weight
+        tgt = query_embed.unsqueeze(1).repeat(1, bs, 1)
+
+        apply_dac = self.transformer.decoder.dac and self.training
+        hs, reference_boxes, dec_presence_out, dec_presence_feats = (
+            self.transformer.decoder(
+                tgt=tgt,
+                memory=memory,
+                memory_key_padding_mask=src_mask,
+                pos=pos_embed,
+                reference_boxes=None,
+                level_start_index=encoder_out["level_start_index"],
+                spatial_shapes=encoder_out["spatial_shapes"],
+                valid_ratios=encoder_out["valid_ratios"],
+                tgt_mask=None,
+                memory_text=prompt,
+                text_attention_mask=prompt_mask,
+                apply_dac=apply_dac,
+            )
+        )
+        hs = hs.transpose(1, 2)  # seq-first to batch-first
+        reference_boxes = reference_boxes.transpose(1, 2)  # seq-first to batch-first
+        if dec_presence_out is not None:
+            # seq-first to batch-first
+            dec_presence_out = dec_presence_out.transpose(1, 2)
+
+        out["presence_feats"] = dec_presence_feats
+        self._update_scores_and_boxes(
+            out,
+            hs,
+            reference_boxes,
+            prompt,
+            prompt_mask,
+            dec_presence_out=dec_presence_out,
+        )
+        return out, hs
+
+    def _update_scores_and_boxes(
+        self,
+        out,
+        hs,
+        reference_boxes,
+        prompt,
+        prompt_mask,
+        dec_presence_out=None,
+        is_instance_prompt=False,
+    ):
+        apply_dac = self.transformer.decoder.dac and self.training
+        num_o2o = (hs.size(2) // 2) if apply_dac else hs.size(2)
+        num_o2m = hs.size(2) - num_o2o
+        assert num_o2m == (num_o2o if apply_dac else 0)
+        out["queries"] = hs[-1][:, :num_o2o]  # remove o2m queries if there are any
+        # score prediction
+        if self.use_dot_prod_scoring:
+            dot_prod_scoring_head = self.dot_prod_scoring
+            if is_instance_prompt and self.instance_dot_prod_scoring is not None:
+                dot_prod_scoring_head = self.instance_dot_prod_scoring
+            outputs_class = dot_prod_scoring_head(hs, prompt, prompt_mask)
+        else:
+            class_embed_head = self.class_embed
+            if is_instance_prompt and self.instance_class_embed is not None:
+                class_embed_head = self.instance_class_embed
+            outputs_class = class_embed_head(hs)
+
+        # box prediction
+        box_head = self.transformer.decoder.bbox_embed
+        if (
+            is_instance_prompt
+            and self.transformer.decoder.instance_bbox_embed is not None
+        ):
+            box_head = self.transformer.decoder.instance_bbox_embed
+        anchor_box_offsets = box_head(hs)
+        reference_boxes_inv_sig = inverse_sigmoid(reference_boxes)
+        outputs_coord = (reference_boxes_inv_sig + anchor_box_offsets).sigmoid()
+        outputs_boxes_xyxy = box_cxcywh_to_xyxy(outputs_coord)
+
+        if dec_presence_out is not None:
+            _update_out(
+                out, "presence_logit_dec", dec_presence_out, update_aux=self.training
+            )
+
+        if self.supervise_joint_box_scores:
+            assert dec_presence_out is not None
+            prob_dec_presence_out = dec_presence_out.clone().sigmoid()
+            if self.detach_presence_in_joint_score:
+                prob_dec_presence_out = prob_dec_presence_out.detach()
+
+            outputs_class = inverse_sigmoid(
+                outputs_class.sigmoid() * prob_dec_presence_out.unsqueeze(2)
+            ).clamp(min=-10.0, max=10.0)
+
+        _update_out(
+            out, "pred_logits", outputs_class[:, :, :num_o2o], update_aux=self.training
+        )
+        _update_out(
+            out, "pred_boxes", outputs_coord[:, :, :num_o2o], update_aux=self.training
+        )
+        _update_out(
+            out,
+            "pred_boxes_xyxy",
+            outputs_boxes_xyxy[:, :, :num_o2o],
+            update_aux=self.training,
+        )
+        if num_o2m > 0 and self.training:
+            _update_out(
+                out,
+                "pred_logits_o2m",
+                outputs_class[:, :, num_o2o:],
+                update_aux=self.training,
+            )
+            _update_out(
+                out,
+                "pred_boxes_o2m",
+                outputs_coord[:, :, num_o2o:],
+                update_aux=self.training,
+            )
+            _update_out(
+                out,
+                "pred_boxes_xyxy_o2m",
+                outputs_boxes_xyxy[:, :, num_o2o:],
+                update_aux=self.training,
+            )
+
+    def _run_segmentation_heads(
+        self,
+        out,
+        backbone_out,
+        img_ids,
+        vis_feat_sizes,
+        encoder_hidden_states,
+        prompt,
+        prompt_mask,
+        hs,
+    ):
+        apply_dac = self.transformer.decoder.dac and self.training
+        if self.segmentation_head is not None:
+            num_o2o = (hs.size(2) // 2) if apply_dac else hs.size(2)
+            num_o2m = hs.size(2) - num_o2o
+            obj_queries = hs if self.o2m_mask_predict else hs[:, :, :num_o2o]
+            seg_head_outputs = activation_ckpt_wrapper(self.segmentation_head)(
+                backbone_feats=backbone_out["backbone_fpn"],
+                obj_queries=obj_queries,
+                image_ids=img_ids,
+                encoder_hidden_states=encoder_hidden_states,
+                act_ckpt_enable=self.training and self.use_act_checkpoint_seg_head,
+                prompt=prompt,
+                prompt_mask=prompt_mask,
+            )
+            aux_masks = False  # self.aux_loss and self.segmentation_head.aux_masks
+            for k, v in seg_head_outputs.items():
+                if k in self.segmentation_head.instance_keys:
+                    _update_out(out, k, v[:, :num_o2o], auxiliary=aux_masks)
+                    if (
+                        self.o2m_mask_predict and num_o2m > 0
+                    ):  # handle o2m mask prediction
+                        _update_out(
+                            out, f"{k}_o2m", v[:, num_o2o:], auxiliary=aux_masks
+                        )
+                else:
+                    out[k] = v
+        else:
+            backbone_out.pop("backbone_fpn", None)
+
+    def _get_best_mask(self, out):
+        prev_mask_idx = out["pred_logits"].argmax(dim=1).squeeze(1)
+        batch_idx = torch.arange(
+            out["pred_logits"].shape[0], device=prev_mask_idx.device
+        )
+        prev_mask_pred = out["pred_masks"][batch_idx, prev_mask_idx][:, None]
+        # Downsample mask to match image resolution.
+        prev_mask_pred = self.geometry_encoder.mask_encoder.mask_downsampler(
+            prev_mask_pred
+        )
+        prev_mask_pred = prev_mask_pred.flatten(-2).permute(2, 0, 1)
+
+        return prev_mask_pred
+
+    def forward_grounding(
+        self,
+        backbone_out,
+        find_input,
+        find_target,
+        geometric_prompt: Prompt,
+    ):
+        with torch.profiler.record_function("SAM3Image._encode_prompt"):
+            prompt, prompt_mask, backbone_out = self._encode_prompt(
+                backbone_out, find_input, geometric_prompt
+            )
+        # Run the encoder
+        with torch.profiler.record_function("SAM3Image._run_encoder"):
+            backbone_out, encoder_out, _ = self._run_encoder(
+                backbone_out, find_input, prompt, prompt_mask
+            )
+        out = {
+            "encoder_hidden_states": encoder_out["encoder_hidden_states"],
+            "prev_encoder_out": {
+                "encoder_out": encoder_out,
+                "backbone_out": backbone_out,
+            },
+        }
+
+        # Run the decoder
+        with torch.profiler.record_function("SAM3Image._run_decoder"):
+            out, hs = self._run_decoder(
+                memory=out["encoder_hidden_states"],
+                pos_embed=encoder_out["pos_embed"],
+                src_mask=encoder_out["padding_mask"],
+                out=out,
+                prompt=prompt,
+                prompt_mask=prompt_mask,
+                encoder_out=encoder_out,
+            )
+
+        # Run segmentation heads
+        with torch.profiler.record_function("SAM3Image._run_segmentation_heads"):
+            self._run_segmentation_heads(
+                out=out,
+                backbone_out=backbone_out,
+                img_ids=find_input.img_ids,
+                vis_feat_sizes=encoder_out["vis_feat_sizes"],
+                encoder_hidden_states=out["encoder_hidden_states"],
+                prompt=prompt,
+                prompt_mask=prompt_mask,
+                hs=hs,
+            )
+
+        if self.training or self.num_interactive_steps_val > 0:
+            self._compute_matching(out, self.back_convert(find_target))
+        return out
+
+    def _postprocess_out(self, out: Dict, multimask_output: bool = False):
+        # For multimask output, during eval we return the single best mask with the dict keys expected by the evaluators, but also return the multimasks output with new keys.
+        num_mask_boxes = out["pred_boxes"].size(1)
+        if not self.training and multimask_output and num_mask_boxes > 1:
+            out["multi_pred_logits"] = out["pred_logits"]
+            if "pred_masks" in out:
+                out["multi_pred_masks"] = out["pred_masks"]
+            out["multi_pred_boxes"] = out["pred_boxes"]
+            out["multi_pred_boxes_xyxy"] = out["pred_boxes_xyxy"]
+
+            best_mask_idx = out["pred_logits"].argmax(1).squeeze(1)
+            batch_idx = torch.arange(len(best_mask_idx), device=best_mask_idx.device)
+
+            out["pred_logits"] = out["pred_logits"][batch_idx, best_mask_idx].unsqueeze(
+                1
+            )
+            if "pred_masks" in out:
+                out["pred_masks"] = out["pred_masks"][
+                    batch_idx, best_mask_idx
+                ].unsqueeze(1)
+            out["pred_boxes"] = out["pred_boxes"][batch_idx, best_mask_idx].unsqueeze(1)
+            out["pred_boxes_xyxy"] = out["pred_boxes_xyxy"][
+                batch_idx, best_mask_idx
+            ].unsqueeze(1)
+
+        return out
+
+    def _get_dummy_prompt(self, num_prompts=1):
+        device = self.device
+        geometric_prompt = Prompt(
+            box_embeddings=torch.zeros(0, num_prompts, 4, device=device),
+            box_mask=torch.zeros(num_prompts, 0, device=device, dtype=torch.bool),
+        )
+        return geometric_prompt
+
+    def forward(self, input: BatchedDatapoint):
+        device = self.device
+        backbone_out = {"img_batch_all_stages": input.img_batch}
+        backbone_out.update(self.backbone.forward_image(input.img_batch))
+        num_frames = len(input.find_inputs)
+        assert num_frames == 1
+
+        text_outputs = self.backbone.forward_text(input.find_text_batch, device=device)
+        backbone_out.update(text_outputs)
+
+        previous_stages_out = SAM3Output(
+            iter_mode=SAM3Output.IterMode.LAST_STEP_PER_STAGE
+        )
+
+        find_input = input.find_inputs[0]
+        find_target = input.find_targets[0]
+
+        if find_input.input_points is not None and find_input.input_points.numel() > 0:
+            print("Warning: Point prompts are ignored in PCS.")
+
+        num_interactive_steps = 0 if self.training else self.num_interactive_steps_val
+        geometric_prompt = Prompt(
+            box_embeddings=find_input.input_boxes,
+            box_mask=find_input.input_boxes_mask,
+            box_labels=find_input.input_boxes_label,
+        )
+
+        # Init vars that are shared across the loop.
+        stage_outs = []
+        for cur_step in range(num_interactive_steps + 1):
+            if cur_step > 0:
+                # We sample interactive geometric prompts (boxes, points)
+                geometric_prompt, _ = self.interactive_prompt_sampler.sample(
+                    geo_prompt=geometric_prompt,
+                    find_target=find_target,
+                    previous_out=stage_outs[-1],
+                )
+            out = self.forward_grounding(
+                backbone_out=backbone_out,
+                find_input=find_input,
+                find_target=find_target,
+                geometric_prompt=geometric_prompt.clone(),
+            )
+            stage_outs.append(out)
+
+        previous_stages_out.append(stage_outs)
+        return previous_stages_out
+
+    def _compute_matching(self, out, targets):
+        out["indices"] = self.matcher(out, targets)
+        for aux_out in out.get("aux_outputs", []):
+            aux_out["indices"] = self.matcher(aux_out, targets)
+
+    def back_convert(self, targets):
+        batched_targets = {
+            "boxes": targets.boxes.view(-1, 4),
+            "boxes_xyxy": box_cxcywh_to_xyxy(targets.boxes.view(-1, 4)),
+            "boxes_padded": targets.boxes_padded,
+            "positive_map": targets.boxes.new_ones(len(targets.boxes), 1),
+            "num_boxes": targets.num_boxes,
+            "masks": targets.segments,
+            "semantic_masks": targets.semantic_segments,
+            "is_valid_mask": targets.is_valid_segment,
+            "is_exhaustive": targets.is_exhaustive,
+            "object_ids_packed": targets.object_ids,
+            "object_ids_padded": targets.object_ids_padded,
+        }
+        return batched_targets
+
+    def predict_inst(
+        self,
+        inference_state,
+        **kwargs,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        orig_h, orig_w = (
+            inference_state["original_height"],
+            inference_state["original_width"],
+        )
+        backbone_out = inference_state["backbone_out"]["sam2_backbone_out"]
+        (
+            _,
+            vision_feats,
+            _,
+            _,
+        ) = self.inst_interactive_predictor.model._prepare_backbone_features(
+            backbone_out
+        )
+        # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos
+        vision_feats[-1] = (
+            vision_feats[-1] + self.inst_interactive_predictor.model.no_mem_embed
+        )
+        feats = [
+            feat.permute(1, 2, 0).view(1, -1, *feat_size)
+            for feat, feat_size in zip(
+                vision_feats[::-1], self.inst_interactive_predictor._bb_feat_sizes[::-1]
+            )
+        ][::-1]
+        self.inst_interactive_predictor._features = {
+            "image_embed": feats[-1],
+            "high_res_feats": feats[:-1],
+        }
+        self.inst_interactive_predictor._is_image_set = True
+        self.inst_interactive_predictor._orig_hw = [(orig_h, orig_w)]
+        res = self.inst_interactive_predictor.predict(**kwargs)
+        self.inst_interactive_predictor._features = None
+        self.inst_interactive_predictor._is_image_set = False
+        return res
+
+    def predict_inst_batch(
+        self,
+        inference_state,
+        *args,
+        **kwargs,
+    ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
+        backbone_out = inference_state["backbone_out"]["sam2_backbone_out"]
+        (
+            _,
+            vision_feats,
+            _,
+            _,
+        ) = self.inst_interactive_predictor.model._prepare_backbone_features(
+            backbone_out
+        )
+        # Add no_mem_embed, which is added to the lowest res feat. map during training on videos
+        vision_feats[-1] = (
+            vision_feats[-1] + self.inst_interactive_predictor.model.no_mem_embed
+        )
+        batch_size = vision_feats[-1].shape[1]
+        orig_heights, orig_widths = (
+            inference_state["original_heights"],
+            inference_state["original_widths"],
+        )
+        assert batch_size == len(orig_heights) == len(orig_widths), (
+            f"Batch size mismatch in predict_inst_batch. Got {batch_size}, {len(orig_heights)}, {len(orig_widths)}"
+        )
+        feats = [
+            feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)
+            for feat, feat_size in zip(
+                vision_feats[::-1], self.inst_interactive_predictor._bb_feat_sizes[::-1]
+            )
+        ][::-1]
+        self.inst_interactive_predictor._features = {
+            "image_embed": feats[-1],
+            "high_res_feats": feats[:-1],
+        }
+        self.inst_interactive_predictor._is_image_set = True
+        self.inst_interactive_predictor._is_batch = True
+        self.inst_interactive_predictor._orig_hw = [
+            (orig_h, orig_w) for orig_h, orig_w in zip(orig_heights, orig_widths)
+        ]
+        res = self.inst_interactive_predictor.predict_batch(*args, **kwargs)
+        self.inst_interactive_predictor._features = None
+        self.inst_interactive_predictor._is_image_set = False
+        self.inst_interactive_predictor._is_batch = False
+        return res
+
+
+class Sam3ImageOnVideoMultiGPU(Sam3Image):
+    def __init__(
+        self, *args, async_all_gather=True, gather_backbone_out=None, **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+        self.rank = int(os.getenv("RANK", "0"))
+        self.world_size = int(os.getenv("WORLD_SIZE", "1"))
+        self.async_all_gather = async_all_gather
+
+        # if gather_backbone is not set, default to gathering only for `SAM3VLBackbone`
+        if gather_backbone_out is None:
+            gather_backbone_out = isinstance(self.backbone, SAM3VLBackbone)
+        self.gather_backbone_out = gather_backbone_out
+
+    def forward_video_grounding_multigpu(
+        self,
+        backbone_out,
+        find_inputs,
+        geometric_prompt: Prompt,
+        frame_idx,
+        num_frames,
+        # `multigpu_buffer` is a dict to cache detector's outputs in a chunk between different calls
+        multigpu_buffer,
+        track_in_reverse=False,
+        # whether to also return the SAM2 backbone features
+        return_sam2_backbone_feats=False,
+        # whether to perform NMS and suppress the scores of those detections removed by NMS
+        run_nms=False,
+        nms_prob_thresh=None,
+        nms_iou_thresh=None,
+        **kwargs,
+    ):
+        """
+        Compute the detector's detection outputs in a distributed manner, where all GPUs process
+        a chunk of frames (equal to the number of GPUs) at once and store them in cache.
+        """
+        # Step 1: fetch the detector outputs in the current chunk from buffer
+        frame_idx_curr_b = frame_idx - frame_idx % self.world_size
+        frame_idx_curr_e = min(frame_idx_curr_b + self.world_size, num_frames)
+        # in case the current frame's detection results are not in the buffer yet, build the current chunk
+        # (this should only happen on the first chunk, since we are also building the next chunk below)
+        if frame_idx not in multigpu_buffer:
+            with torch.profiler.record_function("build_multigpu_buffer_next_chunk1"):
+                self._build_multigpu_buffer_next_chunk(
+                    backbone_out=backbone_out,
+                    find_inputs=find_inputs,
+                    geometric_prompt=geometric_prompt,
+                    frame_idx_begin=frame_idx_curr_b,
+                    frame_idx_end=frame_idx_curr_e,
+                    num_frames=num_frames,
+                    multigpu_buffer=multigpu_buffer,
+                    run_nms=run_nms,
+                    nms_prob_thresh=nms_prob_thresh,
+                    nms_iou_thresh=nms_iou_thresh,
+                )
+
+        # read out the current frame's results from `multigpu_buffer`
+        out = {}
+        for k, (v, handle) in multigpu_buffer[frame_idx].items():
+            if k.startswith("sam2_backbone_") and not return_sam2_backbone_feats:
+                continue
+            if handle is not None:
+                handle.wait()  # wait for async all-gather to finish
+            out[k] = v
+
+        # Step 2: remove detection outputs of the previous chunk from cache to save GPU memory
+        if not track_in_reverse and frame_idx_curr_b - self.world_size >= 0:
+            frame_idx_prev_e = frame_idx_curr_b
+            frame_idx_prev_b = frame_idx_curr_b - self.world_size
+        elif track_in_reverse and frame_idx_curr_e < num_frames:
+            frame_idx_prev_b = frame_idx_curr_e
+            frame_idx_prev_e = min(frame_idx_prev_b + self.world_size, num_frames)
+        else:
+            frame_idx_prev_b = frame_idx_prev_e = None
+        if frame_idx_prev_b is not None:
+            for frame_idx_rm in range(frame_idx_prev_b, frame_idx_prev_e):
+                multigpu_buffer.pop(frame_idx_rm, None)
+
+        # Step 3: compute and cache detection outputs of the next chunk ahead of time
+        # (so that we can overlap computation with all-gather transfer)
+        if not track_in_reverse and frame_idx_curr_e < num_frames:
+            frame_idx_next_b = frame_idx_curr_e
+            frame_idx_next_e = min(frame_idx_next_b + self.world_size, num_frames)
+        elif track_in_reverse and frame_idx_curr_b - self.world_size >= 0:
+            frame_idx_next_e = frame_idx_curr_b
+            frame_idx_next_b = frame_idx_curr_b - self.world_size
+        else:
+            frame_idx_next_b = frame_idx_next_e = None
+        if frame_idx_next_b is not None and frame_idx_next_b not in multigpu_buffer:
+            with torch.profiler.record_function("build_multigpu_buffer_next_chunk2"):
+                self._build_multigpu_buffer_next_chunk(
+                    backbone_out=backbone_out,
+                    find_inputs=find_inputs,
+                    geometric_prompt=geometric_prompt,
+                    frame_idx_begin=frame_idx_next_b,
+                    frame_idx_end=frame_idx_next_e,
+                    num_frames=num_frames,
+                    multigpu_buffer=multigpu_buffer,
+                    run_nms=run_nms,
+                    nms_prob_thresh=nms_prob_thresh,
+                    nms_iou_thresh=nms_iou_thresh,
+                )
+
+        return out, backbone_out
+
+    def _build_multigpu_buffer_next_chunk(
+        self,
+        backbone_out,
+        find_inputs,
+        geometric_prompt: Prompt,
+        frame_idx_begin,
+        frame_idx_end,
+        num_frames,
+        multigpu_buffer,
+        run_nms=False,
+        nms_prob_thresh=None,
+        nms_iou_thresh=None,
+    ):
+        """Compute detection outputs on a chunk of frames and store their results in multigpu_buffer."""
+        # each GPU computes detections on one frame in the chunk (in a round-robin manner)
+        frame_idx_local_gpu = min(frame_idx_begin + self.rank, frame_idx_end - 1)
+        # `forward_grounding` (from base class `Sam3ImageOnVideo`) runs the detector on a single frame
+        with torch.profiler.record_function("forward_grounding"):
+            out_local = self.forward_grounding(
+                backbone_out=backbone_out,
+                find_input=find_inputs[frame_idx_local_gpu],
+                find_target=None,
+                geometric_prompt=geometric_prompt,
+            )
+        if run_nms:
+            with torch.profiler.record_function("nms_masks"):
+                # run NMS as a post-processing step on top of the detection outputs
+                assert nms_prob_thresh is not None and nms_iou_thresh is not None
+                pred_probs = out_local["pred_logits"].squeeze(-1).sigmoid()
+                pred_masks = out_local["pred_masks"]
+                # loop over text prompts (not an overhead for demo where there's only 1 prompt)
+                for prompt_idx in range(pred_probs.size(0)):
+                    keep = nms_masks(
+                        pred_probs=pred_probs[prompt_idx],
+                        pred_masks=pred_masks[prompt_idx],
+                        prob_threshold=nms_prob_thresh,
+                        iou_threshold=nms_iou_thresh,
+                    )
+                    # set a very low threshold for those detections removed by NMS
+                    out_local["pred_logits"][prompt_idx, :, 0] -= 1e4 * (~keep).float()
+
+        if self.gather_backbone_out:
+            # gather the SAM 2 backbone features across GPUs
+            feats = out_local["prev_encoder_out"]["backbone_out"]["sam2_backbone_out"]
+            assert len(feats["backbone_fpn"]) == 3  # SAM2 backbone always have 3 levels
+            # cast the SAM2 backbone features to bfloat16 for all-gather (this is usually
+            # a no-op, SAM2 backbone features are likely already in bfloat16 due to AMP)
+            backbone_fpn_bf16 = [x.to(torch.bfloat16) for x in feats["backbone_fpn"]]
+            fpn0, fpn_handle0 = self._gather_tensor(backbone_fpn_bf16[0])
+            fpn1, fpn_handle1 = self._gather_tensor(backbone_fpn_bf16[1])
+            fpn2, fpn_handle2 = self._gather_tensor(backbone_fpn_bf16[2])
+            # vision_pos_enc is the same on all frames, so no need to all-gather them
+            vision_pos_enc = feats["vision_pos_enc"]
+
+        # trim the detector output to only include the necessary keys
+        out_local = {
+            "pred_logits": out_local["pred_logits"],
+            "pred_boxes": out_local["pred_boxes"],
+            "pred_boxes_xyxy": out_local["pred_boxes_xyxy"],
+            "pred_masks": out_local["pred_masks"],
+        }
+
+        # gather the results: after this step, each GPU will receive detector outputs on
+        # all frames in the chunk and store them in `multigpu_buffer`
+        out_gathered = {k: self._gather_tensor(v) for k, v in out_local.items()}
+        for rank in range(self.world_size):
+            frame_idx_to_save = frame_idx_begin + rank
+            if frame_idx_to_save >= num_frames:
+                continue
+            frame_buffer = {
+                k: (v[rank], handle) for k, (v, handle) in out_gathered.items()
+            }
+            if self.gather_backbone_out:
+                # also add gathered SAM 2 backbone features to frame_buffer
+                frame_buffer["tracker_backbone_fpn_0"] = (fpn0[rank], fpn_handle0)
+                frame_buffer["tracker_backbone_fpn_1"] = (fpn1[rank], fpn_handle1)
+                frame_buffer["tracker_backbone_fpn_2"] = (fpn2[rank], fpn_handle2)
+                frame_buffer["tracker_backbone_pos_enc"] = (vision_pos_enc, None)
+
+            multigpu_buffer[frame_idx_to_save] = frame_buffer
+
+    def _gather_tensor(self, x):
+        if self.world_size == 1:
+            return [x], None
+
+        async_op = self.async_all_gather
+        # here `.contiguous()` is required -- otherwise NCCL all_gather
+        # sometimes gives wrong results
+        x = x.contiguous()  # ensure contiguous memory for NCCL
+        output_list = [torch.empty_like(x) for _ in range(self.world_size)]
+        handle = torch.distributed.all_gather(output_list, x, async_op=async_op)
+        return output_list, handle

third_party/GraspGen/sam3/sam3/model/sam3_image_processor.py

@@ -0,0 +1,222 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+from typing import Dict, List
+
+import numpy as np
+import PIL
+import torch
+from sam3.model import box_ops
+from sam3.model.data_misc import FindStage, interpolate
+from torchvision.transforms import v2
+
+
+class Sam3Processor:
+    """ """
+
+    def __init__(self, model, resolution=1008, device="cuda", confidence_threshold=0.5):
+        self.model = model
+        self.resolution = resolution
+        self.device = device
+        self.transform = v2.Compose(
+            [
+                v2.ToDtype(torch.uint8, scale=True),
+                v2.Resize(size=(resolution, resolution)),
+                v2.ToDtype(torch.float32, scale=True),
+                v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+            ]
+        )
+        self.confidence_threshold = confidence_threshold
+
+        self.find_stage = FindStage(
+            img_ids=torch.tensor([0], device=device, dtype=torch.long),
+            text_ids=torch.tensor([0], device=device, dtype=torch.long),
+            input_boxes=None,
+            input_boxes_mask=None,
+            input_boxes_label=None,
+            input_points=None,
+            input_points_mask=None,
+        )
+
+    @torch.inference_mode()
+    def set_image(self, image, state=None):
+        """Sets the image on which we want to do predictions."""
+        if state is None:
+            state = {}
+
+        if isinstance(image, PIL.Image.Image):
+            width, height = image.size
+        elif isinstance(image, (torch.Tensor, np.ndarray)):
+            height, width = image.shape[-2:]
+        else:
+            raise ValueError("Image must be a PIL image or a tensor")
+
+        image = v2.functional.to_image(image).to(self.device)
+        image = self.transform(image).unsqueeze(0)
+
+        state["original_height"] = height
+        state["original_width"] = width
+        state["backbone_out"] = self.model.backbone.forward_image(image)
+        inst_interactivity_en = self.model.inst_interactive_predictor is not None
+        if inst_interactivity_en and "sam2_backbone_out" in state["backbone_out"]:
+            sam2_backbone_out = state["backbone_out"]["sam2_backbone_out"]
+            sam2_backbone_out["backbone_fpn"][0] = (
+                self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s0(
+                    sam2_backbone_out["backbone_fpn"][0]
+                )
+            )
+            sam2_backbone_out["backbone_fpn"][1] = (
+                self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s1(
+                    sam2_backbone_out["backbone_fpn"][1]
+                )
+            )
+        return state
+
+    @torch.inference_mode()
+    def set_image_batch(self, images: List[np.ndarray], state=None):
+        """Sets the image batch on which we want to do predictions."""
+        if state is None:
+            state = {}
+
+        if not isinstance(images, list):
+            raise ValueError("Images must be a list of PIL images or tensors")
+        assert len(images) > 0, "Images list must not be empty"
+        assert isinstance(images[0], PIL.Image.Image), (
+            "Images must be a list of PIL images"
+        )
+
+        state["original_heights"] = [image.height for image in images]
+        state["original_widths"] = [image.width for image in images]
+
+        images = [
+            self.transform(v2.functional.to_image(image).to(self.device))
+            for image in images
+        ]
+        images = torch.stack(images, dim=0)
+        state["backbone_out"] = self.model.backbone.forward_image(images)
+        inst_interactivity_en = self.model.inst_interactive_predictor is not None
+        if inst_interactivity_en and "sam2_backbone_out" in state["backbone_out"]:
+            sam2_backbone_out = state["backbone_out"]["sam2_backbone_out"]
+            sam2_backbone_out["backbone_fpn"][0] = (
+                self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s0(
+                    sam2_backbone_out["backbone_fpn"][0]
+                )
+            )
+            sam2_backbone_out["backbone_fpn"][1] = (
+                self.model.inst_interactive_predictor.model.sam_mask_decoder.conv_s1(
+                    sam2_backbone_out["backbone_fpn"][1]
+                )
+            )
+        return state
+
+    @torch.inference_mode()
+    def set_text_prompt(self, prompt: str, state: Dict):
+        """Sets the text prompt and run the inference"""
+
+        if "backbone_out" not in state:
+            raise ValueError("You must call set_image before set_text_prompt")
+
+        text_outputs = self.model.backbone.forward_text([prompt], device=self.device)
+        # will erase the previous text prompt if any
+        state["backbone_out"].update(text_outputs)
+        if "geometric_prompt" not in state:
+            state["geometric_prompt"] = self.model._get_dummy_prompt()
+
+        return self._forward_grounding(state)
+
+    @torch.inference_mode()
+    def add_geometric_prompt(self, box: List, label: bool, state: Dict):
+        """Adds a box prompt and run the inference.
+        The image needs to be set, but not necessarily the text prompt.
+        The box is assumed to be in [center_x, center_y, width, height] format and normalized in [0, 1] range.
+        The label is True for a positive box, False for a negative box.
+        """
+        if "backbone_out" not in state:
+            raise ValueError("You must call set_image before set_text_prompt")
+
+        if "language_features" not in state["backbone_out"]:
+            # Looks like we don't have a text prompt yet. This is allowed, but we need to set the text prompt to "visual" for the model to rely only on the geometric prompt
+            dummy_text_outputs = self.model.backbone.forward_text(
+                ["visual"], device=self.device
+            )
+            state["backbone_out"].update(dummy_text_outputs)
+
+        if "geometric_prompt" not in state:
+            state["geometric_prompt"] = self.model._get_dummy_prompt()
+
+        # adding a batch and sequence dimension
+        boxes = torch.tensor(box, device=self.device, dtype=torch.float32).view(1, 1, 4)
+        labels = torch.tensor([label], device=self.device, dtype=torch.bool).view(1, 1)
+        state["geometric_prompt"].append_boxes(boxes, labels)
+
+        return self._forward_grounding(state)
+
+    def reset_all_prompts(self, state: Dict):
+        """Removes all the prompts and results"""
+        if "backbone_out" in state:
+            backbone_keys_to_del = [
+                "language_features",
+                "language_mask",
+                "language_embeds",
+            ]
+            for key in backbone_keys_to_del:
+                if key in state["backbone_out"]:
+                    del state["backbone_out"][key]
+
+        keys_to_del = ["geometric_prompt", "boxes", "masks", "masks_logits", "scores"]
+        for key in keys_to_del:
+            if key in state:
+                del state[key]
+
+    @torch.inference_mode()
+    def set_confidence_threshold(self, threshold: float, state=None):
+        """Sets the confidence threshold for the masks"""
+        self.confidence_threshold = threshold
+        if state is not None and "boxes" in state:
+            # we need to filter the boxes again
+            # In principle we could do this more efficiently since we would only need
+            # to rerun the heads. But this is simpler and not too inefficient
+            return self._forward_grounding(state)
+        return state
+
+    @torch.inference_mode()
+    def _forward_grounding(self, state: Dict):
+        outputs = self.model.forward_grounding(
+            backbone_out=state["backbone_out"],
+            find_input=self.find_stage,
+            geometric_prompt=state["geometric_prompt"],
+            find_target=None,
+        )
+
+        out_bbox = outputs["pred_boxes"]
+        out_logits = outputs["pred_logits"]
+        out_masks = outputs["pred_masks"]
+        out_probs = out_logits.sigmoid()
+        presence_score = outputs["presence_logit_dec"].sigmoid().unsqueeze(1)
+        out_probs = (out_probs * presence_score).squeeze(-1)
+
+        keep = out_probs > self.confidence_threshold
+        out_probs = out_probs[keep]
+        out_masks = out_masks[keep]
+        out_bbox = out_bbox[keep]
+
+        # convert to [x0, y0, x1, y1] format
+        boxes = box_ops.box_cxcywh_to_xyxy(out_bbox)
+
+        img_h = state["original_height"]
+        img_w = state["original_width"]
+        scale_fct = torch.tensor([img_w, img_h, img_w, img_h]).to(self.device)
+        boxes = boxes * scale_fct[None, :]
+
+        out_masks = interpolate(
+            out_masks.unsqueeze(1),
+            (img_h, img_w),
+            mode="bilinear",
+            align_corners=False,
+        ).sigmoid()
+
+        state["masks_logits"] = out_masks
+        state["masks"] = out_masks > 0.5
+        state["boxes"] = boxes
+        state["scores"] = out_probs
+        return state

third_party/GraspGen/sam3/sam3/model/sam3_tracker_base.py

@@ -0,0 +1,1185 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import logging
+
+import torch
+import torch.nn.functional as F
+from sam3.model.memory import SimpleMaskEncoder
+from sam3.model.sam3_tracker_utils import get_1d_sine_pe, select_closest_cond_frames
+from sam3.sam.mask_decoder import MaskDecoder, MLP
+from sam3.sam.prompt_encoder import PromptEncoder
+from sam3.sam.transformer import TwoWayTransformer
+from sam3.train.data.collator import BatchedDatapoint
+
+try:
+    from timm.layers import trunc_normal_
+except ModuleNotFoundError:
+    # compatibility for older timm versions
+    from timm.models.layers import trunc_normal_
+
+# a large negative value as a placeholder score for missing objects
+NO_OBJ_SCORE = -1024.0
+
+
+class Sam3TrackerBase(torch.nn.Module):
+    def __init__(
+        self,
+        backbone,
+        transformer,
+        maskmem_backbone,
+        num_maskmem=7,  # default 1 input frame + 6 previous frames as in CAE
+        image_size=1008,
+        backbone_stride=14,  # stride of the image backbone output
+        # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit,
+        # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model
+        # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM.
+        max_cond_frames_in_attn=-1,
+        # Whether to always keep the first conditioning frame in case we exceed the maximum number of conditioning frames allowed
+        keep_first_cond_frame=False,
+        # whether to output multiple (3) masks for the first click on initial conditioning frames
+        multimask_output_in_sam=False,
+        # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`;
+        # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points)
+        multimask_min_pt_num=1,
+        multimask_max_pt_num=1,
+        # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`)
+        multimask_output_for_tracking=False,
+        # whether to forward image features per frame (as it's being tracked) during evaluation, instead of forwarding image features
+        # of all frames at once. This avoids backbone OOM errors on very long videos in evaluation, but could be slightly slower.
+        forward_backbone_per_frame_for_eval=False,
+        # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5).
+        # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of
+        # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame.
+        memory_temporal_stride_for_eval=1,
+        # whether to offload outputs to CPU memory during evaluation, to avoid GPU OOM on very long videos or very large resolutions or too many objects
+        # (it's recommended to use `forward_backbone_per_frame_for_eval=True` first before setting this option to True)
+        offload_output_to_cpu_for_eval=False,
+        # whether to trim the output of past non-conditioning frames (num_maskmem frames before the current frame) during evaluation
+        # (this helps save GPU or CPU memory on very long videos for semi-supervised VOS eval, where only the first frame receives prompts)
+        trim_past_non_cond_mem_for_eval=False,
+        # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks)
+        non_overlap_masks_for_mem_enc=False,
+        # the maximum number of object pointers from other frames in encoder cross attention
+        max_obj_ptrs_in_encoder=16,
+        # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class.
+        sam_mask_decoder_extra_args=None,
+        # whether to compile all the model compoents
+        compile_all_components=False,
+        # select the frame with object existence
+        use_memory_selection=False,
+        # when using memory selection, the threshold to determine if the frame is good
+        mf_threshold=0.01,
+    ):
+        super().__init__()
+
+        # Part 1: the image backbone
+        self.backbone = backbone
+        self.num_feature_levels = 3
+        self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder
+        # A conv layer to downsample the GT mask prompt to stride 4 (the same stride as
+        # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale,
+        # so that it can be fed into the SAM mask decoder to generate a pointer.
+        self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4)
+
+        # Part 2: encoder-only transformer to fuse current frame's visual features
+        # with memories from past frames
+        assert transformer.decoder is None, "transformer should be encoder-only"
+        self.transformer = transformer
+        self.hidden_dim = transformer.d_model
+
+        # Part 3: memory encoder for the previous frame's outputs
+        self.maskmem_backbone = maskmem_backbone
+        self.mem_dim = self.hidden_dim
+        if hasattr(self.maskmem_backbone, "out_proj") and hasattr(
+            self.maskmem_backbone.out_proj, "weight"
+        ):
+            # if there is compression of memories along channel dim
+            self.mem_dim = self.maskmem_backbone.out_proj.weight.shape[0]
+        self.num_maskmem = num_maskmem  # Number of memories accessible
+
+        # Temporal encoding of the memories
+        self.maskmem_tpos_enc = torch.nn.Parameter(
+            torch.zeros(num_maskmem, 1, 1, self.mem_dim)
+        )
+        trunc_normal_(self.maskmem_tpos_enc, std=0.02)
+
+        # a single token to indicate no memory embedding from previous frames
+        self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
+        self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
+        trunc_normal_(self.no_mem_embed, std=0.02)
+        trunc_normal_(self.no_mem_pos_enc, std=0.02)
+        # Apply sigmoid to the output raw mask logits (to turn them from
+        # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder
+        self.sigmoid_scale_for_mem_enc = 20.0
+        self.sigmoid_bias_for_mem_enc = -10.0
+        self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc
+        self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval
+        # On frames with mask input, whether to directly output the input mask without
+        # using a SAM prompt encoder + mask decoder
+        self.multimask_output_in_sam = multimask_output_in_sam
+        self.multimask_min_pt_num = multimask_min_pt_num
+        self.multimask_max_pt_num = multimask_max_pt_num
+        self.multimask_output_for_tracking = multimask_output_for_tracking
+
+        # Part 4: SAM-style prompt encoder (for both mask and point inputs)
+        # and SAM-style mask decoder for the final mask output
+        self.image_size = image_size
+        self.backbone_stride = backbone_stride
+        self.low_res_mask_size = self.image_size // self.backbone_stride * 4
+        # we resize the mask if it doesn't match `self.input_mask_size` (which is always 4x
+        # the low-res mask size, regardless of the actual input image size); this is because
+        # `_use_mask_as_output` always downsamples the input masks by 4x
+        self.input_mask_size = self.low_res_mask_size * 4
+        self.forward_backbone_per_frame_for_eval = forward_backbone_per_frame_for_eval
+        self.offload_output_to_cpu_for_eval = offload_output_to_cpu_for_eval
+        self.trim_past_non_cond_mem_for_eval = trim_past_non_cond_mem_for_eval
+        self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args
+        self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim))
+        trunc_normal_(self.no_obj_ptr, std=0.02)
+        self.no_obj_embed_spatial = torch.nn.Parameter(torch.zeros(1, self.mem_dim))
+        trunc_normal_(self.no_obj_embed_spatial, std=0.02)
+
+        self._build_sam_heads()
+        self.max_cond_frames_in_attn = max_cond_frames_in_attn
+        self.keep_first_cond_frame = keep_first_cond_frame
+
+        # Use frame filtering according to SAM2Long
+        self.use_memory_selection = use_memory_selection
+        self.mf_threshold = mf_threshold
+
+        # Compile all components of the model
+        self.compile_all_components = compile_all_components
+        if self.compile_all_components:
+            self._compile_all_components()
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def _get_tpos_enc(self, rel_pos_list, device, max_abs_pos=None, dummy=False):
+        if dummy:
+            return torch.zeros(len(rel_pos_list), self.mem_dim, device=device)
+
+        t_diff_max = max_abs_pos - 1 if max_abs_pos is not None else 1
+        pos_enc = (
+            torch.tensor(rel_pos_list).pin_memory().to(device=device, non_blocking=True)
+            / t_diff_max
+        )
+        tpos_dim = self.hidden_dim
+        pos_enc = get_1d_sine_pe(pos_enc, dim=tpos_dim)
+        pos_enc = self.obj_ptr_tpos_proj(pos_enc)
+
+        return pos_enc
+
+    def _build_sam_heads(self):
+        """Build SAM-style prompt encoder and mask decoder."""
+        self.sam_prompt_embed_dim = self.hidden_dim
+        self.sam_image_embedding_size = self.image_size // self.backbone_stride
+
+        # build PromptEncoder and MaskDecoder from SAM
+        # (their hyperparameters like `mask_in_chans=16` are from SAM code)
+        self.sam_prompt_encoder = PromptEncoder(
+            embed_dim=self.sam_prompt_embed_dim,
+            image_embedding_size=(
+                self.sam_image_embedding_size,
+                self.sam_image_embedding_size,
+            ),
+            input_image_size=(self.image_size, self.image_size),
+            mask_in_chans=16,
+        )
+        self.sam_mask_decoder = MaskDecoder(
+            num_multimask_outputs=3,
+            transformer=TwoWayTransformer(
+                depth=2,
+                embedding_dim=self.sam_prompt_embed_dim,
+                mlp_dim=2048,
+                num_heads=8,
+            ),
+            transformer_dim=self.sam_prompt_embed_dim,
+            iou_head_depth=3,
+            iou_head_hidden_dim=256,
+            use_high_res_features=True,
+            iou_prediction_use_sigmoid=True,
+            pred_obj_scores=True,
+            pred_obj_scores_mlp=True,
+            use_multimask_token_for_obj_ptr=True,
+            **(self.sam_mask_decoder_extra_args or {}),
+        )
+        # a linear projection on SAM output tokens to turn them into object pointers
+        self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim)
+        self.obj_ptr_proj = MLP(self.hidden_dim, self.hidden_dim, self.hidden_dim, 3)
+        # a linear projection on temporal positional encoding in object pointers to
+        # avoid potential interference with spatial positional encoding
+        self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim)
+
+    def _forward_sam_heads(
+        self,
+        backbone_features,
+        point_inputs=None,
+        mask_inputs=None,
+        high_res_features=None,
+        multimask_output=False,
+        gt_masks=None,
+    ):
+        """
+        Forward SAM prompt encoders and mask heads.
+
+        Inputs:
+        - backbone_features: image features of [B, C, H, W] shape
+        - point_inputs: a dictionary with "point_coords" and "point_labels", where
+          1) "point_coords" has [B, P, 2] shape and float32 dtype and contains the
+             absolute pixel-unit coordinate in (x, y) format of the P input points
+          2) "point_labels" has shape [B, P] and int32 dtype, where 1 means
+             positive clicks, 0 means negative clicks, and -1 means padding
+        - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the
+          same spatial size as the image.
+        - high_res_features: either 1) None or 2) or a list of length 2 containing
+          two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively,
+          which will be used as high-resolution feature maps for SAM decoder.
+        - multimask_output: if it's True, we output 3 candidate masks and their 3
+          corresponding IoU estimates, and if it's False, we output only 1 mask and
+          its corresponding IoU estimate.
+
+        Outputs:
+        - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if
+          `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM
+          output mask logits (before sigmoid) for the low-resolution masks, with 4x
+          the resolution (1/4 stride) of the input backbone_features.
+        - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3
+          if `multimask_output=True` and M = 1 if `multimask_output=False`),
+          upsampled from the low-resolution masks, with shape size as the image
+          (stride is 1 pixel).
+        - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1
+          if `multimask_output=False`), the estimated IoU of each output mask.
+        - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`.
+          If `multimask_output=True`, it's the mask with the highest IoU estimate.
+          If `multimask_output=False`, it's the same as `low_res_multimasks`.
+        - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`.
+          If `multimask_output=True`, it's the mask with the highest IoU estimate.
+          If `multimask_output=False`, it's the same as `high_res_multimasks`.
+        - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted
+          based on the output token from the SAM mask decoder.
+        """
+        B = backbone_features.size(0)
+        device = backbone_features.device
+        assert backbone_features.size(1) == self.sam_prompt_embed_dim
+        assert backbone_features.size(2) == self.sam_image_embedding_size
+        assert backbone_features.size(3) == self.sam_image_embedding_size
+
+        # a) Handle point prompts
+        if point_inputs is not None:
+            sam_point_coords = point_inputs["point_coords"]
+            sam_point_labels = point_inputs["point_labels"]
+            assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B
+        else:
+            # If no points are provide, pad with an empty point (with label -1)
+            sam_point_coords = torch.zeros(B, 1, 2, device=device)
+            sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device)
+
+        # b) Handle mask prompts
+        if mask_inputs is not None:
+            # If mask_inputs is provided, downsize it into low-res mask input if needed
+            # and feed it as a dense mask prompt into the SAM mask encoder
+            assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1)
+            if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size:
+                sam_mask_prompt = F.interpolate(
+                    mask_inputs.float(),
+                    size=self.sam_prompt_encoder.mask_input_size,
+                    align_corners=False,
+                    mode="bilinear",
+                    antialias=True,  # use antialias for downsampling
+                )
+            else:
+                sam_mask_prompt = mask_inputs
+        else:
+            # Otherwise, simply feed None (and SAM's prompt encoder will add
+            # a learned `no_mask_embed` to indicate no mask input in this case).
+            sam_mask_prompt = None
+
+        sparse_embeddings, dense_embeddings = self.sam_prompt_encoder(
+            points=(sam_point_coords, sam_point_labels),
+            boxes=None,
+            masks=sam_mask_prompt,
+        )
+        # Clone image_pe and the outputs of sam_prompt_encoder
+        # to enable compilation
+        sparse_embeddings = self._maybe_clone(sparse_embeddings)
+        dense_embeddings = self._maybe_clone(dense_embeddings)
+        image_pe = self._maybe_clone(self.sam_prompt_encoder.get_dense_pe())
+        with torch.profiler.record_function("sam_mask_decoder"):
+            (
+                low_res_multimasks,
+                ious,
+                sam_output_tokens,
+                object_score_logits,
+            ) = self.sam_mask_decoder(
+                image_embeddings=backbone_features,
+                image_pe=image_pe,
+                sparse_prompt_embeddings=sparse_embeddings,
+                dense_prompt_embeddings=dense_embeddings,
+                multimask_output=multimask_output,
+                repeat_image=False,  # the image is already batched
+                high_res_features=high_res_features,
+            )
+        # Clone the output of sam_mask_decoder
+        # to enable compilation
+        low_res_multimasks = self._maybe_clone(low_res_multimasks)
+        ious = self._maybe_clone(ious)
+        sam_output_tokens = self._maybe_clone(sam_output_tokens)
+        object_score_logits = self._maybe_clone(object_score_logits)
+
+        if self.training and self.teacher_force_obj_scores_for_mem:
+            # we use gt to detect if there is an object or not to
+            # select no obj ptr and use an empty mask for spatial memory
+            is_obj_appearing = torch.any(gt_masks.float().flatten(1) > 0, dim=1)
+            is_obj_appearing = is_obj_appearing[..., None]
+        else:
+            is_obj_appearing = object_score_logits > 0
+
+        # Mask used for spatial memories is always a *hard* choice between obj and no obj,
+        # consistent with the actual mask prediction
+        low_res_multimasks = torch.where(
+            is_obj_appearing[:, None, None],
+            low_res_multimasks,
+            NO_OBJ_SCORE,
+        )
+
+        # convert masks from possibly bfloat16 (or float16) to float32
+        # (older PyTorch versions before 2.1 don't support `interpolate` on bf16)
+        low_res_multimasks = low_res_multimasks.float()
+        high_res_multimasks = F.interpolate(
+            low_res_multimasks,
+            size=(self.image_size, self.image_size),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+        sam_output_token = sam_output_tokens[:, 0]
+        if multimask_output:
+            # take the best mask prediction (with the highest IoU estimation)
+            best_iou_inds = torch.argmax(ious, dim=-1)
+            batch_inds = torch.arange(B, device=device)
+            low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)
+            high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)
+            if sam_output_tokens.size(1) > 1:
+                sam_output_token = sam_output_tokens[batch_inds, best_iou_inds]
+        else:
+            low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks
+
+        # Extract object pointer from the SAM output token (with occlusion handling)
+        obj_ptr = self.obj_ptr_proj(sam_output_token)
+        lambda_is_obj_appearing = is_obj_appearing.float()
+
+        obj_ptr = lambda_is_obj_appearing * obj_ptr
+        obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
+
+        return (
+            low_res_multimasks,
+            high_res_multimasks,
+            ious,
+            low_res_masks,
+            high_res_masks,
+            obj_ptr,
+            object_score_logits,
+        )
+
+    def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs):
+        """
+        Directly turn binary `mask_inputs` into a output mask logits without using SAM.
+        (same input and output shapes as in _forward_sam_heads above).
+        """
+        # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid).
+        out_scale, out_bias = 20.0, -10.0  # sigmoid(-10.0)=4.5398e-05
+        mask_inputs_float = mask_inputs.float()
+        high_res_masks = mask_inputs_float * out_scale + out_bias
+        low_res_masks = F.interpolate(
+            high_res_masks,
+            size=(
+                high_res_masks.size(-2) // self.backbone_stride * 4,
+                high_res_masks.size(-1) // self.backbone_stride * 4,
+            ),
+            align_corners=False,
+            mode="bilinear",
+            antialias=True,  # use antialias for downsampling
+        )
+        # a dummy IoU prediction of all 1's under mask input
+        ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float()
+        # produce an object pointer using the SAM decoder from the mask input
+        _, _, _, _, _, obj_ptr, _ = self._forward_sam_heads(
+            backbone_features=backbone_features,
+            mask_inputs=self.mask_downsample(mask_inputs_float),
+            high_res_features=high_res_features,
+            gt_masks=mask_inputs,
+        )
+        # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem;
+        # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying
+        # on the object_scores from the SAM decoder.
+        is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1)
+        is_obj_appearing = is_obj_appearing[..., None]
+        lambda_is_obj_appearing = is_obj_appearing.float()
+        object_score_logits = out_scale * lambda_is_obj_appearing + out_bias
+        obj_ptr = lambda_is_obj_appearing * obj_ptr
+        obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
+
+        return (
+            low_res_masks,
+            high_res_masks,
+            ious,
+            low_res_masks,
+            high_res_masks,
+            obj_ptr,
+            object_score_logits,
+        )
+
+    def forward(self, input: BatchedDatapoint, is_inference=False):
+        raise NotImplementedError(
+            "Please use the corresponding methods in SAM3VideoPredictor for inference."
+            "See examples/sam3_dense_video_tracking.ipynb for an inference example."
+        )
+
+    def forward_image(self, img_batch):
+        """Get the image feature on the input batch."""
+        # This line is the only change from the parent class
+        # to use the SAM3 backbone instead of the SAM2 backbone.
+        backbone_out = self.backbone.forward_image(img_batch)["sam2_backbone_out"]
+        # precompute projected level 0 and level 1 features in SAM decoder
+        # to avoid running it again on every SAM click
+        backbone_out["backbone_fpn"][0] = self.sam_mask_decoder.conv_s0(
+            backbone_out["backbone_fpn"][0]
+        )
+        backbone_out["backbone_fpn"][1] = self.sam_mask_decoder.conv_s1(
+            backbone_out["backbone_fpn"][1]
+        )
+        # Clone to help torch.compile
+        for i in range(len(backbone_out["backbone_fpn"])):
+            backbone_out["backbone_fpn"][i] = self._maybe_clone(
+                backbone_out["backbone_fpn"][i]
+            )
+            backbone_out["vision_pos_enc"][i] = self._maybe_clone(
+                backbone_out["vision_pos_enc"][i]
+            )
+        return backbone_out
+
+    def _prepare_backbone_features(self, backbone_out):
+        """Prepare and flatten visual features (same as in MDETR_API model)."""
+        backbone_out = backbone_out.copy()
+        assert len(backbone_out["backbone_fpn"]) == len(backbone_out["vision_pos_enc"])
+        assert len(backbone_out["backbone_fpn"]) >= self.num_feature_levels
+
+        feature_maps = backbone_out["backbone_fpn"][-self.num_feature_levels :]
+        vision_pos_embeds = backbone_out["vision_pos_enc"][-self.num_feature_levels :]
+
+        feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds]
+        # flatten NxCxHxW to HWxNxC
+        vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps]
+        vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds]
+
+        return backbone_out, vision_feats, vision_pos_embeds, feat_sizes
+
+    def _prepare_backbone_features_per_frame(self, img_batch, img_ids):
+        """Compute the image backbone features on the fly for the given img_ids."""
+        # Only forward backbone on unique image ids to avoid repeatitive computation
+        # (if `img_ids` has only one element, it's already unique so we skip this step).
+        if img_ids.numel() > 1:
+            unique_img_ids, inv_ids = torch.unique(img_ids, return_inverse=True)
+        else:
+            unique_img_ids, inv_ids = img_ids, None
+
+        # Compute the image features on those unique image ids
+        image = img_batch[unique_img_ids]
+        backbone_out = self.forward_image(image)
+        (
+            _,
+            vision_feats,
+            vision_pos_embeds,
+            feat_sizes,
+        ) = self._prepare_backbone_features(backbone_out)
+        # Inverse-map image features for `unique_img_ids` to the final image features
+        # for the original input `img_ids`.
+        if inv_ids is not None:
+            image = image[inv_ids]
+            vision_feats = [x[:, inv_ids] for x in vision_feats]
+            vision_pos_embeds = [x[:, inv_ids] for x in vision_pos_embeds]
+
+        return image, vision_feats, vision_pos_embeds, feat_sizes
+
+    def cal_mem_score(self, object_score_logits, iou_score):
+        object_score_norm = torch.where(
+            object_score_logits > 0,
+            object_score_logits.sigmoid() * 2 - 1,  ## rescale to [0, 1]
+            torch.zeros_like(object_score_logits),
+        )
+        score_per_frame = (object_score_norm * iou_score).mean()
+        return score_per_frame
+
+    def frame_filter(self, output_dict, track_in_reverse, frame_idx, num_frames, r):
+        if (frame_idx == 0 and not track_in_reverse) or (
+            frame_idx == num_frames - 1 and track_in_reverse
+        ):
+            return []
+
+        max_num = min(
+            num_frames, self.max_obj_ptrs_in_encoder
+        )  ## maximum number of pointer memory frames to consider
+
+        if not track_in_reverse:
+            start = frame_idx - 1
+            end = 0
+            step = -r
+            must_include = frame_idx - 1
+        else:
+            start = frame_idx + 1
+            end = num_frames
+            step = r
+            must_include = frame_idx + 1
+
+        valid_indices = []
+        for i in range(start, end, step):
+            if (
+                i not in output_dict["non_cond_frame_outputs"]
+                or "eff_iou_score" not in output_dict["non_cond_frame_outputs"][i]
+            ):
+                continue
+
+            score_per_frame = output_dict["non_cond_frame_outputs"][i]["eff_iou_score"]
+
+            if score_per_frame > self.mf_threshold:  # threshold
+                valid_indices.insert(0, i)
+
+            if len(valid_indices) >= max_num - 1:
+                break
+
+        if must_include not in valid_indices:
+            valid_indices.append(must_include)
+
+        return valid_indices
+
+    def _prepare_memory_conditioned_features(
+        self,
+        frame_idx,
+        is_init_cond_frame,
+        current_vision_feats,
+        current_vision_pos_embeds,
+        feat_sizes,
+        output_dict,
+        num_frames,
+        track_in_reverse=False,  # tracking in reverse time order (for demo usage)
+        use_prev_mem_frame=True,
+    ):
+        """Fuse the current frame's visual feature map with previous memory."""
+        B = current_vision_feats[-1].size(1)  # batch size on this frame
+        C = self.hidden_dim
+        H, W = feat_sizes[-1]  # top-level (lowest-resolution) feature size
+        device = current_vision_feats[-1].device
+        # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images.
+        # In this case, we skip the fusion with any memory.
+        if self.num_maskmem == 0:  # Disable memory and skip fusion
+            pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
+            return pix_feat
+
+        num_obj_ptr_tokens = 0
+        tpos_sign_mul = -1 if track_in_reverse else 1
+        # Step 1: condition the visual features of the current frame on previous memories
+        if not is_init_cond_frame and use_prev_mem_frame:
+            # Retrieve the memories encoded with the maskmem backbone
+            to_cat_prompt, to_cat_prompt_mask, to_cat_prompt_pos_embed = [], [], []
+            # Add conditioning frames's output first (all cond frames have t_pos=0 for
+            # when getting temporal positional embedding below)
+            assert len(output_dict["cond_frame_outputs"]) > 0
+            # Select a maximum number of temporally closest cond frames for cross attention
+            cond_outputs = output_dict["cond_frame_outputs"]
+            selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames(
+                frame_idx,
+                cond_outputs,
+                self.max_cond_frames_in_attn,
+                keep_first_cond_frame=self.keep_first_cond_frame,
+            )
+            t_pos_and_prevs = [
+                ((frame_idx - t) * tpos_sign_mul, out, True)
+                for t, out in selected_cond_outputs.items()
+            ]
+            # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory
+            # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1
+            # We also allow taking the memory frame non-consecutively (with r>1), in which case
+            # we take (self.num_maskmem - 2) frames among every r-th frames plus the last frame.
+            r = 1 if self.training else self.memory_temporal_stride_for_eval
+
+            if self.use_memory_selection:
+                valid_indices = self.frame_filter(
+                    output_dict, track_in_reverse, frame_idx, num_frames, r
+                )
+
+            for t_pos in range(1, self.num_maskmem):
+                t_rel = self.num_maskmem - t_pos  # how many frames before current frame
+                if self.use_memory_selection:
+                    if t_rel > len(valid_indices):
+                        continue
+                    prev_frame_idx = valid_indices[-t_rel]
+                else:
+                    if t_rel == 1:
+                        # for t_rel == 1, we take the last frame (regardless of r)
+                        if not track_in_reverse:
+                            # the frame immediately before this frame (i.e. frame_idx - 1)
+                            prev_frame_idx = frame_idx - t_rel
+                        else:
+                            # the frame immediately after this frame (i.e. frame_idx + 1)
+                            prev_frame_idx = frame_idx + t_rel
+                    else:
+                        # for t_rel >= 2, we take the memory frame from every r-th frames
+                        if not track_in_reverse:
+                            # first find the nearest frame among every r-th frames before this frame
+                            # for r=1, this would be (frame_idx - 2)
+                            prev_frame_idx = ((frame_idx - 2) // r) * r
+                            # then seek further among every r-th frames
+                            prev_frame_idx = prev_frame_idx - (t_rel - 2) * r
+                        else:
+                            # first find the nearest frame among every r-th frames after this frame
+                            # for r=1, this would be (frame_idx + 2)
+                            prev_frame_idx = -(-(frame_idx + 2) // r) * r
+                            # then seek further among every r-th frames
+                            prev_frame_idx = prev_frame_idx + (t_rel - 2) * r
+
+                out = output_dict["non_cond_frame_outputs"].get(prev_frame_idx, None)
+                if out is None:
+                    # If an unselected conditioning frame is among the last (self.num_maskmem - 1)
+                    # frames, we still attend to it as if it's a non-conditioning frame.
+                    out = unselected_cond_outputs.get(prev_frame_idx, None)
+                t_pos_and_prevs.append((t_pos, out, False))
+
+            for t_pos, prev, is_selected_cond_frame in t_pos_and_prevs:
+                if prev is None:
+                    continue  # skip padding frames
+                # "maskmem_features" might have been offloaded to CPU in demo use cases,
+                # so we load it back to GPU (it's a no-op if it's already on GPU).
+                feats = prev["maskmem_features"].cuda(non_blocking=True)
+                seq_len = feats.shape[-2] * feats.shape[-1]
+                to_cat_prompt.append(feats.flatten(2).permute(2, 0, 1))
+                to_cat_prompt_mask.append(
+                    torch.zeros(B, seq_len, device=device, dtype=bool)
+                )
+                # Spatial positional encoding (it might have been offloaded to CPU in eval)
+                maskmem_enc = prev["maskmem_pos_enc"][-1].cuda()
+                maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1)
+
+                if (
+                    is_selected_cond_frame
+                    and getattr(self, "cond_frame_spatial_embedding", None) is not None
+                ):
+                    # add a spatial embedding for the conditioning frame
+                    maskmem_enc = maskmem_enc + self.cond_frame_spatial_embedding
+
+                # Temporal positional encoding
+                t = t_pos if not is_selected_cond_frame else 0
+                maskmem_enc = (
+                    maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t - 1]
+                )
+                to_cat_prompt_pos_embed.append(maskmem_enc)
+
+            # Construct the list of past object pointers
+            # Optionally, select only a subset of spatial memory frames during trainining
+            if (
+                self.training
+                and self.prob_to_dropout_spatial_mem > 0
+                and self.rng.random() < self.prob_to_dropout_spatial_mem
+            ):
+                num_spatial_mem_keep = self.rng.integers(len(to_cat_prompt) + 1)
+                keep = self.rng.choice(
+                    range(len(to_cat_prompt)), num_spatial_mem_keep, replace=False
+                ).tolist()
+                to_cat_prompt = [to_cat_prompt[i] for i in keep]
+                to_cat_prompt_mask = [to_cat_prompt_mask[i] for i in keep]
+                to_cat_prompt_pos_embed = [to_cat_prompt_pos_embed[i] for i in keep]
+
+            max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder)
+            # First add those object pointers from selected conditioning frames
+            # (optionally, only include object pointers in the past during evaluation)
+            if not self.training:
+                ptr_cond_outputs = {
+                    t: out
+                    for t, out in selected_cond_outputs.items()
+                    if (t >= frame_idx if track_in_reverse else t <= frame_idx)
+                }
+            else:
+                ptr_cond_outputs = selected_cond_outputs
+            pos_and_ptrs = [
+                # Temporal pos encoding contains how far away each pointer is from current frame
+                (
+                    (frame_idx - t) * tpos_sign_mul,
+                    out["obj_ptr"],
+                    True,  # is_selected_cond_frame
+                )
+                for t, out in ptr_cond_outputs.items()
+            ]
+
+            # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame
+            for t_diff in range(1, max_obj_ptrs_in_encoder):
+                if not self.use_memory_selection:
+                    t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff
+                    if t < 0 or (num_frames is not None and t >= num_frames):
+                        break
+                else:
+                    if -t_diff <= -len(valid_indices):
+                        break
+                    t = valid_indices[-t_diff]
+
+                out = output_dict["non_cond_frame_outputs"].get(
+                    t, unselected_cond_outputs.get(t, None)
+                )
+                if out is not None:
+                    pos_and_ptrs.append((t_diff, out["obj_ptr"], False))
+
+            # If we have at least one object pointer, add them to the across attention
+            if len(pos_and_ptrs) > 0:
+                pos_list, ptrs_list, is_selected_cond_frame_list = zip(*pos_and_ptrs)
+                # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape
+                obj_ptrs = torch.stack(ptrs_list, dim=0)
+                if getattr(self, "cond_frame_obj_ptr_embedding", None) is not None:
+                    obj_ptrs = (
+                        obj_ptrs
+                        + self.cond_frame_obj_ptr_embedding
+                        * torch.tensor(is_selected_cond_frame_list, device=device)[
+                            ..., None, None
+                        ].float()
+                    )
+                # a temporal positional embedding based on how far each object pointer is from
+                # the current frame (sine embedding normalized by the max pointer num).
+                obj_pos = self._get_tpos_enc(
+                    pos_list,
+                    max_abs_pos=max_obj_ptrs_in_encoder,
+                    device=device,
+                )
+                # expand to batch size
+                obj_pos = obj_pos.unsqueeze(1).expand(-1, B, -1)
+
+                if self.mem_dim < C:
+                    # split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C
+                    obj_ptrs = obj_ptrs.reshape(-1, B, C // self.mem_dim, self.mem_dim)
+                    obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1)
+                    obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0)
+                to_cat_prompt.append(obj_ptrs)
+                to_cat_prompt_mask.append(None)  # "to_cat_prompt_mask" is not used
+                to_cat_prompt_pos_embed.append(obj_pos)
+                num_obj_ptr_tokens = obj_ptrs.shape[0]
+            else:
+                num_obj_ptr_tokens = 0
+        else:
+            # directly add no-mem embedding (instead of using the transformer encoder)
+            pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed
+            pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)
+            return pix_feat_with_mem
+
+            # Use a dummy token on the first grame (to avoid emtpy memory input to tranformer encoder)
+            to_cat_prompt = [self.no_mem_embed.expand(1, B, self.mem_dim)]
+            to_cat_prompt_mask = [torch.zeros(B, 1, device=device, dtype=bool)]
+            to_cat_prompt_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)]
+
+        # Step 2: Concatenate the memories and forward through the transformer encoder
+        prompt = torch.cat(to_cat_prompt, dim=0)
+        prompt_mask = None  # For now, we always masks are zeros anyways
+        prompt_pos_embed = torch.cat(to_cat_prompt_pos_embed, dim=0)
+        encoder_out = self.transformer.encoder(
+            src=current_vision_feats,
+            src_key_padding_mask=[None],
+            src_pos=current_vision_pos_embeds,
+            prompt=prompt,
+            prompt_pos=prompt_pos_embed,
+            prompt_key_padding_mask=prompt_mask,
+            feat_sizes=feat_sizes,
+            num_obj_ptr_tokens=num_obj_ptr_tokens,
+        )
+        # reshape the output (HW)BC => BCHW
+        pix_feat_with_mem = encoder_out["memory"].permute(1, 2, 0).view(B, C, H, W)
+        return pix_feat_with_mem
+
+    def _encode_new_memory(
+        self,
+        image,
+        current_vision_feats,
+        feat_sizes,
+        pred_masks_high_res,
+        object_score_logits,
+        is_mask_from_pts,
+        output_dict=None,
+        is_init_cond_frame=False,
+    ):
+        """Encode the current image and its prediction into a memory feature."""
+        B = current_vision_feats[-1].size(1)  # batch size on this frame
+        C = self.hidden_dim
+        H, W = feat_sizes[-1]  # top-level (lowest-resolution) feature size
+        # top-level feature, (HW)BC => BCHW
+        pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
+        if self.non_overlap_masks_for_mem_enc and not self.training:
+            # optionally, apply non-overlapping constraints to the masks (it's applied
+            # in the batch dimension and should only be used during eval, where all
+            # the objects come from the same video under batch size 1).
+            pred_masks_high_res = self._apply_non_overlapping_constraints(
+                pred_masks_high_res
+            )
+        # scale the raw mask logits with a temperature before applying sigmoid
+        if is_mask_from_pts and not self.training:
+            mask_for_mem = (pred_masks_high_res > 0).float()
+        else:
+            # apply sigmoid on the raw mask logits to turn them into range (0, 1)
+            mask_for_mem = torch.sigmoid(pred_masks_high_res)
+        # apply scale and bias terms to the sigmoid probabilities
+        if self.sigmoid_scale_for_mem_enc != 1.0:
+            mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc
+        if self.sigmoid_bias_for_mem_enc != 0.0:
+            mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc
+
+        if isinstance(self.maskmem_backbone, SimpleMaskEncoder):
+            pix_feat = pix_feat.view_as(pix_feat)
+            maskmem_out = self.maskmem_backbone(
+                pix_feat, mask_for_mem, skip_mask_sigmoid=True
+            )
+        else:
+            maskmem_out = self.maskmem_backbone(image, pix_feat, mask_for_mem)
+        # Clone the feats and pos_enc to enable compilation
+        maskmem_features = self._maybe_clone(maskmem_out["vision_features"])
+        maskmem_pos_enc = [self._maybe_clone(m) for m in maskmem_out["vision_pos_enc"]]
+        # add a no-object embedding to the spatial memory to indicate that the frame
+        # is predicted to be occluded (i.e. no object is appearing in the frame)
+        is_obj_appearing = (object_score_logits > 0).float()
+        maskmem_features += (
+            1 - is_obj_appearing[..., None, None]
+        ) * self.no_obj_embed_spatial[..., None, None].expand(*maskmem_features.shape)
+
+        return maskmem_features, maskmem_pos_enc
+
+    def forward_tracking(self, backbone_out, input, return_dict=False):
+        """Forward video tracking on each frame (and sample correction clicks)."""
+        img_feats_already_computed = backbone_out["backbone_fpn"] is not None
+        if img_feats_already_computed:
+            # Prepare the backbone features
+            # - vision_feats and vision_pos_embeds are in (HW)BC format
+            (
+                _,
+                vision_feats,
+                vision_pos_embeds,
+                feat_sizes,
+            ) = self._prepare_backbone_features(backbone_out)
+
+        # Starting the stage loop
+        num_frames = backbone_out["num_frames"]
+        init_cond_frames = backbone_out["init_cond_frames"]
+        frames_to_add_correction_pt = backbone_out["frames_to_add_correction_pt"]
+        # first process all the initial conditioning frames to encode them as memory,
+        # and then conditioning on them to track the remaining frames
+        processing_order = init_cond_frames + backbone_out["frames_not_in_init_cond"]
+        output_dict = {
+            "cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+            "non_cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+        }
+        for stage_id in processing_order:
+            # Get the image features for the current frames
+            img_ids = input.find_inputs[stage_id].img_ids
+            if img_feats_already_computed:
+                # Retrieve image features according to img_ids (if they are already computed).
+                current_image = input.img_batch[img_ids]
+                current_vision_feats = [x[:, img_ids] for x in vision_feats]
+                current_vision_pos_embeds = [x[:, img_ids] for x in vision_pos_embeds]
+            else:
+                # Otherwise, compute the image features on the fly for the given img_ids
+                # (this might be used for evaluation on long videos to avoid backbone OOM).
+                (
+                    current_image,
+                    current_vision_feats,
+                    current_vision_pos_embeds,
+                    feat_sizes,
+                ) = self._prepare_backbone_features_per_frame(input.img_batch, img_ids)
+            # Get output masks based on this frame's prompts and previous memory
+            current_out = self.track_step(
+                frame_idx=stage_id,
+                is_init_cond_frame=stage_id in init_cond_frames,
+                current_vision_feats=current_vision_feats,
+                current_vision_pos_embeds=current_vision_pos_embeds,
+                feat_sizes=feat_sizes,
+                image=current_image,
+                point_inputs=backbone_out["point_inputs_per_frame"].get(stage_id, None),
+                mask_inputs=backbone_out["mask_inputs_per_frame"].get(stage_id, None),
+                output_dict=output_dict,
+                num_frames=num_frames,
+            )
+            # Append the output, depending on whether it's a conditioning frame
+            add_output_as_cond_frame = stage_id in init_cond_frames or (
+                self.add_all_frames_to_correct_as_cond
+                and stage_id in frames_to_add_correction_pt
+            )
+            if add_output_as_cond_frame:
+                output_dict["cond_frame_outputs"][stage_id] = current_out
+            else:
+                output_dict["non_cond_frame_outputs"][stage_id] = current_out
+
+        if return_dict:
+            return output_dict
+        # turn `output_dict` into a list for loss function
+        all_frame_outputs = {}
+        all_frame_outputs.update(output_dict["cond_frame_outputs"])
+        all_frame_outputs.update(output_dict["non_cond_frame_outputs"])
+        all_frame_outputs = [all_frame_outputs[t] for t in range(num_frames)]
+        # Make DDP happy with activation checkpointing by removing unused keys
+        all_frame_outputs = [
+            {k: v for k, v in d.items() if k != "obj_ptr"} for d in all_frame_outputs
+        ]
+
+        return all_frame_outputs
+
+    def track_step(
+        self,
+        frame_idx,
+        is_init_cond_frame,
+        current_vision_feats,
+        current_vision_pos_embeds,
+        feat_sizes,
+        image,
+        point_inputs,
+        mask_inputs,
+        output_dict,
+        num_frames,
+        track_in_reverse=False,  # tracking in reverse time order (for demo usage)
+        # Whether to run the memory encoder on the predicted masks. Sometimes we might want
+        # to skip the memory encoder with `run_mem_encoder=False`. For example,
+        # in demo we might call `track_step` multiple times for each user click,
+        # and only encode the memory when the user finalizes their clicks. And in ablation
+        # settings like SAM training on static images, we don't need the memory encoder.
+        run_mem_encoder=True,
+        # The previously predicted SAM mask logits (which can be fed together with new clicks in demo).
+        prev_sam_mask_logits=None,
+        use_prev_mem_frame=True,
+    ):
+        current_out = {"point_inputs": point_inputs, "mask_inputs": mask_inputs}
+        # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW
+        if len(current_vision_feats) > 1:
+            high_res_features = [
+                x.permute(1, 2, 0).view(x.size(1), x.size(2), *s)
+                for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1])
+            ]
+        else:
+            high_res_features = None
+        if mask_inputs is not None:
+            # (see it as a GT mask) without using a SAM prompt encoder + mask decoder.
+            pix_feat = current_vision_feats[-1].permute(1, 2, 0)
+            pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1])
+            sam_outputs = self._use_mask_as_output(
+                pix_feat, high_res_features, mask_inputs
+            )
+        else:
+            # fused the visual feature with previous memory features in the memory bank
+            pix_feat_with_mem = self._prepare_memory_conditioned_features(
+                frame_idx=frame_idx,
+                is_init_cond_frame=is_init_cond_frame,
+                current_vision_feats=current_vision_feats[-1:],
+                current_vision_pos_embeds=current_vision_pos_embeds[-1:],
+                feat_sizes=feat_sizes[-1:],
+                output_dict=output_dict,
+                num_frames=num_frames,
+                track_in_reverse=track_in_reverse,
+                use_prev_mem_frame=use_prev_mem_frame,
+            )
+            # apply SAM-style segmentation head
+            # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder,
+            # e.g. in demo where such logits come from earlier interaction instead of correction sampling
+            # (in this case, the SAM mask decoder should have `self.iter_use_prev_mask_pred=True`, and
+            # any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead)
+            if prev_sam_mask_logits is not None:
+                assert self.iter_use_prev_mask_pred
+                assert point_inputs is not None and mask_inputs is None
+                mask_inputs = prev_sam_mask_logits
+            multimask_output = self._use_multimask(is_init_cond_frame, point_inputs)
+            sam_outputs = self._forward_sam_heads(
+                backbone_features=pix_feat_with_mem,
+                point_inputs=point_inputs,
+                mask_inputs=mask_inputs,
+                high_res_features=high_res_features,
+                multimask_output=multimask_output,
+            )
+        (
+            _,
+            high_res_multimasks,
+            ious,
+            low_res_masks,
+            high_res_masks,
+            obj_ptr,
+            object_score_logits,
+        ) = sam_outputs
+        # Use the final prediction (after all correction steps for output and eval)
+        current_out["pred_masks"] = low_res_masks
+        current_out["pred_masks_high_res"] = high_res_masks
+        current_out["obj_ptr"] = obj_ptr
+        if self.use_memory_selection:
+            current_out["object_score_logits"] = object_score_logits
+            iou_score = ious.max(-1)[0]
+            current_out["iou_score"] = iou_score
+            current_out["eff_iou_score"] = self.cal_mem_score(
+                object_score_logits, iou_score
+            )
+        if not self.training:
+            # Only add this in inference (to avoid unused param in activation checkpointing;
+            # it's mainly used in the demo to encode spatial memories w/ consolidated masks)
+            current_out["object_score_logits"] = object_score_logits
+
+        # Finally run the memory encoder on the predicted mask to encode
+        # it into a new memory feature (that can be used in future frames)
+        # (note that `self.num_maskmem == 0` is primarily used for reproducing SAM on
+        # images, in which case we'll just skip memory encoder to save compute).
+        if run_mem_encoder and self.num_maskmem > 0:
+            high_res_masks_for_mem_enc = high_res_masks
+            maskmem_features, maskmem_pos_enc = self._encode_new_memory(
+                image=image,
+                current_vision_feats=current_vision_feats,
+                feat_sizes=feat_sizes,
+                pred_masks_high_res=high_res_masks_for_mem_enc,
+                object_score_logits=object_score_logits,
+                is_mask_from_pts=(point_inputs is not None),
+                output_dict=output_dict,
+                is_init_cond_frame=is_init_cond_frame,
+            )
+            current_out["maskmem_features"] = maskmem_features
+            current_out["maskmem_pos_enc"] = maskmem_pos_enc
+        else:
+            current_out["maskmem_features"] = None
+            current_out["maskmem_pos_enc"] = None
+
+        # Optionally, offload the outputs to CPU memory during evaluation to avoid
+        # GPU OOM on very long videos or very large resolution or too many objects
+        if self.offload_output_to_cpu_for_eval and not self.training:
+            # Here we only keep those keys needed for evaluation to get a compact output
+            trimmed_out = {
+                "pred_masks": current_out["pred_masks"].cpu(),
+                "pred_masks_high_res": current_out["pred_masks_high_res"].cpu(),
+                # other items for evaluation (these are small tensors so we keep them on GPU)
+                "obj_ptr": current_out["obj_ptr"],
+                "object_score_logits": current_out["object_score_logits"],
+            }
+            if run_mem_encoder and self.num_maskmem > 0:
+                trimmed_out["maskmem_features"] = maskmem_features.cpu()
+                trimmed_out["maskmem_pos_enc"] = [x.cpu() for x in maskmem_pos_enc]
+            if self.use_memory_selection:
+                trimmed_out["iou_score"] = current_out["iou_score"].cpu()
+                trimmed_out["eff_iou_score"] = current_out["eff_iou_score"].cpu()
+            current_out = trimmed_out
+
+        # Optionally, trim the output of past non-conditioning frame (r * num_maskmem frames
+        # before the current frame) during evaluation. This is intended to save GPU or CPU
+        # memory for semi-supervised VOS eval, where only the first frame receives prompts.
+        def _trim_past_out(past_out, current_out):
+            if past_out is None:
+                return None
+            return {
+                "pred_masks": past_out["pred_masks"],
+                "obj_ptr": past_out["obj_ptr"],
+                "object_score_logits": past_out["object_score_logits"],
+            }
+
+        if self.trim_past_non_cond_mem_for_eval and not self.training:
+            r = self.memory_temporal_stride_for_eval
+            past_frame_idx = frame_idx - r * self.num_maskmem
+            past_out = output_dict["non_cond_frame_outputs"].get(past_frame_idx, None)
+
+            if past_out is not None:
+                print(past_out.get("eff_iou_score", 0))
+                if (
+                    self.use_memory_selection
+                    and past_out.get("eff_iou_score", 0) < self.mf_threshold
+                ) or not self.use_memory_selection:
+                    output_dict["non_cond_frame_outputs"][past_frame_idx] = (
+                        _trim_past_out(past_out, current_out)
+                    )
+
+            if (
+                self.use_memory_selection and not self.offload_output_to_cpu_for_eval
+            ):  ## design for memory selection, trim too old frames to save memory
+                far_old_frame_idx = frame_idx - 20 * self.max_obj_ptrs_in_encoder
+                past_out = output_dict["non_cond_frame_outputs"].get(
+                    far_old_frame_idx, None
+                )
+                if past_out is not None:
+                    output_dict["non_cond_frame_outputs"][far_old_frame_idx] = (
+                        _trim_past_out(past_out, current_out)
+                    )
+
+        return current_out
+
+    def _use_multimask(self, is_init_cond_frame, point_inputs):
+        """Whether to use multimask output in the SAM head."""
+        num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1)
+        multimask_output = (
+            self.multimask_output_in_sam
+            and (is_init_cond_frame or self.multimask_output_for_tracking)
+            and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num)
+        )
+        return multimask_output
+
+    def _apply_non_overlapping_constraints(self, pred_masks):
+        """
+        Apply non-overlapping constraints to the object scores in pred_masks. Here we
+        keep only the highest scoring object at each spatial location in pred_masks.
+        """
+        batch_size = pred_masks.size(0)
+        if batch_size == 1:
+            return pred_masks
+
+        device = pred_masks.device
+        # "max_obj_inds": object index of the object with the highest score at each location
+        max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True)
+        # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks`
+        batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None]
+        keep = max_obj_inds == batch_obj_inds
+        # suppress overlapping regions' scores below -10.0 so that the foreground regions
+        # don't overlap (here sigmoid(-10.0)=4.5398e-05)
+        pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0))
+        return pred_masks
+
+    def _compile_all_components(self):
+        """Compile all model components for faster inference."""
+        # a larger cache size to hold varying number of shapes for torch.compile
+        # see https://github.com/pytorch/pytorch/blob/v2.5.1/torch/_dynamo/config.py#L42-L49
+        torch._dynamo.config.cache_size_limit = 64
+        torch._dynamo.config.accumulated_cache_size_limit = 2048
+        from sam3.perflib.compile import compile_wrapper
+
+        logging.info("Compiling all components. First time may be very slow.")
+
+        self.maskmem_backbone.forward = compile_wrapper(
+            self.maskmem_backbone.forward,
+            mode="max-autotune",
+            fullgraph=True,
+            dynamic=False,
+        )
+        self.transformer.encoder.forward = compile_wrapper(
+            self.transformer.encoder.forward,
+            mode="max-autotune",
+            fullgraph=True,
+            dynamic=True,  # Num. of memories varies
+        )
+        # We disable compilation of sam_prompt_encoder as it sometimes gives a large accuracy regression,
+        # especially when sam_mask_prompt (previous mask logits) is not None
+        # self.sam_prompt_encoder.forward = torch.compile(
+        #     self.sam_prompt_encoder.forward,
+        #     mode="max-autotune",
+        #     fullgraph=True,
+        #     dynamic=False,  # Accuracy regression on True
+        # )
+        self.sam_mask_decoder.forward = compile_wrapper(
+            self.sam_mask_decoder.forward,
+            mode="max-autotune",
+            fullgraph=True,
+            dynamic=False,  # Accuracy regression on True
+        )
+
+    def _maybe_clone(self, x):
+        """Clone a tensor if and only if `self.compile_all_components` is True."""
+        return x.clone() if self.compile_all_components else x
+
+
+def concat_points(old_point_inputs, new_points, new_labels):
+    """Add new points and labels to previous point inputs (add at the end)."""
+    if old_point_inputs is None:
+        points, labels = new_points, new_labels
+    else:
+        points = torch.cat([old_point_inputs["point_coords"], new_points], dim=1)
+        labels = torch.cat([old_point_inputs["point_labels"], new_labels], dim=1)
+
+    return {"point_coords": points, "point_labels": labels}

third_party/GraspGen/sam3/sam3/model/sam3_tracker_utils.py

@@ -0,0 +1,428 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from numpy.typing import NDArray
+from sam3.model.edt import edt_triton
+
+
+def sample_box_points(
+    masks: torch.Tensor,
+    noise: float = 0.1,  # SAM default
+    noise_bound: int = 20,  # SAM default
+    top_left_label: int = 2,
+    bottom_right_label: int = 3,
+) -> tuple[NDArray, NDArray]:
+    """
+    Sample a noised version of the top left and bottom right corners of a given `bbox`
+
+    Inputs:
+    - masks: [B, 1, H, W] tensor
+    - noise: noise as a fraction of box width and height, dtype=float
+    - noise_bound: maximum amount of noise (in pure pixels), dtype=int
+
+    Returns:
+    - box_coords: [B, num_pt, 2], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.float
+    - box_labels: [B, num_pt], label 2 is reserverd for top left and 3 for bottom right corners, dtype=torch.int32
+    """
+    device = masks.device
+    box_coords = mask_to_box(masks)
+    B, _, H, W = masks.shape
+    box_labels = torch.tensor(
+        [top_left_label, bottom_right_label], dtype=torch.int, device=device
+    ).repeat(B)
+    if noise > 0.0:
+        if not isinstance(noise_bound, torch.Tensor):
+            noise_bound = torch.tensor(noise_bound, device=device)
+        bbox_w = box_coords[..., 2] - box_coords[..., 0]
+        bbox_h = box_coords[..., 3] - box_coords[..., 1]
+        max_dx = torch.min(bbox_w * noise, noise_bound)
+        max_dy = torch.min(bbox_h * noise, noise_bound)
+        box_noise = 2 * torch.rand(B, 1, 4, device=device) - 1
+        box_noise = box_noise * torch.stack((max_dx, max_dy, max_dx, max_dy), dim=-1)
+
+        box_coords = box_coords + box_noise
+        img_bounds = (
+            torch.tensor([W, H, W, H], device=device) - 1
+        )  # uncentered pixel coords
+        box_coords.clamp_(torch.zeros_like(img_bounds), img_bounds)  # In place clamping
+
+    box_coords = box_coords.reshape(-1, 2, 2)  # always 2 points
+    box_labels = box_labels.reshape(-1, 2)
+    return box_coords, box_labels
+
+
+def mask_to_box(masks: torch.Tensor):
+    """
+    compute bounding box given an input mask
+
+    Inputs:
+    - masks: [B, 1, H, W] tensor
+
+    Returns:
+    - box_coords: [B, 1, 4], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.Tensor
+    """
+    B, _, h, w = masks.shape
+    device = masks.device
+    mask_area = masks.sum(dim=(-1, -2))
+    xs = torch.arange(w, device=device, dtype=torch.int32)
+    ys = torch.arange(h, device=device, dtype=torch.int32)
+    grid_xs, grid_ys = torch.meshgrid(xs, ys, indexing="xy")
+    grid_xs = grid_xs[None, None, ...].expand(B, 1, h, w)
+    grid_ys = grid_ys[None, None, ...].expand(B, 1, h, w)
+    min_xs, _ = torch.min(torch.where(masks, grid_xs, w).flatten(-2), dim=-1)
+    max_xs, _ = torch.max(torch.where(masks, grid_xs, -1).flatten(-2), dim=-1)
+    min_ys, _ = torch.min(torch.where(masks, grid_ys, h).flatten(-2), dim=-1)
+    max_ys, _ = torch.max(torch.where(masks, grid_ys, -1).flatten(-2), dim=-1)
+    bbox_coords = torch.stack((min_xs, min_ys, max_xs, max_ys), dim=-1)
+    bbox_coords = torch.where(
+        mask_area[..., None] > 0, bbox_coords, torch.zeros_like(bbox_coords)
+    )
+    return bbox_coords
+
+
+def sample_random_points_from_errors(gt_masks, pred_masks, num_pt=1):
+    """
+    Sample `num_pt` random points (along with their labels) independently from the error regions.
+
+    Inputs:
+    - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool
+    - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None
+    - num_pt: int, number of points to sample independently for each of the B error maps
+
+    Outputs:
+    - points: [B, num_pt, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point
+    - labels: [B, num_pt], dtype=torch.int32, where 1 means positive clicks and 0 means
+      negative clicks
+    """
+    if pred_masks is None:  # if pred_masks is not provided, treat it as empty
+        pred_masks = torch.zeros_like(gt_masks)
+    assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1
+    assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape
+    assert num_pt >= 0
+
+    B, _, H_im, W_im = gt_masks.shape
+    device = gt_masks.device
+
+    # false positive region, a new point sampled in this region should have
+    # negative label to correct the FP error
+    fp_masks = ~gt_masks & pred_masks
+    # false negative region, a new point sampled in this region should have
+    # positive label to correct the FN error
+    fn_masks = gt_masks & ~pred_masks
+    # whether the prediction completely match the ground-truth on each mask
+    all_correct = torch.all((gt_masks == pred_masks).flatten(2), dim=2)
+    all_correct = all_correct[..., None, None]
+
+    # channel 0 is FP map, while channel 1 is FN map
+    pts_noise = torch.rand(B, num_pt, H_im, W_im, 2, device=device)
+    # sample a negative new click from FP region or a positive new click
+    # from FN region, depend on where the maximum falls,
+    # and in case the predictions are all correct (no FP or FN), we just
+    # sample a negative click from the background region
+    pts_noise[..., 0] *= fp_masks | (all_correct & ~gt_masks)
+    pts_noise[..., 1] *= fn_masks
+    pts_idx = pts_noise.flatten(2).argmax(dim=2)
+    labels = (pts_idx % 2).to(torch.int32)
+    pts_idx = pts_idx // 2
+    pts_x = pts_idx % W_im
+    pts_y = pts_idx // W_im
+    points = torch.stack([pts_x, pts_y], dim=2).to(torch.float)
+    return points, labels
+
+
+def sample_one_point_from_error_center(gt_masks, pred_masks, padding=True):
+    """
+    Sample 1 random point (along with its label) from the center of each error region,
+    that is, the point with the largest distance to the boundary of each error region.
+    This is the RITM sampling method from https://github.com/saic-vul/ritm_interactive_segmentation/blob/master/isegm/inference/clicker.py
+
+    Inputs:
+    - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool
+    - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None
+    - padding: if True, pad with boundary of 1 px for distance transform
+
+    Outputs:
+    - points: [B, 1, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point
+    - labels: [B, 1], dtype=torch.int32, where 1 means positive clicks and 0 means negative clicks
+    """
+    if pred_masks is None:
+        pred_masks = torch.zeros_like(gt_masks)
+    assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1
+    assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape
+
+    B, _, H, W = gt_masks.shape
+
+    # false positive region, a new point sampled in this region should have
+    # negative label to correct the FP error
+    fp_masks = (~gt_masks & pred_masks).squeeze(1)
+    # false negative region, a new point sampled in this region should have
+    # positive label to correct the FN error
+    fn_masks = (gt_masks & ~pred_masks).squeeze(1)
+
+    if padding:
+        padded_fp_masks = torch.zeros(
+            B, H + 2, W + 2, dtype=fp_masks.dtype, device=fp_masks.device
+        )
+        padded_fp_masks[:, 1 : H + 1, 1 : W + 1] = fp_masks
+        padded_fn_masks = torch.zeros(
+            B, H + 2, W + 2, dtype=fp_masks.dtype, device=fp_masks.device
+        )
+        padded_fn_masks[:, 1 : H + 1, 1 : W + 1] = fn_masks
+    else:
+        padded_fp_masks = fp_masks
+        padded_fn_masks = fn_masks
+
+    fn_mask_dt = edt_triton(padded_fn_masks)
+    fp_mask_dt = edt_triton(padded_fp_masks)
+    if padding:
+        fn_mask_dt = fn_mask_dt[:, 1:-1, 1:-1]
+        fp_mask_dt = fp_mask_dt[:, 1:-1, 1:-1]
+
+    fn_max, fn_argmax = fn_mask_dt.reshape(B, -1).max(dim=-1)
+    fp_max, fp_argmax = fp_mask_dt.reshape(B, -1).max(dim=-1)
+    is_positive = fn_max > fp_max
+    chosen = torch.where(is_positive, fn_argmax, fp_argmax)
+    points_x = chosen % W
+    points_y = chosen // W
+
+    labels = is_positive.long()
+    points = torch.stack([points_x, points_y], -1)
+    return points.unsqueeze(1), labels.unsqueeze(1)
+
+
+def sample_one_point_from_error_center_slow(gt_masks, pred_masks, padding=True):
+    """
+    Sample 1 random point (along with its label) from the center of each error region,
+    that is, the point with the largest distance to the boundary of each error region.
+    This is the RITM sampling method from https://github.com/saic-vul/ritm_interactive_segmentation/blob/master/isegm/inference/clicker.py
+
+    Inputs:
+    - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool
+    - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None
+    - padding: if True, pad with boundary of 1 px for distance transform
+
+    Outputs:
+    - points: [B, 1, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point
+    - labels: [B, 1], dtype=torch.int32, where 1 means positive clicks and 0 means negative clicks
+    """
+    import cv2  # delay OpenCV import to avoid unnecessary dependency
+
+    if pred_masks is None:
+        pred_masks = torch.zeros_like(gt_masks)
+    assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1
+    assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape
+
+    B, _, _, W_im = gt_masks.shape
+    device = gt_masks.device
+
+    # false positive region, a new point sampled in this region should have
+    # negative label to correct the FP error
+    fp_masks = ~gt_masks & pred_masks
+    # false negative region, a new point sampled in this region should have
+    # positive label to correct the FN error
+    fn_masks = gt_masks & ~pred_masks
+
+    fp_masks = fp_masks.cpu().numpy()
+    fn_masks = fn_masks.cpu().numpy()
+    points = torch.zeros(B, 1, 2, dtype=torch.float)
+    labels = torch.ones(B, 1, dtype=torch.int32)
+    for b in range(B):
+        fn_mask = fn_masks[b, 0]
+        fp_mask = fp_masks[b, 0]
+        if padding:
+            fn_mask = np.pad(fn_mask, ((1, 1), (1, 1)), "constant")
+            fp_mask = np.pad(fp_mask, ((1, 1), (1, 1)), "constant")
+        # compute the distance of each point in FN/FP region to its boundary
+        fn_mask_dt = cv2.distanceTransform(fn_mask.astype(np.uint8), cv2.DIST_L2, 0)
+        fp_mask_dt = cv2.distanceTransform(fp_mask.astype(np.uint8), cv2.DIST_L2, 0)
+        if padding:
+            fn_mask_dt = fn_mask_dt[1:-1, 1:-1]
+            fp_mask_dt = fp_mask_dt[1:-1, 1:-1]
+
+        # take the point in FN/FP region with the largest distance to its boundary
+        fn_mask_dt_flat = fn_mask_dt.reshape(-1)
+        fp_mask_dt_flat = fp_mask_dt.reshape(-1)
+        fn_argmax = np.argmax(fn_mask_dt_flat)
+        fp_argmax = np.argmax(fp_mask_dt_flat)
+        is_positive = fn_mask_dt_flat[fn_argmax] > fp_mask_dt_flat[fp_argmax]
+        pt_idx = fn_argmax if is_positive else fp_argmax
+        points[b, 0, 0] = pt_idx % W_im  # x
+        points[b, 0, 1] = pt_idx // W_im  # y
+        labels[b, 0] = int(is_positive)
+
+    points = points.to(device)
+    labels = labels.to(device)
+    return points, labels
+
+
+def get_next_point(gt_masks, pred_masks, method):
+    if method == "uniform":
+        return sample_random_points_from_errors(gt_masks, pred_masks)
+    elif method == "center":
+        return sample_one_point_from_error_center(gt_masks, pred_masks)
+    else:
+        raise ValueError(f"unknown sampling method {method}")
+
+
+def select_closest_cond_frames(
+    frame_idx, cond_frame_outputs, max_cond_frame_num, keep_first_cond_frame=False
+):
+    """
+    Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs`
+    that are temporally closest to the current frame at `frame_idx`. Here, we take
+    - a) the closest conditioning frame before `frame_idx` (if any);
+    - b) the closest conditioning frame after `frame_idx` (if any);
+    - c) any other temporally closest conditioning frames until reaching a total
+         of `max_cond_frame_num` conditioning frames.
+
+    Outputs:
+    - selected_outputs: selected items (keys & values) from `cond_frame_outputs`.
+    - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`.
+    """
+    if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:
+        selected_outputs = cond_frame_outputs
+        unselected_outputs = {}
+    else:
+        assert max_cond_frame_num >= 2, "we should allow using 2+ conditioning frames"
+        selected_outputs = {}
+        if keep_first_cond_frame:
+            idx_first = min(
+                (t for t in cond_frame_outputs if t < frame_idx), default=None
+            )
+            if idx_first is None:
+                # Maybe we are tracking in reverse
+                idx_first = max(
+                    (t for t in cond_frame_outputs if t > frame_idx), default=None
+                )
+            if idx_first is not None:
+                selected_outputs[idx_first] = cond_frame_outputs[idx_first]
+        # the closest conditioning frame before `frame_idx` (if any)
+        idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)
+        if idx_before is not None:
+            selected_outputs[idx_before] = cond_frame_outputs[idx_before]
+
+        # the closest conditioning frame after `frame_idx` (if any)
+        idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)
+        if idx_after is not None:
+            selected_outputs[idx_after] = cond_frame_outputs[idx_after]
+
+        # add other temporally closest conditioning frames until reaching a total
+        # of `max_cond_frame_num` conditioning frames.
+        num_remain = max_cond_frame_num - len(selected_outputs)
+        inds_remain = sorted(
+            (t for t in cond_frame_outputs if t not in selected_outputs),
+            key=lambda x: abs(x - frame_idx),
+        )[:num_remain]
+        selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)
+        unselected_outputs = {
+            t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs
+        }
+
+    return selected_outputs, unselected_outputs
+
+
+def get_1d_sine_pe(pos_inds, dim, temperature=10000):
+    """
+    Get 1D sine positional embedding as in the original Transformer paper.
+    """
+    pe_dim = dim // 2
+    dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device)
+    dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)
+
+    pos_embed = pos_inds.unsqueeze(-1) / dim_t
+    pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)
+    return pos_embed
+
+
+def get_best_gt_match_from_multimasks(pred_multimasks, gt_masks, pred_scores=None):
+    """
+    Get the mask with the best match to GT masks (based on IoU) from pred_multimasks.
+    Optionally, use `pred_scores` to break ties in case all IoUs are zeros.
+    """
+    assert pred_multimasks.ndim == 4 and gt_masks.ndim == 4
+    if pred_multimasks.size(1) == 1:
+        return pred_multimasks  # only a single mask channel, nothing to select
+
+    pred_multimasks_binary = pred_multimasks > 0
+    area_i = torch.sum(pred_multimasks_binary & gt_masks, dim=(2, 3)).float()
+    area_u = torch.sum(pred_multimasks_binary | gt_masks, dim=(2, 3)).float()
+    ious = area_i / torch.clamp(area_u, min=1.0)
+
+    # In case all IoUs are zeros (e.g. because the GT mask is empty), use pred_scores
+    # to break ties and select the best mask
+    if pred_scores is not None:
+        has_nonzero_ious = torch.any(ious > 0).expand_as(ious)
+        scores = torch.where(has_nonzero_ious, ious, pred_scores)
+    else:
+        scores = ious
+
+    # Finally, take the best mask prediction (with the highest score)
+    best_scores_inds = torch.argmax(scores, dim=-1)
+    batch_inds = torch.arange(scores.size(0), device=scores.device)
+    best_pred_mask = pred_multimasks[batch_inds, best_scores_inds].unsqueeze(1)
+    return best_pred_mask
+
+
+def fill_holes_in_mask_scores(mask, max_area, fill_holes=True, remove_sprinkles=True):
+    """
+    A post processor to fill small holes in mask scores with area under `max_area`.
+    Holes are those small connected components in either background or foreground.
+
+    Note that it relies on the "cc_torch" package to find connected components fast. You can
+    install it via the following command (`TORCH_CUDA_ARCH_LIST=8.0` is for A100 GPUs):
+    ```
+    pip uninstall -y cc_torch; TORCH_CUDA_ARCH_LIST=8.0 9.0 pip install git+https://github.com/ronghanghu/cc_torch
+    ```
+    Otherwise, it will fallback to a slightly slower triton implementation, or skimage if the tensor is on cpu
+    """
+
+    if max_area <= 0:
+        return mask  # nothing to fill in this case
+
+    if fill_holes:
+        # We remove small connected components in background by changing them to foreground
+        # with a small positive mask score (0.1).
+        mask_bg = mask <= 0
+        bg_area_thresh = max_area
+        _, areas_bg = _get_connected_components_with_padding(mask_bg)
+        small_components_bg = mask_bg & (areas_bg <= bg_area_thresh)
+        mask = torch.where(small_components_bg, 0.1, mask)
+
+    if remove_sprinkles:
+        # We remove small connected components in foreground by changing them to background
+        # with a small negative mask score (-0.1). Here we only remove connected components
+        # whose areas are under both `max_area` and half of the entire mask's area. This
+        # removes sprinkles while avoids filtering out tiny objects that we want to track.
+        mask_fg = mask > 0
+        fg_area_thresh = torch.sum(mask_fg, dim=(2, 3), keepdim=True, dtype=torch.int32)
+        fg_area_thresh.floor_divide_(2).clamp_(max=max_area)
+        _, areas_fg = _get_connected_components_with_padding(mask_fg)
+        small_components_fg = mask_fg & (areas_fg <= fg_area_thresh)
+        mask = torch.where(small_components_fg, -0.1, mask)
+    return mask
+
+
+def _get_connected_components_with_padding(mask):
+    """Get connected components from masks (possibly padding them to an even size)."""
+    from sam3.perflib.connected_components import connected_components
+
+    mask = mask.to(torch.uint8)
+    _, _, H, W = mask.shape
+    # make sure both height and width are even (to be compatible with cc_torch)
+    pad_h = H % 2
+    pad_w = W % 2
+    if pad_h == 0 and pad_w == 0:
+        labels, counts = connected_components(mask)
+    else:
+        # pad the mask to make its height and width even
+        # padding format is (padding_left,padding_right,padding_top,padding_bottom)
+        mask_pad = F.pad(mask, (0, pad_w, 0, pad_h), mode="constant", value=0)
+        labels, counts = connected_components(mask_pad)
+        labels = labels[:, :, :H, :W]
+        counts = counts[:, :, :H, :W]
+
+    return labels, counts

third_party/GraspGen/sam3/sam3/model/sam3_tracking_predictor.py

@@ -0,0 +1,1369 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import logging
+from collections import OrderedDict
+
+import torch
+from sam3.model.sam3_tracker_base import concat_points, NO_OBJ_SCORE, Sam3TrackerBase
+from sam3.model.sam3_tracker_utils import fill_holes_in_mask_scores
+from sam3.model.utils.sam2_utils import load_video_frames
+from tqdm.auto import tqdm
+
+
+class Sam3TrackerPredictor(Sam3TrackerBase):
+    """
+    The demo class that extends the `Sam3TrackerBase` to handle user interactions
+    and manage inference states, with support for multi-object tracking.
+    """
+
+    def __init__(
+        self,
+        # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks;
+        # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True)
+        clear_non_cond_mem_around_input=False,
+        # whether to also clear non-conditioning memory of the surrounding frames (only effective when `clear_non_cond_mem_around_input` is True).
+        clear_non_cond_mem_for_multi_obj=False,
+        # if fill_hole_area > 0, we fill small holes in the final masks up to this area (after resizing them to the original video resolution)
+        fill_hole_area=0,
+        # if always_start_from_first_ann_frame is True, we always start tracking from the frame where we receive the first annotation (clicks or mask)
+        # and ignore the `start_frame_idx` passed to `propagate_in_video`
+        always_start_from_first_ann_frame=False,
+        # the maximum number of points to be used in the prompt encoder, which reduce the domain gap between training (that only has 8 points)
+        # - if it's set to a positive integer, we only take the `max_point_num_in_prompt_enc//2` points and
+        #   the last `(max_point_num_in_prompt_enc - max_point_num_in_prompt_enc//2)` points in the prompt encoder
+        # - if it's set to 0 or negative, this option is turned off and we use all points in the prompt encoder
+        max_point_num_in_prompt_enc=16,
+        non_overlap_masks_for_output=True,
+        # checkpoint_file=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input
+        self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj
+        self.fill_hole_area = fill_hole_area
+        self.always_start_from_first_ann_frame = always_start_from_first_ann_frame
+        self.max_point_num_in_prompt_enc = max_point_num_in_prompt_enc
+        self.non_overlap_masks_for_output = non_overlap_masks_for_output
+
+        self.bf16_context = torch.autocast(device_type="cuda", dtype=torch.bfloat16)
+        self.bf16_context.__enter__()  # keep using for the entire model process
+
+        self.iter_use_prev_mask_pred = True
+        self.add_all_frames_to_correct_as_cond = True
+
+    @torch.inference_mode()
+    def init_state(
+        self,
+        video_height=None,
+        video_width=None,
+        num_frames=None,
+        video_path=None,
+        cached_features=None,
+        offload_video_to_cpu=False,
+        offload_state_to_cpu=False,
+        async_loading_frames=False,
+    ):
+        """Initialize a inference state."""
+        inference_state = {}
+        # whether to offload the video frames to CPU memory
+        # turning on this option saves the GPU memory with only a very small overhead
+        inference_state["offload_video_to_cpu"] = offload_video_to_cpu
+        # whether to offload the inference state to CPU memory
+        # turning on this option saves the GPU memory at the cost of a lower tracking fps
+        # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object
+        # and from 24 to 21 when tracking two objects)
+        inference_state["offload_state_to_cpu"] = offload_state_to_cpu
+        inference_state["device"] = self.device
+        if offload_state_to_cpu:
+            inference_state["storage_device"] = torch.device("cpu")
+        else:
+            inference_state["storage_device"] = torch.device("cuda")
+
+        if video_path is not None:
+            images, video_height, video_width = load_video_frames(
+                video_path=video_path,
+                image_size=self.image_size,
+                offload_video_to_cpu=offload_video_to_cpu,
+                async_loading_frames=async_loading_frames,
+                compute_device=inference_state["storage_device"],
+            )
+            inference_state["images"] = images
+            inference_state["num_frames"] = len(images)
+            inference_state["video_height"] = video_height
+            inference_state["video_width"] = video_width
+        else:
+            # the original video height and width, used for resizing final output scores
+            inference_state["video_height"] = video_height
+            inference_state["video_width"] = video_width
+            inference_state["num_frames"] = num_frames
+        # inputs on each frame
+        inference_state["point_inputs_per_obj"] = {}
+        inference_state["mask_inputs_per_obj"] = {}
+        # visual features on a small number of recently visited frames for quick interactions
+        inference_state["cached_features"] = (
+            {} if cached_features is None else cached_features
+        )
+        # values that don't change across frames (so we only need to hold one copy of them)
+        inference_state["constants"] = {}
+        # mapping between client-side object id and model-side object index
+        inference_state["obj_id_to_idx"] = OrderedDict()
+        inference_state["obj_idx_to_id"] = OrderedDict()
+        inference_state["obj_ids"] = []
+        # A storage to hold the model's tracking results and states on each frame
+        inference_state["output_dict"] = {
+            "cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+            "non_cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+        }
+        # The index of the frame that received the first annotation
+        inference_state["first_ann_frame_idx"] = None
+        # Slice (view) of each object tracking results, sharing the same memory with "output_dict"
+        inference_state["output_dict_per_obj"] = {}
+        # A temporary storage to hold new outputs when user interact with a frame
+        # to add clicks or mask (it's merged into "output_dict" before propagation starts)
+        inference_state["temp_output_dict_per_obj"] = {}
+        # Frames that already holds consolidated outputs from click or mask inputs
+        # (we directly use their consolidated outputs during tracking)
+        inference_state["consolidated_frame_inds"] = {
+            "cond_frame_outputs": set(),  # set containing frame indices
+            "non_cond_frame_outputs": set(),  # set containing frame indices
+        }
+        # metadata for each tracking frame (e.g. which direction it's tracked)
+        inference_state["tracking_has_started"] = False
+        inference_state["frames_already_tracked"] = {}
+        self.clear_all_points_in_video(inference_state)
+        return inference_state
+
+    def _obj_id_to_idx(self, inference_state, obj_id):
+        """Map client-side object id to model-side object index."""
+        obj_idx = inference_state["obj_id_to_idx"].get(obj_id, None)
+        if obj_idx is not None:
+            return obj_idx
+
+        # This is a new object id not sent to the server before. We only allow adding
+        # new objects *before* the tracking starts.
+        allow_new_object = not inference_state["tracking_has_started"]
+        if allow_new_object:
+            # get the next object slot
+            obj_idx = len(inference_state["obj_id_to_idx"])
+            inference_state["obj_id_to_idx"][obj_id] = obj_idx
+            inference_state["obj_idx_to_id"][obj_idx] = obj_id
+            inference_state["obj_ids"] = list(inference_state["obj_id_to_idx"])
+            # set up input and output structures for this object
+            inference_state["point_inputs_per_obj"][obj_idx] = {}
+            inference_state["mask_inputs_per_obj"][obj_idx] = {}
+            inference_state["output_dict_per_obj"][obj_idx] = {
+                "cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+                "non_cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+            }
+            inference_state["temp_output_dict_per_obj"][obj_idx] = {
+                "cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+                "non_cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+            }
+            return obj_idx
+        else:
+            raise RuntimeError(
+                f"Cannot add new object id {obj_id} after tracking starts. "
+                f"All existing object ids: {inference_state['obj_ids']}."
+            )
+
+    def _obj_idx_to_id(self, inference_state, obj_idx):
+        """Map model-side object index to client-side object id."""
+        return inference_state["obj_idx_to_id"][obj_idx]
+
+    def _get_obj_num(self, inference_state):
+        """Get the total number of unique object ids received so far in this session."""
+        return len(inference_state["obj_idx_to_id"])
+
+    @torch.inference_mode()
+    def add_new_points_or_box(
+        self,
+        inference_state,
+        frame_idx,
+        obj_id,
+        points=None,
+        labels=None,
+        clear_old_points=True,
+        rel_coordinates=True,
+        use_prev_mem_frame=False,
+        normalize_coords=True,
+        box=None,
+    ):
+        """Add new points to a frame."""
+        obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+        point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx]
+        mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx]
+
+        if (points is not None) != (labels is not None):
+            raise ValueError("points and labels must be provided together")
+        if points is None and box is None:
+            raise ValueError("at least one of points or box must be provided as input")
+
+        if points is None:
+            points = torch.zeros(0, 2, dtype=torch.float32)
+        elif not isinstance(points, torch.Tensor):
+            points = torch.tensor(points, dtype=torch.float32)
+        if labels is None:
+            labels = torch.zeros(0, dtype=torch.int32)
+        elif not isinstance(labels, torch.Tensor):
+            labels = torch.tensor(labels, dtype=torch.int32)
+        if points.dim() == 2:
+            points = points.unsqueeze(0)  # add batch dimension
+        if labels.dim() == 1:
+            labels = labels.unsqueeze(0)  # add batch dimension
+
+        if rel_coordinates:
+            # convert the points from relative coordinates to absolute coordinates
+            if points is not None:
+                points = points * self.image_size
+            if box is not None:
+                box = box * self.image_size
+
+        # If `box` is provided, we add it as the first two points with labels 2 and 3
+        # along with the user-provided points (consistent with how SAM 2 is trained).
+        if box is not None:
+            if not clear_old_points:
+                raise ValueError(
+                    "cannot add box without clearing old points, since "
+                    "box prompt must be provided before any point prompt "
+                    "(please use clear_old_points=True instead)"
+                )
+            if not isinstance(box, torch.Tensor):
+                box = torch.tensor(box, dtype=torch.float32, device=points.device)
+            box_coords = box.reshape(1, 2, 2)
+            box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device)
+            box_labels = box_labels.reshape(1, 2)
+            points = torch.cat([box_coords, points], dim=1)
+            labels = torch.cat([box_labels, labels], dim=1)
+
+        points = points.to(inference_state["device"])
+        labels = labels.to(inference_state["device"])
+
+        if not clear_old_points:
+            point_inputs = point_inputs_per_frame.get(frame_idx, None)
+        else:
+            point_inputs = None
+        point_inputs = concat_points(point_inputs, points, labels)
+
+        point_inputs_per_frame[frame_idx] = point_inputs
+        mask_inputs_per_frame.pop(frame_idx, None)
+        # If this frame hasn't been tracked before, we treat it as an initial conditioning
+        # frame, meaning that the inputs points are to generate segments on this frame without
+        # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),
+        # the input points will be used to correct the already tracked masks.
+        is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"]
+        # whether to track in reverse time order
+        if is_init_cond_frame:
+            reverse = False
+        else:
+            reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"]
+        obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+        obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+        # Add a frame to conditioning output if it's an initial conditioning frame or
+        # if the model sees all frames receiving clicks/mask as conditioning frames.
+        is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond
+        storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+
+        # Limit to a maximum number of input points to the prompt encoder (to reduce domain gap)
+        num_points = point_inputs["point_coords"].size(1)
+        if num_points > self.max_point_num_in_prompt_enc > 0:
+            num_first = self.max_point_num_in_prompt_enc // 2
+            num_last = self.max_point_num_in_prompt_enc - num_first
+            point_inputs["point_coords"] = torch.cat(
+                [
+                    point_inputs["point_coords"][:, :num_first],
+                    point_inputs["point_coords"][:, -num_last:],
+                ],
+                dim=1,
+            )
+            point_inputs["point_labels"] = torch.cat(
+                [
+                    point_inputs["point_labels"][:, :num_first],
+                    point_inputs["point_labels"][:, -num_last:],
+                ],
+                dim=1,
+            )
+            logging.warning(
+                f"Too many points ({num_points}) are provided on frame {frame_idx}. Only "
+                f"the first {num_first} points and the last {num_last} points will be used."
+            )
+        # Get any previously predicted mask logits on this object and feed it along with
+        # the new clicks into the SAM mask decoder when `self.iter_use_prev_mask_pred=True`.
+        prev_sam_mask_logits = None
+        if self.iter_use_prev_mask_pred:
+            # lookup temporary output dict first, which contains the most recent output
+            # (if not found, then lookup conditioning and non-conditioning frame output)
+            prev_out = obj_temp_output_dict[storage_key].get(frame_idx)
+            if prev_out is None:
+                prev_out = obj_output_dict["cond_frame_outputs"].get(frame_idx)
+                if prev_out is None:
+                    prev_out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx)
+
+            if prev_out is not None and prev_out["pred_masks"] is not None:
+                prev_sam_mask_logits = prev_out["pred_masks"].cuda(non_blocking=True)
+                # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues.
+                prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0)
+        current_out, _ = self._run_single_frame_inference(
+            inference_state=inference_state,
+            output_dict=obj_output_dict,  # run on the slice of a single object
+            frame_idx=frame_idx,
+            batch_size=1,  # run on the slice of a single object
+            is_init_cond_frame=is_init_cond_frame,
+            point_inputs=point_inputs,
+            mask_inputs=None,
+            reverse=reverse,
+            # Skip the memory encoder when adding clicks or mask. We execute the memory encoder
+            # at the beginning of `propagate_in_video` (after user finalize their clicks). This
+            # allows us to enforce non-overlapping constraints on all objects before encoding
+            # them into memory.
+            run_mem_encoder=False,
+            prev_sam_mask_logits=prev_sam_mask_logits,
+            use_prev_mem_frame=use_prev_mem_frame,
+        )
+        # Add the output to the output dict (to be used as future memory)
+        obj_temp_output_dict[storage_key][frame_idx] = current_out
+
+        # Resize the output mask to the original video resolution
+        obj_ids = inference_state["obj_ids"]
+        consolidated_out = self._consolidate_temp_output_across_obj(
+            inference_state,
+            frame_idx,
+            is_cond=is_cond,
+            run_mem_encoder=False,
+            consolidate_at_video_res=True,
+        )
+        _, video_res_masks = self._get_orig_video_res_output(
+            inference_state, consolidated_out["pred_masks_video_res"]
+        )
+        low_res_masks = None  # not needed by the demo
+        return frame_idx, obj_ids, low_res_masks, video_res_masks
+
+    @torch.inference_mode()
+    def add_new_mask(
+        self,
+        inference_state,
+        frame_idx,
+        obj_id,
+        mask,
+        add_mask_to_memory=False,
+    ):
+        """Add new mask to a frame."""
+        obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+        point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx]
+        mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx]
+
+        assert mask.dim() == 2
+        mask_H, mask_W = mask.shape
+        mask_inputs_orig = mask[None, None]  # add batch and channel dimension
+        mask_inputs_orig = mask_inputs_orig.float().to(inference_state["device"])
+
+        # resize the mask if it doesn't match the model's input mask size
+        if mask_H != self.input_mask_size or mask_W != self.input_mask_size:
+            mask_inputs = torch.nn.functional.interpolate(
+                mask_inputs_orig,
+                size=(self.input_mask_size, self.input_mask_size),
+                align_corners=False,
+                mode="bilinear",
+                antialias=True,  # use antialias for downsampling
+            )
+        else:
+            mask_inputs = mask_inputs_orig
+
+        # also get the mask at the original video resolution (for outputting)
+        video_H = inference_state["video_height"]
+        video_W = inference_state["video_width"]
+        if mask_H != video_H or mask_W != video_W:
+            mask_inputs_video_res = torch.nn.functional.interpolate(
+                mask_inputs_orig,
+                size=(video_H, video_W),
+                align_corners=False,
+                mode="bilinear",
+                antialias=True,  # use antialias for potential downsampling
+            )
+        else:
+            mask_inputs_video_res = mask_inputs_orig
+        # convert mask_inputs_video_res to binary (threshold at 0.5 as it is in range 0~1)
+        mask_inputs_video_res = mask_inputs_video_res > 0.5
+
+        mask_inputs_per_frame[frame_idx] = mask_inputs_video_res
+        point_inputs_per_frame.pop(frame_idx, None)
+        # If this frame hasn't been tracked before, we treat it as an initial conditioning
+        # frame, meaning that the inputs points are to generate segments on this frame without
+        # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),
+        # the input points will be used to correct the already tracked masks.
+        is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"]
+        # whether to track in reverse time order
+        if is_init_cond_frame:
+            reverse = False
+        else:
+            reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"]
+        obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+        obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+        # Add a frame to conditioning output if it's an initial conditioning frame or
+        # if the model sees all frames receiving clicks/mask as conditioning frames.
+        is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond
+        storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+
+        current_out, _ = self._run_single_frame_inference(
+            inference_state=inference_state,
+            output_dict=obj_output_dict,  # run on the slice of a single object
+            frame_idx=frame_idx,
+            batch_size=1,  # run on the slice of a single object
+            is_init_cond_frame=is_init_cond_frame,
+            point_inputs=None,
+            mask_inputs=mask_inputs,
+            reverse=reverse,
+            # Skip the memory encoder when adding clicks or mask. We execute the memory encoder
+            # at the beginning of `propagate_in_video` (after user finalize their clicks). This
+            # allows us to enforce non-overlapping constraints on all objects before encoding
+            # them into memory.
+            run_mem_encoder=False,
+        )
+        # We directly use the input mask at video resolution as the output mask for a better
+        # video editing experience (so that the masks don't change after each brushing).
+        # Here NO_OBJ_SCORE is a large negative value to represent the background and
+        # similarly -NO_OBJ_SCORE is a large positive value to represent the foreground.
+        current_out["pred_masks"] = None
+        current_out["pred_masks_video_res"] = torch.where(
+            mask_inputs_video_res, -NO_OBJ_SCORE, NO_OBJ_SCORE
+        )
+        # Add the output to the output dict (to be used as future memory)
+        obj_temp_output_dict[storage_key][frame_idx] = current_out
+        # Remove the overlapping proportion of other objects' input masks on this frame
+        temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+        for obj_idx2, obj_temp_output_dict2 in temp_output_dict_per_obj.items():
+            if obj_idx2 == obj_idx:
+                continue
+            current_out2 = obj_temp_output_dict2[storage_key].get(frame_idx, None)
+            if current_out2 is not None and "pred_masks_video_res" in current_out2:
+                current_out2["pred_masks_video_res"] = torch.where(
+                    mask_inputs_video_res,
+                    NO_OBJ_SCORE,
+                    current_out2["pred_masks_video_res"],
+                )
+
+        # Resize the output mask to the original video resolution
+        obj_ids = inference_state["obj_ids"]
+        consolidated_out = self._consolidate_temp_output_across_obj(
+            inference_state,
+            frame_idx,
+            is_cond=is_cond,
+            run_mem_encoder=False,
+            consolidate_at_video_res=True,
+        )
+        _, video_res_masks = self._get_orig_video_res_output(
+            inference_state, consolidated_out["pred_masks_video_res"]
+        )
+        low_res_masks = None  # not needed by the demo
+        return frame_idx, obj_ids, low_res_masks, video_res_masks
+
+    def add_new_points(self, *args, **kwargs):
+        """Deprecated method. Please use `add_new_points_or_box` instead."""
+        return self.add_new_points_or_box(*args, **kwargs)
+
+    def _get_orig_video_res_output(self, inference_state, any_res_masks):
+        """
+        Resize the object scores to the original video resolution (video_res_masks)
+        and apply non-overlapping constraints for final output.
+        """
+        device = inference_state["device"]
+        video_H = inference_state["video_height"]
+        video_W = inference_state["video_width"]
+        any_res_masks = any_res_masks.to(device, non_blocking=True)
+        if any_res_masks.shape[-2:] == (video_H, video_W):
+            video_res_masks = any_res_masks
+        else:
+            video_res_masks = torch.nn.functional.interpolate(
+                any_res_masks,
+                size=(video_H, video_W),
+                mode="bilinear",
+                align_corners=False,
+            )
+        if self.non_overlap_masks_for_output:
+            video_res_masks = self._apply_non_overlapping_constraints(video_res_masks)
+        # potentially fill holes in the predicted masks
+        if self.fill_hole_area > 0:
+            video_res_masks = fill_holes_in_mask_scores(
+                video_res_masks, self.fill_hole_area
+            )
+        return any_res_masks, video_res_masks
+
+    def _consolidate_temp_output_across_obj(
+        self,
+        inference_state,
+        frame_idx,
+        is_cond,
+        run_mem_encoder,
+        consolidate_at_video_res=False,
+    ):
+        """
+        Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on
+        a frame into a single output for all objects, including
+        1) fill any missing objects either from `output_dict_per_obj` (if they exist in
+           `output_dict_per_obj` for this frame) or leave them as placeholder values
+           (if they don't exist in `output_dict_per_obj` for this frame);
+        2) if specified, rerun memory encoder after apply non-overlapping constraints
+           on the object scores.
+        """
+        batch_size = self._get_obj_num(inference_state)
+        storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+        # Optionally, we allow consolidating the temporary outputs at the original
+        # video resolution (to provide a better editing experience for mask prompts).
+        if consolidate_at_video_res:
+            assert not run_mem_encoder, "memory encoder cannot run at video resolution"
+            consolidated_H = inference_state["video_height"]
+            consolidated_W = inference_state["video_width"]
+            consolidated_mask_key = "pred_masks_video_res"
+        else:
+            consolidated_H = consolidated_W = self.low_res_mask_size
+            consolidated_mask_key = "pred_masks"
+
+        # Initialize `consolidated_out`. Its "maskmem_features" and "maskmem_pos_enc"
+        # will be added when rerunning the memory encoder after applying non-overlapping
+        # constraints to object scores. Its "pred_masks" are prefilled with a large
+        # negative value (NO_OBJ_SCORE) to represent missing objects.
+        consolidated_out = {
+            "maskmem_features": None,
+            "maskmem_pos_enc": None,
+            consolidated_mask_key: torch.full(
+                size=(batch_size, 1, consolidated_H, consolidated_W),
+                fill_value=NO_OBJ_SCORE,
+                dtype=torch.float32,
+                device=inference_state["storage_device"],
+            ),
+            "obj_ptr": torch.full(
+                size=(batch_size, self.hidden_dim),
+                fill_value=NO_OBJ_SCORE,
+                dtype=torch.float32,
+                device=inference_state["device"],
+            ),
+            "object_score_logits": torch.full(
+                size=(batch_size, 1),
+                # default to 10.0 for object_score_logits, i.e. assuming the object is
+                # present as sigmoid(10)=1, same as in `predict_masks` of `MaskDecoder`
+                fill_value=10.0,
+                dtype=torch.float32,
+                device=inference_state["device"],
+            ),
+        }
+        if self.use_memory_selection:
+            consolidated_out["iou_score"] = torch.full(
+                size=(batch_size, 1),
+                fill_value=0.0,
+                dtype=torch.float32,
+                device=inference_state["device"],
+            )
+        empty_mask_ptr = None
+        for obj_idx in range(batch_size):
+            obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+            obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+            out = obj_temp_output_dict[storage_key].get(frame_idx, None)
+            # If the object doesn't appear in "temp_output_dict_per_obj" on this frame,
+            # we fall back and look up its previous output in "output_dict_per_obj".
+            # We look up both "cond_frame_outputs" and "non_cond_frame_outputs" in
+            # "output_dict_per_obj" to find a previous output for this object.
+            if out is None:
+                out = obj_output_dict["cond_frame_outputs"].get(frame_idx, None)
+            if out is None:
+                out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx, None)
+            # If the object doesn't appear in "output_dict_per_obj" either, we skip it
+            # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE
+            # placeholder above) and set its object pointer to be a dummy pointer.
+            if out is None:
+                # Fill in dummy object pointers for those objects without any inputs or
+                # tracking outcomes on this frame (only do it under `run_mem_encoder=True`,
+                # i.e. when we need to build the memory for tracking).
+                if run_mem_encoder:
+                    if empty_mask_ptr is None:
+                        empty_mask_ptr = self._get_empty_mask_ptr(
+                            inference_state, frame_idx
+                        )
+                    # fill object pointer with a dummy pointer (based on an empty mask)
+                    consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = empty_mask_ptr
+                continue
+            # Add the temporary object output mask to consolidated output mask
+            # (use "pred_masks_video_res" if it's available)
+            obj_mask = out.get("pred_masks_video_res", out["pred_masks"])
+            consolidated_pred_masks = consolidated_out[consolidated_mask_key]
+            if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]:
+                consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask
+            else:
+                # Resize first if temporary object mask has a different resolution
+                is_downsampling = "pred_masks_video_res" in out
+                resized_obj_mask = torch.nn.functional.interpolate(
+                    obj_mask,
+                    size=consolidated_pred_masks.shape[-2:],
+                    mode="bilinear",
+                    align_corners=False,
+                    antialias=is_downsampling,  # use antialias for downsampling
+                )
+                consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask
+            consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = out["obj_ptr"]
+            consolidated_out["object_score_logits"][obj_idx : obj_idx + 1] = out[
+                "object_score_logits"
+            ]
+            if self.use_memory_selection:
+                consolidated_out["iou_score"][obj_idx : obj_idx + 1] = out["iou_score"]
+        # Optionally, apply non-overlapping constraints on the consolidated scores
+        # and rerun the memory encoder
+        if run_mem_encoder:
+            device = inference_state["device"]
+            high_res_masks = torch.nn.functional.interpolate(
+                consolidated_out["pred_masks"].to(device, non_blocking=True),
+                size=(self.image_size, self.image_size),
+                mode="bilinear",
+                align_corners=False,
+            )
+            high_res_masks = self._apply_non_overlapping_constraints(high_res_masks)
+            maskmem_features, maskmem_pos_enc = self._run_memory_encoder(
+                inference_state=inference_state,
+                frame_idx=frame_idx,
+                batch_size=batch_size,
+                high_res_masks=high_res_masks,
+                object_score_logits=consolidated_out["object_score_logits"],
+                is_mask_from_pts=True,  # these frames are what the user interacted with
+            )
+            consolidated_out["maskmem_features"] = maskmem_features
+            consolidated_out["maskmem_pos_enc"] = maskmem_pos_enc
+
+        return consolidated_out
+
+    def _get_empty_mask_ptr(self, inference_state, frame_idx):
+        """Get a dummy object pointer based on an empty mask on the current frame."""
+        # A dummy (empty) mask with a single object
+        batch_size = 1
+        mask_inputs = torch.zeros(
+            (batch_size, 1, self.image_size, self.image_size),
+            dtype=torch.float32,
+            device=inference_state["device"],
+        )
+
+        # Retrieve correct image features
+        (
+            image,
+            _,
+            current_vision_feats,
+            current_vision_pos_embeds,
+            feat_sizes,
+        ) = self._get_image_feature(inference_state, frame_idx, batch_size)
+
+        # Feed the empty mask and image feature above to get a dummy object pointer
+        current_out = self.track_step(
+            frame_idx=frame_idx,
+            is_init_cond_frame=True,
+            current_vision_feats=current_vision_feats,
+            current_vision_pos_embeds=current_vision_pos_embeds,
+            feat_sizes=feat_sizes,
+            image=image,
+            point_inputs=None,
+            mask_inputs=mask_inputs,
+            output_dict={
+                "cond_frame_outputs": {},
+                "non_cond_frame_outputs": {},
+            },
+            num_frames=inference_state["num_frames"],
+            track_in_reverse=False,
+            run_mem_encoder=False,
+            prev_sam_mask_logits=None,
+        )
+        return current_out["obj_ptr"]
+
+    @torch.inference_mode()
+    def propagate_in_video_preflight(self, inference_state, run_mem_encoder=True):
+        """Prepare inference_state and consolidate temporary outputs before tracking."""
+        # Tracking has started and we don't allow adding new objects until session is reset.
+        inference_state["tracking_has_started"] = True
+        batch_size = self._get_obj_num(inference_state)
+
+        # Consolidate per-object temporary outputs in "temp_output_dict_per_obj" and
+        # add them into "output_dict".
+        temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+        output_dict = inference_state["output_dict"]
+        # "consolidated_frame_inds" contains indices of those frames where consolidated
+        # temporary outputs have been added (either in this call or any previous calls
+        # to `propagate_in_video_preflight`).
+        consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+        for is_cond in [False, True]:
+            # Separately consolidate conditioning and non-conditioning temp outptus
+            storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+            # Find all the frames that contain temporary outputs for any objects
+            # (these should be the frames that have just received clicks for mask inputs
+            # via `add_new_points` or `add_new_mask`)
+            temp_frame_inds = set()
+            for obj_temp_output_dict in temp_output_dict_per_obj.values():
+                temp_frame_inds.update(obj_temp_output_dict[storage_key].keys())
+            consolidated_frame_inds[storage_key].update(temp_frame_inds)
+            # consolidate the temprary output across all objects on this frame
+            for frame_idx in temp_frame_inds:
+                consolidated_out = self._consolidate_temp_output_across_obj(
+                    inference_state,
+                    frame_idx,
+                    is_cond=is_cond,
+                    run_mem_encoder=run_mem_encoder,
+                )
+                # merge them into "output_dict" and also create per-object slices
+                output_dict[storage_key][frame_idx] = consolidated_out
+                self._add_output_per_object(
+                    inference_state, frame_idx, consolidated_out, storage_key
+                )
+                clear_non_cond_mem = self.clear_non_cond_mem_around_input and (
+                    self.clear_non_cond_mem_for_multi_obj or batch_size <= 1
+                )
+                if clear_non_cond_mem:
+                    # clear non-conditioning memory of the surrounding frames
+                    self._clear_non_cond_mem_around_input(inference_state, frame_idx)
+
+            # clear temporary outputs in `temp_output_dict_per_obj`
+            for obj_temp_output_dict in temp_output_dict_per_obj.values():
+                obj_temp_output_dict[storage_key].clear()
+
+        # edge case: if an output is added to "cond_frame_outputs", we remove any prior
+        # output on the same frame in "non_cond_frame_outputs"
+        for frame_idx in output_dict["cond_frame_outputs"]:
+            output_dict["non_cond_frame_outputs"].pop(frame_idx, None)
+        for obj_output_dict in inference_state["output_dict_per_obj"].values():
+            for frame_idx in obj_output_dict["cond_frame_outputs"]:
+                obj_output_dict["non_cond_frame_outputs"].pop(frame_idx, None)
+        for frame_idx in consolidated_frame_inds["cond_frame_outputs"]:
+            assert frame_idx in output_dict["cond_frame_outputs"]
+            consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx)
+
+        # Make sure that the frame indices in "consolidated_frame_inds" are exactly those frames
+        # with either points or mask inputs (which should be true under a correct demo workflow).
+        all_consolidated_frame_inds = (
+            consolidated_frame_inds["cond_frame_outputs"]
+            | consolidated_frame_inds["non_cond_frame_outputs"]
+        )
+        input_frames_inds = set()
+        for point_inputs_per_frame in inference_state["point_inputs_per_obj"].values():
+            input_frames_inds.update(point_inputs_per_frame.keys())
+        for mask_inputs_per_frame in inference_state["mask_inputs_per_obj"].values():
+            input_frames_inds.update(mask_inputs_per_frame.keys())
+        assert all_consolidated_frame_inds == input_frames_inds
+        # Record the first interacted frame index (for tracking start)
+        if inference_state["first_ann_frame_idx"] is None:
+            inference_state["first_ann_frame_idx"] = min(
+                input_frames_inds, default=None
+            )
+        # In case `first_ann_frame_idx` is not in the conditioning frames (e.g. because
+        # we cleared the input points on that frame), pick the first conditioning frame
+        if (
+            inference_state["first_ann_frame_idx"]
+            not in output_dict["cond_frame_outputs"]
+        ):
+            inference_state["first_ann_frame_idx"] = min(
+                output_dict["cond_frame_outputs"], default=None
+            )
+
+    def _get_processing_order(
+        self, inference_state, start_frame_idx, max_frame_num_to_track, reverse
+    ):
+        num_frames = inference_state["num_frames"]
+        # set start index, end index, and processing order
+        if self.always_start_from_first_ann_frame:
+            # in this case, we always start tracking from the frame where we receive
+            # the initial annotation and ignore the provided start_frame_idx
+            start_frame_idx = inference_state["first_ann_frame_idx"]
+        if start_frame_idx is None:
+            # default: start from the earliest frame with input points
+            start_frame_idx = min(inference_state["output_dict"]["cond_frame_outputs"])
+        if max_frame_num_to_track is None:
+            # default: track all the frames in the video
+            max_frame_num_to_track = num_frames
+        if reverse:
+            end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0)
+            if start_frame_idx > 0:
+                processing_order = range(start_frame_idx, end_frame_idx - 1, -1)
+            else:
+                # this is the edge case where we start from frame 0 and track in reverse order;
+                # in this case, we track a single frame (frame 0)
+                processing_order = [0]
+        else:
+            end_frame_idx = min(
+                start_frame_idx + max_frame_num_to_track, num_frames - 1
+            )
+            processing_order = range(start_frame_idx, end_frame_idx + 1)
+        return processing_order
+
+    @torch.inference_mode()
+    def propagate_in_video(
+        self,
+        inference_state,
+        start_frame_idx,
+        max_frame_num_to_track,
+        reverse,
+        tqdm_disable=False,
+        obj_ids=None,
+        run_mem_encoder=True,
+        propagate_preflight=False,
+    ):
+        """Propagate the input points across frames to track in the entire video."""
+        if propagate_preflight:
+            self.propagate_in_video_preflight(inference_state)
+        # NOTE: This is a copy from the parent class, except that we return object scores as well.
+        output_dict = inference_state["output_dict"]
+        consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+        if obj_ids is not None:
+            raise NotImplementedError(
+                "Per-object tracking yet for batched inference if not implemented."
+            )
+        obj_ids = inference_state["obj_ids"]
+        batch_size = self._get_obj_num(inference_state)
+        if len(output_dict["cond_frame_outputs"]) == 0:
+            raise RuntimeError("No points are provided; please add points first")
+        clear_non_cond_mem = self.clear_non_cond_mem_around_input and (
+            self.clear_non_cond_mem_for_multi_obj or batch_size <= 1
+        )
+
+        processing_order = self._get_processing_order(
+            inference_state,
+            start_frame_idx,
+            max_frame_num_to_track,
+            reverse,
+        )
+
+        for frame_idx in tqdm(
+            processing_order, desc="propagate in video", disable=tqdm_disable
+        ):
+            # We skip those frames already in consolidated outputs (these are frames
+            # that received input clicks or mask). Note that we cannot directly run
+            # batched forward on them via `_run_single_frame_inference` because the
+            # number of clicks on each object might be different.
+            if frame_idx in consolidated_frame_inds["cond_frame_outputs"]:
+                storage_key = "cond_frame_outputs"
+                current_out = output_dict[storage_key][frame_idx]
+                pred_masks = current_out["pred_masks"]
+                obj_scores = current_out["object_score_logits"]
+                if clear_non_cond_mem:
+                    # clear non-conditioning memory of the surrounding frames
+                    self._clear_non_cond_mem_around_input(inference_state, frame_idx)
+            elif frame_idx in consolidated_frame_inds["non_cond_frame_outputs"]:
+                storage_key = "non_cond_frame_outputs"
+                current_out = output_dict[storage_key][frame_idx]
+                pred_masks = current_out["pred_masks"]
+                obj_scores = current_out["object_score_logits"]
+            else:
+                storage_key = "non_cond_frame_outputs"
+                current_out, pred_masks = self._run_single_frame_inference(
+                    inference_state=inference_state,
+                    output_dict=output_dict,
+                    frame_idx=frame_idx,
+                    batch_size=batch_size,
+                    is_init_cond_frame=False,
+                    point_inputs=None,
+                    mask_inputs=None,
+                    reverse=reverse,
+                    run_mem_encoder=run_mem_encoder,
+                )
+                obj_scores = current_out["object_score_logits"]
+                output_dict[storage_key][frame_idx] = current_out
+            # Create slices of per-object outputs for subsequent interaction with each
+            # individual object after tracking.
+            self._add_output_per_object(
+                inference_state, frame_idx, current_out, storage_key
+            )
+            inference_state["frames_already_tracked"][frame_idx] = {"reverse": reverse}
+
+            # Resize the output mask to the original video resolution (we directly use
+            # the mask scores on GPU for output to avoid any CPU conversion in between)
+            low_res_masks, video_res_masks = self._get_orig_video_res_output(
+                inference_state, pred_masks
+            )
+            yield frame_idx, obj_ids, low_res_masks, video_res_masks, obj_scores
+
+    def _add_output_per_object(
+        self, inference_state, frame_idx, current_out, storage_key
+    ):
+        """
+        Split a multi-object output into per-object output slices and add them into
+        `output_dict_per_obj`. The resulting slices share the same tensor storage.
+        """
+        maskmem_features = current_out["maskmem_features"]
+        assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor)
+
+        maskmem_pos_enc = current_out["maskmem_pos_enc"]
+        assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list)
+
+        output_dict_per_obj = inference_state["output_dict_per_obj"]
+        for obj_idx, obj_output_dict in output_dict_per_obj.items():
+            obj_slice = slice(obj_idx, obj_idx + 1)
+            obj_out = {
+                "maskmem_features": None,
+                "maskmem_pos_enc": None,
+                "pred_masks": current_out["pred_masks"][obj_slice],
+                "obj_ptr": current_out["obj_ptr"][obj_slice],
+                "object_score_logits": current_out["object_score_logits"][obj_slice],
+            }
+            if self.use_memory_selection:
+                obj_out["iou_score"] = current_out["iou_score"][obj_slice]
+            if maskmem_features is not None:
+                obj_out["maskmem_features"] = maskmem_features[obj_slice]
+            if maskmem_pos_enc is not None:
+                obj_out["maskmem_pos_enc"] = [x[obj_slice] for x in maskmem_pos_enc]
+            obj_output_dict[storage_key][frame_idx] = obj_out
+
+    @torch.inference_mode()
+    def clear_all_points_in_frame(
+        self, inference_state, frame_idx, obj_id, need_output=True
+    ):
+        """Remove all input points or mask in a specific frame for a given object."""
+        obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+
+        # Clear the conditioning information on the given frame
+        inference_state["point_inputs_per_obj"][obj_idx].pop(frame_idx, None)
+        inference_state["mask_inputs_per_obj"][obj_idx].pop(frame_idx, None)
+
+        temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+        temp_output_dict_per_obj[obj_idx]["cond_frame_outputs"].pop(frame_idx, None)
+        temp_output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].pop(frame_idx, None)
+
+        # Check and see if there are still any inputs left on this frame
+        batch_size = self._get_obj_num(inference_state)
+        frame_has_input = False
+        for obj_idx2 in range(batch_size):
+            if frame_idx in inference_state["point_inputs_per_obj"][obj_idx2]:
+                frame_has_input = True
+                break
+            if frame_idx in inference_state["mask_inputs_per_obj"][obj_idx2]:
+                frame_has_input = True
+                break
+
+        # If this frame has no remaining inputs for any objects, we further clear its
+        # conditioning frame status
+        if not frame_has_input:
+            output_dict = inference_state["output_dict"]
+            consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+            consolidated_frame_inds["cond_frame_outputs"].discard(frame_idx)
+            consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx)
+            # Remove the frame's conditioning output (possibly downgrading it to non-conditioning)
+            out = output_dict["cond_frame_outputs"].pop(frame_idx, None)
+            if out is not None:
+                # The frame is not a conditioning frame anymore since it's not receiving inputs,
+                # so we "downgrade" its output (if exists) to a non-conditioning frame output.
+                output_dict["non_cond_frame_outputs"][frame_idx] = out
+                inference_state["frames_already_tracked"].pop(frame_idx, None)
+            # Similarly, do it for the sliced output on each object.
+            for obj_idx2 in range(batch_size):
+                obj_output_dict = inference_state["output_dict_per_obj"][obj_idx2]
+                obj_out = obj_output_dict["cond_frame_outputs"].pop(frame_idx, None)
+                if obj_out is not None:
+                    obj_output_dict["non_cond_frame_outputs"][frame_idx] = obj_out
+
+            # If all the conditioning frames have been removed, we also clear the tracking outputs
+            if len(output_dict["cond_frame_outputs"]) == 0:
+                self._reset_tracking_results(inference_state)
+
+        if not need_output:
+            return
+        # Finally, output updated masks per object (after removing the inputs above)
+        obj_ids = inference_state["obj_ids"]
+        is_cond = any(
+            frame_idx in obj_temp_output_dict["cond_frame_outputs"]
+            for obj_temp_output_dict in temp_output_dict_per_obj.values()
+        )
+        consolidated_out = self._consolidate_temp_output_across_obj(
+            inference_state,
+            frame_idx,
+            is_cond=is_cond,
+            run_mem_encoder=False,
+            consolidate_at_video_res=True,
+        )
+        _, video_res_masks = self._get_orig_video_res_output(
+            inference_state, consolidated_out["pred_masks_video_res"]
+        )
+        low_res_masks = None  # not needed by the demo
+        return frame_idx, obj_ids, low_res_masks, video_res_masks
+
+    @torch.inference_mode()
+    def clear_all_points_in_video(self, inference_state):
+        """Remove all input points or mask in all frames throughout the video."""
+        self._reset_tracking_results(inference_state)
+        # Remove all object ids
+        inference_state["obj_id_to_idx"].clear()
+        inference_state["obj_idx_to_id"].clear()
+        inference_state["obj_ids"].clear()
+        inference_state["point_inputs_per_obj"].clear()
+        inference_state["mask_inputs_per_obj"].clear()
+        inference_state["output_dict_per_obj"].clear()
+        inference_state["temp_output_dict_per_obj"].clear()
+
+    def _reset_tracking_results(self, inference_state):
+        """Reset all tracking inputs and results across the videos."""
+        for v in inference_state["point_inputs_per_obj"].values():
+            v.clear()
+        for v in inference_state["mask_inputs_per_obj"].values():
+            v.clear()
+        for v in inference_state["output_dict_per_obj"].values():
+            v["cond_frame_outputs"].clear()
+            v["non_cond_frame_outputs"].clear()
+        for v in inference_state["temp_output_dict_per_obj"].values():
+            v["cond_frame_outputs"].clear()
+            v["non_cond_frame_outputs"].clear()
+        inference_state["output_dict"]["cond_frame_outputs"].clear()
+        inference_state["output_dict"]["non_cond_frame_outputs"].clear()
+        inference_state["consolidated_frame_inds"]["cond_frame_outputs"].clear()
+        inference_state["consolidated_frame_inds"]["non_cond_frame_outputs"].clear()
+        inference_state["tracking_has_started"] = False
+        inference_state["frames_already_tracked"].clear()
+        inference_state["first_ann_frame_idx"] = None
+
+    def _get_image_feature(self, inference_state, frame_idx, batch_size):
+        """Compute the image features on a given frame."""
+        # Look up in the cache
+        image, backbone_out = inference_state["cached_features"].get(
+            frame_idx, (None, None)
+        )
+        if backbone_out is None:
+            if self.backbone is None:
+                raise RuntimeError(
+                    f"Image features for frame {frame_idx} are not cached. "
+                    "Please run inference on this frame first."
+                )
+            else:
+                # Cache miss -- we will run inference on a single image
+                image = inference_state["images"][frame_idx].cuda().float().unsqueeze(0)
+                backbone_out = self.forward_image(image)
+                # Cache the most recent frame's feature (for repeated interactions with
+                # a frame; we can use an LRU cache for more frames in the future).
+                inference_state["cached_features"] = {frame_idx: (image, backbone_out)}
+        if "tracker_backbone_out" in backbone_out:
+            backbone_out = backbone_out["tracker_backbone_out"]  # get backbone output
+
+        # expand the features to have the same dimension as the number of objects
+        expanded_image = image.expand(batch_size, -1, -1, -1)
+        expanded_backbone_out = {
+            "backbone_fpn": backbone_out["backbone_fpn"].copy(),
+            "vision_pos_enc": backbone_out["vision_pos_enc"].copy(),
+        }
+        for i, feat in enumerate(expanded_backbone_out["backbone_fpn"]):
+            feat = feat.expand(batch_size, -1, -1, -1)
+            expanded_backbone_out["backbone_fpn"][i] = feat
+        for i, pos in enumerate(expanded_backbone_out["vision_pos_enc"]):
+            pos = pos.expand(batch_size, -1, -1, -1)
+            expanded_backbone_out["vision_pos_enc"][i] = pos
+
+        features = self._prepare_backbone_features(expanded_backbone_out)
+        features = (expanded_image,) + features
+        return features
+
+    def _run_single_frame_inference(
+        self,
+        inference_state,
+        output_dict,
+        frame_idx,
+        batch_size,
+        is_init_cond_frame,
+        point_inputs,
+        mask_inputs,
+        reverse,
+        run_mem_encoder,
+        prev_sam_mask_logits=None,
+        use_prev_mem_frame=True,
+    ):
+        """Run tracking on a single frame based on current inputs and previous memory."""
+        # Retrieve correct image features
+        (
+            image,
+            _,
+            current_vision_feats,
+            current_vision_pos_embeds,
+            feat_sizes,
+        ) = self._get_image_feature(inference_state, frame_idx, batch_size)
+
+        # point and mask should not appear as input simultaneously on the same frame
+        assert point_inputs is None or mask_inputs is None
+        current_out = self.track_step(
+            frame_idx=frame_idx,
+            is_init_cond_frame=is_init_cond_frame,
+            current_vision_feats=current_vision_feats,
+            current_vision_pos_embeds=current_vision_pos_embeds,
+            feat_sizes=feat_sizes,
+            image=image,
+            point_inputs=point_inputs,
+            mask_inputs=mask_inputs,
+            output_dict=output_dict,
+            num_frames=inference_state["num_frames"],
+            track_in_reverse=reverse,
+            run_mem_encoder=run_mem_encoder,
+            prev_sam_mask_logits=prev_sam_mask_logits,
+            use_prev_mem_frame=use_prev_mem_frame,
+        )
+
+        # optionally offload the output to CPU memory to save GPU space
+        storage_device = inference_state["storage_device"]
+        maskmem_features = current_out["maskmem_features"]
+        if maskmem_features is not None:
+            maskmem_features = maskmem_features.to(torch.bfloat16)
+            maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
+        pred_masks_gpu = current_out["pred_masks"]
+        pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True)
+        # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it
+        maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)
+        # object pointer is a small tensor, so we always keep it on GPU memory for fast access
+        obj_ptr = current_out["obj_ptr"]
+        object_score_logits = current_out["object_score_logits"]
+        # make a compact version of this frame's output to reduce the state size
+        compact_current_out = {
+            "maskmem_features": maskmem_features,
+            "maskmem_pos_enc": maskmem_pos_enc,
+            "pred_masks": pred_masks,
+            "obj_ptr": obj_ptr,
+            "object_score_logits": object_score_logits,
+        }
+        if self.use_memory_selection:
+            compact_current_out["iou_score"] = current_out["iou_score"]
+            compact_current_out["eff_iou_score"] = current_out["eff_iou_score"]
+        return compact_current_out, pred_masks_gpu
+
+    def _run_memory_encoder(
+        self,
+        inference_state,
+        frame_idx,
+        batch_size,
+        high_res_masks,
+        object_score_logits,
+        is_mask_from_pts,
+    ):
+        """
+        Run the memory encoder on `high_res_masks`. This is usually after applying
+        non-overlapping constraints to object scores. Since their scores changed, their
+        memory also need to be computed again with the memory encoder.
+        """
+        # Retrieve correct image features
+        image, _, current_vision_feats, _, feat_sizes = self._get_image_feature(
+            inference_state, frame_idx, batch_size
+        )
+        maskmem_features, maskmem_pos_enc = self._encode_new_memory(
+            image=image,
+            current_vision_feats=current_vision_feats,
+            feat_sizes=feat_sizes,
+            pred_masks_high_res=high_res_masks,
+            object_score_logits=object_score_logits,
+            is_mask_from_pts=is_mask_from_pts,
+        )
+
+        # optionally offload the output to CPU memory to save GPU space
+        storage_device = inference_state["storage_device"]
+        maskmem_features = maskmem_features.to(torch.bfloat16)
+        maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
+        # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it
+        maskmem_pos_enc = self._get_maskmem_pos_enc(
+            inference_state, {"maskmem_pos_enc": maskmem_pos_enc}
+        )
+        return maskmem_features, maskmem_pos_enc
+
+    def _get_maskmem_pos_enc(self, inference_state, current_out):
+        """
+        `maskmem_pos_enc` is the same across frames and objects, so we cache it as
+        a constant in the inference session to reduce session storage size.
+        """
+        model_constants = inference_state["constants"]
+        # "out_maskmem_pos_enc" should be either a list of tensors or None
+        out_maskmem_pos_enc = current_out["maskmem_pos_enc"]
+        if out_maskmem_pos_enc is not None:
+            if "maskmem_pos_enc" not in model_constants:
+                assert isinstance(out_maskmem_pos_enc, list)
+                # only take the slice for one object, since it's same across objects
+                maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]
+                model_constants["maskmem_pos_enc"] = maskmem_pos_enc
+            else:
+                maskmem_pos_enc = model_constants["maskmem_pos_enc"]
+            # expand the cached maskmem_pos_enc to the actual batch size
+            batch_size = out_maskmem_pos_enc[0].size(0)
+            expanded_maskmem_pos_enc = [
+                x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc
+            ]
+        else:
+            expanded_maskmem_pos_enc = None
+        return expanded_maskmem_pos_enc
+
+    @torch.inference_mode()
+    def remove_object(self, inference_state, obj_id, strict=False, need_output=True):
+        """
+        Remove an object id from the tracking state. If strict is True, we check whether
+        the object id actually exists and raise an error if it doesn't exist.
+        """
+        old_obj_idx_to_rm = inference_state["obj_id_to_idx"].get(obj_id, None)
+        updated_frames = []
+        # Check whether this object_id to remove actually exists and possibly raise an error.
+        if old_obj_idx_to_rm is None:
+            if not strict:
+                return inference_state["obj_ids"], updated_frames
+            raise RuntimeError(
+                f"Cannot remove object id {obj_id} as it doesn't exist. "
+                f"All existing object ids: {inference_state['obj_ids']}."
+            )
+
+        # If this is the only remaining object id, we simply reset the state.
+        if len(inference_state["obj_id_to_idx"]) == 1:
+            self.clear_all_points_in_video(inference_state)
+            return inference_state["obj_ids"], updated_frames
+
+        # There are still remaining objects after removing this object id. In this case,
+        # we need to delete the object storage from inference state tensors.
+        # Step 0: clear the input on those frames where this object id has point or mask input
+        # (note that this step is required as it might downgrade conditioning frames to
+        # non-conditioning ones)
+        obj_input_frames_inds = set()
+        obj_input_frames_inds.update(
+            inference_state["point_inputs_per_obj"][old_obj_idx_to_rm]
+        )
+        obj_input_frames_inds.update(
+            inference_state["mask_inputs_per_obj"][old_obj_idx_to_rm]
+        )
+        for frame_idx in obj_input_frames_inds:
+            self.clear_all_points_in_frame(
+                inference_state, frame_idx, obj_id, need_output=False
+            )
+
+        # Step 1: Update the object id mapping (note that it must be done after Step 0,
+        # since Step 0 still requires the old object id mappings in inference_state)
+        old_obj_ids = inference_state["obj_ids"]
+        old_obj_inds = list(range(len(old_obj_ids)))
+        remain_old_obj_inds = old_obj_inds.copy()
+        remain_old_obj_inds.remove(old_obj_idx_to_rm)
+        new_obj_ids = [old_obj_ids[old_idx] for old_idx in remain_old_obj_inds]
+        new_obj_inds = list(range(len(new_obj_ids)))
+        # build new mappings
+        old_idx_to_new_idx = dict(zip(remain_old_obj_inds, new_obj_inds))
+        inference_state["obj_id_to_idx"] = dict(zip(new_obj_ids, new_obj_inds))
+        inference_state["obj_idx_to_id"] = dict(zip(new_obj_inds, new_obj_ids))
+        inference_state["obj_ids"] = new_obj_ids
+
+        # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.
+        # (note that "consolidated_frame_inds" doesn't need to be updated in this step as
+        # it's already handled in Step 0)
+        def _map_keys(container):
+            new_kvs = []
+            for k in old_obj_inds:
+                v = container.pop(k)
+                if k in old_idx_to_new_idx:
+                    new_kvs.append((old_idx_to_new_idx[k], v))
+            container.update(new_kvs)
+
+        _map_keys(inference_state["point_inputs_per_obj"])
+        _map_keys(inference_state["mask_inputs_per_obj"])
+        _map_keys(inference_state["output_dict_per_obj"])
+        _map_keys(inference_state["temp_output_dict_per_obj"])
+
+        # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices.
+        def _slice_state(output_dict, storage_key):
+            for frame_idx, out in output_dict[storage_key].items():
+                out["maskmem_features"] = out["maskmem_features"][remain_old_obj_inds]
+                out["maskmem_pos_enc"] = [
+                    x[remain_old_obj_inds] for x in out["maskmem_pos_enc"]
+                ]
+                # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it
+                out["maskmem_pos_enc"] = self._get_maskmem_pos_enc(inference_state, out)
+                out["pred_masks"] = out["pred_masks"][remain_old_obj_inds]
+                out["obj_ptr"] = out["obj_ptr"][remain_old_obj_inds]
+                out["object_score_logits"] = out["object_score_logits"][
+                    remain_old_obj_inds
+                ]
+                if self.use_memory_selection:
+                    out["iou_score"] = out["iou_score"][remain_old_obj_inds]
+                    out["eff_iou_score"] = self.cal_mem_score(
+                        out["object_score_logits"], out["iou_score"]
+                    )  # recalculate the memory frame score
+                # also update the per-object slices
+                self._add_output_per_object(
+                    inference_state, frame_idx, out, storage_key
+                )
+
+        _slice_state(inference_state["output_dict"], "cond_frame_outputs")
+        _slice_state(inference_state["output_dict"], "non_cond_frame_outputs")
+
+        # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, which
+        # could show an updated mask for objects previously occluded by the object being removed
+        if need_output:
+            temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+            for frame_idx in obj_input_frames_inds:
+                is_cond = any(
+                    frame_idx in obj_temp_output_dict["cond_frame_outputs"]
+                    for obj_temp_output_dict in temp_output_dict_per_obj.values()
+                )
+                consolidated_out = self._consolidate_temp_output_across_obj(
+                    inference_state,
+                    frame_idx,
+                    is_cond=is_cond,
+                    run_mem_encoder=False,
+                    consolidate_at_video_res=True,
+                )
+                _, video_res_masks = self._get_orig_video_res_output(
+                    inference_state, consolidated_out["pred_masks_video_res"]
+                )
+                updated_frames.append((frame_idx, video_res_masks))
+
+        return inference_state["obj_ids"], updated_frames
+
+    def _clear_non_cond_mem_around_input(self, inference_state, frame_idx):
+        """
+        Remove the non-conditioning memory around the input frame. When users provide
+        correction clicks, the surrounding frames' non-conditioning memories can still
+        contain outdated object appearance information and could confuse the model.
+
+        This method clears those non-conditioning memories surrounding the interacted
+        frame to avoid giving the model both old and new information about the object.
+        """
+        r = self.memory_temporal_stride_for_eval
+        frame_idx_begin = frame_idx - r * self.num_maskmem
+        frame_idx_end = frame_idx + r * self.num_maskmem
+        batch_size = self._get_obj_num(inference_state)
+        for obj_idx in range(batch_size):
+            obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+            non_cond_frame_outputs = obj_output_dict["non_cond_frame_outputs"]
+            for t in range(frame_idx_begin, frame_idx_end + 1):
+                non_cond_frame_outputs.pop(t, None)
+
+    def _suppress_shrinked_masks(
+        self, pred_masks, new_pred_masks, shrink_threshold=0.3
+    ):
+        area_before = (pred_masks > 0).sum(dim=(-1, -2))
+        area_after = (new_pred_masks > 0).sum(dim=(-1, -2))
+        area_before = torch.clamp(area_before, min=1.0)
+        area_ratio = area_after / area_before
+        keep = area_ratio >= shrink_threshold
+        keep_mask = keep[..., None, None].expand_as(pred_masks)
+        pred_masks_after = torch.where(
+            keep_mask, pred_masks, torch.clamp(pred_masks, max=-10.0)
+        )
+        return pred_masks_after
+
+    def _suppress_object_pw_area_shrinkage(self, pred_masks):
+        """
+        This function suppresses masks that shrink in area after applying pixelwise non-overlapping constriants.
+        Note that the final output can still be overlapping.
+        """
+        # Apply pixel-wise non-overlapping constraint based on mask scores
+        pixel_level_non_overlapping_masks = super()._apply_non_overlapping_constraints(
+            pred_masks
+        )
+        # Fully suppress masks with high shrinkage (probably noisy) based on the pixel wise non-overlapping constraints
+        # NOTE: The output of this function can be a no op if none of the masks shrinked by a large factor.
+        pred_masks = self._suppress_shrinked_masks(
+            pred_masks, pixel_level_non_overlapping_masks
+        )
+        return pred_masks
+
+    def _apply_object_wise_non_overlapping_constraints(
+        self, pred_masks, obj_scores, background_value=-10.0
+    ):
+        """
+        Applies non-overlapping constraints object wise (i.e. only one object can claim the overlapping region)
+        """
+        # Replace pixel scores with object scores
+        pred_masks_single_score = torch.where(
+            pred_masks > 0, obj_scores[..., None, None], background_value
+        )
+        # Apply pixel-wise non-overlapping constraint based on mask scores
+        pixel_level_non_overlapping_masks = super()._apply_non_overlapping_constraints(
+            pred_masks_single_score
+        )
+        # Replace object scores with pixel scores. Note, that now only one object can claim the overlapping region
+        pred_masks = torch.where(
+            pixel_level_non_overlapping_masks > 0,
+            pred_masks,
+            torch.clamp(pred_masks, max=background_value),
+        )
+        return pred_masks

third_party/GraspGen/sam3/sam3/model/sam3_video_base.py

@@ -0,0 +1,1768 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
+
+# pyre-unsafe
+
+import datetime
+import logging
+import math
+import os
+from collections import defaultdict
+from copy import deepcopy
+from enum import Enum
+from typing import Any, Dict, List, Set
+
+import numpy as np
+import numpy.typing as npt
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from sam3 import perflib
+from sam3.logger import get_logger
+from sam3.model.box_ops import fast_diag_box_iou
+from sam3.model.data_misc import BatchedDatapoint
+from sam3.model.sam3_tracker_utils import fill_holes_in_mask_scores, mask_to_box
+from sam3.perflib.masks_ops import mask_iou
+from sam3.train.masks_ops import rle_encode
+from torch import nn, Tensor
+
+logger = get_logger(__name__)
+
+
+class MaskletConfirmationStatus(Enum):
+    UNCONFIRMED = 1  # newly added masklet, not confirmed by any detection yet
+    CONFIRMED = 2  # confirmed by at least one detection
+
+
+class Sam3VideoBase(nn.Module):
+    def __init__(
+        self,
+        detector: nn.Module,
+        tracker: nn.Module,
+        # prob threshold for detection outputs -- only keep detections above this threshold
+        # enters NMS and det-to-track matching
+        score_threshold_detection=0.5,
+        # IoU threshold for detection NMS
+        det_nms_thresh=0.0,
+        # IoU threshold for det-to-track matching -- a detection is considered "matched" to a tracklet it
+        # overlaps with a tracklet above this threshold -- it is often a loose threshold like 0.1
+        assoc_iou_thresh=0.5,
+        # IoU threshold for det-to-track matching, which is used to determine whether a masklet is "unmatched"
+        # by any detections -- it is often a stricter threshold like 0.5
+        trk_assoc_iou_thresh=0.5,
+        # prob threshold for a detection to be added as a new object
+        new_det_thresh=0.0,
+        # hotstart parameters: we hold off the outputs for `hotstart_delay` frames and
+        # 1) remove those tracklets unmatched by any detections based on `hotstart_unmatch_thresh`
+        # 2) remove those tracklets overlapping with one another based on `hotstart_dup_thresh`
+        hotstart_delay=0,
+        hotstart_unmatch_thresh=3,
+        hotstart_dup_thresh=3,
+        # Whether to suppress masks only within hotstart. If False, we can suppress masks even if they start before hotstart period.
+        suppress_unmatched_only_within_hotstart=True,
+        init_trk_keep_alive=0,
+        max_trk_keep_alive=8,
+        min_trk_keep_alive=-4,
+        # Threshold for suppressing overlapping objects based on recent occlusion
+        suppress_overlapping_based_on_recent_occlusion_threshold=0.0,
+        decrease_trk_keep_alive_for_empty_masklets=False,
+        o2o_matching_masklets_enable=False,  # Enable hungarian matching to match existing masklets
+        suppress_det_close_to_boundary=False,
+        fill_hole_area=16,
+        # The maximum number of objects (masklets) to track across all GPUs (for no limit, set it to -1)
+        max_num_objects=-1,
+        recondition_every_nth_frame=-1,
+        # masket confirmation status (to suppress unconfirmed masklets)
+        masklet_confirmation_enable=False,
+        # a masklet is confirmed after being consecutively detected and matched for
+        # `masklet_confirmation_consecutive_det_thresh`
+        masklet_confirmation_consecutive_det_thresh=3,
+        # bbox heuristic parameters
+        reconstruction_bbox_iou_thresh=0.0,
+        reconstruction_bbox_det_score=0.0,
+    ):
+        super().__init__()
+        self.detector = detector
+        self.tracker = tracker
+        self.score_threshold_detection = score_threshold_detection
+        self.det_nms_thresh = det_nms_thresh
+        self.assoc_iou_thresh = assoc_iou_thresh
+        self.trk_assoc_iou_thresh = trk_assoc_iou_thresh
+        self.new_det_thresh = new_det_thresh
+
+        # hotstart parameters
+        if hotstart_delay > 0:
+            assert hotstart_unmatch_thresh <= hotstart_delay
+            assert hotstart_dup_thresh <= hotstart_delay
+        self.hotstart_delay = hotstart_delay
+        self.hotstart_unmatch_thresh = hotstart_unmatch_thresh
+        self.hotstart_dup_thresh = hotstart_dup_thresh
+        self.suppress_unmatched_only_within_hotstart = (
+            suppress_unmatched_only_within_hotstart
+        )
+        self.init_trk_keep_alive = init_trk_keep_alive
+        self.max_trk_keep_alive = max_trk_keep_alive
+        self.min_trk_keep_alive = min_trk_keep_alive
+        self.suppress_overlapping_based_on_recent_occlusion_threshold = (
+            suppress_overlapping_based_on_recent_occlusion_threshold
+        )
+        self.suppress_det_close_to_boundary = suppress_det_close_to_boundary
+        self.decrease_trk_keep_alive_for_empty_masklets = (
+            decrease_trk_keep_alive_for_empty_masklets
+        )
+        self.o2o_matching_masklets_enable = o2o_matching_masklets_enable
+        self.fill_hole_area = fill_hole_area
+        self.eval()
+        self.rank = int(os.getenv("RANK", "0"))
+        self.world_size = int(os.getenv("WORLD_SIZE", "1"))
+        self._dist_pg_cpu = None  # CPU process group (lazy-initialized on first use)
+
+        # the maximum object number
+        if max_num_objects > 0:
+            num_obj_for_compile = math.ceil(max_num_objects / self.world_size)
+        else:
+            max_num_objects = 10000  # no limit
+            num_obj_for_compile = 16
+        logger.info(f"setting {max_num_objects=} and {num_obj_for_compile=}")
+        self.max_num_objects = max_num_objects
+        self.num_obj_for_compile = num_obj_for_compile
+        self.recondition_every_nth_frame = recondition_every_nth_frame
+        self.masklet_confirmation_enable = masklet_confirmation_enable
+        self.masklet_confirmation_consecutive_det_thresh = (
+            masklet_confirmation_consecutive_det_thresh
+        )
+        self.reconstruction_bbox_iou_thresh = reconstruction_bbox_iou_thresh
+        self.reconstruction_bbox_det_score = reconstruction_bbox_det_score
+
+    @property
+    def device(self):
+        self._device = getattr(self, "_device", None) or next(self.parameters()).device
+        return self._device
+
+    def _init_dist_pg_cpu(self):
+        # a short 3-min timeout to quickly detect any synchronization failures
+        timeout_sec = int(os.getenv("SAM3_COLLECTIVE_OP_TIMEOUT_SEC", "180"))
+        timeout = datetime.timedelta(seconds=timeout_sec)
+        self._dist_pg_cpu = dist.new_group(backend="gloo", timeout=timeout)
+
+    def broadcast_python_obj_cpu(self, python_obj_list, src):
+        if self._dist_pg_cpu is None:
+            self._init_dist_pg_cpu()
+        dist.broadcast_object_list(python_obj_list, src=src, group=self._dist_pg_cpu)
+
+    def _det_track_one_frame(
+        self,
+        frame_idx: int,
+        num_frames: int,
+        reverse: bool,
+        input_batch: BatchedDatapoint,
+        geometric_prompt: Any,
+        tracker_states_local: List[Any],
+        tracker_metadata_prev: Dict[str, Any],
+        feature_cache: Dict,
+        orig_vid_height: int,
+        orig_vid_width: int,
+        is_image_only: bool = False,
+        allow_new_detections: bool = True,
+    ):
+        """
+        This function handles one-step inference for the DenseTracking model in an SPMD manner.
+        At a high-level, all GPUs execute the same function calls as if it's done on a single GPU,
+        while under the hood, some function calls involve distributed computation based on sharded
+        SAM2 states.
+
+        - `input_batch` contains image and other inputs on the entire video; it should be identical across GPUs
+        - `tracker_states_local` holds the local masklet information in this GPU shard
+        - `tracker_metadata_prev` manages the metadata for SAM2 objects, such as which masklet is hold on which GPUs
+          it contains both global and local masklet information
+        """
+
+        # Step 1: run backbone and detector in a distributed manner -- this is done via Sam3ImageOnVideoMultiGPU,
+        # a MultiGPU model (assigned to `self.detector`) that shards frames in a round-robin manner.
+        # It returns a "det_out" dict for `frame_idx` and fills SAM2 backbone features for `frame_idx`
+        # into `feature_cache`. Despite its distributed inference under the hood, the results would be
+        # the same as if it is running backbone and detector for every frame on a single GPU.
+        det_out = self.run_backbone_and_detection(
+            frame_idx=frame_idx,
+            num_frames=num_frames,
+            reverse=reverse,
+            input_batch=input_batch,
+            geometric_prompt=geometric_prompt,
+            feature_cache=feature_cache,
+            allow_new_detections=allow_new_detections,
+        )
+
+        # Step 2: each GPU propagates its local SAM2 states to get the SAM2 prediction masks.
+        # the returned `tracker_low_res_masks_global` contains the concatenated masklet predictions
+        # gathered from all GPUs (as if they are propagated on a single GPU). Note that this step only
+        # runs the SAM2 propagation step, but doesn't encode new memory for the predicted masks;
+        # we defer memory encoding to `run_tracker_update_execution_phase` after resolving all heuristics.
+        if tracker_metadata_prev == {}:
+            # initialize masklet metadata if it's uninitialized (empty dict)
+            tracker_metadata_prev.update(self._initialize_metadata())
+        tracker_low_res_masks_global, tracker_obj_scores_global = (
+            self.run_tracker_propagation(
+                frame_idx=frame_idx,
+                num_frames=num_frames,
+                reverse=reverse,
+                tracker_states_local=tracker_states_local,
+                tracker_metadata_prev=tracker_metadata_prev,
+            )
+        )
+
+        # Step 3: based on detection outputs and the propagated SAM2 prediction masks, we make plans
+        # for SAM2 masklet updates (i.e. which objects to add and remove, how to load-balance them, etc).
+        # We also run SAM2 memory encoder globally in this step to resolve non-overlapping constraints.
+        # **This step should involve all the heuristics needed for any updates.** Most of the update
+        # planning will be done on the master rank (GPU 0) and the resulting plan `tracker_update_plan` is
+        # broadcasted to other GPUs (to be executed in a distributed manner). This step also generates the
+        # new masklet metadata `tracker_metadata_new` (based on its previous version `tracker_metadata_prev`).
+        tracker_update_plan, tracker_metadata_new = (
+            self.run_tracker_update_planning_phase(
+                frame_idx=frame_idx,
+                num_frames=num_frames,
+                reverse=reverse,
+                det_out=det_out,
+                tracker_low_res_masks_global=tracker_low_res_masks_global,
+                tracker_obj_scores_global=tracker_obj_scores_global,
+                tracker_metadata_prev=tracker_metadata_prev,
+                tracker_states_local=tracker_states_local,
+                is_image_only=is_image_only,
+            )
+        )
+
+        # Get reconditioning info from the update plan
+        reconditioned_obj_ids = tracker_update_plan.get("reconditioned_obj_ids", set())
+        det_to_matched_trk_obj_ids = tracker_update_plan.get(
+            "det_to_matched_trk_obj_ids", {}
+        )
+
+        # Step 4: based on `tracker_update_plan`, each GPU executes the update w.r.t. its local SAM2 inference states
+        tracker_states_local_new = self.run_tracker_update_execution_phase(
+            frame_idx=frame_idx,
+            num_frames=num_frames,
+            reverse=reverse,
+            det_out=det_out,
+            tracker_states_local=tracker_states_local,
+            tracker_update_plan=tracker_update_plan,
+            orig_vid_height=orig_vid_height,
+            orig_vid_width=orig_vid_width,
+            feature_cache=feature_cache,
+        )
+
+        # Step 5: finally, build the outputs for this frame (it only needs to be done on GPU 0 since
+        # only GPU 0 will send outputs to the server).
+        if self.rank == 0:
+            obj_id_to_mask = self.build_outputs(
+                frame_idx=frame_idx,
+                num_frames=num_frames,
+                reverse=reverse,
+                det_out=det_out,
+                tracker_low_res_masks_global=tracker_low_res_masks_global,
+                tracker_obj_scores_global=tracker_obj_scores_global,
+                tracker_metadata_prev=tracker_metadata_prev,
+                tracker_update_plan=tracker_update_plan,
+                orig_vid_height=orig_vid_height,
+                orig_vid_width=orig_vid_width,
+                reconditioned_obj_ids=reconditioned_obj_ids,
+                det_to_matched_trk_obj_ids=det_to_matched_trk_obj_ids,
+            )
+            obj_id_to_score = tracker_metadata_new["obj_id_to_score"]
+        else:
+            obj_id_to_mask, obj_id_to_score = {}, {}  # dummy outputs on other GPUs
+        # a few statistics for the current frame as a part of the output
+        frame_stats = {
+            "num_obj_tracked": np.sum(tracker_metadata_new["num_obj_per_gpu"]),
+            "num_obj_dropped": tracker_update_plan["num_obj_dropped_due_to_limit"],
+        }
+        # add tracker scores to metadata, it should be fired for frames except the first frame
+        if tracker_obj_scores_global.shape[0] > 0:
+            # Convert tracker_obj_scores_global to sigmoid scores before updating
+            tracker_obj_scores_global = tracker_obj_scores_global.sigmoid().tolist()
+            tracker_obj_ids = tracker_metadata_prev["obj_ids_all_gpu"]
+            tracker_metadata_new["obj_id_to_tracker_score_frame_wise"][
+                frame_idx
+            ].update(dict(zip(tracker_obj_ids, tracker_obj_scores_global)))
+        return (
+            obj_id_to_mask,  # a dict: obj_id --> output mask
+            obj_id_to_score,  # a dict: obj_id --> output score (prob)
+            tracker_states_local_new,
+            tracker_metadata_new,
+            frame_stats,
+            tracker_obj_scores_global,  # a dict: obj_id --> tracker frame-level scores
+        )
+
+    def _suppress_detections_close_to_boundary(self, boxes, margin=0.025):
+        """
+        Suppress detections too close to image edges (for normalized boxes).
+
+        boxes: (N, 4) in xyxy format, normalized [0,1]
+        margin: fraction of image
+        """
+        x_min, y_min, x_max, y_max = boxes.unbind(-1)
+        x_c = (x_min + x_max) / 2
+        y_c = (y_min + y_max) / 2
+        keep = (
+            (x_c > margin)
+            & (x_c < 1.0 - margin)
+            & (y_c > margin)
+            & (y_c < 1.0 - margin)
+        )
+
+        return keep
+
+    def run_backbone_and_detection(
+        self,
+        frame_idx: int,
+        num_frames: int,
+        input_batch: BatchedDatapoint,
+        geometric_prompt: Any,
+        feature_cache: Dict,
+        reverse: bool,
+        allow_new_detections: bool,
+    ):
+        # Step 1: if text feature is not cached in `feature_cache`, compute and cache it
+        text_batch_key = tuple(input_batch.find_text_batch)
+        if "text" not in feature_cache or text_batch_key not in feature_cache["text"]:
+            text_outputs = self.detector.backbone.forward_text(
+                input_batch.find_text_batch, device=self.device
+            )
+            # note: we only cache the text feature of the most recent prompt
+            feature_cache["text"] = {text_batch_key: text_outputs}
+        else:
+            text_outputs = feature_cache["text"][text_batch_key]
+
+        # Step 2: run backbone, detector, and post-processing with NMS
+        if "multigpu_buffer" not in feature_cache:
+            # "multigpu_buffer" is a buffer cache used by `self.detector` and it needs
+            # to be passed to `forward_video_grounding_multigpu` for every call
+            feature_cache["multigpu_buffer"] = {}
+
+        # Extract max_frame_num_to_track from feature_cache if available
+        tracking_bounds = feature_cache.get("tracking_bounds", {})
+        max_frame_num_to_track = tracking_bounds.get("max_frame_num_to_track")
+        start_frame_idx = tracking_bounds.get("propagate_in_video_start_frame_idx")
+
+        sam3_image_out, _ = self.detector.forward_video_grounding_multigpu(
+            backbone_out={
+                "img_batch_all_stages": input_batch.img_batch,
+                **text_outputs,
+            },
+            find_inputs=input_batch.find_inputs,
+            geometric_prompt=geometric_prompt,
+            frame_idx=frame_idx,
+            num_frames=num_frames,
+            multigpu_buffer=feature_cache["multigpu_buffer"],
+            track_in_reverse=reverse,
+            # also get the SAM2 backbone features
+            return_tracker_backbone_feats=True,
+            # run NMS as a part of distributed computation
+            run_nms=self.det_nms_thresh > 0.0,
+            nms_prob_thresh=self.score_threshold_detection,
+            nms_iou_thresh=self.det_nms_thresh,
+            # pass max_frame_num_to_track to respect tracking limits
+            max_frame_num_to_track=max_frame_num_to_track,
+            propagate_in_video_start_frame_idx=start_frame_idx,
+        )
+        # note: detections in `sam3_image_out` has already gone through NMS
+        pred_probs = sam3_image_out["pred_logits"].squeeze(-1).sigmoid()
+        if not allow_new_detections:
+            pred_probs = pred_probs - 1e8  # make sure no detections are kept
+        pred_boxes_xyxy = sam3_image_out["pred_boxes_xyxy"]
+        pred_masks = sam3_image_out["pred_masks"]
+        # get the positive detection outputs above threshold
+        pos_pred_idx = torch.where(pred_probs > self.score_threshold_detection)
+        det_out = {
+            "bbox": pred_boxes_xyxy[pos_pred_idx[0], pos_pred_idx[1]],
+            "mask": pred_masks[pos_pred_idx[0], pos_pred_idx[1]],
+            "scores": pred_probs[pos_pred_idx[0], pos_pred_idx[1]],
+        }
+
+        # Step 3: build SAM2 backbone features and store them in `feature_cache`
+        backbone_cache = {}
+        sam_mask_decoder = self.tracker.sam_mask_decoder
+        tracker_backbone_fpn = [
+            sam_mask_decoder.conv_s0(sam3_image_out["tracker_backbone_fpn_0"]),
+            sam_mask_decoder.conv_s1(sam3_image_out["tracker_backbone_fpn_1"]),
+            sam3_image_out["tracker_backbone_fpn_2"],  # fpn_2 doesn't need conv
+        ]
+        tracker_backbone_out = {
+            "vision_features": tracker_backbone_fpn[-1],  # top-level feature
+            "vision_pos_enc": sam3_image_out["tracker_backbone_pos_enc"],
+            "backbone_fpn": tracker_backbone_fpn,
+        }
+        backbone_cache["tracker_backbone_out"] = tracker_backbone_out
+        feature_cache[frame_idx] = (
+            input_batch.img_batch[frame_idx],
+            backbone_cache,
+        )
+        # remove from `feature_cache` old features to save GPU memory
+        feature_cache.pop(frame_idx - 1 if not reverse else frame_idx + 1, None)
+        return det_out
+
+    def run_tracker_propagation(
+        self,
+        frame_idx: int,
+        num_frames: int,
+        reverse: bool,
+        tracker_states_local: List[Any],
+        tracker_metadata_prev: Dict[str, npt.NDArray],
+    ):
+        # Step 1: propagate the local SAM2 states to get the current frame's prediction
+        # `low_res_masks_local` of the existing masklets on this GPU
+        # - obj_ids_local: List[int] -- list of object IDs
+        # - low_res_masks_local: Tensor -- (num_local_obj, H_mask, W_mask)
+        obj_ids_local, low_res_masks_local, obj_scores_local = (
+            self._propogate_tracker_one_frame_local_gpu(
+                tracker_states_local, frame_idx=frame_idx, reverse=reverse
+            )
+        )
+
+        assert np.all(
+            obj_ids_local == tracker_metadata_prev["obj_ids_per_gpu"][self.rank]
+        ), "{} != {}".format(
+            obj_ids_local, tracker_metadata_prev["obj_ids_per_gpu"][self.rank]
+        )
+
+        # Step 2: all-gather `low_res_masks_local` into `low_res_masks_global`
+        # - low_res_masks_global: Tensor -- (num_global_obj, H_mask, W_mask)
+        _, H_mask, W_mask = low_res_masks_local.shape
+        if self.world_size > 1:
+            # `low_res_masks_local` and `obj_scores_local` need to be contiguous and float32
+            # (they could be non-contiguous due to slicing and/or bfloat16 due to autocast)
+            low_res_masks_local = low_res_masks_local.float().contiguous()
+            obj_scores_local = obj_scores_local.float().contiguous()
+            num_obj_this_gpu = tracker_metadata_prev["num_obj_per_gpu"][self.rank]
+            assert low_res_masks_local.size(0) == num_obj_this_gpu
+            assert obj_scores_local.size(0) == num_obj_this_gpu
+            low_res_masks_peers = [
+                low_res_masks_local.new_empty(num_obj, H_mask, W_mask)
+                for num_obj in tracker_metadata_prev["num_obj_per_gpu"]
+            ]
+            obj_scores_peers = [
+                obj_scores_local.new_empty(num_obj)
+                for num_obj in tracker_metadata_prev["num_obj_per_gpu"]
+            ]
+            dist.all_gather(low_res_masks_peers, low_res_masks_local)
+            dist.all_gather(obj_scores_peers, obj_scores_local)
+            low_res_masks_global = torch.cat(low_res_masks_peers, dim=0)
+            obj_scores_global = torch.cat(obj_scores_peers, dim=0)
+        else:
+            low_res_masks_global = low_res_masks_local
+            obj_scores_global = obj_scores_local
+        return low_res_masks_global, obj_scores_global
+
+    def _recondition_masklets(
+        self,
+        frame_idx,
+        det_out: Dict[str, Tensor],
+        trk_id_to_max_iou_high_conf_det: List[int],
+        tracker_states_local: List[Any],
+        tracker_metadata: Dict[str, npt.NDArray],
+        tracker_obj_scores_global: Tensor,
+    ):
+        # Recondition the masklets based on the new detections
+        for trk_obj_id, det_idx in trk_id_to_max_iou_high_conf_det.items():
+            new_mask = det_out["mask"][det_idx : det_idx + 1]
+            input_mask_res = self.tracker.input_mask_size
+            new_mask_binary = (
+                F.interpolate(
+                    new_mask.unsqueeze(1),
+                    size=(input_mask_res, input_mask_res),
+                    mode="bilinear",
+                    align_corners=False,
+                ).squeeze(1)[0]
+                > 0
+            )
+            HIGH_CONF_THRESH = 0.8
+            reconditioned_states_idx = set()
+            obj_idx = np.where(tracker_metadata["obj_ids_all_gpu"] == trk_obj_id)[
+                0
+            ].item()
+            obj_score = tracker_obj_scores_global[obj_idx]
+            for state_idx, inference_state in enumerate(tracker_states_local):
+                if (
+                    trk_obj_id in inference_state["obj_ids"]
+                    # NOTE: Goal of this condition is to avoid reconditioning masks that are occluded/low qualiy.
+                    # Unfortunately, these can get reconditioned anyway due to batching. We should consider removing these heuristics.
+                    and obj_score > HIGH_CONF_THRESH
+                ):
+                    logger.debug(
+                        f"Adding new mask for track {trk_obj_id} at frame {frame_idx}. Objects {inference_state['obj_ids']} are all reconditioned."
+                    )
+                    self.tracker.add_new_mask(
+                        inference_state=inference_state,
+                        frame_idx=frame_idx,
+                        obj_id=trk_obj_id,
+                        mask=new_mask_binary,
+                    )
+                    reconditioned_states_idx.add(state_idx)
+
+            for idx in reconditioned_states_idx:
+                self.tracker.propagate_in_video_preflight(
+                    tracker_states_local[idx], run_mem_encoder=True
+                )
+        return tracker_states_local
+
+    def run_tracker_update_planning_phase(
+        self,
+        frame_idx: int,
+        num_frames: int,
+        reverse: bool,
+        det_out: Dict[str, Tensor],
+        tracker_low_res_masks_global: Tensor,
+        tracker_obj_scores_global: Tensor,
+        tracker_metadata_prev: Dict[str, npt.NDArray],
+        tracker_states_local: List[Any],
+        is_image_only: bool = False,
+    ):
+        # initialize new metadata from previous metadata (its values will be updated later)
+        tracker_metadata_new = {
+            "obj_ids_per_gpu": deepcopy(tracker_metadata_prev["obj_ids_per_gpu"]),
+            "obj_ids_all_gpu": None,  # will be filled later
+            "num_obj_per_gpu": deepcopy(tracker_metadata_prev["num_obj_per_gpu"]),
+            "obj_id_to_score": deepcopy(tracker_metadata_prev["obj_id_to_score"]),
+            "obj_id_to_tracker_score_frame_wise": deepcopy(
+                tracker_metadata_prev["obj_id_to_tracker_score_frame_wise"]
+            ),
+            "obj_id_to_last_occluded": {},  # will be filled later
+            "max_obj_id": deepcopy(tracker_metadata_prev["max_obj_id"]),
+        }
+
+        # Initialize reconditioned_obj_ids early to avoid UnboundLocalError
+        reconditioned_obj_ids = set()
+
+        # Step 1: make the update plan and resolve heuristics on GPU 0
+        det_mask_preds: Tensor = det_out["mask"]  # low-res mask logits
+        det_scores_np: npt.NDArray = det_out["scores"].float().cpu().numpy()
+        det_bbox_xyxy: Tensor = det_out["bbox"]
+        if self.rank == 0:
+            # a) match detector and tracker masks and find new objects
+            (
+                new_det_fa_inds,
+                unmatched_trk_obj_ids,
+                det_to_matched_trk_obj_ids,
+                trk_id_to_max_iou_high_conf_det,
+                empty_trk_obj_ids,
+            ) = self._associate_det_trk(
+                det_masks=det_mask_preds,
+                det_scores_np=det_scores_np,
+                trk_masks=tracker_low_res_masks_global,
+                trk_obj_ids=tracker_metadata_prev["obj_ids_all_gpu"],
+            )
+            if self.suppress_det_close_to_boundary:
+                keep = self._suppress_detections_close_to_boundary(
+                    det_bbox_xyxy[new_det_fa_inds]
+                )
+                new_det_fa_inds = new_det_fa_inds[keep.cpu().numpy()]
+
+            # check whether we've hit the maximum number of objects we can track (and if so, drop some detections)
+            prev_obj_num = np.sum(tracker_metadata_prev["num_obj_per_gpu"])
+            new_det_num = len(new_det_fa_inds)
+            num_obj_dropped_due_to_limit = 0
+            if not is_image_only and prev_obj_num + new_det_num > self.max_num_objects:
+                logger.warning(
+                    f"hitting {self.max_num_objects=} with {new_det_num=} and {prev_obj_num=}"
+                )
+                new_det_num_to_keep = self.max_num_objects - prev_obj_num
+                num_obj_dropped_due_to_limit = new_det_num - new_det_num_to_keep
+                new_det_fa_inds = self._drop_new_det_with_obj_limit(
+                    new_det_fa_inds, det_scores_np, new_det_num_to_keep
+                )
+                assert len(new_det_fa_inds) == new_det_num_to_keep
+                new_det_num = len(new_det_fa_inds)
+
+            # assign object IDs to new detections and decide which GPU to place them
+            new_det_start_obj_id = tracker_metadata_prev["max_obj_id"] + 1
+            new_det_obj_ids = new_det_start_obj_id + np.arange(new_det_num)
+            prev_workload_per_gpu = tracker_metadata_prev["num_obj_per_gpu"]
+            new_det_gpu_ids = self._assign_new_det_to_gpus(
+                new_det_num=new_det_num,
+                prev_workload_per_gpu=prev_workload_per_gpu,
+            )
+
+            # b) handle hotstart heuristics to remove objects
+            # here `rank0_metadata` contains metadata stored on (and only accessible to) GPU 0;
+            # we avoid broadcasting them to other GPUs to save communication cost, assuming
+            # that `rank0_metadata` is not needed by other GPUs
+            rank0_metadata_new = deepcopy(tracker_metadata_prev["rank0_metadata"])
+            if not hasattr(self, "_warm_up_complete") or self._warm_up_complete:
+                obj_ids_newly_removed, rank0_metadata_new = self._process_hotstart(
+                    frame_idx=frame_idx,
+                    num_frames=num_frames,
+                    reverse=reverse,
+                    det_to_matched_trk_obj_ids=det_to_matched_trk_obj_ids,
+                    new_det_obj_ids=new_det_obj_ids,
+                    empty_trk_obj_ids=empty_trk_obj_ids,
+                    unmatched_trk_obj_ids=unmatched_trk_obj_ids,
+                    rank0_metadata=rank0_metadata_new,
+                    tracker_metadata=tracker_metadata_prev,
+                )
+            else:
+                # if warm-up is not complete, we don't remove any objects
+                obj_ids_newly_removed = set()
+            tracker_metadata_new["rank0_metadata"] = rank0_metadata_new
+
+        # Step 2: broadcast the update plan to other GPUs
+        NUM_BROADCAST_ITEMS = 9
+        if self.rank == 0 and self.world_size > 1:
+            # `num_obj_per_gpu_on_rank0` is used for metadata consistency check on other GPUs
+            # (it's a small array with length==self.world_size, so broadcasting it is cheap)
+            num_obj_per_gpu_on_rank0 = tracker_metadata_prev["num_obj_per_gpu"]
+            update_plan = [
+                new_det_fa_inds,
+                new_det_obj_ids,
+                new_det_gpu_ids,
+                num_obj_per_gpu_on_rank0,
+                unmatched_trk_obj_ids,
+                det_to_matched_trk_obj_ids,
+                obj_ids_newly_removed,
+                num_obj_dropped_due_to_limit,
+                trk_id_to_max_iou_high_conf_det,
+            ]
+            assert len(update_plan) == NUM_BROADCAST_ITEMS, (
+                f"Manually update NUM_BROADCAST_ITEMS to be: {len(update_plan)}"
+            )
+            self.broadcast_python_obj_cpu(update_plan, src=0)
+        elif self.rank > 0 and self.world_size > 1:
+            update_plan = [
+                None
+            ] * NUM_BROADCAST_ITEMS  # other ranks receive the plan from rank 0
+            self.broadcast_python_obj_cpu(update_plan, src=0)
+            (
+                new_det_fa_inds,
+                new_det_obj_ids,
+                new_det_gpu_ids,
+                num_obj_per_gpu_on_rank0,
+                unmatched_trk_obj_ids,
+                det_to_matched_trk_obj_ids,
+                obj_ids_newly_removed,
+                num_obj_dropped_due_to_limit,
+                trk_id_to_max_iou_high_conf_det,
+            ) = update_plan
+            # metadata consistency check: verify that the received `num_obj_per_gpu_on_rank0` is consistent with the local metadata
+            # it's critical that all GPUs agree on the previous number of objects (otherwise the inference might hang or fail silently)
+            if not np.all(
+                num_obj_per_gpu_on_rank0 == tracker_metadata_prev["num_obj_per_gpu"]
+            ):
+                raise RuntimeError(
+                    f"{self.rank=} received {num_obj_per_gpu_on_rank0=}, which is inconsistent with local record "
+                    f"{tracker_metadata_prev['num_obj_per_gpu']=}. There's likely a bug in update planning or execution."
+                )
+
+        # `tracker_update_plan` should be identical on all GPUs after broadcasting
+        tracker_update_plan = {
+            "new_det_fa_inds": new_det_fa_inds,  # npt.NDArray
+            "new_det_obj_ids": new_det_obj_ids,  # npt.NDArray
+            "new_det_gpu_ids": new_det_gpu_ids,  # npt.NDArray
+            "unmatched_trk_obj_ids": unmatched_trk_obj_ids,  # npt.NDArray
+            "det_to_matched_trk_obj_ids": det_to_matched_trk_obj_ids,  # dict
+            "obj_ids_newly_removed": obj_ids_newly_removed,  # set
+            "num_obj_dropped_due_to_limit": num_obj_dropped_due_to_limit,  # int
+            "trk_id_to_max_iou_high_conf_det": trk_id_to_max_iou_high_conf_det,  # dict
+            "reconditioned_obj_ids": reconditioned_obj_ids,  # set
+        }
+
+        # Step 3 (optional): recondition masklets based on high-confidence detections before memory encoding
+        # NOTE: Running this in execution phase (after memory encoding) can lead to suboptimal results
+        should_recondition_iou = False
+
+        # Evaluate tracklets for reconditioning based on bbox IoU mismatch with detections
+        if (
+            self.reconstruction_bbox_iou_thresh > 0
+            and len(trk_id_to_max_iou_high_conf_det) > 0
+        ):
+            for trk_obj_id, det_idx in trk_id_to_max_iou_high_conf_det.items():
+                det_box = det_out["bbox"][det_idx]
+                det_score = det_out["scores"][det_idx]
+
+                try:
+                    trk_idx = list(tracker_metadata_prev["obj_ids_all_gpu"]).index(
+                        trk_obj_id
+                    )
+                except ValueError:
+                    continue  # Skip if tracklet not found
+
+                tracker_mask = tracker_low_res_masks_global[trk_idx]
+                mask_binary = tracker_mask > 0
+                mask_area = mask_binary.sum().item()
+
+                if mask_area == 0:
+                    continue  # Skip tracklets with zero mask area
+
+                # Get bounding box from SAM2 mask and convert to normalized coordinates
+                tracker_box_pixels = (
+                    mask_to_box(mask_binary.unsqueeze(0).unsqueeze(0))
+                    .squeeze(0)
+                    .squeeze(0)
+                )
+                mask_height, mask_width = tracker_mask.shape[-2:]
+                tracker_box_normalized = torch.tensor(
+                    [
+                        tracker_box_pixels[0] / mask_width,
+                        tracker_box_pixels[1] / mask_height,
+                        tracker_box_pixels[2] / mask_width,
+                        tracker_box_pixels[3] / mask_height,
+                    ],
+                    device=tracker_box_pixels.device,
+                )
+
+                # Compute IoU between detection and SAM2 tracklet bounding boxes
+                det_box_batch = det_box.unsqueeze(0)
+                tracker_box_batch = tracker_box_normalized.unsqueeze(0)
+                iou = fast_diag_box_iou(det_box_batch, tracker_box_batch)[0]
+
+                if (
+                    iou < self.reconstruction_bbox_iou_thresh
+                    and det_score >= self.reconstruction_bbox_det_score
+                ):
+                    should_recondition_iou = True
+                    reconditioned_obj_ids.add(trk_obj_id)
+
+        should_recondition_periodic = (
+            self.recondition_every_nth_frame > 0
+            and frame_idx % self.recondition_every_nth_frame == 0
+            and len(trk_id_to_max_iou_high_conf_det) > 0
+        )
+
+        # Recondition if periodic or IoU condition met
+        if should_recondition_periodic or should_recondition_iou:
+            self._recondition_masklets(
+                frame_idx,
+                det_out,
+                trk_id_to_max_iou_high_conf_det,
+                tracker_states_local,
+                tracker_metadata_prev,
+                tracker_obj_scores_global,
+            )
+
+        # Step 4: Run SAM2 memory encoder on the current frame's prediction masks
+        # This is done on all GPUs
+        batch_size = tracker_low_res_masks_global.size(0)
+        if batch_size > 0:
+            if not hasattr(self, "_warm_up_complete") or self._warm_up_complete:
+                if self.suppress_overlapping_based_on_recent_occlusion_threshold > 0.0:
+                    # NOTE: tracker_low_res_masks_global is updated in-place then returned
+                    tracker_low_res_masks_global = (
+                        self._suppress_overlapping_based_on_recent_occlusion(
+                            frame_idx,
+                            tracker_low_res_masks_global,
+                            tracker_metadata_prev,
+                            tracker_metadata_new,
+                            obj_ids_newly_removed,
+                            reverse,
+                        )
+                    )
+
+            self._tracker_update_memories(
+                tracker_states_local,
+                frame_idx,
+                tracker_metadata=tracker_metadata_prev,
+                low_res_masks=tracker_low_res_masks_global,
+            )
+
+        # Step 4: update the SAM2 metadata based on the update plan
+        # note: except for "rank0_metadata" (that is only available on GPU 0),
+        # the updated `tracker_metadata_new` should be identical on all GPUs
+        for rank in range(self.world_size):
+            new_det_obj_ids_this_gpu = new_det_obj_ids[new_det_gpu_ids == rank]
+            updated_obj_ids_this_gpu = tracker_metadata_new["obj_ids_per_gpu"][rank]
+            if len(new_det_obj_ids_this_gpu) > 0:
+                updated_obj_ids_this_gpu = np.concatenate(
+                    [updated_obj_ids_this_gpu, new_det_obj_ids_this_gpu]
+                )
+            if len(obj_ids_newly_removed) > 0:
+                is_removed = np.isin(
+                    updated_obj_ids_this_gpu, list(obj_ids_newly_removed)
+                )
+                updated_obj_ids_this_gpu = updated_obj_ids_this_gpu[~is_removed]
+            tracker_metadata_new["obj_ids_per_gpu"][rank] = updated_obj_ids_this_gpu
+            tracker_metadata_new["num_obj_per_gpu"][rank] = len(
+                updated_obj_ids_this_gpu
+            )
+        tracker_metadata_new["obj_ids_all_gpu"] = np.concatenate(
+            tracker_metadata_new["obj_ids_per_gpu"]
+        )
+        # update object scores and the maximum object ID assigned so far
+        if len(new_det_obj_ids) > 0:
+            tracker_metadata_new["obj_id_to_score"].update(
+                zip(new_det_obj_ids, det_scores_np[new_det_fa_inds])
+            )
+            # tracker scores are not available for new objects, use det score instead.
+            tracker_metadata_new["obj_id_to_tracker_score_frame_wise"][
+                frame_idx
+            ].update(zip(new_det_obj_ids, det_scores_np[new_det_fa_inds]))
+            tracker_metadata_new["max_obj_id"] = max(
+                tracker_metadata_new["max_obj_id"],
+                np.max(new_det_obj_ids),
+            )
+        # for removed objects, we set their scores to a very low value (-1e4) but still
+        # keep them in "obj_id_to_score" (it's easier to handle outputs this way)
+        for obj_id in obj_ids_newly_removed:
+            tracker_metadata_new["obj_id_to_score"][obj_id] = -1e4
+            tracker_metadata_new["obj_id_to_tracker_score_frame_wise"][frame_idx][
+                obj_id
+            ] = -1e4
+            tracker_metadata_new["obj_id_to_last_occluded"].pop(obj_id, None)
+        # check that "rank0_metadata" is in tracker_metadata_new if and only if it's GPU 0
+        assert ("rank0_metadata" in tracker_metadata_new) == (self.rank == 0)
+        if self.rank == 0 and self.masklet_confirmation_enable:
+            rank0_metadata = self.update_masklet_confirmation_status(
+                rank0_metadata=tracker_metadata_new["rank0_metadata"],
+                obj_ids_all_gpu_prev=tracker_metadata_prev["obj_ids_all_gpu"],
+                obj_ids_all_gpu_updated=tracker_metadata_new["obj_ids_all_gpu"],
+                det_to_matched_trk_obj_ids=det_to_matched_trk_obj_ids,
+                new_det_obj_ids=new_det_obj_ids,
+            )
+            tracker_metadata_new["rank0_metadata"] = rank0_metadata
+
+        return tracker_update_plan, tracker_metadata_new
+
+    def _suppress_overlapping_based_on_recent_occlusion(
+        self,
+        frame_idx: int,
+        tracker_low_res_masks_global: Tensor,
+        tracker_metadata_prev: Dict[str, Any],
+        tracker_metadata_new: Dict[str, Any],
+        obj_ids_newly_removed: Set[int],
+        reverse: bool = False,
+    ):
+        """
+        Suppress overlapping masks based on the most recent occlusion information. If an object is removed by hotstart, we always suppress it if it overlaps with any other object.
+        Args:
+            frame_idx (int): The current frame index.
+            tracker_low_res_masks_global (Tensor): The low-resolution masks for the current frame.
+            tracker_metadata_prev (Dict[str, Any]): The metadata from the previous frame.
+            tracker_metadata_new (Dict[str, Any]): The metadata for the current frame.
+            obj_ids_newly_removed (Set[int]): The object IDs that have been removed.
+        Return:
+            Tensor: The updated low-resolution masks with some objects suppressed.
+        """
+        obj_ids_global = tracker_metadata_prev["obj_ids_all_gpu"]
+        binary_tracker_low_res_masks_global = tracker_low_res_masks_global > 0
+        batch_size = tracker_low_res_masks_global.size(0)
+        if batch_size > 0:
+            assert len(obj_ids_global) == batch_size, (
+                f"Mismatch in number of objects: {len(obj_ids_global)} vs {batch_size}"
+            )
+            NEVER_OCCLUDED = -1
+            ALWAYS_OCCLUDED = 100000  # This value should be larger than any possible frame index, indicates that the object was removed by hotstart logic
+            last_occluded_prev = torch.cat(
+                [
+                    tracker_metadata_prev["obj_id_to_last_occluded"].get(
+                        obj_id,
+                        torch.full(
+                            (1,),
+                            fill_value=(
+                                NEVER_OCCLUDED
+                                if obj_id not in obj_ids_newly_removed
+                                else ALWAYS_OCCLUDED
+                            ),
+                            device=binary_tracker_low_res_masks_global.device,
+                            dtype=torch.long,
+                        ),
+                    )
+                    for obj_id in obj_ids_global
+                ],
+                dim=0,
+            )
+            to_suppress = self._get_objects_to_suppress_based_on_most_recently_occluded(
+                binary_tracker_low_res_masks_global,
+                last_occluded_prev,
+                obj_ids_global,
+                frame_idx,
+                reverse,
+            )
+
+            # Update metadata with occlusion information
+            is_obj_occluded = ~(binary_tracker_low_res_masks_global.any(dim=(-1, -2)))
+            is_obj_occluded_or_suppressed = is_obj_occluded | to_suppress
+            last_occluded_new = last_occluded_prev.clone()
+            last_occluded_new[is_obj_occluded_or_suppressed] = frame_idx
+            # Slice out the last occluded frame for each object
+            tracker_metadata_new["obj_id_to_last_occluded"] = {
+                obj_id: last_occluded_new[obj_idx : obj_idx + 1]
+                for obj_idx, obj_id in enumerate(obj_ids_global)
+            }
+
+            # Zero out suppressed masks before memory encoding
+            NO_OBJ_LOGIT = -10
+            tracker_low_res_masks_global[to_suppress] = NO_OBJ_LOGIT
+
+        return tracker_low_res_masks_global
+
+    def run_tracker_update_execution_phase(
+        self,
+        frame_idx: int,
+        num_frames: int,
+        reverse: bool,
+        det_out: Dict[str, Tensor],
+        tracker_states_local: List[Any],
+        tracker_update_plan: Dict[str, npt.NDArray],
+        orig_vid_height: int,
+        orig_vid_width: int,
+        feature_cache: Dict,
+    ):
+        # initialize tracking scores with detection scores
+        new_det_fa_inds: npt.NDArray = tracker_update_plan["new_det_fa_inds"]
+        new_det_obj_ids: npt.NDArray = tracker_update_plan["new_det_obj_ids"]
+        new_det_gpu_ids: npt.NDArray = tracker_update_plan["new_det_gpu_ids"]
+        is_on_this_gpu: npt.NDArray = new_det_gpu_ids == self.rank
+        new_det_obj_ids_local: npt.NDArray = new_det_obj_ids[is_on_this_gpu]
+        new_det_fa_inds_local: npt.NDArray = new_det_fa_inds[is_on_this_gpu]
+        obj_ids_newly_removed: Set[int] = tracker_update_plan["obj_ids_newly_removed"]
+
+        # Step 1: add new objects from the detector to SAM2 inference states
+        if len(new_det_fa_inds_local) > 0:
+            new_det_fa_inds_local_t = torch.from_numpy(new_det_fa_inds_local)
+            new_det_masks: Tensor = det_out["mask"][new_det_fa_inds_local_t]
+            # initialize SAM2 with new object masks
+            tracker_states_local = self._tracker_add_new_objects(
+                frame_idx=frame_idx,
+                num_frames=num_frames,
+                new_obj_ids=new_det_obj_ids_local,
+                new_obj_masks=new_det_masks,
+                tracker_states_local=tracker_states_local,
+                orig_vid_height=orig_vid_height,
+                orig_vid_width=orig_vid_width,
+                feature_cache=feature_cache,
+            )
+
+        # Step 2: remove from SAM2 inference states those objects removed by heuristics
+        if len(obj_ids_newly_removed) > 0:
+            self._tracker_remove_objects(tracker_states_local, obj_ids_newly_removed)
+
+        return tracker_states_local
+
+    def build_outputs(
+        self,
+        frame_idx: int,
+        num_frames: int,
+        reverse: bool,
+        det_out: Dict[str, Tensor],
+        tracker_low_res_masks_global: Tensor,
+        tracker_obj_scores_global: Tensor,
+        tracker_metadata_prev: Dict[str, npt.NDArray],
+        tracker_update_plan: Dict[str, npt.NDArray],
+        orig_vid_height: int,
+        orig_vid_width: int,
+        reconditioned_obj_ids: set = None,
+        det_to_matched_trk_obj_ids: dict = None,
+    ):
+        new_det_fa_inds: npt.NDArray = tracker_update_plan["new_det_fa_inds"]
+        new_det_obj_ids: npt.NDArray = tracker_update_plan["new_det_obj_ids"]
+        obj_id_to_mask = {}  # obj_id --> output mask tensor
+
+        # Part 1: masks from previous SAM2 propagation
+        existing_masklet_obj_ids = tracker_metadata_prev["obj_ids_all_gpu"]
+        existing_masklet_video_res_masks = F.interpolate(
+            tracker_low_res_masks_global.unsqueeze(1),
+            size=(orig_vid_height, orig_vid_width),
+            mode="bilinear",
+            align_corners=False,
+        )  # (num_obj, 1, H_video, W_video)
+        existing_masklet_binary = existing_masklet_video_res_masks > 0
+        assert len(existing_masklet_obj_ids) == len(existing_masklet_binary)
+        for obj_id, mask in zip(existing_masklet_obj_ids, existing_masklet_binary):
+            obj_id_to_mask[obj_id] = mask  # (1, H_video, W_video)
+
+        # Part 2: masks from new detections
+        new_det_fa_inds_t = torch.from_numpy(new_det_fa_inds)
+        new_det_low_res_masks = det_out["mask"][new_det_fa_inds_t].unsqueeze(1)
+        new_det_low_res_masks = fill_holes_in_mask_scores(
+            new_det_low_res_masks,
+            max_area=self.fill_hole_area,
+            fill_holes=True,
+            remove_sprinkles=True,
+        )
+        new_masklet_video_res_masks = F.interpolate(
+            new_det_low_res_masks,
+            size=(orig_vid_height, orig_vid_width),
+            mode="bilinear",
+            align_corners=False,
+        )  # (num_obj, 1, H_video, W_video)
+
+        new_masklet_binary = new_masklet_video_res_masks > 0
+        assert len(new_det_obj_ids) == len(new_masklet_video_res_masks)
+        for obj_id, mask in zip(new_det_obj_ids, new_masklet_binary):
+            obj_id_to_mask[obj_id] = mask  # (1, H_video, W_video)
+
+        # Part 3: Override masks for reconditioned objects using detection masks
+        if reconditioned_obj_ids is not None and len(reconditioned_obj_ids) > 0:
+            trk_id_to_max_iou_high_conf_det = tracker_update_plan.get(
+                "trk_id_to_max_iou_high_conf_det", {}
+            )
+
+            for obj_id in reconditioned_obj_ids:
+                det_idx = trk_id_to_max_iou_high_conf_det.get(obj_id)
+
+                if det_idx is not None:
+                    det_mask = det_out["mask"][det_idx]
+                    det_mask = det_mask.unsqueeze(0).unsqueeze(0)
+                    det_mask_resized = (
+                        F.interpolate(
+                            det_mask.float(),
+                            size=(orig_vid_height, orig_vid_width),
+                            mode="bilinear",
+                            align_corners=False,
+                        )
+                        > 0
+                    )
+
+                    det_mask_final = det_mask_resized.squeeze(0)
+                    obj_id_to_mask[obj_id] = det_mask_final
+
+        return obj_id_to_mask
+
+    def _get_objects_to_suppress_based_on_most_recently_occluded(
+        self,
+        binary_low_res_masks: Tensor,
+        last_occluded: List[int],
+        obj_ids: List[int],
+        frame_idx: int = None,
+        reverse: bool = False,
+    ):
+        # Suppress overlapping masks for objects that were most recently occluded
+        assert binary_low_res_masks.dtype == torch.bool, (
+            f"Expected boolean tensor, got {binary_low_res_masks.dtype}"
+        )
+        to_suppress = torch.zeros(
+            binary_low_res_masks.size(0),
+            device=binary_low_res_masks.device,
+            dtype=torch.bool,
+        )
+        if len(obj_ids) <= 1:
+            return to_suppress
+
+        iou = mask_iou(binary_low_res_masks, binary_low_res_masks)  # [N,N]
+
+        # Create masks for upper triangular matrix (i < j) and IoU threshold
+        mask_iou_thresh = (
+            iou >= self.suppress_overlapping_based_on_recent_occlusion_threshold
+        )
+        overlapping_pairs = torch.triu(mask_iou_thresh, diagonal=1)  # [N,N]
+
+        last_occ_expanded_i = last_occluded.unsqueeze(1)  # (N, 1)
+        last_occ_expanded_j = last_occluded.unsqueeze(0)  # (1, N)
+        # Suppress most recently occluded
+        cmp_op = torch.gt if not reverse else torch.lt
+        suppress_i_mask = (
+            overlapping_pairs
+            & cmp_op(
+                last_occ_expanded_i, last_occ_expanded_j
+            )  # (last_occ_expanded_i > last_occ_expanded_j)
+            & (
+                last_occ_expanded_j > -1
+            )  # j can suppress i only if i was previously occluded
+        )
+        suppress_j_mask = (
+            overlapping_pairs
+            & cmp_op(last_occ_expanded_j, last_occ_expanded_i)
+            & (
+                last_occ_expanded_i > -1
+            )  # i can suppress j only if j was previously occluded
+        )
+        # Apply suppression
+        to_suppress = suppress_i_mask.any(dim=1) | suppress_j_mask.any(dim=0)
+
+        # Log for debugging
+        if (
+            self.rank == 0
+            and logger.isEnabledFor(logging.DEBUG)
+            and frame_idx is not None
+        ):
+            suppress_i_mask = suppress_i_mask.cpu().numpy()
+            suppress_j_mask = suppress_j_mask.cpu().numpy()
+            last_occluded = last_occluded.cpu().numpy()
+
+            # Find all suppression pairs without using torch.where
+            batch_size = suppress_i_mask.shape[0]
+
+            # Log i-suppression cases (where i gets suppressed in favor of j)
+            for i in range(batch_size):
+                for j in range(batch_size):
+                    if suppress_i_mask[i, j]:
+                        logger.debug(
+                            f"{frame_idx=}: Suppressing obj {obj_ids[i]} last occluded {last_occluded[i]} in favor of {obj_ids[j]} last occluded {last_occluded[j]}"
+                        )
+
+            # Log j-suppression cases (where j gets suppressed in favor of i)
+            for i in range(batch_size):
+                for j in range(batch_size):
+                    if suppress_j_mask[i, j]:
+                        logger.debug(
+                            f"{frame_idx=}: Suppressing obj {obj_ids[j]} last occluded {last_occluded[j]} in favor of {obj_ids[i]} last occluded {last_occluded[i]}"
+                        )
+
+        return to_suppress
+
+    def _propogate_tracker_one_frame_local_gpu(
+        self,
+        inference_states: List[Any],
+        frame_idx: int,
+        reverse: bool,
+        # by default, we disable memory encoding until we gather all outputs
+        run_mem_encoder: bool = False,
+    ):
+        """
+        inference_states: List of inference states, each state corresponds to a different set of objects.
+        """
+        obj_ids_local = []
+        low_res_masks_list = []
+        obj_scores_list = []
+        for inference_state in inference_states:
+            if len(inference_state["obj_ids"]) == 0:
+                continue  # skip propagation on empty inference states
+
+            # propagate one frame
+            num_frames_propagated = 0
+            for out in self.tracker.propagate_in_video(
+                inference_state,
+                start_frame_idx=frame_idx,
+                # end_frame_idx = start_frame_idx + max_frame_num_to_track
+                # (i.e. propagating 1 frame since end_frame_idx is inclusive)
+                max_frame_num_to_track=0,
+                reverse=reverse,
+                tqdm_disable=True,
+                run_mem_encoder=run_mem_encoder,
+            ):
+                out_frame_idx, out_obj_ids, out_low_res_masks, _, out_obj_scores = out
+                num_frames_propagated += 1
+
+            # only 1 frames should be propagated
+            assert num_frames_propagated == 1 and out_frame_idx == frame_idx, (
+                f"num_frames_propagated: {num_frames_propagated}, out_frame_idx: {out_frame_idx}, frame_idx: {frame_idx}"
+            )
+            assert isinstance(out_obj_ids, list)
+            obj_ids_local.extend(out_obj_ids)
+            low_res_masks_list.append(out_low_res_masks.squeeze(1))
+            obj_scores_list.append(out_obj_scores.squeeze(1))
+
+        # concatenate the output masklets from all local inference states
+        H_mask = W_mask = self.tracker.low_res_mask_size
+        if len(low_res_masks_list) > 0:
+            low_res_masks_local = torch.cat(low_res_masks_list, dim=0)
+            obj_scores_local = torch.cat(obj_scores_list, dim=0)
+            assert low_res_masks_local.shape[1:] == (H_mask, W_mask)
+
+            # Apply hole filling to the masks
+            low_res_masks_local = fill_holes_in_mask_scores(
+                low_res_masks_local.unsqueeze(1),
+                max_area=self.fill_hole_area,
+                fill_holes=True,
+                remove_sprinkles=True,
+            )
+            low_res_masks_local = low_res_masks_local.squeeze(1)
+        else:
+            low_res_masks_local = torch.zeros(0, H_mask, W_mask, device=self.device)
+            obj_scores_local = torch.zeros(0, device=self.device)
+
+        return obj_ids_local, low_res_masks_local, obj_scores_local
+
+    def _associate_det_trk(
+        self,
+        det_masks: Tensor,
+        det_scores_np: npt.NDArray,
+        trk_masks: Tensor,
+        trk_obj_ids: npt.NDArray,
+    ):
+        """
+        Match detections on the current frame with the existing masklets.
+
+        Args:
+          - det_masks: (N, H, W) tensor of predicted masks
+          - det_scores_np: (N,) array of detection scores
+          - trk_masks: (M, H, W) tensor of track masks
+          - trk_obj_ids: (M,) array of object IDs corresponding to trk_masks
+
+        Returns:
+          - new_det_fa_inds: array of new object indices.
+          - unmatched_trk_obj_ids: array of existing masklet object IDs that are not matched
+            to any detections on this frame (for unmatched, we only count masklets with >0 area)
+          - det_to_matched_trk_obj_ids: dict[int, npt.NDArray]: mapping from detector's detection indices
+            to the list of matched tracklet object IDs
+          - empty_trk_obj_ids: array of existing masklet object IDs with zero area in SAM2 prediction
+        """
+        iou_threshold = self.assoc_iou_thresh
+        iou_threshold_trk = self.trk_assoc_iou_thresh
+        new_det_thresh = self.new_det_thresh
+
+        assert det_masks.is_floating_point(), "float tensor expected (do not binarize)"
+        assert trk_masks.is_floating_point(), "float tensor expected (do not binarize)"
+        assert trk_masks.size(0) == len(trk_obj_ids), (
+            f"trk_masks and trk_obj_ids should have the same length, {trk_masks.size(0)} vs {len(trk_obj_ids)}"
+        )
+        if trk_masks.size(0) == 0:
+            # all detections are new
+            new_det_fa_inds = np.arange(det_masks.size(0))
+            unmatched_trk_obj_ids = np.array([], np.int64)
+            empty_trk_obj_ids = np.array([], np.int64)
+            det_to_matched_trk_obj_ids = {}
+            trk_id_to_max_iou_high_conf_det = {}
+            return (
+                new_det_fa_inds,
+                unmatched_trk_obj_ids,
+                det_to_matched_trk_obj_ids,
+                trk_id_to_max_iou_high_conf_det,
+                empty_trk_obj_ids,
+            )
+        elif det_masks.size(0) == 0:
+            # all previous tracklets are unmatched if they have a non-zero area
+            new_det_fa_inds = np.array([], np.int64)
+            trk_is_nonempty = (trk_masks > 0).any(dim=(1, 2)).cpu().numpy()
+            unmatched_trk_obj_ids = trk_obj_ids[trk_is_nonempty]
+            empty_trk_obj_ids = trk_obj_ids[~trk_is_nonempty]
+            det_to_matched_trk_obj_ids = {}
+            trk_id_to_max_iou_high_conf_det = {}
+            return (
+                new_det_fa_inds,
+                unmatched_trk_obj_ids,
+                det_to_matched_trk_obj_ids,
+                trk_id_to_max_iou_high_conf_det,
+                empty_trk_obj_ids,
+            )
+
+        if det_masks.shape[-2:] != trk_masks.shape[-2:]:
+            # resize to the smaller size to save GPU memory
+            if np.prod(det_masks.shape[-2:]) < np.prod(trk_masks.shape[-2:]):
+                trk_masks = F.interpolate(
+                    trk_masks.unsqueeze(1),
+                    size=det_masks.shape[-2:],
+                    mode="bilinear",
+                    align_corners=False,
+                ).squeeze(1)
+            else:
+                # resize detections to track size
+                det_masks = F.interpolate(
+                    det_masks.unsqueeze(1),
+                    size=trk_masks.shape[-2:],
+                    mode="bilinear",
+                    align_corners=False,
+                ).squeeze(1)
+
+        det_masks_binary = det_masks > 0
+        trk_masks_binary = trk_masks > 0
+        ious = mask_iou(det_masks_binary, trk_masks_binary)  # (N, M)
+
+        ious_np = ious.cpu().numpy()
+        if self.o2o_matching_masklets_enable:
+            from scipy.optimize import linear_sum_assignment
+
+            # Hungarian matching for tracks (one-to-one: each track matches at most one detection)
+            cost_matrix = 1 - ious_np  # Hungarian solves for minimum cost
+            row_ind, col_ind = linear_sum_assignment(cost_matrix)
+            trk_is_matched = np.zeros(trk_masks.size(0), dtype=bool)
+            for d, t in zip(row_ind, col_ind):
+                if ious_np[d, t] >= iou_threshold_trk:
+                    trk_is_matched[t] = True
+        else:
+            trk_is_matched = (ious_np >= iou_threshold_trk).any(axis=0)
+        # Non-empty tracks not matched by Hungarian assignment above threshold are unmatched
+        trk_is_nonempty = trk_masks_binary.any(dim=(1, 2)).cpu().numpy()
+        trk_is_unmatched = np.logical_and(trk_is_nonempty, ~trk_is_matched)
+        unmatched_trk_obj_ids = trk_obj_ids[trk_is_unmatched]
+        # also record masklets that have zero area in SAM 2 prediction
+        empty_trk_obj_ids = trk_obj_ids[~trk_is_nonempty]
+
+        # For detections: allow many tracks to match to the same detection (many-to-one)
+        # So, a detection is 'new' if it does not match any track above threshold
+        is_new_det = np.logical_and(
+            det_scores_np >= new_det_thresh,
+            np.logical_not(np.any(ious_np >= iou_threshold, axis=1)),
+        )
+        new_det_fa_inds = np.nonzero(is_new_det)[0]
+
+        # for each detection, which tracks it matched to (above threshold)
+        det_to_matched_trk_obj_ids = {}
+        trk_id_to_max_iou_high_conf_det = {}  # trk id --> exactly one detection idx
+        HIGH_CONF_THRESH = 0.8
+        HIGH_IOU_THRESH = 0.8
+        det_to_max_iou_trk_idx = np.argmax(ious_np, axis=1)
+        det_is_high_conf = (det_scores_np >= HIGH_CONF_THRESH) & ~is_new_det
+        det_is_high_iou = np.max(ious_np, axis=1) >= HIGH_IOU_THRESH
+        det_is_high_conf_and_iou = set(
+            np.nonzero(det_is_high_conf & det_is_high_iou)[0]
+        )
+        for d in range(det_masks.size(0)):
+            det_to_matched_trk_obj_ids[d] = trk_obj_ids[ious_np[d, :] >= iou_threshold]
+            if d in det_is_high_conf_and_iou:
+                trk_obj_id = trk_obj_ids[det_to_max_iou_trk_idx[d]].item()
+                trk_id_to_max_iou_high_conf_det[trk_obj_id] = d
+
+        return (
+            new_det_fa_inds,
+            unmatched_trk_obj_ids,
+            det_to_matched_trk_obj_ids,
+            trk_id_to_max_iou_high_conf_det,
+            empty_trk_obj_ids,
+        )
+
+    def _assign_new_det_to_gpus(self, new_det_num, prev_workload_per_gpu):
+        """Distribute the new objects to the GPUs with the least workload."""
+        workload_per_gpu: npt.NDArray = prev_workload_per_gpu.copy()
+        new_det_gpu_ids = np.zeros(new_det_num, np.int64)
+
+        # assign the objects one by one
+        for i in range(len(new_det_gpu_ids)):
+            # find the GPU with the least workload
+            min_gpu = np.argmin(workload_per_gpu)
+            new_det_gpu_ids[i] = min_gpu
+            workload_per_gpu[min_gpu] += 1
+        return new_det_gpu_ids
+
+    def _process_hotstart(
+        self,
+        frame_idx: int,
+        num_frames: int,
+        reverse: bool,
+        det_to_matched_trk_obj_ids: Dict[int, npt.NDArray],
+        new_det_obj_ids: npt.NDArray,
+        empty_trk_obj_ids: npt.NDArray,
+        unmatched_trk_obj_ids: npt.NDArray,
+        rank0_metadata: Dict[str, Any],
+        tracker_metadata: Dict[str, Any],
+    ):
+        """Handle hotstart heuristics to remove unmatched or duplicated objects."""
+        # obj_id --> first frame index where the object was detected
+        obj_first_frame_idx = rank0_metadata["obj_first_frame_idx"]
+        # obj_id --> [mismatched frame indices]
+        unmatched_frame_inds = rank0_metadata["unmatched_frame_inds"]
+        trk_keep_alive = rank0_metadata["trk_keep_alive"]
+        # (first_appear_obj_id, obj_id) --> [overlap frame indices]
+        overlap_pair_to_frame_inds = rank0_metadata["overlap_pair_to_frame_inds"]
+        # removed_obj_ids: object IDs that are suppressed via hot-start
+        removed_obj_ids = rank0_metadata["removed_obj_ids"]
+        suppressed_obj_ids = rank0_metadata["suppressed_obj_ids"][frame_idx]
+
+        obj_ids_newly_removed = set()  # object IDs to be newly removed on this frame
+        hotstart_diff = (
+            frame_idx - self.hotstart_delay
+            if not reverse
+            else frame_idx + self.hotstart_delay
+        )
+
+        # Step 1: log the frame index where each object ID first appears
+        for obj_id in new_det_obj_ids:
+            if obj_id not in obj_first_frame_idx:
+                obj_first_frame_idx[obj_id] = frame_idx
+            assert obj_id not in trk_keep_alive
+            trk_keep_alive[obj_id] = self.init_trk_keep_alive
+
+        matched_trks = set()
+        # We use the det-->tracks list to check for matched objects. Otherwise, we need to compute areas to decide whether they're occluded
+        for matched_trks_per_det in det_to_matched_trk_obj_ids.values():
+            matched_trks.update(matched_trks_per_det)
+        for obj_id in matched_trks:
+            # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the max value of trk_keep_alive
+            trk_keep_alive[obj_id] = min(
+                self.max_trk_keep_alive, trk_keep_alive[obj_id] + 1
+            )
+        for obj_id in unmatched_trk_obj_ids:
+            unmatched_frame_inds[obj_id].append(frame_idx)
+            # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the min value of trk_keep_alive
+            # The max keep alive is 2x the min, means the model prefers to keep the prediction rather than suppress it if it was matched long enough.
+            trk_keep_alive[obj_id] = max(
+                self.min_trk_keep_alive, trk_keep_alive[obj_id] - 1
+            )
+        if self.decrease_trk_keep_alive_for_empty_masklets:
+            for obj_id in empty_trk_obj_ids:
+                # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the min value of trk_keep_alive
+                trk_keep_alive[obj_id] = max(
+                    self.min_trk_keep_alive, trk_keep_alive[obj_id] - 1
+                )
+
+        # Step 2: removed tracks that has not matched with detections for `hotstart_unmatch_thresh` frames with hotstart period
+        # a) add unmatched frame indices for each existing object ID
+        # note that `unmatched_trk_obj_ids` contains those frames where the SAM2 output mask
+        # doesn't match any detection; it excludes those frames where SAM2 gives an empty mask
+        # b) remove a masklet if it first appears after `hotstart_diff` and is unmatched for more
+        # than `self.hotstart_unmatch_thresh` frames
+        for obj_id, frame_indices in unmatched_frame_inds.items():
+            if obj_id in removed_obj_ids or obj_id in obj_ids_newly_removed:
+                continue  # skip if the object is already removed
+            if len(frame_indices) >= self.hotstart_unmatch_thresh:
+                is_within_hotstart = (
+                    obj_first_frame_idx[obj_id] > hotstart_diff and not reverse
+                ) or (obj_first_frame_idx[obj_id] < hotstart_diff and reverse)
+                if is_within_hotstart:
+                    obj_ids_newly_removed.add(obj_id)
+                    logger.debug(
+                        f"Removing object {obj_id} at frame {frame_idx} "
+                        f"since it is unmatched for frames: {frame_indices}"
+                    )
+            if (
+                trk_keep_alive[obj_id] <= 0  # Object has not been matched for too long
+                and not self.suppress_unmatched_only_within_hotstart
+                and obj_id not in removed_obj_ids
+                and obj_id not in obj_ids_newly_removed
+            ):
+                logger.debug(
+                    f"Suppressing object {obj_id} at frame {frame_idx}, due to being unmatched"
+                )
+                suppressed_obj_ids.add(obj_id)
+
+        # Step 3: removed tracks that overlaps with another track for `hotstart_dup_thresh` frames
+        # a) find overlaps tracks -- we consider overlap if they match to the same detection
+        for _, matched_trk_obj_ids in det_to_matched_trk_obj_ids.items():
+            if len(matched_trk_obj_ids) < 2:
+                continue  # only count detections that are matched to multiple (>=2) masklets
+            # if there are multiple matched track ids, we need to find the one that appeared first;
+            # these later appearing ids may be removed since they may be considered as duplicates
+            first_appear_obj_id = (
+                min(matched_trk_obj_ids, key=lambda x: obj_first_frame_idx[x])
+                if not reverse
+                else max(matched_trk_obj_ids, key=lambda x: obj_first_frame_idx[x])
+            )
+            for obj_id in matched_trk_obj_ids:
+                if obj_id != first_appear_obj_id:
+                    key = (first_appear_obj_id, obj_id)
+                    overlap_pair_to_frame_inds[key].append(frame_idx)
+
+        # b) remove a masklet if it first appears after `hotstart_diff` and it overlaps with another
+        # masklet (that appears earlier) for more than `self.hotstart_dup_thresh` frames
+        for (first_obj_id, obj_id), frame_indices in overlap_pair_to_frame_inds.items():
+            if obj_id in removed_obj_ids or obj_id in obj_ids_newly_removed:
+                continue  # skip if the object is already removed
+            if (obj_first_frame_idx[obj_id] > hotstart_diff and not reverse) or (
+                obj_first_frame_idx[obj_id] < hotstart_diff and reverse
+            ):
+                if len(frame_indices) >= self.hotstart_dup_thresh:
+                    obj_ids_newly_removed.add(obj_id)
+                    logger.debug(
+                        f"Removing object {obj_id} at frame {frame_idx} "
+                        f"since it overlaps with another track {first_obj_id} at frames: {frame_indices}"
+                    )
+
+        removed_obj_ids.update(obj_ids_newly_removed)
+        return obj_ids_newly_removed, rank0_metadata
+
+    def _tracker_update_memories(
+        self,
+        tracker_inference_states: List[Any],
+        frame_idx: int,
+        tracker_metadata: Dict[str, Any],
+        low_res_masks: Tensor,
+    ):
+        """
+        Run Sam2 memory encoder, enforcing non-overlapping constraints globally.
+        """
+        if len(tracker_inference_states) == 0:
+            return
+        # Avoid an extra interpolation step by directly interpolating to `interpol_size`
+        high_res_H, high_res_W = (
+            self.tracker.maskmem_backbone.mask_downsampler.interpol_size
+        )
+        # NOTE: inspect this part if we observe OOMs in the demo
+        high_res_masks = F.interpolate(
+            low_res_masks.unsqueeze(1),
+            size=(high_res_H, high_res_W),
+            mode="bilinear",
+            align_corners=False,
+        )
+        # We first apply non-overlapping constraints before memory encoding. This may include some suppression heuristics.
+        if not hasattr(self, "_warm_up_complete") or self._warm_up_complete:
+            high_res_masks = self.tracker._suppress_object_pw_area_shrinkage(
+                high_res_masks
+            )
+        # Instead of gathering the predicted object scores, we use mask areas as a proxy.
+        object_score_logits = torch.where(
+            (high_res_masks > 0).any(dim=(-1, -2)), 10.0, -10.0
+        )
+
+        # Run the memory encoder on local slices for each GPU
+        start_idx_gpu = sum(tracker_metadata["num_obj_per_gpu"][: self.rank])
+        start_idx_state = start_idx_gpu
+        for tracker_state in tracker_inference_states:
+            num_obj_per_state = len(tracker_state["obj_ids"])
+            if num_obj_per_state == 0:
+                continue
+            # Get the local high-res masks and object score logits for this inference state
+            end_idx_state = start_idx_state + num_obj_per_state
+            local_high_res_masks = high_res_masks[start_idx_state:end_idx_state]
+            local_object_score_logits = object_score_logits[
+                start_idx_state:end_idx_state
+            ]
+            local_batch_size = local_high_res_masks.size(0)
+            # Run Sam2 memory encoder. Note that we do not re-enforce the non-overlapping constraint as it is turned off by default
+
+            encoded_mem = self.tracker._run_memory_encoder(
+                tracker_state,
+                frame_idx,
+                local_batch_size,
+                local_high_res_masks,
+                local_object_score_logits,
+                is_mask_from_pts=False,
+            )
+            local_maskmem_features, local_maskmem_pos_enc = encoded_mem
+            # Store encoded memories in the local inference state
+            output_dict = tracker_state["output_dict"]
+            for storage_key in ["cond_frame_outputs", "non_cond_frame_outputs"]:
+                if frame_idx not in output_dict[storage_key]:
+                    continue
+                output_dict[storage_key][frame_idx]["maskmem_features"] = (
+                    local_maskmem_features
+                )
+                output_dict[storage_key][frame_idx]["maskmem_pos_enc"] = [
+                    pos for pos in local_maskmem_pos_enc
+                ]
+                # for batched inference state, we also need to add per-object
+                # memory slides to support instance interactivity
+                self.tracker._add_output_per_object(
+                    inference_state=tracker_state,
+                    frame_idx=frame_idx,
+                    current_out=output_dict[storage_key][frame_idx],
+                    storage_key=storage_key,
+                )
+            start_idx_state += num_obj_per_state
+
+    def _tracker_add_new_objects(
+        self,
+        frame_idx: int,
+        num_frames: int,
+        new_obj_ids: List[int],
+        new_obj_masks: Tensor,
+        tracker_states_local: List[Any],
+        orig_vid_height: int,
+        orig_vid_width: int,
+        feature_cache: Dict,
+    ):
+        """Add a new object to SAM2 inference states."""
+        prev_tracker_state = (
+            tracker_states_local[0] if len(tracker_states_local) > 0 else None
+        )
+
+        # prepare inference_state
+        # batch objects that first appear on the same frame together
+        # Clear inference state. Keep the cached image features if available.
+        new_tracker_state = self.tracker.init_state(
+            cached_features=feature_cache,
+            video_height=orig_vid_height,
+            video_width=orig_vid_width,
+            num_frames=num_frames,
+        )
+        new_tracker_state["backbone_out"] = (
+            prev_tracker_state.get("backbone_out", None)
+            if prev_tracker_state is not None
+            else None
+        )
+
+        assert len(new_obj_ids) == new_obj_masks.size(0)
+        assert new_obj_masks.is_floating_point()
+        input_mask_res = self.tracker.input_mask_size
+        new_obj_masks = F.interpolate(
+            new_obj_masks.unsqueeze(1),
+            size=(input_mask_res, input_mask_res),
+            mode="bilinear",
+            align_corners=False,
+        ).squeeze(1)
+        new_obj_masks = new_obj_masks > 0
+
+        # add object one by one
+        for new_obj_id, new_mask in zip(new_obj_ids, new_obj_masks):
+            self.tracker.add_new_mask(
+                inference_state=new_tracker_state,
+                frame_idx=frame_idx,
+                obj_id=new_obj_id,
+                mask=new_mask,
+                add_mask_to_memory=True,
+            )
+        # NOTE: we skip enforcing the non-overlapping constraint **globally** when adding new objects.
+        self.tracker.propagate_in_video_preflight(
+            new_tracker_state, run_mem_encoder=True
+        )
+        tracker_states_local.append(new_tracker_state)
+        return tracker_states_local
+
+    def _tracker_remove_object(self, tracker_states_local: List[Any], obj_id: int):
+        """
+        Remove an object from SAM2 inference states. This would remove the object from
+        all frames in the video.
+        """
+        tracker_states_local_before_removal = tracker_states_local.copy()
+        tracker_states_local.clear()
+        for tracker_inference_state in tracker_states_local_before_removal:
+            # we try to remove `obj_id` on every inference state with `strict=False`
+            # it will not do anything if an inference state doesn't contain `obj_id`
+            new_obj_ids, _ = self.tracker.remove_object(
+                tracker_inference_state, obj_id, strict=False, need_output=False
+            )
+            # only keep an inference state if it's non-empty after object removal
+            if len(new_obj_ids) > 0:
+                tracker_states_local.append(tracker_inference_state)
+
+    def _tracker_remove_objects(
+        self, tracker_states_local: List[Any], obj_ids: list[int]
+    ):
+        """
+        Remove an object from SAM2 inference states. This would remove the object from
+        all frames in the video.
+        """
+        for obj_id in obj_ids:
+            self._tracker_remove_object(tracker_states_local, obj_id)
+
+    def _initialize_metadata(self):
+        """Initialize metadata for the masklets."""
+        tracker_metadata = {
+            "obj_ids_per_gpu": [np.array([], np.int64) for _ in range(self.world_size)],
+            "obj_ids_all_gpu": np.array([], np.int64),
+            "num_obj_per_gpu": np.zeros(self.world_size, np.int64),
+            "max_obj_id": -1,
+            "obj_id_to_score": {},
+            "obj_id_to_tracker_score_frame_wise": defaultdict(dict),
+            "obj_id_to_last_occluded": {},
+        }
+        if self.rank == 0:
+            # "rank0_metadata" contains metadata that is only stored on (and accessible to) GPU 0
+            # - obj_first_frame_idx: obj_id --> first frame index where the object was detected
+            # - unmatched_frame_inds: obj_id --> [mismatched frame indices]
+            # - overlap_pair_to_frame_inds: (first_appear_obj_id, obj_id) --> [overlap frame indices]
+            # - removed_obj_ids: object IDs that are suppressed via hot-start
+            rank0_metadata = {
+                "obj_first_frame_idx": {},
+                "unmatched_frame_inds": defaultdict(list),
+                "trk_keep_alive": defaultdict(
+                    int
+                ),  # This is used only for object suppression not for removal
+                "overlap_pair_to_frame_inds": defaultdict(list),
+                "removed_obj_ids": set(),
+                "suppressed_obj_ids": defaultdict(
+                    set
+                ),  # frame_idx --> set of objects with suppressed outputs, but still continue to be tracked
+            }
+            if self.masklet_confirmation_enable:
+                # all the following are npt.NDArray with the same shape as `obj_ids_all_gpu`
+                rank0_metadata["masklet_confirmation"] = {
+                    # "status" is the confirmation status of each masklet (in `MaskletConfirmationStatus`)
+                    "status": np.array([], np.int64),
+                    # "consecutive_det_num" is the number of consecutive frames where the masklet is
+                    # detected by the detector (with a matched detection)
+                    "consecutive_det_num": np.array([], np.int64),
+                }
+            tracker_metadata["rank0_metadata"] = rank0_metadata
+
+        return tracker_metadata
+
+    def update_masklet_confirmation_status(
+        self,
+        rank0_metadata: Dict[str, Any],
+        obj_ids_all_gpu_prev: npt.NDArray,
+        obj_ids_all_gpu_updated: npt.NDArray,
+        det_to_matched_trk_obj_ids: Dict[int, npt.NDArray],
+        new_det_obj_ids: npt.NDArray,
+    ):
+        confirmation_data = rank0_metadata["masklet_confirmation"]
+
+        # a) first, expand "confirmation_data" to include new masklets added in this frame
+        status_prev = confirmation_data["status"]
+        consecutive_det_num_prev = confirmation_data["consecutive_det_num"]
+        assert status_prev.shape == obj_ids_all_gpu_prev.shape, (
+            f"Got {status_prev.shape} vs {obj_ids_all_gpu_prev.shape}"
+        )
+
+        obj_id_to_updated_idx = {
+            obj_id: idx for idx, obj_id in enumerate(obj_ids_all_gpu_updated)
+        }
+        prev_elem_is_in_updated = np.isin(obj_ids_all_gpu_prev, obj_ids_all_gpu_updated)
+        prev_elem_obj_ids_in_updated = obj_ids_all_gpu_prev[prev_elem_is_in_updated]
+        prev_elem_inds_in_updated = np.array(
+            [obj_id_to_updated_idx[obj_id] for obj_id in prev_elem_obj_ids_in_updated],
+            dtype=np.int64,
+        )
+        # newly added masklets are initialized to "UNCONFIRMED" status
+        unconfirmed_val = MaskletConfirmationStatus.UNCONFIRMED.value
+        status = np.full_like(obj_ids_all_gpu_updated, fill_value=unconfirmed_val)
+        status[prev_elem_inds_in_updated] = status_prev[prev_elem_is_in_updated]
+        consecutive_det_num = np.zeros_like(obj_ids_all_gpu_updated)
+        consecutive_det_num[prev_elem_inds_in_updated] = consecutive_det_num_prev[
+            prev_elem_is_in_updated
+        ]
+
+        # b) update the confirmation status of all masklets based on the current frame
+        # b.1) update "consecutive_det_num"
+        # "is_matched": whether a masklet is matched to a detection on this frame
+        is_matched = np.isin(obj_ids_all_gpu_updated, new_det_obj_ids)
+        for matched_trk_obj_ids in det_to_matched_trk_obj_ids.values():
+            is_matched |= np.isin(obj_ids_all_gpu_updated, matched_trk_obj_ids)
+        consecutive_det_num = np.where(is_matched, consecutive_det_num + 1, 0)
+
+        # b.2) update "status"
+        change_to_confirmed = (
+            consecutive_det_num >= self.masklet_confirmation_consecutive_det_thresh
+        )
+        status[change_to_confirmed] = MaskletConfirmationStatus.CONFIRMED.value
+
+        confirmation_data["status"] = status
+        confirmation_data["consecutive_det_num"] = consecutive_det_num
+        return rank0_metadata
+
+    def forward(self, input: BatchedDatapoint, is_inference: bool = False):
+        raise NotImplementedError("Evaluation outside demo is not implemented yet")
+
+    def _load_checkpoint(self, ckpt_path: str, strict: bool = True):
+        sd = torch.load(ckpt_path, map_location="cpu", weights_only=True)["model"]
+        missing_keys, unexpected_keys = self.load_state_dict(sd, strict=strict)
+        if len(missing_keys) > 0 or len(unexpected_keys) > 0:
+            logger.warning(f"Loaded ckpt with {missing_keys=}, {unexpected_keys=}")
+        else:
+            logger.info("Loaded ckpt successfully without missing or unexpected keys")
+
+    def prep_for_evaluator(self, video_frames, tracking_res, scores_labels):
+        """This method is only used for benchmark eval (not used in the demo)."""
+        num_frames = len(video_frames)
+        w, h = video_frames[0].size
+        zero_mask = torch.zeros((1, h, w), dtype=torch.bool)
+        object_ids = list(scores_labels.keys())
+        preds = {"scores": [], "labels": [], "boxes": [], "masks_rle": []}
+        for oid in object_ids:
+            o_masks = []
+            o_score = scores_labels[oid][0].item()
+            o_label = scores_labels[oid][1]
+            for frame_idx in range(num_frames):
+                if frame_idx not in tracking_res:
+                    o_masks.append(zero_mask)
+                else:
+                    o_masks.append(tracking_res[frame_idx].get(oid, zero_mask))
+
+            o_masks = torch.cat(o_masks, dim=0)  # (n_frames, H, W)
+            preds["scores"].append(o_score)
+            preds["labels"].append(o_label)
+            preds["boxes"].append(mask_to_box(o_masks.unsqueeze(1)).squeeze())
+            preds["masks_rle"].append(rle_encode(o_masks, return_areas=True))
+
+        preds["boxes"] = (
+            torch.stack(preds["boxes"], dim=0)
+            if len(preds["boxes"]) > 0
+            else torch.empty(
+                (0, num_frames, 4), dtype=torch.float32, device=self.device
+            )
+        )
+        preds["scores"] = (
+            torch.tensor(preds["scores"], device=self.device)
+            if len(preds["scores"]) > 0
+            else torch.empty((0,), device=self.device)
+        )
+        preds["per_frame_scores"] = preds["scores"]
+        preds["labels"] = (
+            torch.tensor(preds["labels"], device=self.device)
+            if len(preds["labels"]) > 0
+            else torch.empty((0,), device=self.device)
+        )
+        return preds
+
+    def _encode_prompt(self, **kwargs):
+        return self.detector._encode_prompt(**kwargs)
+
+    def _drop_new_det_with_obj_limit(self, new_det_fa_inds, det_scores_np, num_to_keep):
+        """
+        Drop a few new detections based on the maximum number of objects. We drop new objects based
+        on their detection scores, keeping the high-scoring ones and dropping the low-scoring ones.
+        """
+        assert 0 <= num_to_keep <= len(new_det_fa_inds)
+        if num_to_keep == 0:
+            return np.array([], np.int64)  # keep none
+        if num_to_keep == len(new_det_fa_inds):
+            return new_det_fa_inds  # keep all
+
+        # keep the top-scoring detections
+        score_order = np.argsort(det_scores_np[new_det_fa_inds])[::-1]
+        new_det_fa_inds = new_det_fa_inds[score_order[:num_to_keep]]
+        return new_det_fa_inds