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# Copyright (R) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
import logging
import warnings
import torch
import torch.nn.functional as F
from image_encoder import SAM2ImageEncoder, random_sam2_input_image
from mask_decoder import SAM2MaskDecoder
from prompt_encoder import SAM2PromptEncoder
from sam2.modeling.sam2_base import SAM2Base
from sam2_utils import compare_tensors_with_tolerance
from torch import nn
logger = logging.getLogger(__name__)
class SAM2ImageDecoder(nn.Module):
def __init__(
self,
sam_model: SAM2Base,
multimask_output: bool,
dynamic_multimask_via_stability: bool = True,
return_logits: bool = False,
mask_threshold: float = 0.0,
) -> None:
super().__init__()
self.prompt_encoder = SAM2PromptEncoder(sam_model)
self.mask_decoder = SAM2MaskDecoder(sam_model, multimask_output, dynamic_multimask_via_stability)
self.return_logits = return_logits
self.mask_threshold = mask_threshold
@torch.no_grad()
def forward(
self,
image_features_0: torch.Tensor,
image_features_1: torch.Tensor,
image_embeddings: torch.Tensor,
point_coords: torch.Tensor,
point_labels: torch.Tensor,
input_masks: torch.Tensor,
has_input_masks: torch.Tensor,
original_image_size: torch.Tensor,
enable_nvtx_profile: bool = False,
):
"""
Decode masks from image features and prompts. Batched images are not supported. H=W=1024.
Args:
image_features_0 (torch.Tensor): [1, 32, H/4, W/4]. high resolution features of level 0 from image encoder.
image_features_1 (torch.Tensor): [1, 64, H/8, W/8]. high resolution features of level 1 from image encoder.
image_embeddings (torch.Tensor): [1, 256, H/16, W/16]. image embedding from image encoder.
point_coords (torch.Tensor): [L, P, 2] shape and float32 dtype and contains the absolute pixel
coordinate in (x, y) format of the P input points in image of size 1024x1024.
point_labels (torch.Tensor): shape [L, P] and int32 dtype, where 1 means
positive (foreground), 0 means negative (background), -1 means padding,
2 (box left upper corner), 3 (box right bottom corner).
input_masks (torch.Tensor): [L, 1, H/4, W/4]. Low resolution mask input to the model.
Typically coming from a previous iteration.
has_input_masks (torch.Tensor): [L]. 1.0 if input_masks is used, 0.0 otherwise.
original_image_size(torch.Tensor): [2]. original image size H_o, W_o.
enable_nvtx_profile (bool): enable NVTX profiling.
Returns:
masks (torch.Tensor): [1, M, H_o, W_o] where M=3 or 1. Masks of original image size.
iou_predictions (torch.Tensor): [1, M]. scores for M masks.
low_res_masks (torch.Tensor, optional): [1, M, H/4, W/4]. low resolution masks.
"""
nvtx_helper = None
if enable_nvtx_profile:
from nvtx_helper import NvtxHelper # noqa: PLC0415
nvtx_helper = NvtxHelper(["prompt_encoder", "mask_decoder", "post_process"])
if nvtx_helper is not None:
nvtx_helper.start_profile("prompt_encoder", color="blue")
sparse_embeddings, dense_embeddings, image_pe = self.prompt_encoder(
point_coords, point_labels, input_masks, has_input_masks
)
if nvtx_helper is not None:
nvtx_helper.stop_profile("prompt_encoder")
nvtx_helper.start_profile("mask_decoder", color="red")
low_res_masks, iou_predictions = self.mask_decoder(
image_features_0, image_features_1, image_embeddings, image_pe, sparse_embeddings, dense_embeddings
)
if nvtx_helper is not None:
nvtx_helper.stop_profile("mask_decoder")
nvtx_helper.start_profile("post_process", color="green")
# Interpolate the low resolution masks back to the original image size.
masks = F.interpolate(
low_res_masks,
(original_image_size[0], original_image_size[1]),
mode="bilinear",
align_corners=False, # Note that align_corners=True has less mismatches during comparing ORT and PyTorch.
)
low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0)
if not self.return_logits:
masks = masks > self.mask_threshold
if nvtx_helper is not None:
nvtx_helper.stop_profile("post_process")
nvtx_helper.print_latency()
return masks, iou_predictions, low_res_masks
def export_decoder_onnx(
sam2_model: SAM2Base,
onnx_model_path: str,
multimask_output: bool = False,
verbose: bool = False,
):
batch_size = 1
image = random_sam2_input_image(batch_size)
sam2_encoder = SAM2ImageEncoder(sam2_model).cpu()
image_features_0, image_features_1, image_embeddings = sam2_encoder(image)
logger.info("image_features_0.shape: %s", image_features_0.shape)
logger.info("image_features_1.shape: %s", image_features_1.shape)
logger.info("image_embeddings.shape: %s", image_embeddings.shape)
sam2_decoder = SAM2ImageDecoder(
sam2_model,
multimask_output=multimask_output,
dynamic_multimask_via_stability=True,
).cpu()
num_labels = 2
num_points = 3
point_coords = torch.randint(low=0, high=1024, size=(num_labels, num_points, 2), dtype=torch.float)
point_labels = torch.randint(low=0, high=1, size=(num_labels, num_points), dtype=torch.int32)
input_masks = torch.zeros(num_labels, 1, 256, 256, dtype=torch.float)
has_input_masks = torch.ones(1, dtype=torch.float)
original_image_size = torch.tensor([1200, 1800], dtype=torch.int32)
example_inputs = (
image_features_0,
image_features_1,
image_embeddings,
point_coords,
point_labels,
input_masks,
has_input_masks,
original_image_size,
)
logger.info("point_coords.shape: %s", point_coords.shape)
logger.info("point_labels.shape: %s", point_labels.shape)
logger.info("input_masks.shape: %s", input_masks.shape)
logger.info("has_input_masks.shape: %s", has_input_masks.shape)
logger.info("original_image_size.shape: %s", original_image_size.shape)
if verbose:
masks, iou_predictions, low_res_masks = sam2_decoder(*example_inputs)
logger.info("masks.shape: %s", masks.shape)
logger.info("iou_predictions.shape: %s", iou_predictions.shape)
logger.info("low_res_masks.shape: %s", low_res_masks.shape)
input_names = [
"image_features_0",
"image_features_1",
"image_embeddings",
"point_coords",
"point_labels",
"input_masks",
"has_input_masks",
"original_image_size",
]
output_names = ["masks", "iou_predictions", "low_res_masks"]
dynamic_axes = {
"point_coords": {0: "num_labels", 1: "num_points"},
"point_labels": {0: "num_labels", 1: "num_points"},
"input_masks": {0: "num_labels"},
"has_input_masks": {0: "num_labels"},
"masks": {0: "num_labels", 2: "original_image_height", 3: "original_image_width"},
"low_res_masks": {0: "num_labels"},
"iou_predictions": {0: "num_labels"},
}
with warnings.catch_warnings():
if not verbose:
warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
warnings.filterwarnings("ignore", category=UserWarning)
torch.onnx.export(
sam2_decoder,
example_inputs,
onnx_model_path,
export_params=True,
opset_version=16,
do_constant_folding=True,
input_names=input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
)
logger.info("decoder onnx model saved to %s", onnx_model_path)
def test_decoder_onnx(
sam2_model: SAM2Base,
onnx_model_path: str,
multimask_output=False,
):
batch_size = 1
image = random_sam2_input_image(batch_size)
sam2_encoder = SAM2ImageEncoder(sam2_model).cpu()
image_features_0, image_features_1, image_embeddings = sam2_encoder(image)
sam2_image_decoder = SAM2ImageDecoder(
sam2_model,
multimask_output=multimask_output,
dynamic_multimask_via_stability=True,
).cpu()
num_labels = 1
num_points = 5
point_coords = torch.randint(low=0, high=1024, size=(num_labels, num_points, 2), dtype=torch.float)
point_labels = torch.randint(low=0, high=1, size=(num_labels, num_points), dtype=torch.int32)
input_masks = torch.zeros(num_labels, 1, 256, 256, dtype=torch.float)
has_input_masks = torch.zeros(1, dtype=torch.float)
original_image_size = torch.tensor([1500, 1500], dtype=torch.int32)
example_inputs = (
image_features_0,
image_features_1,
image_embeddings,
point_coords,
point_labels,
input_masks,
has_input_masks,
original_image_size,
)
masks, iou_predictions, low_res_masks = sam2_image_decoder(*example_inputs)
import onnxruntime # noqa: PLC0415
ort_session = onnxruntime.InferenceSession(onnx_model_path, providers=["CPUExecutionProvider"])
model_inputs = ort_session.get_inputs()
input_names = [model_inputs[i].name for i in range(len(model_inputs))]
logger.info("input_names: %s", input_names)
model_outputs = ort_session.get_outputs()
output_names = [model_outputs[i].name for i in range(len(model_outputs))]
logger.info("output_names: %s", output_names)
inputs = {model_inputs[i].name: example_inputs[i].numpy() for i in range(len(model_inputs))}
outputs = ort_session.run(output_names, inputs)
for i, output_name in enumerate(output_names):
logger.info(f"{output_name}.shape: %s", outputs[i].shape)
ort_masks, ort_iou_predictions, ort_low_res_masks = outputs
if (
compare_tensors_with_tolerance("masks", masks.float(), torch.tensor(ort_masks).float())
and compare_tensors_with_tolerance("iou_predictions", iou_predictions, torch.tensor(ort_iou_predictions))
and compare_tensors_with_tolerance("low_res_masks", low_res_masks, torch.tensor(ort_low_res_masks))
):
print("onnx model has been verified:", onnx_model_path)
else:
print("onnx model verification failed:", onnx_model_path)
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