diff --git a/.gitattributes b/.gitattributes index 1897e55a2867ed073920b030850d557b42999eb0..150f625722ad7b4cd63af86a5c298b6b16960d4b 100644 --- a/.gitattributes +++ b/.gitattributes @@ -3998,3 +3998,4 @@ OVOW/engine/oneposeviagen/fpose/fpose/bundlesdf/mycuda/gridencoder.cpython-311-x OVOW/engine/oneposeviagen/fpose/fpose/mycpp/build/CMakeFiles/mycpp.dir/src/Utils.cpp.o filter=lfs diff=lfs merge=lfs -text OVOW/engine/oneposeviagen/fpose/fpose/mycpp/build/CMakeFiles/mycpp.dir/src/app/pybind_api.cpp.o filter=lfs diff=lfs merge=lfs -text OVOW/engine/oneposeviagen/fpose/fpose/mycpp/build/mycpp.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.cpython-311-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text diff --git a/OVOW/third_party/Motion324/configs/dyscene.yaml b/OVOW/third_party/Motion324/configs/dyscene.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9765c44d1222c781b928afe06e024630e75cd5ea --- /dev/null +++ b/OVOW/third_party/Motion324/configs/dyscene.yaml @@ -0,0 +1,67 @@ +model: + class_name: model.Pcd_motion.Motion_Latent_Model + feat_dim: 768 + tokens: 64 + pcd_layers: 4 + video_encoder: + image_tokenizer: + image_size: 224 + patch_size: 14 + patch_length: 1 + in_channels: 3 + transformer: + d: 768 + d_head: 64 + n_layer: 16 + special_init: true + depth_init: true + use_qk_norm: true + +training: + amp_dtype: bf16 + api_key_path: ./configs/api_keys.yaml + batch_size_per_gpu: 16 + beta1: 0.9 + beta2: 0.95 + allowed_gradnorm_factor: 5 + dataset_name: dataset.dyscene.Dyscene16k_Dataset + dataset_path: /path/to/your/dataset + train_lst: dataset/train.lst + dataset_end: -1 + dataset_begin: 1000 + num_shape_samples: 4096 + num_pcd_samples: 4096 + replica: 1 + frames: 12 + grad_accum_steps: 1 + use_checkpoint: True + grad_checkpoint_every: 1 + grad_clip_norm: 1.0 + coord_mse_loss_weight: 1.0 + lr: 0.0004 + num_workers: 8 + prefetch_factor: 2 + print_every: 20 + use_amp: true + use_tf32: true + checkpoint_every: 10000 + train_steps: 30000 + stop_steps: 30000 + warmup: 1000 + wandb_log_every: 1 + wandb_exp_name: test + wandb_project: Motion324 + resume_ckpt: '' + checkpoint_dir: ./experiments/checkpoints/${training.wandb_exp_name} + weight_decay: 0.05 + +# evaluation: +# dataset_name: dataset.dyscene.Dyscene16k_Dataset +# dataset_path: /path/to/your/dataset +# dataset_begin: 0 +# dataset_end: 1000 +# batch_size_per_gpu: 8 +# frames: 12 +# replica: 1 +# num_shape_samples: 4096 +# num_pcd_samples: 4096 \ No newline at end of file diff --git a/OVOW/third_party/Motion324/dataset/dataset_utils.py b/OVOW/third_party/Motion324/dataset/dataset_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..61b26c629970569a6789e5cabfedd6f79cf1c566 --- /dev/null +++ b/OVOW/third_party/Motion324/dataset/dataset_utils.py @@ -0,0 +1,136 @@ +import os +from typing import Dict, Tuple + +import numpy as np +import torch +import trimesh + + +def load_uv_preprocessing_data(save_dir: str) -> Dict[str, np.ndarray]: + """Load preprocessed UV data.""" + + data = np.load(save_dir) + return { + 'face_uvs': data['face_uvs'], + 'texture_array': data['texture_array'] + } + + +def sample_texture_color_vectorized( + uvs: np.ndarray, + texture_array: np.ndarray +) -> np.ndarray: + """ + Sample colors from texture map in batch based on UV coordinates. + + Args: + uvs (np.ndarray): UV coordinate array with shape (N, 2). + texture_array (np.ndarray): Texture map with shape (H, W, 3). + + Returns: + np.ndarray: Sampled RGB color array with shape (N, 3). + """ + u, v = uvs[:, 0], uvs[:, 1] + + x = (u * (texture_array.shape[1] - 1)).astype(int) + y = ((1 - v) * (texture_array.shape[0] - 1)).astype(int) + + x = np.clip(x, 0, texture_array.shape[1] - 1) + y = np.clip(y, 0, texture_array.shape[0] - 1) + + return texture_array[y, x] + + +def track_with_normal_rgb( + init_mesh: trimesh.Trimesh, + vertex_frames: np.ndarray, + faces: np.ndarray, + num_samples: int, + face_uvs: np.ndarray, + texture_array: np.ndarray +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, np.ndarray]: + """ + Sample points on a deforming mesh sequence and track their positions, normals, and RGB colors. + + This method first samples points on the first frame (t=0) and computes their barycentric coordinates. + Then it uses these fixed coordinates to compute the positions of sampled points in all subsequent frames, + interpolated normals, and RGB colors based on UV mapping. + + Args: + init_mesh (trimesh.Trimesh): Trimesh mesh object of the first frame, used for initial sampling. + vertex_frames (np.ndarray): Mesh vertex sequence with shape (T, N, 3). + faces (np.ndarray): Mesh faces with shape (F, 3), shared across all frames. + num_samples (int): Number of points to sample and track. + face_uvs (np.ndarray): UV coordinates for three vertices of each face, with shape (F, 3, 2). + texture_array (np.ndarray): Texture map with shape (H, W, 3). + + Returns: + Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + - tracked_points (torch.Tensor): Tracked point cloud sequence with shape (T, num_samples, 3). + - tracked_normals (torch.Tensor): Corresponding normal vector sequence with shape (T, num_samples, 3). + - tracked_rgbs (torch.Tensor): Corresponding RGB color sequence with shape (T, num_samples, 3). + Colors are fixed across all frames. + """ + num_frames = vertex_frames.shape[0] + + + sampled_points_t0, face_indices = trimesh.sample.sample_surface(init_mesh, num_samples) + + triangles_t0 = init_mesh.vertices[faces[face_indices]] + + barycentric_coords = trimesh.triangles.points_to_barycentric( + triangles=triangles_t0, points=sampled_points_t0 + ) + + # face_uvs: (F, 3, 2) -> sampled_face_uvs: (num_samples, 3, 2) + sampled_face_uvs = face_uvs[face_indices] + + # barycentric_coords: (num_samples, 3), sampled_face_uvs: (num_samples, 3, 2) + interpolated_uvs = np.einsum( + 'ij,ijk->ik', + barycentric_coords, + sampled_face_uvs + ) + + tracked_rgbs_np = sample_texture_color_vectorized(interpolated_uvs, texture_array) + tracked_rgbs_np = tracked_rgbs_np / 255.0 + + all_tracked_points = [] + all_tracked_normals = [] + + tracking_mesh = trimesh.Trimesh(faces=faces, process=False) + + for t in range(num_frames): + tracking_mesh.vertices = vertex_frames[t] + + current_triangles = tracking_mesh.vertices[faces[face_indices]] + current_points = trimesh.triangles.barycentric_to_points(current_triangles, barycentric_coords) + all_tracked_points.append(current_points) + + vertex_normals_of_triangles = tracking_mesh.vertex_normals[faces[face_indices]] + + interpolated_normals = np.einsum( + 'ij,ijk->ik', + barycentric_coords, + vertex_normals_of_triangles + ) + + norms = np.linalg.norm(interpolated_normals, axis=1, keepdims=True) + non_zero_norms = np.where(norms == 0, 1.0, norms) + interpolated_normals /= non_zero_norms + + all_tracked_normals.append(interpolated_normals) + + tracked_points_np = np.stack(all_tracked_points, axis=0) + tracked_normals_np = np.stack(all_tracked_normals, axis=0) + + tracked_rgbs_np_expanded = np.tile( + tracked_rgbs_np[np.newaxis, :, :], + (num_frames, 1, 1) + ) + + tracked_points = torch.from_numpy(tracked_points_np).float() + tracked_normals = torch.from_numpy(tracked_normals_np).float() + tracked_rgbs = torch.from_numpy(tracked_rgbs_np_expanded).float() + + return tracked_points, tracked_normals, tracked_rgbs, face_indices diff --git a/OVOW/third_party/Motion324/dataset/dyscene.py b/OVOW/third_party/Motion324/dataset/dyscene.py new file mode 100644 index 0000000000000000000000000000000000000000..c4626d2fd3bbc5f158420152743e8373fb101182 --- /dev/null +++ b/OVOW/third_party/Motion324/dataset/dyscene.py @@ -0,0 +1,383 @@ +import os +import random +import re +from pathlib import Path + +import numpy as np +import torch +import trimesh +from torch.utils.data import Dataset + +from dataset.dataset_utils import load_uv_preprocessing_data, track_with_normal_rgb + + +class Dyscene16k_Dataset(Dataset): + def __init__(self, config, pcd_subdir="pcds", transform=None): + """ + Args: + root_dir (string): Directory with all the .glb files, .mp4 files, and the pcd_subdir. + e.g., /path/to/Dataset/3d_object/dyobjs/Arti-XL/glbs/tracks/ + pcd_subdir (string): Subdirectory under root_dir where object-named folders + containing .npy point clouds are located. Default is "pcds". + transform (callable, optional): Optional transform to be applied on a sample. + """ + self.root_dir = Path(config.dataset_path) + self.pcd_base_dir = self.root_dir / pcd_subdir + self.image_base_dir = self.root_dir / "all_images" + if hasattr(config, 'train_lst') and config.train_lst: + train_lst_path = config.train_lst + else: + train_lst_path = 'dataset/train.lst' + self.transform = transform + self.frames = config.frames + self.replica = config.replica + self.num_shape_samples = config.num_shape_samples + self.num_pcd_samples = config.num_pcd_samples + + # Find all .glb files and derive object names all_train + with open(train_lst_path, 'r') as f: #valid all_unique_fliter 2kagain.lst + self.obj_names = [line.strip() for line in f if line.strip()] + + self.obj_names = self.obj_names[config.dataset_begin:config.dataset_end] + + if not self.obj_names: + raise RuntimeError(f"No .glb files found in {self.root_dir}") + + print(f"Found {len(self.obj_names)} objects: {self.obj_names[0]}...") # Print first 5 for brevity + + def __len__(self): + return len(self.obj_names) * self.replica + + def _get_sequence_length(self, obj_name): + """Get the total number of frames for a given object""" + # Check point cloud files + pcd_path = obj_name + '_pointclouds' + obj_pcd_dir = self.pcd_base_dir / pcd_path + pcd_files = [] + if obj_pcd_dir.is_dir(): + pcd_files = sorted( + list(obj_pcd_dir.glob("frame_*.npy")), + key=self._extract_frame_number + ) + + # Check image files + image_path = obj_name + '_images' + obj_image_dir = self.image_base_dir / image_path / 'camera_0' + image_files = [] + if obj_image_dir.is_dir(): + image_files = sorted( + list(obj_image_dir.glob("frame_*.jpg")) + list(obj_image_dir.glob("frame_*.png")), + key=self._extract_frame_number + ) + + # Return the minimum length to ensure both modalities have data + return max(len(pcd_files), len(image_files)) if pcd_files and image_files else 0 + + def _generate_frame_indices(self, T): + """Generate frame indices based on sampling strategies""" + if T < self.frames: + return None + + possible_options = [] + if T >= self.frames: + possible_options.append({ + 'name': 'skip1', + 'skip_interval': 1, + 'probability_weight': 0.4, + 'span': self.frames + }) + required_span_skip2 = (self.frames - 1) * 2 + 1 + if T >= required_span_skip2: + possible_options.append({ + 'name': 'skip2', + 'skip_interval': 2, + 'probability_weight': 0.4, + 'span': required_span_skip2 + }) + required_span_skip4 = (self.frames - 1) * 4 + 1 + if T >= required_span_skip4: + possible_options.append({ + 'name': 'skip4', + 'skip_interval': 4, + 'probability_weight': 0.2, + 'span': required_span_skip4 + }) + + if not possible_options: + return None + + total_weight = sum(opt['probability_weight'] for opt in possible_options) + probabilities = [opt['probability_weight'] / total_weight for opt in possible_options] + + chosen_opt_index = np.random.choice(len(possible_options), p=probabilities) + chosen_strategy = possible_options[chosen_opt_index] + + skip = chosen_strategy['skip_interval'] + span_needed = chosen_strategy['span'] + + max_start_idx = T - span_needed + if max_start_idx <= 0: + start_idx = np.random.randint(0, T - self.frames + 1) + return list(range(start_idx, start_idx + self.frames)) + else: + start_idx = np.random.randint(0, max_start_idx + 1) + if skip == 1: + return list(range(start_idx, start_idx + self.frames)) + else: + return [start_idx + i * skip for i in range(self.frames)] + + def _load_single_image(self, image_path): + """Load a single image file""" + try: + from PIL import Image + image = Image.open(image_path).convert('RGB') + # Convert to numpy array and normalize to [0, 1] + image_array = np.array(image).astype(np.float32) / 255.0 + return image_array + except Exception as e: + #print(f"Warning: Could not load image {image_path}: {e}") + return None + + def _load_single_pointcloud(self, pcd_path): + """Load a single point cloud file""" + try: + point_cloud = np.load(pcd_path) + return point_cloud + except Exception as e: + print(f"Warning: Could not load point cloud {pcd_path}: {e}") + return None + + def _extract_frame_number(self, filename): + """Extracts frame number from filenames like frame_0001.npy""" + match = re.search(r'frame_(\d+)\.npy', filename.name) + if not match: + match = re.search(r'frame_(\d+)\.(jpg|png)', filename.name) + return int(match.group(1)) if match else -1 + + def __getitem__(self, idx): + if torch.is_tensor(idx): + idx = idx.tolist() + + obj_name = self.obj_names[idx % len(self.obj_names)] + + # Get total sequence length + T = self._get_sequence_length(obj_name) + + if T < self.frames: + #print(f"Warning: No data found for object {obj_name}. Returning empty sample.") + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + # Generate frame indices + frame_indices = self._generate_frame_indices(T) + + if frame_indices is None: + print(f"Warning: Cannot generate valid frame indices for {obj_name} with T={T}") + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + if len(frame_indices) != self.frames: + print(f"Warning: Cannot generate valid frame indices for {obj_name} with T={T}") + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + # assert len(frame_indices) == self.frames + # Load images and point clouds based on frame indices + image_path = obj_name + '_images' + camera = random.choice([f'camera_{i}' for i in range(15)]) + obj_image_dir = self.image_base_dir / image_path / camera + + pcd_path = obj_name + '_pointclouds' + obj_pcd_dir = self.pcd_base_dir / pcd_path + + faces_path = obj_pcd_dir / 'faces.npy' + faces_np = np.load(faces_path) + faces = torch.from_numpy(faces_np) + + # Get sorted file lists + image_files = sorted( + list(obj_image_dir.glob("frame_*.jpg")) + list(obj_image_dir.glob("frame_*.png")), + key=self._extract_frame_number + ) + pcd_files = sorted( + list(obj_pcd_dir.glob("frame_*.npy")), + key=self._extract_frame_number + ) + + # Load selected frames + rgb_frames = [] + point_clouds_list = [] + + #assert len(frame_indices) == self.frames + + for frame_idx in frame_indices: + # Load image + if frame_idx < len(image_files): + image_data = self._load_single_image(image_files[frame_idx]) + if image_data is not None: + rgb_frames.append(image_data) + else: + #print(f"Warning: Failed to load image frame {frame_idx} for {obj_name}") + continue + else: + #print(f"Warning: Image frame {frame_idx} not found for {obj_name}") + continue + + # Load point cloud + if frame_idx < len(pcd_files): + pcd_data = self._load_single_pointcloud(pcd_files[frame_idx]) + if pcd_data is not None: + point_clouds_list.append(pcd_data) + else: + print(f"Warning: Failed to load point cloud frame {frame_idx} for {obj_name}") + continue + else: + print(f"Warning: Point cloud frame {frame_idx} not found for {obj_name}") + continue + + if len(point_clouds_list) != self.frames or len(rgb_frames) !=self.frames: + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + # Convert to tensors + video_np = np.stack(rgb_frames, axis=0).astype(np.float32) + vertex_np = np.stack(point_clouds_list, axis=0) + + if video_np.shape[0] != vertex_np.shape[0]: + #print(f"Warning: video_np.shape[0] ({video_np.shape[0]}) != vertex_np.shape[0] ({vertex_np.shape[0]}), resampling for {obj_name}") + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + uv_data_dir = obj_pcd_dir + data_path = os.path.join(uv_data_dir, 'uv_face_texture.npz') + + if not os.path.exists(data_path): + print(f"Warning: UV data not found for {obj_name}") + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + uv_data = load_uv_preprocessing_data(data_path) + face_uvs = uv_data['face_uvs'] + texture_array = uv_data['texture_array'] + + try: + final_rgb_frames = torch.from_numpy(video_np).float() + + ref_shape_pcd, ref_shape_normals, ref_shape_rgbs, ref_shape_face_indices = track_with_normal_rgb( + init_mesh=trimesh.Trimesh(vertices=vertex_np[0], faces=faces_np, process=False), + vertex_frames=vertex_np[0][None, ...], + faces=faces, + num_samples=self.num_shape_samples, + face_uvs=face_uvs, + texture_array=texture_array + ) + ref_shape_pcd = ref_shape_pcd[0] + ref_shape_normals = ref_shape_normals[0] + ref_shape_rgbs = ref_shape_rgbs[0] + + if ( + torch.isnan(ref_shape_pcd).any() or torch.isinf(ref_shape_pcd).any() or + torch.isnan(ref_shape_normals).any() or torch.isinf(ref_shape_normals).any() + ): + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + if isinstance(faces, torch.Tensor): + faces = faces.cpu().numpy() + else: + faces = faces + + initial_vertices = vertex_np[0] + initial_mesh = trimesh.Trimesh(vertices=initial_vertices, faces=faces_np, process=False) + + final_point_clouds, final_point_normals, final_point_rgbs, face_indices = track_with_normal_rgb( + init_mesh=initial_mesh, + vertex_frames=vertex_np, + faces=faces, + num_samples=self.num_pcd_samples, + face_uvs=face_uvs, + texture_array=texture_array + ) + + + if ( + torch.isnan(final_point_clouds).any() or torch.isinf(final_point_clouds).any() or + torch.isnan(final_point_normals).any() or torch.isinf(final_point_normals).any() + ): + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + except Exception as e: + #print(f"Error processing object {obj_name} at index {idx}: {str(e)}. Skipping and trying a new one.") + new_idx = random.randint(0, len(self) - 1) + return self[new_idx] + + sample = { + 'obj_name': obj_name, + 'rgb_video': final_rgb_frames, + 'point_clouds': final_point_clouds, + 'point_rgbs': final_point_rgbs, + 'ref_shape_pcd': ref_shape_pcd, + 'ref_shape_normals': ref_shape_normals, + 'ref_shape_rgbs': ref_shape_rgbs, + 'ref_pcd': final_point_clouds[0], + 'ref_normal': final_point_normals[0], + 'ref_rgb': final_point_rgbs[0], + + } + + return sample + +def collate_fn_with_topology(batch): + """ + Batch processing function supporting topology information. + """ + collated = {} + + tensor_keys = [ + 'rgb_video', + 'point_clouds', + 'point_rgbs', + 'ref_shape_pcd', + 'ref_shape_normals', + 'ref_shape_rgbs', + 'ref_pcd', + 'ref_normal', + 'ref_rgb', + ] + + for key in tensor_keys: + if key in batch[0]: + try: + collated[key] = torch.stack([item[key] for item in batch]) + except RuntimeError as e: + # shapes = [item[key].shape for item in batch] + for i, item in enumerate(batch): + if item[key].shape != batch[0][key].shape: + expected_shape = batch[0][key].shape + actual_shape = item[key].shape + raise RuntimeError( + f"Shape mismatch in tensor '{key}' for object {item['obj_name']}. " + f"Expected {expected_shape} (from {batch[0]['obj_name']}), " + f"got {actual_shape}" + ) + raise RuntimeError(f"Failed to stack tensor '{key}' for batch. Original error: {str(e)}") + + collated['obj_name'] = [item['obj_name'] for item in batch] + + edge_indices = [] + num_nodes_offset = 0 + num_nodes_per_sample = batch[0]['point_clouds'].size(1) + if 'edge_indices' in batch[0]: + for sample in batch: + edge_index_offset = sample['edge_indices'] + num_nodes_offset + edge_indices.append(edge_index_offset) + + num_nodes_offset += num_nodes_per_sample + + # final shape: (2, B * E) + batched_edge_index = torch.cat(edge_indices, dim=1) + + collated['edge_indices'] = batched_edge_index + + return collated diff --git a/OVOW/third_party/Motion324/dataset/long_videos.txt b/OVOW/third_party/Motion324/dataset/long_videos.txt new file mode 100644 index 0000000000000000000000000000000000000000..99498c8fd701c6266a8b772647ccbc8581cd7874 --- /dev/null +++ b/OVOW/third_party/Motion324/dataset/long_videos.txt @@ -0,0 +1,16 @@ +bca5350dd54847eea333362dd7997c43-0-mixamo.com-_rootJoint 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load_i3d_pretrained + + print("calculate_fvd...") + + # videos [batch_size, timestamps, channel, h, w] + + assert videos1.shape == videos2.shape + + # Only load model if not provided + if i3d_model is None: + i3d = load_i3d_pretrained(device=device) + else: + i3d = i3d_model + + fvd_results = [] + + # support grayscale input, if grayscale -> channel*3 + # BTCHW -> BCTHW + # videos -> [batch_size, channel, timestamps, h, w] + + videos1 = trans(videos1) + videos2 = trans(videos2) + + fvd_results = [] + + if only_final: + + assert videos1.shape[2] >= 10, "for calculate FVD, each clip_timestamp must >= 10" + + # videos_clip [batch_size, channel, timestamps, h, w] + videos_clip1 = videos1 + videos_clip2 = videos2 + + # get FVD features + feats1 = get_fvd_feats(videos_clip1, i3d=i3d, device=device) + feats2 = get_fvd_feats(videos_clip2, i3d=i3d, device=device) + + # calculate FVD + fvd_results.append(frechet_distance(feats1, feats2)) + + else: + + # for calculate FVD, each clip_timestamp must >= 10 + for clip_timestamp in tqdm(range(10, videos1.shape[-3]+1)): + + # get a video clip + # videos_clip [batch_size, channel, timestamps[:clip], h, w] + videos_clip1 = videos1[:, :, : clip_timestamp] + videos_clip2 = videos2[:, :, : clip_timestamp] + + # get FVD features + feats1 = get_fvd_feats(videos_clip1, i3d=i3d, device=device) + feats2 = get_fvd_feats(videos_clip2, i3d=i3d, device=device) + + # calculate FVD when timestamps[:clip] + fvd_results.append(frechet_distance(feats1, feats2)) + + result = { + "value": fvd_results, + } + + return result + +# test code / using example + +def main(): + NUMBER_OF_VIDEOS = 8 + VIDEO_LENGTH = 30 + CHANNEL = 3 + SIZE = 64 + videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False) + videos2 = torch.ones(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False) + device = torch.device("cuda") + # device = torch.device("cpu") + + result = calculate_fvd(videos1, videos2, device, method='videogpt', only_final=False) + print("[fvd-videogpt ]", result["value"]) + + result = calculate_fvd(videos1, videos2, device, method='styleganv', only_final=False) + print("[fvd-styleganv]", result["value"]) + +if __name__ == "__main__": + main() diff --git a/OVOW/third_party/Motion324/evaluation/calculate_lpips.py b/OVOW/third_party/Motion324/evaluation/calculate_lpips.py new file mode 100644 index 0000000000000000000000000000000000000000..7da992412bf787d1eaccabe7f7b7a3f3a83f4fc8 --- /dev/null +++ b/OVOW/third_party/Motion324/evaluation/calculate_lpips.py @@ -0,0 +1,219 @@ +import numpy as np +import torch +from tqdm import tqdm +import math + +import torch +import lpips + +# ignore torchvision UserWarning of 'weights' +import warnings +warnings.filterwarnings("ignore") + +spatial = True # Return a spatial map of perceptual distance. + +# Linearly calibrated models (LPIPS) +loss_fn = lpips.LPIPS(net='vgg', spatial=spatial) # Can also set net = 'squeeze' or 'vgg' +# loss_fn = lpips.LPIPS(net='alex', spatial=spatial, lpips=False) # Can also set net = 'squeeze' or 'vgg' + +def trans(x): + # if greyscale images add channel + if x.shape[-3] == 1: + x = x.repeat(1, 1, 3, 1, 1) + + # value range [0, 1] -> [-1, 1] + x = x * 2 - 1 + + return x + +import open_clip + +from dreamsim import dreamsim +from torchvision import transforms + +def calculate_dreamsim_loss(videos1, videos2, device, only_final=False, dreamsim_model=None): + print("calculate_dreamsim_loss...") + + assert videos1.shape == videos2.shape + + # Only load model if not provided + if dreamsim_model is None: + dreamsim_model, _ = dreamsim(pretrained=True, device=device) + + transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor() + ]) + + dreamsim_loss_results = [] + + for video_num in tqdm(range(videos1.shape[0])): + video1 = videos1[video_num] + video2 = videos2[video_num] + + dreamsim_loss_results_of_a_video = [] + for clip_timestamp in range(len(video1)): + img1 = transforms.Resize((224, 224))(video1[clip_timestamp]) + img2 = transforms.Resize((224, 224))(video2[clip_timestamp]) + + img1_tensor = img1.unsqueeze(0).to(device) + img2_tensor = img2.unsqueeze(0).to(device) + + loss = dreamsim_model(img1_tensor, img2_tensor).item() + + dreamsim_loss_results_of_a_video.append(loss) + + dreamsim_loss_results.append(dreamsim_loss_results_of_a_video) + + dreamsim_loss_results = np.array(dreamsim_loss_results) + + dreamsim_loss = [] + dreamsim_loss_std = [] + + if only_final: + dreamsim_loss.append(np.mean(dreamsim_loss_results)) + dreamsim_loss_std.append(np.std(dreamsim_loss_results)) + else: + for clip_timestamp in range(len(video1)): + dreamsim_loss.append(np.mean(dreamsim_loss_results[:, clip_timestamp])) + dreamsim_loss_std.append(np.std(dreamsim_loss_results[:, clip_timestamp])) + + result = { + "value": dreamsim_loss, + "value_std": dreamsim_loss_std, + } + + # return mean for each video + return result, dreamsim_loss_results.mean(axis=1) + + +def calculate_clip_loss(videos1, videos2, device, only_final=False, clip_model=None, clip_preprocess=None): + print("Calculating CLIP loss with OpenCLIP's best model...") + + assert videos1.shape == videos2.shape, "Input videos must have the same shape." + + # Only load model if not provided + if clip_model is None or clip_preprocess is None: + model, _, preprocess = open_clip.create_model_and_transforms("ViT-bigG-14", pretrained="laion2B_s39B_b160k") + model = model.to(device).eval() + else: + model = clip_model + preprocess = clip_preprocess + + clip_loss_results = [] + + for video_num in tqdm(range(videos1.shape[0])): + video1 = videos1[video_num] + video2 = videos2[video_num] + + clip_loss_results_of_a_video = [] + for clip_timestamp in range(len(video1)): + img1 = video1[clip_timestamp].cpu() + img2 = video2[clip_timestamp].cpu() + + # Convert numpy images to PIL images for preprocessing + img1 = preprocess(transforms.ToPILImage()(torch.tensor(img1))) + img2 = preprocess(transforms.ToPILImage()(torch.tensor(img2))) + + # Stack and move to device + images = torch.stack([img1, img2]).to(device) + + # Compute image features + with torch.no_grad(): + image_features = model.encode_image(images) + image_features = torch.nn.functional.normalize(image_features, dim=-1) + + # Compute cosine similarity + similarity = torch.cosine_similarity(image_features[0], image_features[1], dim=0) + clip_loss_results_of_a_video.append(similarity.cpu().item()) + + clip_loss_results.append(clip_loss_results_of_a_video) + + clip_loss_results = np.array(clip_loss_results) + clip_loss = np.mean(clip_loss_results, axis=0) if not only_final else [np.mean(clip_loss_results)] + clip_loss_std = np.std(clip_loss_results, axis=0) if not only_final else [np.std(clip_loss_results)] + + return {"value": clip_loss, "value_std": clip_loss_std}, clip_loss_results.mean(axis=1) + + +def calculate_lpips(videos1, videos2, device, only_final=False, lpips_model=None): + # image should be RGB, IMPORTANT: normalized to [-1,1] + print("calculate_lpips...") + + assert videos1.shape == videos2.shape + + # videos [batch_size, timestamps, channel, h, w] + + # support grayscale input, if grayscale -> channel*3 + # value range [0, 1] -> [-1, 1] + videos1 = trans(videos1) + videos2 = trans(videos2) + + lpips_results = [] + + # Use provided model or global loss_fn + if lpips_model is None: + lpips_model = loss_fn + + lpips_model.to(device) + + for video_num in tqdm(range(videos1.shape[0])): + # get a video + # video [timestamps, channel, h, w] + video1 = videos1[video_num] + video2 = videos2[video_num] + + lpips_results_of_a_video = [] + for clip_timestamp in range(len(video1)): + # get a img + # img [timestamps[x], channel, h, w] + # img [channel, h, w] tensor + + img1 = video1[clip_timestamp].unsqueeze(0).to(device) + img2 = video2[clip_timestamp].unsqueeze(0).to(device) + + # calculate lpips of a video + lpips_results_of_a_video.append(lpips_model.forward(img1, img2).mean().detach().cpu().tolist()) + lpips_results.append(lpips_results_of_a_video) + + lpips_results = np.array(lpips_results) + + lpips = [] + lpips_std = [] + + if only_final: + + lpips.append(np.mean(lpips_results)) + lpips_std.append(np.std(lpips_results)) + + else: + + for clip_timestamp in range(len(video1)): + lpips.append(np.mean(lpips_results[:,clip_timestamp])) + lpips_std.append(np.std(lpips_results[:,clip_timestamp])) + + result = { + "value": lpips, + "value_std": lpips_std, + } + + return result, lpips_results.mean(axis=1) + +# test code / using example + +def main(): + NUMBER_OF_VIDEOS = 8 + VIDEO_LENGTH = 30 + CHANNEL = 3 + SIZE = 64 + videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False) + videos2 = torch.ones(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False) + device = torch.device("cuda") + # device = torch.device("cpu") + + result = calculate_lpips(videos1, videos2, device) + print("[lpips avg]", result["value"]) + print("[lpips std]", result["value_std"]) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/OVOW/third_party/Motion324/evaluation/evaluation.py b/OVOW/third_party/Motion324/evaluation/evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..01d9dd6dc75238b0fc3a6d11f14df7dcc293fe84 --- /dev/null +++ b/OVOW/third_party/Motion324/evaluation/evaluation.py @@ -0,0 +1,274 @@ +import torch +import torchvision.transforms as transforms +import cv2 +import os +import json +import numpy as np +from calculate_fvd import calculate_fvd +from calculate_lpips import calculate_lpips, calculate_dreamsim_loss, calculate_clip_loss +import argparse +from glob import glob +from PIL import Image +import lpips +import open_clip +from dreamsim import dreamsim + +# Constants +FRAME_SIZE = 512 # Resize all frames to 512x512 +TIMESTAMP_LIMIT = 32 # Split videos into 32-frame subvideos + +# Define transformation +transform = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((FRAME_SIZE, FRAME_SIZE)), + transforms.ToTensor() # Converts to [0,1] range +]) + +def load_video_as_tensor(video_path): + """Load a video and convert it into a tensor [timestamp, channel, width, height].""" + cap = cv2.VideoCapture(video_path) + frames = [] + + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Convert BGR to RGB + frame = transform(frame) # Resize & normalize + frames.append(frame) + + cap.release() + + #if len(frames) < TIMESTAMP_LIMIT: + # return None # Ignore videos with fewer than 32 frames + + video_tensor = torch.stack(frames) # Shape: [timestamp, channel, width, height] + return video_tensor + +def load_images_as_tensor(image_dir): + """Load images from a directory and convert them into a tensor [timestamp, channel, width, height].""" + # Find all image files (png, jpg, jpeg) + image_files = sorted(glob(os.path.join(image_dir, "*.png")) + + glob(os.path.join(image_dir, "*.jpg")) + + glob(os.path.join(image_dir, "*.jpeg"))) + + #if len(image_files) < TIMESTAMP_LIMIT: + # return None # Ignore if fewer than required frames + + frames = [] + for img_path in image_files: + img = Image.open(img_path).convert('RGB') + img_array = np.array(img) + frame = transform(img_array) # Resize & normalize + frames.append(frame) + + video_tensor = torch.stack(frames) # Shape: [timestamp, channel, width, height] + return video_tensor + +def is_video_file(path): + """Check if path is a video file""" + video_extensions = ['.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv', '.webm'] + return os.path.isfile(path) and any(path.lower().endswith(ext) for ext in video_extensions) + +def load_input(path): + """Load input from video file or image directory""" + if is_video_file(path): + return load_video_as_tensor(path) + elif os.path.isdir(path): + return load_images_as_tensor(path) + else: + raise ValueError(f"Path {path} is neither a video file nor a directory") + +def get_frame_count(path): + """Get frame count from video file or image directory""" + if is_video_file(path): + cap = cv2.VideoCapture(path) + count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + cap.release() + return count + elif os.path.isdir(path): + image_files = sorted(glob(os.path.join(path, "*.png")) + + glob(os.path.join(path, "*.jpg")) + + glob(os.path.join(path, "*.jpeg"))) + return len(image_files) + else: + return 0 + +def process_single_video(video_path_or_dir): + """Load a single video/image directory and convert it into a tensor [timestamp, channel, width, height].""" + tensor = load_input(video_path_or_dir) + if tensor.shape[0] == 0: + raise ValueError(f"Input {video_path_or_dir} has no frames") + # If video has fewer frames than TIMESTAMP_LIMIT, pad by repeating the last frame + if tensor.shape[0] < TIMESTAMP_LIMIT: + num_frames_needed = TIMESTAMP_LIMIT - tensor.shape[0] + # Take the last num_frames_needed frames and reverse them for padding + padding = tensor[-num_frames_needed:].flip(0) # Reverse the last num_frames_needed frames + tensor = torch.cat([tensor, padding], dim=0) # Concatenate along timestamp dimension + print(f"Input {video_path_or_dir} padded from {tensor.shape[0] - num_frames_needed} to {tensor.shape[0]} frames") + # Split into 32-frame subvideos + subvideos = [tensor[i:i+TIMESTAMP_LIMIT] for i in range(0, tensor.shape[0] - TIMESTAMP_LIMIT + 1, TIMESTAMP_LIMIT)] + print(f"Loaded {len(subvideos)} subvideos from {video_path_or_dir}") + + # Stack into batch: [batch, timestamp, channel, width, height] + return torch.stack(subvideos) + +parser = argparse.ArgumentParser(description="Evaluate video metrics between videos/images.") +parser.add_argument('--gt_paths', type=str, required=True, nargs='+', help='Path(s) to ground truth video(s) or image directory(ies)') +parser.add_argument('--result_paths', type=str, required=True, nargs='+', help='Path(s) to generated/result video(s) or image directory(ies)') +parser.add_argument('--output_dir', type=str, default=None, help='Directory to save results (optional)') +args = parser.parse_args() + +gt_paths = args.gt_paths +result_paths = args.result_paths +output_dir = args.output_dir + +# Ensure same number of paths +if len(gt_paths) != len(result_paths): + raise ValueError(f"Number of GT paths ({len(gt_paths)}) must match number of result paths ({len(result_paths)})") + +# Move tensors to device +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# Load all models once before the loop +print("Loading all models...") +print(" - Loading LPIPS model...") +lpips_model = lpips.LPIPS(net='vgg', spatial=True) +lpips_model = lpips_model.to(device) + +print(" - Loading I3D model for FVD...") +from fvd.styleganv.fvd import load_i3d_pretrained +i3d_model = load_i3d_pretrained(device=device) + +print(" - Loading DreamSim model...") +dreamsim_model, _ = dreamsim(pretrained=True, device=device) + +print(" - Loading OpenCLIP model...") +clip_model, _, clip_preprocess = open_clip.create_model_and_transforms("ViT-bigG-14", pretrained="laion2B_s39B_b160k") +clip_model = clip_model.to(device).eval() + +print("All models loaded successfully!\n") + +# Create output directory if specified +if output_dir: + os.makedirs(output_dir, exist_ok=True) + +# Process each pair of paths +all_results = [] +for idx, (gt_path, result_path) in enumerate(zip(gt_paths, result_paths)): + print(f"\n{'='*60}") + print(f"Processing pair {idx+1}/{len(gt_paths)}") + print(f"{'='*60}") + + # Check if paths exist + if not os.path.exists(gt_path): + print(f"Warning: GT path not found at {gt_path}, skipping...") + continue + if not os.path.exists(result_path): + print(f"Warning: Result path not found at {result_path}, skipping...") + continue + + print(f"GT: {gt_path}") + print(f"Result: {result_path}") + + try: + # Get frame counts + gt_frame_count = get_frame_count(gt_path) + result_frame_count = get_frame_count(result_path) + + if gt_frame_count == 0: + print(f"Warning: GT path {gt_path} has no frames, skipping...") + continue + if result_frame_count == 0: + print(f"Warning: Result path {result_path} has no frames, skipping...") + continue + + if gt_frame_count != result_frame_count: + print(f"Warning: Frame count mismatch: GT has {gt_frame_count} frames, but result has {result_frame_count} frames.") + + print(f"GT has {gt_frame_count} frames") + print(f"Result has {result_frame_count} frames") + print(f"Processing...") + + videos1 = process_single_video(gt_path) + videos2 = process_single_video(result_path) + + # Compute metrics + only_final = True + + print("Computing metrics...") + lpips_result, lpips_eachvideo = calculate_lpips(videos1, videos2, device, only_final=only_final, lpips_model=lpips_model) + fvd_result = calculate_fvd(videos1, videos2, device, method='styleganv', only_final=only_final, i3d_model=i3d_model) + dreamsim_result, dreamsim_eachvideo = calculate_dreamsim_loss(videos1, videos2, device, only_final=only_final, dreamsim_model=dreamsim_model) + clip_loss_result, clip_loss_eachvideo = calculate_clip_loss(videos1, videos2, device, only_final=only_final, clip_model=clip_model, clip_preprocess=clip_preprocess) + + # Extract scalar values from result dictionaries + # When only_final=True, value is a list with single element + fvd_value = fvd_result['value'][0] if isinstance(fvd_result['value'], list) else fvd_result['value'] + lpips_value = lpips_result['value'][0] if isinstance(lpips_result['value'], list) else lpips_result['value'] + dreamsim_value = dreamsim_result['value'][0] if isinstance(dreamsim_result['value'], list) else dreamsim_result['value'] + clip_loss_value = clip_loss_result['value'] if isinstance(clip_loss_result['value'], (int, float)) else clip_loss_result['value'][0] + + result = { + 'gt_path': gt_path, + 'result_path': result_path, + 'fvd': fvd_value, + 'lpips': lpips_value, + 'dreamsim': dreamsim_value, + 'clip_loss': clip_loss_value, + } + all_results.append(result) + + # Print results + print(f"\nResults:") + print(json.dumps({k: v for k, v in result.items() if k not in ['gt_path', 'result_path']}, indent=4)) + + # Save results to file + # Default: save to result_path's dirname with basename.txt + result_basename = os.path.basename(result_path.rstrip('/')) + # Remove extension if it's a file + if os.path.isfile(result_path): + result_basename = os.path.splitext(result_basename)[0] + + if output_dir: + # If output_dir is specified, save there + output_file = os.path.join(output_dir, f'{result_basename}.txt') + os.makedirs(output_dir, exist_ok=True) + else: + # Otherwise, save to result_path's directory + result_dir = os.path.dirname(result_path) + if result_dir: + output_file = os.path.join(result_dir, f'{result_basename}.txt') + os.makedirs(result_dir, exist_ok=True) + else: + # If result_path has no dirname (current directory), save to current directory + output_file = f'{result_basename}.txt' + with open(output_file, 'w') as f: + f.write(f"GT Source: {gt_path}\n") + f.write(f"Result Source: {result_path}\n") + f.write(f"FVD: {fvd_value}\n") + f.write(f"LPIPS: {lpips_value}\n") + f.write(f"DreamSim: {dreamsim_value}\n") + f.write(f"CLIP Loss: {clip_loss_value}\n") + f.write("-" * 50 + "\n") + print(f"Results saved to {output_file}") + + except Exception as e: + print(f"Error processing pair {idx+1}: {str(e)}") + import traceback + traceback.print_exc() + continue + +# Print summary +print(f"\n{'='*60}") +print(f"Summary: Processed {len(all_results)}/{len(gt_paths)} pairs") +if len(all_results) > 0: + avg_fvd = np.mean([r['fvd'] for r in all_results]) + avg_lpips = np.mean([r['lpips'] for r in all_results]) + avg_dreamsim = np.mean([r['dreamsim'] for r in all_results]) + avg_clip = np.mean([r['clip_loss'] for r in all_results]) + print(f"Average FVD: {avg_fvd:.6f}") + print(f"Average LPIPS: {avg_lpips:.6f}") + print(f"Average DreamSim: {avg_dreamsim:.6f}") + print(f"Average CLIP Loss: {avg_clip:.6f}") +print(f"{'='*60}") \ No newline at end of file diff --git a/OVOW/third_party/Motion324/evaluation/evaluation_pcd.py b/OVOW/third_party/Motion324/evaluation/evaluation_pcd.py new file mode 100644 index 0000000000000000000000000000000000000000..8e1b5d0121e1338a3398b3dd8775964d9d322bde --- /dev/null +++ b/OVOW/third_party/Motion324/evaluation/evaluation_pcd.py @@ -0,0 +1,940 @@ +import argparse +import numpy as np +import trimesh +from pathlib import Path +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from scipy.spatial import cKDTree +import os + +# Try to import bpy for GLB/FBX loading +try: + import bpy + BLENDER_AVAILABLE = True +except ImportError: + BLENDER_AVAILABLE = False + print("Warning: bpy not available. GLB/FBX loading will not work.") + + +def load_pred_mesh_from_file(pred_file_path, frame_idx, pred_mesh_cache=None): + """ + Load predicted mesh from GLB/FBX file using Blender. + pred_mesh_cache should be a dict containing 'obj', 'depsgraph', 'scene', 'file_ext', 'frames' + """ + if not BLENDER_AVAILABLE: + raise RuntimeError("bpy is not available. Cannot load GLB/FBX files.") + + if pred_mesh_cache is None: + raise ValueError("pred_mesh_cache must be provided for file loading") + + obj = pred_mesh_cache['obj'] + depsgraph = pred_mesh_cache['depsgraph'] + scene = pred_mesh_cache['scene'] + file_ext = pred_mesh_cache['file_ext'] + frames = pred_mesh_cache['frames'] + + # Get the actual frame number (handle FBX frame offset) + if frame_idx < len(frames): + actual_frame = frames[frame_idx] + else: + # If frame_idx exceeds available frames, use last frame + actual_frame = frames[-1] + + # Set scene to the target frame + scene.frame_set(actual_frame) + obj_eval = obj.evaluated_get(depsgraph) + me = bpy.data.meshes.new_from_object( + obj_eval, + preserve_all_data_layers=True, + depsgraph=depsgraph, + ) + + # Extract vertices + vertices = np.zeros((len(me.vertices), 3), dtype=np.float32) + for i, v in enumerate(me.vertices): + vertices[i] = v.co + + # Extract faces + faces = [] + for poly in me.polygons: + faces.append(list(poly.vertices)) + faces = np.array(faces, dtype=np.int32) + + # Clean up temporary mesh + bpy.data.meshes.remove(me) + + mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False) + return mesh + + +def load_pred_mesh_from_dir(pred_path, frame_idx): + """Load predicted mesh from faces.npy and frame_{frame_idx:04d}.npy""" + faces = np.load(Path(pred_path) / "faces.npy") + vertices = np.load(Path(pred_path) / f"frame_{frame_idx:04d}.npy") + mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False) + return mesh + + +def load_pred_mesh(pred_path, frame_idx, pred_mesh_cache=None): + """ + Load predicted mesh. Supports both: + 1. Directory with faces.npy and frame_*.npy files + 2. GLB/FBX file (requires Blender) + """ + pred_path_obj = Path(pred_path) + + # Check if it's a file (GLB/FBX) or directory (npy files) + if pred_path_obj.is_file(): + # It's a file, use Blender to load + if pred_mesh_cache is None: + raise ValueError("pred_mesh_cache must be provided when loading from GLB/FBX file") + return load_pred_mesh_from_file(pred_path, frame_idx, pred_mesh_cache) + else: + # It's a directory, load from npy files + return load_pred_mesh_from_dir(pred_path, frame_idx) + + +def initialize_pred_mesh_cache(pred_path): + """ + Initialize Blender scene and load predicted mesh file. + Returns a cache dict containing necessary Blender objects and frame information. + Returns None if pred_path is not a file or Blender is not available. + """ + pred_path_obj = Path(pred_path) + + # If it's not a file, return None (will use directory loading) + if not pred_path_obj.is_file(): + return None + + if not BLENDER_AVAILABLE: + raise RuntimeError("bpy is not available. Cannot load GLB/FBX files.") + + file_ext = pred_path_obj.suffix.lower() + if file_ext not in ['.glb', '.gltf', '.fbx']: + return None # Not a supported file format, will use directory loading + + print(f"Loading predicted mesh from {pred_path_obj}...") + + # Clear scene and import file + bpy.ops.object.select_all(action='SELECT') + bpy.ops.object.delete() + + if file_ext == '.glb' or file_ext == '.gltf': + bpy.ops.import_scene.gltf(filepath=str(pred_path_obj)) + elif file_ext == '.fbx': + bpy.ops.import_scene.fbx(filepath=str(pred_path_obj)) + + # Get the mesh object + obj = None + for o in bpy.context.scene.objects: + if o.type == 'MESH': + obj = o + break + + if obj is None: + raise ValueError("No mesh object found in the imported file") + + # Gather keyframes + def gather_keyframes(o): + frames = set() + ad = o.animation_data + if ad and ad.action: + for fc in ad.action.fcurves: + for kp in fc.keyframe_points: + frames.add(int(round(kp.co.x))) + sk = getattr(o.data, "shape_keys", None) + if sk and sk.animation_data and sk.animation_data.action: + for fc in sk.animation_data.action.fcurves: + for kp in fc.keyframe_points: + frames.add(int(round(kp.co.x))) + return sorted(frames) + + frames = gather_keyframes(obj) + + # If no keyframes, use scene frame range + if not frames: + frames = list(range(bpy.context.scene.frame_start, bpy.context.scene.frame_end + 1)) + + print(f"Found {len(frames)} frames in predicted mesh file: {frames[:10]}..." if len(frames) > 10 else f"Found {len(frames)} frames in predicted mesh file: {frames}") + + scene = bpy.context.scene + depsgraph = bpy.context.evaluated_depsgraph_get() + + return { + 'obj': obj, + 'depsgraph': depsgraph, + 'scene': scene, + 'file_ext': file_ext, + 'frames': frames + } + +def normalize_mesh(mesh): + """ + Normalize mesh similar to bpyrenderer's normalize_scene method. + Uses CUBE range type normalization. + """ + vertices = mesh.vertices + + # Calculate bounding box + bbox_min = vertices.min(axis=0) + bbox_max = vertices.max(axis=0) + + # Calculate offset to center (similar to normalize_scene) + offset = -(bbox_min + bbox_max) / 2 + center = -offset # Store original center for later use + + # Calculate scale (CUBE range type: normalize_range / max(bbox_max - bbox_min)) + normalize_range = 2.0 + scale = normalize_range / np.max(bbox_max - bbox_min) + + # Apply normalization: first translate to center, then scale + vertices_centered = vertices + offset + vertices_normalized = vertices_centered * scale + + return vertices_normalized, center, 1.0 / scale + +def apply_normalization(vertices, center, scale): + """Apply pre-computed normalization parameters""" + return (vertices - center) / scale + +def apply_icp_alignment(vertices_norm, R, t, s): + """Apply ICP alignment transformation: s * (vertices_norm @ R.T) + t""" + return s * (vertices_norm @ R.T) + t + + +def icp_alignment(source_points, target_points, max_iterations=1000, tolerance=1e-7, output_dir=None, optimize_scale=False, visualize=False): + """ + Use standard Point-to-Point ICP algorithm to align source to target. + Returns: rotation matrix R, translation vector t, scale factor s + such that: aligned = s * (source @ R.T) + t + + Args: + source_points: source point cloud + target_points: target point cloud + max_iterations: maximum number of iterations + tolerance: convergence threshold + output_dir: output directory (for saving visualizations) + optimize_scale: whether to optimize scale factor (default False, scale limited to 0.95-1.05 range) + visualize: whether to generate visualizations + """ + # Initialize + source = source_points.copy() + target = target_points.copy() + + # Calculate initial scale (based on bbox max range) + source_bbox_min = np.min(source, axis=0) + source_bbox_max = np.max(source, axis=0) + source_max_range = np.max((source_bbox_max - source_bbox_min)[:2]) # Only consider x and y axes + + target_bbox_min = np.min(target, axis=0) + target_bbox_max = np.max(target, axis=0) + target_max_range = np.max((target_bbox_max - target_bbox_min)[:2]) # Only consider x and y axes + + print(f"Source max range: {source_max_range}, Target max range: {target_max_range}") + # Calculate scale factor based on bbox max range + if source_max_range > 1e-10: + scale = target_max_range / source_max_range + # Limit initial scale to 0.95-1.05 range + scale = np.clip(scale, 0.95, 1.05) + else: + print(f"Warning: source_max_range is too small: {source_max_range}") + scale = 1.0 + + # ICP iteration + R = np.eye(3) + t = np.zeros(3) + prev_error = float('inf') + + # List to save intermediate results + iteration_data = [] + + # Create output directory (only when visualizing) + if visualize and output_dir is not None: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + temp_dir = output_dir / "icp_iterations" + temp_dir.mkdir(exist_ok=True) + + for iteration in range(max_iterations): + # Apply current transformation: scale * (source @ R.T) + t + source_transformed = scale * (source @ R.T) + t + + # Find nearest neighbors + tree = cKDTree(target) + distances, indices = tree.query(source_transformed) + + # Get corresponding target points + matched_target = target[indices] + + # Calculate point-to-point distance + error = np.mean(distances) + + if visualize: + print(f"ICP Iteration {iteration+1}: Mean Distance = {error:.6f}, Scale = {scale:.6f}") + + # Save iteration data (only when visualizing) + if visualize: + iteration_data.append({ + 'iteration': iteration, + 'error': error, + 'R': R.copy(), + 't': t.copy(), + 'scale': scale, + 'source_transformed': source_transformed.copy() + }) + + # Visualize current iteration state (only when visualizing) + if visualize and output_dir is not None and (iteration % 5 == 0 or iteration == 0 or iteration == max_iterations - 1): + fig = plt.figure(figsize=(15, 5)) + + # Downsample for visualization + sample_rate = max(1, len(source_transformed) // 5000) + source_sample = source_transformed[::sample_rate] + target_sample = target[::sample_rate] + + # 3D view - front + ax1 = fig.add_subplot(131, projection='3d') + ax1.scatter(source_sample[:, 0], source_sample[:, 1], source_sample[:, 2], + c='red', s=1, alpha=0.5, label='Source') + ax1.scatter(target_sample[:, 0], target_sample[:, 1], target_sample[:, 2], + c='blue', s=1, alpha=0.5, label='Target') + ax1.set_xlabel('X') + ax1.set_ylabel('Y') + ax1.set_zlabel('Z') + ax1.set_title(f'3D View - Iter {iteration+1}') + ax1.legend() + ax1.view_init(elev=20, azim=45) + + # Set same axis range + all_points = np.vstack([source_transformed, target]) + max_range = np.array([all_points[:, 0].max()-all_points[:, 0].min(), + all_points[:, 1].max()-all_points[:, 1].min(), + all_points[:, 2].max()-all_points[:, 2].min()]).max() / 2.0 + mid_x = (all_points[:, 0].max()+all_points[:, 0].min()) * 0.5 + mid_y = (all_points[:, 1].max()+all_points[:, 1].min()) * 0.5 + mid_z = (all_points[:, 2].max()+all_points[:, 2].min()) * 0.5 + ax1.set_xlim(mid_x - max_range, mid_x + max_range) + ax1.set_ylim(mid_y - max_range, mid_y + max_range) + ax1.set_zlim(mid_z - max_range, mid_z + max_range) + + # 3D view - side + ax2 = fig.add_subplot(132, projection='3d') + ax2.scatter(source_sample[:, 0], source_sample[:, 1], source_sample[:, 2], + c='red', s=1, alpha=0.5, label='Source') + ax2.scatter(target_sample[:, 0], target_sample[:, 1], target_sample[:, 2], + c='blue', s=1, alpha=0.5, label='Target') + ax2.set_xlabel('X') + ax2.set_ylabel('Y') + ax2.set_zlabel('Z') + ax2.set_title(f'Side View - Iter {iteration+1}') + ax2.legend() + ax2.view_init(elev=0, azim=0) + ax2.set_xlim(mid_x - max_range, mid_x + max_range) + ax2.set_ylim(mid_y - max_range, mid_y + max_range) + ax2.set_zlim(mid_z - max_range, mid_z + max_range) + + # Error curve + ax3 = fig.add_subplot(133) + errors = [d['error'] for d in iteration_data] + ax3.plot(range(len(errors)), errors, 'b-', linewidth=2) + ax3.scatter(len(errors)-1, errors[-1], c='red', s=50, zorder=5) + ax3.set_xlabel('Iteration') + ax3.set_ylabel('Mean Distance Error') + ax3.set_title(f'Convergence Curve\nCurrent Error: {error:.6f}') + ax3.grid(True, alpha=0.3) + + plt.tight_layout() + + # Save image + img_path = temp_dir / f"iteration_{iteration:04d}.png" + plt.savefig(img_path, dpi=100, bbox_inches='tight') + plt.close() + print(f" Saved visualization to {img_path}") + + # Check convergence + if abs(prev_error - error) < tolerance: + if visualize: + print(f"ICP converged at iteration {iteration+1}") + break + prev_error = error + + # Calculate centroids + source_centroid = np.mean(source_transformed, axis=0) + target_centroid = np.mean(matched_target, axis=0) + + # Center + source_centered_iter = source_transformed - source_centroid + target_centered_iter = matched_target - target_centroid + + # Calculate covariance matrix + H = source_centered_iter.T @ target_centered_iter + + # SVD decomposition + U, S, Vt = np.linalg.svd(H) + + # Calculate rotation matrix + R_delta = Vt.T @ U.T + + # Ensure right-handed coordinate system (determinant = 1) + if np.linalg.det(R_delta) < 0: + Vt[-1, :] *= -1 + R_delta = Vt.T @ U.T + + # Calculate translation increment + t_delta = target_centroid - source_centroid @ R_delta.T + + # Update rotation and translation + R = R @ R_delta + t = t @ R_delta.T + t_delta + + # Orthogonalize rotation matrix (ensure numerical stability) + U, _, Vt = np.linalg.svd(R) + R = U @ Vt + + # Optimize scale factor (if enabled) + if optimize_scale: + # After applying current R and t, optimize scale + source_rotated_translated = source @ R.T + t + # Optimal scale = sum(target * source) / sum(source * source) + numerator = np.sum(target[indices] * source_rotated_translated) + denominator = np.sum(source_rotated_translated * source_rotated_translated) + if denominator > 1e-10: + scale_new = numerator / denominator + # Limit scale to 0.95-1.05 range + scale_new = np.clip(scale_new, 0.95, 1.05) + # Smooth scale update to avoid oscillation + scale = 0.8 * scale + 0.2 * scale_new + # Ensure scale is in range again + scale = np.clip(scale, 0.95, 1.05) + + # Print final error + print(f"Final Mean Distance: {error:.6f}") + + # Create final comparison plot (only when visualizing) + if visualize and output_dir is not None: + print("\nCreating final comparison visualization...") + + # Select key frames for display + key_iterations = [0] + step = max(1, len(iteration_data) // 8) + for i in range(step, len(iteration_data), step): + key_iterations.append(i) + if len(iteration_data) - 1 not in key_iterations: + key_iterations.append(len(iteration_data) - 1) + + n_images = len(key_iterations) + n_cols = min(4, n_images) + n_rows = (n_images + n_cols - 1) // n_cols + + fig = plt.figure(figsize=(5*n_cols, 5*n_rows)) + + for idx, iter_idx in enumerate(key_iterations): + ax = fig.add_subplot(n_rows, n_cols, idx+1, projection='3d') + + data = iteration_data[iter_idx] + source_transformed = data['source_transformed'] + + sample_rate = max(1, len(source_transformed) // 3000) + source_sample = source_transformed[::sample_rate] + target_sample = target[::sample_rate] + + ax.scatter(source_sample[:, 0], source_sample[:, 1], source_sample[:, 2], + c='red', s=1, alpha=0.5, label='Source') + ax.scatter(target_sample[:, 0], target_sample[:, 1], target_sample[:, 2], + c='blue', s=1, alpha=0.5, label='Target') + + ax.set_xlabel('X') + ax.set_ylabel('Y') + ax.set_zlabel('Z') + ax.set_title(f"Iter {data['iteration']+1}\nError: {data['error']:.6f}, Scale: {data['scale']:.6f}") + ax.legend() + ax.view_init(elev=20, azim=45) + + # Set same axis range + all_points = np.vstack([source_transformed, target]) + max_range = np.array([all_points[:, 0].max()-all_points[:, 0].min(), + all_points[:, 1].max()-all_points[:, 1].min(), + all_points[:, 2].max()-all_points[:, 2].min()]).max() / 2.0 + mid_x = (all_points[:, 0].max()+all_points[:, 0].min()) * 0.5 + mid_y = (all_points[:, 1].max()+all_points[:, 1].min()) * 0.5 + mid_z = (all_points[:, 2].max()+all_points[:, 2].min()) * 0.5 + ax.set_xlim(mid_x - max_range, mid_x + max_range) + ax.set_ylim(mid_y - max_range, mid_y + max_range) + ax.set_zlim(mid_z - max_range, mid_z + max_range) + + plt.tight_layout() + final_grid_path = output_dir / "icp_iterations_grid.png" + plt.savefig(final_grid_path, dpi=150, bbox_inches='tight') + plt.close() + print(f"Final grid saved to {final_grid_path}") + + # Save error curve and scale curve + fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10)) + + errors = [d['error'] for d in iteration_data] + ax1.plot(range(len(errors)), errors, 'b-', linewidth=2, label='Error') + ax1.scatter(range(len(errors)), errors, c='blue', s=20, alpha=0.5) + ax1.set_xlabel('Iteration', fontsize=12) + ax1.set_ylabel('Mean Distance Error', fontsize=12) + ax1.set_title('ICP Convergence Curve', fontsize=14) + ax1.grid(True, alpha=0.3) + ax1.legend() + + if optimize_scale: + scales = [d['scale'] for d in iteration_data] + ax2.plot(range(len(scales)), scales, 'r-', linewidth=2, label='Scale') + ax2.scatter(range(len(scales)), scales, c='red', s=20, alpha=0.5) + ax2.axhline(y=0.95, color='g', linestyle='--', alpha=0.5, label='Scale limits') + ax2.axhline(y=1.05, color='g', linestyle='--', alpha=0.5) + ax2.set_xlabel('Iteration', fontsize=12) + ax2.set_ylabel('Scale Factor', fontsize=12) + ax2.set_title('Scale Optimization (limited to 0.95-1.05)', fontsize=14) + ax2.grid(True, alpha=0.3) + ax2.legend() + + plt.tight_layout() + error_curve_path = output_dir / "icp_error_curve.png" + plt.savefig(error_curve_path, dpi=150, bbox_inches='tight') + plt.close() + print(f"Error curve saved to {error_curve_path}") + + return R, t, scale + + +def compute_alignment_from_first_frame(gt_path, pred_path, pred_mesh_cache=None, visualize=False): + """ + Compute ICP alignment parameters from the first frame. + Returns: rotation matrix R, translation vector t, scale factor s, and normalization parameters. + + Args: + gt_path: path to GT data directory (contains faces.npy and frame_*.npy) + pred_path: path to predicted data (can be directory with frame_*.npy or GLB/FBX file) + pred_mesh_cache: cache for predicted mesh (if loading from file) + visualize: whether to generate visualizations + """ + gt_path = Path(gt_path) + pred_path = Path(pred_path) + + # Load GT first frame + print(f"Loading GT frame 0 from {gt_path}...") + gt_faces_path = gt_path / "faces.npy" + if gt_faces_path.exists(): + gt_faces = np.load(gt_faces_path) + else: + gt_faces = None + + gt_frame_0 = np.load(gt_path / "frame_0000.npy") + if gt_faces is not None: + gt_mesh_0 = trimesh.Trimesh(vertices=gt_frame_0, faces=gt_faces, process=False) + else: + gt_mesh_0 = trimesh.Trimesh(vertices=gt_frame_0, process=False) + + # Normalize GT + gt_vertices_0_norm, gt_center, gt_scale = normalize_mesh(gt_mesh_0) + print(f"GT Normalization - Center: {gt_center}, Scale: {gt_scale}") + + # Sample 10000 points from GT mesh surface for ICP + print("Sampling 10000 points from GT mesh surface...") + gt_sampled_points, _ = trimesh.sample.sample_surface(gt_mesh_0, 10000) + # Apply same normalization to sampled points + gt_sampled_points_norm = (gt_sampled_points - gt_center) / gt_scale + + # Load predicted first frame + print(f"Loading predicted frame 0 from {pred_path}...") + pred_mesh_0 = load_pred_mesh(pred_path, 0, pred_mesh_cache) + + # Normalize prediction + pred_vertices_0_norm, pred_center, pred_scale = normalize_mesh(pred_mesh_0) + print(f"Pred Normalization - Center: {pred_center}, Scale: {pred_scale}") + + # Use standard Point-to-Point ICP alignment (using GT sampled points) + print("\nRunning Point-to-Point ICP alignment...") + output_dir = gt_path / "icp_visualization" if visualize else None + R, t, s = icp_alignment(pred_vertices_0_norm, gt_sampled_points_norm, + output_dir=output_dir, visualize=visualize) + print(f"\nICP Alignment Results:") + print(f"Rotation Matrix R:\n{R}") + print(f"Translation t: {t}") + print(f"Scale s: {s}") + + # Save alignment parameters + # Save to GT path (as npz format for easy loading) + np.savez(gt_path / 'icp_alignment_params.npz', + R=R, t=t, s=s) + print(f"Alignment parameters saved to: {gt_path / 'icp_alignment_params.npz'}") + + return R, t, s + + +def sample_points_from_mesh(mesh, num_points=50000): + """Sample points uniformly from mesh surface""" + points, _ = trimesh.sample.sample_surface(mesh, num_points) + return points + + +def compute_chamfer_distance(points1, points2): + """Compute bidirectional Chamfer distance""" + # Build KD-trees + tree1 = cKDTree(points1) + tree2 = cKDTree(points2) + + # Compute distances from points1 to points2 + dist1, _ = tree1.query(points2, k=1) + # Compute distances from points2 to points1 + dist2, _ = tree2.query(points1, k=1) + + # Chamfer distance is the mean of both directions + chamfer_dist = (np.mean(dist1) + np.mean(dist2)) + return chamfer_dist + + +def compute_fscore(points1, points2, threshold=0.02): + """Compute F-score at given threshold""" + # Build KD-trees + tree1 = cKDTree(points1) + tree2 = cKDTree(points2) + + # Compute distances + dist1, _ = tree1.query(points2, k=1) + dist2, _ = tree2.query(points1, k=1) + + # Compute precision and recall + precision = np.mean(dist1 < threshold) + recall = np.mean(dist2 < threshold) + + # Compute F-score + if precision + recall == 0: + return 0.0 + fscore = 2 * precision * recall / (precision + recall) + return fscore + + +def compute_iou_voxel(mesh1, mesh2, resolution=128): + """Compute IoU on voxel grids""" + # Voxelize both meshes + voxel1 = mesh1.voxelized(pitch=1.0/resolution) + voxel2 = mesh2.voxelized(pitch=1.0/resolution) + + # Get filled voxel grids + grid1 = voxel1.matrix + grid2 = voxel2.matrix + + # Ensure same shape + max_shape = np.maximum(grid1.shape, grid2.shape) + padded_grid1 = np.zeros(max_shape, dtype=bool) + padded_grid2 = np.zeros(max_shape, dtype=bool) + + padded_grid1[:grid1.shape[0], :grid1.shape[1], :grid1.shape[2]] = grid1 + padded_grid2[:grid2.shape[0], :grid2.shape[1], :grid2.shape[2]] = grid2 + + # Compute IoU + intersection = np.logical_and(padded_grid1, padded_grid2).sum() + union = np.logical_or(padded_grid1, padded_grid2).sum() + + if union == 0: + return 0.0 + iou = intersection / union + return iou + + +def visualize_mesh_comparison(gt_mesh, pred_mesh, frame_idx, viz_dir): + """Visualize ground truth and predicted meshes""" + fig = plt.figure(figsize=(15, 5)) + + # Plot GT mesh + ax1 = fig.add_subplot(131, projection='3d') + ax1.plot_trisurf(gt_mesh.vertices[:, 0], gt_mesh.vertices[:, 1], gt_mesh.vertices[:, 2], + triangles=gt_mesh.faces, alpha=0.7, color='blue', edgecolor='none') + ax1.set_title(f'GT Mesh (Frame {frame_idx})') + ax1.set_xlabel('X') + ax1.set_ylabel('Y') + ax1.set_zlabel('Z') + ax1.set_xlim(-1, 1) + ax1.set_ylim(-1, 1) + ax1.set_zlim(-1, 1) + + # Plot predicted mesh + ax2 = fig.add_subplot(132, projection='3d') + ax2.plot_trisurf(pred_mesh.vertices[:, 0], pred_mesh.vertices[:, 1], pred_mesh.vertices[:, 2], + triangles=pred_mesh.faces, alpha=0.7, color='red', edgecolor='none') + ax2.set_title(f'Predicted Mesh (Frame {frame_idx})') + ax2.set_xlabel('X') + ax2.set_ylabel('Y') + ax2.set_zlabel('Z') + ax2.set_xlim(-1, 1) + ax2.set_ylim(-1, 1) + ax2.set_zlim(-1, 1) + + # Plot both together (wireframe for better visibility) + ax3 = fig.add_subplot(133, projection='3d') + ax3.plot_trisurf(gt_mesh.vertices[:, 0], gt_mesh.vertices[:, 1], gt_mesh.vertices[:, 2], + triangles=gt_mesh.faces, alpha=0.3, color='blue', edgecolor='none') + ax3.plot_trisurf(pred_mesh.vertices[:, 0], pred_mesh.vertices[:, 1], pred_mesh.vertices[:, 2], + triangles=pred_mesh.faces, alpha=0.3, color='red', edgecolor='none') + ax3.set_title(f'Mesh Overlay (Frame {frame_idx})') + ax3.set_xlabel('X') + ax3.set_ylabel('Y') + ax3.set_zlabel('Z') + ax3.set_xlim(-1, 1) + ax3.set_ylim(-1, 1) + ax3.set_zlim(-1, 1) + # Set viewing angle to 45 degrees + for ax in [ax1, ax2, ax3]: + ax.view_init(elev=30, azim=45) + plt.tight_layout() + save_path = os.path.join(viz_dir, f'mesh_comparison_frame_{frame_idx:04d}.png') + plt.savefig(save_path, dpi=150, bbox_inches='tight') + plt.close() + print(f"Saved mesh comparison to {save_path}") + + +def visualize_pointcloud_comparison(gt_points, pred_points, frame_idx, viz_dir): + """Visualize ground truth and predicted point clouds""" + fig = plt.figure(figsize=(15, 5)) + + # Plot GT points + ax1 = fig.add_subplot(131, projection='3d') + ax1.scatter(gt_points[:, 0], gt_points[:, 1], gt_points[:, 2], + c='blue', s=1, alpha=0.5, label='GT Points') + ax1.set_title(f'GT Point Cloud (Frame {frame_idx})') + ax1.set_xlabel('X') + ax1.set_ylabel('Y') + ax1.set_zlabel('Z') + ax1.set_xlim(-1, 1) + ax1.set_ylim(-1, 1) + ax1.set_zlim(-1, 1) + ax1.legend() + + # Plot predicted points + ax2 = fig.add_subplot(132, projection='3d') + ax2.scatter(pred_points[:, 0], pred_points[:, 1], pred_points[:, 2], + c='red', s=1, alpha=0.5, label='Pred Points') + ax2.set_title(f'Predicted Point Cloud (Frame {frame_idx})') + ax2.set_xlabel('X') + ax2.set_ylabel('Y') + ax2.set_zlabel('Z') + ax2.set_xlim(-1, 1) + ax2.set_ylim(-1, 1) + ax2.set_zlim(-1, 1) + ax2.legend() + + # Plot both together + ax3 = fig.add_subplot(133, projection='3d') + ax3.scatter(gt_points[:, 0], gt_points[:, 1], gt_points[:, 2], + c='blue', s=1, alpha=0.3, label='GT') + ax3.scatter(pred_points[:, 0], pred_points[:, 1], pred_points[:, 2], + c='red', s=1, alpha=0.3, label='Pred') + ax3.set_title(f'Point Cloud Overlay (Frame {frame_idx})') + ax3.set_xlabel('X') + ax3.set_ylabel('Y') + ax3.set_zlabel('Z') + ax3.set_xlim(-1, 1) + ax3.set_ylim(-1, 1) + ax3.set_zlim(-1, 1) + ax3.legend() + + for ax in [ax1, ax2, ax3]: + ax.view_init(elev=30, azim=45) + + plt.tight_layout() + save_path = os.path.join(viz_dir, f'pointcloud_comparison_frame_{frame_idx:04d}.png') + plt.savefig(save_path, dpi=150, bbox_inches='tight') + plt.close() + print(f"Saved point cloud comparison to {save_path}") + + +def evaluate_sequence(gt_path, pred_path, num_samples=2048, viz=False, icp_viz=False): + """Evaluate entire sequence""" + pred_path = Path(pred_path) + gt_path = Path(gt_path) + + # Create visualization directory + viz_dir = Path(pred_path).parent.parent / 'viz' + viz_dir.mkdir(exist_ok=True) + print(f"Visualization directory: {viz_dir}") + + # Initialize predicted mesh cache if loading from file + pred_mesh_cache = initialize_pred_mesh_cache(pred_path) + is_pred_file = pred_mesh_cache is not None + + # Load GT frames from point cloud directory + print(f"Loading GT frames from {gt_path}...") + gt_frame_files = sorted(gt_path.glob("frame_*.npy")) + gt_frames = [np.load(f) for f in gt_frame_files] + print(f"Loaded {len(gt_frames)} frames from GT point clouds") + + # Load GT faces + gt_faces_path = gt_path / "faces.npy" + if gt_faces_path.exists(): + gt_faces = np.load(gt_faces_path) + print(f"Loaded GT faces from {gt_faces_path}") + else: + print(f"Warning: No faces.npy found in {gt_path}") + gt_faces = None + + # Get number of frames from predicted mesh + if is_pred_file: + # Get frame count from Blender cache + num_frames = len(pred_mesh_cache['frames']) + print(f"Found {num_frames} frames in predicted mesh file") + # Extract predicted faces from first frame (they should be consistent) + pred_mesh_first = load_pred_mesh_from_file(pred_path, 0, pred_mesh_cache) + pred_faces = pred_mesh_first.faces + print(f"Loaded predicted faces from first frame: {len(pred_faces)} faces") + else: + # Get frame count from directory + frame_files = sorted(pred_path.glob("frame_*.npy")) + num_frames = len(frame_files) + print(f"Found {num_frames} frames in predicted data directory") + + # Load predicted faces + pred_faces_path = pred_path / "faces.npy" + if pred_faces_path.exists(): + pred_faces = np.load(pred_faces_path) + print(f"Loaded predicted faces from {pred_faces_path}") + else: + print(f"Warning: No faces.npy found in {pred_path}") + pred_faces = None + + # Ensure we have enough GT frames + if len(gt_frames) < num_frames: + print(f"Warning: GT data has {len(gt_frames)} frames but prediction has {num_frames} frames") + print(f"Will repeat last frame to match predicted length") + while len(gt_frames) < num_frames: + gt_frames.append(gt_frames[-1].copy()) + + # Load first predicted frame for normalization + pred_mesh_0 = load_pred_mesh(pred_path, 0, pred_mesh_cache) + _, pred_center, pred_scale = normalize_mesh(pred_mesh_0) + + print(f"Pred Normalization params - Center: {pred_center}, Scale: {pred_scale}") + + # Normalize predicted frame 0 + pred_vertices_0_norm = apply_normalization(pred_mesh_0.vertices, pred_center, pred_scale) + + # Normalize GT frame 0 + if gt_faces is not None: + gt_mesh_0 = trimesh.Trimesh(vertices=gt_frames[0], faces=gt_faces, process=False) + else: + gt_mesh_0 = trimesh.Trimesh(vertices=gt_frames[0], process=False) + _, gt_center, gt_scale = normalize_mesh(gt_mesh_0) + + gt_vertices_0_norm = apply_normalization(gt_frames[0], gt_center, gt_scale) + print(f"GT Normalization params - Center: {gt_center}, Scale: {gt_scale}") + print(f"GT Normalization params - Min: {min(gt_vertices_0_norm[:, 0])}, Max: {max(gt_vertices_0_norm[:, 0])}") + print(f"GT Normalization params - Min: {min(gt_vertices_0_norm[:, 1])}, Max: {max(gt_vertices_0_norm[:, 1])}") + print(f"GT Normalization params - Min: {min(gt_vertices_0_norm[:, 2])}, Max: {max(gt_vertices_0_norm[:, 2])}") + # Load ICP alignment parameters + icp_params_path = viz_dir / 'icp_alignment_params.npz' + if icp_params_path.exists(): + icp_params = np.load(icp_params_path) + R = icp_params['R'] + t = icp_params['t'] + s = icp_params['s'] + print(f"Loaded ICP alignment parameters from {icp_params_path}") + print(f"R shape: {R.shape}, t shape: {t.shape}, s: {s}") + else: + print(f"No icp_alignment_params.npz found in {gt_path}") + print("Computing ICP alignment from first frame...") + # Make visualize for compute_alignment_from_first_frame configurable via arg + R, t, s = compute_alignment_from_first_frame(gt_path, pred_path, pred_mesh_cache, visualize=icp_viz) + print(f"ICP alignment computed - R shape: {R.shape}, t shape: {t.shape}, s: {s}") + + # Store metrics for all frames + chamfer_distances = [] + fscores = [] + ious = [] + if viz: + print(f"Visualization directory: {viz_dir}") + os.makedirs(viz_dir, exist_ok=True) + + for frame_idx in range(num_frames): + print(f"\nProcessing frame {frame_idx}/{num_frames-1}...") + + # Load predicted mesh and apply prediction normalization + pred_mesh = load_pred_mesh(pred_path, frame_idx, pred_mesh_cache) + pred_vertices_norm = apply_normalization(pred_mesh.vertices, pred_center, pred_scale) + # Use predicted faces from cache or from mesh + pred_faces_to_use = pred_faces if pred_faces is not None else pred_mesh.faces + pred_mesh_norm = trimesh.Trimesh(vertices=pred_vertices_norm, faces=pred_faces_to_use, process=False) + + # Get GT frame, apply GT normalization, then apply ICP alignment + gt_vertices = gt_frames[frame_idx] + gt_vertices_norm = apply_normalization(gt_vertices, gt_center, gt_scale) + gt_vertices_aligned = apply_icp_alignment(gt_vertices_norm, R, t, s) + if gt_faces is not None: + gt_mesh_aligned = trimesh.Trimesh(vertices=gt_vertices_aligned, faces=gt_faces, process=False) + else: + gt_mesh_aligned = trimesh.Trimesh(vertices=gt_vertices_aligned, process=False) + + # Visualize mesh comparison for every frame + #print(f"Visualizing mesh comparison for frame {frame_idx}...") + if viz: + visualize_mesh_comparison(gt_mesh_aligned, pred_mesh_norm, frame_idx, viz_dir) + + # Sample points + gt_points = sample_points_from_mesh(gt_mesh_aligned, num_samples) + pred_points = sample_points_from_mesh(pred_mesh_norm, num_samples) + + # Visualize point cloud comparison for every frame + #print(f"Visualizing point cloud comparison for frame {frame_idx}...") + + #visualize_pointcloud_comparison(gt_points, pred_points, frame_idx, viz_dir) + # Compute metrics + chamfer = compute_chamfer_distance(gt_points, pred_points) + fscore = compute_fscore(gt_points, pred_points, threshold=0.02) + # iou = compute_iou_voxel(gt_mesh_aligned, pred_mesh_norm, resolution=128) + + chamfer_distances.append(chamfer) + fscores.append(fscore) + # ious.append(iou) + + print(f"Frame {frame_idx} - Chamfer: {chamfer:.6f}, F-score: {fscore:.4f}") + + # Print summary statistics + print("\n" + "="*50) + print("Summary Statistics:") + print("="*50) + print(f"Chamfer Distance - Mean: {np.mean(chamfer_distances):.6f}, Std: {np.std(chamfer_distances):.6f}") + print(f"F-score - Mean: {np.mean(fscores):.4f}, Std: {np.std(fscores):.4f}") + # print(f"IoU - Mean: {np.mean(ious):.4f}, Std: {np.std(ious):.4f}") + + # Save results to txt file in gt_path + gt_path_obj = Path(gt_path) + os.makedirs(gt_path_obj, exist_ok=True) + results_txt_path = gt_path_obj / 'evaluation_results.txt' + with open(results_txt_path, 'w') as f: + f.write(f"{gt_path_obj.name}\n") + f.write(f"cd_mean_{np.mean(chamfer_distances):.6f}\n") + f.write(f"fs_mean_{np.mean(fscores):.6f}\n") + print(f"\nResults saved to {results_txt_path}") + + return { + 'chamfer_distances': chamfer_distances, + 'fscores': fscores, + # 'ious': ious + } + + +def main(): + parser = argparse.ArgumentParser(description='Evaluate mesh reconstruction quality') + parser.add_argument('--gt_path', type=str, required=True, + help='Path to ground-truth point cloud directory (contains faces.npy and frame_*.npy)') + parser.add_argument('--pred_path', type=str, required=True, + help='Path to predicted data (contains faces.npy and frame_*.npy)') + parser.add_argument('--num_samples', type=int, default=50000, + help='Number of points to sample from each mesh') + parser.add_argument('--viz', action='store_true', + help='Visualize point clouds and meshes') + parser.add_argument('--icp_viz', action='store_true', + help='Visualize ICP alignment procedure for first frame') + + args = parser.parse_args() + + results = evaluate_sequence(args.gt_path, args.pred_path, args.num_samples, args.viz, icp_viz=args.icp_viz) + + print(f"\nResults saved to {args.pred_path}") + + +if __name__ == '__main__': + main() diff --git a/OVOW/third_party/Motion324/evaluation/fvd/styleganv/fvd.py b/OVOW/third_party/Motion324/evaluation/fvd/styleganv/fvd.py new file mode 100644 index 0000000000000000000000000000000000000000..6ed4b3414514052d43c22cc7db8d5dd0241185b9 --- /dev/null +++ b/OVOW/third_party/Motion324/evaluation/fvd/styleganv/fvd.py @@ -0,0 +1,90 @@ +import torch +import os +import math +import torch.nn.functional as F + +# https://github.com/universome/fvd-comparison + + +def load_i3d_pretrained(device=torch.device('cpu')): + i3D_WEIGHTS_URL = "https://www.dropbox.com/s/ge9e5ujwgetktms/i3d_torchscript.pt" + filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'i3d_torchscript.pt') + print('Loading model from: %s'%filepath) + if not os.path.exists(filepath): + print(f"preparing for download {i3D_WEIGHTS_URL}, you can download it by yourself.") + os.system(f"wget {i3D_WEIGHTS_URL} -O {filepath}") + i3d = torch.jit.load(filepath).eval().to(device) + i3d = torch.nn.DataParallel(i3d) + return i3d + + +def get_feats(videos, detector, device, bs=10): + # videos : torch.tensor BCTHW [0, 1] + detector_kwargs = dict(rescale=False, resize=False, return_features=True) # Return raw features before the softmax layer. + feats = np.empty((0, 400)) + with torch.no_grad(): + for i in range((len(videos)-1)//bs + 1): + feats = np.vstack([feats, detector(x=torch.stack([preprocess_single(video) for video in videos[i*bs:(i+1)*bs]]).to(device), **detector_kwargs).detach().cpu().numpy()]) + return feats + + +def get_fvd_feats(videos, i3d, device, bs=10): + # videos in [0, 1] as torch tensor BCTHW + # videos = [preprocess_single(video) for video in videos] + embeddings = get_feats(videos, i3d, device, bs) + return embeddings + + +def preprocess_single(video, resolution=224, sequence_length=None): + # video: CTHW, [0, 1] + c, t, h, w = video.shape + + # temporal crop + if sequence_length is not None: + assert sequence_length <= t + video = video[:, :sequence_length] + + # scale shorter side to resolution + scale = resolution / min(h, w) + if h < w: + target_size = (resolution, math.ceil(w * scale)) + else: + target_size = (math.ceil(h * scale), resolution) + video = F.interpolate(video, size=target_size, mode='bilinear', align_corners=False) + + # center crop + c, t, h, w = video.shape + w_start = (w - resolution) // 2 + h_start = (h - resolution) // 2 + video = video[:, :, h_start:h_start + resolution, w_start:w_start + resolution] + + # [0, 1] -> [-1, 1] + video = (video - 0.5) * 2 + + return video.contiguous() + + +""" +Copy-pasted from https://github.com/cvpr2022-stylegan-v/stylegan-v/blob/main/src/metrics/frechet_video_distance.py +""" +from typing import Tuple +from scipy.linalg import sqrtm +import numpy as np + + +def compute_stats(feats: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + mu = feats.mean(axis=0) # [d] + sigma = np.cov(feats, rowvar=False) # [d, d] + return mu, sigma + + +def frechet_distance(feats_fake: np.ndarray, feats_real: np.ndarray) -> float: + mu_gen, sigma_gen = compute_stats(feats_fake) + mu_real, sigma_real = compute_stats(feats_real) + m = np.square(mu_gen - mu_real).sum() + if feats_fake.shape[0]>1: + s, _ = sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member + fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) + else: + fid = np.real(m) + return float(fid) \ No newline at end of file diff --git a/OVOW/third_party/Motion324/evaluation/lpips/__init__.py b/OVOW/third_party/Motion324/evaluation/lpips/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3ab722ec89014e29c3b9439165d8e03c33a527c0 --- /dev/null +++ b/OVOW/third_party/Motion324/evaluation/lpips/__init__.py @@ -0,0 +1,126 @@ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import torch +# from torch.autograd import Variable + +from lpips.trainer import * +from lpips.lpips import * + +def normalize_tensor(in_feat,eps=1e-10): + norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True)) + return in_feat/(norm_factor+eps) + +def l2(p0, p1, range=255.): + return .5*np.mean((p0 / range - p1 / range)**2) + +def psnr(p0, p1, peak=255.): + return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2)) + +def dssim(p0, p1, range=255.): + from skimage.measure import compare_ssim + return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2. + +def tensor2np(tensor_obj): + # change dimension of a tensor object into a numpy array + return tensor_obj[0].cpu().float().numpy().transpose((1,2,0)) + +def np2tensor(np_obj): + # change dimenion of np array into tensor array + return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + +def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False): + # image tensor to lab tensor + from skimage import color + + img = tensor2im(image_tensor) + img_lab = color.rgb2lab(img) + if(mc_only): + img_lab[:,:,0] = img_lab[:,:,0]-50 + if(to_norm and not mc_only): + img_lab[:,:,0] = img_lab[:,:,0]-50 + img_lab = img_lab/100. + + return np2tensor(img_lab) + +def tensorlab2tensor(lab_tensor,return_inbnd=False): + from skimage import color + import warnings + warnings.filterwarnings("ignore") + + lab = tensor2np(lab_tensor)*100. + lab[:,:,0] = lab[:,:,0]+50 + + rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1) + if(return_inbnd): + # convert back to lab, see if we match + lab_back = color.rgb2lab(rgb_back.astype('uint8')) + mask = 1.*np.isclose(lab_back,lab,atol=2.) + mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis]) + return (im2tensor(rgb_back),mask) + else: + return im2tensor(rgb_back) + +def load_image(path): + if(path[-3:] == 'dng'): + import rawpy + with rawpy.imread(path) as raw: + img = raw.postprocess() + elif(path[-3:]=='bmp' or path[-3:]=='jpg' or path[-3:]=='png' or path[-4:]=='jpeg'): + import cv2 + return cv2.imread(path)[:,:,::-1] + else: + import matplotlib.pyplot as plt + img = (255*plt.imread(path)[:,:,:3]).astype('uint8') + + return img + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + +def tensor2vec(vector_tensor): + return vector_tensor.data.cpu().numpy()[:, :, 0, 0] + + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + diff --git a/OVOW/third_party/Motion324/evaluation/lpips/lpips.py b/OVOW/third_party/Motion324/evaluation/lpips/lpips.py new file mode 100644 index 0000000000000000000000000000000000000000..97a0a22e7ba5b4b1ca0af09e036f232da6ee9b64 --- /dev/null +++ b/OVOW/third_party/Motion324/evaluation/lpips/lpips.py @@ -0,0 +1,241 @@ + +from __future__ import absolute_import + +import torch +import torch.nn as nn +import torch.nn.init as init +from torch.autograd import Variable +import numpy as np +from . import pretrained_networks as pn +import torch.nn + +import lpips + +def spatial_average(in_tens, keepdim=True): + return in_tens.mean([2,3],keepdim=keepdim) + +def upsample(in_tens, out_HW=(64,64)): # assumes scale factor is same for H and W + in_H, in_W = in_tens.shape[2], in_tens.shape[3] + return nn.Upsample(size=out_HW, mode='bilinear', align_corners=False)(in_tens) + +# Learned perceptual metric +class LPIPS(nn.Module): + def __init__(self, pretrained=True, net='alex', version='0.1', lpips=True, spatial=False, + pnet_rand=False, pnet_tune=False, use_dropout=True, model_path=None, eval_mode=True, verbose=True): + """ Initializes a perceptual loss torch.nn.Module + + Parameters (default listed first) + --------------------------------- + lpips : bool + [True] use linear layers on top of base/trunk network + [False] means no linear layers; each layer is averaged together + pretrained : bool + This flag controls the linear layers, which are only in effect when lpips=True above + [True] means linear layers are calibrated with human perceptual judgments + [False] means linear layers are randomly initialized + pnet_rand : bool + [False] means trunk loaded with ImageNet classification weights + [True] means randomly initialized trunk + net : str + ['alex','vgg','squeeze'] are the base/trunk networks available + version : str + ['v0.1'] is the default and latest + ['v0.0'] contained a normalization bug; corresponds to old arxiv v1 (https://arxiv.org/abs/1801.03924v1) + model_path : 'str' + [None] is default and loads the pretrained weights from paper https://arxiv.org/abs/1801.03924v1 + + The following parameters should only be changed if training the network + + eval_mode : bool + [True] is for test mode (default) + [False] is for training mode + pnet_tune + [False] keep base/trunk frozen + [True] tune the base/trunk network + use_dropout : bool + [True] to use dropout when training linear layers + [False] for no dropout when training linear layers + """ + + super(LPIPS, self).__init__() + if(verbose): + print('Setting up [%s] perceptual loss: trunk [%s], v[%s], spatial [%s]'% + ('LPIPS' if lpips else 'baseline', net, version, 'on' if spatial else 'off')) + + self.pnet_type = net + self.pnet_tune = pnet_tune + self.pnet_rand = pnet_rand + self.spatial = spatial + self.lpips = lpips # false means baseline of just averaging all layers + self.version = version + self.scaling_layer = ScalingLayer() + + if(self.pnet_type in ['vgg','vgg16']): + net_type = pn.vgg16 + self.chns = [64,128,256,512,512] + elif(self.pnet_type=='alex'): + net_type = pn.alexnet + self.chns = [64,192,384,256,256] + elif(self.pnet_type=='squeeze'): + net_type = pn.squeezenet + self.chns = [64,128,256,384,384,512,512] + self.L = len(self.chns) + + self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune) + + if(lpips): + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4] + if(self.pnet_type=='squeeze'): # 7 layers for squeezenet + self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout) + self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout) + self.lins+=[self.lin5,self.lin6] + self.lins = nn.ModuleList(self.lins) + + if(pretrained): + if(model_path is None): + import inspect + import os + model_path = os.path.abspath(os.path.join(inspect.getfile(self.__init__), '..', 'weights/v%s/%s.pth'%(version,net))) + + if(verbose): + print('Loading model from: %s'%model_path) + self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) + + if(eval_mode): + self.eval() + + def forward(self, in0, in1, retPerLayer=False, normalize=False): + if normalize: # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1] + in0 = 2 * in0 - 1 + in1 = 2 * in1 - 1 + + # v0.0 - original release had a bug, where input was not scaled + in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version=='0.1' else (in0, in1) + outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input) + feats0, feats1, diffs = {}, {}, {} + + for kk in range(self.L): + feats0[kk], feats1[kk] = lpips.normalize_tensor(outs0[kk]), lpips.normalize_tensor(outs1[kk]) + diffs[kk] = (feats0[kk]-feats1[kk])**2 + + if(self.lpips): + if(self.spatial): + res = [upsample(self.lins[kk](diffs[kk]), out_HW=in0.shape[2:]) for kk in range(self.L)] + else: + res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)] + else: + if(self.spatial): + res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_HW=in0.shape[2:]) for kk in range(self.L)] + else: + res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)] + + val = 0 + for l in range(self.L): + val += res[l] + + if(retPerLayer): + return (val, res) + else: + return val + + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + self.register_buffer('shift', torch.Tensor([-.030,-.088,-.188])[None,:,None,None]) + self.register_buffer('scale', torch.Tensor([.458,.448,.450])[None,:,None,None]) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + ''' A single linear layer which does a 1x1 conv ''' + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + + layers = [nn.Dropout(),] if(use_dropout) else [] + layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),] + self.model = nn.Sequential(*layers) + + def forward(self, x): + return self.model(x) + +class Dist2LogitLayer(nn.Module): + ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) ''' + def __init__(self, chn_mid=32, use_sigmoid=True): + super(Dist2LogitLayer, self).__init__() + + layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),] + layers += [nn.LeakyReLU(0.2,True),] + layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),] + layers += [nn.LeakyReLU(0.2,True),] + layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),] + if(use_sigmoid): + layers += [nn.Sigmoid(),] + self.model = nn.Sequential(*layers) + + def forward(self,d0,d1,eps=0.1): + return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1)) + +class BCERankingLoss(nn.Module): + def __init__(self, chn_mid=32): + super(BCERankingLoss, self).__init__() + self.net = Dist2LogitLayer(chn_mid=chn_mid) + # self.parameters = list(self.net.parameters()) + self.loss = torch.nn.BCELoss() + + def forward(self, d0, d1, judge): + per = (judge+1.)/2. + self.logit = self.net.forward(d0,d1) + return self.loss(self.logit, per) + +# L2, DSSIM metrics +class FakeNet(nn.Module): + def __init__(self, use_gpu=True, colorspace='Lab'): + super(FakeNet, self).__init__() + self.use_gpu = use_gpu + self.colorspace = colorspace + +class L2(FakeNet): + def forward(self, in0, in1, retPerLayer=None): + assert(in0.size()[0]==1) # currently only supports batchSize 1 + + if(self.colorspace=='RGB'): + (N,C,X,Y) = in0.size() + value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N) + return value + elif(self.colorspace=='Lab'): + value = lpips.l2(lpips.tensor2np(lpips.tensor2tensorlab(in0.data,to_norm=False)), + lpips.tensor2np(lpips.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float') + ret_var = Variable( torch.Tensor((value,) ) ) + if(self.use_gpu): + ret_var = ret_var.cuda() + return ret_var + +class DSSIM(FakeNet): + + def forward(self, in0, in1, retPerLayer=None): + assert(in0.size()[0]==1) # currently only supports batchSize 1 + + if(self.colorspace=='RGB'): + value = lpips.dssim(1.*lpips.tensor2im(in0.data), 1.*lpips.tensor2im(in1.data), range=255.).astype('float') + elif(self.colorspace=='Lab'): + value = lpips.dssim(lpips.tensor2np(lpips.tensor2tensorlab(in0.data,to_norm=False)), + lpips.tensor2np(lpips.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float') + ret_var = Variable( torch.Tensor((value,) ) ) + if(self.use_gpu): + ret_var = ret_var.cuda() + return ret_var + +def print_network(net): + num_params = 0 + for param in net.parameters(): + num_params += param.numel() + print('Network',net) + print('Total number of parameters: %d' % num_params) diff --git a/OVOW/third_party/Motion324/evaluation/lpips/pretrained_networks.py b/OVOW/third_party/Motion324/evaluation/lpips/pretrained_networks.py new file mode 100644 index 0000000000000000000000000000000000000000..82f14e206e7b395ac9d989cb2536e17b17e65184 --- /dev/null +++ b/OVOW/third_party/Motion324/evaluation/lpips/pretrained_networks.py @@ -0,0 +1,180 @@ +from collections import namedtuple +import torch +from torchvision import models as tv + +class squeezenet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(squeezenet, self).__init__() + pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.slice6 = torch.nn.Sequential() + self.slice7 = torch.nn.Sequential() + self.N_slices = 7 + for x in range(2): + self.slice1.add_module(str(x), pretrained_features[x]) + for x in range(2,5): + self.slice2.add_module(str(x), pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), pretrained_features[x]) + for x in range(10, 11): + self.slice5.add_module(str(x), pretrained_features[x]) + for x in range(11, 12): + self.slice6.add_module(str(x), pretrained_features[x]) + for x in range(12, 13): + self.slice7.add_module(str(x), pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + h = self.slice6(h) + h_relu6 = h + h = self.slice7(h) + h_relu7 = h + vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7']) + out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7) + + return out + + +class alexnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(alexnet, self).__init__() + alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(2): + self.slice1.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(2, 5): + self.slice2.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(5, 8): + self.slice3.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(8, 10): + self.slice4.add_module(str(x), alexnet_pretrained_features[x]) + for x in range(10, 12): + self.slice5.add_module(str(x), alexnet_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1 = h + h = self.slice2(h) + h_relu2 = h + h = self.slice3(h) + h_relu3 = h + h = self.slice4(h) + h_relu4 = h + h = self.slice5(h) + h_relu5 = h + alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5']) + out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5) + + return out + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(vgg16, self).__init__() + vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + + return out + + + +class resnet(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True, num=18): + super(resnet, self).__init__() + if(num==18): + self.net = tv.resnet18(pretrained=pretrained) + elif(num==34): + self.net = tv.resnet34(pretrained=pretrained) + elif(num==50): + self.net = tv.resnet50(pretrained=pretrained) + elif(num==101): + self.net = tv.resnet101(pretrained=pretrained) + elif(num==152): + self.net = tv.resnet152(pretrained=pretrained) + self.N_slices = 5 + + self.conv1 = self.net.conv1 + self.bn1 = self.net.bn1 + self.relu = self.net.relu + self.maxpool = self.net.maxpool + self.layer1 = self.net.layer1 + self.layer2 = self.net.layer2 + self.layer3 = self.net.layer3 + self.layer4 = self.net.layer4 + + def forward(self, X): + h = self.conv1(X) + h = self.bn1(h) + h = self.relu(h) + h_relu1 = h + h = self.maxpool(h) + h = self.layer1(h) + h_conv2 = h + h = self.layer2(h) + h_conv3 = h + h = self.layer3(h) + h_conv4 = h + h = self.layer4(h) + h_conv5 = h + + outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5']) + out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5) + + return out diff --git a/OVOW/third_party/Motion324/evaluation/lpips/trainer.py b/OVOW/third_party/Motion324/evaluation/lpips/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..f37f13bd33cf920522927d052c8410a8d9be9e9b --- /dev/null +++ b/OVOW/third_party/Motion324/evaluation/lpips/trainer.py @@ -0,0 +1,280 @@ + +from __future__ import absolute_import + +import numpy as np +import torch +from torch import nn +from collections import OrderedDict +from torch.autograd import Variable +from scipy.ndimage import zoom +from tqdm import tqdm +import lpips +import os + + +class Trainer(): + def name(self): + return self.model_name + + def initialize(self, model='lpips', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, model_path=None, + use_gpu=True, printNet=False, spatial=False, + is_train=False, lr=.0001, beta1=0.5, version='0.1', gpu_ids=[0]): + ''' + INPUTS + model - ['lpips'] for linearly calibrated network + ['baseline'] for off-the-shelf network + ['L2'] for L2 distance in Lab colorspace + ['SSIM'] for ssim in RGB colorspace + net - ['squeeze','alex','vgg'] + model_path - if None, will look in weights/[NET_NAME].pth + colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM + use_gpu - bool - whether or not to use a GPU + printNet - bool - whether or not to print network architecture out + spatial - bool - whether to output an array containing varying distances across spatial dimensions + is_train - bool - [True] for training mode + lr - float - initial learning rate + beta1 - float - initial momentum term for adam + version - 0.1 for latest, 0.0 was original (with a bug) + gpu_ids - int array - [0] by default, gpus to use + ''' + self.use_gpu = use_gpu + self.gpu_ids = gpu_ids + self.model = model + self.net = net + self.is_train = is_train + self.spatial = spatial + self.model_name = '%s [%s]'%(model,net) + + if(self.model == 'lpips'): # pretrained net + linear layer + self.net = lpips.LPIPS(pretrained=not is_train, net=net, version=version, lpips=True, spatial=spatial, + pnet_rand=pnet_rand, pnet_tune=pnet_tune, + use_dropout=True, model_path=model_path, eval_mode=False) + elif(self.model=='baseline'): # pretrained network + self.net = lpips.LPIPS(pnet_rand=pnet_rand, net=net, lpips=False) + elif(self.model in ['L2','l2']): + self.net = lpips.L2(use_gpu=use_gpu,colorspace=colorspace) # not really a network, only for testing + self.model_name = 'L2' + elif(self.model in ['DSSIM','dssim','SSIM','ssim']): + self.net = lpips.DSSIM(use_gpu=use_gpu,colorspace=colorspace) + self.model_name = 'SSIM' + else: + raise ValueError("Model [%s] not recognized." % self.model) + + self.parameters = list(self.net.parameters()) + + if self.is_train: # training mode + # extra network on top to go from distances (d0,d1) => predicted human judgment (h*) + self.rankLoss = lpips.BCERankingLoss() + self.parameters += list(self.rankLoss.net.parameters()) + self.lr = lr + self.old_lr = lr + self.optimizer_net = torch.optim.Adam(self.parameters, lr=lr, betas=(beta1, 0.999)) + else: # test mode + self.net.eval() + + if(use_gpu): + self.net.to(gpu_ids[0]) + self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids) + if(self.is_train): + self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0 + + if(printNet): + print('---------- Networks initialized -------------') + networks.print_network(self.net) + print('-----------------------------------------------') + + def forward(self, in0, in1, retPerLayer=False): + ''' Function computes the distance between image patches in0 and in1 + INPUTS + in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1] + OUTPUT + computed distances between in0 and in1 + ''' + + return self.net.forward(in0, in1, retPerLayer=retPerLayer) + + # ***** TRAINING FUNCTIONS ***** + def optimize_parameters(self): + self.forward_train() + self.optimizer_net.zero_grad() + self.backward_train() + self.optimizer_net.step() + self.clamp_weights() + + def clamp_weights(self): + for module in self.net.modules(): + if(hasattr(module, 'weight') and module.kernel_size==(1,1)): + module.weight.data = torch.clamp(module.weight.data,min=0) + + def set_input(self, data): + self.input_ref = data['ref'] + self.input_p0 = data['p0'] + self.input_p1 = data['p1'] + self.input_judge = data['judge'] + + if(self.use_gpu): + self.input_ref = self.input_ref.to(device=self.gpu_ids[0]) + self.input_p0 = self.input_p0.to(device=self.gpu_ids[0]) + self.input_p1 = self.input_p1.to(device=self.gpu_ids[0]) + self.input_judge = self.input_judge.to(device=self.gpu_ids[0]) + + self.var_ref = Variable(self.input_ref,requires_grad=True) + self.var_p0 = Variable(self.input_p0,requires_grad=True) + self.var_p1 = Variable(self.input_p1,requires_grad=True) + + def forward_train(self): # run forward pass + self.d0 = self.forward(self.var_ref, self.var_p0) + self.d1 = self.forward(self.var_ref, self.var_p1) + self.acc_r = self.compute_accuracy(self.d0,self.d1,self.input_judge) + + self.var_judge = Variable(1.*self.input_judge).view(self.d0.size()) + + self.loss_total = self.rankLoss.forward(self.d0, self.d1, self.var_judge*2.-1.) + + return self.loss_total + + def backward_train(self): + torch.mean(self.loss_total).backward() + + def compute_accuracy(self,d0,d1,judge): + ''' d0, d1 are Variables, judge is a Tensor ''' + d1_lt_d0 = (d1 %f' % (type,self.old_lr, lr)) + self.old_lr = lr + + + def get_image_paths(self): + return self.image_paths + + def save_done(self, flag=False): + np.save(os.path.join(self.save_dir, 'done_flag'),flag) + np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i') + + +def score_2afc_dataset(data_loader, func, name=''): + ''' Function computes Two Alternative Forced Choice (2AFC) score using + distance function 'func' in dataset 'data_loader' + INPUTS + data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside + func - callable distance function - calling d=func(in0,in1) should take 2 + pytorch tensors with shape Nx3xXxY, and return numpy array of length N + OUTPUTS + [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators + [1] - dictionary with following elements + d0s,d1s - N arrays containing distances between reference patch to perturbed patches + gts - N array in [0,1], preferred patch selected by human evaluators + (closer to "0" for left patch p0, "1" for right patch p1, + "0.6" means 60pct people preferred right patch, 40pct preferred left) + scores - N array in [0,1], corresponding to what percentage function agreed with humans + CONSTS + N - number of test triplets in data_loader + ''' + + d0s = [] + d1s = [] + gts = [] + + for data in tqdm(data_loader.load_data(), desc=name): + d0s+=func(data['ref'],data['p0']).data.cpu().numpy().flatten().tolist() + d1s+=func(data['ref'],data['p1']).data.cpu().numpy().flatten().tolist() + gts+=data['judge'].cpu().numpy().flatten().tolist() + + d0s = np.array(d0s) + d1s = np.array(d1s) + gts = np.array(gts) + scores = (d0s None: + """The initialization""" + + super().__init__() + + if logspace: + frequencies = 2.0 ** torch.arange(num_freqs, dtype=torch.float32) + else: + frequencies = torch.linspace( + 1.0, 2.0 ** (num_freqs - 1), num_freqs, dtype=torch.float32 + ) + + if include_pi: + frequencies *= torch.pi + + self.register_buffer("frequencies", frequencies, persistent=False) + self.include_input = include_input + self.num_freqs = num_freqs + + self.out_dim = self._get_dims(input_dim) + + def _get_dims(self, input_dim): + temp = 1 if self.include_input or self.num_freqs == 0 else 0 + out_dim = input_dim * (self.num_freqs * 2 + temp) + + return out_dim + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward process. + + Args: + x: tensor of shape [..., dim] + + Returns: + embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)] + where temp is 1 if include_input is True and 0 otherwise. + """ + + if self.num_freqs > 0: + embed = (x[..., None].contiguous() * self.frequencies.to(device=x.device)).view( + *x.shape[:-1], -1 + ) + if self.include_input: + return torch.cat((x, embed.sin(), embed.cos()), dim=-1) + else: + return torch.cat((embed.sin(), embed.cos()), dim=-1) + else: + return x + +def get_sinusoidal_time_embed(num_frames, embed_dim, device): + """ + Generate time embedding with shape=[num_frames, embed_dim]. + """ + position = torch.arange(num_frames, dtype=torch.float32, device=device).unsqueeze(1) + div_term = torch.exp(torch.arange(0, embed_dim, 2, device=device).float() * (-math.log(10000.0) / embed_dim)) + pe = torch.zeros(num_frames, embed_dim, device=device) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + return pe # [T, C] + +class SinusoidalPositionalEncoding(nn.Module): + """ + Sinusoidal positional encoding module. + """ + def __init__(self, d_model: int, max_len: int = 4096, dropout: float = 0.0): + """ + Args: + d_model (int): Model dimension (embedding dimension). + max_len (int): Maximum sequence length supported. + dropout (float): Dropout rate. + """ + super().__init__() + self.dropout = nn.Dropout(p=dropout) + + if d_model % 2 != 0: + raise ValueError(f"d_model ({d_model}) must be even to use paired sin/cos for sinusoidal positional encoding.") + + position = torch.arange(max_len).unsqueeze(1) # Shape: [max_len, 1] + div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) # Shape: [d_model / 2] + + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2] = torch.sin(position * div_term) + pe[0, :, 1::2] = torch.cos(position * div_term) + + self.register_buffer('pe', pe) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward propagation. + Args: + x: Input tensor with shape [batch_size, seq_len, embedding_dim] + Returns: + torch.Tensor: Output tensor with positional encoding added, same shape as input. + """ + x = x + self.pe[:, :x.size(1), :] + return self.dropout(x) + +class PointEmbed(nn.Module): + def __init__(self, hidden_dim=48, dim=768): + super().__init__() + + assert hidden_dim % 6 == 0 + + self.embedding_dim = hidden_dim + e = torch.pow(2, torch.arange(self.embedding_dim // 6)).float() * np.pi + e = torch.stack([ + torch.cat([e, torch.zeros(self.embedding_dim // 6), + torch.zeros(self.embedding_dim // 6)]), + torch.cat([torch.zeros(self.embedding_dim // 6), e, + torch.zeros(self.embedding_dim // 6)]), + torch.cat([torch.zeros(self.embedding_dim // 6), + torch.zeros(self.embedding_dim // 6), e]), + ]) + self.register_buffer('basis', e) # 3 x 16 + + self.mlp = nn.Linear(self.embedding_dim+3, dim) + + @staticmethod + def embed(input, basis): + projections = torch.einsum( + 'bnd,de->bne', input, basis) + embeddings = torch.cat([projections.sin(), projections.cos()], dim=2) + return embeddings + + def forward(self, input): + # input: B x N x 3 + embed = self.mlp(torch.cat([self.embed(input, self.basis), input], dim=2)) # B x N x C + return embed + +class PatchEmbed(nn.Module):#4 patch_length normal + """Video patch embedding layer.""" + def __init__(self, video_size=224, video_length=16, patch_size=16, patch_length=4, + in_chans=3, embed_dim=768): + super().__init__() + self.video_size = video_size + self.video_length = video_length + self.patch_size = patch_size + self.patch_length = patch_length + self.in_chans = in_chans + self.embed_dim = embed_dim + + # Calculate number of patches + self.num_patches_h = video_size // patch_size + self.num_patches_w = video_size // patch_size + self.num_patches_t = video_length // patch_length + self.num_patches = self.num_patches_t * self.num_patches_h * self.num_patches_w + + # Patch embedding layer + self.proj = nn.Conv3d(in_chans, embed_dim, + kernel_size=(patch_length, patch_size, patch_size), + stride=(patch_length, patch_size, patch_size)) + + def forward(self, x): + # Input: B, C, T, H, W + B, C, T, H, W = x.shape + assert T == self.video_length and H == self.video_size and W == self.video_size, \ + f"Input size mismatch: got {T}x{H}x{W}, expected {self.video_length}x{self.video_size}x{self.video_size}" + + x = self.proj(x) # B, embed_dim, T/pT, H/pH, W/pW + return x + +def resize_pos_embed(posemb, src_shape, target_shape): + """Resize position embedding to match target shape.""" + posemb = posemb.reshape(1, src_shape[0], src_shape[1], src_shape[2], -1) + posemb = posemb.permute(0, 4, 1, 2, 3) + posemb = nn.functional.interpolate(posemb, size=target_shape, mode='trilinear', align_corners=False) + posemb = posemb.permute(0, 2, 3, 4, 1) + posemb = posemb.reshape(1, target_shape[0] * target_shape[1] * target_shape[2], -1) + return posemb + +def generate_pos_embed(T, H, W, embed_dim): + """Generate 3D position embedding.""" + latent_t = torch.arange(T, dtype=torch.float32) + latent_h = torch.arange(H, dtype=torch.float32) + latent_w = torch.arange(W, dtype=torch.float32) + + if T > 1: + latent_t = 2 * (latent_t / (T - 1)) - 1 + else: + latent_t = torch.tensor([0.0], dtype=torch.float32) + + if H > 1: + latent_h = 2 * (latent_h / (H - 1)) - 1 + else: + latent_h = torch.tensor([0.0], dtype=torch.float32) + + if W > 1: + latent_w = 2 * (latent_w / (W - 1)) - 1 + else: + latent_w = torch.tensor([0.0], dtype=torch.float32) + + t, h, w = torch.meshgrid(latent_t, latent_h, latent_w, indexing='ij') + + pos = torch.stack([t, h, w], dim=-1) + + # Apply Fourier feature mapping + freq_bands = 2.0 ** torch.linspace(0.0, 7.0, embed_dim // 6) + freq_bands = freq_bands.view(1, 1, 1, 1, -1) + + # Project to higher dimensions using sin and cos + pos = pos.unsqueeze(-1) * freq_bands + pos = torch.cat([torch.sin(pos), torch.cos(pos)], dim=-1) + + # Reshape to 1D sequence + pos = pos.reshape(1, -1, embed_dim) + + return pos + +class Motion_Latent_Model(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.feat_dim = self.config.model.feat_dim + + self._init_video_patchify() + + transformer_config = self.config.model.video_encoder.transformer + use_qk_norm = transformer_config.get("use_qk_norm", True) + d_model = transformer_config.d + d_head = transformer_config.d_head + + use_qk_norm = transformer_config.get("use_qk_norm", True) + + self.point_embed = PointEmbed(dim=transformer_config.d) + self.point_normal_rgb_proj = nn.Linear(transformer_config.d + 3 + 3, transformer_config.d) + self.point_normal_rgb_proj.apply(init_weights) + + self.num_learnable_tokens = config.model.tokens + self.learnable_tokens = nn.Parameter(torch.randn(1, self.num_learnable_tokens, d_model)) + + # Special tokens for t=0 and t>0. Each is (1, 4, d_model). + self.special_token_0 = nn.Parameter(torch.randn(1, 4, d_model)) + self.special_token_rest = nn.Parameter(torch.randn(1, 4, d_model)) + + self.encoder_cross_attn = QK_Norm_CrossAttentionBlock( + dim=d_model, + head_dim=d_head, + kv_dim=d_model, + use_qk_norm=use_qk_norm + ) + + self.points_transformer_blocks = nn.ModuleList([ + QK_Norm_TransformerBlock( + transformer_config.d, transformer_config.d_head, use_qk_norm=use_qk_norm + ) for _ in range(config.model.pcd_layers) + ]) + self.points_transformer_blocks.apply(init_weights) + + self.image_encoder = DinoEncoder(patch_size=14) + self.alternating_layers = transformer_config.get("n_layer", 12) + assert self.alternating_layers % 2 == 0, "Alternating layers should be even." + + self.global_transformer_blocks = nn.ModuleList([ + QK_Norm_TransformerBlock( + transformer_config.d, transformer_config.d_head, use_qk_norm=use_qk_norm + ) for _ in range(self.alternating_layers // 2) + ]) + self.global_transformer_blocks.apply(init_weights) + + self.local_transformer_blocks = nn.ModuleList([ + QK_Norm_TransformerBlock( + transformer_config.d, transformer_config.d_head, use_qk_norm=use_qk_norm + ) for _ in range(self.alternating_layers // 2) + ]) + self.local_transformer_blocks.apply(init_weights) + + self.transformer_input_layernorm = nn.LayerNorm(transformer_config.d, bias=False) + self.transformer_input_layernorm.apply(init_weights) + + self.decoder_cross_attn = QK_Norm_CrossAttentionBlock( + dim=d_model, + head_dim=d_head, + kv_dim=d_model, + use_qk_norm=use_qk_norm + ) + + self.shared_mlp_output = nn.Sequential( + nn.LayerNorm(self.feat_dim), + nn.Linear(self.feat_dim, self.feat_dim), + nn.GELU(), + nn.Linear(self.feat_dim, 3), + ) + self.shared_mlp_output.apply(init_weights) + + self.loss_computer = MSELossComputer(self.config) + + def _init_video_patchify(self): + video_config = self.config.model.video_encoder + transformer_config = video_config.transformer + token_config = video_config.image_tokenizer + + self.image_size = token_config.get("image_size", 224) + self.video_length = self.config.training.frames + self.patch_size = token_config.get("patch_size", 14) + self.patch_length = token_config.get("patch_length", 1) + self.embed_dim = transformer_config.d + + self.num_patches_h = self.image_size // self.patch_size + self.num_patches_w = self.image_size // self.patch_size + self.num_patches_t = self.video_length // self.patch_length + + self.latent_length = self.num_patches_t + self.latent_size = self.num_patches_h # Assuming square patches + + pos_embed = generate_pos_embed( + self.latent_length, self.latent_size, self.latent_size, self.embed_dim + ) + self.register_buffer('pos_embed', pos_embed) + + drop_rate = transformer_config.get("drop_rate", 0.1) + self.pos_drop = nn.Dropout(p=drop_rate) + + def train(self, mode=True): + super().train(mode) + + def pass_transformer_layers(self, blocks, input_tokens, gradient_checkpoint=False, checkpoint_every=1): + num_layers = len(blocks) + if not gradient_checkpoint: + for layer in blocks: + input_tokens = layer(input_tokens) + return input_tokens + + def _process_layer_group(tokens, start_idx, end_idx): + for idx in range(start_idx, end_idx): + tokens = blocks[idx](tokens) + return tokens + + for start_idx in range(0, num_layers, checkpoint_every): + end_idx = min(start_idx + checkpoint_every, num_layers) + input_tokens = torch.utils.checkpoint.checkpoint( + _process_layer_group, input_tokens, start_idx, end_idx, use_reentrant=False + ) + return input_tokens + + def pass_alternating_attention(self, input_tokens, gradient_checkpoint=False, checkpoint_every=1): + B, T, L, C = input_tokens.shape + num_loops = len(self.global_transformer_blocks) + + if not gradient_checkpoint: + processed_tokens = input_tokens + for i in range(num_loops): + processed_tokens = processed_tokens.view(B, T * L, C) + processed_tokens = self.global_transformer_blocks[i]( + processed_tokens + ) + processed_tokens = processed_tokens.view(B, T, L, C) + processed_tokens_reshaped = processed_tokens.view(B * T, L, C) + processed_tokens_local = self.local_transformer_blocks[i](processed_tokens_reshaped) + processed_tokens = processed_tokens_local.view(B, T, L, C) + return processed_tokens + + def _process_alternating_group(tokens, start_idx, end_idx): + for i in range(start_idx, end_idx): + tokens = tokens.view(B, T * L, C) + tokens = self.global_transformer_blocks[i]( + tokens + ) + tokens = tokens.view(B, T, L, C) + tokens_reshaped = tokens.view(B * T, L, C) + tokens_processed = self.local_transformer_blocks[i](tokens_reshaped) + tokens = tokens_processed.view(B, T, L, C) + return tokens + + processed_tokens = input_tokens + for start_idx in range(0, num_loops, checkpoint_every): + end_idx = min(start_idx + checkpoint_every, num_loops) + processed_tokens = torch.utils.checkpoint.checkpoint( + _process_alternating_group, processed_tokens, start_idx, end_idx, use_reentrant=False + ) + return processed_tokens + + def pass_pcd_layers(self, input_tokens, gradient_checkpoint=False, checkpoint_every=1): + num_layers = len(self.points_transformer_blocks) + if not gradient_checkpoint: + for layer in self.points_transformer_blocks: + input_tokens = layer(input_tokens) + return input_tokens + + def _process_layer_group(tokens, start_idx, end_idx): + for idx in range(start_idx, end_idx): + tokens = self.points_transformer_blocks[idx](tokens) + return tokens + + for start_idx in range(0, num_layers, checkpoint_every): + end_idx = min(start_idx + checkpoint_every, num_layers) + input_tokens = torch.utils.checkpoint.checkpoint( + _process_layer_group, input_tokens, start_idx, end_idx, use_reentrant=False + ) + return input_tokens + + def forward(self, sample): + B = sample['ref_pcd'].shape[0] + N_points = sample['ref_pcd'].shape[1] + device = sample['ref_pcd'].device + tokens = self.learnable_tokens.shape[1] + + point_embedding = self.point_embed(sample['ref_shape_pcd']) + checkpoint_every = self.config.training.grad_checkpoint_every + + point_normal_rgb_embedding = self.point_normal_rgb_proj(torch.cat([point_embedding, sample['ref_shape_normals'], sample['ref_shape_rgbs']], dim=-1)) + + query_tokens = self.learnable_tokens.expand(B, -1, -1).to(device) + mesh_point_feat = self.encoder_cross_attn(query_tokens, point_normal_rgb_embedding, point_normal_rgb_embedding) + use_checkpoint = self.config.training.get('use_checkpoint', True) + mesh_feat = self.pass_pcd_layers(mesh_point_feat, gradient_checkpoint=use_checkpoint, checkpoint_every=checkpoint_every) + + rgb_video = sample['rgb_video'] # B, T, H, W, C + B_vid, T_in, H_in, W_in, C_rgb = rgb_video.shape + #assert self.config.training.frames == T_in, "Mismatch between configured frames and input video frames." + + tokenizer_input = rgb_video.permute(0, 1, 4, 2, 3) + tokenizer_input = tokenizer_input.reshape(B_vid * T_in, tokenizer_input.shape[-3], tokenizer_input.shape[-2], tokenizer_input.shape[-1]) + tokenizer_input = F.interpolate(tokenizer_input, (self.image_size, self.image_size), mode="bilinear", align_corners=False) + + with torch.no_grad(): + image_tokens_raw = self.image_encoder(tokenizer_input) + + x = image_tokens_raw.view(B_vid, T_in, self.num_patches_h, self.num_patches_w, image_tokens_raw.shape[-1]) + x = x.permute(0, 4, 1, 2, 3) + latentT, latentH, latentW = x.shape[2], x.shape[3], x.shape[4] + x = x.flatten(2).transpose(1, 2) + if latentT != self.latent_length or latentH != self.latent_size or latentW != self.latent_size: + pos_embed = resize_pos_embed( + self.pos_embed, + src_shape=(self.latent_length, self.latent_size, self.latent_size), + target_shape=(latentT, latentH, latentW), + ) + else: + pos_embed = self.pos_embed + x = x + pos_embed + x = self.pos_drop(x) + video_tokens_projected = x + num_video_patches_per_frame = video_tokens_projected.shape[1] // T_in + video_tokens_per_frame = video_tokens_projected.view(B, T_in, num_video_patches_per_frame, -1) # [B,T,L_img,C] + + special_token_0 = self.special_token_0.expand(B, 4, -1) # [B, 4, C] + special_token_rest = self.special_token_rest.expand(B, 4, -1) # [B, 4, C] + + all_special_tokens = torch.stack([special_token_0] + [special_token_rest]*(T_in-1), dim=1) # [B, T_in, 4, C] + + # mesh_feat: [B, tokens, C] -> [B, T_in, tokens, C] + pcd_tokens_repeated = mesh_feat.unsqueeze(1).expand(B, T_in, tokens, mesh_feat.shape[-1]) # [B, T_in, tokens, C] + # [B, T_in, tokens+4+L_img, C] + concatenated_tokens = torch.cat([ + all_special_tokens, + pcd_tokens_repeated, # [B, T_in, tokens, C] + video_tokens_per_frame # [B,T_in,L_img,C] + ], dim=2) + + concatenated_tokens = self.transformer_input_layernorm(concatenated_tokens) + B, T, L, C = concatenated_tokens.shape + + use_checkpoint = self.config.training.get('use_checkpoint', True) + checkpoint_every = self.config.training.get('grad_checkpoint_every', 1) + + processed_tokens_BTLC = self.pass_alternating_attention( + input_tokens=concatenated_tokens, + gradient_checkpoint=use_checkpoint, + checkpoint_every=checkpoint_every + ) + pcd_tokens_final = processed_tokens_BTLC[:, :, 4:4+tokens, :] + + output_pcds = sample['ref_pcd'] + output_normals = sample['ref_normal'] + output_rgbs = sample['ref_rgb'] + + B, N, _ = output_pcds.shape + chunk_size = 4096 + + def decode_chunk(pcd_chunk, normal_chunk, rgb_chunk, pcd_tokens_for_decode): + B = pcd_chunk.shape[0] + N = pcd_chunk.shape[1] + T = T_in + # Repeat to [B, T_in, N, C] + pcd_chunk_proj = pcd_chunk[:, None, :, :].expand(-1, T, -1, -1) + normal_chunk_proj = normal_chunk[:, None, :, :].expand(-1, T, -1, -1) + rgb_chunk_proj = rgb_chunk[:, None, :, :].expand(-1, T, -1, -1) + + outputs = [] + for t in range(T): + # For each time step, get corresponding token slice + # pcd_tokens_for_decode: [B, T_in, tokens, C] + pcd_tokens_t = pcd_tokens_for_decode[:, t, :, :] # [B, tokens, C] + + # The inputs for this time step + pcd_input = pcd_chunk_proj[:, t, :, :] # [B, N, C] + normal_input = normal_chunk_proj[:, t, :, :] + rgb_input = rgb_chunk_proj[:, t, :, :] + + # Project and concat features + out_ref_emb = self.point_embed(pcd_input) + point_feat = self.point_normal_rgb_proj(torch.cat([ + out_ref_emb, normal_input, rgb_input + ], dim=-1)) + + # query: [B, chunk_size, C], key/value: [B, num_pcd_tokens, C] + decoded_out_t = self.decoder_cross_attn( + query=point_feat, + key=pcd_tokens_t, + value=pcd_tokens_t + ) + output_t = self.shared_mlp_output(decoded_out_t) # [B, N, 3] + outputs.append(output_t) + decoded_chunk = torch.stack(outputs, dim=1).contiguous().view(-1, outputs[0].shape[1], outputs[0].shape[2]) + return decoded_chunk + + if not self.training and N > chunk_size: + pcds_chunks = [] + for i in range(0, N, chunk_size): + pcd_chunk_slice = output_pcds[:, i:i + chunk_size, :] + normal_chunk_slice = output_normals[:, i:i + chunk_size, :] + rgb_chunk_slice = output_rgbs[:, i:i + chunk_size, :] + + decoded_chunk = decode_chunk(pcd_chunk_slice, normal_chunk_slice, rgb_chunk_slice, pcd_tokens_final) + pcds_chunks.append(decoded_chunk) + pcds = torch.cat(pcds_chunks, dim=1) # [B*T, N, C] + else: + pcds = decode_chunk(output_pcds, output_normals, output_rgbs, pcd_tokens_final) # [B*T, N, C] + + output = pcds.view(B, T_in, N_points, 3) + + # Check if ground truth is available for loss computation + if 'point_clouds' in sample: + xyz_loss_metrics = self.loss_computer(output[..., :3], sample['point_clouds']) + loss_metrics = edict() + loss_metrics.loss = xyz_loss_metrics.loss + loss_metrics.xyz_loss = xyz_loss_metrics.coord_mse_loss + + result = edict( + input_data=sample, + pcd_moved=output[..., :3], + loss_metrics=loss_metrics + ) + else: + result = edict( + input_data=sample, + pcd_moved=output[..., :3] + ) + return result diff --git a/OVOW/third_party/Motion324/model/__init__.py b/OVOW/third_party/Motion324/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/OVOW/third_party/Motion324/model/image_encoder/__init__.py b/OVOW/third_party/Motion324/model/image_encoder/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/OVOW/third_party/Motion324/model/image_encoder/dino/__init__.py b/OVOW/third_party/Motion324/model/image_encoder/dino/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/OVOW/third_party/Motion324/model/image_encoder/dino/model_dino.py b/OVOW/third_party/Motion324/model/image_encoder/dino/model_dino.py new file mode 100644 index 0000000000000000000000000000000000000000..5e6888322ce56e782cee1517099bea34f0fa6f42 --- /dev/null +++ b/OVOW/third_party/Motion324/model/image_encoder/dino/model_dino.py @@ -0,0 +1,859 @@ +# coding=utf-8 +# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch DINOv2 model.""" + + +import collections.abc +import math +from typing import Dict, List, Optional, Set, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.modeling_outputs import ( + BackboneOutput, + BaseModelOutput, + BaseModelOutputWithPooling, + ImageClassifierOutput, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from transformers.utils.backbone_utils import BackboneMixin +from transformers.models.dinov2.configuration_dinov2 import Dinov2Config + + +logger = logging.get_logger(__name__) + +# General docstring +_CONFIG_FOR_DOC = "Dinov2Config" + +# Base docstring +_CHECKPOINT_FOR_DOC = "facebook/dinov2-base" +_EXPECTED_OUTPUT_SHAPE = [1, 257, 768] + +# Image classification docstring +_IMAGE_CLASS_CHECKPOINT = "facebook/dinov2-small-imagenet1k-1-layer" +_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" + + +DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "facebook/dinov2-base", + # See all DINOv2 models at https://huggingface.co/models?filter=dinov2 +] + + +class Dinov2Embeddings(nn.Module): + """ + Construct the CLS token, mask token, position and patch embeddings. + """ + + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size)) + self.patch_embeddings = Dinov2PatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size)) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.config = config + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher + resolution images. + + Source: + https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 + """ + + num_patches = embeddings.shape[1] - 1 + num_positions = self.position_embeddings.shape[1] - 1 + if num_patches == num_positions and height == width: + return self.position_embeddings + class_pos_embed = self.position_embeddings[:, 0] + patch_pos_embed = self.position_embeddings[:, 1:] + dim = embeddings.shape[-1] + height = height // self.config.patch_size + width = width // self.config.patch_size + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + height, width = height + 0.1, width + 0.1 + patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + target_dtype = patch_pos_embed.dtype + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.to(dtype=torch.float32), + scale_factor=(float(height / math.sqrt(num_positions)), float(width / math.sqrt(num_positions))), + mode="bicubic", + align_corners=False, + ).to(dtype=target_dtype) + if int(height) != patch_pos_embed.shape[-2] or int(width) != patch_pos_embed.shape[-1]: + raise ValueError("Width or height does not match with the interpolated position embeddings") + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) + + def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor: + batch_size, _, height, width = pixel_values.shape + target_dtype = self.patch_embeddings.projection.weight.dtype + embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype)) + + if bool_masked_pos is not None: + embeddings = torch.where( + bool_masked_pos.unsqueeze(-1), self.mask_token.to(embeddings.dtype).unsqueeze(0), embeddings + ) + + # add the [CLS] token to the embedded patch tokens + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + # add positional encoding to each token + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + + embeddings = self.dropout(embeddings) + + return embeddings + + +class Dinov2PatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + num_channels = pixel_values.shape[1] + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + f" Expected {self.num_channels} but got {num_channels}." + ) + embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) + return embeddings + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Dinov2 +class Dinov2SelfAttention(nn.Module): + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Dinov2 +class Dinov2SelfOutput(nn.Module): + """ + The residual connection is defined in Dinov2Layer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Dinov2 +class Dinov2Attention(nn.Module): + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + self.attention = Dinov2SelfAttention(config) + self.output = Dinov2SelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads: Set[int]) -> None: + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) + self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_outputs = self.attention(hidden_states, head_mask, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class Dinov2LayerScale(nn.Module): + def __init__(self, config) -> None: + super().__init__() + self.lambda1 = nn.Parameter(config.layerscale_value * torch.ones(config.hidden_size)) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + return hidden_state * self.lambda1 + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, + however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the + layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the + argument. + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.beit.modeling_beit.BeitDropPath +class Dinov2DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: Optional[float] = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +class Dinov2MLP(nn.Module): + def __init__(self, config) -> None: + super().__init__() + in_features = out_features = config.hidden_size + hidden_features = int(config.hidden_size * config.mlp_ratio) + self.fc1 = nn.Linear(in_features, hidden_features, bias=True) + if isinstance(config.hidden_act, str): + self.activation = ACT2FN[config.hidden_act] + else: + self.activation = config.hidden_act + self.fc2 = nn.Linear(hidden_features, out_features, bias=True) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + hidden_state = self.fc1(hidden_state) + hidden_state = self.activation(hidden_state) + hidden_state = self.fc2(hidden_state) + return hidden_state + + +class Dinov2SwiGLUFFN(nn.Module): + def __init__(self, config) -> None: + super().__init__() + in_features = out_features = config.hidden_size + hidden_features = int(config.hidden_size * config.mlp_ratio) + hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 + + self.weights_in = nn.Linear(in_features, 2 * hidden_features, bias=True) + self.weights_out = nn.Linear(hidden_features, out_features, bias=True) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + hidden_state = self.weights_in(hidden_state) + x1, x2 = hidden_state.chunk(2, dim=-1) + hidden = nn.functional.silu(x1) * x2 + return self.weights_out(hidden) + + +class Dinov2Layer(nn.Module): + """This corresponds to the Block class in the original implementation.""" + + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + + self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attention = Dinov2Attention(config) + self.layer_scale1 = Dinov2LayerScale(config) + self.drop_path = Dinov2DropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity() + + self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + if config.use_swiglu_ffn: + self.mlp = Dinov2SwiGLUFFN(config) + else: + self.mlp = Dinov2MLP(config) + self.layer_scale2 = Dinov2LayerScale(config) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: + self_attention_outputs = self.attention( + self.norm1(hidden_states), # in Dinov2, layernorm is applied before self-attention + head_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + + attention_output = self.layer_scale1(attention_output) + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + hidden_states = self.drop_path(attention_output) + hidden_states + + # in Dinov2, layernorm is also applied after self-attention + layer_output = self.norm2(hidden_states) + layer_output = self.mlp(layer_output) + layer_output = self.layer_scale2(layer_output) + + # second residual connection + layer_output = self.drop_path(layer_output) + hidden_states + + outputs = (layer_output,) + outputs + + return outputs + + +# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->Dinov2 +class Dinov2Encoder(nn.Module): + def __init__(self, config: Dinov2Config) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([Dinov2Layer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> Union[tuple, BaseModelOutput]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + layer_module.__call__, + hidden_states, + layer_head_mask, + output_attentions, + ) + else: + layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class Dinov2PreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = Dinov2Config + base_model_prefix = "dinov2" + main_input_name = "pixel_values" + supports_gradient_checkpointing = True + + def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid + # `trunc_normal_cpu` not implemented in `half` issues + module.weight.data = nn.init.trunc_normal_( + module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range + ).to(module.weight.dtype) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + elif isinstance(module, Dinov2Embeddings): + module.position_embeddings.data = nn.init.trunc_normal_( + module.position_embeddings.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.position_embeddings.dtype) + + module.cls_token.data = nn.init.trunc_normal_( + module.cls_token.data.to(torch.float32), + mean=0.0, + std=self.config.initializer_range, + ).to(module.cls_token.dtype) + + +DINOV2_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`Dinov2Config`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +DINOV2_BASE_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`BitImageProcessor.preprocess`] for details. + + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Only relevant for + pre-training. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +DINOV2_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See + [`BitImageProcessor.preprocess`] for details. + + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top.", + DINOV2_START_DOCSTRING, +) +class Dinov2Model(Dinov2PreTrainedModel): + def __init__(self, config: Dinov2Config): + super().__init__(config) + self.config = config + + self.embeddings = Dinov2Embeddings(config) + self.encoder = Dinov2Encoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> Dinov2PatchEmbeddings: + return self.embeddings.patch_embeddings + + def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + @add_start_docstrings_to_model_forward(DINOV2_BASE_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPooling, + config_class=_CONFIG_FOR_DOC, + modality="vision", + expected_output=_EXPECTED_OUTPUT_SHAPE, + ) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + bool_masked_pos: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos) + + encoder_outputs = self.encoder( + embedding_output, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + pooled_output = sequence_output[:, 0, :] + + if not return_dict: + head_outputs = (sequence_output, pooled_output) + return head_outputs + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@add_start_docstrings( + """ + Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state + of the [CLS] token) e.g. for ImageNet. + """, + DINOV2_START_DOCSTRING, +) +class Dinov2ForImageClassification(Dinov2PreTrainedModel): + def __init__(self, config: Dinov2Config) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.dinov2 = Dinov2Model(config) + + # Classifier head + self.classifier = ( + nn.Linear(config.hidden_size * 2, config.num_labels) if config.num_labels > 0 else nn.Identity() + ) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_IMAGE_CLASS_CHECKPOINT, + output_type=ImageClassifierOutput, + config_class=_CONFIG_FOR_DOC, + expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, + ) + def forward( + self, + pixel_values: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[tuple, ImageClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.dinov2( + pixel_values, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] # batch_size, sequence_length, hidden_size + + cls_token = sequence_output[:, 0] + patch_tokens = sequence_output[:, 1:] + + linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1) + + logits = self.classifier(linear_input) + + loss = None + if labels is not None: + # move labels to correct device to enable model parallelism + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + Dinov2 backbone, to be used with frameworks like DETR and MaskFormer. + """, + DINOV2_START_DOCSTRING, +) +class Dinov2Backbone(Dinov2PreTrainedModel, BackboneMixin): + def __init__(self, config): + super().__init__(config) + super()._init_backbone(config) + + self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)] + self.embeddings = Dinov2Embeddings(config) + self.encoder = Dinov2Encoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> Dinov2PatchEmbeddings: + return self.embeddings.patch_embeddings + + @add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.Tensor, + output_hidden_states: Optional[bool] = None, + output_attentions: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> BackboneOutput: + """ + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoBackbone + >>> import torch + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base") + >>> model = AutoBackbone.from_pretrained( + ... "facebook/dinov2-base", out_features=["stage2", "stage5", "stage8", "stage11"] + ... ) + + >>> inputs = processor(image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + [1, 768, 16, 16] + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + + embedding_output = self.embeddings(pixel_values) + + outputs = self.encoder( + embedding_output, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict + ) + + hidden_states = outputs.hidden_states if return_dict else outputs[1] + + feature_maps = () + for stage, hidden_state in zip(self.stage_names, hidden_states): + if stage in self.out_features: + if self.config.apply_layernorm: + hidden_state = self.layernorm(hidden_state) + if self.config.reshape_hidden_states: + hidden_state = hidden_state[:, 1:] + # this was actually a bug in the original implementation that we copied here, + # cause normally the order is height, width + batch_size, _, height, width = pixel_values.shape + patch_size = self.config.patch_size + hidden_state = hidden_state.reshape(batch_size, height // patch_size, width // patch_size, -1) + hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() + feature_maps += (hidden_state,) + + if not return_dict: + if output_hidden_states: + output = (feature_maps,) + outputs[1:] + else: + output = (feature_maps,) + outputs[2:] + return output + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=outputs.hidden_states if output_hidden_states else None, + attentions=outputs.attentions if output_attentions else None, + ) \ No newline at end of file diff --git a/OVOW/third_party/Motion324/model/image_encoder/dinov2.py b/OVOW/third_party/Motion324/model/image_encoder/dinov2.py new file mode 100644 index 0000000000000000000000000000000000000000..a38f9af918b57bb8ffa5d4c1909444219bc3f0dc --- /dev/null +++ b/OVOW/third_party/Motion324/model/image_encoder/dinov2.py @@ -0,0 +1,144 @@ +import os +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Dict, Tuple + + +_RESNET_MEAN = [0.485, 0.456, 0.406] +_RESNET_STD = [0.229, 0.224, 0.225] + + +class PositionGetter: + """Generates and caches 2D spatial positions for patches in a grid.""" + + def __init__(self): + self.position_cache: Dict[Tuple[int, int], torch.Tensor] = {} + + def __call__(self, batch_size: int, height: int, width: int, device: torch.device) -> torch.Tensor: + """ + Generate spatial positions for a batch of patches. + + Args: + batch_size: Number of samples in the batch + height: Height of the grid in patches + width: Width of the grid in patches + device: Target device for the position tensor + + Returns: + Tensor of shape (batch_size, height*width, 2) containing y,x coordinates + """ + if (height, width) not in self.position_cache: + y_coords = torch.arange(height, device=device) + x_coords = torch.arange(width, device=device) + positions = torch.cartesian_prod(y_coords, x_coords) + self.position_cache[height, width] = positions + + cached_positions = self.position_cache[height, width] + return cached_positions.view(1, height * width, 2).expand(batch_size, -1, -1).clone() + +class DinoEncoder(nn.Module): + """DINOv2 vision transformer encoder for extracting image features.""" + + def __init__(self, patch_size=14, model_name='dinov2_vitb14'): + super(DinoEncoder, self).__init__() + repo_override = os.environ.get("DINOv2_REPO") + local_repo_candidates = [ + repo_override, + os.path.join(torch.hub.get_dir(), "facebookresearch_dinov2_main"), + "~/.cache/torch/hub/facebookresearch_dinov2_main", + ] + local_repo = next((p for p in local_repo_candidates if p and os.path.isdir(p)), None) + # pretrained=False: the backbone weights are restored from the Motion324 + # checkpoint (image_encoder.model.*), so skip the network download that + # fails offline (SSL/CA) and would be overwritten anyway. + if local_repo: + self.model = torch.hub.load(local_repo, model_name, source="local", pretrained=False) + else: + self.model = torch.hub.load("facebookresearch/dinov2", model_name, pretrained=False) + if hasattr(self.model, 'patch_size'): + assert self.model.patch_size == patch_size, \ + f"DINOv2 model's patch size ({self.model.patch_size}) must match provided patch_size ({patch_size})" + else: + print(f"Warning: DINOv2 model does not expose patch_size. Assuming it is {patch_size}.") + + self.patch_size = patch_size + self.position_getter = PositionGetter() + + # For fixed 224x224 input with patch_size=14 + self.image_size = 224 + self.num_patches_per_dim = self.image_size // self.patch_size # 16 + self.num_patches_total = self.num_patches_per_dim * self.num_patches_per_dim # 256 + + for param in self.model.parameters(): + param.requires_grad = False + + self.model.eval() + + def forward(self, image): + """ + Forward pass through DINOv2 encoder. + + Args: + image: Input images of shape (B, C, H, W), e.g., (B, 3, 224, 224) + + Returns: + encoded_feats: Patch features of shape (B, N_patches, D_embed) + """ + B, C, H, W = image.shape + device = image.device + + resnet_mean = torch.tensor(_RESNET_MEAN, device=device).view(1, 3, 1, 1) + resnet_std = torch.tensor(_RESNET_STD, device=device).view(1, 3, 1, 1) + images_resnet_norm = (image - resnet_mean) / resnet_std + + assert H == self.image_size and W == self.image_size, \ + f"Input image size must be {self.image_size}x{self.image_size}, but got {H}x{W}" + + encoded_feats = self._process_images(images_resnet_norm) + return encoded_feats + + def _process_images(self, images): + """ + Process a batch of images through DINO encoder. + + Args: + images: Input images of shape (B, C, H, W), e.g., (B, 3, 224, 224) + + Returns: + encoded_features: Patch features + """ + B, C, H, W = images.shape + features_dict = self.model.forward_features(images) + + # Get patch tokens + if 'x_norm_patchtokens' in features_dict: + encoded_features = features_dict['x_norm_patchtokens'] + elif 'x_patchtokens' in features_dict: + encoded_features = features_dict['x_patchtokens'] + else: + if isinstance(features_dict, torch.Tensor) and features_dict.ndim == 3 and features_dict.shape[0] == B: + num_expected_patches = (H // self.patch_size) * (W // self.patch_size) + if features_dict.shape[1] == num_expected_patches: + encoded_features = features_dict + else: + raise KeyError(f"DINOv2 output shape {features_dict.shape} does not match expected patch tokens") + else: + available_keys = list(features_dict.keys()) if isinstance(features_dict, dict) else 'N/A' + raise KeyError(f"Could not find patch tokens in DINOv2 output. Available keys: {available_keys}") + + if hasattr(self.model, 'embed_dim'): + assert encoded_features.shape[-1] == self.model.embed_dim, \ + f"Feature dimension {encoded_features.shape[-1]} does not match model embed_dim {self.model.embed_dim}" + + assert encoded_features.shape[1] == self.num_patches_total, \ + f"Number of patches {encoded_features.shape[1]} does not match expected {self.num_patches_total}" + + return encoded_features + + def train(self, mode: bool = False): + """Override train method to keep DINO model in eval mode.""" + super(DinoEncoder, self).train(mode) + if self.model is not None: + self.model.eval() + return self diff --git a/OVOW/third_party/Motion324/model/loss.py b/OVOW/third_party/Motion324/model/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..bdadb02713f7fa5cacfe6ac780f5ac3f89bf642d --- /dev/null +++ b/OVOW/third_party/Motion324/model/loss.py @@ -0,0 +1,66 @@ +import torch.nn as nn +import torch +import torch.nn.functional as F +from easydict import EasyDict as edict + +class MSELossComputer(nn.Module): + def __init__(self, config): + """ + Initializes the LossComputer. + + Args: + config: Configuration object. Expected to have config.training.coord_mse_loss_weight. + """ + super().__init__() + self.config = config + + # Ensure the config has 'coord_mse_loss_weight' defined. + if not hasattr(self.config, 'training') or \ + not hasattr(self.config.training, 'coord_mse_loss_weight'): + raise ValueError( + "Configuration must have 'config.training.coord_mse_loss_weight' defined." + ) + + def forward( + self, + coords_pred: torch.Tensor, + coords_target: torch.Tensor, + ): + """ + Calculate Mean Squared Error (MSE) loss for coordinates. + + Args: + coords_pred (torch.Tensor): Predicted coordinates. Shape [B, T, N, C]. + coords_target (torch.Tensor): Ground truth coordinates. Shape [B, T, N, C]. + + Returns: + edict: Dictionary of loss metrics, containing 'loss' and 'coord_mse_loss'. + """ + + # Determine device from input tensor + current_device = coords_pred.device + + total_loss = torch.tensor(0.0, device=current_device) + loss_metrics = edict() + + # --- Coordinate MSE Loss --- + coord_mse_loss_value = torch.tensor(0.0, device=current_device) + + # Validate shapes + if not (coords_pred.ndim == 4 and \ + coords_target.ndim == 4 and \ + coords_pred.shape == coords_target.shape): + raise ValueError( + f"Shape mismatch or invalid shape for coordinate MSE. " + f"Expected both tensors of shape (B, T, N, C). " + f"Got pred: {coords_pred.shape}, target: {coords_target.shape}" + ) + + if self.config.training.coord_mse_loss_weight > 0.0: + coord_mse_loss_value = F.mse_loss(coords_pred, coords_target) + total_loss += self.config.training.coord_mse_loss_weight * coord_mse_loss_value + + loss_metrics.coord_mse_loss = coord_mse_loss_value + loss_metrics.loss = total_loss # The final combined loss (which is just coord_mse_loss weighted) + + return loss_metrics diff --git a/OVOW/third_party/Motion324/model/transformer.py b/OVOW/third_party/Motion324/model/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..1bff755510e6eae09cf2e18d5e62fc3a808da799 --- /dev/null +++ b/OVOW/third_party/Motion324/model/transformer.py @@ -0,0 +1,423 @@ +# Copyright (c) 2025 Haian Jin. Created for the LVSM project (ICLR 2025). +# Modifications Copyright (c) 2025 Hongyuan Chen. + +import torch +import torch.nn as nn +from einops import rearrange + +try: + import xformers.ops as xops +except ImportError: + raise ImportError("Please use flashatt v2") + + + +def init_weights(module, std=0.02): + """Initialize weights for linear and embedding layers. + + Args: + module: Module to initialize + std: Standard deviation for normal initialization + """ + if isinstance(module, (nn.Linear, nn.Embedding)): + torch.nn.init.normal_(module.weight, mean=0.0, std=std) + if isinstance(module, nn.Linear) and module.bias is not None: + torch.nn.init.zeros_(module.bias) + + + +# src: https://github.com/pytorch/benchmark/blob/main/torchbenchmark/models/llama/model.py#L28 +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-5): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + output = self._norm(x.float()).type_as(x) + + return output * self.weight.type_as(x) + + + +class MLP(nn.Module): + """ + Multi-Layer Perceptron block. + Reference: https://github.com/facebookresearch/dino/blob/7c446df5b9f45747937fb0d72314eb9f7b66930a/vision_transformer.py#L49-L65 + """ + + def __init__( + self, + dim, + mlp_ratio=4, + bias=False, + dropout=0.0, + activation=nn.GELU, + mlp_dim=None, + ): + """ + Args: + dim: Input dimension + mlp_ratio: Multiplier for hidden dimension + bias: Whether to use bias in linear layers + dropout: Dropout probability + activation: Activation function + mlp_dim: Optional explicit hidden dimension (overrides mlp_ratio) + """ + super().__init__() + hidden_dim = mlp_dim if mlp_dim is not None else int(dim * mlp_ratio) + + self.mlp = nn.Sequential( + nn.Linear(dim, hidden_dim, bias=bias), + activation(), + nn.Linear(hidden_dim, dim, bias=bias), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.mlp(x) + + +class QK_Norm_CrossAttention(nn.Module): + """ + Cross-attention module with optional Q-K normalization. + Takes separate query, key, and value inputs. + """ + def __init__( + self, + dim, + head_dim, + kv_dim=None, + qkv_bias=False, + fc_bias=True, + attn_dropout=0.0, + fc_dropout=0.0, + use_qk_norm=True, + ): + super().__init__() + assert dim % head_dim == 0, f"Token dimension {dim} should be divisible by head dimension {head_dim}" + if kv_dim is None: + kv_dim = dim + + self.dim = dim + self.kv_dim = kv_dim + self.head_dim = head_dim + self.num_heads = dim // head_dim + self.attn_dropout = attn_dropout + self.use_qk_norm = use_qk_norm + + self.to_q = nn.Linear(dim, dim, bias=qkv_bias) + self.to_k = nn.Linear(kv_dim, dim, bias=qkv_bias) + self.to_v = nn.Linear(kv_dim, dim, bias=qkv_bias) + + self.fc = nn.Linear(dim, dim, bias=fc_bias) + self.attn_fc_dropout = nn.Dropout(fc_dropout) + + if self.use_qk_norm: + self.q_norm = RMSNorm(head_dim) + self.k_norm = RMSNorm(head_dim) + + def forward(self, query, key, value, attn_bias=None): + q = self.to_q(query) + k = self.to_k(key) + v = self.to_v(value) + + q, k, v = (rearrange(t, "b l (nh dh) -> b l nh dh", dh=self.head_dim) for t in (q, k, v)) + + if self.use_qk_norm: + q = self.q_norm(q) + k = self.k_norm(k) + + x = xops.memory_efficient_attention( + q, k, v, + attn_bias=attn_bias, + p=self.attn_dropout if self.training else 0.0, + op=(xops.fmha.flash.FwOp, xops.fmha.flash.BwOp), + ) + + x = rearrange(x, "b l nh dh -> b l (nh dh)") + x = self.attn_fc_dropout(self.fc(x)) + + return x + +class QK_Norm_SelfAttention(nn.Module): + """ + Self-attention with optional Q-K normalization. + Reference: https://github.com/facebookresearch/dino/blob/7c446df5b9f45747937fb0d72314eb9f7b66930a/vision_transformer.py#L68-L92 + """ + + def __init__( + self, + dim, + head_dim, + qkv_bias=False, + fc_bias=True, + attn_dropout=0.0, + fc_dropout=0.0, + use_qk_norm=True, + ): + """ + Args: + dim: Input dimension + head_dim: Dimension of each attention head + qkv_bias: Whether to use bias in QKV projection + fc_bias: Whether to use bias in output projection + attn_dropout: Dropout probability for attention weights + fc_dropout: Dropout probability for output projection + use_qk_norm: Whether to use Q-K normalization + We use flash attention V2 for efficiency. + """ + super().__init__() + assert dim % head_dim == 0, f"Token dimension {dim} should be divisible by head dimension {head_dim}" + + self.dim = dim + self.head_dim = head_dim + self.num_heads = dim // head_dim + self.attn_dropout = attn_dropout + self.use_qk_norm = use_qk_norm + + self.to_qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) + self.fc = nn.Linear(dim, dim, bias=fc_bias) + self.attn_fc_dropout = nn.Dropout(fc_dropout) + + # Optional Q-K normalization + if self.use_qk_norm: + self.q_norm = RMSNorm(head_dim) + self.k_norm = RMSNorm(head_dim) + + def forward(self, x, attn_bias=None): + """ + Args: + x: Input tensor of shape (batch, seq_len, dim) + attn_bias: Optional attention bias mask + + Returns: + Output tensor of shape (batch, seq_len, dim) + """ + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + + q, k, v = (rearrange(t, "b l (nh dh) -> b l nh dh", dh=self.head_dim) for t in (q, k, v)) + + # Apply qk normalization if enabled + if self.use_qk_norm: + q = self.q_norm(q) + k = self.k_norm(k) + + x = xops.memory_efficient_attention( + q, k, v, + attn_bias=attn_bias, + p=self.attn_dropout if self.training else 0.0, + op=(xops.fmha.flash.FwOp, xops.fmha.flash.BwOp), + ) + + x = rearrange(x, "b l nh dh -> b l (nh dh)") + x = self.attn_fc_dropout(self.fc(x)) + + return x + +class SubsetAttention(nn.Module): + """Attention that can attend to subsets of queries or keys/values.""" + + def __init__( + self, + dim, + head_dim, + qkv_bias=False, + attn_dropout=0.0, + fc_bias=False, + fc_dropout=0.0, + use_qk_norm=False + ): + """ + Args: + dim: Input dimension + head_dim: Dimension of each attention head + qkv_bias: Whether to use bias in QKV projection + attn_dropout: Dropout probability for attention weights + fc_bias: Whether to use bias in output projection + fc_dropout: Dropout probability for output projection + use_qk_norm: Whether to use Q-K normalization + We use flash attention V2 for efficiency. + """ + super().__init__() + assert dim % head_dim == 0, f"Token dimension {dim} should be divisible by head dimension {head_dim}" + + self.dim = dim + self.head_dim = head_dim + self.num_heads = dim // head_dim + self.attn_dropout = attn_dropout + self.use_qk_norm = use_qk_norm + + # Projections + self.to_qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) + self.fc = nn.Linear(dim, dim, bias=fc_bias) + self.attn_fc_dropout = nn.Dropout(fc_dropout) + + # Optional Q-K normalization + if self.use_qk_norm: + self.q_norm = RMSNorm(head_dim) + self.k_norm = RMSNorm(head_dim) + + def forward(self, x, subset_kv_size=None, subset_q_size=None): + """ + Args: + x: Input tensor of shape (batch, seq_len, dim) + subset_kv_size: If provided, only attend to tokens after this index in KV + subset_q_size: If provided, only compute attention for queries up to this index + + Returns: + Output tensor of shape (batch, seq_len, dim) + """ + # Only one subset parameter can be provided + assert not (subset_kv_size is not None and subset_q_size is not None), \ + "Only one of subset_kv_size or subset_q_size can be provided" + + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + + q, k, v = (rearrange(t, "b l (nh dh) -> b l nh dh", dh=self.head_dim) for t in (q, k, v)) + + if self.use_qk_norm: + q = self.q_norm(q) + k = self.k_norm(k) + + # Handle subset attention cases + if subset_kv_size is not None and subset_kv_size < k.shape[1]: + # Attend to subset of key/value tokens + k_subset = k[:, subset_kv_size:, :, :].contiguous() + v_subset = v[:, subset_kv_size:, :, :].contiguous() + + x = xops.memory_efficient_attention( + q, k_subset, v_subset, + attn_bias=None, + p=self.attn_dropout if self.training else 0.0, + op=(xops.fmha.flash.FwOp, xops.fmha.flash.BwOp), + ) + elif subset_q_size is not None and subset_q_size < q.shape[1]: + # Only compute attention for subset of query tokens + q_subset = q[:, :subset_q_size, :, :].contiguous() + + x = xops.memory_efficient_attention( + q_subset, k, v, + attn_bias=None, + p=self.attn_dropout if self.training else 0.0, + op=(xops.fmha.flash.FwOp, xops.fmha.flash.BwOp), + ) + else: + # Regular attention for all tokens + x = xops.memory_efficient_attention( + q, k, v, + attn_bias=None, + p=self.attn_dropout if self.training else 0.0, + op=(xops.fmha.flash.FwOp, xops.fmha.flash.BwOp), + ) + + x = rearrange(x, "b l nh dh -> b l (nh dh)") + + # Final projection + x = self.attn_fc_dropout(self.fc(x)) + + return x + +class QK_Norm_CrossAttentionBlock(nn.Module): + """ + Transformer block using cross-attention. + Query is processed, then interacts with Key/Value. + """ + def __init__( + self, + dim, + head_dim, + kv_dim=None, + ln_bias=False, + attn_qkv_bias=False, + attn_dropout=0.0, + attn_fc_bias=False, + attn_fc_dropout=0.0, + mlp_ratio=4, + mlp_bias=False, + mlp_dropout=0.0, + use_qk_norm=True, + ): + super().__init__() + self.norm_q = nn.LayerNorm(dim, bias=ln_bias) + self.norm_kv = nn.LayerNorm(kv_dim if kv_dim is not None else dim, bias=ln_bias) + self.attn = QK_Norm_CrossAttention( + dim=dim, + head_dim=head_dim, + kv_dim=kv_dim, + qkv_bias=attn_qkv_bias, + fc_bias=attn_fc_bias, + attn_dropout=attn_dropout, + fc_dropout=attn_fc_dropout, + use_qk_norm=use_qk_norm, + ) + self.norm2 = nn.LayerNorm(dim, bias=ln_bias) + self.mlp = MLP( + dim=dim, + mlp_ratio=mlp_ratio, + bias=mlp_bias, + dropout=mlp_dropout, + ) + + def forward(self, query, key, value): + # Pre-normalization + q_norm = self.norm_q(query) + k_norm = self.norm_kv(key) + v_norm = self.norm_kv(value) + + # Cross-attention + attn_out = self.attn(q_norm, k_norm, v_norm) + + # Residual connection and MLP + x = query + attn_out + x = x + self.mlp(self.norm2(x)) + return x + +class QK_Norm_TransformerBlock(nn.Module): + """ + Standard transformer block with pre-normalization architecture. + Reference: https://github.com/facebookresearch/dino/blob/7c446df5b9f45747937fb0d72314eb9f7b66930a/vision_transformer.py#L95-L113 + """ + + def __init__( + self, + dim, + head_dim, + ln_bias=False, + attn_qkv_bias=False, + attn_dropout=0.0, + attn_fc_bias=False, + attn_fc_dropout=0.0, + mlp_ratio=4, + mlp_bias=False, + mlp_dropout=0.0, + use_qk_norm=True, + ): + super().__init__() + self.norm1 = nn.LayerNorm(dim, bias=ln_bias) + self.attn = QK_Norm_SelfAttention( + dim=dim, + head_dim=head_dim, + qkv_bias=attn_qkv_bias, + fc_bias=attn_fc_bias, + attn_dropout=attn_dropout, + fc_dropout=attn_fc_dropout, + use_qk_norm=use_qk_norm, + ) + + self.norm2 = nn.LayerNorm(dim, bias=ln_bias) + self.mlp = MLP( + dim=dim, + mlp_ratio=mlp_ratio, + bias=mlp_bias, + dropout=mlp_dropout, + ) + + + def forward(self, x): + x = x + self.attn(self.norm1(x)) + x = x + self.mlp(self.norm2(x)) + return x \ No newline at end of file diff --git a/OVOW/third_party/Motion324/pyrightconfig.json b/OVOW/third_party/Motion324/pyrightconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..497b6ceaddcaf7594599bcb0685517b4140d1c0e --- /dev/null +++ b/OVOW/third_party/Motion324/pyrightconfig.json @@ -0,0 +1,12 @@ +{ + "exclude": [ + "experiments/**", + "log/**", + "metric_checkpoint/**", + "preprocessed_data/**" ], + "typeCheckingMode": "off" + // "reportMissingImports": false, + // "reportUnknownMemberType": false, + // "reportAttributeAccessIssue": false, + // "reportUnknownVariableType": false +} \ No newline at end of file diff --git a/OVOW/third_party/Motion324/requirements.txt b/OVOW/third_party/Motion324/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea23c889b627649087541add5e803ccd4aabaf77 --- /dev/null +++ b/OVOW/third_party/Motion324/requirements.txt @@ -0,0 +1,36 @@ +# --extra-index-url https://download.pytorch.org/whl/cu124 +torch==2.4.1 +torchvision==0.19.1+cu124 +xformers==0.0.28 +imageio==2.33.0 +Pillow==10.0.1 +imageio-ffmpeg==0.5.0 +omegaconf==2.1.1 +einops>= 0.4.1 +transformers==4.56.1 +peft==0.17.0 +torchmetrics>= 0.11.0 +datasets==2.14.5 +rich>=13.3.2 +wandb==0.21.1 +ipdb +tqdm +easydict==1.13 +lpips==0.1.4 +dreamsim==0.2.1 +open-clip-torch==3.1.0 +jaxtyping==0.3.1 +scikit-image==0.23.2 +rembg==2.0.68 +onnxruntime +trimesh==4.6.9 +bpy==4.4.0 +natsort +scikit-learn +numpy==1.26.4 +matplotlib +pybind11==2.13.6 +diffusers==0.35.2 +xatlas==0.0.11 +pymeshlab +pygltflib \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/4D_from_existing.sh b/OVOW/third_party/Motion324/scripts/4D_from_existing.sh new file mode 100644 index 0000000000000000000000000000000000000000..95a565d371204149b51e34083c9d83993310588f --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/4D_from_existing.sh @@ -0,0 +1,71 @@ +#!/bin/bash + +# Script to generate 4D animation from existing mesh and video +# Usage: ./scripts/4D_from_existing.sh +# Example: ./scripts/4D_from_existing.sh ./examples/mesh.glb ./examples/video.mp4 ./examples/output +set -e + +# Check arguments +if [ $# -lt 3 ]; then + echo "Usage: $0 " + echo " data_dir: Path to .glb or .fbx mesh file (FBX will be automatically converted to GLB)" + echo " video_path: Path to video file (.mp4/.avi/.mov) or directory containing images" + echo " output_dir: Output directory for animation results" + echo "" + echo "Example: $0 ./examples/chili.glb ./examples/chili.mp4 ./examples/output" + exit 1 +fi + +DATA_DIR="$1" +VIDEO_PATH="$2" +OUTPUT_DIR="$3" + +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" +PROJECT_ROOT="${SCRIPT_DIR}/.." + +# Validate data_dir +if [ ! -f "$DATA_DIR" ]; then + echo "Error: Mesh file not found: $DATA_DIR" + echo "Supported formats: .glb, .fbx" + exit 1 +fi + +# Validate video_path +if [ ! -f "$VIDEO_PATH" ] && [ ! -d "$VIDEO_PATH" ]; then + echo "Error: Video file or directory not found: $VIDEO_PATH" + echo "Supported: .mp4/.avi/.mov video files or directory containing images" + exit 1 +fi + +# Create output directory +mkdir -p "$OUTPUT_DIR" + +echo "==========================================" +echo "Generating 4D animation from existing mesh and video" +echo "Mesh file: $DATA_DIR" +echo "Video path: $VIDEO_PATH" +echo "Output directory: $OUTPUT_DIR" +echo "==========================================" + +# Change to project root directory +cd "$PROJECT_ROOT" + +# Run inference +python scripts/inference_with_video_mesh.py \ + --config=configs/dyscene.yaml \ + training.resume_ckpt="$PROJECT_ROOT/experiments/checkpoints/ckpt_0000000000060000.pt" \ + model.class_name=model.Pcd_motion.Motion_Latent_Model \ + training.num_shape_samples=16384 \ + training.frames=256 \ + start_frame=0 \ + use_segmentation=True \ + data_dir="$DATA_DIR" \ + video_path="$VIDEO_PATH" \ + output_dir="$OUTPUT_DIR" + +echo "" +echo "==========================================" +echo "Processing complete!" +echo "Animation output: $OUTPUT_DIR" +echo "==========================================" + diff --git a/OVOW/third_party/Motion324/scripts/4D_from_video.sh b/OVOW/third_party/Motion324/scripts/4D_from_video.sh new file mode 100644 index 0000000000000000000000000000000000000000..b037f182fe5c1971d875f1779e9dfb3caa5f220f --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/4D_from_video.sh @@ -0,0 +1,145 @@ +#!/bin/bash + +# Script to convert video input to 4D animation +# Usage: ./scripts/4D_from_video.sh [--split_only] [--backend hunyuan|trellis2] +# ./scripts/4D_from_video.sh ./examples/tiger.mp4 +# ./scripts/4D_from_video.sh ./examples/tiger.mp4 --split_only +# ./scripts/4D_from_video.sh ./examples/tiger.mp4 --backend trellis2 +set -e + +# Check arguments +if [ $# -lt 1 ]; then + echo "Usage: $0 [--split_only] [--backend hunyuan|trellis2]" + echo "Example: $0 ./examples/tiger.mp4" + echo " $0 ./examples/tiger.mp4 --split_only" + echo " $0 ./examples/tiger.mp4 --backend trellis2" + exit 1 +fi + +INPUT_VIDEO="$1" +shift + +# Defaults +SPLIT_ONLY="false" +BACKEND="hunyuan" + +while [ $# -gt 0 ]; do + case "$1" in + --split_only) + SPLIT_ONLY="true" + shift + ;; + --backend) + if [ -z "$2" ]; then + echo "Error: --backend requires a value (hunyuan|trellis2)" + exit 1 + fi + BACKEND="$2" + shift 2 + ;; + --trellis2) + BACKEND="trellis2" + shift + ;; + *) + echo "Error: Unknown option '$1'" + exit 1 + ;; + esac +done + +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" +PROJECT_ROOT="${SCRIPT_DIR}/.." +OUTPUT_BASE_DIR="$(dirname "$INPUT_VIDEO")" + +# Keep Hunyuan weights under /data to avoid repeated downloads into /mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/.cache. +CKPT_ROOT="${MOTION324_CKPT_ROOT:-/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/jiayin/OVOW/ckpts}" +export HY3DGEN_MODELS="${HY3DGEN_MODELS:-${CKPT_ROOT}/hy3dgen}" +mkdir -p "$HY3DGEN_MODELS" + +# Validate input video +if [ ! -f "$INPUT_VIDEO" ]; then + echo "Error: Input video not found: $INPUT_VIDEO" + exit 1 +fi + +# Set up paths +VIDEO_NAME=$(basename "$INPUT_VIDEO" | sed 's/\.[^.]*$//') +PROCESSED_DIR="$OUTPUT_BASE_DIR/${VIDEO_NAME}_processed" +MASKED_RGB_DIR="$PROCESSED_DIR/masked_rgb" +ANIMATION_OUTPUT_DIR="$PROCESSED_DIR/animation" + +echo "==========================================" +echo "Processing video: $INPUT_VIDEO" +echo "Base directory: $OUTPUT_BASE_DIR" +echo "Processed directory: $PROCESSED_DIR" +echo "Split only: $SPLIT_ONLY" +echo "Backend: $BACKEND" +echo "HY3DGEN_MODELS: $HY3DGEN_MODELS" +echo "==========================================" + +# Step 1: Remove background and extract frames or only split frames +echo "" +if [ "$SPLIT_ONLY" = "true" ]; then + echo "Step 1: Extracting frames only (split_only mode)..." + python "$PROJECT_ROOT/utils/rmbg_for_black_bg.py" --input "$INPUT_VIDEO" --split_only + echo "Split only mode done. Exiting pipeline after video frame extraction." +else + echo "Step 1: Removing background and extracting frames..." + python "$PROJECT_ROOT/utils/rmbg_for_black_bg.py" --input "$INPUT_VIDEO" +fi + +HUNYUAN_INPUT_DIR="$OUTPUT_BASE_DIR/${VIDEO_NAME}_processed/Hunyuan_Gen_Input" +# Step 2: Generate Hunyuan mesh +echo "" +echo "Step 2: Generating mesh ($BACKEND)..." +FBX_HUNYUAN_O="$(find "$HUNYUAN_INPUT_DIR" -name "*_hunyuan_original_wo_remap_converted.fbx" | head -1)" +if [ -f "$FBX_HUNYUAN_O" ]; then + echo "Hunyuan mesh already exists at $FBX_HUNYUAN_O, skipping generation." +else + if [ "$BACKEND" = "trellis2" ]; then + export HF_ENDPOINT=https://huggingface.co + else + export HF_ENDPOINT=https://hf-mirror.com + fi + python "$PROJECT_ROOT/scripts/hunyuan_Gen.py" \ + --root "$OUTPUT_BASE_DIR" \ + --output "$OUTPUT_BASE_DIR" \ + --N 1 \ + --n 0 \ + --skip 256 \ + --seed 42 \ + --backend "$BACKEND" +fi + +if [ "$BACKEND" != "trellis2" ]; then + python "$PROJECT_ROOT/utils/convert_fbx.py" "$HUNYUAN_INPUT_DIR" +fi +# Step 3: Find the generated FBX file +FBX_FILE=$(find "$HUNYUAN_INPUT_DIR" -name "*_hunyuan_original_wo_remap_converted.fbx" | head -1) +if [ -z "$FBX_FILE" ]; then + echo "Error: FBX file not found in $HUNYUAN_INPUT_DIR" + exit 1 +fi + +# Step 4: Generate animation +echo "" +echo "Step 3: Generating animation..." +python "$PROJECT_ROOT/scripts/inference_with_video_only.py" \ + --config="$PROJECT_ROOT/configs/dyscene.yaml" \ + training.resume_ckpt="$PROJECT_ROOT/experiments/checkpoints/ckpt_0000000000060000.pt" \ + model.class_name=model.Pcd_motion.Motion_Latent_Model \ + data_dir="$FBX_FILE" \ + image_path="$MASKED_RGB_DIR" \ + training.num_shape_samples=16384 \ + training.num_pcd_samples=4096 \ + training.frames=256 \ + start_frame=0 \ + training.use_fbx=True \ + output_dir="$ANIMATION_OUTPUT_DIR" + +echo "" +echo "==========================================" +echo "Processing complete!" +echo "Animation output: $ANIMATION_OUTPUT_DIR" +echo "==========================================" diff --git a/OVOW/third_party/Motion324/scripts/hunyuan_Gen.py b/OVOW/third_party/Motion324/scripts/hunyuan_Gen.py new file mode 100644 index 0000000000000000000000000000000000000000..886d9ff5e369ea7f2544689c11cab43a107114ae --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hunyuan_Gen.py @@ -0,0 +1,375 @@ +# Modified from V2M4 +import os +# os.environ['ATTN_BACKEND'] = 'xformers' # Can be 'flash-attn' or 'xformers', default is 'flash-attn' +os.environ['SPCONV_ALGO'] = 'native' # Can be 'native' or 'auto', default is 'auto'. + # 'auto' is faster but will do benchmarking at the beginning. + # Recommended to set to 'native' if run only once. +import imageio +import torch +import random +import numpy as np +import rembg +from PIL import Image +from natsort import ns, natsorted +import argparse +import trimesh +import shutil +import subprocess +import sys + +# Work around transformers torch.load safety gate (CVE-2025-32434) for local trusted checkpoints. +if os.environ.get("MOTION324_DISABLE_TORCH_LOAD_CHECK", "1") == "1": + try: + import transformers.modeling_utils as _tf_mu + _tf_mu.check_torch_load_is_safe = lambda: None + except Exception: + pass + +def preprocess_image(input: Image.Image, return_rgba=False, return_all_rbga=False) -> Image.Image: + """ + Preprocess the input image. + """ + # If the input has an alpha channel, use it directly; otherwise, remove background + has_alpha = False + if input.mode == 'RGBA': + alpha = np.array(input)[:, :, 3] + if not np.all(alpha == 255): + has_alpha = True + if has_alpha: + output = input + else: + input = input.convert('RGB') + max_size = max(input.size) + scale = min(1, 1024 / max_size) + if scale < 1: + input = input.resize((int(input.width * scale), int(input.height * scale)), Image.Resampling.LANCZOS) + # Use birefnet-massive instead of u2net for better performance + rembg_session = rembg.new_session('birefnet-massive') + output = rembg.remove(input, session=rembg_session) + output_np = np.array(output) + alpha = output_np[:, :, 3] + bbox = np.argwhere(alpha > 0.8 * 255) + bbox = np.min(bbox[:, 1]), np.min(bbox[:, 0]), np.max(bbox[:, 1]), np.max(bbox[:, 0]) + center = (bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2 + size = max(bbox[2] - bbox[0], bbox[3] - bbox[1]) + size = int(size * 1.2) + bbox = center[0] - size // 2, center[1] - size // 2, center[0] + size // 2, center[1] + size // 2 + + # Uncropped image + before_crop = output.resize((518, 518), Image.Resampling.LANCZOS) + before_crop = np.array(before_crop).astype(np.float32) / 255 + + # If the crop region is out of the image, it will be automatically padded with black. + output = output.crop(bbox) # type: ignore + output = output.resize((518, 518), Image.Resampling.LANCZOS) + output = np.array(output).astype(np.float32) / 255 + + if return_all_rbga: + before_crop_rgba = Image.fromarray((before_crop * 255).astype(np.uint8), mode='RGBA') + + before_crop = before_crop[:, :, :3] * before_crop[:, :, 3:4] + before_crop = Image.fromarray((before_crop * 255).astype(np.uint8)) + + output = Image.fromarray((output * 255).astype(np.uint8), mode='RGBA') + return output, before_crop_rgba, before_crop + + before_crop = before_crop[:, :, :3] * before_crop[:, :, 3:4] + before_crop = Image.fromarray((before_crop * 255).astype(np.uint8)) + + if return_rgba: + output = Image.fromarray((output * 255).astype(np.uint8), mode='RGBA') + return output, before_crop + + output = output[:, :, :3] * output[:, :, 3:4] + output = Image.fromarray((output * 255).astype(np.uint8)) + return output, before_crop + +def seed_torch(seed=0): + print("Seed Fixed!") + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + +def parse_args(): + parser = argparse.ArgumentParser(description='HunyuanGen Pipeline') + parser.add_argument('--root', type=str, default='', help='Root directory of the dataset') + parser.add_argument('--output', type=str, default='', help='Output directory of the results') + parser.add_argument('--N', type=int, default=1, help='Total number of parallel processes') + parser.add_argument('--n', type=int, default=0, help='Index of the current process') + parser.add_argument('--model', type=str, default='Hunyuan', help='Base model', choices=['Hunyuan']) + parser.add_argument('--seed', type=int, default=42, help='Random seed for reproducibility') + parser.add_argument('--skip', type=int, default=256, help='Skip every N frames for large movement of the object (default: 5)') + parser.add_argument('--max_faces', type=int, default=10000, help='Maximum number of faces for the generated mesh (default: 10000)') + parser.add_argument('--backend', type=str, default='hunyuan', choices=['hunyuan', 'trellis2'], help='Mesh generation backend') + parser.add_argument('--trellis2_env', type=str, default=os.environ.get('TRELLIS2_ENV', 'opg'), help='Conda env for Trellis2') + parser.add_argument('--trellis2_ckpt', type=str, default=os.environ.get('TRELLIS2_CKPT', '/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/jiayin/OVOW/engine/checkpoints/TRELLIS.2-4B'), help='Trellis2 checkpoint path') + parser.add_argument('--trellis2_pipeline_type', type=str, default=os.environ.get('TRELLIS2_PIPELINE_TYPE', '1024'), help='Trellis2 pipeline type') + parser.add_argument('--trellis2_max_num_tokens', type=int, default=int(os.environ.get('TRELLIS2_MAX_NUM_TOKENS', '49152')), help='Trellis2 max tokens') + parser.add_argument('--trellis2_texture_size', type=int, default=int(os.environ.get('TRELLIS2_TEXTURE_SIZE', '1024')), help='Trellis2 texture size') + parser.add_argument('--blender_bin', type=str, default=os.environ.get('BLENDER_BIN', '/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/jiayin/OVOW/blender-5.0.0-linux-x64/blender'), help='Blender binary for FBX export') + return parser.parse_args() + +def get_folder_size(folder): + return len([f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]) + + +def _init_hunyuan_pipelines(): + from hy3dgen.texgen import Hunyuan3DPaintPipeline + from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline + from hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover + + pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') + pipeline_paint = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2') + + pipeline.model.requires_grad = False + pipeline.vae.requires_grad = False + pipeline.conditioner.requires_grad = False + pipeline_paint.models['delight_model'].pipeline.feature_extractor.requires_grad = False + pipeline_paint.models['delight_model'].pipeline.text_encoder.requires_grad = False + pipeline_paint.models['delight_model'].pipeline.unet.requires_grad = False + pipeline_paint.models['delight_model'].pipeline.vae.requires_grad = False + pipeline_paint.models['multiview_model'].pipeline.unet.requires_grad = False + pipeline_paint.models['multiview_model'].pipeline.vae.requires_grad = False + pipeline_paint.models['multiview_model'].pipeline.text_encoder.requires_grad = False + pipeline_paint.models['multiview_model'].pipeline.feature_extractor.requires_grad = False + + return pipeline, pipeline_paint, (FaceReducer, FloaterRemover, DegenerateFaceRemover) + + +def _resolve_conda_exe(): + conda_exe = os.environ.get("CONDA_EXE") + if conda_exe and os.path.exists(conda_exe): + return conda_exe + for candidate in ["conda", "/root/miniconda3/bin/conda", "conda"]: + if os.path.isabs(candidate): + if os.path.exists(candidate): + return candidate + else: + return candidate + return "conda" + + +def _run_trellis2( + trellis2_script, + image_path, + output_glb, + ckpt_path, + env_name, + pipeline_type, + max_num_tokens, + simplify_target, + texture_size, + seed, + log_path=None, +): + conda_exe = _resolve_conda_exe() + cmd = [ + conda_exe, + "run", + "-n", + env_name, + "python", + "-u", + trellis2_script, + "--input", + image_path, + "--output_glb", + output_glb, + "--ckpt", + ckpt_path, + "--seed", + str(seed), + "--pipeline_type", + str(pipeline_type), + "--max_num_tokens", + str(max_num_tokens), + "--simplify_target", + str(simplify_target), + "--texture_size", + str(texture_size), + "--disable_rembg", + ] + print("Running Trellis2:", " ".join(cmd)) + env = os.environ.copy() + env["PYTHONUNBUFFERED"] = "1" + # Prefer spconv backend to avoid flex_gemm/triton incompatibilities. + env.setdefault("SPARSE_CONV_BACKEND", "spconv") + env.setdefault("SPCONV_ALGO", "native") + if log_path: + os.makedirs(os.path.dirname(log_path) or ".", exist_ok=True) + with open(log_path, "a", encoding="utf-8") as log_f: + log_f.write("CMD: " + " ".join(cmd) + "\n") + log_f.flush() + proc = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + env=env, + ) + assert proc.stdout is not None + for line in proc.stdout: + sys.stdout.write(line) + sys.stdout.flush() + log_f.write(line) + log_f.flush() + ret = proc.wait() + if ret != 0: + raise subprocess.CalledProcessError(ret, cmd) + else: + subprocess.run(cmd, check=True, env=env) + + +def _convert_glb_to_fbx(convert_script, glb_path, fbx_path, blender_bin): + cmd = [ + sys.executable, + convert_script, + "--input", + glb_path, + "--output", + fbx_path, + "--blender_bin", + blender_bin, + ] + print("Converting GLB to FBX:", " ".join(cmd)) + subprocess.run(cmd, check=True) + +if __name__ == "__main__": + args = parse_args() + backend = args.backend.lower() + root = "/path/to/obj_motion/Eval/1/viz" if args.root == "" else args.root + default_output_root = "results_examples" + print(f"Root viz dir: {root}") + + output_root = args.output if args.output != "" else default_output_root + + processed_dirs = [] + for name in os.listdir(root): + sub_path = os.path.join(root, name) + if not os.path.isdir(sub_path): + continue + processed_path = os.path.join(sub_path, "masked_rgb") + animation_path = os.path.join(sub_path, "animation") + if os.path.isdir(processed_path) and not os.path.isdir(animation_path): + processed_dirs.append(processed_path) + print("Found processed masked_rgb folders:") + print(processed_dirs) + animations = natsorted(processed_dirs, alg=ns.PATH) + + folder_sizes = [(anim, get_folder_size(anim)) for anim in animations] + folder_sizes.sort(key=lambda x: x[1], reverse=True) + + assignments = [[] for _ in range(args.N)] + workload = [0] * args.N + + for folder, size in folder_sizes: + min_index = workload.index(min(workload)) + assignments[min_index].append(folder) + workload[min_index] += size + + assigned_animations = assignments[args.n] + + total_imgs = sum(get_folder_size(f) for f in assigned_animations) + print(f"Process {args.n} assigned {len(assigned_animations)} folders with total images: {total_imgs}") + + if backend == "hunyuan": + pipeline, pipeline_paint, cleaners = _init_hunyuan_pipelines() + + for animation in assigned_animations: + source_path = animation + output_path = source_path if args.output == "" else os.path.join(args.output, source_path.split("/")[-2], 'Hunyuan_Gen_Input') + + print("/n/n ============= Start processing: ", animation, " =============/n") + os.makedirs(output_path, exist_ok=True) + + seed = args.seed + seed_torch(seed) + + imgs_list = os.listdir(source_path) + imgs_list = [img for img in imgs_list if not os.path.isdir(source_path + "/" + img)] + imgs_list = natsorted(imgs_list, alg=ns.PATH) + + outputs_list = [] + base_name_list = [] + extrinsics_list = [] + visual_list = [] + params = None + + for ind, img in enumerate(imgs_list): + if ind % args.skip != 0: + continue + + image = Image.open(source_path + "/" + img) + base_name = image.filename.split("/")[-1].split(".")[0] + + save_path = output_path + "/" + base_name + "_rmbg.png" + cropped_image, rmbg_image = preprocess_image(image, return_rgba=True) + rmbg_image.save(save_path) + cropped_image.save(save_path.replace(".png", "_cropped.png")) + if backend == "trellis2": + trellis2_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "trellis2_gen.py") + trellis2_input = save_path.replace(".png", "_cropped.png") + glb_path = output_path + "/" + base_name + "_hunyuan_original_wo_remap.glb" + fbx_path = output_path + "/" + base_name + "_hunyuan_original_wo_remap_converted.fbx" + if os.path.exists(fbx_path): + print(f"FBX already exists, skipping: {fbx_path}") + continue + _run_trellis2( + trellis2_script, + trellis2_input, + glb_path, + args.trellis2_ckpt, + args.trellis2_env, + args.trellis2_pipeline_type, + args.trellis2_max_num_tokens, + args.max_faces, + args.trellis2_texture_size, + args.seed, + log_path=os.path.join(output_path, f"{base_name}_trellis2.log"), + ) + watertight_path = output_path + "/" + base_name + "_hunyuan_original_watertight.glb" + if not os.path.exists(watertight_path): + shutil.copy(glb_path, watertight_path) + convert_script = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "utils", "convert_glb_to_fbx.py") + _convert_glb_to_fbx(convert_script, glb_path, fbx_path, args.blender_bin) + print(f"Trellis2 GLB exported: {glb_path}") + print(f"Trellis2 FBX exported: {fbx_path}") + continue + import time + start_time = time.time() + + torch.manual_seed(seed) + mesh = pipeline(image=cropped_image)[0] + + for cleaner in [cleaners[1](), cleaners[2]()]: + mesh = cleaner(mesh) + + mesh = cleaners[0]()(mesh, max_facenum=args.max_faces) + + vertices_watertight = mesh.vertices @ np.array([[1, 0, 0], [0, 0, 1], [0, -1, 0]]) + faces_watertight = mesh.faces + mean_point = mesh.vertices.mean(axis=0) + vertices_watertight = (vertices_watertight - mean_point) * 0.5 + mean_point + mesh_watertight = trimesh.Trimesh(vertices_watertight, faces_watertight, process=False) + mesh_watertight.export(output_path + "/" + base_name + "_hunyuan_original_watertight.glb") + print("mesh_watertight exported:", output_path + "/" + base_name + "_hunyuan_original_watertight.glb") + + mesh, vmapping = pipeline_paint(mesh, image=cropped_image) + # Save vmapping for later use + vmapping_path = output_path + "/" + base_name + "_vmapping.npy" + np.save(vmapping_path, vmapping) + print(f"vmapping saved to: {vmapping_path}") + + end_time = time.time() + generation_time = end_time - start_time + print(f"Hunyuan generation time: {generation_time:.4f} seconds") + # Save the original Hunyuan mesh before further processing + mesh_original = mesh.copy() + mesh_original.export(output_path + "/" + base_name + "_hunyuan_original_wo_remap.glb") + print("mesh_original exported:", output_path + "/" + base_name + "_hunyuan_original_wo_remap.glb") diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8bb2bf86e283e50f0df2ecfba8fc66289df09901 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/__init__.py @@ -0,0 +1,13 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/rembg.py b/OVOW/third_party/Motion324/scripts/hy3dgen/rembg.py new file mode 100644 index 0000000000000000000000000000000000000000..6247f060c9f325b1e267668baf236ec8e4c2dae9 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/rembg.py @@ -0,0 +1,25 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +from PIL import Image +from rembg import remove, new_session + + +class BackgroundRemover(): + def __init__(self): + self.session = new_session() + + def __call__(self, image: Image.Image): + output = remove(image, session=self.session, bgcolor=[255, 255, 255, 0]) + return output diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1b1f9cce42c1d2bef94bbb2d7b088d24e76fe01e --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/__init__.py @@ -0,0 +1,17 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +from .pipelines import Hunyuan3DDiTPipeline, Hunyuan3DDiTFlowMatchingPipeline +from .postprocessors import FaceReducer, FloaterRemover, DegenerateFaceRemover, MeshSimplifier +from .preprocessors import ImageProcessorV2, IMAGE_PROCESSORS, DEFAULT_IMAGEPROCESSOR diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/pipelines.py b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/pipelines.py new file mode 100644 index 0000000000000000000000000000000000000000..c4976e7618ece63c6e97b5e51c11881c220ea42f --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/pipelines.py @@ -0,0 +1,765 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import copy +import importlib +import inspect +import os +from typing import List, Optional, Union + +import numpy as np +import torch +import trimesh +import yaml +from PIL import Image +from diffusers.utils.torch_utils import randn_tensor +from diffusers.utils.import_utils import is_accelerate_version, is_accelerate_available +from tqdm import tqdm + +from .models.autoencoders import ShapeVAE +from .models.autoencoders import SurfaceExtractors +from .utils import logger, synchronize_timer, smart_load_model + + +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + """ + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + if timesteps is not None: + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +@synchronize_timer('Export to trimesh') +def export_to_trimesh(mesh_output): + if isinstance(mesh_output, list): + outputs = [] + for mesh in mesh_output: + if mesh is None: + outputs.append(None) + else: + mesh.mesh_f = mesh.mesh_f[:, ::-1] + mesh_output = trimesh.Trimesh(mesh.mesh_v, mesh.mesh_f) + outputs.append(mesh_output) + return outputs + else: + mesh_output.mesh_f = mesh_output.mesh_f[:, ::-1] + mesh_output = trimesh.Trimesh(mesh_output.mesh_v, mesh_output.mesh_f) + return mesh_output + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def instantiate_from_config(config, **kwargs): + if "target" not in config: + raise KeyError("Expected key `target` to instantiate.") + cls = get_obj_from_str(config["target"]) + params = config.get("params", dict()) + kwargs.update(params) + instance = cls(**kwargs) + return instance + + +class Hunyuan3DDiTPipeline: + model_cpu_offload_seq = "conditioner->model->vae" + _exclude_from_cpu_offload = [] + + @classmethod + @synchronize_timer('Hunyuan3DDiTPipeline Model Loading') + def from_single_file( + cls, + ckpt_path, + config_path, + device='cuda', + dtype=torch.float16, + use_safetensors=None, + **kwargs, + ): + # load config + with open(config_path, 'r') as f: + config = yaml.safe_load(f) + + # load ckpt + if use_safetensors: + ckpt_path = ckpt_path.replace('.ckpt', '.safetensors') + if not os.path.exists(ckpt_path): + raise FileNotFoundError(f"Model file {ckpt_path} not found") + logger.info(f"Loading model from {ckpt_path}") + + if use_safetensors: + # parse safetensors + import safetensors.torch + safetensors_ckpt = safetensors.torch.load_file(ckpt_path, device='cpu') + ckpt = {} + for key, value in safetensors_ckpt.items(): + model_name = key.split('.')[0] + new_key = key[len(model_name) + 1:] + if model_name not in ckpt: + ckpt[model_name] = {} + ckpt[model_name][new_key] = value + else: + ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=True) + # load model + model = instantiate_from_config(config['model']) + model.load_state_dict(ckpt['model']) + vae = instantiate_from_config(config['vae']) + vae.load_state_dict(ckpt['vae'], strict=False) + conditioner = instantiate_from_config(config['conditioner']) + if 'conditioner' in ckpt: + conditioner.load_state_dict(ckpt['conditioner']) + image_processor = instantiate_from_config(config['image_processor']) + scheduler = instantiate_from_config(config['scheduler']) + + model_kwargs = dict( + vae=vae, + model=model, + scheduler=scheduler, + conditioner=conditioner, + image_processor=image_processor, + device=device, + dtype=dtype, + ) + model_kwargs.update(kwargs) + + return cls( + **model_kwargs + ) + + @classmethod + def from_pretrained( + cls, + model_path, + device='cuda', + dtype=torch.float16, + use_safetensors=True, + variant='fp16', + subfolder='hunyuan3d-dit-v2-0', + **kwargs, + ): + kwargs['from_pretrained_kwargs'] = dict( + model_path=model_path, + subfolder=subfolder, + use_safetensors=use_safetensors, + variant=variant, + dtype=dtype, + device=device, + ) + config_path, ckpt_path = smart_load_model( + model_path, + subfolder=subfolder, + use_safetensors=use_safetensors, + variant=variant + ) + return cls.from_single_file( + ckpt_path, + config_path, + device=device, + dtype=dtype, + use_safetensors=use_safetensors, + **kwargs + ) + + def __init__( + self, + vae, + model, + scheduler, + conditioner, + image_processor, + device='cuda', + dtype=torch.float16, + **kwargs + ): + self.vae = vae + self.model = model + self.scheduler = scheduler + self.conditioner = conditioner + self.image_processor = image_processor + self.kwargs = kwargs + self.to(device, dtype) + + def compile(self): + self.vae = torch.compile(self.vae) + self.model = torch.compile(self.model) + self.conditioner = torch.compile(self.conditioner) + + def enable_flashvdm( + self, + enabled: bool = True, + adaptive_kv_selection=True, + topk_mode='mean', + mc_algo='mc', + replace_vae=True, + ): + if enabled: + model_path = self.kwargs['from_pretrained_kwargs']['model_path'] + turbo_vae_mapping = { + 'Hunyuan3D-2': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0-turbo'), + 'Hunyuan3D-2mv': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0-turbo'), + 'Hunyuan3D-2mini': ('tencent/Hunyuan3D-2mini', 'hunyuan3d-vae-v2-mini-turbo'), + } + model_name = model_path.split('/')[-1] + if replace_vae and model_name in turbo_vae_mapping: + model_path, subfolder = turbo_vae_mapping[model_name] + self.vae = ShapeVAE.from_pretrained( + model_path, subfolder=subfolder, + use_safetensors=self.kwargs['from_pretrained_kwargs']['use_safetensors'], + device=self.device, + ) + self.vae.enable_flashvdm_decoder( + enabled=enabled, + adaptive_kv_selection=adaptive_kv_selection, + topk_mode=topk_mode, + mc_algo=mc_algo + ) + else: + model_path = self.kwargs['from_pretrained_kwargs']['model_path'] + vae_mapping = { + 'Hunyuan3D-2': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0'), + 'Hunyuan3D-2mv': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0'), + 'Hunyuan3D-2mini': ('tencent/Hunyuan3D-2mini', 'hunyuan3d-vae-v2-mini'), + } + model_name = model_path.split('/')[-1] + if model_name in vae_mapping: + model_path, subfolder = vae_mapping[model_name] + self.vae = ShapeVAE.from_pretrained(model_path, subfolder=subfolder) + self.vae.enable_flashvdm_decoder(enabled=False) + + def to(self, device=None, dtype=None): + if dtype is not None: + self.dtype = dtype + self.vae.to(dtype=dtype) + self.model.to(dtype=dtype) + self.conditioner.to(dtype=dtype) + if device is not None: + self.device = torch.device(device) + self.vae.to(device) + self.model.to(device) + self.conditioner.to(device) + + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + [`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from + Accelerate's module hooks. + """ + for name, model in self.components.items(): + if not isinstance(model, torch.nn.Module) or name in self._exclude_from_cpu_offload: + continue + + if not hasattr(model, "_hf_hook"): + return self.device + for module in model.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"): + r""" + Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared + to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` + method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with + `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. + + Arguments: + gpu_id (`int`, *optional*): + The ID of the accelerator that shall be used in inference. If not specified, it will default to 0. + device (`torch.Device` or `str`, *optional*, defaults to "cuda"): + The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will + default to "cuda". + """ + if self.model_cpu_offload_seq is None: + raise ValueError( + "Model CPU offload cannot be enabled because no `model_cpu_offload_seq` class attribute is set." + ) + + if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): + from accelerate import cpu_offload_with_hook + else: + raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") + + torch_device = torch.device(device) + device_index = torch_device.index + + if gpu_id is not None and device_index is not None: + raise ValueError( + f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}" + f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}" + ) + + # _offload_gpu_id should be set to passed gpu_id (or id in passed `device`) or default to previously set id or default to 0 + self._offload_gpu_id = gpu_id or torch_device.index or getattr(self, "_offload_gpu_id", 0) + + device_type = torch_device.type + device = torch.device(f"{device_type}:{self._offload_gpu_id}") + + if self.device.type != "cpu": + self.to("cpu") + device_mod = getattr(torch, self.device.type, None) + if hasattr(device_mod, "empty_cache") and device_mod.is_available(): + device_mod.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + all_model_components = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)} + + self._all_hooks = [] + hook = None + for model_str in self.model_cpu_offload_seq.split("->"): + model = all_model_components.pop(model_str, None) + if not isinstance(model, torch.nn.Module): + continue + + _, hook = cpu_offload_with_hook(model, device, prev_module_hook=hook) + self._all_hooks.append(hook) + + # CPU offload models that are not in the seq chain unless they are explicitly excluded + # these models will stay on CPU until maybe_free_model_hooks is called + # some models cannot be in the seq chain because they are iteratively called, such as controlnet + for name, model in all_model_components.items(): + if not isinstance(model, torch.nn.Module): + continue + + if name in self._exclude_from_cpu_offload: + model.to(device) + else: + _, hook = cpu_offload_with_hook(model, device) + self._all_hooks.append(hook) + + def maybe_free_model_hooks(self): + r""" + Function that offloads all components, removes all model hooks that were added when using + `enable_model_cpu_offload` and then applies them again. In case the model has not been offloaded this function + is a no-op. Make sure to add this function to the end of the `__call__` function of your pipeline so that it + functions correctly when applying enable_model_cpu_offload. + """ + if not hasattr(self, "_all_hooks") or len(self._all_hooks) == 0: + # `enable_model_cpu_offload` has not be called, so silently do nothing + return + + for hook in self._all_hooks: + # offload model and remove hook from model + hook.offload() + hook.remove() + + # make sure the model is in the same state as before calling it + self.enable_model_cpu_offload() + + @synchronize_timer('Encode cond') + def encode_cond(self, image, additional_cond_inputs, do_classifier_free_guidance, dual_guidance): + bsz = image.shape[0] + cond = self.conditioner(image=image, **additional_cond_inputs) + + if do_classifier_free_guidance: + un_cond = self.conditioner.unconditional_embedding(bsz, **additional_cond_inputs) + + if dual_guidance: + un_cond_drop_main = copy.deepcopy(un_cond) + un_cond_drop_main['additional'] = cond['additional'] + + def cat_recursive(a, b, c): + if isinstance(a, torch.Tensor): + return torch.cat([a, b, c], dim=0).to(self.dtype) + out = {} + for k in a.keys(): + out[k] = cat_recursive(a[k], b[k], c[k]) + return out + + cond = cat_recursive(cond, un_cond_drop_main, un_cond) + else: + def cat_recursive(a, b): + if isinstance(a, torch.Tensor): + return torch.cat([a, b], dim=0).to(self.dtype) + out = {} + for k in a.keys(): + out[k] = cat_recursive(a[k], b[k]) + return out + + cond = cat_recursive(cond, un_cond) + return cond + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def prepare_latents(self, batch_size, dtype, device, generator, latents=None): + shape = (batch_size, *self.vae.latent_shape) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * getattr(self.scheduler, 'init_noise_sigma', 1.0) + return latents + + def prepare_image(self, image) -> dict: + if isinstance(image, str) and not os.path.exists(image): + raise FileNotFoundError(f"Couldn't find image at path {image}") + + if not isinstance(image, list): + image = [image] + + outputs = [] + for img in image: + output = self.image_processor(img) + outputs.append(output) + + cond_input = {k: [] for k in outputs[0].keys()} + for output in outputs: + for key, value in output.items(): + cond_input[key].append(value) + for key, value in cond_input.items(): + if isinstance(value[0], torch.Tensor): + cond_input[key] = torch.cat(value, dim=0) + + return cond_input + + def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32): + """ + See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 + + Args: + timesteps (`torch.Tensor`): + generate embedding vectors at these timesteps + embedding_dim (`int`, *optional*, defaults to 512): + dimension of the embeddings to generate + dtype: + data type of the generated embeddings + + Returns: + `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` + """ + assert len(w.shape) == 1 + w = w * 1000.0 + + half_dim = embedding_dim // 2 + emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) + emb = w.to(dtype)[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1)) + assert emb.shape == (w.shape[0], embedding_dim) + return emb + + def set_surface_extractor(self, mc_algo): + if mc_algo is None: + return + logger.info('The parameters `mc_algo` is deprecated, and will be removed in future versions.\n' + 'Please use: \n' + 'from hy3dgen.shapegen.models.autoencoders import SurfaceExtractors\n' + 'pipeline.vae.surface_extractor = SurfaceExtractors[mc_algo]() instead\n') + if mc_algo not in SurfaceExtractors.keys(): + raise ValueError(f"Unknown mc_algo {mc_algo}") + self.vae.surface_extractor = SurfaceExtractors[mc_algo]() + + @torch.no_grad() + def __call__( + self, + image: Union[str, List[str], Image.Image] = None, + num_inference_steps: int = 50, + timesteps: List[int] = None, + sigmas: List[float] = None, + eta: float = 0.0, + guidance_scale: float = 7.5, + dual_guidance_scale: float = 10.5, + dual_guidance: bool = True, + generator=None, + box_v=1.01, + octree_resolution=384, + mc_level=-1 / 512, + num_chunks=8000, + mc_algo=None, + output_type: Optional[str] = "trimesh", + enable_pbar=True, + **kwargs, + ) -> List[List[trimesh.Trimesh]]: + callback = kwargs.pop("callback", None) + callback_steps = kwargs.pop("callback_steps", None) + + self.set_surface_extractor(mc_algo) + + device = self.device + dtype = self.dtype + do_classifier_free_guidance = guidance_scale >= 0 and \ + getattr(self.model, 'guidance_cond_proj_dim', None) is None + dual_guidance = dual_guidance_scale >= 0 and dual_guidance + + cond_inputs = self.prepare_image(image) + image = cond_inputs.pop('image') + cond = self.encode_cond( + image=image, + additional_cond_inputs=cond_inputs, + do_classifier_free_guidance=do_classifier_free_guidance, + dual_guidance=False, + ) + batch_size = image.shape[0] + + t_dtype = torch.long + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, num_inference_steps, device, timesteps, sigmas) + + latents = self.prepare_latents(batch_size, dtype, device, generator) + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + guidance_cond = None + if getattr(self.model, 'guidance_cond_proj_dim', None) is not None: + logger.info('Using lcm guidance scale') + guidance_scale_tensor = torch.tensor(guidance_scale - 1).repeat(batch_size) + guidance_cond = self.get_guidance_scale_embedding( + guidance_scale_tensor, embedding_dim=self.model.guidance_cond_proj_dim + ).to(device=device, dtype=latents.dtype) + with synchronize_timer('Diffusion Sampling'): + for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:", leave=False)): + # expand the latents if we are doing classifier free guidance + if do_classifier_free_guidance: + latent_model_input = torch.cat([latents] * (3 if dual_guidance else 2)) + else: + latent_model_input = latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + timestep_tensor = torch.tensor([t], dtype=t_dtype, device=device) + timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0]) + noise_pred = self.model(latent_model_input, timestep_tensor, cond, guidance_cond=guidance_cond) + + # no drop, drop clip, all drop + if do_classifier_free_guidance: + if dual_guidance: + noise_pred_clip, noise_pred_dino, noise_pred_uncond = noise_pred.chunk(3) + noise_pred = ( + noise_pred_uncond + + guidance_scale * (noise_pred_clip - noise_pred_dino) + + dual_guidance_scale * (noise_pred_dino - noise_pred_uncond) + ) + else: + noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + outputs = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs) + latents = outputs.prev_sample + + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, outputs) + + return self._export( + latents, + output_type, + box_v, mc_level, num_chunks, octree_resolution, mc_algo, + ) + + def _export( + self, + latents, + output_type='trimesh', + box_v=1.01, + mc_level=0.0, + num_chunks=20000, + octree_resolution=256, + mc_algo='mc', + enable_pbar=True + ): + if not output_type == "latent": + latents = 1. / self.vae.scale_factor * latents + latents = self.vae(latents) + outputs = self.vae.latents2mesh( + latents, + bounds=box_v, + mc_level=mc_level, + num_chunks=num_chunks, + octree_resolution=octree_resolution, + mc_algo=mc_algo, + enable_pbar=enable_pbar, + ) + else: + outputs = latents + + if output_type == 'trimesh': + outputs = export_to_trimesh(outputs) + + return outputs + + +class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline): + + @torch.inference_mode() + def __call__( + self, + image: Union[str, List[str], Image.Image, dict, List[dict]] = None, + num_inference_steps: int = 50, + timesteps: List[int] = None, + sigmas: List[float] = None, + eta: float = 0.0, + guidance_scale: float = 5.0, + generator=None, + box_v=1.01, + octree_resolution=384, + mc_level=0.0, + mc_algo=None, + num_chunks=8000, + output_type: Optional[str] = "trimesh", + enable_pbar=True, + **kwargs, + ) -> List[List[trimesh.Trimesh]]: + callback = kwargs.pop("callback", None) + callback_steps = kwargs.pop("callback_steps", None) + + self.set_surface_extractor(mc_algo) + + device = self.device + dtype = self.dtype + do_classifier_free_guidance = guidance_scale >= 0 and not ( + hasattr(self.model, 'guidance_embed') and + self.model.guidance_embed is True + ) + + cond_inputs = self.prepare_image(image) + image = cond_inputs.pop('image') + cond = self.encode_cond( + image=image, + additional_cond_inputs=cond_inputs, + do_classifier_free_guidance=do_classifier_free_guidance, + dual_guidance=False, + ) + batch_size = image.shape[0] + + # 5. Prepare timesteps + # NOTE: this is slightly different from common usage, we start from 0. + sigmas = np.linspace(0, 1, num_inference_steps) if sigmas is None else sigmas + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, + num_inference_steps, + device, + sigmas=sigmas, + ) + latents = self.prepare_latents(batch_size, dtype, device, generator) + + guidance = None + if hasattr(self.model, 'guidance_embed') and \ + self.model.guidance_embed is True: + guidance = torch.tensor([guidance_scale] * batch_size, device=device, dtype=dtype) + # logger.info(f'Using guidance embed with scale {guidance_scale}') + + with synchronize_timer('Diffusion Sampling'): + for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:")): + # expand the latents if we are doing classifier free guidance + if do_classifier_free_guidance: + latent_model_input = torch.cat([latents] * 2) + else: + latent_model_input = latents + + # NOTE: we assume model get timesteps ranged from 0 to 1 + timestep = t.expand(latent_model_input.shape[0]).to( + latents.dtype) / self.scheduler.config.num_train_timesteps + noise_pred = self.model(latent_model_input, timestep, cond, guidance=guidance) + + if do_classifier_free_guidance: + noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + outputs = self.scheduler.step(noise_pred, t, latents) + latents = outputs.prev_sample + + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, outputs) + + return self._export( + latents, + output_type, + box_v, mc_level, num_chunks, octree_resolution, mc_algo, + enable_pbar=enable_pbar, + ) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/postprocessors.py b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/postprocessors.py new file mode 100644 index 0000000000000000000000000000000000000000..0c96cfb4937bfe3f9d460279d60c5f3b7ba99e18 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/postprocessors.py @@ -0,0 +1,204 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import os +import tempfile +from typing import Union + +import numpy as np +import pymeshlab +import torch +import trimesh + +from .models.autoencoders import Latent2MeshOutput +from .utils import synchronize_timer + + +def load_mesh(path): + if path.endswith(".glb"): + mesh = trimesh.load(path) + else: + mesh = pymeshlab.MeshSet() + mesh.load_new_mesh(path) + return mesh + + +def reduce_face(mesh: pymeshlab.MeshSet, max_facenum: int = 200000): + if max_facenum > mesh.current_mesh().face_number(): + return mesh + + mesh.apply_filter( + "meshing_decimation_quadric_edge_collapse", + targetfacenum=max_facenum, + qualitythr=1.0, + preserveboundary=True, + boundaryweight=3, + preservenormal=True, + preservetopology=True, + autoclean=True + ) + return mesh + + +def remove_floater(mesh: pymeshlab.MeshSet): + mesh.apply_filter("compute_selection_by_small_disconnected_components_per_face", + nbfaceratio=0.005) + mesh.apply_filter("compute_selection_transfer_face_to_vertex", inclusive=False) + mesh.apply_filter("meshing_remove_selected_vertices_and_faces") + return mesh + + +def pymeshlab2trimesh(mesh: pymeshlab.MeshSet): + tmp_dir = os.path.join(os.environ.get('TMPDIR', tempfile.gettempdir()), 'hy3dgen_tmp') + os.makedirs(tmp_dir, exist_ok=True) + temp_file_path = os.path.join(tmp_dir, f'temp_mesh_{os.getpid()}.ply') + mesh.save_current_mesh(temp_file_path) + mesh = trimesh.load(temp_file_path) + # Check loaded object type + if isinstance(mesh, trimesh.Scene): + combined_mesh = trimesh.Trimesh() + # If it's a Scene, iterate through all geometries and merge them + for geom in mesh.geometry.values(): + combined_mesh = trimesh.util.concatenate([combined_mesh, geom]) + mesh = combined_mesh + return mesh + + +def trimesh2pymeshlab(mesh: trimesh.Trimesh): + with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file: + if isinstance(mesh, trimesh.scene.Scene): + for idx, obj in enumerate(mesh.geometry.values()): + if idx == 0: + temp_mesh = obj + else: + temp_mesh = temp_mesh + obj + mesh = temp_mesh + mesh.export(temp_file.name) + mesh = pymeshlab.MeshSet() + mesh.load_new_mesh(temp_file.name) + return mesh + + +def export_mesh(input, output): + if isinstance(input, pymeshlab.MeshSet): + mesh = output + elif isinstance(input, Latent2MeshOutput): + output = Latent2MeshOutput() + output.mesh_v = output.current_mesh().vertex_matrix() + output.mesh_f = output.current_mesh().face_matrix() + mesh = output + else: + mesh = pymeshlab2trimesh(output) + return mesh + + +def import_mesh(mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str]) -> pymeshlab.MeshSet: + if isinstance(mesh, str): + mesh = load_mesh(mesh) + elif isinstance(mesh, Latent2MeshOutput): + mesh = pymeshlab.MeshSet() + mesh_pymeshlab = pymeshlab.Mesh(vertex_matrix=mesh.mesh_v, face_matrix=mesh.mesh_f) + mesh.add_mesh(mesh_pymeshlab, "converted_mesh") + + if isinstance(mesh, (trimesh.Trimesh, trimesh.scene.Scene)): + mesh = trimesh2pymeshlab(mesh) + + return mesh + + +class FaceReducer: + @synchronize_timer('FaceReducer') + def __call__( + self, + mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], + max_facenum: int = 40000 + ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh]: + ms = import_mesh(mesh) + ms = reduce_face(ms, max_facenum=max_facenum) + mesh = export_mesh(mesh, ms) + return mesh + + +class FloaterRemover: + @synchronize_timer('FloaterRemover') + def __call__( + self, + mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], + ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]: + ms = import_mesh(mesh) + ms = remove_floater(ms) + mesh = export_mesh(mesh, ms) + return mesh + + +class DegenerateFaceRemover: + @synchronize_timer('DegenerateFaceRemover') + def __call__( + self, + mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], + ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]: + ms = import_mesh(mesh) + + with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file: + ms.save_current_mesh(temp_file.name) + ms = pymeshlab.MeshSet() + ms.load_new_mesh(temp_file.name) + + mesh = export_mesh(mesh, ms) + return mesh + + +def mesh_normalize(mesh): + """ + Normalize mesh vertices to sphere + """ + scale_factor = 1.2 + vtx_pos = np.asarray(mesh.vertices) + max_bb = (vtx_pos - 0).max(0)[0] + min_bb = (vtx_pos - 0).min(0)[0] + + center = (max_bb + min_bb) / 2 + + scale = torch.norm(torch.tensor(vtx_pos - center, dtype=torch.float32), dim=1).max() * 2.0 + + vtx_pos = (vtx_pos - center) * (scale_factor / float(scale)) + mesh.vertices = vtx_pos + + return mesh + + +class MeshSimplifier: + def __init__(self, executable: str = None): + if executable is None: + CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) + executable = os.path.join(CURRENT_DIR, "mesh_simplifier.bin") + self.executable = executable + + @synchronize_timer('MeshSimplifier') + def __call__( + self, + mesh: Union[trimesh.Trimesh], + ) -> Union[trimesh.Trimesh]: + with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as temp_input: + with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as temp_output: + mesh.export(temp_input.name) + os.system(f'{self.executable} {temp_input.name} {temp_output.name}') + ms = trimesh.load(temp_output.name, process=False) + if isinstance(ms, trimesh.Scene): + combined_mesh = trimesh.Trimesh() + for geom in ms.geometry.values(): + combined_mesh = trimesh.util.concatenate([combined_mesh, geom]) + ms = combined_mesh + ms = mesh_normalize(ms) + return ms diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/preprocessors.py b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/preprocessors.py new file mode 100644 index 0000000000000000000000000000000000000000..8a9cb9ea1591363fb77f4e02351a0f945e3bc1ab --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/preprocessors.py @@ -0,0 +1,167 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import cv2 +import numpy as np +import torch +from PIL import Image +from einops import repeat, rearrange + + +def array_to_tensor(np_array): + image_pt = torch.tensor(np_array).float() + image_pt = image_pt / 255 * 2 - 1 + image_pt = rearrange(image_pt, "h w c -> c h w") + image_pts = repeat(image_pt, "c h w -> b c h w", b=1) + return image_pts + + +class ImageProcessorV2: + def __init__(self, size=512, border_ratio=None): + self.size = size + self.border_ratio = border_ratio + + @staticmethod + def recenter(image, border_ratio: float = 0.2): + """ recenter an image to leave some empty space at the image border. + + Args: + image (ndarray): input image, float/uint8 [H, W, 3/4] + mask (ndarray): alpha mask, bool [H, W] + border_ratio (float, optional): border ratio, image will be resized to (1 - border_ratio). Defaults to 0.2. + + Returns: + ndarray: output image, float/uint8 [H, W, 3/4] + """ + + if image.shape[-1] == 4: + mask = image[..., 3] + else: + mask = np.ones_like(image[..., 0:1]) * 255 + image = np.concatenate([image, mask], axis=-1) + mask = mask[..., 0] + + H, W, C = image.shape + + size = max(H, W) + result = np.zeros((size, size, C), dtype=np.uint8) + + coords = np.nonzero(mask) + x_min, x_max = coords[0].min(), coords[0].max() + y_min, y_max = coords[1].min(), coords[1].max() + h = x_max - x_min + w = y_max - y_min + if h == 0 or w == 0: + raise ValueError('input image is empty') + desired_size = int(size * (1 - border_ratio)) + scale = desired_size / max(h, w) + h2 = int(h * scale) + w2 = int(w * scale) + x2_min = (size - h2) // 2 + x2_max = x2_min + h2 + + y2_min = (size - w2) // 2 + y2_max = y2_min + w2 + + result[x2_min:x2_max, y2_min:y2_max] = cv2.resize(image[x_min:x_max, y_min:y_max], (w2, h2), + interpolation=cv2.INTER_AREA) + + bg = np.ones((result.shape[0], result.shape[1], 3), dtype=np.uint8) * 255 + + mask = result[..., 3:].astype(np.float32) / 255 + result = result[..., :3] * mask + bg * (1 - mask) + + mask = mask * 255 + result = result.clip(0, 255).astype(np.uint8) + mask = mask.clip(0, 255).astype(np.uint8) + return result, mask + + def load_image(self, image, border_ratio=0.15, to_tensor=True): + if isinstance(image, str): + image = cv2.imread(image, cv2.IMREAD_UNCHANGED) + image, mask = self.recenter(image, border_ratio=border_ratio) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + elif isinstance(image, Image.Image): + image = image.convert("RGBA") + image = np.asarray(image) + image, mask = self.recenter(image, border_ratio=border_ratio) + + image = cv2.resize(image, (self.size, self.size), interpolation=cv2.INTER_CUBIC) + mask = cv2.resize(mask, (self.size, self.size), interpolation=cv2.INTER_NEAREST) + mask = mask[..., np.newaxis] + + if to_tensor: + image = array_to_tensor(image) + mask = array_to_tensor(mask) + return image, mask + + def __call__(self, image, border_ratio=0.15, to_tensor=True, **kwargs): + if self.border_ratio is not None: + border_ratio = self.border_ratio + image, mask = self.load_image(image, border_ratio=border_ratio, to_tensor=to_tensor) + outputs = { + 'image': image, + 'mask': mask + } + return outputs + + +class MVImageProcessorV2(ImageProcessorV2): + """ + view order: front, front clockwise 90, back, front clockwise 270 + """ + return_view_idx = True + + def __init__(self, size=512, border_ratio=None): + super().__init__(size, border_ratio) + self.view2idx = { + 'front': 0, + 'left': 1, + 'back': 2, + 'right': 3 + } + + def __call__(self, image_dict, border_ratio=0.15, to_tensor=True, **kwargs): + if self.border_ratio is not None: + border_ratio = self.border_ratio + + images = [] + masks = [] + view_idxs = [] + for idx, (view_tag, image) in enumerate(image_dict.items()): + view_idxs.append(self.view2idx[view_tag]) + image, mask = self.load_image(image, border_ratio=border_ratio, to_tensor=to_tensor) + images.append(image) + masks.append(mask) + + zipped_lists = zip(view_idxs, images, masks) + sorted_zipped_lists = sorted(zipped_lists) + view_idxs, images, masks = zip(*sorted_zipped_lists) + + image = torch.cat(images, 0).unsqueeze(0) + mask = torch.cat(masks, 0).unsqueeze(0) + outputs = { + 'image': image, + 'mask': mask, + 'view_idxs': view_idxs + } + return outputs + + +IMAGE_PROCESSORS = { + "v2": ImageProcessorV2, + 'mv_v2': MVImageProcessorV2, +} + +DEFAULT_IMAGEPROCESSOR = 'v2' diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/schedulers.py b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/schedulers.py new file mode 100644 index 0000000000000000000000000000000000000000..13f0da86b278679d427498128392a7a1ad69b2e6 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/schedulers.py @@ -0,0 +1,480 @@ +# Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import math +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import SchedulerMixin +from diffusers.utils import BaseOutput, logging + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + """ + + prev_sample: torch.FloatTensor + + +class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin): + """ + NOTE: this is very similar to diffusers.FlowMatchEulerDiscreteScheduler. Except our timesteps are reversed + + Euler scheduler. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + timestep_spacing (`str`, defaults to `"linspace"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + shift (`float`, defaults to 1.0): + The shift value for the timestep schedule. + """ + + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + shift: float = 1.0, + use_dynamic_shifting=False, + ): + timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32).copy() + timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) + + sigmas = timesteps / num_train_timesteps + if not use_dynamic_shifting: + # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution + sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) + + self.timesteps = sigmas * num_train_timesteps + + self._step_index = None + self._begin_index = None + + self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def scale_noise( + self, + sample: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + noise: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + """ + Forward process in flow-matching + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + # Make sure sigmas and timesteps have the same device and dtype as original_samples + sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype) + + if sample.device.type == "mps" and torch.is_floating_point(timestep): + # mps does not support float64 + schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32) + timestep = timestep.to(sample.device, dtype=torch.float32) + else: + schedule_timesteps = self.timesteps.to(sample.device) + timestep = timestep.to(sample.device) + + # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index + if self.begin_index is None: + step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep] + elif self.step_index is not None: + # add_noise is called after first denoising step (for inpainting) + step_indices = [self.step_index] * timestep.shape[0] + else: + # add noise is called before first denoising step to create initial latent(img2img) + step_indices = [self.begin_index] * timestep.shape[0] + + sigma = sigmas[step_indices].flatten() + while len(sigma.shape) < len(sample.shape): + sigma = sigma.unsqueeze(-1) + + sample = sigma * noise + (1.0 - sigma) * sample + + return sample + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def time_shift(self, mu: float, sigma: float, t: torch.Tensor): + return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) + + def set_timesteps( + self, + num_inference_steps: int = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + mu: Optional[float] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + + if self.config.use_dynamic_shifting and mu is None: + raise ValueError(" you have a pass a value for `mu` when `use_dynamic_shifting` is set to be `True`") + + if sigmas is None: + self.num_inference_steps = num_inference_steps + timesteps = np.linspace( + self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps + ) + + sigmas = timesteps / self.config.num_train_timesteps + + if self.config.use_dynamic_shifting: + sigmas = self.time_shift(mu, 1.0, sigmas) + else: + sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas) + + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) + timesteps = sigmas * self.config.num_train_timesteps + + self.timesteps = timesteps.to(device=device) + self.sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)]) + + self._step_index = None + self._begin_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + s_churn: float = 0.0, + s_tmin: float = 0.0, + s_tmax: float = float("inf"), + s_noise: float = 1.0, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[FlowMatchEulerDiscreteSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + s_churn (`float`): + s_tmin (`float`): + s_tmax (`float`): + s_noise (`float`, defaults to 1.0): + Scaling factor for noise added to the sample. + generator (`torch.Generator`, *optional*): + A random number generator. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or + tuple. + + Returns: + [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is + returned, otherwise a tuple is returned where the first element is the sample tensor. + """ + + if ( + isinstance(timestep, int) + or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor) + ): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if self.step_index is None: + self._init_step_index(timestep) + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + + sigma = self.sigmas[self.step_index] + sigma_next = self.sigmas[self.step_index + 1] + + prev_sample = sample + (sigma_next - sigma) * model_output + + # Cast sample back to model compatible dtype + prev_sample = prev_sample.to(model_output.dtype) + + # upon completion increase step index by one + self._step_index += 1 + + if not return_dict: + return (prev_sample,) + + return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample) + + def __len__(self): + return self.config.num_train_timesteps + + +@dataclass +class ConsistencyFlowMatchEulerDiscreteSchedulerOutput(BaseOutput): + prev_sample: torch.FloatTensor + pred_original_sample: torch.FloatTensor + + +class ConsistencyFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin): + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + pcm_timesteps: int = 50, + ): + sigmas = np.linspace(0, 1, num_train_timesteps) + step_ratio = num_train_timesteps // pcm_timesteps + + euler_timesteps = (np.arange(1, pcm_timesteps) * step_ratio).round().astype(np.int64) - 1 + euler_timesteps = np.asarray([0] + euler_timesteps.tolist()) + + self.euler_timesteps = euler_timesteps + self.sigmas = sigmas[self.euler_timesteps] + self.sigmas = torch.from_numpy((self.sigmas.copy())).to(dtype=torch.float32) + self.timesteps = self.sigmas * num_train_timesteps + self._step_index = None + self._begin_index = None + self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def set_timesteps( + self, + num_inference_steps: int = None, + device: Union[str, torch.device] = None, + sigmas: Optional[List[float]] = None, + ): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + self.num_inference_steps = num_inference_steps if num_inference_steps is not None else len(sigmas) + inference_indices = np.linspace( + 0, self.config.pcm_timesteps, num=self.num_inference_steps, endpoint=False + ) + inference_indices = np.floor(inference_indices).astype(np.int64) + inference_indices = torch.from_numpy(inference_indices).long() + + self.sigmas_ = self.sigmas[inference_indices] + timesteps = self.sigmas_ * self.config.num_train_timesteps + self.timesteps = timesteps.to(device=device) + self.sigmas_ = torch.cat( + [self.sigmas_, torch.ones(1, device=self.sigmas_.device)] + ) + + self._step_index = None + self._begin_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[ConsistencyFlowMatchEulerDiscreteSchedulerOutput, Tuple]: + if ( + isinstance(timestep, int) + or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor) + ): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if self.step_index is None: + self._init_step_index(timestep) + + sample = sample.to(torch.float32) + + sigma = self.sigmas_[self.step_index] + sigma_next = self.sigmas_[self.step_index + 1] + + prev_sample = sample + (sigma_next - sigma) * model_output + prev_sample = prev_sample.to(model_output.dtype) + + pred_original_sample = sample + (1.0 - sigma) * model_output + pred_original_sample = pred_original_sample.to(model_output.dtype) + + self._step_index += 1 + + if not return_dict: + return (prev_sample,) + + return ConsistencyFlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample, + pred_original_sample=pred_original_sample) + + def __len__(self): + return self.config.num_train_timesteps diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..86b66eea242d5a8ffd61f1c6a6618bce20608588 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/shapegen/utils.py @@ -0,0 +1,126 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import logging +import os +from functools import wraps + +import torch + + +def get_logger(name): + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + console_handler.setFormatter(formatter) + logger.addHandler(console_handler) + return logger + + +logger = get_logger('hy3dgen.shapgen') + + +class synchronize_timer: + """ Synchronized timer to count the inference time of `nn.Module.forward`. + + Supports both context manager and decorator usage. + + Example as context manager: + ```python + with synchronize_timer('name') as t: + run() + ``` + + Example as decorator: + ```python + @synchronize_timer('Export to trimesh') + def export_to_trimesh(mesh_output): + pass + ``` + """ + + def __init__(self, name=None): + self.name = name + + def __enter__(self): + """Context manager entry: start timing.""" + if os.environ.get('HY3DGEN_DEBUG', '0') == '1': + self.start = torch.cuda.Event(enable_timing=True) + self.end = torch.cuda.Event(enable_timing=True) + self.start.record() + return lambda: self.time + + def __exit__(self, exc_type, exc_value, exc_tb): + """Context manager exit: stop timing and log results.""" + if os.environ.get('HY3DGEN_DEBUG', '0') == '1': + self.end.record() + torch.cuda.synchronize() + self.time = self.start.elapsed_time(self.end) + if self.name is not None: + logger.info(f'{self.name} takes {self.time} ms') + + def __call__(self, func): + """Decorator: wrap the function to time its execution.""" + + @wraps(func) + def wrapper(*args, **kwargs): + with self: + result = func(*args, **kwargs) + return result + + return wrapper + + +def smart_load_model( + model_path, + subfolder, + use_safetensors, + variant, +): + original_model_path = model_path + # try local path + base_dir = os.environ.get('HY3DGEN_MODELS', '~/.cache/hy3dgen') + model_path = os.path.expanduser(os.path.join(base_dir, model_path, subfolder)) + logger.info(f'Try to load model from local path: {model_path}') + if not os.path.exists(model_path): + logger.info('Model path not exists, try to download from huggingface') + try: + from huggingface_hub import snapshot_download + # Only download specified subdirectory + path = snapshot_download( + repo_id=original_model_path, + allow_patterns=[f"{subfolder}/*"], # Key modification: pattern match subfolder + ) + model_path = os.path.join(path, subfolder) # Keep path concatenation logic unchanged + except ImportError: + logger.warning( + "You need to install HuggingFace Hub to load models from the hub." + ) + raise RuntimeError(f"Model path {model_path} not found") + except Exception as e: + raise e + + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model path {original_model_path} not found") + + extension = 'ckpt' if not use_safetensors else 'safetensors' + variant = '' if variant is None else f'.{variant}' + ckpt_name = f'model{variant}.{extension}' + config_path = os.path.join(model_path, 'config.yaml') + ckpt_path = os.path.join(model_path, ckpt_name) + return config_path, ckpt_path diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7054c5797257839532eb03b05a474ddf020b1695 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/__init__.py @@ -0,0 +1,16 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + + +from .pipelines import Hunyuan3DPaintPipeline, Hunyuan3DTexGenConfig diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f471e1ac289d962613553fed2ba6e177e5af3ab9 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py @@ -0,0 +1,22 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +''' +from .hierarchy import BuildHierarchy, BuildHierarchyWithColor +from .io_obj import LoadObj, LoadObjWithTexture +from .render import rasterize, interpolate +''' +from .io_glb import * +from .io_obj import * +from .render import * diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py new file mode 100644 index 0000000000000000000000000000000000000000..f1daf7f29fe91b8fffd00790f1e6c4bd05ee00be --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py @@ -0,0 +1,241 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import base64 +import io +import os + +import numpy as np +from PIL import Image as PILImage +from pygltflib import GLTF2 +from scipy.spatial.transform import Rotation as R + + +# Function to extract buffer data +def get_buffer_data(gltf, buffer_view): + buffer = gltf.buffers[buffer_view.buffer] + buffer_data = gltf.get_data_from_buffer_uri(buffer.uri) + byte_offset = buffer_view.byteOffset if buffer_view.byteOffset else 0 + byte_length = buffer_view.byteLength + return buffer_data[byte_offset:byte_offset + byte_length] + + +# Function to extract attribute data +def get_attribute_data(gltf, accessor_index): + accessor = gltf.accessors[accessor_index] + buffer_view = gltf.bufferViews[accessor.bufferView] + buffer_data = get_buffer_data(gltf, buffer_view) + + comptype = {5120: np.int8, 5121: np.uint8, 5122: np.int16, 5123: np.uint16, 5125: np.uint32, 5126: np.float32} + dtype = comptype[accessor.componentType] + + t2n = {'SCALAR': 1, 'VEC2': 2, 'VEC3': 3, 'VEC4': 4, 'MAT2': 4, 'MAT3': 9, 'MAT4': 16} + num_components = t2n[accessor.type] + + # Calculate the correct slice of data + byte_offset = accessor.byteOffset if accessor.byteOffset else 0 + byte_stride = buffer_view.byteStride if buffer_view.byteStride else num_components * np.dtype(dtype).itemsize + count = accessor.count + + # Extract the attribute data + attribute_data = np.zeros((count, num_components), dtype=dtype) + for i in range(count): + start = byte_offset + i * byte_stride + end = start + num_components * np.dtype(dtype).itemsize + attribute_data[i] = np.frombuffer(buffer_data[start:end], dtype=dtype) + + return attribute_data + + +# Function to extract image data +def get_image_data(gltf, image, folder): + if image.uri: + if image.uri.startswith('data:'): + # Data URI + header, encoded = image.uri.split(',', 1) + data = base64.b64decode(encoded) + else: + # External file + fn = image.uri + if not os.path.isabs(fn): + fn = folder + '/' + fn + with open(fn, 'rb') as f: + data = f.read() + else: + buffer_view = gltf.bufferViews[image.bufferView] + data = get_buffer_data(gltf, buffer_view) + return data + + +# Function to convert triangle strip to triangles +def convert_triangle_strip_to_triangles(indices): + triangles = [] + for i in range(len(indices) - 2): + if i % 2 == 0: + triangles.append([indices[i], indices[i + 1], indices[i + 2]]) + else: + triangles.append([indices[i], indices[i + 2], indices[i + 1]]) + return np.array(triangles).reshape(-1, 3) + + +# Function to convert triangle fan to triangles +def convert_triangle_fan_to_triangles(indices): + triangles = [] + for i in range(1, len(indices) - 1): + triangles.append([indices[0], indices[i], indices[i + 1]]) + return np.array(triangles).reshape(-1, 3) + + +# Function to get the transformation matrix from a node +def get_node_transform(node): + if node.matrix: + return np.array(node.matrix).reshape(4, 4).T + else: + T = np.eye(4) + if node.translation: + T[:3, 3] = node.translation + if node.rotation: + R_mat = R.from_quat(node.rotation).as_matrix() + T[:3, :3] = R_mat + if node.scale: + S = np.diag(node.scale + [1]) + T = T @ S + return T + + +def get_world_transform(gltf, node_index, parents, world_transforms): + if parents[node_index] == -2: + return world_transforms[node_index] + + node = gltf.nodes[node_index] + if parents[node_index] == -1: + world_transforms[node_index] = get_node_transform(node) + parents[node_index] = -2 + return world_transforms[node_index] + + parent_index = parents[node_index] + parent_transform = get_world_transform(gltf, parent_index, parents, world_transforms) + world_transforms[node_index] = parent_transform @ get_node_transform(node) + parents[node_index] = -2 + return world_transforms[node_index] + + +def LoadGlb(path): + # Load the GLB file using pygltflib + gltf = GLTF2().load(path) + + primitives = [] + images = {} + # Iterate through the meshes in the GLB file + + world_transforms = [np.identity(4) for i in range(len(gltf.nodes))] + parents = [-1 for i in range(len(gltf.nodes))] + for node_index, node in enumerate(gltf.nodes): + for idx in node.children: + parents[idx] = node_index + # for i in range(len(gltf.nodes)): + # get_world_transform(gltf, i, parents, world_transform) + + for node_index, node in enumerate(gltf.nodes): + if node.mesh is not None: + world_transform = get_world_transform(gltf, node_index, parents, world_transforms) + # Iterate through the primitives in the mesh + mesh = gltf.meshes[node.mesh] + for primitive in mesh.primitives: + # Access the attributes of the primitive + attributes = primitive.attributes.__dict__ + mode = primitive.mode if primitive.mode is not None else 4 # Default to TRIANGLES + result = {} + if primitive.indices is not None: + indices = get_attribute_data(gltf, primitive.indices) + if mode == 4: # TRIANGLES + face_indices = indices.reshape(-1, 3) + elif mode == 5: # TRIANGLE_STRIP + face_indices = convert_triangle_strip_to_triangles(indices) + elif mode == 6: # TRIANGLE_FAN + face_indices = convert_triangle_fan_to_triangles(indices) + else: + continue + result['F'] = face_indices + + # Extract vertex positions + if 'POSITION' in attributes and attributes['POSITION'] is not None: + positions = get_attribute_data(gltf, attributes['POSITION']) + # Apply the world transformation to the positions + positions_homogeneous = np.hstack([positions, np.ones((positions.shape[0], 1))]) + transformed_positions = (world_transform @ positions_homogeneous.T).T[:, :3] + result['V'] = transformed_positions + + # Extract vertex colors + if 'COLOR_0' in attributes and attributes['COLOR_0'] is not None: + colors = get_attribute_data(gltf, attributes['COLOR_0']) + if colors.shape[-1] > 3: + colors = colors[..., :3] + result['VC'] = colors + + # Extract UVs + if 'TEXCOORD_0' in attributes and not attributes['TEXCOORD_0'] is None: + uvs = get_attribute_data(gltf, attributes['TEXCOORD_0']) + result['UV'] = uvs + + if primitive.material is not None: + material = gltf.materials[primitive.material] + if ( + material.pbrMetallicRoughness is not None + and material.pbrMetallicRoughness.baseColorTexture is not None + ): + texture_index = material.pbrMetallicRoughness.baseColorTexture.index + texture = gltf.textures[texture_index] + image_index = texture.source + if not image_index in images: + image = gltf.images[image_index] + image_data = get_image_data(gltf, image, os.path.dirname(path)) + pil_image = PILImage.open(io.BytesIO(image_data)) + if pil_image.mode != 'RGB': + pil_image = pil_image.convert('RGB') + images[image_index] = pil_image + result['TEX'] = image_index + elif material.emissiveTexture is not None: + texture_index = material.emissiveTexture.index + texture = gltf.textures[texture_index] + image_index = texture.source + if not image_index in images: + image = gltf.images[image_index] + image_data = get_image_data(gltf, image, os.path.dirname(path)) + pil_image = PILImage.open(io.BytesIO(image_data)) + if pil_image.mode != 'RGB': + pil_image = pil_image.convert('RGB') + images[image_index] = pil_image + result['TEX'] = image_index + else: + if material.pbrMetallicRoughness is not None: + base_color = material.pbrMetallicRoughness.baseColorFactor + else: + base_color = np.array([0.8, 0.8, 0.8], dtype=np.float32) + result['MC'] = base_color + + primitives.append(result) + + return primitives, images + + +def RotatePrimitives(primitives, transform): + for i in range(len(primitives)): + if 'V' in primitives[i]: + primitives[i]['V'] = primitives[i]['V'] @ transform.T + + +if __name__ == '__main__': + path = 'data/test.glb' + LoadGlb(path) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py new file mode 100644 index 0000000000000000000000000000000000000000..e40d50050456a3a3d5cb3fbed516c4d4bd0bdb8f --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py @@ -0,0 +1,66 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import cv2 +import numpy as np + + +def LoadObj(fn): + lines = [l.strip() for l in open(fn)] + vertices = [] + faces = [] + for l in lines: + words = [w for w in l.split(' ') if w != ''] + if len(words) == 0: + continue + if words[0] == 'v': + v = [float(words[i]) for i in range(1, 4)] + vertices.append(v) + elif words[0] == 'f': + f = [int(words[i]) - 1 for i in range(1, 4)] + faces.append(f) + + return np.array(vertices).astype('float32'), np.array(faces).astype('int32') + + +def LoadObjWithTexture(fn, tex_fn): + lines = [l.strip() for l in open(fn)] + vertices = [] + vertex_textures = [] + faces = [] + face_textures = [] + for l in lines: + words = [w for w in l.split(' ') if w != ''] + if len(words) == 0: + continue + if words[0] == 'v': + v = [float(words[i]) for i in range(1, len(words))] + vertices.append(v) + elif words[0] == 'vt': + v = [float(words[i]) for i in range(1, len(words))] + vertex_textures.append(v) + elif words[0] == 'f': + f = [] + ft = [] + for i in range(1, len(words)): + t = words[i].split('/') + f.append(int(t[0]) - 1) + ft.append(int(t[1]) - 1) + for i in range(2, len(f)): + faces.append([f[0], f[i - 1], f[i]]) + face_textures.append([ft[0], ft[i - 1], ft[i]]) + + tex_image = cv2.cvtColor(cv2.imread(tex_fn), cv2.COLOR_BGR2RGB) + return np.array(vertices).astype('float32'), np.array(vertex_textures).astype('float32'), np.array(faces).astype( + 'int32'), np.array(face_textures).astype('int32'), tex_image diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py new file mode 100644 index 0000000000000000000000000000000000000000..2d4d3f7ee6ba13ff7df1000eb3dd3e978d2d6fc4 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py @@ -0,0 +1,31 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import custom_rasterizer_kernel +import torch + + +def rasterize(pos, tri, resolution, clamp_depth=torch.zeros(0), use_depth_prior=0): + assert (pos.device == tri.device) + findices, barycentric = custom_rasterizer_kernel.rasterize_image(pos[0], tri, clamp_depth, resolution[1], + resolution[0], 1e-6, use_depth_prior) + return findices, barycentric + + +def interpolate(col, findices, barycentric, tri): + f = findices - 1 + (findices == 0) + vcol = col[0, tri.long()[f.long()]] + result = barycentric.view(*barycentric.shape, 1) * vcol + result = torch.sum(result, axis=-2) + return result.view(1, *result.shape) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1614ff826f65b4720649d343c43fb09dbb6b9fa5 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py @@ -0,0 +1,13 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..65ab321f397ab8642146795af53b0b8d91374607 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp @@ -0,0 +1,574 @@ +#include "rasterizer.h" +#include + +inline int pos2key(float* p, int resolution) { + int x = (p[0] * 0.5 + 0.5) * resolution; + int y = (p[1] * 0.5 + 0.5) * resolution; + int z = (p[2] * 0.5 + 0.5) * resolution; + return (x * resolution + y) * resolution + z; +} + +inline void key2pos(int key, int resolution, float* p) { + int x = key / resolution / resolution; + int y = key / resolution % resolution; + int z = key % resolution; + p[0] = ((x + 0.5) / resolution - 0.5) * 2; + p[1] = ((y + 0.5) / resolution - 0.5) * 2; + p[2] = ((z + 0.5) / resolution - 0.5) * 2; +} + +inline void key2cornerpos(int key, int resolution, float* p) { + int x = key / resolution / resolution; + int y = key / resolution % resolution; + int z = key % resolution; + p[0] = ((x + 0.75) / resolution - 0.5) * 2; + p[1] = ((y + 0.25) / resolution - 0.5) * 2; + p[2] = ((z + 0.75) / resolution - 0.5) * 2; +} + +inline float* pos_ptr(int l, int i, int j, torch::Tensor t) { + float* pdata = t.data_ptr(); + int height = t.size(1); + int width = t.size(2); + return &pdata[((l * height + i) * width + j) * 4]; +} + +struct Grid +{ + std::vector seq2oddcorner; + std::vector seq2evencorner; + std::vector seq2grid; + std::vector seq2normal; + std::vector seq2neighbor; + std::unordered_map grid2seq; + std::vector downsample_seq; + int num_origin_seq; + int resolution; + int stride; +}; + +inline void pos_from_seq(Grid& grid, int seq, float* p) { + auto k = grid.seq2grid[seq]; + key2pos(k, grid.resolution, p); +} + +inline int fetch_seq(Grid& grid, int l, int i, int j, torch::Tensor pdata) { + float* p = pos_ptr(l, i, j, pdata); + if (p[3] == 0) + return -1; + auto key = pos2key(p, grid.resolution); + int seq = grid.grid2seq[key]; + return seq; +} + +inline int fetch_last_seq(Grid& grid, int i, int j, torch::Tensor pdata) { + int num_layers = pdata.size(0); + int l = 0; + int idx = fetch_seq(grid, l, i, j, pdata); + while (l < num_layers - 1) { + l += 1; + int new_idx = fetch_seq(grid, l, i, j, pdata); + if (new_idx == -1) + break; + idx = new_idx; + } + return idx; +} + +inline int fetch_nearest_seq(Grid& grid, int i, int j, int dim, float d, torch::Tensor pdata) { + float p[3]; + float max_dist = 1e10; + int best_idx = -1; + int num_layers = pdata.size(0); + for (int l = 0; l < num_layers; ++l) { + int idx = fetch_seq(grid, l, i, j, pdata); + if (idx == -1) + break; + pos_from_seq(grid, idx, p); + float dist = std::abs(d - p[(dim + 2) % 3]); + if (dist < max_dist) { + max_dist = dist; + best_idx = idx; + } + } + return best_idx; +} + +inline int fetch_nearest_seq_layer(Grid& grid, int i, int j, int dim, float d, torch::Tensor pdata) { + float p[3]; + float max_dist = 1e10; + int best_layer = -1; + int num_layers = pdata.size(0); + for (int l = 0; l < num_layers; ++l) { + int idx = fetch_seq(grid, l, i, j, pdata); + if (idx == -1) + break; + pos_from_seq(grid, idx, p); + float dist = std::abs(d - p[(dim + 2) % 3]); + if (dist < max_dist) { + max_dist = dist; + best_layer = l; + } + } + return best_layer; +} + +void FetchNeighbor(Grid& grid, int seq, float* pos, int dim, int boundary_info, std::vector& view_layer_positions, + int* output_indices) +{ + auto t = view_layer_positions[dim]; + int height = t.size(1); + int width = t.size(2); + int top = 0; + int ci = 0; + int cj = 0; + if (dim == 0) { + ci = (pos[1]/2+0.5)*height; + cj = (pos[0]/2+0.5)*width; + } + else if (dim == 1) { + ci = (pos[1]/2+0.5)*height; + cj = (pos[2]/2+0.5)*width; + } + else { + ci = (-pos[2]/2+0.5)*height; + cj = (pos[0]/2+0.5)*width; + } + int stride = grid.stride; + for (int ni = ci + stride; ni >= ci - stride; ni -= stride) { + for (int nj = cj - stride; nj <= cj + stride; nj += stride) { + int idx = -1; + if (ni == ci && nj == cj) + idx = seq; + else if (!(ni < 0 || ni >= height || nj < 0 || nj >= width)) { + if (boundary_info == -1) + idx = fetch_seq(grid, 0, ni, nj, t); + else if (boundary_info == 1) + idx = fetch_last_seq(grid, ni, nj, t); + else + idx = fetch_nearest_seq(grid, ni, nj, dim, pos[(dim + 2) % 3], t); + } + output_indices[top] = idx; + top += 1; + } + } +} + +void DownsampleGrid(Grid& src, Grid& tar) +{ + src.downsample_seq.resize(src.seq2grid.size(), -1); + tar.resolution = src.resolution / 2; + tar.stride = src.stride * 2; + float pos[3]; + std::vector seq2normal_count; + for (int i = 0; i < src.seq2grid.size(); ++i) { + key2pos(src.seq2grid[i], src.resolution, pos); + int k = pos2key(pos, tar.resolution); + int s = seq2normal_count.size(); + if (!tar.grid2seq.count(k)) { + tar.grid2seq[k] = tar.seq2grid.size(); + tar.seq2grid.emplace_back(k); + seq2normal_count.emplace_back(0); + seq2normal_count.emplace_back(0); + seq2normal_count.emplace_back(0); + //tar.seq2normal.emplace_back(src.seq2normal[i]); + } else { + s = tar.grid2seq[k] * 3; + } + seq2normal_count[s + src.seq2normal[i]] += 1; + src.downsample_seq[i] = tar.grid2seq[k]; + } + tar.seq2normal.resize(seq2normal_count.size() / 3); + for (int i = 0; i < seq2normal_count.size(); i += 3) { + int t = 0; + for (int j = 1; j < 3; ++j) { + if (seq2normal_count[i + j] > seq2normal_count[i + t]) + t = j; + } + tar.seq2normal[i / 3] = t; + } +} + +void NeighborGrid(Grid& grid, std::vector view_layer_positions, int v) +{ + grid.seq2evencorner.resize(grid.seq2grid.size(), 0); + grid.seq2oddcorner.resize(grid.seq2grid.size(), 0); + std::unordered_set visited_seq; + for (int vd = 0; vd < 3; ++vd) { + auto t = view_layer_positions[vd]; + auto t0 = view_layer_positions[v]; + int height = t.size(1); + int width = t.size(2); + int num_layers = t.size(0); + int num_view_layers = t0.size(0); + for (int i = 0; i < height; ++i) { + for (int j = 0; j < width; ++j) { + for (int l = 0; l < num_layers; ++l) { + int seq = fetch_seq(grid, l, i, j, t); + if (seq == -1) + break; + int dim = grid.seq2normal[seq]; + if (dim != v) + continue; + + float pos[3]; + pos_from_seq(grid, seq, pos); + + int ci = 0; + int cj = 0; + if (dim == 0) { + ci = (pos[1]/2+0.5)*height; + cj = (pos[0]/2+0.5)*width; + } + else if (dim == 1) { + ci = (pos[1]/2+0.5)*height; + cj = (pos[2]/2+0.5)*width; + } + else { + ci = (-pos[2]/2+0.5)*height; + cj = (pos[0]/2+0.5)*width; + } + + if ((ci % (grid.stride * 2) < grid.stride) && (cj % (grid.stride * 2) >= grid.stride)) + grid.seq2evencorner[seq] = 1; + + if ((ci % (grid.stride * 2) >= grid.stride) && (cj % (grid.stride * 2) < grid.stride)) + grid.seq2oddcorner[seq] = 1; + + bool is_boundary = false; + if (vd == v) { + if (l == 0 || l == num_layers - 1) + is_boundary = true; + else { + int seq_new = fetch_seq(grid, l + 1, i, j, t); + if (seq_new == -1) + is_boundary = true; + } + } + int boundary_info = 0; + if (is_boundary && (l == 0)) + boundary_info = -1; + else if (is_boundary) + boundary_info = 1; + if (visited_seq.count(seq)) + continue; + visited_seq.insert(seq); + + FetchNeighbor(grid, seq, pos, dim, boundary_info, view_layer_positions, &grid.seq2neighbor[seq * 9]); + } + } + } + } +} + +void PadGrid(Grid& src, Grid& tar, std::vector& view_layer_positions) { + auto& downsample_seq = src.downsample_seq; + auto& seq2evencorner = src.seq2evencorner; + auto& seq2oddcorner = src.seq2oddcorner; + int indices[9]; + std::vector mapped_even_corners(tar.seq2grid.size(), 0); + std::vector mapped_odd_corners(tar.seq2grid.size(), 0); + for (int i = 0; i < downsample_seq.size(); ++i) { + if (seq2evencorner[i] > 0) { + mapped_even_corners[downsample_seq[i]] = 1; + } + if (seq2oddcorner[i] > 0) { + mapped_odd_corners[downsample_seq[i]] = 1; + } + } + auto& tar_seq2normal = tar.seq2normal; + auto& tar_seq2grid = tar.seq2grid; + for (int i = 0; i < tar_seq2grid.size(); ++i) { + if (mapped_even_corners[i] == 1 && mapped_odd_corners[i] == 1) + continue; + auto k = tar_seq2grid[i]; + float p[3]; + key2cornerpos(k, tar.resolution, p); + + int src_key = pos2key(p, src.resolution); + if (!src.grid2seq.count(src_key)) { + int seq = src.seq2grid.size(); + src.grid2seq[src_key] = seq; + src.seq2evencorner.emplace_back((mapped_even_corners[i] == 0)); + src.seq2oddcorner.emplace_back((mapped_odd_corners[i] == 0)); + src.seq2grid.emplace_back(src_key); + src.seq2normal.emplace_back(tar_seq2normal[i]); + FetchNeighbor(src, seq, p, tar_seq2normal[i], 0, view_layer_positions, indices); + for (int j = 0; j < 9; ++j) { + src.seq2neighbor.emplace_back(indices[j]); + } + src.downsample_seq.emplace_back(i); + } else { + int seq = src.grid2seq[src_key]; + if (mapped_even_corners[i] == 0) + src.seq2evencorner[seq] = 1; + if (mapped_odd_corners[i] == 0) + src.seq2oddcorner[seq] = 1; + } + } +} + +std::vector> build_hierarchy(std::vector view_layer_positions, + std::vector view_layer_normals, int num_level, int resolution) +{ + if (view_layer_positions.size() != 3 || num_level < 1) { + printf("Alert! We require 3 layers and at least 1 level! (%d %d)\n", view_layer_positions.size(), num_level); + return {{},{},{},{}}; + } + + std::vector grids; + grids.resize(num_level); + + std::vector seq2pos; + auto& seq2grid = grids[0].seq2grid; + auto& seq2normal = grids[0].seq2normal; + auto& grid2seq = grids[0].grid2seq; + grids[0].resolution = resolution; + grids[0].stride = 1; + + auto int64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); + auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); + + for (int v = 0; v < 3; ++v) { + int num_layers = view_layer_positions[v].size(0); + int height = view_layer_positions[v].size(1); + int width = view_layer_positions[v].size(2); + float* data = view_layer_positions[v].data_ptr(); + float* data_normal = view_layer_normals[v].data_ptr(); + for (int l = 0; l < num_layers; ++l) { + for (int i = 0; i < height; ++i) { + for (int j = 0; j < width; ++j) { + float* p = &data[(i * width + j) * 4]; + float* n = &data_normal[(i * width + j) * 3]; + if (p[3] == 0) + continue; + auto k = pos2key(p, resolution); + if (!grid2seq.count(k)) { + int dim = 0; + for (int d = 0; d < 3; ++d) { + if (std::abs(n[d]) > std::abs(n[dim])) + dim = d; + } + dim = (dim + 1) % 3; + grid2seq[k] = seq2grid.size(); + seq2grid.emplace_back(k); + seq2pos.push_back(p[0]); + seq2pos.push_back(p[1]); + seq2pos.push_back(p[2]); + seq2normal.emplace_back(dim); + } + } + } + data += (height * width * 4); + data_normal += (height * width * 3); + } + } + + for (int i = 0; i < num_level - 1; ++i) { + DownsampleGrid(grids[i], grids[i + 1]); + } + + for (int l = 0; l < num_level; ++l) { + grids[l].seq2neighbor.resize(grids[l].seq2grid.size() * 9, -1); + grids[l].num_origin_seq = grids[l].seq2grid.size(); + for (int d = 0; d < 3; ++d) { + NeighborGrid(grids[l], view_layer_positions, d); + } + } + + for (int i = num_level - 2; i >= 0; --i) { + PadGrid(grids[i], grids[i + 1], view_layer_positions); + } + for (int i = grids[0].num_origin_seq; i < grids[0].seq2grid.size(); ++i) { + int k = grids[0].seq2grid[i]; + float p[3]; + key2pos(k, grids[0].resolution, p); + seq2pos.push_back(p[0]); + seq2pos.push_back(p[1]); + seq2pos.push_back(p[2]); + } + + std::vector texture_positions(2); + std::vector grid_neighbors(grids.size()); + std::vector grid_downsamples(grids.size() - 1); + std::vector grid_evencorners(grids.size()); + std::vector grid_oddcorners(grids.size()); + + texture_positions[0] = torch::zeros({static_cast(seq2pos.size() / 3), static_cast(3)}, float_options); + texture_positions[1] = torch::zeros({static_cast(seq2pos.size() / 3)}, float_options); + float* positions_out_ptr = texture_positions[0].data_ptr(); + memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size()); + positions_out_ptr = texture_positions[1].data_ptr(); + for (int i = 0; i < grids[0].seq2grid.size(); ++i) { + positions_out_ptr[i] = (i < grids[0].num_origin_seq); + } + + for (int i = 0; i < grids.size(); ++i) { + grid_neighbors[i] = torch::zeros({static_cast(grids[i].seq2grid.size()), static_cast(9)}, int64_options); + int64_t* nptr = grid_neighbors[i].data_ptr(); + for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) { + nptr[j] = grids[i].seq2neighbor[j]; + } + + grid_evencorners[i] = torch::zeros({static_cast(grids[i].seq2evencorner.size())}, int64_options); + grid_oddcorners[i] = torch::zeros({static_cast(grids[i].seq2oddcorner.size())}, int64_options); + int64_t* dptr = grid_evencorners[i].data_ptr(); + for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) { + dptr[j] = grids[i].seq2evencorner[j]; + } + dptr = grid_oddcorners[i].data_ptr(); + for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) { + dptr[j] = grids[i].seq2oddcorner[j]; + } + if (i + 1 < grids.size()) { + grid_downsamples[i] = torch::zeros({static_cast(grids[i].downsample_seq.size())}, int64_options); + int64_t* dptr = grid_downsamples[i].data_ptr(); + for (int j = 0; j < grids[i].downsample_seq.size(); ++j) { + dptr[j] = grids[i].downsample_seq[j]; + } + } + + } + return {texture_positions, grid_neighbors, grid_downsamples, grid_evencorners, grid_oddcorners}; +} + +std::vector> build_hierarchy_with_feat( + std::vector view_layer_positions, + std::vector view_layer_normals, + std::vector view_layer_feats, + int num_level, int resolution) +{ + if (view_layer_positions.size() != 3 || num_level < 1) { + printf("Alert! We require 3 layers and at least 1 level! (%d %d)\n", view_layer_positions.size(), num_level); + return {{},{},{},{}}; + } + + std::vector grids; + grids.resize(num_level); + + std::vector seq2pos; + std::vector seq2feat; + auto& seq2grid = grids[0].seq2grid; + auto& seq2normal = grids[0].seq2normal; + auto& grid2seq = grids[0].grid2seq; + grids[0].resolution = resolution; + grids[0].stride = 1; + + auto int64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); + auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); + + int feat_channel = 3; + for (int v = 0; v < 3; ++v) { + int num_layers = view_layer_positions[v].size(0); + int height = view_layer_positions[v].size(1); + int width = view_layer_positions[v].size(2); + float* data = view_layer_positions[v].data_ptr(); + float* data_normal = view_layer_normals[v].data_ptr(); + float* data_feat = view_layer_feats[v].data_ptr(); + feat_channel = view_layer_feats[v].size(3); + for (int l = 0; l < num_layers; ++l) { + for (int i = 0; i < height; ++i) { + for (int j = 0; j < width; ++j) { + float* p = &data[(i * width + j) * 4]; + float* n = &data_normal[(i * width + j) * 3]; + float* f = &data_feat[(i * width + j) * feat_channel]; + if (p[3] == 0) + continue; + auto k = pos2key(p, resolution); + if (!grid2seq.count(k)) { + int dim = 0; + for (int d = 0; d < 3; ++d) { + if (std::abs(n[d]) > std::abs(n[dim])) + dim = d; + } + dim = (dim + 1) % 3; + grid2seq[k] = seq2grid.size(); + seq2grid.emplace_back(k); + seq2pos.push_back(p[0]); + seq2pos.push_back(p[1]); + seq2pos.push_back(p[2]); + for (int c = 0; c < feat_channel; ++c) { + seq2feat.emplace_back(f[c]); + } + seq2normal.emplace_back(dim); + } + } + } + data += (height * width * 4); + data_normal += (height * width * 3); + data_feat += (height * width * feat_channel); + } + } + + for (int i = 0; i < num_level - 1; ++i) { + DownsampleGrid(grids[i], grids[i + 1]); + } + + for (int l = 0; l < num_level; ++l) { + grids[l].seq2neighbor.resize(grids[l].seq2grid.size() * 9, -1); + grids[l].num_origin_seq = grids[l].seq2grid.size(); + for (int d = 0; d < 3; ++d) { + NeighborGrid(grids[l], view_layer_positions, d); + } + } + + for (int i = num_level - 2; i >= 0; --i) { + PadGrid(grids[i], grids[i + 1], view_layer_positions); + } + for (int i = grids[0].num_origin_seq; i < grids[0].seq2grid.size(); ++i) { + int k = grids[0].seq2grid[i]; + float p[3]; + key2pos(k, grids[0].resolution, p); + seq2pos.push_back(p[0]); + seq2pos.push_back(p[1]); + seq2pos.push_back(p[2]); + for (int c = 0; c < feat_channel; ++c) { + seq2feat.emplace_back(0.5); + } + } + + std::vector texture_positions(2); + std::vector texture_feats(1); + std::vector grid_neighbors(grids.size()); + std::vector grid_downsamples(grids.size() - 1); + std::vector grid_evencorners(grids.size()); + std::vector grid_oddcorners(grids.size()); + + texture_positions[0] = torch::zeros({static_cast(seq2pos.size() / 3), static_cast(3)}, float_options); + texture_positions[1] = torch::zeros({static_cast(seq2pos.size() / 3)}, float_options); + texture_feats[0] = torch::zeros({static_cast(seq2feat.size() / feat_channel), static_cast(feat_channel)}, float_options); + float* positions_out_ptr = texture_positions[0].data_ptr(); + memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size()); + positions_out_ptr = texture_positions[1].data_ptr(); + for (int i = 0; i < grids[0].seq2grid.size(); ++i) { + positions_out_ptr[i] = (i < grids[0].num_origin_seq); + } + float* feats_out_ptr = texture_feats[0].data_ptr(); + memcpy(feats_out_ptr, seq2feat.data(), sizeof(float) * seq2feat.size()); + + for (int i = 0; i < grids.size(); ++i) { + grid_neighbors[i] = torch::zeros({static_cast(grids[i].seq2grid.size()), static_cast(9)}, int64_options); + int64_t* nptr = grid_neighbors[i].data_ptr(); + for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) { + nptr[j] = grids[i].seq2neighbor[j]; + } + grid_evencorners[i] = torch::zeros({static_cast(grids[i].seq2evencorner.size())}, int64_options); + grid_oddcorners[i] = torch::zeros({static_cast(grids[i].seq2oddcorner.size())}, int64_options); + int64_t* dptr = grid_evencorners[i].data_ptr(); + for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) { + dptr[j] = grids[i].seq2evencorner[j]; + } + dptr = grid_oddcorners[i].data_ptr(); + for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) { + dptr[j] = grids[i].seq2oddcorner[j]; + } + if (i + 1 < grids.size()) { + grid_downsamples[i] = torch::zeros({static_cast(grids[i].downsample_seq.size())}, int64_options); + int64_t* dptr = grid_downsamples[i].data_ptr(); + for (int j = 0; j < grids[i].downsample_seq.size(); ++j) { + dptr[j] = grids[i].downsample_seq[j]; + } + } + } + return {texture_positions, texture_feats, grid_neighbors, grid_downsamples, grid_evencorners, grid_oddcorners}; +} diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4af6eebfeb44f1578ae99f07601f2e2fa1c3c0d8 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp @@ -0,0 +1,139 @@ +#include "rasterizer.h" + +void rasterizeTriangleCPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) { + float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0])); + float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0])); + float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1])); + float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1])); + + for (int px = x_min; px < x_max + 1; ++px) { + if (px < 0 || px >= width) + continue; + for (int py = y_min; py < y_max + 1; ++py) { + if (py < 0 || py >= height) + continue; + float vt[2] = {px + 0.5, py + 0.5}; + float baryCentricCoordinate[3]; + calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate); + if (isBarycentricCoordInBounds(baryCentricCoordinate)) { + int pixel = py * width + px; + if (zbuffer == 0) { + zbuffer[pixel] = (INT64)(idx + 1); + continue; + } + + float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2]; + float depth_thres = 0; + if (d) { + depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation; + } + + int z_quantize = depth * (2<<17); + INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1); + if (depth < depth_thres) + continue; + zbuffer[pixel] = std::min(zbuffer[pixel], token); + } + } + } +} + +void barycentricFromImgcoordCPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces, + float* barycentric_map, int pix) +{ + INT64 f = zbuffer[pix] % MAXINT; + if (f == (MAXINT-1)) { + findices[pix] = 0; + barycentric_map[pix * 3] = 0; + barycentric_map[pix * 3 + 1] = 0; + barycentric_map[pix * 3 + 2] = 0; + return; + } + findices[pix] = f; + f -= 1; + float barycentric[3] = {0, 0, 0}; + if (f >= 0) { + float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f}; + float* vt0_ptr = V + (F[f * 3] * 4); + float* vt1_ptr = V + (F[f * 3 + 1] * 4); + float* vt2_ptr = V + (F[f * 3 + 2] * 4); + + float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f}; + float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f}; + float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f}; + + calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric); + + barycentric[0] = barycentric[0] / vt0_ptr[3]; + barycentric[1] = barycentric[1] / vt1_ptr[3]; + barycentric[2] = barycentric[2] / vt2_ptr[3]; + float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]); + barycentric[0] *= w; + barycentric[1] *= w; + barycentric[2] *= w; + + } + barycentric_map[pix * 3] = barycentric[0]; + barycentric_map[pix * 3 + 1] = barycentric[1]; + barycentric_map[pix * 3 + 2] = barycentric[2]; +} + +void rasterizeImagecoordsKernelCPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces, int f) +{ + float* vt0_ptr = V + (F[f * 3] * 4); + float* vt1_ptr = V + (F[f * 3 + 1] * 4); + float* vt2_ptr = V + (F[f * 3 + 2] * 4); + + float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f}; + float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f}; + float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f}; + + rasterizeTriangleCPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc); +} + +std::vector rasterize_image_cpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, + int width, int height, float occlusion_truncation, int use_depth_prior) +{ + int num_faces = F.size(0); + int num_vertices = V.size(0); + auto options = torch::TensorOptions().dtype(torch::kInt32).requires_grad(false); + auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); + auto findices = torch::zeros({height, width}, options); + INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1); + auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint; + + if (!use_depth_prior) { + for (int i = 0; i < num_faces; ++i) { + rasterizeImagecoordsKernelCPU(V.data_ptr(), F.data_ptr(), 0, + (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces, i); + } + } else { + for (int i = 0; i < num_faces; ++i) + rasterizeImagecoordsKernelCPU(V.data_ptr(), F.data_ptr(), D.data_ptr(), + (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces, i); + } + + auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); + auto barycentric = torch::zeros({height, width, 3}, float_options); + for (int i = 0; i < width * height; ++i) + barycentricFromImgcoordCPU(V.data_ptr(), F.data_ptr(), + findices.data_ptr(), (INT64*)z_min.data_ptr(), width, height, num_vertices, num_faces, barycentric.data_ptr(), i); + + return {findices, barycentric}; +} + +std::vector rasterize_image(torch::Tensor V, torch::Tensor F, torch::Tensor D, + int width, int height, float occlusion_truncation, int use_depth_prior) +{ + int device_id = V.get_device(); + if (device_id == -1) + return rasterize_image_cpu(V, F, D, width, height, occlusion_truncation, use_depth_prior); + else + return rasterize_image_gpu(V, F, D, width, height, occlusion_truncation, use_depth_prior); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("rasterize_image", &rasterize_image, "Custom image rasterization"); + m.def("build_hierarchy", &build_hierarchy, "Custom image rasterization"); + m.def("build_hierarchy_with_feat", &build_hierarchy_with_feat, "Custom image rasterization"); +} diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h new file mode 100644 index 0000000000000000000000000000000000000000..cf4f9870bda0714763e4236f85293ca7cef7d51f --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h @@ -0,0 +1,54 @@ +#ifndef RASTERIZER_H_ +#define RASTERIZER_H_ + +#include +#include +#include +#include // For CUDA context + +#define INT64 unsigned long long +#define MAXINT 2147483647 + +__host__ __device__ inline float calculateSignedArea2(float* a, float* b, float* c) { + return ((c[0] - a[0]) * (b[1] - a[1]) - (b[0] - a[0]) * (c[1] - a[1])); +} + +__host__ __device__ inline void calculateBarycentricCoordinate(float* a, float* b, float* c, float* p, + float* barycentric) +{ + float beta_tri = calculateSignedArea2(a, p, c); + float gamma_tri = calculateSignedArea2(a, b, p); + float area = calculateSignedArea2(a, b, c); + if (area == 0) { + barycentric[0] = -1.0; + barycentric[1] = -1.0; + barycentric[2] = -1.0; + return; + } + float tri_inv = 1.0 / area; + float beta = beta_tri * tri_inv; + float gamma = gamma_tri * tri_inv; + float alpha = 1.0 - beta - gamma; + barycentric[0] = alpha; + barycentric[1] = beta; + barycentric[2] = gamma; +} + +__host__ __device__ inline bool isBarycentricCoordInBounds(float* barycentricCoord) { + return barycentricCoord[0] >= 0.0 && barycentricCoord[0] <= 1.0 && + barycentricCoord[1] >= 0.0 && barycentricCoord[1] <= 1.0 && + barycentricCoord[2] >= 0.0 && barycentricCoord[2] <= 1.0; +} + +std::vector rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, + int width, int height, float occlusion_truncation, int use_depth_prior); + +std::vector> build_hierarchy(std::vector view_layer_positions, std::vector view_layer_normals, int num_level, int resolution); + +std::vector> build_hierarchy_with_feat( + std::vector view_layer_positions, + std::vector view_layer_normals, + std::vector view_layer_feats, + int num_level, int resolution); + +#endif \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu new file mode 100644 index 0000000000000000000000000000000000000000..cc6f354c0e2801b9ac84ec4547845c8edb606a60 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu @@ -0,0 +1,127 @@ +#include "rasterizer.h" + +__device__ void rasterizeTriangleGPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) { + float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0])); + float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0])); + float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1])); + float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1])); + + for (int px = x_min; px < x_max + 1; ++px) { + if (px < 0 || px >= width) + continue; + for (int py = y_min; py < y_max + 1; ++py) { + if (py < 0 || py >= height) + continue; + float vt[2] = {px + 0.5f, py + 0.5f}; + float baryCentricCoordinate[3]; + calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate); + if (isBarycentricCoordInBounds(baryCentricCoordinate)) { + int pixel = py * width + px; + if (zbuffer == 0) { + atomicExch(&zbuffer[pixel], (INT64)(idx + 1)); + continue; + } + float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2]; + float depth_thres = 0; + if (d) { + depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation; + } + + int z_quantize = depth * (2<<17); + INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1); + if (depth < depth_thres) + continue; + atomicMin(&zbuffer[pixel], token); + } + } + } +} + +__global__ void barycentricFromImgcoordGPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces, + float* barycentric_map) +{ + int pix = blockIdx.x * blockDim.x + threadIdx.x; + if (pix >= width * height) + return; + INT64 f = zbuffer[pix] % MAXINT; + if (f == (MAXINT-1)) { + findices[pix] = 0; + barycentric_map[pix * 3] = 0; + barycentric_map[pix * 3 + 1] = 0; + barycentric_map[pix * 3 + 2] = 0; + return; + } + findices[pix] = f; + f -= 1; + float barycentric[3] = {0, 0, 0}; + if (f >= 0) { + float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f}; + float* vt0_ptr = V + (F[f * 3] * 4); + float* vt1_ptr = V + (F[f * 3 + 1] * 4); + float* vt2_ptr = V + (F[f * 3 + 2] * 4); + + float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f}; + float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f}; + float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f}; + + calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric); + + barycentric[0] = barycentric[0] / vt0_ptr[3]; + barycentric[1] = barycentric[1] / vt1_ptr[3]; + barycentric[2] = barycentric[2] / vt2_ptr[3]; + float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]); + barycentric[0] *= w; + barycentric[1] *= w; + barycentric[2] *= w; + + } + barycentric_map[pix * 3] = barycentric[0]; + barycentric_map[pix * 3 + 1] = barycentric[1]; + barycentric_map[pix * 3 + 2] = barycentric[2]; +} + +__global__ void rasterizeImagecoordsKernelGPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces) +{ + int f = blockIdx.x * blockDim.x + threadIdx.x; + if (f >= num_faces) + return; + + float* vt0_ptr = V + (F[f * 3] * 4); + float* vt1_ptr = V + (F[f * 3 + 1] * 4); + float* vt2_ptr = V + (F[f * 3 + 2] * 4); + + float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f}; + float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f}; + float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f}; + + rasterizeTriangleGPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc); +} + +std::vector rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, + int width, int height, float occlusion_truncation, int use_depth_prior) +{ + int device_id = V.get_device(); + cudaSetDevice(device_id); + int num_faces = F.size(0); + int num_vertices = V.size(0); + auto options = torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA, device_id).requires_grad(false); + auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA, device_id).requires_grad(false); + auto findices = torch::zeros({height, width}, options); + INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1); + auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint; + + if (!use_depth_prior) { + rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr(), F.data_ptr(), 0, + (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces); + } else { + rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr(), F.data_ptr(), D.data_ptr(), + (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces); + } + + auto float_options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA, device_id).requires_grad(false); + auto barycentric = torch::zeros({height, width, 3}, float_options); + barycentricFromImgcoordGPU<<<(width * height + 255)/256, 256>>>(V.data_ptr(), F.data_ptr(), + findices.data_ptr(), (INT64*)z_min.data_ptr(), width, height, num_vertices, num_faces, barycentric.data_ptr()); + + return {findices, barycentric}; +} diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/setup.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..3e312a7f45689753b5ba3ed4befff1fefecff6fd --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/setup.py @@ -0,0 +1,26 @@ +from setuptools import setup, find_packages +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +# build custom rasterizer +# build with `python setup.py install` +# nvcc is needed + +custom_rasterizer_module = CUDAExtension('custom_rasterizer_kernel', [ + 'lib/custom_rasterizer_kernel/rasterizer.cpp', + 'lib/custom_rasterizer_kernel/grid_neighbor.cpp', + 'lib/custom_rasterizer_kernel/rasterizer_gpu.cu', +]) + +setup( + packages=find_packages(), + version='0.1', + name='custom_rasterizer', + include_package_data=True, + package_dir={'': '.'}, + ext_modules=[ + custom_rasterizer_module, + ], + cmdclass={ + 'build_ext': BuildExtension + } +) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/setup.py.hy3d b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/setup.py.hy3d new file mode 100644 index 0000000000000000000000000000000000000000..3e312a7f45689753b5ba3ed4befff1fefecff6fd --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/custom_rasterizer/setup.py.hy3d @@ -0,0 +1,26 @@ +from setuptools import setup, find_packages +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +# build custom rasterizer +# build with `python setup.py install` +# nvcc is needed + +custom_rasterizer_module = CUDAExtension('custom_rasterizer_kernel', [ + 'lib/custom_rasterizer_kernel/rasterizer.cpp', + 'lib/custom_rasterizer_kernel/grid_neighbor.cpp', + 'lib/custom_rasterizer_kernel/rasterizer_gpu.cu', +]) + +setup( + packages=find_packages(), + version='0.1', + name='custom_rasterizer', + include_package_data=True, + package_dir={'': '.'}, + ext_modules=[ + custom_rasterizer_module, + ], + cmdclass={ + 'build_ext': BuildExtension + } +) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8bb2bf86e283e50f0df2ecfba8fc66289df09901 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/__init__.py @@ -0,0 +1,13 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/camera_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/camera_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fd60e3ef7c7188e7a0372aba6b68ad84691a7e6a --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/camera_utils.py @@ -0,0 +1,106 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import math + +import numpy as np +import torch + + +def transform_pos(mtx, pos, keepdim=False): + t_mtx = torch.from_numpy(mtx).to( + pos.device) if isinstance( + mtx, np.ndarray) else mtx + if pos.shape[-1] == 3: + posw = torch.cat( + [pos, torch.ones([pos.shape[0], 1]).to(pos.device)], axis=1) + else: + posw = pos + + if keepdim: + return torch.matmul(posw, t_mtx.t())[...] + else: + return torch.matmul(posw, t_mtx.t())[None, ...] + + +def get_mv_matrix(elev, azim, camera_distance, center=None): + elev = -elev + azim += 90 + + elev_rad = math.radians(elev) + azim_rad = math.radians(azim) + + camera_position = np.array([camera_distance * math.cos(elev_rad) * math.cos(azim_rad), + camera_distance * + math.cos(elev_rad) * math.sin(azim_rad), + camera_distance * math.sin(elev_rad)]) + + if center is None: + center = np.array([0, 0, 0]) + else: + center = np.array(center) + + lookat = center - camera_position + lookat = lookat / np.linalg.norm(lookat) + + up = np.array([0, 0, 1.0]) + right = np.cross(lookat, up) + right = right / np.linalg.norm(right) + up = np.cross(right, lookat) + up = up / np.linalg.norm(up) + + c2w = np.concatenate( + [np.stack([right, up, -lookat], axis=-1), camera_position[:, None]], axis=-1) + + w2c = np.zeros((4, 4)) + w2c[:3, :3] = np.transpose(c2w[:3, :3], (1, 0)) + w2c[:3, 3:] = -np.matmul(np.transpose(c2w[:3, :3], (1, 0)), c2w[:3, 3:]) + w2c[3, 3] = 1.0 + + return w2c.astype(np.float32) + + +def get_orthographic_projection_matrix( + left=-1, right=1, bottom=-1, top=1, near=0, far=2): + """ + Calculate orthographic projection matrix. + + Args: + left (float): Left boundary of projection region. + right (float): Right boundary of projection region. + bottom (float): Bottom boundary of projection region. + top (float): Top boundary of projection region. + near (float): Near clipping plane distance. + far (float): Far clipping plane distance. + + Returns: + numpy.ndarray: Orthographic projection matrix. + """ + ortho_matrix = np.eye(4, dtype=np.float32) + ortho_matrix[0, 0] = 2 / (right - left) + ortho_matrix[1, 1] = 2 / (top - bottom) + ortho_matrix[2, 2] = -2 / (far - near) + ortho_matrix[0, 3] = -(right + left) / (right - left) + ortho_matrix[1, 3] = -(top + bottom) / (top - bottom) + ortho_matrix[2, 3] = -(far + near) / (far - near) + return ortho_matrix + + +def get_perspective_projection_matrix(fovy, aspect_wh, near, far): + fovy_rad = math.radians(fovy) + return np.array([[1.0 / (math.tan(fovy_rad / 2.0) * aspect_wh), 0, 0, 0], + [0, 1.0 / math.tan(fovy_rad / 2.0), 0, 0], + [0, 0, -(far + near) / (far - near), - + 2.0 * far * near / (far - near)], + [0, 0, -1, 0]]).astype(np.float32) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.bat b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.bat new file mode 100644 index 0000000000000000000000000000000000000000..3947b0f03f9f6245dac95db7460703076444a304 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.bat @@ -0,0 +1,3 @@ +FOR /F "tokens=*" %%i IN ('python -m pybind11 --includes') DO SET PYINCLUDES=%%i +echo %PYINCLUDES% +g++ -O3 -Wall -shared -std=c++11 -fPIC %PYINCLUDES% mesh_processor.cpp -o mesh_processor.pyd -lpython3.12 \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.cpp b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ca8650fada02099d3fce0f551fa4f953f278cf34 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.cpp @@ -0,0 +1,161 @@ +#include +#include +#include +#include +#include +#include +#include + +namespace py = pybind11; +using namespace std; + +std::pair, + py::array_t> meshVerticeInpaint_smooth(py::array_t texture, +py::array_t mask, + py::array_t vtx_pos, py::array_t vtx_uv, + py::array_t pos_idx, py::array_t uv_idx) { + auto texture_buf = texture.request(); + auto mask_buf = mask.request(); + auto vtx_pos_buf = vtx_pos.request(); + auto vtx_uv_buf = vtx_uv.request(); + auto pos_idx_buf = pos_idx.request(); + auto uv_idx_buf = uv_idx.request(); + + int texture_height = texture_buf.shape[0]; + int texture_width = texture_buf.shape[1]; + int texture_channel = texture_buf.shape[2]; + float* texture_ptr = static_cast(texture_buf.ptr); + uint8_t* mask_ptr = static_cast(mask_buf.ptr); + + int vtx_num = vtx_pos_buf.shape[0]; + float* vtx_pos_ptr = static_cast(vtx_pos_buf.ptr); + float* vtx_uv_ptr = static_cast(vtx_uv_buf.ptr); + int* pos_idx_ptr = static_cast(pos_idx_buf.ptr); + int* uv_idx_ptr = static_cast(uv_idx_buf.ptr); + + vector vtx_mask(vtx_num, 0.0f); + vector> vtx_color(vtx_num, vector(texture_channel, 0.0f)); + vector uncolored_vtxs; + + vector> G(vtx_num); + + for (int i = 0; i < uv_idx_buf.shape[0]; ++i) { + for (int k = 0; k < 3; ++k) { + int vtx_uv_idx = uv_idx_ptr[i * 3 + k]; + int vtx_idx = pos_idx_ptr[i * 3 + k]; + int uv_v = round(vtx_uv_ptr[vtx_uv_idx * 2] * (texture_width - 1)); + int uv_u = round((1.0 - vtx_uv_ptr[vtx_uv_idx * 2 + 1]) * (texture_height - 1)); + + if (mask_ptr[uv_u * texture_width + uv_v] > 0) { + vtx_mask[vtx_idx] = 1.0f; + for (int c = 0; c < texture_channel; ++c) { + vtx_color[vtx_idx][c] = texture_ptr[(uv_u * texture_width + uv_v) * texture_channel + c]; + } + }else{ + uncolored_vtxs.push_back(vtx_idx); + } + + G[pos_idx_ptr[i * 3 + k]].push_back(pos_idx_ptr[i * 3 + (k + 1) % 3]); + } + } + + int smooth_count = 2; + int last_uncolored_vtx_count = 0; + while (smooth_count>0) { + int uncolored_vtx_count = 0; + + for (int vtx_idx : uncolored_vtxs) { + + vector sum_color(texture_channel, 0.0f); + float total_weight = 0.0f; + + array vtx_0 = {vtx_pos_ptr[vtx_idx * 3], +vtx_pos_ptr[vtx_idx * 3 + 1], vtx_pos_ptr[vtx_idx * 3 + 2]}; + for (int connected_idx : G[vtx_idx]) { + if (vtx_mask[connected_idx] > 0) { + array vtx1 = {vtx_pos_ptr[connected_idx * 3], + vtx_pos_ptr[connected_idx * 3 + 1], vtx_pos_ptr[connected_idx * 3 + 2]}; + float dist_weight = 1.0f / max(sqrt(pow(vtx_0[0] - vtx1[0], 2) + pow(vtx_0[1] - vtx1[1], 2) + \ + pow(vtx_0[2] - vtx1[2], 2)), 1E-4); + dist_weight = dist_weight * dist_weight; + for (int c = 0; c < texture_channel; ++c) { + sum_color[c] += vtx_color[connected_idx][c] * dist_weight; + } + total_weight += dist_weight; + } + } + + if (total_weight > 0.0f) { + for (int c = 0; c < texture_channel; ++c) { + vtx_color[vtx_idx][c] = sum_color[c] / total_weight; + } + vtx_mask[vtx_idx] = 1.0f; + } else { + uncolored_vtx_count++; + } + + } + + if(last_uncolored_vtx_count==uncolored_vtx_count){ + smooth_count--; + }else{ + smooth_count++; + } + last_uncolored_vtx_count = uncolored_vtx_count; + } + + // Create new arrays for the output + py::array_t new_texture(texture_buf.size); + py::array_t new_mask(mask_buf.size); + + auto new_texture_buf = new_texture.request(); + auto new_mask_buf = new_mask.request(); + + float* new_texture_ptr = static_cast(new_texture_buf.ptr); + uint8_t* new_mask_ptr = static_cast(new_mask_buf.ptr); + // Copy original texture and mask to new arrays + std::copy(texture_ptr, texture_ptr + texture_buf.size, new_texture_ptr); + std::copy(mask_ptr, mask_ptr + mask_buf.size, new_mask_ptr); + + for (int face_idx = 0; face_idx < uv_idx_buf.shape[0]; ++face_idx) { + for (int k = 0; k < 3; ++k) { + int vtx_uv_idx = uv_idx_ptr[face_idx * 3 + k]; + int vtx_idx = pos_idx_ptr[face_idx * 3 + k]; + + if (vtx_mask[vtx_idx] == 1.0f) { + int uv_v = round(vtx_uv_ptr[vtx_uv_idx * 2] * (texture_width - 1)); + int uv_u = round((1.0 - vtx_uv_ptr[vtx_uv_idx * 2 + 1]) * (texture_height - 1)); + + for (int c = 0; c < texture_channel; ++c) { + new_texture_ptr[(uv_u * texture_width + uv_v) * texture_channel + c] = vtx_color[vtx_idx][c]; + } + new_mask_ptr[uv_u * texture_width + uv_v] = 255; + } + } + } + + // Reshape the new arrays to match the original texture and mask shapes + new_texture.resize({texture_height, texture_width, 3}); + new_mask.resize({texture_height, texture_width}); + return std::make_pair(new_texture, new_mask); +} + + +std::pair, py::array_t> meshVerticeInpaint(py::array_t texture, + py::array_t mask, + py::array_t vtx_pos, py::array_t vtx_uv, + py::array_t pos_idx, py::array_t uv_idx, const std::string& method = "smooth") { + if (method == "smooth") { + return meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx); + } else { + throw std::invalid_argument("Invalid method. Use 'smooth' or 'forward'."); + } +} + +PYBIND11_MODULE(mesh_processor, m) { + m.def("meshVerticeInpaint", &meshVerticeInpaint, "A function to process mesh", + py::arg("texture"), py::arg("mask"), + py::arg("vtx_pos"), py::arg("vtx_uv"), + py::arg("pos_idx"), py::arg("uv_idx"), + py::arg("method") = "smooth"); +} \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.cpython-311-x86_64-linux-gnu.so b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.cpython-311-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..b51e5e6e8662498ab7e509bb0042d4369adf9674 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.cpython-311-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:59e8f5a43ede59fcb29c54967ebe8a44cc82a3c5a13bd18d228f6da596ddfbc6 +size 192312 diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..5a731ccea4237c4605f94c7b52ace42d590b6aa0 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_processor.py @@ -0,0 +1,84 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import numpy as np + +def meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx): + texture_height, texture_width, texture_channel = texture.shape + vtx_num = vtx_pos.shape[0] + + vtx_mask = np.zeros(vtx_num, dtype=np.float32) + vtx_color = [np.zeros(texture_channel, dtype=np.float32) for _ in range(vtx_num)] + uncolored_vtxs = [] + G = [[] for _ in range(vtx_num)] + + for i in range(uv_idx.shape[0]): + for k in range(3): + vtx_uv_idx = uv_idx[i, k] + vtx_idx = pos_idx[i, k] + uv_v = int(round(vtx_uv[vtx_uv_idx, 0] * (texture_width - 1))) + uv_u = int(round((1.0 - vtx_uv[vtx_uv_idx, 1]) * (texture_height - 1))) + if mask[uv_u, uv_v] > 0: + vtx_mask[vtx_idx] = 1.0 + vtx_color[vtx_idx] = texture[uv_u, uv_v] + else: + uncolored_vtxs.append(vtx_idx) + G[pos_idx[i, k]].append(pos_idx[i, (k + 1) % 3]) + + smooth_count = 2 + last_uncolored_vtx_count = 0 + while smooth_count > 0: + uncolored_vtx_count = 0 + for vtx_idx in uncolored_vtxs: + sum_color = np.zeros(texture_channel, dtype=np.float32) + total_weight = 0.0 + vtx_0 = vtx_pos[vtx_idx] + for connected_idx in G[vtx_idx]: + if vtx_mask[connected_idx] > 0: + vtx1 = vtx_pos[connected_idx] + dist = np.sqrt(np.sum((vtx_0 - vtx1) ** 2)) + dist_weight = 1.0 / max(dist, 1e-4) + dist_weight *= dist_weight + sum_color += vtx_color[connected_idx] * dist_weight + total_weight += dist_weight + if total_weight > 0: + vtx_color[vtx_idx] = sum_color / total_weight + vtx_mask[vtx_idx] = 1.0 + else: + uncolored_vtx_count += 1 + + if last_uncolored_vtx_count == uncolored_vtx_count: + smooth_count -= 1 + else: + smooth_count += 1 + last_uncolored_vtx_count = uncolored_vtx_count + + new_texture = texture.copy() + new_mask = mask.copy() + for face_idx in range(uv_idx.shape[0]): + for k in range(3): + vtx_uv_idx = uv_idx[face_idx, k] + vtx_idx = pos_idx[face_idx, k] + if vtx_mask[vtx_idx] == 1.0: + uv_v = int(round(vtx_uv[vtx_uv_idx, 0] * (texture_width - 1))) + uv_u = int(round((1.0 - vtx_uv[vtx_uv_idx, 1]) * (texture_height - 1))) + new_texture[uv_u, uv_v] = vtx_color[vtx_idx] + new_mask[uv_u, uv_v] = 255 + return new_texture, new_mask + +def meshVerticeInpaint(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx, method="smooth"): + if method == "smooth": + return meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx) + else: + raise ValueError("Invalid method. Use 'smooth' or 'forward'.") \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_render.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_render.py new file mode 100644 index 0000000000000000000000000000000000000000..b07290fa2917e736784ba5280712aaddf0800a04 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_render.py @@ -0,0 +1,823 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +import trimesh +from PIL import Image + +from .camera_utils import ( + transform_pos, + get_mv_matrix, + get_orthographic_projection_matrix, + get_perspective_projection_matrix, +) +from .mesh_processor import meshVerticeInpaint +from .mesh_utils import load_mesh, save_mesh + + +def stride_from_shape(shape): + stride = [1] + for x in reversed(shape[1:]): + stride.append(stride[-1] * x) + return list(reversed(stride)) + + +def scatter_add_nd_with_count(input, count, indices, values, weights=None): + # input: [..., C], D dimension + C channel + # count: [..., 1], D dimension + # indices: [N, D], long + # values: [N, C] + + D = indices.shape[-1] + C = input.shape[-1] + size = input.shape[:-1] + stride = stride_from_shape(size) + + assert len(size) == D + + input = input.view(-1, C) # [HW, C] + count = count.view(-1, 1) + + flatten_indices = (indices * torch.tensor(stride, + dtype=torch.long, device=indices.device)).sum(-1) # [N] + + if weights is None: + weights = torch.ones_like(values[..., :1]) + + input.scatter_add_(0, flatten_indices.unsqueeze(1).repeat(1, C), values) + count.scatter_add_(0, flatten_indices.unsqueeze(1), weights) + + return input.view(*size, C), count.view(*size, 1) + + +def linear_grid_put_2d(H, W, coords, values, return_count=False): + # coords: [N, 2], float in [0, 1] + # values: [N, C] + + C = values.shape[-1] + + indices = coords * torch.tensor( + [H - 1, W - 1], dtype=torch.float32, device=coords.device + ) + indices_00 = indices.floor().long() # [N, 2] + indices_00[:, 0].clamp_(0, H - 2) + indices_00[:, 1].clamp_(0, W - 2) + indices_01 = indices_00 + torch.tensor( + [0, 1], dtype=torch.long, device=indices.device + ) + indices_10 = indices_00 + torch.tensor( + [1, 0], dtype=torch.long, device=indices.device + ) + indices_11 = indices_00 + torch.tensor( + [1, 1], dtype=torch.long, device=indices.device + ) + + h = indices[..., 0] - indices_00[..., 0].float() + w = indices[..., 1] - indices_00[..., 1].float() + w_00 = (1 - h) * (1 - w) + w_01 = (1 - h) * w + w_10 = h * (1 - w) + w_11 = h * w + + result = torch.zeros(H, W, C, device=values.device, + dtype=values.dtype) # [H, W, C] + count = torch.zeros(H, W, 1, device=values.device, + dtype=values.dtype) # [H, W, 1] + weights = torch.ones_like(values[..., :1]) # [N, 1] + + result, count = scatter_add_nd_with_count( + result, count, indices_00, values * w_00.unsqueeze(1), weights * w_00.unsqueeze(1)) + result, count = scatter_add_nd_with_count( + result, count, indices_01, values * w_01.unsqueeze(1), weights * w_01.unsqueeze(1)) + result, count = scatter_add_nd_with_count( + result, count, indices_10, values * w_10.unsqueeze(1), weights * w_10.unsqueeze(1)) + result, count = scatter_add_nd_with_count( + result, count, indices_11, values * w_11.unsqueeze(1), weights * w_11.unsqueeze(1)) + + if return_count: + return result, count + + mask = (count.squeeze(-1) > 0) + result[mask] = result[mask] / count[mask].repeat(1, C) + + return result + + +class MeshRender(): + def __init__( + self, + camera_distance=1.45, camera_type='orth', + default_resolution=1024, texture_size=1024, + use_antialias=True, max_mip_level=None, filter_mode='linear', + bake_mode='linear', raster_mode='cr', device='cuda'): + + self.device = device + + self.set_default_render_resolution(default_resolution) + self.set_default_texture_resolution(texture_size) + + self.camera_distance = camera_distance + self.use_antialias = use_antialias + self.max_mip_level = max_mip_level + self.filter_mode = filter_mode + + self.bake_angle_thres = 75 + self.bake_unreliable_kernel_size = int( + (2 / 512) * max(self.default_resolution[0], self.default_resolution[1])) + self.bake_mode = bake_mode + + self.raster_mode = raster_mode + if self.raster_mode == 'cr': + import custom_rasterizer as cr + self.raster = cr + else: + raise f'No raster named {self.raster_mode}' + + if camera_type == 'orth': + self.ortho_scale = 1.2 + self.camera_proj_mat = get_orthographic_projection_matrix( + left=-self.ortho_scale * 0.5, right=self.ortho_scale * 0.5, + bottom=-self.ortho_scale * 0.5, top=self.ortho_scale * 0.5, + near=0.1, far=100 + ) + elif camera_type == 'perspective': + self.camera_proj_mat = get_perspective_projection_matrix( + 49.13, self.default_resolution[1] / self.default_resolution[0], + 0.01, 100.0 + ) + else: + raise f'No camera type {camera_type}' + + def raster_rasterize(self, pos, tri, resolution, ranges=None, grad_db=True): + + if self.raster_mode == 'cr': + rast_out_db = None + if pos.dim() == 2: + pos = pos.unsqueeze(0) + findices, barycentric = self.raster.rasterize(pos, tri, resolution) + rast_out = torch.cat((barycentric, findices.unsqueeze(-1)), dim=-1) + rast_out = rast_out.unsqueeze(0) + else: + raise f'No raster named {self.raster_mode}' + + return rast_out, rast_out_db + + def raster_interpolate(self, uv, rast_out, uv_idx, rast_db=None, diff_attrs=None): + + if self.raster_mode == 'cr': + textd = None + barycentric = rast_out[0, ..., :-1] + findices = rast_out[0, ..., -1] + if uv.dim() == 2: + uv = uv.unsqueeze(0) + textc = self.raster.interpolate(uv, findices, barycentric, uv_idx) + else: + raise f'No raster named {self.raster_mode}' + + return textc, textd + + def raster_texture(self, tex, uv, uv_da=None, mip_level_bias=None, mip=None, filter_mode='auto', + boundary_mode='wrap', max_mip_level=None): + + if self.raster_mode == 'cr': + raise f'Texture is not implemented in cr' + else: + raise f'No raster named {self.raster_mode}' + + return color + + def raster_antialias(self, color, rast, pos, tri, topology_hash=None, pos_gradient_boost=1.0): + + if self.raster_mode == 'cr': + # Antialias has not been supported yet + color = color + else: + raise f'No raster named {self.raster_mode}' + + return color + + def load_mesh( + self, + mesh, + scale_factor=1.15, + auto_center=True, + ): + vtx_pos, pos_idx, vtx_uv, uv_idx, texture_data = load_mesh(mesh) + self.mesh_copy = mesh + self.set_mesh(vtx_pos, pos_idx, + vtx_uv=vtx_uv, uv_idx=uv_idx, + scale_factor=scale_factor, auto_center=auto_center + ) + if texture_data is not None: + self.set_texture(texture_data) + + def save_mesh(self): + texture_data = self.get_texture() + texture_data = Image.fromarray((texture_data * 255).astype(np.uint8)) + return save_mesh(self.mesh_copy, texture_data) + + def set_mesh( + self, + vtx_pos, pos_idx, + vtx_uv=None, uv_idx=None, + scale_factor=1.15, auto_center=True + ): + + self.vtx_pos = torch.from_numpy(vtx_pos).to(self.device).float() + self.pos_idx = torch.from_numpy(pos_idx).to(self.device).to(torch.int) + if (vtx_uv is not None) and (uv_idx is not None): + self.vtx_uv = torch.from_numpy(vtx_uv).to(self.device).float() + self.uv_idx = torch.from_numpy(uv_idx).to(self.device).to(torch.int) + else: + self.vtx_uv = None + self.uv_idx = None + + self.vtx_pos[:, [0, 1]] = -self.vtx_pos[:, [0, 1]] + self.vtx_pos[:, [1, 2]] = self.vtx_pos[:, [2, 1]] + if (vtx_uv is not None) and (uv_idx is not None): + self.vtx_uv[:, 1] = 1.0 - self.vtx_uv[:, 1] + + if auto_center: + max_bb = (self.vtx_pos - 0).max(0)[0] + min_bb = (self.vtx_pos - 0).min(0)[0] + center = (max_bb + min_bb) / 2 + scale = torch.norm(self.vtx_pos - center, dim=1).max() * 2.0 + self.vtx_pos = (self.vtx_pos - center) * \ + (scale_factor / float(scale)) + self.scale_factor = scale_factor + + def set_texture(self, tex): + if isinstance(tex, np.ndarray): + tex = Image.fromarray((tex * 255).astype(np.uint8)) + elif isinstance(tex, torch.Tensor): + tex = tex.cpu().numpy() + tex = Image.fromarray((tex * 255).astype(np.uint8)) + + tex = tex.resize(self.texture_size).convert('RGB') + tex = np.array(tex) / 255.0 + self.tex = torch.from_numpy(tex).to(self.device) + self.tex = self.tex.float() + + def set_default_render_resolution(self, default_resolution): + if isinstance(default_resolution, int): + default_resolution = (default_resolution, default_resolution) + self.default_resolution = default_resolution + + def set_default_texture_resolution(self, texture_size): + if isinstance(texture_size, int): + texture_size = (texture_size, texture_size) + self.texture_size = texture_size + + def get_mesh(self): + vtx_pos = self.vtx_pos.cpu().numpy() + pos_idx = self.pos_idx.cpu().numpy() + vtx_uv = self.vtx_uv.cpu().numpy() + uv_idx = self.uv_idx.cpu().numpy() + + # Inverse coordinate transformation + vtx_pos[:, [1, 2]] = vtx_pos[:, [2, 1]] + vtx_pos[:, [0, 1]] = -vtx_pos[:, [0, 1]] + + vtx_uv[:, 1] = 1.0 - vtx_uv[:, 1] + return vtx_pos, pos_idx, vtx_uv, uv_idx + + def get_texture(self): + return self.tex.cpu().numpy() + + def to(self, device): + self.device = device + + for attr_name in dir(self): + attr_value = getattr(self, attr_name) + if isinstance(attr_value, torch.Tensor): + setattr(self, attr_name, attr_value.to(self.device)) + + def color_rgb_to_srgb(self, image): + if isinstance(image, Image.Image): + image_rgb = torch.tesnor( + np.array(image) / + 255.0).float().to( + self.device) + elif isinstance(image, np.ndarray): + image_rgb = torch.tensor(image).float() + else: + image_rgb = image.to(self.device) + + image_srgb = torch.where( + image_rgb <= 0.0031308, + 12.92 * image_rgb, + 1.055 * torch.pow(image_rgb, 1 / 2.4) - 0.055 + ) + + if isinstance(image, Image.Image): + image_srgb = Image.fromarray( + (image_srgb.cpu().numpy() * + 255).astype( + np.uint8)) + elif isinstance(image, np.ndarray): + image_srgb = image_srgb.cpu().numpy() + else: + image_srgb = image_srgb.to(image.device) + + return image_srgb + + def _render( + self, + glctx, + mvp, + pos, + pos_idx, + uv, + uv_idx, + tex, + resolution, + max_mip_level, + keep_alpha, + filter_mode + ): + pos_clip = transform_pos(mvp, pos) + if isinstance(resolution, (int, float)): + resolution = [resolution, resolution] + rast_out, rast_out_db = self.raster_rasterize( + glctx, pos_clip, pos_idx, resolution=resolution) + + tex = tex.contiguous() + if filter_mode == 'linear-mipmap-linear': + texc, texd = self.raster_interpolate( + uv[None, ...], rast_out, uv_idx, rast_db=rast_out_db, diff_attrs='all') + color = self.raster_texture( + tex[None, ...], texc, texd, filter_mode='linear-mipmap-linear', max_mip_level=max_mip_level) + else: + texc, _ = self.raster_interpolate(uv[None, ...], rast_out, uv_idx) + color = self.raster_texture(tex[None, ...], texc, filter_mode=filter_mode) + + visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) + color = color * visible_mask # Mask out background. + if self.use_antialias: + color = self.raster_antialias(color, rast_out, pos_clip, pos_idx) + + if keep_alpha: + color = torch.cat([color, visible_mask], dim=-1) + return color[0, ...] + + def render( + self, + elev, + azim, + camera_distance=None, + center=None, + resolution=None, + tex=None, + keep_alpha=True, + bgcolor=None, + filter_mode=None, + return_type='th' + ): + + proj = self.camera_proj_mat + r_mv = get_mv_matrix( + elev=elev, + azim=azim, + camera_distance=self.camera_distance if camera_distance is None else camera_distance, + center=center) + r_mvp = np.matmul(proj, r_mv).astype(np.float32) + if tex is not None: + if isinstance(tex, Image.Image): + tex = torch.tensor(np.array(tex) / 255.0) + elif isinstance(tex, np.ndarray): + tex = torch.tensor(tex) + if tex.dim() == 2: + tex = tex.unsqueeze(-1) + tex = tex.float().to(self.device) + image = self._render(r_mvp, self.vtx_pos, self.pos_idx, self.vtx_uv, self.uv_idx, + self.tex if tex is None else tex, + self.default_resolution if resolution is None else resolution, + self.max_mip_level, True, filter_mode if filter_mode else self.filter_mode) + mask = (image[..., [-1]] == 1).float() + if bgcolor is None: + bgcolor = [0 for _ in range(image.shape[-1] - 1)] + image = image * mask + (1 - mask) * \ + torch.tensor(bgcolor + [0]).to(self.device) + if keep_alpha == False: + image = image[..., :-1] + if return_type == 'np': + image = image.cpu().numpy() + elif return_type == 'pl': + image = image.squeeze(-1).cpu().numpy() * 255 + image = Image.fromarray(image.astype(np.uint8)) + return image + + def render_normal( + self, + elev, + azim, + camera_distance=None, + center=None, + resolution=None, + bg_color=[1, 1, 1], + use_abs_coor=False, + normalize_rgb=True, + return_type='th' + ): + + pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center) + if resolution is None: + resolution = self.default_resolution + if isinstance(resolution, (int, float)): + resolution = [resolution, resolution] + rast_out, rast_out_db = self.raster_rasterize( + pos_clip, self.pos_idx, resolution=resolution) + + if use_abs_coor: + mesh_triangles = self.vtx_pos[self.pos_idx[:, :3], :] + else: + pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4] + mesh_triangles = pos_camera[self.pos_idx[:, :3], :] + face_normals = F.normalize( + torch.cross(mesh_triangles[:, + 1, + :] - mesh_triangles[:, + 0, + :], + mesh_triangles[:, + 2, + :] - mesh_triangles[:, + 0, + :], + dim=-1), + dim=-1) + + vertex_normals = trimesh.geometry.mean_vertex_normals(vertex_count=self.vtx_pos.shape[0], + faces=self.pos_idx.cpu(), + face_normals=face_normals.cpu(), ) + vertex_normals = torch.from_numpy( + vertex_normals).float().to(self.device).contiguous() + + # Interpolate normal values across the rasterized pixels + normal, _ = self.raster_interpolate( + vertex_normals[None, ...], rast_out, self.pos_idx) + + visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) + normal = normal * visible_mask + \ + torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 - + visible_mask) + + if normalize_rgb: + normal = (normal + 1) * 0.5 + if self.use_antialias: + normal = self.raster_antialias(normal, rast_out, pos_clip, self.pos_idx) + + image = normal[0, ...] + if return_type == 'np': + image = image.cpu().numpy() + elif return_type == 'pl': + image = image.cpu().numpy() * 255 + image = Image.fromarray(image.astype(np.uint8)) + + return image + + def convert_normal_map(self, image): + # blue is front, red is left, green is top + if isinstance(image, Image.Image): + image = np.array(image) + mask = (image == [255, 255, 255]).all(axis=-1) + + image = (image / 255.0) * 2.0 - 1.0 + + image[..., [1]] = -image[..., [1]] + image[..., [1, 2]] = image[..., [2, 1]] + image[..., [0]] = -image[..., [0]] + + image = (image + 1.0) * 0.5 + + image = (image * 255).astype(np.uint8) + image[mask] = [127, 127, 255] + + return Image.fromarray(image) + + def get_pos_from_mvp(self, elev, azim, camera_distance, center): + proj = self.camera_proj_mat + r_mv = get_mv_matrix( + elev=elev, + azim=azim, + camera_distance=self.camera_distance if camera_distance is None else camera_distance, + center=center) + + pos_camera = transform_pos(r_mv, self.vtx_pos, keepdim=True) + pos_clip = transform_pos(proj, pos_camera) + + return pos_camera, pos_clip + + def render_depth( + self, + elev, + azim, + camera_distance=None, + center=None, + resolution=None, + return_type='th' + ): + pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center) + + if resolution is None: + resolution = self.default_resolution + if isinstance(resolution, (int, float)): + resolution = [resolution, resolution] + rast_out, rast_out_db = self.raster_rasterize( + pos_clip, self.pos_idx, resolution=resolution) + + pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4] + tex_depth = pos_camera[:, 2].reshape(1, -1, 1).contiguous() + + # Interpolate depth values across the rasterized pixels + depth, _ = self.raster_interpolate(tex_depth, rast_out, self.pos_idx) + + visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) + depth_max, depth_min = depth[visible_mask > + 0].max(), depth[visible_mask > 0].min() + depth = (depth - depth_min) / (depth_max - depth_min) + + depth = depth * visible_mask # Mask out background. + if self.use_antialias: + depth = self.raster_antialias(depth, rast_out, pos_clip, self.pos_idx) + + image = depth[0, ...] + if return_type == 'np': + image = image.cpu().numpy() + elif return_type == 'pl': + image = image.squeeze(-1).cpu().numpy() * 255 + image = Image.fromarray(image.astype(np.uint8)) + return image + + def render_position(self, elev, azim, camera_distance=None, center=None, + resolution=None, bg_color=[1, 1, 1], return_type='th'): + pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center) + if resolution is None: + resolution = self.default_resolution + if isinstance(resolution, (int, float)): + resolution = [resolution, resolution] + rast_out, rast_out_db = self.raster_rasterize( + pos_clip, self.pos_idx, resolution=resolution) + + tex_position = 0.5 - self.vtx_pos[:, :3] / self.scale_factor + tex_position = tex_position.contiguous() + + # Interpolate depth values across the rasterized pixels + position, _ = self.raster_interpolate( + tex_position[None, ...], rast_out, self.pos_idx) + + visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) + + position = position * visible_mask + \ + torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 - + visible_mask) + if self.use_antialias: + position = self.raster_antialias(position, rast_out, pos_clip, self.pos_idx) + + image = position[0, ...] + + if return_type == 'np': + image = image.cpu().numpy() + elif return_type == 'pl': + image = image.squeeze(-1).cpu().numpy() * 255 + image = Image.fromarray(image.astype(np.uint8)) + return image + + def render_uvpos(self, return_type='th'): + image = self.uv_feature_map(self.vtx_pos * 0.5 + 0.5) + if return_type == 'np': + image = image.cpu().numpy() + elif return_type == 'pl': + image = image.cpu().numpy() * 255 + image = Image.fromarray(image.astype(np.uint8)) + return image + + def uv_feature_map(self, vert_feat, bg=None): + vtx_uv = self.vtx_uv * 2 - 1.0 + vtx_uv = torch.cat( + [vtx_uv, torch.zeros_like(self.vtx_uv)], dim=1).unsqueeze(0) + vtx_uv[..., -1] = 1 + uv_idx = self.uv_idx + rast_out, rast_out_db = self.raster_rasterize( + vtx_uv, uv_idx, resolution=self.texture_size) + feat_map, _ = self.raster_interpolate(vert_feat[None, ...], rast_out, uv_idx) + feat_map = feat_map[0, ...] + if bg is not None: + visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)[0, ...] + feat_map[visible_mask == 0] = bg + return feat_map + + def render_sketch_from_geometry(self, normal_image, depth_image): + normal_image_np = normal_image.cpu().numpy() + depth_image_np = depth_image.cpu().numpy() + + normal_image_np = (normal_image_np * 255).astype(np.uint8) + depth_image_np = (depth_image_np * 255).astype(np.uint8) + normal_image_np = cv2.cvtColor(normal_image_np, cv2.COLOR_RGB2GRAY) + + normal_edges = cv2.Canny(normal_image_np, 80, 150) + depth_edges = cv2.Canny(depth_image_np, 30, 80) + + combined_edges = np.maximum(normal_edges, depth_edges) + + sketch_image = torch.from_numpy(combined_edges).to( + normal_image.device).float() / 255.0 + sketch_image = sketch_image.unsqueeze(-1) + + return sketch_image + + def render_sketch_from_depth(self, depth_image): + depth_image_np = depth_image.cpu().numpy() + depth_image_np = (depth_image_np * 255).astype(np.uint8) + depth_edges = cv2.Canny(depth_image_np, 30, 80) + combined_edges = depth_edges + sketch_image = torch.from_numpy(combined_edges).to( + depth_image.device).float() / 255.0 + sketch_image = sketch_image.unsqueeze(-1) + return sketch_image + + def back_project(self, image, elev, azim, + camera_distance=None, center=None, method=None): + if isinstance(image, Image.Image): + image = torch.tensor(np.array(image) / 255.0) + elif isinstance(image, np.ndarray): + image = torch.tensor(image) + if image.dim() == 2: + image = image.unsqueeze(-1) + image = image.float().to(self.device) + resolution = image.shape[:2] + channel = image.shape[-1] + texture = torch.zeros(self.texture_size + (channel,)).to(self.device) + cos_map = torch.zeros(self.texture_size + (1,)).to(self.device) + + proj = self.camera_proj_mat + r_mv = get_mv_matrix( + elev=elev, + azim=azim, + camera_distance=self.camera_distance if camera_distance is None else camera_distance, + center=center) + pos_camera = transform_pos(r_mv, self.vtx_pos, keepdim=True) + pos_clip = transform_pos(proj, pos_camera) + pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4] + v0 = pos_camera[self.pos_idx[:, 0], :] + v1 = pos_camera[self.pos_idx[:, 1], :] + v2 = pos_camera[self.pos_idx[:, 2], :] + face_normals = F.normalize( + torch.cross( + v1 - v0, + v2 - v0, + dim=-1), + dim=-1) + vertex_normals = trimesh.geometry.mean_vertex_normals(vertex_count=self.vtx_pos.shape[0], + faces=self.pos_idx.cpu(), + face_normals=face_normals.cpu(), ) + vertex_normals = torch.from_numpy( + vertex_normals).float().to(self.device).contiguous() + tex_depth = pos_camera[:, 2].reshape(1, -1, 1).contiguous() + rast_out, rast_out_db = self.raster_rasterize( + pos_clip, self.pos_idx, resolution=resolution) + visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)[0, ...] + + normal, _ = self.raster_interpolate( + vertex_normals[None, ...], rast_out, self.pos_idx) + normal = normal[0, ...] + uv, _ = self.raster_interpolate(self.vtx_uv[None, ...], rast_out, self.uv_idx) + depth, _ = self.raster_interpolate(tex_depth, rast_out, self.pos_idx) + depth = depth[0, ...] + + depth_max, depth_min = depth[visible_mask > + 0].max(), depth[visible_mask > 0].min() + depth_normalized = (depth - depth_min) / (depth_max - depth_min) + depth_image = depth_normalized * visible_mask # Mask out background. + + sketch_image = self.render_sketch_from_depth(depth_image) + + lookat = torch.tensor([[0, 0, -1]], device=self.device) + cos_image = torch.nn.functional.cosine_similarity( + lookat, normal.view(-1, 3)) + cos_image = cos_image.view(normal.shape[0], normal.shape[1], 1) + + cos_thres = np.cos(self.bake_angle_thres / 180 * np.pi) + cos_image[cos_image < cos_thres] = 0 + + # shrink + kernel_size = self.bake_unreliable_kernel_size * 2 + 1 + kernel = torch.ones( + (1, 1, kernel_size, kernel_size), dtype=torch.float32).to( + sketch_image.device) + + visible_mask = visible_mask.permute(2, 0, 1).unsqueeze(0).float() + visible_mask = F.conv2d( + 1.0 - visible_mask, + kernel, + padding=kernel_size // 2) + visible_mask = 1.0 - (visible_mask > 0).float() # Binarize + visible_mask = visible_mask.squeeze(0).permute(1, 2, 0) + + sketch_image = sketch_image.permute(2, 0, 1).unsqueeze(0) + sketch_image = F.conv2d(sketch_image, kernel, padding=kernel_size // 2) + sketch_image = (sketch_image > 0).float() # Binarize + sketch_image = sketch_image.squeeze(0).permute(1, 2, 0) + visible_mask = visible_mask * (sketch_image < 0.5) + + cos_image[visible_mask == 0] = 0 + + method = self.bake_mode if method is None else method + + if method == 'linear': + proj_mask = (visible_mask != 0).view(-1) + uv = uv.squeeze(0).contiguous().view(-1, 2)[proj_mask] + image = image.squeeze(0).contiguous().view(-1, channel)[proj_mask] + cos_image = cos_image.contiguous().view(-1, 1)[proj_mask] + sketch_image = sketch_image.contiguous().view(-1, 1)[proj_mask] + + texture = linear_grid_put_2d( + self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], image) + cos_map = linear_grid_put_2d( + self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], cos_image) + boundary_map = linear_grid_put_2d( + self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], sketch_image) + else: + raise f'No bake mode {method}' + + return texture, cos_map, boundary_map + + def bake_texture(self, colors, elevs, azims, + camera_distance=None, center=None, exp=6, weights=None): + for i in range(len(colors)): + if isinstance(colors[i], Image.Image): + colors[i] = torch.tensor( + np.array( + colors[i]) / 255.0, + device=self.device).float() + if weights is None: + weights = [1.0 for _ in range(colors)] + textures = [] + cos_maps = [] + for color, elev, azim, weight in zip(colors, elevs, azims, weights): + texture, cos_map, _ = self.back_project( + color, elev, azim, camera_distance, center) + cos_map = weight * (cos_map ** exp) + textures.append(texture) + cos_maps.append(cos_map) + + texture_merge, trust_map_merge = self.fast_bake_texture( + textures, cos_maps) + return texture_merge, trust_map_merge + + @torch.no_grad() + def fast_bake_texture(self, textures, cos_maps): + + channel = textures[0].shape[-1] + texture_merge = torch.zeros( + self.texture_size + (channel,)).to(self.device) + trust_map_merge = torch.zeros(self.texture_size + (1,)).to(self.device) + for texture, cos_map in zip(textures, cos_maps): + view_sum = (cos_map > 0).sum() + painted_sum = ((cos_map > 0) * (trust_map_merge > 0)).sum() + if painted_sum / view_sum > 0.99: + continue + texture_merge += texture * cos_map + trust_map_merge += cos_map + texture_merge = texture_merge / torch.clamp(trust_map_merge, min=1E-8) + + return texture_merge, trust_map_merge > 1E-8 + + def uv_inpaint(self, texture, mask): + + if isinstance(texture, torch.Tensor): + texture_np = texture.cpu().numpy() + elif isinstance(texture, np.ndarray): + texture_np = texture + elif isinstance(texture, Image.Image): + texture_np = np.array(texture) / 255.0 + + vtx_pos, pos_idx, vtx_uv, uv_idx = self.get_mesh() + + texture_np, mask = meshVerticeInpaint( + texture_np, mask, vtx_pos, vtx_uv, pos_idx, uv_idx) + + texture_np = cv2.inpaint( + (texture_np * + 255).astype( + np.uint8), + 255 - + mask, + 3, + cv2.INPAINT_NS) + + return texture_np diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fa5694a66a501262b3d50acdf1340d0fa8487dec --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/mesh_utils.py @@ -0,0 +1,34 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import trimesh + + +def load_mesh(mesh): + vtx_pos = mesh.vertices if hasattr(mesh, 'vertices') else None + pos_idx = mesh.faces if hasattr(mesh, 'faces') else None + + vtx_uv = mesh.visual.uv if hasattr(mesh.visual, 'uv') else None + uv_idx = mesh.faces if hasattr(mesh, 'faces') else None + + texture_data = None + + return vtx_pos, pos_idx, vtx_uv, uv_idx, texture_data + + +def save_mesh(mesh, texture_data): + material = trimesh.visual.texture.SimpleMaterial(image=texture_data, diffuse=(255, 255, 255)) + texture_visuals = trimesh.visual.TextureVisuals(uv=mesh.visual.uv, image=texture_data, material=material) + mesh.visual = texture_visuals + return mesh diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/setup.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..1bfdb10a559dc63f56502f45efdef0470dd41bc5 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/differentiable_renderer/setup.py @@ -0,0 +1,62 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +from setuptools import setup, Extension +import pybind11 +import sys +import platform + +def get_platform_specific_args(): + system = platform.system().lower() + cpp_std = 'c++14' # Make configurable if needed + + if sys.platform == 'win32': + compile_args = ['/O2', f'/std:{cpp_std}', '/EHsc', '/MP', '/DWIN32_LEAN_AND_MEAN', '/bigobj'] + link_args = [] + extra_includes = [] + elif system == 'linux': + compile_args = ['-O3', f'-std={cpp_std}', '-fPIC', '-Wall', '-Wextra', '-pthread'] + link_args = ['-fPIC', '-pthread'] + extra_includes = [] + elif sys.platform == 'darwin': + compile_args = ['-O3', f'-std={cpp_std}', '-fPIC', '-Wall', '-Wextra', + '-stdlib=libc++', '-mmacosx-version-min=10.14'] + link_args = ['-fPIC', '-stdlib=libc++', '-mmacosx-version-min=10.14', '-dynamiclib'] + extra_includes = [] + else: + raise RuntimeError(f"Unsupported platform: {system}") + + return compile_args, link_args, extra_includes + +extra_compile_args, extra_link_args, platform_includes = get_platform_specific_args() +include_dirs = [pybind11.get_include(), pybind11.get_include(user=True)] +include_dirs.extend(platform_includes) + +ext_modules = [ + Extension( + "mesh_processor", + ["mesh_processor.cpp"], + include_dirs=include_dirs, + language='c++', + extra_compile_args=extra_compile_args, + extra_link_args=extra_link_args, + ), +] + +setup( + name="mesh_processor", + ext_modules=ext_modules, + install_requires=['pybind11>=2.6.0'], + python_requires='>=3.6', +) \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8bb2bf86e283e50f0df2ecfba8fc66289df09901 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/__init__.py @@ -0,0 +1,13 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/pipeline.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..38f3777448d0fe026f6456a71cd51a8b013446ec --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/pipeline.py @@ -0,0 +1,722 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +from typing import Any, Callable, Dict, List, Optional, Union + +import numpy +import numpy as np +import torch +import torch.distributed +import torch.utils.checkpoint +import transformers +from PIL import Image +import diffusers +from diffusers import ( + AutoencoderKL, + DDPMScheduler, + DiffusionPipeline, + EulerAncestralDiscreteScheduler, + UNet2DConditionModel, + ImagePipelineOutput +) +from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback +from diffusers.image_processor import PipelineImageInput +from diffusers.image_processor import VaeImageProcessor +from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput +from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline, \ + retrieve_timesteps, rescale_noise_cfg +from diffusers.schedulers import KarrasDiffusionSchedulers, LCMScheduler +from diffusers.utils import deprecate +from einops import rearrange +from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection + +from .unet.modules import UNet2p5DConditionModel, \ + compute_multi_resolution_mask, compute_multi_resolution_discrete_voxel_indice + +def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32): + """ + See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 + + Args: + timesteps (`torch.Tensor`): + generate embedding vectors at these timesteps + embedding_dim (`int`, *optional*, defaults to 512): + dimension of the embeddings to generate + dtype: + data type of the generated embeddings + + Returns: + `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` + """ + assert len(w.shape) == 1 + w = w * 1000.0 + + half_dim = embedding_dim // 2 + emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) + emb = w.to(dtype)[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1)) + assert emb.shape == (w.shape[0], embedding_dim) + return emb + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less") + return x[(...,) + (None,) * dims_to_append] + + +# From LCMScheduler.get_scalings_for_boundary_condition_discrete +def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0): + scaled_timestep = timestep_scaling * timestep + c_skip = sigma_data ** 2 / (scaled_timestep ** 2 + sigma_data ** 2) + c_out = scaled_timestep / (scaled_timestep ** 2 + sigma_data ** 2) ** 0.5 + return c_skip, c_out + + +# Compare LCMScheduler.step, Step 4 +def get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas, N_gen): + alphas = extract_into_tensor(alphas, timesteps, sample.shape, N_gen) + sigmas = extract_into_tensor(sigmas, timesteps, sample.shape, N_gen) + model_output = rearrange(model_output, '(b n) c h w -> b n c h w', n=N_gen) + if prediction_type == "epsilon": + pred_x_0 = (sample - sigmas * model_output) / alphas + elif prediction_type == "sample": + pred_x_0 = model_output + elif prediction_type == "v_prediction": + pred_x_0 = alphas * sample - sigmas * model_output + else: + raise ValueError( + f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`" + f" are supported." + ) + + return pred_x_0 + + +# Based on step 4 in DDIMScheduler.step +def get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas, N_gen): + alphas = extract_into_tensor(alphas, timesteps, sample.shape, N_gen) + sigmas = extract_into_tensor(sigmas, timesteps, sample.shape, N_gen) + model_output = rearrange(model_output, '(b n) c h w -> b n c h w', n=N_gen) + if prediction_type == "epsilon": + pred_epsilon = model_output + elif prediction_type == "sample": + pred_epsilon = (sample - alphas * model_output) / sigmas + elif prediction_type == "v_prediction": + pred_epsilon = alphas * model_output + sigmas * sample + else: + raise ValueError( + f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`" + f" are supported." + ) + + return pred_epsilon + +def extract_into_tensor(a, t, x_shape, N_gen): + # b, *_ = t.shape + out = a.gather(-1, t) + out = out.repeat(N_gen) + out = rearrange(out, '(b n) -> b n', n=N_gen) + b, c, *_ = out.shape + return out.reshape(b, c, *((1,) * (len(x_shape) - 2))) + +class DDIMSolver: + def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50): + # DDIM sampling parameters + step_ratio = timesteps // ddim_timesteps + self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1 + self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps] + self.ddim_alpha_cumprods_prev = np.asarray( + [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist() + ) + # convert to torch tensors + self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long() + self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods) + self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev) + + def to(self, device): + self.ddim_timesteps = self.ddim_timesteps.to(device) + self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device) + self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device) + return self + + def ddim_step(self, pred_x0, pred_noise, timestep_index, N_gen): + alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape, N_gen) + dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise + x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt + return x_prev + + +@torch.no_grad() +def update_ema(target_params, source_params, rate=0.99): + """ + Update target parameters to be closer to those of source parameters using + an exponential moving average. + + :param target_params: the target parameter sequence. + :param source_params: the source parameter sequence. + :param rate: the EMA rate (closer to 1 means slower). + """ + + for targ, src in zip(target_params, source_params): + targ.detach().mul_(rate).add_(src, alpha=1 - rate) + +def to_rgb_image(maybe_rgba: Image.Image): + if maybe_rgba.mode == 'RGB': + return maybe_rgba + elif maybe_rgba.mode == 'RGBA': + rgba = maybe_rgba + img = numpy.random.randint(127, 128, size=[rgba.size[1], rgba.size[0], 3], dtype=numpy.uint8) + img = Image.fromarray(img, 'RGB') + img.paste(rgba, mask=rgba.getchannel('A')) + return img + else: + raise ValueError("Unsupported image type.", maybe_rgba.mode) + + +class HunyuanPaintPipeline(StableDiffusionPipeline): + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2p5DConditionModel, + scheduler: KarrasDiffusionSchedulers, + feature_extractor: CLIPImageProcessor, + safety_checker=None, + use_torch_compile=False, + ): + DiffusionPipeline.__init__(self) + + safety_checker = None + self.register_modules( + vae=torch.compile(vae) if use_torch_compile else vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=torch.compile(feature_extractor) if use_torch_compile else feature_extractor, + ) + self.solver = DDIMSolver( + scheduler.alphas_cumprod.numpy(), + timesteps=scheduler.config.num_train_timesteps, + ddim_timesteps=30, + ).to('cuda') + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.is_turbo = False + + def set_turbo(self, is_turbo: bool): + self.is_turbo = is_turbo + + @torch.no_grad() + def encode_images(self, images): + B = images.shape[0] + images = rearrange(images, 'b n c h w -> (b n) c h w') + + dtype = next(self.vae.parameters()).dtype + images = (images - 0.5) * 2.0 + posterior = self.vae.encode(images.to(dtype)).latent_dist + latents = posterior.sample() * self.vae.config.scaling_factor + + latents = rearrange(latents, '(b n) c h w -> b n c h w', b=B) + return latents + + @torch.no_grad() + def __call__( + self, + image: Image.Image = None, + prompt=None, + negative_prompt='watermark, ugly, deformed, noisy, blurry, low contrast', + *args, + num_images_per_prompt: Optional[int] = 1, + guidance_scale=2.0, + output_type: Optional[str] = "pil", + width=512, + height=512, + num_inference_steps=28, + return_dict=True, + **cached_condition, + ): + device = self._execution_device + + if image is None: + raise ValueError("Inputting embeddings not supported for this pipeline. Please pass an image.") + assert not isinstance(image, torch.Tensor) + + if not isinstance(image, List): + image = [image] + + image = [to_rgb_image(img) for img in image] + + image_vae = [torch.tensor(np.array(img) / 255.0) for img in image] + image_vae = [img_vae.unsqueeze(0).permute(0, 3, 1, 2).unsqueeze(0) for img_vae in image_vae] + image_vae = torch.cat(image_vae, dim=1) + image_vae = image_vae.to(device=device, dtype=self.vae.dtype) + + batch_size, N_ref = image_vae.shape[0], image_vae.shape[1] + assert batch_size == 1 + assert num_images_per_prompt == 1 + + ref_latents = self.encode_images(image_vae) + + def convert_pil_list_to_tensor(images): + bg_c = [1., 1., 1.] + images_tensor = [] + for batch_imgs in images: + view_imgs = [] + for pil_img in batch_imgs: + img = numpy.asarray(pil_img, dtype=numpy.float32) / 255. + if img.shape[2] > 3: + alpha = img[:, :, 3:] + img = img[:, :, :3] * alpha + bg_c * (1 - alpha) + img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).contiguous().half().to("cuda") + view_imgs.append(img) + view_imgs = torch.cat(view_imgs, dim=0) + images_tensor.append(view_imgs.unsqueeze(0)) + + images_tensor = torch.cat(images_tensor, dim=0) + return images_tensor + + if "normal_imgs" in cached_condition: + + if isinstance(cached_condition["normal_imgs"], List): + cached_condition["normal_imgs"] = convert_pil_list_to_tensor(cached_condition["normal_imgs"]) + + cached_condition['normal_imgs'] = self.encode_images(cached_condition["normal_imgs"]) + + if "position_imgs" in cached_condition: + + if isinstance(cached_condition["position_imgs"], List): + cached_condition["position_imgs"] = convert_pil_list_to_tensor(cached_condition["position_imgs"]) + + cached_condition['position_maps'] = cached_condition['position_imgs'] + cached_condition["position_imgs"] = self.encode_images(cached_condition["position_imgs"]) + + if 'camera_info_gen' in cached_condition: + camera_info = cached_condition['camera_info_gen'] # B,N + if isinstance(camera_info, List): + camera_info = torch.tensor(camera_info) + camera_info = camera_info.to(device).to(torch.int64) + cached_condition['camera_info_gen'] = camera_info + if 'camera_info_ref' in cached_condition: + camera_info = cached_condition['camera_info_ref'] # B,N + if isinstance(camera_info, List): + camera_info = torch.tensor(camera_info) + camera_info = camera_info.to(device).to(torch.int64) + cached_condition['camera_info_ref'] = camera_info + + cached_condition['ref_latents'] = ref_latents + + if self.is_turbo: + if 'position_maps' in cached_condition: + cached_condition['position_attn_mask'] = ( + compute_multi_resolution_mask(cached_condition['position_maps']) + ) + cached_condition['position_voxel_indices'] = ( + compute_multi_resolution_discrete_voxel_indice(cached_condition['position_maps']) + ) + + if (guidance_scale > 1) and (not self.is_turbo): + negative_ref_latents = torch.zeros_like(cached_condition['ref_latents']) + cached_condition['ref_latents'] = torch.cat([negative_ref_latents, cached_condition['ref_latents']]) + cached_condition['ref_scale'] = torch.as_tensor([0.0, 1.0]).to(cached_condition['ref_latents']) + if "normal_imgs" in cached_condition: + cached_condition['normal_imgs'] = torch.cat( + (cached_condition['normal_imgs'], cached_condition['normal_imgs'])) + + if "position_imgs" in cached_condition: + cached_condition['position_imgs'] = torch.cat( + (cached_condition['position_imgs'], cached_condition['position_imgs'])) + + if 'position_maps' in cached_condition: + cached_condition['position_maps'] = torch.cat( + (cached_condition['position_maps'], cached_condition['position_maps'])) + + if 'camera_info_gen' in cached_condition: + cached_condition['camera_info_gen'] = torch.cat( + (cached_condition['camera_info_gen'], cached_condition['camera_info_gen'])) + if 'camera_info_ref' in cached_condition: + cached_condition['camera_info_ref'] = torch.cat( + (cached_condition['camera_info_ref'], cached_condition['camera_info_ref'])) + + prompt_embeds = self.unet.learned_text_clip_gen.repeat(num_images_per_prompt, 1, 1) + negative_prompt_embeds = torch.zeros_like(prompt_embeds) + + latents: torch.Tensor = self.denoise( + None, + *args, + cross_attention_kwargs=None, + guidance_scale=guidance_scale, + num_images_per_prompt=num_images_per_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + num_inference_steps=num_inference_steps, + output_type='latent', + width=width, + height=height, + **cached_condition + ).images + + if not output_type == "latent": + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + else: + image = latents + + image = self.image_processor.postprocess(image, output_type=output_type) + if not return_dict: + return (image,) + + return ImagePipelineOutput(images=image) + + def denoise( + self, + prompt: Union[str, List[str]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + timesteps: List[int] = None, + sigmas: List[float] = None, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + ip_adapter_image: Optional[PipelineImageInput] = None, + ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guidance_rescale: float = 0.0, + clip_skip: Optional[int] = None, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + **kwargs, + ): + r""" + The call function to the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. + height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + timesteps (`List[int]`, *optional*): + Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument + in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is + passed will be used. Must be in descending order. + sigmas (`List[float]`, *optional*): + Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in + their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed + will be used. + guidance_scale (`float`, *optional*, defaults to 7.5): + A higher guidance scale value encourages the model to generate images closely linked to the text + `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide what to not include in image generation. If not defined, you need to + pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies + to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.Tensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not + provided, text embeddings are generated from the `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If + not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. + ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. + ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): + Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of + IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should + contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not + provided, embeddings are computed from the `ip_adapter_image` input argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generated image. Choose between `PIL.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in + [`self.processor`] + (https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + guidance_rescale (`float`, *optional*, defaults to 0.0): + Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are + Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when + using zero terminal SNR. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): + A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of + each denoising step during the inference. with the following arguments: `callback_on_step_end(self: + DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a + list of all tensors as specified by `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + + Examples: + + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, + otherwise a `tuple` is returned where the first element is a list with the generated images and the + second element is a list of `bool`s indicating whether the corresponding generated image contains + "not-safe-for-work" (nsfw) content. + """ + + callback = kwargs.pop("callback", None) + callback_steps = kwargs.pop("callback_steps", None) + + if callback is not None: + deprecate( + "callback", + "1.0.0", + "Passing `callback` as an input argument to `__call__` is deprecated,", + "consider using `callback_on_step_end`", + ) + if callback_steps is not None: + deprecate( + "callback_steps", + "1.0.0", + "Passing `callback_steps` as an input argument to `__call__` is deprecated,", + "consider using `callback_on_step_end`", + ) + + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + # to deal with lora scaling and other possible forward hooks + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + height, + width, + callback_steps, + negative_prompt, + prompt_embeds, + negative_prompt_embeds, + ip_adapter_image, + ip_adapter_image_embeds, + callback_on_step_end_tensor_inputs, + ) + + self._guidance_scale = guidance_scale + self._guidance_rescale = guidance_rescale + self._clip_skip = clip_skip + self._cross_attention_kwargs = cross_attention_kwargs + self._interrupt = False + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # 3. Encode input prompt + lora_scale = ( + self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None + ) + + prompt_embeds, negative_prompt_embeds = self.encode_prompt( + prompt, + device, + num_images_per_prompt, + self.do_classifier_free_guidance if self.is_turbo else False, + negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + lora_scale=lora_scale, + clip_skip=self.clip_skip, + ) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + if (self.do_classifier_free_guidance) and (not self.is_turbo): + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + + if ip_adapter_image is not None or ip_adapter_image_embeds is not None: + image_embeds = self.prepare_ip_adapter_image_embeds( + ip_adapter_image, + ip_adapter_image_embeds, + device, + batch_size * num_images_per_prompt, + self.do_classifier_free_guidance if self.is_turbo else False, + ) + + # 4. Prepare + if self.is_turbo: + bsz = 3 + N_gen = 15 + index = torch.range(29, 0, -bsz, device='cuda').long() + timesteps = self.solver.ddim_timesteps[index] + self.scheduler.set_timesteps(timesteps=timesteps.cpu(), device='cuda') + else: + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, num_inference_steps, device, timesteps, sigmas + ) + + assert num_images_per_prompt == 1 + # 5. Prepare latent variables + num_channels_latents = self.unet.config.in_channels + latents = self.prepare_latents( + batch_size * kwargs['num_in_batch'], # num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 6.1 Add image embeds for IP-Adapter + added_cond_kwargs = ( + {"image_embeds": image_embeds} + if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) + else None + ) + + # 6.2 Optionally get Guidance Scale Embedding + timestep_cond = None + if self.unet.config.time_cond_proj_dim is not None: + guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) + timestep_cond = self.get_guidance_scale_embedding( + guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim + ).to(device=device, dtype=latents.dtype) + + # 7. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + self._num_timesteps = len(timesteps) + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + # expand the latents if we are doing classifier free guidance + latents = rearrange(latents, '(b n) c h w -> b n c h w', n=kwargs['num_in_batch']) + latent_model_input = ( + torch.cat([latents] * 2) + if ((self.do_classifier_free_guidance) and (not self.is_turbo)) + else latents + ) + latent_model_input = rearrange(latent_model_input, 'b n c h w -> (b n) c h w') + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = rearrange(latent_model_input, '(b n) c h w ->b n c h w', n=kwargs['num_in_batch']) + + # predict the noise residual + + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + timestep_cond=timestep_cond, + cross_attention_kwargs=self.cross_attention_kwargs, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, **kwargs + )[0] + latents = rearrange(latents, 'b n c h w -> (b n) c h w') + # perform guidance + if (self.do_classifier_free_guidance) and (not self.is_turbo): + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) + + if (self.do_classifier_free_guidance) and (self.guidance_rescale > 0.0) and (not self.is_turbo): + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) + + # compute the previous noisy sample x_t -> x_t-1 + latents = \ + self.scheduler.step(noise_pred, t, latents[:, :num_channels_latents, :, :], **extra_step_kwargs, + return_dict=False)[0] + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, latents) + + if not output_type == "latent": + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[ + 0 + ] + image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) + else: + image = latents + has_nsfw_concept = None + + if has_nsfw_concept is None: + do_denormalize = [True] * image.shape[0] + else: + do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] + + image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image, has_nsfw_concept) + + return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/unet/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/unet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8bb2bf86e283e50f0df2ecfba8fc66289df09901 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/unet/__init__.py @@ -0,0 +1,13 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/unet/modules.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/unet/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..70977b6925bd2ea0e9f0dddfee6cd56eeafac664 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/hunyuanpaint/unet/modules.py @@ -0,0 +1,599 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import copy +import json +import os +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from diffusers.models import UNet2DConditionModel +from diffusers.models.attention_processor import Attention +from diffusers.models.transformers.transformer_2d import BasicTransformerBlock +from einops import rearrange + + +def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int): + # "feed_forward_chunk_size" can be used to save memory + if hidden_states.shape[chunk_dim] % chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]}" + f"has to be divisible by chunk size: {chunk_size}." + f" Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = hidden_states.shape[chunk_dim] // chunk_size + ff_output = torch.cat( + [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)], + dim=chunk_dim, + ) + return ff_output + + +class Basic2p5DTransformerBlock(torch.nn.Module): + def __init__(self, transformer: BasicTransformerBlock,layer_name,use_ma=True,use_ra=True,is_turbo=False) -> None: + super().__init__() + self.transformer = transformer + self.layer_name = layer_name + self.use_ma = use_ma + self.use_ra = use_ra + self.is_turbo = is_turbo + + # multiview attn + if self.use_ma: + self.attn_multiview = Attention( + query_dim=self.dim, + heads=self.num_attention_heads, + dim_head=self.attention_head_dim, + dropout=self.dropout, + bias=self.attention_bias, + cross_attention_dim=None, + upcast_attention=self.attn1.upcast_attention, + out_bias=True, + ) + + # ref attn + if self.use_ra: + self.attn_refview = Attention( + query_dim=self.dim, + heads=self.num_attention_heads, + dim_head=self.attention_head_dim, + dropout=self.dropout, + bias=self.attention_bias, + cross_attention_dim=None, + upcast_attention=self.attn1.upcast_attention, + out_bias=True, + ) + if self.is_turbo: + self._initialize_attn_weights() + + def _initialize_attn_weights(self): + + if self.use_ma: + self.attn_multiview.load_state_dict(self.attn1.state_dict()) + with torch.no_grad(): + for layer in self.attn_multiview.to_out: + for param in layer.parameters(): + param.zero_() + if self.use_ra: + self.attn_refview.load_state_dict(self.attn1.state_dict()) + with torch.no_grad(): + for layer in self.attn_refview.to_out: + for param in layer.parameters(): + param.zero_() + + def __getattr__(self, name: str): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.transformer, name) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, + ) -> torch.Tensor: + + # Notice that normalization is always applied before the real computation in the following blocks. + # 0. Self-Attention + batch_size = hidden_states.shape[0] + + cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} + num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1) + mode = cross_attention_kwargs.pop('mode', None) + if not self.is_turbo: + mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0) + ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0) + else: + position_attn_mask = cross_attention_kwargs.pop("position_attn_mask", None) + position_voxel_indices = cross_attention_kwargs.pop("position_voxel_indices", None) + mva_scale = 1.0 + ref_scale = 1.0 + + condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None) + + if self.norm_type == "ada_norm": + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.norm_type == "ada_norm_zero": + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]: + norm_hidden_states = self.norm1(hidden_states) + elif self.norm_type == "ada_norm_continuous": + norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"]) + elif self.norm_type == "ada_norm_single": + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) + ).chunk(6, dim=1) + norm_hidden_states = self.norm1(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + else: + raise ValueError("Incorrect norm used") + + if self.pos_embed is not None: + norm_hidden_states = self.pos_embed(norm_hidden_states) + + # 1. Prepare GLIGEN inputs + cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} + gligen_kwargs = cross_attention_kwargs.pop("gligen", None) + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + if self.norm_type == "ada_norm_zero": + attn_output = gate_msa.unsqueeze(1) * attn_output + elif self.norm_type == "ada_norm_single": + attn_output = gate_msa * attn_output + + hidden_states = attn_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 1.2 Reference Attention + if 'w' in mode: + condition_embed_dict[self.layer_name] = rearrange( + norm_hidden_states, '(b n) l c -> b (n l) c', + n=num_in_batch + ) # B, (N L), C + + if 'r' in mode and self.use_ra: + condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1, num_in_batch, 1, + 1) # B N L C + condition_embed = rearrange(condition_embed, 'b n l c -> (b n) l c') + + attn_output = self.attn_refview( + norm_hidden_states, + encoder_hidden_states=condition_embed, + attention_mask=None, + **cross_attention_kwargs + ) + if not self.is_turbo: + ref_scale_timing = ref_scale + if isinstance(ref_scale, torch.Tensor): + ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1) + for _ in range(attn_output.ndim - 1): + ref_scale_timing = ref_scale_timing.unsqueeze(-1) + + hidden_states = ref_scale_timing * attn_output + hidden_states + + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 1.3 Multiview Attention + if num_in_batch > 1 and self.use_ma: + multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch) + + if self.is_turbo: + position_mask = None + if position_attn_mask is not None: + if multivew_hidden_states.shape[1] in position_attn_mask: + position_mask = position_attn_mask[multivew_hidden_states.shape[1]] + position_indices = None + if position_voxel_indices is not None: + if multivew_hidden_states.shape[1] in position_voxel_indices: + position_indices = position_voxel_indices[multivew_hidden_states.shape[1]] + attn_output = self.attn_multiview( + multivew_hidden_states, + encoder_hidden_states=multivew_hidden_states, + attention_mask=position_mask, + position_indices=position_indices, + **cross_attention_kwargs + ) + else: + attn_output = self.attn_multiview( + multivew_hidden_states, + encoder_hidden_states=multivew_hidden_states, + **cross_attention_kwargs + ) + + attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch) + + hidden_states = mva_scale * attn_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 1.2 GLIGEN Control + if gligen_kwargs is not None: + hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) + + # 3. Cross-Attention + if self.attn2 is not None: + if self.norm_type == "ada_norm": + norm_hidden_states = self.norm2(hidden_states, timestep) + elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]: + norm_hidden_states = self.norm2(hidden_states) + elif self.norm_type == "ada_norm_single": + # For PixArt norm2 isn't applied here: + # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 + norm_hidden_states = hidden_states + elif self.norm_type == "ada_norm_continuous": + norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"]) + else: + raise ValueError("Incorrect norm") + + if self.pos_embed is not None and self.norm_type != "ada_norm_single": + norm_hidden_states = self.pos_embed(norm_hidden_states) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + + hidden_states = attn_output + hidden_states + + # 4. Feed-forward + # i2vgen doesn't have this norm 🤷‍♂️ + if self.norm_type == "ada_norm_continuous": + norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"]) + elif not self.norm_type == "ada_norm_single": + norm_hidden_states = self.norm3(hidden_states) + + if self.norm_type == "ada_norm_zero": + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self.norm_type == "ada_norm_single": + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size) + else: + ff_output = self.ff(norm_hidden_states) + + if self.norm_type == "ada_norm_zero": + ff_output = gate_mlp.unsqueeze(1) * ff_output + elif self.norm_type == "ada_norm_single": + ff_output = gate_mlp * ff_output + + hidden_states = ff_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + return hidden_states + +@torch.no_grad() +def compute_voxel_grid_mask(position, grid_resolution=8): + + position = position.half() + B,N,_,H,W = position.shape + assert H%grid_resolution==0 and W%grid_resolution==0 + + valid_mask = (position != 1).all(dim=2, keepdim=True) + valid_mask = valid_mask.expand_as(position) + position[valid_mask==False] = 0 + + + position = rearrange( + position, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + valid_mask = rearrange( + valid_mask, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + + grid_position = position.sum(dim=(-2, -1)) + count_masked = valid_mask.sum(dim=(-2, -1)) + + grid_position = grid_position / count_masked.clamp(min=1) + grid_position[count_masked<5] = 0 + + grid_position = grid_position.permute(0,1,4,2,3) + grid_position = rearrange(grid_position, 'b n c h w -> b n (h w) c') + + grid_position_expanded_1 = grid_position.unsqueeze(2).unsqueeze(4) # Shape becomes B, N, 1, L, 1, 3 + grid_position_expanded_2 = grid_position.unsqueeze(1).unsqueeze(3) # Shape becomes B, 1, N, 1, L, 3 + + # Calculate Euclidean distance + distances = torch.norm(grid_position_expanded_1 - grid_position_expanded_2, dim=-1) # Shape: B, N, N, L, L + + weights = distances + grid_distance = 1.73/grid_resolution + + #weights = weights*-32 + #weights = weights.clamp(min=-10000.0) + + weights = weights< grid_distance + + return weights + +def compute_multi_resolution_mask(position_maps, grid_resolutions=[32, 16, 8]): + position_attn_mask = {} + with torch.no_grad(): + for grid_resolution in grid_resolutions: + position_mask = compute_voxel_grid_mask(position_maps, grid_resolution) + position_mask = rearrange(position_mask, 'b ni nj li lj -> b (ni li) (nj lj)') + position_attn_mask[position_mask.shape[1]] = position_mask + return position_attn_mask + +@torch.no_grad() +def compute_discrete_voxel_indice(position, grid_resolution=8, voxel_resolution=128): + + position = position.half() + B,N,_,H,W = position.shape + assert H%grid_resolution==0 and W%grid_resolution==0 + + valid_mask = (position != 1).all(dim=2, keepdim=True) + valid_mask = valid_mask.expand_as(position) + position[valid_mask==False] = 0 + + position = rearrange( + position, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + valid_mask = rearrange( + valid_mask, + 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', + num_h=grid_resolution, num_w=grid_resolution + ) + + grid_position = position.sum(dim=(-2, -1)) + count_masked = valid_mask.sum(dim=(-2, -1)) + + grid_position = grid_position / count_masked.clamp(min=1) + grid_position[count_masked<5] = 0 + + grid_position = grid_position.permute(0,1,4,2,3).clamp(0, 1) # B N C H W + voxel_indices = grid_position * (voxel_resolution - 1) + voxel_indices = torch.round(voxel_indices).long() + return voxel_indices + +def compute_multi_resolution_discrete_voxel_indice( + position_maps, + grid_resolutions=[64, 32, 16, 8], + voxel_resolutions=[512, 256, 128, 64] +): + voxel_indices = {} + with torch.no_grad(): + for grid_resolution, voxel_resolution in zip(grid_resolutions, voxel_resolutions): + voxel_indice = compute_discrete_voxel_indice(position_maps, grid_resolution, voxel_resolution) + voxel_indice = rearrange(voxel_indice, 'b n c h w -> b (n h w) c') + voxel_indices[voxel_indice.shape[1]] = {'voxel_indices':voxel_indice, 'voxel_resolution':voxel_resolution} + return voxel_indices + +class UNet2p5DConditionModel(torch.nn.Module): + def __init__(self, unet: UNet2DConditionModel) -> None: + super().__init__() + self.unet = unet + + self.use_ma = True + self.use_ra = True + self.use_camera_embedding = True + self.use_dual_stream = True + self.is_turbo = False + + if self.use_dual_stream: + self.unet_dual = copy.deepcopy(unet) + self.init_attention(self.unet_dual) + self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra, is_turbo=self.is_turbo) + self.init_condition() + self.init_camera_embedding() + + @staticmethod + def from_pretrained(pretrained_model_name_or_path, **kwargs): + torch_dtype = kwargs.pop('torch_dtype', torch.float32) + config_path = os.path.join(pretrained_model_name_or_path, 'config.json') + unet_ckpt_path = os.path.join(pretrained_model_name_or_path, 'diffusion_pytorch_model.bin') + with open(config_path, 'r', encoding='utf-8') as file: + config = json.load(file) + unet = UNet2DConditionModel(**config) + unet = UNet2p5DConditionModel(unet) + unet_ckpt = torch.load(unet_ckpt_path, map_location='cpu', weights_only=True) + unet.load_state_dict(unet_ckpt, strict=True) + unet = unet.to(torch_dtype) + return unet + + def init_condition(self): + self.unet.conv_in = torch.nn.Conv2d( + 12, + self.unet.conv_in.out_channels, + kernel_size=self.unet.conv_in.kernel_size, + stride=self.unet.conv_in.stride, + padding=self.unet.conv_in.padding, + dilation=self.unet.conv_in.dilation, + groups=self.unet.conv_in.groups, + bias=self.unet.conv_in.bias is not None) + + self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1, 77, 1024)) + self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1, 77, 1024)) + + def init_camera_embedding(self): + + if self.use_camera_embedding: + time_embed_dim = 1280 + self.max_num_ref_image = 5 + self.max_num_gen_image = 12 * 3 + 4 * 2 + self.unet.class_embedding = nn.Embedding(self.max_num_ref_image + self.max_num_gen_image, time_embed_dim) + + def init_attention(self, unet, use_ma=False, use_ra=False, is_turbo=False): + + for down_block_i, down_block in enumerate(unet.down_blocks): + if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention: + for attn_i, attn in enumerate(down_block.attentions): + for transformer_i, transformer in enumerate(attn.transformer_blocks): + if isinstance(transformer, BasicTransformerBlock): + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'down_{down_block_i}_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) + + if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention: + for attn_i, attn in enumerate(unet.mid_block.attentions): + for transformer_i, transformer in enumerate(attn.transformer_blocks): + if isinstance(transformer, BasicTransformerBlock): + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'mid_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) + + for up_block_i, up_block in enumerate(unet.up_blocks): + if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention: + for attn_i, attn in enumerate(up_block.attentions): + for transformer_i, transformer in enumerate(attn.transformer_blocks): + if isinstance(transformer, BasicTransformerBlock): + attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( + transformer, + f'up_{up_block_i}_{attn_i}_{transformer_i}', + use_ma, use_ra, is_turbo + ) + + def __getattr__(self, name: str): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.unet, name) + + def forward( + self, sample, timestep, encoder_hidden_states, + *args, down_intrablock_additional_residuals=None, + down_block_res_samples=None, mid_block_res_sample=None, + **cached_condition, + ): + B, N_gen, _, H, W = sample.shape + assert H == W + + if self.use_camera_embedding: + camera_info_gen = cached_condition['camera_info_gen'] + self.max_num_ref_image + camera_info_gen = rearrange(camera_info_gen, 'b n -> (b n)') + else: + camera_info_gen = None + + sample = [sample] + if 'normal_imgs' in cached_condition: + sample.append(cached_condition["normal_imgs"]) + if 'position_imgs' in cached_condition: + sample.append(cached_condition["position_imgs"]) + sample = torch.cat(sample, dim=2) + + sample = rearrange(sample, 'b n c h w -> (b n) c h w') + + encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(1, N_gen, 1, 1) + encoder_hidden_states_gen = rearrange(encoder_hidden_states_gen, 'b n l c -> (b n) l c') + + if self.use_ra: + if 'condition_embed_dict' in cached_condition: + condition_embed_dict = cached_condition['condition_embed_dict'] + else: + condition_embed_dict = {} + ref_latents = cached_condition['ref_latents'] + N_ref = ref_latents.shape[1] + if self.use_camera_embedding: + camera_info_ref = cached_condition['camera_info_ref'] + camera_info_ref = rearrange(camera_info_ref, 'b n -> (b n)') + else: + camera_info_ref = None + + ref_latents = rearrange(ref_latents, 'b n c h w -> (b n) c h w') + + encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(1).repeat(B, N_ref, 1, 1) + encoder_hidden_states_ref = rearrange(encoder_hidden_states_ref, 'b n l c -> (b n) l c') + + noisy_ref_latents = ref_latents + timestep_ref = 0 + + if self.use_dual_stream: + unet_ref = self.unet_dual + else: + unet_ref = self.unet + unet_ref( + noisy_ref_latents, timestep_ref, + encoder_hidden_states=encoder_hidden_states_ref, + class_labels=camera_info_ref, + # **kwargs + return_dict=False, + cross_attention_kwargs={ + 'mode': 'w', 'num_in_batch': N_ref, + 'condition_embed_dict': condition_embed_dict}, + ) + cached_condition['condition_embed_dict'] = condition_embed_dict + else: + condition_embed_dict = None + + mva_scale = cached_condition.get('mva_scale', 1.0) + ref_scale = cached_condition.get('ref_scale', 1.0) + + if self.is_turbo: + cross_attention_kwargs_ = { + 'mode': 'r', 'num_in_batch': N_gen, + 'condition_embed_dict': condition_embed_dict, + 'position_attn_mask':position_attn_mask, + 'position_voxel_indices':position_voxel_indices, + 'mva_scale': mva_scale, + 'ref_scale': ref_scale, + } + else: + cross_attention_kwargs_ = { + 'mode': 'r', 'num_in_batch': N_gen, + 'condition_embed_dict': condition_embed_dict, + 'mva_scale': mva_scale, + 'ref_scale': ref_scale, + } + return self.unet( + sample, timestep, + encoder_hidden_states_gen, *args, + class_labels=camera_info_gen, + down_intrablock_additional_residuals=[ + sample.to(dtype=self.unet.dtype) for sample in down_intrablock_additional_residuals + ] if down_intrablock_additional_residuals is not None else None, + down_block_additional_residuals=[ + sample.to(dtype=self.unet.dtype) for sample in down_block_res_samples + ] if down_block_res_samples is not None else None, + mid_block_additional_residual=( + mid_block_res_sample.to(dtype=self.unet.dtype) + if mid_block_res_sample is not None else None + ), + return_dict=False, + cross_attention_kwargs=cross_attention_kwargs_, + ) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/pipelines.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/pipelines.py new file mode 100644 index 0000000000000000000000000000000000000000..d9bdd88a82890f4128cdda7806008b47a60ce0b0 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/pipelines.py @@ -0,0 +1,237 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + + +import logging +import numpy as np +import os +import torch +from PIL import Image +from typing import List, Union, Optional + + +from .differentiable_renderer.mesh_render import MeshRender +from .utils.dehighlight_utils import Light_Shadow_Remover +from .utils.multiview_utils import Multiview_Diffusion_Net +from .utils.imagesuper_utils import Image_Super_Net +from .utils.uv_warp_utils import mesh_uv_wrap + +logger = logging.getLogger(__name__) + + +class Hunyuan3DTexGenConfig: + + def __init__(self, light_remover_ckpt_path, multiview_ckpt_path, subfolder_name): + self.device = 'cuda' + self.light_remover_ckpt_path = light_remover_ckpt_path + self.multiview_ckpt_path = multiview_ckpt_path + + self.candidate_camera_azims = [0, 90, 180, 270, 0, 180] + self.candidate_camera_elevs = [0, 0, 0, 0, 90, -90] + self.candidate_view_weights = [1, 0.1, 0.5, 0.1, 0.05, 0.05] + + self.render_size = 2048 + self.texture_size = 2048 + self.bake_exp = 4 + self.merge_method = 'fast' + + self.pipe_dict = {'hunyuan3d-paint-v2-0': 'hunyuanpaint', 'hunyuan3d-paint-v2-0-turbo': 'hunyuanpaint-turbo'} + self.pipe_name = self.pipe_dict[subfolder_name] + + +class Hunyuan3DPaintPipeline: + @classmethod + def from_pretrained(cls, model_path, subfolder='hunyuan3d-paint-v2-0-turbo'): + original_model_path = model_path + if not os.path.exists(model_path): + # try local path + base_dir = os.environ.get('HY3DGEN_MODELS', '~/.cache/hy3dgen') + model_path = os.path.expanduser(os.path.join(base_dir, model_path)) + + delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') + multiview_model_path = os.path.join(model_path, subfolder) + + if not os.path.exists(delight_model_path) or not os.path.exists(multiview_model_path): + try: + import huggingface_hub + # download from huggingface + model_path = huggingface_hub.snapshot_download( + repo_id=original_model_path, allow_patterns=["hunyuan3d-delight-v2-0/*"] + ) + model_path = huggingface_hub.snapshot_download( + repo_id=original_model_path, allow_patterns=[f'{subfolder}/*'] + ) + delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') + multiview_model_path = os.path.join(model_path, subfolder) + return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path, subfolder)) + except ImportError: + logger.warning( + "You need to install HuggingFace Hub to load models from the hub." + ) + raise RuntimeError(f"Model path {model_path} not found") + else: + return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path, subfolder)) + + raise FileNotFoundError(f"Model path {original_model_path} not found and we could not find it at huggingface") + + def __init__(self, config): + self.config = config + self.models = {} + self.render = MeshRender( + default_resolution=self.config.render_size, + texture_size=self.config.texture_size) + + self.load_models() + + def load_models(self): + # empty cude cache + torch.cuda.empty_cache() + # Load model + self.models['delight_model'] = Light_Shadow_Remover(self.config) + self.models['multiview_model'] = Multiview_Diffusion_Net(self.config) + # self.models['super_model'] = Image_Super_Net(self.config) + + def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"): + self.models['delight_model'].pipeline.enable_model_cpu_offload(gpu_id=gpu_id, device=device) + self.models['multiview_model'].pipeline.enable_model_cpu_offload(gpu_id=gpu_id, device=device) + + def render_normal_multiview(self, camera_elevs, camera_azims, use_abs_coor=True): + normal_maps = [] + for elev, azim in zip(camera_elevs, camera_azims): + normal_map = self.render.render_normal( + elev, azim, use_abs_coor=use_abs_coor, return_type='pl') + normal_maps.append(normal_map) + + return normal_maps + + def render_position_multiview(self, camera_elevs, camera_azims): + position_maps = [] + for elev, azim in zip(camera_elevs, camera_azims): + position_map = self.render.render_position( + elev, azim, return_type='pl') + position_maps.append(position_map) + + return position_maps + + def bake_from_multiview(self, views, camera_elevs, + camera_azims, view_weights, method='graphcut'): + project_textures, project_weighted_cos_maps = [], [] + project_boundary_maps = [] + for view, camera_elev, camera_azim, weight in zip( + views, camera_elevs, camera_azims, view_weights): + project_texture, project_cos_map, project_boundary_map = self.render.back_project( + view, camera_elev, camera_azim) + project_cos_map = weight * (project_cos_map ** self.config.bake_exp) + project_textures.append(project_texture) + project_weighted_cos_maps.append(project_cos_map) + project_boundary_maps.append(project_boundary_map) + + if method == 'fast': + texture, ori_trust_map = self.render.fast_bake_texture( + project_textures, project_weighted_cos_maps) + else: + raise f'no method {method}' + return texture, ori_trust_map > 1E-8 + + def texture_inpaint(self, texture, mask): + + texture_np = self.render.uv_inpaint(texture, mask) + texture = torch.tensor(texture_np / 255).float().to(texture.device) + + return texture + + def recenter_image(self, image, border_ratio=0.2): + if image.mode == 'RGB': + return image + elif image.mode == 'L': + image = image.convert('RGB') + return image + + alpha_channel = np.array(image)[:, :, 3] + non_zero_indices = np.argwhere(alpha_channel > 0) + if non_zero_indices.size == 0: + raise ValueError("Image is fully transparent") + + min_row, min_col = non_zero_indices.min(axis=0) + max_row, max_col = non_zero_indices.max(axis=0) + + cropped_image = image.crop((min_col, min_row, max_col + 1, max_row + 1)) + + width, height = cropped_image.size + border_width = int(width * border_ratio) + border_height = int(height * border_ratio) + + new_width = width + 2 * border_width + new_height = height + 2 * border_height + + square_size = max(new_width, new_height) + + new_image = Image.new('RGBA', (square_size, square_size), (255, 255, 255, 0)) + + paste_x = (square_size - new_width) // 2 + border_width + paste_y = (square_size - new_height) // 2 + border_height + + new_image.paste(cropped_image, (paste_x, paste_y)) + return new_image + + @torch.no_grad() + def __call__(self, mesh, image): + + if not isinstance(image, List): + image = [image] + + images_prompt = [] + for i in range(len(image)): + if isinstance(image[i], str): + image_prompt = Image.open(image[i]) + else: + image_prompt = image[i] + images_prompt.append(image_prompt) + + images_prompt = [self.recenter_image(image_prompt) for image_prompt in images_prompt] + + mesh, vmapping = mesh_uv_wrap(mesh) + + self.render.load_mesh(mesh) + + selected_camera_elevs, selected_camera_azims, selected_view_weights = \ + self.config.candidate_camera_elevs, self.config.candidate_camera_azims, self.config.candidate_view_weights + + normal_maps = self.render_normal_multiview( + selected_camera_elevs, selected_camera_azims, use_abs_coor=True) + position_maps = self.render_position_multiview( + selected_camera_elevs, selected_camera_azims) + + camera_info = [(((azim // 30) + 9) % 12) // {-20: 1, 0: 1, 20: 1, -90: 3, 90: 3}[ + elev] + {-20: 0, 0: 12, 20: 24, -90: 36, 90: 40}[elev] for azim, elev in + zip(selected_camera_azims, selected_camera_elevs)] + multiviews = self.models['multiview_model'](images_prompt, normal_maps + position_maps, camera_info) + + for i in range(len(multiviews)): + # multiviews[i] = self.models['super_model'](multiviews[i]) + multiviews[i] = multiviews[i].resize( + (self.config.render_size, self.config.render_size)) + + texture, mask = self.bake_from_multiview(multiviews, + selected_camera_elevs, selected_camera_azims, selected_view_weights, + method=self.config.merge_method) + + mask_np = (mask.squeeze(-1).cpu().numpy() * 255).astype(np.uint8) + + texture = self.texture_inpaint(texture, mask_np) + + self.render.set_texture(texture) + textured_mesh = self.render.save_mesh() + + return textured_mesh, vmapping diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/__init__.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8bb2bf86e283e50f0df2ecfba8fc66289df09901 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/__init__.py @@ -0,0 +1,13 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/alignImg4Tex_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/alignImg4Tex_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..34df2041e598b2cbf92a345f4d003c53437dfb92 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/alignImg4Tex_utils.py @@ -0,0 +1,121 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import torch +from diffusers import EulerAncestralDiscreteScheduler +from diffusers import StableDiffusionControlNetPipeline, StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, \ + AutoencoderKL + + +class Img2img_Control_Ip_adapter: + def __init__(self, device): + controlnet = ControlNetModel.from_pretrained('lllyasviel/control_v11f1p_sd15_depth', torch_dtype=torch.float16, + variant="fp16", use_safetensors=True) + pipe = StableDiffusionControlNetPipeline.from_pretrained( + 'runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True + ) + pipe.load_ip_adapter('h94/IP-Adapter', subfolder="models", weight_name="ip-adapter-plus_sd15.safetensors") + pipe.set_ip_adapter_scale(0.7) + + pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) + # pipe.enable_model_cpu_offload() + self.pipe = pipe.to(device) + + def __call__( + self, + prompt, + control_image, + ip_adapter_image, + negative_prompt, + height=512, + width=512, + num_inference_steps=20, + guidance_scale=8.0, + controlnet_conditioning_scale=1.0, + output_type="pil", + **kwargs, + ): + results = self.pipe( + prompt=prompt, + negative_prompt=negative_prompt, + image=control_image, + ip_adapter_image=ip_adapter_image, + generator=torch.manual_seed(42), + seed=42, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + controlnet_conditioning_scale=controlnet_conditioning_scale, + strength=1, + # clip_skip=2, + height=height, + width=width, + output_type=output_type, + **kwargs, + ).images[0] + return results + + +################################################################ + +class HesModel: + def __init__(self, ): + controlnet_depth = ControlNetModel.from_pretrained( + 'diffusers/controlnet-depth-sdxl-1.0', + torch_dtype=torch.float16, + variant="fp16", + use_safetensors=True + ) + self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained( + 'stabilityai/stable-diffusion-xl-base-1.0', + torch_dtype=torch.float16, + variant="fp16", + controlnet=controlnet_depth, + use_safetensors=True, + ) + self.pipe.vae = AutoencoderKL.from_pretrained( + 'madebyollin/sdxl-vae-fp16-fix', + torch_dtype=torch.float16 + ) + + self.pipe.load_ip_adapter('h94/IP-Adapter', subfolder="sdxl_models", weight_name="ip-adapter_sdxl.safetensors") + self.pipe.set_ip_adapter_scale(0.7) + self.pipe.to("cuda") + + def __call__(self, + init_image, + control_image, + ip_adapter_image=None, + prompt='3D image', + negative_prompt='2D image', + seed=42, + strength=0.8, + num_inference_steps=40, + guidance_scale=7.5, + controlnet_conditioning_scale=0.5, + **kwargs + ): + image = self.pipe( + prompt=prompt, + image=init_image, + control_image=control_image, + ip_adapter_image=ip_adapter_image, + negative_prompt=negative_prompt, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + strength=strength, + controlnet_conditioning_scale=controlnet_conditioning_scale, + seed=seed, + **kwargs + ).images[0] + return image diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/counter_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/counter_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..383a5151cb22e1c965a8432cc5a0ac312eaa9ab2 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/counter_utils.py @@ -0,0 +1,48 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + + +class RunningStats(): + def __init__(self) -> None: + self.count = 0 + self.sum = 0 + self.mean = 0 + self.min = None + self.max = None + + def add_value(self, value): + self.count += 1 + self.sum += value + self.mean = self.sum / self.count + + if self.min is None or value < self.min: + self.min = value + + if self.max is None or value > self.max: + self.max = value + + def get_count(self): + return self.count + + def get_sum(self): + return self.sum + + def get_mean(self): + return self.mean + + def get_min(self): + return self.min + + def get_max(self): + return self.max diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/dehighlight_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/dehighlight_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9b5236882ac7fce1484e5c410372d6abe3f8030b --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/dehighlight_utils.py @@ -0,0 +1,110 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import cv2 +import numpy as np +import torch +from PIL import Image +from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler + + +class Light_Shadow_Remover(): + def __init__(self, config): + self.device = config.device + self.cfg_image = 1.5 + self.cfg_text = 1.0 + + pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained( + config.light_remover_ckpt_path, + torch_dtype=torch.float16, + safety_checker=None, + ) + pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config) + pipeline.set_progress_bar_config(disable=True) + + self.pipeline = pipeline.to(self.device, torch.float16) + + def recorrect_rgb(self, src_image, target_image, alpha_channel, scale=0.95): + + def flat_and_mask(bgr, a): + mask = torch.where(a > 0.5, True, False) + bgr_flat = bgr.reshape(-1, bgr.shape[-1]) + mask_flat = mask.reshape(-1) + bgr_flat_masked = bgr_flat[mask_flat, :] + return bgr_flat_masked + + src_flat = flat_and_mask(src_image, alpha_channel) + target_flat = flat_and_mask(target_image, alpha_channel) + corrected_bgr = torch.zeros_like(src_image) + + for i in range(3): + src_mean, src_stddev = torch.mean(src_flat[:, i]), torch.std(src_flat[:, i]) + target_mean, target_stddev = torch.mean(target_flat[:, i]), torch.std(target_flat[:, i]) + corrected_bgr[:, :, i] = torch.clamp( + (src_image[:, :, i] - scale * src_mean) * + (target_stddev / src_stddev) + scale * target_mean, + 0, 1) + + src_mse = torch.mean((src_image - target_image) ** 2) + modify_mse = torch.mean((corrected_bgr - target_image) ** 2) + if src_mse < modify_mse: + corrected_bgr = torch.cat([src_image, alpha_channel], dim=-1) + else: + corrected_bgr = torch.cat([corrected_bgr, alpha_channel], dim=-1) + + return corrected_bgr + + @torch.no_grad() + def __call__(self, image): + + image = image.resize((512, 512)) + + if image.mode == 'RGBA': + image_array = np.array(image) + alpha_channel = image_array[:, :, 3] + erosion_size = 3 + kernel = np.ones((erosion_size, erosion_size), np.uint8) + alpha_channel = cv2.erode(alpha_channel, kernel, iterations=1) + image_array[alpha_channel == 0, :3] = 255 + image_array[:, :, 3] = alpha_channel + image = Image.fromarray(image_array) + + image_tensor = torch.tensor(np.array(image) / 255.0).to(self.device) + alpha = image_tensor[:, :, 3:] + rgb_target = image_tensor[:, :, :3] + else: + image_tensor = torch.tensor(np.array(image) / 255.0).to(self.device) + alpha = torch.ones_like(image_tensor)[:, :, :1] + rgb_target = image_tensor[:, :, :3] + + image = image.convert('RGB') + + image = self.pipeline( + prompt="", + image=image, + generator=torch.manual_seed(42), + height=512, + width=512, + num_inference_steps=50, + image_guidance_scale=self.cfg_image, + guidance_scale=self.cfg_text, + ).images[0] + + image_tensor = torch.tensor(np.array(image)/255.0).to(self.device) + rgb_src = image_tensor[:,:,:3] + image = self.recorrect_rgb(rgb_src, rgb_target, alpha) + image = image[:,:,:3]*image[:,:,3:] + torch.ones_like(image[:,:,:3])*(1.0-image[:,:,3:]) + image = Image.fromarray((image.cpu().numpy()*255).astype(np.uint8)) + + return image diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/imagesuper_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/imagesuper_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0b893c53a4571b4d7eb5adf7b199e4152c0a227b --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/imagesuper_utils.py @@ -0,0 +1,34 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import torch +from diffusers import StableDiffusionUpscalePipeline + +class Image_Super_Net(): + def __init__(self, config): + self.up_pipeline_x4 = StableDiffusionUpscalePipeline.from_pretrained( + 'stabilityai/stable-diffusion-x4-upscaler', + torch_dtype=torch.float16, + ).to(config.device) + self.up_pipeline_x4.set_progress_bar_config(disable=True) + + def __call__(self, image, prompt=''): + with torch.no_grad(): + upscaled_image = self.up_pipeline_x4( + prompt=[prompt], + image=image, + num_inference_steps=5, + ).images[0] + + return upscaled_image diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/multiview_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/multiview_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2bb01dc6987ea05ba5a93c1e75c951d6ee7243 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/multiview_utils.py @@ -0,0 +1,89 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import os +import random + +import numpy as np +import torch +from typing import List +from diffusers import DiffusionPipeline +from diffusers import EulerAncestralDiscreteScheduler, LCMScheduler + + +class Multiview_Diffusion_Net(): + def __init__(self, config) -> None: + self.device = config.device + self.view_size = 512 + multiview_ckpt_path = config.multiview_ckpt_path + + current_file_path = os.path.abspath(__file__) + custom_pipeline_path = os.path.join(os.path.dirname(current_file_path), '..', 'hunyuanpaint') + + pipeline = DiffusionPipeline.from_pretrained( + multiview_ckpt_path, + custom_pipeline=custom_pipeline_path, + torch_dtype=torch.float16, + use_safetensors=False) + + if config.pipe_name in ['hunyuanpaint']: + pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, + timestep_spacing='trailing') + elif config.pipe_name in ['hunyuanpaint-turbo']: + pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config, + timestep_spacing='trailing') + pipeline.set_turbo(True) + # pipeline.prepare() + + pipeline.set_progress_bar_config(disable=True) + self.pipeline = pipeline.to(self.device) + + def seed_everything(self, seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + os.environ["PL_GLOBAL_SEED"] = str(seed) + + def __call__(self, input_images, control_images, camera_info): + + self.seed_everything(0) + + if not isinstance(input_images, List): + input_images = [input_images] + + input_images = [input_image.resize((self.view_size, self.view_size)) for input_image in input_images] + for i in range(len(control_images)): + control_images[i] = control_images[i].resize((self.view_size, self.view_size)) + if control_images[i].mode == 'L': + control_images[i] = control_images[i].point(lambda x: 255 if x > 1 else 0, mode='1') + + kwargs = dict(generator=torch.Generator(device=self.pipeline.device).manual_seed(0)) + + num_view = len(control_images) // 2 + normal_image = [[control_images[i] for i in range(num_view)]] + position_image = [[control_images[i + num_view] for i in range(num_view)]] + + camera_info_gen = [camera_info] + camera_info_ref = [[0]] + kwargs['width'] = self.view_size + kwargs['height'] = self.view_size + kwargs['num_in_batch'] = num_view + kwargs['camera_info_gen'] = camera_info_gen + kwargs['camera_info_ref'] = camera_info_ref + kwargs["normal_imgs"] = normal_image + kwargs["position_imgs"] = position_image + + mvd_image = self.pipeline(input_images, num_inference_steps=30, **kwargs).images + + return mvd_image diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/simplify_mesh_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/simplify_mesh_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5c239993dd1498feb1d95e32f6b0df4a3e88da1e --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/simplify_mesh_utils.py @@ -0,0 +1,36 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import trimesh + + +def remesh_mesh(mesh_path, remesh_path, method='trimesh'): + if method == 'trimesh': + mesh_simplify_trimesh(mesh_path, remesh_path) + else: + raise f'Method {method} has not been implemented.' + + +def mesh_simplify_trimesh(inputpath, outputpath): + import pymeshlab + ms = pymeshlab.MeshSet() + ms.load_new_mesh(inputpath, load_in_a_single_layer=True) + ms.save_current_mesh(outputpath.replace('.glb', '.obj'), save_textures=False) + + courent = trimesh.load(outputpath.replace('.glb', '.obj'), force='mesh') + face_num = courent.faces.shape[0] + + if face_num > 100000: + courent = courent.simplify_quadric_decimation(40000) + courent.export(outputpath) diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/uv_warp_utils.py b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/uv_warp_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e9ec16fb34b8f509b4376e3450cde9620e796dbe --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/texgen/utils/uv_warp_utils.py @@ -0,0 +1,36 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import trimesh +import xatlas + + +def mesh_uv_wrap(mesh): + if isinstance(mesh, trimesh.Scene): + mesh = mesh.dump(concatenate=True) + + if len(mesh.faces) > 500000000: + raise ValueError("The mesh has more than 500,000,000 faces, which is not supported.") + + vmapping, indices, uvs = xatlas.parametrize(mesh.vertices, mesh.faces) + + # get vmapping here for watertight processing + mesh.vertices = mesh.vertices[vmapping] + mesh.faces = indices + mesh.visual.uv = uvs + # mesh.vertices = mesh.vertices + # mesh.faces = vmapping[indices] + # mesh.visual.uv = uvs + + return mesh, vmapping diff --git a/OVOW/third_party/Motion324/scripts/hy3dgen/text2image.py b/OVOW/third_party/Motion324/scripts/hy3dgen/text2image.py new file mode 100644 index 0000000000000000000000000000000000000000..2c8a3ab373750ab8ab12a497f15a03179f49ea07 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/hy3dgen/text2image.py @@ -0,0 +1,81 @@ +# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT +# except for the third-party components listed below. +# Hunyuan 3D does not impose any additional limitations beyond what is outlined +# in the repsective licenses of these third-party components. +# Users must comply with all terms and conditions of original licenses of these third-party +# components and must ensure that the usage of the third party components adheres to +# all relevant laws and regulations. + +# For avoidance of doubts, Hunyuan 3D means the large language models and +# their software and algorithms, including trained model weights, parameters (including +# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, +# fine-tuning enabling code and other elements of the foregoing made publicly available +# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. + +import os +import random + +import numpy as np +import torch +from diffusers import AutoPipelineForText2Image + + +def seed_everything(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + os.environ["PL_GLOBAL_SEED"] = str(seed) + + +class HunyuanDiTPipeline: + def __init__( + self, + model_path="Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled", + device='cuda' + ): + self.device = device + self.pipe = AutoPipelineForText2Image.from_pretrained( + model_path, + torch_dtype=torch.float16, + enable_pag=True, + pag_applied_layers=["blocks.(16|17|18|19)"] + ).to(device) + self.pos_txt = ",白色背景,3D风格,最佳质量" + self.neg_txt = "文本,特写,裁剪,出框,最差质量,低质量,JPEG伪影,PGLY,重复,病态," \ + "残缺,多余的手指,变异的手,画得不好的手,画得不好的脸,变异,畸形,模糊,脱水,糟糕的解剖学," \ + "糟糕的比例,多余的肢体,克隆的脸,毁容,恶心的比例,畸形的肢体,缺失的手臂,缺失的腿," \ + "额外的手臂,额外的腿,融合的手指,手指太多,长脖子" + + def compile(self): + # accelarate hunyuan-dit transformer,first inference will cost long time + torch.set_float32_matmul_precision('high') + self.pipe.transformer = torch.compile(self.pipe.transformer, fullgraph=True) + # self.pipe.vae.decode = torch.compile(self.pipe.vae.decode, fullgraph=True) + generator = torch.Generator(device=self.pipe.device) # infer once for hot-start + out_img = self.pipe( + prompt='美少女战士', + negative_prompt='模糊', + num_inference_steps=25, + pag_scale=1.3, + width=1024, + height=1024, + generator=generator, + return_dict=False + )[0][0] + + @torch.no_grad() + def __call__(self, prompt, seed=0): + seed_everything(seed) + generator = torch.Generator(device=self.pipe.device) + generator = generator.manual_seed(int(seed)) + out_img = self.pipe( + prompt=prompt[:60] + self.pos_txt, + negative_prompt=self.neg_txt, + num_inference_steps=25, + pag_scale=1.3, + width=1024, + height=1024, + generator=generator, + return_dict=False + )[0][0] + return out_img diff --git a/OVOW/third_party/Motion324/scripts/images2video.py b/OVOW/third_party/Motion324/scripts/images2video.py new file mode 100644 index 0000000000000000000000000000000000000000..29794f8da9c39b5d66c52e0ec01e1a99e49a16f1 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/images2video.py @@ -0,0 +1,97 @@ +""" +Simple script to convert images in a directory to a video. +Usage: python images2video.py [--fps FPS] +The output video name will be the basename of image_dir with .mp4 suffix, placed inside output_dir. +""" + +import os +import sys +import argparse +import imageio +from PIL import Image +from natsort import natsorted +""" +python scripts/images2video.py /path/to/your/image_name_folder /path/to/your/video_folder +""" +def images_to_video(image_dir, output_dir, fps=12): + """ + Convert images in a directory to a video. + + Args: + image_dir: Directory containing images + output_dir: Directory to save the output video + fps: Frames per second (default: 12) + """ + if not os.path.isdir(image_dir): + print(f"Error: Directory not found: {image_dir}") + return False + + if not os.path.isdir(output_dir): + try: + os.makedirs(output_dir, exist_ok=True) + except Exception as e: + print(f"Error creating output directory {output_dir}: {e}") + return False + + # Get all image files + image_files = [] + for ext in ['.png', '.jpg', '.jpeg', '.PNG', '.JPG', '.JPEG']: + image_files.extend([f for f in os.listdir(image_dir) if f.endswith(ext)]) + + if not image_files: + print(f"Error: No image files found in {image_dir}") + return False + + # Sort images naturally + image_files = natsorted(image_files) + + if len(image_files) < 12: + print(f"Warning: Found only {len(image_files)} images, will create video with {len(image_files)} frames") + + # Set output path: /.mp4 + base_name = os.path.basename(os.path.abspath(image_dir.rstrip("/\\"))) + output_path = os.path.join(output_dir, f"{base_name}.mp4") + + print(f"Converting {len(image_files)} images to video: {output_path}") + print(f"FPS: {fps}") + + # Read images + frames = [] + for img_file in image_files: + img_path = os.path.join(image_dir, img_file) + try: + img = Image.open(img_path).convert("RGB") + frames.append(img) + print(f" Loaded: {img_file}") + except Exception as e: + print(f" Error loading {img_file}: {e}") + continue + + if not frames: + print("Error: No valid images loaded") + return False + + # Write video + try: + imageio.mimsave(output_path, frames, fps=fps, codec='libx264', quality=8) + print(f"✓ Video saved successfully: {output_path}") + return True + except Exception as e: + print(f"Error saving video: {e}") + return False + +def main(): + parser = argparse.ArgumentParser(description="Convert images to video") + parser.add_argument("image_dir", type=str, help="Directory containing images") + parser.add_argument("output_dir", type=str, help="Directory to save the output video") + parser.add_argument("--fps", type=int, default=12, help="Frames per second (default: 12)") + + args = parser.parse_args() + + images_to_video(args.image_dir, args.output_dir, args.fps) + +if __name__ == "__main__": + main() +""" +python scripts/images2video.py /path/to/your/image_name_folder /path/to/your/video_folder +""" \ No newline at end of file diff --git a/OVOW/third_party/Motion324/scripts/inference_with_video_mesh.py b/OVOW/third_party/Motion324/scripts/inference_with_video_mesh.py new file mode 100644 index 0000000000000000000000000000000000000000..b7c297e50be7891ae5a84a86babe5879e5bf6ab5 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/inference_with_video_mesh.py @@ -0,0 +1,452 @@ +# Animate existing mesh with existing video. + +import os +import sys +import json +# Add project root directory to Python path to enable imports from root +project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +print(project_root) +sys.path.append(project_root) + +import importlib +import torch +import numpy as np +import imageio +from natsort import natsorted +from PIL import Image + +from setup import init_config, init_distributed +from utils.render import clear_scene, import_glb, drive_mesh_with_trajs_frames_gt +from utils.mesh_processing import convert_fbx_to_glb_with_blender, sample_pointcloud_with_albedo +from utils.inference_utils import seed_everything, load_checkpoint, smooth_trajectories, load_u2net_model, segment_foreground_with_u2net +from utils.visualization import visualize_input_data + +os.environ['SPCONV_ALGO'] = 'native' +config = init_config() + +def load_video_from_path(video_path): + """ + Load video from file or image directory. + + Args: + video_path: Path to video file (.mp4, .avi, .mov) or directory containing images + + Returns: + video_np: numpy array of shape (T, H, W, C), range [0, 255] + """ + if video_path.lower().endswith(('.mp4', '.avi', '.mov')): + print(f"Loading video file: {video_path}") + reader = imageio.get_reader(video_path) + frames = [frame for frame in reader] + reader.close() + video_np = np.stack(frames, axis=0) + print(f"✓ Loaded {len(frames)} frames from video, shape: {video_np.shape}") + + elif os.path.isdir(video_path): + print(f"Loading images from directory: {video_path}") + image_paths = natsorted([ + os.path.join(video_path, f) + for f in os.listdir(video_path) + if f.lower().endswith(('.png', '.jpg', '.jpeg')) + ]) + frames = [np.array(Image.open(img_path).convert("RGB")) for img_path in image_paths] + video_np = np.stack(frames, axis=0) + print(f"✓ Loaded {len(frames)} images from directory, shape: {video_np.shape}") + else: + raise ValueError(f"video_path must be a video file or image directory: {video_path}") + + return video_np + + +def prepare_mesh_data(config, glb_path, device): + """ + Load and prepare mesh data for inference. + + Args: + config: Configuration object + glb_path: Path to GLB file + device: Device to load data on + + Returns: + input_data: Dictionary containing mesh data + mesh: trimesh object + faces: Face indices + """ + import trimesh + from scipy.spatial import cKDTree + + print(f"Loading GLB file: {glb_path}") + mesh = trimesh.load(glb_path, force='mesh') + mesh.fix_normals() + + if isinstance(mesh, trimesh.Scene): + print(f"Detected scene with {len(mesh.geometry)} geometries") + for name in mesh.geometry.keys(): + print(f" - {name}") + mesh = trimesh.util.concatenate(mesh.geometry.values()) + else: + print(f"Detected single geometry, vertices shape: {mesh.vertices.shape}") + + # Normalize mesh + vertices = mesh.vertices.astype(np.float32) + faces = mesh.faces.astype(np.int64) + vertex_normals_np = mesh.vertex_normals.astype(np.float32) + + center = (vertices.max(axis=0) + vertices.min(axis=0)) / 2 + vertices = vertices - center + v_max = np.abs(vertices).max() + scale_norm = float(2 * (v_max + 1e-8)) + vertices = vertices / scale_norm + + R_example = np.eye(3, dtype=np.float32) + vertices = vertices @ R_example.T + vertex_normals_np = vertex_normals_np @ R_example.T + + mesh.vertices = mesh.vertices - center + mesh.vertices = mesh.vertices / scale_norm + mesh.vertices = mesh.vertices @ R_example.T + + # Sample surface points + num_shape_samples = getattr(config.training, "num_shape_samples", 16384) + print(f"Sampling {num_shape_samples} surface points with texture interpolation...") + samples_xyz, samples_normals, samples_rgb = sample_pointcloud_with_albedo(mesh, num=num_shape_samples) + print(f"✓ Sampling complete: points {samples_xyz.shape}, normals {samples_normals.shape}, colors {samples_rgb.shape}") + + # Assign colors to vertices + tree = cKDTree(samples_xyz.numpy()) + _, nearest_indices = tree.query(vertices, k=1) + vert_rgb = samples_rgb[nearest_indices].numpy() + + # Prepare input data + input_data = { + 'ref_shape_pcd': samples_xyz[None].float().to(device), + 'ref_shape_normals': samples_normals[None].float().to(device), + 'ref_shape_rgbs': samples_rgb[None].float().to(device), + 'ref_pcd': torch.from_numpy(vertices)[None].float().to(device), + 'ref_normal': torch.from_numpy(vertex_normals_np)[None].float().to(device), + 'ref_rgb': torch.from_numpy(vert_rgb)[None].float().to(device), + 'faces': torch.from_numpy(faces)[None].long().to(device) + } + + norm_params = { + "center": center.tolist(), + "scale": scale_norm, + "R_example": R_example.tolist(), + "R_out": np.eye(3, dtype=np.float32).tolist(), + } + + return input_data, mesh, faces, norm_params + + +def run_model_inference(model, input_data, video_tensor, config, device): + """ + Run model inference on video with chunking support. + + Args: + model: Loaded model + input_data: Dictionary containing mesh data + video_tensor: Video frames tensor + config: Configuration object + device: Device to run on + + Returns: + trajs: Predicted trajectories (1, T, N, 3) + """ + chunk_size = config.training.get('frames', 12) + total_T = video_tensor.shape[0] + print(f"Total frames: {total_T}, chunk size: {chunk_size}") + + amp_dtype_mapping = { + "fp16": torch.float16, + "bf16": torch.bfloat16, + "fp32": torch.float32, + 'tf32': torch.float32 + } + + if total_T <= chunk_size: + print(f"Total frames ({total_T}) <= chunk size ({chunk_size}), processing all frames at once") + input_data_chunk = input_data.copy() + input_data_chunk['rgb_video'] = video_tensor[None].float().to(device) + + with torch.autocast( + enabled=config.training.get('use_amp', False), + device_type="cuda", + dtype=amp_dtype_mapping[config.training.get('amp_dtype', 'bf16')], + ): + output_chunk = model(input_data_chunk) + + if isinstance(output_chunk, dict) and 'pcd_moved' in output_chunk: + trajs = output_chunk['pcd_moved'].float() + print(f"Trajectories shape: {trajs.shape}") + else: + trajs = None + print("Warning: No pcd_moved in output") + else: + # Process with overlapping chunks + slide_size = chunk_size - 1 + chunk_start_indices = list(range(0, total_T - chunk_size + 1, slide_size)) + if chunk_start_indices and (chunk_start_indices[-1] + chunk_size < total_T): + chunk_start_indices.append(total_T - chunk_size) + print(f"Chunk start indices: {chunk_start_indices}") + + out_trajs_lst = [] + + for i, start_idx in enumerate(chunk_start_indices): + end_idx = start_idx + chunk_size + + if i == 0: + rgb_window = video_tensor[0:chunk_size] + else: + rgb_window = torch.cat([ + video_tensor[0:1], + video_tensor[start_idx+1:end_idx] + ], dim=0) + + input_data_chunk = input_data.copy() + input_data_chunk['rgb_video'] = rgb_window[None].float().to(device) + + if rgb_window.shape[0] != chunk_size: + print(f"Warning: Chunk rgb_video frames != chunk_size, skipping") + continue + + print(f"Processing chunk {i+1}/{len(chunk_start_indices)}: frames {start_idx}-{end_idx}") + + with torch.autocast( + enabled=config.training.get('use_amp', False), + device_type="cuda", + dtype=amp_dtype_mapping[config.training.get('amp_dtype', 'fp16')], + ): + output_chunk = model(input_data_chunk) + + if isinstance(output_chunk, dict) and 'pcd_moved' in output_chunk: + trajs_chunk = output_chunk['pcd_moved'].float() + out_trajs_lst.append(trajs_chunk) + else: + print("Warning: No pcd_moved in chunk output, skipping") + + # Merge trajectories + if len(out_trajs_lst) > 0: + if len(chunk_start_indices) >= 2: + merged_trajs_lst = [] + for i in range(len(out_trajs_lst)): + if i == 0 and i != len(out_trajs_lst) - 2: # if len==2 should keep first N frames + chunk_trajs = out_trajs_lst[i].clone() + chunk_trajs[:, 0, :, :] = input_data['ref_pcd'] + merged_trajs_lst.append(chunk_trajs) + elif i < len(out_trajs_lst) - 2: + merged_trajs_lst.append(out_trajs_lst[i][:, 1:, ...]) + elif i == len(out_trajs_lst) - 2: + start_a = chunk_start_indices[-2] + start_b = chunk_start_indices[-1] + keep_len = max(start_b - start_a, 0) + if keep_len > 0 and len(out_trajs_lst) != 2: + merged_trajs_lst.append(out_trajs_lst[i][:, 1:1+keep_len, ...]) + elif keep_len > 0 and i == 0 and len(out_trajs_lst) == 2: + chunk_trajs = out_trajs_lst[i].clone() + chunk_trajs[:, 0, :, :] = input_data['ref_pcd'] + merged_trajs_lst.append(chunk_trajs[:, :1+keep_len, ...]) + elif i == len(out_trajs_lst) - 1: + merged_trajs_lst.append(out_trajs_lst[i][:, 1:, ...]) + + if len(merged_trajs_lst) > 0: + trajs = torch.cat(merged_trajs_lst, dim=1) + print(f"Merged trajectories shape: {trajs.shape}") + else: + trajs = None + print("No trajectories obtained") + else: + trajs = out_trajs_lst[0].clone() + trajs[:, 0, :, :] = input_data['ref_pcd'] + print(f"Trajectories shape: {trajs.shape}") + else: + trajs = None + print("No trajectories obtained") + + return trajs + + +def save_segmented_videos(video_tensor, mask_tensor, output_dir, fps=12): + """ + Save segmented videos (black background, white background) and mask video. + + Args: + video_tensor: Video frames tensor (T, H, W, C), range [0, 1] + mask_tensor: Mask tensor (T, H, W, 1), range [0, 1] + output_dir: Output directory to save videos + fps: Frames per second for output videos + """ + os.makedirs(output_dir, exist_ok=True) + + # Save segmented video with black background + output_black_path = os.path.join(output_dir, "input_rgb_video_segmented_black.mp4") + with imageio.get_writer(output_black_path, fps=fps) as writer: + for i in range(video_tensor.shape[0]): + frame_uint8 = (video_tensor[i].numpy() * 255).astype(np.uint8) + writer.append_data(frame_uint8) + print(f"✓ Saved segmented video (black bg): {output_black_path}") + + # Save segmented video with white background + output_white_path = os.path.join(output_dir, "input_rgb_video_segmented_white.mp4") + with imageio.get_writer(output_white_path, fps=fps) as writer: + for i in range(video_tensor.shape[0]): + frame = video_tensor[i].numpy() + mask = mask_tensor[i].numpy() + frame_white = frame * mask + (1 - mask) + frame_uint8 = (frame_white * 255).astype(np.uint8) + writer.append_data(frame_uint8) + print(f"✓ Saved segmented video (white bg): {output_white_path}") + + # Save foreground mask video + output_mask_path = os.path.join(output_dir, "input_foreground_mask.mp4") + with imageio.get_writer(output_mask_path, fps=fps) as writer: + for i in range(mask_tensor.shape[0]): + mask = mask_tensor[i].numpy() + mask_gray = (mask.squeeze(-1) * 255).astype(np.uint8) + mask_rgb = np.stack([mask_gray] * 3, axis=-1) + writer.append_data(mask_rgb) + print(f"✓ Saved foreground mask video: {output_mask_path}") + + +def run_inference_on_video(config): + """Main inference function for custom mesh and video input.""" + seed_everything(777) + ddp_info = init_distributed(seed=777) + torch.backends.cuda.matmul.allow_tf32 = config.training.get('use_tf32', True) + torch.backends.cudnn.allow_tf32 = config.training.get('use_tf32', True) + + # Load model + module, class_name = config.model.class_name.rsplit(".", 1) + Motion324 = importlib.import_module(module).__dict__[class_name] + model = Motion324(config).to(ddp_info.device) + + ckpt_path = config.training.get("resume_ckpt", "") + if not ckpt_path: + ckpt_path = os.path.join(config.training.checkpoint_dir, "latest.pt") + + step_info = load_checkpoint(ckpt_path, model, ddp_info.device) + print(f"Loaded checkpoint from step {step_info['param_update_step']}") + model.eval() + + # Load U2Net for segmentation if enabled + use_segmentation = getattr(config, 'use_segmentation', True) + u2net_model = None + if use_segmentation: + print("Loading U2Net model for foreground segmentation...") + u2net_model = load_u2net_model(device=ddp_info.device) + if u2net_model is None: + print("Warning: U2Net model loading failed, will not perform foreground segmentation") + else: + print("Foreground segmentation disabled (use_segmentation=False)") + + # Prepare mesh + filepath = config.data_dir + if filepath.lower().endswith('.fbx'): + print(f"Detected FBX file, converting to GLB...") + output_dir = config.output_dir if hasattr(config, 'output_dir') else "./" + os.makedirs(output_dir, exist_ok=True) + glb_path = os.path.join(output_dir, "converted_mesh.glb") + convert_fbx_to_glb_with_blender(filepath, glb_path) + print(f"✓ GLB file saved to: {glb_path}") + elif filepath.lower().endswith('.glb'): + print(f"Detected GLB file, using directly") + glb_path = filepath + else: + raise ValueError(f"Unsupported file format: {filepath}, only .fbx and .glb are supported") + + with torch.no_grad(): + input_data, mesh, faces, norm_params = prepare_mesh_data(config, glb_path, ddp_info.device) + + # Load video + if not hasattr(config, 'video_path') or config.video_path is None: + raise ValueError("config.video_path not specified, cannot load video") + + video_np = load_video_from_path(config.video_path) + + # Apply foreground segmentation + if u2net_model is not None: + masked_frames, masks = segment_foreground_with_u2net(video_np, u2net_model, device=ddp_info.device) + print("✓ Applied foreground segmentation, background as black") + else: + masked_frames = video_np + masks = np.ones(video_np.shape[:3] + (1,), dtype=np.float32) + + video_tensor = torch.from_numpy(masked_frames.astype(np.float32)).float() / 255.0 + mask_tensor = torch.from_numpy(masks.astype(np.float32)).float() + + # Save Motion324 normalization params for downstream inverse transform + output_dir = config.output_dir if hasattr(config, 'output_dir') else "./" + os.makedirs(output_dir, exist_ok=True) + if norm_params is not None: + norm_path = os.path.join(output_dir, "motion324_norm.json") + with open(norm_path, "w") as f: + json.dump(norm_params, f, indent=2) + print(f"Saved motion324_norm to: {norm_path}") + + # Select frames + T = config.training.frames + start_frame = getattr(config, 'start_frame', 0) + + if start_frame + T <= video_tensor.shape[0]: + video_tensor = video_tensor[start_frame:start_frame + T] + mask_tensor = mask_tensor[start_frame:start_frame + T] + else: + print(f"Warning: Insufficient frames, using {video_tensor.shape[0] - start_frame} frames from frame {start_frame}") + video_tensor = video_tensor[start_frame:] + mask_tensor = mask_tensor[start_frame:] + + # Save input videos + output_video_dir = config.output_dir if hasattr(config, 'output_dir') else "./" + save_segmented_videos(video_tensor, mask_tensor, output_video_dir, fps=12) + + total_T = video_tensor.shape[0] + input_data['rgb_video'] = video_tensor + + # Visualize input data + visualize_input_data(input_data, save_path=os.path.join(output_video_dir, "input_data_vis.png")) + + # Run inference + trajs = run_model_inference(model, input_data, video_tensor, config, ddp_info.device) + + # Apply smoothing + if trajs is not None: + # apply smoothing to trajectories to avoid jitter for static vertices + trajs = smooth_trajectories( + trajs, + method='combined', + motion_threshold=0.002, + window_size=3, + sigma=1.0, + savgol_polyorder=2, + oneeuro_mincutoff=1.0, + oneeuro_beta=0.007, + visualization_dir=None + ) + print(f"Trajectory smoothing complete") + + # Generate animation + if trajs is not None: + trajs_to_use = trajs[:, :total_T, :, :] + # convert coords to blender coordinate system + trajs_b = trajs_to_use.clone() + trajs_b[..., 0], trajs_b[..., 1], trajs_b[..., 2] = ( + trajs_to_use[..., 0], # x -> x + -trajs_to_use[..., 2], # y -> -z + trajs_to_use[..., 1] # z -> y + ) + trajs_to_use = trajs_b + clear_scene() + mesh_objects = import_glb(glb_path) + + drive_mesh_with_trajs_frames_gt( + mesh_objects, + trajs_to_use.cpu(), + os.path.join(output_video_dir, 'output_animation'), + azi=0, + ele=0, + export_format='glb' + ) + print(f"✓ Animation saved to: {os.path.join(output_video_dir, 'output_animation.glb')}") + + +if __name__ == "__main__": + run_inference_on_video(config) diff --git a/OVOW/third_party/Motion324/scripts/inference_with_video_only.py b/OVOW/third_party/Motion324/scripts/inference_with_video_only.py new file mode 100644 index 0000000000000000000000000000000000000000..de9bc62e7f894a83568e5e1a0d8a04d64ae6a6dd --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/inference_with_video_only.py @@ -0,0 +1,635 @@ +# Animate hunyuan genereated fbx mesh with input video. + +import os +import sys +import json +# Add project root directory to Python path to enable imports from root +project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +print(project_root) +sys.path.append(project_root) + +os.environ['SPCONV_ALGO'] = 'native' + +import importlib +import torch +import numpy as np +import imageio +from natsort import natsorted +from PIL import Image +import trimesh + +from setup import init_config, init_distributed +from utils.render import (clear_scene, import_fbx, get_all_vertices, + get_all_faces, drive_mesh_with_trajs_frames) +from utils.visualization import visualize_input_data +from utils.inference_utils import seed_everything, load_checkpoint, smooth_trajectories + +config = init_config() +seed_everything(777) + + +def load_model(config, device): + """ + Load model and checkpoint. + + Args: + config: Model configuration + device: Device to load model on + + Returns: + model: Loaded model + """ + module, class_name = config.model.class_name.rsplit(".", 1) + Motion324 = importlib.import_module(module).__dict__[class_name] + model = Motion324(config).to(device) + + ckpt_path = config.training.get("resume_ckpt", "") + if not ckpt_path: + ckpt_path = os.path.join(config.training.checkpoint_dir, "latest.pt") + + step_info = load_checkpoint(ckpt_path, model, device) + print(f"Loaded checkpoint from step {step_info['param_update_step']}") + model.eval() + + return model + + +def prepare_mesh_data_fbx(filepath, config): + """ + Load and prepare mesh data from FBX file. + + Args: + filepath: Path to FBX file + config: Configuration object + + Returns: + vertices: Vertex positions (N, 3) + faces: Face indices (M, 3) + samples_xyz: Sampled surface points (num_samples, 3) + samples_normals: Normals at sampled points (num_samples, 3) + samples_rgb: RGB colors at sampled points (num_samples, 3) + vertex_normals_np: Vertex normals (N, 3) + vert_rgb: Vertex RGB colors (N, 3) + mesh_objects: Blender mesh objects + """ + clear_scene() + mesh_objects = import_fbx(filepath) + + all_vertices = np.array(get_all_vertices(mesh_objects))[0] + all_faces = np.array(get_all_faces(mesh_objects))[0] + faces = all_faces + + # Normalize mesh + center = (all_vertices.max(axis=0) + all_vertices.min(axis=0)) / 2 + all_vertices = all_vertices - center + v_max = np.abs(all_vertices).max() + scale_norm = float(2 * (v_max + 1e-8)) + all_vertices = all_vertices / scale_norm + mesh_combined = trimesh.Trimesh(vertices=all_vertices, faces=all_faces, process=False) + + # Sample surface points + num_samples = getattr(config.training, "num_shape_samples", 4096) + samples_xyz, face_indices = trimesh.sample.sample_surface(mesh_combined, num_samples) + samples_normals = mesh_combined.face_normals[face_indices] # (num_samples, 3) + + vertex_normals_np = mesh_combined.vertex_normals.astype(np.float32) + + # Initialize colors + samples_rgb = np.full(samples_xyz.shape, [128, 128, 128], dtype=np.uint8) + vert_rgb = np.full(all_vertices.shape, [128, 128, 128], dtype=np.uint8) + + # Extract texture colors + for mesh_obj in mesh_objects: + if mesh_obj.data.materials: + mat = mesh_obj.data.materials[0] + if mat and mat.use_nodes: + nodes = mat.node_tree.nodes + for node in nodes: + if node.type == 'TEX_IMAGE' and node.image: + try: + from PIL import Image as PILImage + temp_path = "/tmp/temp_texture.png" + node.image.save_render(temp_path) + texture = PILImage.open(temp_path) + if texture.mode != 'RGB': + texture_np = np.array(texture.convert('RGB')) + else: + texture_np = np.array(texture) + + if mesh_obj.data.uv_layers: + uv_layer = mesh_obj.data.uv_layers.active.data + tex_height, tex_width, _ = texture_np.shape + + # Sample colors for surface points + for i, face_idx in enumerate(face_indices): + poly = mesh_obj.data.polygons[face_idx] + face_uvs = [] + for loop_idx in poly.loop_indices: + face_uvs.append(uv_layer[loop_idx].uv[:]) + face_uvs = np.array(face_uvs) # (3, 2) + + triangle = all_vertices[all_faces[face_idx]] + point = samples_xyz[i] + bary = trimesh.triangles.points_to_barycentric([triangle], [point])[0] + + interpolated_uv = np.dot(bary, face_uvs) + interpolated_uv[1] = 1.0 - interpolated_uv[1] + + pixel_x = int(np.clip(interpolated_uv[0] * (tex_width-1), 0, tex_width-1)) + pixel_y = int(np.clip(interpolated_uv[1] * (tex_height-1), 0, tex_height-1)) + samples_rgb[i] = texture_np[pixel_y, pixel_x] + + # Sample colors for vertices + vert_to_faces = {} + for face_idx, face in enumerate(all_faces): + for local_idx, vert_idx in enumerate(face): + if vert_idx not in vert_to_faces: + vert_to_faces[vert_idx] = [] + vert_to_faces[vert_idx].append((face_idx, local_idx)) + + for vert_idx in range(len(all_vertices)): + if vert_idx in vert_to_faces: + face_idx, local_idx = vert_to_faces[vert_idx][0] + poly = mesh_obj.data.polygons[face_idx] + loop_idx = poly.loop_indices[local_idx] + uv = uv_layer[loop_idx].uv[:] + uv_flipped = [uv[0], 1.0 - uv[1]] + pixel_x = int(np.clip(uv_flipped[0] * (tex_width-1), 0, tex_width-1)) + pixel_y = int(np.clip(uv_flipped[1] * (tex_height-1), 0, tex_height-1)) + vert_rgb[vert_idx] = texture_np[pixel_y, pixel_x] + break + except Exception as e: + print(f"Failed to load texture: {e}") + pass + + # Ensure RGB has 3 channels + if samples_rgb.shape[1] == 4: + samples_rgb = samples_rgb[:, :3] + if vert_rgb.shape[1] == 4: + vert_rgb = vert_rgb[:, :3] + + # Convert coordinates + R_example = np.array([ + [1.0, 0.0, 0.0], + [0.0, 0.0, 1.0], + [0.0, -1.0, 0.0] + ]) + vertices = all_vertices @ R_example.T + vertex_normals_np = vertex_normals_np @ R_example.T + samples_xyz = samples_xyz @ R_example.T + samples_normals = samples_normals @ R_example.T + + norm_params = { + "center": center.tolist(), + "scale": scale_norm, + "R_example": R_example.tolist(), + "R_out": np.eye(3, dtype=np.float32).tolist(), + } + + return vertices, faces, samples_xyz, samples_normals, samples_rgb, vertex_normals_np, vert_rgb, mesh_objects, norm_params + + +def prepare_mesh_data_trimesh(filepath, config): + """ + Load and prepare mesh data from trimesh-compatible file. + + Args: + filepath: Path to mesh file + config: Configuration object + + Returns: + vertices: Vertex positions (N, 3) + faces: Face indices (M, 3) + samples_xyz: Sampled surface points (num_samples, 3) + samples_normals: Normals at sampled points (num_samples, 3) + samples_rgb: RGB colors at sampled points (num_samples, 3) + vertex_normals_np: Vertex normals (N, 3) + vert_rgb: Vertex RGB colors (N, 3) + """ + mesh = trimesh.load(filepath, process=False) + + if isinstance(mesh, trimesh.Scene): + mesh = trimesh.util.concatenate(mesh.geometry.values()) + + vertices = mesh.vertices.astype(np.float32) # (N, 3) + faces = mesh.faces.astype(np.int64) # (M, 3) + vertex_normals_np = mesh.vertex_normals.astype(np.float32) # (N, 3) + + # Normalize mesh + center = (vertices.max(axis=0) + vertices.min(axis=0)) / 2 + vertices = vertices - center + v_max = np.abs(vertices).max() + scale_norm = float(2 * (v_max + 1e-8)) + vertices = vertices / scale_norm + + # Sample surface points + num_samples = getattr(config.training, "num_shape_samples", 2048) + samples_xyz, face_indices = trimesh.sample.sample_surface(mesh, num_samples) + samples_xyz = samples_xyz - center + samples_xyz = samples_xyz / scale_norm + samples_normals = mesh.face_normals[face_indices] # (num_samples, 3) + + # Initialize colors + samples_rgb = np.full(samples_xyz.shape, [128, 128, 128], dtype=np.uint8) + vert_rgb = np.full(vertices.shape, [128, 128, 128], dtype=np.uint8) + + # Extract texture or vertex colors + print(mesh.visual.kind) + if mesh.visual.kind == 'texture' and hasattr(mesh.visual, 'uv'): + Image = None + try: + from PIL import Image as PILImage + Image = PILImage + except ImportError: + pass + + if Image is not None: + if hasattr(mesh.visual.material, 'baseColorTexture'): + texture = mesh.visual.material.baseColorTexture + elif hasattr(mesh.visual.material, 'image'): + texture = mesh.visual.material.image + else: + texture = None + + if texture is not None: + if texture.mode != 'RGB': + texture_np = np.array(texture.convert('RGB')) + else: + texture_np = np.array(texture) + tex_height, tex_width, _ = texture_np.shape + + # Sample colors for surface points + triangles = mesh.vertices[mesh.faces[face_indices]] + bary_coords = trimesh.triangles.points_to_barycentric(triangles, samples_xyz) + face_uvs = mesh.visual.uv[mesh.faces[face_indices]] + interpolated_uvs = np.einsum('ij,ijk->ik', bary_coords, face_uvs) + interpolated_uvs[:, 1] = 1.0 - interpolated_uvs[:, 1] + pixel_coords_f = interpolated_uvs * [tex_width-1, tex_height-1] + pixel_indices = np.round(pixel_coords_f).astype(int) + pixel_indices[:, 0] = np.clip(pixel_indices[:, 0], 0, tex_width-1) + pixel_indices[:, 1] = np.clip(pixel_indices[:, 1], 0, tex_height-1) + samples_rgb = texture_np[pixel_indices[:, 1], pixel_indices[:, 0]] # (num_samples, 3) + + # Sample colors for vertices + vertex_uvs = mesh.visual.uv + vertex_uvs_flipped = vertex_uvs.copy() + vertex_uvs_flipped[:, 1] = 1.0 - vertex_uvs_flipped[:, 1] + pixel_coords_f = vertex_uvs_flipped * [tex_width-1, tex_height-1] + pixel_indices = np.round(pixel_coords_f).astype(int) + pixel_indices[:, 0] = np.clip(pixel_indices[:, 0], 0, tex_width-1) + pixel_indices[:, 1] = np.clip(pixel_indices[:, 1], 0, tex_height-1) + vert_rgb = texture_np[pixel_indices[:, 1], pixel_indices[:, 0]] + + elif mesh.visual.kind == 'vertex' and hasattr(mesh.visual, 'vertex_colors'): + vertex_colors = mesh.visual.vertex_colors + if vertex_colors is not None: + triangles = mesh.vertices[mesh.faces[face_indices]] + bary_coords = trimesh.triangles.points_to_barycentric(triangles, samples_xyz) + face_vertex_colors = vertex_colors[mesh.faces[face_indices]] # (num_samples, 3, 3/4) + interpolated_colors = np.einsum('ij,ijk->ik', bary_coords, face_vertex_colors) + samples_rgb = interpolated_colors[:, :3] + + vert_rgb = vertex_colors[:, :3] + + # Ensure RGB has 3 channels + if samples_rgb.shape[1] == 4: + samples_rgb = samples_rgb[:, :3] + if vert_rgb.shape[1] == 4: + vert_rgb = vert_rgb[:, :3] + + # Convert coordinates + R_example = np.array([ + [1.0, 0.0, 0.0], + [0.0, 0.0, 1.0], + [0.0, -1.0, 0.0] + ]) + vertices = vertices @ R_example.T + vertex_normals_np = vertex_normals_np @ R_example.T + samples_xyz = samples_xyz @ R_example.T + samples_normals = samples_normals @ R_example.T + + norm_params = { + "center": center.tolist(), + "scale": scale_norm, + "R_example": R_example.tolist(), + "R_out": np.eye(3, dtype=np.float32).tolist(), + } + + return vertices, faces, samples_xyz, samples_normals, samples_rgb, vertex_normals_np, vert_rgb, norm_params + + +def load_images_from_path(image_path, start_frame=0): + """ + Load images from directory or list of paths. + + Args: + image_path: Path to image directory or list of image paths + start_frame: Starting frame index + + Returns: + video_np: numpy array of shape (T, H, W, C), range [0, 255] + video_tensor: torch tensor of shape (T, H, W, C), range [0, 1] + """ + if isinstance(image_path, list): + image_paths = image_path + elif os.path.isdir(image_path): + image_paths = natsorted([ + os.path.join(image_path, f) + for f in os.listdir(image_path) + if f.lower().endswith(('.png', '.jpg', '.jpeg')) + ]) + else: + raise ValueError("image_path must be a list of image paths or a directory") + + frames = [] + sel_image_paths = image_paths[start_frame:] + for img_path in sel_image_paths: + img = Image.open(img_path).convert("RGB") + frames.append(np.array(img)) + + video_np = np.stack(frames, axis=0) # (T, H, W, C) + video_tensor = torch.from_numpy(video_np).float() / 255.0 + + return video_np, video_tensor + + +def prepare_input_data(vertices, faces, samples_xyz, samples_normals, samples_rgb, + vertex_normals_np, vert_rgb, device): + """ + Prepare input data dictionary for model inference. + + Args: + vertices: Vertex positions (N, 3) + faces: Face indices (M, 3) + samples_xyz: Sampled surface points (num_samples, 3) + samples_normals: Normals at sampled points (num_samples, 3) + samples_rgb: RGB colors at sampled points (num_samples, 3) + vertex_normals_np: Vertex normals (N, 3) + vert_rgb: Vertex RGB colors (N, 3) + device: Device to load data on + + Returns: + input_data: Dictionary containing mesh data + """ + vertices_torch = torch.tensor(vertices, dtype=torch.float32) + faces_torch = torch.tensor(faces, dtype=torch.int64) + + ref_shape_pcd = torch.from_numpy(samples_xyz.astype(np.float32)) # (num_samples, 3) + ref_shape_normals = torch.from_numpy(samples_normals.astype(np.float32)) # (num_samples, 3) + ref_shape_rgbs = torch.from_numpy(samples_rgb.astype(np.float32) / 255.0) # (num_samples, 3) + ref_rgb = torch.from_numpy(vert_rgb.astype(np.float32) / 255.0) # (N, 3) + ref_normal = torch.from_numpy(vertex_normals_np.astype(np.float32)) + ref_pcd = vertices_torch + faces = faces_torch + + input_data = {} + input_data['faces'] = torch.from_numpy(faces).unsqueeze(0).to(device) if isinstance(faces, np.ndarray) else faces[None].to(device) # (1, F, 3) + input_data['ref_shape_pcd'] = ref_shape_pcd[None].to(device) # (1, num_shape_samples, 3) + input_data['ref_shape_normals'] = ref_shape_normals[None].to(device) # (1, num_shape_samples, 3) + input_data['ref_shape_rgbs'] = ref_shape_rgbs[None].to(device) # (1, num_shape_samples, 3) + input_data['ref_pcd'] = ref_pcd[None].to(device) # (1, N_points, 3) + input_data['ref_normal'] = ref_normal[None].to(device) # (1, N_points, 3) + input_data['ref_rgb'] = ref_rgb[None].to(device) # (1, N_points, 3) + + return input_data + + +def run_model_inference(model, input_data, video_tensor, config, device): + """ + Run model inference on video with chunking support. + + Args: + model: Loaded model + input_data: Dictionary containing mesh data + video_tensor: Video frames tensor (T, H, W, C) + config: Configuration object + device: Device to run on + + Returns: + trajs: Predicted trajectories (1, T, N, 3) + """ + total_T = video_tensor.shape[0] + chunk_size = config.training.frames + rgb_video = video_tensor # (total_T, H, W, C) + + amp_dtype_mapping = { + "fp16": torch.float16, + "bf16": torch.bfloat16, + "fp32": torch.float32, + 'tf32': torch.float32 + } + + print(f"Total frames: {total_T}, frames per chunk: {chunk_size}") + + # Process video in chunks + if total_T <= chunk_size: + print(f"Total frames ({total_T}) <= chunk_size ({chunk_size}), processing all frames directly") + input_data_chunk = input_data.copy() + input_data_chunk['rgb_video'] = rgb_video[None].to(device) + + with torch.autocast( + enabled=config.training.get('use_amp', False), + device_type="cuda", + dtype=amp_dtype_mapping[config.training.get('amp_dtype', 'fp16')], + ): + output_chunk = model(input_data_chunk) + + if isinstance(output_chunk, dict) and 'pcd_moved' in output_chunk: + trajs = output_chunk['pcd_moved'].float() # (1, total_T, N, 3) + print(f"trajs.shape: {trajs.shape}") + else: + trajs = None + print("Warning: pcd_moved not in output") + else: + slide_size = chunk_size - 1 + chunk_start_indices = list(range(0, total_T - chunk_size + 1, slide_size)) + print(chunk_start_indices) + if chunk_start_indices and (chunk_start_indices[-1] + chunk_size < total_T): + chunk_start_indices.append(total_T - chunk_size) + print(chunk_start_indices) + out_trajs_lst = [] + + for i, start_idx in enumerate(chunk_start_indices): + end_idx = start_idx + chunk_size + + if i == 0: + rgb_window = rgb_video[0:chunk_size] + else: + rgb_window = torch.cat( + [ + rgb_video[0:1], + rgb_video[start_idx+1:end_idx] + ], + dim=0 + ) + input_data_chunk = input_data.copy() + input_data_chunk['rgb_video'] = rgb_window[None].to(device) + if rgb_window.shape[0] != chunk_size: + print(f"Warning: chunk rgb_video frame count ({rgb_window.shape[0]}) != T ({chunk_size}), skipping") + continue + print(f"Inferring chunk: {start_idx}-{end_idx}, rgb_video.shape: {input_data_chunk['rgb_video'].shape}") + + with torch.autocast( + enabled=config.training.get('use_amp', False), + device_type="cuda", + dtype=amp_dtype_mapping[config.training.get('amp_dtype', 'fp16')], + ): + output_chunk = model(input_data_chunk) + + if isinstance(output_chunk, dict) and 'pcd_moved' in output_chunk: + trajs_chunk = output_chunk['pcd_moved'].float() # (1, chunk, N, 3) + print(f"trajs_chunk.shape: {trajs_chunk.shape}") + out_trajs_lst.append(trajs_chunk) + else: + print("Warning: chunk did not output pcd_moved, skipping") + + # Merge chunks + if len(out_trajs_lst) > 0: + if len(chunk_start_indices) >= 2: + merged_trajs_lst = [] + for i in range(len(out_trajs_lst)): + if i == 0 and len(out_trajs_lst) != 2: # if len==2 should keep first N frames + chunk_trajs = out_trajs_lst[i].clone() # (1, chunk_size, N, 3) + chunk_trajs[:, 0, :, :] = input_data['ref_pcd'] + merged_trajs_lst.append(chunk_trajs) + elif i < len(out_trajs_lst) - 2: + merged_trajs_lst.append(out_trajs_lst[i][:, 1:, ...]) + elif i == len(out_trajs_lst) - 2: + start_a = chunk_start_indices[-2] + start_b = chunk_start_indices[-1] + keep_len = max(start_b - start_a, 0) + if keep_len > 0 and len(out_trajs_lst) != 2: + merged_trajs_lst.append(out_trajs_lst[i][:, 1:1+keep_len, ...]) + elif keep_len > 0 and i == 0 and len(out_trajs_lst) == 2: + chunk_trajs = out_trajs_lst[i].clone() + chunk_trajs[:, 0, :, :] = input_data['ref_pcd'] + merged_trajs_lst.append(chunk_trajs[:, :1+keep_len, ...]) + elif i == len(out_trajs_lst) - 1: + merged_trajs_lst.append(out_trajs_lst[i][:, 1:, ...]) + if len(merged_trajs_lst) > 0: + trajs = torch.cat(merged_trajs_lst, dim=1) + print(f"Concatenated trajs.shape: {trajs.shape}") + else: + trajs = None + print("No trajs obtained") + else: + trajs = out_trajs_lst[0].clone() + trajs[:, 0, :, :] = input_data['ref_pcd'] + print(f"Concatenated trajs.shape: {trajs.shape}") + else: + trajs = None + print("No trajs obtained") + + return trajs + + +def run_inference_on_video(config): + """ + Main inference function for video with mesh data. + + Args: + config: Model configuration + Returns: + Model output + """ + ddp_info = init_distributed(seed=777) + torch.backends.cuda.matmul.allow_tf32 = config.training.get('use_tf32', True) + torch.backends.cudnn.allow_tf32 = config.training.get('use_tf32', True) + + # Load model + model = load_model(config, ddp_info.device) + + with torch.no_grad(): + # Prepare mesh data + filepath = config.data_dir + use_fbx = config.training.get('use_fbx', True) + + if use_fbx: + vertices, faces, samples_xyz, samples_normals, samples_rgb, \ + vertex_normals_np, vert_rgb, mesh_objects, norm_params = prepare_mesh_data_fbx(filepath, config) + else: + vertices, faces, samples_xyz, samples_normals, samples_rgb, \ + vertex_normals_np, vert_rgb, norm_params = prepare_mesh_data_trimesh(filepath, config) + mesh_objects = None + + # Prepare input data + input_data = prepare_input_data( + vertices, faces, samples_xyz, samples_normals, samples_rgb, + vertex_normals_np, vert_rgb, ddp_info.device + ) + + # Load images + if not hasattr(config, 'image_path') or config.image_path is None: + raise ValueError("config.image_path not specified, cannot load rgb_video") + + start_frame = getattr(config, 'start_frame', 0) + video_np, video_tensor = load_images_from_path(config.image_path, start_frame) + + # Save input video + output_video_dir = config.output_dir if hasattr(config, 'output_dir') else "./" + os.makedirs(output_video_dir, exist_ok=True) + # Save Motion324 normalization params for downstream inverse transform + if norm_params is not None: + norm_path = os.path.join(output_video_dir, "motion324_norm.json") + with open(norm_path, "w") as f: + json.dump(norm_params, f, indent=2) + print(f"Saved motion324_norm to: {norm_path}") + output_video_path = os.path.join(output_video_dir, "input_rgb_video.mp4") + T = config.training.frames + with imageio.get_writer(output_video_path, fps=12) as writer: + for frame in video_np[:T]: + writer.append_data(frame) + print(f"Saved first {T} frames to video file: {output_video_path}") + + # Visualize input data + visualize_input_data(input_data, save_path=os.path.join(output_video_dir, "input_data_vis.png")) + + # Run inference + trajs = run_model_inference(model, input_data, video_tensor, config, ddp_info.device) + + # Apply smoothing + if trajs is not None: + trajs = smooth_trajectories( + trajs, + method='combined', + motion_threshold=0.002, + window_size=3, + sigma=1.0, + savgol_polyorder=2, + oneeuro_mincutoff=1.0, + oneeuro_beta=0.007, + #visualization_dir=config.output_dir + ) + print(f"Trajectory smoothing completed") + + # Generate animation + if trajs is not None: + total_T = video_tensor.shape[0] + trajs_to_use = trajs[:, :total_T, :, :] + + # Convert coords to blender coordinate system + trajs_b = trajs_to_use.clone() + trajs_b[..., 0], trajs_b[..., 1], trajs_b[..., 2] = ( + trajs_to_use[..., 0], # x -> x + -trajs_to_use[..., 2], # y -> -z + trajs_to_use[..., 1] # z -> y + ) + trajs_to_use = trajs_b + + if use_fbx and mesh_objects is not None: + drive_mesh_with_trajs_frames( + mesh_objects, + trajs_to_use.cpu(), + os.path.join(output_video_dir, 'output_animation'), + azi=0, + ele=0, + export_format='fbx' + ) + else: + print("Warning: mesh_objects not available, cannot generate animation") + + return trajs + + +if __name__ == "__main__": + run_inference_on_video(config) diff --git a/OVOW/third_party/Motion324/scripts/trellis2_gen.py b/OVOW/third_party/Motion324/scripts/trellis2_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..5f7adcf7eed3ece635ae80bdbf0d4d855b7abba4 --- /dev/null +++ b/OVOW/third_party/Motion324/scripts/trellis2_gen.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +import argparse +import os +import sys +from datetime import datetime + +os.environ.setdefault("OPENCV_IO_ENABLE_OPENEXR", "1") +os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True") +os.environ["HF_ENDPOINT"] = "https://huggingface.co" + +trellis2_root = os.environ.get( + "TRELLIS2_ROOT", + "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/jiayin/OVOW/engine/oneposeviagen/TRELLIS2", +) +if os.path.isdir(trellis2_root) and trellis2_root not in sys.path: + sys.path.insert(0, trellis2_root) + +import numpy as np +import torch +from PIL import Image + + + +def _load_image_with_alpha(path: str) -> Image.Image: + img = Image.open(path) + if img.mode == "RGBA": + arr = np.array(img).astype(np.float32) / 255.0 + rgb = arr[..., :3] * arr[..., 3:4] + return Image.fromarray((rgb * 255).astype(np.uint8), mode="RGB") + return img.convert("RGB") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Trellis2 GLB generator (single image).") + parser.add_argument("--input", required=True, help="Input image path (RGB or RGBA).") + parser.add_argument("--output_glb", required=True, help="Output GLB path.") + parser.add_argument("--ckpt", required=True, help="Checkpoint path or repo id.") + parser.add_argument("--seed", type=int, default=42, help="Random seed.") + parser.add_argument("--pipeline_type", default="1024", help="Pipeline type (e.g., 1024).") + parser.add_argument("--max_num_tokens", type=int, default=49152, help="Max tokens.") + parser.add_argument("--simplify_target", type=int, default=1000000, help="Decimation target.") + parser.add_argument("--texture_size", type=int, default=1024, help="Texture size.") + parser.add_argument("--preprocess_image", action="store_true", help="Enable pipeline preprocessing.") + parser.add_argument("--disable_rembg", action="store_true", help="Disable Trellis2 rembg model download.") + return parser.parse_args() + + +def log(msg: str) -> None: + ts = datetime.now().strftime("%H:%M:%S") + print(f"[{ts}] {msg}", flush=True) + + +def main() -> None: + args = parse_args() + os.makedirs(os.path.dirname(args.output_glb) or ".", exist_ok=True) + + if args.seed >= 0: + torch.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + + log("Importing Trellis2 and o_voxel") + from trellis2.pipelines import Trellis2ImageTo3DPipeline + from trellis2.pipelines import rembg as rembg_mod + try: + import o_voxel + except Exception as exc: # noqa: BLE001 + raise RuntimeError( + "Failed to import o_voxel. Please build/install it in the Trellis2 env.\n" + "Example:\n" + " conda run -n opg pip install -e " + "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/jiayin/OVOW/engine/oneposeviagen/TRELLIS2/o-voxel" + ) from exc + + log(f"Loading image: {args.input}") + image = _load_image_with_alpha(args.input) + + disable_rembg = args.disable_rembg or os.getenv("TRELLIS2_DISABLE_REMBG", "0") == "1" + if disable_rembg: + class _NoRembg: + def __init__(self, *_, **__): + pass + def to(self, *_): + return self + def cuda(self): + return self + def cpu(self): + return self + def __call__(self, image): + return image + rembg_mod.BiRefNet = _NoRembg + log("RMBG disabled (using no-op rembg model)") + + log(f"Loading Trellis2 pipeline from: {args.ckpt}") + pipeline = Trellis2ImageTo3DPipeline.from_pretrained(args.ckpt) + pipeline.cuda() + + log("Running Trellis2 inference") + run_kwargs = { + "num_samples": 1, + "seed": int(args.seed), + "preprocess_image": bool(args.preprocess_image), + "pipeline_type": str(args.pipeline_type), + "max_num_tokens": int(args.max_num_tokens), + } + try: + outputs = pipeline.run(image, **run_kwargs) + except TypeError: + outputs = pipeline.run(image) + + log("Converting to GLB") + mesh = outputs[0] + mesh.simplify(int(args.simplify_target)) + + glb_obj = o_voxel.postprocess.to_glb( + vertices=mesh.vertices, + faces=mesh.faces, + attr_volume=mesh.attrs, + coords=mesh.coords, + attr_layout=mesh.layout, + voxel_size=mesh.voxel_size, + aabb=[[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]], + decimation_target=int(args.simplify_target), + texture_size=int(args.texture_size), + remesh=True, + remesh_band=1, + remesh_project=0, + verbose=False, + ) + glb_obj.export(args.output_glb, extension_webp=True) + log(f"GLB saved: {args.output_glb}") + + +if __name__ == "__main__": + main() diff --git a/OVOW/third_party/Motion324/setup.py b/OVOW/third_party/Motion324/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..4f159f0ec34d4c2bf4edfb5125f629b81d9f0646 --- /dev/null +++ b/OVOW/third_party/Motion324/setup.py @@ -0,0 +1,300 @@ +# Copyright (c) 2025 Haian Jin. Created for the LVSM project (ICLR 2025). +# Modifications Copyright (c) 2025 Hongyuan Chen. + +from omegaconf import OmegaConf +import argparse +from easydict import EasyDict as edict +import re +import os +import datetime +import torch +import torch.distributed as dist +import numpy as np +import random +import yaml +import wandb +import shutil +import copy +from pathlib import Path +import time +import builtins + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + builtin_print = builtins.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + force = force or (get_world_size() > 8) + if is_master or force: + now = datetime.datetime.now().time() + builtin_print('[{}] '.format(now), end='') # print with time stamp + builtin_print(*args, **kwargs) + + builtins.print = print + +#################Init Config Begins################# +def process_overrides(overrides): + """ + Handle space around "=" + """ + # First, join all items with spaces to create a single string + combined = ' '.join(overrides) + + # Use regex to identify and fix patterns like 'param = value' to 'param=value' + # This handles various spacing around the equals sign + fixed_string = re.sub(r'(\S+)\s*=\s*(\S+)', r'\1=\2', combined) + + # Split the fixed string back into a list, preserving properly formatted args + # We split on spaces that are not within a parameter=value pair + processed = re.findall(r'[^\s=]+=\S+|\S+', fixed_string) + + return processed + +def init_config(): + parser = argparse.ArgumentParser() + + parser.add_argument("--config", "-c", required=True) + parser.add_argument("overrides", nargs="*") # Capture all "key=value" args + args = parser.parse_args() + + # Load base config + config = OmegaConf.load(args.config) + + # Parse CLI overrides using OmegaConf's native CLI parser + processed_overrides = process_overrides(args.overrides) + cli_overrides = OmegaConf.from_cli(processed_overrides) + + # Merge configs (with type-safe automatic conversion) + config = OmegaConf.merge(config, cli_overrides) + + # Convert to EasyDict if needed + config = OmegaConf.to_container(config, resolve=True) + config = edict(config) + return config + +#################Init Config End################# + + +def init_distributed(seed=42): + """ + Initialize distributed training environment and set random seeds for reproducibility. + + Args: + seed (int): Random seed for PyTorch, NumPy, and Python's random module. + Default is 42. + + Returns: + edict: Dictionary with attribute access containing: + - local_rank: GPU rank within the current node + - global_rank: Global rank of the process + - world_size: Total number of processes + - device: The CUDA device assigned to this process + - is_main_process: Flag to identify the main process + - seed: The random seed used for this process + """ + if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + global_rank = int(os.environ["RANK"]) + world_size = int(os.environ['WORLD_SIZE']) + local_rank = int(os.environ['LOCAL_RANK']) + elif 'SLURM_PROCID' in os.environ: + global_rank = int(os.environ['SLURM_PROCID']) + local_rank = global_rank % torch.cuda.device_count() + else: + print('Not using distributed mode') + setup_for_distributed(is_master=True) + return edict({ + 'local_rank': 0, + 'global_rank': 0, + 'world_size': 1, + 'device': torch.device('cuda'), + 'is_main_process': True, + 'seed': seed, + 'is_distributed': False + }) + + device = torch.device(f"cuda:{local_rank}") + torch.cuda.set_device(device) + + dist.init_process_group( + backend="nccl", + world_size=world_size,# + rank=global_rank,# + device_id=device,# + timeout=datetime.timedelta(seconds=3600) + ) + + # Set random seeds + # Each process gets a different seed derived from the base seed + process_seed = seed + global_rank + torch.manual_seed(process_seed) + torch.cuda.manual_seed(process_seed) + torch.cuda.manual_seed_all(process_seed) + np.random.seed(process_seed) + random.seed(process_seed) + + # Optional: For better performance + torch.backends.cudnn.benchmark = True + + return edict({ + 'local_rank': local_rank, + 'global_rank': global_rank, + 'world_size': world_size, + 'device': device, + 'is_main_process': global_rank == 0, + 'seed': process_seed, + 'is_distributed': True + }) + +def local_backup_src_code( + src_dir, + dst_dir, + max_size_MB=20.0, + extension_to_backup=(".py", ".yaml", ".sh", ".bash", ".json"), + exclude_dirs=("wandb", ".git", "checkpoints", "experiments"), + verbose=True, +): + """ + Backup source code files with size limit check. + + Args: + src_dir: Source directory to backup + dst_dir: Destination directory for backups + max_size_MB: Maximum total size allowed for backup in MB + extension_to_backup: File extensions to include in backup + exclude_dirs: Directories to exclude from backup + verbose: Whether to print progress information + + Returns: + tuple: (num_files_backed_up, total_size_in_bytes) + + Raises: + ValueError: If total size exceeds max_size_MB + """ + start_time = time.time() + src_path = Path(src_dir).resolve() + dst_path = Path(dst_dir).resolve() + + # Convert to set for faster lookup + extension_set = set(extension_to_backup) + ignore_paths = {(src_path / d).resolve() for d in exclude_dirs} + + max_bytes = int(max_size_MB * 1024 * 1024) + + if not src_path.exists(): + raise FileNotFoundError(f"Source directory does not exist: {src_path}") + + files = [] + total_size = 0 + + for dirpath, dirnames, filenames in os.walk(src_path): + current_path = Path(dirpath).resolve() + + # Skip excluded directories + if any(parent in ignore_paths for parent in current_path.parents) or current_path in ignore_paths: + dirnames.clear() + continue + + # Filter files by extension + for filename in filenames: + file_ext = os.path.splitext(filename)[1] + if file_ext not in extension_set: + continue + + src_file = current_path / filename + rel_path = current_path.relative_to(src_path) + dst_file = dst_path / rel_path / filename + + try: + file_size = src_file.stat().st_size + total_size += file_size + files.append((src_file, dst_file, file_size)) + except (FileNotFoundError, PermissionError) as e: + if verbose: + print(f"Warning: Could not access {src_file}: {e}") + + if total_size > max_bytes: + if verbose: + print(f"Size limit exceeded: {total_size / (1024*1024):.2f} MB > {max_size_MB} MB") + print("Largest files:") + for src_file, _, size in sorted(files, key=lambda x: x[2], reverse=True)[:5]: + print(f"{src_file}: {size / 1024:.1f} KB") + raise ValueError(f"Size limit exceeded: {total_size / (1024*1024):.2f} MB > {max_size_MB} MB") + + if verbose: + print(f"Backing up {len(files)} files ({total_size / (1024*1024):.2f} MB)") + + dst_path.mkdir(parents=True, exist_ok=True) + + # Copy files + successful_copies = 0 + for src_file, dst_file, _ in files: + try: + dst_file.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src_file, dst_file) + successful_copies += 1 + except Exception as e: + if verbose: + print(f"Error copying {src_file} to {dst_file}: {e}") + + elapsed_time = time.time() - start_time + if verbose: + print(f"Backup completed: {successful_copies}/{len(files)} files copied in {elapsed_time:.2f} seconds") + + return successful_copies, total_size + + + +def init_wandb_and_backup(config): + # API key validation + assert os.path.exists( + config.training.api_key_path + ), f"API key file does not exist: {config.training.api_key_path}" + api_keys = edict(yaml.safe_load(open(config.training.api_key_path, "r"))) + assert api_keys.wandb is not None, "Wandb API key not found in api key file" + + # WandB setup and login + os.environ["WANDB_API_KEY"] = api_keys.wandb + + # WandB initialization + config_copy = copy.deepcopy(config) + wandb.init( + project=config.training.wandb_project, + name=config.training.wandb_exp_name, + config=config_copy, + ) + + # Source code backup + cur_dir = os.path.dirname(os.path.realpath(__file__)) + trgt_dir = os.path.join(config.training.checkpoint_dir, "src", os.path.basename(cur_dir)) + os.makedirs(trgt_dir, exist_ok=True) + extension_to_backup=(".py", ".yaml", ".sh") # ".bash", ".json" + exclude_dirs=("wandb", ".git", "checkpoints", "experiments", ".history") + local_backup_src_code(cur_dir, trgt_dir, extension_to_backup=extension_to_backup, exclude_dirs=exclude_dirs) + + # Save config file + config_save_path = os.path.join(config.training.checkpoint_dir, "config.yaml") + with open(config_save_path, 'w') as f: + yaml.dump(dict(config), f) + + wandb.run.log_code( + trgt_dir, + include_fn=lambda path: path.endswith(extension_to_backup), + ) + + diff --git a/OVOW/third_party/Motion324/train.py b/OVOW/third_party/Motion324/train.py new file mode 100644 index 0000000000000000000000000000000000000000..7e2bee209c803c2b90bff6b083e93ae08d6ab967 --- /dev/null +++ b/OVOW/third_party/Motion324/train.py @@ -0,0 +1,273 @@ +# Copyright (c) 2025 Haian Jin. Created for the LVSM project (ICLR 2025). +# Modifications Copyright (c) 2025 Hongyuan Chen. + +import importlib +import os +import sys +sys.setrecursionlimit(100000) + +import time +import wandb +import torch +from rich import print +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader, DistributedSampler +import torch.distributed as dist +from setup import init_config, init_distributed, init_wandb_and_backup +from utils.training_utils import create_optimizer, create_lr_scheduler, auto_resume_job, print_rank0, check_and_handle_global_nan_loss +from dataset.dyscene import collate_fn_with_topology + +n_thread = 2 +os.environ["MKL_NUM_THREADS"] = f"{n_thread}" +os.environ["NUMEXPR_NUM_THREADS"] = f"{n_thread}" +os.environ["OMP_NUM_THREADS"] = f"4" +os.environ["VECLIB_MAXIMUM_THREADS"] = f"{n_thread}" +os.environ["OPENBLAS_NUM_THREADS"] = f"{n_thread}" +os.environ["OMP_NUM_THREADS"] = f"{n_thread}" + +# Load config and read(override) arguments from CLI +config = init_config() + +ddp_info = init_distributed(seed=777) + +if ddp_info.is_distributed: + dist.barrier() +if ddp_info.is_main_process: + init_wandb_and_backup(config) +if ddp_info.is_distributed: + dist.barrier() + +torch.backends.cuda.matmul.allow_tf32 = config.training.use_tf32 +torch.backends.cudnn.allow_tf32 = config.training.use_tf32 +amp_dtype_mapping = { + "fp16": torch.float16, + "bf16": torch.bfloat16, + "fp32": torch.float32, + 'tf32': torch.float32 +} + +# Load dataset, model, training settings and scheduler +dataset_name = config.training.get("dataset_name", "dataset.dyscene.Dyscene16k_Dataset") +module, class_name = dataset_name.rsplit(".", 1) +Dataset = importlib.import_module(module).__dict__[class_name] +dataset = Dataset(config.training) +batch_size_per_gpu = config.training.batch_size_per_gpu + +print_rank0(f"Training Dataset class_name: {class_name}") + +if ddp_info.is_distributed: + datasampler = DistributedSampler(dataset, shuffle=True) +else: + datasampler = torch.utils.data.RandomSampler(dataset) + +dataloader = DataLoader( + dataset, + batch_size=batch_size_per_gpu, + shuffle=False, + num_workers=config.training.num_workers, + persistent_workers=True, + pin_memory=True, + drop_last=True, + prefetch_factor=config.training.prefetch_factor, + sampler=datasampler, + collate_fn=collate_fn_with_topology, +) + +total_train_steps = config.training.train_steps +grad_accum_steps = config.training.grad_accum_steps +stop_steps = config.training.stop_steps * grad_accum_steps +total_param_update_steps = total_train_steps +total_train_steps = total_train_steps * grad_accum_steps +total_batch_size = batch_size_per_gpu * ddp_info.world_size * grad_accum_steps +total_num_epochs = int(total_param_update_steps * total_batch_size / len(dataset)) + +module, class_name = config.model.class_name.rsplit(".", 1) +Motion324 = importlib.import_module(module).__dict__[class_name] +model = Motion324(config).to(ddp_info.device) + +if ddp_info.is_distributed: + model = DDP(model, device_ids=[ddp_info.local_rank]) +else: + model = model.to(ddp_info.device) + +optimizer, optimized_param_dict, all_param_dict = create_optimizer( + model, + config.training.weight_decay, + config.training.lr, + (config.training.beta1, config.training.beta2), +) +optim_param_list = list(optimized_param_dict.values()) + +scheduler_type = config.training.get("scheduler_type", "cosine") +lr_scheduler = create_lr_scheduler( + optimizer, + total_param_update_steps, + config.training.warmup, + scheduler_type=scheduler_type, +) + +if config.training.get("resume_ckpt", "") != "": + ckpt_load_path = config.training.resume_ckpt +else: + ckpt_load_path = config.training.checkpoint_dir +reset_training_state = config.training.get("reset_training_state", False) +optimizer, lr_scheduler, cur_train_step, cur_param_update_step = auto_resume_job( + ckpt_load_path, + model, + optimizer, + lr_scheduler, + reset_training_state, +) + +enable_grad_scaler = config.training.use_amp and config.training.amp_dtype == "fp16" +scaler = torch.amp.GradScaler('cuda', enabled=enable_grad_scaler) + +print_rank0(f"Grad scaler enabled: {enable_grad_scaler}") +print_rank0(config) + +if ddp_info.is_distributed: + dist.barrier() + +start_train_step = cur_train_step +model.train() + +dataloader_iter = iter(dataloader) +while cur_train_step <= stop_steps or cur_param_update_step <= total_param_update_steps: + tic = time.time() + cur_epoch = int(cur_train_step * (total_batch_size / grad_accum_steps) // len(dataset) ) + try: + data = next(dataloader_iter) + except StopIteration: + print(f"Current Rank {ddp_info.local_rank} Ran out of data. Resetting dataloader epoch to {cur_epoch}; might take a while...") + if ddp_info.is_distributed: + datasampler.set_epoch(cur_epoch) + dataloader_iter = iter(dataloader) + data = next(dataloader_iter) + + batch = {k: v.to(ddp_info.device) if type(v) == torch.Tensor else v for k, v in data.items()} + batch["cur_train_step"] = cur_train_step + try: + with torch.autocast( + enabled=config.training.use_amp, + device_type="cuda", + dtype=amp_dtype_mapping[config.training.amp_dtype], + ): + ret_dict = model(batch) + + update_grads = (cur_train_step + 1) % grad_accum_steps == 0 or cur_train_step == total_train_steps + + if not update_grads: + if ddp_info.is_distributed: + with model.no_sync(): # no sync grads for efficiency + scaler.scale(ret_dict.loss_metrics.loss / grad_accum_steps).backward() + else: + scaler.scale(ret_dict.loss_metrics.loss / grad_accum_steps).backward() + else: + scaler.scale(ret_dict.loss_metrics.loss / grad_accum_steps).backward() + + cur_train_step += 1 + + skip_optimizer_step = False + if torch.isnan(ret_dict.loss_metrics.loss) or torch.isinf(ret_dict.loss_metrics.loss): + print(f"NaN or Inf loss detected, skip this iteration") + skip_optimizer_step = True + ret_dict.loss_metrics.loss.data = torch.zeros_like(ret_dict.loss_metrics.loss) + + # Check gradient norm and update optimizer if everything is fine + total_grad_norm = None + if update_grads: + scaler.unscale_(optimizer) + with torch.no_grad(): + for n, p in optimized_param_dict.items(): + if p.requires_grad and (p.grad is not None): + p.grad.nan_to_num_(nan=0.0, posinf=1e-6, neginf=-1e-6) + + # visualize the grad norm of each layer of our model (FOR DEBUG) + if ddp_info.is_main_process and config.training.get("log_grad_norm_details", False): + grad_norms = {} + for name, param in model.named_parameters(): + if param.grad is not None: + grad_norms[name] = param.grad.detach().norm().item() + for layer_name, grad_norm in grad_norms.items(): + wandb.log({"grad_norm_details/" + layer_name: grad_norm}, step=cur_param_update_step) + + total_grad_norm = 0.0 + if config.training.grad_clip_norm > 0: + total_grad_norm = torch.nn.utils.clip_grad_norm_(optim_param_list, max_norm=config.training.grad_clip_norm).item() + + allowed_gradnorm = config.training.grad_clip_norm * config.training.get("allowed_gradnorm_factor", 5) + if total_grad_norm > allowed_gradnorm: + skip_optimizer_step = True + print(f"WARNING: step {cur_param_update_step} grad norm too large {total_grad_norm} > {allowed_gradnorm}, skipping optimizer step") + + if ddp_info.is_main_process: + if not skip_optimizer_step: + wandb.log({"grad_norm": total_grad_norm}, step=cur_param_update_step) + + if not skip_optimizer_step: + scaler.step(optimizer) + scaler.update() + cur_param_update_step += 1 + + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + except RuntimeError as e: + print_rank0(f"Step {cur_train_step}: RuntimeError during backward: {e}. Zeroing grads and skipping step.") + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + torch.cuda.empty_cache() + continue + + # log and save checkpoint + if ddp_info.is_main_process: + loss_dict = {k: float(f"{v.item():.6f}") for k, v in ret_dict.loss_metrics.items()} + if (cur_train_step % config.training.print_every == 0) or (cur_train_step < 100 + start_train_step): + iter_time = time.time() - tic + print_str = f"[Epoch {int(cur_epoch):>3d}] | Forwad step: {int(cur_train_step):>6d} (Param update step: {int(cur_param_update_step):>6d})" + print_str += f" | Iter Time: {iter_time:.2f}s | LR: {optimizer.param_groups[0]['lr']:.6f}\n" + if config.training.grad_clip_norm > 0 and total_grad_norm is not None: + print_str += f"Grad Norm: {total_grad_norm:.10f} | " + for k, v in loss_dict.items(): + print_str += f"{k}: {v:.6f} | " + print(print_str) + + if (cur_train_step % config.training.wandb_log_every == 0) or ( + cur_train_step < 2000 + start_train_step + ): + iter_time = time.time() - tic + log_dict = { + "iter": cur_train_step, + "forward_pass_step": cur_train_step, + "param_update_step": cur_param_update_step, + "lr": optimizer.param_groups[0]["lr"], + "iter_time": iter_time, + "grad_norm": total_grad_norm if total_grad_norm is not None else 0.0, + "epoch": cur_epoch, + } + log_dict.update({"train/" + k: v for k, v in loss_dict.items()}) + if not skip_optimizer_step and update_grads: + wandb.log( + log_dict, + step=cur_param_update_step, + ) + + if (cur_param_update_step % config.training.checkpoint_every == 0) or (cur_train_step == total_train_steps): + if isinstance(model, DDP): + model_weights = model.module.state_dict() + else: + model_weights = model.state_dict() + checkpoint = { + "model": model_weights, + "optimizer": optimizer.state_dict(), + "lr_scheduler": lr_scheduler.state_dict(), + "fwdbwd_pass_step": cur_train_step, + "param_update_step": cur_param_update_step, + } + os.makedirs(config.training.checkpoint_dir, exist_ok=True) + ckpt_path = os.path.join(config.training.checkpoint_dir, f"ckpt_{cur_param_update_step:016}.pt") + torch.save(checkpoint, ckpt_path) + print(f"Saved checkpoint at step {cur_param_update_step} to {os.path.abspath(ckpt_path)}") + +if ddp_info.is_distributed: + dist.barrier() + dist.destroy_process_group() diff --git a/OVOW/third_party/Motion324/utils/convert_fbx.py b/OVOW/third_party/Motion324/utils/convert_fbx.py new file mode 100644 index 0000000000000000000000000000000000000000..2960806b757ea73b3e8d0d8074a9d4475ec0c4c6 --- /dev/null +++ b/OVOW/third_party/Motion324/utils/convert_fbx.py @@ -0,0 +1,560 @@ +""" +Create FBX files directly using FBX SDK, keeping original vertices and faces unchanged +Also supports independent UV indices (ByPolygonVertex mapping mode) +""" +import os +import sys +import numpy as np +from pathlib import Path +import trimesh +import subprocess +import glob + +def _load_mesh_any(path): + """Load mesh/scene and return a single Trimesh.""" + mesh = trimesh.load(path, force="scene", process=False) + if isinstance(mesh, trimesh.Scene): + geos = [g for g in mesh.geometry.values() if isinstance(g, trimesh.Trimesh)] + if not geos: + raise RuntimeError(f"No mesh geometry found in: {path}") + mesh = trimesh.util.concatenate(geos) + if not isinstance(mesh, trimesh.Trimesh): + raise RuntimeError(f"Invalid mesh type for: {path}") + return mesh + +def load_obj_with_independent_uv(obj_path): + """ + Load an OBJ file with independent vertex and UV indices (v/vt format). + + Args: + obj_path: Path to the OBJ file + + Returns: + vertices: numpy array of vertex positions (N, 3) + faces: numpy array of face vertex indices (F, 3) + uvs: numpy array of UV coordinates (M, 2) + uv_indices: numpy array of face UV indices (F, 3) + texture_path: path to the texture image (if found in MTL) + """ + vertices = [] + uvs = [] + faces = [] + uv_indices = [] + texture_path = None + mtl_file = None + + with open(obj_path, 'r') as f: + for line in f: + line = line.strip() + if not line or line.startswith('#'): + continue + + parts = line.split() + if not parts: + continue + + if parts[0] == 'v': + # Vertex position + vertices.append([float(parts[1]), float(parts[2]), float(parts[3])]) + elif parts[0] == 'vt': + # UV coordinate + uvs.append([float(parts[1]), float(parts[2])]) + elif parts[0] == 'f': + # Face with format v/vt or v/vt/vn + face_verts = [] + face_uvs = [] + for i in range(1, len(parts)): + indices = parts[i].split('/') + # Vertex index (1-based in OBJ, convert to 0-based) + face_verts.append(int(indices[0]) - 1) + # UV index (1-based in OBJ, convert to 0-based) + if len(indices) > 1 and indices[1]: + face_uvs.append(int(indices[1]) - 1) + + # Only add triangular faces + if len(face_verts) == 3: + faces.append(face_verts) + if len(face_uvs) == 3: + uv_indices.append(face_uvs) + elif parts[0] == 'mtllib': + # Material library file + mtl_file = parts[1] + + # Try to load texture from MTL file + if mtl_file: + mtl_path = os.path.join(os.path.dirname(obj_path), mtl_file) + if os.path.exists(mtl_path): + with open(mtl_path, 'r') as f: + for line in f: + line = line.strip() + if line.startswith('map_Kd'): + texture_filename = line.split()[1] + texture_path = os.path.join(os.path.dirname(obj_path), texture_filename) + break + + vertices = np.array(vertices, dtype=np.float32) + faces = np.array(faces, dtype=np.int32) + uvs = np.array(uvs, dtype=np.float32) if uvs else None + uv_indices = np.array(uv_indices, dtype=np.int32) if uv_indices else None + + return vertices, faces, uvs, uv_indices, texture_path + + +def create_fbx_with_independent_uv_via_sdk(obj_path, output_fbx_path): + """ + Create FBX using Blender's low-level API via Python script + Ensures vertex and face structure remains completely unchanged + """ + + # First load OBJ data + vertices, faces, uvs, uv_indices, texture_path = load_obj_with_independent_uv(obj_path) + + print("=== Original OBJ Data ===") + print(f"Vertex count: {len(vertices)}") + print(f"Face count: {len(faces)}") + print(f"UV count: {len(uvs)}") + print(f"UV index shape: {uv_indices.shape}") + print(f"Texture path: {texture_path}") + + # Try in-process bpy export first (works when bpy is installed in the env) + try: + import bpy + import bmesh # noqa: F401 + + # Clear scene + bpy.ops.wm.read_factory_settings(use_empty=True) + + # Create new mesh data + mesh_data = bpy.data.meshes.new("PreservedMesh") + mesh_data.from_pydata(vertices.tolist(), [], faces.tolist()) + mesh_data.update() + + obj = bpy.data.objects.new("PreservedObject", mesh_data) + bpy.context.collection.objects.link(obj) + bpy.context.view_layer.objects.active = obj + + # Create UV layer + uv_layer = mesh_data.uv_layers.new(name="UVMap") + loop_index = 0 + for face_idx, face in enumerate(faces): + for corner_idx in range(len(face)): + uv_idx = uv_indices[face_idx][corner_idx] + uv_layer.data[loop_index].uv = uvs[uv_idx] + loop_index += 1 + + # Create material and load texture + if texture_path and os.path.exists(texture_path): + mat = bpy.data.materials.new(name="TexturedMaterial") + mat.use_nodes = True + nodes = mat.node_tree.nodes + links = mat.node_tree.links + nodes.clear() + bsdf = nodes.new(type='ShaderNodeBsdfPrincipled') + bsdf.location = (0, 0) + tex_image = nodes.new(type='ShaderNodeTexImage') + tex_image.location = (-300, 0) + try: + img = bpy.data.images.load(texture_path) + tex_image.image = img + except Exception: + pass + links.new(tex_image.outputs['Color'], bsdf.inputs['Base Color']) + output = nodes.new(type='ShaderNodeOutputMaterial') + output.location = (300, 0) + links.new(bsdf.outputs['BSDF'], output.inputs['Surface']) + if obj.data.materials: + obj.data.materials[0] = mat + else: + obj.data.materials.append(mat) + + # Export as FBX + bpy.ops.export_scene.fbx( + filepath=output_fbx_path, + use_selection=False, + use_mesh_modifiers=False, + mesh_smooth_type='OFF', + use_tspace=True, + path_mode='COPY', + embed_textures=True, + bake_space_transform=False, + ) + return True + except Exception: + pass + + # Create Blender script, manually build mesh to preserve topology + blender_script = f""" +import bpy +import bmesh +import numpy as np +import os + +# Clear scene +bpy.ops.wm.read_factory_settings(use_empty=True) + +# Load data (from numpy arrays) +vertices = {vertices.tolist()} +faces = {faces.tolist()} +uvs = {uvs.tolist()} +uv_indices = {uv_indices.tolist()} +texture_path = "{texture_path}" + +print(f"Blender: Loaded {{len(vertices)}} vertices, {{len(faces)}} faces, {{len(uvs)}} UVs") +print(f"Blender: Texture path: {{texture_path}}") + +# Create new mesh data +mesh_data = bpy.data.meshes.new("PreservedMesh") + +# Use from_pydata to create mesh (this preserves original vertex indices) +mesh_data.from_pydata(vertices, [], faces) +mesh_data.update() + +# Create object +obj = bpy.data.objects.new("PreservedObject", mesh_data) +bpy.context.collection.objects.link(obj) +bpy.context.view_layer.objects.active = obj + +# Ensure mesh data is updated +mesh_data.update() +mesh_data.calc_loop_triangles() + +# Create UV layer +uv_layer = mesh_data.uv_layers.new(name="UVMap") + +# Set UV coordinates (per-loop, i.e., per-face-vertex) +# Blender's loop corresponds to face corner +loop_index = 0 +for face_idx, face in enumerate(faces): + for corner_idx in range(len(face)): + # Get UV index for this corner + uv_idx = uv_indices[face_idx][corner_idx] + + # Set UV (Blender's loops are sequential) + uv_layer.data[loop_index].uv = uvs[uv_idx] + loop_index += 1 + +print(f"Blender: Set {{loop_index}} UV loops") + +# Create material and load texture +if texture_path and os.path.exists(texture_path): + print(f"Blender: Loading texture {{texture_path}}") + + # Create material + mat = bpy.data.materials.new(name="TexturedMaterial") + mat.use_nodes = True + nodes = mat.node_tree.nodes + links = mat.node_tree.links + + # Clear default nodes + nodes.clear() + + # Create Principled BSDF + bsdf = nodes.new(type='ShaderNodeBsdfPrincipled') + bsdf.location = (0, 0) + + # Create image texture node + tex_image = nodes.new(type='ShaderNodeTexImage') + tex_image.location = (-300, 0) + + # Load texture image + try: + img = bpy.data.images.load(texture_path) + tex_image.image = img + print(f"Blender: Successfully loaded texture image") + except Exception as e: + print(f"Blender: Failed to load texture: {{e}}") + + # Connect texture to Base Color + links.new(tex_image.outputs['Color'], bsdf.inputs['Base Color']) + + # Create output node + output = nodes.new(type='ShaderNodeOutputMaterial') + output.location = (300, 0) + links.new(bsdf.outputs['BSDF'], output.inputs['Surface']) + + # Assign material to object + if obj.data.materials: + obj.data.materials[0] = mat + else: + obj.data.materials.append(mat) + + print(f"Blender: Material created and assigned") +else: + print(f"Blender: Texture file not found or path is empty") + +# Verify vertex and face counts +print(f"Blender: Mesh has {{len(mesh_data.vertices)}} vertices, {{len(mesh_data.polygons)}} faces") +assert len(mesh_data.vertices) == len(vertices), "Vertex count mismatch!" +assert len(mesh_data.polygons) == len(faces), "Face count mismatch!" + +# Export as FBX +bpy.ops.export_scene.fbx( + filepath="{output_fbx_path}", + use_selection=False, + use_mesh_modifiers=False, # Don't apply modifiers + mesh_smooth_type='OFF', # No smoothing + use_tspace=True, # Use tangent space + path_mode='COPY', # Copy textures + embed_textures=True, # Embed textures + bake_space_transform=False # Don't bake transform +) + +print(f"Blender: Successfully exported to {{'{output_fbx_path}'}}") +print(f"Blender: Final vertex count: {{len(mesh_data.vertices)}}") +print(f"Blender: Final face count: {{len(mesh_data.polygons)}}") + """ + + # Save Blender script + script_path = obj_path.replace('.obj', '_fbx_export.py') + with open(script_path, 'w') as f: + f.write(blender_script) + + print(f"\nSaved Blender script: {script_path}") + + # Run Blender + print("\nRunning Blender to export FBX...") + try: #modify the path to the blender executable HERE + blender_bin = os.environ.get( + "BLENDER_BIN", + "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/jiayin/OVOW/blender-5.0.0-linux-x64/blender", + ) + result = subprocess.run( + [blender_bin, '--background', '--python', script_path], + capture_output=True, + text=True, + timeout=60 + ) + + # Find key information in output + output_lines = result.stdout.split('\n') + for line in output_lines: + print(line) + + os.remove(script_path) + + return result.returncode == 0 + + except FileNotFoundError: + print("✗ Blender not found, please ensure it is installed") + return False + except Exception as e: + print(f"✗ Error: {e}") + return False + + +def export_mesh_with_texture(mesh, output_path): + """ + Export mesh with texture as OBJ format + + Args: + mesh: trimesh.Trimesh object with visual attributes + output_path: Path to save OBJ file (without extension) + """ + + # Ensure output directory exists + os.makedirs(os.path.dirname(output_path) if os.path.dirname(output_path) else ".", exist_ok=True) + + # Get mesh components + vertices = mesh.vertices + faces = mesh.faces + + # Get UV coordinates and indices + if hasattr(mesh.visual, 'uv') and mesh.visual.uv is not None: + uvs = mesh.visual.uv + if hasattr(mesh.visual, 'uv_indices') and mesh.visual.uv_indices is not None: + uv_indices = mesh.visual.uv_indices + else: + uv_indices = faces + else: + print("Warning: No UV coordinates found") + uvs = None + uv_indices = None + + # Write OBJ file + obj_path = output_path if output_path.endswith('.obj') else output_path + '.obj' + mtl_path = obj_path.replace('.obj', '.mtl') + texture_path = obj_path.replace('.obj', '.png') + + with open(obj_path, 'w') as f: + # Write MTL reference + f.write(f"mtllib {os.path.basename(mtl_path)}\n") + + # Write vertices + for v in vertices: + f.write(f"v {v[0]} {v[1]} {v[2]}\n") + + # Write UV coordinates + if uvs is not None: + for uv in uvs: + f.write(f"vt {uv[0]} {uv[1]}\n") + + f.write("usemtl material_0\n") + + # Write faces + for i, face in enumerate(faces): + if uv_indices is not None: + uv_face = uv_indices[i] + f.write(f"f {face[0]+1}/{uv_face[0]+1} {face[1]+1}/{uv_face[1]+1} {face[2]+1}/{uv_face[2]+1}\n") + else: + f.write(f"f {face[0]+1} {face[1]+1} {face[2]+1}\n") + + # Write MTL file + with open(mtl_path, 'w') as f: + f.write("newmtl material_0\n") + f.write("Ka 1.0 1.0 1.0\n") + f.write("Kd 1.0 1.0 1.0\n") + f.write("Ks 0.0 0.0 0.0\n") + f.write(f"map_Kd material_0.png\n") + + print(f"Exported mesh to: {obj_path}") + print(f" Vertices: {len(vertices)}") + print(f" Faces: {len(faces)}") + if uvs is not None: + print(f" UV coordinates: {len(uvs)}") + + return obj_path + + +def process_mesh_conversion(base_path, non_water_filename, water_filename, vmapping_filename): + """ + Main function for processing mesh conversion + + Args: + base_path: Base path + non_water_filename: Non-watertight mesh filename (e.g., 000000_hunyuan_original_wo_remap.glb) + water_filename: Watertight mesh filename (e.g., 000000_hunyuan_original_watertight.glb) + vmapping_filename: Vmapping filename (e.g., 000000_vmapping.npy) + + Returns: + success: Whether conversion was successful + """ + # Build full paths + non_water_path = os.path.join(base_path, non_water_filename) + water_path = os.path.join(base_path, water_filename) + vmapping_path = os.path.join(base_path, vmapping_filename) + + # Check if files exist + if not os.path.exists(non_water_path): + print(f"Error: Non-watertight mesh file not found {non_water_path}") + return False + if not os.path.exists(water_path): + print(f"Error: Watertight mesh file not found {water_path}") + return False + if not os.path.exists(vmapping_path): + print(f"Error: Vmapping file not found {vmapping_path}") + return False + + print(f"=== Starting Mesh Conversion ===") + print(f"Base path: {base_path}") + print(f"Non-watertight mesh: {non_water_filename}") + print(f"Watertight mesh: {water_filename}") + print(f"Vmapping: {vmapping_filename}") + + # Load vmapping file + vmapping = np.load(vmapping_path) + print(f"Vmapping shape: {vmapping.shape}") + + # Load watertight mesh (before xatlas) + original_mesh = _load_mesh_any(water_path) + print(f"Original watertight mesh: {len(original_mesh.vertices)} vertices, {len(original_mesh.faces)} faces") + + # Load non-watertight mesh (after xatlas, contains UV) + unwrapped_mesh = _load_mesh_any(non_water_path) + print(f"Unwrapped mesh: {len(unwrapped_mesh.vertices)} vertices, {len(unwrapped_mesh.faces)} faces") + # Export the unwrapped mesh for comparison + unwrapped_mesh_export_path = os.path.join(base_path, f"{os.path.splitext(non_water_filename)[0]}_unwrapped.obj") + unwrapped_mesh.export(unwrapped_mesh_export_path) + print(f"Unwrapped mesh exported: {unwrapped_mesh_export_path}") + # Use vmapping to map unwrapped mesh back to original mesh structure + # Create new mesh with original vertices but containing unwrapped faces + remapped_mesh = trimesh.Trimesh( + vertices=original_mesh.vertices, + faces=vmapping[unwrapped_mesh.faces], + process=False + ) + + # Copy texture and UV information + remapped_mesh.visual.material = unwrapped_mesh.visual.material + remapped_mesh.visual.uv = unwrapped_mesh.visual.uv + remapped_mesh.visual.uv_indices = unwrapped_mesh.faces + + print(f"Remapped mesh: {len(remapped_mesh.vertices)} vertices, {len(remapped_mesh.faces)} faces") + + # Extract base filename (remove extension) + base_filename = os.path.splitext(non_water_filename)[0] + + # Export intermediate OBJ file (in same directory) + obj_output_path = os.path.join(base_path, f"{base_filename}_converted") + obj_path = export_mesh_with_texture(remapped_mesh, obj_output_path) + + print(f"\n=== Intermediate OBJ File Generated ===") + print(f"OBJ path: {obj_path}") + + # Convert to FBX + fbx_output_path = os.path.join(base_path, f"{base_filename}_converted.fbx") + print(f"\n=== Starting FBX Conversion ===") + print(f"FBX output path: {fbx_output_path}") + + success = create_fbx_with_independent_uv_via_sdk(obj_path, fbx_output_path) + + if success: + print("\n✓ Conversion completed successfully!") + print(f"Output files:") + print(f" - OBJ: {obj_path}") + print(f" - FBX: {fbx_output_path}") + else: + print("\n✗ Conversion failed!") + + return success + + +def main(): + """Main function - example usage""" + + # If arguments passed from command line + if len(sys.argv) == 2: + base_path = sys.argv[1] + print(f"base_path: {base_path}") + # Extract filename prefix from base_path + # Find matching files + non_water_pattern = os.path.join(base_path, "*hunyuan_original_wo_remap.glb") + water_pattern = os.path.join(base_path, "*_hunyuan_original_watertight.glb") + vmapping_pattern = os.path.join(base_path, "*vmapping.npy") + + non_water_files = glob.glob(non_water_pattern) + water_files = glob.glob(water_pattern) + vmapping_files = glob.glob(vmapping_pattern) + + if not non_water_files: + print(f"Error: No matching files found: {non_water_pattern}") + sys.exit(1) + if not water_files: + print(f"Error: No matching files found: {water_pattern}") + sys.exit(1) + if not vmapping_files: + print(f"Error: No matching files found: {vmapping_pattern}") + sys.exit(1) + + non_water_filename = os.path.basename(non_water_files[0]) + water_filename = os.path.basename(water_files[0]) + vmapping_filename = os.path.basename(vmapping_files[0]) + else: + # Default parameters (example) + base_path = "./example/wild_processed/Hunyuan_Gen_Input" + non_water_filename = "000000_hunyuan_original_wo_remap.glb" + water_filename = "000000_hunyuan_original_watertight.glb" + vmapping_filename = "000000_vmapping.npy" + + print("Usage:") + print(" python convert_fbx.py ") + print("\nExample:") + print(f" python convert_fbx.py {base_path} {non_water_filename} {water_filename} {vmapping_filename}") + print("\nUsing default parameters...\n") + + # Execute conversion + success = process_mesh_conversion(base_path, non_water_filename, water_filename, vmapping_filename) + + return success + +if __name__ == "__main__": + main() diff --git a/OVOW/third_party/Motion324/utils/convert_glb_to_fbx.py b/OVOW/third_party/Motion324/utils/convert_glb_to_fbx.py new file mode 100644 index 0000000000000000000000000000000000000000..14778d9d55ec1b91839c619a4b48ec00f3f6cfb7 --- /dev/null +++ b/OVOW/third_party/Motion324/utils/convert_glb_to_fbx.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python3 +import argparse +import os +import subprocess +import tempfile + + +def _write_blender_script(glb_path: str, fbx_path: str) -> str: + script = f""" +import bpy + +bpy.ops.wm.read_factory_settings(use_empty=True) +bpy.ops.import_scene.gltf(filepath=r\"{glb_path}\") + +for obj in bpy.context.scene.objects: + obj.select_set(False) + +mesh_objects = [obj for obj in bpy.context.scene.objects if obj.type == 'MESH'] +for obj in mesh_objects: + obj.select_set(True) + +if mesh_objects: + bpy.context.view_layer.objects.active = mesh_objects[0] + +bpy.ops.export_scene.fbx( + filepath=r\"{fbx_path}\", + use_selection=True, + use_mesh_modifiers=False, + mesh_smooth_type='OFF', + use_tspace=True, + path_mode='COPY', + embed_textures=True, + bake_space_transform=False +) +""" + fd, path = tempfile.mkstemp(prefix="glb_to_fbx_", suffix=".py") + with os.fdopen(fd, "w") as f: + f.write(script) + return path + + +def _convert_glb_to_fbx_with_bpy(glb_path: str, fbx_path: str) -> None: + import bpy + + bpy.ops.wm.read_factory_settings(use_empty=True) + bpy.ops.import_scene.gltf(filepath=glb_path) + + for obj in bpy.context.scene.objects: + obj.select_set(False) + + mesh_objects = [obj for obj in bpy.context.scene.objects if obj.type == "MESH"] + for obj in mesh_objects: + obj.select_set(True) + + if mesh_objects: + bpy.context.view_layer.objects.active = mesh_objects[0] + + bpy.ops.export_scene.fbx( + filepath=fbx_path, + use_selection=True, + use_mesh_modifiers=False, + mesh_smooth_type="OFF", + use_tspace=True, + path_mode="COPY", + embed_textures=True, + bake_space_transform=False, + ) + + +def convert_glb_to_fbx(glb_path: str, fbx_path: str, blender_bin: str, timeout: int) -> None: + if not os.path.isfile(glb_path): + raise FileNotFoundError(f"GLB not found: {glb_path}") + os.makedirs(os.path.dirname(fbx_path) or ".", exist_ok=True) + + try: + _convert_glb_to_fbx_with_bpy(glb_path, fbx_path) + return + except Exception: + pass + + script_path = _write_blender_script(glb_path, fbx_path) + try: + result = subprocess.run( + [blender_bin, "--background", "--python", script_path], + capture_output=True, + text=True, + timeout=timeout, + check=False, + ) + finally: + if os.path.exists(script_path): + os.remove(script_path) + + if result.returncode != 0: + raise RuntimeError( + "Blender FBX export failed\\n" + f"stdout:\\n{result.stdout}\\n" + f"stderr:\\n{result.stderr}" + ) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert GLB to FBX via Blender.") + parser.add_argument("--input", required=True, help="Input GLB path.") + parser.add_argument("--output", required=True, help="Output FBX path.") + parser.add_argument( + "--blender_bin", + default=os.environ.get( + "BLENDER_BIN", + "/mnt/einsia/aws01-nvme/einsia-shared/homes/gaomingju/jiayin/OVOW/blender-5.0.0-linux-x64/blender", + ), + help="Blender binary path.", + ) + parser.add_argument("--timeout", type=int, default=600, help="Timeout in seconds.") + return parser.parse_args() + + +def main() -> None: + args = parse_args() + blender_bin = args.blender_bin + if not os.path.isfile(blender_bin): + raise FileNotFoundError(f"Blender binary not found: {blender_bin}") + convert_glb_to_fbx(args.input, args.output, blender_bin, args.timeout) + + +if __name__ == "__main__": + main() diff --git a/OVOW/third_party/Motion324/utils/inference_utils.py b/OVOW/third_party/Motion324/utils/inference_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f96046482e759e1a370e055b51afe2084da69623 --- /dev/null +++ b/OVOW/third_party/Motion324/utils/inference_utils.py @@ -0,0 +1,296 @@ +import os +import random +import torch +import numpy as np +from scipy.ndimage import gaussian_filter1d +from scipy.signal import savgol_filter +from .visualization import plot_smoothing_comparison + + +def seed_everything(seed=0): + """Set random seed for reproducibility.""" + print(f"Setting random seed: {seed}") + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + + +def load_checkpoint(ckpt_path, model, device): + """ + Load checkpoint and return model state. + + Args: + ckpt_path: Path to checkpoint file + model: Model to load weights into + device: Device to load checkpoint on + + Returns: + step_info: Dictionary containing fwdbwd_pass_step and param_update_step + """ + print(f"Loading checkpoint from: {ckpt_path}") + checkpoint = torch.load(ckpt_path, map_location=device) + model_state_dict = checkpoint['model'] + + if 'pos_embed' in model_state_dict: + del model_state_dict['pos_embed'] + + try: + model.load_state_dict(model_state_dict, strict=True) + print("✓ Model loaded successfully with strict=True") + except RuntimeError as e: + print(f"Warning: Strict loading failed: {e}") + print("Attempting to load with strict=False...") + model.load_state_dict(model_state_dict, strict=False) + print("✓ Model loaded with strict=False") + + step_info = { + 'fwdbwd_pass_step': checkpoint.get('fwdbwd_pass_step', 0), + 'param_update_step': checkpoint.get('param_update_step', 0) + } + return step_info + + +class OneEuroFilter: + """ + One Euro Filter for smooth trajectory tracking. + + Args: + mincutoff: Minimum cutoff frequency + beta: Speed coefficient + dcutoff: Cutoff frequency for derivative + """ + def __init__(self, mincutoff=1.0, beta=0.007, dcutoff=1.0): + self.mincutoff = mincutoff + self.beta = beta + self.dcutoff = dcutoff + self.x_prev = None + self.dx_prev = 0.0 + + def __call__(self, x, alpha_d=None): + if self.x_prev is None: + self.x_prev = x + return x + + dx = x - self.x_prev + if alpha_d is None: + alpha_d = self.smoothing_factor(1.0, self.dcutoff) + dx_hat = alpha_d * dx + (1 - alpha_d) * self.dx_prev + + cutoff = self.mincutoff + self.beta * abs(dx_hat) + alpha = self.smoothing_factor(1.0, cutoff) + + x_hat = alpha * x + (1 - alpha) * self.x_prev + + self.x_prev = x_hat + self.dx_prev = dx_hat + return x_hat + + @staticmethod + def smoothing_factor(te, cutoff): + r = 2 * np.pi * cutoff * te + return r / (r + 1) + + +def smooth_trajectories(trajs, method='combined', motion_threshold=0.005, + window_size=3, sigma=1.0, savgol_polyorder=2, + oneeuro_mincutoff=1.0, oneeuro_beta=0.007, + visualization_dir=None): + """ + Apply smoothing to trajectories to reduce jitter. + + Args: + trajs: (B, T, N, 3) trajectory tensor + method: Smoothing method ('threshold', 'gaussian', 'savgol', 'oneeuro', 'combined') + motion_threshold: Threshold for motion filtering + window_size: Window size for gaussian and moving average + sigma: Standard deviation for gaussian smoothing + savgol_polyorder: Polynomial order for Savitzky-Golay filter + oneeuro_mincutoff: Minimum cutoff frequency for One Euro Filter + oneeuro_beta: Speed coefficient for One Euro Filter + visualization_dir: Directory to save visualization plots + + Returns: + Smoothed trajectories (B, T, N, 3) + """ + trajs_smoothed = trajs.clone() + B, T, N, _ = trajs.shape + + # Threshold filtering + if method in ['threshold', 'combined']: + print(f"Applying threshold filtering, threshold={motion_threshold}") + for b in range(B): + for t in range(1, T): + displacement = trajs[b, t] - trajs[b, t-1] + displacement_magnitude = torch.norm(displacement, dim=-1) + mask = displacement_magnitude < motion_threshold + trajs_smoothed[b, t][mask] = trajs_smoothed[b, t-1][mask] + + # Gaussian smoothing + if method in ['gaussian', 'combined']: + print(f"Applying gaussian smoothing, window_size={window_size}, sigma={sigma}") + trajs_np = trajs_smoothed.cpu().numpy() + for b in range(B): + for n in range(N): + for dim in range(3): + trajs_np[b, :, n, dim] = gaussian_filter1d( + trajs_np[b, :, n, dim], + sigma=sigma, + mode='nearest' + ) + trajs_smoothed = torch.from_numpy(trajs_np).to(trajs.device).type(trajs.dtype) + + # Savitzky-Golay filter + if method == 'savgol': + print(f"Applying Savitzky-Golay filter, window_size={window_size}, polyorder={savgol_polyorder}") + if window_size % 2 == 0: + window_size += 1 + if T >= window_size: + trajs_np = trajs_smoothed.cpu().numpy() + for b in range(B): + for n in range(N): + for dim in range(3): + trajs_np[b, :, n, dim] = savgol_filter( + trajs_np[b, :, n, dim], + window_length=window_size, + polyorder=min(savgol_polyorder, window_size-1), + mode='nearest' + ) + trajs_smoothed = torch.from_numpy(trajs_np).to(trajs.device).type(trajs.dtype) + + # One Euro Filter + if method == 'oneeuro': + print(f"Applying One Euro Filter, mincutoff={oneeuro_mincutoff}, beta={oneeuro_beta}") + trajs_np = trajs_smoothed.cpu().numpy() + for b in range(B): + for n in range(N): + for dim in range(3): + one_euro = OneEuroFilter(mincutoff=oneeuro_mincutoff, beta=oneeuro_beta) + for t in range(T): + trajs_np[b, t, n, dim] = one_euro(trajs_np[b, t, n, dim]) + trajs_smoothed = torch.from_numpy(trajs_np).to(trajs.device).type(trajs.dtype) + + # Visualization + if visualization_dir is not None: + displacements_before = [] + displacements_after = [] + + for b in range(B): + for t in range(1, T): + disp_before = trajs[b, t] - trajs[b, t-1] + mag_before = torch.norm(disp_before, dim=-1).cpu().numpy() + displacements_before.append(mag_before) + + disp_after = trajs_smoothed[b, t] - trajs_smoothed[b, t-1] + mag_after = torch.norm(disp_after, dim=-1).cpu().numpy() + displacements_after.append(mag_after) + + plot_smoothing_comparison(displacements_before, displacements_after, + motion_threshold, method, visualization_dir) + + return trajs_smoothed + + +def load_u2net_model(model_path=None, device='cuda'): + """ + Load U2Net model for foreground segmentation. + + Args: + model_path: Path to U2Net model weights (if None, will try to auto-download) + device: Device to run on + + Returns: + U2Net model session or None if loading fails + """ + try: + import sys + import importlib + import builtins + + setup_module_backup = sys.modules.get('setup') + removed_setup = False + # Keep setup module when builtins.print comes from it; numba expects it to be resolvable. + if 'setup' in sys.modules and getattr(builtins.print, "__module__", None) == "setup": + if setup_module_backup is not None and not hasattr(setup_module_backup, "print"): + setattr(setup_module_backup, "print", builtins.print) + elif 'setup' in sys.modules: + del sys.modules['setup'] + removed_setup = True + + try: + rembg = importlib.import_module('rembg') + print("Using rembg library for foreground segmentation") + session = rembg.new_session('u2net') + print("✓ U2Net model loaded successfully") + return session + finally: + if removed_setup and setup_module_backup is not None: + sys.modules['setup'] = setup_module_backup + except ImportError as e: + print(f"rembg not installed or import failed: {e}") + print("Hint: Install rembg with: pip install rembg") + return None + except Exception as e: + print(f"Error loading U2Net model: {e}") + import traceback + traceback.print_exc() + return None + + +def segment_foreground_with_u2net(frames, u2net_model=None, device='cuda'): + """ + Use U2Net to perform foreground segmentation on video frames. + + Args: + frames: numpy array, shape (T, H, W, 3), range [0, 255] + u2net_model: U2Net model or rembg session + device: Device to run on + + Returns: + masked_frames: numpy array, shape (T, H, W, 3), background as black + masks: numpy array, shape (T, H, W, 1), segmentation mask, range [0,1] + """ + if u2net_model is None: + print("No U2Net model provided, skipping foreground segmentation") + return frames, np.ones(frames.shape[:-1] + (1,), dtype=np.float32) + + print(f"Applying U2Net foreground segmentation to {len(frames)} frames...") + masked_frames = [] + masks = [] + + try: + import importlib + rembg_module = importlib.import_module('rembg') + remove_func = rembg_module.remove + + for i, frame in enumerate(frames): + if i % 10 == 0: + print(f"Processing frame {i+1}/{len(frames)}...") + + from PIL import Image as PILImage + frame_pil = PILImage.fromarray(frame.astype(np.uint8)) + output_pil = remove_func(frame_pil, session=u2net_model) + output_np = np.array(output_pil) + + if output_np.shape[2] == 4: # RGBA + mask = output_np[:, :, 3:4] / 255.0 + rgb = output_np[:, :, :3] + else: + mask = np.ones((output_np.shape[0], output_np.shape[1], 1), dtype=np.float32) + rgb = output_np[:, :, :3] + + masked_frame = (rgb * mask).astype(np.uint8) + masked_frames.append(masked_frame) + masks.append(mask.astype(np.float32)) + + print("✓ Foreground segmentation complete") + masked_frames = np.stack(masked_frames, axis=0) + masks = np.stack(masks, axis=0) + return masked_frames, masks + except Exception as e: + print(f"Error during segmentation: {e}") + return frames, np.ones(frames.shape[:-1] + (1,), dtype=np.float32) diff --git a/OVOW/third_party/Motion324/utils/mesh_processing.py b/OVOW/third_party/Motion324/utils/mesh_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..511e7da2667cac7d280fad3e4b20036151ed6c56 --- /dev/null +++ b/OVOW/third_party/Motion324/utils/mesh_processing.py @@ -0,0 +1,219 @@ +import os +import numpy as np +import torch +import subprocess +import tempfile +from PIL import Image +import trimesh + + +def convert_fbx_to_glb_with_blender(fbx_path, output_glb_path): + """ + Convert FBX file to GLB using Blender. + + Args: + fbx_path: Input FBX file path + output_glb_path: Output GLB file path + """ + python_script = f""" +import bpy +import bmesh + +def reset_scene(): + for obj in bpy.data.objects: + bpy.data.objects.remove(obj, do_unlink=True) + for material in bpy.data.materials: + bpy.data.materials.remove(material, do_unlink=True) + for texture in bpy.data.textures: + bpy.data.textures.remove(texture, do_unlink=True) + for image in bpy.data.images: + bpy.data.images.remove(image, do_unlink=True) + +if __name__ == "__main__": + reset_scene() + bpy.ops.import_scene.fbx(filepath='{fbx_path}') + mesh_objs = [obj for obj in bpy.context.scene.objects if obj.type == 'MESH'] + + for obj in mesh_objs: + for mat_slot in obj.data.materials: + if not mat_slot.use_nodes: + continue + nodes = mat_slot.node_tree.nodes + img_nodes = [n for n in nodes if n.type == 'TEX_IMAGE'] + if img_nodes: + base_img = img_nodes[0].image + new_mat = bpy.data.materials.new(name="MergedMat") + new_mat.use_nodes = True + new_nodes = new_mat.node_tree.nodes + new_nodes.clear() + bsdf = new_nodes.new('ShaderNodeBsdfPrincipled') + tex_node = new_nodes.new('ShaderNodeTexImage') + tex_node.image = base_img + output = new_nodes.new('ShaderNodeOutputMaterial') + new_mat.node_tree.links.new(bsdf.outputs['BSDF'], output.inputs['Surface']) + new_mat.node_tree.links.new(tex_node.outputs['Color'], bsdf.inputs['Base Color']) + obj.data.materials[0] = new_mat + + bpy.ops.object.select_all(action='DESELECT') + for obj in bpy.context.scene.objects: + if obj.type != 'MESH': + continue + obj.select_set(True) + bpy.context.view_layer.objects.active = obj + if obj.parent: + bpy.ops.object.parent_clear(type='CLEAR_KEEP_TRANSFORM') + bpy.ops.object.transform_apply(location=True, rotation=True, scale=True) + for mod in obj.modifiers: + try: + bpy.ops.object.modifier_apply(modifier=mod.name) + except: + pass + obj.select_set(False) + + bpy.ops.export_scene.gltf( + filepath='{output_glb_path}', + export_format='GLB', + export_materials='EXPORT', + use_selection=False, + export_yup=True, + export_apply=True, + ) +""" + + with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f: + temp_script = f.name + f.write(python_script) + + try: + subprocess.run( + f'python "{temp_script}"', + shell=True, + check=True + ) + print(f"✓ Successfully converted FBX to GLB: {output_glb_path}") + except subprocess.CalledProcessError as e: + print(f"✗ Blender conversion failed: {e}") + raise + finally: + if os.path.exists(temp_script): + os.remove(temp_script) + + +def barycentric_coords(tri, p): + """ + Calculate barycentric coordinates of point p in triangle tri. + + Args: + tri: (3, 3) triangle vertex coordinates + p: (3,) point coordinates + + Returns: + (3,) barycentric coordinates [u, v, w] where u+v+w=1 + """ + v0 = tri[1] - tri[0] + v1 = tri[2] - tri[0] + v2 = p - tri[0] + d00 = np.dot(v0, v0) + d01 = np.dot(v0, v1) + d11 = np.dot(v1, v1) + d20 = np.dot(v2, v0) + d21 = np.dot(v2, v1) + denom = d00 * d11 - d01 * d01 + if denom == 0: + return np.array([1/3, 1/3, 1/3]) + v = (d11 * d20 - d01 * d21) / denom + w = (d00 * d21 - d01 * d20) / denom + u = 1 - v - w + return np.array([u, v, w]) + + +def sample_pointcloud_with_albedo(mesh, num=200000): + """ + Sample point cloud from mesh with positions, normals, and colors using barycentric interpolation. + + Args: + mesh: trimesh.Trimesh object + num: Number of points to sample + + Returns: + points: (num, 3) point cloud coordinates + normals: (num, 3) normals + colors: (num, 3) RGB colors in range [0, 1] + """ + points, face_idx = mesh.sample(num, return_index=True) + normals = mesh.face_normals[face_idx] + colors = None + + # Try vertex colors first + if hasattr(mesh.visual, "vertex_colors") and len(mesh.visual.vertex_colors) == len(mesh.vertices): + vertex_colors = mesh.visual.vertex_colors + if vertex_colors.ndim == 2 and vertex_colors.shape[1] >= 3: + vertex_colors = vertex_colors[:, :3] / 255.0 + tri_colors = vertex_colors[mesh.faces[face_idx]] + colors = tri_colors.mean(axis=1) + + # Try texture if no vertex colors + if colors is None and hasattr(mesh.visual, "material") and hasattr(mesh.visual.material, "baseColorTexture") and mesh.visual.material.baseColorTexture: + tex = mesh.visual.material.baseColorTexture + image = tex.image if hasattr(tex, "image") else tex + + if isinstance(image, str): + image = Image.open(image) + if image.mode not in ["RGB", "RGBA"]: + image = image.convert("RGB") + tex_np = np.array(image).astype(np.float32) / 255.0 + if tex_np.ndim == 2: + tex_np = np.stack([tex_np] * 3, axis=-1) + elif tex_np.shape[2] > 3: + tex_np = tex_np[:, :, :3] + tex_w, tex_h = image.size + + if hasattr(mesh.visual, "uv") and mesh.visual.uv is not None: + uv = mesh.visual.uv % 1.0 + tri_uvs = uv[mesh.faces[face_idx]] + colors = np.zeros((num, 3), dtype=np.float32) + for i in range(num): + tri = mesh.triangles[face_idx[i]] + p = points[i] + w = barycentric_coords(tri, p) + uv_sample = w[0] * tri_uvs[i, 0] + w[1] * tri_uvs[i, 1] + w[2] * tri_uvs[i, 2] + u = int(np.clip(uv_sample[0] * tex_w, 0, tex_w - 1)) + v = int(np.clip((1 - uv_sample[1]) * tex_h, 0, tex_h - 1)) + colors[i] = tex_np[v, u, :] + + if colors is None: + colors = np.full((num, 3), 0.5, dtype=np.float32) + + points = torch.from_numpy(points.astype(np.float32)) + normals = torch.from_numpy(normals.astype(np.float32)) + colors = torch.from_numpy(colors.astype(np.float32)) + + return points, normals, colors + + +def normalize_mesh(mesh, return_params=False): + """ + Normalize mesh to fit in a unit cube centered at origin. + + Args: + mesh: trimesh.Trimesh object + return_params: If True, return normalization parameters + + Returns: + mesh: Normalized mesh (or vertices if return_params=True) + center: Center point (only if return_params=True) + scale: Scale factor (only if return_params=True) + """ + vertices = mesh.vertices.astype(np.float32) + center = (vertices.max(axis=0) + vertices.min(axis=0)) / 2 + vertices = vertices - center + v_max = np.abs(vertices).max() + scale = 2 * (v_max + 1e-8) + vertices = vertices / scale + + if return_params: + return vertices, center, scale + else: + mesh.vertices = vertices + return mesh + diff --git a/OVOW/third_party/Motion324/utils/render.py b/OVOW/third_party/Motion324/utils/render.py new file mode 100644 index 0000000000000000000000000000000000000000..220d03d3bc71bfe26a880de5d42f4b85f1297b83 --- /dev/null +++ b/OVOW/third_party/Motion324/utils/render.py @@ -0,0 +1,345 @@ +import bpy +import bmesh +import os +import numpy as np +import torch +import math +import mathutils +from mathutils import Vector +import imageio +import imageio.v3 as iio +import glob + +def clear_scene(): + """Clear the current scene.""" + bpy.ops.wm.read_homefile(use_empty=True) + for obj in bpy.data.objects: + bpy.data.objects.remove(obj, do_unlink=True) + +def import_glb(filepath): + """Import GLB/GLTF file and return mesh objects.""" + bpy.ops.import_scene.gltf(filepath=filepath) + mesh_objects = [obj for obj in bpy.context.scene.objects if (obj.type == 'MESH' and obj.name != 'Cube' and obj.name != 'Icosphere')] + if not mesh_objects: + raise ValueError("No mesh objects found") + for obj in mesh_objects: + if obj.animation_data: + obj.animation_data_clear() + if obj.data.shape_keys: + obj.shape_key_clear() + bpy.context.view_layer.objects.active = obj + bpy.ops.object.transform_apply(location=True, rotation=True, scale=True) + print(f"Loaded {len(mesh_objects)} mesh objects") + for mesh_obj in mesh_objects: + print(f"Mesh: {mesh_obj.name}, Vertices: {len(mesh_obj.data.vertices)}") + return mesh_objects + +def import_fbx(filepath): + """Import FBX file and align to trimesh coordinate system.""" + bpy.ops.import_scene.fbx(filepath=filepath) + mesh_objects = [obj for obj in bpy.context.scene.objects if (obj.type == 'MESH' and obj.name != 'Cube' and obj.name != 'Icosphere')] + if not mesh_objects: + raise ValueError("No mesh objects found") + for obj in mesh_objects: + if obj.animation_data: + obj.animation_data_clear() + if obj.data.shape_keys: + obj.shape_key_clear() + bpy.context.view_layer.objects.active = obj + bpy.ops.object.transform_apply(location=True, rotation=True, scale=True) + bpy.ops.object.transform_apply(location=True, rotation=True, scale=True) + print(f"Loaded {len(mesh_objects)} mesh objects from FBX") + for mesh_obj in mesh_objects: + print(f"Mesh: {mesh_obj.name}, Vertices: {len(mesh_obj.data.vertices)}") + return mesh_objects + +def get_mesh_vertices(mesh_obj): + """Extract vertex coordinates from a mesh object.""" + vertices = [] + for vertex in mesh_obj.data.vertices: + vertices.append([vertex.co[0], vertex.co[1], vertex.co[2]]) + return np.array(vertices) + +def get_all_vertices(mesh_objects): + """Extract vertex coordinates from multiple mesh objects.""" + all_vertices = [] + for mesh_obj in mesh_objects: + vertices = get_mesh_vertices(mesh_obj) + all_vertices.append(torch.from_numpy(vertices)) + return all_vertices + +def get_mesh_faces(mesh_obj): + """Extract face indices from a mesh object.""" + faces = [] + for face in mesh_obj.data.polygons: + faces.append(list(face.vertices)) + return np.array(faces) + +def get_all_faces(mesh_objects): + """Extract face indices from multiple mesh objects, adjusting vertex indices for merged meshes.""" + all_faces = [] + vertex_count = 0 + for mesh_obj in mesh_objects: + faces = get_mesh_faces(mesh_obj) + if len(all_faces) > 0: + faces = faces + vertex_count + all_faces.append(torch.from_numpy(faces)) + vertex_count += len(mesh_obj.data.vertices) + return all_faces + +def move_vertices_with_trajectory(mesh_obj, frame, trajectories): + """Move mesh vertices according to trajectory data for a specific frame.""" + bpy.context.view_layer.objects.active = mesh_obj + bpy.ops.object.mode_set(mode='OBJECT') + if mesh_obj.data.shape_keys is None: + basis = mesh_obj.shape_key_add(name='Basis') + basis.interpolation = 'KEY_LINEAR' + + bpy.context.scene.frame_set(frame) + shape_key_name = f"Frame_{frame}" + shape_key = mesh_obj.data.shape_keys.key_blocks.get(shape_key_name) + if not shape_key: + shape_key = mesh_obj.shape_key_add(name=shape_key_name) + shape_key.interpolation = 'KEY_LINEAR' + if hasattr(trajectories[frame], 'numpy'): + positions = trajectories[frame].numpy() + else: + positions = trajectories[frame] + for idx, pos in enumerate(positions): + shape_key.data[idx].co = Vector(pos) + + shape_key.value = 1.0 + shape_key.keyframe_insert(data_path="value", frame=frame) + if frame > 0: + shape_key.value = 0.0 + shape_key.keyframe_insert(data_path="value", frame=frame - 1) + +def drive_mesh_with_trajs_frames(mesh_objects, trajs, output_dir, azi=0.0, ele=0.0, export_format="fbx"): + """Drive mesh animation with trajectory data and optionally export to various formats.""" + try: + print(output_dir) + os.makedirs(output_dir, exist_ok=True) + print("Setting up animations for all meshes...") + for i in range(len(mesh_objects)): + mesh_obj = mesh_objects[i] + traj = trajs[i] + num_frames = traj.shape[0] + print(traj.shape) + print(traj.max(), traj.min()) + for frame in range(num_frames): + move_vertices_with_trajectory(mesh_obj, frame, traj) + # Insert all keyframes first + for frame in range(num_frames): + for shape_key in mesh_obj.data.shape_keys.key_blocks[1:]: + shape_key.value = 0 + shape_key.keyframe_insert("value", frame=frame) + + current_shape_key = mesh_obj.data.shape_keys.key_blocks[f"Frame_{frame}"] + current_shape_key.value = 1 + current_shape_key.keyframe_insert("value", frame=frame) + + # Set interpolation to CONSTANT for all keyframes at once (AFTER all keyframes are inserted) + if mesh_obj.data.shape_keys.animation_data and mesh_obj.data.shape_keys.animation_data.action: + fcurves = mesh_obj.data.shape_keys.animation_data.action.fcurves + for fc in fcurves: + for kf in fc.keyframe_points: + kf.interpolation = 'CONSTANT' + + print("Setting up scene...") + bpy.context.scene.frame_start = 0 + bpy.context.scene.frame_end = trajs.shape[1] - 1 + if export_format != "none": + print(f"Exporting animated mesh to {export_format.upper()} format...") + bpy.ops.object.select_all(action='DESELECT') + for obj in mesh_objects: + obj.select_set(True) + filename = os.path.basename(output_dir) + export_path = os.path.join(os.path.dirname(output_dir), f"{filename}.{export_format}") + if export_format == "abc": + bpy.ops.wm.alembic_export( + filepath=export_path, + start=bpy.context.scene.frame_start, + end=bpy.context.scene.frame_end, + selected=True, + uvs=True, + face_sets=True + ) + elif export_format == "fbx": + # Optimized FBX export for shape key animations + bpy.ops.export_scene.fbx( + filepath=export_path, + use_selection=True, + bake_anim=True, + bake_anim_use_all_bones=False, + bake_anim_use_nla_strips=False, + bake_anim_use_all_actions=False, + bake_anim_force_startend_keying=False, # Disabled: not needed for shape keys + bake_anim_step=1.0, # Set to 1.0 to bake only existing keyframes + bake_anim_simplify_factor=0.0, # Disable simplification for speed + add_leaf_bones=False, + path_mode='COPY', + embed_textures=True, + use_mesh_modifiers=True, + use_mesh_edges=True, + use_tspace=True, + use_custom_props=True, + use_active_collection=False, + ) + elif export_format == "glb": + bpy.ops.export_scene.gltf( + filepath=export_path, + use_selection=True, + export_format='GLB', + export_animations=True, + export_frame_range=True, + ) + print(f"Successfully exported animated mesh to: {export_path}") + bpy.ops.object.select_all(action='DESELECT') + except Exception as e: + print(f"Error occurred: {str(e)}") + raise + +def move_vertices_with_trajectory_gt(mesh_obj, frame, trajectories): + """Move mesh vertices according to ground truth trajectory data for a specific frame.""" + bpy.context.view_layer.objects.active = mesh_obj + bpy.ops.object.mode_set(mode='OBJECT') + if mesh_obj.data.shape_keys is None: + basis = mesh_obj.shape_key_add(name='Basis') + basis.interpolation = 'KEY_LINEAR' + + shape_key_name = f"Frame_{frame}" + shape_key = mesh_obj.data.shape_keys.key_blocks.get(shape_key_name) + if not shape_key: + shape_key = mesh_obj.shape_key_add(name=shape_key_name) + shape_key.interpolation = 'KEY_LINEAR' + if hasattr(trajectories[frame], 'numpy'): + positions = trajectories[frame].numpy() + else: + positions = trajectories[frame] + for idx, pos in enumerate(positions): + shape_key.data[idx].co = Vector(pos) + +def drive_mesh_with_trajs_frames_gt(mesh_objects, trajs, output_dir, azi=0.0, ele=0.0, export_format="glb"): + """ + Drive mesh animation by merging multiple meshes into one and using shape keys. + This approach is most compatible with GLB export as it only requires handling a single object. + + Args: + mesh_objects: List of Blender mesh objects + trajs: Trajectory data with shape (1, T, N, 3), where N is the total number of vertices + output_dir: Output directory + export_format: Export format ('glb', 'fbx', 'abc', 'none') + """ + try: + print(output_dir) + os.makedirs(output_dir, exist_ok=True) + print("Setting up animations for all meshes (V2 - Merged Mesh with Shape Keys)...") + + vertex_counts = [] + for mesh_obj in mesh_objects: + vertex_counts.append(len(mesh_obj.data.vertices)) + + print(f"Mesh objects count: {len(mesh_objects)}") + print(f"Vertex counts per mesh: {vertex_counts}") + print(f"Total vertices: {sum(vertex_counts)}") + print(f"Trajectories shape: {trajs.shape}") + + total_vertices = sum(vertex_counts) + if trajs.shape[2] != total_vertices: + print(f"Warning: Total vertices ({total_vertices}) does not match trajectory points ({trajs.shape[2]})") + + num_frames = trajs.shape[1] + trajs = trajs[0] + + print("Merging all meshes into one...") + bpy.ops.object.select_all(action='DESELECT') + for obj in mesh_objects: + obj.select_set(True) + bpy.context.view_layer.objects.active = mesh_objects[0] + + bpy.ops.object.join() + merged_mesh = bpy.context.active_object + merged_mesh.name = "MergedAnimatedMesh" + print(f"Merged mesh created: {merged_mesh.name} with {len(merged_mesh.data.vertices)} vertices") + + print("Creating shape keys for merged mesh...") + bpy.ops.object.mode_set(mode='OBJECT') + + basis = merged_mesh.shape_key_add(name='Basis') + basis.interpolation = 'KEY_LINEAR' + + for frame_idx in range(num_frames): + shape_key_name = f"Frame_{frame_idx}" + shape_key = merged_mesh.shape_key_add(name=shape_key_name) + shape_key.interpolation = 'KEY_LINEAR' + + if hasattr(trajs[frame_idx], 'numpy'): + positions = trajs[frame_idx].numpy() + else: + positions = trajs[frame_idx] + + for vert_idx, pos in enumerate(positions): + shape_key.data[vert_idx].co = Vector(pos) + + print(f"Created {num_frames} shape keys") + + print("Setting up shape key animation...") + for frame_idx in range(num_frames): + for shape_key in merged_mesh.data.shape_keys.key_blocks[1:]: + shape_key.value = 0 + shape_key.keyframe_insert("value", frame=frame_idx) + + current_shape_key = merged_mesh.data.shape_keys.key_blocks[f"Frame_{frame_idx}"] + current_shape_key.value = 1 + current_shape_key.keyframe_insert("value", frame=frame_idx) + + if merged_mesh.data.shape_keys.animation_data and merged_mesh.data.shape_keys.animation_data.action: + fcurves = merged_mesh.data.shape_keys.animation_data.action.fcurves + for fc in fcurves: + for kf in fc.keyframe_points: + kf.interpolation = 'CONSTANT' + + print("Shape key animation setup complete") + + bpy.context.scene.frame_start = 0 + bpy.context.scene.frame_end = num_frames - 1 + + print("Setting up scene...") + + if export_format != "none": + print(f"Exporting animated mesh to {export_format.upper()} format...") + bpy.ops.object.select_all(action='DESELECT') + merged_mesh.select_set(True) + filename = os.path.basename(output_dir) + export_path = os.path.join(os.path.dirname(output_dir), f"{filename}.{export_format}") + + if export_format == "abc": + bpy.ops.wm.alembic_export( + filepath=export_path, + start=bpy.context.scene.frame_start, + end=bpy.context.scene.frame_end, + selected=True, + uvs=True, + face_sets=True + ) + elif export_format == "glb": + bpy.ops.export_scene.gltf( + filepath=export_path, + use_selection=True, + export_format='GLB', + export_animations=True, + export_frame_range=True, + export_morph=True, + export_morph_animation=True, + ) + + print(f"Successfully exported animated mesh to: {export_path}") + bpy.ops.object.select_all(action='DESELECT') + + print("Animation setup complete!") + + except Exception as e: + print(f"Error occurred: {str(e)}") + import traceback + traceback.print_exc() + raise diff --git a/OVOW/third_party/Motion324/utils/rmbg_for_black_bg.py b/OVOW/third_party/Motion324/utils/rmbg_for_black_bg.py new file mode 100644 index 0000000000000000000000000000000000000000..45d68b10505c15120863be42f93517e744dffe4e --- /dev/null +++ b/OVOW/third_party/Motion324/utils/rmbg_for_black_bg.py @@ -0,0 +1,411 @@ +import os +import argparse +import numpy as np +from tqdm import tqdm +from easydict import EasyDict as edict +from PIL import Image +import rembg +import cv2 # for video processing +from pathlib import Path # for path handling + +# rembg session models (sorted by quality/speed trade-off): +# +# Highest Quality (slower): +# - 'birefnet-massive' : Best quality, slowest, large model +# - 'isnet-general-use' : Very high quality, balanced speed +# - 'birefnet' : High quality, good balance + +# +# Fast: +# - 'u2net' : Good quality, fast (default choice) +# - 'u2net_human_seg' : Optimized for human segmentation +# - 'u2net_cloth_seg' : Optimized for clothing segmentation + +# rembg_session = rembg.new_session('birefnet-massive') +# rembg_session = rembg.new_session('u2net') +rembg_session = rembg.new_session('isnet-general-use') +# rembg_session = rembg.new_session('u2net_human_seg') + +def remove_background(image): + # Remove background from given PIL Image. Returns: + # - RGBA image with black background on transparent regions + # - mask as numpy array (float [0, 1]) + # - mask image as PIL Image (mode 'L') + # - bounding box (min_x, min_y, max_x+1, max_y+1), or None for empty foreground + + # Step 1: Generate the mask using rembg + if image.mode != 'RGBA': + image_rgb = image.convert('RGB') + out = rembg.remove(image_rgb, session=rembg_session) + else: + arr = np.array(image) + if arr.shape[2] != 4 or np.all(arr[..., 3] == 255): + image_rgb = image.convert('RGB') + out = rembg.remove(image_rgb, session=rembg_session) + else: + out = image + + arr = np.array(out) + alpha = arr[..., 3] + mask = (alpha > int(0.8 * 255)).astype(np.uint8) * 255 + mask_img = Image.fromarray(mask, mode='L') + + # Calculate tight bounding box of the foreground (do not expand) + pos = np.argwhere(mask > 0) + if pos.size == 0: + w, h = image.size + empty_mask = np.zeros((h, w), dtype=np.uint8) + mask_img = Image.fromarray(empty_mask, mode='L') + mask_np = empty_mask.astype(np.float32) + empty_rgba = np.zeros((h, w, 4), dtype=np.uint8) + return Image.fromarray(empty_rgba, mode='RGBA'), mask_np, mask_img, None + min_x, min_y = np.min(pos[:, 1]), np.min(pos[:, 0]) + max_x, max_y = np.max(pos[:, 1]), np.max(pos[:, 0]) + bbox = (min_x, min_y, max_x + 1, max_y + 1) # left, top, right (open), bottom (open) + + # Step 2: Apply mask to original image, set background to black and keep alpha = mask + img_rgba = image.convert('RGBA') + img_np = np.array(img_rgba).astype(np.float32) / 255.0 # shape H,W,4 + mask_np = (mask / 255.0).astype(np.float32) # shape H,W + + result_np = np.zeros_like(img_np) + result_np[..., :3] = img_np[..., :3] * mask_np[..., None] + result_np[..., 3] = mask_np + + new_image_blackbg = Image.fromarray((result_np * 255).astype(np.uint8), mode='RGBA') + + return new_image_blackbg, mask_np, mask_img, bbox + +def compute_mask_bbox(mask_img): + mask_arr = np.array(mask_img) + pos = np.argwhere(mask_arr > 0) + if pos.size == 0: + return None + min_y, min_x = pos.min(axis=0) + max_y, max_x = pos.max(axis=0) + return (int(min_x), int(min_y), int(max_x) + 1, int(max_y) + 1) + +def merge_bbox(global_bbox, new_bbox): + if new_bbox is None: + return global_bbox + if global_bbox is None: + return list(new_bbox) + g_left, g_top, g_right, g_bottom = global_bbox + n_left, n_top, n_right, n_bottom = new_bbox + return [ + min(g_left, n_left), + min(g_top, n_top), + max(g_right, n_right), + max(g_bottom, n_bottom), + ] + +def crop_and_center_to_512(img, bbox, fill_value): + # crop by bbox, resize to fit within 512 keeping aspect, then center-pad + left, top, right, bottom = bbox + cropped = img.crop((left, top, right, bottom)) + w, h = cropped.size + if w == 0 or h == 0: + return Image.new(img.mode, (512, 512), fill_value) + scale = 512 / max(w, h) + new_w = max(1, int(round(w * scale))) + new_h = max(1, int(round(h * scale))) + resized = cropped.resize((new_w, new_h), Image.Resampling.LANCZOS) + canvas = Image.new(img.mode, (512, 512), fill_value) + offset_x = (512 - new_w) // 2 + offset_y = (512 - new_h) // 2 + canvas.paste(resized, (offset_x, offset_y)) + return canvas + +def process_images_in_folder(folder_path, out_base): + """ + Process all images (png/jpg/jpeg) in a folder and save outputs to subfolders within out_base. + - Saves full original image to /origin + - Saves masked RGBA image to /masked + - Saves binary mask image to /mask + - Saves resized (512x512) binary mask to /mask_512 + - Saves RGB masked (512x512) image to /masked_rgb and /frames/masked_rgb + """ + os.makedirs(out_base, exist_ok=True) + orig_out_base = os.path.join(out_base, "origin") + masked_out_base = os.path.join(out_base, "masked") + mask_out_base = os.path.join(out_base, "mask") + mask512_out_base = os.path.join(out_base, "mask_512") + masked_rgb_out_base = os.path.join(out_base, "masked_rgb") + masked_rgb_out_base2 = os.path.join(out_base, "frames", "masked_rgb") + for subdir in [orig_out_base, masked_out_base, mask_out_base, mask512_out_base, masked_rgb_out_base, masked_rgb_out_base2]: + os.makedirs(subdir, exist_ok=True) + + image_files = sorted([f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]) + if len(image_files) == 0: + return + bbox = None + processed_items = [] + for idx, image_file in enumerate(image_files): + image_path = os.path.join(folder_path, image_file) + image_name = os.path.splitext(os.path.basename(image_file))[0] + image = Image.open(image_path) + # Save the original image + orig_save_path = os.path.join(orig_out_base, image_file) + image.save(orig_save_path) + if bbox is None: + image_masked, mask_np, mask_img, first_bbox = remove_background(image) + curr_bbox = first_bbox + else: + # Only generate mask, apply it to current image to get RGBA black bkg + if image.mode != 'RGBA': + image = image.convert('RGB') + out = rembg.remove(image, session=rembg_session) + else: + arr = np.array(image) + if arr.shape[2] != 4 or np.all(arr[..., 3] == 255): + image = image.convert('RGB') + out = rembg.remove(image, session=rembg_session) + else: + out = image + arr = np.array(out) + alpha = arr[..., 3] + mask_img = Image.fromarray((alpha > int(0.8 * 255)).astype(np.uint8) * 255, mode='L') + + # Apply mask to out, output RGBA, black where mask==0 + out_np = np.array(out).astype(np.float32) / 255.0 + mask_np = np.array(mask_img).astype(np.float32) / 255.0 + if mask_np.ndim == 2: + mask_np = mask_np[..., None] + image_masked_np = out_np.copy() + image_masked_np[..., :3] = image_masked_np[..., :3] * mask_np + image_masked_np[..., 3] = mask_np[..., 0] + image_masked = Image.fromarray((image_masked_np * 255).astype(np.uint8), mode='RGBA') + curr_bbox = compute_mask_bbox(mask_img) + + bbox = merge_bbox(bbox, curr_bbox) + + # Save masked RGBA image + masked_save_path = os.path.join(masked_out_base, f'{image_name}_masked.png') + image_masked.save(masked_save_path) + + # Save mask as image (L) + mask_save_path = os.path.join(mask_out_base, f'{image_name}_mask.png') + mask_img.save(mask_save_path) + + # Record data for later global bbox crop + center to 512 + processed_items.append((image_name, mask_img, image_masked, image_path)) + + if bbox is None: + print("No valid masks found; skip 512 outputs.") + return + for image_name, mask_img, image_masked, image_path in processed_items: + mask512 = crop_and_center_to_512(mask_img, bbox, fill_value=0) + mask512_save_path = os.path.join(mask512_out_base, f'{image_name}_mask_512.png') + mask512.save(mask512_save_path) + + rgb_np = np.array(image_masked)[..., :3] + image_masked_rgb = Image.fromarray(rgb_np) + image_masked_512 = crop_and_center_to_512(image_masked_rgb, bbox, fill_value=(0, 0, 0)) + masked_rgb_save_path = os.path.join(masked_rgb_out_base, f'{image_name}_masked_rgb.png') + image_masked_512.save(masked_rgb_save_path) + masked_rgb_save_path2 = os.path.join(masked_rgb_out_base2, f'{image_name}_masked_rgb.png') + image_masked_512.save(masked_rgb_save_path2) + + # Also crop and center the original images to 512x512 using the computed global bbox + for idx, (image_name, mask_img, image_masked, image_path) in enumerate(processed_items): + image_file = image_name + ".png" + if os.path.exists(image_path): + orig_image = Image.open(image_path) + # Crop and center to 512 + orig512 = crop_and_center_to_512(orig_image, bbox, fill_value=(0, 0, 0)) + # Save to separate output folder for origin_cropped results + orig_cropped_save_path = os.path.join(orig_out_base + "_cropped", f'{image_name}_origin_cropped_512.png') + os.makedirs(os.path.dirname(orig_cropped_save_path), exist_ok=True) + orig512.save(orig_cropped_save_path) + else: + print(f"Warning: original image not found: {image_path}, cannot save cropped origin 512.") + +def extract_frames_from_video(video_path, output_dir): + """ + Extracts frames from a video and saves them to output_dir as PNG images. + Returns True on success, False if fails. + """ + os.makedirs(output_dir, exist_ok=True) + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + print(f"Error: Cannot open video {video_path}") + return False + + frame_count = 0 + print(f"Extracting frames from video: {video_path}") + + with tqdm(desc="Extracting frames") as pbar: + while True: + ret, frame = cap.read() + if not ret: + break + # Convert BGR to RGB for PIL + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame_pil = Image.fromarray(frame_rgb) + frame_path = os.path.join(output_dir, f"{frame_count:06d}.png") + frame_pil.save(frame_path) + frame_count += 1 + pbar.update(1) + cap.release() + print(f"Extracted {frame_count} frames to {output_dir}") + return True + +def process_recursively(root_dir, out_base): + """ + Recursively process all images in all subfolders of root_dir (ignores images directly in root_dir). + Skips subfolders named 'resized_images' or 'masked_images'. + """ + for dirpath, dirnames, filenames in os.walk(root_dir): + # Skip the root dir itself (only process subfolders) + if os.path.abspath(dirpath) == os.path.abspath(root_dir): + continue + # Skip 'resized_images' and 'masked_images' subtrees + rel_path = os.path.relpath(dirpath, root_dir) + if rel_path.split(os.sep)[0] == "resized_images" or rel_path.split(os.sep)[0] == "masked_images": + continue + out_folder = os.path.join(out_base, rel_path) + process_images_in_folder(dirpath, out_folder) + +def main_split_only(opt): + """ + Only extract frames from input video and save to output directory. + No background removal is performed. + """ + input_path = opt.input + if os.path.isfile(input_path): + if not input_path.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')): + print(f"Error: {input_path} is not a supported video format") + return + + video_name = Path(input_path).stem + if opt.output_dir: + base_dir = opt.output_dir + else: + base_dir = os.path.join(os.path.dirname(input_path), f"{video_name}_processed") + + # Skip if output directory already exists + if os.path.exists(base_dir) and os.path.isdir(base_dir): + print(f"Output directory already exists: {base_dir}") + print(f"Skipping processing for {input_path}") + return + + # Output structure: base_dir/masked_images/video/origin/ + masked_rgb_dir = os.path.join(base_dir, "masked_images", "video", "origin") + if not extract_frames_from_video(input_path, masked_rgb_dir): + return + + # Also save each frame to base_dir/masked_rgb/ for compatibility + masked_rgb_dir2 = os.path.join(base_dir, "masked_rgb") + os.makedirs(masked_rgb_dir2, exist_ok=True) + for f in os.listdir(masked_rgb_dir): + if f.endswith('.png'): + src = os.path.join(masked_rgb_dir, f) + dst = os.path.join(masked_rgb_dir2, f) + Image.open(src).save(dst) + + masked_rgb_dir3 = os.path.join(base_dir, "frames", "video") + os.makedirs(masked_rgb_dir3, exist_ok=True) + for f in os.listdir(masked_rgb_dir): + if f.endswith('.png'): + src = os.path.join(masked_rgb_dir, f) + dst = os.path.join(masked_rgb_dir3, f) + Image.open(src).save(dst) + + print(f"Video splitting complete.") + print(f"Frames saved in: {masked_rgb_dir}") + print(f"Frames also saved in: {masked_rgb_dir3}") + else: + print(f"Error: {input_path} is not a video file") + +def main(opt): + input_path = opt.input + + # If split_only is specified, only extract frames from video + if opt.split_only: + main_split_only(opt) + return + + # Check if input is a video file or a directory + if os.path.isfile(input_path): + if not input_path.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')): + print(f"Error: {input_path} is not a supported video format") + return + + video_name = Path(input_path).stem + if opt.output_dir: + base_dir = opt.output_dir + else: + base_dir = os.path.join(os.path.dirname(input_path), f"{video_name}_processed") + + # Skip if output directory already exists + if os.path.exists(base_dir) and os.path.isdir(base_dir): + print(f"Output directory already exists: {base_dir}") + print(f"Skipping processing for {input_path}") + return + + # Extract frames + frames_dir = os.path.join(base_dir, "frames") + if not extract_frames_from_video(input_path, frames_dir): + return + + # Move extracted frames into a "video" subfolder for processing convention + out_base = os.path.join(base_dir, "masked_images") + frames_subdir = os.path.join(frames_dir, "video") + os.makedirs(frames_subdir, exist_ok=True) + import errno + import shutil + for f in os.listdir(frames_dir): + if f.endswith('.png'): + src = os.path.join(frames_dir, f) + dst = os.path.join(frames_subdir, f) + try: + os.rename(src, dst) + except OSError as e: + # Some deployments put temp/output dirs on different mounts. + # Fall back to copy+remove when cross-device rename is not possible. + if e.errno == errno.EXDEV: + shutil.move(src, dst) + else: + raise + + # Also save to base_dir/masked_rgb/ for compatibility after mask processing + masked_rgb_dir = os.path.join(base_dir, "masked_rgb") + os.makedirs(masked_rgb_dir, exist_ok=True) + + # Copy processed masked RGB files to masked_rgb_dir (strip "_masked_rgb" suffix) + masked_rgb_video_dir = os.path.join(out_base, "video", "masked_rgb") + if os.path.exists(masked_rgb_video_dir): + for fname in os.listdir(masked_rgb_video_dir): + if fname.endswith("_masked_rgb.png"): + src_path = os.path.join(masked_rgb_video_dir, fname) + new_name = fname.replace("_masked_rgb", "") + dst_path = os.path.join(masked_rgb_dir, new_name) + shutil.copy(src_path, dst_path) + print(f'copied {masked_rgb_video_dir} to {masked_rgb_dir}') + else: + process_recursively(frames_dir, out_base) + if os.path.exists(masked_rgb_video_dir): + for fname in os.listdir(masked_rgb_video_dir): + if fname.endswith("_masked_rgb.png"): + src_path = os.path.join(masked_rgb_video_dir, fname) + new_name = fname.replace("_masked_rgb", "") + dst_path = os.path.join(masked_rgb_dir, new_name) + shutil.copy(src_path, dst_path) + print(f'copied {masked_rgb_video_dir} to {masked_rgb_dir}') + print(f"Video processing complete. Output in: {base_dir}") + else: + print(f"Error: {input_path} is not a video file") + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--input', type=str, required=True, + help='Input video file or directory containing images') + parser.add_argument('--output_dir', type=str, default=None, + help='Output directory (optional, will auto-generate if not provided)') + parser.add_argument('--split_only', action='store_true', + help='Only extract frames from video without background removal (split_only mode)') + opt = parser.parse_args() + opt = edict(vars(opt)) + main(opt) +# python ./utils/rmbg_for_black_bg.py --input /path/to/video.mp4 +# python ./utils/rmbg_for_black_bg.py --input /path/to/video.mp4 --split_only diff --git a/OVOW/third_party/Motion324/utils/training_utils.py b/OVOW/third_party/Motion324/utils/training_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7d19c851acaa38ee0ce4d23cd7da93b8bec6cdc4 --- /dev/null +++ b/OVOW/third_party/Motion324/utils/training_utils.py @@ -0,0 +1,221 @@ +# Copyright (c) 2025 Haian Jin. Created for the LVSM project (ICLR 2025). +# Modifications Copyright (c) 2025 Hongyuan Chen. + +import torch +from transformers import ( + get_constant_schedule_with_warmup, + get_cosine_schedule_with_warmup, + get_linear_schedule_with_warmup, +) +import torch.distributed as dist +import os +from rich import print +import traceback +from torch.nn.parallel import DistributedDataParallel as DDP +import torch.distributed as dist +import gc + +def is_dist_avail_and_initialized(): + return dist.is_available() and dist.is_initialized() + +def print_rank0(*args, **kwargs): + if dist.is_initialized(): + if dist.get_rank() == 0: + print(*args, **kwargs) + else: + print(*args, **kwargs) + + +def format_number(num): + if num >= 1_000_000_000: + return f"{num / 1_000_000_000:.2f}B" + elif num >= 1_000_000: + return f"{num / 1_000_000:.2f}M" + elif num >= 1_000: + return f"{num / 1_000:.2f}K" + return str(num) + +def create_optimizer(model, weight_decay, learning_rate, betas): + all_param_dict = {name: param for name, param in model.named_parameters()} + optimized_param_dict = {name: param for name, param in all_param_dict.items() if param.requires_grad} + + decay_params, nodecay_params = [], [] + for name, param in optimized_param_dict.items(): + if param.dim() == 1 or getattr(param, '_no_weight_decay', False): + nodecay_params.append(param) + else: + decay_params.append(param) + optim_groups = [ + {'params': decay_params, 'weight_decay': weight_decay}, + {'params': nodecay_params, 'weight_decay': 0.0} + ] + optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas,fused=True) + + if is_dist_avail_and_initialized(): + if dist.get_rank() == 0: + def get_module_name(name): + parts = name.split('.') + if len(parts) > 2 and parts[0] == 'module': + return parts[1] + '.' + parts[2] + return parts[0] + print(f'Optimizer: AdamW, learning rate: {learning_rate}, weight_decay: {weight_decay}, betas: {betas}') + total_params = sum(p.numel() for p in model.parameters()) + trainable_params = sum(p.numel() for p in optimized_param_dict.values()) + optim_module_names = sorted(set(get_module_name(name) for name in optimized_param_dict.keys())) + frozen_module_names = sorted(set(get_module_name(name) for name in set(all_param_dict.keys()) - set(optimized_param_dict.keys()))) + + print(f'Total parameters: {format_number(total_params)}, Trainable parameters: {format_number(trainable_params)}') + print(f'Optimized parameters: {optim_module_names}') + print(f'Frozen parameters: {frozen_module_names}') + + return optimizer, optimized_param_dict, all_param_dict + +def create_lr_scheduler(optimizer, param_update_steps, warm_up_steps, scheduler_type='cosine'): + if scheduler_type == 'linear': + scheduler = get_linear_schedule_with_warmup(optimizer, warm_up_steps, param_update_steps) + elif scheduler_type == 'cosine': + scheduler = get_cosine_schedule_with_warmup(optimizer, warm_up_steps, param_update_steps) + elif scheduler_type == 'constant': + scheduler = get_constant_schedule_with_warmup(optimizer, warm_up_steps) + else: + raise ValueError(f'Invalid scheduler type: {scheduler_type}') + return scheduler + + + +def find_checkpoints(load_path): + if os.path.isdir(load_path): + ckpt_names = [file_name for file_name in os.listdir(load_path) if file_name.endswith(".pt")] + ckpt_names = sorted(ckpt_names, key=lambda x: x) + ckpt_paths = [os.path.join(load_path, ckpt_name) for ckpt_name in ckpt_names] + else: + if load_path.endswith(".pt"): + ckpt_paths = [load_path] + else: + ckpt_paths = [] + return ckpt_paths + + + +def auto_resume_job( + load_path, + model, + optimizer, + lr_scheduler, + reset_training_state +): + """ + Resume training from the latest checkpoint in the specified directory. + Returns the fwdbwd_pass_step and param_update_step. + + Args: + load_path: If dir, load the last checkpoint in the directory. + O.w., assume it's a ckpt and load it. + model: model to be loaded + optimizer: optimizer to be loaded + lr_scheduler: lr scheduler to be loaded + reset_training_state: whether to reset the training state + + Returns: + optimizer, lr_scheduler, forward_pass_step, param_update_step + + """ + forward_pass_step = 0 + param_update_step = 0 + all_ckpt_paths = find_checkpoints(load_path) + if len(all_ckpt_paths) == 0: + print_rank0(f"No checkpoint found in {load_path}, we will start from scratch") + return optimizer, lr_scheduler, forward_pass_step, param_update_step + try: + ckpt_path = all_ckpt_paths[-1] + checkpoint = torch.load(ckpt_path, map_location="cpu") + except: + traceback.print_exc() + print_rank0(f"Failed to load {ckpt_path}, we will start from scratch") + return optimizer, lr_scheduler, forward_pass_step, param_update_step + + # Load model weights + if isinstance(model, DDP): + status = model.module.load_state_dict(checkpoint['model'], strict=False) + else: + status = model.load_state_dict(checkpoint['model'], strict=False) + print_rank0(f"Loaded model from {os.path.abspath(ckpt_path)}, the status is {status}") + + if not reset_training_state: + try: + optimizer.load_state_dict(checkpoint["optimizer"]) + lr_scheduler.load_state_dict(checkpoint["lr_scheduler"]) + forward_pass_step = checkpoint["fwdbwd_pass_step"] + param_update_step = checkpoint["param_update_step"] + print_rank0(f"Resumed optimizer and lr_scheduler from {ckpt_path}") + except: + traceback.print_exc() + print_rank0(f"Failed to load optimizer and lr_scheduler from {ckpt_path}") + + return optimizer, lr_scheduler, forward_pass_step, param_update_step + + +def check_and_handle_global_nan_loss(loss, ddp_info, cur_train_step, grad_accum_steps, total_train_steps, + ret_dict, batch, lr_scheduler, optimizer): + """ + Check if loss contains NaN/Inf values and handle cleanup if detected. + + Args: + loss: The loss tensor to check + ddp_info: Distributed training info object with is_distributed, device, etc. + cur_train_step: Current training step + grad_accum_steps: Gradient accumulation steps + total_train_steps: Total training steps + ret_dict: Return dictionary from model forward pass + batch: Input batch + lr_scheduler: Learning rate scheduler + optimizer: Optimizer + + Returns: + bool: True if NaN/Inf was detected and cleanup was performed, False otherwise + """ + loss_is_nan = torch.isnan(loss) or torch.isinf(loss) + if ddp_info.is_distributed: + has_nan_tensor = torch.tensor(float(loss_is_nan), device=ddp_info.device) + dist.all_reduce(has_nan_tensor, op=dist.ReduceOp.MAX) + global_has_nan = has_nan_tensor.item() > 0.5 + else: + global_has_nan = loss_is_nan + + if global_has_nan: + print_rank0(f"Step {cur_train_step}: Detected NaN/Inf loss on at least one rank. Skipping backward and optimizer step for all ranks.") + if (cur_train_step + 1) % grad_accum_steps == 0 or cur_train_step == total_train_steps: + print(f"Zeroing grads for step {cur_train_step}") + + def deep_detach(obj): + if isinstance(obj, torch.Tensor): + return obj.detach() + elif isinstance(obj, dict): + return {k: deep_detach(v) for k, v in obj.items()} + elif isinstance(obj, (list, tuple)): + return type(obj)(deep_detach(item) for item in obj) + elif hasattr(obj, '__dict__'): + for k, v in obj.__dict__.items(): + setattr(obj, k, deep_detach(v)) + return obj + else: + return obj + + with torch.no_grad(): + ret_dict = deep_detach(ret_dict) + + del ret_dict + del batch + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + gc.collect() + torch.cuda.empty_cache() + + if ddp_info.is_distributed: + dist.barrier() + + return True + + return False + + diff --git a/OVOW/third_party/Motion324/utils/visualization.py b/OVOW/third_party/Motion324/utils/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..8474ffe499676f7f008019b91c532a4d3369474e --- /dev/null +++ b/OVOW/third_party/Motion324/utils/visualization.py @@ -0,0 +1,307 @@ +""" +Visualization utilities for inference +""" + +import os +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.animation as animation +from matplotlib.gridspec import GridSpec +from mpl_toolkits.mplot3d import Axes3D +from mpl_toolkits.mplot3d.art3d import Poly3DCollection +from pathlib import Path + +try: + import bpy + from mathutils import Vector, Matrix, Euler + BLENDER_AVAILABLE = True +except ImportError: + BLENDER_AVAILABLE = False + +def visualize_input_data(input_data, save_path="input_data_vis.png"): + """ + Visualize input data including colors, normals (as colors), and ref_pcd. + + Args: + input_data: Dictionary containing ref_shape_pcd, ref_shape_rgbs, ref_shape_normals, + ref_pcd, ref_rgb, ref_normal + save_path: Path to save the visualization image + """ + fig = plt.figure(figsize=(14, 10)) + gs = GridSpec(2, 2, figure=fig) + + ax1 = fig.add_subplot(gs[0, 0], projection='3d') + points1 = input_data['ref_shape_pcd'][0].detach().cpu().numpy() + rgbs1 = input_data['ref_shape_rgbs'][0].detach().cpu().numpy() + rgbs1_clip = np.clip(rgbs1, 0, 1) + ax1.scatter(points1[:, 0], points1[:, 1], points1[:, 2], c=rgbs1_clip, s=1) + ax1.set_title("ref_shape_pcd RGB") + ax1.set_xlabel('X') + ax1.set_ylabel('Y') + ax1.set_zlabel('Z') + ax1.set_xlim([-0.5, 0.5]) + ax1.set_ylim([-0.5, 0.5]) + ax1.set_zlim([-0.5, 0.5]) + + ax2 = fig.add_subplot(gs[0, 1], projection='3d') + normals1 = input_data['ref_shape_normals'][0].detach().cpu().numpy() + normals_vis1 = (normals1 + 1) * 0.5 + ax2.scatter(points1[:, 0], points1[:, 1], points1[:, 2], c=normals_vis1, s=1) + ax2.set_title("ref_shape_pcd Normals (as color)") + ax2.set_xlabel('X') + ax2.set_ylabel('Y') + ax2.set_zlabel('Z') + ax2.set_xlim([-0.5, 0.5]) + ax2.set_ylim([-0.5, 0.5]) + ax2.set_zlim([-0.5, 0.5]) + + ax3 = fig.add_subplot(gs[1, 0], projection='3d') + points2 = input_data['ref_pcd'][0].detach().cpu().numpy() + rgbs2 = input_data['ref_rgb'][0].detach().cpu().numpy() + rgbs2_clip = np.clip(rgbs2, 0, 1) + ax3.scatter(points2[:, 0], points2[:, 1], points2[:, 2], c=rgbs2_clip, s=3) + ax3.set_title("ref_pcd RGB") + ax3.set_xlabel('X') + ax3.set_ylabel('Y') + ax3.set_zlabel('Z') + ax3.set_xlim([-0.5, 0.5]) + ax3.set_ylim([-0.5, 0.5]) + ax3.set_zlim([-0.5, 0.5]) + + ax4 = fig.add_subplot(gs[1, 1], projection='3d') + normals2 = input_data['ref_normal'][0].detach().cpu().numpy() + normals_vis2 = (normals2 + 1) * 0.5 + ax4.scatter(points2[:, 0], points2[:, 1], points2[:, 2], c=normals_vis2, s=3) + ax4.set_title("ref_pcd Normals (as color)") + ax4.set_xlabel('X') + ax4.set_ylabel('Y') + ax4.set_zlabel('Z') + ax4.set_xlim([-0.5, 0.5]) + ax4.set_ylim([-0.5, 0.5]) + ax4.set_zlim([-0.5, 0.5]) + + plt.tight_layout() + plt.savefig(save_path, dpi=180) + plt.close(fig) + print(f"Input data visualization saved to: {save_path}") + +def visualize_pointcloud_prediction(result, sample, save_dir="./visualization_results", idx = 0, fps=8): + """ + Visualize point cloud prediction results. + + Args: + result: Model prediction results containing input_data, rgb, pcd_moved, loss_metrics. + sample: Input data containing obj_name, rgb_video, point_clouds, uv_gt, color_gt. + save_dir: Directory to save visualization results. + fps: Animation frame rate. + """ + save_dir = Path(save_dir) + save_dir.mkdir(exist_ok=True, parents=True) + + gt_point_clouds = sample['point_clouds'][idx].float().cpu().numpy() # Shape: (T, N_points, D_features) + pred_point_clouds = result['pcd_moved'][idx].float().cpu().numpy() # Shape: (T, N_points, 3) + rgb_frames = sample['rgb_video'][idx].float().cpu().numpy() # Shape: (T, H, W, C) + obj_name = sample['obj_name'][idx] + + T, N_points, _ = gt_point_clouds.shape + + if gt_point_clouds.shape[-1] > 3: + gt_coords = gt_point_clouds[..., :3] + else: + gt_coords = gt_point_clouds + + pred_coords = pred_point_clouds + + + all_coords = np.concatenate([gt_coords.reshape(-1, 3), pred_coords.reshape(-1, 3)], axis=0) + min_vals = np.min(all_coords, axis=0) + max_vals = np.max(all_coords, axis=0) + center = (min_vals + max_vals) / 2 + max_range = np.max(max_vals - min_vals) / 2 * 1.2 + + fig = plt.figure(figsize=(24, 6)) + gs = GridSpec(1, 4, figure=fig) + + ax1 = fig.add_subplot(gs[0, 0], projection='3d') + ax1.set_title('Ground Truth', fontsize=14, fontweight='bold') + + ax2 = fig.add_subplot(gs[0, 1], projection='3d') + ax2.set_title('Prediction', fontsize=14, fontweight='bold') + + ax3 = fig.add_subplot(gs[0, 2], projection='3d') + ax3.set_title('GT (Blue) vs Pred (Red)', fontsize=14, fontweight='bold') + + ax4 = fig.add_subplot(gs[0, 3]) + ax4.set_title('RGB Video', fontsize=14, fontweight='bold') + ax4.axis('off') + + for ax in [ax1, ax2, ax3]: + ax.set_xlim([center[0] - max_range, center[0] + max_range]) + ax.set_ylim([center[1] - max_range, center[1] + max_range]) + ax.set_zlim([center[2] - max_range, center[2] + max_range]) + ax.set_xlabel('X') + ax.set_ylabel('Y') + ax.set_zlabel('Z') + + if len(rgb_frames.shape) == 4 and rgb_frames.shape[-1] == 3: + if rgb_frames.max() > 1.0: + rgb_frames_normalized = rgb_frames / 255.0 + else: + rgb_frames_normalized = rgb_frames + else: + print(f"Warning: RGB frames shape {rgb_frames.shape} is unexpected") + rgb_frames_normalized = np.zeros((T, 224, 224, 3)) + + im = ax4.imshow(rgb_frames_normalized[0]) + + def animate(frame): + ax1.clear() + ax2.clear() + ax3.clear() + + ax1.set_title(f'GT (Frame {frame+1}/{T})', fontsize=14, fontweight='bold') + ax2.set_title(f'Pred (Frame {frame+1}/{T})', fontsize=14, fontweight='bold') + ax3.set_title(f'GT vs Pred (Frame {frame+1}/{T})', fontsize=14, fontweight='bold') + ax4.set_title(f'RGB (Frame {frame+1}/{T})', fontsize=14, fontweight='bold') + + for ax in [ax1, ax2, ax3]: + ax.set_xlim([center[0] - max_range, center[0] + max_range]) + ax.set_ylim([center[1] - max_range, center[1] + max_range]) + ax.set_zlim([center[2] - max_range, center[2] + max_range]) + ax.set_xlabel('X') + ax.set_ylabel('Y') + ax.set_zlabel('Z') + + gt_coords_frame = gt_coords[frame] + pred_coords_frame = pred_coords[frame] + + # GT subplot + ax1.scatter(gt_coords_frame[:, 0], gt_coords_frame[:, 1], gt_coords_frame[:, 2], + c='blue', s=2, alpha=0.6) + + # Prediction subplot + ax2.scatter(pred_coords_frame[:, 0], pred_coords_frame[:, 1], pred_coords_frame[:, 2], + c='red', s=2, alpha=0.6) + + # Comparison subplot + ax3.scatter(gt_coords_frame[:, 0], gt_coords_frame[:, 1], gt_coords_frame[:, 2], + c='blue', s=2, alpha=0.6, label='GT') + ax3.scatter(pred_coords_frame[:, 0], pred_coords_frame[:, 1], pred_coords_frame[:, 2], + c='red', s=2, alpha=0.6, label='Pred') + ax3.legend() + + im.set_array(rgb_frames_normalized[frame]) + + return [] + + anim = animation.FuncAnimation(fig, animate, frames=T, interval=1000//fps, blit=False) + + save_path = save_dir / f"{obj_name}_combined_visualization.gif" + anim.save(str(save_path), writer='pillow', fps=fps, dpi=100) + plt.close(fig) + + mse_per_frame = np.mean((gt_coords - pred_coords) ** 2, axis=(1, 2)) + avg_mse = np.mean(mse_per_frame) + + return { + 'visualization_path': str(save_path), + } + + +def visualize_point_cloud_motion(ref_pcd, pcd_moved, trajs, save_path): + """ + Visualize point cloud positions before and after movement with motion trajectory arrows. + + Args: + ref_pcd: Original point cloud, shape (N, 3) + pcd_moved: Moved point cloud, shape (N, 3) + trajs: Motion trajectory vectors, shape (N, 3) + save_path: Path to save the image + """ + ref_pcd_np = ref_pcd.cpu().numpy() + pcd_moved_np = pcd_moved.cpu().numpy() + trajs_np = trajs.cpu().numpy() + + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + ax.scatter(ref_pcd_np[:, 0], ref_pcd_np[:, 1], ref_pcd_np[:, 2], s=5, c='b', label='ref_pcd') + ax.scatter(pcd_moved_np[:, 0], pcd_moved_np[:, 1], pcd_moved_np[:, 2], s=5, c='r', label='pcd_moved') + ax.quiver( + ref_pcd_np[:, 0], ref_pcd_np[:, 1], ref_pcd_np[:, 2], + trajs_np[:, 0], trajs_np[:, 1], trajs_np[:, 2], + length=1.0, normalize=False, color='g', linewidth=0.5, alpha=0.7 + ) + ax.set_title("Point Cloud Motion Comparison") + ax.legend() + plt.savefig(save_path, bbox_inches='tight') + plt.close(fig) + + +def plot_smoothing_comparison(displacements_before, displacements_after, + motion_threshold, method, visualization_dir): + """ + Plot comparison of trajectory smoothing before and after. + + Args: + displacements_before: List of displacement arrays before smoothing + displacements_after: List of displacement arrays after smoothing + motion_threshold: Motion threshold used for filtering + method: Smoothing method name + visualization_dir: Directory to save the plot + """ + fig, axes = plt.subplots(2, 2, figsize=(14, 10)) + + point_mean_before = np.mean(np.stack(displacements_before, axis=0), axis=0) + point_mean_after = np.mean(np.stack(displacements_after, axis=0), axis=0) + + axes[0, 0].scatter(range(len(point_mean_before)), point_mean_before, s=1, alpha=0.5, color='b', label='Before') + axes[0, 0].scatter(range(len(point_mean_after)), point_mean_after, s=1, alpha=0.5, color='r', label='After') + axes[0, 0].set_xlabel('Point Index') + axes[0, 0].set_ylabel('Mean Displacement Across Frames') + axes[0, 0].set_title('Per-Point Average Displacement') + axes[0, 0].grid(True) + axes[0, 0].legend() + + all_before = np.concatenate(displacements_before) + axes[0, 1].hist(all_before, bins=100, color='b', alpha=0.7, edgecolor='black') + axes[0, 1].set_xlabel('Displacement Magnitude') + axes[0, 1].set_ylabel('Frequency') + axes[0, 1].set_title('Displacement Distribution (Before)') + axes[0, 1].axvline(x=motion_threshold, color='r', linestyle='--', label=f'Threshold={motion_threshold}') + axes[0, 1].set_yscale('log') + axes[0, 1].legend() + axes[0, 1].grid(True) + + all_after = np.concatenate(displacements_after) + axes[1, 0].hist(all_after, bins=100, color='r', alpha=0.7, edgecolor='black') + axes[1, 0].set_xlabel('Displacement Magnitude') + axes[1, 0].set_ylabel('Frequency') + axes[1, 0].set_title('Displacement Distribution (After)') + axes[1, 0].axvline(x=motion_threshold, color='r', linestyle='--', label=f'Threshold={motion_threshold}') + axes[1, 0].set_yscale('log') + axes[1, 0].legend() + axes[1, 0].grid(True) + + point_std_before = np.std(np.stack(displacements_before, axis=0), axis=0) + point_std_after = np.std(np.stack(displacements_after, axis=0), axis=0) + + axes[1, 1].scatter(range(len(point_std_before)), point_std_before, s=1, alpha=0.5, color='b', label='Before') + axes[1, 1].scatter(range(len(point_std_after)), point_std_after, s=1, alpha=0.5, color='r', label='After') + axes[1, 1].set_xlabel('Point Index') + axes[1, 1].set_ylabel('Std Displacement Across Frames') + axes[1, 1].set_title('Per-Point Displacement Std') + axes[1, 1].grid(True) + axes[1, 1].legend() + + plt.tight_layout() + smooth_plot_path = os.path.join(visualization_dir, f'smooth_comparison_{method}.png') + plt.savefig(smooth_plot_path, dpi=150) + plt.close() + print(f"Smoothing comparison plot saved to: {smooth_plot_path}") + + print(f"\n=== Smoothing Statistics ===") + print(f"Average displacement before: {np.mean(all_before):.6f} ± {np.std(all_before):.6f}") + print(f"Average displacement after: {np.mean(all_after):.6f} ± {np.std(all_after):.6f}") + print(f"Jitter reduction rate: {(1 - np.std(all_after) / np.std(all_before)) * 100:.2f}%") + print(f"Per-point average displacement - before: {np.mean(point_mean_before):.6f} ± {np.std(point_mean_before):.6f}") + print(f"Per-point average displacement - after: {np.mean(point_mean_after):.6f} ± {np.std(point_mean_after):.6f}") diff --git a/OVOW/third_party/RoMaV2/.github/actions/uv-build/action.yml b/OVOW/third_party/RoMaV2/.github/actions/uv-build/action.yml new file mode 100644 index 0000000000000000000000000000000000000000..38d4c2bb2b70ef98b85f09a015250f5153b079cd --- /dev/null +++ b/OVOW/third_party/RoMaV2/.github/actions/uv-build/action.yml @@ -0,0 +1,23 @@ +name: uv-build +description: Build the project with uv and run smoke tests +inputs: + python-version: + description: Python version to install + required: true +runs: + using: composite + steps: + - name: Install uv + uses: astral-sh/setup-uv@v6 + - name: Install Python + shell: bash + run: uv python install ${{ inputs.python-version }} + - name: Build + shell: bash + run: uv build + - name: Smoke test (wheel) + shell: bash + run: uv run --isolated --no-project --with dist/*.whl tests/test_smoke.py + - name: Smoke test (source distribution) + shell: bash + run: uv run --isolated --no-project --with dist/*.tar.gz tests/test_smoke.py \ No newline at end of file diff --git a/OVOW/third_party/RoMaV2/.github/workflows/build.yml b/OVOW/third_party/RoMaV2/.github/workflows/build.yml new file mode 100644 index 0000000000000000000000000000000000000000..d1179d2b4309f316ef0011881f80d70f95f1411c --- /dev/null +++ b/OVOW/third_party/RoMaV2/.github/workflows/build.yml @@ -0,0 +1,22 @@ +name: Build + +on: + push: + branches: + - main + pull_request: + branches: + - main +jobs: + run: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ['3.9', '3.10', '3.11', '3.12', '3.13'] + steps: + - name: Checkout + uses: actions/checkout@v5 + - name: Build and smoke test + uses: ./.github/actions/uv-build + with: + python-version: ${{ matrix.python-version }} \ No newline at end of file diff --git a/OVOW/third_party/RoMaV2/.github/workflows/publish.yml b/OVOW/third_party/RoMaV2/.github/workflows/publish.yml new file mode 100644 index 0000000000000000000000000000000000000000..1526da9bd17ee8c0821036ce4d60ad6ce29d496b --- /dev/null +++ b/OVOW/third_party/RoMaV2/.github/workflows/publish.yml @@ -0,0 +1,41 @@ +name: PyPI Publish + +on: + push: + tags: + # Publish on any tag starting with a `v`, e.g., v0.1.0 + - v* + workflow_dispatch: + release: + types: [published] +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ['3.9', '3.10', '3.11', '3.12', '3.13'] + steps: + - name: Checkout + uses: actions/checkout@v5 + - name: Build and smoke test + uses: ./.github/actions/uv-build + with: + python-version: ${{ matrix.python-version }} + + publish: + runs-on: ubuntu-latest + needs: build + environment: + name: pypi + permissions: + id-token: write + contents: read + steps: + - name: Checkout + uses: actions/checkout@v5 + - name: Build and smoke test + uses: ./.github/actions/uv-build + with: + python-version: '3.12' + - name: Publish + run: uv publish \ No newline at end of file diff --git a/OVOW/third_party/RoMaV2/.gitignore b/OVOW/third_party/RoMaV2/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b0bb0f64a8b048a0988c25ea92d0bed295b655bb --- /dev/null +++ b/OVOW/third_party/RoMaV2/.gitignore @@ -0,0 +1,6 @@ +__pycache__/ +.vscode +.venv +*.pt +wandb/ +uv.lock \ No newline at end of file diff --git a/OVOW/third_party/RoMaV2/.python-version b/OVOW/third_party/RoMaV2/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..e4fba2183587225f216eeada4c78dfab6b2e65f5 --- /dev/null +++ b/OVOW/third_party/RoMaV2/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/OVOW/third_party/RoMaV2/LICENSE b/OVOW/third_party/RoMaV2/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..e5e8310db5b6aca14e0aa9d5cd89b763c3b84e23 --- /dev/null +++ b/OVOW/third_party/RoMaV2/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Johan Edstedt + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/OVOW/third_party/RoMaV2/README.md b/OVOW/third_party/RoMaV2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d5b695b307a1e5e5bd6dcc17bd08bbe292475664 --- /dev/null +++ b/OVOW/third_party/RoMaV2/README.md @@ -0,0 +1,134 @@ +

+

RoMa v2 🤖: Harder Better Faster Denser Feature Matching +

+ Johan Edstedt + · + David Nordström + · + Yushan Zhang + · + Georg Bökman + · + Jonathan Astermark + · + Viktor Larsson + · + Anders Heyden + · + Fredrik Kahl + · + Mårten Wadenbäck + · + Michael Felsberg +

+

+ Paper | + Project Page +

+
+

+
+

+ example +

+ +## How to Use +```python +from romav2 import RoMaV2 + +# load pretrained model +model = RoMaV2() +# Match densely for any image-like pair of inputs +preds = model.match(img_A_path, img_B_path) + +# you can also run the forward method directly as +# preds = model(img_A, img_B) + +# Sample 5000 matches for estimation +matches, overlaps, precision_AB, precision_BA = model.sample(preds, 5000) + +# Convert to pixel coordinates (RoMaV2 produces matches in [-1,1]x[-1,1]) +kptsA, kptsB = model.to_pixel_coordinates(matches, H_A, W_A, H_B, W_B) + +# Find a fundamental matrix (or anything else of interest) +F, mask = cv2.findFundamentalMat( + kptsA.cpu().numpy(), kptsB.cpu().numpy(), ransacReprojThreshold=0.2, method=cv2.USAC_MAGSAC, confidence=0.999999, maxIters=10000 +) +``` +We additionally provide two demos in the [demos folder](demo), which might help in understanding. + + +## Setup/Install +In your python environment (tested on Linux python 3.12), run: +```bash +uv pip install -e . +``` +or +```bash +uv sync +``` + +## Benchmarks +If you do not already have MegaDepth and ScanNet, you can the following to download them: +```bash +source scripts/eval_prep.sh +``` +### Mega-1500 +```bash +uv run tests/test_mega1500.py +``` +### ScanNet-1500 +```bash +uv run tests/test_scannet1500.py +``` +### Expected Results +Experiments on ScanNet-1500 and MegaDepth-1500 are provided in the [tests folder](tests). +Running these gave me `ScanNet-1500: [34.0, 56.5, 73.9]`, and `Mega-1500: [62.8, 76,8, 86.5]`, which are similar to the results of the paper. + + +## Fused local correlation kernel +Include the `--extra fused-local-corr` flag as: +```bash +uv sync --extra fused-local-corr +``` +or +```bash +uv pip install romav2[fused-local-corr] +``` +or +```bash +uv add romav2[fused-local-corr] +``` + +## Settings +By twiddling with some different settings you may reach better results on your task of interest. +Some important ones, which we enable setting to some reasonable defaults through `model.apply_setting`, are: + +`model.H_lr, model.W_lr`: height and width for the image pair. + +`model.H_hr, model.W_hr`: height and width for a high resolution version of the image pair (used for upsampling as in RoMa) + +`model.bidirectional`: Useful for getting more diverse matches, and for estimating the covariance matrix in both directions. + +`model.threshold`: Value between [0,1]. Used to set overlap prediction above it to 1. Useful for Mega1500. + +`model.balanced_sampling`: Diverse sampling, same as RoMa. Typically helps to get better RANSAC estimates. + +## License +All our code except DINOv3 is MIT license. +DINOv3 has a custom license, see [DINOv3](https://github.com/facebookresearch/dinov3/tree/main?tab=License-1-ov-file#readme). + +## Acknowledgement +Our codebase builds mainly on the code in [RoMa](https://github.com/Parskatt/RoMa). +We were additionally inspired by [UFM](https://github.com/UniFlowMatch/UFM) and [MapAnything](https://github.com/facebookresearch/map-anything), particularly for the datasets used to train the models. + +## BibTeX +If you find our models useful, please consider citing our paper! +``` +@article{edstedt2025romav2, + title={{RoMa v2: Harder Better Faster Denser Feature Matching}}, + author={Johan Edstedt, David Nordström, Yushan Zhang, Georg Bökman, Jonathan Astermark, Viktor Larsson, Anders Heyden, Fredrik Kahl, Mårten Wadenbäck, Michael Felsberg}, + journal={arXiv preprint arXiv:2511.15706}, + year={2025} +} +``` diff --git a/OVOW/third_party/RoMaV2/data/.gitignore b/OVOW/third_party/RoMaV2/data/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..c96a04f008ee21e260b28f7701595ed59e2839e3 --- /dev/null +++ b/OVOW/third_party/RoMaV2/data/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/OVOW/third_party/RoMaV2/demo/demo_covariance.py b/OVOW/third_party/RoMaV2/demo/demo_covariance.py new file mode 100644 index 0000000000000000000000000000000000000000..17db2c911389079c641c2ca756074598f8711594 --- /dev/null +++ b/OVOW/third_party/RoMaV2/demo/demo_covariance.py @@ -0,0 +1,52 @@ +from pathlib import Path + +import torch +from PIL import Image +from romav2.io import tensor_to_pil +from romav2.device import device +from romav2 import RoMaV2 + +if __name__ == "__main__": + from argparse import ArgumentParser + + parser = ArgumentParser() + parser.add_argument("--im_A_path", default="assets/toronto_A.jpg", type=str) + parser.add_argument("--im_B_path", default="assets/toronto_B.jpg", type=str) + parser.add_argument("--save_path", default="demo/roma_v2_std_toronto.jpg", type=str) + parser.add_argument("--std_max", default=8.0, type=float) + + args, _ = parser.parse_known_args() + im1_path = args.im_A_path + im2_path = args.im_B_path + save_path = args.save_path + + # Create model + + model = RoMaV2() + model.apply_setting("precise") + + H, W = model.H_hr, model.W_hr + + im1 = Image.open(im1_path).resize((W, H)) + im2 = Image.open(im2_path).resize((W, H)) + + # Match + preds = model.match(im1_path, im2_path) + warp_AB, overlap_AB = preds["warp_AB"][0], preds["overlap_AB"][0] + warp_BA, overlap_BA = preds["warp_BA"][0], preds["overlap_BA"][0] + precision_AB, precision_BA = ( + preds["precision_AB"][0], + preds["precision_BA"][0], + ) + + std_AB = torch.linalg.det(precision_AB) ** (-1 / 4) + std_BA = torch.linalg.det(precision_BA) ** (-1 / 4) + + std_im = torch.cat((std_AB, std_BA), dim=1) + overlap = torch.cat((overlap_AB, overlap_BA), dim=1)[..., 0] + white_im = torch.ones((H, 2 * W), device=device) + std_im = (std_im / args.std_max).clamp(0, 1) + vis_im = overlap * std_im + (1 - overlap) * white_im + if not Path(save_path).exists(): + Path(save_path).parent.mkdir(parents=True, exist_ok=True) + tensor_to_pil(vis_im).save(save_path) diff --git a/OVOW/third_party/RoMaV2/demo/demo_match.py b/OVOW/third_party/RoMaV2/demo/demo_match.py new file mode 100644 index 0000000000000000000000000000000000000000..c79b9651916f8c4b9bba82af212464b391c34b55 --- /dev/null +++ b/OVOW/third_party/RoMaV2/demo/demo_match.py @@ -0,0 +1,55 @@ +from pathlib import Path + +import torch +from PIL import Image +import torch.nn.functional as F +import numpy as np +from romav2.io import tensor_to_pil +from romav2.device import device +from romav2 import RoMaV2 + +if __name__ == "__main__": + from argparse import ArgumentParser + + parser = ArgumentParser() + parser.add_argument("--im_A_path", default="assets/toronto_A.jpg", type=str) + parser.add_argument("--im_B_path", default="assets/toronto_B.jpg", type=str) + parser.add_argument( + "--save_path", default="demo/roma_v2_warp_toronto.jpg", type=str + ) + + args, _ = parser.parse_known_args() + im1_path = args.im_A_path + im2_path = args.im_B_path + save_path = args.save_path + + # Create model + + model = RoMaV2() + model.apply_setting("precise") + H, W = (model.H_lr, model.W_lr) if (model.H_hr is None or model.W_hr is None) else (model.H_hr, model.W_hr) + im1 = Image.open(im1_path).resize((W, H)) + im2 = Image.open(im2_path).resize((W, H)) + + # Match + preds = model.match(im1_path, im2_path) + warp_AB, overlap_AB = preds["warp_AB"][0], preds["overlap_AB"][0] + warp_BA, overlap_BA = preds["warp_BA"][0], preds["overlap_BA"][0] + # Sampling not needed, but can be done with model.sample(warp, certainty) + + x1 = (torch.tensor(np.array(im1)) / 255).to(device).permute(2, 0, 1) + x2 = (torch.tensor(np.array(im2)) / 255).to(device).permute(2, 0, 1) + + im2_transfer_rgb = F.grid_sample( + x2[None], warp_AB[None], mode="bilinear", align_corners=False + )[0] + im1_transfer_rgb = F.grid_sample( + x1[None], warp_BA[None], mode="bilinear", align_corners=False + )[0] + warp_im = torch.cat((im2_transfer_rgb, im1_transfer_rgb), dim=2) + overlap = torch.cat((overlap_AB, overlap_BA), dim=1)[..., 0] + white_im = torch.ones((H, 2 * W), device=device) + vis_im = overlap * warp_im + (1 - overlap) * white_im + if not Path(save_path).exists(): + Path(save_path).parent.mkdir(parents=True, exist_ok=True) + tensor_to_pil(vis_im).save(save_path) diff --git a/OVOW/third_party/RoMaV2/pyproject.toml b/OVOW/third_party/RoMaV2/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..baf33e7f183448084a85f9c018003392506c8e22 --- /dev/null +++ b/OVOW/third_party/RoMaV2/pyproject.toml @@ -0,0 +1,35 @@ +[project] +name = "romav2" +version = "2.0.1" +description = "RoMa v2: Harder Better Faster Denser Feature Matching" +readme = "README.md" +authors = [ + { name = "Johan Edstedt", email = "johan.edstedt@liu.se" } +] +requires-python = ">=3.10" +dependencies = [ + "einops>=0.8.1", + "pillow>=12.0.0", + "rich>=14.2.0", + "torch", + "torchvision>=0.23.0", + "tqdm>=4.67.1", + "fused-local-corr ; sys_platform == 'linux'", +] + +[build-system] +requires = ["uv_build>=0.8.15,<0.9.0"] +build-backend = "uv_build" + +[project.optional-dependencies] +eval = [ + "kornia>=0.8.2", + "matplotlib>=3.10.7", + "opencv-python>=4.12.0.88", + "wandb>=0.23.0", + "wxbs-benchmark>=0.0.4", +] +dev = [ + "slurm-util>=0.2.7", + "ruff>=0.14.5", +] diff --git a/OVOW/third_party/RoMaV2/scripts/eval_prep.sh b/OVOW/third_party/RoMaV2/scripts/eval_prep.sh new file mode 100644 index 0000000000000000000000000000000000000000..d81bedbad3dc1f7c962d5da22cd117e77cacb0f3 --- /dev/null +++ b/OVOW/third_party/RoMaV2/scripts/eval_prep.sh @@ -0,0 +1,11 @@ +mkdir -p data/megadepth/ +mkdir -p data/scannet/scans/ +wget https://github.com/Parskatt/storage/releases/download/mega1500/0015_0.1_0.3.npz && mv 0015_0.1_0.3.npz data/megadepth/ +wget https://github.com/Parskatt/storage/releases/download/mega1500/0015_0.3_0.5.npz && mv 0015_0.3_0.5.npz data/megadepth/ +wget https://github.com/Parskatt/storage/releases/download/mega1500/0022_0.1_0.3.npz && mv 0022_0.1_0.3.npz data/megadepth/ +wget https://github.com/Parskatt/storage/releases/download/mega1500/0022_0.3_0.5.npz && mv 0022_0.3_0.5.npz data/megadepth/ +wget https://github.com/Parskatt/storage/releases/download/mega1500/0022_0.5_0.7.npz && mv 0022_0.5_0.7.npz data/megadepth/ +wget https://github.com/Parskatt/storage/releases/download/mega1500/megadepth_test_1500.tar && tar -xvf megadepth_test_1500.tar && mv megadepth_test_1500/Undistorted_SfM data/megadepth/ && rmdir megadepth_test_1500 && rm megadepth_test_1500.tar + +wget https://github.com/Parskatt/storage/releases/download/scannet1500/test.npz && mv test.npz data/scannet/scans/ +wget https://github.com/Parskatt/storage/releases/download/scannet1500/scannet_test_1500.tar && tar -xvf scannet_test_1500.tar && mv scannet_test_1500 data/scannet/scans/ && mv data/scannet/scans/scannet_test_1500 data/scannet/scans/scans_test && rm scannet_test_1500.tar \ No newline at end of file diff --git a/OVOW/third_party/RoMaV2/src/romav2/__init__.py b/OVOW/third_party/RoMaV2/src/romav2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5a8eb95a46e14728388df732de643c017b771147 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/__init__.py @@ -0,0 +1,8 @@ +import logging as _logging +from .logging import configure_logger as configure_logger +from .logging import logger as _logger + +if not any(not isinstance(h, _logging.NullHandler) for h in _logger.handlers): + configure_logger() + +from .romav2 import RoMaV2 as RoMaV2 diff --git a/OVOW/third_party/RoMaV2/src/romav2/benchmarks/__init__.py b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..08acd178b992dd518dc692622b73963953187c24 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/__init__.py @@ -0,0 +1,4 @@ +from .scannet1500 import ScanNet1500 as ScanNet1500 +from .mega1500 import Mega1500 as Mega1500 +from .wxbs import WxBSBenchmark as WxBSBenchmark +from .satast import SatAst as SatAst diff --git a/OVOW/third_party/RoMaV2/src/romav2/benchmarks/mega1500.py b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/mega1500.py new file mode 100644 index 0000000000000000000000000000000000000000..8ddbea0878545fd5a37bda8dbedeeded37212068 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/mega1500.py @@ -0,0 +1,115 @@ +import numpy as np +import logging +from romav2.geometry import ( + compute_pose_error, + pose_auc, + estimate_pose_cv2_ransac, + compute_relative_pose, +) +from PIL import Image +from tqdm import tqdm + +logger = logging.getLogger(__name__) + + +class Mega1500: + def __init__(self, data_root="data/megadepth") -> None: + self.scene_names = [ + "0015_0.1_0.3.npz", + "0015_0.3_0.5.npz", + "0022_0.1_0.3.npz", + "0022_0.3_0.5.npz", + "0022_0.5_0.7.npz", + ] + self.scenes = [ + np.load(f"{data_root}/{scene}", allow_pickle=True) + for scene in self.scene_names + ] + self.data_root = data_root + + def benchmark(self, model, model_name=None): + data_root = self.data_root + tot_e_t, tot_e_R, tot_e_pose = [], [], [] + thresholds = [5, 10, 20] + for scene_ind in range(len(self.scenes)): + scene = self.scenes[scene_ind] + pairs = scene["pair_infos"] + intrinsics = scene["intrinsics"] + poses = scene["poses"] + im_paths = scene["image_paths"] + pair_inds = range(len(pairs)) + for pairind in (pbar := tqdm(pair_inds, desc="Mega1500 eval")): + idx1, idx2 = pairs[pairind][0] + K1 = intrinsics[idx1].copy() + T1 = poses[idx1].copy() + R1, t1 = T1[:3, :3], T1[:3, 3] + K2 = intrinsics[idx2].copy() + T2 = poses[idx2].copy() + R2, t2 = T2[:3, :3], T2[:3, 3] + R, t = compute_relative_pose(R1, t1, R2, t2) + im_A_path = f"{data_root}/{im_paths[idx1]}" + im_B_path = f"{data_root}/{im_paths[idx2]}" + preds = model.match(im_A_path, im_B_path) + im_A = Image.open(im_A_path) + w1, h1 = im_A.size + im_B = Image.open(im_B_path) + w2, h2 = im_B.size + if True: # Note: we keep this true as it was used in DKM/RoMa papers. There is very little difference compared to setting to False. + scale1 = 1200 / max(w1, h1) + scale2 = 1200 / max(w2, h2) + w1, h1 = scale1 * w1, scale1 * h1 + w2, h2 = scale2 * w2, scale2 * h2 + K1, K2 = K1.copy(), K2.copy() + K1[:2] = K1[:2] * scale1 + K2[:2] = K2[:2] * scale2 + for _ in range(5): + matches, _, _, _ = model.sample(preds, 5_000) + kpts1, kpts2 = model.to_pixel_coordinates(matches, h1, w1, h2, w2) + kpts1, kpts2 = kpts1.cpu().numpy(), kpts2.cpu().numpy() + shuffling = np.random.permutation(np.arange(len(kpts1))) + kpts1 = kpts1[shuffling] + kpts2 = kpts2[shuffling] + try: + threshold = 0.5 + norm_threshold = threshold / ( + np.mean(np.abs(K1[:2, :2])) + np.mean(np.abs(K2[:2, :2])) + ) + R_est, t_est, _ = estimate_pose_cv2_ransac( + kpts1, + kpts2, + K1, + K2, + norm_threshold, + conf=0.99999, + ) + e_t, e_R = compute_pose_error(R_est, t_est[:, 0], R, t) + e_pose = max(e_t, e_R) + except Exception as e: + logger.debug(f"Pose estimation error: {e}") + e_t, e_R = 90, 90 + e_pose = max(e_t, e_R) + tot_e_t.append(e_t) + tot_e_R.append(e_R) + tot_e_pose.append(e_pose) + pbar.set_postfix( + auc=f"{[f'{a.item():.3f}' for a in pose_auc(tot_e_pose, thresholds)]}" + ) + + tot_e_pose = np.array(tot_e_pose) + auc = pose_auc(tot_e_pose, thresholds) + acc_5 = (tot_e_pose < 5).mean() + acc_10 = (tot_e_pose < 10).mean() + acc_15 = (tot_e_pose < 15).mean() + acc_20 = (tot_e_pose < 20).mean() + map_5 = acc_5 + map_10 = np.mean([acc_5, acc_10]) + map_20 = np.mean([acc_5, acc_10, acc_15, acc_20]) + logger.info("%s auc: %s", model_name, auc) + return { + "auc_5": auc[0], + "auc_10": auc[1], + "auc_20": auc[2], + "map_5": map_5, + "map_10": map_10, + "map_20": map_20, + } diff --git a/OVOW/third_party/RoMaV2/src/romav2/benchmarks/satast.py b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/satast.py new file mode 100644 index 0000000000000000000000000000000000000000..c780b769419b671933aaae6a0e6530fd4843a7eb --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/satast.py @@ -0,0 +1,463 @@ +import os +import logging +import torch +import tempfile +import json +import numpy as np +import cv2 +from PIL import Image +from tqdm import tqdm +from romav2.geometry import pose_auc +import matplotlib.pyplot as plt + +OFFSET = 0.5 + +logger = logging.getLogger(__name__) + + +class SatAst: + """ + A benchmark class to evaluate image matchers using a folder of JSON files. + + The final metric is the Area Under the Curve (AUC) of the cumulative + distribution of *all* individual errors, calculated at different + pixel thresholds. + + Args: + json_folder_path (str): Path to the directory containing the .json files. + image_dataset_root (str): The root directory to prepend to the + image paths (query_path, pred_path) + found inside the JSON files. + """ + + def __init__( + self, + json_folder_path="data/satast/annotations-v1", + image_dataset_root="data/satast", + ) -> None: + self.json_folder_path = json_folder_path + self.image_dataset_root = image_dataset_root + + try: + self.json_files = sorted( + [f for f in os.listdir(json_folder_path) if f.endswith(".json")] + ) + if not self.json_files: + logger.warning("No .json files found in %s", json_folder_path) + except FileNotFoundError: + logger.error("Directory not found: %s", json_folder_path) + self.json_files = [] + + logger.info("Found %d JSON files.", len(self.json_files)) + + def _pixel_to_normalized(self, pts_pix, w, h, offset=OFFSET): + """ + Converts pixel coordinates [0, n-1] to normalized [-1, 1]. + """ + pts_norm = np.zeros_like(pts_pix, dtype=np.float64) + pts_norm[..., 0] = (2.0 * (pts_pix[..., 0] + offset) / w) - 1.0 + pts_norm[..., 1] = (2.0 * (pts_pix[..., 1] + offset) / h) - 1.0 + return pts_norm + + def _normalized_to_pixel(self, pts_norm, w, h, offset=OFFSET): + """ + Converts normalized coordinates [-1, 1] to pixel [0, n-1]. + """ + pts_pix = np.zeros_like(pts_norm, dtype=np.float64) + pts_pix[..., 0] = (w * (pts_norm[..., 0] + 1.0) / 2.0) - offset + pts_pix[..., 1] = (h * (pts_norm[..., 1] + 1.0) / 2.0) - offset + return pts_pix + + def benchmark(self, model, model_name=None, visualize=False, num_visualize=10): + """ + Runs the full benchmark on the provided model. + This version benchmarks correspondences through an estimated homography. + + Args: + model: The matcher model object. Must have a 'match(im_A_path, im_B_path)' + method and a 'sample(matches, certainty, num_samples)' method. + Assumed to return matches in normalized [-1, 1] coordinates. + model_name (str, optional): A name for the model. + visualize (bool, optional): If True, plots the first `num_visualize` + successful homography estimations. + num_visualize (int, optional): The maximum number of pairs to visualize. + + Returns: + dict: A dictionary containing the AUC scores. + """ + # This list will store *all* individual reprojection errors + all_reprojection_errors = [] + viz_counter = 0 + temp_file = tempfile.NamedTemporaryFile(suffix=".jpeg", delete=False) + im_B_path = temp_file.name + + for json_name in tqdm(self.json_files, desc="Running Benchmark"): + json_path = os.path.join(self.json_folder_path, json_name) + + # 1. Load JSON and Image Paths + with open(json_path, "r") as f: + json_data = json.load(f) + + im_A_path_rel = json_data["query_path"].replace("\\", "/") + im_B_path_rel = json_data["pred_path"].replace("\\", "/") + + im_A_path = os.path.join(self.image_dataset_root, im_A_path_rel) + im_B_path0 = os.path.join(self.image_dataset_root, im_B_path_rel) + + im_A = Image.open(im_A_path) + w1, h1 = im_A.size + im_B0 = Image.open(im_B_path0) + # if "__rot0__" not in im_B_path0: + # continue + w2, h2 = im_B0.size + + for rot_idx in range(1): + im_B0 = Image.open(im_B_path0) + + if rot_idx == 0: + rotated_im_B = im_B0 + elif rot_idx == 1: + rotated_im_B = im_B0.transpose(method=Image.Transpose.ROTATE_90) + elif rot_idx == 2: + rotated_im_B = im_B0.transpose(method=Image.Transpose.ROTATE_180) + elif rot_idx == 3: + rotated_im_B = im_B0.transpose(method=Image.Transpose.ROTATE_270) + rotated_im_B.save(im_B_path, format="JPEG") + + # 2. Run the matcher model + dense_preds = model.match(im_A_path, im_B_path) + if isinstance(dense_preds, dict): + sparse_preds = model.sample(dense_preds, 10_000) + else: + sparse_preds = model.sample(*dense_preds, 10_000) + good_matches = sparse_preds[0] + pos_a_norm = good_matches[:, :2] + pos_b_norm = good_matches[:, 2:] + + # Rotate back + if rot_idx == 1: + pos_b_norm = torch.stack( + [-pos_b_norm[:, 1], pos_b_norm[:, 0]], dim=1 + ) + elif rot_idx == 2: + pos_b_norm = -pos_b_norm + elif rot_idx == 3: + pos_b_norm = torch.stack( + [pos_b_norm[:, 1], -pos_b_norm[:, 0]], dim=1 + ) + + # 3. Estimate Homography in Normalized Coordinates + H_pred_norm = None + if len(pos_a_norm) >= 4: + try: + norm_thresh = 0.001 + + H_pred_norm, inliers = cv2.findHomography( + pos_a_norm.cpu().numpy(), + pos_b_norm.cpu().numpy(), + method=cv2.USAC_DEFAULT, + confidence=0.99999999, + maxIters=100_000, + ransacReprojThreshold=norm_thresh, + ) + except cv2.error as e: + print(e) + H_pred_norm = None + + # 4. Get Ground Truth Correspondences (Pixel) + last_iteration_corrs = json_data["correspondences"][-1] + pts_src_pix = np.array(last_iteration_corrs["pts_src"]) + pts_dst_pix = np.array(last_iteration_corrs["pts_dst"]) + + if pts_src_pix.shape[0] == 0: + continue # No GT points, skip this pair + + if H_pred_norm is None: + # If RANSAC fails, append 'inf' for all GT points + all_reprojection_errors.extend( + [float("inf")] * pts_src_pix.shape[0] + ) + continue + + if visualize and viz_counter < num_visualize: + logger.info("Visualizing pair: %s", json_name) + + # 1. Create pixel-to-norm (M1) and norm-to-pixel (M2_inv) matrices + + # M1: P1 -> N1 + M1 = np.array( + [ + [2.0 / w1, 0, (2.0 * OFFSET / w1) - 1.0], + [0, 2.0 / h1, (2.0 * OFFSET / h1) - 1.0], + [0, 0, 1.0], + ], + dtype=np.float64, + ) + + # M2_inv: N2 -> P2 + M2_inv = np.array( + [ + [w2 / 2.0, 0, w2 / 2.0 - OFFSET], + [0, h2 / 2.0, h2 / 2.0 - OFFSET], + [0, 0, 1.0], + ], + dtype=np.float64, + ) + + # 2. Convert H_pred_norm (N1 -> N2) to H_pix (P1 -> P2) + H_pix = M2_inv @ H_pred_norm @ M1 + + # 3. Invert H_pix to get (P2 -> P1) for warping Image 2 to 1 + try: + H_pix_inv = np.linalg.inv(H_pix) + except np.linalg.LinAlgError: + logger.warning( + "Could not invert homography for %s. Skipping visualization.", + json_name, + ) + continue # Skip this viz + + # --- NEW: Extract and sample inliers for visualization --- + plot_pts_a = None + plot_pts_b = None + plot_colors = None + + # Get inlier points from the RANSAC + inliers_mask = inliers.flatten().astype(bool) + inlier_pts_a_norm = pos_a_norm.cpu().numpy()[inliers_mask] + inlier_pts_b_norm = pos_b_norm.cpu().numpy()[inliers_mask] + num_inliers = inlier_pts_a_norm.shape[0] + + if num_inliers > 0: + # Sample up to 10 inliers + num_to_sample = min(num_inliers, 10) + indices = np.random.choice( + num_inliers, num_to_sample, replace=False + ) + + sampled_pts_a_norm = inlier_pts_a_norm[indices] + sampled_pts_b_norm = inlier_pts_b_norm[indices] + + # Convert to pixel coordinates for plotting + plot_pts_a = self._normalized_to_pixel( + sampled_pts_a_norm, w1, h1 + ) + plot_pts_b = self._normalized_to_pixel( + sampled_pts_b_norm, w2, h2 + ) + + # Generate random colors for each pair + plot_colors = np.random.rand(num_to_sample, 3) + + # 4. Load images with OpenCV for warping + img1_cv = cv2.imread(im_A_path) # Target + img2_cv = cv2.imread(im_B_path0) # Source + + # 5. Warp image 2 to image 1's frame + warped_img2 = cv2.warpPerspective(img2_cv, H_pix_inv, (w1, h1)) + + # 6. Plot + img1_rgb = cv2.cvtColor(img1_cv, cv2.COLOR_BGR2RGB) + img2_rgb = cv2.cvtColor(img2_cv, cv2.COLOR_BGR2RGB) + warped_img2_rgb = cv2.cvtColor(warped_img2, cv2.COLOR_BGR2RGB) + + fig, axes = plt.subplots(1, 3, figsize=(21, 7)) + fig.suptitle( + f"Homography Visualization: {os.path.basename(im_B_path0)} -> {os.path.basename(im_A_path)}", + fontsize=16, + ) + + axes[0].imshow(img1_rgb) + axes[0].set_title("Image 1 (Target)") + axes[0].axis("off") + if plot_pts_a is not None: + axes[0].scatter( + plot_pts_a[:, 0], + plot_pts_a[:, 1], + s=40, + c=plot_colors, + marker="x", + ) + + axes[1].imshow(img2_rgb) + axes[1].set_title("Image 2 (Source)") + axes[1].axis("off") + if plot_pts_b is not None: + axes[1].scatter( + plot_pts_b[:, 0], + plot_pts_b[:, 1], + s=40, + c=plot_colors, + marker="x", + ) + + axes[2].imshow(warped_img2_rgb) + axes[2].set_title("Image 2 Warped to Image 1") + axes[2].axis("off") + + plt.tight_layout() + plt.show() + + viz_counter += 1 + + # 5. Normalize GT Source Points + pts_src_norm = self._pixel_to_normalized(pts_src_pix, w1, h1) + + # 6. Warp Normalized GT Points + pts_src_norm_h = np.hstack( + (pts_src_norm, np.ones((pts_src_norm.shape[0], 1))) + ) + warped_pts_norm_h = np.dot(pts_src_norm_h, H_pred_norm.T) + + # De-homogenize + epsilon = 1e-8 + warped_pts_norm = warped_pts_norm_h[:, :2] / ( + warped_pts_norm_h[:, 2, np.newaxis] + epsilon + ) + + # 7. Convert Warped Points back to Pixel Coords + warped_pts_pix = self._normalized_to_pixel(warped_pts_norm, w2, h2) + + # 8. Calculate and Store All Errors + errors = np.linalg.norm(warped_pts_pix - pts_dst_pix, axis=1) + all_reprojection_errors.extend(errors) + + temp_file.close() + + # 9. Compute AUC over *all* errors + thresholds = np.arange(1, 31) + auc = pose_auc(np.array(all_reprojection_errors), thresholds) + logger.info("=== AUC OF ERRORS ===") + logger.info("%s", auc) + + results = { + "reprojection_auc_5px": auc[4], + "reprojection_auc_10px": auc[9], + "reprojection_auc_20px": auc[19], + "reprojection_auc_30px": auc[29], + } + logger.info("=== MAIN RESULTS ===") + logger.info("%s", results) + + return results + + def benchmark_warp(self, model, model_name=None, visualize=False, num_visualize=10): + """ + Runs the full benchmark on the provided model. + This version benchmarks correspondences through an estimated dense warp. + + Args: + model: The matcher model object. Must have a 'match(im_A_path, im_B_path)' + method and a 'sample(matches, certainty, num_samples)' method. + Assumed to return matches in normalized [-1, 1] coordinates. + model_name (str, optional): A name for the model. + visualize (bool, optional): If True, plots the first `num_visualize` + successful homography estimations. + num_visualize (int, optional): The maximum number of pairs to visualize. + + Returns: + dict: A dictionary containing the AUC scores. + """ + # This list will store *all* individual reprojection errors + all_reprojection_errors = [] + temp_file = tempfile.NamedTemporaryFile(suffix=".jpeg", delete=False) + im_B_path = temp_file.name + + for json_name in tqdm(self.json_files, desc="Running Benchmark"): + json_path = os.path.join(self.json_folder_path, json_name) + + # 1. Load JSON and Image Paths + with open(json_path, "r") as f: + json_data = json.load(f) + + im_A_path_rel = json_data["query_path"].replace("\\", "/") + im_B_path_rel = json_data["pred_path"].replace("\\", "/") + + im_A_path = os.path.join(self.image_dataset_root, im_A_path_rel) + im_B_path0 = os.path.join(self.image_dataset_root, im_B_path_rel) + + im_A = Image.open(im_A_path) + w1, h1 = im_A.size + im_B0 = Image.open(im_B_path0) + w2, h2 = im_B0.size + + for rot_idx in range(4): + im_B0 = Image.open(im_B_path0) + + if rot_idx == 0: + rotated_im_B = im_B0 + elif rot_idx == 1: + rotated_im_B = im_B0.transpose(method=Image.Transpose.ROTATE_90) + elif rot_idx == 2: + rotated_im_B = im_B0.transpose(method=Image.Transpose.ROTATE_180) + elif rot_idx == 3: + rotated_im_B = im_B0.transpose(method=Image.Transpose.ROTATE_270) + rotated_im_B.save(im_B_path, format="JPEG") + + # 2. Run the matcher model + preds = model.match(im_A_path, im_B_path) + warp = preds["warp_AB"] + + # 3. Get Ground Truth Correspondences (Pixel) + last_iteration_corrs = json_data["correspondences"][-1] + pts_src_pix = np.array(last_iteration_corrs["pts_src"]) + pts_dst_pix = np.array(last_iteration_corrs["pts_dst"]) + + if pts_src_pix.shape[0] == 0: + continue # No GT points, skip this pair + + # 5. Normalize GT Source Points + pts_src_norm = torch.tensor( + self._pixel_to_normalized(pts_src_pix, w1, h1), + device=warp.device, + dtype=warp.dtype, + ) + + # 6. Warp Normalized GT Points + warp_src_to_pred = warp[0, :, : warp.shape[2] // 2, -2:] + warped_pts_norm = torch.nn.functional.grid_sample( + warp_src_to_pred.permute(2, 0, 1)[None], + pts_src_norm[None, None], + align_corners=False, + mode="bilinear", + )[0, :, 0].mT + + # Rotate back + if rot_idx == 1: + warped_pts_norm = torch.stack( + [-warped_pts_norm[:, 1], warped_pts_norm[:, 0]], dim=1 + ) + elif rot_idx == 2: + warped_pts_norm = -warped_pts_norm + elif rot_idx == 3: + warped_pts_norm = torch.stack( + [warped_pts_norm[:, 1], -warped_pts_norm[:, 0]], dim=1 + ) + + # 7. Convert Warped Points back to Pixel Coords + warped_pts_pix = self._normalized_to_pixel( + warped_pts_norm.cpu().numpy(), w2, h2 + ) + + # 8. Calculate and Store All Errors + errors = np.linalg.norm(warped_pts_pix - pts_dst_pix, axis=1) + all_reprojection_errors.extend(errors) + + temp_file.close() + + # 9. Compute AUC over *all* errors + thresholds = np.arange(1, 31) + auc = pose_auc(np.array(all_reprojection_errors), thresholds) + logger.info("=== AUC OF ERRORS ===") + logger.info("%s", auc) + + results = { + "reprojection_auc_5px": auc[4], + "reprojection_auc_10px": auc[9], + "reprojection_auc_20px": auc[19], + "reprojection_auc_30px": auc[29], + } + logger.info("=== MAIN RESULTS ===") + logger.info("%s", results) + + return results diff --git a/OVOW/third_party/RoMaV2/src/romav2/benchmarks/scannet1500.py b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/scannet1500.py new file mode 100644 index 0000000000000000000000000000000000000000..e5fc0902763f6531de2c4f12cd680738921c8965 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/scannet1500.py @@ -0,0 +1,125 @@ +import os.path as osp +import logging +import numpy as np +from romav2.geometry import compute_pose_error, pose_auc, estimate_pose_cv2_ransac +from PIL import Image +from tqdm import tqdm + +logger = logging.getLogger(__name__) + + +class ScanNet1500: + def __init__(self, data_root="data/scannet/scans") -> None: + self.data_root = data_root + + def benchmark(self, model, model_name=None): + model.train(False) + thresholds = [5, 10, 20] + data_root = self.data_root + tmp = np.load(osp.join(data_root, "test.npz")) + pairs, rel_pose = tmp["name"], tmp["rel_pose"] + tot_e_t, tot_e_R, tot_e_pose = [], [], [] + pair_inds = range(len(pairs)) + for pairind in (pbar := tqdm(pair_inds, smoothing=0.9)): + scene = pairs[pairind] + scene_name = f"scene0{scene[0]}_00" + im_A_path = osp.join( + self.data_root, + "scans_test", + scene_name, + "color", + f"{scene[2]}.jpg", + ) + im_A = Image.open(im_A_path) + im_B_path = osp.join( + self.data_root, + "scans_test", + scene_name, + "color", + f"{scene[3]}.jpg", + ) + im_B = Image.open(im_B_path) + T_gt = rel_pose[pairind].reshape(3, 4) + R, t = T_gt[:3, :3], T_gt[:3, 3] + K = np.stack( + [ + np.array([float(i) for i in r.split()]) + for r in open( + osp.join( + self.data_root, + "scans_test", + scene_name, + "intrinsic", + "intrinsic_color.txt", + ), + "r", + ) + .read() + .split("\n") + if r + ] + ) + w1, h1 = im_A.size + w2, h2 = im_B.size + preds = model.match(im_A_path, im_B_path) + sparse_matches, _, _, _ = model.sample(preds, 5000) + scale1 = 480 / min(w1, h1) + scale2 = 480 / min(w2, h2) + w1, h1 = scale1 * w1, scale1 * h1 + w2, h2 = scale2 * w2, scale2 * h2 + K1 = K.copy() + K2 = K.copy() + K1[:2] = K1[:2] * scale1 + K2[:2] = K2[:2] * scale2 + + offset = 0.5 + kpts1, kpts2 = model.to_pixel_coordinates(sparse_matches, h1, w1, h2, w2) + kpts1, kpts2 = kpts1.cpu().numpy(), kpts2.cpu().numpy() + kpts1 = kpts1 - offset + kpts2 = kpts2 - offset + + for _ in range(5): + shuffling = np.random.permutation(np.arange(len(kpts1))) + kpts1 = kpts1[shuffling] + kpts2 = kpts2[shuffling] + try: + norm_threshold = 0.5 / ( + np.mean(np.abs(K1[:2, :2])) + np.mean(np.abs(K2[:2, :2])) + ) + R_est, t_est, mask = estimate_pose_cv2_ransac( + kpts1, + kpts2, + K1, + K2, + norm_threshold, + conf=0.99999, + ) + e_t, e_R = compute_pose_error(R_est, t_est[:, 0], R, t) + e_pose = max(e_t, e_R) + except Exception as e: + logger.debug(f"Pose estimation error: {e}") + e_t, e_R = 90, 90 + e_pose = max(e_t, e_R) + tot_e_t.append(e_t) + tot_e_R.append(e_R) + tot_e_pose.append(e_pose) + pbar.set_postfix( + auc=f"{[f'{a.item():.3f}' for a in pose_auc(tot_e_pose, thresholds)]}" + ) + tot_e_pose = np.array(tot_e_pose) + auc = pose_auc(tot_e_pose, thresholds) + acc_5 = (tot_e_pose < 5).mean() + acc_10 = (tot_e_pose < 10).mean() + acc_15 = (tot_e_pose < 15).mean() + acc_20 = (tot_e_pose < 20).mean() + map_5 = acc_5 + map_10 = np.mean([acc_5, acc_10]) + map_20 = np.mean([acc_5, acc_10, acc_15, acc_20]) + return { + "auc_5": auc[0], + "auc_10": auc[1], + "auc_20": auc[2], + "map_5": map_5, + "map_10": map_10, + "map_20": map_20, + } diff --git a/OVOW/third_party/RoMaV2/src/romav2/benchmarks/wxbs.py b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/wxbs.py new file mode 100644 index 0000000000000000000000000000000000000000..a6e83742bbb12ad3985ff77e97218faef1ae373d --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/benchmarks/wxbs.py @@ -0,0 +1,104 @@ +from dataclasses import dataclass + +import torch + +from tqdm import tqdm +from wxbs_benchmark.dataset import WxBSDataset +from wxbs_benchmark.evaluation import evaluate_corrs + +from romav2.romav2 import RoMaV2 +from romav2.io import tensor_to_pil +from romav2.device import device +from pathlib import Path +from romav2.geometry import to_normalized, to_pixel, bhwc_grid_sample +from romav2.vis import vis + + +class WxBSBenchmark: + @dataclass(frozen=True) + class Cfg: + subset: str = "test" + dataset_path: str = "data/WxBS" + download: bool = True + + def __init__(self, cfg: Cfg): + self.subset = cfg.subset + WxBSDataset.urls["v1.1"][0] = ( + "https://github.com/Parskatt/storage/releases/download/wxbs/WxBS_v1.1.zip" + ) + + def wrap(f): + def __getitem__(self, idx): + out = f(self, idx) + return { + **out, + "imgfname1": self.pairs[idx][0], + "imgfname2": self.pairs[idx][1], + } + + return __getitem__ + + WxBSDataset.__getitem__ = wrap(WxBSDataset.__getitem__) + self.dataset = WxBSDataset( + cfg.dataset_path, subset=self.subset, download=cfg.download + ) + + def __call__(self, model: RoMaV2, step: int): + estimated_right = [] + estimated_left = [] + for pair_dict in tqdm(self.dataset): + H_A, W_A = pair_dict["img1"].shape[:2] + H_B, W_B = pair_dict["img2"].shape[:2] + points_left = torch.from_numpy(pair_dict["pts"][:, :2]).to(device).float() + points_right = torch.from_numpy(pair_dict["pts"][:, 2:4]).to(device).float() + n_points_left = to_normalized(points_left, H=H_A, W=W_A) + n_points_right = to_normalized(points_right, H=H_B, W=W_B) + if model.name.lower() == "roma": + preds: tuple[torch.Tensor, torch.Tensor] = model.match( + pair_dict["imgfname1"], pair_dict["imgfname2"] + ) # type: ignore + warp_bidirectional = preds[0] + W_pred = warp_bidirectional.shape[2] + warp_AB = warp_bidirectional[:, :, : W_pred // 2, 2:] + warp_BA = warp_bidirectional[:, :, W_pred // 2 :, :2] + overlap_AB = preds[1][:, :, : W_pred // 2] + overlap_BA = preds[1][:, :, W_pred // 2 :] + else: + preds = model.match(pair_dict["img1"], pair_dict["img2"]) + warp_AB = preds["warp_AB"] + warp_BA = preds["warp_BA"] + overlap_AB = preds["overlap_AB"] + overlap_BA = preds["overlap_BA"] + n_est_points_right = bhwc_grid_sample( + warp_AB, + n_points_left[None, None], + mode="bilinear", + align_corners=False, + ) + n_est_points_left = bhwc_grid_sample( + warp_BA, + n_points_right[None, None], + mode="bilinear", + align_corners=False, + ) + est_points_right = to_pixel(n_est_points_right, H=H_B, W=W_B) + est_points_left = to_pixel(n_est_points_left, H=H_A, W=W_A) + # model.to_pixel_coordinates(preds["warp_AB"], H_A, W_A, H_B, W_B) + vis_im_path = Path(f"vis/wxbs_{model.name}_{pair_dict['name']}.jpg") + if not vis_im_path.exists(): + vis_im = vis( + pair_dict["img1"], + pair_dict["img2"], + warp_AB, + warp_BA, + overlap_AB, + overlap_BA, + ) + vis_im_path.parent.mkdir(parents=True, exist_ok=True) + tensor_to_pil(vis_im).save(vis_im_path) + estimated_right.append(est_points_right.cpu().numpy()[0, 0]) + estimated_left.append(est_points_left.cpu().numpy()[0, 0]) + result_dict, thresholds = evaluate_corrs( + estimated_right, estimated_left, self.subset + ) + return result_dict, thresholds diff --git a/OVOW/third_party/RoMaV2/src/romav2/device.py b/OVOW/third_party/RoMaV2/src/romav2/device.py new file mode 100644 index 0000000000000000000000000000000000000000..41d26cd1641d819e2ef4a52e2077df245bd7b5fd --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/device.py @@ -0,0 +1,9 @@ +import torch + +device = torch.device("cpu") +if torch.cuda.is_available(): + device = torch.device("cuda") +elif torch.backends.mps.is_available(): + device = torch.device("mps") +else: + device = torch.device("cpu") diff --git a/OVOW/third_party/RoMaV2/src/romav2/dpt.py b/OVOW/third_party/RoMaV2/src/romav2/dpt.py new file mode 100644 index 0000000000000000000000000000000000000000..6c07cbb8eb7cfdfc999773749a63495e949234f2 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/dpt.py @@ -0,0 +1,516 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +# Inspired by https://github.com/DepthAnything/Depth-Anything-V2 + + +from typing import List, Tuple, Union + +import torch +from romav2.device import device +import torch.nn as nn + + +class DPTHead(nn.Module): + """ + DPT Head for dense prediction tasks. + + This implementation follows the architecture described in "Vision Transformers for Dense Prediction" + (https://arxiv.org/abs/2103.13413). The DPT head processes features from a vision transformer + backbone and produces dense predictions by fusing multi-scale features. + """ + + def __init__( + self, + dim_in: int, + out_dim: int, + patch_size: int = 16, + features: int = 256, + out_channels: List[int] = [256, 512, 1024, 1024], + pos_embed: bool = True, + feature_only: bool = False, + down_ratio: int = 1, + align_corners: bool = True, + ) -> None: + super(DPTHead, self).__init__() + self.out_channels = out_channels + self.patch_size = patch_size + self.pos_embed = pos_embed + self.feature_only = feature_only + self.down_ratio = down_ratio + self.align_corners = align_corners + self.norm = nn.LayerNorm(dim_in) + + # Projection layers for each output channel from tokens. + self.projects = nn.ModuleList( + [ + nn.Conv2d( + in_channels=dim_in, + out_channels=oc, + kernel_size=1, + stride=1, + padding=0, + ) + for oc in out_channels + ] + ) + + # Resize layers for upsampling feature maps. + self.resize_layers = nn.ModuleList( + [ + nn.ConvTranspose2d( + in_channels=out_channels[0], + out_channels=out_channels[0], + kernel_size=4, + stride=4, + padding=0, + ), + nn.ConvTranspose2d( + in_channels=out_channels[1], + out_channels=out_channels[1], + kernel_size=2, + stride=2, + padding=0, + ), + nn.Identity(), + nn.Conv2d( + in_channels=out_channels[3], + out_channels=out_channels[3], + kernel_size=3, + stride=2, + padding=1, + ), + ] + ) + + self.scratch = _make_scratch(out_channels, features, expand=False) + + # Attach additional modules to scratch. + self.scratch.refinenet1 = _make_fusion_block(features) + self.scratch.refinenet2 = _make_fusion_block(features) + self.scratch.refinenet3 = _make_fusion_block(features) + self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False) + + head_features_1 = features + head_features_2 = 32 + + if feature_only: + self.scratch.output_conv1 = nn.Conv2d( + head_features_1, head_features_1, kernel_size=3, stride=1, padding=1 + ) + else: + self.scratch.output_conv1 = nn.Conv2d( + head_features_1, + head_features_1 // 2, + kernel_size=3, + stride=1, + padding=1, + ) + conv2_in_channels = head_features_1 // 2 + + self.scratch.output_conv2 = nn.Sequential( + nn.Conv2d( + conv2_in_channels, + head_features_2, + kernel_size=3, + stride=1, + padding=1, + ), + nn.ReLU(inplace=True), + nn.Conv2d(head_features_2, out_dim, kernel_size=1, stride=1, padding=0), + ) + + def forward( + self, + tokens: torch.Tensor | List[torch.Tensor], + *, + img_A: torch.Tensor | None = None, + img_B: torch.Tensor | None = None, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + if isinstance(tokens, torch.Tensor): + B, H, W, D = tokens.shape + else: + B, H, W, D = tokens[0].shape + return self._forward_impl(tokens, H, W) + + def _forward_impl( + self, + aggregated_tokens_list_or_tokens: List[torch.Tensor] | torch.Tensor, + patch_h: int, + patch_w: int, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """ + Implementation of the forward pass through the DPT head. + + This method processes a specific chunk of frames from the sequence. + + Args: + aggregated_tokens_list (List[Tensor]): List of token tensors from different transformer layers. + images (Tensor): Input images with shape [B, S, 3, H, W]. + patch_start_idx (int): Starting index for patch tokens. + frames_start_idx (int, optional): Starting index for frames to process. + frames_end_idx (int, optional): Ending index for frames to process. + + Returns: + Tensor or Tuple[Tensor, Tensor]: Feature maps or (predictions, confidence). + """ + with torch.autocast(device_type=device.type, dtype=torch.bfloat16): + assert not isinstance(aggregated_tokens_list_or_tokens, torch.Tensor), ( + "aggregated_tokens_list_or_tokens should be a list of tensors" + ) + aggregated_tokens_list = aggregated_tokens_list_or_tokens + if len(aggregated_tokens_list) != len(self.out_channels): + assert len(self.out_channels) % len(aggregated_tokens_list) == 0, ( + "out_channels must be a multiple of intermediate_layer_idx" + ) + factor = len(self.out_channels) // len(aggregated_tokens_list) + aggregated_tokens_list = [ + x + for xs in [ + [aggregated_tokens_list[i]] * factor + for i in range(len(aggregated_tokens_list)) + ] + for x in xs + ] + + B = aggregated_tokens_list[0].shape[0] + + H, W = patch_h * self.patch_size, patch_w * self.patch_size + + out = [] + + for dpt_idx in range(len(self.out_channels)): + x = aggregated_tokens_list[dpt_idx] + + x = x.reshape(B, -1, x.shape[-1]) + + x = self.norm(x) + + x = x.permute(0, 2, 1).reshape( + (x.shape[0], x.shape[-1], patch_h, patch_w) + ) + + x = self.projects[dpt_idx](x) + if self.pos_embed: + x = self._apply_pos_embed(x, W, H) + x = self.resize_layers[dpt_idx](x) + + out.append(x) + + # Fuse features from multiple layers. + out = self.scratch_forward(out) + # Interpolate fused output to match target image resolution. + out = custom_interpolate( + out, + ( + int(patch_h * self.patch_size / self.down_ratio), + int(patch_w * self.patch_size / self.down_ratio), + ), + mode="bilinear", + align_corners=self.align_corners, + ) + + if self.pos_embed: + out = self._apply_pos_embed(out, W, H) + + if self.feature_only: + return out.view(B, *out.shape[1:]) + # float it for precision + out = out.float() + out = self.scratch.output_conv2(out) + out = out.permute(0, 2, 3, 1) + return out + + def scratch_forward(self, features: List[torch.Tensor]) -> torch.Tensor: + """ + Forward pass through the fusion blocks. + + Args: + features (List[Tensor]): List of feature maps from different layers. + + Returns: + Tensor: Fused feature map. + """ + layer_1, layer_2, layer_3, layer_4 = features + + layer_1_rn = self.scratch.layer1_rn(layer_1) + layer_2_rn = self.scratch.layer2_rn(layer_2) + layer_3_rn = self.scratch.layer3_rn(layer_3) + layer_4_rn = self.scratch.layer4_rn(layer_4) + + out = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:]) + del layer_4_rn, layer_4 + + out = self.scratch.refinenet3(out, layer_3_rn, size=layer_2_rn.shape[2:]) + del layer_3_rn, layer_3 + + out = self.scratch.refinenet2(out, layer_2_rn, size=layer_1_rn.shape[2:]) + del layer_2_rn, layer_2 + + out = self.scratch.refinenet1(out, layer_1_rn) + del layer_1_rn, layer_1 + + out = self.scratch.output_conv1(out) + return out + + +################################################################################ +# Modules +################################################################################ + + +def _make_fusion_block( + features: int, + size: int = None, + has_residual: bool = True, + groups: int = 1, + align_corners: bool = True, +) -> nn.Module: + return FeatureFusionBlock( + features, + nn.ReLU(inplace=True), + deconv=False, + bn=False, + expand=False, + align_corners=align_corners, + size=size, + has_residual=has_residual, + groups=groups, + ) + + +def _make_scratch( + in_shape: List[int], out_shape: int, groups: int = 1, expand: bool = False +) -> nn.Module: + scratch = nn.Module() + out_shape1 = out_shape + out_shape2 = out_shape + out_shape3 = out_shape + if len(in_shape) >= 4: + out_shape4 = out_shape + + if expand: + out_shape1 = out_shape + out_shape2 = out_shape * 2 + out_shape3 = out_shape * 4 + if len(in_shape) >= 4: + out_shape4 = out_shape * 8 + + scratch.layer1_rn = nn.Conv2d( + in_shape[0], + out_shape1, + kernel_size=3, + stride=1, + padding=1, + bias=False, + groups=groups, + ) + scratch.layer2_rn = nn.Conv2d( + in_shape[1], + out_shape2, + kernel_size=3, + stride=1, + padding=1, + bias=False, + groups=groups, + ) + scratch.layer3_rn = nn.Conv2d( + in_shape[2], + out_shape3, + kernel_size=3, + stride=1, + padding=1, + bias=False, + groups=groups, + ) + if len(in_shape) >= 4: + scratch.layer4_rn = nn.Conv2d( + in_shape[3], + out_shape4, + kernel_size=3, + stride=1, + padding=1, + bias=False, + groups=groups, + ) + return scratch + + +class ResidualConvUnit(nn.Module): + """Residual convolution module.""" + + def __init__(self, features, activation, bn, groups=1): + """Init. + + Args: + features (int): number of features + """ + super().__init__() + + self.bn = bn + self.groups = groups + self.conv1 = nn.Conv2d( + features, + features, + kernel_size=3, + stride=1, + padding=1, + bias=True, + groups=self.groups, + ) + self.conv2 = nn.Conv2d( + features, + features, + kernel_size=3, + stride=1, + padding=1, + bias=True, + groups=self.groups, + ) + + self.norm1 = None + self.norm2 = None + + self.activation = activation + self.skip_add = nn.quantized.FloatFunctional() + + def forward(self, x): + """Forward pass. + + Args: + x (tensor): input + + Returns: + tensor: output + """ + + out = self.activation(x) + out = self.conv1(out) + if self.norm1 is not None: + out = self.norm1(out) + + out = self.activation(out) + out = self.conv2(out) + if self.norm2 is not None: + out = self.norm2(out) + + return self.skip_add.add(out, x) + + +class FeatureFusionBlock(nn.Module): + """Feature fusion block.""" + + def __init__( + self, + features, + activation, + deconv=False, + bn=False, + expand=False, + align_corners=True, + size=None, + has_residual=True, + groups=1, + ): + """Init. + + Args: + features (int): number of features + """ + super(FeatureFusionBlock, self).__init__() + + self.deconv = deconv + self.align_corners = align_corners + self.groups = groups + self.expand = expand + out_features = features + if self.expand: + out_features = features // 2 + + self.out_conv = nn.Conv2d( + features, + out_features, + kernel_size=1, + stride=1, + padding=0, + bias=True, + groups=self.groups, + ) + + if has_residual: + self.resConfUnit1 = ResidualConvUnit( + features, activation, bn, groups=self.groups + ) + + self.has_residual = has_residual + self.resConfUnit2 = ResidualConvUnit( + features, activation, bn, groups=self.groups + ) + + self.skip_add = nn.quantized.FloatFunctional() + self.size = size + + def forward(self, *xs, size=None): + """Forward pass. + + Returns: + tensor: output + """ + output = xs[0] + + if self.has_residual: + res = self.resConfUnit1(xs[1]) + output = self.skip_add.add(output, res) + + output = self.resConfUnit2(output) + + if (size is None) and (self.size is None): + modifier = {"scale_factor": 2} + elif size is None: + modifier = {"size": self.size} + else: + modifier = {"size": size} + + output = custom_interpolate( + output, **modifier, mode="bilinear", align_corners=self.align_corners + ) + output = self.out_conv(output) + + return output + + +def custom_interpolate( + x: torch.Tensor, + size: Tuple[int, int] = None, + scale_factor: float = None, + mode: str = "bilinear", + align_corners: bool = True, +) -> torch.Tensor: + """ + Custom interpolate to avoid INT_MAX issues in nn.functional.interpolate. + """ + if size is None: + size = (int(x.shape[-2] * scale_factor), int(x.shape[-1] * scale_factor)) + + INT_MAX = 1610612736 + + input_elements = size[0] * size[1] * x.shape[0] * x.shape[1] + + if input_elements > INT_MAX: + chunks = torch.chunk(x, chunks=(input_elements // INT_MAX) + 1, dim=0) + interpolated_chunks = [ + nn.functional.interpolate( + chunk, size=size, mode=mode, align_corners=align_corners + ) + for chunk in chunks + ] + x = torch.cat(interpolated_chunks, dim=0) + return x.contiguous() + else: + return nn.functional.interpolate( + x, size=size, mode=mode, align_corners=align_corners + ) diff --git a/OVOW/third_party/RoMaV2/src/romav2/features.py b/OVOW/third_party/RoMaV2/src/romav2/features.py new file mode 100644 index 0000000000000000000000000000000000000000..6836eaa3b58bd34e2d149ffa4b14f35c24fd4491 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/features.py @@ -0,0 +1,191 @@ +from dataclasses import dataclass, field +from typing import Any, Callable, Literal +import torch +from einops import rearrange +from romav2.normalizers import imagenet +from romav2.types import Normalizer, DescriptorName +from romav2.device import device +from torch import nn +from functools import partial +import torchvision.models as models +from torch.nn import functional as F + + +def wrap_with_normalize( + forward: Callable[[torch.Tensor], list[torch.Tensor]], + *, + normalizer: Normalizer, + patch_size: int, + enable_amp: bool, + frozen: bool, + normalize_feats: bool, +): + def wrapped_forward(self, img: torch.Tensor) -> list[torch.Tensor]: + with ( + torch.autocast(device.type, torch.bfloat16, enabled=enable_amp), + torch.set_grad_enabled(not frozen), + ): + if self.training and frozen: + self.eval() + B, C, H, W = img.shape + assert C == 3, f"Image must have 3 channels, but got shape {img.shape=}" + img_n = normalizer(img) + H = H // patch_size + W = W // patch_size + raw_outs = forward(img_n) + maybe_feat_normalizer = ( + F.normalize if normalize_feats else lambda x, dim=-1: x + ) + return [ + maybe_feat_normalizer( + rearrange(x, "B (H W) D -> B H W D", H=H, W=W), dim=-1 + ) + for x in raw_outs + ] + + return wrapped_forward + + +def wrap_model( + model: nn.Module, + *, + normalizer: Normalizer, + patch_size: int, + enable_amp: bool, + frozen: bool, + normalize_feats: bool, + func: Any, +): + if enable_amp and frozen: # if training we want params in fp32 + model = model.to(torch.bfloat16) + if frozen: + for param in model.parameters(): + param.requires_grad = False + model.frozen = frozen + type(model).forward = wrap_with_normalize( + func, + normalizer=normalizer, + patch_size=patch_size, + enable_amp=enable_amp, + frozen=frozen, + normalize_feats=normalize_feats, + ) + return model + + +def _get_layers(layers, model): + return [b if b > 0 else len(model.blocks) + b for b in layers] + + +class Descriptor: + @dataclass(frozen=True) + class Cfg: + name: DescriptorName = "dinov3_vitl16" + enable_amp: bool = True + frozen: bool = True + normalize_feats: bool = False + layer_idx: list[int] = field( + default_factory=lambda: [11, 17] + ) # [4, 11, 17, 23] for dinov3 style + weights_path: str | None = None + + def __new__(cls, cfg: Cfg) -> nn.Module: + partial_wrap = partial( + wrap_model, + enable_amp=cfg.enable_amp, + frozen=cfg.frozen, + normalize_feats=cfg.normalize_feats, + ) + match cfg.name: + case "dinov3_vitl16": + normalizer = imagenet + # TODO: this will break in distributed if not available locally + dinov3_vitl16: nn.Module = torch.hub.load( + repo_or_dir="facebookresearch/dinov3:adc254450203739c8149213a7a69d8d905b4fcfa", + model="dinov3_vitl16", + pretrained=cfg.weights_path is not None, + weights=cfg.weights_path, + skip_validation=True, + ).to(device) + layers = _get_layers(cfg.layer_idx, dinov3_vitl16) + return partial_wrap( + dinov3_vitl16, + normalizer=normalizer, + patch_size=16, + func=partial(dinov3_vitl16.get_intermediate_layers, n=layers), + ) + case "dinov2_vitl14": + normalizer = imagenet + + dinov2_vit14: nn.Module = torch.hub.load( + "facebookresearch/dinov2", "dinov2_vitl14" + ).to(device) + dinov2_vit14.mask_token = None + layers = _get_layers(cfg.layer_idx, dinov2_vit14) + return partial_wrap( + dinov2_vit14, + normalizer=normalizer, + patch_size=14, + func=partial(dinov2_vit14.get_intermediate_layers, n=layers), + ) + case "mum_vitl16": + raise NotImplementedError("MUM is not supported") + case _: + raise ValueError(f"Unknown descriptor name: {cfg.name}") + + +class VGG(nn.Module): + def forward(self, x): + x = imagenet(x) + with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16): + feats = {} + scale = 1 + for layer in self.layers: + if isinstance(layer, nn.MaxPool2d): + feats[scale] = x.permute(0, 2, 3, 1) + scale = scale * 2 + x = layer(x) + return feats + + +class VGG19(VGG): + def __init__(self, patch_size: int) -> None: + super().__init__() + if patch_size not in [8]: + raise NotImplementedError( + f"VGG19 is not supported for patch size {patch_size}" + ) + last_layer = {8: 28}[patch_size] + self.layers = nn.ModuleList( + models.vgg19(weights=models.VGG19_Weights.IMAGENET1K_V1).features[ + :last_layer + ] + ) + + +class VGG19BN(VGG): + def __init__(self, patch_size: int, pretrained: bool) -> None: + super().__init__() + last_layer = {1: 7, 2: 14, 4: 27, 8: 40, 16: 52}[patch_size] + self.layers = nn.ModuleList( + models.vgg19_bn(weights=models.VGG19_BN_Weights.IMAGENET1K_V1 if pretrained else None).features[ + :last_layer + ] + ) + + +class FineFeatures(nn.Module): + @dataclass(frozen=True) + class Cfg: + type: Literal["vgg19", "vgg19bn"] = "vgg19bn" + patch_size: int = 4 + pretrained: bool = False + + def __new__(cls, cfg: Cfg): + match cfg.type: + case "vgg19": + return VGG19(cfg.patch_size, cfg.pretrained) + case "vgg19bn": + return VGG19BN(cfg.patch_size, cfg.pretrained) + case _: + raise ValueError(f"Unknown refiner features type: {cfg.type}") diff --git a/OVOW/third_party/RoMaV2/src/romav2/geometry.py b/OVOW/third_party/RoMaV2/src/romav2/geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..25ed71f9072c54c72afcd35491364906c4688f81 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/geometry.py @@ -0,0 +1,261 @@ +from typing import Literal +import torch +import torch.nn.functional as F +from romav2.device import device +from einops import einsum +from romav2.types import ConfidenceMode +import numpy as np + + +def to_homogeneous(x: torch.Tensor) -> torch.Tensor: + return torch.cat((x, torch.ones_like(x[..., :1])), dim=-1) + + +def from_homogeneous(x: torch.Tensor) -> torch.Tensor: + return x[..., :-1] / x[..., -1:] + + +def get_normalized_grid( + B: int, + H: int, + W: int, + overload_device: torch.device | None = None, +) -> torch.Tensor: + x1_n = torch.meshgrid( + *[ + torch.linspace(-1 + 1 / n, 1 - 1 / n, n, device=overload_device or device) + for n in (B, H, W) + ], + indexing="ij", + ) + x1_n = torch.stack((x1_n[2], x1_n[1]), dim=-1).reshape(B, H, W, 2) + return x1_n + + +def get_pixel_grid( + B: int, + *, + H: int, + W: int, + overload_device: torch.device | None = None, +) -> torch.Tensor: + x1_n = torch.meshgrid( + *[torch.arange(n, device=overload_device or device) + 0.5 for n in (B, H, W)], + indexing="ij", + ) + x1_n = torch.stack((x1_n[2], x1_n[1]), dim=-1).reshape(B, H, W, 2) + return x1_n + + +def to_normalized(x: torch.Tensor, *, H: int, W: int) -> torch.Tensor: + return torch.stack((2 * x[..., 0] / W, 2 * x[..., 1] / H), dim=-1) - 1 + + +def to_pixel(x: torch.Tensor, *, H: int, W: int) -> torch.Tensor: + return torch.stack(((x[..., 0] + 1) / 2 * W, (x[..., 1] + 1) / 2 * H), dim=-1) + + +def warp_and_overlap_from_depth( + *, + depth_A: torch.Tensor, + depth_B: torch.Tensor, + K_A: torch.Tensor, + K_B: torch.Tensor, + T_AB: torch.Tensor, + rel_depth_error_threshold: float | None, + mode: ConfidenceMode, +) -> tuple[torch.Tensor, torch.Tensor]: + device = depth_A.device + if mode == "covis" and rel_depth_error_threshold is None: + raise ValueError("rel_depth_error_threshold must be provided for covis mode") + B, H_A, W_A, one = depth_A.shape + B, H_B, W_B, one = depth_B.shape + assert one == 1, "depth_A and depth_B must have shape (B, H, W, 1)" + grid = to_homogeneous(get_pixel_grid(B, H=H_A, W=W_A, overload_device=device)) + x = einsum( + grid, K_A.inverse(), "batch H W pixel, batch calib pixel -> batch H W calib" + ) + x = x * depth_A + x = to_homogeneous(x) + x = einsum(x, T_AB, "batch H W calib, batch world calib -> batch H W world") + x_AB = from_homogeneous(x) + x = einsum(x_AB, K_B, "batch H W world, batch pixel world -> batch H W pixel") + x = from_homogeneous(x) + warp = to_normalized(x, H=H_B, W=W_B) + z_AB = x_AB[..., -1:] + pos_and_finite_depth = ( + (z_AB > 0.0).logical_and(z_AB.isfinite()).logical_and(~depth_B.isnan()) + ) + z_B = bhwc_grid_sample(depth_B, warp, mode="bilinear", align_corners=False) + rel_depth_error = (z_B - z_AB) / z_B + pos_depth_and_within_frame = (pos_and_finite_depth).logical_and( + warp.abs().amax(dim=-1, keepdim=True) < 1.0 + ) + if mode == "covis" or mode == "covis_and_correct": + assert rel_depth_error_threshold is not None + consistent_depth = ( + rel_depth_error.abs() < rel_depth_error_threshold + ).logical_and(pos_and_finite_depth) + conf = consistent_depth + elif mode == "frame": + conf = pos_depth_and_within_frame + elif mode == "positive": + conf = pos_and_finite_depth + else: + raise ValueError(f"Unknown confidence mode: {mode}") + return warp, conf.float() + + +def bhwc_interpolate( + x: torch.Tensor, + size: tuple[int, int], + mode: str = "bilinear", + align_corners: bool | None = None, +) -> torch.Tensor: + return F.interpolate( + x.permute(0, 3, 1, 2), size=size, mode=mode, align_corners=align_corners + ).permute(0, 2, 3, 1) + + +def bhwc_grid_sample( + x: torch.Tensor, + grid: torch.Tensor, + mode: str = "bilinear", + align_corners: bool | None = None, +) -> torch.Tensor: + return F.grid_sample( + x.permute(0, 3, 1, 2), grid, mode=mode, align_corners=align_corners + ).permute(0, 2, 3, 1) + + +def angle_error_mat(R1, R2): + cos = (np.trace(np.dot(R1.T, R2)) - 1) / 2 + cos = np.clip(cos, -1.0, 1.0) # numercial errors can make it out of bounds + return np.rad2deg(np.abs(np.arccos(cos))) + + +def angle_error_vec(v1, v2): + n = np.linalg.norm(v1) * np.linalg.norm(v2) + return np.rad2deg(np.arccos(np.clip(np.dot(v1, v2) / n, -1.0, 1.0))) + + +def compute_pose_error(R_gt, t_gt, R, t): + error_t = angle_error_vec(t.squeeze(), t_gt) + error_t = np.minimum(error_t, 180 - error_t) # ambiguity of E estimation + error_R = angle_error_mat(R, R_gt) + return error_t, error_R + + +def pose_auc(errors, thresholds): + sort_idx = np.argsort(errors) + errors = np.array(errors.copy())[sort_idx] + recall = (np.arange(len(errors)) + 1) / len(errors) + errors = np.r_[0.0, errors] + recall = np.r_[0.0, recall] + aucs = [] + for t in thresholds: + last_index = np.searchsorted(errors, t) + r = np.r_[recall[:last_index], recall[last_index - 1]] + e = np.r_[errors[:last_index], t] + aucs.append(np.trapz(r, x=e) / t) # type: ignore + return aucs + + +def cov_mat_from_cov_params(c: torch.Tensor) -> torch.Tensor: + return prec_mat_from_prec_params(c) + + +def prec_mat_from_prec_params(p: torch.Tensor) -> torch.Tensor: + P = p.new_zeros(p.shape[0], p.shape[1], p.shape[2], 2, 2) + P[..., 0, 0] = p[..., 0] + P[..., 1, 0] = p[..., 1] + P[..., 0, 1] = p[..., 1] + P[..., 1, 1] = p[..., 2] + return P + + +def to_double_angle_rep(v: torch.Tensor) -> torch.Tensor: + angle = torch.atan2(v[..., 1], v[..., 0]) + double_angle_rep = torch.stack((torch.cos(2 * angle), torch.sin(2 * angle)), dim=-1) + return double_angle_rep + + +def prec_mat_to_flow( + P: torch.Tensor, vis_max: float, mode: Literal["smallest", "largest"] = "largest" +) -> torch.Tensor: + vals, vecs = torch.linalg.eigh(P) + if mode == "smallest": + vis_vec = vecs[..., 0] + elif mode == "largest": + vis_vec = vecs[..., -1] + else: + raise ValueError(f"Invalid mode: {mode}") + # select upper half-plane + vis_vec = torch.where(vis_vec[..., 1:2] >= 0, vis_vec, -vis_vec) + double_angle_rep = to_double_angle_rep(vis_vec) + scale = (vals[..., 0] * vals[..., 1]).pow(0.25).clamp(0, vis_max)[..., None] + flow = scale * double_angle_rep + return flow + + +def prec_params_to_flow( + p: torch.Tensor, vis_max: float, mode: Literal["smallest", "largest"] = "largest" +) -> torch.Tensor: + P = prec_mat_from_prec_params(p) + return prec_mat_to_flow(P, vis_max, mode) + + +def overlap_from_warps( + warp_A_to_B: torch.Tensor, warp_B_to_A: torch.Tensor, error_threshold: float = 5e-3 +) -> torch.Tensor: + B, H, W, _ = warp_A_to_B.shape + grid = get_normalized_grid(B, H, W, overload_device=warp_A_to_B.device) + in_frame = warp_A_to_B.abs().le(1.0).all(dim=-1, keepdim=True) + fwd_bwd = bhwc_grid_sample( + warp_B_to_A, warp_A_to_B, mode="bilinear", align_corners=False + ) + fwd_bwd_error = (fwd_bwd - grid).norm(dim=-1, keepdim=True) + overlap = (fwd_bwd_error < error_threshold).logical_and(in_frame) + return overlap.float() + + +# Code taken from https://github.com/PruneTruong/DenseMatching/blob/40c29a6b5c35e86b9509e65ab0cd12553d998e5f/validation/utils_pose_estimation.py +# --- GEOMETRY --- +def estimate_pose_cv2_ransac( + kpts0: np.ndarray, + kpts1: np.ndarray, + K0: np.ndarray, + K1: np.ndarray, + norm_thresh: float, + conf: float = 0.99999, +): + import cv2 + + if len(kpts0) < 5: + return None + K0inv = np.linalg.inv(K0[:2, :2]) + K1inv = np.linalg.inv(K1[:2, :2]) + + kpts0 = (K0inv @ (kpts0 - K0[None, :2, 2]).T).T + kpts1 = (K1inv @ (kpts1 - K1[None, :2, 2]).T).T + E, mask = cv2.findEssentialMat( + kpts0, kpts1, np.eye(3), threshold=norm_thresh, prob=conf + ) + + ret = None + if E is not None: + best_num_inliers = 0 + + for _E in np.split(E, len(E) // 3): + n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask) # type: ignore + if n > best_num_inliers: + best_num_inliers = n + ret = (R, t, mask.ravel() > 0) + assert ret is not None + return ret + + +def compute_relative_pose(R1, t1, R2, t2): + rots = R2 @ (R1.mT) + trans = -rots @ t1 + t2 + return rots, trans diff --git a/OVOW/third_party/RoMaV2/src/romav2/io.py b/OVOW/third_party/RoMaV2/src/romav2/io.py new file mode 100644 index 0000000000000000000000000000000000000000..394698874877370ba7a203a90d993bbadbd800c0 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/io.py @@ -0,0 +1,24 @@ +import numpy as np +from PIL import Image + + +def numpy_to_pil(x: np.ndarray): + assert x.dtype in [np.float32, np.uint8] + if x.dtype == np.float32: + assert x.min() >= 0.0 and x.max() <= 1.0 + x *= 255 + x = x.astype(np.uint8) + return Image.fromarray(x) + + +def tensor_to_pil(x): + x = x.clone().detach().cpu().numpy() + if len(x.shape) == 3: + x = np.transpose(x, (1, 2, 0)) + x = np.clip(x, 0.0, 1.0) + return numpy_to_pil(x) + + +def check_not_i16(pil_img: Image.Image): + if pil_img.mode == "I;16": + raise NotImplementedError("Can't handle 16 bit images") diff --git a/OVOW/third_party/RoMaV2/src/romav2/local_correlation.py b/OVOW/third_party/RoMaV2/src/romav2/local_correlation.py new file mode 100644 index 0000000000000000000000000000000000000000..37d8bcae66e835a9e12225723fc6a767889847bf --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/local_correlation.py @@ -0,0 +1,152 @@ +from typing import Literal +import torch +import torch.nn.functional as F +try: + import local_corr +except ImportError: + local_corr = None + + +def local_corr_wrapper( + feature0: torch.Tensor, + feature1: torch.Tensor, + coords: torch.Tensor, + local_window: torch.Tensor, + B, + K, + c, + r, + h, + w, + device, + padding_mode="zeros", + sample_mode: Literal["bilinear", "nearest"] = "bilinear", + dtype=torch.float32, +): + assert local_corr is not None + assert padding_mode == "zeros" + warp = (coords[..., None, :] + local_window[:, None, None]).reshape(B, h * w, K, 2) + corr = ( + local_corr.local_corr( + feature0.reshape(B, c, h * w).permute(0, 2, 1).float() / (c**0.5), + feature1.permute(0, 2, 3, 1).clone().detach().float(), + warp.clone().detach(), + mode=sample_mode, + normalized_coords=True, + ) + .permute(0, 2, 1) + .reshape(B, K, h, w) + ) + return corr + + +def native_torch_local_corr( + feature0, + feature1, + warp, + local_window, + B, + K, + c, + r, + h, + w, + device, + padding_mode="zeros", + sample_mode="bilinear", + dtype=torch.float32, +): + corr = torch.empty((B, K, h, w), device=device, dtype=dtype) + for _ in range(B): + with torch.no_grad(): + local_window_coords = ( + warp[_, :, :, None] + local_window[:, None, None] + ).reshape(1, h, w * K, 2) + window_feature = F.grid_sample( + feature1[_ : _ + 1], + local_window_coords, + padding_mode=padding_mode, + align_corners=False, + mode=sample_mode, # + ) + window_feature = window_feature.reshape(c, h, w, K) + corr[_] = ( + (feature0[_, ..., None] / (c**0.5) * window_feature) + .sum(dim=0) + .permute(2, 0, 1) + ) + return corr + + +def local_correlation( + feature0: torch.Tensor, # (B x C x H x W) + feature1: torch.Tensor, # (B x C x H x W) + local_radius: int, + warp: torch.Tensor, # (B x H x W x 2) + scale_factor: torch.Tensor, + padding_mode="zeros", + sample_mode: Literal["bilinear", "nearest"] = "bilinear", +): + r = local_radius + K = (2 * r + 1) ** 2 + B, c, h, w = feature0.size() + local_h, local_w = h, w + device = feature0.device + dtype = feature0.dtype + local_window = torch.meshgrid( + [ + torch.linspace( + -2 * local_radius / local_h, + 2 * local_radius / local_h, + 2 * r + 1, + device=device, + ), + torch.linspace( + -2 * local_radius / local_w, + 2 * local_radius / local_w, + 2 * r + 1, + device=device, + ), + ], + indexing="ij", + ) + local_window = ( + torch.stack((local_window[1], local_window[0]), dim=-1)[None] + .expand(1, 2 * r + 1, 2 * r + 1, 2) + .reshape(1, K, 2) + ) + if local_corr is None: + corr = native_torch_local_corr( + feature0, + feature1, + warp, + local_window, + B, + K, + c, + r, + h, + w, + device, + padding_mode, + sample_mode, + dtype, + ) + else: + corr = local_corr_wrapper( + feature0, + feature1, + warp, + local_window, + B, + K, + c, + r, + h, + w, + device, + padding_mode, + sample_mode, + dtype, + ) + return corr diff --git a/OVOW/third_party/RoMaV2/src/romav2/logging.py b/OVOW/third_party/RoMaV2/src/romav2/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..22c7dd4025615b3a8bd9104d6ec2ef6f1fba5f9e --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/logging.py @@ -0,0 +1,97 @@ +import logging +import sys +from logging.handlers import RotatingFileHandler + + +class _RichFormatter(logging.Formatter): + """Formatter that injects Rich markup to color the romav2 package name.""" + + def format(self, record: logging.LogRecord) -> str: # type: ignore[override] + original_name = record.name + try: + if original_name.startswith("romav2"): + parts = original_name.split(".") + parts[0] = "[bold cyan]" + parts[0] + "[/]" + record.name = ".".join(parts) + return super().format(record) + finally: + record.name = original_name + + +logger = logging.getLogger("romav2") +logger.propagate = False # Prevent propagation to avoid double logging +logger.addHandler(logging.NullHandler()) # Default null handler + + +def configure_logger( + level=logging.INFO, + log_format=("%(name)s - %(module)s:%(lineno)d in %(funcName)s - %(message)s"), + date_format="%Y-%m-%d %H:%M:%S", + file_path=None, + file_max_bytes=10485760, # 10MB + file_backup_count=3, + stream=sys.stderr, + propagate=False, # Default to False to prevent double logging + use_rich: bool | None = None, # Auto-detect by default +): + """ + Configure the package logger with handlers similar to basicConfig. + This does NOT use basicConfig() and only affects this package's logger. + """ + # Clear any existing handlers + for handler in logger.handlers[:]: + if not isinstance(handler, logging.NullHandler): + logger.removeHandler(handler) + + # Set propagation + logger.propagate = propagate + + # Set level + logger.setLevel(level) + + # Decide Rich usage + rich_available = False + if use_rich is None or use_rich: + try: + from rich.logging import RichHandler # type: ignore + + rich_available = True + except Exception: + rich_available = False + + # Create formatters: colored for console, plain for files + console_formatter = _RichFormatter(log_format) + file_formatter = logging.Formatter( + f"%(asctime)s - %(name)s - %(levelname)s - {log_format}", date_format + ) + + # Add console handler if stream is specified + if stream: + if rich_available: + from rich.logging import RichHandler # type: ignore + + console_handler = RichHandler( + show_time=True, + show_level=True, + show_path=False, + rich_tracebacks=True, + markup=True, + log_time_format=date_format, + console=None, # default console + ) + # Rich will render time/level; our formatter supplies name/caller/message + console_handler.setFormatter(console_formatter) + else: + console_handler = logging.StreamHandler(stream) + console_handler.setFormatter(console_formatter) + logger.addHandler(console_handler) + + # Add file handler if file path is specified + if file_path: + file_handler = RotatingFileHandler( + file_path, maxBytes=file_max_bytes, backupCount=file_backup_count + ) + file_handler.setFormatter(file_formatter) + logger.addHandler(file_handler) + + return logger diff --git a/OVOW/third_party/RoMaV2/src/romav2/matcher.py b/OVOW/third_party/RoMaV2/src/romav2/matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..1c1ef6d8de48c6f804a8770befb0a764d2ea6bb5 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/matcher.py @@ -0,0 +1,207 @@ +import torch +import torch.nn as nn +from dataclasses import dataclass +from typing import Literal +from romav2.geometry import get_normalized_grid +from einops import einsum +from romav2.device import device +from romav2.vit import ViTModel, vit_from_name +from romav2.types import HeadType, MatcherStyle +from romav2.dpt import DPTHead + + +def normalize(x: torch.Tensor, dim: int): + return x / x.norm(dim=dim, keepdim=True) + + +def cosine_similarity(f_A: torch.Tensor, f_B: torch.Tensor) -> torch.Tensor: + f_A = normalize(f_A, dim=-1) + f_B = normalize(f_B, dim=-1) + res = einsum(f_A, f_B, "B H_A W_A D, B H_B W_B D -> B H_A W_A H_B W_B") + return res + + +def _compute_match_embeddings( + *, + f_A: torch.Tensor, + f_B: torch.Tensor, + pos_emb_grid: torch.Tensor, + temp: float, + B: int, + H_A: int, + W_A: int, + H_B: int, + W_B: int, +) -> torch.Tensor: + attn_AB_logits = (1 / temp * cosine_similarity(f_A, f_B)).reshape( + B, H_A * W_A, H_B * W_B + ) + attn_AB = torch.softmax(attn_AB_logits, dim=2) + attn_AB = attn_AB.reshape(B, H_A, W_A, H_B, W_B) + + match_emb = einsum( + attn_AB, pos_emb_grid, "B H_A W_A H_B W_B, B H_B W_B D -> B H_A W_A D" + ) + attn_AB_logits = attn_AB_logits.reshape(B, H_A, W_A, H_B, W_B) + return attn_AB_logits, attn_AB, match_emb + + +def _compute_head_preds( + *, + f_list_A: list[torch.Tensor], + match_emb_AB: torch.Tensor, + f_mv_A: torch.Tensor, + img_A: torch.Tensor, + img_B: torch.Tensor, + head: DPTHead, +) -> torch.Tensor: + head_input = f_list_A + head_input[-1] = f_list_A[-1] + f_mv_A + match_emb_AB + warp_and_confidence = head(head_input, img_A=img_A, img_B=img_B) + B, H_out, W_out, D_warp_and_confidence = warp_and_confidence.shape + warp = warp_and_confidence[:, :, :, :2] + confidence = warp_and_confidence[:, :, :, 2:] + return warp, confidence + + +class Matcher(nn.Module): + @dataclass(frozen=True) + class Cfg: + mv_vit: ViTModel = "vit_base" + mv_vit_use_rope: bool = True + mv_vit_position_mode: Literal["same"] = "same" + mv_vit_attention_mode: Literal["alternating"] = "alternating" + head: HeadType = "dpt-no-pos" + # NOTE: 0.2 in RoMa + temp: float = 0.1 + # NOTE: 8 in RoMa + scale: float = 1 + dim: int = 1024 + warp_dim: int = 2 + confidence_dim: int = 1 + num_feature_layers: int = 2 + feat_dim: int = 1024 + pos_emb_dim: int = 1024 + enable_amp: bool = True + style: MatcherStyle = "romav2" + # ufm uses pos embedings for view B and no attn + pos_embed_rope_rescale_coords: float | None = None + + def __init__(self, cfg: Cfg): + super().__init__() + self.cfg = cfg + omega = 2 * torch.pi * torch.randn(cfg.dim // 2, 2) + self.omega = nn.Buffer(omega) + self.scale = nn.Buffer(torch.tensor(cfg.scale)) + + self.temp = nn.Buffer(torch.tensor(cfg.temp)) + self.mv_vit = vit_from_name( + cfg.mv_vit, + device=device, + in_dim=cfg.feat_dim * cfg.num_feature_layers, + out_dim=cfg.dim, + multiview=True, + use_rope=cfg.mv_vit_use_rope, + mv_position_mode=cfg.mv_vit_position_mode, + mv_attention_mode=cfg.mv_vit_attention_mode, + pos_embed_rope_rescale_coords=cfg.pos_embed_rope_rescale_coords, + ) + self.head = DPTHead( + dim_in=cfg.dim, + out_dim=cfg.warp_dim + cfg.confidence_dim, + pos_embed=False, + feature_only=False, + down_ratio=4, + ) + + def forward( + self, + f_list_A: list[torch.Tensor], + f_list_B: list[torch.Tensor], + img_A: torch.Tensor, + img_B: torch.Tensor, + bidirectional: bool, + ): + preds = {} + f_A = torch.cat(f_list_A, dim=-1) + f_B = torch.cat(f_list_B, dim=-1) + B, H_A, W_A, D_feat = f_A.shape + B, H_B, W_B, D_feat = f_B.shape + assert D_feat == self.cfg.feat_dim * self.cfg.num_feature_layers, ( + "Feature dimension mismatch" + ) + x = get_normalized_grid(B, H_B, W_B) + x_emb = nn.functional.linear( + x.reshape(B, H_B * W_B, 2), self.scale * self.omega + ).reshape(B, H_B, W_B, -1) + pos_emb_grid = torch.cat((x_emb.sin(), x_emb.cos()), dim=-1) + + with torch.autocast(device.type, torch.bfloat16, enabled=self.cfg.enable_amp): + assert self.mv_vit is not None + f_mv_AB = self.mv_vit(torch.stack((f_A, f_B), dim=1))[ + "x_norm_patchtokens" + ].reshape(B, 2, H_A, W_A, self.cfg.dim) + f_mv_A = f_mv_AB[:, 0] + f_mv_B = f_mv_AB[:, 1] + + f_mv_A = f_mv_A.float() + f_mv_B = f_mv_B.float() + + assert H_A == H_B and W_A == W_B, "H_A and W_A must be equal to H_B and W_B" + attn_AB_logits, attn_AB, match_emb_AB = _compute_match_embeddings( + f_A=f_mv_A, + f_B=f_mv_B, + pos_emb_grid=pos_emb_grid, + temp=self.temp, + B=B, + H_A=H_A, + W_A=W_A, + H_B=H_B, + W_B=W_B, + ) + warp_AB, confidence_AB = _compute_head_preds( + f_list_A=f_list_A, + match_emb_AB=match_emb_AB, + f_mv_A=f_mv_A, + img_A=img_A, + img_B=img_B, + head=self.head, + ) + + if bidirectional: + attn_BA_logits, attn_BA, match_emb_BA = _compute_match_embeddings( + f_A=f_mv_B, + f_B=f_mv_A, + pos_emb_grid=pos_emb_grid, + temp=self.temp, + B=B, + H_A=H_B, + W_A=W_B, + H_B=H_A, + W_B=W_A, + ) + warp_BA, confidence_BA = _compute_head_preds( + f_list_A=f_list_B, + match_emb_AB=match_emb_BA, + f_mv_A=f_mv_B, + img_A=img_B, + img_B=img_A, + head=self.head, + ) + else: + match_emb_BA = None + attn_BA = None + attn_BA_logits = None + warp_BA = None + confidence_BA = None + + preds["attn_AB_logits"] = attn_AB_logits + preds["attn_AB"] = attn_AB + preds["warp_AB"] = warp_AB + preds["confidence_AB"] = confidence_AB + + preds["attn_BA_logits"] = attn_BA_logits + preds["attn_BA"] = attn_BA + preds["warp_BA"] = warp_BA + preds["confidence_BA"] = confidence_BA + return preds diff --git a/OVOW/third_party/RoMaV2/src/romav2/normalizers.py b/OVOW/third_party/RoMaV2/src/romav2/normalizers.py new file mode 100644 index 0000000000000000000000000000000000000000..4ac811ddbb2bbdf620d21ec46931672213a8e8c4 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/normalizers.py @@ -0,0 +1,17 @@ +import torch + + +def imagenet(img: torch.Tensor) -> torch.Tensor: + imagenet_mean = torch.tensor([0.485, 0.456, 0.406], device=img.device) + imagenet_std = torch.tensor([0.229, 0.224, 0.225], device=img.device) + return (img - imagenet_mean[None, :, None, None]) / imagenet_std[ + None, :, None, None + ] + + +def inception(img: torch.Tensor) -> torch.Tensor: + inception_mean = torch.tensor([0.5, 0.5, 0.5], device=img.device) + inception_std = torch.tensor([0.5, 0.5, 0.5], device=img.device) + return (img - inception_mean[None, :, None, None]) / inception_std[ + None, :, None, None + ] diff --git a/OVOW/third_party/RoMaV2/src/romav2/refiner.py b/OVOW/third_party/RoMaV2/src/romav2/refiner.py new file mode 100644 index 0000000000000000000000000000000000000000..0de76515885803df1000b8509f8172536a2c37dd --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/refiner.py @@ -0,0 +1,276 @@ +from functools import partial + +from torch.nn import functional as F +from dataclasses import dataclass +from typing import Literal +import torch +import torch.nn as nn +from romav2.local_correlation import local_correlation +from romav2.geometry import get_normalized_grid +from romav2.device import device +from romav2.geometry import bhwc_grid_sample +from romav2.types import RefinersType, NormType + + +def create_block( + in_dim: int, + out_dim: int, + kernel_size: int, + bn_momentum: float, + norm_type_name: NormType = "batch", +): + num_groups = in_dim + assert out_dim % in_dim == 0, "outdim must be divisible by indim for depthwise" + conv_depthwise = nn.Conv2d( + in_dim, + out_dim, + kernel_size=kernel_size, + padding=kernel_size // 2, + groups=num_groups, + ) + if norm_type_name == "batch": + norm = nn.BatchNorm2d(out_dim, momentum=bn_momentum) + else: + raise TypeError(f"Unknown norm type: {norm_type_name}") + relu = nn.ReLU(inplace=True) + # conv_pointwise = nn.Linear(out_dim, out_dim) + conv_pointwise = nn.Conv2d(out_dim, out_dim, 1, 1, 0) + return [conv_depthwise, norm, relu, conv_pointwise] + + +class Block(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + kernel_size: int, + bn_momentum: float, + enable_amp: bool, + norm_type_name: NormType = "batch", + ): + super().__init__() + self.conv_depthwise, self.norm, self.relu, self.conv_pointwise = create_block( + in_dim, out_dim, kernel_size, bn_momentum, norm_type_name + ) + self.enable_amp = enable_amp + + def forward(self, x: torch.Tensor) -> torch.Tensor: + with torch.autocast( + device_type=device.type, enabled=self.enable_amp, dtype=torch.bfloat16 + ): + x = self.conv_depthwise(x) + x = self.norm(x) + x = self.relu(x) + x = self.conv_pointwise(x) + return x + + +class ConvRefiner(nn.Module): + @dataclass(frozen=True) + class Cfg: + feat_dim: int + proj_dim: int + displacement_emb_dim: int + local_corr_radius: int | None + patch_size: int + warp_dim: int = 2 + confidence_dim: int = 4 + kernel_size: int = 5 + hidden_blocks: int = 8 + norm_type_name: NormType = "batch" + bn_momentum: float = 0.01 + enable_amp: bool = True + refine_init: float = 4.0 + channels_last: bool = False + block_type: Literal["roma"] = "roma" + grid_sample_mode: Literal["bilinear", "bicubic"] = "bilinear" + + def __init__( + self, + cfg: Cfg, + ): + super().__init__() + self.cfg = cfg + self.proj = nn.Linear(cfg.feat_dim, cfg.proj_dim) + hidden_dim = ( + 2 * cfg.proj_dim + + cfg.displacement_emb_dim + + ( + (2 * cfg.local_corr_radius + 1) ** 2 + if cfg.local_corr_radius is not None + else 0 + ) + ) + self.hidden_dim = hidden_dim + if cfg.block_type == "roma": + self.block1 = Block( + in_dim=hidden_dim, + out_dim=hidden_dim, + norm_type_name=cfg.norm_type_name, + bn_momentum=cfg.bn_momentum, + kernel_size=cfg.kernel_size, + enable_amp=cfg.enable_amp, + ) + self.hidden_blocks = nn.Sequential( + *[ + Block( + hidden_dim, + hidden_dim, + norm_type_name=cfg.norm_type_name, + bn_momentum=cfg.bn_momentum, + kernel_size=cfg.kernel_size, + enable_amp=cfg.enable_amp, + ) + for _ in range(cfg.hidden_blocks) + ] + ) + else: + raise ValueError(f"Unknown block type: {cfg.block_type}") + + self.disp_emb = nn.Conv2d(2, cfg.displacement_emb_dim, 1, 1, 0) + self.warp_head = nn.Conv2d(hidden_dim, cfg.warp_dim, 1, 1, 0) + self.confidence_head = nn.Conv2d(hidden_dim, cfg.confidence_dim, 1, 1, 0) + + def forward( + self, + *, + f_A: torch.Tensor, + f_B: torch.Tensor, + prev_warp: torch.Tensor, + prev_confidence: torch.Tensor | None, + scale_factor: torch.Tensor, + ): + B, H_A, W_A, _ = f_A.shape + B, H_B, W_B, D = f_B.shape + assert H_A == H_B and W_A == W_B, "Images must have the same height and width" + prev_warp = prev_warp.detach() + if prev_confidence is not None: + prev_confidence = prev_confidence.detach() + B, H_A, W_A, D = f_A.shape + assert D == self.cfg.feat_dim, ( + f"Config feature dimension {self.cfg.feat_dim=} must be the same as the input feature dimension {D=}" + ) + + f_A = self.proj(f_A.reshape(B, H_A * W_A, self.cfg.feat_dim).float()).reshape( + B, H_A, W_A, self.cfg.proj_dim + ) + f_B = self.proj(f_B.reshape(B, H_B * W_B, self.cfg.feat_dim).float()).reshape( + B, H_B, W_B, self.cfg.proj_dim + ) + with torch.no_grad(): + f_BA = bhwc_grid_sample( + f_B, prev_warp, mode=self.cfg.grid_sample_mode, align_corners=False + ) + im_A_coords = get_normalized_grid(B, H_A, W_A) + in_displacement = prev_warp - im_A_coords + in_displacement_bdhw = in_displacement.permute(0, 3, 1, 2) + emb_in_displacement = self.disp_emb( + scale_factor[None, :, None, None] * in_displacement_bdhw + ) + # Corr in other means take a kxk grid around the predicted coordinate in other image + f_A_bdhw = f_A.permute(0, 3, 1, 2) + f_BA_bdhw = f_BA.permute(0, 3, 1, 2) + d = torch.cat((f_A_bdhw, f_BA_bdhw, emb_in_displacement), dim=1) + if self.cfg.local_corr_radius is not None: + local_corr = local_correlation( + f_A_bdhw, + f_B.permute(0, 3, 1, 2), + local_radius=self.cfg.local_corr_radius, + warp=prev_warp, + scale_factor=scale_factor, + ) + d = torch.cat((d, local_corr), dim=1) + if self.cfg.channels_last: + d = d.to(memory_format=torch.channels_last) + z = self.block1(d) + z = self.hidden_blocks(z) + z = z.float() + + displacement = self.warp_head(z) + delta_confidence = self.confidence_head(z) + displacement = displacement.permute(0, 2, 3, 1) + delta_confidence = delta_confidence.permute(0, 2, 3, 1) + displacement = displacement.view(B, H_A, W_A, self.cfg.warp_dim) + delta_confidence = delta_confidence.view(B, H_A, W_A, self.cfg.confidence_dim) + warp = prev_warp + displacement / ( + self.cfg.refine_init + * torch.tensor((W_A, H_A), device=device)[None, None, None] + ) + + if delta_confidence.shape[-1] == 4: + chol_eps = 1e-6 + l00 = F.softplus(delta_confidence[..., 1]) + chol_eps # this is in pixels + l10 = delta_confidence[..., 2] + l11 = F.softplus(delta_confidence[..., 3]) + chol_eps + p00 = l00 * l00 + p10 = l00 * l10 + p11 = l10 * l10 + l11 * l11 + delta_confidence = torch.stack( + [delta_confidence[..., 0], p00, p10, p11], dim=-1 + ) + + if prev_confidence is not None: + if prev_confidence.shape[-1] == delta_confidence.shape[-1]: + confidence = prev_confidence + delta_confidence + else: + padded_prev_confidence = torch.zeros_like(delta_confidence) + padded_prev_confidence[..., :1] = prev_confidence + confidence = padded_prev_confidence + delta_confidence + else: + confidence = delta_confidence + + return {"warp": warp, "confidence": confidence} + + +class Refiners: + @dataclass(frozen=True) + class Cfg: + refiner_type: RefinersType = "roma-4-pow2" + confidence_dim: int = 4 + grid_sample_mode: Literal["bilinear", "bicubic"] = "bilinear" + + def __new__(cls, cfg: Cfg): + partial_refiner_coarse = partial( + ConvRefiner.Cfg, + confidence_dim=cfg.confidence_dim, + grid_sample_mode=cfg.grid_sample_mode, + ) + match cfg.refiner_type: + case "roma-4-pow2": + refiner_configs = { + # tot: 192 * 2 + 79 + 7^2 = 512 + 4: partial_refiner_coarse( + feat_dim=256, + proj_dim=192, + displacement_emb_dim=79, + local_corr_radius=3, + patch_size=4, + block_type="roma", + ), + # tot: 48 * 2 + 23 + 3^2 = 128 + 2: partial_refiner_coarse( + feat_dim=128, + proj_dim=48, + displacement_emb_dim=23, + local_corr_radius=1, + patch_size=2, + block_type="roma", + ), + # tot: 12 * 2 + 8 = 32 + 1: partial_refiner_coarse( + feat_dim=64, + proj_dim=12, + displacement_emb_dim=8, + local_corr_radius=None, + patch_size=1, + block_type="roma", + ), + } + # assert set(cfg.patch_sizes) == set(refiner_configs.keys()) + patch_sizes = list(refiner_configs.keys()) + patch_sizes.sort(reverse=True) + refiners = nn.ModuleDict() + # explicit for-loop for order + for patch_size in patch_sizes: + refiners[str(patch_size)] = ConvRefiner(refiner_configs[patch_size]) + return refiners diff --git a/OVOW/third_party/RoMaV2/src/romav2/romav2.py b/OVOW/third_party/RoMaV2/src/romav2/romav2.py new file mode 100644 index 0000000000000000000000000000000000000000..65a1a3c72e770194fc4a659ec46265237c58e24f --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/romav2.py @@ -0,0 +1,532 @@ +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from collections import OrderedDict + + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from PIL import Image + +import logging +from romav2.device import device +from romav2.features import Descriptor, FineFeatures +from romav2.geometry import ( + bhwc_grid_sample, + bhwc_interpolate, + get_normalized_grid, + prec_mat_from_prec_params, + to_pixel, +) +from romav2.io import check_not_i16 +from romav2.matcher import Matcher +from romav2.refiner import Refiners +from romav2.types import Setting, ImageLike + +logger = logging.getLogger(__name__) + + +def _interpolate_warp_and_confidence( + *, + warp: torch.Tensor, + confidence: torch.Tensor, + H: int, + W: int, + patch_size: int, + zero_out_precision: bool, +): + warp = bhwc_interpolate( + warp.detach(), + size=(H // patch_size, W // patch_size), + mode="bilinear", + align_corners=False, + ) + if zero_out_precision: + # delta at 4 is absolute, and if we + # for the second pass we therefore can't use first pred. + # overlap is fine since it's relative to matcher pred. + confidence[..., 1:] = 0.0 + + confidence = bhwc_interpolate( + confidence.detach(), + size=(H // patch_size, W // patch_size), + mode="bilinear", + align_corners=False, + ) + return warp, confidence + + +def _map_confidence(*, confidence: torch.Tensor, threshold: float | None): + overlap = confidence[..., :1].sigmoid() + if threshold is not None: + overlap[overlap > threshold] = 1.0 + precision = prec_mat_from_prec_params(confidence[..., 1:4]) + return overlap, precision + + +class RoMaV2(nn.Module): + @dataclass(frozen=True) + class Cfg: + descriptor: Descriptor.Cfg = Descriptor.Cfg() + matcher: Matcher.Cfg = Matcher.Cfg() + refiners: Refiners.Cfg = Refiners.Cfg() + refiner_features: FineFeatures.Cfg = FineFeatures.Cfg() + anchor_width: int = 512 + anchor_height: int = 512 + setting: Setting = "precise" + compile: bool = False + name: str = "RoMa v2" + + # settings + H_lr: int + W_lr: int + H_hr: int | None + W_hr: int | None + bidirectional: bool + threshold: float | None + balanced_sampling: bool + + def __init__(self, cfg: Cfg | None = None): + super().__init__() + if cfg is None: + # default + cfg = RoMaV2.Cfg() + + weights = torch.hub.load_state_dict_from_url( + "https://github.com/Parskatt/RoMaV2/releases/download/v2.0.1/romav2.0.1.pt", + map_location=device + ) + self.f = Descriptor(cfg.descriptor) + self.matcher = Matcher(cfg.matcher) + self.cfg = cfg + self.anchor_width = cfg.anchor_width + self.anchor_height = cfg.anchor_height + self.refiners = Refiners(cfg.refiners) + self.refiner_features = FineFeatures(cfg.refiner_features) + self.to(device) + self.eval() + self.apply_setting(cfg.setting) + self.name = cfg.name + self.load_state_dict(weights) + if cfg.compile: + logger.info(f"Compiling {self.name}...") + self.compile() + logger.info(f"{self.name} initialized.") + + def apply_setting(self, setting: Setting): + if setting in ["mega1500", "scannet1500", "wxbs", "satast"]: + self.H_lr = 800 + self.W_lr = 800 + self.H_hr = 1024 + self.W_hr = 1024 + self.bidirectional = True + self.threshold = 0.05 + self.balanced_sampling = True + elif setting == "turbo": + self.H_lr = 320 + self.W_lr = 320 + self.H_hr = None + self.W_hr = None + self.bidirectional = False + self.threshold = None + self.balanced_sampling = True + elif setting == "fast": + self.H_lr = 512 + self.W_lr = 512 + self.H_hr = None + self.W_hr = None + self.bidirectional = False + self.threshold = None + self.balanced_sampling = True + elif setting == "base": + self.H_lr = 640 + self.W_lr = 640 + self.H_hr = None + self.W_hr = None + self.bidirectional = False + self.threshold = None + self.balanced_sampling = True + elif setting == "precise": + self.H_lr = 800 + self.W_lr = 800 + self.H_hr = 1280 + self.W_hr = 1280 + self.bidirectional = True + self.threshold = None + self.balanced_sampling = True + else: + raise TypeError(f"Invalid setting: {setting}") + + @torch.inference_mode() + def forward( + self, + img_A_lr: torch.Tensor, + img_B_lr: torch.Tensor, + img_A_hr: torch.Tensor | None = None, + img_B_hr: torch.Tensor | None = None, + ) -> dict[str, tuple[torch.Tensor, torch.Tensor] | torch.Tensor]: + if torch.get_float32_matmul_precision() != "highest": + raise RuntimeError("Float32 matmul precision must be set to highest") + assert not self.training, "Currently only inference mode released" + # assumes images between [0, 1] + # init preds + predictions = OrderedDict() + # extract feats + f_A = self.f(img_A_lr) + f_B = self.f(img_B_lr) + # match feats + matcher_output = self.matcher( + f_A, f_B, img_A=img_A_lr, img_B=img_B_lr, bidirectional=self.bidirectional + ) + # return matcher_output + predictions["matcher"] = matcher_output + warp_AB, confidence_AB = ( + matcher_output["warp_AB"], + matcher_output["confidence_AB"], + ) + if self.bidirectional: + warp_BA, confidence_BA = ( + matcher_output["warp_BA"], + matcher_output["confidence_BA"], + ) + else: + warp_BA = None + confidence_BA = None + # refine warp, maybe twice (if hr is available) + for stage, (img_A, img_B) in enumerate( + zip([img_A_lr, img_A_hr], [img_B_lr, img_B_hr]) + ): + if img_A is None or img_B is None: + continue + B, C, H, W = img_A.shape + scale_factor = torch.tensor( + (W / self.anchor_width, H / self.anchor_height), device=device + ) + refiner_features_A = self.refiner_features(img_A) + refiner_features_B = self.refiner_features(img_B) + for patch_size_str, refiner in self.refiners.items(): + patch_size = int(patch_size_str) + zero_out_precision = ( + img_A_hr is not None and patch_size == 4 and stage == 1 + ) + warp_AB, confidence_AB = _interpolate_warp_and_confidence( + warp=warp_AB, + confidence=confidence_AB, + H=H, + W=W, + patch_size=patch_size, + zero_out_precision=zero_out_precision, + ) + if self.bidirectional: + warp_BA, confidence_BA = _interpolate_warp_and_confidence( + warp=warp_BA, + confidence=confidence_BA, + H=H, + W=W, + patch_size=patch_size, + zero_out_precision=zero_out_precision, + ) + + f_patch_A = refiner_features_A[patch_size] + f_patch_B = refiner_features_B[patch_size] + refiner_output_AB = refiner( + f_A=f_patch_A, + f_B=f_patch_B, + prev_warp=warp_AB, + prev_confidence=confidence_AB, + scale_factor=scale_factor, + ) + if self.bidirectional: + refiner_output_BA = refiner( + f_A=f_patch_B, + f_B=f_patch_A, + prev_warp=warp_BA, + prev_confidence=confidence_BA, + scale_factor=scale_factor, + ) + else: + refiner_output_BA = None + predictions[f"refiner_{patch_size}_AB"] = refiner_output_AB + predictions[f"refiner_{patch_size}_BA"] = refiner_output_BA + warp_AB, confidence_AB = ( + refiner_output_AB["warp"], + refiner_output_AB["confidence"], + ) + if self.bidirectional: + warp_BA, confidence_BA = ( + refiner_output_BA["warp"], + refiner_output_BA["confidence"], + ) + predictions["warp_AB"] = warp_AB + predictions["confidence_AB"] = confidence_AB + if self.bidirectional: + predictions["warp_BA"] = warp_BA + predictions["confidence_BA"] = confidence_BA + else: + predictions["warp_BA"] = None + predictions["confidence_BA"] = None + return predictions + + def _load_image(self, img_like: ImageLike) -> torch.Tensor: + if isinstance(img_like, str) or isinstance(img_like, Path): + img_pil = Image.open(img_like) + check_not_i16(img_pil) + img_pil = img_pil.convert("RGB") + img = torch.from_numpy(np.array(img_pil)).permute(2, 0, 1).to(device) + elif isinstance(img_like, Image.Image): + img = torch.from_numpy(np.array(img_like)).permute(2, 0, 1).to(device) + elif isinstance(img_like, np.ndarray): + assert img_like.shape[-1] == 3, ( + f"Image must have 3 channels, but got shape {img_like.shape=}" + ) + img = torch.from_numpy(img_like).permute(2, 0, 1).to(device) + elif isinstance(img_like, torch.Tensor): + assert img_like.shape[1] == 3, ( + f"Image must have 3 channels, but got shape {img_like.shape=}" + ) + img = img_like + else: + raise ValueError(f"Unsupported image type: {type(img_like)}") + + if img.dtype == torch.uint8: + img = img.float() / 255.0 + if len(img.shape) == 3: + img = img[None] + return img + + @torch.inference_mode() + def match( + self, + img_like_A: ImageLike, + img_like_B: ImageLike, + ) -> dict[str, torch.Tensor]: + self.eval() + img_A = self._load_image(img_like_A) + img_B = self._load_image(img_like_B) + + img_A_lr = F.interpolate( + img_A, + size=(self.H_lr, self.W_lr), + mode="bicubic", + align_corners=False, + antialias=True, + ) + img_B_lr = F.interpolate( + img_B, + size=(self.H_lr, self.W_lr), + mode="bicubic", + align_corners=False, + antialias=True, + ) + if self.H_hr is not None and self.W_hr is not None: + img_A_hr = F.interpolate( + img_A, + size=(self.H_hr, self.W_hr), + mode="bicubic", + align_corners=False, + antialias=True, + ) + img_B_hr = F.interpolate( + img_B, + size=(self.H_hr, self.W_hr), + mode="bicubic", + align_corners=False, + antialias=True, + ) + else: + img_A_hr = None + img_B_hr = None + + preds = self(img_A_lr, img_B_lr, img_A_hr=img_A_hr, img_B_hr=img_B_hr) + + warp_AB = preds["warp_AB"] + confidence_AB = preds["confidence_AB"] + warp_BA = preds["warp_BA"] + confidence_BA = preds["confidence_BA"] + overlap_AB, precision_AB = _map_confidence( + confidence=confidence_AB, threshold=self.threshold + ) + if self.bidirectional: + overlap_BA, precision_BA = _map_confidence( + confidence=confidence_BA, threshold=self.threshold + ) + else: + overlap_BA = None + precision_BA = None + + preds = { + "warp_AB": warp_AB.clone(), + "confidence_AB": confidence_AB.clone(), + "overlap_AB": overlap_AB.clone(), + "precision_AB": precision_AB.clone(), + "warp_BA": warp_BA.clone() if warp_BA is not None else None, + "confidence_BA": confidence_BA.clone() if confidence_BA is not None else None, + "overlap_BA": overlap_BA.clone() if overlap_BA is not None else None, + "precision_BA": precision_BA.clone() if precision_BA is not None else None, + } + return preds + + def sample(self, preds: dict[str, torch.Tensor], num_corresp: int): + warp = preds["warp_AB"] + confidence_AB = preds["overlap_AB"] + precision_AB = preds["precision_AB"] if "precision_AB" in preds else None + + warp = warp[0] + confidence_AB = confidence_AB[0].reshape(-1) + if precision_AB is not None: + precision_AB = precision_AB[0] + + H_A, W_A, two = warp.shape + grid = get_normalized_grid(1, H_A, W_A)[0] + matches_AB = torch.cat((grid, warp), dim=-1).reshape(-1, 4) + if self.bidirectional: + confidence_BA = preds["overlap_BA"] + warp_BA = preds["warp_BA"] + + precision_BA = ( + preds["precision_BA"] if "precision_BA" in preds else None + ) + warp_BA = warp_BA[0] + confidence_BA = confidence_BA[0] + if precision_BA is not None: + precision_BA = precision_BA[0] + + if precision_BA is not None and precision_AB is not None: + precision_A = bhwc_grid_sample( + precision_BA[None].reshape(1, H_A, W_A, -1), + warp[None], + mode="bilinear", + align_corners=False, + ).reshape(H_A, W_A, 2, 2) + precision_B = bhwc_grid_sample( + precision_AB[None].reshape(1, H_A, W_A, -1), + warp_BA[None], + mode="bilinear", + align_corners=False, + ).reshape(H_A, W_A, 2, 2) + precision_fwd = torch.stack( + (precision_A, precision_AB), dim=-3 + ).reshape(-1, 2, 2, 2) + precision_bwd = torch.stack( + (precision_BA, precision_B), dim=-3 + ).reshape(-1, 2, 2, 2) + precision = torch.cat((precision_fwd, precision_bwd), dim=0) + else: + precision = None + # let's hope H_A is equal to H_B + grid = get_normalized_grid(1, H_A, W_A)[0] + matches_BA = torch.cat((warp_BA, grid), dim=-1).reshape(-1, 4) + confidence = torch.cat( + (confidence_AB.reshape(-1), confidence_BA.reshape(-1)), dim=0 + ) + matches = torch.cat((matches_AB, matches_BA), dim=0) + else: + matches = matches_AB + confidence = confidence_AB.reshape(-1) + precision = precision_AB.reshape(-1, 2, 2) + + expansion_factor = 4 + confidence = confidence * matches.abs().amax(dim=-1).le(1 - 1 / H_A).float() + corresp_inds = torch.multinomial( + confidence, expansion_factor * num_corresp, replacement=False + ) + sampled_matches = matches[corresp_inds] + sampled_confidence = confidence[corresp_inds] + if precision is not None: + sampled_precision = precision[corresp_inds] + else: + sampled_precision = None + # return sampled_matches, sampled_confidence + density = kde(sampled_matches) + + p = 1 / (density + 1) + p[density < 10] = ( + 1e-7 # Basically should have at least 10 perfect neighbours, or around 100 ok ones + ) + balanced_samples = torch.multinomial( + p, num_samples=min(num_corresp, len(sampled_confidence)), replacement=False + ) + return ( + sampled_matches[balanced_samples], + sampled_confidence[balanced_samples], + sampled_precision[balanced_samples][:, 0] + if sampled_precision is not None + else None, + sampled_precision[balanced_samples][:, 1] + if sampled_precision is not None + else None, + ) + + @classmethod + def prec_map_coordinates( + cls, precision: torch.Tensor, *, H_in: int, W_in: int, H_out: int, W_out: int + ): + W_ratio = W_in / W_out + H_ratio = H_in / H_out + ratio = torch.tensor([W_ratio, H_ratio], device=precision.device) + precision = precision * ratio[None, :, None] * ratio[None, None, :] + return precision + + @classmethod + def to_pixel_coordinates( + cls, warp: torch.Tensor, H_A: int, W_A: int, H_B: int, W_B: int + ): + return to_pixel(warp[..., :2], H=H_A, W=W_A), to_pixel( + warp[..., 2:], H=H_B, W=W_B + ) + + @classmethod + def match_keypoints( + cls, + x_A: torch.Tensor, + x_B: torch.Tensor, + warp: torch.Tensor, + certainty: torch.Tensor, + return_tuple: bool = True, + return_inds: bool = False, + max_dist: float = 0.005, + cert_th: float = 0.0, + ): + x_A_to_B = F.grid_sample( + warp[..., -2:].permute(2, 0, 1)[None], + x_A[None, None], + align_corners=False, + mode="bilinear", + )[0, :, 0].mT + cert_A_to_B = F.grid_sample( + certainty[None, None, ...], + x_A[None, None], + align_corners=False, + mode="bilinear", + )[0, 0, 0] + D = torch.cdist(x_A_to_B, x_B) + inds_A, inds_B = torch.nonzero( + (D == D.min(dim=-1, keepdim=True).values) + * (D == D.min(dim=-2, keepdim=True).values) + * (cert_A_to_B[:, None] > cert_th) + * (D < max_dist), + as_tuple=True, + ) + + if return_tuple: + if return_inds: + return inds_A, inds_B + else: + return x_A[inds_A], x_B[inds_B] + else: + if return_inds: + return torch.cat((inds_A, inds_B), dim=-1) + else: + return torch.cat((x_A[inds_A], x_B[inds_B]), dim=-1) + + +def kde(x: torch.Tensor, std: float = 0.1, half: bool = True) -> torch.Tensor: + # use a gaussian kernel to estimate density + if half: + x = x.half() + scores = (-(torch.cdist(x, x) ** 2) / (2 * std**2)).exp() + density = scores.sum(dim=-1) + return density diff --git a/OVOW/third_party/RoMaV2/src/romav2/types.py b/OVOW/third_party/RoMaV2/src/romav2/types.py new file mode 100644 index 0000000000000000000000000000000000000000..06a95264d2a09e7b4c5739dd4a7884cb63b8e0df --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/types.py @@ -0,0 +1,75 @@ +from __future__ import annotations +from typing import Callable +from dataclasses import dataclass +from pathlib import Path +import torch +from typing import Literal +import numpy as np +from PIL import Image + + +@dataclass +class Batch: + img_A: torch.Tensor + img_B: torch.Tensor + depth_A: torch.Tensor + depth_B: torch.Tensor + K_A: torch.Tensor + K_B: torch.Tensor + pose_A: torch.Tensor + pose_B: torch.Tensor + T_AB: torch.Tensor + img_A_path: Path + img_B_path: Path + source: GTSource | list[GTSource] + warp_A_to_B: torch.Tensor + warp_B_to_A: torch.Tensor + mask_A_to_B: torch.Tensor + mask_B_to_A: torch.Tensor + + def to(self, device: torch.device) -> Batch: + return Batch( + img_A=self.img_A.to(device), + img_B=self.img_B.to(device), + K_A=self.K_A.to(device), + K_B=self.K_B.to(device), + pose_A=self.pose_A.to(device), + pose_B=self.pose_B.to(device), + depth_A=self.depth_A.to(device), + depth_B=self.depth_B.to(device), + warp_A_to_B=self.warp_A_to_B.to(device), + warp_B_to_A=self.warp_B_to_A.to(device), + T_AB=self.T_AB.to(device), + img_A_path=self.img_A_path, + img_B_path=self.img_B_path, + source=self.source, + mask_A_to_B=self.mask_A_to_B.to(device), + mask_B_to_A=self.mask_B_to_A.to(device), + ) + + @classmethod + def collate(cls, samples: list[Batch]) -> Batch: + keys = samples[0].__dict__.keys() + batch = {} + for k in keys: + if isinstance(samples[0].__dict__[k], torch.Tensor): + batch[k] = torch.stack([s.__dict__[k] for s in samples]) + else: + batch[k] = [s.__dict__[k] for s in samples] + return Batch(**batch) # type: ignore[missing-arguments] + + +GTSource = Literal["depth", "warp"] +HeadType = Literal["dpt-no-pos"] +SampleMode = Literal["frame_distance", "overlap"] +ConfidenceMode = Literal["covis", "frame", "positive"] +NormType = Literal["batch"] +RefinersType = Literal["roma-4-pow2"] +MatcherStyle = Literal["romav2"] +DescriptorName = Literal["dinov3_vitl16", "dinov2_vitl14"] +Normalizer = Callable[[torch.Tensor], torch.Tensor] +OptimizerName = Literal["adamw"] +ImageLike = torch.Tensor | np.ndarray | str | Path | Image.Image +Setting = Literal[ + "mega1500", "scannet1500", "wxbs", "satast", "base", "precise", "turbo", "fast" +] diff --git a/OVOW/third_party/RoMaV2/src/romav2/vis.py b/OVOW/third_party/RoMaV2/src/romav2/vis.py new file mode 100644 index 0000000000000000000000000000000000000000..fee780435cc1d7aa0284372f317b9cd97fee1987 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vis.py @@ -0,0 +1,36 @@ +import torch +import torch.nn.functional as F +import numpy as np +from romav2.device import device + + +def vis(img1, img2, warp_AB, warp_BA, certainty_AB, certainty_BA): + H, W = warp_AB.shape[1:3] + x1 = F.interpolate( + (torch.tensor(np.array(img1)) / 255).to(device).permute(2, 0, 1)[None], + size=(H, W), + mode="bicubic", + align_corners=False, + antialias=True, + ) + x2 = F.interpolate( + (torch.tensor(np.array(img2)) / 255).to(device).permute(2, 0, 1)[None], + size=(H, W), + mode="bicubic", + align_corners=False, + antialias=True, + ) + im2_transfer_rgb = F.grid_sample( + x2, warp_AB, mode="bilinear", align_corners=False + )[0] + im1_transfer_rgb = F.grid_sample( + x1, warp_BA, mode="bilinear", align_corners=False + )[0] + warp_im = torch.cat((im2_transfer_rgb, im1_transfer_rgb), dim=2) # .permute(1,2,0) + white_im = torch.ones((H, 2 * W), device=device) + certainty = torch.cat((certainty_AB, certainty_BA), dim=2) + + vis_im = certainty[0] * warp_im + (1 - certainty[0]) * white_im + x = torch.cat((x1[0], x2[0]), dim=2) + vis_im = torch.cat((x, vis_im), dim=1) + return vis_im diff --git a/OVOW/third_party/RoMaV2/src/romav2/vit/__init__.py b/OVOW/third_party/RoMaV2/src/romav2/vit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..211ef182a540bb5120a45546ebecf8a9fcc1aa99 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vit/__init__.py @@ -0,0 +1,304 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This software may be used and distributed in accordance with +# the terms of the DINOv3 License Agreement. + + +import logging +from functools import partial +from typing import Any, Dict, List, Literal, Optional, Tuple + +import torch +import torch.nn.init +from einops import rearrange +from torch import Tensor, nn + +from .block import SelfAttentionBlock +from .ffn_layers import Mlp, SwiGLUFFN +from .layer_scale import LayerScale +from .rms_norm import RMSNorm +from .rope import RopePositionEmbedding +from .utils import named_apply + +logger = logging.getLogger(__name__) + +ffn_layer_dict = { + "mlp": Mlp, + "swiglu": SwiGLUFFN, + "swiglu32": partial(SwiGLUFFN, align_to=32), + "swiglu64": partial(SwiGLUFFN, align_to=64), + "swiglu128": partial(SwiGLUFFN, align_to=128), +} + +norm_layer_dict = { + "layernorm": partial(nn.LayerNorm, eps=1e-6), + "layernormbf16": partial(nn.LayerNorm, eps=1e-5), + "rmsnorm": RMSNorm, +} + +dtype_dict = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + + +def init_weights_vit(module: nn.Module, name: str = ""): + if isinstance(module, nn.Linear): + torch.nn.init.trunc_normal_(module.weight, std=0.02) + if module.bias is not None: + nn.init.zeros_(module.bias) + if isinstance(module, nn.LayerNorm): + module.reset_parameters() + if isinstance(module, LayerScale): + module.reset_parameters() + if isinstance(module, RMSNorm): + module.reset_parameters() + + +ViTOutput = Literal[ + "x_norm_clstoken", "x_storage_tokens", "x_norm_patchtokens", "x_prenorm", "masks" +] + + +class MatchTransformer(nn.Module): + def __init__( + self, + *, + in_dim: int, + out_dim: int, + use_rope: bool, + dim: int, + depth: int, + num_heads: int, + ffn_ratio: float, + multiview: bool, + mv_position_mode: Literal["same", None], + mv_attention_mode: Literal["alternating", None], + pos_embed_rope_base: float = 100.0, + pos_embed_rope_min_period: float | None = None, + pos_embed_rope_max_period: float | None = None, + pos_embed_rope_normalize_coords: Literal["min", "max", "separate"] = "separate", + pos_embed_rope_shift_coords: float | None = None, + pos_embed_rope_jitter_coords: float | None = None, + pos_embed_rope_rescale_coords: float | None = None, + pos_embed_rope_dtype: str = "bf16", + qkv_bias: bool = True, + drop_path_rate: float = 0.0, + layerscale_init: float | None = None, + norm_layer: str = "layernorm", + ffn_layer: str = "mlp", + ffn_bias: bool = True, + proj_bias: bool = True, + n_storage_tokens: int = 0, + mask_k_bias: bool = False, + untie_cls_and_patch_norms: bool = False, + untie_global_and_local_cls_norm: bool = False, + device: Any | None = None, + **ignored_kwargs, + ): + super().__init__() + self.projector = nn.Linear(in_dim, dim) + self.output_projector = nn.Linear(dim, out_dim) + self.multiview = multiview + self.mv_position_mode = mv_position_mode + self.mv_attention_mode = mv_attention_mode + if len(ignored_kwargs) > 0: + logger.warning(f"Ignored kwargs: {ignored_kwargs}") + del ignored_kwargs + + norm_layer_cls = norm_layer_dict[norm_layer] + + self.num_features = self.embed_dim = ( + dim # num_features for consistency with other models + ) + self.n_blocks = depth + self.num_heads = num_heads + + self.n_storage_tokens = n_storage_tokens + if self.n_storage_tokens > 0: + self.storage_tokens = nn.Parameter( + torch.empty(1, n_storage_tokens, dim, device=device) + ) + logger.debug(f"using {ffn_layer} layer as FFN") + ffn_layer_cls = ffn_layer_dict[ffn_layer] + ffn_ratio_sequence = [ffn_ratio] * depth + blocks_list = [ + SelfAttentionBlock( + dim=dim, + num_heads=num_heads, + ffn_ratio=ffn_ratio_sequence[i], + qkv_bias=qkv_bias, + proj_bias=proj_bias, + ffn_bias=ffn_bias, + drop_path=drop_path_rate, + norm_layer=norm_layer_cls, + act_layer=nn.GELU, + ffn_layer=ffn_layer_cls, + init_values=layerscale_init, + mask_k_bias=mask_k_bias, + device=device, + ) + for i in range(depth) + ] + + self.chunked_blocks = False + self.blocks = nn.ModuleList(blocks_list) + + # This norm is applied to everything, or when untying, to patch and mask tokens. + self.norm = norm_layer_cls(dim) + + self.untie_cls_and_patch_norms = untie_cls_and_patch_norms + if untie_cls_and_patch_norms: + # When untying, this norm is applied to CLS tokens and registers. + self.cls_norm = norm_layer_cls(dim) + else: + self.cls_norm = None + + self.untie_global_and_local_cls_norm = untie_global_and_local_cls_norm + if untie_global_and_local_cls_norm: + # When untying, this norm is applied to local CLS tokens and registers. + # This norm is never used during eval. + self.local_cls_norm = norm_layer_cls(dim) + else: + self.local_cls_norm = None + # self.mask_token = nn.Parameter(torch.empty(1, dim, device=device)) + if use_rope: + self.rope_embed = RopePositionEmbedding( + dim, + num_heads=num_heads, + device=device, + base=pos_embed_rope_base, + min_period=pos_embed_rope_min_period, + max_period=pos_embed_rope_max_period, + normalize_coords=pos_embed_rope_normalize_coords, + shift_coords=pos_embed_rope_shift_coords, + jitter_coords=pos_embed_rope_jitter_coords, + rescale_coords=pos_embed_rope_rescale_coords, + dtype=dtype_dict[pos_embed_rope_dtype], + ) + else: + self.rope_embed = None + + def init_weights(self): + if self.rope_embed is not None: + self.rope_embed._init_weights() + if self.n_storage_tokens > 0: + nn.init.normal_(self.storage_tokens, std=0.02) + named_apply(init_weights_vit, self) + + def prepare_tokens(self, x: Tensor) -> Tuple[Tensor, Tuple[int]]: + if self.multiview: + B, V, H, W, _ = x.shape + else: + B, H, W, _ = x.shape + x = x.flatten(1, -2) + x = self.projector(x) + return x, (H, W) + + def forward_features( + self, x: Tensor, masks: Optional[Tensor] = None + ) -> List[Dict[ViTOutput, Tensor]]: + assert masks is None + t2_x, hw_tuple = self.prepare_tokens(x) + x = t2_x + H, W = hw_tuple + for idx, blk in enumerate(self.blocks): + if self.rope_embed is not None: + rope_sincos: tuple[Tensor, Tensor] = self.rope_embed(H=H, W=W) + else: + rope_sincos = None + if self.multiview and self.mv_attention_mode == "alternating": + # every kth block run only self attention + if idx % 2 == 1: + x = rearrange(x, "B (V H W) D -> (B V) (H W) D", V=2, H=H, W=W) + x = blk(x, rope_sincos) + x = rearrange(x, "(B V) (H W) D -> B (V H W) D", V=2, H=H, W=W) + else: + x = blk(x, None) + else: + x = blk(x, rope_sincos) + x_norm = self.norm(x) + output = { + "x_norm_patchtokens": self.output_projector(x_norm), + "x_prenorm": x, + } + return output + + def forward(self, *args, **kwargs) -> List[Dict[ViTOutput, Tensor]]: + ret = self.forward_features(*args, **kwargs) + return ret + + +ViTModel = Literal[ + "vit_tiny", + "vit_small", + "vit_base", + "vit_large", + "vit_so400m", + "vit_huge2", + "vit_giant2", + "vit_7b", +] + + +def vit_from_name( + name: ViTModel, + in_dim: int, + out_dim: int, + use_rope: bool, + mv_position_mode: Literal["same", None], + mv_attention_mode: Literal["alternating", None], + multiview: bool, + pos_embed_rope_rescale_coords: float | None, + device: torch.device | None = None, +) -> MatchTransformer: + match name: + case "vit_tiny": + vit_cls = partial( + MatchTransformer, dim=192, depth=12, num_heads=3, ffn_ratio=4 + ) + case "vit_small": + vit_cls = partial( + MatchTransformer, dim=384, depth=12, num_heads=6, ffn_ratio=4 + ) + case "vit_base": + vit_cls = partial( + MatchTransformer, dim=768, depth=12, num_heads=12, ffn_ratio=4 + ) + case "vit_large": + vit_cls = partial( + MatchTransformer, dim=1024, depth=24, num_heads=16, ffn_ratio=4 + ) + case "vit_so400m": + vit_cls = partial( + MatchTransformer, + dim=1152, + depth=27, + num_heads=18, + ffn_ratio=3.777777778, + ) + case "vit_huge2": + vit_cls = partial( + MatchTransformer, dim=1280, depth=32, num_heads=20, ffn_ratio=4 + ) + case "vit_giant2": + vit_cls = partial( + MatchTransformer, dim=1536, depth=40, num_heads=24, ffn_ratio=4 + ) + case "vit_7b": + vit_cls = partial( + MatchTransformer, dim=4096, depth=40, num_heads=32, ffn_ratio=3 + ) + case _: + raise TypeError(f"Invalid ViT model: {name}") + return vit_cls( + in_dim=in_dim, + out_dim=out_dim, + use_rope=use_rope, + device=device, + multiview=multiview, + mv_position_mode=mv_position_mode, + mv_attention_mode=mv_attention_mode, + pos_embed_rope_rescale_coords=pos_embed_rope_rescale_coords, + ) diff --git a/OVOW/third_party/RoMaV2/src/romav2/vit/attention.py b/OVOW/third_party/RoMaV2/src/romav2/vit/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..a34c98f811718adafea0c6d8191208fef5488a1c --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vit/attention.py @@ -0,0 +1,181 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This software may be used and distributed in accordance with +# the terms of the DINOv3 License Agreement. + +import math +from typing import List, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from romav2.vit.utils import cat_keep_shapes, uncat_with_shapes + + +# RoPE-related functions: +def rope_rotate_half(x: Tensor) -> Tensor: + # x: [ x0 x1 x2 x3 x4 x5] + # out: [-x3 -x4 -x5 x0 x1 x2] + x1, x2 = x.chunk(2, dim=-1) + return torch.cat([-x2, x1], dim=-1) + + +def rope_apply(x: Tensor, sin: Tensor, cos: Tensor) -> Tensor: + # x: [..., D], eg [x0, x1, x2, x3, x4, x5] + # sin: [..., D], eg [sin0, sin1, sin2, sin0, sin1, sin2] + # cos: [..., D], eg [cos0, cos1, cos2, cos0, cos1, cos2] + return (x * cos) + (rope_rotate_half(x) * sin) + + +class LinearKMaskedBias(nn.Linear): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + o = self.out_features + assert o % 3 == 0 + if self.bias is not None: + self.register_buffer( + "bias_mask", torch.full_like(self.bias, fill_value=math.nan) + ) + + def forward(self, input: Tensor) -> Tensor: + masked_bias = ( + self.bias * self.bias_mask.to(self.bias.dtype) + if self.bias is not None + else None + ) + return F.linear(input, self.weight, masked_bias) + + +class SelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + mask_k_bias: bool = False, + device=None, + ) -> None: + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + linear_class = LinearKMaskedBias if mask_k_bias else nn.Linear + self.qkv = linear_class(dim, dim * 3, bias=qkv_bias, device=device) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim, bias=proj_bias, device=device) + self.proj_drop = nn.Dropout(proj_drop) + + def apply_rope( + self, q: Tensor, k: Tensor, rope: Tensor | Tuple[Tensor, Tensor] + ) -> Tuple[Tensor, Tensor]: + # All operations will use the dtype of rope, the output is cast back to the dtype of q and k + q_dtype = q.dtype + k_dtype = k.dtype + sin, cos = rope + rope_dtype = sin.dtype + q = q.to(dtype=rope_dtype) + k = k.to(dtype=rope_dtype) + N = q.shape[-2] + prefix = N - sin.shape[-2] + assert prefix >= 0 + q_prefix = q[:, :, :prefix, :] + q = rope_apply(q[:, :, prefix:, :], sin, cos) # [B, head, hw, D//head] + q = torch.cat((q_prefix, q), dim=-2) # [B, head, N, D//head] + k_prefix = k[:, :, :prefix, :] + k = rope_apply(k[:, :, prefix:, :], sin, cos) # [B, head, hw, D//head] + k = torch.cat((k_prefix, k), dim=-2) # [B, head, N, D//head] + q = q.to(dtype=q_dtype) + k = k.to(dtype=k_dtype) + return q, k + + def forward(self, x: Tensor, attn_bias=None, rope: Tensor = None) -> Tensor: + qkv = self.qkv(x) + attn_v = self.compute_attention(qkv=qkv, attn_bias=attn_bias, rope=rope) + x = self.proj(attn_v) + x = self.proj_drop(x) + return x + + def forward_list(self, x_list, attn_bias=None, rope_list=None) -> List[Tensor]: + assert len(x_list) == len(rope_list) # should be enforced by the Block + x_flat, shapes, num_tokens = cat_keep_shapes(x_list) + qkv_flat = self.qkv(x_flat) + qkv_list = uncat_with_shapes(qkv_flat, shapes, num_tokens) + att_out = [] + for _, (qkv, _, rope) in enumerate(zip(qkv_list, shapes, rope_list)): + att_out.append(self.compute_attention(qkv, attn_bias=attn_bias, rope=rope)) + x_flat, shapes, num_tokens = cat_keep_shapes(att_out) + x_flat = self.proj(x_flat) + return uncat_with_shapes(x_flat, shapes, num_tokens) + + def compute_attention(self, qkv: Tensor, attn_bias=None, rope=None) -> Tensor: + assert attn_bias is None + B, N, _ = qkv.shape + C = self.qkv.in_features + + qkv = qkv.reshape(B, N, 3, self.num_heads, C // self.num_heads) + q, k, v = torch.unbind(qkv, 2) + q, k, v = [t.transpose(1, 2) for t in [q, k, v]] + if rope is not None: + q, k = self.apply_rope(q, k, rope) + x = torch.nn.functional.scaled_dot_product_attention(q, k, v) + x = x.transpose(1, 2) + return x.reshape([B, N, C]) + + +class CausalSelfAttention(nn.Module): + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = False, + proj_bias: bool = True, + attn_drop: float = 0.0, + proj_drop: float = 0.0, + ) -> None: + super().__init__() + self.dim = dim + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = attn_drop + self.proj = nn.Linear(dim, dim, bias=proj_bias) + self.proj_drop = nn.Dropout(proj_drop) + + def init_weights( + self, + init_attn_std: float | None = None, + init_proj_std: float | None = None, + factor: float = 1.0, + ) -> None: + init_attn_std = init_attn_std or (self.dim**-0.5) + init_proj_std = init_proj_std or init_attn_std * factor + nn.init.normal_(self.qkv.weight, std=init_attn_std) + nn.init.normal_(self.proj.weight, std=init_proj_std) + if self.qkv.bias is not None: + nn.init.zeros_(self.qkv.bias) + if self.proj.bias is not None: + nn.init.zeros_(self.proj.bias) + + def forward(self, x: Tensor, is_causal: bool = True) -> Tensor: + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + q, k, v = torch.unbind(qkv, 2) + q, k, v = [t.transpose(1, 2) for t in [q, k, v]] + x = torch.nn.functional.scaled_dot_product_attention( + q, + k, + v, + attn_mask=None, + dropout_p=self.attn_drop if self.training else 0, + is_causal=is_causal, + ) + x = x.transpose(1, 2).contiguous().view(B, N, C) + x = self.proj_drop(self.proj(x)) + return x diff --git a/OVOW/third_party/RoMaV2/src/romav2/vit/block.py b/OVOW/third_party/RoMaV2/src/romav2/vit/block.py new file mode 100644 index 0000000000000000000000000000000000000000..40d48ac167ae2825ec89ab681ec484e68b713a44 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vit/block.py @@ -0,0 +1,293 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This software may be used and distributed in accordance with +# the terms of the DINOv3 License Agreement. + +from typing import Callable, List, Optional + +import torch +from torch import Tensor, nn + +from romav2.vit.utils import cat_keep_shapes, uncat_with_shapes + +from .attention import CausalSelfAttention, SelfAttention +from .ffn_layers import Mlp +from .layer_scale import LayerScale + +torch._dynamo.config.automatic_dynamic_shapes = False +torch._dynamo.config.accumulated_cache_size_limit = 1024 + + +class SelfAttentionBlock(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + ffn_ratio: float = 4.0, + qkv_bias: bool = False, + proj_bias: bool = True, + ffn_bias: bool = True, + drop: float = 0.0, + attn_drop: float = 0.0, + init_values=None, + drop_path: float = 0.0, + act_layer: Callable[..., nn.Module] = nn.GELU, + norm_layer: Callable[..., nn.Module] = nn.LayerNorm, + attn_class: Callable[..., nn.Module] = SelfAttention, + ffn_layer: Callable[..., nn.Module] = Mlp, + mask_k_bias: bool = False, + device=None, + ) -> None: + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = attn_class( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + proj_bias=proj_bias, + attn_drop=attn_drop, + proj_drop=drop, + mask_k_bias=mask_k_bias, + device=device, + ) + self.ls1 = ( + LayerScale(dim, init_values=init_values, device=device) + if init_values + else nn.Identity() + ) + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * ffn_ratio) + self.mlp = ffn_layer( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + bias=ffn_bias, + device=device, + ) + self.ls2 = ( + LayerScale(dim, init_values=init_values, device=device) + if init_values + else nn.Identity() + ) + + self.sample_drop_ratio = drop_path + + @staticmethod + def _maybe_index_rope( + rope: tuple[Tensor, Tensor] | None, indices: Tensor + ) -> tuple[Tensor, Tensor] | None: + if rope is None: + return None + + sin, cos = rope + assert sin.ndim == cos.ndim + if sin.ndim == 4: + # If the rope embedding has a batch dimension (is different for each batch element), index into it + return sin[indices], cos[indices] # [batch, heads, patches, embed_dim] + else: + # No batch dimension, do not index + return sin, cos # [heads, patches, embed_dim] or [patches, embed_dim] + + def _forward(self, x: Tensor, rope=None) -> Tensor: + """ + This is the reference implementation for a single tensor, matching what is done below for a list. + We call the list op on [x] instead of this function. + """ + b, _, _ = x.shape + sample_subset_size = max(int(b * (1 - self.sample_drop_ratio)), 1) + residual_scale_factor = b / sample_subset_size + + if self.training and self.sample_drop_ratio > 0.0: + indices_1 = (torch.randperm(b, device=x.device))[:sample_subset_size] + + x_subset_1 = x[indices_1] + rope_subset = self._maybe_index_rope(rope, indices_1) + residual_1 = self.attn(self.norm1(x_subset_1), rope=rope_subset) + + x_attn = torch.index_add( + x, + dim=0, + source=self.ls1(residual_1), + index=indices_1, + alpha=residual_scale_factor, + ) + + indices_2 = (torch.randperm(b, device=x.device))[:sample_subset_size] + + x_subset_2 = x_attn[indices_2] + residual_2 = self.mlp(self.norm2(x_subset_2)) + + x_ffn = torch.index_add( + x_attn, + dim=0, + source=self.ls2(residual_2), + index=indices_2, + alpha=residual_scale_factor, + ) + else: + x_attn = x + self.ls1(self.attn(self.norm1(x), rope=rope)) + x_ffn = x_attn + self.ls2(self.mlp(self.norm2(x_attn))) + + return x_ffn + + def _forward_list(self, x_list: List[Tensor], rope_list=None) -> List[Tensor]: + """ + This list operator concatenates the tokens from the list of inputs together to save + on the elementwise operations. Torch-compile memory-planning allows hiding the overhead + related to concat ops. + """ + b_list = [x.shape[0] for x in x_list] + sample_subset_sizes = [ + max(int(b * (1 - self.sample_drop_ratio)), 1) for b in b_list + ] + residual_scale_factors = [ + b / sample_subset_size + for b, sample_subset_size in zip(b_list, sample_subset_sizes) + ] + + if self.training and self.sample_drop_ratio > 0.0: + indices_1_list = [ + (torch.randperm(b, device=x.device))[:sample_subset_size] + for x, b, sample_subset_size in zip(x_list, b_list, sample_subset_sizes) + ] + x_subset_1_list = [ + x[indices_1] for x, indices_1 in zip(x_list, indices_1_list) + ] + + if rope_list is not None: + rope_subset_list = [ + self._maybe_index_rope(rope, indices_1) + for rope, indices_1 in zip(rope_list, indices_1_list) + ] + else: + rope_subset_list = rope_list + + flattened, shapes, num_tokens = cat_keep_shapes(x_subset_1_list) + norm1 = uncat_with_shapes(self.norm1(flattened), shapes, num_tokens) + residual_1_list = self.attn.forward_list(norm1, rope_list=rope_subset_list) + + x_attn_list = [ + torch.index_add( + x, + dim=0, + source=self.ls1(residual_1), + index=indices_1, + alpha=residual_scale_factor, + ) + for x, residual_1, indices_1, residual_scale_factor in zip( + x_list, residual_1_list, indices_1_list, residual_scale_factors + ) + ] + + indices_2_list = [ + (torch.randperm(b, device=x.device))[:sample_subset_size] + for x, b, sample_subset_size in zip(x_list, b_list, sample_subset_sizes) + ] + x_subset_2_list = [ + x[indices_2] for x, indices_2 in zip(x_attn_list, indices_2_list) + ] + flattened, shapes, num_tokens = cat_keep_shapes(x_subset_2_list) + norm2_flat = self.norm2(flattened) + norm2_list = uncat_with_shapes(norm2_flat, shapes, num_tokens) + + residual_2_list = self.mlp.forward_list(norm2_list) + + x_ffn = [ + torch.index_add( + x_attn, + dim=0, + source=self.ls2(residual_2), + index=indices_2, + alpha=residual_scale_factor, + ) + for x_attn, residual_2, indices_2, residual_scale_factor in zip( + x_attn_list, residual_2_list, indices_2_list, residual_scale_factors + ) + ] + else: + x_out = [] + for x, rope in zip(x_list, rope_list): + x_attn = x + self.ls1(self.attn(self.norm1(x), rope=rope)) + x_ffn = x_attn + self.ls2(self.mlp(self.norm2(x_attn))) + x_out.append(x_ffn) + x_ffn = x_out + + return x_ffn + + def forward(self, x_or_x_list, rope_or_rope_list=None) -> List[Tensor]: + if isinstance(x_or_x_list, Tensor): + return self._forward_list([x_or_x_list], rope_list=[rope_or_rope_list])[0] + elif isinstance(x_or_x_list, list): + if rope_or_rope_list is None: + rope_or_rope_list = [None for x in x_or_x_list] + return self._forward_list(x_or_x_list, rope_list=rope_or_rope_list) + else: + raise AssertionError + + +class CausalSelfAttentionBlock(nn.Module): + def __init__( + self, + dim: int, + num_heads: int, + ffn_ratio: float = 4.0, + ls_init_value: Optional[float] = None, + is_causal: bool = True, + act_layer: Callable = nn.GELU, + norm_layer: Callable = nn.LayerNorm, + dropout_prob: float = 0.0, + ): + super().__init__() + + self.dim = dim + self.is_causal = is_causal + self.ls1 = ( + LayerScale(dim, init_values=ls_init_value) + if ls_init_value + else nn.Identity() + ) + self.attention_norm = norm_layer(dim) + self.attention = CausalSelfAttention( + dim, num_heads, attn_drop=dropout_prob, proj_drop=dropout_prob + ) + + self.ffn_norm = norm_layer(dim) + ffn_hidden_dim = int(dim * ffn_ratio) + self.feed_forward = Mlp( + in_features=dim, + hidden_features=ffn_hidden_dim, + drop=dropout_prob, + act_layer=act_layer, + ) + + self.ls2 = ( + LayerScale(dim, init_values=ls_init_value) + if ls_init_value + else nn.Identity() + ) + + def init_weights( + self, + init_attn_std: float | None = None, + init_proj_std: float | None = None, + init_fc_std: float | None = None, + factor: float = 1.0, + ) -> None: + init_attn_std = init_attn_std or (self.dim**-0.5) + init_proj_std = init_proj_std or init_attn_std * factor + init_fc_std = init_fc_std or (2 * self.dim) ** -0.5 + self.attention.init_weights(init_attn_std, init_proj_std) + self.attention_norm.reset_parameters() + nn.init.normal_(self.feed_forward.fc1.weight, std=init_fc_std) + nn.init.normal_(self.feed_forward.fc2.weight, std=init_proj_std) + self.ffn_norm.reset_parameters() + + def forward( + self, + x: torch.Tensor, + ): + x_attn = x + self.ls1(self.attention(self.attention_norm(x), self.is_causal)) + x_ffn = x_attn + self.ls2(self.feed_forward(self.ffn_norm(x_attn))) + return x_ffn diff --git a/OVOW/third_party/RoMaV2/src/romav2/vit/ffn_layers.py b/OVOW/third_party/RoMaV2/src/romav2/vit/ffn_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..3f088d71c847e3d82cd182f27c756550cfb9747b --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vit/ffn_layers.py @@ -0,0 +1,83 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This software may be used and distributed in accordance with +# the terms of the DINOv3 License Agreement. + +from typing import Callable, List, Optional + +import torch.nn.functional as F +from torch import Tensor, nn + +from romav2.vit.utils import cat_keep_shapes, uncat_with_shapes + + +class ListForwardMixin(object): + def forward(self, x: Tensor): + raise NotImplementedError + + def forward_list(self, x_list: List[Tensor]) -> List[Tensor]: + x_flat, shapes, num_tokens = cat_keep_shapes(x_list) + x_flat = self.forward(x_flat) + return uncat_with_shapes(x_flat, shapes, num_tokens) + + +class Mlp(nn.Module, ListForwardMixin): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Callable[..., nn.Module] = nn.GELU, + drop: float = 0.0, + bias: bool = True, + device=None, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features, bias=bias, device=device) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features, bias=bias, device=device) + self.drop = nn.Dropout(drop) + + def forward(self, x: Tensor) -> Tensor: + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class SwiGLUFFN(nn.Module, ListForwardMixin): + def __init__( + self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_layer: Optional[Callable[..., nn.Module]] = None, + drop: float = 0.0, + bias: bool = True, + align_to: int = 8, + device=None, + ) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + d = int(hidden_features * 2 / 3) + swiglu_hidden_features = d + (-d % align_to) + self.w1 = nn.Linear( + in_features, swiglu_hidden_features, bias=bias, device=device + ) + self.w2 = nn.Linear( + in_features, swiglu_hidden_features, bias=bias, device=device + ) + self.w3 = nn.Linear( + swiglu_hidden_features, out_features, bias=bias, device=device + ) + + def forward(self, x: Tensor) -> Tensor: + x1 = self.w1(x) + x2 = self.w2(x) + hidden = F.silu(x1) * x2 + return self.w3(hidden) diff --git a/OVOW/third_party/RoMaV2/src/romav2/vit/layer_scale.py b/OVOW/third_party/RoMaV2/src/romav2/vit/layer_scale.py new file mode 100644 index 0000000000000000000000000000000000000000..0b72b7c64c9cc38fd4e3db63e9c90f0158caa36c --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vit/layer_scale.py @@ -0,0 +1,29 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This software may be used and distributed in accordance with +# the terms of the DINOv3 License Agreement. + +from typing import Union + +import torch +from torch import Tensor, nn + + +class LayerScale(nn.Module): + def __init__( + self, + dim: int, + init_values: Union[float, Tensor] = 1e-5, + inplace: bool = False, + device=None, + ) -> None: + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(torch.empty(dim, device=device)) + self.init_values = init_values + + def reset_parameters(self): + nn.init.constant_(self.gamma, self.init_values) + + def forward(self, x: Tensor) -> Tensor: + return x.mul_(self.gamma) if self.inplace else x * self.gamma diff --git a/OVOW/third_party/RoMaV2/src/romav2/vit/patch_embed.py b/OVOW/third_party/RoMaV2/src/romav2/vit/patch_embed.py new file mode 100644 index 0000000000000000000000000000000000000000..d377de946abcf3f48ab49200733f79c4d5c7d848 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vit/patch_embed.py @@ -0,0 +1,94 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This software may be used and distributed in accordance with +# the terms of the DINOv3 License Agreement. + +import math +from typing import Callable, Tuple, Union + +from torch import Tensor, nn + + +def make_2tuple(x): + if isinstance(x, tuple): + assert len(x) == 2 + return x + + assert isinstance(x, int) + return (x, x) + + +class PatchEmbed(nn.Module): + """ + 2D image to patch embedding: (B,C,H,W) -> (B,N,D) + + Args: + img_size: Image size. + patch_size: Patch token size. + in_chans: Number of input image channels. + embed_dim: Number of linear projection output channels. + norm_layer: Normalization layer. + """ + + def __init__( + self, + img_size: Union[int, Tuple[int, int]] = 224, + patch_size: Union[int, Tuple[int, int]] = 16, + in_chans: int = 3, + embed_dim: int = 768, + norm_layer: Callable | None = None, + flatten_embedding: bool = True, + ) -> None: + super().__init__() + + image_HW = make_2tuple(img_size) + patch_HW = make_2tuple(patch_size) + patch_grid_size = ( + image_HW[0] // patch_HW[0], + image_HW[1] // patch_HW[1], + ) + + self.img_size = image_HW + self.patch_size = patch_HW + self.patches_resolution = patch_grid_size + self.num_patches = patch_grid_size[0] * patch_grid_size[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.flatten_embedding = flatten_embedding + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW + ) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x: Tensor) -> Tensor: + _, _, H, W = x.shape + + x = self.proj(x) # B C H W + H, W = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) # B HW C + x = self.norm(x) + if not self.flatten_embedding: + x = x.reshape(-1, H, W, self.embed_dim) # B H W C + return x + + def flops(self) -> float: + Ho, Wo = self.patches_resolution + flops = ( + Ho + * Wo + * self.embed_dim + * self.in_chans + * (self.patch_size[0] * self.patch_size[1]) + ) + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + + def reset_parameters(self): + k = 1 / (self.in_chans * (self.patch_size[0] ** 2)) + nn.init.uniform_(self.proj.weight, -math.sqrt(k), math.sqrt(k)) + if self.proj.bias is not None: + nn.init.uniform_(self.proj.bias, -math.sqrt(k), math.sqrt(k)) diff --git a/OVOW/third_party/RoMaV2/src/romav2/vit/rms_norm.py b/OVOW/third_party/RoMaV2/src/romav2/vit/rms_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..1d0a89c47c5e71687cadbf47fef567b2c6a2b3b4 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vit/rms_norm.py @@ -0,0 +1,24 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This software may be used and distributed in accordance with +# the terms of the DINOv3 License Agreement. + +import torch +from torch import Tensor, nn + + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-5): + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.eps = eps + + def reset_parameters(self) -> None: + nn.init.constant_(self.weight, 1) + + def _norm(self, x: Tensor) -> Tensor: + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x: Tensor) -> Tensor: + output = self._norm(x.float()).type_as(x) + return output * self.weight diff --git a/OVOW/third_party/RoMaV2/src/romav2/vit/rope.py b/OVOW/third_party/RoMaV2/src/romav2/vit/rope.py new file mode 100644 index 0000000000000000000000000000000000000000..114dc76e7c66bccb1663a9a64d287f3b2cee8c56 --- /dev/null +++ b/OVOW/third_party/RoMaV2/src/romav2/vit/rope.py @@ -0,0 +1,133 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This software may be used and distributed in accordance with +# the terms of the DINOv3 License Agreement. + +import math +from typing import Literal + +import numpy as np +import torch +from torch import Tensor, nn + + +# RoPE positional embedding with no mixing of coordinates (axial) and no learnable weights +# Supports two parametrizations of the rope parameters: either using `base` or `min_period` and `max_period`. +class RopePositionEmbedding(nn.Module): + def __init__( + self, + embed_dim: int, + *, + num_heads: int, + base: float | None = 100.0, + min_period: float | None = None, + max_period: float | None = None, + normalize_coords: Literal["min", "max", "separate"] = "separate", + shift_coords: float | None = None, + jitter_coords: float | None = None, + rescale_coords: float | None = None, + dtype: torch.dtype | None = None, + device: torch.device | None = None, + ): + super().__init__() + assert embed_dim % (4 * num_heads) == 0 + both_periods = min_period is not None and max_period is not None + if (base is None and not both_periods) or (base is not None and both_periods): + raise ValueError( + "Either `base` or `min_period`+`max_period` must be provided." + ) + + D_head = embed_dim // num_heads + self.base = base + self.min_period = min_period + self.max_period = max_period + self.D_head = D_head + self.normalize_coords = normalize_coords + self.shift_coords = shift_coords + self.jitter_coords = jitter_coords + self.rescale_coords = rescale_coords + + # Needs persistent=True because we do teacher.load_state_dict(student.state_dict()) to initialize the teacher + self.dtype = dtype # Don't rely on self.periods.dtype + self.register_buffer( + "periods", + torch.empty(D_head // 4, device=device, dtype=dtype), + persistent=True, + ) + self._init_weights() + + def forward(self, *, H: int, W: int) -> tuple[Tensor, Tensor]: + device = self.periods.device + dtype = self.dtype + dd = {"device": device, "dtype": dtype} + + # Prepare coords in range [-1, +1] + if self.normalize_coords == "max": + max_HW = max(H, W) + coords_h = torch.arange(0.5, H, **dd) / max_HW # [H] + coords_w = torch.arange(0.5, W, **dd) / max_HW # [W] + elif self.normalize_coords == "min": + min_HW = min(H, W) + coords_h = torch.arange(0.5, H, **dd) / min_HW # [H] + coords_w = torch.arange(0.5, W, **dd) / min_HW # [W] + elif self.normalize_coords == "separate": + coords_h = torch.arange(0.5, H, **dd) / H # [H] + coords_w = torch.arange(0.5, W, **dd) / W # [W] + else: + raise ValueError(f"Unknown normalize_coords: {self.normalize_coords}") + coords = torch.stack( + torch.meshgrid(coords_h, coords_w, indexing="ij"), dim=-1 + ) # [H, W, 2] + coords = coords.flatten(0, 1) # [HW, 2] + coords = 2.0 * coords - 1.0 # Shift range [0, 1] to [-1, +1] + + # Shift coords by adding a uniform value in [-shift, shift] + if self.training and self.shift_coords is not None: + shift_hw = torch.empty(2, **dd).uniform_( + -self.shift_coords, self.shift_coords + ) + coords += shift_hw[None, :] + + # Jitter coords by multiplying the range [-1, 1] by a log-uniform value in [1/jitter, jitter] + if self.training and self.jitter_coords is not None: + jitter_max = np.log(self.jitter_coords) + jitter_min = -jitter_max + jitter_hw = torch.empty(2, **dd).uniform_(jitter_min, jitter_max).exp() + coords *= jitter_hw[None, :] + + # Rescale coords by multiplying the range [-1, 1] by a log-uniform value in [1/rescale, rescale] + if self.training and self.rescale_coords is not None: + rescale_max = np.log(self.rescale_coords) + rescale_min = -rescale_max + rescale_hw = torch.empty(1, **dd).uniform_(rescale_min, rescale_max).exp() + coords *= rescale_hw + + # Prepare angles and sin/cos + angles = ( + 2 * math.pi * coords[:, :, None] / self.periods[None, None, :] + ) # [HW, 2, D//4] + angles = angles.flatten(1, 2) # [HW, D//2] + angles = angles.tile(2) # [HW, D] + cos = torch.cos(angles) # [HW, D] + sin = torch.sin(angles) # [HW, D] + + return (sin, cos) # 2 * [HW, D] + + def _init_weights(self): + device = self.periods.device + dtype = self.dtype + if self.base is not None: + periods = self.base ** ( + 2 + * torch.arange(self.D_head // 4, device=device, dtype=dtype) + / (self.D_head // 2) + ) # [D//4] + else: + base = self.max_period / self.min_period + exponents = torch.linspace( + 0, 1, self.D_head // 4, device=device, dtype=dtype + ) # [D//4] range [0, 1] + periods = base**exponents # range [1, max_period / min_period] + periods = periods / base # range [min_period / max_period, 1] + periods = periods * self.max_period # range [min_period, max_period] + self.periods.data = periods