# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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. """ Run this command to interactively debug: PYTHONPATH=. python cosmos_predict1/autoregressive/datasets/video_dataset.py """ import os import traceback import warnings from concurrent.futures import ThreadPoolExecutor, as_completed import numpy as np import torch from decord import VideoReader, cpu from torch.utils.data import Dataset from tqdm import tqdm from cosmos_predict1.autoregressive.configs.base.dataset import VideoDatasetConfig from cosmos_predict1.autoregressive.datasets.dataset_utils import ( CenterCrop, Normalize, ResizeSmallestSideAspectPreserving, ) class VideoDataset(Dataset): def __init__(self, config: VideoDatasetConfig): """Video Dataset class for loading video-to-video generation data.""" super().__init__() self.dataset_dir = config.dataset_dir self.sequence_interval = config.sequence_interval self.sequence_length = config.num_frames self.video_size = config.video_size self.start_frame_interval = config.start_frame_interval self.video_dir = self.dataset_dir self.video_paths = [os.path.join(self.video_dir, f) for f in os.listdir(self.video_dir) if f.endswith(".mp4")] print(f"{len(self.video_paths)} videos in total") self.samples = self._init_samples(self.video_paths) self.samples = sorted(self.samples, key=lambda x: (x["video_path"], x["frame_ids"][0])) print(f"{len(self.samples)} samples in total") self.wrong_number = 0 self.resize_transform = ResizeSmallestSideAspectPreserving( input_keys=["video"], args={"img_w": self.video_size[1], "img_h": self.video_size[0]}, ) self.crop_transform = CenterCrop( input_keys=["video"], args={"img_w": self.video_size[1], "img_h": self.video_size[0]}, ) self.normalize_transform = Normalize( input_keys=["video"], args={"mean": 0.5, "std": 0.5}, ) def __str__(self): return f"{len(self.video_paths)} samples from {self.dataset_dir}" def _init_samples(self, video_paths): samples = [] with ThreadPoolExecutor(32) as executor: future_to_video_path = { executor.submit(self._load_and_process_video_path, video_path): video_path for video_path in video_paths } for future in tqdm(as_completed(future_to_video_path), total=len(video_paths)): samples.extend(future.result()) return samples def _load_and_process_video_path(self, video_path): vr = VideoReader(video_path, ctx=cpu(0), num_threads=2) n_frames = len(vr) samples = [] for frame_i in range(0, n_frames, self.start_frame_interval): sample = dict() sample["video_path"] = video_path sample["orig_num_frames"] = n_frames sample["chunk_index"] = -1 sample["frame_ids"] = [] curr_frame_i = frame_i while True: if curr_frame_i > (n_frames - 1): break sample["frame_ids"].append(curr_frame_i) if len(sample["frame_ids"]) == self.sequence_length: break curr_frame_i += self.sequence_interval # make sure there are sequence_length number of frames if len(sample["frame_ids"]) == self.sequence_length: sample["chunk_index"] += 1 samples.append(sample) return samples def __len__(self): return len(self.samples) def _load_video(self, video_path, frame_ids): vr = VideoReader(video_path, ctx=cpu(0), num_threads=2) assert (np.array(frame_ids) < len(vr)).all(), "Some frame_ids are out of range." assert (np.array(frame_ids) >= 0).all(), "Some frame_ids are negative." vr.seek(0) frame_data = vr.get_batch(frame_ids).asnumpy() fps = vr.get_avg_fps() return frame_data, fps def _get_frames(self, video_path, frame_ids): frames, fps = self._load_video(video_path, frame_ids) frames = frames.astype(np.uint8) frames = torch.from_numpy(frames) frames = frames.permute(0, 3, 1, 2) # Rearrange from [T, H, W, C] to [T, C, H, W] return frames, fps def __getitem__(self, index): try: sample = self.samples[index] video_path = sample["video_path"] frame_ids = sample["frame_ids"] data = dict() video, fps = self._get_frames(video_path, frame_ids) data["video"] = video data["fps"] = fps data["num_frames"] = self.sequence_length data["orig_num_frames"] = sample["orig_num_frames"] data["chunk_index"] = sample["chunk_index"] data["frame_start"] = frame_ids[0] data["frame_end"] = frame_ids[-1] data["video_name"] = { "video_path": video_path, "start_frame_id": str(frame_ids[0]), } # resize video to smallest side aspect preserving data = self.resize_transform(data) # center crop video data = self.crop_transform(data) # normalize video data = self.normalize_transform(data) data["video"] = data["video"].permute(1, 0, 2, 3) # Rearrange from [T, C, H, W] to [C, T, H, W] return data except Exception: warnings.warn( f"Invalid data encountered: {self.samples[index]['video_path']}. Skipped " f"(by randomly sampling another sample in the same dataset)." ) warnings.warn("FULL TRACEBACK:") warnings.warn(traceback.format_exc()) self.wrong_number += 1 print(self.wrong_number) return self[np.random.randint(len(self.samples))] if __name__ == "__main__": config = VideoDatasetConfig(dataset_dir="datasets/cosmos_nemo_assets/videos/") dataset = VideoDataset(config) indices = [0, 1, 2, -1] for idx in indices: data = dataset[idx] print( ( f"{idx=} " f"{data['video'].sum()=}\n" f"{data['video'].shape=}\n" f"{data['video_name']=}\n" f"{data.keys()=}\n" "---" ) )