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# 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"
"---"
)
)
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