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217
tools/decode/vendor/cosmos_tokenizer/video_cli.py
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tools/decode/vendor/cosmos_tokenizer/video_cli.py
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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""A CLI to run CausalVideoTokenizer on plain videos based on torch.jit.
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Usage:
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python3 -m cosmos_tokenizer.video_cli \
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--video_pattern 'path/to/video/samples/*.mp4' \
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--output_dir ./reconstructions \
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--checkpoint_enc ./pretrained_ckpts/CosmosCV_f4x8x8/encoder.jit \
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--checkpoint_dec ./pretrained_ckpts/CosmosCV_f4x8x8/decoder.jit
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Optionally, you can run the model in pure PyTorch mode:
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python3 -m cosmos_tokenizer.video_cli \
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--video_pattern 'path/to/video/samples/*.mp4' \
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--mode=torch \
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--tokenizer_type=CV \
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--temporal_compression=4 \
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--spatial_compression=8 \
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--checkpoint_enc ./pretrained_ckpts/CosmosCV_f4x8x8/encoder.jit \
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--checkpoint_dec ./pretrained_ckpts/CosmosCV_f4x8x8/decoder.jit
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"""
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import os
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from argparse import ArgumentParser, Namespace
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from typing import Any
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import sys
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import numpy as np
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from loguru import logger as logging
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from cosmos_tokenizer.networks import TokenizerConfigs
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from cosmos_tokenizer.utils import (
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get_filepaths,
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get_output_filepath,
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read_video,
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resize_video,
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write_video,
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)
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from cosmos_tokenizer.video_lib import CausalVideoTokenizer
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def _parse_args() -> tuple[Namespace, dict[str, Any]]:
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parser = ArgumentParser(description="A CLI for CausalVideoTokenizer.")
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parser.add_argument(
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"--video_pattern",
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type=str,
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default="path/to/videos/*.mp4",
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help="Glob pattern.",
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)
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parser.add_argument(
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"--checkpoint",
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type=str,
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default=None,
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help="JIT full Autoencoder model filepath.",
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)
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parser.add_argument(
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"--checkpoint_enc",
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type=str,
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default=None,
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help="JIT Encoder model filepath.",
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)
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parser.add_argument(
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"--checkpoint_dec",
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type=str,
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default=None,
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help="JIT Decoder model filepath.",
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)
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parser.add_argument(
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"--tokenizer_type",
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type=str,
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choices=["CV", "DV"],
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help="Specifies the tokenizer type.",
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)
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parser.add_argument(
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"--spatial_compression",
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type=int,
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choices=[8, 16],
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default=8,
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help="The spatial compression factor.",
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)
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parser.add_argument(
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"--temporal_compression",
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type=int,
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choices=[4, 8],
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default=4,
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help="The temporal compression factor.",
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)
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parser.add_argument(
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"--mode",
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type=str,
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choices=["torch", "jit"],
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default="jit",
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help="Specify the backend: native 'torch' or 'jit' (default: 'jit')",
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)
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parser.add_argument(
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"--short_size",
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type=int,
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default=None,
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help="The size to resample inputs. None, by default.",
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)
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parser.add_argument(
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"--temporal_window",
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type=int,
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default=17,
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help="The temporal window to operate at a time.",
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)
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parser.add_argument(
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"--dtype",
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type=str,
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default="bfloat16",
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help="Sets the precision, default bfloat16.",
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)
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parser.add_argument(
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"--device",
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type=str,
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default="cuda",
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help="Device for invoking the model.",
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)
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parser.add_argument(
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"--output_dir", type=str, default=None, help="Output directory."
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)
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parser.add_argument(
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"--output_fps",
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type=float,
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default=24.0,
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help="Output frames-per-second (FPS).",
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)
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parser.add_argument(
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"--save_input",
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action="store_true",
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help="If on, the input video will be be outputted too.",
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)
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args = parser.parse_args()
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return args
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logging.info("Initializes args ...")
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args = _parse_args()
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if args.mode == "torch" and args.tokenizer_type not in ["CV", "DV"]:
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logging.error("'torch' backend requires the tokenizer_type of 'CV' or 'DV'.")
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sys.exit(1)
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def _run_eval() -> None:
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"""Invokes JIT-compiled CausalVideoTokenizer on an input video."""
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if (
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args.checkpoint_enc is None
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and args.checkpoint_dec is None
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and args.checkpoint is None
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):
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logging.warning(
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"Aborting. Both encoder or decoder JIT required. Or provide the full autoencoder JIT model."
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)
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return
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if args.mode == "torch":
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tokenizer_config = TokenizerConfigs[args.tokenizer_type].value
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tokenizer_config.update(dict(spatial_compression=args.spatial_compression))
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tokenizer_config.update(dict(temporal_compression=args.temporal_compression))
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else:
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tokenizer_config = None
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logging.info(
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f"Loading a torch.jit model `{os.path.dirname(args.checkpoint or args.checkpoint_enc or args.checkpoint_dec)}` ..."
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)
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autoencoder = CausalVideoTokenizer(
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checkpoint=args.checkpoint,
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checkpoint_enc=args.checkpoint_enc,
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checkpoint_dec=args.checkpoint_dec,
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tokenizer_config=tokenizer_config,
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device=args.device,
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dtype=args.dtype,
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)
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logging.info(f"Looking for files matching video_pattern={args.video_pattern} ...")
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filepaths = get_filepaths(args.video_pattern)
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logging.info(f"Found {len(filepaths)} videos from {args.video_pattern}.")
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for filepath in filepaths:
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logging.info(f"Reading video {filepath} ...")
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video = read_video(filepath)
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video = resize_video(video, short_size=args.short_size)
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logging.info("Invoking the autoencoder model in ... ")
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batch_video = video[np.newaxis, ...]
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output_video = autoencoder(batch_video, temporal_window=args.temporal_window)[0]
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logging.info("Constructing output filepath ...")
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output_filepath = get_output_filepath(filepath, output_dir=args.output_dir)
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logging.info(f"Outputing {output_filepath} ...")
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write_video(output_filepath, output_video, fps=args.output_fps)
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if args.save_input:
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ext = os.path.splitext(output_filepath)[-1]
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input_filepath = output_filepath.replace(ext, "_input" + ext)
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write_video(input_filepath, video, fps=args.output_fps)
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@logging.catch(reraise=True)
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def main() -> None:
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_run_eval()
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if __name__ == "__main__":
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main()
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