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""" |
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python inference.py \ |
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--variant mobilenetv3 \ |
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--checkpoint "CHECKPOINT" \ |
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--device cuda \ |
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--input-source "input.mp4" \ |
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--output-type video \ |
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--output-composition "composition.mp4" \ |
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--output-alpha "alpha.mp4" \ |
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--output-foreground "foreground.mp4" \ |
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--output-video-mbps 4 \ |
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--seq-chunk 1 |
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""" |
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|
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import torch |
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import os |
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from torch.utils.data import DataLoader |
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from torchvision import transforms |
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from typing import Optional, Tuple |
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from tqdm.auto import tqdm |
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|
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from inference_utils import VideoReader, VideoWriter, ImageSequenceReader, ImageSequenceWriter |
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|
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def convert_video(model, |
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input_source: str, |
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input_resize: Optional[Tuple[int, int]] = None, |
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downsample_ratio: Optional[float] = None, |
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output_type: str = 'video', |
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output_composition: Optional[str] = None, |
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output_alpha: Optional[str] = None, |
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output_foreground: Optional[str] = None, |
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output_video_mbps: Optional[float] = None, |
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seq_chunk: int = 1, |
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num_workers: int = 0, |
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progress: bool = True, |
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device: Optional[str] = None, |
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dtype: Optional[torch.dtype] = None): |
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|
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""" |
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Args: |
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input_source:A video file, or an image sequence directory. Images must be sorted in accending order, support png and jpg. |
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input_resize: If provided, the input are first resized to (w, h). |
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downsample_ratio: The model's downsample_ratio hyperparameter. If not provided, model automatically set one. |
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output_type: Options: ["video", "png_sequence"]. |
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output_composition: |
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The composition output path. File path if output_type == 'video'. Directory path if output_type == 'png_sequence'. |
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If output_type == 'video', the composition has green screen background. |
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If output_type == 'png_sequence'. the composition is RGBA png images. |
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output_alpha: The alpha output from the model. |
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output_foreground: The foreground output from the model. |
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seq_chunk: Number of frames to process at once. Increase it for better parallelism. |
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num_workers: PyTorch's DataLoader workers. Only use >0 for image input. |
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progress: Show progress bar. |
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device: Only need to manually provide if model is a TorchScript freezed model. |
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dtype: Only need to manually provide if model is a TorchScript freezed model. |
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""" |
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|
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assert downsample_ratio is None or (downsample_ratio > 0 and downsample_ratio <= 1), 'Downsample ratio must be between 0 (exclusive) and 1 (inclusive).' |
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assert any([output_composition, output_alpha, output_foreground]), 'Must provide at least one output.' |
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assert output_type in ['video', 'png_sequence'], 'Only support "video" and "png_sequence" output modes.' |
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assert seq_chunk >= 1, 'Sequence chunk must be >= 1' |
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assert num_workers >= 0, 'Number of workers must be >= 0' |
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|
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|
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if input_resize is not None: |
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transform = transforms.Compose([ |
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transforms.Resize(input_resize[::-1]), |
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transforms.ToTensor() |
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]) |
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else: |
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transform = transforms.ToTensor() |
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|
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if os.path.isfile(input_source): |
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source = VideoReader(input_source, transform) |
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else: |
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source = ImageSequenceReader(input_source, transform) |
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reader = DataLoader(source, batch_size=seq_chunk, pin_memory=True, num_workers=num_workers) |
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|
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if output_type == 'video': |
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frame_rate = source.frame_rate if isinstance(source, VideoReader) else 30 |
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output_video_mbps = 1 if output_video_mbps is None else output_video_mbps |
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if output_composition is not None: |
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writer_com = VideoWriter( |
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path=output_composition, |
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frame_rate=frame_rate, |
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bit_rate=int(output_video_mbps * 1000000)) |
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if output_alpha is not None: |
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writer_pha = VideoWriter( |
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path=output_alpha, |
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frame_rate=frame_rate, |
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bit_rate=int(output_video_mbps * 1000000)) |
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if output_foreground is not None: |
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writer_fgr = VideoWriter( |
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path=output_foreground, |
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frame_rate=frame_rate, |
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bit_rate=int(output_video_mbps * 1000000)) |
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else: |
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if output_composition is not None: |
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writer_com = ImageSequenceWriter(output_composition, 'png') |
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if output_alpha is not None: |
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writer_pha = ImageSequenceWriter(output_alpha, 'png') |
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if output_foreground is not None: |
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writer_fgr = ImageSequenceWriter(output_foreground, 'png') |
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|
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model = model.eval() |
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if device is None or dtype is None: |
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param = next(model.parameters()) |
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dtype = param.dtype |
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device = param.device |
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|
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if (output_composition is not None) and (output_type == 'video'): |
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bgr = torch.tensor([120, 255, 155], device=device, dtype=dtype).div(255).view(1, 1, 3, 1, 1) |
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|
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try: |
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with torch.no_grad(): |
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bar = tqdm(total=len(source), disable=not progress, dynamic_ncols=True) |
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rec = [None] * 4 |
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for src in reader: |
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|
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if downsample_ratio is None: |
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downsample_ratio = auto_downsample_ratio(*src.shape[2:]) |
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|
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src = src.to(device, dtype, non_blocking=True).unsqueeze(0) |
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fgr, pha, *rec = model(src, *rec, downsample_ratio) |
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|
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if output_foreground is not None: |
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writer_fgr.write(fgr[0]) |
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if output_alpha is not None: |
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writer_pha.write(pha[0]) |
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if output_composition is not None: |
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if output_type == 'video': |
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com = fgr * pha + bgr * (1 - pha) |
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else: |
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fgr = fgr * pha.gt(0) |
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com = torch.cat([fgr, pha], dim=-3) |
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writer_com.write(com[0]) |
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|
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bar.update(src.size(1)) |
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|
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finally: |
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|
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if output_composition is not None: |
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writer_com.close() |
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if output_alpha is not None: |
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writer_pha.close() |
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if output_foreground is not None: |
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writer_fgr.close() |
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|
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def auto_downsample_ratio(h, w): |
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""" |
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Automatically find a downsample ratio so that the largest side of the resolution be 512px. |
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""" |
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return min(512 / max(h, w), 1) |
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|
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|
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class Converter: |
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def __init__(self, variant: str, checkpoint: str, device: str): |
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self.model = MattingNetwork(variant).eval().to(device) |
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self.model.load_state_dict(torch.load(checkpoint, map_location=device)) |
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self.model = torch.jit.script(self.model) |
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self.model = torch.jit.freeze(self.model) |
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self.device = device |
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|
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def convert(self, *args, **kwargs): |
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convert_video(self.model, device=self.device, dtype=torch.float32, *args, **kwargs) |
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|
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if __name__ == '__main__': |
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import argparse |
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from model import MattingNetwork |
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|
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parser = argparse.ArgumentParser() |
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parser.add_argument('--variant', type=str, required=True, choices=['mobilenetv3', 'resnet50']) |
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parser.add_argument('--checkpoint', type=str, required=True) |
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parser.add_argument('--device', type=str, required=True) |
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parser.add_argument('--input-source', type=str, required=True) |
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parser.add_argument('--input-resize', type=int, default=None, nargs=2) |
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parser.add_argument('--downsample-ratio', type=float) |
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parser.add_argument('--output-composition', type=str) |
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parser.add_argument('--output-alpha', type=str) |
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parser.add_argument('--output-foreground', type=str) |
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parser.add_argument('--output-type', type=str, required=True, choices=['video', 'png_sequence']) |
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parser.add_argument('--output-video-mbps', type=int, default=1) |
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parser.add_argument('--seq-chunk', type=int, default=1) |
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parser.add_argument('--num-workers', type=int, default=0) |
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parser.add_argument('--disable-progress', action='store_true') |
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args = parser.parse_args() |
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|
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converter = Converter(args.variant, args.checkpoint, args.device) |
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converter.convert( |
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input_source=args.input_source, |
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input_resize=args.input_resize, |
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downsample_ratio=args.downsample_ratio, |
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output_type=args.output_type, |
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output_composition=args.output_composition, |
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output_alpha=args.output_alpha, |
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output_foreground=args.output_foreground, |
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output_video_mbps=args.output_video_mbps, |
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seq_chunk=args.seq_chunk, |
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num_workers=args.num_workers, |
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progress=not args.disable_progress |
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) |
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