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import timm |
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import torchvision |
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data_config = {'input_size': (3, 384, 384), |
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'interpolation': 'bicubic', |
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'mean': (0.48145466, 0.4578275, 0.40821073), |
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'std': (0.26862954, 0.26130258, 0.27577711), |
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'crop_pct': 1.0, |
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'crop_mode': 'squash'} |
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transform_synthetic = timm.data.create_transform(**data_config, is_training=False) |
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transform_200M = torchvision.transforms.Compose([ |
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torchvision.transforms.Resize((640, 640)), |
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torchvision.transforms.ToTensor(), |
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torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], |
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std=[0.229, 0.224, 0.225]), |
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]) |
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transform_5M = torchvision.transforms.Compose([ |
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torchvision.transforms.Resize((224, 224)), |
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torchvision.transforms.ToTensor(), |
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torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], |
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std=[0.229, 0.224, 0.225]), |
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]) |