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import torch
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class ImageResizeTransform:
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"""
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Transform that resizes images loaded from a dataset
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(BGR data in NCHW channel order, typically uint8) to a format ready to be
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consumed by DensePose training (BGR float32 data in NCHW channel order)
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"""
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def __init__(self, min_size: int = 800, max_size: int = 1333):
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self.min_size = min_size
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self.max_size = max_size
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def __call__(self, images: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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images (torch.Tensor): tensor of size [N, 3, H, W] that contains
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BGR data (typically in uint8)
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Returns:
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images (torch.Tensor): tensor of size [N, 3, H1, W1] where
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H1 and W1 are chosen to respect the specified min and max sizes
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and preserve the original aspect ratio, the data channels
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follow BGR order and the data type is `torch.float32`
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"""
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images = images.float()
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min_size = min(images.shape[-2:])
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max_size = max(images.shape[-2:])
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scale = min(self.min_size / min_size, self.max_size / max_size)
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images = torch.nn.functional.interpolate(
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images,
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scale_factor=scale,
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mode="bilinear",
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align_corners=False,
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)
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return images
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