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