(self, masks: torch.Tensor, orig_im_size: torch.Tensor)
| 74 | return mask_embedding |
| 75 | |
| 76 | def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor: |
| 77 | masks = F.interpolate( |
| 78 | masks, |
| 79 | size=(self.img_size, self.img_size), |
| 80 | mode="bilinear", |
| 81 | align_corners=False, |
| 82 | ) |
| 83 | |
| 84 | prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size) |
| 85 | masks = masks[..., : int(prepadded_size[0]), : int(prepadded_size[1])] |
| 86 | |
| 87 | orig_im_size = orig_im_size.to(torch.int64) |
| 88 | h, w = orig_im_size[0], orig_im_size[1] |
| 89 | masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False) |
| 90 | return masks |
| 91 | |
| 92 | def select_masks( |
| 93 | self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int |
no test coverage detected