Input: pch_res: n x c x pch_size x pch_size, float index_infos: (h_start, h_end, w_start, w_end)
(self, pch_res, index_infos)
| 768 | return torch.tile(torch.tensor(weights, device=device), (nbatches, 3, 1, 1)) |
| 769 | |
| 770 | def update(self, pch_res, index_infos): |
| 771 | ''' |
| 772 | Input: |
| 773 | pch_res: n x c x pch_size x pch_size, float |
| 774 | index_infos: (h_start, h_end, w_start, w_end) |
| 775 | ''' |
| 776 | if index_infos is None: |
| 777 | w_start, w_end = self.w_start, self.w_end |
| 778 | h_start, h_end = self.h_start, self.h_end |
| 779 | else: |
| 780 | h_start, h_end, w_start, w_end = index_infos |
| 781 | |
| 782 | self.im_res[:, :, h_start:h_end, w_start:w_end] += pch_res |
| 783 | self.pixel_count[:, :, h_start:h_end, w_start:w_end] += 1 |
| 784 | |
| 785 | def update_gaussian(self, pch_res, index_infos): |
| 786 | ''' |