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Running
on
Zero
| import torch | |
| from .position import PositionEmbeddingSine | |
| def split_feature(feature, | |
| num_splits=2, | |
| channel_last=False, | |
| ): | |
| if channel_last: # [B, H, W, C] | |
| b, h, w, c = feature.size() | |
| # if h % num_splits: | |
| # feature = feature[:, :, :-1, :] | |
| # if w % num_splits: | |
| # feature = feature[:, :, :, :-1] | |
| # b, h, w, c = feature.size() | |
| assert h % num_splits == 0 and w % num_splits == 0 | |
| b_new = b * num_splits * num_splits | |
| h_new = h // num_splits | |
| w_new = w // num_splits | |
| feature = feature.view(b, num_splits, h // num_splits, num_splits, w // num_splits, c | |
| ).permute(0, 1, 3, 2, 4, 5).reshape(b_new, h_new, w_new, c) # [B*K*K, H/K, W/K, C] | |
| else: # [B, C, H, W] | |
| b, c, h, w = feature.size() | |
| # if h % num_splits: | |
| # feature = feature[:, :, :-1, :] | |
| # if w % num_splits: | |
| # feature = feature[:, :, :, :-1] | |
| # b, c, h, w = feature.size() | |
| assert h % num_splits == 0 and w % num_splits == 0 | |
| b_new = b * num_splits * num_splits | |
| h_new = h // num_splits | |
| w_new = w // num_splits | |
| feature = feature.view(b, c, num_splits, h // num_splits, num_splits, w // num_splits | |
| ).permute(0, 2, 4, 1, 3, 5).reshape(b_new, c, h_new, w_new) # [B*K*K, C, H/K, W/K] | |
| return feature | |
| def merge_splits(splits, | |
| num_splits=2, | |
| channel_last=False, | |
| ): | |
| if channel_last: # [B*K*K, H/K, W/K, C] | |
| b, h, w, c = splits.size() | |
| new_b = b // num_splits // num_splits | |
| splits = splits.view(new_b, num_splits, num_splits, h, w, c) | |
| merge = splits.permute(0, 1, 3, 2, 4, 5).contiguous().view( | |
| new_b, num_splits * h, num_splits * w, c) # [B, H, W, C] | |
| else: # [B*K*K, C, H/K, W/K] | |
| b, c, h, w = splits.size() | |
| new_b = b // num_splits // num_splits | |
| splits = splits.view(new_b, num_splits, num_splits, c, h, w) | |
| merge = splits.permute(0, 3, 1, 4, 2, 5).contiguous().view( | |
| new_b, c, num_splits * h, num_splits * w) # [B, C, H, W] | |
| return merge | |
| def normalize_img(img0, img1): | |
| # loaded images are in [0, 255] | |
| # normalize by ImageNet mean and std | |
| mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(img1.device) | |
| std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(img1.device) | |
| img0 = (img0 / 255. - mean) / std | |
| img1 = (img1 / 255. - mean) / std | |
| return img0, img1 | |
| def feature_add_position(feature0, feature1, attn_splits, feature_channels): | |
| pos_enc = PositionEmbeddingSine(num_pos_feats=feature_channels // 2) | |
| if attn_splits > 1: # add position in splited window | |
| # import ipdb; ipdb.set_trace() | |
| feature0_splits = split_feature(feature0, num_splits=attn_splits) | |
| feature1_splits = split_feature(feature1, num_splits=attn_splits) | |
| position = pos_enc(feature0_splits) | |
| feature0_splits = feature0_splits + position | |
| feature1_splits = feature1_splits + position | |
| feature0 = merge_splits(feature0_splits, num_splits=attn_splits) | |
| feature1 = merge_splits(feature1_splits, num_splits=attn_splits) | |
| else: | |
| position = pos_enc(feature0) | |
| feature0 = feature0 + position | |
| feature1 = feature1 + position | |
| return feature0, feature1 | |