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| from packaging import version | |
| import torch | |
| from torch import nn | |
| class PatchNCELoss(nn.Module): | |
| def __init__(self, opt): | |
| super().__init__() | |
| self.opt = opt | |
| self.cross_entropy_loss = torch.nn.CrossEntropyLoss(reduction='none') | |
| self.mask_dtype = torch.uint8 if version.parse(torch.__version__) < version.parse('1.2.0') else torch.bool | |
| def forward(self, feat_q, feat_k): | |
| batchSize = feat_q.shape[0] | |
| dim = feat_q.shape[1] | |
| feat_k = feat_k.detach() | |
| # pos logit | |
| l_pos = torch.bmm(feat_q.view(batchSize, 1, -1), feat_k.view(batchSize, -1, 1)) | |
| l_pos = l_pos.view(batchSize, 1) | |
| # neg logit | |
| # Should the negatives from the other samples of a minibatch be utilized? | |
| # In CUT and FastCUT, we found that it's best to only include negatives | |
| # from the same image. Therefore, we set | |
| # --nce_includes_all_negatives_from_minibatch as False | |
| # However, for single-image translation, the minibatch consists of | |
| # crops from the "same" high-resolution image. | |
| # Therefore, we will include the negatives from the entire minibatch. | |
| if self.opt.nce_includes_all_negatives_from_minibatch: | |
| # reshape features as if they are all negatives of minibatch of size 1. | |
| batch_dim_for_bmm = 1 | |
| else: | |
| batch_dim_for_bmm = self.opt.batch_size | |
| # reshape features to batch size | |
| feat_q = feat_q.view(batch_dim_for_bmm, -1, dim) | |
| feat_k = feat_k.view(batch_dim_for_bmm, -1, dim) | |
| npatches = feat_q.size(1) | |
| l_neg_curbatch = torch.bmm(feat_q, feat_k.transpose(2, 1)) | |
| # diagonal entries are similarity between same features, and hence meaningless. | |
| # just fill the diagonal with very small number, which is exp(-10) and almost zero | |
| diagonal = torch.eye(npatches, device=feat_q.device, dtype=self.mask_dtype)[None, :, :] | |
| l_neg_curbatch.masked_fill_(diagonal, -10.0) | |
| l_neg = l_neg_curbatch.view(-1, npatches) | |
| out = torch.cat((l_pos, l_neg), dim=1) / self.opt.nce_T | |
| loss = self.cross_entropy_loss(out, torch.zeros(out.size(0), dtype=torch.long, | |
| device=feat_q.device)) | |
| return loss | |