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