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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
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