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| import time | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| class AdaptiveSupervisedPatchNCELoss(nn.Module): | |
| def __init__(self, opt): | |
| super().__init__() | |
| self.opt = opt | |
| self.cross_entropy_loss = torch.nn.CrossEntropyLoss(reduction='none') | |
| self.mask_dtype = torch.bool | |
| self.total_epochs = opt.n_epochs + opt.n_epochs_decay | |
| def forward(self, feat_q, feat_k, current_epoch=-1): | |
| num_patches = feat_q.shape[0] | |
| dim = feat_q.shape[1] | |
| feat_k = feat_k.detach() | |
| # pos logit | |
| l_pos = torch.bmm( | |
| feat_q.view(num_patches, 1, -1), feat_k.view(num_patches, -1, 1)) | |
| l_pos = l_pos.view(num_patches, 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)) | |
| if self.opt.asp_loss_mode == 'none': | |
| return loss | |
| scheduler, lookup = self.opt.asp_loss_mode.split('_')[:2] | |
| # Compute scheduling | |
| t = (current_epoch - 1) / self.total_epochs | |
| if scheduler == 'sigmoid': | |
| p = 1 / (1 + np.exp((t - 0.5) * 10)) | |
| elif scheduler == 'linear': | |
| p = 1 - t | |
| elif scheduler == 'lambda': | |
| k = 1 - self.opt.n_epochs_decay / self.total_epochs | |
| m = 1 / (1 - k) | |
| p = m - m * t if t >= k else 1.0 | |
| elif scheduler == 'zero': | |
| p = 1.0 | |
| else: | |
| raise ValueError(f"Unrecognized scheduler: {scheduler}") | |
| # Weight lookups | |
| w0 = 1.0 | |
| x = l_pos.squeeze().detach() | |
| if lookup == 'top': | |
| x = torch.where(x > 0.0, x, torch.zeros_like(x)) | |
| w1 = torch.sqrt(1 - (x - 1) ** 2) | |
| elif lookup == 'linear': | |
| w1 = torch.relu(x) | |
| elif lookup == 'bell': | |
| sigma, mu, sc = 1, 0, 4 | |
| w1 = 1 / (sigma * np.sqrt(2 * torch.pi)) * torch.exp(-((x - 0.5) * sc - mu) ** 2 / (2 * sigma ** 2)) | |
| elif lookup == 'uniform': | |
| w1 = torch.ones_like(x) | |
| else: | |
| raise ValueError(f"Unrecognized lookup: {lookup}") | |
| # Apply weights with schedule | |
| w = p * w0 + (1 - p) * w1 | |
| # Normalize | |
| w = w / w.sum() * len(w) | |
| loss = loss * w | |
| return loss | |