224 lines
7.9 KiB
Python
224 lines
7.9 KiB
Python
"""
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Author: Yonglong Tian (yonglong@mit.edu)
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Date: May 07, 2020
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"""
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from __future__ import print_function
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import torch
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import torch.nn as nn
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import numpy as np
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class SupConLoss(nn.Module):
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"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
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It also supports the unsupervised contrastive loss in SimCLR"""
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def __init__(self, temperature=0.07, contrast_mode='all',
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base_temperature=0.07):
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super(SupConLoss, self).__init__()
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self.temperature = temperature
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self.contrast_mode = contrast_mode
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self.base_temperature = base_temperature
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def forward(self, features, labels=None, mask=None):
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"""Compute loss for model. If both `labels` and `mask` are None,
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it degenerates to SimCLR unsupervised loss:
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https://arxiv.org/pdf/2002.05709.pdf
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Args:
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features: hidden vector of shape [bsz, n_views, ...].
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labels: ground truth of shape [bsz].
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mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
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has the same class as sample i. Can be asymmetric.
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Returns:
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A loss scalar.
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"""
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device = (torch.device('cuda')
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if features.is_cuda
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else torch.device('cpu'))
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if len(features.shape) < 3:
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raise ValueError('`features` needs to be [bsz, n_views, ...],'
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'at least 3 dimensions are required')
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if len(features.shape) > 3:
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features = features.view(features.shape[0], features.shape[1], -1)
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batch_size = features.shape[0]
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if labels is not None and mask is not None:
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raise ValueError('Cannot define both `labels` and `mask`')
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elif labels is None and mask is None:
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mask = torch.eye(batch_size, dtype=torch.float32).to(device)
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elif labels is not None:
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labels = labels.contiguous().view(-1, 1)
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if labels.shape[0] != batch_size:
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raise ValueError('Num of labels does not match num of features')
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mask = torch.eq(labels, labels.T).float().to(device)
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else:
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mask = mask.float().to(device)
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contrast_count = features.shape[1]
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contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
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if self.contrast_mode == 'one':
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anchor_feature = features[:, 0]
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anchor_count = 1
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elif self.contrast_mode == 'all':
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anchor_feature = contrast_feature
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anchor_count = contrast_count
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else:
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raise ValueError('Unknown mode: {}'.format(self.contrast_mode))
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# compute logits
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anchor_dot_contrast = torch.div(
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torch.matmul(anchor_feature, contrast_feature.T),
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self.temperature)
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# for numerical stability
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logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
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logits = anchor_dot_contrast - logits_max.detach()
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# tile mask
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mask = mask.repeat(anchor_count, contrast_count)
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# mask-out self-contrast cases
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logits_mask = torch.scatter(
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torch.ones_like(mask),
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1,
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torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
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0
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)
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mask = mask * logits_mask
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# compute log_prob
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exp_logits = torch.exp(logits) * logits_mask
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log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
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# compute mean of log-likelihood over positive
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mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
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# loss
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loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
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loss = loss.view(anchor_count, batch_size).mean()
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return loss
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class SupConLoss1View(nn.Module):
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"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
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It also supports the unsupervised contrastive loss in SimCLR"""
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def __init__(self, temperature=0.07, base_temperature=0.07):
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super(SupConLoss1View, self).__init__()
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self.temperature = temperature
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self.base_temperature = base_temperature
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def forward(self, features, labels):
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"""Compute loss for model. If both `labels` and `mask` are None,
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it degenerates to SimCLR unsupervised loss:
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https://arxiv.org/pdf/2002.05709.pdf
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Args:
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features: hidden vector of shape [bsz, ndim].
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labels: ground truth of shape [bsz].
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Returns:
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A loss scalar.
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"""
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device = (torch.device('cuda')
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if features.is_cuda
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else torch.device('cpu'))
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if len(features.shape) != 2:
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raise ValueError('`features` needs to be [bsz, ndim]')
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batch_size = features.shape[0]
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labels = labels.contiguous().view(-1, 1)
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if labels.shape[0] != batch_size:
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raise ValueError('Num of labels does not match num of features')
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mask = torch.eq(labels, labels.T).float().to(device)
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cross = torch.matmul(features, features.T)
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# frobenius_loss = torch.norm(mask-cross)
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upper_diag = torch.triu_indices(batch_size,batch_size,+1)
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cross_upper = cross[upper_diag[0], upper_diag[1]]
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mask_upper = mask[upper_diag[0], upper_diag[1]]
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npos = int(mask_upper.sum().item())
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# weight = torch.from_numpy(np.asarray([1-pos, pos], dtype=float)).to(device)
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#return torch.nn.functional.binary_cross_entropy_with_logits(cross_upper, mask_upper)
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#print('mask min-max:', mask.min(), mask.max())
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#print('cross min-max:', cross.min(), cross.max())
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#return torch.norm(cross-mask, p='fro') # <-- diagonal signal (trivial) should be too strong
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pos_loss = mse(cross_upper, mask_upper, label=1, k=-1)
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neg_loss = mse(cross_upper, mask_upper, label=0, k=npos)
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# return frobenius_loss, neg_loss, pos_loss
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#return neg_loss, pos_loss
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# balanced_loss = pos_loss + neg_loss
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# return balanced_loss, neg_loss, pos_loss
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# loss = torch.nn.functional.binary_cross_entropy(cross_upper, mask_upper)
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# return loss, neg_loss, pos_loss
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return torch.mean((cross_upper-mask_upper)**2), neg_loss, pos_loss
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def choice(tensor, k):
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perm = torch.randperm(tensor.size(0))
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idx = perm[:k]
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return tensor[idx]
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def mse(input, target, label, k=-1):
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input = input[target==label]
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if k>-1:
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input = choice(input, k)
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if label==0:
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return torch.mean(input**2)
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else:
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return torch.mean((1-input)**2)
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# index = target==label
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# return torch.mean((input[index] - target[index]) ** 2)
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# # compute logits
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# anchor_dot_contrast = torch.div(torch.matmul(features, features.T),self.temperature)
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# # for numerical stability
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# # logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
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# # logits = anchor_dot_contrast - logits_max.detach()
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# logits = anchor_dot_contrast
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#
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# # mask-out self-contrast cases
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# # logits_mask = torch.scatter(
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# # torch.ones_like(mask),
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# # 1,
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# # torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
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# # 0
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# # )
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# # mask = mask * logits_mask
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# logits_mask = torch.ones_like(mask)
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# logits_mask.fill_diagonal_(0)
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# mask.fill_diagonal_(0)
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#
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# # compute log_prob
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# exp_logits = torch.exp(logits) * logits_mask
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# log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
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#
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# # compute mean of log-likelihood over positive
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# div = mask.sum(1)
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# div=torch.clamp(div, min=1)
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# mean_log_prob_pos = (mask * log_prob).sum(1) / div
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#
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# # loss
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# loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
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# # loss = loss.view(anchor_count, batch_size).mean()
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# loss = loss.view(-1, batch_size).mean()
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#
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# return loss
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