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import torch
from torch import nn
class SupConLoss(nn.Module):
"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
It also supports the unsupervised contrastive loss in SimCLR.
"""
def __init__(self, model, temperature=0.07, contrast_mode="all", base_temperature=0.07):
super(SupConLoss, self).__init__()
self.model = model
self.temperature = temperature
self.contrast_mode = contrast_mode
self.base_temperature = base_temperature
def forward(self, sentence_features, labels=None, mask=None):
"""Computes loss for model.
If both `labels` and `mask` are None, it degenerates to SimCLR unsupervised loss:
https://arxiv.org/pdf/2002.05709.pdf
Args:
features: hidden vector of shape [bsz, n_views, ...].
labels: ground truth of shape [bsz].
mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
has the same class as sample i. Can be asymmetric.
Returns:
A loss scalar.
"""
features = self.model(sentence_features[0])["sentence_embedding"]
# Normalize embeddings
features = torch.nn.functional.normalize(features, p=2, dim=1)
# Add n_views dimension
features = torch.unsqueeze(features, 1)
device = features.device
if len(features.shape) < 3:
raise ValueError("`features` needs to be [bsz, n_views, ...]," "at least 3 dimensions are required")
if len(features.shape) > 3:
features = features.view(features.shape[0], features.shape[1], -1)
batch_size = features.shape[0]
if labels is not None and mask is not None:
raise ValueError("Cannot define both `labels` and `mask`")
elif labels is None and mask is None:
mask = torch.eye(batch_size, dtype=torch.float32).to(device)
elif labels is not None:
labels = labels.contiguous().view(-1, 1)
if labels.shape[0] != batch_size:
raise ValueError("Num of labels does not match num of features")
mask = torch.eq(labels, labels.T).float().to(device)
else:
mask = mask.float().to(device)
contrast_count = features.shape[1]
contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
if self.contrast_mode == "one":
anchor_feature = features[:, 0]
anchor_count = 1
elif self.contrast_mode == "all":
anchor_feature = contrast_feature
anchor_count = contrast_count
else:
raise ValueError("Unknown mode: {}".format(self.contrast_mode))
# Compute logits
anchor_dot_contrast = torch.div(torch.matmul(anchor_feature, contrast_feature.T), self.temperature)
# For numerical stability
logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
logits = anchor_dot_contrast - logits_max.detach()
# Tile mask
mask = mask.repeat(anchor_count, contrast_count)
# Mask-out self-contrast cases
logits_mask = torch.scatter(
torch.ones_like(mask),
1,
torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
0,
)
mask = mask * logits_mask
# Compute log_prob
exp_logits = torch.exp(logits) * logits_mask
log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
# Compute mean of log-likelihood over positive
mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
# Loss
loss = -(self.temperature / self.base_temperature) * mean_log_prob_pos
loss = loss.view(anchor_count, batch_size).mean()
return loss
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