| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108 |
- import paddle
- from paddle import nn
- class SupConLoss(nn.Layer):
- """Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
- It also supports the unsupervised contrastive loss in SimCLR"""
- def __init__(self,
- views=16,
- temperature=0.07,
- contrast_mode='all',
- base_temperature=0.07,
- normalize_feature=True):
- super(SupConLoss, self).__init__()
- self.temperature = paddle.to_tensor(temperature)
- self.contrast_mode = contrast_mode
- self.base_temperature = paddle.to_tensor(base_temperature)
- self.num_ids = None
- self.views = views
- self.normalize_feature = normalize_feature
- def forward(self, features, labels, mask=None):
- """Compute 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 = features["features"]
- if self.num_ids is None:
- self.num_ids = int(features.shape[0] / self.views)
- if self.normalize_feature:
- features = 1. * features / (paddle.expand_as(
- paddle.norm(
- features, p=2, axis=-1, keepdim=True), features) + 1e-12)
- features = features.reshape([self.num_ids, self.views, -1])
- labels = labels.reshape([self.num_ids, self.views])[:, 0]
- 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.reshape(
- [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 = paddle.eye(batch_size, dtype='float32')
- elif labels is not None:
- labels = labels.reshape([-1, 1])
- if labels.shape[0] != batch_size:
- raise ValueError(
- 'Num of labels does not match num of features')
- mask = paddle.cast(
- paddle.equal(labels, paddle.t(labels)), 'float32')
- else:
- mask = paddle.cast(mask, 'float32')
- contrast_count = features.shape[1]
- contrast_feature = paddle.concat(
- paddle.unbind(
- features, axis=1), axis=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 = paddle.divide(
- paddle.matmul(anchor_feature, paddle.t(contrast_feature)),
- self.temperature)
- # for numerical stability
- logits_max = paddle.max(anchor_dot_contrast, axis=1, keepdim=True)
- logits = anchor_dot_contrast - logits_max.detach()
- # tile mask
- mask = paddle.tile(mask, [anchor_count, contrast_count])
- logits_mask = 1 - paddle.eye(batch_size * anchor_count)
- mask = mask * logits_mask
- # compute log_prob
- exp_logits = paddle.exp(logits) * logits_mask
- log_prob = logits - paddle.log(
- paddle.sum(exp_logits, axis=1, keepdim=True))
- # compute mean of log-likelihood over positive
- mean_log_prob_pos = paddle.sum((mask * log_prob),
- axis=1) / paddle.sum(mask, axis=1)
- # loss
- loss = -(self.temperature / self.base_temperature) * mean_log_prob_pos
- loss = paddle.mean(loss.reshape([anchor_count, batch_size]))
- return {"SupConLoss": loss}
|