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# -*- coding: utf-8 -*-
# Author: Gaojian Wang@ZJUICSR
# --------------------------------------------------------
# This source code is licensed under the Attribution-NonCommercial 4.0 International License.
# You can find the license in the LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
from __future__ import print_function
import torch
import torch.nn as nn
import math
class SimSiamLoss(nn.Module):
def __init__(self):
super(SimSiamLoss, self).__init__()
self.criterion = nn.CosineSimilarity(dim=1)
def forward(self, cl_features):
if len(cl_features.shape) < 3:
raise ValueError('`features` needs to be [bsz, n_views, ...],'
'at least 3 dimensions are required')
if len(cl_features.shape) > 3:
cl_features = cl_features.view(cl_features.shape[0], cl_features.shape[1], -1) # [BS, 2, feat_cl_dim]
cl_features_1 = cl_features[:, 0] # [BS, feat_cl_dim]
cl_features_2 = cl_features[:, 1] # [BS, feat_cl_dim]
loss = -(self.criterion(cl_features_1, cl_features_2).mean()) * 0.5
# if not math.isfinite(loss):
# print(cl_features_1, '\n', cl_features_2)
# print(self.criterion(cl_features_1, cl_features_2))
return loss
class BYOLLoss(nn.Module):
def __init__(self):
super(BYOLLoss, self).__init__()
@staticmethod
def forward(cl_features):
if len(cl_features.shape) < 3:
raise ValueError('`features` needs to be [bsz, n_views, ...],'
'at least 3 dimensions are required')
if len(cl_features.shape) > 3:
cl_features = cl_features.view(cl_features.shape[0], cl_features.shape[1], -1) # [BS, 2, feat_cl_dim]
cl_features_1 = cl_features[:, 0] # [BS, feat_cl_dim]
cl_features_2 = cl_features[:, 1] # [BS, feat_cl_dim]
loss = 2 - 2 * (cl_features_1 * cl_features_2).sum(dim=-1)
# loss = 1 - (cl_features_1 * cl_features_2).sum(dim=-1)
loss = loss.mean()
if not math.isfinite(loss):
print(cl_features_1, '\n', cl_features_2)
print(2 - 2 * (cl_features_1 * cl_features_2).sum(dim=-1))
return loss
# different implementation of InfoNCELoss, including MOCOV3Loss; SupConLoss
class InfoNCELoss(nn.Module):
def __init__(self, temperature=0.1, contrast_sample='all'):
"""
from CMAE: https://github.com/ZhichengHuang/CMAE/issues/5
:param temperature: 0.1 0.5 1.0, 1.5 2.0
"""
super(InfoNCELoss, self).__init__()
self.temperature = temperature
self.criterion = nn.CrossEntropyLoss()
self.contrast_sample = contrast_sample
def forward(self, cl_features):
"""
Args:
:param cl_features: : hidden vector of shape [bsz, n_views, ...]
Returns:
A loss scalar.
"""
device = (torch.device('cuda')
if cl_features.is_cuda
else torch.device('cpu'))
if len(cl_features.shape) < 3:
raise ValueError('`features` needs to be [bsz, n_views, ...],'
'at least 3 dimensions are required')
if len(cl_features.shape) > 3:
cl_features = cl_features.view(cl_features.shape[0], cl_features.shape[1], -1) # [BS, 2, feat_cl_dim]
cl_features_1 = cl_features[:, 0] # [BS, feat_cl_dim]
cl_features_2 = cl_features[:, 1] # [BS, feat_cl_dim]
score_all = torch.matmul(cl_features_1, cl_features_2.transpose(1, 0)) # [BS, BS]
score_all = score_all / self.temperature
bs = score_all.size(0)
if self.contrast_sample == 'all':
score = score_all
elif self.contrast_sample == 'positive':
mask = torch.eye(bs, dtype=torch.float).to(device) # torch.Size([BS, BS])
score = score_all * mask
else:
raise ValueError('Contrastive sample: all{pos&neg} or positive(positive)')
# label = (torch.arange(bs, dtype=torch.long) +
# bs * torch.distributed.get_rank()).to(device)
label = torch.arange(bs, dtype=torch.long).to(device)
loss = 2 * self.temperature * self.criterion(score, label)
if not math.isfinite(loss):
print(cl_features_1, '\n', cl_features_2)
print(score_all, '\n', score, '\n', mask)
return loss
class MOCOV3Loss(nn.Module):
def __init__(self, temperature=0.1):
super(MOCOV3Loss, self).__init__()
self.temperature = temperature
def forward(self, cl_features):
if len(cl_features.shape) < 3:
raise ValueError('`features` needs to be [bsz, n_views, ...],'
'at least 3 dimensions are required')
if len(cl_features.shape) > 3:
cl_features = cl_features.view(cl_features.shape[0], cl_features.shape[1], -1) # [BS, 2, feat_cl_dim]
cl_features_1 = cl_features[:, 0] # [BS, feat_cl_dim]
cl_features_2 = cl_features[:, 1] # [BS, feat_cl_dim]
# normalize
cl_features_1 = nn.functional.normalize(cl_features_1, dim=1)
cl_features_2 = nn.functional.normalize(cl_features_2, dim=1)
# Einstein sum is more intuitive
logits = torch.einsum('nc,mc->nm', [cl_features_1, cl_features_2]) / self.temperature
N = logits.shape[0]
labels = (torch.arange(N, dtype=torch.long)).cuda()
return nn.CrossEntropyLoss()(logits, labels) * (2 * self.temperature)
class SupConLoss(nn.Module):
"""
from: https://github.com/HobbitLong/SupContrast
Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
It also supports the unsupervised contrastive loss in SimCLR"""
def __init__(self, temperature=0.1, contrast_mode='all', contrast_sample='all',
base_temperature=0.1):
super(SupConLoss, self).__init__()
self.temperature = temperature
self.contrast_mode = contrast_mode
self.contrast_sample = contrast_sample
self.base_temperature = base_temperature
def forward(self, features, labels=None, 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.
"""
device = (torch.device('cuda')
if features.is_cuda
else torch.device('cpu'))
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) # [BS, 2, feat_cl_dim]
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) # torch.Size([BS, BS])
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_count(2)
contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) # [BS*contrast_count, D]
if self.contrast_mode == 'one':
anchor_feature = features[:, 0] # [BS, D]
anchor_count = 1
elif self.contrast_mode == 'all':
anchor_feature = contrast_feature # [BS*contrast_count, D]
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) # [BS*contrast_count, BS*contrast_count]
# for numerical stability
logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) # [BS*contrast_count, 1]
logits = anchor_dot_contrast - logits_max.detach() # [BS*contrast_count, BS*contrast_count]
# tile mask
mask = mask.repeat(anchor_count, contrast_count) # [BS*anchor_count, BS*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
) # [BS*anchor_count, BS*contrast_count]
mask = mask * logits_mask # [BS*anchor_count, BS*contrast_count]
"""
logits_mask is used to get the denominator(positives and negatives).
mask is used to get the numerator(positives). mask is applied to log_prob.
"""
# compute log_prob，logits_mask is contrast anchor with both positives and negatives
exp_logits = torch.exp(logits) * logits_mask # [BS*anchor_count, BS*contrast_count]
# compute log_prob，logits_mask is contrast anchor with negatives, i.e., denominator only negatives contrast:
# exp_logits = torch.exp(logits) * (logits_mask-mask)
if self.contrast_sample == 'all':
log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True)) # [BS*anchor_count, BS*anchor_count]
# compute mean of log-likelihood over positive
mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) # [BS*anchor_count]
elif self.contrast_sample == 'positive':
mean_log_prob_pos = (mask * logits).sum(1) / mask.sum(1)
else:
raise ValueError('Contrastive sample: all{pos&neg} or positive(positive)')
# loss
loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
loss = loss.view(anchor_count, batch_size).mean()
return loss
class InfoNCELossPatchLevel(nn.Module):
"""
test: ref ConMIM: https://github.com/TencentARC/ConMIM.
"""
def __init__(self, temperature=0.1, contrast_sample='all'):
"""
:param temperature: 0.1 0.5 1.0, 1.5 2.0
"""
super(InfoNCELossPatchLevel, self).__init__()
self.temperature = temperature
self.criterion = nn.CrossEntropyLoss()
self.contrast_sample = contrast_sample
self.facial_region_group = [
[2, 3], # eyebrows
[4, 5], # eyes
[6], # nose
[7, 8, 9], # mouth
[10, 1, 0], # face boundaries
[10], # hair
[1], # facial skin
[0] # background
]
def forward(self, cl_features, parsing_map=None):
"""
Args:
:param parsing_map:
:param cl_features: : hidden vector of shape [bsz, n_views, ...]
Returns:
A loss scalar.
"""
device = (torch.device('cuda')
if cl_features.is_cuda
else torch.device('cpu'))
if len(cl_features.shape) < 4:
raise ValueError('`features` needs to be [bsz, n_views, n_cl_patches, ...],'
'at least 4 dimensions are required')
if len(cl_features.shape) > 4:
cl_features = cl_features.view(cl_features.shape[0], cl_features.shape[1], cl_features.shape[2], -1)
# [BS, 2, num_cl_patches, feat_cl_dim]
cl_features_1 = cl_features[:, 0]
cl_features_2 = cl_features[:, 1]
score = torch.matmul(cl_features_1, cl_features_2.permute(0, 2, 1)) # [BS, num_cl_patches, num_cl_patches]
score = score / self.temperature
bs = score.size(0)
num_cl_patches = score.size(1)
if self.contrast_sample == 'all':
score = score
elif self.contrast_sample == 'positive':
mask = torch.eye(num_cl_patches, dtype=torch.float32) # torch.Size([num_cl_patches, num_cl_patches])
mask_batch = mask.unsqueeze(0).expand(bs, -1).to(device) # [bs, num_cl_patches, num_cl_patches]
score = score*mask_batch
elif self.contrast_sample == 'region':
cl_features_1_fr = []
cl_features_2_fr = []
for facial_region_index in self.facial_region_group:
fr_mask = (parsing_map == facial_region_index).unsqueeze(2).expand(-1, -1, cl_features_1.size(-1))
cl_features_1_fr.append((cl_features_1 * fr_mask).mean(dim=1, keepdim=False))
cl_features_2_fr.append((cl_features_1 * fr_mask).mean(dim=1, keepdim=False))
cl_features_1_fr = torch.stack(cl_features_1_fr, dim=1)
cl_features_2_fr = torch.stack(cl_features_2_fr, dim=1)
score = torch.matmul(cl_features_1_fr, cl_features_2_fr.permute(0, 2, 1)) # [BS, 8, 8]
score = score / self.temperature
# mask = torch.eye(cl_features_1_fr.size(1), dtype=torch.bool)
# torch.Size([cl_features_1_fr.size(1), cl_features_1_fr.size(1)])
# mask_batch = mask.unsqueeze(0).expand(bs, -1).to(device)
# [bs, cl_features_1_fr.size(1), cl_features_1_fr.size(1)]
# score = score*mask_batch
label = torch.arange(cl_features_1_fr.size(1), dtype=torch.long).to(device)
labels_batch = label.unsqueeze(0).expand(bs, -1)
loss = 2 * self.temperature * self.criterion(score, labels_batch)
return loss
else:
raise ValueError('Contrastive sample: all{pos&neg} or positive(positive)')
# label = (torch.arange(bs, dtype=torch.long) +
# bs * torch.distributed.get_rank()).to(device)
label = torch.arange(num_cl_patches, dtype=torch.long).to(device)
labels_batch = label.unsqueeze(0).expand(bs, -1)
loss = 2 * self.temperature * self.criterion(score, labels_batch)
return loss
class MSELoss(nn.Module):
"""
test: unused
"""
def __init__(self):
super(MSELoss, self).__init__()
@staticmethod
def forward(cl_features):
if len(cl_features.shape) < 3:
raise ValueError('`features` needs to be [bsz, n_views, n_patches, ...],'
'at least 3 dimensions are required')
if len(cl_features.shape) > 3:
cl_features = cl_features.view(cl_features.shape[0], cl_features.shape[1], -1) # [BS, 2, feat_cl_dim]
cl_features_1 = cl_features[:, 0].float() # [BS, feat_cl_dim]
cl_features_2 = cl_features[:, 1].float() # [BS, feat_cl_dim]
return torch.nn.functional.mse_loss(cl_features_1, cl_features_2, reduction='mean')
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