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loss_contrast.py
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loss_contrast.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from abc import ABC
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.loss.loss_helper import FSAuxCELoss, FSAuxRMILoss, FSCELoss
from lib.utils.tools.logger import Logger as Log
class PixelContrastLoss(nn.Module, ABC):
def __init__(self, configer):
super(PixelContrastLoss, self).__init__()
self.configer = configer
self.temperature = self.configer.get('contrast', 'temperature')
self.base_temperature = self.configer.get('contrast', 'base_temperature')
self.ignore_label = -1
if self.configer.exists('loss', 'params') and 'ce_ignore_index' in self.configer.get('loss', 'params'):
self.ignore_label = self.configer.get('loss', 'params')['ce_ignore_index']
self.max_samples = self.configer.get('contrast', 'max_samples')
self.max_views = self.configer.get('contrast', 'max_views')
def _hard_anchor_sampling(self, X, y_hat, y):
batch_size, feat_dim = X.shape[0], X.shape[-1]
classes = []
total_classes = 0
for ii in range(batch_size):
this_y = y_hat[ii]
this_classes = torch.unique(this_y)
this_classes = [x for x in this_classes if x != self.ignore_label]
this_classes = [x for x in this_classes if (this_y == x).nonzero().shape[0] > self.max_views]
classes.append(this_classes)
total_classes += len(this_classes)
if total_classes == 0:
return None, None
n_view = self.max_samples // total_classes
n_view = min(n_view, self.max_views)
X_ = torch.zeros((total_classes, n_view, feat_dim), dtype=torch.float).cuda()
y_ = torch.zeros(total_classes, dtype=torch.float).cuda()
X_ptr = 0
for ii in range(batch_size):
this_y_hat = y_hat[ii]
this_y = y[ii]
this_classes = classes[ii]
for cls_id in this_classes:
hard_indices = ((this_y_hat == cls_id) & (this_y != cls_id)).nonzero()
easy_indices = ((this_y_hat == cls_id) & (this_y == cls_id)).nonzero()
num_hard = hard_indices.shape[0]
num_easy = easy_indices.shape[0]
if num_hard >= n_view / 2 and num_easy >= n_view / 2:
num_hard_keep = n_view // 2
num_easy_keep = n_view - num_hard_keep
elif num_hard >= n_view / 2:
num_easy_keep = num_easy
num_hard_keep = n_view - num_easy_keep
elif num_easy >= n_view / 2:
num_hard_keep = num_hard
num_easy_keep = n_view - num_hard_keep
else:
Log.info('this shoud be never touched! {} {} {}'.format(num_hard, num_easy, n_view))
raise Exception
perm = torch.randperm(num_hard)
hard_indices = hard_indices[perm[:num_hard_keep]]
perm = torch.randperm(num_easy)
easy_indices = easy_indices[perm[:num_easy_keep]]
indices = torch.cat((hard_indices, easy_indices), dim=0)
X_[X_ptr, :, :] = X[ii, indices, :].squeeze(1)
y_[X_ptr] = cls_id
X_ptr += 1
return X_, y_
def _contrastive(self, feats_, labels_):
anchor_num, n_view = feats_.shape[0], feats_.shape[1]
labels_ = labels_.contiguous().view(-1, 1)
mask = torch.eq(labels_, torch.transpose(labels_, 0, 1)).float().cuda()
contrast_count = n_view
contrast_feature = torch.cat(torch.unbind(feats_, dim=1), dim=0)
anchor_feature = contrast_feature
anchor_count = contrast_count
anchor_dot_contrast = torch.div(torch.matmul(anchor_feature, torch.transpose(contrast_feature, 0, 1)),
self.temperature)
logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
logits = anchor_dot_contrast - logits_max.detach()
mask = mask.repeat(anchor_count, contrast_count)
neg_mask = 1 - mask
logits_mask = torch.ones_like(mask).scatter_(1,
torch.arange(anchor_num * anchor_count).view(-1, 1).cuda(),
0)
mask = mask * logits_mask
neg_logits = torch.exp(logits) * neg_mask
neg_logits = neg_logits.sum(1, keepdim=True)
exp_logits = torch.exp(logits)
log_prob = logits - torch.log(exp_logits + neg_logits)
mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
loss = loss.mean()
return loss
def forward(self, feats, labels=None, predict=None):
labels = labels.unsqueeze(1).float().clone()
labels = torch.nn.functional.interpolate(labels,
(feats.shape[2], feats.shape[3]), mode='nearest')
labels = labels.squeeze(1).long()
assert labels.shape[-1] == feats.shape[-1], '{} {}'.format(labels.shape, feats.shape)
batch_size = feats.shape[0]
labels = labels.contiguous().view(batch_size, -1)
predict = predict.contiguous().view(batch_size, -1)
feats = feats.permute(0, 2, 3, 1)
feats = feats.contiguous().view(feats.shape[0], -1, feats.shape[-1])
feats_, labels_ = self._hard_anchor_sampling(feats, labels, predict)
loss = self._contrastive(feats_, labels_)
return loss
class ContrastCELoss(nn.Module, ABC):
def __init__(self, configer=None):
super(ContrastCELoss, self).__init__()
self.configer = configer
ignore_index = -1
if self.configer.exists('loss', 'params') and 'ce_ignore_index' in self.configer.get('loss', 'params'):
ignore_index = self.configer.get('loss', 'params')['ce_ignore_index']
Log.info('ignore_index: {}'.format(ignore_index))
self.loss_weight = self.configer.get('contrast', 'loss_weight')
self.use_rmi = self.configer.get('contrast', 'use_rmi')
if self.use_rmi:
self.seg_criterion = FSAuxRMILoss(configer=configer)
else:
self.seg_criterion = FSCELoss(configer=configer)
self.contrast_criterion = PixelContrastLoss(configer=configer)
def forward(self, preds, target, with_embed=False):
h, w = target.size(1), target.size(2)
assert "seg" in preds
assert "embed" in preds
seg = preds['seg']
embedding = preds['embed']
pred = F.interpolate(input=seg, size=(h, w), mode='bilinear', align_corners=True)
loss = self.seg_criterion(pred, target)
_, predict = torch.max(seg, 1)
loss_contrast = self.contrast_criterion(embedding, target, predict)
if with_embed is True:
return loss + self.loss_weight * loss_contrast
return loss + 0 * loss_contrast # just a trick to avoid errors in distributed training
class ContrastAuxCELoss(nn.Module, ABC):
def __init__(self, configer=None):
super(ContrastAuxCELoss, self).__init__()
self.configer = configer
ignore_index = -1
if self.configer.exists('loss', 'params') and 'ce_ignore_index' in self.configer.get('loss', 'params'):
ignore_index = self.configer.get('loss', 'params')['ce_ignore_index']
Log.info('ignore_index: {}'.format(ignore_index))
self.loss_weight = self.configer.get('contrast', 'loss_weight')
self.use_rmi = self.configer.get('contrast', 'use_rmi')
if self.use_rmi:
self.seg_criterion = FSAuxRMILoss(configer=configer)
else:
self.seg_criterion = FSAuxCELoss(configer=configer)
self.contrast_criterion = PixelContrastLoss(configer=configer)
def forward(self, preds, target, with_embed=False):
h, w = target.size(1), target.size(2)
assert "seg" in preds
assert "seg_aux" in preds
assert "embed" in preds
seg = preds['seg']
seg_aux = preds['seg_aux']
embedding = preds['embed']
pred = F.interpolate(input=seg, size=(h, w), mode='bilinear', align_corners=True)
pred_aux = F.interpolate(input=seg_aux, size=(h, w), mode='bilinear', align_corners=True)
loss = self.seg_criterion([pred_aux, pred], target)
_, predict = torch.max(seg, 1)
loss_contrast = self.contrast_criterion(embedding, target, predict)
if with_embed is True:
return loss + self.loss_weight * loss_contrast
return loss + 0 * loss_contrast # just a trick to avoid errors in distributed training