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| #!/usr/bin/env python | |
| # -*- encoding: utf-8 -*- | |
| """ | |
| @Author : Peike Li | |
| @Contact : [email protected] | |
| @File : criterion.py | |
| @Time : 8/30/19 8:59 PM | |
| @Desc : | |
| @License : This source code is licensed under the license found in the | |
| LICENSE file in the root directory of this source tree. | |
| """ | |
| import torch.nn as nn | |
| import torch | |
| import numpy as np | |
| from torch.nn import functional as F | |
| from .lovasz_softmax import LovaszSoftmax | |
| from .kl_loss import KLDivergenceLoss | |
| from .consistency_loss import ConsistencyLoss | |
| NUM_CLASSES = 20 | |
| class CriterionAll(nn.Module): | |
| def __init__(self, use_class_weight=False, ignore_index=255, lambda_1=1, lambda_2=1, lambda_3=1, | |
| num_classes=20): | |
| super(CriterionAll, self).__init__() | |
| self.ignore_index = ignore_index | |
| self.use_class_weight = use_class_weight | |
| self.criterion = torch.nn.CrossEntropyLoss(ignore_index=ignore_index) | |
| self.lovasz = LovaszSoftmax(ignore_index=ignore_index) | |
| self.kldiv = KLDivergenceLoss(ignore_index=ignore_index) | |
| self.reg = ConsistencyLoss(ignore_index=ignore_index) | |
| self.lamda_1 = lambda_1 | |
| self.lamda_2 = lambda_2 | |
| self.lamda_3 = lambda_3 | |
| self.num_classes = num_classes | |
| def parsing_loss(self, preds, target, cycle_n=None): | |
| """ | |
| Loss function definition. | |
| Args: | |
| preds: [[parsing result1, parsing result2],[edge result]] | |
| target: [parsing label, egde label] | |
| soft_preds: [[parsing result1, parsing result2],[edge result]] | |
| Returns: | |
| Calculated Loss. | |
| """ | |
| h, w = target[0].size(1), target[0].size(2) | |
| pos_num = torch.sum(target[1] == 1, dtype=torch.float) | |
| neg_num = torch.sum(target[1] == 0, dtype=torch.float) | |
| weight_pos = neg_num / (pos_num + neg_num) | |
| weight_neg = pos_num / (pos_num + neg_num) | |
| weights = torch.tensor([weight_neg, weight_pos]) # edge loss weight | |
| loss = 0 | |
| # loss for segmentation | |
| preds_parsing = preds[0] | |
| for pred_parsing in preds_parsing: | |
| scale_pred = F.interpolate(input=pred_parsing, size=(h, w), | |
| mode='bilinear', align_corners=True) | |
| loss += 0.5 * self.lamda_1 * self.lovasz(scale_pred, target[0]) | |
| if target[2] is None: | |
| loss += 0.5 * self.lamda_1 * self.criterion(scale_pred, target[0]) | |
| else: | |
| soft_scale_pred = F.interpolate(input=target[2], size=(h, w), | |
| mode='bilinear', align_corners=True) | |
| soft_scale_pred = moving_average(soft_scale_pred, to_one_hot(target[0], num_cls=self.num_classes), | |
| 1.0 / (cycle_n + 1.0)) | |
| loss += 0.5 * self.lamda_1 * self.kldiv(scale_pred, soft_scale_pred, target[0]) | |
| # loss for edge | |
| preds_edge = preds[1] | |
| for pred_edge in preds_edge: | |
| scale_pred = F.interpolate(input=pred_edge, size=(h, w), | |
| mode='bilinear', align_corners=True) | |
| if target[3] is None: | |
| loss += self.lamda_2 * F.cross_entropy(scale_pred, target[1], | |
| weights.cuda(), ignore_index=self.ignore_index) | |
| else: | |
| soft_scale_edge = F.interpolate(input=target[3], size=(h, w), | |
| mode='bilinear', align_corners=True) | |
| soft_scale_edge = moving_average(soft_scale_edge, to_one_hot(target[1], num_cls=2), | |
| 1.0 / (cycle_n + 1.0)) | |
| loss += self.lamda_2 * self.kldiv(scale_pred, soft_scale_edge, target[0]) | |
| # consistency regularization | |
| preds_parsing = preds[0] | |
| preds_edge = preds[1] | |
| for pred_parsing in preds_parsing: | |
| scale_pred = F.interpolate(input=pred_parsing, size=(h, w), | |
| mode='bilinear', align_corners=True) | |
| scale_edge = F.interpolate(input=preds_edge[0], size=(h, w), | |
| mode='bilinear', align_corners=True) | |
| loss += self.lamda_3 * self.reg(scale_pred, scale_edge, target[0]) | |
| return loss | |
| def forward(self, preds, target, cycle_n=None): | |
| loss = self.parsing_loss(preds, target, cycle_n) | |
| return loss | |
| def _generate_weights(self, masks, num_classes): | |
| """ | |
| masks: torch.Tensor with shape [B, H, W] | |
| """ | |
| masks_label = masks.data.cpu().numpy().astype(np.int64) | |
| pixel_nums = [] | |
| tot_pixels = 0 | |
| for i in range(num_classes): | |
| pixel_num_of_cls_i = np.sum(masks_label == i).astype(np.float) | |
| pixel_nums.append(pixel_num_of_cls_i) | |
| tot_pixels += pixel_num_of_cls_i | |
| weights = [] | |
| for i in range(num_classes): | |
| weights.append( | |
| (tot_pixels - pixel_nums[i]) / tot_pixels / (num_classes - 1) | |
| ) | |
| weights = np.array(weights, dtype=np.float) | |
| # weights = torch.from_numpy(weights).float().to(masks.device) | |
| return weights | |
| def moving_average(target1, target2, alpha=1.0): | |
| target = 0 | |
| target += (1.0 - alpha) * target1 | |
| target += target2 * alpha | |
| return target | |
| def to_one_hot(tensor, num_cls, dim=1, ignore_index=255): | |
| b, h, w = tensor.shape | |
| tensor[tensor == ignore_index] = 0 | |
| onehot_tensor = torch.zeros(b, num_cls, h, w).cuda() | |
| onehot_tensor.scatter_(dim, tensor.unsqueeze(dim), 1) | |
| return onehot_tensor | |