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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| try: | |
| from itertools import ifilterfalse | |
| except ImportError: # py3k | |
| from itertools import filterfalse as ifilterfalse | |
| def dice_loss(probas, labels, smooth=1): | |
| C = probas.size(1) | |
| losses = [] | |
| for c in list(range(C)): | |
| fg = (labels == c).float() | |
| if fg.sum() == 0: | |
| continue | |
| class_pred = probas[:, c] | |
| p0 = class_pred | |
| g0 = fg | |
| numerator = 2 * torch.sum(p0 * g0) + smooth | |
| denominator = torch.sum(p0) + torch.sum(g0) + smooth | |
| losses.append(1 - ((numerator) / (denominator))) | |
| return mean(losses) | |
| def tversky_loss(probas, labels, alpha=0.5, beta=0.5, epsilon=1e-6): | |
| ''' | |
| Tversky loss function. | |
| probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1) | |
| labels: [P] Tensor, ground truth labels (between 0 and C - 1) | |
| Same as soft dice loss when alpha=beta=0.5. | |
| Same as Jaccord loss when alpha=beta=1.0. | |
| See `Tversky loss function for image segmentation using 3D fully convolutional deep networks` | |
| https://arxiv.org/pdf/1706.05721.pdf | |
| ''' | |
| C = probas.size(1) | |
| losses = [] | |
| for c in list(range(C)): | |
| fg = (labels == c).float() | |
| if fg.sum() == 0: | |
| continue | |
| class_pred = probas[:, c] | |
| p0 = class_pred | |
| p1 = 1 - class_pred | |
| g0 = fg | |
| g1 = 1 - fg | |
| numerator = torch.sum(p0 * g0) | |
| denominator = numerator + alpha * \ | |
| torch.sum(p0*g1) + beta*torch.sum(p1*g0) | |
| losses.append(1 - ((numerator) / (denominator + epsilon))) | |
| return mean(losses) | |
| def flatten_probas(probas, labels, ignore=255): | |
| """ | |
| Flattens predictions in the batch | |
| """ | |
| B, C, H, W = probas.size() | |
| probas = probas.permute(0, 2, 3, | |
| 1).contiguous().view(-1, C) # B * H * W, C = P, C | |
| labels = labels.view(-1) | |
| if ignore is None: | |
| return probas, labels | |
| valid = (labels != ignore) | |
| vprobas = probas[valid.view(-1, 1).expand(-1, C)].reshape(-1, C) | |
| # vprobas = probas[torch.nonzero(valid).squeeze()] | |
| vlabels = labels[valid] | |
| return vprobas, vlabels | |
| def isnan(x): | |
| return x != x | |
| def mean(l, ignore_nan=False, empty=0): | |
| """ | |
| nanmean compatible with generators. | |
| """ | |
| l = iter(l) | |
| if ignore_nan: | |
| l = ifilterfalse(isnan, l) | |
| try: | |
| n = 1 | |
| acc = next(l) | |
| except StopIteration: | |
| if empty == 'raise': | |
| raise ValueError('Empty mean') | |
| return empty | |
| for n, v in enumerate(l, 2): | |
| acc += v | |
| if n == 1: | |
| return acc | |
| return acc / n | |
| class DiceLoss(nn.Module): | |
| def __init__(self, ignore_index=255): | |
| super(DiceLoss, self).__init__() | |
| self.ignore_index = ignore_index | |
| def forward(self, tmp_dic, label_dic, step=None): | |
| total_loss = [] | |
| for idx in range(len(tmp_dic)): | |
| pred = tmp_dic[idx] | |
| label = label_dic[idx] | |
| pred = F.softmax(pred, dim=1) | |
| label = label.view(1, 1, pred.size()[2], pred.size()[3]) | |
| loss = dice_loss( | |
| *flatten_probas(pred, label, ignore=self.ignore_index)) | |
| total_loss.append(loss.unsqueeze(0)) | |
| total_loss = torch.cat(total_loss, dim=0) | |
| return total_loss | |
| class SoftJaccordLoss(nn.Module): | |
| def __init__(self, ignore_index=255): | |
| super(SoftJaccordLoss, self).__init__() | |
| self.ignore_index = ignore_index | |
| def forward(self, tmp_dic, label_dic, step=None): | |
| total_loss = [] | |
| for idx in range(len(tmp_dic)): | |
| pred = tmp_dic[idx] | |
| label = label_dic[idx] | |
| pred = F.softmax(pred, dim=1) | |
| label = label.view(1, 1, pred.size()[2], pred.size()[3]) | |
| loss = tversky_loss(*flatten_probas(pred, | |
| label, | |
| ignore=self.ignore_index), | |
| alpha=1.0, | |
| beta=1.0) | |
| total_loss.append(loss.unsqueeze(0)) | |
| total_loss = torch.cat(total_loss, dim=0) | |
| return total_loss | |
| class CrossEntropyLoss(nn.Module): | |
| def __init__(self, | |
| top_k_percent_pixels=None, | |
| hard_example_mining_step=100000): | |
| super(CrossEntropyLoss, self).__init__() | |
| self.top_k_percent_pixels = top_k_percent_pixels | |
| if top_k_percent_pixels is not None: | |
| assert (top_k_percent_pixels > 0 and top_k_percent_pixels < 1) | |
| self.hard_example_mining_step = hard_example_mining_step + 1e-5 | |
| if self.top_k_percent_pixels is None: | |
| self.celoss = nn.CrossEntropyLoss(ignore_index=255, | |
| reduction='mean') | |
| else: | |
| self.celoss = nn.CrossEntropyLoss(ignore_index=255, | |
| reduction='none') | |
| def forward(self, dic_tmp, y, step): | |
| total_loss = [] | |
| for i in range(len(dic_tmp)): | |
| pred_logits = dic_tmp[i] | |
| gts = y[i] | |
| if self.top_k_percent_pixels is None: | |
| final_loss = self.celoss(pred_logits, gts) | |
| else: | |
| # Only compute the loss for top k percent pixels. | |
| # First, compute the loss for all pixels. Note we do not put the loss | |
| # to loss_collection and set reduction = None to keep the shape. | |
| num_pixels = float(pred_logits.size(2) * pred_logits.size(3)) | |
| pred_logits = pred_logits.view( | |
| -1, pred_logits.size(1), | |
| pred_logits.size(2) * pred_logits.size(3)) | |
| gts = gts.view(-1, gts.size(1) * gts.size(2)) | |
| pixel_losses = self.celoss(pred_logits, gts) | |
| if self.hard_example_mining_step == 0: | |
| top_k_pixels = int(self.top_k_percent_pixels * num_pixels) | |
| else: | |
| ratio = min(1.0, | |
| step / float(self.hard_example_mining_step)) | |
| top_k_pixels = int((ratio * self.top_k_percent_pixels + | |
| (1.0 - ratio)) * num_pixels) | |
| top_k_loss, top_k_indices = torch.topk(pixel_losses, | |
| k=top_k_pixels, | |
| dim=1) | |
| final_loss = torch.mean(top_k_loss) | |
| final_loss = final_loss.unsqueeze(0) | |
| total_loss.append(final_loss) | |
| total_loss = torch.cat(total_loss, dim=0) | |
| return total_loss | |