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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import paddle | |
| from paddle import nn | |
| import paddle.nn.functional as F | |
| from paddleseg.cvlibs import manager | |
| from paddleseg.models import losses | |
| class OhemEdgeAttentionLoss(nn.Layer): | |
| """ | |
| Implements the cross entropy loss function. It only compute the edge part. | |
| Args: | |
| edge_threshold (float, optional): The pixels greater edge_threshold as edges. Default: 0.8. | |
| thresh (float, optional): The threshold of ohem. Default: 0.7. | |
| min_kept (int, optional): The min number to keep in loss computation. Default: 5000. | |
| ignore_index (int64, optional): Specifies a target value that is ignored | |
| and does not contribute to the input gradient. Default ``255``. | |
| """ | |
| def __init__(self, | |
| edge_threshold=0.8, | |
| thresh=0.7, | |
| min_kept=5000, | |
| ignore_index=255): | |
| super().__init__() | |
| self.edge_threshold = edge_threshold | |
| self.thresh = thresh | |
| self.min_kept = min_kept | |
| self.ignore_index = ignore_index | |
| self.EPS = 1e-10 | |
| def forward(self, logits, label): | |
| """ | |
| Forward computation. | |
| Args: | |
| logits (tuple|list): (seg_logit, edge_logit) Tensor, the data type is float32, float64. Shape is | |
| (N, C), where C is number of classes, and if shape is more than 2D, this | |
| is (N, C, D1, D2,..., Dk), k >= 1. C =1 of edge_logit . | |
| label (Tensor): Label tensor, the data type is int64. Shape is (N, C), where each | |
| value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is | |
| (N, C, D1, D2,..., Dk), k >= 1. | |
| """ | |
| seg_logit, edge_logit = logits[0], logits[1] | |
| if len(label.shape) != len(seg_logit.shape): | |
| label = paddle.unsqueeze(label, 1) | |
| if edge_logit.shape != label.shape: | |
| raise ValueError( | |
| 'The shape of edge_logit should equal to the label, but they are {} != {}' | |
| .format(edge_logit.shape, label.shape)) | |
| # Filter out edge | |
| filler = paddle.ones_like(label) * self.ignore_index | |
| label = paddle.where(edge_logit > self.edge_threshold, label, filler) | |
| # ohem | |
| n, c, h, w = seg_logit.shape | |
| label = label.reshape((-1, )) | |
| valid_mask = (label != self.ignore_index).astype('int64') | |
| num_valid = valid_mask.sum() | |
| label = label * valid_mask | |
| prob = F.softmax(seg_logit, axis=1) | |
| prob = prob.transpose((1, 0, 2, 3)).reshape((c, -1)) | |
| if self.min_kept < num_valid and num_valid > 0: | |
| # let the value which ignored greater than 1 | |
| prob = prob + (1 - valid_mask) | |
| # get the prob of relevant label | |
| label_onehot = F.one_hot(label, c) | |
| label_onehot = label_onehot.transpose((1, 0)) | |
| prob = prob * label_onehot | |
| prob = paddle.sum(prob, axis=0) | |
| threshold = self.thresh | |
| if self.min_kept > 0: | |
| index = prob.argsort() | |
| threshold_index = index[min(len(index), self.min_kept) - 1] | |
| threshold_index = int(threshold_index.numpy()[0]) | |
| if prob[threshold_index] > self.thresh: | |
| threshold = prob[threshold_index] | |
| kept_mask = (prob < threshold).astype('int64') | |
| label = label * kept_mask | |
| valid_mask = valid_mask * kept_mask | |
| # make the invalid region as ignore | |
| label = label + (1 - valid_mask) * self.ignore_index | |
| label = label.reshape((n, 1, h, w)) | |
| valid_mask = valid_mask.reshape((n, 1, h, w)).astype('float32') | |
| loss = F.softmax_with_cross_entropy( | |
| seg_logit, label, ignore_index=self.ignore_index, axis=1) | |
| loss = loss * valid_mask | |
| avg_loss = paddle.mean(loss) / (paddle.mean(valid_mask) + self.EPS) | |
| label.stop_gradient = True | |
| valid_mask.stop_gradient = True | |
| return avg_loss | |