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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
-
-
- @manager.LOSSES.add_component
- class EdgeAttentionLoss(nn.Layer):
- """
- Implements the cross entropy loss function. It only compute the edge part.
-
- Args:
- edge_threshold (float): The pixels greater edge_threshold as edges.
- ignore_index (int64): Specifies a target value that is ignored
- and does not contribute to the input gradient. Default ``255``.
- """
-
- def __init__(self, edge_threshold=0.8, ignore_index=255):
- super().__init__()
- self.edge_threshold = edge_threshold
- self.ignore_index = ignore_index
- self.EPS = 1e-10
- self.mean_mask = 1
-
- 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))
-
- filler = paddle.ones_like(label) * self.ignore_index
- label = paddle.where(edge_logit > self.edge_threshold, label, filler)
-
- seg_logit = paddle.transpose(seg_logit, [0, 2, 3, 1])
- label = paddle.transpose(label, [0, 2, 3, 1])
- loss = F.softmax_with_cross_entropy(
- seg_logit, label, ignore_index=self.ignore_index, axis=-1)
-
- mask = label != self.ignore_index
- mask = paddle.cast(mask, 'float32')
- loss = loss * mask
- avg_loss = paddle.mean(loss) / (paddle.mean(mask) + self.EPS)
- if paddle.mean(mask) < self.mean_mask:
- self.mean_mask = paddle.mean(mask)
-
- label.stop_gradient = True
- mask.stop_gradient = True
- return avg_loss
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