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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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