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# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.cvlibs import manager
@manager.LOSSES.add_component
class DetailAggregateLoss(nn.Layer):
"""
DetailAggregateLoss's implementation based on PaddlePaddle.
The original article refers to Meituan
Fan, Mingyuan, et al. "Rethinking BiSeNet For Real-time Semantic Segmentation."
(https://arxiv.org/abs/2104.13188)
Args:
ignore_index (int64, optional): Specifies a target value that is ignored
and does not contribute to the input gradient. Default ``255``.
"""
def __init__(self, ignore_index=255):
super(DetailAggregateLoss, self).__init__()
self.ignore_index = ignore_index
self.laplacian_kernel = paddle.to_tensor(
[-1, -1, -1, -1, 8, -1, -1, -1, -1], dtype='float32').reshape(
(1, 1, 3, 3))
self.fuse_kernel = paddle.create_parameter(
[1, 3, 1, 1], dtype='float32')
def forward(self, logits, label):
"""
Args:
logits (Tensor): 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.
label (Tensor): Label tensor, the data type is int64. Shape is (N), where each
value is 0 <= label[i] <= C-1, and if shape is more than 2D, this is
(N, D1, D2,..., Dk), k >= 1.
Returns: loss
"""
boundary_targets = F.conv2d(
paddle.unsqueeze(
label, axis=1).astype('float32'),
self.laplacian_kernel,
padding=1)
boundary_targets = paddle.clip(boundary_targets, min=0)
boundary_targets = boundary_targets > 0.1
boundary_targets = boundary_targets.astype('float32')
boundary_targets_x2 = F.conv2d(
paddle.unsqueeze(
label, axis=1).astype('float32'),
self.laplacian_kernel,
stride=2,
padding=1)
boundary_targets_x2 = paddle.clip(boundary_targets_x2, min=0)
boundary_targets_x4 = F.conv2d(
paddle.unsqueeze(
label, axis=1).astype('float32'),
self.laplacian_kernel,
stride=4,
padding=1)
boundary_targets_x4 = paddle.clip(boundary_targets_x4, min=0)
boundary_targets_x8 = F.conv2d(
paddle.unsqueeze(
label, axis=1).astype('float32'),
self.laplacian_kernel,
stride=8,
padding=1)
boundary_targets_x8 = paddle.clip(boundary_targets_x8, min=0)
boundary_targets_x8_up = F.interpolate(
boundary_targets_x8, boundary_targets.shape[2:], mode='nearest')
boundary_targets_x4_up = F.interpolate(
boundary_targets_x4, boundary_targets.shape[2:], mode='nearest')
boundary_targets_x2_up = F.interpolate(
boundary_targets_x2, boundary_targets.shape[2:], mode='nearest')
boundary_targets_x2_up = boundary_targets_x2_up > 0.1
boundary_targets_x2_up = boundary_targets_x2_up.astype('float32')
boundary_targets_x4_up = boundary_targets_x4_up > 0.1
boundary_targets_x4_up = boundary_targets_x4_up.astype('float32')
boundary_targets_x8_up = boundary_targets_x8_up > 0.1
boundary_targets_x8_up = boundary_targets_x8_up.astype('float32')
boudary_targets_pyramids = paddle.stack(
(boundary_targets, boundary_targets_x2_up, boundary_targets_x4_up),
axis=1)
boudary_targets_pyramids = paddle.squeeze(
boudary_targets_pyramids, axis=2)
boudary_targets_pyramid = F.conv2d(boudary_targets_pyramids,
self.fuse_kernel)
boudary_targets_pyramid = boudary_targets_pyramid > 0.1
boudary_targets_pyramid = boudary_targets_pyramid.astype('float32')
if logits.shape[-1] != boundary_targets.shape[-1]:
logits = F.interpolate(
logits,
boundary_targets.shape[2:],
mode='bilinear',
align_corners=True)
bce_loss = F.binary_cross_entropy_with_logits(logits,
boudary_targets_pyramid)
dice_loss = self.fixed_dice_loss_func(
F.sigmoid(logits), boudary_targets_pyramid)
detail_loss = bce_loss + dice_loss
label.stop_gradient = True
return detail_loss
def fixed_dice_loss_func(self, input, target):
"""
simplified diceloss for DetailAggregateLoss.
"""
smooth = 1.
n = input.shape[0]
iflat = paddle.reshape(input, [n, -1])
tflat = paddle.reshape(target, [n, -1])
intersection = paddle.sum((iflat * tflat), axis=1)
loss = 1 - (
(2. * intersection + smooth) /
(paddle.sum(iflat, axis=1) + paddle.sum(tflat, axis=1) + smooth))
return paddle.mean(loss)
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