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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
@manager.LOSSES.add_component
class CrossEntropyLoss(nn.Layer):
"""
Implements the cross entropy loss function.
Args:
weight (tuple|list|ndarray|Tensor, optional): A manual rescaling weight
given to each class. Its length must be equal to the number of classes.
Default ``None``.
ignore_index (int64, optional): Specifies a target value that is ignored
and does not contribute to the input gradient. Default ``255``.
top_k_percent_pixels (float, optional): the value lies in [0.0, 1.0].
When its value < 1.0, only compute the loss for the top k percent pixels
(e.g., the top 20% pixels). This is useful for hard pixel mining. Default ``1.0``.
data_format (str, optional): The tensor format to use, 'NCHW' or 'NHWC'. Default ``'NCHW'``.
"""
def __init__(self,
weight=None,
ignore_index=255,
top_k_percent_pixels=1.0,
data_format='NCHW'):
super(CrossEntropyLoss, self).__init__()
self.ignore_index = ignore_index
self.top_k_percent_pixels = top_k_percent_pixels
self.EPS = 1e-8
self.data_format = data_format
if weight is not None:
self.weight = paddle.to_tensor(weight, dtype='float32')
else:
self.weight = None
def forward(self, logit, label, semantic_weights=None):
"""
Forward computation.
Args:
logit (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.
semantic_weights (Tensor, optional): Weights about loss for each pixels,
shape is the same as label. Default: None.
Returns:
(Tensor): The average loss.
"""
channel_axis = 1 if self.data_format == 'NCHW' else -1
if self.weight is not None and logit.shape[channel_axis] != len(
self.weight):
raise ValueError(
'The number of weights = {} must be the same as the number of classes = {}.'
.format(len(self.weight), logit.shape[channel_axis]))
if channel_axis == 1:
logit = paddle.transpose(logit, [0, 2, 3, 1])
label = label.astype('int64')
loss = F.cross_entropy(
logit,
label,
ignore_index=self.ignore_index,
reduction='none',
weight=self.weight)
return self._post_process_loss(logit, label, semantic_weights, loss)
def _post_process_loss(self, logit, label, semantic_weights, loss):
"""
Consider mask and top_k to calculate the final loss.
Args:
logit (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.
semantic_weights (Tensor, optional): Weights about loss for each pixels,
shape is the same as label.
loss (Tensor): Loss tensor which is the output of cross_entropy. If soft_label
is False in cross_entropy, the shape of loss should be the same as the label.
If soft_label is True in cross_entropy, the shape of loss should be
(N, D1, D2,..., Dk, 1).
Returns:
(Tensor): The average loss.
"""
mask = label != self.ignore_index
mask = paddle.cast(mask, 'float32')
label.stop_gradient = True
mask.stop_gradient = True
if loss.ndim > mask.ndim:
loss = paddle.squeeze(loss, axis=-1)
loss = loss * mask
if semantic_weights is not None:
loss = loss * semantic_weights
if self.weight is not None:
_one_hot = F.one_hot(label * mask, logit.shape[-1])
coef = paddle.sum(_one_hot * self.weight, axis=-1)
else:
coef = paddle.ones_like(label)
if self.top_k_percent_pixels == 1.0:
avg_loss = paddle.mean(loss) / (paddle.mean(mask * coef) + self.EPS)
else:
loss = loss.reshape((-1, ))
top_k_pixels = int(self.top_k_percent_pixels * loss.numel())
loss, indices = paddle.topk(loss, top_k_pixels)
coef = coef.reshape((-1, ))
coef = paddle.gather(coef, indices)
coef.stop_gradient = True
coef = coef.astype('float32')
avg_loss = loss.mean() / (paddle.mean(coef) + self.EPS)
return avg_loss
@manager.LOSSES.add_component
class DistillCrossEntropyLoss(CrossEntropyLoss):
"""
The implementation of distill cross entropy loss.
Args:
weight (tuple|list|ndarray|Tensor, optional): A manual rescaling weight
given to each class. Its length must be equal to the number of classes.
Default ``None``.
ignore_index (int64, optional): Specifies a target value that is ignored
and does not contribute to the input gradient. Default ``255``.
top_k_percent_pixels (float, optional): the value lies in [0.0, 1.0].
When its value < 1.0, only compute the loss for the top k percent pixels
(e.g., the top 20% pixels). This is useful for hard pixel mining.
Default ``1.0``.
data_format (str, optional): The tensor format to use, 'NCHW' or 'NHWC'.
Default ``'NCHW'``.
"""
def __init__(self,
weight=None,
ignore_index=255,
top_k_percent_pixels=1.0,
data_format='NCHW'):
super().__init__(weight, ignore_index, top_k_percent_pixels,
data_format)
def forward(self,
student_logit,
teacher_logit,
label,
semantic_weights=None):
"""
Forward computation.
Args:
student_logit (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.
teacher_logit (Tensor): Logit tensor, the data type is float32, float64. The shape
is the same as the student_logit.
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.
semantic_weights (Tensor, optional): Weights about loss for each pixels,
shape is the same as label. Default: None.
"""
if student_logit.shape != teacher_logit.shape:
raise ValueError(
'The shape of student_logit = {} must be the same as the shape of teacher_logit = {}.'
.format(student_logit.shape, teacher_logit.shape))
channel_axis = 1 if self.data_format == 'NCHW' else -1
if self.weight is not None and student_logit.shape[channel_axis] != len(
self.weight):
raise ValueError(
'The number of weights = {} must be the same as the number of classes = {}.'
.format(len(self.weight), student_logit.shape[channel_axis]))
if channel_axis == 1:
student_logit = paddle.transpose(student_logit, [0, 2, 3, 1])
teacher_logit = paddle.transpose(teacher_logit, [0, 2, 3, 1])
teacher_logit = F.softmax(teacher_logit)
loss = F.cross_entropy(
student_logit,
teacher_logit,
weight=self.weight,
reduction='none',
soft_label=True)
return self._post_process_loss(student_logit, label, semantic_weights,
loss)
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