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99 lines
3.4 KiB
99 lines
3.4 KiB
3 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class FocalLossHX(nn.Layer):
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def __init__(self, gamma=0, size_average=True):
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super(FocalLoss, self).__init__()
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self.gamma = gamma
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self.size_average = size_average
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def forward(self, input, target):
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# print('input')
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# print(input.shape)
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# print(target.shape)
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if input.dim() > 2:
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input = paddle.reshape(
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input,
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shape=[input.size(0), input.size(1), -1]) # N,C,H,W => N,C,H*W
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input = input.transpose(1, 2) # N,C,H*W => N,H*W,C
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input = paddle.reshape(
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input, shape=[-1, input.size(2)]) # N,H*W,C => N*H*W,C
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target = paddle.reshape(target, shape=[-1])
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logpt = F.log_softmax(input)
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# print('logpt')
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# print(logpt.shape)
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# print(logpt)
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# get true class column from each row
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all_rows = paddle.arange(len(input))
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# print(target)
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log_pt = logpt.numpy()[all_rows.numpy(), target.numpy()]
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pt = paddle.to_tensor(log_pt, dtype='float64').exp()
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ce = F.cross_entropy(input, target, reduction='none')
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# print('ce')
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# print(ce.shape)
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loss = (1 - pt)**self.gamma * ce
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# print('ce:%f'%ce.mean())
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# print('fl:%f'%loss.mean())
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if self.size_average:
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return loss.mean()
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else:
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return loss.sum()
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class FocalLoss(nn.Layer):
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"""
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Focal Loss.
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Code referenced from:
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https://github.com/clcarwin/focal_loss_pytorch/blob/master/focalloss.py
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Args:
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gamma (float): the coefficient of Focal Loss.
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ignore_index (int64): Specifies a target value that is ignored
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and does not contribute to the input gradient. Default ``255``.
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"""
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def __init__(self, gamma=2.0):
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super(FocalLoss, self).__init__()
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self.gamma = gamma
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def forward(self, logit, label):
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#####logit = F.softmax(logit)
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# logit = paddle.reshape(
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# logit, [logit.shape[0], logit.shape[1], -1]) # N,C,H,W => N,C,H*W
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# logit = paddle.transpose(logit, [0, 2, 1]) # N,C,H*W => N,H*W,C
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# logit = paddle.reshape(logit,
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# [-1, logit.shape[2]]) # N,H*W,C => N*H*W,C
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label = paddle.reshape(label, [-1, 1])
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range_ = paddle.arange(0, label.shape[0])
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range_ = paddle.unsqueeze(range_, axis=-1)
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label = paddle.cast(label, dtype='int64')
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label = paddle.concat([range_, label], axis=-1)
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logpt = F.log_softmax(logit)
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logpt = paddle.gather_nd(logpt, label)
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pt = paddle.exp(logpt.detach())
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loss = -1 * (1 - pt)**self.gamma * logpt
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loss = paddle.mean(loss)
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# print(loss)
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# print(logpt)
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return loss
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