forked from PulseFocusPlatform/PulseFocusPlatform
68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
# Copyright (c) 2020 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from ppdet.core.workspace import register, serializable
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__all__ = ['CTFocalLoss']
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@register
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@serializable
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class CTFocalLoss(object):
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"""
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CTFocalLoss: CornerNet & CenterNet Focal Loss
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Args:
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loss_weight (float): loss weight
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gamma (float): gamma parameter for Focal Loss
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"""
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def __init__(self, loss_weight=1., gamma=2.0):
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self.loss_weight = loss_weight
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self.gamma = gamma
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def __call__(self, pred, target):
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"""
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Calculate the loss
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Args:
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pred (Tensor): heatmap prediction
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target (Tensor): target for positive samples
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Return:
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ct_focal_loss (Tensor): Focal Loss used in CornerNet & CenterNet.
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Note that the values in target are in [0, 1] since gaussian is
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used to reduce the punishment and we treat [0, 1) as neg example.
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"""
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fg_map = paddle.cast(target == 1, 'float32')
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fg_map.stop_gradient = True
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bg_map = paddle.cast(target < 1, 'float32')
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bg_map.stop_gradient = True
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neg_weights = paddle.pow(1 - target, 4) * bg_map
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pos_loss = 0 - paddle.log(pred) * paddle.pow(1 - pred,
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self.gamma) * fg_map
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neg_loss = 0 - paddle.log(1 - pred) * paddle.pow(
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pred, self.gamma) * neg_weights
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pos_loss = paddle.sum(pos_loss)
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neg_loss = paddle.sum(neg_loss)
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fg_num = paddle.sum(fg_map)
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ct_focal_loss = (pos_loss + neg_loss) / (
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fg_num + paddle.cast(fg_num == 0, 'float32'))
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return ct_focal_loss * self.loss_weight
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