forked from PulseFocusPlatform/PulseFocusPlatform
74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
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# Copyright (c) 2019 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 numpy as np
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from paddle import fluid
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from ppdet.core.workspace import register, serializable
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__all__ = ['BalancedL1Loss']
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@register
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@serializable
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class BalancedL1Loss(object):
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"""
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Balanced L1 Loss, see https://arxiv.org/abs/1904.02701
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Args:
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alpha (float): hyper parameter of BalancedL1Loss, see more details in the paper
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gamma (float): hyper parameter of BalancedL1Loss, see more details in the paper
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beta (float): hyper parameter of BalancedL1Loss, see more details in the paper
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loss_weights (float): loss weight
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"""
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def __init__(self, alpha=0.5, gamma=1.5, beta=1.0, loss_weight=1.0):
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super(BalancedL1Loss, self).__init__()
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self.alpha = alpha
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self.gamma = gamma
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self.beta = beta
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self.loss_weight = loss_weight
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def __call__(
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self,
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x,
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y,
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inside_weight=None,
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outside_weight=None, ):
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alpha = self.alpha
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gamma = self.gamma
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beta = self.beta
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loss_weight = self.loss_weight
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diff = fluid.layers.abs(x - y)
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b = np.e**(gamma / alpha) - 1
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less_beta = diff < beta
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ge_beta = diff >= beta
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less_beta = fluid.layers.cast(x=less_beta, dtype='float32')
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ge_beta = fluid.layers.cast(x=ge_beta, dtype='float32')
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less_beta.stop_gradient = True
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ge_beta.stop_gradient = True
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loss_1 = less_beta * (
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alpha / b * (b * diff + 1) * fluid.layers.log(b * diff / beta + 1) -
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alpha * diff)
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loss_2 = ge_beta * (gamma * diff + gamma / b - alpha * beta)
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iou_weights = 1.0
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if inside_weight is not None and outside_weight is not None:
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iou_weights = inside_weight * outside_weight
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loss = (loss_1 + loss_2) * iou_weights
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loss = fluid.layers.reduce_sum(loss, dim=-1) * loss_weight
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return loss
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