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61 lines
2.2 KiB
61 lines
2.2 KiB
# Copyright (c) 2022 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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import paddle.nn as nn |
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import paddle.nn.functional as F |
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from paddlers.models.ppdet.core.workspace import register |
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__all__ = ['SmoothL1Loss'] |
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@register |
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class SmoothL1Loss(nn.Layer): |
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"""Smooth L1 Loss. |
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Args: |
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beta (float): controls smooth region, it becomes L1 Loss when beta=0.0 |
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loss_weight (float): the final loss will be multiplied by this |
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""" |
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def __init__(self, beta=1.0, loss_weight=1.0): |
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super(SmoothL1Loss, self).__init__() |
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assert beta >= 0 |
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self.beta = beta |
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self.loss_weight = loss_weight |
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def forward(self, pred, target, reduction='none'): |
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"""forward function, based on fvcore. |
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Args: |
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pred (Tensor): prediction tensor |
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target (Tensor): target tensor, pred.shape must be the same as target.shape |
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reduction (str): the way to reduce loss, one of (none, sum, mean) |
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""" |
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assert reduction in ('none', 'sum', 'mean') |
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target = target.detach() |
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if self.beta < 1e-5: |
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loss = paddle.abs(pred - target) |
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else: |
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n = paddle.abs(pred - target) |
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cond = n < self.beta |
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loss = paddle.where(cond, 0.5 * n**2 / self.beta, |
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n - 0.5 * self.beta) |
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if reduction == 'mean': |
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loss = loss.mean() if loss.size > 0 else 0.0 * loss.sum() |
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elif reduction == 'sum': |
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loss = loss.sum() |
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return loss * self.loss_weight
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