139 lines
5.5 KiB
Python
139 lines
5.5 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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import paddle.nn.functional as F
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import paddle.nn as nn
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from ppdet.core.workspace import register
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__all__ = ['FocalLoss', 'Weighted_FocalLoss']
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@register
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class FocalLoss(nn.Layer):
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"""A wrapper around paddle.nn.functional.sigmoid_focal_loss.
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Args:
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use_sigmoid (bool): currently only support use_sigmoid=True
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alpha (float): parameter alpha in Focal Loss
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gamma (float): parameter gamma in Focal Loss
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loss_weight (float): final loss will be multiplied by this
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"""
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def __init__(self,
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use_sigmoid=True,
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alpha=0.25,
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gamma=2.0,
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loss_weight=1.0):
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super(FocalLoss, self).__init__()
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assert use_sigmoid == True, \
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'Focal Loss only supports sigmoid at the moment'
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self.use_sigmoid = use_sigmoid
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self.alpha = alpha
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self.gamma = gamma
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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.
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Args:
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pred (Tensor): logits of class prediction, of shape (N, num_classes)
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target (Tensor): target class label, of shape (N, )
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reduction (str): the way to reduce loss, one of (none, sum, mean)
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"""
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num_classes = pred.shape[1]
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target = F.one_hot(target, num_classes+1).cast(pred.dtype)
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target = target[:, :-1].detach()
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loss = F.sigmoid_focal_loss(
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pred, target, alpha=self.alpha, gamma=self.gamma,
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reduction=reduction)
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return loss * self.loss_weight
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@register
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class Weighted_FocalLoss(FocalLoss):
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"""A wrapper around paddle.nn.functional.sigmoid_focal_loss.
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Args:
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use_sigmoid (bool): currently only support use_sigmoid=True
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alpha (float): parameter alpha in Focal Loss
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gamma (float): parameter gamma in Focal Loss
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loss_weight (float): final loss will be multiplied by this
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"""
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def __init__(self,
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use_sigmoid=True,
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alpha=0.25,
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gamma=2.0,
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loss_weight=1.0,
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reduction="mean"):
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super(FocalLoss, self).__init__()
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assert use_sigmoid == True, \
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'Focal Loss only supports sigmoid at the moment'
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self.use_sigmoid = use_sigmoid
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self.alpha = alpha
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self.gamma = gamma
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self.loss_weight = loss_weight
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self.reduction = reduction
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def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None):
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"""forward function.
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Args:
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pred (Tensor): logits of class prediction, of shape (N, num_classes)
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target (Tensor): target class label, of shape (N, )
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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_override in (None, 'none', 'mean', 'sum')
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reduction = (
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reduction_override if reduction_override else self.reduction)
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num_classes = pred.shape[1]
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target = F.one_hot(target, num_classes + 1).astype(pred.dtype)
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target = target[:, :-1].detach()
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loss = F.sigmoid_focal_loss(
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pred, target, alpha=self.alpha, gamma=self.gamma,
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reduction='none')
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if weight is not None:
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if weight.shape != loss.shape:
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if weight.shape[0] == loss.shape[0]:
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# For most cases, weight is of shape (num_priors, ),
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# which means it does not have the second axis num_class
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weight = weight.reshape((-1, 1))
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else:
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# Sometimes, weight per anchor per class is also needed. e.g.
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# in FSAF. But it may be flattened of shape
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# (num_priors x num_class, ), while loss is still of shape
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# (num_priors, num_class).
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assert weight.numel() == loss.numel()
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weight = weight.reshape((loss.shape[0], -1))
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assert weight.ndim == loss.ndim
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loss = loss * weight
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# if avg_factor is not specified, just reduce the loss
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if avg_factor is None:
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if reduction == 'mean':
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loss = loss.mean()
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elif reduction == 'sum':
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loss = loss.sum()
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else:
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# if reduction is mean, then average the loss by avg_factor
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if reduction == 'mean':
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# Avoid causing ZeroDivisionError when avg_factor is 0.0,
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# i.e., all labels of an image belong to ignore index.
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eps = 1e-10
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loss = loss.sum() / (avg_factor + eps)
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# if reduction is 'none', then do nothing, otherwise raise an error
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elif reduction != 'none':
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raise ValueError('avg_factor can not be used with reduction="sum"')
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return loss * self.loss_weight
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