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152 lines
5.8 KiB
152 lines
5.8 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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# The code is based on: |
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# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/varifocal_loss.py |
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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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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_slim.models.ppdet.core.workspace import register, serializable |
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from paddlers_slim.models.ppdet.modeling import ops |
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__all__ = ['VarifocalLoss'] |
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def varifocal_loss(pred, |
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target, |
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alpha=0.75, |
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gamma=2.0, |
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iou_weighted=True, |
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use_sigmoid=True): |
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"""`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_ |
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Args: |
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pred (Tensor): The prediction with shape (N, C), C is the |
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number of classes |
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target (Tensor): The learning target of the iou-aware |
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classification score with shape (N, C), C is the number of classes. |
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alpha (float, optional): A balance factor for the negative part of |
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Varifocal Loss, which is different from the alpha of Focal Loss. |
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Defaults to 0.75. |
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gamma (float, optional): The gamma for calculating the modulating |
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factor. Defaults to 2.0. |
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iou_weighted (bool, optional): Whether to weight the loss of the |
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positive example with the iou target. Defaults to True. |
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""" |
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# pred and target should be of the same size |
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assert pred.shape == target.shape |
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if use_sigmoid: |
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pred_new = F.sigmoid(pred) |
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else: |
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pred_new = pred |
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target = target.cast(pred.dtype) |
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if iou_weighted: |
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focal_weight = target * (target > 0.0).cast('float32') + \ |
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alpha * (pred_new - target).abs().pow(gamma) * \ |
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(target <= 0.0).cast('float32') |
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else: |
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focal_weight = (target > 0.0).cast('float32') + \ |
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alpha * (pred_new - target).abs().pow(gamma) * \ |
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(target <= 0.0).cast('float32') |
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if use_sigmoid: |
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loss = F.binary_cross_entropy_with_logits( |
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pred, target, reduction='none') * focal_weight |
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else: |
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loss = F.binary_cross_entropy( |
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pred, target, reduction='none') * focal_weight |
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loss = loss.sum(axis=1) |
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return loss |
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@register |
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@serializable |
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class VarifocalLoss(nn.Layer): |
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def __init__(self, |
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use_sigmoid=True, |
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alpha=0.75, |
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gamma=2.0, |
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iou_weighted=True, |
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reduction='mean', |
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loss_weight=1.0): |
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"""`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_ |
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Args: |
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use_sigmoid (bool, optional): Whether the prediction is |
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used for sigmoid or softmax. Defaults to True. |
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alpha (float, optional): A balance factor for the negative part of |
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Varifocal Loss, which is different from the alpha of Focal |
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Loss. Defaults to 0.75. |
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gamma (float, optional): The gamma for calculating the modulating |
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factor. Defaults to 2.0. |
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iou_weighted (bool, optional): Whether to weight the loss of the |
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positive examples with the iou target. Defaults to True. |
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reduction (str, optional): The method used to reduce the loss into |
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a scalar. Defaults to 'mean'. Options are "none", "mean" and |
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"sum". |
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loss_weight (float, optional): Weight of loss. Defaults to 1.0. |
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""" |
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super(VarifocalLoss, self).__init__() |
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assert alpha >= 0.0 |
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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.iou_weighted = iou_weighted |
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self.reduction = reduction |
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self.loss_weight = loss_weight |
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def forward(self, pred, target, weight=None, avg_factor=None): |
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"""Forward function. |
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Args: |
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pred (Tensor): The prediction. |
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target (Tensor): The learning target of the prediction. |
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weight (Tensor, optional): The weight of loss for each |
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prediction. Defaults to None. |
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avg_factor (int, optional): Average factor that is used to average |
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the loss. Defaults to None. |
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Returns: |
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Tensor: The calculated loss |
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""" |
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loss = self.loss_weight * varifocal_loss( |
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pred, |
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target, |
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alpha=self.alpha, |
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gamma=self.gamma, |
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iou_weighted=self.iou_weighted, |
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use_sigmoid=self.use_sigmoid) |
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if weight is not None: |
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loss = loss * weight |
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if avg_factor is None: |
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if self.reduction == 'none': |
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return loss |
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elif self.reduction == 'mean': |
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return loss.mean() |
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elif self.reduction == 'sum': |
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return 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 self.reduction == 'mean': |
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loss = loss.sum() / avg_factor |
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# if reduction is 'none', then do nothing, otherwise raise an error |
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elif self.reduction != 'none': |
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raise ValueError( |
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'avg_factor can not be used with reduction="sum"') |
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return loss
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