更换文档检测模型
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paddle_detection/ppdet/modeling/losses/gfocal_loss.py
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paddle_detection/ppdet/modeling/losses/gfocal_loss.py
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# Copyright (c) 2021 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/gfocal_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 ppdet.core.workspace import register, serializable
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from ppdet.modeling import ops
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__all__ = ['QualityFocalLoss', 'DistributionFocalLoss']
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def quality_focal_loss(pred, target, beta=2.0, use_sigmoid=True):
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"""
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Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
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Qualified and Distributed Bounding Boxes for Dense Object Detection
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<https://arxiv.org/abs/2006.04388>`_.
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Args:
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pred (Tensor): Predicted joint representation of classification
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and quality (IoU) estimation with shape (N, C), C is the number of
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classes.
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target (tuple([Tensor])): Target category label with shape (N,)
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and target quality label with shape (N,).
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beta (float): The beta parameter for calculating the modulating factor.
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Defaults to 2.0.
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Returns:
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Tensor: Loss tensor with shape (N,).
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"""
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assert len(target) == 2, """target for QFL must be a tuple of two elements,
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including category label and quality label, respectively"""
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# label denotes the category id, score denotes the quality score
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label, score = target
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if use_sigmoid:
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func = F.binary_cross_entropy_with_logits
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else:
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func = F.binary_cross_entropy
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# negatives are supervised by 0 quality score
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pred_sigmoid = F.sigmoid(pred) if use_sigmoid else pred
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scale_factor = pred_sigmoid
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zerolabel = paddle.zeros(pred.shape, dtype='float32')
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loss = func(pred, zerolabel, reduction='none') * scale_factor.pow(beta)
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# FG cat_id: [0, num_classes -1], BG cat_id: num_classes
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bg_class_ind = pred.shape[1]
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pos = paddle.logical_and((label >= 0),
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(label < bg_class_ind)).nonzero().squeeze(1)
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if pos.shape[0] == 0:
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return loss.sum(axis=1)
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pos_label = paddle.gather(label, pos, axis=0)
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pos_mask = np.zeros(pred.shape, dtype=np.int32)
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pos_mask[pos.numpy(), pos_label.numpy()] = 1
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pos_mask = paddle.to_tensor(pos_mask, dtype='bool')
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score = score.unsqueeze(-1).expand([-1, pred.shape[1]]).cast('float32')
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# positives are supervised by bbox quality (IoU) score
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scale_factor_new = score - pred_sigmoid
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loss_pos = func(
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pred, score, reduction='none') * scale_factor_new.abs().pow(beta)
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loss = loss * paddle.logical_not(pos_mask) + loss_pos * pos_mask
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loss = loss.sum(axis=1)
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return loss
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def distribution_focal_loss(pred, label):
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"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning
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Qualified and Distributed Bounding Boxes for Dense Object Detection
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<https://arxiv.org/abs/2006.04388>`_.
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Args:
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pred (Tensor): Predicted general distribution of bounding boxes
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(before softmax) with shape (N, n+1), n is the max value of the
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integral set `{0, ..., n}` in paper.
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label (Tensor): Target distance label for bounding boxes with
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shape (N,).
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Returns:
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Tensor: Loss tensor with shape (N,).
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"""
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dis_left = label.cast('int64')
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dis_right = dis_left + 1
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weight_left = dis_right.cast('float32') - label
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weight_right = label - dis_left.cast('float32')
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loss = F.cross_entropy(pred, dis_left, reduction='none') * weight_left \
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+ F.cross_entropy(pred, dis_right, reduction='none') * weight_right
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return loss
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@register
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@serializable
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class QualityFocalLoss(nn.Layer):
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r"""Quality Focal Loss (QFL) is a variant of `Generalized Focal Loss:
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Learning Qualified and Distributed Bounding Boxes for Dense Object
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Detection <https://arxiv.org/abs/2006.04388>`_.
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Args:
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use_sigmoid (bool): Whether sigmoid operation is conducted in QFL.
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Defaults to True.
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beta (float): The beta parameter for calculating the modulating factor.
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Defaults to 2.0.
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reduction (str): Options are "none", "mean" and "sum".
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loss_weight (float): Loss weight of current loss.
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"""
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def __init__(self,
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use_sigmoid=True,
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beta=2.0,
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reduction='mean',
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loss_weight=1.0):
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super(QualityFocalLoss, self).__init__()
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self.use_sigmoid = use_sigmoid
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self.beta = beta
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assert reduction in ('none', 'mean', 'sum')
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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): Predicted joint representation of
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classification and quality (IoU) estimation with shape (N, C),
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C is the number of classes.
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target (tuple([Tensor])): Target category label with shape
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(N,) and target quality label with shape (N,).
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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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"""
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loss = self.loss_weight * quality_focal_loss(
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pred, target, beta=self.beta, 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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@register
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@serializable
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class DistributionFocalLoss(nn.Layer):
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"""Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss:
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Learning Qualified and Distributed Bounding Boxes for Dense Object
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Detection <https://arxiv.org/abs/2006.04388>`_.
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Args:
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reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
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loss_weight (float): Loss weight of current loss.
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"""
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def __init__(self, reduction='mean', loss_weight=1.0):
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super(DistributionFocalLoss, self).__init__()
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assert reduction in ('none', 'mean', 'sum')
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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): Predicted general distribution of bounding
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boxes (before softmax) with shape (N, n+1), n is the max value
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of the integral set `{0, ..., n}` in paper.
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target (Tensor): Target distance label for bounding boxes
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with shape (N,).
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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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"""
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loss = self.loss_weight * distribution_focal_loss(pred, target)
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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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