102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
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# 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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from ppdet.core.workspace import register, serializable
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__all__ = ['SOLOv2Loss']
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@register
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@serializable
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class SOLOv2Loss(object):
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"""
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SOLOv2Loss
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Args:
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ins_loss_weight (float): Weight of instance loss.
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focal_loss_gamma (float): Gamma parameter for focal loss.
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focal_loss_alpha (float): Alpha parameter for focal loss.
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"""
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def __init__(self,
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ins_loss_weight=3.0,
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focal_loss_gamma=2.0,
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focal_loss_alpha=0.25):
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self.ins_loss_weight = ins_loss_weight
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self.focal_loss_gamma = focal_loss_gamma
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self.focal_loss_alpha = focal_loss_alpha
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def _dice_loss(self, input, target):
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input = paddle.reshape(input, shape=(paddle.shape(input)[0], -1))
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target = paddle.reshape(target, shape=(paddle.shape(target)[0], -1))
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a = paddle.sum(input * target, axis=1)
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b = paddle.sum(input * input, axis=1) + 0.001
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c = paddle.sum(target * target, axis=1) + 0.001
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d = (2 * a) / (b + c)
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return 1 - d
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def __call__(self, ins_pred_list, ins_label_list, cate_preds, cate_labels,
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num_ins):
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"""
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Get loss of network of SOLOv2.
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Args:
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ins_pred_list (list): Variable list of instance branch output.
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ins_label_list (list): List of instance labels pre batch.
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cate_preds (list): Concat Variable list of categroy branch output.
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cate_labels (list): Concat list of categroy labels pre batch.
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num_ins (int): Number of positive samples in a mini-batch.
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Returns:
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loss_ins (Variable): The instance loss Variable of SOLOv2 network.
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loss_cate (Variable): The category loss Variable of SOLOv2 network.
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"""
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#1. Ues dice_loss to calculate instance loss
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loss_ins = []
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total_weights = paddle.zeros(shape=[1], dtype='float32')
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for input, target in zip(ins_pred_list, ins_label_list):
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if input is None:
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continue
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target = paddle.cast(target, 'float32')
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target = paddle.reshape(
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target,
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shape=[-1, paddle.shape(input)[-2], paddle.shape(input)[-1]])
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weights = paddle.cast(
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paddle.sum(target, axis=[1, 2]) > 0, 'float32')
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input = F.sigmoid(input)
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dice_out = paddle.multiply(self._dice_loss(input, target), weights)
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total_weights += paddle.sum(weights)
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loss_ins.append(dice_out)
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loss_ins = paddle.sum(paddle.concat(loss_ins)) / total_weights
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loss_ins = loss_ins * self.ins_loss_weight
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#2. Ues sigmoid_focal_loss to calculate category loss
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# expand onehot labels
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num_classes = cate_preds.shape[-1]
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cate_labels_bin = F.one_hot(cate_labels, num_classes=num_classes + 1)
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cate_labels_bin = cate_labels_bin[:, 1:]
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loss_cate = F.sigmoid_focal_loss(
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cate_preds,
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label=cate_labels_bin,
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normalizer=num_ins + 1.,
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gamma=self.focal_loss_gamma,
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alpha=self.focal_loss_alpha)
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return loss_ins, loss_cate
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