223 lines
7.4 KiB
Python
223 lines
7.4 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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from ppdet.core.workspace import register, create
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from .meta_arch import BaseArch
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__all__ = ['FCOS', 'ARSL_FCOS']
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@register
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class FCOS(BaseArch):
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"""
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FCOS network, see https://arxiv.org/abs/1904.01355
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Args:
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backbone (object): backbone instance
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neck (object): 'FPN' instance
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fcos_head (object): 'FCOSHead' instance
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ssod_loss (object): 'SSODFCOSLoss' instance, only used for semi-det(ssod) by DenseTeacher
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"""
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__category__ = 'architecture'
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__inject__ = ['ssod_loss']
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def __init__(self,
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backbone='ResNet',
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neck='FPN',
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fcos_head='FCOSHead',
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ssod_loss='SSODFCOSLoss'):
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super(FCOS, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.fcos_head = fcos_head
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# for ssod, semi-det
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self.is_teacher = False
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self.ssod_loss = ssod_loss
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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backbone = create(cfg['backbone'])
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kwargs = {'input_shape': backbone.out_shape}
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neck = create(cfg['neck'], **kwargs)
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kwargs = {'input_shape': neck.out_shape}
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fcos_head = create(cfg['fcos_head'], **kwargs)
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return {
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'backbone': backbone,
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'neck': neck,
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"fcos_head": fcos_head,
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}
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def _forward(self):
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body_feats = self.backbone(self.inputs)
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fpn_feats = self.neck(body_feats)
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self.is_teacher = self.inputs.get('is_teacher', False)
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if self.training or self.is_teacher:
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losses = self.fcos_head(fpn_feats, self.inputs)
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return losses
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else:
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fcos_head_outs = self.fcos_head(fpn_feats)
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bbox_pred, bbox_num = self.fcos_head.post_process(
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fcos_head_outs, self.inputs['scale_factor'])
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return {'bbox': bbox_pred, 'bbox_num': bbox_num}
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def get_loss(self):
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return self._forward()
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def get_pred(self):
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return self._forward()
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def get_loss_keys(self):
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return ['loss_cls', 'loss_box', 'loss_quality']
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def get_ssod_loss(self, student_head_outs, teacher_head_outs, train_cfg):
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ssod_losses = self.ssod_loss(student_head_outs, teacher_head_outs,
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train_cfg)
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return ssod_losses
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@register
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class ARSL_FCOS(BaseArch):
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"""
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FCOS ARSL network, see https://arxiv.org/abs/
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Args:
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backbone (object): backbone instance
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neck (object): 'FPN' instance
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fcos_head (object): 'FCOSHead_ARSL' instance
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fcos_cr_loss (object): 'FCOSLossCR' instance, only used for semi-det(ssod) by ARSL
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"""
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__category__ = 'architecture'
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__inject__ = ['fcos_cr_loss']
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def __init__(self,
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backbone,
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neck,
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fcos_head='FCOSHead_ARSL',
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fcos_cr_loss='FCOSLossCR'):
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super(ARSL_FCOS, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.fcos_head = fcos_head
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self.fcos_cr_loss = fcos_cr_loss
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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backbone = create(cfg['backbone'])
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kwargs = {'input_shape': backbone.out_shape}
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neck = create(cfg['neck'], **kwargs)
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kwargs = {'input_shape': neck.out_shape}
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fcos_head = create(cfg['fcos_head'], **kwargs)
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# consistency regularization loss
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fcos_cr_loss = create(cfg['fcos_cr_loss'])
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return {
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'backbone': backbone,
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'neck': neck,
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'fcos_head': fcos_head,
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'fcos_cr_loss': fcos_cr_loss,
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}
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def forward(self, inputs, branch="supervised", teacher_prediction=None):
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assert branch in ['supervised', 'semi_supervised'], \
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print('In ARSL, type must be supervised or semi_supervised.')
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if self.data_format == 'NHWC':
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image = inputs['image']
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inputs['image'] = paddle.transpose(image, [0, 2, 3, 1])
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self.inputs = inputs
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if self.training:
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if branch == "supervised":
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out = self.get_loss()
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else:
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out = self.get_pseudo_loss(teacher_prediction)
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else:
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# norm test
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if branch == "supervised":
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out = self.get_pred()
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# predict pseudo labels
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else:
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out = self.get_pseudo_pred()
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return out
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# model forward
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def model_forward(self):
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body_feats = self.backbone(self.inputs)
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fpn_feats = self.neck(body_feats)
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fcos_head_outs = self.fcos_head(fpn_feats)
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return fcos_head_outs
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# supervised loss for labeled data
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def get_loss(self):
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loss = {}
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tag_labels, tag_bboxes, tag_centerness = [], [], []
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for i in range(len(self.fcos_head.fpn_stride)):
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# labels, reg_target, centerness
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k_lbl = 'labels{}'.format(i)
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if k_lbl in self.inputs:
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tag_labels.append(self.inputs[k_lbl])
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k_box = 'reg_target{}'.format(i)
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if k_box in self.inputs:
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tag_bboxes.append(self.inputs[k_box])
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k_ctn = 'centerness{}'.format(i)
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if k_ctn in self.inputs:
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tag_centerness.append(self.inputs[k_ctn])
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fcos_head_outs = self.model_forward()
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loss_fcos = self.fcos_head.get_loss(fcos_head_outs, tag_labels,
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tag_bboxes, tag_centerness)
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loss.update(loss_fcos)
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return loss
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# unsupervised loss for unlabeled data
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def get_pseudo_loss(self, teacher_prediction):
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loss = {}
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fcos_head_outs = self.model_forward()
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unsup_loss = self.fcos_cr_loss(fcos_head_outs, teacher_prediction)
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for k in unsup_loss.keys():
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loss[k + '_pseudo'] = unsup_loss[k]
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return loss
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# get detection results for test, decode and rescale the results to original size
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def get_pred(self):
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fcos_head_outs = self.model_forward()
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scale_factor = self.inputs['scale_factor']
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bbox_pred, bbox_num = self.fcos_head.post_process(fcos_head_outs,
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scale_factor)
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output = {'bbox': bbox_pred, 'bbox_num': bbox_num}
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return output
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# generate pseudo labels to guide student
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def get_pseudo_pred(self):
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fcos_head_outs = self.model_forward()
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pred_cls, pred_loc, pred_iou = fcos_head_outs[1:] # 0 is locations
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for lvl, _ in enumerate(pred_loc):
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pred_loc[lvl] = pred_loc[lvl] / self.fcos_head.fpn_stride[lvl]
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return [pred_cls, pred_loc, pred_iou, self.fcos_head.fpn_stride]
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