105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# Copyright (c) 2023 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__ = ['QueryInst']
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@register
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class QueryInst(BaseArch):
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__category__ = 'architecture'
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__inject__ = ['post_process']
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def __init__(self,
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backbone,
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neck,
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rpn_head,
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roi_head,
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post_process='SparsePostProcess'):
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super(QueryInst, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.rpn_head = rpn_head
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self.roi_head = roi_head
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self.post_process = post_process
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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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rpn_head = create(cfg['rpn_head'], **kwargs)
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roi_head = create(cfg['roi_head'], **kwargs)
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return {
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'backbone': backbone,
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'neck': neck,
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'rpn_head': rpn_head,
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"roi_head": roi_head
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}
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def _forward(self, targets=None):
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features = self.backbone(self.inputs)
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features = self.neck(features)
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proposal_bboxes, proposal_features = self.rpn_head(self.inputs[
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'img_whwh'])
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outputs = self.roi_head(features, proposal_bboxes, proposal_features,
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targets)
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if self.training:
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return outputs
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else:
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bbox_pred, bbox_num, mask_pred = self.post_process(
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outputs['class_logits'], outputs['bbox_pred'],
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self.inputs['scale_factor_whwh'], self.inputs['ori_shape'],
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outputs['mask_logits'])
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return bbox_pred, bbox_num, mask_pred
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def get_loss(self):
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targets = []
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for i in range(len(self.inputs['img_whwh'])):
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boxes = self.inputs['gt_bbox'][i]
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labels = self.inputs['gt_class'][i].squeeze(-1)
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img_whwh = self.inputs['img_whwh'][i]
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if boxes.shape[0] != 0:
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img_whwh_tgt = img_whwh.unsqueeze(0).tile([boxes.shape[0], 1])
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else:
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img_whwh_tgt = paddle.zeros_like(boxes)
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gt_segm = self.inputs['gt_segm'][i].astype('float32')
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targets.append({
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'boxes': boxes,
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'labels': labels,
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'img_whwh': img_whwh,
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'img_whwh_tgt': img_whwh_tgt,
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'gt_segm': gt_segm
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})
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losses = self._forward(targets)
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losses.update({'loss': sum(losses.values())})
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return losses
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def get_pred(self):
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bbox_pred, bbox_num, mask_pred = self._forward()
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return {'bbox': bbox_pred, 'bbox_num': bbox_num, 'mask': mask_pred}
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