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106 lines
3.7 KiB
106 lines
3.7 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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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 paddlers_slim.models.ppdet.core.workspace import register, create |
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from .meta_arch import BaseArch |
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__all__ = ['FasterRCNN'] |
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@register |
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class FasterRCNN(BaseArch): |
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""" |
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Faster R-CNN network, see https://arxiv.org/abs/1506.01497 |
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Args: |
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backbone (object): backbone instance |
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rpn_head (object): `RPNHead` instance |
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bbox_head (object): `BBoxHead` instance |
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bbox_post_process (object): `BBoxPostProcess` instance |
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neck (object): 'FPN' instance |
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""" |
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__category__ = 'architecture' |
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__inject__ = ['bbox_post_process'] |
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def __init__(self, |
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backbone, |
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rpn_head, |
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bbox_head, |
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bbox_post_process, |
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neck=None): |
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super(FasterRCNN, 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.bbox_head = bbox_head |
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self.bbox_post_process = bbox_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 = cfg['neck'] and create(cfg['neck'], **kwargs) |
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out_shape = neck and neck.out_shape or backbone.out_shape |
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kwargs = {'input_shape': out_shape} |
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rpn_head = create(cfg['rpn_head'], **kwargs) |
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bbox_head = create(cfg['bbox_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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"bbox_head": bbox_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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if self.neck is not None: |
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body_feats = self.neck(body_feats) |
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if self.training: |
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rois, rois_num, rpn_loss = self.rpn_head(body_feats, self.inputs) |
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bbox_loss, _ = self.bbox_head(body_feats, rois, rois_num, |
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self.inputs) |
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return rpn_loss, bbox_loss |
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else: |
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rois, rois_num, _ = self.rpn_head(body_feats, self.inputs) |
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preds, _ = self.bbox_head(body_feats, rois, rois_num, None) |
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im_shape = self.inputs['im_shape'] |
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scale_factor = self.inputs['scale_factor'] |
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bbox, bbox_num = self.bbox_post_process(preds, (rois, rois_num), |
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im_shape, scale_factor) |
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# rescale the prediction back to origin image |
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bboxes, bbox_pred, bbox_num = self.bbox_post_process.get_pred( |
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bbox, bbox_num, im_shape, scale_factor) |
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return bbox_pred, bbox_num |
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def get_loss(self, ): |
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rpn_loss, bbox_loss = self._forward() |
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loss = {} |
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loss.update(rpn_loss) |
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loss.update(bbox_loss) |
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total_loss = paddle.add_n(list(loss.values())) |
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loss.update({'loss': total_loss}) |
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return loss |
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def get_pred(self): |
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bbox_pred, bbox_num = self._forward() |
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output = {'bbox': bbox_pred, 'bbox_num': bbox_num} |
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return output
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