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93 lines
2.9 KiB
93 lines
2.9 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 .meta_arch import BaseArch |
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from paddlers_slim.models.ppdet.core.workspace import register, create |
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__all__ = ['DETR'] |
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@register |
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class DETR(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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transformer, |
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detr_head, |
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post_process='DETRBBoxPostProcess'): |
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super(DETR, self).__init__() |
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self.backbone = backbone |
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self.transformer = transformer |
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self.detr_head = detr_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 |
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backbone = create(cfg['backbone']) |
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# transformer |
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kwargs = {'input_shape': backbone.out_shape} |
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transformer = create(cfg['transformer'], **kwargs) |
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# head |
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kwargs = { |
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'hidden_dim': transformer.hidden_dim, |
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'nhead': transformer.nhead, |
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'input_shape': backbone.out_shape |
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} |
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detr_head = create(cfg['detr_head'], **kwargs) |
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return { |
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'backbone': backbone, |
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'transformer': transformer, |
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"detr_head": detr_head, |
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} |
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def _forward(self): |
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# Backbone |
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body_feats = self.backbone(self.inputs) |
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# Transformer |
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out_transformer = self.transformer(body_feats, self.inputs['pad_mask']) |
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# DETR Head |
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if self.training: |
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return self.detr_head(out_transformer, body_feats, self.inputs) |
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else: |
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preds = self.detr_head(out_transformer, body_feats) |
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bbox, bbox_num = self.post_process(preds, self.inputs['im_shape'], |
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self.inputs['scale_factor']) |
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return bbox, bbox_num |
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def get_loss(self, ): |
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losses = self._forward() |
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losses.update({ |
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'loss': |
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paddle.add_n([v for k, v in losses.items() if 'log' not in k]) |
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}) |
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return losses |
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def get_pred(self): |
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bbox_pred, bbox_num = self._forward() |
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output = { |
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"bbox": bbox_pred, |
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"bbox_num": bbox_num, |
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} |
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return output
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