100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
# Copyright (c) 2021 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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from ppdet.core.workspace import register, create
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from .meta_arch import BaseArch
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__all__ = ["SparseRCNN"]
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@register
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class SparseRCNN(BaseArch):
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__category__ = 'architecture'
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__inject__ = ["postprocess"]
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def __init__(self,
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backbone,
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neck,
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head="SparsercnnHead",
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postprocess="SparsePostProcess"):
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super(SparseRCNN, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.head = head
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self.postprocess = postprocess
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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 = {'roi_input_shape': neck.out_shape}
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head = create(cfg['head'], **kwargs)
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return {
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'backbone': backbone,
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'neck': neck,
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"head": 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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head_outs = self.head(fpn_feats, self.inputs["img_whwh"])
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if not self.training:
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bbox_pred, bbox_num = self.postprocess(
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head_outs["pred_logits"], head_outs["pred_boxes"],
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self.inputs["scale_factor_whwh"], self.inputs["ori_shape"])
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return bbox_pred, bbox_num
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else:
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return head_outs
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def get_loss(self):
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batch_gt_class = self.inputs["gt_class"]
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batch_gt_box = self.inputs["gt_bbox"]
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batch_whwh = self.inputs["img_whwh"]
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targets = []
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for i in range(len(batch_gt_class)):
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boxes = batch_gt_box[i]
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labels = batch_gt_class[i].squeeze(-1)
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img_whwh = batch_whwh[i]
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img_whwh_tgt = img_whwh.unsqueeze(0).tile([int(boxes.shape[0]), 1])
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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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})
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outputs = self._forward()
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loss_dict = self.head.get_loss(outputs, targets)
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acc = loss_dict["acc"]
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loss_dict.pop("acc")
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total_loss = sum(loss_dict.values())
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loss_dict.update({"loss": total_loss, "acc": acc})
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return loss_dict
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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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