168 lines
6.1 KiB
Python
168 lines
6.1 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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import numpy as np
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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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def init_cot_head(self, relationship):
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self.bbox_head.init_cot_head(relationship)
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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, nms_keep_idx = self.bbox_post_process(
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preds, (rois, rois_num), 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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if self.use_extra_data:
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extra_data = {
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} # record the bbox output before nms, such like scores and nms_keep_idx
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"""extra_data:{
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'scores': predict scores,
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'nms_keep_idx': bbox index before nms,
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}
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"""
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extra_data['scores'] = preds[1] # predict scores (probability)
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# Todo: get logits output
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extra_data[
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'nms_keep_idx'] = nms_keep_idx # bbox index before nms
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return bbox_pred, bbox_num, extra_data
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else:
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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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if self.use_extra_data:
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bbox_pred, bbox_num, extra_data = 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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'extra_data': extra_data
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}
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else:
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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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def target_bbox_forward(self, data):
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body_feats = self.backbone(data)
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if self.neck is not None:
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body_feats = self.neck(body_feats)
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rois = [roi for roi in data['gt_bbox']]
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rois_num = paddle.concat([paddle.shape(roi)[0:1] for roi in rois])
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preds, _ = self.bbox_head(body_feats, rois, rois_num, None, cot=True)
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return preds
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def relationship_learning(self, loader, num_classes_novel):
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print('computing relationship')
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train_labels_list = []
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label_list = []
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for step_id, data in enumerate(loader):
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_, bbox_prob = self.target_bbox_forward(data)
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batch_size = data['im_id'].shape[0]
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for i in range(batch_size):
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num_bbox = data['gt_class'][i].shape[0]
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train_labels = data['gt_class'][i]
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train_labels_list.append(train_labels.numpy().squeeze(1))
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base_labels = bbox_prob.detach().numpy()[:, :-1]
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label_list.append(base_labels)
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labels = np.concatenate(train_labels_list, 0)
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probabilities = np.concatenate(label_list, 0)
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N_t = np.max(labels) + 1
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conditional = []
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for i in range(N_t):
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this_class = probabilities[labels == i]
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average = np.mean(this_class, axis=0, keepdims=True)
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conditional.append(average)
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return np.concatenate(conditional)
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