更换文档检测模型

paddle_detection/ppdet/modeling/architectures/faster_rcnn.py

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+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 
+#   
+# Licensed under the Apache License, Version 2.0 (the "License");   
+# you may not use this file except in compliance with the License.  
+# You may obtain a copy of the License at   
+#   
+#     http://www.apache.org/licenses/LICENSE-2.0    
+#   
+# Unless required by applicable law or agreed to in writing, software   
+# distributed under the License is distributed on an "AS IS" BASIS, 
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
+# See the License for the specific language governing permissions and   
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+import numpy as np
+
+__all__ = ['FasterRCNN']
+
+
+@register
+class FasterRCNN(BaseArch):
+    """
+    Faster R-CNN network, see https://arxiv.org/abs/1506.01497
+
+    Args:
+        backbone (object): backbone instance
+        rpn_head (object): `RPNHead` instance
+        bbox_head (object): `BBoxHead` instance
+        bbox_post_process (object): `BBoxPostProcess` instance
+        neck (object): 'FPN' instance
+    """
+    __category__ = 'architecture'
+    __inject__ = ['bbox_post_process']
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 bbox_head,
+                 bbox_post_process,
+                 neck=None):
+        super(FasterRCNN, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.rpn_head = rpn_head
+        self.bbox_head = bbox_head
+        self.bbox_post_process = bbox_post_process
+
+    def init_cot_head(self, relationship):
+        self.bbox_head.init_cot_head(relationship)
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        backbone = create(cfg['backbone'])
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = cfg['neck'] and create(cfg['neck'], **kwargs)
+
+        out_shape = neck and neck.out_shape or backbone.out_shape
+        kwargs = {'input_shape': out_shape}
+        rpn_head = create(cfg['rpn_head'], **kwargs)
+        bbox_head = create(cfg['bbox_head'], **kwargs)
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            "rpn_head": rpn_head,
+            "bbox_head": bbox_head,
+        }
+
+    def _forward(self):
+        body_feats = self.backbone(self.inputs)
+        if self.neck is not None:
+            body_feats = self.neck(body_feats)
+        if self.training:
+            rois, rois_num, rpn_loss = self.rpn_head(body_feats, self.inputs)
+            bbox_loss, _ = self.bbox_head(body_feats, rois, rois_num,
+                                          self.inputs)
+            return rpn_loss, bbox_loss
+        else:
+            rois, rois_num, _ = self.rpn_head(body_feats, self.inputs)
+            preds, _ = self.bbox_head(body_feats, rois, rois_num, None)
+            im_shape = self.inputs['im_shape']
+            scale_factor = self.inputs['scale_factor']
+            bbox, bbox_num, nms_keep_idx = self.bbox_post_process(
+                preds, (rois, rois_num), im_shape, scale_factor)
+
+            # rescale the prediction back to origin image
+            bboxes, bbox_pred, bbox_num = self.bbox_post_process.get_pred(
+                bbox, bbox_num, im_shape, scale_factor)
+
+            if self.use_extra_data:
+                extra_data = {
+                }  # record the bbox output before nms, such like scores and nms_keep_idx
+                """extra_data:{
+                            'scores': predict scores,
+                            'nms_keep_idx': bbox index before nms,
+                           }
+                """
+                extra_data['scores'] = preds[1]  # predict scores (probability)
+                # Todo: get logits output
+                extra_data[
+                    'nms_keep_idx'] = nms_keep_idx  # bbox index before nms
+                return bbox_pred, bbox_num, extra_data
+            else:
+                return bbox_pred, bbox_num
+
+    def get_loss(self, ):
+        rpn_loss, bbox_loss = self._forward()
+        loss = {}
+        loss.update(rpn_loss)
+        loss.update(bbox_loss)
+        total_loss = paddle.add_n(list(loss.values()))
+        loss.update({'loss': total_loss})
+        return loss
+
+    def get_pred(self):
+        if self.use_extra_data:
+            bbox_pred, bbox_num, extra_data = self._forward()
+            output = {
+                'bbox': bbox_pred,
+                'bbox_num': bbox_num,
+                'extra_data': extra_data
+            }
+        else:
+            bbox_pred, bbox_num = self._forward()
+            output = {'bbox': bbox_pred, 'bbox_num': bbox_num}
+        return output
+
+    def target_bbox_forward(self, data):
+        body_feats = self.backbone(data)
+        if self.neck is not None:
+            body_feats = self.neck(body_feats)
+        rois = [roi for roi in data['gt_bbox']]
+        rois_num = paddle.concat([paddle.shape(roi)[0:1] for roi in rois])
+
+        preds, _ = self.bbox_head(body_feats, rois, rois_num, None, cot=True)
+        return preds
+
+    def relationship_learning(self, loader, num_classes_novel):
+        print('computing relationship')
+        train_labels_list = []
+        label_list = []
+
+        for step_id, data in enumerate(loader):
+            _, bbox_prob = self.target_bbox_forward(data)
+            batch_size = data['im_id'].shape[0]
+            for i in range(batch_size):
+                num_bbox = data['gt_class'][i].shape[0]
+                train_labels = data['gt_class'][i]
+                train_labels_list.append(train_labels.numpy().squeeze(1))
+            base_labels = bbox_prob.detach().numpy()[:, :-1]
+            label_list.append(base_labels)
+
+        labels = np.concatenate(train_labels_list, 0)
+        probabilities = np.concatenate(label_list, 0)
+        N_t = np.max(labels) + 1
+        conditional = []
+        for i in range(N_t):
+            this_class = probabilities[labels == i]
+            average = np.mean(this_class, axis=0, keepdims=True)
+            conditional.append(average)
+        return np.concatenate(conditional)