# Copyright (c) 2019 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 from collections import OrderedDict from paddle import fluid from ppdet.experimental import mixed_precision_global_state from ppdet.core.workspace import register __all__ = ['FasterRCNN'] @register class FasterRCNN(object): """ Faster R-CNN architecture, see https://arxiv.org/abs/1506.01497 Args: backbone (object): backbone instance rpn_head (object): `RPNhead` instance bbox_assigner (object): `BBoxAssigner` instance roi_extractor (object): ROI extractor instance bbox_head (object): `BBoxHead` instance fpn (object): feature pyramid network instance """ __category__ = 'architecture' __inject__ = [ 'backbone', 'rpn_head', 'bbox_assigner', 'roi_extractor', 'bbox_head', 'fpn' ] def __init__(self, backbone, rpn_head, roi_extractor, bbox_head='BBoxHead', bbox_assigner='BBoxAssigner', rpn_only=False, fpn=None): super(FasterRCNN, self).__init__() self.backbone = backbone self.rpn_head = rpn_head self.bbox_assigner = bbox_assigner self.roi_extractor = roi_extractor self.bbox_head = bbox_head self.fpn = fpn self.rpn_only = rpn_only def build(self, feed_vars, mode='train'): if mode == 'train': required_fields = ['gt_label', 'gt_box', 'is_crowd', 'im_info'] else: required_fields = ['im_shape', 'im_info'] self._input_check(required_fields, feed_vars) im = feed_vars['image'] im_info = feed_vars['im_info'] if mode == 'train': gt_box = feed_vars['gt_box'] is_crowd = feed_vars['is_crowd'] else: im_shape = feed_vars['im_shape'] mixed_precision_enabled = mixed_precision_global_state() is not None # cast inputs to FP16 if mixed_precision_enabled: im = fluid.layers.cast(im, 'float16') body_feats = self.backbone(im) body_feat_names = list(body_feats.keys()) # cast features back to FP32 if mixed_precision_enabled: body_feats = OrderedDict((k, fluid.layers.cast(v, 'float32')) for k, v in body_feats.items()) if self.fpn is not None: body_feats, spatial_scale = self.fpn.get_output(body_feats) rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) if mode == 'train': rpn_loss = self.rpn_head.get_loss(im_info, gt_box, is_crowd) # sampled rpn proposals for var in ['gt_label', 'is_crowd', 'gt_box', 'im_info']: assert var in feed_vars, "{} has no {}".format(feed_vars, var) outs = self.bbox_assigner( rpn_rois=rois, gt_classes=feed_vars['gt_label'], is_crowd=feed_vars['is_crowd'], gt_boxes=feed_vars['gt_box'], im_info=feed_vars['im_info']) rois = outs[0] labels_int32 = outs[1] bbox_targets = outs[2] bbox_inside_weights = outs[3] bbox_outside_weights = outs[4] else: if self.rpn_only: im_scale = fluid.layers.slice( im_info, [1], starts=[2], ends=[3]) im_scale = fluid.layers.sequence_expand(im_scale, rois) rois = rois / im_scale return {'proposal': rois} if self.fpn is None: # in models without FPN, roi extractor only uses the last level of # feature maps. And body_feat_names[-1] represents the name of # last feature map. body_feat = body_feats[body_feat_names[-1]] roi_feat = self.roi_extractor(body_feat, rois) else: roi_feat = self.roi_extractor(body_feats, rois, spatial_scale) if mode == 'train': loss = self.bbox_head.get_loss(roi_feat, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights) loss.update(rpn_loss) total_loss = fluid.layers.sum(list(loss.values())) loss.update({'loss': total_loss}) return loss else: pred = self.bbox_head.get_prediction(roi_feat, rois, im_info, im_shape) return pred def build_multi_scale(self, feed_vars): required_fields = ['image', 'im_info', 'im_shape'] self._input_check(required_fields, feed_vars) ims = [] for k in feed_vars.keys(): if 'image' in k: ims.append(feed_vars[k]) result = {} result.update(feed_vars) for i, im in enumerate(ims): im_info = fluid.layers.slice( input=feed_vars['im_info'], axes=[1], starts=[3 * i], ends=[3 * i + 3]) im_shape = feed_vars['im_shape'] body_feats = self.backbone(im) result.update(body_feats) body_feat_names = list(body_feats.keys()) if self.fpn is not None: body_feats, spatial_scale = self.fpn.get_output(body_feats) rois = self.rpn_head.get_proposals(body_feats, im_info, mode='test') if self.fpn is None: # in models without FPN, roi extractor only uses the last level of # feature maps. And body_feat_names[-1] represents the name of # last feature map. body_feat = body_feats[body_feat_names[-1]] roi_feat = self.roi_extractor(body_feat, rois) else: roi_feat = self.roi_extractor(body_feats, rois, spatial_scale) pred = self.bbox_head.get_prediction( roi_feat, rois, im_info, im_shape, return_box_score=True) bbox_name = 'bbox_' + str(i) score_name = 'score_' + str(i) if 'flip' in im.name: bbox_name += '_flip' score_name += '_flip' result[bbox_name] = pred['bbox'] result[score_name] = pred['score'] return result def _input_check(self, require_fields, feed_vars): for var in require_fields: assert var in feed_vars, \ "{} has no {} field".format(feed_vars, var) def train(self, feed_vars): return self.build(feed_vars, 'train') def eval(self, feed_vars, multi_scale=None): if multi_scale: return self.build_multi_scale(feed_vars) return self.build(feed_vars, 'test') def test(self, feed_vars): return self.build(feed_vars, 'test')