# 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 import copy from collections import OrderedDict import paddle.fluid as fluid from ppdet.experimental import mixed_precision_global_state from ppdet.core.workspace import register from ppdet.utils.check import check_version from .input_helper import multiscale_def __all__ = ['CascadeRCNN'] @register class CascadeRCNN(object): """ Cascade R-CNN architecture, see https://arxiv.org/abs/1712.00726 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', 'fpn', 'rpn_head', 'bbox_assigner', 'roi_extractor', 'bbox_head' ] def __init__(self, backbone, rpn_head, roi_extractor='FPNRoIAlign', bbox_head='CascadeBBoxHead', bbox_assigner='CascadeBBoxAssigner', rpn_only=False, fpn='FPN'): super(CascadeRCNN, self).__init__() check_version('2.0.0-rc0') assert fpn is not None, "cascade RCNN requires FPN" self.backbone = backbone self.fpn = fpn self.rpn_head = rpn_head self.bbox_assigner = bbox_assigner self.roi_extractor = roi_extractor self.bbox_head = bbox_head self.rpn_only = rpn_only # Cascade local cfg self.cls_agnostic_bbox_reg = 2 (brw0, brw1, brw2) = self.bbox_assigner.bbox_reg_weights self.cascade_bbox_reg_weights = [ [1. / brw0, 1. / brw0, 2. / brw0, 2. / brw0], [1. / brw1, 1. / brw1, 2. / brw1, 2. / brw1], [1. / brw2, 1. / brw2, 2. / brw2, 2. / brw2] ] self.cascade_rcnn_loss_weight = [1.0, 0.5, 0.25] def build(self, feed_vars, mode='train'): if mode == 'train': required_fields = ['gt_class', 'gt_bbox', '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_bbox = feed_vars['gt_bbox'] is_crowd = feed_vars['is_crowd'] mixed_precision_enabled = mixed_precision_global_state() is not None # cast inputs to FP16 if mixed_precision_enabled: im = fluid.layers.cast(im, 'float16') # backbone body_feats = self.backbone(im) # 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()) # FPN if self.fpn is not None: body_feats, spatial_scale = self.fpn.get_output(body_feats) # rpn proposals rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) if mode == 'train': #fluid.layers.Print(gt_bbox) #fluid.layers.Print(is_crowd) rpn_loss = self.rpn_head.get_loss(im_info, gt_bbox, is_crowd) 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, rpn_rois) rois = rpn_rois / im_scale return {'proposal': rois} proposal_list = [] roi_feat_list = [] rcnn_pred_list = [] rcnn_target_list = [] proposals = None bbox_pred = None max_overlap = None for i in range(3): if i > 0: refined_bbox = self._decode_box( proposals, bbox_pred, curr_stage=i - 1, ) else: refined_bbox = rpn_rois if mode == 'train': outs = self.bbox_assigner( input_rois=refined_bbox, feed_vars=feed_vars, curr_stage=i, max_overlap=max_overlap) proposals = outs[0] max_overlap = outs[-1] rcnn_target_list.append(outs[:-1]) else: proposals = refined_bbox proposal_list.append(proposals) # extract roi features roi_feat = self.roi_extractor(body_feats, proposals, spatial_scale) roi_feat_list.append(roi_feat) # bbox head cls_score, bbox_pred = self.bbox_head.get_output( roi_feat, wb_scalar=1.0 / self.cascade_rcnn_loss_weight[i], name='_' + str(i + 1) if i > 0 else '') rcnn_pred_list.append((cls_score, bbox_pred)) if mode == 'train': loss = self.bbox_head.get_loss(rcnn_pred_list, rcnn_target_list, self.cascade_rcnn_loss_weight) 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( im_info, feed_vars['im_shape'], roi_feat_list, rcnn_pred_list, proposal_list, self.cascade_bbox_reg_weights, self.cls_agnostic_bbox_reg) return pred def build_multi_scale(self, feed_vars): required_fields = ['image', 'im_shape', 'im_info'] self._input_check(required_fields, feed_vars) result = {} im_shape = feed_vars['im_shape'] result['im_shape'] = im_shape for i in range(len(self.im_info_names) // 2): im = feed_vars[self.im_info_names[2 * i]] im_info = feed_vars[self.im_info_names[2 * i + 1]] # backbone body_feats = self.backbone(im) result.update(body_feats) # FPN if self.fpn is not None: body_feats, spatial_scale = self.fpn.get_output(body_feats) # rpn proposals rpn_rois = self.rpn_head.get_proposals( body_feats, im_info, mode='test') proposal_list = [] roi_feat_list = [] rcnn_pred_list = [] proposals = None bbox_pred = None for i in range(3): if i > 0: refined_bbox = self._decode_box( proposals, bbox_pred, curr_stage=i - 1, ) else: refined_bbox = rpn_rois proposals = refined_bbox proposal_list.append(proposals) # extract roi features roi_feat = self.roi_extractor(body_feats, proposals, spatial_scale) roi_feat_list.append(roi_feat) # bbox head cls_score, bbox_pred = self.bbox_head.get_output( roi_feat, wb_scalar=1.0 / self.cascade_rcnn_loss_weight[i], name='_' + str(i + 1) if i > 0 else '') rcnn_pred_list.append((cls_score, bbox_pred)) # get mask rois rois = proposal_list[2] if self.fpn is None: last_feat = body_feats[list(body_feats.keys())[-1]] roi_feat = self.roi_extractor(last_feat, rois) else: roi_feat = self.roi_extractor(body_feats, rois, spatial_scale) pred = self.bbox_head.get_prediction( im_info, im_shape, roi_feat_list, rcnn_pred_list, proposal_list, self.cascade_bbox_reg_weights, self.cls_agnostic_bbox_reg, 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 _decode_box(self, proposals, bbox_pred, curr_stage): rcnn_loc_delta_r = fluid.layers.reshape( bbox_pred, (-1, self.cls_agnostic_bbox_reg, 4)) # only use fg box delta to decode box rcnn_loc_delta_s = fluid.layers.slice( rcnn_loc_delta_r, axes=[1], starts=[1], ends=[2]) refined_bbox = fluid.layers.box_coder( prior_box=proposals, prior_box_var=self.cascade_bbox_reg_weights[curr_stage], target_box=rcnn_loc_delta_s, code_type='decode_center_size', box_normalized=False, axis=1, ) refined_bbox = fluid.layers.reshape(refined_bbox, shape=[-1, 4]) return refined_bbox def _inputs_def(self, image_shape): im_shape = [None] + image_shape # yapf: disable inputs_def = { 'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0}, 'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0}, 'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0}, 'im_id': {'shape': [None, 1], 'dtype': 'int64', 'lod_level': 0}, 'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1}, 'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1}, 'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1}, 'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1}, } # yapf: enable return inputs_def def build_inputs(self, image_shape=[3, None, None], fields=[ 'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd' ], multi_scale=False, num_scales=-1, use_flip=None, use_dataloader=True, iterable=False): inputs_def = self._inputs_def(image_shape) fields = copy.deepcopy(fields) if multi_scale: ms_def, ms_fields = multiscale_def(image_shape, num_scales, use_flip) inputs_def.update(ms_def) fields += ms_fields self.im_info_names = ['image', 'im_info'] + ms_fields feed_vars = OrderedDict([(key, fluid.data( name=key, shape=inputs_def[key]['shape'], dtype=inputs_def[key]['dtype'], lod_level=inputs_def[key]['lod_level'])) for key in fields]) loader = fluid.io.DataLoader.from_generator( feed_list=list(feed_vars.values()), capacity=16, use_double_buffer=True, iterable=iterable) if use_dataloader else None return feed_vars, loader 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, exclude_nms=False): assert not exclude_nms, "exclude_nms for {} is not support currently".format( self.__class__.__name__) return self.build(feed_vars, 'test')