# 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 numpy as np import sys from collections import OrderedDict import copy import paddle.fluid as fluid from ppdet.core.workspace import register from .input_helper import multiscale_def __all__ = ['CascadeRCNNClsAware'] @register class CascadeRCNNClsAware(object): """ Cascade R-CNN architecture, see https://arxiv.org/abs/1712.00726 This is a kind of modification of Cascade R-CNN. Specifically, it predicts bboxes for all classes with different weights, while the standard vesion just predicts bboxes for foreground 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', fpn='FPN', ): super(CascadeRCNNClsAware, self).__init__() 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.bbox_clip = np.log(1000. / 16.) # Cascade local cfg (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'): 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'] gt_class = feed_vars['gt_class'] else: im_shape = feed_vars['im_shape'] # backbone body_feats = self.backbone(im) # 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': rpn_loss = self.rpn_head.get_loss(im_info, gt_bbox, is_crowd) proposal_list = [] roi_feat_list = [] rcnn_pred_list = [] rcnn_target_list = [] bbox_pred = None self.cascade_var_v = [] for stage in range(3): var_v = np.array( self.cascade_bbox_reg_weights[stage], dtype="float32") prior_box_var = fluid.layers.create_tensor(dtype="float32") fluid.layers.assign(input=var_v, output=prior_box_var) self.cascade_var_v.append(prior_box_var) self.cascade_decoded_box = [] self.cascade_cls_prob = [] for stage in range(3): if stage > 0: pool_rois = decoded_assign_box else: pool_rois = rpn_rois if mode == "train": self.cascade_var_v[stage].stop_gradient = True outs = self.bbox_assigner( input_rois=pool_rois, feed_vars=feed_vars, curr_stage=stage) pool_rois = outs[0] rcnn_target_list.append(outs) # extract roi features roi_feat = self.roi_extractor(body_feats, pool_rois, spatial_scale) roi_feat_list.append(roi_feat) # bbox head cls_score, bbox_pred = self.bbox_head.get_output( roi_feat, cls_agnostic_bbox_reg=self.bbox_head.num_classes, wb_scalar=1.0 / self.cascade_rcnn_loss_weight[stage], name='_' + str(stage + 1)) cls_prob = fluid.layers.softmax(cls_score, use_cudnn=False) decoded_box, decoded_assign_box = fluid.layers.box_decoder_and_assign( pool_rois, self.cascade_var_v[stage], bbox_pred, cls_prob, self.bbox_clip) if mode == "train": decoded_box.stop_gradient = True decoded_assign_box.stop_gradient = True else: self.cascade_cls_prob.append(cls_prob) self.cascade_decoded_box.append(decoded_box) rcnn_pred_list.append((cls_score, bbox_pred)) # out loop 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_cls_aware( im_info, im_shape, self.cascade_cls_prob, self.cascade_decoded_box, self.cascade_bbox_reg_weights) 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 = [] rcnn_target_list = [] bbox_pred = None self.cascade_var_v = [] for stage in range(3): var_v = np.array( self.cascade_bbox_reg_weights[stage], dtype="float32") prior_box_var = fluid.layers.create_tensor(dtype="float32") fluid.layers.assign(input=var_v, output=prior_box_var) self.cascade_var_v.append(prior_box_var) self.cascade_decoded_box = [] self.cascade_cls_prob = [] for stage in range(3): if stage > 0: pool_rois = decoded_assign_box else: pool_rois = rpn_rois # extract roi features roi_feat = self.roi_extractor(body_feats, pool_rois, spatial_scale) roi_feat_list.append(roi_feat) # bbox head cls_score, bbox_pred = self.bbox_head.get_output( roi_feat, cls_agnostic_bbox_reg=self.bbox_head.num_classes, wb_scalar=1.0 / self.cascade_rcnn_loss_weight[stage], name='_' + str(stage + 1)) cls_prob = fluid.layers.softmax(cls_score, use_cudnn=False) decoded_box, decoded_assign_box = fluid.layers.box_decoder_and_assign( pool_rois, self.cascade_var_v[stage], bbox_pred, cls_prob, self.bbox_clip) self.cascade_cls_prob.append(cls_prob) self.cascade_decoded_box.append(decoded_box) rcnn_pred_list.append((cls_score, bbox_pred)) pred = self.bbox_head.get_prediction_cls_aware( im_info, im_shape, self.cascade_cls_prob, self.cascade_decoded_box, self.cascade_bbox_reg_weights, 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 _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_id': {'shape': [None, 1], 'dtype': 'int64', 'lod_level': 0}, 'im_shape': {'shape': [None, 3], 'dtype': 'float32', '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', 'gt_mask' ], 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 _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, exclude_nms=False): assert not exclude_nms, "exclude_nms for {} is not support currently".format( self.__class__.__name__) return self.build(feed_vars, 'test')