# 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 import copy import paddle.fluid as fluid from ppdet.experimental import mixed_precision_global_state from ppdet.core.workspace import register from .input_helper import multiscale_def __all__ = ['MaskRCNN'] @register class MaskRCNN(object): """ Mask R-CNN architecture, see https://arxiv.org/abs/1703.06870 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 mask_assigner (object): `MaskAssigner` instance mask_head (object): `MaskHead` instance fpn (object): feature pyramid network instance """ __category__ = 'architecture' __inject__ = [ 'backbone', 'rpn_head', 'bbox_assigner', 'roi_extractor', 'bbox_head', 'mask_assigner', 'mask_head', 'fpn' ] def __init__(self, backbone, rpn_head, bbox_head='BBoxHead', bbox_assigner='BBoxAssigner', roi_extractor='RoIAlign', mask_assigner='MaskAssigner', mask_head='MaskHead', rpn_only=False, fpn=None): super(MaskRCNN, 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.mask_assigner = mask_assigner self.mask_head = mask_head self.rpn_only = rpn_only self.fpn = fpn def build(self, feed_vars, mode='train'): if mode == 'train': required_fields = [ 'gt_class', 'gt_bbox', 'gt_mask', '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'] 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 spatial_scale = None if self.fpn is not None: body_feats, spatial_scale = self.fpn.get_output(body_feats) # RPN proposals rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode) if mode == 'train': rpn_loss = self.rpn_head.get_loss(im_info, feed_vars['gt_bbox'], feed_vars['is_crowd']) outs = self.bbox_assigner( rpn_rois=rois, gt_classes=feed_vars['gt_class'], is_crowd=feed_vars['is_crowd'], gt_boxes=feed_vars['gt_bbox'], im_info=feed_vars['im_info']) rois = outs[0] labels_int32 = outs[1] 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) loss = self.bbox_head.get_loss(roi_feat, labels_int32, *outs[2:]) loss.update(rpn_loss) mask_rois, roi_has_mask_int32, mask_int32 = self.mask_assigner( rois=rois, gt_classes=feed_vars['gt_class'], is_crowd=feed_vars['is_crowd'], gt_segms=feed_vars['gt_mask'], im_info=feed_vars['im_info'], labels_int32=labels_int32) if self.fpn is None: bbox_head_feat = self.bbox_head.get_head_feat() feat = fluid.layers.gather(bbox_head_feat, roi_has_mask_int32) else: feat = self.roi_extractor( body_feats, mask_rois, spatial_scale, is_mask=True) mask_loss = self.mask_head.get_loss(feat, mask_int32) loss.update(mask_loss) total_loss = fluid.layers.sum(list(loss.values())) loss.update({'loss': total_loss}) return loss 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} mask_name = 'mask_pred' mask_pred, bbox_pred = self.single_scale_eval( body_feats, mask_name, rois, im_info, feed_vars['im_shape'], spatial_scale) return {'bbox': bbox_pred, 'mask': mask_pred} def build_multi_scale(self, feed_vars, mask_branch=False): required_fields = ['image', 'im_info'] self._input_check(required_fields, feed_vars) result = {} if not mask_branch: assert 'im_shape' in feed_vars, \ "{} has no im_shape field".format(feed_vars) result.update(feed_vars) 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]] body_feats = self.backbone(im) # FPN 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 not mask_branch: im_shape = feed_vars['im_shape'] body_feat_names = list(body_feats.keys()) if self.fpn is None: 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'] else: mask_name = 'mask_pred_' + str(i) bbox_pred = feed_vars['bbox'] #result.update({im.name: im}) if 'flip' in im.name: mask_name += '_flip' bbox_pred = feed_vars['bbox_flip'] mask_pred, bbox_pred = self.single_scale_eval( body_feats, mask_name, rois, im_info, feed_vars['im_shape'], spatial_scale, bbox_pred) result[mask_name] = mask_pred return result def single_scale_eval(self, body_feats, mask_name, rois, im_info, im_shape, spatial_scale, bbox_pred=None): if not bbox_pred: 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) bbox_pred = self.bbox_head.get_prediction(roi_feat, rois, im_info, im_shape) bbox_pred = bbox_pred['bbox'] # share weight bbox_shape = fluid.layers.shape(bbox_pred) bbox_size = fluid.layers.reduce_prod(bbox_shape) bbox_size = fluid.layers.reshape(bbox_size, [1, 1]) size = fluid.layers.fill_constant([1, 1], value=6, dtype='int32') cond = fluid.layers.less_than(x=bbox_size, y=size) mask_pred = fluid.layers.create_global_var( shape=[1], value=0.0, dtype='float32', persistable=False, name=mask_name) def noop(): fluid.layers.assign(input=bbox_pred, output=mask_pred) def process_boxes(): bbox = fluid.layers.slice(bbox_pred, [1], starts=[2], ends=[6]) im_scale = fluid.layers.slice(im_info, [1], starts=[2], ends=[3]) im_scale = fluid.layers.sequence_expand(im_scale, bbox) mask_rois = bbox * im_scale if self.fpn is None: last_feat = body_feats[list(body_feats.keys())[-1]] mask_feat = self.roi_extractor(last_feat, mask_rois) mask_feat = self.bbox_head.get_head_feat(mask_feat) else: mask_feat = self.roi_extractor( body_feats, mask_rois, spatial_scale, is_mask=True) mask_out = self.mask_head.get_prediction(mask_feat, bbox) fluid.layers.assign(input=mask_out, output=mask_pred) fluid.layers.cond(cond, noop, process_boxes) return mask_pred, bbox_pred 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 _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}, 'gt_mask': {'shape': [None, 2], 'dtype': 'float32', 'lod_level': 3}, # polygon coordinates '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, mask_branch=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 if mask_branch: box_fields = ['bbox', 'bbox_flip'] if use_flip else ['bbox'] for key in box_fields: inputs_def[key] = { 'shape': [6], 'dtype': 'float32', 'lod_level': 1 } fields += box_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]) use_dataloader = use_dataloader and not mask_branch 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, mask_branch=False): if multi_scale: return self.build_multi_scale(feed_vars, mask_branch) return self.build(feed_vars, 'test') def test(self, feed_vars): return self.build(feed_vars, 'test')