forked from PulseFocusPlatform/PulseFocusPlatform
344 lines
13 KiB
Python
344 lines
13 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from collections import OrderedDict
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import copy
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import paddle.fluid as fluid
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from ppdet.experimental import mixed_precision_global_state
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from ppdet.core.workspace import register
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from .input_helper import multiscale_def
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__all__ = ['MaskRCNN']
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@register
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class MaskRCNN(object):
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"""
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Mask R-CNN architecture, see https://arxiv.org/abs/1703.06870
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Args:
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backbone (object): backbone instance
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rpn_head (object): `RPNhead` instance
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bbox_assigner (object): `BBoxAssigner` instance
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roi_extractor (object): ROI extractor instance
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bbox_head (object): `BBoxHead` instance
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mask_assigner (object): `MaskAssigner` instance
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mask_head (object): `MaskHead` instance
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fpn (object): feature pyramid network instance
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"""
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__category__ = 'architecture'
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__inject__ = [
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'backbone', 'rpn_head', 'bbox_assigner', 'roi_extractor', 'bbox_head',
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'mask_assigner', 'mask_head', 'fpn'
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]
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def __init__(self,
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backbone,
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rpn_head,
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bbox_head='BBoxHead',
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bbox_assigner='BBoxAssigner',
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roi_extractor='RoIAlign',
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mask_assigner='MaskAssigner',
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mask_head='MaskHead',
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rpn_only=False,
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fpn=None):
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super(MaskRCNN, self).__init__()
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self.backbone = backbone
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self.rpn_head = rpn_head
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self.bbox_assigner = bbox_assigner
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self.roi_extractor = roi_extractor
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self.bbox_head = bbox_head
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self.mask_assigner = mask_assigner
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self.mask_head = mask_head
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self.rpn_only = rpn_only
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self.fpn = fpn
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def build(self, feed_vars, mode='train'):
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if mode == 'train':
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required_fields = [
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'gt_class', 'gt_bbox', 'gt_mask', 'is_crowd', 'im_info'
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]
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else:
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required_fields = ['im_shape', 'im_info']
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self._input_check(required_fields, feed_vars)
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im = feed_vars['image']
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im_info = feed_vars['im_info']
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mixed_precision_enabled = mixed_precision_global_state() is not None
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# cast inputs to FP16
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if mixed_precision_enabled:
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im = fluid.layers.cast(im, 'float16')
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# backbone
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body_feats = self.backbone(im)
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# cast features back to FP32
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if mixed_precision_enabled:
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body_feats = OrderedDict((k, fluid.layers.cast(v, 'float32'))
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for k, v in body_feats.items())
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# FPN
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spatial_scale = None
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if self.fpn is not None:
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body_feats, spatial_scale = self.fpn.get_output(body_feats)
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# RPN proposals
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rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode)
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if mode == 'train':
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rpn_loss = self.rpn_head.get_loss(im_info, feed_vars['gt_bbox'],
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feed_vars['is_crowd'])
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outs = self.bbox_assigner(
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rpn_rois=rois,
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gt_classes=feed_vars['gt_class'],
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is_crowd=feed_vars['is_crowd'],
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gt_boxes=feed_vars['gt_bbox'],
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im_info=feed_vars['im_info'])
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rois = outs[0]
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labels_int32 = outs[1]
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if self.fpn is None:
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last_feat = body_feats[list(body_feats.keys())[-1]]
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roi_feat = self.roi_extractor(last_feat, rois)
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else:
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roi_feat = self.roi_extractor(body_feats, rois, spatial_scale)
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loss = self.bbox_head.get_loss(roi_feat, labels_int32, *outs[2:])
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loss.update(rpn_loss)
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mask_rois, roi_has_mask_int32, mask_int32 = self.mask_assigner(
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rois=rois,
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gt_classes=feed_vars['gt_class'],
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is_crowd=feed_vars['is_crowd'],
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gt_segms=feed_vars['gt_mask'],
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im_info=feed_vars['im_info'],
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labels_int32=labels_int32)
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if self.fpn is None:
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bbox_head_feat = self.bbox_head.get_head_feat()
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feat = fluid.layers.gather(bbox_head_feat, roi_has_mask_int32)
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else:
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feat = self.roi_extractor(
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body_feats, mask_rois, spatial_scale, is_mask=True)
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mask_loss = self.mask_head.get_loss(feat, mask_int32)
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loss.update(mask_loss)
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total_loss = fluid.layers.sum(list(loss.values()))
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loss.update({'loss': total_loss})
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return loss
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else:
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if self.rpn_only:
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im_scale = fluid.layers.slice(
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im_info, [1], starts=[2], ends=[3])
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im_scale = fluid.layers.sequence_expand(im_scale, rois)
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rois = rois / im_scale
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return {'proposal': rois}
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mask_name = 'mask_pred'
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mask_pred, bbox_pred = self.single_scale_eval(
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body_feats, mask_name, rois, im_info, feed_vars['im_shape'],
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spatial_scale)
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return {'bbox': bbox_pred, 'mask': mask_pred}
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def build_multi_scale(self, feed_vars, mask_branch=False):
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required_fields = ['image', 'im_info']
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self._input_check(required_fields, feed_vars)
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result = {}
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if not mask_branch:
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assert 'im_shape' in feed_vars, \
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"{} has no im_shape field".format(feed_vars)
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result.update(feed_vars)
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for i in range(len(self.im_info_names) // 2):
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im = feed_vars[self.im_info_names[2 * i]]
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im_info = feed_vars[self.im_info_names[2 * i + 1]]
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body_feats = self.backbone(im)
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# FPN
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if self.fpn is not None:
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body_feats, spatial_scale = self.fpn.get_output(body_feats)
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rois = self.rpn_head.get_proposals(body_feats, im_info, mode='test')
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if not mask_branch:
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im_shape = feed_vars['im_shape']
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body_feat_names = list(body_feats.keys())
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if self.fpn is None:
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body_feat = body_feats[body_feat_names[-1]]
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roi_feat = self.roi_extractor(body_feat, rois)
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else:
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roi_feat = self.roi_extractor(body_feats, rois,
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spatial_scale)
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pred = self.bbox_head.get_prediction(
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roi_feat, rois, im_info, im_shape, return_box_score=True)
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bbox_name = 'bbox_' + str(i)
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score_name = 'score_' + str(i)
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if 'flip' in im.name:
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bbox_name += '_flip'
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score_name += '_flip'
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result[bbox_name] = pred['bbox']
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result[score_name] = pred['score']
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else:
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mask_name = 'mask_pred_' + str(i)
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bbox_pred = feed_vars['bbox']
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#result.update({im.name: im})
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if 'flip' in im.name:
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mask_name += '_flip'
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bbox_pred = feed_vars['bbox_flip']
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mask_pred, bbox_pred = self.single_scale_eval(
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body_feats, mask_name, rois, im_info, feed_vars['im_shape'],
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spatial_scale, bbox_pred)
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result[mask_name] = mask_pred
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return result
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def single_scale_eval(self,
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body_feats,
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mask_name,
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rois,
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im_info,
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im_shape,
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spatial_scale,
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bbox_pred=None):
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if not bbox_pred:
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if self.fpn is None:
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last_feat = body_feats[list(body_feats.keys())[-1]]
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roi_feat = self.roi_extractor(last_feat, rois)
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else:
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roi_feat = self.roi_extractor(body_feats, rois, spatial_scale)
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bbox_pred = self.bbox_head.get_prediction(roi_feat, rois, im_info,
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im_shape)
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bbox_pred = bbox_pred['bbox']
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# share weight
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bbox_shape = fluid.layers.shape(bbox_pred)
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bbox_size = fluid.layers.reduce_prod(bbox_shape)
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bbox_size = fluid.layers.reshape(bbox_size, [1, 1])
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size = fluid.layers.fill_constant([1, 1], value=6, dtype='int32')
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cond = fluid.layers.less_than(x=bbox_size, y=size)
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mask_pred = fluid.layers.create_global_var(
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shape=[1],
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value=0.0,
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dtype='float32',
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persistable=False,
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name=mask_name)
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def noop():
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fluid.layers.assign(input=bbox_pred, output=mask_pred)
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def process_boxes():
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bbox = fluid.layers.slice(bbox_pred, [1], starts=[2], ends=[6])
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im_scale = fluid.layers.slice(im_info, [1], starts=[2], ends=[3])
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im_scale = fluid.layers.sequence_expand(im_scale, bbox)
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mask_rois = bbox * im_scale
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if self.fpn is None:
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last_feat = body_feats[list(body_feats.keys())[-1]]
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mask_feat = self.roi_extractor(last_feat, mask_rois)
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mask_feat = self.bbox_head.get_head_feat(mask_feat)
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else:
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mask_feat = self.roi_extractor(
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body_feats, mask_rois, spatial_scale, is_mask=True)
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mask_out = self.mask_head.get_prediction(mask_feat, bbox)
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fluid.layers.assign(input=mask_out, output=mask_pred)
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fluid.layers.cond(cond, noop, process_boxes)
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return mask_pred, bbox_pred
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def _input_check(self, require_fields, feed_vars):
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for var in require_fields:
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assert var in feed_vars, \
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"{} has no {} field".format(feed_vars, var)
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def _inputs_def(self, image_shape):
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im_shape = [None] + image_shape
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# yapf: disable
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inputs_def = {
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'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
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'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
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'im_id': {'shape': [None, 1], 'dtype': 'int64', 'lod_level': 0},
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'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
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'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
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'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
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'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
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'gt_mask': {'shape': [None, 2], 'dtype': 'float32', 'lod_level': 3}, # polygon coordinates
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'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
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}
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# yapf: enable
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return inputs_def
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def build_inputs(self,
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image_shape=[3, None, None],
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fields=[
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'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class',
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'is_crowd', 'gt_mask'
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],
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multi_scale=False,
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num_scales=-1,
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use_flip=None,
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use_dataloader=True,
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iterable=False,
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mask_branch=False):
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inputs_def = self._inputs_def(image_shape)
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fields = copy.deepcopy(fields)
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if multi_scale:
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ms_def, ms_fields = multiscale_def(image_shape, num_scales,
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use_flip)
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inputs_def.update(ms_def)
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fields += ms_fields
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self.im_info_names = ['image', 'im_info'] + ms_fields
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if mask_branch:
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box_fields = ['bbox', 'bbox_flip'] if use_flip else ['bbox']
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for key in box_fields:
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inputs_def[key] = {
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'shape': [None, 6],
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'dtype': 'float32',
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'lod_level': 1
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}
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fields += box_fields
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feed_vars = OrderedDict([(key, fluid.data(
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name=key,
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shape=inputs_def[key]['shape'],
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dtype=inputs_def[key]['dtype'],
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lod_level=inputs_def[key]['lod_level'])) for key in fields])
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use_dataloader = use_dataloader and not mask_branch
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loader = fluid.io.DataLoader.from_generator(
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feed_list=list(feed_vars.values()),
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capacity=16,
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use_double_buffer=True,
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iterable=iterable) if use_dataloader else None
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return feed_vars, loader
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def train(self, feed_vars):
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return self.build(feed_vars, 'train')
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def eval(self, feed_vars, multi_scale=None, mask_branch=False):
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if multi_scale:
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return self.build_multi_scale(feed_vars, mask_branch)
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return self.build(feed_vars, 'test')
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def test(self, feed_vars, exclude_nms=False):
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assert not exclude_nms, "exclude_nms for {} is not support currently".format(
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self.__class__.__name__)
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return self.build(feed_vars, 'test')
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