forked from PulseFocusPlatform/PulseFocusPlatform
146 lines
5.3 KiB
Python
146 lines
5.3 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 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 ppdet.modeling.ops import SSDOutputDecoder
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__all__ = ['SSD']
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@register
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class SSD(object):
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"""
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Single Shot MultiBox Detector, see https://arxiv.org/abs/1512.02325
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Args:
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backbone (object): backbone instance
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multi_box_head (object): `MultiBoxHead` instance
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output_decoder (object): `SSDOutputDecoder` instance
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num_classes (int): number of output classes
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"""
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__category__ = 'architecture'
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__inject__ = ['backbone', 'multi_box_head', 'output_decoder', 'fpn']
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__shared__ = ['num_classes']
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def __init__(self,
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backbone,
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fpn=None,
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multi_box_head='MultiBoxHead',
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output_decoder=SSDOutputDecoder().__dict__,
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num_classes=21):
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super(SSD, self).__init__()
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self.backbone = backbone
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self.fpn = fpn
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self.multi_box_head = multi_box_head
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self.num_classes = num_classes
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self.output_decoder = output_decoder
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if isinstance(output_decoder, dict):
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self.output_decoder = SSDOutputDecoder(**output_decoder)
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def build(self, feed_vars, mode='train'):
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im = feed_vars['image']
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if mode == 'train' or mode == 'eval':
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gt_bbox = feed_vars['gt_bbox']
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gt_class = feed_vars['gt_class']
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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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if self.fpn is not None:
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body_feats, spatial_scale = self.fpn.get_output(body_feats)
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if isinstance(body_feats, OrderedDict):
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body_feat_names = list(body_feats.keys())
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body_feats = [body_feats[name] for name in body_feat_names]
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# cast features back to FP32
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if mixed_precision_enabled:
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body_feats = [fluid.layers.cast(v, 'float32') for v in body_feats]
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locs, confs, box, box_var = self.multi_box_head(
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inputs=body_feats, image=im, num_classes=self.num_classes)
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if mode == 'train':
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loss = fluid.layers.ssd_loss(locs, confs, gt_bbox, gt_class, box,
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box_var)
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loss = fluid.layers.reduce_sum(loss)
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return {'loss': loss}
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else:
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pred = self.output_decoder(locs, confs, box, box_var)
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return {'bbox': pred}
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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_id': {'shape': [None, 1], 'dtype': 'int64', '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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'im_shape': {'shape': [None, 3], 'dtype': 'int32', 'lod_level': 0},
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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(
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self,
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image_shape=[3, None, None],
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fields=['image', 'im_id', 'gt_bbox', 'gt_class'], # for train
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use_dataloader=True,
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iterable=False):
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inputs_def = self._inputs_def(image_shape)
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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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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):
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return self.build(feed_vars, 'eval')
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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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def is_bbox_normalized(self):
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# SSD use output_decoder in output layers, bbox is normalized
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# to range [0, 1], is_bbox_normalized is used in eval.py and infer.py
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return True
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