forked from PulseFocusPlatform/PulseFocusPlatform
153 lines
5.0 KiB
Python
153 lines
5.0 KiB
Python
# Copyright (c) 2020 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 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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__all__ = ['EfficientDet']
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@register
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class EfficientDet(object):
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"""
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EfficientDet architecture, see https://arxiv.org/abs/1911.09070
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Args:
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backbone (object): backbone instance
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fpn (object): feature pyramid network instance
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retina_head (object): `RetinaHead` instance
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"""
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__category__ = 'architecture'
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__inject__ = ['backbone', 'fpn', 'efficient_head', 'anchor_grid']
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def __init__(self,
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backbone,
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fpn,
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efficient_head,
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anchor_grid,
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box_loss_weight=50.):
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super(EfficientDet, self).__init__()
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self.backbone = backbone
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self.fpn = fpn
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self.efficient_head = efficient_head
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self.anchor_grid = anchor_grid
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self.box_loss_weight = box_loss_weight
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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':
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gt_labels = feed_vars['gt_label']
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gt_targets = feed_vars['gt_target']
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fg_num = feed_vars['fg_num']
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else:
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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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if mixed_precision_enabled:
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im = fluid.layers.cast(im, 'float16')
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body_feats = self.backbone(im)
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if mixed_precision_enabled:
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body_feats = [fluid.layers.cast(f, 'float32') for f in body_feats]
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body_feats = self.fpn(body_feats)
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# XXX not used for training, but the parameters are needed when
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# exporting inference model
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anchors = self.anchor_grid()
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if mode == 'train':
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loss = self.efficient_head.get_loss(body_feats, gt_labels,
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gt_targets, fg_num)
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loss_cls = loss['loss_cls']
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loss_bbox = loss['loss_bbox']
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total_loss = loss_cls + self.box_loss_weight * loss_bbox
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loss.update({'loss': total_loss})
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return loss
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else:
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pred = self.efficient_head.get_prediction(body_feats, anchors,
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im_info)
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return pred
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def _inputs_def(self, image_shape):
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im_shape = [None] + image_shape
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inputs_def = {
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'image': {
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'shape': im_shape,
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'dtype': 'float32'
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},
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'im_info': {
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'shape': [None, 3],
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'dtype': 'float32'
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},
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'im_id': {
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'shape': [None, 1],
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'dtype': 'int64'
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},
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'im_shape': {
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'shape': [None, 3],
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'dtype': 'float32'
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},
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'fg_num': {
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'shape': [None, 1],
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'dtype': 'int32'
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},
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'gt_label': {
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'shape': [None, None, 1],
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'dtype': 'int32'
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},
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'gt_target': {
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'shape': [None, None, 4],
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'dtype': 'float32'
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},
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}
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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', 'fg_num', 'gt_label',
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'gt_target'
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],
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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'])) 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, '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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