forked from PulseFocusPlatform/PulseFocusPlatform
192 lines
7.0 KiB
Python
192 lines
7.0 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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from paddle import fluid
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.regularizer import L2Decay
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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__ = ['FaceBoxes']
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@register
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class FaceBoxes(object):
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"""
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FaceBoxes: A CPU Real-time Face Detector with High Accuracy.
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see https://arxiv.org/abs/1708.05234
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Args:
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backbone (object): backbone instance
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output_decoder (object): `SSDOutputDecoder` instance
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densities (list|None): the densities of generated density prior boxes,
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this attribute should be a list or tuple of integers.
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fixed_sizes (list|None): the fixed sizes of generated density prior boxes,
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this attribute should a list or tuple of same length with `densities`.
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num_classes (int): number of output classes.
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steps (list|None): step size of adjacent prior boxes on each feature map.
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"""
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__category__ = 'architecture'
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__inject__ = ['backbone', 'output_decoder']
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__shared__ = ['num_classes']
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def __init__(self,
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backbone="FaceBoxNet",
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output_decoder=SSDOutputDecoder().__dict__,
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densities=[[4, 2, 1], [1], [1]],
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fixed_sizes=[[32., 64., 128.], [256.], [512.]],
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num_classes=2,
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steps=[8., 16., 32.]):
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super(FaceBoxes, self).__init__()
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self.backbone = backbone
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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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self.densities = densities
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self.fixed_sizes = fixed_sizes
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self.steps = steps
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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_bbox = feed_vars['gt_bbox']
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gt_class = feed_vars['gt_class']
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body_feats = self.backbone(im)
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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(
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locs,
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confs,
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gt_bbox,
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gt_class,
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box,
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box_var,
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overlap_threshold=0.35,
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neg_overlap=0.35)
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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 _multi_box_head(self, inputs, image, num_classes=2):
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def permute_and_reshape(input, last_dim):
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trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
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compile_shape = [0, -1, last_dim]
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return fluid.layers.reshape(trans, shape=compile_shape)
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def _is_list_or_tuple_(data):
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return (isinstance(data, list) or isinstance(data, tuple))
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locs, confs = [], []
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boxes, vars = [], []
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b_attr = ParamAttr(learning_rate=2., regularizer=L2Decay(0.))
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for i, input in enumerate(inputs):
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densities = self.densities[i]
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fixed_sizes = self.fixed_sizes[i]
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box, var = fluid.layers.density_prior_box(
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input,
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image,
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densities=densities,
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fixed_sizes=fixed_sizes,
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fixed_ratios=[1.],
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clip=False,
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offset=0.5,
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steps=[self.steps[i]] * 2)
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num_boxes = box.shape[2]
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box = fluid.layers.reshape(box, shape=[-1, 4])
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var = fluid.layers.reshape(var, shape=[-1, 4])
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num_loc_output = num_boxes * 4
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num_conf_output = num_boxes * num_classes
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# get loc
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mbox_loc = fluid.layers.conv2d(
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input, num_loc_output, 3, 1, 1, bias_attr=b_attr)
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loc = permute_and_reshape(mbox_loc, 4)
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# get conf
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mbox_conf = fluid.layers.conv2d(
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input, num_conf_output, 3, 1, 1, bias_attr=b_attr)
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conf = permute_and_reshape(mbox_conf, 2)
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locs.append(loc)
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confs.append(conf)
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boxes.append(box)
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vars.append(var)
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face_mbox_loc = fluid.layers.concat(locs, axis=1)
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face_mbox_conf = fluid.layers.concat(confs, axis=1)
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prior_boxes = fluid.layers.concat(boxes)
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box_vars = fluid.layers.concat(vars)
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return face_mbox_loc, face_mbox_conf, prior_boxes, box_vars
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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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}
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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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return True
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