forked from PulseFocusPlatform/PulseFocusPlatform
131 lines
4.3 KiB
Python
131 lines
4.3 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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from paddle import fluid
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from paddle.fluid.param_attr import ParamAttr
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from ppdet.core.workspace import register
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__all__ = ['HRFPN']
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@register
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class HRFPN(object):
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"""
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HRNet, see https://arxiv.org/abs/1908.07919
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Args:
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num_chan (int): number of feature channels
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pooling_type (str): pooling type of downsampling
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share_conv (bool): whethet to share conv for different layers' reduction
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spatial_scale (list): feature map scaling factor
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"""
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def __init__(
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self,
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num_chan=256,
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pooling_type="avg",
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share_conv=False,
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spatial_scale=[1. / 64, 1. / 32, 1. / 16, 1. / 8, 1. / 4], ):
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self.num_chan = num_chan
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self.pooling_type = pooling_type
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self.share_conv = share_conv
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self.spatial_scale = spatial_scale
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return
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def get_output(self, body_dict):
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num_out = len(self.spatial_scale)
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body_name_list = list(body_dict.keys())
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num_backbone_stages = len(body_name_list)
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outs = []
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outs.append(body_dict[body_name_list[0]])
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# resize
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for i in range(1, len(body_dict)):
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resized = self.resize_input_tensor(body_dict[body_name_list[i]],
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outs[0], 2**i)
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outs.append(resized)
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# concat
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out = fluid.layers.concat(outs, axis=1)
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# reduction
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out = fluid.layers.conv2d(
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input=out,
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num_filters=self.num_chan,
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filter_size=1,
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stride=1,
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padding=0,
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param_attr=ParamAttr(name='hrfpn_reduction_weights'),
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bias_attr=False)
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# conv
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outs = [out]
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for i in range(1, num_out):
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outs.append(
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self.pooling(
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out, size=2**i, stride=2**i,
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pooling_type=self.pooling_type))
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outputs = []
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for i in range(num_out):
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conv_name = "shared_fpn_conv" if self.share_conv else "shared_fpn_conv_" + str(
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i)
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conv = fluid.layers.conv2d(
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input=outs[i],
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num_filters=self.num_chan,
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filter_size=3,
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stride=1,
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padding=1,
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param_attr=ParamAttr(name=conv_name + "_weights"),
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bias_attr=False)
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outputs.append(conv)
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for idx in range(0, num_out - len(body_name_list)):
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body_name_list.append("fpn_res5_sum_subsampled_{}x".format(2**(idx +
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1)))
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outputs = outputs[::-1]
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body_name_list = body_name_list[::-1]
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res_dict = OrderedDict([(body_name_list[k], outputs[k])
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for k in range(len(body_name_list))])
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return res_dict, self.spatial_scale
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def resize_input_tensor(self, body_input, ref_output, scale):
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shape = fluid.layers.shape(ref_output)
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shape_hw = fluid.layers.slice(shape, axes=[0], starts=[2], ends=[4])
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out_shape_ = shape_hw
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out_shape = fluid.layers.cast(out_shape_, dtype='int32')
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out_shape.stop_gradient = True
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body_output = fluid.layers.resize_bilinear(
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body_input, scale=scale, out_shape=out_shape)
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return body_output
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def pooling(self, input, size, stride, pooling_type):
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pool = fluid.layers.pool2d(
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input=input,
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pool_size=size,
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pool_stride=stride,
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pool_type=pooling_type)
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return pool
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