460 lines
15 KiB
Python
460 lines
15 KiB
Python
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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import math
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.initializer import MSRA
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from paddle.fluid.param_attr import ParamAttr
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__all__ = [
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"HRNet", "HRNet_W18_C", "HRNet_W30_C", "HRNet_W32_C", "HRNet_W40_C",
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"HRNet_W44_C", "HRNet_W48_C", "HRNet_W60_C", "HRNet_W64_C",
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"SE_HRNet_W18_C", "SE_HRNet_W30_C", "SE_HRNet_W32_C", "SE_HRNet_W40_C",
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"SE_HRNet_W44_C", "SE_HRNet_W48_C", "SE_HRNet_W60_C", "SE_HRNet_W64_C"
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]
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class HRNet():
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def __init__(self, width=18, has_se=False):
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self.width = width
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self.has_se = has_se
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self.channels = {
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18: [[18, 36], [18, 36, 72], [18, 36, 72, 144]],
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30: [[30, 60], [30, 60, 120], [30, 60, 120, 240]],
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32: [[32, 64], [32, 64, 128], [32, 64, 128, 256]],
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40: [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
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44: [[44, 88], [44, 88, 176], [44, 88, 176, 352]],
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48: [[48, 96], [48, 96, 192], [48, 96, 192, 384]],
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60: [[60, 120], [60, 120, 240], [60, 120, 240, 480]],
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64: [[64, 128], [64, 128, 256], [64, 128, 256, 512]]
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}
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def net(self, input, class_dim=1000):
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width = self.width
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channels_2, channels_3, channels_4 = self.channels[width]
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num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3
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x = self.conv_bn_layer(
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input=input,
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filter_size=3,
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num_filters=64,
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stride=2,
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if_act=True,
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name='layer1_1')
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x = self.conv_bn_layer(
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input=x,
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filter_size=3,
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num_filters=64,
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stride=2,
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if_act=True,
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name='layer1_2')
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la1 = self.layer1(x, name='layer2')
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tr1 = self.transition_layer([la1], [256], channels_2, name='tr1')
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st2 = self.stage(tr1, num_modules_2, channels_2, name='st2')
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tr2 = self.transition_layer(st2, channels_2, channels_3, name='tr2')
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st3 = self.stage(tr2, num_modules_3, channels_3, name='st3')
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tr3 = self.transition_layer(st3, channels_3, channels_4, name='tr3')
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st4 = self.stage(tr3, num_modules_4, channels_4, name='st4')
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#classification
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last_cls = self.last_cls_out(x=st4, name='cls_head')
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y = last_cls[0]
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last_num_filters = [256, 512, 1024]
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for i in range(3):
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y = fluid.layers.elementwise_add(
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last_cls[i + 1],
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self.conv_bn_layer(
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input=y,
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filter_size=3,
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num_filters=last_num_filters[i],
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stride=2,
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name='cls_head_add' + str(i + 1)))
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y = self.conv_bn_layer(
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input=y,
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filter_size=1,
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num_filters=2048,
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stride=1,
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name='cls_head_last_conv')
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pool = fluid.layers.pool2d(
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input=y, pool_type='avg', global_pooling=True)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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out = fluid.layers.fc(
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input=pool,
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size=class_dim,
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param_attr=ParamAttr(
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name='fc_weights',
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initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name='fc_offset'))
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return out
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def layer1(self, input, name=None):
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conv = input
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for i in range(4):
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conv = self.bottleneck_block(
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conv,
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num_filters=64,
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downsample=True if i == 0 else False,
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name=name + '_' + str(i + 1))
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return conv
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def transition_layer(self, x, in_channels, out_channels, name=None):
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num_in = len(in_channels)
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num_out = len(out_channels)
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out = []
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for i in range(num_out):
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if i < num_in:
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if in_channels[i] != out_channels[i]:
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residual = self.conv_bn_layer(
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x[i],
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filter_size=3,
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num_filters=out_channels[i],
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name=name + '_layer_' + str(i + 1))
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out.append(residual)
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else:
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out.append(x[i])
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else:
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residual = self.conv_bn_layer(
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x[-1],
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filter_size=3,
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num_filters=out_channels[i],
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stride=2,
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name=name + '_layer_' + str(i + 1))
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out.append(residual)
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return out
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def branches(self, x, block_num, channels, name=None):
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out = []
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for i in range(len(channels)):
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residual = x[i]
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for j in range(block_num):
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residual = self.basic_block(
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residual,
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channels[i],
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name=name + '_branch_layer_' + str(i + 1) + '_' +
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str(j + 1))
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out.append(residual)
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return out
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def fuse_layers(self, x, channels, multi_scale_output=True, name=None):
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out = []
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for i in range(len(channels) if multi_scale_output else 1):
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residual = x[i]
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for j in range(len(channels)):
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if j > i:
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y = self.conv_bn_layer(
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x[j],
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filter_size=1,
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num_filters=channels[i],
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if_act=False,
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name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
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y = fluid.layers.resize_nearest(input=y, scale=2**(j - i))
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residual = fluid.layers.elementwise_add(
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x=residual, y=y, act=None)
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elif j < i:
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y = x[j]
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for k in range(i - j):
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if k == i - j - 1:
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y = self.conv_bn_layer(
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y,
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filter_size=3,
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num_filters=channels[i],
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stride=2,
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if_act=False,
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name=name + '_layer_' + str(i + 1) + '_' +
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str(j + 1) + '_' + str(k + 1))
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else:
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y = self.conv_bn_layer(
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y,
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filter_size=3,
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num_filters=channels[j],
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stride=2,
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name=name + '_layer_' + str(i + 1) + '_' +
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str(j + 1) + '_' + str(k + 1))
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residual = fluid.layers.elementwise_add(
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x=residual, y=y, act=None)
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residual = fluid.layers.relu(residual)
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out.append(residual)
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return out
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def high_resolution_module(self,
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x,
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channels,
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multi_scale_output=True,
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name=None):
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residual = self.branches(x, 4, channels, name=name)
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out = self.fuse_layers(
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residual,
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channels,
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multi_scale_output=multi_scale_output,
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name=name)
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return out
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def stage(self,
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x,
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num_modules,
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channels,
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multi_scale_output=True,
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name=None):
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out = x
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for i in range(num_modules):
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if i == num_modules - 1 and multi_scale_output == False:
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out = self.high_resolution_module(
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out,
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channels,
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multi_scale_output=False,
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name=name + '_' + str(i + 1))
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else:
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out = self.high_resolution_module(
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out, channels, name=name + '_' + str(i + 1))
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return out
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def last_cls_out(self, x, name=None):
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out = []
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num_filters_list = [32, 64, 128, 256]
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for i in range(len(x)):
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out.append(
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self.bottleneck_block(
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input=x[i],
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num_filters=num_filters_list[i],
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name=name + 'conv_' + str(i + 1),
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downsample=True))
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return out
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def basic_block(self,
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input,
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num_filters,
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stride=1,
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downsample=False,
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name=None):
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residual = input
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conv = self.conv_bn_layer(
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input=input,
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filter_size=3,
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num_filters=num_filters,
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stride=stride,
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name=name + '_conv1')
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conv = self.conv_bn_layer(
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input=conv,
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filter_size=3,
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num_filters=num_filters,
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if_act=False,
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name=name + '_conv2')
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if downsample:
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residual = self.conv_bn_layer(
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input=input,
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filter_size=1,
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num_filters=num_filters,
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if_act=False,
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name=name + '_downsample')
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if self.has_se:
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conv = self.squeeze_excitation(
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input=conv,
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num_channels=num_filters,
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reduction_ratio=16,
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name=name + '_fc')
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return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
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def bottleneck_block(self,
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input,
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num_filters,
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stride=1,
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downsample=False,
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name=None):
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residual = input
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conv = self.conv_bn_layer(
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input=input,
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filter_size=1,
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num_filters=num_filters,
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name=name + '_conv1')
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conv = self.conv_bn_layer(
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input=conv,
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filter_size=3,
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num_filters=num_filters,
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stride=stride,
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name=name + '_conv2')
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conv = self.conv_bn_layer(
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input=conv,
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filter_size=1,
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num_filters=num_filters * 4,
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if_act=False,
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name=name + '_conv3')
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if downsample:
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residual = self.conv_bn_layer(
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input=input,
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filter_size=1,
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num_filters=num_filters * 4,
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if_act=False,
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name=name + '_downsample')
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if self.has_se:
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conv = self.squeeze_excitation(
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input=conv,
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num_channels=num_filters * 4,
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reduction_ratio=16,
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name=name + '_fc')
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return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
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def squeeze_excitation(self,
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input,
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num_channels,
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reduction_ratio,
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name=None):
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pool = fluid.layers.pool2d(
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input=input, pool_size=0, pool_type='avg', global_pooling=True)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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squeeze = fluid.layers.fc(
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input=pool,
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size=num_channels / reduction_ratio,
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act='relu',
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + '_sqz_weights'),
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bias_attr=ParamAttr(name=name + '_sqz_offset'))
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stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
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excitation = fluid.layers.fc(
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input=squeeze,
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size=num_channels,
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act='sigmoid',
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + '_exc_weights'),
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bias_attr=ParamAttr(name=name + '_exc_offset'))
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scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
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return scale
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def conv_bn_layer(self,
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input,
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filter_size,
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num_filters,
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stride=1,
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padding=1,
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num_groups=1,
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if_act=True,
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name=None):
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=num_groups,
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act=None,
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param_attr=ParamAttr(
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initializer=MSRA(), name=name + '_weights'),
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bias_attr=False)
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bn_name = name + '_bn'
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bn = fluid.layers.batch_norm(
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input=conv,
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param_attr=ParamAttr(
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name=bn_name + "_scale",
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initializer=fluid.initializer.Constant(1.0)),
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bias_attr=ParamAttr(
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name=bn_name + "_offset",
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initializer=fluid.initializer.Constant(0.0)),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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if if_act:
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bn = fluid.layers.relu(bn)
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return bn
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def HRNet_W18_C():
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model = HRNet(width=18)
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return model
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def HRNet_W30_C():
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model = HRNet(width=30)
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return model
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def HRNet_W32_C():
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model = HRNet(width=32)
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return model
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def HRNet_W40_C():
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model = HRNet(width=40)
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return model
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def HRNet_W44_C():
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model = HRNet(width=44)
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return model
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def HRNet_W48_C():
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model = HRNet(width=48)
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return model
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def HRNet_W60_C():
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model = HRNet(width=60)
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return model
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def HRNet_W64_C():
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model = HRNet(width=64)
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return model
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def SE_HRNet_W18_C():
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model = HRNet(width=18, has_se=True)
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return model
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def SE_HRNet_W30_C():
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model = HRNet(width=30, has_se=True)
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return model
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def SE_HRNet_W32_C():
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model = HRNet(width=32, has_se=True)
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return model
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def SE_HRNet_W40_C():
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model = HRNet(width=40, has_se=True)
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return model
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def SE_HRNet_W44_C():
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model = HRNet(width=44, has_se=True)
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return model
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def SE_HRNet_W48_C():
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model = HRNet(width=48, has_se=True)
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return model
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def SE_HRNet_W60_C():
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model = HRNet(width=60, has_se=True)
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return model
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def SE_HRNet_W64_C():
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model = HRNet(width=64, has_se=True)
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return model
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