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192 lines
6.5 KiB
Python
192 lines
6.5 KiB
Python
# Copyright (c) 2021 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 paddle import ParamAttr
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from paddle import fluid
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import paddle.nn as nn
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from paddle.nn import Conv3D, BatchNorm3D
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from functools import partial
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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padding_mode='zeros',
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weight_attr=None,
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bias_attr=None,
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name=None,
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data_format="NCDHW"):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv3D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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padding_mode=padding_mode,
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weight_attr=ParamAttr(initializer=nn.initializer.KaimingNormal(
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fan_in=num_filters * filter_size * filter_size), name=name+'_weights'),
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bias_attr=bias_attr,
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data_format=data_format)
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bn_name = "bn_" + name
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self._batch_norm = BatchNorm3D(
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num_filters,
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momentum=0.9,
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epsilon=1e-05,
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weight_attr=ParamAttr(initializer=nn.initializer.Constant(
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1.), name=bn_name + '_scale'),
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bias_attr=ParamAttr(initializer=nn.initializer.Constant(
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0.), name=bn_name + '_offset'),
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data_format=data_format)
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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def _downsample_basic_block(self, x, planes, stride):
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out = fluid.layers.pool3d(
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x, pool_size=1, pool_stride=stride, pool_type='avg')
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shape = out.shape
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zero_pads = fluid.layers.zeros([shape[0], planes - shape[1], shape[2], shape[3], shape[4]],
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dtype='float32')
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out = fluid.layers.concat([out, zero_pads], axis=1)
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class BottleneckBlock(nn.Layer):
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expansion = 2
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def __init__(self, inplanes, planes, cardinality, stride=1, downsample=None, name=None):
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super(BottleneckBlock, self).__init__()
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mid_planes = cardinality * int(planes / 32)
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self.conv0 = ConvBNLayer(
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inplanes, mid_planes, filter_size=1, bias_attr=False, name=name+'_branch2a')
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self.conv1 = ConvBNLayer(mid_planes, mid_planes, filter_size=3, stride=stride,
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padding=1, groups=cardinality, bias_attr=False, name=name+'_branch2b')
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self.conv2 = ConvBNLayer(mid_planes, planes * self.expansion,
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filter_size=1, bias_attr=False, name=name+'_branch2c')
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self.downsample = downsample
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self.stride = stride
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self.relu = nn.ReLU()
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def forward(self, x):
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residual = x
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out = self.conv0(x)
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out = self.relu(out)
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out = self.conv1(out)
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out = self.relu(out)
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out = self.conv2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNeXt(nn.Layer):
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def __init__(self,
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block,
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layers,
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shortcut_type='B',
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cardinality=32):
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self.inplanes = 64
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super(ResNeXt, self).__init__()
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self.conv = ConvBNLayer(
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3,
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64,
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filter_size=7,
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stride=(1, 2, 2),
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padding=(3, 3, 3),
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bias_attr=False,
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name="res_conv1"
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)
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self.relu = nn.ReLU()
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self.maxpool = nn.MaxPool3D(kernel_size=(3, 3, 3), stride=2, padding=1)
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self.layer1 = self._make_layer(block, 128, layers[0], shortcut_type,
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cardinality, stride=1, name='layer1')
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self.layer2 = self._make_layer(
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block, 256, layers[1], shortcut_type, cardinality, stride=2, name='layer2')
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self.layer3 = self._make_layer(
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block, 512, layers[2], shortcut_type, cardinality, stride=2, name='layer3')
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self.layer4 = self._make_layer(
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block, 1024, layers[3], shortcut_type, cardinality, stride=2, name='layer4')
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self.avgpool = nn.AvgPool3D((2, 1, 1), stride=1, exclusive=False)
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def _make_layer(self,
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block,
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planes,
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blocks,
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shortcut_type,
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cardinality,
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stride=1,
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name=None):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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if shortcut_type == 'A':
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downsample = partial(self._downsample_basic_block,
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planes=planes * block.expansion,
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stride=stride)
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else:
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downsample = ConvBNLayer(
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self.inplanes,
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planes * block.expansion,
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1,
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stride=stride,
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bias_attr=False,
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name=name+'downsample'
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)
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layers = []
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layers.append(
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block(self.inplanes, planes, cardinality, stride, downsample, name=name+'_downsample'))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes,
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cardinality, name=name+'_res_block'+str(i)))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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return x
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def ResNext101():
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"""Constructs a ResNext-101 model.
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"""
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model = ResNeXt(BottleneckBlock, [3, 4, 23, 3])
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return model
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