254 lines
8.3 KiB
Python
254 lines
8.3 KiB
Python
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#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.param_attr import ParamAttr
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__all__ = [
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"SE_ResNeXt", "SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d",
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"SE_ResNeXt152_32x4d"
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]
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class SE_ResNeXt():
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def __init__(self, layers=50):
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self.layers = layers
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def net(self, input, class_dim=1000):
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layers = self.layers
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supported_layers = [50, 101, 152]
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assert layers in supported_layers, \
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"supported layers are {} but input layer is {}".format(supported_layers, layers)
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if layers == 50:
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cardinality = 32
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reduction_ratio = 16
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depth = [3, 4, 6, 3]
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num_filters = [128, 256, 512, 1024]
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conv = self.conv_bn_layer(
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input=input,
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num_filters=64,
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filter_size=7,
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stride=2,
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act='relu',
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name='conv1', )
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conv = fluid.layers.pool2d(
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input=conv,
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pool_size=3,
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pool_stride=2,
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pool_padding=1,
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pool_type='max',
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use_cudnn=False)
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elif layers == 101:
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cardinality = 32
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reduction_ratio = 16
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depth = [3, 4, 23, 3]
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num_filters = [128, 256, 512, 1024]
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conv = self.conv_bn_layer(
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input=input,
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num_filters=64,
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filter_size=7,
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stride=2,
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act='relu',
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name="conv1", )
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conv = fluid.layers.pool2d(
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input=conv,
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pool_size=3,
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pool_stride=2,
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pool_padding=1,
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pool_type='max',
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use_cudnn=False)
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elif layers == 152:
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cardinality = 64
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reduction_ratio = 16
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depth = [3, 8, 36, 3]
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num_filters = [128, 256, 512, 1024]
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conv = self.conv_bn_layer(
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input=input,
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num_filters=64,
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filter_size=3,
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stride=2,
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act='relu',
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name='conv1')
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conv = self.conv_bn_layer(
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input=conv,
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num_filters=64,
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filter_size=3,
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stride=1,
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act='relu',
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name='conv2')
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conv = self.conv_bn_layer(
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input=conv,
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num_filters=128,
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filter_size=3,
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stride=1,
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act='relu',
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name='conv3')
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conv = fluid.layers.pool2d(
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input=conv, pool_size=3, pool_stride=2, pool_padding=1, \
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pool_type='max', use_cudnn=False)
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n = 1 if layers == 50 or layers == 101 else 3
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for block in range(len(depth)):
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n += 1
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for i in range(depth[block]):
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conv = self.bottleneck_block(
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input=conv,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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cardinality=cardinality,
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reduction_ratio=reduction_ratio,
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name=str(n) + '_' + str(i + 1))
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pool = fluid.layers.pool2d(
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input=conv, pool_type='avg', global_pooling=True, use_cudnn=False)
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drop = fluid.layers.dropout(x=pool, dropout_prob=0.5)
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stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
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out = fluid.layers.fc(
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input=drop,
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size=class_dim,
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param_attr=ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name='fc6_weights'),
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bias_attr=ParamAttr(name='fc6_offset'))
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return out
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def shortcut(self, input, ch_out, stride, name):
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ch_in = input.shape[1]
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if ch_in != ch_out or stride != 1:
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filter_size = 1
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return self.conv_bn_layer(
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input,
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ch_out,
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filter_size,
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stride,
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name='conv' + name + '_prj')
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else:
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return input
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def bottleneck_block(self,
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input,
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num_filters,
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stride,
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cardinality,
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reduction_ratio,
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name=None):
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conv0 = self.conv_bn_layer(
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input=input,
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num_filters=num_filters,
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filter_size=1,
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act='relu',
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name='conv' + name + '_x1')
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conv1 = self.conv_bn_layer(
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input=conv0,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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groups=cardinality,
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act='relu',
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name='conv' + name + '_x2')
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conv2 = self.conv_bn_layer(
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input=conv1,
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num_filters=num_filters * 2,
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filter_size=1,
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act=None,
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name='conv' + name + '_x3')
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scale = self.squeeze_excitation(
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input=conv2,
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num_channels=num_filters * 2,
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reduction_ratio=reduction_ratio,
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name='fc' + name)
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short = self.shortcut(input, num_filters * 2, stride, name=name)
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return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
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def conv_bn_layer(self,
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input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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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=groups,
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act=None,
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bias_attr=False,
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param_attr=ParamAttr(name=name + '_weights'), )
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bn_name = name + "_bn"
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return fluid.layers.batch_norm(
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input=conv,
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act=act,
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param_attr=ParamAttr(name=bn_name + '_scale'),
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bias_attr=ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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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_type='avg', global_pooling=True, use_cudnn=False)
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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 SE_ResNeXt50_32x4d():
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model = SE_ResNeXt(layers=50)
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return model
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def SE_ResNeXt101_32x4d():
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model = SE_ResNeXt(layers=101)
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return model
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def SE_ResNeXt152_32x4d():
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model = SE_ResNeXt(layers=152)
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return model
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