Update se_resnext.py
cuicheng01
GitHub
parent
39c24c1cb6
commit
b7b5a0c3c6
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@ -1,253 +1,293 @@
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#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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# 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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# 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 numpy as np
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
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from paddle.nn.initializer import Uniform
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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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__all__ = ["SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d", "SE_ResNeXt152_64x4d"]
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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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class ConvBNLayer(nn.Layer):
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def __init__(
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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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groups=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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name=None):
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super(ConvBNLayer, self).__init__()
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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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self._conv = Conv2d(
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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=(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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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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bn_name = name + '_bn'
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self._batch_norm = BatchNorm(
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num_filters,
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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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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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class BottleneckBlock(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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stride,
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cardinality,
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reduction_ratio,
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shortcut=True,
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if_first=False,
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name=None):
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super(BottleneckBlock, self).__init__()
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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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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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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name='conv' + name + '_x1')
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters,
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filter_size=3,
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groups=cardinality,
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stride=stride,
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act='relu',
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name='conv' + name + '_x2')
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self.conv2 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters * 2 if cardinality == 32 else num_filters,
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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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self.scale = SELayer(
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num_channels=num_filters * 2 if cardinality == 32 else num_filters,
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num_filters=num_filters * 2 if cardinality == 32 else num_filters,
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reduction_ratio=reduction_ratio,
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name='fc' + name)
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if not shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters * 2
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if cardinality == 32 else num_filters,
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filter_size=1,
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stride=stride,
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name='conv' + name + '_prj')
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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conv2 = self.conv2(conv1)
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scale = self.scale(conv2)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.elementwise_add(x=short, y=scale, act='relu')
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return y
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class SELayer(nn.Layer):
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def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
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super(SELayer, self).__init__()
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self.pool2d_gap = AdaptiveAvgPool2d(1)
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self._num_channels = num_channels
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med_ch = int(num_channels / reduction_ratio)
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stdv = 1.0 / math.sqrt(num_channels * 1.0)
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self.squeeze = Linear(
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num_channels,
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med_ch,
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weight_attr=ParamAttr(
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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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self.relu = nn.ReLU()
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stdv = 1.0 / math.sqrt(med_ch * 1.0)
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self.excitation = Linear(
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med_ch,
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num_filters,
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weight_attr=ParamAttr(
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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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self.sigmoid = nn.Sigmoid()
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def forward(self, input):
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pool = self.pool2d_gap(input)
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pool = paddle.reshape(pool, shape=[-1, self._num_channels])
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squeeze = self.squeeze(pool)
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squeeze = self.relu(squeeze)
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excitation = self.excitation(squeeze)
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excitation = self.sigmoid(excitation)
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excitation = paddle.reshape(
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excitation, shape=[-1, self._num_channels, 1, 1])
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out = input * excitation
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return out
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def SE_ResNeXt50_32x4d():
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model = SE_ResNeXt(layers=50)
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class ResNeXt(nn.Layer):
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def __init__(self, layers=50, class_dim=1000, cardinality=32):
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super(ResNeXt, self).__init__()
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self.layers = layers
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self.cardinality = cardinality
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self.reduction_ratio = 16
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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(
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supported_layers, layers)
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supported_cardinality = [32, 64]
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assert cardinality in supported_cardinality, \
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"supported cardinality is {} but input cardinality is {}" \
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.format(supported_cardinality, cardinality)
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if layers == 50:
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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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num_channels = [64, 256, 512, 1024]
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num_filters = [128, 256, 512,
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1024] if cardinality == 32 else [256, 512, 1024, 2048]
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if layers < 152:
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self.conv = ConvBNLayer(
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num_channels=3,
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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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else:
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self.conv1_1 = ConvBNLayer(
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num_channels=3,
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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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self.conv1_2 = ConvBNLayer(
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num_channels=64,
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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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self.conv1_3 = ConvBNLayer(
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num_channels=64,
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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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self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.block_list = []
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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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shortcut = False
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for i in range(depth[block]):
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bottleneck_block = self.add_sublayer(
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'bb_%d_%d' % (block, i),
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BottleneckBlock(
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num_channels=num_channels[block] if i == 0 else
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num_filters[block] * int(64 // self.cardinality),
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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=self.cardinality,
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reduction_ratio=self.reduction_ratio,
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shortcut=shortcut,
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if_first=block == 0,
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name=str(n) + '_' + str(i + 1)))
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self.block_list.append(bottleneck_block)
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shortcut = True
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self.pool2d_avg = AdaptiveAvgPool2d(1)
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self.pool2d_avg_channels = num_channels[-1] * 2
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stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
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self.out = Linear(
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self.pool2d_avg_channels,
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class_dim,
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weight_attr=ParamAttr(
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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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def forward(self, inputs):
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if self.layers < 152:
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y = self.conv(inputs)
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else:
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y = self.conv1_1(inputs)
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y = self.conv1_2(y)
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y = self.conv1_3(y)
|
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y = self.pool2d_max(y)
|
||||
|
||||
for block in self.block_list:
|
||||
y = block(y)
|
||||
y = self.pool2d_avg(y)
|
||||
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
|
||||
y = self.out(y)
|
||||
return y
|
||||
|
||||
|
||||
def SE_ResNeXt50_32x4d(**args):
|
||||
model = ResNeXt(layers=50, cardinality=32, **args)
|
||||
return model
|
||||
|
||||
|
||||
def SE_ResNeXt101_32x4d():
|
||||
model = SE_ResNeXt(layers=101)
|
||||
def SE_ResNeXt101_32x4d(**args):
|
||||
model = ResNeXt(layers=101, cardinality=32, **args)
|
||||
return model
|
||||
|
||||
|
||||
def SE_ResNeXt152_32x4d():
|
||||
model = SE_ResNeXt(layers=152)
|
||||
def SE_ResNeXt152_64x4d(**args):
|
||||
model = ResNeXt(layers=152, cardinality=64, **args)
|
||||
return model
|
||||
|
|
|
|||
Loading…
Reference in New Issue