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未验证 提交 24b8453b 编写于 作者: L littletomatodonkey 提交者： GitHub
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Merge pull request #283 from cuicheng01/dygraph 

Add se-resnext and vd architecture
上级 39c24c1c b5b0ff17
隐藏空白更改
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Showing with 276 addition and 235 deletion
ppcls/modeling/architectures/__init__.py
浏览文件 @ 24b8453b
...@@ -21,6 +21,7 @@ from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2N ...@@ -21,6 +21,7 @@ from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2N
from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_48w_2s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_48w_2s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s
from .se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd, SE_ResNet101_vd, SE_ResNet152_vd, SE_ResNet200_vd from .se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd, SE_ResNet101_vd, SE_ResNet152_vd, SE_ResNet200_vd
from .se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd from .se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d
from .dpn import DPN68 from .dpn import DPN68
from .densenet import DenseNet121 from .densenet import DenseNet121
from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C
......
ppcls/modeling/architectures/se_resnext.py
浏览文件 @ 24b8453b
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# #
#Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
#You may obtain a copy of the License at # You may obtain a copy of the License at
# #
# http://www.apache.org/licenses/LICENSE-2.0 # http://www.apache.org/licenses/LICENSE-2.0
# #
#Unless required by applicable law or agreed to in writing, software # Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS, # distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
#limitations under the License. # limitations under the License.
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math import math
import paddle __all__ = ["SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d", "SE_ResNeXt152_64x4d"]
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
__all__ = [
"SE_ResNeXt", "SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d",
"SE_ResNeXt152_32x4d"
]
class ConvBNLayer(nn.Layer):
def __init__(
self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
class SE_ResNeXt(): self._conv = Conv2d(
def __init__(self, layers=50): in_channels=num_channels,
self.layers = layers out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
bn_name = name + '_bn'
self._batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
cardinality,
reduction_ratio,
shortcut=True,
if_first=False,
name=None):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act='relu',
name='conv' + name + '_x1')
self.conv1 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
groups=cardinality,
stride=stride,
act='relu',
name='conv' + name + '_x2')
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters * 2 if cardinality == 32 else num_filters,
filter_size=1,
act=None,
name='conv' + name + '_x3')
self.scale = SELayer(
num_channels=num_filters * 2 if cardinality == 32 else num_filters,
num_filters=num_filters * 2 if cardinality == 32 else num_filters,
reduction_ratio=reduction_ratio,
name='fc' + name)
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters * 2
if cardinality == 32 else num_filters,
filter_size=1,
stride=stride,
name='conv' + name + '_prj')
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
scale = self.scale(conv2)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.elementwise_add(x=short, y=scale, act='relu')
return y
class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
self.pool2d_gap = AdaptiveAvgPool2d(1)
self._num_channels = num_channels
med_ch = int(num_channels / reduction_ratio)
stdv = 1.0 / math.sqrt(num_channels * 1.0)
self.squeeze = Linear(
num_channels,
med_ch,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv),
name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
self.relu = nn.ReLU()
stdv = 1.0 / math.sqrt(med_ch * 1.0)
self.excitation = Linear(
med_ch,
num_filters,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv),
name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset'))
self.sigmoid = nn.Sigmoid()
def forward(self, input):
pool = self.pool2d_gap(input)
pool = paddle.reshape(pool, shape=[-1, self._num_channels])
squeeze = self.squeeze(pool)
squeeze = self.relu(squeeze)
excitation = self.excitation(squeeze)
excitation = self.sigmoid(excitation)
excitation = paddle.reshape(
excitation, shape=[-1, self._num_channels, 1, 1])
out = input * excitation
return out
class ResNeXt(nn.Layer):
def __init__(self, layers=50, class_dim=1000, cardinality=32):
super(ResNeXt, self).__init__()
def net(self, input, class_dim=1000): self.layers = layers
layers = self.layers self.cardinality = cardinality
self.reduction_ratio = 16
supported_layers = [50, 101, 152] supported_layers = [50, 101, 152]
assert layers in supported_layers, \ assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers) "supported layers are {} but input layer is {}".format(
supported_layers, layers)
supported_cardinality = [32, 64]
assert cardinality in supported_cardinality, \
"supported cardinality is {} but input cardinality is {}" \
.format(supported_cardinality, cardinality)
if layers == 50: if layers == 50:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 6, 3] depth = [3, 4, 6, 3]
num_filters = [128, 256, 512, 1024]
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act='relu',
name='conv1', )
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max',
use_cudnn=False)
elif layers == 101: elif layers == 101:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 23, 3] depth = [3, 4, 23, 3]
num_filters = [128, 256, 512, 1024] elif layers == 152:
depth = [3, 8, 36, 3]
conv = self.conv_bn_layer( num_channels = [64, 256, 512, 1024]
input=input, num_filters = [128, 256, 512,
1024] if cardinality == 32 else [256, 512, 1024, 2048]
if layers < 152:
self.conv = ConvBNLayer(
num_channels=3,
num_filters=64, num_filters=64,
filter_size=7, filter_size=7,
stride=2, stride=2,
act='relu', act='relu',
name="conv1", ) name="conv1")
conv = fluid.layers.pool2d( else:
input=conv, self.conv1_1 = ConvBNLayer(
pool_size=3, num_channels=3,
pool_stride=2,
pool_padding=1,
pool_type='max',
use_cudnn=False)
elif layers == 152:
cardinality = 64
reduction_ratio = 16
depth = [3, 8, 36, 3]
num_filters = [128, 256, 512, 1024]
conv = self.conv_bn_layer(
input=input,
num_filters=64, num_filters=64,
filter_size=3, filter_size=3,
stride=2, stride=2,
act='relu', act='relu',
name='conv1') name="conv1")
conv = self.conv_bn_layer( self.conv1_2 = ConvBNLayer(
input=conv, num_channels=64,
num_filters=64, num_filters=64,
filter_size=3, filter_size=3,
stride=1, stride=1,
act='relu', act='relu',
name='conv2') name="conv2")
conv = self.conv_bn_layer( self.conv1_3 = ConvBNLayer(
input=conv, num_channels=64,
num_filters=128, num_filters=128,
filter_size=3, filter_size=3,
stride=1, stride=1,
act='relu', act='relu',
name='conv3') name="conv3")
conv = fluid.layers.pool2d(
input=conv, pool_size=3, pool_stride=2, pool_padding=1, \ self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
pool_type='max', use_cudnn=False)
self.block_list = []
n = 1 if layers == 50 or layers == 101 else 3 n = 1 if layers == 50 or layers == 101 else 3
for block in range(len(depth)): for block in range(len(depth)):
n += 1 n += 1
shortcut = False
for i in range(depth[block]): for i in range(depth[block]):
conv = self.bottleneck_block( bottleneck_block = self.add_sublayer(
input=conv, 'bb_%d_%d' % (block, i),
num_filters=num_filters[block], BottleneckBlock(
stride=2 if i == 0 and block != 0 else 1, num_channels=num_channels[block] if i == 0 else
cardinality=cardinality, num_filters[block] * int(64 // self.cardinality),
reduction_ratio=reduction_ratio, num_filters=num_filters[block],
name=str(n) + '_' + str(i + 1)) stride=2 if i == 0 and block != 0 else 1,
cardinality=self.cardinality,
pool = fluid.layers.pool2d( reduction_ratio=self.reduction_ratio,
input=conv, pool_type='avg', global_pooling=True, use_cudnn=False) shortcut=shortcut,
drop = fluid.layers.dropout(x=pool, dropout_prob=0.5) if_first=block == 0,
stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0) name=str(n) + '_' + str(i + 1)))
out = fluid.layers.fc( self.block_list.append(bottleneck_block)
input=drop, shortcut = True
size=class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name='fc6_weights'),
bias_attr=ParamAttr(name='fc6_offset'))
return out
def shortcut(self, input, ch_out, stride, name): self.pool2d_avg = AdaptiveAvgPool2d(1)
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
filter_size = 1
return self.conv_bn_layer(
input,
ch_out,
filter_size,
stride,
name='conv' + name + '_prj')
else:
return input
def bottleneck_block(self,
input,
num_filters,
stride,
cardinality,
reduction_ratio,
name=None):
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=1,
act='relu',
name='conv' + name + '_x1')
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
groups=cardinality,
act='relu',
name='conv' + name + '_x2')
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters * 2,
filter_size=1,
act=None,
name='conv' + name + '_x3')
scale = self.squeeze_excitation(
input=conv2,
num_channels=num_filters * 2,
reduction_ratio=reduction_ratio,
name='fc' + name)
short = self.shortcut(input, num_filters * 2, stride, name=name) self.pool2d_avg_channels = num_channels[-1] * 2
return fluid.layers.elementwise_add(x=short, y=scale, act='relu') stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
def conv_bn_layer(self, self.out = Linear(
input, self.pool2d_avg_channels,
num_filters, class_dim,
filter_size, weight_attr=ParamAttr(
stride=1, initializer=Uniform(-stdv, stdv),
groups=1, name="fc6_weights"),
act=None, bias_attr=ParamAttr(name="fc6_offset"))
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
bias_attr=False,
param_attr=ParamAttr(name=name + '_weights'), )
bn_name = name + "_bn"
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def squeeze_excitation(self, def forward(self, inputs):
input, if self.layers < 152:
num_channels, y = self.conv(inputs)
reduction_ratio, else:
name=None): y = self.conv1_1(inputs)
pool = fluid.layers.pool2d( y = self.conv1_2(y)
input=input, pool_type='avg', global_pooling=True, use_cudnn=False) y = self.conv1_3(y)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) y = self.pool2d_max(y)
squeeze = fluid.layers.fc(
input=pool, for block in self.block_list:
size=num_channels // reduction_ratio, y = block(y)
act='relu', y = self.pool2d_avg(y)
param_attr=fluid.param_attr.ParamAttr( y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
initializer=fluid.initializer.Uniform(-stdv, stdv), y = self.out(y)
name=name + '_sqz_weights'), return y
bias_attr=ParamAttr(name=name + '_sqz_offset'))
stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
excitation = fluid.layers.fc(
input=squeeze,
size=num_channels,
act='sigmoid',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + '_exc_weights'),
bias_attr=ParamAttr(name=name + '_exc_offset'))
scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
return scale
def SE_ResNeXt50_32x4d(): def SE_ResNeXt50_32x4d(**args):
model = SE_ResNeXt(layers=50) model = ResNeXt(layers=50, cardinality=32, **args)
return model return model
def SE_ResNeXt101_32x4d(): def SE_ResNeXt101_32x4d(**args):
model = SE_ResNeXt(layers=101) model = ResNeXt(layers=101, cardinality=32, **args)
return model return model
def SE_ResNeXt152_32x4d(): def SE_ResNeXt152_64x4d(**args):
model = SE_ResNeXt(layers=152) model = ResNeXt(layers=152, cardinality=64, **args)
return model return model
ppcls/modeling/architectures/se_resnext_vd.py
浏览文件 @ 24b8453b
...@@ -18,16 +18,18 @@ from __future__ import print_function ...@@ -18,16 +18,18 @@ from __future__ import print_function
import numpy as np import numpy as np
import paddle import paddle
import paddle.fluid as fluid from paddle import ParamAttr
from paddle.fluid.param_attr import ParamAttr import paddle.nn as nn
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math import math
__all__ = ["SE_ResNeXt50_vd_32x4d", "SE_ResNeXt50_vd_32x4d", "SENet154_vd"] __all__ = ["SE_ResNeXt50_vd_32x4d", "SE_ResNeXt50_vd_32x4d", "SENet154_vd"]
class ConvBNLayer(fluid.dygraph.Layer): class ConvBNLayer(nn.Layer):
def __init__( def __init__(
self, self,
num_channels, num_channels,
...@@ -37,21 +39,20 @@ class ConvBNLayer(fluid.dygraph.Layer): ...@@ -37,21 +39,20 @@ class ConvBNLayer(fluid.dygraph.Layer):
groups=1, groups=1,
is_vd_mode=False, is_vd_mode=False,
act=None, act=None,
name=None, ): name=None):
super(ConvBNLayer, self).__init__() super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode self.is_vd_mode = is_vd_mode
self._pool2d_avg = Pool2D( self._pool2d_avg = AvgPool2d(
pool_size=2, pool_stride=2, pool_padding=0, pool_type='avg') kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = Conv2D( self._conv = Conv2d(
num_channels=num_channels, in_channels=num_channels,
num_filters=num_filters, out_channels=num_filters,
filter_size=filter_size, kernel_size=filter_size,
stride=stride, stride=stride,
padding=(filter_size - 1) // 2, padding=(filter_size - 1) // 2,
groups=groups, groups=groups,
act=None, weight_attr=ParamAttr(name=name + "_weights"),
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False) bias_attr=False)
bn_name = name + '_bn' bn_name = name + '_bn'
self._batch_norm = BatchNorm( self._batch_norm = BatchNorm(
...@@ -70,7 +71,7 @@ class ConvBNLayer(fluid.dygraph.Layer): ...@@ -70,7 +71,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y return y
class BottleneckBlock(fluid.dygraph.Layer): class BottleneckBlock(nn.Layer):
def __init__(self, def __init__(self,
num_channels, num_channels,
num_filters, num_filters,
...@@ -106,7 +107,7 @@ class BottleneckBlock(fluid.dygraph.Layer): ...@@ -106,7 +107,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
num_channels=num_filters * 2 if cardinality == 32 else num_filters, num_channels=num_filters * 2 if cardinality == 32 else num_filters,
num_filters=num_filters * 2 if cardinality == 32 else num_filters, num_filters=num_filters * 2 if cardinality == 32 else num_filters,
reduction_ratio=reduction_ratio, reduction_ratio=reduction_ratio,
name='fc_' + name) name='fc' + name)
if not shortcut: if not shortcut:
self.short = ConvBNLayer( self.short = ConvBNLayer(
...@@ -130,15 +131,15 @@ class BottleneckBlock(fluid.dygraph.Layer): ...@@ -130,15 +131,15 @@ class BottleneckBlock(fluid.dygraph.Layer):
short = inputs short = inputs
else: else:
short = self.short(inputs) short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=scale, act='relu') y = paddle.elementwise_add(x=short, y=scale, act='relu')
return y return y
class SELayer(fluid.dygraph.Layer): class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None): def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__() super(SELayer, self).__init__()
self.pool2d_gap = Pool2D(pool_type='avg', global_pooling=True) self.pool2d_gap = AdaptiveAvgPool2d(1)
self._num_channels = num_channels self._num_channels = num_channels
...@@ -147,34 +148,35 @@ class SELayer(fluid.dygraph.Layer): ...@@ -147,34 +148,35 @@ class SELayer(fluid.dygraph.Layer):
self.squeeze = Linear( self.squeeze = Linear(
num_channels, num_channels,
med_ch, med_ch,
act="relu", weight_attr=ParamAttr(
param_attr=ParamAttr( initializer=Uniform(-stdv, stdv),
initializer=fluid.initializer.Uniform(-stdv, stdv), name=name + "_sqz_weights"),
name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset')) bias_attr=ParamAttr(name=name + '_sqz_offset'))
self.relu = nn.ReLU()
stdv = 1.0 / math.sqrt(med_ch * 1.0) stdv = 1.0 / math.sqrt(med_ch * 1.0)
self.excitation = Linear( self.excitation = Linear(
med_ch, med_ch,
num_filters, num_filters,
act="sigmoid", weight_attr=ParamAttr(
param_attr=ParamAttr( initializer=Uniform(-stdv, stdv),
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_exc_weights"), name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset')) bias_attr=ParamAttr(name=name + '_exc_offset'))
self.sigmoid = nn.Sigmoid()
def forward(self, input): def forward(self, input):
pool = self.pool2d_gap(input) pool = self.pool2d_gap(input)
pool = fluid.layers.reshape(pool, shape=[-1, self._num_channels]) pool = paddle.reshape(pool, shape=[-1, self._num_channels])
squeeze = self.squeeze(pool) squeeze = self.squeeze(pool)
squeeze = self.relu(squeeze)
excitation = self.excitation(squeeze) excitation = self.excitation(squeeze)
excitation = fluid.layers.reshape( excitation = self.sigmoid(excitation)
excitation = paddle.reshape(
excitation, shape=[-1, self._num_channels, 1, 1]) excitation, shape=[-1, self._num_channels, 1, 1])
out = input * excitation out = input * excitation
return out return out
class ResNeXt(fluid.dygraph.Layer): class ResNeXt(nn.Layer):
def __init__(self, layers=50, class_dim=1000, cardinality=32): def __init__(self, layers=50, class_dim=1000, cardinality=32):
super(ResNeXt, self).__init__() super(ResNeXt, self).__init__()
...@@ -221,8 +223,7 @@ class ResNeXt(fluid.dygraph.Layer): ...@@ -221,8 +223,7 @@ class ResNeXt(fluid.dygraph.Layer):
act='relu', act='relu',
name="conv1_3") name="conv1_3")
self.pool2d_max = Pool2D( self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.block_list = [] self.block_list = []
n = 1 if layers == 50 or layers == 101 else 3 n = 1 if layers == 50 or layers == 101 else 3
...@@ -245,8 +246,7 @@ class ResNeXt(fluid.dygraph.Layer): ...@@ -245,8 +246,7 @@ class ResNeXt(fluid.dygraph.Layer):
self.block_list.append(bottleneck_block) self.block_list.append(bottleneck_block)
shortcut = True shortcut = True
self.pool2d_avg = Pool2D( self.pool2d_avg = AdaptiveAvgPool2d(1)
pool_size=7, pool_type='avg', global_pooling=True)
self.pool2d_avg_channels = num_channels[-1] * 2 self.pool2d_avg_channels = num_channels[-1] * 2
...@@ -255,8 +255,8 @@ class ResNeXt(fluid.dygraph.Layer): ...@@ -255,8 +255,8 @@ class ResNeXt(fluid.dygraph.Layer):
self.out = Linear( self.out = Linear(
self.pool2d_avg_channels, self.pool2d_avg_channels,
class_dim, class_dim,
param_attr=ParamAttr( weight_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv), initializer=Uniform(-stdv, stdv),
name="fc6_weights"), name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset")) bias_attr=ParamAttr(name="fc6_offset"))
...@@ -268,7 +268,7 @@ class ResNeXt(fluid.dygraph.Layer): ...@@ -268,7 +268,7 @@ class ResNeXt(fluid.dygraph.Layer):
for block in self.block_list: for block in self.block_list:
y = block(y) y = block(y)
y = self.pool2d_avg(y) y = self.pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels]) y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = self.out(y) y = self.out(y)
return y return y
......
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