Skip to content

P
PaddleClas

PaddlePaddle / PaddleClas
接近 2 年 前同步成功

通知 116
Star 4999
Fork 1114
P
PaddleClas
提交 0dfe15d2 编写于 作者: W wqz960
浏览文件
操作

add Inception, ResNeXt101_wsl, EfficientNet and other models

上级 be35b7cc
展开全部 隐藏空白更改
内联 并排
Showing with 2803 addition and 1934 deletion
ppcls/modeling/architectures/alexnet.py
浏览文件 @ 0dfe15d2
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid import framework
import math
import sys
import time
__all__ = ['Xception41', 'Xception65', 'Xception71']
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
bn_name = "bn_" + name
self._batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=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 Separable_Conv(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, stride=1, name=None):
super(Separable_Conv, self).__init__()
self._pointwise_conv = ConvBNLayer(
input_channels, output_channels, 1, name=name + "_sep")
self._depthwise_conv = ConvBNLayer(
output_channels,
output_channels,
3,
stride=stride,
groups=output_channels,
name=name + "_dw")
def forward(self, inputs):
x = self._pointwise_conv(inputs)
x = self._depthwise_conv(x)
return x
class Entry_Flow_Bottleneck_Block(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
stride=2,
name=None,
relu_first=False):
super(Entry_Flow_Bottleneck_Block, self).__init__()
self.relu_first = relu_first
self._short = Conv2D(
num_channels=input_channels,
num_filters=output_channels,
filter_size=1,
stride=stride,
padding=0,
act=None,
param_attr=ParamAttr(name + "_branch1_weights"),
bias_attr=False)
self._conv1 = Separable_Conv(
input_channels,
output_channels,
stride=1,
name=name + "_branch2a_weights")
self._conv2 = Separable_Conv(
output_channels,
output_channels,
stride=1,
name=name + "_branch2b_weights")
self._pool = Pool2D(
pool_size=3, pool_stride=stride, pool_padding=1, pool_type="max")
def forward(self, inputs):
conv0 = inputs
short = self._short(inputs)
layer_helper = LayerHelper(self.full_name(), act="relu")
if self.relu_first:
conv0 = layer_helper.append_activation(conv0)
conv1 = self._conv1(conv0)
conv2 = layer_helper.append_activation(conv1)
conv2 = self._conv2(conv2)
pool = self._pool(conv2)
return fluid.layers.elementwise_add(x=short, y=pool)
class Entry_Flow(fluid.dygraph.Layer):
def __init__(self, block_num=3):
super(Entry_Flow, self).__init__()
name = "entry_flow"
self.block_num = block_num
self._conv1 = ConvBNLayer(
3, 32, 3, stride=2, act="relu", name=name + "_conv1")
self._conv2 = ConvBNLayer(32, 64, 3, act="relu", name=name + "_conv2")
if block_num == 3:
self._conv_0 = Entry_Flow_Bottleneck_Block(
64, 128, stride=2, name=name + "_0", relu_first=False)
self._conv_1 = Entry_Flow_Bottleneck_Block(
128, 256, stride=2, name=name + "_1", relu_first=True)
self._conv_2 = Entry_Flow_Bottleneck_Block(
256, 728, stride=2, name=name + "_2", relu_first=True)
elif block_num == 5:
self._conv_0 = Entry_Flow_Bottleneck_Block(
64, 128, stride=2, name=name + "_0", relu_first=False)
self._conv_1 = Entry_Flow_Bottleneck_Block(
128, 256, stride=1, name=name + "_1", relu_first=True)
self._conv_2 = Entry_Flow_Bottleneck_Block(
256, 256, stride=2, name=name + "_2", relu_first=True)
self._conv_3 = Entry_Flow_Bottleneck_Block(
256, 728, stride=1, name=name + "_3", relu_first=True)
self._conv_4 = Entry_Flow_Bottleneck_Block(
728, 728, stride=2, name=name + "_4", relu_first=True)
else:
sys.exit(-1)
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
if self.block_num == 3:
x = self._conv_0(x)
x = self._conv_1(x)
x = self._conv_2(x)
elif self.block_num == 5:
x = self._conv_0(x)
x = self._conv_1(x)
x = self._conv_2(x)
x = self._conv_3(x)
x = self._conv_4(x)
return x
import paddle
import paddle.fluid as fluid
__all__ = ['AlexNet']
class AlexNet():
def __init__(self):
pass
def net(self, input, class_dim=1000):
stdv = 1.0 / math.sqrt(input.shape[1] * 11 * 11)
layer_name = [
"conv1", "conv2", "conv3", "conv4", "conv5", "fc6", "fc7", "fc8"
]
conv1 = fluid.layers.conv2d(
input=input,
num_filters=64,
filter_size=11,
stride=4,
padding=2,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[0] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[0] + "_weights"))
pool1 = fluid.layers.pool2d(
input=conv1,
pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
stdv = 1.0 / math.sqrt(pool1.shape[1] * 5 * 5)
conv2 = fluid.layers.conv2d(
input=pool1,
num_filters=192,
filter_size=5,
class Middle_Flow_Bottleneck_Block(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, name):
super(Middle_Flow_Bottleneck_Block, self).__init__()
self._conv_0 = Separable_Conv(
input_channels,
output_channels,
stride=1,
padding=2,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[1] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[1] + "_weights"))
pool2 = fluid.layers.pool2d(
input=conv2,
pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
stdv = 1.0 / math.sqrt(pool2.shape[1] * 3 * 3)
conv3 = fluid.layers.conv2d(
input=pool2,
num_filters=384,
filter_size=3,
name=name + "_branch2a_weights")
self._conv_1 = Separable_Conv(
output_channels,
output_channels,
stride=1,
padding=1,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[2] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[2] + "_weights"))
stdv = 1.0 / math.sqrt(conv3.shape[1] * 3 * 3)
conv4 = fluid.layers.conv2d(
input=conv3,
num_filters=256,
filter_size=3,
name=name + "_branch2b_weights")
self._conv_2 = Separable_Conv(
output_channels,
output_channels,
stride=1,
padding=1,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[3] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[3] + "_weights"))
stdv = 1.0 / math.sqrt(conv4.shape[1] * 3 * 3)
conv5 = fluid.layers.conv2d(
input=conv4,
num_filters=256,
filter_size=3,
name=name + "_branch2c_weights")
def forward(self, inputs):
layer_helper = LayerHelper(self.full_name(), act="relu")
conv0 = layer_helper.append_activation(inputs)
conv0 = self._conv_0(conv0)
conv1 = layer_helper.append_activation(conv0)
conv1 = self._conv_1(conv1)
conv2 = layer_helper.append_activation(conv1)
conv2 = self._conv_2(conv2)
return fluid.layers.elementwise_add(x=inputs, y=conv2)
class Middle_Flow(fluid.dygraph.Layer):
def __init__(self, block_num=8):
super(Middle_Flow, self).__init__()
self.block_num = block_num
self._conv_0 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_0")
self._conv_1 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_1")
self._conv_2 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_2")
self._conv_3 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_3")
self._conv_4 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_4")
self._conv_5 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_5")
self._conv_6 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_6")
self._conv_7 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_7")
if block_num == 16:
self._conv_8 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_8")
self._conv_9 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_9")
self._conv_10 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_10")
self._conv_11 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_11")
self._conv_12 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_12")
self._conv_13 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_13")
self._conv_14 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_14")
self._conv_15 = Middle_Flow_Bottleneck_Block(
728, 728, name="middle_flow_15")
def forward(self, inputs):
x = self._conv_0(inputs)
x = self._conv_1(x)
x = self._conv_2(x)
x = self._conv_3(x)
x = self._conv_4(x)
x = self._conv_5(x)
x = self._conv_6(x)
x = self._conv_7(x)
if self.block_num == 16:
x = self._conv_8(x)
x = self._conv_9(x)
x = self._conv_10(x)
x = self._conv_11(x)
x = self._conv_12(x)
x = self._conv_13(x)
x = self._conv_14(x)
x = self._conv_15(x)
return x
class Exit_Flow_Bottleneck_Block(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels1, output_channels2,
name):
super(Exit_Flow_Bottleneck_Block, self).__init__()
self._short = Conv2D(
num_channels=input_channels,
num_filters=output_channels2,
filter_size=1,
stride=2,
padding=0,
act=None,
param_attr=ParamAttr(name + "_branch1_weights"),
bias_attr=False)
self._conv_1 = Separable_Conv(
input_channels,
output_channels1,
stride=1,
name=name + "_branch2a_weights")
self._conv_2 = Separable_Conv(
output_channels1,
output_channels2,
stride=1,
padding=1,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[4] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[4] + "_weights"))
pool5 = fluid.layers.pool2d(
input=conv5,
pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
drop6 = fluid.layers.dropout(x=pool5, dropout_prob=0.5)
stdv = 1.0 / math.sqrt(drop6.shape[1] * drop6.shape[2] *
drop6.shape[3] * 1.0)
fc6 = fluid.layers.fc(
input=drop6,
size=4096,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[5] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[5] + "_weights"))
drop7 = fluid.layers.dropout(x=fc6, dropout_prob=0.5)
stdv = 1.0 / math.sqrt(drop7.shape[1] * 1.0)
fc7 = fluid.layers.fc(
input=drop7,
size=4096,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[6] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[6] + "_weights"))
stdv = 1.0 / math.sqrt(fc7.shape[1] * 1.0)
out = fluid.layers.fc(
input=fc7,
size=class_dim,
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[7] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[7] + "_weights"))
name=name + "_branch2b_weights")
self._pool = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type="max")
def forward(self, inputs):
short = self._short(inputs)
layer_helper = LayerHelper(self.full_name(), act="relu")
conv0 = layer_helper.append_activation(inputs)
conv1 = self._conv_1(conv0)
conv2 = layer_helper.append_activation(conv1)
conv2 = self._conv_2(conv2)
pool = self._pool(conv2)
return fluid.layers.elementwise_add(x=short, y=pool)
class Exit_Flow(fluid.dygraph.Layer):
def __init__(self, class_dim):
super(Exit_Flow, self).__init__()
name = "exit_flow"
self._conv_0 = Exit_Flow_Bottleneck_Block(
728, 728, 1024, name=name + "_1")
self._conv_1 = Separable_Conv(1024, 1536, stride=1, name=name + "_2")
self._conv_2 = Separable_Conv(1536, 2048, stride=1, name=name + "_3")
self._pool = Pool2D(pool_type="avg", global_pooling=True)
stdv = 1.0 / math.sqrt(2048 * 1.0)
self._out = Linear(
2048,
class_dim,
param_attr=ParamAttr(
name="fc_weights",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
layer_helper = LayerHelper(self.full_name(), act="relu")
conv0 = self._conv_0(inputs)
conv1 = self._conv_1(conv0)
conv1 = layer_helper.append_activation(conv1)
conv2 = self._conv_2(conv1)
conv2 = layer_helper.append_activation(conv2)
pool = self._pool(conv2)
pool = fluid.layers.reshape(pool, [0, -1])
out = self._out(pool)
return out
class Xception(fluid.dygraph.Layer):
def __init__(self,
entry_flow_block_num=3,
middle_flow_block_num=8,
class_dim=1000):
super(Xception, self).__init__()
self.entry_flow_block_num = entry_flow_block_num
self.middle_flow_block_num = middle_flow_block_num
self._entry_flow = Entry_Flow(entry_flow_block_num)
self._middle_flow = Middle_Flow(middle_flow_block_num)
self._exit_flow = Exit_Flow(class_dim)
def forward(self, inputs):
x = self._entry_flow(inputs)
x = self._middle_flow(x)
x = self._exit_flow(x)
return x
def Xception41():
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8)
return model
def Xception65():
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16)
return model
def Xception71():
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16)
return model
ppcls/modeling/architectures/darknet.py
浏览文件 @ 0dfe15d2
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# coding=UTF-8
import numpy as np
import argparse
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid import framework
import math
import sys
import time
__all__ = ["DarkNet53"]
class DarkNet53():
def __init__(self):
pass
def net(self, input, class_dim=1000):
DarkNet_cfg = {53: ([1, 2, 8, 8, 4], self.basicblock)}
stages, block_func = DarkNet_cfg[53]
stages = stages[0:5]
conv1 = self.conv_bn_layer(
input,
ch_out=32,
filter_size=3,
stride=1,
padding=1,
name="yolo_input")
conv = self.downsample(
conv1, ch_out=conv1.shape[1] * 2, name="yolo_input.downsample")
for i, stage in enumerate(stages):
conv = self.layer_warp(
block_func,
conv,
32 * (2**i),
stage,
name="stage.{}".format(i))
if i < len(stages) - 1: # do not downsaple in the last stage
conv = self.downsample(
conv,
ch_out=conv.shape[1] * 2,
name="stage.{}.downsample".format(i))
pool = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name='fc_weights'),
bias_attr=ParamAttr(name='fc_offset'))
return out
def conv_bn_layer(self,
input,
ch_out,
filter_size,
stride,
padding,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=ch_out,
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
stride,
padding,
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=input_channels,
num_filters=output_channels,
filter_size=filter_size,
stride=stride,
padding=padding,
......@@ -82,39 +38,133 @@ class DarkNet53():
bias_attr=False)
bn_name = name + ".bn"
out = fluid.layers.batch_norm(
input=conv,
act='relu',
param_attr=ParamAttr(name=bn_name + '.scale'),
bias_attr=ParamAttr(name=bn_name + '.offset'),
moving_mean_name=bn_name + '.mean',
moving_variance_name=bn_name + '.var')
return out
def downsample(self,
input,
ch_out,
filter_size=3,
stride=2,
padding=1,
name=None):
return self.conv_bn_layer(
input,
ch_out=ch_out,
filter_size=filter_size,
stride=stride,
padding=padding,
name=name)
def basicblock(self, input, ch_out, name=None):
conv1 = self.conv_bn_layer(input, ch_out, 1, 1, 0, name=name + ".0")
conv2 = self.conv_bn_layer(
conv1, ch_out * 2, 3, 1, 1, name=name + ".1")
out = fluid.layers.elementwise_add(x=input, y=conv2, act=None)
return out
def layer_warp(self, block_func, input, ch_out, count, name=None):
res_out = block_func(input, ch_out, name='{}.0'.format(name))
for j in range(1, count):
res_out = block_func(res_out, ch_out, name='{}.{}'.format(name, j))
return res_out
self._bn = BatchNorm(
num_channels=output_channels,
act="relu",
param_attr=ParamAttr(name=bn_name + ".scale"),
bias_attr=ParamAttr(name=bn_name + ".offset"),
moving_mean_name=bn_name + ".mean",
moving_variance_name=bn_name + ".var")
def forward(self, inputs):
x = self._conv(inputs)
x = self._bn(x)
return x
class Basic_Block(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, name=None):
super(Basic_Block, self).__init__()
self._conv1 = ConvBNLayer(
input_channels, output_channels, 1, 1, 0, name=name + ".0")
self._conv2 = ConvBNLayer(
output_channels, output_channels * 2, 3, 1, 1, name=name + ".1")
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
return fluid.layers.elementwise_add(x=inputs, y=x)
class DarkNet(fluid.dygraph.Layer):
def __init__(self, class_dim=1000):
super(DarkNet53, self).__init__()
self.stages = [1, 2, 8, 8, 4]
self._conv1 = ConvBNLayer(3, 32, 3, 1, 1, name="yolo_input")
self._conv2 = ConvBNLayer(
32, 64, 3, 2, 1, name="yolo_input.downsample")
self._basic_block_01 = Basic_Block(64, 32, name="stage.0.0")
self._downsample_0 = ConvBNLayer(
64, 128, 3, 2, 1, name="stage.0.downsample")
self._basic_block_11 = Basic_Block(128, 64, name="stage.1.0")
self._basic_block_12 = Basic_Block(128, 64, name="stage.1.1")
self._downsample_1 = ConvBNLayer(
128, 256, 3, 2, 1, name="stage.1.downsample")
self._basic_block_21 = Basic_Block(256, 128, name="stage.2.0")
self._basic_block_22 = Basic_Block(256, 128, name="stage.2.1")
self._basic_block_23 = Basic_Block(256, 128, name="stage.2.2")
self._basic_block_24 = Basic_Block(256, 128, name="stage.2.3")
self._basic_block_25 = Basic_Block(256, 128, name="stage.2.4")
self._basic_block_26 = Basic_Block(256, 128, name="stage.2.5")
self._basic_block_27 = Basic_Block(256, 128, name="stage.2.6")
self._basic_block_28 = Basic_Block(256, 128, name="stage.2.7")
self._downsample_2 = ConvBNLayer(
256, 512, 3, 2, 1, name="stage.2.downsample")
self._basic_block_31 = Basic_Block(512, 256, name="stage.3.0")
self._basic_block_32 = Basic_Block(512, 256, name="stage.3.1")
self._basic_block_33 = Basic_Block(512, 256, name="stage.3.2")
self._basic_block_34 = Basic_Block(512, 256, name="stage.3.3")
self._basic_block_35 = Basic_Block(512, 256, name="stage.3.4")
self._basic_block_36 = Basic_Block(512, 256, name="stage.3.5")
self._basic_block_37 = Basic_Block(512, 256, name="stage.3.6")
self._basic_block_38 = Basic_Block(512, 256, name="stage.3.7")
self._downsample_3 = ConvBNLayer(
512, 1024, 3, 2, 1, name="stage.3.downsample")
self._basic_block_41 = Basic_Block(1024, 512, name="stage.4.0")
self._basic_block_42 = Basic_Block(1024, 512, name="stage.4.1")
self._basic_block_43 = Basic_Block(1024, 512, name="stage.4.2")
self._basic_block_44 = Basic_Block(1024, 512, name="stage.4.3")
self._pool = Pool2D(pool_type="avg", global_pooling=True)
stdv = 1.0 / math.sqrt(1024.0)
self._out = Linear(
input_dim=1024,
output_dim=class_dim,
param_attr=ParamAttr(
name="fc_weights",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
x = self._basic_block_01(x)
x = self._downsample_0(x)
x = self._basic_block_11(x)
x = self._basic_block_12(x)
x = self._downsample_1(x)
x = self._basic_block_21(x)
x = self._basic_block_22(x)
x = self._basic_block_23(x)
x = self._basic_block_24(x)
x = self._basic_block_25(x)
x = self._basic_block_26(x)
x = self._basic_block_27(x)
x = self._basic_block_28(x)
x = self._downsample_2(x)
x = self._basic_block_31(x)
x = self._basic_block_32(x)
x = self._basic_block_33(x)
x = self._basic_block_34(x)
x = self._basic_block_35(x)
x = self._basic_block_36(x)
x = self._basic_block_37(x)
x = self._basic_block_38(x)
x = self._downsample_3(x)
x = self._basic_block_41(x)
x = self._basic_block_42(x)
x = self._basic_block_43(x)
x = self._basic_block_44(x)
x = self._pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = self._out(x)
return x
def DarkNet53(**args):
model = DarkNet(**args)
return model
ppcls/modeling/architectures/googlenet.py
浏览文件 @ 0dfe15d2
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid import framework
import math
import sys
import time
__all__ = ['GoogLeNet_DY']
def xavier(channels, filter_size, name):
stdv = (3.0 / (filter_size**2 * channels))**0.5
param_attr = ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_weights")
__all__ = ['GoogLeNet']
class GoogLeNet():
def __init__(self):
pass
def conv_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
channels = input.shape[1]
stdv = (3.0 / (filter_size**2 * channels))**0.5
param_attr = ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_weights")
conv = fluid.layers.conv2d(
input=input,
return param_attr
class ConvLayer(fluid.dygraph.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvLayer, self).__init__()
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=act,
param_attr=param_attr,
bias_attr=False,
name=name)
return conv
def xavier(self, channels, filter_size, name):
stdv = (3.0 / (filter_size**2 * channels))**0.5
param_attr = ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_weights")
return param_attr
def inception(self,
input,
channels,
filter1,
filter3R,
filter3,
filter5R,
filter5,
proj,
name=None):
conv1 = self.conv_layer(
input=input,
num_filters=filter1,
filter_size=1,
stride=1,
act=None,
name="inception_" + name + "_1x1")
conv3r = self.conv_layer(
input=input,
num_filters=filter3R,
filter_size=1,
stride=1,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
def forward(self, inputs):
y = self._conv(inputs)
return y
class Inception(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter1,
filter3R,
filter3,
filter5R,
filter5,
proj,
name=None):
super(Inception, self).__init__()
self._conv1 = ConvLayer(
input_channels, filter1, 1, name="inception_" + name + "_1x1")
self._conv3r = ConvLayer(
input_channels,
filter3R,
1,
name="inception_" + name + "_3x3_reduce")
conv3 = self.conv_layer(
input=conv3r,
num_filters=filter3,
filter_size=3,
stride=1,
act=None,
name="inception_" + name + "_3x3")
conv5r = self.conv_layer(
input=input,
num_filters=filter5R,
filter_size=1,
stride=1,
act=None,
self._conv3 = ConvLayer(
filter3R, filter3, 3, name="inception_" + name + "_3x3")
self._conv5r = ConvLayer(
input_channels,
filter5R,
1,
name="inception_" + name + "_5x5_reduce")
conv5 = self.conv_layer(
input=conv5r,
num_filters=filter5,
filter_size=5,
stride=1,
act=None,
name="inception_" + name + "_5x5")
pool = fluid.layers.pool2d(
input=input,
pool_size=3,
pool_stride=1,
pool_padding=1,
pool_type='max')
convprj = fluid.layers.conv2d(
input=pool,
filter_size=1,
num_filters=proj,
stride=1,
padding=0,
name="inception_" + name + "_3x3_proj",
param_attr=ParamAttr(
name="inception_" + name + "_3x3_proj_weights"),
bias_attr=False)
cat = fluid.layers.concat(input=[conv1, conv3, conv5, convprj], axis=1)
cat = fluid.layers.relu(cat)
return cat
def net(self, input, class_dim=1000):
conv = self.conv_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act=None,
name="conv1")
pool = fluid.layers.pool2d(
input=conv, pool_size=3, pool_type='max', pool_stride=2)
conv = self.conv_layer(
input=pool,
num_filters=64,
filter_size=1,
stride=1,
act=None,
name="conv2_1x1")
conv = self.conv_layer(
input=conv,
num_filters=192,
filter_size=3,
stride=1,
act=None,
name="conv2_3x3")
pool = fluid.layers.pool2d(
input=conv, pool_size=3, pool_type='max', pool_stride=2)
ince3a = self.inception(pool, 192, 64, 96, 128, 16, 32, 32, "ince3a")
ince3b = self.inception(ince3a, 256, 128, 128, 192, 32, 96, 64,
"ince3b")
pool3 = fluid.layers.pool2d(
input=ince3b, pool_size=3, pool_type='max', pool_stride=2)
ince4a = self.inception(pool3, 480, 192, 96, 208, 16, 48, 64, "ince4a")
ince4b = self.inception(ince4a, 512, 160, 112, 224, 24, 64, 64,
"ince4b")
ince4c = self.inception(ince4b, 512, 128, 128, 256, 24, 64, 64,
"ince4c")
ince4d = self.inception(ince4c, 512, 112, 144, 288, 32, 64, 64,
"ince4d")
ince4e = self.inception(ince4d, 528, 256, 160, 320, 32, 128, 128,
"ince4e")
pool4 = fluid.layers.pool2d(
input=ince4e, pool_size=3, pool_type='max', pool_stride=2)
ince5a = self.inception(pool4, 832, 256, 160, 320, 32, 128, 128,
"ince5a")
ince5b = self.inception(ince5a, 832, 384, 192, 384, 48, 128, 128,
"ince5b")
pool5 = fluid.layers.pool2d(
input=ince5b, pool_size=7, pool_type='avg', pool_stride=7)
dropout = fluid.layers.dropout(x=pool5, dropout_prob=0.4)
out = fluid.layers.fc(input=dropout,
size=class_dim,
act='softmax',
param_attr=self.xavier(1024, 1, "out"),
name="out",
bias_attr=ParamAttr(name="out_offset"))
pool_o1 = fluid.layers.pool2d(
input=ince4a, pool_size=5, pool_type='avg', pool_stride=3)
conv_o1 = self.conv_layer(
input=pool_o1,
num_filters=128,
filter_size=1,
stride=1,
act=None,
name="conv_o1")
fc_o1 = fluid.layers.fc(input=conv_o1,
size=1024,
act='relu',
param_attr=self.xavier(2048, 1, "fc_o1"),
name="fc_o1",
bias_attr=ParamAttr(name="fc_o1_offset"))
dropout_o1 = fluid.layers.dropout(x=fc_o1, dropout_prob=0.7)
out1 = fluid.layers.fc(input=dropout_o1,
size=class_dim,
act='softmax',
param_attr=self.xavier(1024, 1, "out1"),
name="out1",
bias_attr=ParamAttr(name="out1_offset"))
pool_o2 = fluid.layers.pool2d(
input=ince4d, pool_size=5, pool_type='avg', pool_stride=3)
conv_o2 = self.conv_layer(
input=pool_o2,
num_filters=128,
filter_size=1,
stride=1,
act=None,
name="conv_o2")
fc_o2 = fluid.layers.fc(input=conv_o2,
size=1024,
act='relu',
param_attr=self.xavier(2048, 1, "fc_o2"),
name="fc_o2",
bias_attr=ParamAttr(name="fc_o2_offset"))
dropout_o2 = fluid.layers.dropout(x=fc_o2, dropout_prob=0.7)
out2 = fluid.layers.fc(input=dropout_o2,
size=class_dim,
act='softmax',
param_attr=self.xavier(1024, 1, "out2"),
name="out2",
bias_attr=ParamAttr(name="out2_offset"))
# last fc layer is "out"
self._conv5 = ConvLayer(
filter5R, filter5, 5, name="inception_" + name + "_5x5")
self._pool = Pool2D(
pool_size=3, pool_type="max", pool_stride=1, pool_padding=1)
self._convprj = ConvLayer(
input_channels, proj, 1, name="inception_" + name + "_3x3_proj")
def forward(self, inputs):
conv1 = self._conv1(inputs)
conv3r = self._conv3r(inputs)
conv3 = self._conv3(conv3r)
conv5r = self._conv5r(inputs)
conv5 = self._conv5(conv5r)
pool = self._pool(inputs)
convprj = self._convprj(pool)
cat = fluid.layers.concat([conv1, conv3, conv5, convprj], axis=1)
layer_helper = LayerHelper(self.full_name(), act="relu")
return layer_helper.append_activation(cat)
class GoogleNet_DY(fluid.dygraph.Layer):
def __init__(self, class_dim=1000):
super(GoogleNet_DY, self).__init__()
self._conv = ConvLayer(3, 64, 7, 2, name="conv1")
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._conv_1 = ConvLayer(64, 64, 1, name="conv2_1x1")
self._conv_2 = ConvLayer(64, 192, 3, name="conv2_3x3")
self._ince3a = Inception(
192, 192, 64, 96, 128, 16, 32, 32, name="ince3a")
self._ince3b = Inception(
256, 256, 128, 128, 192, 32, 96, 64, name="ince3b")
self._ince4a = Inception(
480, 480, 192, 96, 208, 16, 48, 64, name="ince4a")
self._ince4b = Inception(
512, 512, 160, 112, 224, 24, 64, 64, name="ince4b")
self._ince4c = Inception(
512, 512, 128, 128, 256, 24, 64, 64, name="ince4c")
self._ince4d = Inception(
512, 512, 112, 144, 288, 32, 64, 64, name="ince4d")
self._ince4e = Inception(
528, 528, 256, 160, 320, 32, 128, 128, name="ince4e")
self._ince5a = Inception(
832, 832, 256, 160, 320, 32, 128, 128, name="ince5a")
self._ince5b = Inception(
832, 832, 384, 192, 384, 48, 128, 128, name="ince5b")
self._pool_5 = Pool2D(pool_size=7, pool_type='avg', pool_stride=7)
self._drop = fluid.dygraph.Dropout(p=0.4)
self._fc_out = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out"),
bias_attr=ParamAttr(name="out_offset"),
act="softmax")
self._pool_o1 = Pool2D(pool_size=5, pool_stride=3, pool_type="avg")
self._conv_o1 = ConvLayer(512, 128, 1, name="conv_o1")
self._fc_o1 = Linear(
1152,
1024,
param_attr=xavier(2048, 1, "fc_o1"),
bias_attr=ParamAttr(name="fc_o1_offset"),
act="relu")
self._drop_o1 = fluid.dygraph.Dropout(p=0.7)
self._out1 = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out1"),
bias_attr=ParamAttr(name="out1_offset"),
act="softmax")
self._pool_o2 = Pool2D(pool_size=5, pool_stride=3, pool_type='avg')
self._conv_o2 = ConvLayer(528, 128, 1, name="conv_o2")
self._fc_o2 = Linear(
1152,
1024,
param_attr=xavier(2048, 1, "fc_o2"),
bias_attr=ParamAttr(name="fc_o2_offset"))
self._drop_o2 = fluid.dygraph.Dropout(p=0.7)
self._out2 = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out2"),
bias_attr=ParamAttr(name="out2_offset"))
def forward(self, inputs):
x = self._conv(inputs)
x = self._pool(x)
x = self._conv_1(x)
x = self._conv_2(x)
x = self._pool(x)
x = self._ince3a(x)
x = self._ince3b(x)
x = self._pool(x)
ince4a = self._ince4a(x)
x = self._ince4b(ince4a)
x = self._ince4c(x)
ince4d = self._ince4d(x)
x = self._ince4e(ince4d)
x = self._pool(x)
x = self._ince5a(x)
ince5b = self._ince5b(x)
x = self._pool_5(ince5b)
x = self._drop(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
out = self._fc_out(x)
x = self._pool_o1(ince4a)
x = self._conv_o1(x)
x = fluid.layers.flatten(x)
x = self._fc_o1(x)
x = self._drop_o1(x)
out1 = self._out1(x)
x = self._pool_o2(ince4d)
x = self._conv_o2(x)
x = fluid.layers.flatten(x)
x = self._fc_o2(x)
x = self._drop_o2(x)
out2 = self._out2(x)
return [out, out1, out2]
def GoogLeNet():
model = GoogleNet_DY()
return model
ppcls/modeling/architectures/resnext101_wsl.py
浏览文件 @ 0dfe15d2
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import argparse
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.base import to_variable
__all__ = [
"ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl",
"ResNeXt101_32x48d_wsl", "Fix_ResNeXt101_32x48d_wsl"
]
from paddle.fluid import framework
import math
import sys
import time
class ResNeXt101_wsl():
def __init__(self, layers=101, cardinality=32, width=48):
self.layers = layers
self.cardinality = cardinality
self.width = width
__all__ = ["ResNeXt101_32x8d_wsl",
"ResNeXt101_wsl_32x16d_wsl",
"ResNeXt101_wsl_32x32d_wsl",
"ResNeXt101_wsl_32x48d_wsl"]
def net(self, input, class_dim=1000):
layers = self.layers
cardinality = self.cardinality
width = self.width
depth = [3, 4, 23, 3]
base_width = cardinality * width
num_filters = [base_width * i for i in [1, 2, 4, 8]]
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act='relu',
name="conv1") #debug
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
for block in range(len(depth)):
for i in range(depth[block]):
conv_name = 'layer' + str(block + 1) + "." + str(i)
conv = self.bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
cardinality=cardinality,
name=conv_name)
pool = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name='fc.weight'),
bias_attr=fluid.param_attr.ParamAttr(name='fc.bias'))
return out
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
if "downsample" in name:
conv_name = name + '.0'
conv_name = name + ".0"
else:
conv_name = name
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,
param_attr=ParamAttr(name=conv_name + ".weight"),
bias_attr=False)
conv_name = name
self._conv = Conv2D(num_channels=input_channels,
num_filters=output_channels,
filter_size=filter_size,
stride=stride,
padding=(filter_size-1)//2,
groups=groups,
act=None,
param_attr=ParamAttr(name=conv_name + ".weight"),
bias_attr=False)
if "downsample" in name:
bn_name = name[:9] + 'downsample' + '.1'
bn_name = name[:9] + "downsample.1"
else:
if "conv1" == name:
bn_name = 'bn' + name[-1]
bn_name = "bn" + name[-1]
else:
bn_name = (name[:10] if name[7:9].isdigit() else name[:9]
) + 'bn' + name[-1]
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + '.weight'),
bias_attr=ParamAttr(bn_name + '.bias'),
moving_mean_name=bn_name + '.running_mean',
moving_variance_name=bn_name + '.running_var', )
def shortcut(self, input, ch_out, stride, name):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
else:
return input
def bottleneck_block(self, input, num_filters, stride, cardinality, name):
cardinality = self.cardinality
width = self.width
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=1,
act='relu',
name=name + ".conv1")
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
groups=cardinality,
act='relu',
name=name + ".conv2")
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters // (width // 8),
filter_size=1,
act=None,
name=name + ".conv3")
short = self.shortcut(
input,
num_filters // (width // 8),
stride,
name=name + ".downsample")
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
bn_name = (name[:10] if name[7:9].isdigit() else name[:9]) + "bn" + name[-1]
self._bn = BatchNorm(num_channels=output_channels,
act=act,
param_attr=ParamAttr(name=bn_name + ".weight"),
bias_attr=ParamAttr(name=bn_name + ".bias"),
moving_mean_name=bn_name + ".running_mean",
moving_variance_name=bn_name + ".running_var")
def forward(self, inputs):
x = self._conv(inputs)
x = self._bn(x)
return x
class Short_Cut(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, stride, name=None):
super(Short_Cut, self).__init__()
self.input_channels = input_channels
self.output_channels = output_channels
self.stride = stride
if input_channels!=output_channels or stride!=1:
self._conv = ConvBNLayer(
input_channels, output_channels, filter_size=1, stride=stride, name=name)
def forward(self, inputs):
if self.input_channels!= self.output_channels or self.stride!=1:
return self._conv(inputs)
return inputs
class Bottleneck_Block(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, stride, cardinality, width, name):
super(Bottleneck_Block, self).__init__()
self._conv0 = ConvBNLayer(
input_channels, output_channels, filter_size=1, act="relu", name=name + ".conv1")
self._conv1 = ConvBNLayer(
output_channels, output_channels, filter_size=3, act="relu", stride=stride, groups=cardinality, name=name + ".conv2")
self._conv2 = ConvBNLayer(
output_channels, output_channels//(width//8), filter_size=1, act=None, name=name + ".conv3")
self._short = Short_Cut(
input_channels, output_channels//(width//8), stride=stride, name=name + ".downsample")
def forward(self, inputs):
x = self._conv0(inputs)
x = self._conv1(x)
x = self._conv2(x)
y = self._short(inputs)
return fluid.layers.elementwise_add(x, y, act="relu")
class ResNeXt101_wsl(fluid.dygraph.Layer):
def __init__(self, layers=101, cardinality=32, width=48, class_dim=1000):
super(ResNeXt101_wsl, self).__init__()
self.class_dim = class_dim
self.layers = layers
self.cardinality = cardinality
self.width = width
self.scale = width//8
self.depth = [3, 4, 23, 3]
self.base_width = cardinality * width
num_filters = [self.base_width*i for i in [1,2,4,8]] #[256, 512, 1024, 2048]
self._conv_stem = ConvBNLayer(
3, 64, 7, stride=2, act="relu", name="conv1")
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type="max")
self._conv1_0 = Bottleneck_Block(
64, num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.0")
self._conv1_1 = Bottleneck_Block(
num_filters[0]//(width//8), num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.1")
self._conv1_2 = Bottleneck_Block(
num_filters[0]//(width//8), num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.2")
self._conv2_0 = Bottleneck_Block(
num_filters[0]//(width//8), num_filters[1], stride=2, cardinality=self.cardinality, width=self.width, name="layer2.0")
self._conv2_1 = Bottleneck_Block(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.1")
self._conv2_2 = Bottleneck_Block(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.2")
self._conv2_3 = Bottleneck_Block(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.3")
self._conv3_0 = Bottleneck_Block(
num_filters[1]//(width//8), num_filters[2], stride=2, cardinality=self.cardinality, width=self.width, name="layer3.0")
self._conv3_1 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.1")
self._conv3_2 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.2")
self._conv3_3 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.3")
self._conv3_4 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.4")
self._conv3_5 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.5")
self._conv3_6 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.6")
self._conv3_7 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.7")
self._conv3_8 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.8")
self._conv3_9 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.9")
self._conv3_10 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.10")
self._conv3_11 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.11")
self._conv3_12 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.12")
self._conv3_13 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.13")
self._conv3_14 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.14")
self._conv3_15 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.15")
self._conv3_16 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.16")
self._conv3_17 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.17")
self._conv3_18 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.18")
self._conv3_19 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.19")
self._conv3_20 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.20")
self._conv3_21 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.21")
self._conv3_22 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.22")
self._conv4_0 = Bottleneck_Block(
num_filters[2]//(width//8), num_filters[3], stride=2, cardinality=self.cardinality, width=self.width, name="layer4.0")
self._conv4_1 = Bottleneck_Block(
num_filters[3]//(width//8), num_filters[3], stride=1, cardinality=self.cardinality, width=self.width, name="layer4.1")
self._conv4_2 = Bottleneck_Block(
num_filters[3]//(width//8), num_filters[3], stride=1, cardinality=self.cardinality, width=self.width, name="layer4.2")
self._avg_pool = Pool2D(pool_type="avg", global_pooling=True)
self._out = Linear(input_dim=num_filters[3]//(width//8),
output_dim=class_dim,
param_attr=ParamAttr(name="fc.weight"),
bias_attr=ParamAttr(name="fc.bias"))
def forward(self, inputs):
x = self._conv_stem(inputs)
x = self._pool(x)
x = self._conv1_0(x)
x = self._conv1_1(x)
x = self._conv1_2(x)
x = self._conv2_0(x)
x = self._conv2_1(x)
x = self._conv2_2(x)
x = self._conv2_3(x)
x = self._conv3_0(x)
x = self._conv3_1(x)
x = self._conv3_2(x)
x = self._conv3_3(x)
x = self._conv3_4(x)
x = self._conv3_5(x)
x = self._conv3_6(x)
x = self._conv3_7(x)
x = self._conv3_8(x)
x = self._conv3_9(x)
x = self._conv3_10(x)
x = self._conv3_11(x)
x = self._conv3_12(x)
x = self._conv3_13(x)
x = self._conv3_14(x)
x = self._conv3_15(x)
x = self._conv3_16(x)
x = self._conv3_17(x)
x = self._conv3_18(x)
x = self._conv3_19(x)
x = self._conv3_20(x)
x = self._conv3_21(x)
x = self._conv3_22(x)
x = self._conv4_0(x)
x = self._conv4_1(x)
x = self._conv4_2(x)
x = self._avg_pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = self._out(x)
return x
def ResNeXt101_32x8d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=8)
return model
return model
def ResNeXt101_32x16d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=16)
return model
return model
def ResNeXt101_32x32d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=32)
return model
return model
def ResNeXt101_32x48d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=48)
return model
def Fix_ResNeXt101_32x48d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=48)
return model
return model
ppcls/modeling/architectures/squeezenet.py
浏览文件 @ 0dfe15d2
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import argparse
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid import framework
import math
import sys
import time
__all__ = ["SqueezeNet1_0", "SqueezeNet1_1"]
class Make_Fire_Conv(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
padding=0,
name=None):
super(Make_Fire_Conv, self).__init__()
self._conv = Conv2D(input_channels,
output_channels,
filter_size,
padding=padding,
act="relu",
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=ParamAttr(name=name + "_offset"))
def forward(self, inputs):
return self._conv(inputs)
__all__ = ["SqueezeNet", "SqueezeNet1_0", "SqueezeNet1_1"]
class Make_Fire(fluid.dygraph.Layer):
def __init__(self,
input_channels,
squeeze_channels,
expand1x1_channels,
expand3x3_channels,
name=None):
super(Make_Fire, self).__init__()
self._conv = Make_Fire_Conv(input_channels,
squeeze_channels,
1,
name=name + "_squeeze1x1")
self._conv_path1 = Make_Fire_Conv(squeeze_channels,
expand1x1_channels,
1,
name=name + "_expand1x1")
self._conv_path2 = Make_Fire_Conv(squeeze_channels,
expand3x3_channels,
3,
padding=1,
name=name + "_expand3x3")
def forward(self, inputs):
x = self._conv(inputs)
x1 = self._conv_path1(x)
x2 = self._conv_path2(x)
return fluid.layers.concat([x1, x2], axis=1)
class SqueezeNet():
def __init__(self, version='1.0'):
class SqueezeNet(fluid.dygraph.Layer):
def __init__(self, version, class_dim=1000):
super(SqueezeNet, self).__init__()
self.version = version
def net(self, input, class_dim=1000):
version = self.version
assert version in ['1.0', '1.1'], \
"supported version are {} but input version is {}".format(['1.0', '1.1'], version)
if version == '1.0':
conv = fluid.layers.conv2d(
input,
num_filters=96,
filter_size=7,
stride=2,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name='conv1_offset'))
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 16, 64, 64, name='fire2')
conv = self.make_fire(conv, 16, 64, 64, name='fire3')
conv = self.make_fire(conv, 32, 128, 128, name='fire4')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 32, 128, 128, name='fire5')
conv = self.make_fire(conv, 48, 192, 192, name='fire6')
conv = self.make_fire(conv, 48, 192, 192, name='fire7')
conv = self.make_fire(conv, 64, 256, 256, name='fire8')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 64, 256, 256, name='fire9')
if self.version == "1.0":
self._conv = Conv2D(3,
96,
7,
stride=2,
act="relu",
param_attr=ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name="conv1_offset"))
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_type="max")
self._conv1 = Make_Fire(96, 16, 64, 64, name="fire2")
self._conv2 = Make_Fire(128, 16, 64, 64, name="fire3")
self._conv3 = Make_Fire(128, 32, 128, 128, name="fire4")
self._conv4 = Make_Fire(256, 32, 128, 128, name="fire5")
self._conv5 = Make_Fire(256, 48, 192, 192, name="fire6")
self._conv6 = Make_Fire(384, 48, 192, 192, name="fire7")
self._conv7 = Make_Fire(384, 64, 256, 256, name="fire8")
self._conv8 = Make_Fire(512, 64, 256, 256, name="fire9")
else:
conv = fluid.layers.conv2d(
input,
num_filters=64,
filter_size=3,
stride=2,
padding=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name='conv1_offset'))
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 16, 64, 64, name='fire2')
conv = self.make_fire(conv, 16, 64, 64, name='fire3')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 32, 128, 128, name='fire4')
conv = self.make_fire(conv, 32, 128, 128, name='fire5')
conv = fluid.layers.pool2d(
conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 48, 192, 192, name='fire6')
conv = self.make_fire(conv, 48, 192, 192, name='fire7')
conv = self.make_fire(conv, 64, 256, 256, name='fire8')
conv = self.make_fire(conv, 64, 256, 256, name='fire9')
conv = fluid.layers.dropout(conv, dropout_prob=0.5)
conv = fluid.layers.conv2d(
conv,
num_filters=class_dim,
filter_size=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv10_weights"),
bias_attr=ParamAttr(name='conv10_offset'))
conv = fluid.layers.pool2d(conv, pool_type='avg', global_pooling=True)
out = fluid.layers.flatten(conv)
return out
def make_fire_conv(self,
input,
num_filters,
filter_size,
padding=0,
name=None):
conv = fluid.layers.conv2d(
input,
num_filters=num_filters,
filter_size=filter_size,
padding=padding,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name=name + "_weights"),
bias_attr=ParamAttr(name=name + '_offset'))
return conv
def make_fire(self,
input,
squeeze_channels,
expand1x1_channels,
expand3x3_channels,
name=None):
conv = self.make_fire_conv(
input, squeeze_channels, 1, name=name + '_squeeze1x1')
conv_path1 = self.make_fire_conv(
conv, expand1x1_channels, 1, name=name + '_expand1x1')
conv_path2 = self.make_fire_conv(
conv, expand3x3_channels, 3, 1, name=name + '_expand3x3')
out = fluid.layers.concat([conv_path1, conv_path2], axis=1)
return out
self._conv = Conv2D(3,
64,
3,
stride=2,
padding=1,
act="relu",
param_attr=ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name="conv1_offset"))
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_type="max")
self._conv1 = Make_Fire(64, 16, 64, 64, name="fire2")
self._conv2 = Make_Fire(128, 16, 64, 64, name="fire3")
self._conv3 = Make_Fire(128, 32, 128, 128, name="fire4")
self._conv4 = Make_Fire(256, 32, 128, 128, name="fire5")
def SqueezeNet1_0():
model = SqueezeNet(version='1.0')
return model
self._conv5 = Make_Fire(256, 48, 192, 192, name="fire6")
self._conv6 = Make_Fire(384, 48, 192, 192, name="fire7")
self._conv7 = Make_Fire(384, 64, 256, 256, name="fire8")
self._conv8 = Make_Fire(512, 64, 256, 256, name="fire9")
self._drop = Dropout(p=0.5)
self._conv9 = Conv2D(512,
class_dim,
1,
act="relu",
param_attr=ParamAttr(name="conv10_weights"),
bias_attr=ParamAttr(name="conv10_offset"))
self._avg_pool = Pool2D(pool_type="avg",
global_pooling=True)
def forward(self, inputs):
x = self._conv(inputs)
x = self._pool(x)
if self.version=="1.0":
x = self._conv1(x)
x = self._conv2(x)
x = self._conv3(x)
x = self._pool(x)
x = self._conv4(x)
x = self._conv5(x)
x = self._conv6(x)
x = self._conv7(x)
x = self._pool(x)
x = self._conv8(x)
else:
x = self._conv1(x)
x = self._conv2(x)
x = self._pool(x)
x = self._conv3(x)
x = self._conv4(x)
x = self._pool(x)
x = self._conv5(x)
x = self._conv6(x)
x = self._conv7(x)
x = self._conv8(x)
x = self._drop(x)
x = self._conv9(x)
x = self._avg_pool(x)
x = fluid.layers.squeeze(x, axes=[2,3])
return x
def SqueezeNet1_0():
model = SqueezeNet(version="1.0")
return model
def SqueezeNet1_1():
model = SqueezeNet(version='1.1')
return model
model = SqueezeNet(version="1.1")
return model
\ No newline at end of file
ppcls/modeling/architectures/vgg.py
浏览文件 @ 0dfe15d2
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
#coding:utf-8
import numpy as np
import argparse
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid import framework
import math
import sys
import time
__all__ = ["VGG11", "VGG13", "VGG16", "VGG19"]
class Conv_Block(fluid.dygraph.Layer):
def __init__(self,
input_channels,
output_channels,
groups,
name=None):
super(Conv_Block, self).__init__()
self.groups = groups
self._conv_1 = Conv2D(num_channels=input_channels,
num_filters=output_channels,
filter_size=3,
stride=1,
padding=1,
act="relu",
param_attr=ParamAttr(name=name + "1_weights"),
bias_attr=False)
if groups == 2 or groups == 3 or groups == 4:
self._conv_2 = Conv2D(num_channels=output_channels,
num_filters=output_channels,
filter_size=3,
stride=1,
padding=1,
act="relu",
param_attr=ParamAttr(name=name + "2_weights"),
bias_attr=False)
if groups == 3 or groups == 4:
self._conv_3 = Conv2D(num_channels=output_channels,
num_filters=output_channels,
filter_size=3,
stride=1,
padding=1,
act="relu",
param_attr=ParamAttr(name=name + "3_weights"),
bias_attr=False)
if groups == 4:
self._conv_4 = Conv2D(number_channels=output_channels,
number_filters=output_channels,
filter_size=3,
stride=1,
padding=1,
act="relu",
param_attr=ParamAttr(name=name + "4_weights"),
bias_attr=False)
self._pool = Pool2D(pool_size=2,
pool_type="max",
pool_stride=2)
def forward(self, inputs):
x = self._conv_1(inputs)
if self.groups == 2 or self.groups == 3 or self.groups == 4:
x = self._conv_2(x)
if self.groups == 3 or self.groups == 4 :
x = self._conv_3(x)
if self.groups == 4:
x = self._conv_4(x)
x = self._pool(x)
return x
class VGGNet(fluid.dygraph.Layer):
def __init__(self, layers=11, class_dim=1000):
super(VGGNet, self).__init__()
__all__ = ["VGGNet", "VGG11", "VGG13", "VGG16", "VGG19"]
class VGGNet():
def __init__(self, layers=16):
self.layers = layers
def net(self, input, class_dim=1000):
layers = self.layers
vgg_spec = {
11: ([1, 1, 2, 2, 2]),
13: ([2, 2, 2, 2, 2]),
16: ([2, 2, 3, 3, 3]),
19: ([2, 2, 4, 4, 4])
}
assert layers in vgg_spec.keys(), \
"supported layers are {} but input layer is {}".format(vgg_spec.keys(), layers)
nums = vgg_spec[layers]
conv1 = self.conv_block(input, 64, nums[0], name="conv1_")
conv2 = self.conv_block(conv1, 128, nums[1], name="conv2_")
conv3 = self.conv_block(conv2, 256, nums[2], name="conv3_")
conv4 = self.conv_block(conv3, 512, nums[3], name="conv4_")
conv5 = self.conv_block(conv4, 512, nums[4], name="conv5_")
fc_dim = 4096
fc_name = ["fc6", "fc7", "fc8"]
fc1 = fluid.layers.fc(
input=conv5,
size=fc_dim,
act='relu',
param_attr=fluid.param_attr.ParamAttr(
name=fc_name[0] + "_weights"),
bias_attr=fluid.param_attr.ParamAttr(name=fc_name[0] + "_offset"))
fc1 = fluid.layers.dropout(x=fc1, dropout_prob=0.5)
fc2 = fluid.layers.fc(
input=fc1,
size=fc_dim,
act='relu',
param_attr=fluid.param_attr.ParamAttr(
name=fc_name[1] + "_weights"),
bias_attr=fluid.param_attr.ParamAttr(name=fc_name[1] + "_offset"))
fc2 = fluid.layers.dropout(x=fc2, dropout_prob=0.5)
out = fluid.layers.fc(
input=fc2,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
name=fc_name[2] + "_weights"),
bias_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_offset"))
return out
def conv_block(self, input, num_filter, groups, name=None):
conv = input
for i in range(groups):
conv = fluid.layers.conv2d(
input=conv,
num_filters=num_filter,
filter_size=3,
stride=1,
padding=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(
name=name + str(i + 1) + "_weights"),
bias_attr=False)
return fluid.layers.pool2d(
input=conv, pool_size=2, pool_type='max', pool_stride=2)
self.vgg_configure = {11: [1, 1, 2, 2, 2],
13: [2, 2, 2, 2, 2],
16: [2, 2, 3, 3, 3],
19: [2, 2, 4, 4, 4]}
assert self.layers in self.vgg_configure.keys(), \
"supported layers are {} but input layer is {}".format(vgg_configure.keys(), layers)
self.groups = self.vgg_configure[self.layers]
self._conv_block_1 = Conv_Block(3, 64, self.groups[0], name="conv1_")
self._conv_block_2 = Conv_Block(64, 128, self.groups[1], name="conv2_")
self._conv_block_3 = Conv_Block(128, 256, self.groups[2], name="conv3_")
self._conv_block_4 = Conv_Block(256, 512, self.groups[3], name="conv4_")
self._conv_block_5 = Conv_Block(512, 512, self.groups[4], name="conv5_")
#self._drop = fluid.dygraph.nn.Dropout(p=0.5)
self._fc1 = Linear(input_dim=7*7*512,
output_dim=4096,
act="relu",
param_attr=ParamAttr(name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
self._fc2 = Linear(input_dim=4096,
output_dim=4096,
act="relu",
param_attr=ParamAttr(name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
self._out = Linear(input_dim=4096,
output_dim=class_dim,
param_attr=ParamAttr(name="fc8_weights"),
bias_attr=ParamAttr(name="fc8_offset"))
def forward(self, inputs):
x = self._conv_block_1(inputs)
x = self._conv_block_2(x)
x = self._conv_block_3(x)
x = self._conv_block_4(x)
x = self._conv_block_5(x)
x = fluid.layers.flatten(x, axis=0)
x = self._fc1(x)
# x = self._drop(x)
x = self._fc2(x)
# x = self._drop(x)
x = self._out(x)
return x
def VGG11():
model = VGGNet(layers=11)
return model
return model
def VGG13():
model = VGGNet(layers=13)
return model
def VGG16():
model = VGGNet(layers=16)
return model
return model
def VGG19():
model = VGGNet(layers=19)
return model
return model
\ No newline at end of file
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或