def WRN_cifar(mode='train', root_path='', batch_size=32, block_n=3, block_k=1):
network = 'name:"WRN-28"' + '\n'
if mode == 'train':
network, last_name = write_prototxt.data(network,
name="Data1",
mirror=True,
scale=0.00390625,
crop_size=32,
batch_size=batch_size,
backend="LMDB",
shuffle=True,
datasets_path=root_path)
use_global_stats = False
dropout_ratio = 0.5
elif mode == 'test':
network, last_name = write_prototxt.data(network,
name="Data1",
mirror=False,
scale=0.00390625,
crop_size=32,
batch_size=batch_size,
backend="LMDB",
shuffle=False,
datasets_path=root_path)
use_global_stats = True
dropout_ratio = 1.0
network, last_name = write_prototxt.Convolution(network,
name="conv1",
bottom_name=last_name,
top_name='conv1',
num_output=16,
bias_term=True,
pad=1,
kernel_size=3,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn="conv1/bn",
name_scale="conv1/scale",
bottom_name="conv1",
top_name="conv1",
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(network,
name="conv1/ReLU",
bottom_name='conv1',
top_name='conv1')
#network=Pooling(network,name="Pooling1",bottom_name='conv1',top_name='Pooling1',pool='MAX')
network, last_name = ResBlock(network,
last_name=last_name,
block_name='conv2_',
block_n=block_n,
num_out=16 * block_k,
downsampling=False,
down_method='conv',
use_global_stats=use_global_stats,
dropout_ratio=dropout_ratio,
first_layer=True)
network, last_name = ResBlock(network,
last_name=last_name,
block_name='conv3_',
block_n=block_n,
num_out=32 * block_k,
downsampling=True,
down_method='conv',
use_global_stats=use_global_stats,
dropout_ratio=dropout_ratio,
first_layer=False)
network, last_name = ResBlock(network,
last_name=last_name,
block_name='conv4_',
block_n=block_n,
num_out=64 * block_k,
downsampling=True,
down_method='conv',
use_global_stats=use_global_stats,
dropout_ratio=dropout_ratio,
first_layer=False)
#network,last_name=ResBlock(network,last_name=last_name,block_name='conv5_',block_n=block_n,num_out=64,downsampling=True,down_method='conv',use_global_stats=use_global_stats,dropout_ratio=dropout_ratio)
network, last_name = write_prototxt.Pooling(network,
name="Pooling1",
bottom_name=last_name,
top_name='Pooling1',
pool='AVE',
global_pooling=True)
network, last_name = write_prototxt.InnerProduct(network,
name="fc1",
bottom_name=last_name,
top_name='fc1',
num_output=10,
weight_type='msra',
bias_type='constant')
#if mode=='train':
network, last_name = write_prototxt.SoftmaxWithLoss(network,
name="Softmax1",
bottom_name1='fc1',
bottom_name2='label',
top_name='Softmax1')
if mode == 'test':
network, last_name = write_prototxt.Accuracy(network,
name="prob",
bottom_name1='fc1',
bottom_name2='label',
top_name='prob')
#
#
print network
return network
def Net(mode='train', root_path='', batch_size=32):
network = 'name:"ResNet-75"' + '\n'
if mode == 'train':
network, last_name = write_prototxt.data(network,
name="data",
mirror=True,
scale=0.00390625,
crop_size=64,
batch_size=batch_size,
backend="LMDB",
shuffle=True,
datasets_path=root_path)
use_global_stats = False
elif mode == 'test':
network, last_name = write_prototxt.data(network,
name="data",
mirror=False,
scale=0.00390625,
crop_size=64,
batch_size=batch_size,
backend="LMDB",
shuffle=False,
datasets_path=root_path)
use_global_stats = True
network, last_name = write_prototxt.Convolution(network,
name="conv1",
bottom_name=last_name,
top_name='conv1',
num_output=24,
bias_term=True,
pad=1,
kernel_size=3,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn="conv1_bn",
name_scale="conv1__scale",
bottom_name="conv1",
top_name="conv1",
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(network,
name="conv1_ReLU",
bottom_name='conv1',
top_name='conv1')
#network=Pooling(network,name="Pooling1",bottom_name='conv1',top_name='Pooling1',pool='MAX')
network, last_name = ResBlock(network,
last_name=last_name,
block_name='conv2_',
block_n=9,
num_out=24,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = ResBlock(network,
last_name=last_name,
block_name='conv3_',
block_n=9,
num_out=32,
downsampling=True,
down_method='conv',
use_global_stats=use_global_stats)
network, last_name = ResBlock(network,
last_name=last_name,
block_name='conv4_',
block_n=9,
num_out=64,
downsampling=True,
down_method='conv',
use_global_stats=use_global_stats)
network, last_name = ResBlock(network,
last_name=last_name,
block_name='conv5_',
block_n=9,
num_out=128,
downsampling=True,
down_method='conv',
use_global_stats=use_global_stats)
network, last_name = write_prototxt.Pooling(network,
name="Pooling1",
bottom_name=last_name,
top_name='Pooling1',
pool='AVE',
global_pooling=True)
network, last_name = write_prototxt.InnerProduct(network,
name="fc1",
bottom_name=last_name,
top_name='fc1',
num_output=230,
weight_type='xavier',
bias_type='constant')
if mode == 'train':
network, last_name = write_prototxt.SoftmaxWithLoss(
network,
name="Softmax1",
bottom_name1='fc1',
bottom_name2='label',
top_name='Softmax1')
if mode == 'test':
network, last_name = write_prototxt.Accuracy(network,
name="prob",
bottom_name1='fc1',
bottom_name2='label',
top_name='prob')
#
#
print network
return network
def ResBlock(network,
last_name='',
block_name='conv2_',
block_n=3,
num_out=16,
downsampling=False,
down_method='pooling',
use_global_stats='False',
dropout_ratio=0.5,
first_layer=False):
input_name = last_name
for i in range(1, block_n + 1, 1):
if i == 1 and downsampling == True:
first_stride = 2
else:
first_stride = 1
network, last_name = write_prototxt.Convolution(
network,
name=block_name + str(i) + '_0',
bottom_name=last_name,
top_name=block_name + str(i) + '_0',
num_output=num_out,
bias_term=True,
pad=1,
kernel_size=3,
stride=first_stride,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + str(i) + "_bn" + '0',
name_scale=block_name + str(i) + "_scale" + '0',
bottom_name=block_name + str(i) + '_0',
top_name=block_name + str(i) + '_0',
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(
network,
name=block_name + str(i) + "_ReLU" + '0',
bottom_name=block_name + str(i) + '_0',
top_name=block_name + str(i) + '_0')
network, last_name = write_prototxt.Dropout(
network,
name=block_name + "Drop" + str(i),
bottom_name1=block_name + str(i) + '_0',
top_name=block_name + str(i) + '_0',
dropout_ratio=dropout_ratio)
network, last_name = write_prototxt.Convolution(
network,
name=block_name + str(i) + '_1',
bottom_name=last_name,
top_name=block_name + str(i) + '_1',
num_output=num_out,
bias_term=True,
pad=1,
kernel_size=3,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + str(i) + "bn" + '1',
name_scale=block_name + str(i) + "_scale" + '1',
bottom_name=block_name + str(i) + '_1',
top_name=block_name + str(i) + '_1',
use_global_stats=use_global_stats)
#network,last_name=ReLU(network,name=block_name+str(i)+"ReLU"+'_1',bottom_name=block_name+str(i)+'_1',top_name=block_name+str(i)+'_1')
origin_name = last_name
network, last_name = write_prototxt.Pooling(
network,
name=block_name + str(i) + "_Pooling",
bottom_name=last_name,
top_name=block_name + str(i) + '_Pooling',
pool='AVE',
global_pooling=True)
num_output_temp = num_out / 16
if num_output_temp < 16: num_output_temp = 16
num_output_temp *= 10 # 需修改
network, last_name = write_prototxt.Convolution(
network,
name=block_name + str(i) + '_2',
bottom_name=last_name,
top_name=block_name + str(i) + '_2',
num_output=num_output_temp,
bias_term=True,
pad=0,
kernel_size=1,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.ReLU(
network,
name=block_name + str(i) + "_ReLU" + '2',
bottom_name=block_name + str(i) + '_2',
top_name=block_name + str(i) + '_2')
network, last_name = write_prototxt.Convolution(
network,
name=block_name + str(i) + '_3',
bottom_name=last_name,
top_name=block_name + str(i) + '_3',
num_output=num_out,
bias_term=True,
pad=0,
kernel_size=1,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.Sigmoid(
network,
name=block_name + str(i) + "_Prob" + '3',
bottom_name=block_name + str(i) + '_3',
top_name=block_name + str(i) + '_3')
SE_name = last_name
if i == 1 and (downsampling == True or first_layer == True):
input_name_temp = last_name
if down_method == 'conv':
network, last_name = write_prototxt.Convolution(
network,
name=block_name + str(i) + '_down',
bottom_name=input_name,
top_name=block_name + str(i) + '_down',
num_output=num_out,
bias_term=True,
pad=0,
kernel_size=1,
stride=first_stride,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + str(i) + "_bn" + '_down',
name_scale=block_name + str(i) + "_scale" + '_down',
bottom_name=block_name + str(i) + '_down',
top_name=block_name + str(i) + '_down',
use_global_stats=use_global_stats)
#network,last_name=ReLU(network,name=block_name+str(i)+"_ReLU"+'_down',bottom_name=block_name+str(i)+'_down',top_name=block_name+str(i)+'_down')
else:
network, last_name = write_prototxt.Pooling(
network,
name=block_name + str(i) + "_Pooling" + '_down',
bottom_name=input_name,
top_name=block_name + str(i) + "_Pooling" + '_down',
pool='MAX',
kernel_size=2,
stride=2)
input_name = last_name
# network,last_name=write_prototxt.Eltwise(network,name=block_name+"Eltwise_"+str(i),bottom_name1=input_name,
# bottom_name2=last_name,top_name=block_name+"Eltwise_"+str(i),operation='SUM')
network, last_name = write_prototxt.Axpy(
network,
name=block_name + "Axpy_" + str(i),
bottom_name1=SE_name,
bottom_name2=origin_name,
bottom_name3=input_name,
top_name=block_name + "Axpy_" + str(i))
network, last_name = write_prototxt.ReLU(network,
name=block_name + str(i) +
"ReLU" + '_1',
bottom_name=last_name,
top_name=last_name)
input_name = last_name
return network, last_name
def ResBlock(network,
last_name='',
block_name='conv2_',
block_n=3,
num_out=16,
downsampling=False,
down_method='pooling',
use_global_stats='False'):
input_name = last_name
for i in range(1, block_n + 1, 1):
if i == 1 and downsampling == True:
first_stride = 2
else:
first_stride = 1
network, last_name = write_prototxt.Convolution(
network,
name=block_name + str(i) + '_0',
bottom_name=last_name,
top_name=block_name + str(i) + '_0',
num_output=num_out,
bias_term=True,
pad=1,
kernel_size=3,
stride=first_stride,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + str(i) + "_bn" + '0',
name_scale=block_name + str(i) + "_scale" + '0',
bottom_name=block_name + str(i) + '_0',
top_name=block_name + str(i) + '_0',
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(
network,
name=block_name + str(i) + "_ReLU" + '0',
bottom_name=block_name + str(i) + '_0',
top_name=block_name + str(i) + '_0')
network, last_name = write_prototxt.Convolution(
network,
name=block_name + str(i) + '_1',
bottom_name=last_name,
top_name=block_name + str(i) + '_1',
num_output=num_out,
bias_term=True,
pad=1,
kernel_size=3,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + str(i) + "bn" + '_1',
name_scale=block_name + str(i) + "_scale" + '1',
bottom_name=block_name + str(i) + '_1',
top_name=block_name + str(i) + '_1',
use_global_stats=use_global_stats)
#network,last_name=ReLU(network,name=block_name+str(i)+"ReLU"+'_1',bottom_name=block_name+str(i)+'_1',top_name=block_name+str(i)+'_1')
if i == 1 and downsampling == True:
input_name_temp = last_name
if down_method == 'conv':
network, last_name = write_prototxt.Convolution(
network,
name=block_name + str(i) + '_down',
bottom_name=input_name,
top_name=block_name + str(i) + '_down',
num_output=num_out,
bias_term=True,
pad=0,
kernel_size=1,
stride=first_stride,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + str(i) + "_bn" + '_down',
name_scale=block_name + str(i) + "_scale" + '_down',
bottom_name=block_name + str(i) + '_down',
top_name=block_name + str(i) + '_down',
use_global_stats=use_global_stats)
#network,last_name=ReLU(network,name=block_name+str(i)+"_ReLU"+'_down',bottom_name=block_name+str(i)+'_down',top_name=block_name+str(i)+'_down')
else:
network, last_name = write_prototxt.Pooling(
network,
name=block_name + str(i) + "_Pooling" + '_down',
bottom_name=input_name,
top_name=block_name + str(i) + "_Pooling" + '_down',
pool='MAX',
kernel_size=2,
stride=2)
input_name = input_name_temp
network, last_name = write_prototxt.Eltwise(
network,
name=block_name + "Eltwise_" + str(i),
bottom_name1=input_name,
bottom_name2=last_name,
top_name=block_name + "Eltwise_" + str(i),
operation='SUM')
network, last_name = write_prototxt.ReLU(network,
name=block_name + str(i) +
"ReLU" + '_1',
bottom_name=last_name,
top_name=last_name)
input_name = last_name
return network, last_name
def Net(mode='train', root_path='', batch_size=32):
network = 'name:"ShuffleNet"' + '\n'
if mode == 'train':
network, last_name = write_prototxt.data(network,
name="Data1",
mirror=True,
crop_size=28,
batch_size=batch_size,
backend="LMDB",
shuffle=True,
datasets_path=root_path)
use_global_stats = False
elif mode == 'test':
network, last_name = write_prototxt.data(network,
name="Data1",
mirror=False,
crop_size=28,
batch_size=batch_size,
backend="LMDB",
shuffle=False,
datasets_path=root_path)
use_global_stats = True
network, last_name = write_prototxt.Convolution(network,
name="conv1",
bottom_name=last_name,
top_name='conv1',
num_output=15,
bias_term=False,
pad=1,
kernel_size=3,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn="conv1_bn",
name_scale="conv1__scale",
bottom_name="conv1",
top_name="conv1",
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(network,
name="conv1_ReLU",
bottom_name='conv1',
top_name='conv1')
network, last_name = ShuffleNet_Unit0(network,
last_name=last_name,
block_name='resx1_conv',
block_n=1,
num_out0=24,
num_out1=45,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx2_conv',
block_n=1,
num_out0=30,
num_out1=60,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx3_conv',
block_n=1,
num_out0=30,
num_out1=60,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx4_conv',
block_n=1,
num_out0=30,
num_out1=60,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx5_conv',
block_n=1,
num_out0=30,
num_out1=60,
downsampling=True,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx6_conv',
block_n=1,
num_out0=48,
num_out1=120,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx7_conv',
block_n=1,
num_out0=48,
num_out1=120,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx10_conv',
block_n=1,
num_out0=48,
num_out1=120,
downsampling=True,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx11_conv',
block_n=1,
num_out0=60,
num_out1=240,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx12_conv',
block_n=1,
num_out0=60,
num_out1=240,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = ShuffleNet_Unit(network,
last_name=last_name,
block_name='resx13_conv',
block_n=1,
num_out0=60,
num_out1=240,
downsampling=False,
group=3,
use_global_stats=use_global_stats)
network, last_name = write_prototxt.Pooling(network,
name="Pooling1",
bottom_name=last_name,
top_name='Pooling1',
pool='AVE',
global_pooling=True)
network, last_name = write_prototxt.InnerProduct(network,
name="fc1",
bottom_name=last_name,
top_name='fc1',
num_output=10,
weight_type='xavier',
bias_type='constant')
if mode == 'train':
network, last_name = write_prototxt.SoftmaxWithLoss(
network,
name="Softmax1",
bottom_name1='fc1',
bottom_name2='Data2',
top_name='Softmax1')
if mode == 'test':
network, last_name = write_prototxt.Accuracy(network,
name="prob",
bottom_name1='fc1',
bottom_name2='Data2',
top_name='prob')
#
#
print network
return network
def ShuffleNet_Unit(network,
last_name='',
block_name='conv2_',
block_n=3,
num_out0=16,
num_out1=16,
downsampling=False,
group=3,
down_method='pooling',
use_global_stats='False'):
input_name = last_name
for i in range(1, block_n + 1, 1):
if i == 1 and downsampling == True:
first_stride = 2
else:
first_stride = 1
network, last_name = write_prototxt.Convolution(network,
name=block_name + '1',
bottom_name=last_name,
top_name=block_name +
'1',
num_output=num_out0,
bias_term=False,
pad=0,
kernel_size=1,
stride=1,
weight_type='msra',
bias_type='constant',
group=group)
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + '1' + "_bn",
name_scale=block_name + '1' + "_scale",
bottom_name=block_name + '1',
top_name=block_name + '1',
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(network,
name=block_name + '1' +
"_ReLU",
bottom_name=block_name + '1',
top_name=block_name + '1')
network, last_name = write_prototxt.ShuffleChannel(
network,
name=block_name + "_shuffle",
bottom_name=last_name,
top_name=block_name + "_shuffle",
group=group)
#ConvolutionDepthwise
network, last_name = write_prototxt.ConvolutionDepthwise(
network,
name=block_name + '2',
bottom_name=last_name,
top_name=block_name + '2',
num_output=num_out0,
bias_term=False,
pad=1,
kernel_size=3,
stride=first_stride,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + '2' + '_bn',
name_scale=block_name + '2' + "_scale",
bottom_name=block_name + '2',
top_name=block_name + '2',
use_global_stats=use_global_stats)
#network,last_name=write_prototxt.ReLU(network,name=block_name+'2'+"_ReLU",bottom_name=block_name+'2',top_name=block_name+'2')
network, last_name = write_prototxt.Convolution(network,
name=block_name + '3',
bottom_name=last_name,
top_name=block_name +
'3',
num_output=num_out1,
bias_term=False,
pad=0,
kernel_size=1,
stride=1,
weight_type='msra',
bias_type='constant',
group=group)
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + '3' + "_bn",
name_scale=block_name + '3' + "_scale",
bottom_name=block_name + '3',
top_name=block_name + '3',
use_global_stats=use_global_stats)
#network,last_name=write_prototxt.ReLU(network,name=block_name+str(i)+'0'+"ReLU/dw",bottom_name=block_name+'0'+'/sep',top_name=block_name+'0'+'/sep')
if (downsampling == True):
network, input_name = write_prototxt.Pooling(
network,
name=block_name + "_match",
bottom_name=input_name,
top_name=block_name + "_match",
pool='AVE',
kernel_size=3,
stride=2)
network, last_name = write_prototxt.Concat(
network,
name=block_name + '_concat',
bottom_name1=input_name,
bottom_name2=last_name,
top_name=block_name + '_concat')
network, last_name = write_prototxt.ReLU(
network,
name=block_name + '_concat_ReLU',
bottom_name=block_name + '_concat',
top_name=block_name + '_concat')
else:
network, last_name = write_prototxt.Eltwise(
network,
name=block_name + '_elewise',
bottom_name1=input_name,
bottom_name2=last_name,
top_name=block_name + '_elewise',
operation='SUM')
network, last_name = write_prototxt.ReLU(
network,
name=block_name + 'elewise_ReLU',
bottom_name=block_name + '_elewise',
top_name=block_name + '_elewise')
return network, last_name
def Net(mode='train', root_path='', batch_size=32):
network = 'name:"MobileNetV2"' + '\n'
if mode == 'train':
network, last_name = write_prototxt.data(network,
name="Data1",
mirror=True,
crop_size=28,
batch_size=batch_size,
backend="LMDB",
shuffle=True,
datasets_path=root_path)
use_global_stats = False
elif mode == 'test':
network, last_name = write_prototxt.data(network,
name="Data1",
mirror=False,
crop_size=28,
batch_size=batch_size,
backend="LMDB",
shuffle=False,
datasets_path=root_path)
use_global_stats = True
network, last_name = write_prototxt.Convolution(network,
name="conv1",
bottom_name=last_name,
top_name='conv1',
num_output=32,
bias_term=False,
pad=1,
kernel_size=3,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn="conv1_bn",
name_scale="conv1__scale",
bottom_name="conv1",
top_name="conv1",
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(network,
name="conv1_ReLU",
bottom_name='conv1',
top_name='conv1')
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv2_',
block_n=1,
num_out0=32,
num_out1=32,
num_out2=16,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv3_',
block_n=1,
num_out0=16,
num_out1=96,
num_out2=24,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv4_',
block_n=1,
num_out0=24,
num_out1=144,
num_out2=24,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv5_',
block_n=1,
num_out0=24,
num_out1=144,
num_out2=32,
downsampling=True,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv6_',
block_n=1,
num_out0=32,
num_out1=144,
num_out2=32,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv7_',
block_n=1,
num_out0=32,
num_out1=192,
num_out2=32,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv8_',
block_n=1,
num_out0=32,
num_out1=192,
num_out2=32,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv9_',
block_n=1,
num_out0=32,
num_out1=192,
num_out2=48,
downsampling=True,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv10_',
block_n=1,
num_out0=48,
num_out1=384,
num_out2=48,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv11_',
block_n=1,
num_out0=48,
num_out1=384,
num_out2=48,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = MobileV2_Unit(network,
last_name=last_name,
block_name='conv12_',
block_n=1,
num_out0=48,
num_out1=576,
num_out2=64,
downsampling=False,
use_global_stats=use_global_stats)
network, last_name = write_prototxt.Pooling(network,
name="Pooling1",
bottom_name=last_name,
top_name='Pooling1',
pool='AVE',
global_pooling=True)
network, last_name = write_prototxt.InnerProduct(network,
name="fc1",
bottom_name=last_name,
top_name='fc1',
num_output=10,
weight_type='xavier',
bias_type='constant')
if mode == 'train':
network, last_name = write_prototxt.SoftmaxWithLoss(
network,
name="Softmax1",
bottom_name1='fc1',
bottom_name2='Data2',
top_name='Softmax1')
if mode == 'test':
network, last_name = write_prototxt.Accuracy(network,
name="prob",
bottom_name1='fc1',
bottom_name2='Data2',
top_name='prob')
#
#
print network
return network
def MobileV2_Unit(network,
last_name='',
block_name='conv2_',
block_n=3,
num_out0=16,
num_out1=16,
num_out2=16,
downsampling=False,
down_method='pooling',
use_global_stats='False'):
input_name = last_name
for i in range(1, block_n + 1, 1):
if i == 1 and downsampling == True:
first_stride = 2
else:
first_stride = 1
network, last_name = write_prototxt.Convolution(
network,
name=block_name + '0' + '/expand',
bottom_name=last_name,
top_name=block_name + '0' + '/expand',
num_output=num_out1,
bias_term=False,
pad=0,
kernel_size=1,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + '0' + "/bn/expand",
name_scale=block_name + '0' + "/scale/expand",
bottom_name=block_name + '0' + '/expand',
top_name=block_name + '0' + '/expand',
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(
network,
name=block_name + str(i) + '0' + "ReLU/dw",
bottom_name=block_name + '0' + '/expand',
top_name=block_name + '0' + '/expand')
#ConvolutionDepthwise
network, last_name = write_prototxt.ConvolutionDepthwise(
network,
name=block_name + '0' + '/dw',
bottom_name=last_name,
top_name=block_name + '0' + '/dw',
num_output=num_out1,
bias_term=False,
pad=1,
kernel_size=3,
stride=first_stride,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + '0' + '/bn/dw',
name_scale=block_name + '0' + "/scale/dw",
bottom_name=block_name + '0' + '/dw',
top_name=block_name + '0' + '/dw',
use_global_stats=use_global_stats)
network, last_name = write_prototxt.ReLU(
network,
name=block_name + str(i) + '0' + "ReLU/dw",
bottom_name=block_name + '0' + '/dw',
top_name=block_name + '0' + '/dw')
network, last_name = write_prototxt.Convolution(
network,
name=block_name + '0' + '/linear',
bottom_name=last_name,
top_name=block_name + '0' + '/linear',
num_output=num_out2,
bias_term=False,
pad=0,
kernel_size=1,
stride=1,
weight_type='msra',
bias_type='constant')
network, last_name = write_prototxt.BatchNorm(
network,
name_bn=block_name + '0' + "/bn/linear",
name_scale=block_name + '0' + "/scale/linear",
bottom_name=block_name + '0' + '/linear',
top_name=block_name + '0' + '/linear',
use_global_stats=use_global_stats)
#network,last_name=write_prototxt.ReLU(network,name=block_name+str(i)+'0'+"ReLU/dw",bottom_name=block_name+'0'+'/sep',top_name=block_name+'0'+'/sep')
if num_out0 == num_out2:
network, last_name = write_prototxt.Eltwise(
network,
name="block_" + block_name + '0',
bottom_name1=input_name,
bottom_name2=last_name,
top_name="block_" + block_name,
operation='')
return network, last_name