Transforming network definition from mxnet.json to caffe.prototxt
'''
import sys
import json
import sys
sys.path.insert(0, '/home/huangxuankun/caffe/caffe/python')
import caffe
from caffe import layers as L
from caffe import params as P
# model_def: Inception-7-symbol.json
model_def = sys.argv[1]
N = caffe.NetSpec()
N.data, N.label = L.Data(batch_size=64,
backend=P.Data.LMDB, source='/home/tmp_data_dir/huangxuankun/batch/imagenet2015/CLS/val_lmdb',
transform_param=dict(scale=1./255, crop_size=299), include=dict(phase=True), ntop=2)
def gen_conv(name, bottom, kernel_size, num_output, stride, pad, group=1):
if kernel_size[0] == kernel_size[1]:
exec(name+'=L.Convolution('+bottom+', kernel_size='+str(kernel_size[0])+', num_output='+str(num_output)+', stride='+str(stride)+', pad='+str(pad[0])+', group='+str(group)+', bias_term=False, weight_filler=dict(type="gaussian", std=0.01))')
else:
exec(name+'=L.Convolution('+bottom+', kernel_h='+str(kernel_size[0])+', kernel_w='+str(kernel_size[1])+', num_output='+str(num_output)+', stride='+str(stride)+', pad_h='+str(pad[0])+', pad_w='+str(pad[1])+', group='+str(group)+', bias_term=False, weight_filler=dict(type="gaussian", std=0.01))')
def gen_pooling(name, bottom, kernel_size, stride, pad, pool_type):
if pool_type == 'max':
exec(name+'=L.Pooling('+bottom+', kernel_size='+str(kernel_size[0])+', stride='+str(stride)+', pad='+str(pad[0])+', pool=P.Pooling.MAX)')
elif pool_type == 'avg':
exec(name+'=L.Pooling('+bottom+', kernel_size='+str(kernel_size[0])+', stride='+str(stride)+', pad='+str(pad[0])+', pool=P.Pooling.AVE)')
def gen_fc(name, bottom, num_output):
def make_resnet(training_data='cifar10_train',
test_data='cifar10_test',
mean_file='mean.binaryproto',
num_res_in_stage=3):
num_feature_maps = np.array([16, 32, 64]) # feature map size: [32, 16, 8]
n = caffe.NetSpec()
# make training data layer
n.data, n.label = L.Data(source=training_data,
backend=P.Data.LMDB,
batch_size=128,
ntop=2,
transform_param=dict(crop_size=32,
mean_file=mean_file,
mirror=True),
image_data_param=dict(shuffle=True),
include=dict(phase=0))
# make test data layer
n.test_data, n.test_label = L.Data(source=test_data,
backend=P.Data.LMDB,
batch_size=100,
ntop=2,
transform_param=dict(
crop_size=32,
mean_file=mean_file,
mirror=False),
include=dict(phase=1))
# conv1 should accept both training and test data layers. But this is inconvenient to code in pycaffe.
# You have to write two conv layers for them. To deal with this, I temporarily ignore the test data layer
# and let conv1 accept the output of training data layer. Then, after making the whole prototxt, I postprocess
# the top name of the two data layers, renaming their names to the same.
n.conv1, n.bn_conv1_train, n.bn_conv1_test, n.scale_conv1, n.relu_conv1 = \
conv_bn_scale_relu(n.data, kernel_size=3, num_out=16, stride=1, pad=1, params=conv_params)
# set up a checkpoint so as to know where we get.
checkpoint = 'n.relu_conv1'
# start making blocks.
# num_feature_maps: the number of feature maps for each stage. Default is [16,32,64],
# suggesting the network has three stages.
# num_res_in_stage: a parameter from the original paper, telling us how many blocks there are in
# each stage.
for num_map in num_feature_maps:
num_map = int(num_map)
for res in list(range(num_res_in_stage)):
# stage name
stage = 'map' + str(num_map) + '_' + str(res + 1) + '_'
# use the projecting block when downsample the feature map
if np.where(num_feature_maps == num_map)[0] >= 1 and res == 0:
make_res = 'n.' + stage + 'conv_proj,' + \
'n.' + stage + 'bn_proj_train,' + \
'n.' + stage + 'bn_proj_test,' + \
'n.' + stage + 'scale_proj,' + \
'n.' + stage + 'conv_a,' + \
'n.' + stage + 'bn_a_train, ' + \
'n.' + stage + 'bn_a_test, ' + \
'n.' + stage + 'scale_a, ' + \
'n.' + stage + 'relu_a, ' + \
'n.' + stage + 'conv_b, ' + \
'n.' + stage + 'bn_b_train, ' + \
'n.' + stage + 'bn_b_test, ' + \
'n.' + stage + 'scale_b, ' + \
'n.' + stage + 'eltsum, ' + \
'n.' + stage + 'relu_after_sum' + \
' = project_residual(' + checkpoint + ', num_out=num_map, stride=2, pad=1)'
exec(make_res)
checkpoint = 'n.' + stage + 'relu_after_sum' # where we get
continue
# most blocks have this shape
make_res = 'n.' + stage + 'conv_a, ' + \
'n.' + stage + 'bn_a_train, ' + \
'n.' + stage + 'bn_a_test, ' + \
'n.' + stage + 'scale_a, ' + \
'n.' + stage + 'relu_a, ' + \
'n.' + stage + 'conv_b, ' + \
'n.' + stage + 'bn_b_train, ' + \
'n.' + stage + 'bn_b_test, ' + \
'n.' + stage + 'scale_b, ' + \
'n.' + stage + 'eltsum, ' + \
'n.' + stage + 'relu_after_sum' + \
' = identity_residual(' + checkpoint + ', num_out=num_map, stride=1, pad=1)'
exec(make_res)
checkpoint = 'n.' + stage + 'relu_after_sum' # where we get
# add the pooling layer
exec('n.pool_global = L.Pooling(' + checkpoint +
', pool=P.Pooling.AVE, global_pooling=True)')
n.score = L.InnerProduct(
n.pool_global,
num_output=10,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
n.loss = L.SoftmaxWithLoss(n.score, n.label)
n.acc = L.Accuracy(n.score, n.label)
return n.to_proto()
def create_neural_net(input_file, batch_size=50):
net = caffe.NetSpec()
net.data, net.label = L.Data(batch_size=batch_size,
source=input_file,
backend=caffe.params.Data.LMDB,
ntop=2,
include=dict(phase=caffe.TEST),
name='juniward04')
## pre-process
net.conv1 = L.Convolution(net.data,
num_output=16,
kernel_size=4,
stride=1,
pad=1,
weight_filler=dict(type='dct4'),
param=[{
'lr_mult': 0,
'decay_mult': 0
}],
bias_term=False)
TRUNCABS = caffe_pb2.QuantTruncAbsParameter.TRUNCABS
net.quanttruncabs = L.QuantTruncAbs(net.conv1,
process=TRUNCABS,
threshold=8,
in_place=True)
## block 1
[
net.conv1_proj, net.bn2, net.scale2, net.conv512_1, net.bn2_1,
net.scale2_1, net.relu512_1, net.conv512_to_256, net.bn2_2,
net.scale2_2, net.res512_to_256, net.relu512_to_256
] = add_downsampling_block(net.quanttruncabs, 12)
## block 2
[
net.conv256_1, net.bn2_3, net.scale2_3, net.relu256_1, net.conv256_2,
net.bn2_4, net.scale2_4, net.res256_2, net.relu256_2
] = add_skip_block(net.res512_to_256, 24)
## block 2_1
[
net.conv256_4, net.bn3_1, net.scale3_1, net.relu256_4, net.conv256_5,
net.bn3_2, net.scale3_2, net.res256_5, net.relu256_5
] = add_skip_block(net.res256_2, 24)
## block 2_2
[
net.conv256_6, net.bn4_1, net.scale4_1, net.relu256_6, net.conv256_7,
net.bn4_2, net.scale4_2, net.res256_7, net.relu256_7
] = add_skip_block(net.res256_5, 24)
## block 3
[
net.res256_2_proj, net.bn2_5, net.scale2_5, net.conv256_3, net.bn2_6,
net.scale2_6, net.relu256_3, net.conv256_to_128, net.bn2_7,
net.scale2_7, net.res256_to_128, net.relu256_to_128
] = add_downsampling_block(net.res256_7, 24)
## ## block 4
## [net.conv128_1, net.bn2_8, net.scale2_8, net.relu128_1, net.conv128_2, net.bn2_9,
## net.scale2_9, net.res128_2, net.relu128_2] = add_skip_block(net.res256_to_128, 48)
## block 4_1
[
net.conv128_4, net.bn3_3, net.scale3_3, net.relu128_4, net.conv128_5,
net.bn3_4, net.scale3_4, net.res128_5, net.relu128_5
] = add_skip_block(net.res256_to_128, 48)
## block 4_2
[
net.conv128_6, net.bn4_3, net.scale4_3, net.relu128_6, net.conv128_7,
net.bn4_4, net.scale4_4, net.res128_7, net.relu128_7
] = add_skip_block(net.res128_5, 48)
## block 5
[
net.res128_2_proj, net.bn2_10, net.scale2_10, net.conv128_3,
net.bn2_11, net.scale2_11, net.relu128_3, net.conv128_to_64,
net.bn2_12, net.scale2_12, net.res128_to_64, net.relu128_to_64
] = add_downsampling_block(net.res128_7, 48)
## block 6
[
net.conv64_1, net.bn2_13, net.scale2_13, net.relu64_1, net.conv64_2,
net.bn2_14, net.scale2_14, net.res64_2, net.relu64_2
] = add_skip_block(net.res128_to_64, 96)
## block 6_1
[
net.conv64_4, net.bn3_5, net.scale3_5, net.relu64_4, net.conv64_5,
net.bn3_6, net.scale3_6, net.res64_5, net.relu64_5
] = add_skip_block(net.res64_2, 96)
## block 6_2
[
net.conv64_6, net.bn4_5, net.scale4_5, net.relu64_6, net.conv64_7,
net.bn4_6, net.scale4_6, net.res64_7, net.relu64_7
] = add_skip_block(net.res64_5, 96)
## block 7
[
net.res64_2_proj, net.bn2_15, net.scale2_15, net.conv64_3, net.bn2_16,
net.scale2_16, net.relu64_3, net.conv64_to_32, net.bn2_17,
net.scale2_17, net.res64_to_32, net.relu64_to_32
] = add_downsampling_block(net.res64_7, 96)
## block 8
[
net.conv32_1, net.bn2_18, net.scale2_18, net.relu32_1, net.conv32_2,
net.bn2_19, net.scale2_19, net.res32_2, net.relu32_2
] = add_skip_block(net.res64_to_32, 192)
## block 8_1
[
net.conv32_4, net.bn3_7, net.scale3_7, net.relu32_4, net.conv32_5,
net.bn3_8, net.scale3_8, net.res32_5, net.relu32_5
] = add_skip_block(net.res32_2, 192)
## block 8_2
[
net.conv32_6, net.bn4_7, net.scale4_7, net.relu32_6, net.conv32_7,
net.bn4_8, net.scale4_8, net.res32_7, net.relu32_7
] = add_skip_block(net.res32_5, 192)
## block 9
[
net.res32_2_proj, net.bn2_20, net.scale2_20, net.conv32_3, net.bn2_21,
net.scale2_21, net.relu32_3, net.conv32_to_16, net.bn2_22,
net.scale2_22, net.res32_to_16, net.relu32_to_16
] = add_downsampling_block(net.res32_7, 192)
## block 10
[
net.conv16_1, net.bn2_23, net.scale2_23, net.relu16_1, net.conv16_2,
net.bn2_24, net.scale2_24, net.res16_2, net.relu16_2
] = add_skip_block(net.res32_to_16, 384)
## block 10_1
[
net.conv16_3, net.bn3_9, net.scale3_9, net.relu16_3, net.conv16_4,
net.bn3_10, net.scale3_10, net.res16_4, net.relu16_4
] = add_skip_block(net.res16_2, 384)
## block 10_2
[
net.conv16_5, net.bn4_9, net.scale4_9, net.relu16_5, net.conv16_6,
net.bn4_10, net.scale4_10, net.res16_6, net.relu16_6
] = add_skip_block(net.res16_4, 384)
## global pool
AVE = caffe_pb2.PoolingParameter.AVE
net.global_pool = L.Pooling(net.res16_6, pool=AVE, kernel_size=8, stride=1)
## full connecting
net.fc = L.InnerProduct(net.global_pool,
param=[{
'lr_mult': 1
}, {
'lr_mult': 2
}],
num_output=2,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
## accuracy
net.accuracy = L.Accuracy(net.fc,
net.label,
include=dict(phase=caffe.TEST))
## loss
net.loss = L.SoftmaxWithLoss(net.fc, net.label)
return net.to_proto()
def resnet_layers_proto(self,
batch_size,
phase='TRAIN',
stages=(3, 4, 6, 3)):
"""
:param batch_size: the batch_size of train and test phase
:param phase: TRAIN or TEST
:param stages: the num of layers = 2 + 3*sum(stages), layers would better be chosen from [50, 101, 152]
{every stage is composed of 1 residual_branch_shortcut module and stage[i]-1 residual_branch
modules, each module consists of 3 conv layers}
(3, 4, 6, 3) for 50 layers; (3, 4, 23, 3) for 101 layers; (3, 8, 36, 3) for 152 layers
"""
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(
crop_size=224,
mean_value=[104, 117, 123],
mirror=mirror))
n.conv1, n.conv1_bn, n.conv1_scale, n.conv1_relu = \
conv_bn_scale_relu(n.data, num_output=64, kernel_size=7, stride=2, pad=3) # 64x112x112
n.pool1 = L.Pooling(n.conv1,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX) # 64x56x56
for num in xrange(len(stages)): # num = 0, 1, 2, 3
for i in xrange(stages[num]):
if i == 0:
stage_string = branch_shortcut_string
bottom_string = [
'n.pool1',
'n.res2b%s' % str(stages[0] - 1),
'n.res3b%s' % str(stages[1] - 1),
'n.res4b%s' % str(stages[2] - 1)
][num]
else:
stage_string = branch_string
if i == 1:
bottom_string = 'n.res%sa' % str(num + 2)
else:
bottom_string = 'n.res%sb%s' % (str(num + 2),
str(i - 1))
exec(
stage_string.replace('(stage)', str(num + 2)).replace(
'(bottom)', bottom_string).replace(
'(num)', str(2**num * 64)).replace(
'(order)',
str(i)).replace('(stride)',
str(int(num > 0) + 1)))
exec 'n.pool5 = L.Pooling((bottom), pool=P.Pooling.AVE, global_pooling=True)'.\
replace('(bottom)', 'n.res5b%s' % str(stages[3] - 1))
n.classifier = L.InnerProduct(n.pool5,
num_output=self.classifier_num,
param=[
dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)
],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant',
value=0))
n.loss = L.SoftmaxWithLoss(n.classifier, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy_top1 = L.Accuracy(n.classifier,
n.label,
include=dict(phase=1))
n.accuracy_top5 = L.Accuracy(n.classifier,
n.label,
include=dict(phase=1),
accuracy_param=dict(top_k=5))
return n.to_proto()
def alexnet_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(
crop_size=227,
mean_value=[104, 117, 123],
mirror=mirror))
n.conv1, n.relu1 = conv_relu(n.data,
num_output=96,
kernel_size=11,
stride=4) # 96x55x55
n.norm1 = L.LRN(n.conv1, local_size=5, alpha=0.0001, beta=0.75)
n.pool1 = L.Pooling(n.norm1,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX) # 96x27x27
n.conv2, n.relu2 = conv_relu(n.pool1,
num_output=256,
kernel_size=5,
pad=2,
group=2) # 256x27x27
n.norm2 = L.LRN(n.conv2, local_size=5, alpha=0.0001, beta=0.75)
n.pool2 = L.Pooling(n.norm2,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX) # 256x13x13
n.conv3, n.relu3 = conv_relu(n.pool2,
num_output=384,
kernel_size=3,
pad=1) # 384x13x13
n.conv4, n.relu4 = conv_relu(n.conv3,
num_output=384,
kernel_size=3,
pad=1,
group=2) # 384x13x13
n.conv5, n.relu5 = conv_relu(n.conv4,
num_output=256,
kernel_size=3,
pad=1,
group=2) # 256x13x13
n.pool5 = L.Pooling(n.conv5,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX) # 256x6x16
n.fc6, n.relu6, n.drop6 = fc_relu_drop(n.pool5,
num_output=4096) # 4096x1x1
n.fc7, n.relu7, n.drop7 = fc_relu_drop(n.fc6,
num_output=4096) # 4096x1x1
n.fc8 = L.InnerProduct(n.fc7,
num_output=self.classifier_num,
param=[
dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
n.loss = L.SoftmaxWithLoss(n.fc8, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy_top1, n.accuracy_top5 = accuracy_top1_top5(
n.pool4, n.label)
return n.to_proto()
def compile_time_operation(self, learning_option, cluster):
option = learning_option.get("option", self.option)
file_format = learning_option.get("file_format", self.file_format)
data_path = learning_option.get("data_path", self.data_path)
label_path = learning_option.get("label_path", self.label_path)
batch_size = learning_option.get("batch_size", self.batch_size)
iteration = learning_option.get("iteration", self.iteration)
image_size = self.image_size
output_shape = self.output_shape
# for shmcaffe
#learning_option["move_rate"] = learning_option.get("move_rate", 0.2)
#learning_option["tau"] = learning_option.get("tau", 1)
# Phase checkpoint setting, PHASE: 0 for trian, 1 for test
isTrainTest = 0
if option.lower() == "test":
temp_include = dict(phase=caffe.TEST)
data_path = learning_option.get("test_data_path", data_path)
test_label_path = learning_option.get("test_label_path", label_path)
batch_size = learning_option.get("test_batch_size", batch_size)
elif option.lower() == "datasets":
temp_include = dict(phase=caffe.TRAIN)
elif option.lower() == "train_test":
temp_include = dict(phase=caffe.TRAIN)
isTrainTest = 1
else:
temp_include = dict(phase=caffe.TRAIN)
# DB Data
if file_format.lower() in ["lmdb", "leveldb"]:
# Backend checkpoint setting, default value 0 (leveldb) for backend
# Data layer setting
image, label = L.Data(name=self.name, source=data_path,
batch_size=batch_size, backend=(0 if file_format.lower()=="leveldb" else 1), include=temp_include, ntop=2)
if isTrainTest == 1:
data_path = learning_option.get("test_data_path", data_path)
batch_size = learning_option.get("test_batch_size", batch_size)
temp_image, temp_label = L.Data(name=self.name, source=data_path,
batch_size=batch_size,
backend=(0 if file_format.lower() == "leveldb" else 1),
include=dict(phase=caffe.TEST), ntop=2)
setattr(tempNet, str(self.name) + '.image', temp_image)
setattr(tempNet, str(self.name) + '.label', temp_label)
# Image Data
# TODO: HDF5 와 같은 형식을 또 다른 개별 종륭의 layer 사용 가능하나 현재 raw image 파일 형식만 들어온다고 가정
else :
# Read and parse the source directory
''' for uninfo -twkim
with open(data_path+'/'+label_path, 'r') as f:
lines = f.readlines()
new_lines = []
for line in lines:
new_lines.append('/'+line.split()[0]+'.'+file_format + ' ' + line.split()[1]+'\n')
with open(data_path+'/'+label_path.split('.')[0]+'_caffelist.txt', 'w') as f:
f.writelines(new_lines)
f.close()
'''
# Image Data layer setting
image, label = L.ImageData(name=self.name,
source=data_path + '/' + label_path.split('.')[0] + '_caffelist.txt',
batch_size=batch_size, include=temp_include, ntop=2, root_folder=data_path,
new_height=image_size[1], new_width=image_size[0])
if isTrainTest == 1:
data_path = learning_option.get("test_data_path", data_path)
batch_size = learning_option.get("test_batch_size", batch_size)
label_path = learning_option.get("test_label_path", label_path)
# Read and parse the source directory
''' for uninfo - twkim
with open(data_path + '/' + label_path, 'r') as f:
lines = f.readlines()
new_lines = []
for line in lines:
new_lines.append('/' + line.split()[0] + '.' + file_format + ' ' + line.split()[1] + '\n')
with open(data_path + '/' + label_path.split('.')[0] + '_caffelist.txt', 'w') as f:
f.writelines(new_lines)
f.close()
'''
# Test image data layer setting
temp_image, temp_label = L.ImageData(name=self.name,
source=data_path + '/' + label_path.split('.')[0] + '_caffelist.txt',
batch_size=batch_size, include=dict(phase=caffe.TEST), ntop=2,
root_folder=data_path, new_height=image_size[1],
new_width=image_size[0])
setattr(tempNet, str(self.name) + '.image', temp_image)
setattr(tempNet, str(self.name) + '.label', temp_label)
# Record the layer output information
self.set_output('image', image)
self.set_output('label', label)
self.set_dimension('image', image_size)
try:
if isTrainTest != 1:
del learning_option['option']
del learning_option['file_format']
del learning_option['data_path']
del learning_option['label_path']
del learning_option['batch_size']
del learning_option['iteration']
learning_option['max_iter'] = iteration
except KeyError:
pass
try:
del learning_option['test_data_path']
del learning_option['test_label_path']
del learning_option['test_batch_size']
learning_option['test_iter'] = learning_option.get("test_iteration", 100)
del learning_option['test_iteration']
except KeyError:
pass
def resnet_layers_proto(self,
batch_size,
phase='TRAIN',
stages=(3, 4, 6, 3)):
"""
(3, 4, 6, 3) for 50 layers; (3, 4, 23, 3) for 101 layers; (3, 8, 36, 3) for 152 layers
"""
global use_global_stats
n = caffe.NetSpec()
if phase == 'TRAIN':
n.data, n.label = L.Data(source=self.train_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
include=dict(phase=0),
transform_param=dict(
crop_size=224,
mean_value=[104, 117, 123],
mirror=True))
use_global_stats = False
else:
n.data, n.label = L.Data(source=self.test_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
include=dict(phase=1),
transform_param=dict(
crop_size=224,
mean_value=[104, 117, 123],
mirror=False))
use_global_stats = True
n.conv1, n.conv1_bn, n.conv1_scale, n.conv1_relu = conv_bn_scale_relu(
n.data,
num_output=64,
kernel_size=7,
stride=2,
pad=3,
bias_term=True)
n.pool1 = L.Pooling(n.conv1,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX) # 64x56x56
for num in xrange(len(stages)): # num = 0, 1, 2, 3
for i in xrange(stages[num]):
if i == 0:
stage_string = branch_shortcut_string
bottom_string = [
'n.pool1',
'n.res2b%s' % str(stages[0] - 1),
'n.res3b%s' % str(stages[1] - 1),
'n.res4b%s' % str(stages[2] - 1)
][num]
else:
stage_string = branch_string
if i == 1:
bottom_string = 'n.res%sa' % str(num + 2)
else:
bottom_string = 'n.res%sb%s' % (str(num + 2),
str(i - 1))
exec(
stage_string.replace('(stage)', str(num + 2)).replace(
'(bottom)', bottom_string).replace(
'(num)', str(2**num * 64)).replace(
'(order)',
str(i)).replace('(stride)',
str(int(num > 0) + 1)))
exec 'n.pool5 = L.Pooling((bottom), pool=P.Pooling.AVE, global_pooling=True)'.replace(
'(bottom)', 'n.res5b%s' % str(stages[3] - 1))
n.classifier = L.InnerProduct(n.pool5, num_output=self.classifier_num)
n.loss = L.SoftmaxWithLoss(n.classifier, n.label)
if phase == 'TEST':
n.accuracy_top1 = L.Accuracy(n.classifier,
n.label,
include=dict(phase=1))
n.accuracy_top5 = L.Accuracy(n.classifier,
n.label,
include=dict(phase=1),
accuracy_param=dict(top_k=5))
return n.to_proto()
print("SVD NET:")
for layer_name, param in netSVD.params.items():
# 0 is weight, 1 is biases
print(layer_name, param[0].data.shape)
print(type(net.params))
print(net.params.keys())
print("layer ip1:")
print("WEIGHT:")
print(net.params["ip1"][0].data.shape)
print("BIASES:")
print(net.params["ip1"][1].data.shape)
data, label = L.Data(source=test_db,
backend=P.Data.LMDB,
batch_size=100,
ntop=2,
mean_file=mean_proto)
model_file = "model/net_SVD%d.caffemodel" % SVD_R
if SVD_R > 0:
# SVD
print("SVD %d" % SVD_R)
u, sigma, vt = la.svd(net.params["ip1"][0].data)
print("Sigma: ", sigma)
if SVD_R > len(sigma):
print("SVD_R is too large :-(")
sys.exit()
U = np.matrix(u[:, :SVD_R])
S = np.matrix(np.diag(sigma[:SVD_R]))
def lenet(lmdb, batch_size):
# our version of LeNet: a series of linear and simple nonlinear transformations
n = caffe.NetSpec()
n.data, n.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb,
transform_param=dict(crop_size=227,
mean_value=[104, 117, 123]),
ntop=2)
n.conv1 = L.Convolution(n.data,
kernel_size=3,
num_output=64,
stride=2,
weight_filler=dict(type='xavier'))
n.relu1 = L.ReLU(n.conv1, in_place=True)
n.pool1 = L.Pooling(n.relu1, kernel_size=3, stride=2, pool=P.Pooling.MAX)
#n.fire2_squeeze1x1 = L.Convolution(n.pool1, kernel_size=1, num_output=16,weight_filler=dict(type='xavier'))
#n.fire2_relu_squeeze1x1 = L.ReLU(n.fire2_squeeze1x1,in_place=True)
#n.fire2_expand1x1 = L.Convolution(n.fire2_relu_squeeze1x1, kernel_size=1, num_output=64,weight_filler=dict(type='xavier'))
#n.fire2_relu_expand1x1 = L.ReLU(n.fire2_expand1x1,in_place=True)
#n.fire2_expand3x3 = L.Convolution(n.fire2_relu_squeeze1x1, kernel_size=3, pad=1, num_output=64,weight_filler=dict(type='xavier'))
#n.fire2_relu_expand3x3 = L.ReLU(n.fire2_expand3x3,in_place=True)
#n.fire2_concat = L.Concat(n.fire2_relu_expand1x1,n.fire2_relu_expand3x3)
suffix = ""
fire_num = 2
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.pool1, kernel_size=1, num_output=16,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
fire_num = 3
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire2_concat, kernel_size=1, num_output=16,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
n.pool3 = L.Pooling(n.fire3_concat,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX)
#n.fire3_squeeze1x1 = L.Convolution(n.fire2_concat, kernel_size=1, num_output=16,weight_filler=dict(type='xavier'))
#n.fire3_relu_squeeze1x1 = L.ReLU(n.fire3_squeeze1x1,in_place=True)
#n.fire3_expand1x1 = L.Convolution(n.fire3_relu_squeeze1x1, kernel_size=1, num_output=64,weight_filler=dict(type='xavier'))
#n.fire3_relu_expand1x1 = L.ReLU(n.fire3_expand1x1,in_place=True)
#n.fire3_expand3x3 = L.Convolution(n.fire3_relu_squeeze1x1, kernel_size=3, pad=1, num_output=64,weight_filler=dict(type='xavier'))
#n.fire3_relu_expand3x3 = L.ReLU(n.fire3_expand3x3,in_place=True)
#n.fire3_concat = L.Concat(n.fire3_relu_expand1x1,n.fire3_relu_expand3x3)
#n.pool3 = L.Pooling(n.fire3_concat, kernel_size=3, stride=2, pool=P.Pooling.MAX)
suffix = '_cluster' # root node branch
fire_num = 4
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.pool3, kernel_size=1, num_output=8,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=32,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=32,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
# n.pool4_cluster = L.Pooling(n.fire4_concat, kernel_size=3, stride=2, pool=P.Pooling.MAX)
fire_num = 5
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire4_concat_cluster, kernel_size=1, num_output=8,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=32,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=32,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
exec 'n.pool{0}{1} = L.Pooling(n.fire{0}_concat{1}, kernel_size=3, stride=2, pool=P.Pooling.MAX)'.format(
fire_num, suffix)
fire_num = 6
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.pool5_cluster, kernel_size=1, num_output=12,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=48,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=48,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
fire_num = 7
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire6_concat_cluster, kernel_size=1, num_output=12,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=48,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=48,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
fire_num = 8
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire7_concat_cluster, kernel_size=1, num_output=16,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
fire_num = 9
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire8_concat_cluster, kernel_size=1, num_output=16,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
exec 'n.drop{0}{1} = L.Dropout(n.fire{0}_concat{1}, in_place=True, dropout_ratio=0.5)'.format(
fire_num, suffix)
fire_num = 10
exec 'n.conv{0}{1} = L.Convolution(n.fire9_concat{1}, kernel_size=1, num_output=4,weight_filler=dict(type="gaussion",mean=0.0,std=0.01))'.format(
fire_num, suffix)
#exec 'n.conv{0}_relu{1} = L.ReLU(n.conv{0}{1},in_place=True)'.format(fire_num,suffix)
exec 'n.pool{0}{1} = L.Pooling(n.conv{0}{1}, global_pooling=True, pool=P.Pooling.AVE)'.format(
fire_num, suffix)
#exec 'n.softmax{1} = L.Softmax(n.pool{0}{1})'.format(fire_num,suffix)
for i in range(1, num_branch + 1):
suffix = '_b{0}'.format(i) # root node branch
fire_num = 4
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.pool3, kernel_size=1, num_output=8,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=32,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=32,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
# n.pool4_cluster = L.Pooling(n.fire4_concat, kernel_size=3, stride=2, pool=P.Pooling.MAX)
fire_num = 5
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire4_concat_cluster, kernel_size=1, num_output=8,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=32,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=32,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
exec 'n.pool{0}{1} = L.Pooling(n.fire{0}_concat{1}, kernel_size=3, stride=2, pool=P.Pooling.MAX)'.format(
fire_num, suffix)
fire_num = 6
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.pool5_cluster, kernel_size=1, num_output=12,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=48,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=48,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
fire_num = 7
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire6_concat_cluster, kernel_size=1, num_output=12,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=48,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=48,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
fire_num = 8
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire7_concat_cluster, kernel_size=1, num_output=16,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
fire_num = 9
exec 'n.fire{0}_squeeze1x1{1} = L.Convolution(n.fire8_concat_cluster, kernel_size=1, num_output=16,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_squeeze1x1{1} = L.ReLU(n.fire{0}_squeeze1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand1x1{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand1x1{1} = L.ReLU(n.fire{0}_expand1x1{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_expand3x3{1} = L.Convolution(n.fire{0}_relu_squeeze1x1{1}, kernel_size=3, pad=1, num_output=64,weight_filler=dict(type="xavier"))'.format(
fire_num, suffix)
exec 'n.fire{0}_relu_expand3x3{1} = L.ReLU(n.fire{0}_expand3x3{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.fire{0}_concat{1} = L.Concat(n.fire{0}_relu_expand1x1{1},n.fire{0}_relu_expand3x3{1})'.format(
fire_num, suffix)
exec 'n.drop{0}{1} = L.Dropout(n.fire{0}_concat{1}, in_place=True, dropout_ratio=0.5)'.format(
fire_num, suffix)
fire_num = 10
exec 'n.conv{0}{1} = L.Convolution(n.fire9_concat{1}, kernel_size=1, num_output=4,weight_filler=dict(type="gaussion",mean=0.0,std=0.01))'.format(
fire_num, suffix)
exec 'n.conv{0}_relu{1} = L.ReLU(n.conv{0}{1},in_place=True)'.format(
fire_num, suffix)
exec 'n.pool{0}{1} = L.Pooling(n.conv{0}_relu{1}, global_pooling=True, pool=P.Pooling.AVE)'.format(
fire_num, suffix)
exec 'n.softmax{1} = L.Softmax(n.pool{0}{1})'.format(
fire_num, suffix)
exec 'n.final_concat = L.Concat(n.softmax_b1,n.softmax_b2,n.softmax_b3,n.softmax_b4)'
exec 'n.final_concat_reshape = L.Reshape(reshape=dict(dim=[0,-1,1000]))'
#exec 'n.matvec = L.Python()'
#n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))
#n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))
#n.loss = L.SoftmaxWithLoss(n.score, n.label)
return n.to_proto()
def alexnet_bn_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(
crop_size=227,
mean_value=[104, 117, 123],
mirror=mirror))
n.conv1, n.conv1_bn, n.conv1_scale, n.conv1_relu = \
factorization_conv_bn_scale_relu(n.data, num_output=96, kernel_size=11, stride=4,) # 96x55x55
n.pool1 = L.Pooling(n.conv1,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX) # 96x27x27
n.conv2, n.conv2_bn, n.conv2_scale, n.conv2_relu = \
factorization_conv_bn_scale_relu(n.pool1, num_output=256, kernel_size=5, pad=2) # 256x27x27
n.pool2 = L.Pooling(n.conv2,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX) # 256x13x13
n.conv3, n.conv3_bn, n.conv3_scale, n.conv3_relu = \
factorization_conv_bn_scale_relu(n.pool2, num_output=384, kernel_size=3, pad=1) # 384x13x13
n.conv4, n.conv4_bn, n.conv4_scale, n.conv4_relu = \
factorization_conv_bn_scale_relu(n.conv3, num_output=384, kernel_size=3, pad=1) # 384x13x13
n.conv5, n.conv5_bn, n.conv5_scale, n.conv5_relu = \
factorization_conv_bn_scale_relu(n.conv4, num_output=256, kernel_size=3, pad=1) # 256x13x13
n.pool5 = L.Pooling(n.conv5,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX) # 256x6x16
n.fc6, n.relu6, n.drop6 = fc_relu_drop(n.pool5,
num_output=2048) # 1024x1x1
n.fc7, n.relu7, n.drop7 = fc_relu_drop(n.fc6,
num_output=2048) # 1024x1x1
n.fc8 = L.InnerProduct(n.fc7,
num_output=self.classifier_num,
param=[
dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
if phase == 'TRAIN':
pass
else:
n.accuracy_top1 = L.Accuracy(n.fc8, n.label, include=dict(phase=1))
n.accuracy_top5 = L.Accuracy(n.fc8,
n.label,
include=dict(phase=1),
accuracy_param=dict(top_k=5))
n.loss = L.SoftmaxWithLoss(n.fc8, n.label)
return n.to_proto()
def InceptionResNetV2(train_lmdb,
test_lmdb,
input_size=299,
batch_size=256,
stages=[0, 5, 10, 5],
first_output=32,
include_acc=False):
# now, this code can't recognize include phase, so there will only be a TEST phase data layer
data, label = L.Data(source=train_lmdb,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(crop_size=input_size,
mean_value=[104, 117, 123],
mirror=True),
include=dict(phase=getattr(caffe_pb2, 'TRAIN')))
data, label = L.Data(source=test_lmdb,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(crop_size=input_size,
mean_value=[104, 117, 123],
mirror=True),
include=dict(phase=getattr(caffe_pb2, 'TEST')))
data, label = L.MemoryData(batch_size=batch_size,
height=input_size,
width=input_size,
channels=3,
ntop=2,
transform_param=dict(mean_value=[104, 117, 123],
mirror=True),
include=dict(phase=getattr(caffe_pb2, 'TEST')))
Inception_ResNet_A_input = stem(bottom=data,
conv1_num=32,
conv2_num=32,
conv3_num=64,
conv4_num=96,
conv5_num=64,
conv6_num=96,
conv7_num=64,
conv8_num=64,
conv9_num=64,
conv10_num=96,
conv11_num=192)
for i in xrange(stages[1]):
Inception_ResNet_A_input = Inception_ResNet_A(
bottom=Inception_ResNet_A_input,
bottom_size=384,
num1x1=32,
num3x3=48,
num3x3double=64)
Inception_ResNet_B_input = ReductionA(bottom=Inception_ResNet_A_input,
num1x1_k=256,
num3x3_l=256,
num3x3_n=384,
num3x3_m=384)
for i in xrange(stages[2]):
Inception_ResNet_B_input = Inception_ResNet_B(
bottom=Inception_ResNet_B_input,
bottom_size=1152,
num1x1=192,
num1x1double=128,
num7x1=160,
num1x7=192)
Inception_ResNet_C_input = ReductionB(bottom=Inception_ResNet_B_input,
num1x1=256,
num3x3=384,
num3x3double=288,
num3x3three=320)
for i in xrange(stages[3]):
Inception_ResNet_C_input = Inception_ResNet_C(
bottom=Inception_ResNet_C_input,
bottom_size=2144,
num1x1=192,
num1x3=224,
num3x1=256)
glb_pool = L.Pooling(Inception_ResNet_C_input,
pool=P.Pooling.AVE,
global_pooling=True)
dropout = L.Dropout(glb_pool, dropout_ratio=0.2)
fc = L.InnerProduct(dropout, num_output=1000)
loss = L.SoftmaxWithLoss(fc, label)
acc = L.Accuracy(fc, label, include=dict(phase=getattr(caffe_pb2, 'TEST')))
return to_proto(loss, acc)
def create_net(lmdb, mean_file, batch_size, include_acc=False):
# 网络规范
net = caffe.NetSpec()
net.data, net.label = L.Data(source=lmdb,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(crop_size=227,
mean_file=mean_file,
mirror=True))
net.conv1 = L.Convolution(
net.data,
num_output=96,
kernel_size=11,
stride=4,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type="constant", value=0))
net.relu1 = L.ReLU(net.conv1, in_place=True)
net.norm1 = L.LRN(net.conv1, local_size=5, alpha=0.0001, beta=0.75)
net.pool1 = L.Pooling(net.norm1,
pool=P.Pooling.MAX,
kernel_size=3,
stride=2)
net.conv2 = L.Convolution(
net.pool1,
num_output=256,
pad=2,
kernel_size=5,
group=2,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type="constant", value=0.1))
net.relu2 = L.ReLU(net.conv2, in_place=True)
net.norm2 = L.LRN(net.conv2, local_size=5, alpha=0.0001, beta=0.75)
net.pool2 = L.Pooling(net.norm2,
pool=P.Pooling.MAX,
kernel_size=3,
stride=2)
net.conv3 = L.Convolution(
net.pool2,
num_output=384,
pad=1,
kernel_size=3,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type="constant", value=0))
net.relu3 = L.ReLU(net.conv3, in_place=True)
net.conv4 = L.Convolution(
net.conv3,
num_output=384,
pad=1,
kernel_size=3,
group=2,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type="constant", value=0.1))
net.relu4 = L.ReLU(net.conv4, in_place=True)
net.conv5 = L.Convolution(
net.conv4,
num_output=256,
pad=1,
kernel_size=3,
group=2,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type="constant", value=0.1))
net.relu5 = L.ReLU(net.conv5, in_place=True)
net.pool5 = L.Pooling(net.conv5,
pool=P.Pooling.MAX,
kernel_size=3,
stride=2)
net.fc6 = L.InnerProduct(
net.pool5,
num_output=4096,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type="gaussian", std=0.005),
bias_filler=dict(type="constant", value=0.1))
net.relu6 = L.ReLU(net.fc6, in_place=True)
net.drop6 = L.Dropout(net.fc6, dropout_ratio=0.5, in_place=True)
net.fc7 = L.InnerProduct(
net.fc6,
num_output=4096,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type="gaussian", std=0.005),
bias_filler=dict(type="constant", value=0.1))
net.relu7 = L.ReLU(net.fc7, in_place=True)
net.drop7 = L.Dropout(net.fc7, dropout_ratio=0.5, in_place=True)
net.fc8 = L.InnerProduct(
net.fc7,
num_output=1000,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type="constant", value=0.1))
net.loss = L.SoftmaxWithLoss(net.fc8, net.label)
if include_acc:
net.acc = L.Accuracy(net.fc8, net.label)
return net.to_proto()
return net.to_proto()
def nin_bn_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(
crop_size=227,
mean_value=[104, 117, 123],
mirror=mirror))
n.conv1, n.conv1_bn, n.relu0, n.cccp1, n.cccp1_bn, n.relu1, n.cccp2, n.cccp2_bn, n.relu2 = \
conv_bn_stack_3(n.data, dict(num_output=[96, 96, 96], kernel_size=[11, 1, 1], stride=[4, 1, 1],
pad=[0, 0, 0], group=[1, 1, 1],
weight_type=['gaussian', 'gaussian', 'gaussian'],
weight_std=[0.01, 0.05, 0.05],
bias_type=['constant', 'constant', 'constant'], bias_value=[0, 0, 0]))
n.pool1 = L.Pooling(n.cccp2_bn,
pool=P.Pooling.MAX,
kernel_size=3,
stride=2)
n.conv2, n.conv2_bn, n.relu3, n.cccp3, n.cccp3_bn, n.relu4, n.cccp4, n.cccp4_bn, n.relu5 = \
conv_bn_stack_3(n.pool1, dict(num_output=[256, 256, 256], kernel_size=[5, 1, 1], stride=[1, 1, 1],
pad=[2, 0, 0], group=[1, 1, 1],
weight_type=['gaussian', 'gaussian', 'gaussian'],
weight_std=[0.05, 0.05, 0.05],
bias_type=['constant', 'constant', 'constant'], bias_value=[0, 0, 0]))
n.pool2 = L.Pooling(n.cccp4_bn,
pool=P.Pooling.MAX,
kernel_size=3,
stride=2)
n.conv3, n.conv3_bn, n.relu6, n.cccp5, n.cccp5_bn, n.relu7, n.cccp6, n.cccp6_bn, n.relu8 = \
conv_bn_stack_3(n.pool2, dict(num_output=[384, 384, 384], kernel_size=[3, 1, 1], stride=[2, 1, 1],
pad=[1, 0, 0], group=[1, 1, 1],
weight_type=['gaussian', 'gaussian', 'gaussian'],
weight_std=[0.01, 0.05, 0.05],
bias_type=['constant', 'constant', 'constant'], bias_value=[0, 0, 0]))
n.pool3 = L.Pooling(n.cccp6_bn,
pool=P.Pooling.MAX,
kernel_size=3,
stride=2)
n.drop7 = L.Dropout(n.pool3,
in_place=True,
dropout_param=dict(dropout_ratio=0.5))
n.conv4, n.conv4_bn, n.relu9, n.cccp7, n.cccp7_bn, n.relu10, n.cccp8, n.cccp8_bn, n.relu11 = \
conv_bn_stack_3(n.pool3, dict(num_output=[1024, 1024, 1024], kernel_size=[3, 1, 1], stride=[1, 1, 1],
pad=[1, 0, 0], group=[1, 1, 1],
weight_type=['gaussian', 'gaussian', 'gaussian'],
weight_std=[0.01, 0.05, 0.05],
bias_type=['constant', 'constant', 'constant'], bias_value=[0, 0, 0]))
n.pool4 = L.Pooling(n.cccp8_bn,
pool=P.Pooling.MAX,
kernel_size=6,
stride=1)
if phase == 'TRAIN':
n.loss = L.SoftmaxWithLoss(n.pool4, n.label)
else:
n.accuracy_top1, n.accuracy_top5 = accuracy_top1_top5(
n.pool4, n.label)
return n.to_proto()
num_classes=20,
coords=4,
confidence_threshold=0.01,
nms_threshold=.45,
biases=[1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52],
include={'phase': caffe.TEST})
if __name__ == '__main__':
net = caffe.NetSpec()
#net.data = L.Input(shape=[dict(dim=[1, 3, 224, 224])])
net.data, net.label = L.Data(
name='data',
batch_size=32,
backend=P.Data.LMDB,
source='examples/imagenet/ilsvrc12_train_lmdb',
transform_param=dict(crop_size=227,
mean_value=[127.5, 127.5, 127.5],
scale=1 / 127.5),
ntop=2,
include={'phase': caffe.TRAIN})
net.test_data, net.test_label = L.Data(
name='data',
batch_size=25,
backend=P.Data.LMDB,
source='examples/imagenet/ilsvrc12_val_lmdb',
transform_param=dict(crop_size=227,
mean_value=[127.5, 127.5, 127.5],
scale=1 / 127.5),
ntop=2,
top=['data', 'label'],
include={'phase': caffe.TEST})
def fcn(split,fname_img_lmdb,fname_map_lmdb,learn_all=False):
param = learned_param if learn_all else frozen_param
n = caffe.NetSpec()
# invoke LMDB data loader
n.data = L.Data(batch_size=1, backend=P.Data.LMDB, source=fname_img_lmdb, ntop=1)
n.label = L.Data(batch_size=1, backend=P.Data.LMDB, source=fname_map_lmdb, ntop=1)
# the base net
n.conv1_1, n.relu1_1 = conv_relu(n.data, 64, pad=100, param=param)
n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64, param=param)
n.pool1 = max_pool(n.relu1_2)
n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128, param=param)
n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128, param=param)
n.pool2 = max_pool(n.relu2_2)
n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256, param=param)
n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256, param=param)
n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256, param=param)
n.pool3 = max_pool(n.relu3_3)
n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512, param=param)
n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512, param=param)
n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512, param=param)
n.pool4 = max_pool(n.relu4_3)
n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512, param=param)
n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512, param=param)
n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512, param=param)
n.pool5 = max_pool(n.relu5_3)
# fully conv
n.fc6, n.relu6 = conv_relu(n.pool5, 4096, ks=7, pad=0, param=param)
n.drop6 = L.Dropout(n.relu6, dropout_ratio=0.5, in_place=True)
n.fc7, n.relu7 = conv_relu(n.drop6, 4096, ks=1, pad=0, param=param)
n.drop7 = L.Dropout(n.relu7, dropout_ratio=0.5, in_place=True)
# the following are parameters being learnt anyway
# made layer named score_sal, upscore_sal to avoid copying new weights
n.score_sal = L.Convolution(n.drop7, num_output=1, kernel_size=1, pad=0,
param=learned_param) # <- learning weights for this layer
# CHANGES DUE TO SKIP CONNECTION:
# don't upscale all the way, only to the previous layer
# replaced kernel_size=64, stride=32 with:
n.upscore_sal2 = L.Deconvolution(n.score_sal,
convolution_param=dict(num_output=1, kernel_size=4, stride=2,
bias_term=False),param=[dict(lr_mult=0)]) # don't learn upscoring; fix it as bilinear
n.score_pool4 = L.Convolution(n.pool4, num_output=1, kernel_size=1, pad=0, param=learned_param) # <- learning weights for this layer
n.score_pool4c = crop(n.score_pool4, n.upscore_sal2)
n.fuse_pool4 = L.Eltwise(n.upscore_sal2, n.score_pool4c, operation=P.Eltwise.SUM)
n.upscore16 = L.Deconvolution(n.fuse_pool4,
convolution_param=dict(num_output=1, kernel_size=32, stride=16,
bias_term=False),param=[dict(lr_mult=0)])
# don't learn any of the upscaling (deconvolution) filters - just fix at bilinear
# n.score = crop(n.upscore_sal, n.data)
n.score = crop(n.upscore16, n.data)
n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label, loss_weight=1)
return n.to_proto()
def resnet_layers_proto(self,
batch_size,
phase='TRAIN',
stages=(3, 4, 6, 3)):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
need_mirror = True
else:
source_data = self.test_data
need_mirror = False
n.data, n.label = L.Data(source=source_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(
crop_size=224,
mean_value=[128, 128, 128],
mirror=need_mirror))
n.conv1, n.conv1_bn, n.conv1_scale, n.conv1_relu = \
block_conv_bn_scale_relu( n.data, num_output = 64, kernel_size = 7, stride = 2, pad = 3 ) # 64x112x112
n.pool1 = L.Pooling(n.conv1,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX)
residual_num = 0
for num in xrange(len(stages)):
for i in xrange(stages[num]):
residual_num = residual_num + 1
if num == 0 and i == 0:
stage_string = skip_connect_with_dimen_match_no_patch_reduce
if residual_num == 1:
bottom_string = 'n.pool1'
else:
bottom_string = 'n.res%s_eletwise' % (
str(residual_num - 1))
elif i == 0 and num > 0:
stage_string = skip_connect_with_dimen_match
if residual_num == 1:
bottom_string = 'n.pool1'
else:
bottom_string = 'n.res%s_eletwise' % (
str(residual_num - 1))
else:
stage_string = skip_connect_no_dimen_match
bottom_string = 'n.res%s_eletwise' % (str(residual_num -
1))
exec(
stage_string.replace('(stage)', str(residual_num)).replace(
'(bottom)',
bottom_string).replace('(num)', str(2**num * 64)))
exec 'n.pool5 = L.Pooling( bottom_string, kernel_size=7, stride=1, pool=P.Pooling.AVE)'.replace(
'bottom_string', 'n.res%s_eletwise' % str(residual_num))
n.classifier = L.InnerProduct(n.pool5,
num_output=self.classifier_num,
param=[
dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)
],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant',
value=0))
n.loss = L.SoftmaxWithLoss(n.classifier, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy_top1 = L.Accuracy(n.classifier,
n.label,
include=dict(phase=1))
n.accuracy_top5 = L.Accuracy(n.classifier,
n.label,
include=dict(phase=1),
accuracy_param=dict(top_k=5))
return n.to_proto()
def segnet_network(data_source, label_source, mode='train'):
""" Builds a Caffe Network Definition object for SegNet
Args:
data_source (str): path to the data LMDB
label_source (str): path to the label LMDB
mode (str, optional): 'train', 'test' or 'deploy' (defaults to 'train')
Returns:
obj: SegNet (Caffe Network Definition object)
"""
n = caffe.NetSpec()
if MEAN_PIXEL is None:
transform_param = {}
else:
transform_param = {'mean_value': MEAN_PIXEL}
if mode == 'deploy':
n.data = L.Input(input_param={ 'shape':\
{ 'dim': [BATCH_SIZE, 3, test_patch_size[0], test_patch_size[1]] }
})
else:
n.data = L.Data(batch_size=BATCH_SIZE, backend=P.Data.LMDB,\
transform_param=transform_param, source=data_source)
n.label = L.Data(batch_size=BATCH_SIZE, backend=P.Data.LMDB, source=label_source)
convolution_block(n, n.data, "conv1_{}", 2, planes=(64,64,64), lr_mult=0.5)
n.pool1, n.pool1_mask = L.Pooling(n.conv1_2, pool=P.Pooling.MAX, kernel_size=2, stride=2, ntop=2)
convolution_block(n, n.pool1, "conv2_{}", 2, planes=(128,128,128), lr_mult=0.5)
n.pool2, n.pool2_mask = L.Pooling(n.conv2_2, pool=P.Pooling.MAX, kernel_size=2, stride=2, ntop=2)
convolution_block(n, n.pool2, "conv3_{}", 3, planes=(256,256,256), lr_mult=0.5)
n.pool3, n.pool3_mask = L.Pooling(n.conv3_3, pool=P.Pooling.MAX, kernel_size=2, stride=2, ntop=2)
convolution_block(n, n.pool3, "conv4_{}", 3, planes=(512,512,512), lr_mult=0.5)
n.pool4, n.pool4_mask = L.Pooling(n.conv4_3, pool=P.Pooling.MAX, kernel_size=2, stride=2, ntop=2)
convolution_block(n, n.pool4, "conv5_{}", 3, planes=(512,512,512), lr_mult=0.5)
n.pool5, n.pool5_mask = L.Pooling(n.conv5_3, pool=P.Pooling.MAX, kernel_size=2, stride=2, ntop=2)
n.upsample5 = L.Upsample(n.pool5, n.pool5_mask, scale=2)
convolution_block(n, n.upsample5, "conv5_{}_D", 3, planes=(512,512,512), lr_mult=1, reverse=True)
n.upsample4 = L.Upsample(n.conv5_1_D, n.pool4_mask, scale=2)
convolution_block(n, n.upsample4, "conv4_{}_D", 3, planes=(512,512,256), lr_mult=1, reverse=True)
n.upsample3 = L.Upsample(n.conv4_1_D, n.pool3_mask, scale=2)
convolution_block(n, n.upsample3, "conv3_{}_D", 3, planes=(256,256,128), lr_mult=1, reverse=True)
n.upsample2 = L.Upsample(n.conv3_1_D, n.pool2_mask, scale=2)
convolution_block(n, n.upsample2, "conv2_{}_D", 2, planes=(128,128,64), lr_mult=1, reverse=True)
n.upsample1 = L.Upsample(n.conv2_1_D, n.pool1_mask, scale=2)
n.conv1_2_D, n.conv1_2_D_bn, n.conv1_2_D_scale, n.conv1_2_D_relu =\
convolution_unit(n.upsample1, 3, 1, 64, lr_mult=1)
n.conv1_1_D, _, _, _ = convolution_unit(n.conv1_2_D, 3, 1, 6, lr_mult=1)
if mode == 'train' or mode == 'test':
n.loss = L.SoftmaxWithLoss(n.conv1_1_D, n.label, loss_param={'ignore_label': IGNORE_LABEL})
n.accuracy = L.Accuracy(n.conv1_1_D, n.label)
return n
'test_interval': 2000,
}
# Check file.
check_if_exist(train_data)
check_if_exist(test_data)
make_if_not_exist(save_dir)
make_if_not_exist(job_dir)
make_if_not_exist(snapshot_dir)
# Create train net
train_net = caffe.NetSpec()
train_net.data, train_net.label = L.Data(
source=train_data,
backend=P.Data.LMDB,
batch_size=train_batch_size,
ntop=2,
transform_param=dict(crop_size=crop_size, mean_file=mean_file,
mirror=True),
include=dict(phase=caffe_pb2.Phase.Value('TRAIN')))
ZFNetBody(train_net, from_layer='data', for_training=True)
with open(train_net_file, 'w') as f:
print('name: "{}_train"'.format(model_name), file=f)
print(train_net.to_proto(), file=f)
shutil.copy(train_net_file, job_dir)
# Create test net
test_net = caffe.NetSpec()
test_net.data, test_net.label = L.Data(
source=test_data,
def gen_net(lmdb, batch_size):
# our version of LeNet: a series of linear and simple nonlinear transformations
n = caffe.NetSpec()
n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,
transform_param=dict(scale=1./255), ntop=2)
return n.to_proto()
def vgg_16(lmdb, bs_train=32, bs_val=10, lmdb_flag=True, not_deploy=True):
n = caffe.NetSpec()
if not_deploy:
if lmdb_flag:
n.data, n.label = L.Data(source=lmdb + 'cub200_2011_train_lmdb',
backend=P.Data.LMDB,
include=dict(phase=caffe_pb2.TRAIN),
batch_size=bs_train,
ntop=2,
transform_param=dict(
crop_size=224,
mean_value=[110, 127, 123],
mirror=True))
data_str = n.to_proto()
n.data, n.label = L.Data(source=lmdb + 'cub200_2011_val_lmdb',
backend=P.Data.LMDB,
include=dict(phase=caffe_pb2.TEST),
batch_size=bs_val,
ntop=2,
transform_param=dict(
crop_size=224,
mean_value=[110, 127, 123],
mirror=False))
else:
n.data, n.label = L.Data(source=lmdb + 'cub200_2011_train_leveldb',
backend=P.Data.LEVELDB,
include=dict(phase=caffe_pb2.TRAIN),
batch_size=bs_train,
ntop=2,
transform_param=dict(
crop_size=224,
mean_value=[110, 127, 123],
mirror=True))
data_str = n.to_proto()
n.data, n.label = L.Data(source=lmdb + 'cub200_2011_val_leveldb',
backend=P.Data.LEVELDB,
include=dict(phase=caffe_pb2.TEST),
batch_size=bs_val,
ntop=2,
transform_param=dict(
crop_size=224,
mean_value=[110, 127, 123],
mirror=False))
else:
data_str = 'input: "data"\ninput_dim: 1\ninput_dim: 3\ninput_dim: 224\ninput_dim: 224'
n.data = L.Data()
# the net itself
n.conv1_1, n.relu1_1 = conv_relu(n.data, nout=64, pad=1, ks=3)
n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, nout=64, pad=1, ks=3)
n.pool1 = max_pool(n.relu1_2, ks=2, stride=2)
n.conv2_1, n.relu2_1 = conv_relu(n.pool1, nout=128, pad=1, ks=3)
n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, nout=128, pad=1, ks=3)
n.pool2 = max_pool(n.relu2_2, ks=2, stride=2)
n.conv3_1, n.relu3_1 = conv_relu(n.pool2, nout=256, pad=1, ks=3)
n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, nout=256, pad=1, ks=3)
n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, nout=256, pad=1, ks=3)
n.pool3 = max_pool(n.relu3_3, ks=2, stride=2)
n.conv4_1, n.relu4_1 = conv_relu(n.pool3, nout=512, pad=1, ks=3)
n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, nout=512, pad=1, ks=3)
n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, nout=512, pad=1, ks=3)
n.pool4 = max_pool(n.relu4_3, ks=2, stride=2)
n.conv5_1, n.relu5_1 = conv_relu(n.pool4, nout=512, pad=1, ks=3)
n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, nout=512, pad=1, ks=3)
n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, nout=512, pad=1, ks=3)
n.pool5 = ave_pool(n.relu5_3, ks=14, stride=1)
n.softmax = L.Convolution(n.pool5,
kernel_size=1,
num_output=200,
param=[
dict(lr_mult=10, decay_mult=10),
dict(lr_mult=20, decay_mult=0)
])
if not_deploy:
n.loss = L.SoftmaxWithLoss(n.softmax, n.label)
n.acc_top_1 = L.Accuracy(n.softmax, n.label, top_k=1)
else:
n.prob = L.Softmax(n.softmax)
model_str = str(n.to_proto())
if not not_deploy:
model_str = model_str[54:-1]
return str(data_str) + '\n' + model_str
net.relu1 = L.ReLU(net.ip1, in_place=True)
net.ip2 = L.InnerProduct(net.relu1, name='ip2', num_output=10, **kwargs)
caffe_root = '../../'
# caffe_root = 'E:/Code/Github/windows_caffe/'
model_root = caffe_root + 'models/mnist/'
# 训练数据
train_data = caffe_root + "data/mnist/mnist_train_lmdb"
# 测试数据
test_data = caffe_root + "data/mnist/mnist_test_lmdb"
# 训练网络
train_net = caffe.NetSpec() # 基础网络
# 带标签的数据输入层
train_net.data, train_net.label = L.Data(source=train_data,backend=P.Data.LMDB, batch_size=64,ntop=2,transform_param=dict(scale=0.00390625))
# 生成LeNet5的主体结构
lenet5_body(train_net, 'data')
# 生成误差损失层
train_net.loss = L.SoftmaxWithLoss(train_net.ip2, train_net.label)
# 测试网络
test_net = caffe.NetSpec() # 基础网络
# 带标签的数据输入层
test_net.data, test_net.label = L.Data(source=test_data, batch_size=100, backend=P.Data.LMDB, ntop=2,transform_param=dict(scale=0.00390625))
# 生成LeNet5的主体结构
lenet5_body(test_net, 'data')
# 生成误差损失层
test_net.loss = L.SoftmaxWithLoss(test_net.ip2, test_net.label)
# 添加一个精确层
test_net.accuracy = L.Accuracy(test_net.ip2, test_net.label)
def construct_fcn(image_lmdb, contour_lmdb, batch_size=1, include_acc=False):
net = caffe.NetSpec()
# args for convlution layers
weight_filler = dict(type='gaussian', mean=0.0, std=0.01)
bias_filler = dict(type='constant', value=0.1)
param = [dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)]
net.data = L.Data(source=image_lmdb,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=1,
transform_param=dict(crop_size=0,
mean_value=[77],
mirror=False))
net.label = L.Data(source=contour_lmdb,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=1)
# conv-relu-pool 1
net.conv1 = L.Convolution(net.data,
kernel_size=5,
stride=2,
num_output=100,
pad=50,
group=1,
weight_filler=weight_filler,
bias_filler=bias_filler,
param=param)
net.relu1 = L.ReLU(net.conv1, in_place=True)
net.pool1 = L.Pooling(net.relu1,
pool=P.Pooling.MAX,
kernel_size=2,
stride=2)
# conv-relu-pool 2
net.conv2 = L.Convolution(net.pool1,
kernel_size=5,
stride=2,
num_output=200,
pad=0,
group=1,
weight_filler=weight_filler,
bias_filler=bias_filler,
param=param)
net.relu2 = L.ReLU(net.conv2, in_place=True)
net.pool2 = L.Pooling(net.relu2,
pool=P.Pooling.MAX,
kernel_size=2,
stride=2)
net.conv3 = L.Convolution(net.pool2,
kernel_size=3,
stride=1,
num_output=300,
pad=0,
group=1,
weight_filler=weight_filler,
bias_filler=bias_filler,
param=param)
net.relu3 = L.ReLU(net.conv3, in_place=True)
net.conv4 = L.Convolution(net.relu3,
kernel_size=3,
stride=1,
num_output=300,
pad=0,
group=1,
weight_filler=weight_filler,
bias_filler=bias_filler,
param=param)
net.relu4 = L.ReLU(net.conv4, in_place=True)
net.drop = L.Dropout(net.relu4, dropout_ratio=0.1, in_place=True)
net.score_classes = L.Convolution(net.drop,
kernel_size=1,
stride=1,
num_output=2,
pad=0,
group=1,
weight_filler=weight_filler,
bias_filler=bias_filler,
param=param)
net.upscore = L.Deconvolution(net.score_classes)
net.score = L.Crop(net.upscore, net.data)
net.loss = L.SoftmaxWithLoss(net.score,
net.label,
loss_param=dict(normalize=True))
if include_acc:
net.accuracy = L.Accuracy(net.score, net.label)
return net.to_proto()
def inception_resnet_v2_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(
crop_size=299,
mean_value=[104, 117, 123],
mirror=mirror))
# stem
n.conv1_3x3_s2, n.conv1_3x3_s2_bn, n.conv1_3x3_s2_scale, n.conv1_3x3_relu, n.conv2_3x3_s1, n.conv2_3x3_s1_bn, \
n.conv2_3x3_s1_scale, n.conv2_3x3_relu, n.conv3_3x3_s1, n.conv3_3x3_s1_bn, n.conv3_3x3_s1_scale, n.conv3_3x3_relu, \
n.pool1_3x3_s2, n.conv4_3x3_reduce, n.conv4_3x3_reduce_bn, n.conv4_3x3_reduce_scale, n.conv4_3x3_reduce_relu, \
n.conv4_3x3, n.conv4_3x3_bn, n.conv4_3x3_scale, n.conv4_relu_3x3, n.pool2_3x3_s2, n.conv5_1x1, n.conv5_1x1_bn, n.conv5_1x1_scale, \
n.conv5_1x1_relu, n.conv5_5x5_reduce, n.conv5_5x5_reduce_bn, n.conv5_5x5_reduce_scale, n.conv5_5x5_reduce_relu, \
n.conv5_5x5, n.conv5_5x5_bn, n.conv5_5x5_scale, n.conv5_5x5_relu, n.conv5_3x3_reduce, n.conv5_3x3_reduce_bn, n.conv5_3x3_reduce_scale, \
n.conv5_3x3_reduce_relu, n.conv5_3x3, n.conv5_3x3_bn, n.conv5_3x3_scale, n.conv5_3x3_relu, n.conv5_3x3_2, n.conv5_3x3_2_bn, \
n.conv5_3x3_2_scale, n.conv5_3x3_2_relu, n.ave_pool, n.conv5_1x1_ave, n.conv5_1x1_ave_bn, n.conv5_1x1_ave_scale, n.conv5_1x1_ave_relu, \
n.stem_concat = stem_resnet_v2_299x299(n.data) # 320x35x35
# 10 x inception_resnet_v2_a
for i in xrange(10):
if i == 0:
bottom = 'n.stem_concat'
else:
bottom = 'n.inception_resnet_v2_a(order)_residual_eltwise'.replace(
'(order)', str(i))
exec(
string_a.replace('(order)',
str(i + 1)).replace('bottom',
bottom)) # 384x35x35
# reduction_resnet_v2_a
n.reduction_a_3x3, n.reduction_a_3x3_bn, n.reduction_a_3x3_scale, n.reduction_a_3x3_relu, \
n.reduction_a_3x3_2_reduce, n.reduction_a_3x3_2_reduce_bn, n.reduction_a_3x3_2_reduce_scale, \
n.reduction_a_3x3_2_reduce_relu, n.reduction_a_3x3_2, n.reduction_a_3x3_2_bn, n.reduction_a_3x3_2_scale, \
n.reduction_a_3x3_2_relu, n.reduction_a_3x3_3, n.reduction_a_3x3_3_bn, n.reduction_a_3x3_3_scale, \
n.reduction_a_3x3_3_relu, n.reduction_a_pool, n.reduction_a_concat = \
reduction_resnet_v2_a(n.inception_resnet_v2_a10_residual_eltwise) # 1088x17x17
# 20 x inception_resnet_v2_b
for i in xrange(20):
if i == 0:
bottom = 'n.reduction_a_concat'
else:
bottom = 'n.inception_resnet_v2_b(order)_residual_eltwise'.replace(
'(order)', str(i))
exec(
string_b.replace('(order)',
str(i + 1)).replace('bottom',
bottom)) # 1088x17x17
# reduction_resnet_v2_b
n.reduction_b_3x3_reduce, n.reduction_b_3x3_reduce_bn, n.reduction_b_3x3_reduce_scale, \
n.reduction_b_3x3_reduce_relu, n.reduction_b_3x3, n.reduction_b_3x3_bn, n.reduction_b_3x3_scale, \
n.reduction_b_3x3_relu, n.reduction_b_3x3_2_reduce, n.reduction_b_3x3_2_reduce_bn, n.reduction_b_3x3_2_reduce_scale, \
n.reduction_b_3x3_2_reduce_relu, n.reduction_b_3x3_2, n.reduction_b_3x3_2_bn, n.reduction_b_3x3_2_scale, \
n.reduction_b_3x3_2_relu, n.reduction_b_3x3_3_reduce, n.reduction_b_3x3_3_reduce_bn, n.reduction_b_3x3_3_reduce_scale, \
n.reduction_b_3x3_3_reduce_relu, n.reduction_b_3x3_3, n.reduction_b_3x3_3_bn, n.reduction_b_3x3_3_scale, \
n.reduction_b_3x3_3_relu, n.reduction_b_3x3_4, n.reduction_b_3x3_4_bn, n.reduction_b_3x3_4_scale, \
n.reduction_b_3x3_4_relu, n.reduction_b_pool, n.reduction_b_concat = \
reduction_resnet_v2_b(n.inception_resnet_v2_b20_residual_eltwise) # 2080x8x8
# 9 x inception_resnet_v2_c
for i in xrange(9):
if i == 0:
bottom = 'n.reduction_b_concat'
else:
bottom = 'n.inception_resnet_v2_c(order)_residual_eltwise'.replace(
'(order)', str(i))
exec(
string_c.replace('(order)',
str(i + 1)).replace('bottom',
bottom)) # 2080x8x8
n.inception_resnet_v2_c10_1x1, n.inception_resnet_v2_c10_1x1_bn, n.inception_resnet_v2_c10_1x1_scale, \
n.inception_resnet_v2_c10_1x1_relu = \
factorization_conv_bn_scale_relu(n.inception_resnet_v2_c9_residual_eltwise, num_output=192,
kernel_size=1) # 192x8x8
n.inception_resnet_v2_c10_1x3_reduce, n.inception_resnet_v2_c10_1x3_reduce_bn, \
n.inception_resnet_v2_c10_1x3_reduce_scale, n.inception_resnet_v2_c10_1x3_reduce_relu = \
factorization_conv_bn_scale_relu(n.inception_resnet_v2_c9_residual_eltwise, num_output=192,
kernel_size=1) # 192x8x8
n.inception_resnet_v2_c10_1x3, n.inception_resnet_v2_c10_1x3_bn, n.inception_resnet_v2_c10_1x3_scale, \
n.inception_resnet_v2_c10_1x3_relu = \
factorization_conv_mxn(n.inception_resnet_v2_c10_1x3_reduce, num_output=224, kernel_h=1, kernel_w=3,
pad_h=0, pad_w=1) # 224x8x8
n.inception_resnet_v2_c10_3x1, n.inception_resnet_v2_c10_3x1_bn, n.inception_resnet_v2_c10_3x1_scale, \
n.inception_resnet_v2_c10_3x1_relu = \
factorization_conv_mxn(n.inception_resnet_v2_c10_1x3, num_output=256, kernel_h=3, kernel_w=1, pad_h=1,
pad_w=0) # 256x8x8
n.inception_resnet_v2_c10_concat = L.Concat(
n.inception_resnet_v2_c10_1x1,
n.inception_resnet_v2_c10_3x1) # 448(192+256)x8x8
n.inception_resnet_v2_c10_up, n.inception_resnet_v2_c10_up_bn, n.inception_resnet_v2_c10_up_scale = \
factorization_conv_bn_scale(n.inception_resnet_v2_c10_concat, num_output=2080,
kernel_size=1) # 2080x8x8
n.inception_resnet_v2_c10_residual_eltwise = \
L.Eltwise(n.inception_resnet_v2_c9_residual_eltwise, n.inception_resnet_v2_c10_up,
eltwise_param=dict(operation=1)) # 2080x8x8
n.conv6_1x1, n.conv6_1x1_bn, n.conv6_1x1_scale, n.conv6_1x1_relu = \
factorization_conv_bn_scale_relu(n.inception_resnet_v2_c10_residual_eltwise, num_output=1536,
kernel_size=1) # 1536x8x8
n.pool_8x8_s1 = L.Pooling(n.conv6_1x1,
pool=P.Pooling.AVE,
global_pooling=True) # 1536x1x1
n.pool_8x8_s1_drop = L.Dropout(n.pool_8x8_s1,
dropout_param=dict(dropout_ratio=0.2))
n.classifier = L.InnerProduct(n.pool_8x8_s1_drop,
num_output=self.classifier_num,
param=[
dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)
],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant',
value=0))
n.loss = L.SoftmaxWithLoss(n.classifier, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy_top1 = L.Accuracy(n.classifier,
n.label,
include=dict(phase=1))
n.accuracy_top5 = L.Accuracy(n.classifier,
n.label,
include=dict(phase=1),
accuracy_param=dict(top_k=5))
return n.to_proto()
def densenet(mode, data_file, bs, nlayer, nclass, first_nout=16, growth_rate=16, dropout=0.2):
net = caffe.NetSpec()
# data layer ---------------------------------------------------------------
mirror = True
shuffle = True
if mode == 1: # TEST phase
mirror = False
shuffle = False
transform = dict(scale = 0.0078125,
mirror = mirror,
#crop_size = 224,
mean_value = [127.5, 127.5, 127.5])
net.data, net.label = L.Data(#include = dict(phase = mode),
transform_param = transform,
source = data_file,
batch_size = bs,
backend = P.Data.LMDB,
ntop = 2)
# net.data, net.label = L.ImageData(#include = dict(phase = mode),
# transform_param = transform,
# source = data_file,
# batch_size = bs,
# shuffle = shuffle,
# #new_height = 256,
# #new_width = 256,
# #is_color = True,
# ntop = 2)
pre_fmap = 0 # total number of previous feature maps
# first convolution --------------------------------------------------------
net.conv_1 = L.Convolution(net.data, num_output=first_nout,
kernel_size=7, stride=2, pad=3,
weight_filler=dict(type='msra'),
bias_filler=dict(type='constant'),
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)])
net.relu_1 = L.PReLU(net.conv_1, in_place=True)
net.pool_1 = L.Pooling(net.relu_1, pool=P.Pooling.MAX,
kernel_size=3, stride=2)
pre_layer = net.pool_1
pre_fmap += first_nout
# DB + TD ------------------------------------------------------------------
# +1 in order to make the index values from 1
for major in xrange(len(nlayer)-1):
# DB
for minor in xrange(nlayer[major]):
pre_layer = cat_layer(net, mode, major+1, minor+1, pre_layer, growth_rate, dropout)
pre_fmap += growth_rate
# TD
pre_layer = transition_down(net, mode, major+1, pre_layer, pre_fmap, dropout)
pre_fmap = pre_fmap // 2
# last DB, without TD
major = len(nlayer)
for minor in xrange(nlayer[-1]):
pre_layer = cat_layer(net, mode, major, minor+1, pre_layer, growth_rate, dropout)
pre_fmap += growth_rate
# final layers -------------------------------------------------------------
use_global_stats = False
if mode == 1: # TEST phase
use_global_stats = True
net.bn_final = L.BatchNorm(pre_layer, in_place=False,
batch_norm_param = dict(use_global_stats=use_global_stats),
param=[dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0)])
net.scale_finel = L.Scale(net.bn_final, bias_term=True, in_place=True,
filler=dict(value=1), bias_filler=dict(value=0))
net.relu_final = L.PReLU(net.scale_finel, in_place=True)
net.pool_final = L.Pooling(net.relu_final, pool=P.Pooling.AVE, global_pooling=True)
net.fc_class = L.InnerProduct(net.pool_final, num_output=nclass,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'),
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)])
net.loss = L.SoftmaxWithLoss(net.fc_class, net.label)
if mode == 1:
net.accuracy = L.Accuracy(net.fc_class, net.label)
return str(net.to_proto())
def make_resnet(training_data='train_data_path',
test_data='test_data_path',
mean_file='mean.binaryproto',
depth=50):
# num_feature_maps = np.array([16, 32, 64]) # feature map size: [32, 16, 8]
configs = {
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3],
200: [3, 24, 36, 3],
}
block_config = configs[depth]
num_feature_maps = [64, 128, 256, 512]
n_stage = len(num_feature_maps)
n = caffe.NetSpec()
# make training data layer
n.data, n.label = L.Data(source=training_data,
backend=P.Data.LMDB,
batch_size=256,
ntop=2,
transform_param=dict(crop_size=224,
mean_file=mean_file,
mirror=True),
image_data_param=dict(shuffle=True),
include=dict(phase=0))
# make test data layer
n.test_data, n.test_label = L.Data(source=test_data,
backend=P.Data.LMDB,
batch_size=100,
ntop=2,
transform_param=dict(
crop_size=224,
mean_file=mean_file,
mirror=False),
include=dict(phase=1))
# conv1 should accept both training and test data layers. But this is inconvenient to code in pycaffe.
# You have to write two conv layers for them. To deal with this, I temporarily ignore the test data layer
# and let conv1 accept the output of training data layer. Then, after making the whole prototxt, I postprocess
# the top name of the two data layers, renaming their names to the same.
n.conv = L.Convolution(
n.data,
kernel_size=7,
stride=2,
num_output=64,
pad=3,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=weight_filler,
bias_filler=bias_filler)
n.bn = L.BatchNorm(n.conv, in_place=True)
n.scale = L.Scale(n.bn, scale_param=dict(bias_term=True), in_place=True)
n.relu = L.ReLU(n.scale, in_place=True)
n.max_pooling = L.Pooling(n.relu,
pool=P.Pooling.MAX,
kernel_size=3,
stride=2,
pad=0)
# set up a checkpoint so as to know where we get.
checkpoint = 'n.max_pooling'
# start making blocks.
# num_feature_maps: the number of feature maps for each stage. Default is [16,32,64],
# suggesting the network has three stages.
# num_block_in_stage: a parameter from the original paper, telling us how many blocks there are in
# each stage.
# depth :
for i in range(n_stage):
num_map = num_feature_maps[i]
nblocks = block_config[i]
if (i == 0):
stride = 1
else:
stride = 2
for res in range(nblocks):
# stage name
stage = 'blk' + str(res + 1) + '_stg' + str(i + 1)
# use the projecting block when downsample the feature map
if res == 0:
make_res = 'n.' + 'conv_' + stage + '_proj,' + \
'n.' + 'bn_' + stage + '_proj,' + \
'n.' + 'scale_' + stage + '_proj,' + \
'n.' + 'conv_' + stage + '_a,' + \
'n.' + 'bn_' + stage + '_a, ' + \
'n.' + 'scale_' + stage + '_a, ' + \
'n.' + 'relu_' + stage + '_a, ' + \
'n.' + 'conv_' + stage + '_b, ' + \
'n.' + 'bn_' + stage + '_b, ' + \
'n.' + 'scale_' + stage + '_b, ' + \
'n.' + 'relu_' + stage + '_b, ' + \
'n.' + 'conv_' + stage + '_c, ' + \
'n.' + 'bn_' + stage + '_c, ' + \
'n.' + 'scale_' + stage + '_c, ' + \
'n.' + 'se_pool_' + stage + '_c, ' + \
'n.' + 'se_reduce_' + stage + ', ' + \
'n.' + 'se_relu_' + stage + ', ' + \
'n.' + 'se_recover_' + stage + ', ' + \
'n.' + 'se_sigmoid_' + stage + ', ' + \
'n.' + 'se_scale_' + stage + ', ' + \
'n.' + 'eltsum_' + stage + ', ' + \
'n.' + 'relu_after_sum_' + stage + \
' = project_residual(' + checkpoint + ', num_out=num_map, stride=' + str(stride) + ')'
exec(make_res)
checkpoint = 'n.' + 'relu_after_sum_' + stage # where we get
continue
# most blocks have this shape
make_res = 'n.' + 'conv_' + stage + '_a, ' + \
'n.' + 'bn_' + stage + '_a, ' + \
'n.' + 'scale_' + stage + '_a, ' + \
'n.' + 'relu_' + stage + '_a, ' + \
'n.' + 'conv_' + stage + '_b, ' + \
'n.' + 'bn_' + stage + '_b, ' + \
'n.' + 'scale_' + stage + '_b, ' + \
'n.' + 'relu_' + stage + '_b, ' + \
'n.' + 'conv_' + stage + '_c, ' + \
'n.' + 'bn_' + stage + '_c, ' + \
'n.' + 'scale_' + stage + '_c, ' + \
'n.' + 'se_pool_' + stage + '_d, ' + \
'n.' + 'se_reduce_' + stage + ', ' + \
'n.' + 'se_relu_' + stage + ', ' + \
'n.' + 'se_recover_' + stage + ', ' + \
'n.' + 'se_sigmoid_' + stage + ', ' + \
'n.' + 'se_scale_' + stage + ', ' + \
'n.' + 'eltsum_' + stage + ', ' + \
'n.' + 'relu_after_sum_' + stage + \
' = identity_residual(' + checkpoint + ', num_out=num_map, stride=1)'
exec(make_res)
checkpoint = 'n.' + 'relu_after_sum_' + stage # where we get
# add the pooling layer
exec('n.pool_global = L.Pooling(' + checkpoint +
', pool=P.Pooling.AVE, global_pooling=True)')
n.score = L.InnerProduct(
n.pool_global,
num_output=1000,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
n.loss = L.SoftmaxWithLoss(n.score, n.label)
n.acc = L.Accuracy(n.score, n.label)
return n.to_proto()
def inception_v3_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data,
backend=P.Data.LMDB,
batch_size=batch_size,
ntop=2,
transform_param=dict(
crop_size=299,
mean_value=[104, 117, 123],
mirror=mirror))
# stage 1
n.conv1_3x3_s2, n.conv1_3x3_s2_bn, n.conv1_3x3_relu, n.conv2_3x3_s1, n.conv2_3x3_s1_bn, n.conv2_3x3_relu, \
n.conv3_3x3_s1, n.conv3_3x3_s1_bn, n.conv3_3x3_relu = \
conv_bn_stack_3(n.data, dict(num_output=[32, 32, 64], kernel_size=[3, 3, 3], stride=[2, 1, 1],
pad=[0, 0, 1], group=[1, 1, 1], weight_type=['xavier', 'xavier', 'xavier'],
weight_std=[0.01, 0.01, 0.01],
bias_type=['constant', 'constant', 'constant'], bias_value=[0.2, 0.2, 0.2]))
n.pool1_3x3_s2 = L.Pooling(n.conv3_3x3_s1_bn,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX)
# stage 2
n.conv4_3x3_reduce, n.conv4_3x3_reduce_bn, n.conv4_relu_3x3_reduce, n.conv4_3x3, n.conv4_3x3_bn, n.conv4_relu_3x3 = \
conv_bn_stack_2(n.pool1_3x3_s2,
dict(num_output=[80, 192], kernel_size=[1, 3], stride=[1, 1], pad=[0, 0], group=[1, 1],
weight_type=['xavier', 'xavier'], weight_std=[0.01, 0.01],
bias_type=['constant', 'constant'], bias_value=[0.2, 0.2]))
n.pool2_3x3_s2 = L.Pooling(n.conv4_3x3_bn,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX)
# stage 3
n.inception_3a_1x1, n.inception_3a_1x1_bn, n.inception_3a_relu_1x1, n.inception_3a_5x5_reduce, \
n.inception_3a_5x5_reduce_bn, n.inception_3a_relu_5x5_reduce, n.inception_3a_5x5, n.inception_3a_5x5_bn, \
n.inception_3a_relu_5x5, n.inception_3a_3x3_reduce, n.inception_3a_3x3_reduce_bn, n.inception_3a_relu_3x3_reduce, \
n.inception_3a_3x3_1, n.inception_3a_3x3_1_bn, n.inception_3a_relu_3x3_1, n.inception_3a_3x3_2, \
n.inception_3a_3x3_2_bn, n.inception_3a_relu_3x3_2, n.inception_3a_pool, n.inception_3a_pool_proj, \
n.inception_3a_pool_proj_bn, n.inception_3a_relu_pool_proj, n.inception_3a_output = \
inception_v3_7a(n.pool2_3x3_s2,
dict(conv_1x1=64, conv_5x5_reduce=48, conv_5x5=64, conv_3x3_reduce=64, conv_3x3_1=96,
conv_3x3_2=96, pool_proj=32))
n.inception_3b_1x1, n.inception_3b_1x1_bn, n.inception_3b_relu_1x1, n.inception_3b_5x5_reduce, \
n.inception_3b_5x5_reduce_bn, n.inception_3b_relu_5x5_reduce, n.inception_3b_5x5, n.inception_3b_5x5_bn, \
n.inception_3b_relu_5x5, n.inception_3b_3x3_reduce, n.inception_3b_3x3_reduce_bn, n.inception_3b_relu_3x3_reduce, \
n.inception_3b_3x3_1, n.inception_3b_3x3_1_bn, n.inception_3b_relu_3x3_1, n.inception_3b_3x3_2, \
n.inception_3b_3x3_2_bn, n.inception_3b_relu_3x3_2, n.inception_3b_pool, n.inception_3b_pool_proj, \
n.inception_3b_pool_proj_bn, n.inception_3b_relu_pool_proj, n.inception_3b_output = \
inception_v3_7a(n.inception_3a_output,
dict(conv_1x1=64, conv_5x5_reduce=48, conv_5x5=64, conv_3x3_reduce=64, conv_3x3_1=96,
conv_3x3_2=96, pool_proj=64))
n.inception_3c_1x1, n.inception_3c_1x1_bn, n.inception_3c_relu_1x1, n.inception_3c_5x5_reduce, \
n.inception_3c_5x5_reduce_bn, n.inception_3c_relu_5x5_reduce, n.inception_3c_5x5, n.inception_3c_5x5_bn, \
n.inception_3c_relu_5x5, n.inception_3c_3x3_reduce, n.inception_3c_3x3_reduce_bn, n.inception_3c_relu_3x3_reduce, \
n.inception_3c_3x3_1, n.inception_3c_3x3_1_bn, n.inception_3c_relu_3x3_1, n.inception_3c_3x3_2, \
n.inception_3c_3x3_2_bn, n.inception_3c_relu_3x3_2, n.inception_3c_pool, n.inception_3c_pool_proj, \
n.inception_3c_pool_proj_bn, n.inception_3c_relu_pool_proj, n.inception_3c_output = \
inception_v3_7a(n.inception_3b_output,
dict(conv_1x1=64, conv_5x5_reduce=48, conv_5x5=64, conv_3x3_reduce=64, conv_3x3_1=96,
conv_3x3_2=96, pool_proj=64))
n.inception_3d_3x3_0, n.inception_3d_3x3_0_bn, n.inception_3d_relu_3x3_0, n.inception_3d_3x3_reduce, \
n.inception_3d_3x3_reduce_bn, n.inception_3d_relu_3x3_reduce, n.inception_3d_3x3_1, n.inception_3d_3x3_1_bn, \
n.inception_3d_relu_3x3_1, n.inception_3d_3x3_2, n.inception_3d_3x3_2_bn, n.inception_3d_relu_3x3_2, \
n.inception_3d_pool, n.inception_3d_output = \
inception_v3_7b(n.inception_3c_output,
dict(conv_3x3_0=384, conv_3x3_reduce=64, conv_3x3_1=96, conv_3x3_2=96))
# stage 4
n.inception_4a_1x1, n.inception_4a_1x1_bn, n.inception_4a_relu_1x1, n.inception_4a_1x7_reduce, \
n.inception_4a_1x7_reduce_bn, n.inception_4a_relu_1x7_reduce, n.inception_4a_1x7_0, n.inception_4a_1x7_0_bn, \
n.inception_4a_relu_1x7_0, n.inception_4a_7x1_0, n.inception_4a_7x1_0_bn, n.inception_4a_relu_7x1_0, \
n.inception_4a_7x1_reduce, n.inception_4a_7x1_reduce_bn, n.inception_4a_relu_7x1_reduce, \
n.inception_4a_7x1_1, n.inception_4a_7x1_1_bn, n.inception_4a_relu_7x1_1, n.inception_4a_1x7_1, \
n.inception_4a_1x7_1_bn, n.inception_4a_relu_1x7_1, n.inception_4a_7x1_2, n.inception_4a_7x1_2_bn, \
n.inception_4a_relu_7x1_2, n.inception_4a_1x7_2, n.inception_4a_1x7_2_bn, n.inception_4a_relu_1x7_2, \
n.inception_4a_pool, n.inception_4a_pool_proj, n.inception_4a_pool_proj_bn, n.inception_4a_relu_pool_proj, \
n.inception_4a_output = \
inception_v3_7c(n.inception_3d_output,
dict(conv_1x1=192, conv_1x7_reduce=128, conv_1x7_0=128, conv_7x1_0=192, conv_7x1_reduce=128,
conv_1x7_1=128, conv_7x1_1=128, conv_1x7_2=128, conv_7x1_2=192, pool_proj=192))
n.inception_4b_1x1, n.inception_4b_1x1_bn, n.inception_4b_relu_1x1, n.inception_4b_1x7_reduce, \
n.inception_4b_1x7_reduce_bn, n.inception_4b_relu_1x7_reduce, n.inception_4b_1x7_0, n.inception_4b_1x7_0_bn, \
n.inception_4b_relu_1x7_0, n.inception_4b_7x1_0, n.inception_4b_7x1_0_bn, n.inception_4b_relu_7x1_0, \
n.inception_4b_7x1_reduce, n.inception_4b_7x1_reduce_bn, n.inception_4b_relu_7x1_reduce, \
n.inception_4b_7x1_1, n.inception_4b_7x1_1_bn, n.inception_4b_relu_7x1_1, n.inception_4b_1x7_1, \
n.inception_4b_1x7_1_bn, n.inception_4b_relu_1x7_1, n.inception_4b_7x1_2, n.inception_4b_7x1_2_bn, \
n.inception_4b_relu_7x1_2, n.inception_4b_1x7_2, n.inception_4b_1x7_2_bn, n.inception_4b_relu_1x7_2, \
n.inception_4b_pool, n.inception_4b_pool_proj, n.inception_4b_pool_proj_bn, n.inception_4b_relu_pool_proj, \
n.inception_4b_output = \
inception_v3_7c(n.inception_4a_output,
dict(conv_1x1=192, conv_1x7_reduce=160, conv_1x7_0=160, conv_7x1_0=192, conv_7x1_reduce=160,
conv_1x7_1=160, conv_7x1_1=160, conv_1x7_2=160, conv_7x1_2=160, pool_proj=192))
n.inception_4c_1x1, n.inception_4c_1x1_bn, n.inception_4c_relu_1x1, n.inception_4c_1x7_reduce, \
n.inception_4c_1x7_reduce_bn, n.inception_4c_relu_1x7_reduce, n.inception_4c_1x7_0, n.inception_4c_1x7_0_bn, \
n.inception_4c_relu_1x7_0, n.inception_4c_7x1_0, n.inception_4c_7x1_0_bn, n.inception_4c_relu_7x1_0, \
n.inception_4c_7x1_reduce, n.inception_4c_7x1_reduce_bn, n.inception_4c_relu_7x1_reduce, \
n.inception_4c_7x1_1, n.inception_4c_7x1_1_bn, n.inception_4c_relu_7x1_1, n.inception_4c_1x7_1, \
n.inception_4c_1x7_1_bn, n.inception_4c_relu_1x7_1, n.inception_4c_7x1_2, n.inception_4c_7x1_2_bn, \
n.inception_4c_relu_7x1_2, n.inception_4c_1x7_2, n.inception_4c_1x7_2_bn, n.inception_4c_relu_1x7_2, \
n.inception_4c_pool, n.inception_4c_pool_proj, n.inception_4c_pool_proj_bn, n.inception_4c_relu_pool_proj, \
n.inception_4c_output = \
inception_v3_7c(n.inception_4b_output,
dict(conv_1x1=192, conv_1x7_reduce=160, conv_1x7_0=160, conv_7x1_0=192, conv_7x1_reduce=160,
conv_1x7_1=160, conv_7x1_1=160, conv_1x7_2=160, conv_7x1_2=160, pool_proj=192))
n.inception_4d_1x1, n.inception_4d_1x1_bn, n.inception_4d_relu_1x1, n.inception_4d_1x7_reduce, \
n.inception_4d_1x7_reduce_bn, n.inception_4d_relu_1x7_reduce, n.inception_4d_1x7_0, n.inception_4d_1x7_0_bn, \
n.inception_4d_relu_1x7_0, n.inception_4d_7x1_0, n.inception_4d_7x1_0_bn, n.inception_4d_relu_7x1_0, \
n.inception_4d_7x1_reduce, n.inception_4d_7x1_reduce_bn, n.inception_4d_relu_7x1_reduce, \
n.inception_4d_7x1_1, n.inception_4d_7x1_1_bn, n.inception_4d_relu_7x1_1, n.inception_4d_1x7_1, \
n.inception_4d_1x7_1_bn, n.inception_4d_relu_1x7_1, n.inception_4d_7x1_2, n.inception_4d_7x1_2_bn, \
n.inception_4d_relu_7x1_2, n.inception_4d_1x7_2, n.inception_4d_1x7_2_bn, n.inception_4d_relu_1x7_2, \
n.inception_4d_pool, n.inception_4d_pool_proj, n.inception_4d_pool_proj_bn, n.inception_4d_relu_pool_proj, \
n.inception_4d_output = \
inception_v3_7c(n.inception_4c_output,
dict(conv_1x1=192, conv_1x7_reduce=192, conv_1x7_0=192, conv_7x1_0=192, conv_7x1_reduce=192,
conv_1x7_1=192, conv_7x1_1=192, conv_1x7_2=192, conv_7x1_2=192, pool_proj=192))
n.inception_4e_3x3_reduce, n.inception_4e_3x3_reduce_bn, n.inception_4e_relu_3x3_reduce, n.inception_4e_3x3_0, \
n.inception_4e_3x3_0_bn, n.inception_4e_relu_3x3_0, n.inception_4e_1x7_reduce, n.inception_4e_1x7_reduce_bn, \
n.inception_4e_relu_1x7_reduce, n.inception_4e_1x7, n.inception_4e_1x7_bn, n.inception_4e_relu_1x7, \
n.inception_4e_7x1, n.inception_4e_7x1_bn, n.inception_4e_relu_7x1, n.inception_4e_3x3_1, \
n.inception_4e_3x3_1_bn, n.inception_4e_relu_3x3_1, n.inception_4e_pool, n.inception_4e_output = \
inception_v3_7d(n.inception_4d_output,
dict(conv_3x3_reduce=192, conv_3x3_0=320, conv_1x7_reduce=192, conv_1x7=192, conv_7x1=192,
conv_3x3_1=192))
# stage 5
n.inception_5a_1x1, n.inception_5a_1x1_bn, n.inception_5a_relu_1x1, n.inception_5a_3x3_0_reduce, \
n.inception_5a_3x3_0_reduce_bn, n.inception_5a_relu_3x3_0_reduce, n.inception_5a_1x3_0, n.inception_5a_1x3_0_bn, \
n.inception_5a_relu_1x3_0, n.inception_5a_3x1_0, n.inception_5a_3x1_0_bn, n.inception_5a_relu_3x1_0, \
n.inception_5a_3x3_1_reduce, n.inception_5a_3x3_1_reduce_bn, n.inception_5a_relu_3x3_1_reduce, n.inception_5a_3x3_1, \
n.inception_5a_3x3_1_bn, n.inception_5a_relu_3x3_1, n.inception_5a_1x3_1, n.inception_5a_1x3_1_bn, \
n.inception_5a_relu_1x3_1, n.inception_5a_3x1_1, n.inception_5a_3x1_1_bn, n.inception_5a_relu_3x1_1, \
n.inception_5a_pool, n.inception_5a_pool_proj, n.inception_5a_pool_proj_bn, n.inception_5a_relu_pool_proj, \
n.inception_5a_output = \
inception_v3_7e(n.inception_4e_output,
dict(conv_1x1=320, conv_3x3_0_reduce=384, conv_1x3_0=384, conv_3x1_0=384,
conv_3x3_1_reduce=448, conv_3x3_1=384, conv_1x3_1=384, conv_3x1_1=384,
pooling=P.Pooling.AVE, pool_proj=192))
n.inception_5b_1x1, n.inception_5b_1x1_bn, n.inception_5b_relu_1x1, n.inception_5b_3x3_0_reduce, \
n.inception_5b_3x3_0_reduce_bn, n.inception_5b_relu_3x3_0_reduce, n.inception_5b_1x3_0, n.inception_5b_1x3_0_bn, \
n.inception_5b_relu_1x3_0, n.inception_5b_3x1_0, n.inception_5b_3x1_0_bn, n.inception_5b_relu_3x1_0, \
n.inception_5b_3x3_1_reduce, n.inception_5b_3x3_1_reduce_bn, n.inception_5b_relu_3x3_1_reduce, n.inception_5b_3x3_1, \
n.inception_5b_3x3_1_bn, n.inception_5b_relu_3x3_1, n.inception_5b_1x3_1, n.inception_5b_1x3_1_bn, \
n.inception_5b_relu_1x3_1, n.inception_5b_3x1_1, n.inception_5b_3x1_1_bn, n.inception_5b_relu_3x1_1, \
n.inception_5b_pool, n.inception_5b_pool_proj, n.inception_5b_pool_proj_bn, n.inception_5b_relu_pool_proj, \
n.inception_5b_output = \
inception_v3_7e(n.inception_5a_output,
dict(conv_1x1=320, conv_3x3_0_reduce=384, conv_1x3_0=384, conv_3x1_0=384,
conv_3x3_1_reduce=448, conv_3x3_1=384, conv_1x3_1=384, conv_3x1_1=384,
pooling=P.Pooling.MAX, pool_proj=192))
n.pool3_7x7_s1 = L.Pooling(n.inception_5b_output,
kernel_size=7,
stride=1,
pool=P.Pooling.AVE)
n.classifier = L.InnerProduct(n.pool3_7x7_s1,
num_output=self.classifier_num,
param=[
dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)
],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant',
value=0))
n.loss = L.SoftmaxWithLoss(n.classifier, n.label)
if phase == 'TRAIN':
pass
else:
n.loss_top1, n.loss_top5 = accuracy_top1_top5(
n.classifier, n.label)
return n.to_proto()
def create_net(phase):
global train_transform_param
global test_transform_param
train_transform_param = {'mirror': False, 'mean_file': Params['mean_file']}
test_transform_param = {'mean_file': Params['mean_file']}
if phase == 'train':
lmdb_file = Params['train_lmdb']
transform_param = train_transform_param
batch_size = Params['batch_size_per_device']
else:
lmdb_file = Params['test_lmdb']
transform_param = test_transform_param
batch_size = Params['test_batch_size']
net = caffe.NetSpec()
net.data, net.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb_file,
transform_param=transform_param,
ntop=2)
#include=dict(phase=caffe_pb2.Phase.Value('TRAIN')),
kwargs = {
'param': [dict(lr_mult=1), dict(lr_mult=2)],
'weight_filler': dict(type='gaussian', std=0.0001),
'bias_filler': dict(type='constant')
}
net.conv1 = L.Convolution(net.data, num_output=16, kernel_size=3, **kwargs)
net.pool1 = L.Pooling(net.conv1,
pool=P.Pooling.MAX,
kernel_size=3,
stride=2)
net.relu1 = L.ReLU(net.pool1, in_place=True)
kwargs = {
'param':
[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
'weight_filler': dict(type='gaussian', std=0.005),
'bias_filler': dict(type='constant')
}
net.fc2 = L.InnerProduct(net.pool1, num_output=128, **kwargs)
net.relu2 = L.ReLU(net.fc2, in_place=True)
net.drop2 = L.Dropout(net.fc2,
in_place=True,
dropout_param=dict(dropout_ratio=0.5))
kwargs = {
'param':
[dict(lr_mult=1, decay_mult=100),
dict(lr_mult=2, decay_mult=0)],
'weight_filler': dict(type='gaussian', std=0.01),
'bias_filler': dict(type='constant', value=0)
}
net.fc3 = L.InnerProduct(net.fc2, num_output=45, **kwargs)
if phase == 'train':
net.loss = L.SoftmaxWithLoss(net.fc3, net.label)
elif phase == 'test':
net.accuracy = L.Accuracy(net.fc3, net.label)
else:
net.prob = L.Softmax(net.fc3)
net_proto = net.to_proto()
if phase == 'deploy':
del net_proto.layer[0]
#del net_proto.layer[-1]
net_proto.input.extend(['data'])
net_proto.input_dim.extend([1, 3, 12, 36])
net_proto.name = '{}_{}'.format(Params['model_name'], phase)
return net_proto
import caffe
from caffe import layers as L
temp_image, temp_label = L.Data(
name='tw',
source='/home/ncl/caffe/examples/mnist/mnist_leveldb',
batch_size=128,
include=dict(phase=caffe.TRAIN),
ntop=2,
backend=0)
def inception_v1_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=mirror))
n.conv1_7x7_s2 = L.Convolution(n.data, num_output=64, kernel_size=7, stride=2, pad=3,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.conv1_relu_7x7 = L.ReLU(n.conv1_7x7_s2, in_place=True)
n.pool1_3x3_s2 = L.Pooling(n.conv1_7x7_s2, kernel_size=3, stride=1, pad=1, pool=P.Pooling.MAX)
n.pool1_norm1 = L.LRN(n.pool1_3x3_s2, local_size=5, alpha=1e-4, beta=0.75)
n.conv2_3x3_reduce = L.Convolution(n.pool1_norm1, kernel_size=1, num_output=64, stride=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.conv2_relu_3x3_reduce = L.ReLU(n.conv2_3x3_reduce, in_place=True)
n.conv2_3x3 = L.Convolution(n.conv2_3x3_reduce, num_output=192, kernel_size=3, stride=1, pad=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.conv2_relu_3x3 = L.ReLU(n.conv2_3x3, in_place=True)
n.conv2_norm2 = L.LRN(n.conv2_3x3, local_size=5, alpha=1e-4, beta=0.75)
n.pool2_3x3_s2 = L.Pooling(n.conv2_norm2, kernel_size=3, stride=1, pad=1, pool=P.Pooling.MAX)
n.inception_3a_1x1, n.inception_3a_relu_1x1, n.inception_3a_3x3_reduce, n.inception_3a_relu_3x3_reduce, \
n.inception_3a_3x3, n.inception_3a_relu_3x3, n.inception_3a_5x5_reduce, n.inception_3a_relu_5x5_reduce, \
n.inception_3a_5x5, n.inception_3a_relu_5x5, n.inception_3a_pool, n.inception_3a_pool_proj, \
n.inception_3a_relu_pool_proj, n.inception_3a_output = \
inception(n.pool2_3x3_s2, dict(conv_1x1=64, conv_3x3_reduce=96, conv_3x3=128, conv_5x5_reduce=16,
conv_5x5=32, pool_proj=32))
n.inception_3b_1x1, n.inception_3b_relu_1x1, n.inception_3b_3x3_reduce, n.inception_3b_relu_3x3_reduce, \
n.inception_3b_3x3, n.inception_3b_relu_3x3, n.inception_3b_5x5_reduce, n.inception_3b_relu_5x5_reduce, \
n.inception_3b_5x5, n.inception_3b_relu_5x5, n.inception_3b_pool, n.inception_3b_pool_proj, \
n.inception_3b_relu_pool_proj, n.inception_3b_output = \
inception(n.inception_3a_output, dict(conv_1x1=128, conv_3x3_reduce=128, conv_3x3=192, conv_5x5_reduce=32,
conv_5x5=96, pool_proj=64))
n.pool3_3x3_s2 = L.Pooling(n.inception_3b_output, kernel_size=3, stride=2, pool=P.Pooling.MAX)
n.inception_4a_1x1, n.inception_4a_relu_1x1, n.inception_4a_3x3_reduce, n.inception_4a_relu_3x3_reduce, \
n.inception_4a_3x3, n.inception_4a_relu_3x3, n.inception_4a_5x5_reduce, n.inception_4a_relu_5x5_reduce, \
n.inception_4a_5x5, n.inception_4a_relu_5x5, n.inception_4a_pool, n.inception_4a_pool_proj, \
n.inception_4a_relu_pool_proj, n.inception_4a_output = \
inception(n.pool3_3x3_s2, dict(conv_1x1=192, conv_3x3_reduce=96, conv_3x3=208, conv_5x5_reduce=16,
conv_5x5=48, pool_proj=64))
# loss 1
n.loss1_ave_pool = L.Pooling(n.inception_4a_output, kernel_size=5, stride=3, pool=P.Pooling.AVE)
n.loss1_conv = L.Convolution(n.loss1_ave_pool, num_output=128, kernel_size=1, stride=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.loss1_relu_conv = L.ReLU(n.loss1_conv, in_place=True)
n.loss1_fc, n.loss1_relu_fc, n.loss1_drop_fc = \
fc_relu_drop(n.loss1_conv, dict(num_output=1024, weight_type='xavier', weight_std=1, bias_type='constant',
bias_value=0.2), dropout_ratio=0.7)
n.loss1_classifier = L.InnerProduct(n.loss1_fc, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss1_loss = L.SoftmaxWithLoss(n.loss1_classifier, n.label, loss_weight=0.3)
if phase == 'TRAIN':
pass
else:
n.loss1_accuracy_top1 = L.Accuracy(n.loss1_classifier, n.label, include=dict(phase=1))
n.loss1_accuracy_top5 = L.Accuracy(n.loss1_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
n.inception_4b_1x1, n.inception_4b_relu_1x1, n.inception_4b_3x3_reduce, n.inception_4b_relu_3x3_reduce, \
n.inception_4b_3x3, n.inception_4b_relu_3x3, n.inception_4b_5x5_reduce, n.inception_4b_relu_5x5_reduce, \
n.inception_4b_5x5, n.inception_4b_relu_5x5, n.inception_4b_pool, n.inception_4b_pool_proj, \
n.inception_4b_relu_pool_proj, n.inception_4b_output = \
inception(n.inception_4a_output, dict(conv_1x1=160, conv_3x3_reduce=112, conv_3x3=224, conv_5x5_reduce=24,
conv_5x5=64, pool_proj=64))
n.inception_4c_1x1, n.inception_4c_relu_1x1, n.inception_4c_3x3_reduce, n.inception_4c_relu_3x3_reduce, \
n.inception_4c_3x3, n.inception_4c_relu_3x3, n.inception_4c_5x5_reduce, n.inception_4c_relu_5x5_reduce, \
n.inception_4c_5x5, n.inception_4c_relu_5x5, n.inception_4c_pool, n.inception_4c_pool_proj, \
n.inception_4c_relu_pool_proj, n.inception_4c_output = \
inception(n.inception_4b_output, dict(conv_1x1=128, conv_3x3_reduce=128, conv_3x3=256, conv_5x5_reduce=24,
conv_5x5=64, pool_proj=64))
n.inception_4d_1x1, n.inception_4d_relu_1x1, n.inception_4d_3x3_reduce, n.inception_4d_relu_3x3_reduce, \
n.inception_4d_3x3, n.inception_4d_relu_3x3, n.inception_4d_5x5_reduce, n.inception_4d_relu_5x5_reduce, \
n.inception_4d_5x5, n.inception_4d_relu_5x5, n.inception_4d_pool, n.inception_4d_pool_proj, \
n.inception_4d_relu_pool_proj, n.inception_4d_output = \
inception(n.inception_4c_output, dict(conv_1x1=112, conv_3x3_reduce=144, conv_3x3=288, conv_5x5_reduce=32,
conv_5x5=64, pool_proj=64))
# loss 2
n.loss2_ave_pool = L.Pooling(n.inception_4d_output, kernel_size=5, stride=3, pool=P.Pooling.AVE)
n.loss2_conv = L.Convolution(n.loss2_ave_pool, num_output=128, kernel_size=1, stride=1,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', weight_std=1),
bias_filler=dict(type='constant', value=0.2))
n.loss2_relu_conv = L.ReLU(n.loss2_conv, in_place=True)
n.loss2_fc, n.loss2_relu_fc, n.loss2_drop_fc = \
fc_relu_drop(n.loss2_conv, dict(num_output=1024, weight_type='xavier', weight_std=1, bias_type='constant',
bias_value=0.2), dropout_ratio=0.7)
n.loss2_classifier = L.InnerProduct(n.loss2_fc, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss2_loss = L.SoftmaxWithLoss(n.loss2_classifier, n.label, loss_weight=0.3)
if phase == 'TRAIN':
pass
else:
n.loss2_accuracy_top1 = L.Accuracy(n.loss2_classifier, n.label, include=dict(phase=1))
n.loss2_accuracy_top5 = L.Accuracy(n.loss2_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
n.inception_4e_1x1, n.inception_4e_relu_1x1, n.inception_4e_3x3_reduce, n.inception_4e_relu_3x3_reduce, \
n.inception_4e_3x3, n.inception_4e_relu_3x3, n.inception_4e_5x5_reduce, n.inception_4e_relu_5x5_reduce, \
n.inception_4e_5x5, n.inception_4e_relu_5x5, n.inception_4e_pool, n.inception_4e_pool_proj, \
n.inception_4e_relu_pool_proj, n.inception_4e_output = \
inception(n.inception_4d_output, dict(conv_1x1=256, conv_3x3_reduce=160, conv_3x3=320, conv_5x5_reduce=32,
conv_5x5=128, pool_proj=128))
n.pool4_3x3_s2 = L.Pooling(n.inception_4e_output, kernel_size=3, stride=2, pool=P.Pooling.MAX)
n.inception_5a_1x1, n.inception_5a_relu_1x1, n.inception_5a_3x3_reduce, n.inception_5a_relu_3x3_reduce, \
n.inception_5a_3x3, n.inception_5a_relu_3x3, n.inception_5a_5x5_reduce, n.inception_5a_relu_5x5_reduce, \
n.inception_5a_5x5, n.inception_5a_relu_5x5, n.inception_5a_pool, n.inception_5a_pool_proj, \
n.inception_5a_relu_pool_proj, n.inception_5a_output = \
inception(n.pool4_3x3_s2, dict(conv_1x1=256, conv_3x3_reduce=160, conv_3x3=320, conv_5x5_reduce=32,
conv_5x5=128, pool_proj=128))
n.inception_5b_1x1, n.inception_5b_relu_1x1, n.inception_5b_3x3_reduce, n.inception_5b_relu_3x3_reduce, \
n.inception_5b_3x3, n.inception_5b_relu_3x3, n.inception_5b_5x5_reduce, n.inception_5b_relu_5x5_reduce, \
n.inception_5b_5x5, n.inception_5b_relu_5x5, n.inception_5b_pool, n.inception_5b_pool_proj, \
n.inception_5b_relu_pool_proj, n.inception_5b_output = \
inception(n.inception_5a_output, dict(conv_1x1=384, conv_3x3_reduce=192, conv_3x3=384, conv_5x5_reduce=48,
conv_5x5=128, pool_proj=128))
n.pool5_7x7_s1 = L.Pooling(n.inception_5b_output, kernel_size=7, stride=1, pool=P.Pooling.AVE)
n.pool5_drop_7x7_s1 = L.Dropout(n.pool5_7x7_s1, in_place=True,
dropout_param=dict(dropout_ratio=0.4))
n.loss3_classifier = L.InnerProduct(n.pool5_7x7_s1, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss3_loss = L.SoftmaxWithLoss(n.loss3_classifier, n.label, loss_weight=1)
if phase == 'TRAIN':
pass
else:
n.loss3_accuracy_top1 = L.Accuracy(n.loss3_classifier, n.label, include=dict(phase=1))
n.loss3_accuracy_top5 = L.Accuracy(n.loss3_classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
return n.to_proto()
def cnn_to_proto_loop(module_options, sample_ranges, dataset, prototxt_file):
if dataset == 'mnist':
prototxt_buffer = 'name: "LeNet"\n'
prototxt_buffer += '"layer {"\n'
prototxt_buffer += ' name: "data"\n'
prototxt_buffer += ' type: "Input"\n'
prototxt_buffer += ' top: "data"\n'
prototxt_buffer += ' input_param { shape: { dim: ' + str(
sample_ranges['train_batch_size_mnist']
) + ' dim: 1 dim: 28 dim: 28 } }\n}\n'
elif dataset == 'cifar10':
prototxt_buffer = 'name: "CIFAR10_full"\n'
prototxt_buffer += 'input: "data"\n'
prototxt_buffer += 'input_dim: ' + str(
int(sample_ranges['train_batch_size_cifar10'])) + '\n'
prototxt_buffer += 'input_dim: 3\n'
prototxt_buffer += 'input_dim: 32\n'
prototxt_buffer += 'input_dim: 32\n'
else:
print "Unknown dataset specified -- Exiting!!"
exit()
prototxt_buffer += '#pragma-off\n'
n = caffe.NetSpec()
if dataset == 'mnist':
n.data, n.label = L.Data(
name="mnist",
batch_size=sample_ranges['train_batch_size_mnist'],
backend=1,
source=sample_ranges['train_source_mnist'],
ntop=2,
transform_param=dict(scale=0.00390625),
include=dict(phase=0))
prototxt_buffer += str(n.to_proto())
n = caffe.NetSpec()
n.data, n.label = L.Data(
name="mnist",
batch_size=sample_ranges['test_batch_size_mnist'],
backend=1,
source=sample_ranges['test_source_mnist'],
ntop=2,
transform_param=dict(scale=0.00390625),
include=dict(phase=1))
previous_layer = n.data
elif dataset == 'cifar10':
n.data, n.label = L.Data(
name="cifar",
batch_size=sample_ranges['train_batch_size_cifar10'],
backend=1,
source=sample_ranges['train_source_cifar10'],
ntop=2,
transform_param=dict(mean_file="./mean.binaryproto"),
include=dict(phase=0))
prototxt_buffer += str(n.to_proto())
n = caffe.NetSpec()
n.data, n.label = L.Data(
name="cifar",
batch_size=sample_ranges['test_batch_size_cifar10'],
backend=1,
source=sample_ranges['test_source_cifar10'],
ntop=2,
transform_param=dict(mean_file="./mean.binaryproto"),
include=dict(phase=1))
previous_layer = n.data
prototxt_buffer += '\n#pragma-on\n'
for num_layer in range(module_options['num_conv_layers']):
layer_name_conv = 'conv' + str(num_layer + 1)
n.conv_ = L.Convolution(
previous_layer,
name=layer_name_conv,
kernel_size=module_options[layer_name_conv]['kernel_size'],
stride=module_options[layer_name_conv]['stride'],
num_output=module_options[layer_name_conv]['num_output'],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'),
param=dict(lr_mult=2))
layer_name_pool = 'pool' + str(num_layer + 1)
n.pool_ = L.Pooling(
n.conv_,
name=layer_name_pool,
kernel_size=module_options[layer_name_pool]['kernel_size'],
stride=module_options[layer_name_pool]['stride'],
pool=P.Pooling.MAX)
previous_layer = n.pool_
for num_layer in range(module_options['num_fc_layers']):
layer_name_fc = 'ip' + str(num_layer + 1)
n.ip_ = L.InnerProduct(
previous_layer,
name=layer_name_fc,
num_output=module_options[layer_name_fc]['num_output'],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'),
param=dict(lr_mult=2))
layer_name_relu = 'relu' + str(num_layer + 1)
n.relu_ = L.ReLU(n.ip_, name=layer_name_relu, in_place=True)
previous_layer = n.relu_
n.ip_last = L.InnerProduct(previous_layer,
num_output=10,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'),
param=dict(lr_mult=2))
n.accuracy = L.Accuracy(n.ip_last, n.label, include=dict(phase=1))
n.loss = L.SoftmaxWithLoss(n.ip_last, n.label)
prototxt_buffer += str(n.to_proto())
write_on_1 = True
past_first_layer = False
write_on_2 = True
lines = prototxt_buffer.split('\n')
# generate model prototxt file
with open(prototxt_file, 'w') as f:
for line in lines:
if line.strip() == '#pragma-off':
write_on_1 = False
continue
if line.strip() == '#pragma-on':
write_on_1 = True
continue
if line.strip() == '"layer {"':
f.write("layer {\n")
continue
if line.strip(
) == 'layer {' and write_on_1 and not past_first_layer:
if dataset == 'cifar10':
f.write("\n")
past_first_layer = True
write_on_2 = False
continue
if line.strip() == 'layer {' and past_first_layer:
write_on_2 = True
if write_on_1 and write_on_2:
#print line
# dstam -- find and replace pycaffe default naming to match paleo syntax
line_ = line
for next_conv in range(module_options['num_conv_layers'] - 1):
if line == ' top: "Convolution' + str(next_conv +
1) + '"':
line_ = ' top: "conv' + str(next_conv + 1) + '"'
if line == ' top: "Pooling' + str(next_conv + 1) + '"':
line_ = ' top: "pool' + str(next_conv + 1) + '"'
if line == ' bottom: "Convolution' + str(next_conv +
1) + '"':
line_ = ' bottom: "conv' + str(next_conv + 1) + '"'
if line == ' bottom: "Pooling' + str(next_conv + 1) + '"':
line_ = ' bottom: "pool' + str(next_conv + 1) + '"'
if line == ' top: "conv_"':
line_ = ' top: "conv' + str(
module_options['num_conv_layers']) + '"'
if line == ' top: "pool_"':
line_ = ' top: "pool' + str(
module_options['num_conv_layers']) + '"'
if line == ' bottom: "conv_"':
line_ = ' bottom: "conv' + str(
module_options['num_conv_layers']) + '"'
if line == ' bottom: "pool_"':
line_ = ' bottom: "pool' + str(
module_options['num_conv_layers']) + '"'
for next_ip in range(module_options['num_fc_layers'] - 1):
if line == ' top: "InnerProduct' + str(next_ip + 1) + '"':
line_ = ' top: "ip' + str(next_ip + 1) + '"'
if line == ' bottom: "InnerProduct' + str(next_ip +
1) + '"':
line_ = ' bottom: "ip' + str(next_ip + 1) + '"'
if line == ' top: "ip_"':
line_ = ' top: "ip' + str(
module_options['num_fc_layers']) + '"'
if line == ' top: "ip_last"':
line_ = ' top: "ip' + str(
module_options['num_fc_layers'] + 1) + '"'
if line == ' bottom: "ip_"':
line_ = ' bottom: "ip' + str(
module_options['num_fc_layers']) + '"'
if line == ' bottom: "ip_last"':
line_ = ' bottom: "ip' + str(
module_options['num_fc_layers'] + 1) + '"'
if line == ' name: "ip_last"':
line_ = ' name: "ip' + str(
module_options['num_fc_layers'] + 1) + '"'
f.write(line_ + "\n")
