def create_net(phase):
global train_transform_param
global test_transform_param
train_transform_param = {
'mirror': True,
'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, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
'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=16, **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=2, **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([64,3,12,36])
net_proto.name = '{}_{}'.format(Params['model_name'], phase)
return net_proto
def mobilenet(split):
train_data_file = root_str + 'train_lmdb'
test_data_file = root_str + 'test_lmdb'
#mean_file = root_str + 'imagenet_mean.binaryproto'
if split == 'train':
data, labels = L.Data(source = train_data_file,
backend = P.Data.LMDB,
ntop = 2,
batch_size = 128,
image_data_param=dict(shuffle=True),
#include={'phase':caffe.TRAIN},
transform_param = dict(#scale=0.00390625,
crop_size = 28,
#mean_file=mean_file,
mirror=True
))
else:
data, labels = L.Data(source = test_data_file,
backend = P.Data.LMDB,
ntop = 2,
batch_size = 100,
image_data_param=dict(shuffle=True),
#include={'phase':caffe.TRAIN},
transform_param = dict(#scale=0.00390625,
crop_size = 28,
#mean_file=mean_file,
#mirror=True
))
if split == 'deploy':
scale, result = conv_BN_scale_relu(split, bottom="data", nout = 32, ks = 3, stride = 1, pad = 1,group=1) #conv1
else:
scale, result = conv_BN_scale_relu(split, bottom=data, nout = 32, ks = 3, stride = 1, pad = 1,group=1) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 32, ks = 3, stride = 1, pad = 1,group=32) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 64, ks = 1, stride = 1, pad = 0,group=1) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 64, ks = 3, stride = 2, pad = 1,group=64) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 128, ks = 1, stride = 1, pad = 0,group=1) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 128, ks = 3, stride = 2, pad = 1,group=128) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 256, ks = 1, stride = 1, pad = 0,group=1) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 256, ks = 3, stride = 2, pad = 1,group=256) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 512, ks = 1, stride = 1, pad = 0,group=1) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 512, ks = 3, stride = 2, pad = 1,group=512) #conv1
scale,result = conv_BN_scale_relu(split, bottom=result, nout = 1024, ks = 1, stride = 1, pad = 0,group=1) #conv1
pool = L.Pooling(result, pool = P.Pooling.AVE, global_pooling = True)
#pool = L.Pooling(result, pool=P.Pooling.AVE, kernel_size=4, stride=1,pad=0)
IP = L.InnerProduct(pool, num_output = 10,
weight_filler=dict(type='xavier'),bias_filler=dict(type='constant'))
if split == 'deploy':
prob=L.Softmax(IP)
return to_proto(prob)
else:
loss = L.SoftmaxWithLoss(IP, labels)
if split == 'train':
return to_proto(loss)
acc = L.Accuracy(IP, labels)
return to_proto(acc, loss)
def max_pool(bottom, ks=2, stride=2): return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def create_net(lmdb, batch_size, mean_file, model):
n = caffe.NetSpec()
#数据层
if model == False:
n.data, n.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb,
include=dict(phase=0),
transform_param=dict(scale=1. / 255,
mirror=True,
crop_size=227,
mean_file=mean_file),
ntop=2)
if model == True:
n.data, n.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb,
include=dict(phase=1),
transform_param=dict(scale=1. / 255,
mirror=True,
crop_size=227,
mean_file=mean_file),
ntop=2)
#卷积层conv1
n.conv1 = L.Convolution(
n.data,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
kernel_size=11,
stride=4,
num_output=96,
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type='constant', value=0))
#ReLu层
n.relu1 = L.ReLU(n.conv1, in_place=True)
#LRN层
n.norm1 = L.LRN(n.conv1, local_size=5, alpha=0.0001, beta=0.75)
#Pooling层
n.pool1 = L.Pooling(n.norm1, kernel_size=3, stride=2, pool=P.Pooling.MAX)
#卷积层conv2
n.conv2 = L.Convolution(
n.pool1,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
kernel_size=5,
num_output=256,
pad=2,
group=2,
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type='constant', value=0.1))
# ReLu2层
n.relu2 = L.ReLU(n.conv2, in_place=True)
# LRN2层
n.norm2 = L.LRN(n.conv2, local_size=5, alpha=0.0001, beta=0.75)
# Pooling2层
n.pool2 = L.Pooling(n.norm2, kernel_size=3, stride=2, pool=P.Pooling.MAX)
# 卷积层conv3
n.conv3 = L.Convolution(
n.pool2,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
kernel_size=3,
num_output=384,
pad=1,
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type='constant', value=0))
# ReLu3层
n.relu3 = L.ReLU(n.conv3, in_place=True)
# 卷积层conv4
n.conv4 = L.Convolution(
n.conv3,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
kernel_size=3,
num_output=384,
pad=1,
group=2,
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type='constant', value=0.1))
# ReLu4层
n.relu4 = L.ReLU(n.conv4, in_place=True)
# 卷积层conv5
n.conv5 = L.Convolution(
n.conv4,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
kernel_size=3,
num_output=256,
pad=1,
group=2,
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type='constant', value=0.1))
# ReLu5层
n.relu5 = L.ReLU(n.conv5, in_place=True)
# Pooling5层
n.pool5 = L.Pooling(n.conv5, kernel_size=3, stride=2, pool=P.Pooling.MAX)
#全连接层fc6
n.fc6 = L.InnerProduct(
n.pool5,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
num_output=4096,
weight_filler=dict(type="gaussian", std=0.005),
bias_filler=dict(type='constant', value=0.1))
n.relu6 = L.ReLU(n.fc6, in_place=True)
#Dropout6层
n.drop6 = L.Dropout(n.fc6, dropout_ratio=0.5, in_place=True) #丢弃数据的概率
# 全连接层fc7
n.fc7 = L.InnerProduct(
n.fc6,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
num_output=4096,
weight_filler=dict(type="gaussian", std=0.005),
bias_filler=dict(type='constant', value=0.1))
# ReLu7层
n.relu7 = L.ReLU(n.fc7, in_place=True)
# Dropout7层
n.drop7 = L.Dropout(n.fc7, dropout_ratio=0.5, in_place=True) # 丢弃数据的概率
# 全连接层fc8
n.fc8 = L.InnerProduct(
n.fc7,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
num_output=1000,
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type='constant', value=0))
if model:
n.acc = L.Accuracy(n.fc8, n.label)
else:
n.loss = L.SoftmaxWithLoss(n.fc8, n.label)
return n.to_proto()
def InceptionV3Body(net, from_layer, output_pred=False):
# scale is fixed to 1, thus we ignore it.
use_scale = False
out_layer = 'conv'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=32, kernel_size=3, pad=0, stride=2, use_scale=use_scale)
from_layer = out_layer
out_layer = 'conv_1'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=32, kernel_size=3, pad=0, stride=1, use_scale=use_scale)
from_layer = out_layer
out_layer = 'conv_2'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=64, kernel_size=3, pad=1, stride=1, use_scale=use_scale)
from_layer = out_layer
out_layer = 'pool'
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX,
kernel_size=3, stride=2, pad=0)
from_layer = out_layer
out_layer = 'conv_3'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=80, kernel_size=1, pad=0, stride=1, use_scale=use_scale)
from_layer = out_layer
out_layer = 'conv_4'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=192, kernel_size=3, pad=0, stride=1, use_scale=use_scale)
from_layer = out_layer
out_layer = 'pool_1'
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX,
kernel_size=3, stride=2, pad=0)
from_layer = out_layer
# inceptions with 1x1, 3x3, 5x5 convolutions
for inception_id in xrange(0, 3):
if inception_id == 0:
out_layer = 'mixed'
tower_2_conv_num_output = 32
else:
out_layer = 'mixed_{}'.format(inception_id)
tower_2_conv_num_output = 64
towers = []
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=48, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=64, kernel_size=5, pad=2, stride=1),
])
towers.append(tower)
tower_name = '{}/tower_1'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=96, kernel_size=3, pad=1, stride=1),
dict(name='conv_2', num_output=96, kernel_size=3, pad=1, stride=1),
])
towers.append(tower)
tower_name = '{}/tower_2'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=P.Pooling.AVE, kernel_size=3, pad=1, stride=1),
dict(name='conv', num_output=tower_2_conv_num_output, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
# inceptions with 1x1, 3x3(in sequence) convolutions
out_layer = 'mixed_3'
towers = []
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=384, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=96, kernel_size=3, pad=1, stride=1),
dict(name='conv_2', num_output=96, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=P.Pooling.MAX, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
# inceptions with 1x1, 7x1, 1x7 convolutions
for inception_id in xrange(4, 8):
if inception_id == 4:
num_output = 128
elif inception_id == 5 or inception_id == 6:
num_output = 160
elif inception_id == 7:
num_output = 192
out_layer = 'mixed_{}'.format(inception_id)
towers = []
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=num_output, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]),
dict(name='conv_2', num_output=192, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]),
])
towers.append(tower)
tower_name = '{}/tower_1'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=num_output, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]),
dict(name='conv_2', num_output=num_output, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]),
dict(name='conv_3', num_output=num_output, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]),
dict(name='conv_4', num_output=192, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]),
])
towers.append(tower)
tower_name = '{}/tower_2'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=P.Pooling.AVE, kernel_size=3, pad=1, stride=1),
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
# inceptions with 1x1, 3x3, 1x7, 7x1 filters
out_layer = 'mixed_8'
towers = []
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=320, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
tower_name = '{}/tower_1'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=192, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]),
dict(name='conv_2', num_output=192, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]),
dict(name='conv_3', num_output=192, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=P.Pooling.MAX, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
for inception_id in xrange(9, 11):
num_output = 384
num_output2 = 448
if inception_id == 9:
pool = P.Pooling.AVE
else:
pool = P.Pooling.MAX
out_layer = 'mixed_{}'.format(inception_id)
towers = []
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=320, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1),
])
subtowers = []
subtower_name = '{}/mixed'.format(tower_name)
subtower = InceptionTower(net, '{}/conv'.format(tower_name), subtower_name, [
dict(name='conv', num_output=num_output, kernel_size=[1, 3], pad=[0, 1], stride=[1, 1]),
])
subtowers.append(subtower)
subtower = InceptionTower(net, '{}/conv'.format(tower_name), subtower_name, [
dict(name='conv_1', num_output=num_output, kernel_size=[3, 1], pad=[1, 0], stride=[1, 1]),
])
subtowers.append(subtower)
net[subtower_name] = L.Concat(*subtowers, axis=1)
towers.append(net[subtower_name])
tower_name = '{}/tower_1'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=num_output2, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=num_output, kernel_size=3, pad=1, stride=1),
])
subtowers = []
subtower_name = '{}/mixed'.format(tower_name)
subtower = InceptionTower(net, '{}/conv_1'.format(tower_name), subtower_name, [
dict(name='conv', num_output=num_output, kernel_size=[1, 3], pad=[0, 1], stride=[1, 1]),
])
subtowers.append(subtower)
subtower = InceptionTower(net, '{}/conv_1'.format(tower_name), subtower_name, [
dict(name='conv_1', num_output=num_output, kernel_size=[3, 1], pad=[1, 0], stride=[1, 1]),
])
subtowers.append(subtower)
net[subtower_name] = L.Concat(*subtowers, axis=1)
towers.append(net[subtower_name])
tower_name = '{}/tower_2'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=pool, kernel_size=3, pad=1, stride=1),
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
if output_pred:
net.pool_3 = L.Pooling(net[from_layer], pool=P.Pooling.AVE, kernel_size=8, pad=0, stride=1)
net.softmax = L.InnerProduct(net.pool_3, num_output=1008)
net.softmax_prob = L.Softmax(net.softmax)
return net
def max_pooling_layer(previous, name, params):
""" create a max pooling layer """
return cl.Pooling(
previous, name=name, pool=cp.Pooling.MAX,
kernel_size=int(params["size"]), stride=int(params["stride"]))
def ResNet(split):
data, labels = L.Python(module='readDataLayer',
layer='input_layer',
ntop=2,
param_str=str(
dict(split=split,
data_dir=this_dir + '/data/',
train_data_name='train_',
test_data_name='test',
train_batches=128,
test_batches=128,
crop_size_x=33,
crop_size_y=33,
train_pack_nums=9,
test_pack_nums=1)))
HGG_1, _ = conv_BN_scale_relu(split, data, 64, 3, 1, 0)
HGG_2, _ = conv_BN_scale_relu(split, HGG_1, 64, 3, 1, 0)
HGG_3, _ = conv_BN_scale_relu(split, HGG_2, 64, 3, 1, 0)
HGG_4 = L.Pooling(HGG_3,
pool=P.Pooling.MAX,
global_pooling=False,
stride=2,
kernel_size=3)
HGG_5, _ = conv_BN_scale_relu(split, HGG_4, 128, 3, 1, 0)
HGG_6, _ = conv_BN_scale_relu(split, HGG_5, 128, 3, 1, 0)
HGG_7, _ = conv_BN_scale_relu(split, HGG_6, 128, 3, 1, 0)
HGG_8 = L.Pooling(HGG_7,
pool=P.Pooling.MAX,
global_pooling=False,
stride=2,
kernel_size=3)
HGG_8a = L.Flatten(HGG_8)
HGG_9 = L.ReLU(HGG_8a)
HGG_9a = L.InnerProduct(L.Dropout(HGG_9, dropout_ratio=0.1),
num_output=256,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
# HGG_9a = L.InnerProduct(HGG_9, num_output = 256)
HGG_10 = L.ReLU(HGG_9a)
HGG_10a = L.InnerProduct(L.Dropout(HGG_10, dropout_ratio=0.1),
num_output=256,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
# HGG_10a = L.InnerProduct(HGG_10,num_output = 256)
HGG_11 = L.Dropout(HGG_10a, dropout_ratio=0.1)
HGG_11a = L.InnerProduct(HGG_11,
num_output=5,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
acc = L.Accuracy(HGG_11a, labels)
loss = L.SoftmaxWithLoss(HGG_11a, labels)
return to_proto(loss, acc)
def generate_net(train_lmdb, val_lmdb, train_batch_size, test_batch_size):
net = caffe.NetSpec()
net.data, net.label = L.Data(source=train_lmdb,
backend=caffe.params.Data.LMDB,
batch_size=train_batch_size,
ntop=2,
transform_param=dict(
crop_size=224,
mean_value=[103.94, 116.78, 123.68]),
scale=0.017,
include=dict(phase=caffe.TRAIN))
# note:
train_data_layer_str = str(net.to_proto())
net.data, net.label = L.Data(source=val_lmdb,
backend=caffe.params.Data.LMDB,
batch_size=test_batch_size,
ntop=2,
transform_param=dict(
crop_size=224,
mean_value=[103.94, 116.78, 123.68]),
scale=0.017,
include=dict(phase=caffe.TEST))
# bone
net.conv1 = L.Convolution(
net.data,
num_output=32,
kernel_size=3,
stride=2,
pad=1,
weight_filler={"type": "xavier"},
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)])
net.tops['conv1/bn'] = L.BatchNorm(net.conv1,
param=[
dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0)
],
in_place=False)
net.tops['conv1/scale'] = L.Scale(
net.tops['conv1/bn'],
param=[dict(lr_mult=1, decay_mult=0),
dict(lr_mult=2, decay_mult=0)],
scale_param={
'filler': {
'value': 1
},
'bias_term': True,
'bias_filler': {
'value': 0
}
},
in_place=True)
net.conv1_relu = L.ReLU6(net.tops['conv1/scale'], in_place=True)
bottleneck(net, net.conv1_relu, 'conv2', 32, 1, 16, 1)
bottleneck(net, net.tops['conv2/1x1_down/scale'], 'conv3_1', 16, 6, 24, 2)
bottleneck(net, net.tops['conv3_1/1x1_down/scale'], 'conv3_2', 24, 6, 24,
1)
bottleneck(net, net.tops['conv3_2/add'], 'conv4_1', 24, 6, 32, 2)
bottleneck(net, net.tops['conv4_1/1x1_down/scale'], 'conv4_2', 32, 6, 32,
1)
bottleneck(net, net.tops['conv4_2/add'], 'conv4_3', 32, 6, 32, 1)
bottleneck(net, net.tops['conv4_3/add'], 'conv5_1', 32, 6, 64, 2)
bottleneck(net, net.tops['conv5_1/1x1_down/scale'], 'conv5_2', 64, 6, 64,
1)
bottleneck(net, net.tops['conv5_2/add'], 'conv5_3', 64, 6, 64, 1)
bottleneck(net, net.tops['conv5_3/add'], 'conv5_4', 64, 6, 64, 1)
bottleneck(net, net.tops['conv5_4/add'], 'conv6_1', 64, 6, 96, 1)
bottleneck(net, net.tops['conv6_1/1x1_down/scale'], 'conv6_2', 96, 6, 96,
1)
bottleneck(net, net.tops['conv6_2/add'], 'conv6_3', 96, 6, 96, 1)
bottleneck(net, net.tops['conv6_3/add'], 'conv7_1', 96, 6, 160, 2)
bottleneck(net, net.tops['conv7_1/1x1_down/scale'], 'conv7_2', 160, 6, 160,
1)
bottleneck(net, net.tops['conv7_2/add'], 'conv7_3', 160, 6, 160, 1)
bottleneck(net, net.tops['conv7_3/add'], 'conv8', 160, 6, 320, 1)
net.conv9 = L.Convolution(
net.tops['conv8/1x1_down/scale'],
num_output=1280,
kernel_size=1,
weight_filler={"type": "xavier"},
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)])
net.tops['conv9/bn'] = L.BatchNorm(net.conv9,
param=[
dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0)
],
in_place=False)
net.tops['conv9/scale'] = L.Scale(
net.tops['conv9/bn'],
param=[dict(lr_mult=1, decay_mult=0),
dict(lr_mult=2, decay_mult=0)],
scale_param={
'filler': {
'value': 1
},
'bias_term': True,
'bias_filler': {
'value': 0
}
},
in_place=True)
net.conv9_relu = caffe.layers.ReLU6(net.tops['conv9/scale'], in_place=True)
# global average pooling
net.pool10 = L.Pooling(net.conv9_relu,
pool=caffe.params.Pooling.AVE,
global_pooling=True)
# 1000 cls
net.conv11 = L.Convolution(
net.pool10,
num_output=1000,
kernel_size=1,
weight_filler={
"type": "gaussian",
"mean": 0,
"std": 0.01
},
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)])
# softmax loss
net.loss = L.SoftmaxWithLoss(net.conv11,
net.label,
include=dict(phase=caffe.TRAIN))
# accuracy
net.accuracy = L.Accuracy(net.conv11,
net.label,
include=dict(phase=caffe.TEST))
net.accuracy_top5 = L.Accuracy(net.conv11,
net.label,
include=dict(phase=caffe.TEST),
accuracy_param=dict(top_k=5))
return train_data_layer_str + str(net.to_proto())
def compile_time_operation(self, learning_option, cluster):
"""
define pooling(max/average pooling) operation for input blob
"""
# get input
input_ = self.get_input('input')
indim = self.get_dimension('input')
print(indim) # twtest
# get attr
# required field
pool_type = self.get_attr('pool_type', default=None)
if pool_type is None:
raise Exception(
'[DLMDL ERROR]: {0} in {1} layer must be declared.'.format(
'type', self.name))
kernel_size = self.get_attr('kernel_size', default=None)
if kernel_size is None:
raise Exception(
'[DLMDL ERROR]: {0} in {1} layer must be declared.'.format(
'kernel_size', self.name))
# optional field
padding = self.get_attr('padding', default='VALID')
stride = self.get_attr('stride', default=1)
engine = self.get_attr('engine', default='DEFAULT')
global_pooling = self.get_attr('global_pooling', default=False)
# padding
if padding == 'SAME':
outdim = [
np.ceil(float(indim[i]) / float(stride)) for i in xrange(2)
]
outdim.insert(0, indim[0])
outdim.insert(1, indim[1])
p = [
int(((outdim[i + 2] - 1) * stride + kernel_size[i] -
indim[i + 2]) / 2) for i in xrange(2)
]
else:
outdim = [
np.ceil(float(indim[i] - kernel_size[i] + 1) / float(stride))
for i in xrange(2)
]
outdim.insert(0, indim[0])
outdim.insert(1, indim[1])
p = [0, 0]
if engine == 'DEFAULT':
engine_idx = 0
elif engine == 'CAFFE':
engine_idx = 1
elif engine == 'CUDNN':
engine_idx = 2
else: #TODO: error handling
pass
# pool=0: max_pool, pool=1: avr_pool
if pool_type == 'MAX':
pool_type_idx = 0
elif pool_type == 'AVG':
pool_type_idx = 1
else: #TODO: error handling
pass
pool = L.Pooling(input_,
name=self.name,
pool=pool_type_idx,
kernel_h=kernel_size[0],
kernel_w=kernel_size[1],
stride=stride,
pad_h=p[0],
pad_w=p[1],
engine=engine_idx,
global_pooling=global_pooling)
self.set_output('output', pool)
self.set_dimension('output', outdim)
def resnet18(split, mean, opt):
n = caffe.NetSpec()
# config python data layer
if split == 'train':
batch_size = opt.train_batch_size
if split == 'val':
batch_size = opt.val_batch_size
if split == 'test':
batch_size = opt.test_batch_size
if split == 'train' or split == 'val':
dataset_name = opt.train_dataset_name
else:
dataset_name = opt.test_dataset_name
pydata_params = dict(split=split,
data_dir=opt.data_dir,
batch_size=batch_size,
mean=mean,
dataset=dataset_name,
use_HSV=opt.use_HSV,
load_size=opt.load_size,
crop_size=opt.crop_size)
n.data, n.label = L.Python(module='faceData_layers',
layer='FaceDataLayer',
ntop=2,
param_str=str(pydata_params))
# start building main body of network
# There main differences:
# 1. do not use 4*nout for certain convolution layers
# 2. do not use bias_term for convolution layer before start of residual blocks
# 3. do not set the BN layer parameter, moving_average_fraction, to 0.9 (using default value 0.999)
# 4. for weight filter initialziation, we do not specify 'msra'
n.conv1, n.bn_conv1, n.scale_conv1 = _conv_bn_scale(n.data,
64,
bias_term=False,
kernel_size=7,
pad=3,
stride=2)
n.conv1_relu = L.ReLU(n.scale_conv1, in_place=True)
n.pool1 = L.Pooling(n.conv1_relu,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX)
_resnet_block_2stages('2a', n, n.pool1, 64, branch1=True, initial_stride=1)
_resnet_block_2stages('2b', n, n.res2a_relu, 64)
_resnet_block_2stages('3a', n, n.res2b_relu, 128, branch1=True)
_resnet_block_2stages('3b', n, n.res3a_relu, 128)
_resnet_block_2stages('4a', n, n.res3b_relu, 256, branch1=True)
_resnet_block_2stages('4b', n, n.res4a_relu, 256)
_resnet_block_2stages('5a', n, n.res4b_relu, 512, branch1=True)
_resnet_block_2stages('5b', n, n.res5a_relu, 512)
n.pool5 = L.Pooling(n.res5b_relu,
kernel_size=7,
stride=1,
pool=P.Pooling.AVE)
# fully connected classifier
lr_ratio = 100 # lr multiplier for truncated layers
n.fc_face1 = L.InnerProduct(n.pool5,
num_output=1000,
param=[
dict(lr_mult=1 * lr_ratio, decay_mult=1),
dict(lr_mult=2 * lr_ratio, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
n.fc_face2 = L.InnerProduct(n.fc_face1,
num_output=2,
param=[
dict(lr_mult=1 * lr_ratio, decay_mult=1),
dict(lr_mult=2 * lr_ratio, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
# loss and accuracy layer
n.loss = L.SoftmaxWithLoss(n.fc_face2, n.label)
n.acc = L.Accuracy(n.fc_face2, n.label)
return n.to_proto()
def resnet50(split, mean, opt):
n = caffe.NetSpec()
# config python data layer
if split == 'train':
batch_size = opt.train_batch_size
if split == 'val':
batch_size = opt.val_batch_size
if split == 'test':
batch_size = opt.test_batch_size
if split == 'train' or split == 'val':
dataset_name = opt.train_dataset_name
else:
dataset_name = opt.test_dataset_name
pydata_params = dict(split=split,
data_dir=opt.data_dir,
batch_size=batch_size,
mean=mean,
dataset=dataset_name,
use_HSV=opt.use_HSV,
load_size=opt.load_size,
crop_size=opt.crop_size)
n.data, n.label = L.Python(module='faceData_layers',
layer='FaceDataLayer',
ntop=2,
param_str=str(pydata_params))
# start building main body of network
n.conv1, n.bn_conv1, n.scale_conv1 = _conv_bn_scale(n.data,
64,
bias_term=True,
kernel_size=7,
pad=3,
stride=2)
n.conv1_relu = L.ReLU(n.scale_conv1)
n.pool1 = L.Pooling(n.conv1_relu,
kernel_size=3,
stride=2,
pool=P.Pooling.MAX)
_resnet_block_3stages('2a', n, n.pool1, 64, branch1=True, initial_stride=1)
_resnet_block_3stages('2b', n, n.res2a_relu, 64)
_resnet_block_3stages('2c', n, n.res2b_relu, 64)
_resnet_block_3stages('3a', n, n.res2c_relu, 128, branch1=True)
_resnet_block_3stages('3b', n, n.res3a_relu, 128)
_resnet_block_3stages('3c', n, n.res3b_relu, 128)
_resnet_block_3stages('3d', n, n.res3c_relu, 128)
_resnet_block_3stages('4a', n, n.res3d_relu, 256, branch1=True)
_resnet_block_3stages('4b', n, n.res4a_relu, 256)
_resnet_block_3stages('4c', n, n.res4b_relu, 256)
_resnet_block_3stages('4d', n, n.res4c_relu, 256)
_resnet_block_3stages('4e', n, n.res4d_relu, 256)
_resnet_block_3stages('4f', n, n.res4e_relu, 256)
_resnet_block_3stages('5a', n, n.res4f_relu, 512, branch1=True)
_resnet_block_3stages('5b', n, n.res5a_relu, 512)
_resnet_block_3stages('5c', n, n.res5b_relu, 512)
n.pool5 = L.Pooling(n.res5c_relu,
kernel_size=7,
stride=1,
pool=P.Pooling.AVE)
# fully connected classifier
lr_ratio = 100 # lr multiplier for truncated layers
n.fc_face1 = L.InnerProduct(n.pool5,
num_output=1000,
param=[
dict(lr_mult=1 * lr_ratio, decay_mult=1),
dict(lr_mult=2 * lr_ratio, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
n.fc_face2 = L.InnerProduct(n.fc_face1,
num_output=2,
param=[
dict(lr_mult=1 * lr_ratio, decay_mult=1),
dict(lr_mult=2 * lr_ratio, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
# loss and accuracy layer
n.loss = L.SoftmaxWithLoss(n.fc_face2, n.label)
n.acc = L.Accuracy(n.fc_face2, n.label)
return n.to_proto()
def mfb_coatt(mode, batchsize, T, question_vocab_size, folder):
n = caffe.NetSpec()
mode_str = json.dumps({
'mode': mode,
'batchsize': batchsize,
'folder': folder
})
if mode == 'val':
n.data, n.cont, n.img_feature, n.label, n.glove = L.Python( \
module='vqa_data_layer', layer='VQADataProviderLayer', \
param_str=mode_str, ntop=5 )
else:
n.data, n.cont, n.img_feature, n.label, n.glove = L.Python(\
module='vqa_data_layer_kld', layer='VQADataProviderLayer', \
param_str=mode_str, ntop=5 )
n.embed = L.Embed(n.data, input_dim=question_vocab_size, num_output=300, \
weight_filler=dict(type='xavier'))
n.embed_tanh = L.TanH(n.embed)
concat_word_embed = [n.embed_tanh, n.glove]
n.concat_embed = L.Concat(*concat_word_embed,
concat_param={'axis': 2}) # T x N x 600
# LSTM
n.lstm1 = L.LSTM(\
n.concat_embed, n.cont,\
recurrent_param=dict(\
num_output=config.LSTM_UNIT_NUM,\
weight_filler=dict(type='xavier')))
n.lstm1_droped = L.Dropout(
n.lstm1, dropout_param={'dropout_ratio': config.LSTM_DROPOUT_RATIO})
n.lstm1_resh = L.Permute(n.lstm1_droped,
permute_param=dict(order=[1, 2, 0]))
n.lstm1_resh2 = L.Reshape(n.lstm1_resh, \
reshape_param=dict(shape=dict(dim=[0,0,0,1])))
'''
Question Attention
'''
n.qatt_conv1 = L.Convolution(n.lstm1_resh2,
kernel_size=1,
stride=1,
num_output=512,
pad=0,
weight_filler=dict(type='xavier'))
n.qatt_relu = L.ReLU(n.qatt_conv1)
n.qatt_conv2 = L.Convolution(n.qatt_relu,
kernel_size=1,
stride=1,
num_output=config.NUM_QUESTION_GLIMPSE,
pad=0,
weight_filler=dict(type='xavier'))
n.qatt_reshape = L.Reshape(
n.qatt_conv2,
reshape_param=dict(shape=dict(dim=[
-1, config.NUM_QUESTION_GLIMPSE, config.MAX_WORDS_IN_QUESTION, 1
]))) # N*NUM_QUESTION_GLIMPSE*15
n.qatt_softmax = L.Softmax(n.qatt_reshape, axis=2)
qatt_maps = L.Slice(n.qatt_softmax,
ntop=config.NUM_QUESTION_GLIMPSE,
slice_param={'axis': 1})
dummy_lstm = L.DummyData(shape=dict(dim=[batchsize, 1]),
data_filler=dict(type='constant', value=1),
ntop=1)
qatt_feature_list = []
for i in xrange(config.NUM_QUESTION_GLIMPSE):
if config.NUM_QUESTION_GLIMPSE == 1:
n.__setattr__(
'qatt_feat%d' % i,
L.SoftAttention(n.lstm1_resh2, qatt_maps, dummy_lstm))
else:
n.__setattr__(
'qatt_feat%d' % i,
L.SoftAttention(n.lstm1_resh2, qatt_maps[i], dummy_lstm))
qatt_feature_list.append(n.__getattr__('qatt_feat%d' % i))
n.qatt_feat_concat = L.Concat(*qatt_feature_list)
'''
Image Attention with MFB
'''
n.q_feat_resh = L.Reshape(
n.qatt_feat_concat, reshape_param=dict(shape=dict(dim=[0, -1, 1, 1])))
n.i_feat_resh = L.Reshape(
n.img_feature,
reshape_param=dict(shape=dict(
dim=[0, -1, config.IMG_FEAT_WIDTH, config.IMG_FEAT_WIDTH])))
n.iatt_q_proj = L.InnerProduct(n.q_feat_resh,
num_output=config.JOINT_EMB_SIZE,
weight_filler=dict(type='xavier'))
n.iatt_q_resh = L.Reshape(
n.iatt_q_proj,
reshape_param=dict(shape=dict(dim=[-1, config.JOINT_EMB_SIZE, 1, 1])))
n.iatt_q_tile1 = L.Tile(n.iatt_q_resh, axis=2, tiles=config.IMG_FEAT_WIDTH)
n.iatt_q_tile2 = L.Tile(n.iatt_q_tile1,
axis=3,
tiles=config.IMG_FEAT_WIDTH)
n.iatt_i_conv = L.Convolution(n.i_feat_resh,
kernel_size=1,
stride=1,
num_output=config.JOINT_EMB_SIZE,
pad=0,
weight_filler=dict(type='xavier'))
n.iatt_i_resh1 = L.Reshape(n.iatt_i_conv,
reshape_param=dict(shape=dict(dim=[
-1, config.JOINT_EMB_SIZE,
config.IMG_FEAT_WIDTH, config.IMG_FEAT_WIDTH
])))
n.iatt_iq_eltwise = L.Eltwise(n.iatt_q_tile2,
n.iatt_i_resh1,
eltwise_param=dict(operation=0))
n.iatt_iq_droped = L.Dropout(
n.iatt_iq_eltwise,
dropout_param={'dropout_ratio': config.MFB_DROPOUT_RATIO})
n.iatt_iq_resh2 = L.Reshape(n.iatt_iq_droped,
reshape_param=dict(shape=dict(
dim=[-1, config.JOINT_EMB_SIZE, 196, 1])))
n.iatt_iq_permute1 = L.Permute(n.iatt_iq_resh2,
permute_param=dict(order=[0, 2, 1, 3]))
n.iatt_iq_resh2 = L.Reshape(
n.iatt_iq_permute1,
reshape_param=dict(shape=dict(dim=[
-1, config.IMG_FEAT_SIZE, config.MFB_OUT_DIM, config.MFB_FACTOR_NUM
])))
n.iatt_iq_sumpool = L.Pooling(n.iatt_iq_resh2, pool=P.Pooling.SUM, \
pooling_param=dict(kernel_w=config.MFB_FACTOR_NUM, kernel_h=1))
n.iatt_iq_permute2 = L.Permute(n.iatt_iq_sumpool,
permute_param=dict(order=[0, 2, 1, 3]))
n.iatt_iq_sqrt = L.SignedSqrt(n.iatt_iq_permute2)
n.iatt_iq_l2 = L.L2Normalize(n.iatt_iq_sqrt)
## 2 conv layers 1000 -> 512 -> 2
n.iatt_conv1 = L.Convolution(n.iatt_iq_l2,
kernel_size=1,
stride=1,
num_output=512,
pad=0,
weight_filler=dict(type='xavier'))
n.iatt_relu = L.ReLU(n.iatt_conv1)
n.iatt_conv2 = L.Convolution(n.iatt_relu,
kernel_size=1,
stride=1,
num_output=config.NUM_IMG_GLIMPSE,
pad=0,
weight_filler=dict(type='xavier'))
n.iatt_resh = L.Reshape(
n.iatt_conv2,
reshape_param=dict(shape=dict(
dim=[-1, config.NUM_IMG_GLIMPSE, config.IMG_FEAT_SIZE])))
n.iatt_softmax = L.Softmax(n.iatt_resh, axis=2)
n.iatt_softmax_resh = L.Reshape(
n.iatt_softmax,
reshape_param=dict(shape=dict(dim=[
-1, config.NUM_IMG_GLIMPSE, config.IMG_FEAT_WIDTH,
config.IMG_FEAT_WIDTH
])))
iatt_maps = L.Slice(n.iatt_softmax_resh,
ntop=config.NUM_IMG_GLIMPSE,
slice_param={'axis': 1})
dummy = L.DummyData(shape=dict(dim=[batchsize, 1]),
data_filler=dict(type='constant', value=1),
ntop=1)
iatt_feature_list = []
for i in xrange(config.NUM_IMG_GLIMPSE):
if config.NUM_IMG_GLIMPSE == 1:
n.__setattr__('iatt_feat%d' % i,
L.SoftAttention(n.i_feat_resh, iatt_maps, dummy))
else:
n.__setattr__('iatt_feat%d' % i,
L.SoftAttention(n.i_feat_resh, iatt_maps[i], dummy))
n.__setattr__('iatt_feat%d_resh'%i, L.Reshape(n.__getattr__('iatt_feat%d'%i), \
reshape_param=dict(shape=dict(dim=[0,-1]))))
iatt_feature_list.append(n.__getattr__('iatt_feat%d_resh' % i))
n.iatt_feat_concat = L.Concat(*iatt_feature_list)
n.iatt_feat_concat_resh = L.Reshape(
n.iatt_feat_concat, reshape_param=dict(shape=dict(dim=[0, -1, 1, 1])))
'''
Fine-grained Image-Question MFB fusion
'''
n.mfb_q_proj = L.InnerProduct(n.q_feat_resh,
num_output=config.JOINT_EMB_SIZE,
weight_filler=dict(type='xavier'))
n.mfb_i_proj = L.InnerProduct(n.iatt_feat_concat_resh,
num_output=config.JOINT_EMB_SIZE,
weight_filler=dict(type='xavier'))
n.mfb_iq_eltwise = L.Eltwise(n.mfb_q_proj,
n.mfb_i_proj,
eltwise_param=dict(operation=0))
n.mfb_iq_drop = L.Dropout(
n.mfb_iq_eltwise,
dropout_param={'dropout_ratio': config.MFB_DROPOUT_RATIO})
n.mfb_iq_resh = L.Reshape(
n.mfb_iq_drop,
reshape_param=dict(shape=dict(
dim=[-1, 1, config.MFB_OUT_DIM, config.MFB_FACTOR_NUM])))
n.mfb_iq_sumpool = L.Pooling(n.mfb_iq_resh, pool=P.Pooling.SUM, \
pooling_param=dict(kernel_w=config.MFB_FACTOR_NUM, kernel_h=1))
n.mfb_out = L.Reshape(n.mfb_iq_sumpool,\
reshape_param=dict(shape=dict(dim=[-1,config.MFB_OUT_DIM])))
n.mfb_sign_sqrt = L.SignedSqrt(n.mfb_out)
n.mfb_l2 = L.L2Normalize(n.mfb_sign_sqrt)
n.prediction = L.InnerProduct(n.mfb_l2,
num_output=config.NUM_OUTPUT_UNITS,
weight_filler=dict(type='xavier'))
if mode == 'val':
n.loss = L.SoftmaxWithLoss(n.prediction, n.label)
else:
n.loss = L.SoftmaxKLDLoss(n.prediction, n.label)
return n.to_proto()
def lenet(lmdb_data, lmdb_label, batch_size, deploy, crop=64, mirror=False):
"""Simple LeNet to predict cdf."""
data_transforms = dict(scale=1.)
if crop: # will crop images to [crop]x[crop] with random center
data_transforms['crop_size'] = crop
if mirror: # will randomly flip images
data_transforms['mirror'] = 1
n = caffe.NetSpec()
if deploy:
input_ = "data"
dim1 = batch_size
dim2 = 3 # need to change these manually
dim3 = 64
dim4 = 64
n.data = L.Layer()
else:
n.data = L.Data(batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb_data,
transform_param=data_transforms,
ntop=1)
n.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb_label,
ntop=1)
# first convolutional layer
n.conv1 = L.Convolution(n.data,
kernel_size=5,
num_output=40,
weight_filler=dict(type='xavier'))
n.norm1 = L.BatchNorm(n.conv1)
n.relu1 = L.ReLU(n.norm1, in_place=True)
n.pool1 = L.Pooling(n.relu1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
# second convolutional layer
n.conv2 = L.Convolution(n.pool1,
kernel_size=5,
num_output=40,
weight_filler=dict(type='xavier'))
n.norm2 = L.BatchNorm(n.conv2)
n.relu2 = L.ReLU(n.norm2, in_place=True)
n.pool2 = L.Pooling(n.relu2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
# fully connected layers
n.drop = L.Dropout(n.pool2, dropout_ratio=0.5)
n.ip1 = L.InnerProduct(n.drop,
num_output=600,
weight_filler=dict(type='xavier'))
n.out = L.Sigmoid(n.ip1)
if deploy:
deploy_str = ('input: {}\ninput_dim: {}\n'
'input_dim: {}\ninput_dim: {}\n'
'input_dim: {}').format('"%s"' % input_, dim1, dim2,
dim3, dim4)
return (deploy_str + '\n' + 'layer {' +
'layer {'.join(str(n.to_proto()).split('layer {')[2:]))
else:
n.loss = L.EuclideanLoss(n.out, n.label)
return str(n.to_proto())
def convert_symbol2proto(symbol):
def looks_like_weight(name):
"""Internal helper to figure out if node should be hidden with `hide_weights`.
"""
if name.endswith("_weight"):
return True
if name.endswith("_bias"):
return True
if name.endswith("_beta") or name.endswith("_gamma") or name.endswith("_moving_var") or name.endswith(
"_moving_mean"):
return True
return False
json_symbol = json.loads(symbol.tojson())
all_nodes = json_symbol['nodes']
no_weight_nodes = []
for node in all_nodes:
op = node['op']
name = node['name']
if op == 'null':
if looks_like_weight(name):
continue
no_weight_nodes.append(node)
# build next node dict
next_node = dict()
for node in no_weight_nodes:
node_name = node['name']
for input in node['inputs']:
last_node_name = all_nodes[input[0]]['name']
if last_node_name in next_node:
next_node[last_node_name].append(node_name)
else:
next_node[last_node_name] = [node_name]
supported_op_type = ['null', 'BatchNorm', 'Convolution', 'Activation', 'Pooling', 'elemwise_add', 'SliceChannel',
'FullyConnected', 'SoftmaxOutput', '_maximum', 'add_n', 'Concat']
top_dict = dict()
caffe_net = caffe.NetSpec()
for node in no_weight_nodes:
if node['op'] == 'null':
input_param = dict()
if node['name'] == 'data':
input_param['shape'] = dict(dim=[1, 3, 160, 160])
else:
input_param['shape'] = dict(dim=[1])
top_data = CL.Input(ntop=1, input_param=input_param)
top_dict[node['name']] = [top_data]
setattr(caffe_net, node['name'], top_data)
elif node['op'].endswith('_copy'):
pass
elif node['op'] == 'BatchNorm':
input = node['inputs'][0]
while True:
if all_nodes[input[0]]['op'] not in supported_op_type:
input = all_nodes[input[0]]['inputs'][0]
else:
break
bottom_node_name = all_nodes[input[0]]['name']
attr = node['attrs']
in_place = False
if len(next_node[bottom_node_name]) == 1:
in_place = True
if 'momentum' in attr:
momentum = float(attr['momentum'])
else:
momentum = 0.9
if 'eps' in attr:
eps = float(attr['eps'])
else:
eps = 0.001
if NO_INPLACE:
in_place = False
bn_top = CL.BatchNorm(top_dict[bottom_node_name][input[1]], ntop=1,
batch_norm_param=dict(use_global_stats=True,
moving_average_fraction=momentum,
eps=eps), in_place=in_place)
setattr(caffe_net, node['name'], bn_top)
scale_top = CL.Scale(bn_top, ntop=1, scale_param=dict(bias_term=True), in_place=not NO_INPLACE)
top_dict[node['name']] = [scale_top]
setattr(caffe_net, node['name'] + '_scale', scale_top)
elif node['op'] == 'Convolution':
input = node['inputs'][0]
while True:
if all_nodes[input[0]]['op'] not in supported_op_type:
input = all_nodes[input[0]]['inputs'][0]
else:
break
bottom_node_name = all_nodes[input[0]]['name']
attr = node['attrs']
convolution_param = dict()
if 'kernel' in attr:
kernel_size = eval(attr['kernel'])
assert kernel_size[0] == kernel_size[1]
convolution_param['kernel_size'] = kernel_size[0]
else:
convolution_param['kernel_size'] = 1
if 'no_bias' in attr:
convolution_param['bias_term'] = not eval(attr['no_bias'])
if 'num_group' in attr:
convolution_param['group'] = int(attr['num_group'])
convolution_param['num_output'] = int(attr['num_filter'])
if 'pad' in attr:
pad_size = eval(attr['pad'])
assert pad_size[0] == pad_size[1]
convolution_param['pad'] = pad_size[0]
if 'stride' in attr:
stride_size = eval(attr['stride'])
assert stride_size[0] == stride_size[1]
convolution_param['stride'] = stride_size[0]
conv_top = CL.Convolution(top_dict[bottom_node_name][input[1]], ntop=1, convolution_param=convolution_param)
top_dict[node['name']] = [conv_top]
setattr(caffe_net, node['name'], conv_top)
elif node['op'] == 'Activation':
input = node['inputs'][0]
while True:
if all_nodes[input[0]]['op'] not in supported_op_type:
input = all_nodes[input[0]]['inputs'][0]
else:
break
bottom_node_name = all_nodes[input[0]]['name']
attr = node['attrs']
in_place = False
if len(next_node[bottom_node_name]) == 1:
in_place = True
if NO_INPLACE:
in_place = False
if attr['act_type'] == 'relu':
ac_top = CL.ReLU(top_dict[bottom_node_name][input[1]], ntop=1, in_place=in_place)
elif attr['act_type'] == 'sigmoid':
ac_top = CL.Sigmoid(top_dict[bottom_node_name][input[1]], ntop=1, in_place=in_place)
elif attr['act_type'] == 'tanh':
ac_top = CL.TanH(top_dict[bottom_node_name][input[1]], ntop=1, in_place=in_place)
top_dict[node['name']] = [ac_top]
setattr(caffe_net, node['name'], ac_top)
elif node['op'] == 'Pooling':
input = node['inputs'][0]
while True:
if all_nodes[input[0]]['op'] not in supported_op_type:
input = all_nodes[input[0]]['inputs'][0]
else:
break
bottom_node_name = all_nodes[input[0]]['name']
attr = node['attrs']
pooling_param = dict()
if attr['pool_type'] == 'avg':
pooling_param['pool'] = 1
elif attr['pool_type'] == 'max':
pooling_param['pool'] = 0
else:
assert False, attr['pool_type']
if 'global_pool' in attr and eval(attr['global_pool']) is True:
pooling_param['global_pooling'] = True
else:
if 'kernel' in attr:
kernel_size = eval(attr['kernel'])
assert kernel_size[0] == kernel_size[1]
pooling_param['kernel_size'] = kernel_size[0]
if 'pad' in attr:
pad_size = eval(attr['pad'])
assert pad_size[0] == pad_size[1]
pooling_param['pad'] = pad_size[0]
if 'stride' in attr:
stride_size = eval(attr['stride'])
assert stride_size[0] == stride_size[1]
pooling_param['stride'] = stride_size[0]
pool_top = CL.Pooling(top_dict[bottom_node_name][input[1]], ntop=1, pooling_param=pooling_param)
top_dict[node['name']] = [pool_top]
setattr(caffe_net, node['name'], pool_top)
elif node['op'] == 'elemwise_add' or node['op'] == 'add_n':
input_a = node['inputs'][0]
while True:
if all_nodes[input_a[0]]['op'] not in supported_op_type:
input_a = all_nodes[input_a[0]]['inputs'][0]
else:
break
input_b = node['inputs'][1]
while True:
if all_nodes[input_b[0]]['op'] not in supported_op_type:
input_b = all_nodes[input_b[0]]['inputs'][0]
else:
break
bottom_node_name_a = all_nodes[input_a[0]]['name']
bottom_node_name_b = all_nodes[input_b[0]]['name']
eltwise_param = dict()
eltwise_param['operation'] = 1
ele_add_top = CL.Eltwise(top_dict[bottom_node_name_a][input_a[1]], top_dict[bottom_node_name_b][input_b[1]],
ntop=1, eltwise_param=eltwise_param)
top_dict[node['name']] = [ele_add_top]
setattr(caffe_net, node['name'], ele_add_top)
elif node['op'] == '_maximum':
input_a = node['inputs'][0]
while True:
if all_nodes[input_a[0]]['op'] not in supported_op_type:
input_a = all_nodes[input_a[0]]['inputs'][0]
else:
break
input_b = node['inputs'][1]
while True:
if all_nodes[input_b[0]]['op'] not in supported_op_type:
input_b = all_nodes[input_b[0]]['inputs'][0]
else:
break
bottom_node_name_a = all_nodes[input_a[0]]['name']
bottom_node_name_b = all_nodes[input_b[0]]['name']
eltwise_param = dict()
eltwise_param['operation'] = 2
ele_add_top = CL.Eltwise(top_dict[bottom_node_name_a][input_a[1]], top_dict[bottom_node_name_b][input_b[1]],
ntop=1, eltwise_param=eltwise_param)
top_dict[node['name']] = [ele_add_top]
setattr(caffe_net, node['name'], ele_add_top)
elif node['op'] == 'SliceChannel':
input = node['inputs'][0]
while True:
if all_nodes[input[0]]['op'] not in supported_op_type:
input = all_nodes[input[0]]['inputs'][0]
else:
break
bottom_node_name = all_nodes[input[0]]['name']
slice_param = dict()
slice_param['slice_dim'] = 1
slice_num = 2
slice_outputs = CL.Slice(top_dict[bottom_node_name][input[1]], ntop=slice_num, slice_param=slice_param)
top_dict[node['name']] = slice_outputs
for idx, output in enumerate(slice_outputs):
setattr(caffe_net, node['name'] + '_' + str(idx), output)
elif node['op'] == 'FullyConnected':
input = node['inputs'][0]
while True:
if all_nodes[input[0]]['op'] not in supported_op_type:
input = all_nodes[input[0]]['inputs'][0]
else:
break
bottom_node_name = all_nodes[input[0]]['name']
attr = node['attrs']
inner_product_param = dict()
inner_product_param['num_output'] = int(attr['num_hidden'])
fc_top = CL.InnerProduct(top_dict[bottom_node_name][input[1]], ntop=1,
inner_product_param=inner_product_param)
top_dict[node['name']] = [fc_top]
setattr(caffe_net, node['name'], fc_top)
elif node['op'] == 'SoftmaxOutput':
input_a = node['inputs'][0]
while True:
if all_nodes[input_a[0]]['op'] not in supported_op_type:
input_a = all_nodes[input_a[0]]['inputs'][0]
else:
break
input_b = node['inputs'][1]
while True:
if all_nodes[input_b[0]]['op'] not in supported_op_type:
input_b = all_nodes[input_b[0]]['inputs'][0]
else:
break
bottom_node_name_a = all_nodes[input_a[0]]['name']
bottom_node_name_b = all_nodes[input_b[0]]['name']
softmax_loss = CL.SoftmaxWithLoss(top_dict[bottom_node_name_a][input_a[1]],
top_dict[bottom_node_name_b][input_b[1]], ntop=1)
top_dict[node['name']] = [softmax_loss]
setattr(caffe_net, node['name'], softmax_loss)
elif node['op'] == 'Concat':
if len(node['inputs']) == 2:
input_a = node['inputs'][0]
while True:
if all_nodes[input_a[0]]['op'] not in supported_op_type:
input_a = all_nodes[input_a[0]]['inputs'][0]
else:
break
input_b = node['inputs'][1]
while True:
if all_nodes[input_b[0]]['op'] not in supported_op_type:
input_b = all_nodes[input_b[0]]['inputs'][0]
else:
break
bottom_node_name_a = all_nodes[input_a[0]]['name']
bottom_node_name_b = all_nodes[input_b[0]]['name']
concat_top = CL.Concat(top_dict[bottom_node_name_a][input_a[1]], top_dict[bottom_node_name_b][input_b[1]], ntop=1)
top_dict[node['name']] = [concat_top]
setattr(caffe_net, node['name'], concat_top)
elif len(node['inputs']) == 3:
input_a = node['inputs'][0]
while True:
if all_nodes[input_a[0]]['op'] not in supported_op_type:
input_a = all_nodes[input_a[0]]['inputs'][0]
else:
break
input_b = node['inputs'][1]
while True:
if all_nodes[input_b[0]]['op'] not in supported_op_type:
input_b = all_nodes[input_b[0]]['inputs'][0]
else:
break
input_c = node['inputs'][2]
while True:
if all_nodes[input_c[0]]['op'] not in supported_op_type:
input_c = all_nodes[input_c[0]]['inputs'][0]
else:
break
bottom_node_name_a = all_nodes[input_a[0]]['name']
bottom_node_name_b = all_nodes[input_b[0]]['name']
bottom_node_name_c = all_nodes[input_c[0]]['name']
concat_top = CL.Concat(top_dict[bottom_node_name_a][input_a[1]],
top_dict[bottom_node_name_b][input_b[1]],
top_dict[bottom_node_name_c][input_c[1]], ntop=1)
top_dict[node['name']] = [concat_top]
setattr(caffe_net, node['name'], concat_top)
else:
logging.warn('unknown op type = %s' % node['op'])
return caffe_net.to_proto()
def setLayers(data_source,
batch_size,
layername,
kernel,
stride,
outCH,
label_name,
transform_param_in,
deploy=False):
# it is tricky to produce the deploy prototxt file, as the data input is not from a layer, so we have to creat a workaround
# producing training and testing prototxt files is pretty straight forward
n = caffe.NetSpec()
assert len(layername) == len(kernel)
assert len(layername) == len(stride)
assert len(layername) == len(outCH)
# produce data definition for deploy net
if deploy == False:
n.data, n.tops['label'] = L.CPMData(data_param=dict(
backend=1, source=data_source, batch_size=batch_size),
transform_param=transform_param_in,
ntop=2)
n.tops[label_name[1]], n.tops[label_name[0]] = L.Slice(
n.label, slice_param=dict(axis=1, slice_point=15), ntop=2)
else:
input = "data"
dim1 = 1
dim2 = 4
dim3 = 368
dim4 = 368
# make an empty "data" layer so the next layer accepting input will be able to take the correct blob name "data",
# we will later have to remove this layer from the serialization string, since this is just a placeholder
n.data = L.Layer()
# something special before everything
n.image, n.center_map = L.Slice(n.data,
slice_param=dict(axis=1, slice_point=3),
ntop=2)
n.pool_center_lower = L.Pooling(n.center_map,
kernel_size=9,
stride=8,
pool=P.Pooling.AVE)
# just follow arrays..CPCPCPCPCCCC....
last_layer = 'image'
stage = 1
conv_counter = 1
pool_counter = 1
drop_counter = 1
state = 'image' # can be image or fuse
share_point = 0
for l in range(0, len(layername)):
if layername[l] == 'C':
if state == 'image':
conv_name = 'conv%d_stage%d' % (conv_counter, stage)
else:
conv_name = 'Mconv%d_stage%d' % (conv_counter, stage)
if stage == 1:
lr_m = 5
else:
lr_m = 1
n.tops[conv_name] = L.Convolution(
n.tops[last_layer],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=1),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer = conv_name
if layername[l + 1] != 'L':
if (state == 'image'):
ReLUname = 'relu%d_stage%d' % (conv_counter, stage)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer],
in_place=True)
else:
ReLUname = 'Mrelu%d_stage%d' % (conv_counter, stage)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer],
in_place=True)
last_layer = ReLUname
conv_counter += 1
elif layername[l] == 'P': # Pooling
n.tops['pool%d_stage%d' % (pool_counter, stage)] = L.Pooling(
n.tops[last_layer],
kernel_size=kernel[l],
stride=stride[l],
pool=P.Pooling.MAX)
last_layer = 'pool%d_stage%d' % (pool_counter, stage)
pool_counter += 1
elif layername[l] == 'L':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
if deploy == False:
if stage == 1:
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops[last_layer], n.tops[label_name[0]])
else:
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops[last_layer], n.tops[label_name[1]])
stage += 1
last_connect = last_layer
last_layer = 'image'
conv_counter = 1
pool_counter = 1
drop_counter = 1
state = 'image'
elif layername[l] == 'D':
if deploy == False:
n.tops['drop%d_stage%d' % (drop_counter, stage)] = L.Dropout(
n.tops[last_layer],
in_place=True,
dropout_param=dict(dropout_ratio=0.5))
drop_counter += 1
elif layername[l] == '@':
n.tops['concat_stage%d' % stage] = L.Concat(
n.tops[last_layer],
n.tops[last_connect],
n.pool_center_lower,
concat_param=dict(axis=1))
conv_counter = 1
state = 'fuse'
last_layer = 'concat_stage%d' % stage
elif layername[l] == '$':
if not share_point:
share_point = last_layer
else:
last_layer = share_point
# final process
stage -= 1
if stage == 1:
n.silence = L.Silence(n.pool_center_lower, ntop=0)
if deploy == False:
return str(n.to_proto())
# for generating the deploy net
else:
# generate the input information header string
deploy_str = 'input: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}'.format(
'"' + input + '"', dim1, dim2, dim3, dim4)
# assemble the input header with the net layers string. remove the first placeholder layer from the net string.
return deploy_str + '\n' + 'layer {' + 'layer {'.join(
str(n.to_proto()).split('layer {')[2:])
def setLayers_twoBranches(data_source,
batch_size,
layername,
kernel,
stride,
outCH,
label_name,
transform_param_in,
deploy=False,
batchnorm=0,
lr_mult_distro=[1, 1, 1]):
# it is tricky to produce the deploy prototxt file, as the data input is not from a layer, so we have to creat a workaround
# producing training and testing prototxt files is pretty straight forward
n = caffe.NetSpec()
assert len(layername) == len(kernel)
assert len(layername) == len(stride)
assert len(layername) == len(outCH)
num_parts = transform_param['num_parts']
if deploy == False and "lmdb" not in data_source:
if (len(label_name) == 1):
n.data, n.tops[label_name[0]] = L.HDF5Data(hdf5_data_param=dict(
batch_size=batch_size, source=data_source),
ntop=2)
elif (len(label_name) == 2):
n.data, n.tops[label_name[0]], n.tops[label_name[1]] = L.HDF5Data(
hdf5_data_param=dict(batch_size=batch_size,
source=data_source),
ntop=3)
# produce data definition for deploy net
elif deploy == False:
n.data, n.tops['label'] = L.CPMData(
data_param=dict(backend=1,
source=data_source,
batch_size=batch_size),
cpm_transform_param=transform_param_in,
ntop=2)
n.tops[label_name[2]], n.tops[label_name[3]], n.tops[
label_name[4]], n.tops[label_name[5]] = L.Slice(
n.label,
slice_param=dict(
axis=1, slice_point=[38, num_parts + 1, num_parts + 39]),
ntop=4)
n.tops[label_name[0]] = L.Eltwise(n.tops[label_name[2]],
n.tops[label_name[4]],
operation=P.Eltwise.PROD)
n.tops[label_name[1]] = L.Eltwise(n.tops[label_name[3]],
n.tops[label_name[5]],
operation=P.Eltwise.PROD)
else:
input = "data"
dim1 = 1
dim2 = 4
dim3 = 368
dim4 = 368
# make an empty "data" layer so the next layer accepting input will be able to take the correct blob name "data",
# we will later have to remove this layer from the serialization string, since this is just a placeholder
n.data = L.Layer()
# something special before everything
n.image, n.center_map = L.Slice(n.data,
slice_param=dict(axis=1, slice_point=3),
ntop=2)
n.silence2 = L.Silence(n.center_map, ntop=0)
#n.pool_center_lower = L.Pooling(n.center_map, kernel_size=9, stride=8, pool=P.Pooling.AVE)
# just follow arrays..CPCPCPCPCCCC....
last_layer = ['image', 'image']
stage = 1
conv_counter = 1
pool_counter = 1
drop_counter = 1
local_counter = 1
state = 'image' # can be image or fuse
share_point = 0
for l in range(0, len(layername)):
if layername[l] == 'V': #pretrained VGG layers
conv_name = 'conv%d_%d' % (pool_counter, local_counter)
lr_m = lr_mult_distro[0]
n.tops[conv_name] = L.Convolution(
n.tops[last_layer[0]],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=1),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer[0] = conv_name
last_layer[1] = conv_name
print '%s\tch=%d\t%.1f' % (last_layer[0], outCH[l], lr_m)
ReLUname = 'relu%d_%d' % (pool_counter, local_counter)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[0]], in_place=True)
local_counter += 1
print ReLUname
if layername[l] == 'B':
pool_counter += 1
local_counter = 1
if layername[l] == 'C':
if state == 'image':
#conv_name = 'conv%d_stage%d' % (conv_counter, stage)
conv_name = 'conv%d_%d_CPM' % (
pool_counter, local_counter
) # no image state in subsequent stages
if stage == 1:
lr_m = lr_mult_distro[1]
else:
lr_m = lr_mult_distro[1]
else: # fuse
conv_name = 'Mconv%d_stage%d' % (conv_counter, stage)
lr_m = lr_mult_distro[2]
conv_counter += 1
#if stage == 1:
# lr_m = 1
#else:
# lr_m = lr_sub
n.tops[conv_name] = L.Convolution(
n.tops[last_layer[0]],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=1),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer[0] = conv_name
last_layer[1] = conv_name
print '%s\tch=%d\t%.1f' % (last_layer[0], outCH[l], lr_m)
if layername[l + 1] != 'L':
if (state == 'image'):
if (batchnorm == 1):
batchnorm_name = 'bn%d_stage%d' % (conv_counter, stage)
n.tops[batchnorm_name] = L.BatchNorm(
n.tops[last_layer[0]],
param=[
dict(lr_mult=0),
dict(lr_mult=0),
dict(lr_mult=0)
])
#scale_filler=dict(type='constant', value=1), shift_filler=dict(type='constant', value=0.001))
last_layer[0] = batchnorm_name
#ReLUname = 'relu%d_stage%d' % (conv_counter, stage)
ReLUname = 'relu%d_%d_CPM' % (pool_counter, local_counter)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[0]],
in_place=True)
else:
if (batchnorm == 1):
batchnorm_name = 'Mbn%d_stage%d' % (conv_counter,
stage)
n.tops[batchnorm_name] = L.BatchNorm(
n.tops[last_layer[0]],
param=[
dict(lr_mult=0),
dict(lr_mult=0),
dict(lr_mult=0)
])
#scale_filler=dict(type='constant', value=1), shift_filler=dict(type='constant', value=0.001))
last_layer[0] = batchnorm_name
ReLUname = 'Mrelu%d_stage%d' % (conv_counter, stage)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[0]],
in_place=True)
#last_layer = ReLUname
print ReLUname
#conv_counter += 1
local_counter += 1
elif layername[l] == 'C2':
for level in range(0, 2):
if state == 'image':
#conv_name = 'conv%d_stage%d' % (conv_counter, stage)
conv_name = 'conv%d_%d_CPM_L%d' % (
pool_counter, local_counter, level + 1
) # no image state in subsequent stages
if stage == 1:
lr_m = lr_mult_distro[1]
else:
lr_m = lr_mult_distro[1]
else: # fuse
conv_name = 'Mconv%d_stage%d_L%d' % (conv_counter, stage,
level + 1)
lr_m = lr_mult_distro[2]
#conv_counter += 1
#if stage == 1:
# lr_m = 1
#else:
# lr_m = lr_sub
if layername[l + 1] == 'L2' or layername[l + 1] == 'L3':
if level == 0:
outCH[l] = 38
else:
outCH[l] = 19
n.tops[conv_name] = L.Convolution(
n.tops[last_layer[level]],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=1),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer[level] = conv_name
print '%s\tch=%d\t%.1f' % (last_layer[level], outCH[l], lr_m)
if layername[l + 1] != 'L2' and layername[l + 1] != 'L3':
if (state == 'image'):
if (batchnorm == 1):
batchnorm_name = 'bn%d_stage%d_L%d' % (
conv_counter, stage, level + 1)
n.tops[batchnorm_name] = L.BatchNorm(
n.tops[last_layer[level]],
param=[
dict(lr_mult=0),
dict(lr_mult=0),
dict(lr_mult=0)
])
#scale_filler=dict(type='constant', value=1), shift_filler=dict(type='constant', value=0.001))
last_layer[level] = batchnorm_name
#ReLUname = 'relu%d_stage%d' % (conv_counter, stage)
ReLUname = 'relu%d_%d_CPM_L%d' % (
pool_counter, local_counter, level + 1)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[level]],
in_place=True)
else:
if (batchnorm == 1):
batchnorm_name = 'Mbn%d_stage%d_L%d' % (
conv_counter, stage, level + 1)
n.tops[batchnorm_name] = L.BatchNorm(
n.tops[last_layer[level]],
param=[
dict(lr_mult=0),
dict(lr_mult=0),
dict(lr_mult=0)
])
#scale_filler=dict(type='constant', value=1), shift_filler=dict(type='constant', value=0.001))
last_layer[level] = batchnorm_name
ReLUname = 'Mrelu%d_stage%d_L%d' % (conv_counter,
stage, level + 1)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[level]],
in_place=True)
print ReLUname
conv_counter += 1
local_counter += 1
elif layername[l] == 'P': # Pooling
n.tops['pool%d_stage%d' % (pool_counter, stage)] = L.Pooling(
n.tops[last_layer[0]],
kernel_size=kernel[l],
stride=stride[l],
pool=P.Pooling.MAX)
last_layer[0] = 'pool%d_stage%d' % (pool_counter, stage)
pool_counter += 1
local_counter = 1
conv_counter += 1
print last_layer[0]
elif layername[l] == 'L':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
if deploy == False and "lmdb" not in data_source:
n.tops['map_vec_stage%d' % stage] = L.Flatten(
n.tops[last_layer[0]])
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops['map_vec_stage%d' % stage], n.tops[label_name[1]])
elif deploy == False:
level = 1
name = 'weight_stage%d' % stage
n.tops[name] = L.Eltwise(n.tops[last_layer[level]],
n.tops[label_name[(level + 2)]],
operation=P.Eltwise.PROD)
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops[name], n.tops[label_name[level]])
print 'loss %d' % stage
stage += 1
conv_counter = 1
pool_counter = 1
drop_counter = 1
local_counter = 1
state = 'image'
elif layername[l] == 'L2':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
weight = [lr_mult_distro[3], 1]
# print lr_mult_distro[3]
for level in range(0, 2):
if deploy == False and "lmdb" not in data_source:
n.tops['map_vec_stage%d_L%d' %
(stage, level + 1)] = L.Flatten(
n.tops[last_layer[level]])
n.tops['loss_stage%d_L%d' %
(stage, level + 1)] = L.EuclideanLoss(
n.tops['map_vec_stage%d' % stage],
n.tops[label_name[level]],
loss_weight=weight[level])
elif deploy == False:
name = 'weight_stage%d_L%d' % (stage, level + 1)
n.tops[name] = L.Eltwise(n.tops[last_layer[level]],
n.tops[label_name[(level + 2)]],
operation=P.Eltwise.PROD)
n.tops['loss_stage%d_L%d' %
(stage, level + 1)] = L.EuclideanLoss(
n.tops[name],
n.tops[label_name[level]],
loss_weight=weight[level])
print 'loss %d level %d' % (stage, level + 1)
stage += 1
#last_connect = last_layer
#last_layer = 'image'
conv_counter = 1
pool_counter = 1
drop_counter = 1
local_counter = 1
state = 'image'
elif layername[l] == 'L3':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
weight = [lr_mult_distro[3], 1]
# print lr_mult_distro[3]
if deploy == False:
level = 0
n.tops['loss_stage%d_L%d' %
(stage, level + 1)] = L.Euclidean2Loss(
n.tops[last_layer[level]],
n.tops[label_name[level]],
n.tops[label_name[2]],
loss_weight=weight[level])
print 'loss %d level %d' % (stage, level + 1)
level = 1
n.tops['loss_stage%d_L%d' %
(stage, level + 1)] = L.EuclideanLoss(
n.tops[last_layer[level]],
n.tops[label_name[level]],
loss_weight=weight[level])
print 'loss %d level %d' % (stage, level + 1)
stage += 1
#last_connect = last_layer
#last_layer = 'image'
conv_counter = 1
pool_counter = 1
drop_counter = 1
local_counter = 1
state = 'image'
elif layername[l] == 'D':
if deploy == False:
n.tops['drop%d_stage%d' % (drop_counter, stage)] = L.Dropout(
n.tops[last_layer[0]],
in_place=True,
dropout_param=dict(dropout_ratio=0.5))
drop_counter += 1
elif layername[l] == '@':
#if not share_point:
# share_point = last_layer
n.tops['concat_stage%d' % stage] = L.Concat(
n.tops[last_layer[0]],
n.tops[last_layer[1]],
n.tops[share_point],
concat_param=dict(axis=1))
local_counter = 1
state = 'fuse'
last_layer[0] = 'concat_stage%d' % stage
last_layer[1] = 'concat_stage%d' % stage
print last_layer
elif layername[l] == '$':
share_point = last_layer[0]
pool_counter += 1
local_counter = 1
print 'share'
# final process
stage -= 1
#if stage == 1:
# n.silence = L.Silence(n.pool_center_lower, ntop=0)
if deploy == False:
return str(n.to_proto())
# for generating the deploy net
else:
# generate the input information header string
deploy_str = 'input: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}'.format(
'"' + input + '"', dim1, dim2, dim3, dim4)
# assemble the input header with the net layers string. remove the first placeholder layer from the net string.
return deploy_str + '\n' + 'layer {' + 'layer {'.join(
str(n.to_proto()).split('layer {')[2:])
def get_caffe_layer(node, net, input_dims):
"""Generate caffe layer for corresponding mxnet op.
Args:
node (iterable from MxnetParser): Mxnet op summary generated by MxnetParser
net (caffe.net): Caffe netspec object
Returns:
caffe.layers: Equivalent caffe layer
"""
print(node)
if node['type'] == 'Convolution':
assert len(node['inputs']) == 1, \
'Convolution layers can have only one input'
conv_params = node['attrs']
kernel_size = make_list(conv_params['kernel'])[0]
num_filters = make_list(conv_params['num_filter'])[0]
if 'stride' in conv_params:
stride = make_list(conv_params['stride'])[0]
else:
stride = 1
padding = make_list(conv_params['pad'])[0]
if 'dilate' in conv_params:
dilation = make_list(conv_params['dilate'])[0]
else:
dilation = 1
convolution_param = {'pad': padding,
'kernel_size': kernel_size,
'num_output': num_filters,
'stride': stride,
'dilation': dilation}
return layers.Convolution(net[node['inputs'][0]],
convolution_param=convolution_param)
if node['type'] == 'Activation':
assert len(node['inputs']) == 1, \
'Activation layers can have only one input'
assert node['attrs']['act_type'] == 'relu'
return layers.ReLU(net[node['inputs'][0]])
if node['type'] == 'Pooling':
assert len(node['inputs']) == 1, \
'Pooling layers can have only one input'
kernel_size = make_list(node['attrs']['kernel'])
stride = make_list(node['attrs']['stride'])
pooling_type = node['attrs']['pool_type']
if 'pad' in node['attrs']:
padding = make_list(node['attrs']['pad'])
else:
padding = [0]
if pooling_type == 'max':
pooling = params.Pooling.MAX
elif pooling_type == 'avg':
pooling = params.Pooling.AVE
pooling_param = {'pool': pooling, 'pad': padding[0],
'kernel_size': kernel_size[0], 'stride': stride[0]}
return layers.Pooling(net[node['inputs'][0]],
pooling_param=pooling_param)
if node['type'] == 'L2Normalization':
across_spatial = node['attrs']['mode'] != 'channel'
channel_shared = False
scale_filler = {
'type': "constant",
'value': constants.NORMALIZATION_FACTOR
}
norm_param = {'across_spatial': across_spatial,
'scale_filler': scale_filler,
'channel_shared': channel_shared}
return layers.Normalize(net[node['inputs'][0]],
norm_param=norm_param)
if node['type'] == 'BatchNorm':
bn_param = {
'moving_average_fraction': 0.90,
'use_global_stats': True,
'eps': 1e-5
}
return layers.BatchNorm(net[node['inputs'][0]],
in_place=True, **bn_param)
# Note - this layer has been implemented
# only in WeiLiu's ssd branch of caffe not in caffe master
if node['type'] == 'transpose':
order = make_list(node['attrs']['axes'])
return layers.Permute(net[node['inputs'][0]],
permute_param={'order': order})
if node['type'] == 'Flatten':
if node['inputs'][0].endswith('anchors'):
axis = 2
else:
axis = 1
return layers.Flatten(net[node['inputs'][0]],
flatten_param={'axis': axis})
if node['type'] == 'Concat':
# In the ssd model, always concatenate along last axis,
# since anchor boxes have an extra dimension in caffe (that includes variance).
axis = -1
concat_inputs = [net[inp] for inp in node['inputs']]
return layers.Concat(*concat_inputs, concat_param={'axis': axis})
if node['type'] == 'Reshape':
if node['name'] == 'multibox_anchors':
reshape_dims = [1, 2, -1]
else:
reshape_dims = make_list(node['attrs']['shape'])
return layers.Reshape(net[node['inputs'][0]],
reshape_param={'shape': {'dim': reshape_dims}})
if node['type'] == '_contrib_MultiBoxPrior':
priorbox_inputs = [net[inp] for inp in node['inputs']] + [net["data"]]
sizes = make_list(node["attrs"]["sizes"])
min_size = sizes[0] * input_dims[0]
max_size = int(round((sizes[1] * input_dims[0]) ** 2 / min_size))
aspect_ratio = make_list(node["attrs"]["ratios"])
steps = make_list(node["attrs"]["steps"])
param = {'clip': node["attrs"]["clip"] == "true",
'flip': False,
'min_size': int(round(min_size)),
'max_size': int(round(max_size)),
'aspect_ratio': aspect_ratio,
'variance': [.1, .1, .2, .2],
'step': int(round(steps[0] * input_dims[0])),
}
return layers.PriorBox(*priorbox_inputs, prior_box_param=param)
if node['type'] == '_contrib_MultiBoxDetection':
multibox_inputs = [net[inp] for inp in node['inputs']]
bottom_order = [1, 0, 2]
multibox_inputs = [multibox_inputs[i] for i in bottom_order]
param = {
'num_classes': constants.NUM_CLASSES,
'share_location': True,
'background_label_id': 0,
'nms_param': {
'nms_threshold': float(node['attrs']['nms_threshold']),
'top_k': int(node['attrs']['nms_topk'])
},
'keep_top_k': make_list(node['attrs']['nms_topk'])[0],
'confidence_threshold': 0.01,
'code_type': params.PriorBox.CENTER_SIZE,
}
return layers.DetectionOutput(*multibox_inputs, detection_output_param=param)
if node['type'] in ['SoftmaxActivation', 'SoftmaxOutput']:
if 'mode' not in node['attrs']:
axis = 1
elif node['attrs']['mode'] == 'channel':
axis = 1
else:
axis = 0
# note: caffe expects confidence scores to be flattened before detection output layer receives it
return layers.Flatten(layers.Permute(layers.Softmax(net[node['inputs'][0]],
axis=axis),
permute_param={'order': [0, 2, 1]}),
flatten_param={'axis': 1})
def pool(inputs, kernel_size=2, stride=2):
pool = L.Pooling(inputs,
kernel_size=kernel_size,
stride=stride,
pool=P.Pooling.MAX)
return pool
def global_pooling_layer(previous, name, mode="avg"):
""" create a Global Pooling Layer """
pool = cp.Pooling.AVE if mode == "avg" else cp.Pooling.MAX
return cl.Pooling(previous, name=name, pool=pool, global_pooling=True)
def mixed_5b(net, common_bottom_layer):
# branch 0
top_layer_branch0 = 'Mixed_5b/Branch_0/Conv2d_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch0,
use_bn=True,
use_relu=True,
num_output=96,
kernel_size=1,
pad=0,
stride=1)
# branch 1
top_layer_branch1 = 'Mixed_5b/Branch_1/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=48,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch1 = top_layer_branch1
top_layer_branch1 = 'Mixed_5b/Branch_1/Conv2d_0b_5x5'
conv_bn_layer(net,
in_layer=bottom_layer_branch1,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=5,
pad=2,
stride=1)
# branch 2
top_layer_branch2 = 'Mixed_5b/Branch_2/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = 'Mixed_5b/Branch_2/Conv2d_0b_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=96,
kernel_size=3,
pad=1,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = 'Mixed_5b/Branch_2/Conv2d_0c_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=96,
kernel_size=3,
pad=1,
stride=1)
# branch 3
top_layer_branch3 = 'mixed5b_branch3_avepool_0a'
net[top_layer_branch3] = layers.Pooling(net[common_bottom_layer],
pool=params.Pooling.AVE,
kernel_size=3,
stride=1,
pad=1)
bottom_layer_branch3 = top_layer_branch3
top_layer_branch3 = 'Mixed_5b/Branch_3/Conv2d_0b_1x1'
conv_bn_layer(net,
in_layer=bottom_layer_branch3,
out_layer=top_layer_branch3,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=1,
pad=0,
stride=1)
top_layer = 'mixed5b'
net[top_layer] = layers.Concat(*[
net[top_layer_branch0], net[top_layer_branch1], net[top_layer_branch2],
net[top_layer_branch3]
],
axis=1)
return top_layer
def ResNet(lmdb, batch_size, mean_file, model):
n = caffe.NetSpec()
#数据层
if model == False:
n.data, n.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb,
include=dict(phase=0),
transform_param=dict(scale=1. / 255,
mirror=True,
crop_size=227,
mean_file=mean_file),
ntop=2)
if model == True:
n.data, n.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB,
source=lmdb,
include=dict(phase=1),
transform_param=dict(scale=1. / 255,
mirror=True,
crop_size=227,
mean_file=mean_file),
ntop=2)
#卷积层conv1
n.conv1 = L.Convolution(
n.data,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
kernel_size=7,
stride=2,
num_output=64,
pad=3,
weight_filler=dict(type="gaussian", std=0.01),
bias_filler=dict(type='constant', value=0),
name="conv1/7x7_s2")
#ReLu层
n.relu1 = L.ReLU(n.conv1, in_place=True, name="conv1/relu_7x7")
#Pooling层
n.pool1 = L.Pooling(n.conv1,
kernel_size=2,
stride=2,
pool=P.Pooling.MAX,
name="pool1/3x3_s2")
n.conv2 = L.Convolution(
n.pool1,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
kernel_size=1,
num_output=64,
weight_filler=dict(type="xavier"),
bias_filler=dict(type='constant', value=0.2),
name="conv2/3x3_reduce")
n.relu2 = L.ReLU(n.conv2, in_place=True, name="conv2/relu_3x3_reduce")
n.conv2_3x3 = L.Convolution(
n.conv2,
param=[dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)],
kernel_size=3,
num_output=192,
pad=1,
weight_filler=dict(type="xavier"),
bias_filler=dict(type='constant', value=0.2),
name="conv2/3x3")
n.relu2 = L.ReLU(n.conv2_3x3, in_place=True, name="conv2/relu_3x3")
n.pool2 = L.Pooling(n.conv2_3x3,
kernel_size=2,
stride=2,
pool=P.Pooling.MAX,
name="pool2/3x3_s2")
def mixed_7a(net, common_bottom_layer):
# branch 0
top_layer_branch0 = 'Mixed_7a/Branch_0/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch0,
use_bn=True,
use_relu=True,
num_output=256,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch0 = top_layer_branch0
top_layer_branch0 = 'Mixed_7a/Branch_0/Conv2d_1a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch0,
out_layer=top_layer_branch0,
use_bn=True,
use_relu=True,
num_output=384,
kernel_size=3,
pad=0,
stride=2)
# branch 1
top_layer_branch1 = 'Mixed_7a/Branch_1/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=256,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch1 = top_layer_branch1
top_layer_branch1 = 'Mixed_7a/Branch_1/Conv2d_1a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch1,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=288,
kernel_size=3,
pad=0,
stride=2)
# branch 2
top_layer_branch2 = 'Mixed_7a/Branch_2/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=256,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = 'Mixed_7a/Branch_2/Conv2d_0b_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=288,
kernel_size=3,
pad=1,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = 'Mixed_7a/Branch_2/Conv2d_1a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=320,
kernel_size=3,
pad=0,
stride=2)
# branch 3
top_layer_branch3 = 'mixed7a_branch3_maxpool_0'
net[top_layer_branch3] = layers.Pooling(net[common_bottom_layer],
pool=params.Pooling.MAX,
kernel_size=3,
stride=2,
pad=0)
top_layer = 'mixed7a'
net[top_layer] = layers.Concat(*[
net[top_layer_branch0], net[top_layer_branch1], net[top_layer_branch2],
net[top_layer_branch3]
],
axis=1)
return top_layer
def VGGNetBody(net, from_layer, need_fc=True, fully_conv=False, reduced=False,
dilated=False, nopool=False, dropout=True, freeze_layers=[]):
kwargs = {
'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)}
assert from_layer in net.keys()
net.conv1_1 = L.Convolution(net[from_layer], num_output=64, pad=1, kernel_size=3, **kwargs)
net.relu1_1 = L.ReLU(net.conv1_1, in_place=True)
net.conv1_2 = L.Convolution(net.relu1_1, num_output=64, pad=1, kernel_size=3, **kwargs)
net.relu1_2 = L.ReLU(net.conv1_2, in_place=True)
if nopool:
name = 'conv1_3'
net[name] = L.Convolution(net.relu1_2, num_output=64, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool1'
net.pool1 = L.Pooling(net.relu1_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv2_1 = L.Convolution(net[name], num_output=128, pad=1, kernel_size=3, **kwargs)
net.relu2_1 = L.ReLU(net.conv2_1, in_place=True)
net.conv2_2 = L.Convolution(net.relu2_1, num_output=128, pad=1, kernel_size=3, **kwargs)
net.relu2_2 = L.ReLU(net.conv2_2, in_place=True)
if nopool:
name = 'conv2_3'
net[name] = L.Convolution(net.relu2_2, num_output=128, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool2'
net[name] = L.Pooling(net.relu2_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv3_1 = L.Convolution(net[name], num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_1 = L.ReLU(net.conv3_1, in_place=True)
net.conv3_2 = L.Convolution(net.relu3_1, num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_2 = L.ReLU(net.conv3_2, in_place=True)
net.conv3_3 = L.Convolution(net.relu3_2, num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_3 = L.ReLU(net.conv3_3, in_place=True)
if nopool:
name = 'conv3_4'
net[name] = L.Convolution(net.relu3_3, num_output=256, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool3'
net[name] = L.Pooling(net.relu3_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv4_1 = L.Convolution(net[name], num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_1 = L.ReLU(net.conv4_1, in_place=True)
net.conv4_2 = L.Convolution(net.relu4_1, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_2 = L.ReLU(net.conv4_2, in_place=True)
net.conv4_3 = L.Convolution(net.relu4_2, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_3 = L.ReLU(net.conv4_3, in_place=True)
if nopool:
name = 'conv4_4'
net[name] = L.Convolution(net.relu4_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool4'
net[name] = L.Pooling(net.relu4_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv5_1 = L.Convolution(net[name], num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu5_1 = L.ReLU(net.conv5_1, in_place=True)
net.conv5_2 = L.Convolution(net.relu5_1, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu5_2 = L.ReLU(net.conv5_2, in_place=True)
net.conv5_3 = L.Convolution(net.relu5_2, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu5_3 = L.ReLU(net.conv5_3, in_place=True)
if need_fc:
if dilated:
if nopool:
name = 'conv5_4'
net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=1, **kwargs)
else:
name = 'pool5'
net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, pad=1, kernel_size=3, stride=1)
else:
if nopool:
name = 'conv5_4'
net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool5'
net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
if fully_conv:
if dilated:
if reduced:
net.fc6 = L.Convolution(net[name], num_output=1024, pad=6, kernel_size=3, dilation=6, **kwargs)
else:
net.fc6 = L.Convolution(net[name], num_output=4096, pad=6, kernel_size=7, dilation=2, **kwargs)
else:
if reduced:
net.fc6 = L.Convolution(net[name], num_output=1024, pad=3, kernel_size=3, dilation=3, **kwargs)
else:
net.fc6 = L.Convolution(net[name], num_output=4096, pad=3, kernel_size=7, **kwargs)
net.relu6 = L.ReLU(net.fc6, in_place=True)
if dropout:
net.drop6 = L.Dropout(net.relu6, dropout_ratio=0.5, in_place=True)
if reduced:
net.fc7 = L.Convolution(net.relu6, num_output=1024, kernel_size=1, **kwargs)
else:
net.fc7 = L.Convolution(net.relu6, num_output=4096, kernel_size=1, **kwargs)
net.relu7 = L.ReLU(net.fc7, in_place=True)
if dropout:
net.drop7 = L.Dropout(net.relu7, dropout_ratio=0.5, in_place=True)
else:
net.fc6 = L.InnerProduct(net.pool5, num_output=4096)
net.relu6 = L.ReLU(net.fc6, in_place=True)
if dropout:
net.drop6 = L.Dropout(net.relu6, dropout_ratio=0.5, in_place=True)
net.fc7 = L.InnerProduct(net.relu6, num_output=4096)
net.relu7 = L.ReLU(net.fc7, in_place=True)
if dropout:
net.drop7 = L.Dropout(net.relu7, dropout_ratio=0.5, in_place=True)
# Update freeze layers.
kwargs['param'] = [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)]
layers = net.keys()
for freeze_layer in freeze_layers:
if freeze_layer in layers:
net.update(freeze_layer, kwargs)
return net
def inception_resnet_v2(net):
net['data'] = layers.DummyData(num=1, channels=3, height=299, width=299)
# 149 x 149 x 32
top_layer = 'Conv2d_1a_3x3'
conv_bn_layer(net,
in_layer='data',
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=32,
kernel_size=3,
pad=0,
stride=2)
bottom_layer = top_layer
# 147 x 147 x 32
top_layer = 'Conv2d_2a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=32,
kernel_size=3,
pad=0,
stride=1)
bottom_layer = top_layer
# 147 x 147 x 64
top_layer = 'Conv2d_2b_3x3'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=3,
pad=0,
stride=1)
bottom_layer = top_layer
# 73 x 73 x 64
top_layer = 'maxpool_3a'
net[top_layer] = layers.Pooling(net[bottom_layer],
pool=params.Pooling.MAX,
kernel_size=3,
stride=2,
pad=0)
bottom_layer = top_layer
# 73 x 73 x 80
top_layer = 'Conv2d_3b_1x1'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=80,
kernel_size=1,
pad=0,
stride=1)
bottom_layer = top_layer
# 71 x 71 x 192
top_layer = 'Conv2d_4a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=192,
kernel_size=3,
pad=0,
stride=1)
bottom_layer = top_layer
# 35 x 35 x 192
top_layer = 'maxpool_5a'
net[top_layer] = layers.Pooling(net[bottom_layer],
pool=params.Pooling.MAX,
kernel_size=3,
stride=2,
pad=0)
bottom_layer = top_layer
bottom_layer = mixed_5b(net, bottom_layer)
# 35 x 35 x 320 (Mixed 5a)
bottom_layer = inception_block_35(net,
bottom_layer,
10,
0.17,
repeat_name='Repeat')
# 17 x 17 x 1088
bottom_layer = mixed_6a(net, bottom_layer)
bottom_layer = inception_block_17(net,
bottom_layer,
20,
0.10,
repeat_name='Repeat_1')
bottom_layer = mixed_7a(net, bottom_layer)
bottom_layer = inception_block_8(net,
bottom_layer,
9,
0.20,
repeat_name='Repeat_2',
apply_last_relu=True)
bottom_layer = inception_block_8(net,
bottom_layer,
1,
0.20,
repeat_name='',
apply_last_relu=False)
top_layer = 'Conv2d_7b_1x1'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=1536,
kernel_size=1,
pad=0,
stride=1)
with open('gg.prototxt', 'w') as f:
print(net.to_proto(), file=f)
def max_pool(data, ks=2, stride=2):
return L.Pooling(data, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def global_avg_pool(bottom, kernelSize=3):
#return L.Pooling(bottom, pool=P.Pooling.AVE,stride=1, kernel_size=kernelSize)
return L.Pooling(bottom, pool=P.Pooling.AVE, global_pooling=True)
def ave_pool(bottom, ks, stride=1):
return L.Pooling(bottom, pool=P.Pooling.AVE, kernel_size=ks, stride=stride)
def inception_bn(bottom, conv_output):
conv_1x1 = L.Convolution(
bottom,
kernel_size=1,
num_output=conv_output['conv_1x1'],
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=0.2))
bn_1x1 = L.BatchNorm(conv_1x1, use_global_stats=False)
relu_1x1 = L.ReLU(bn_1x1, in_place=True)
conv_3x3_reduce = L.Convolution(
bottom,
kernel_size=1,
num_output=conv_output['conv_3x3_reduce'],
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=0.2))
bn_3x3_reduce = L.BatchNorm(conv_3x3_reduce, use_global_stats=False)
relu_3x3_reduce = L.ReLU(bn_3x3_reduce, in_place=True)
conv_3x3 = L.Convolution(
bn_3x3_reduce,
kernel_size=3,
num_output=conv_output['conv_3x3'],
pad=1,
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=0.2))
bn_3x3 = L.BatchNorm(conv_3x3, use_global_stats=False)
relu_3x3 = L.ReLU(bn_3x3, in_place=True)
conv_5x5_reduce = L.Convolution(
bottom,
kernel_size=1,
num_output=conv_output['conv_5x5_reduce'],
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=0.2))
bn_5x5_reduce = L.BatchNorm(conv_5x5_reduce, use_global_stats=False)
relu_5x5_reduce = L.ReLU(bn_5x5_reduce, in_place=True)
conv_5x5 = L.Convolution(
bn_5x5_reduce,
kernel_size=5,
num_output=conv_output['conv_5x5'],
pad=2,
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=0.2))
bn_5x5 = L.BatchNorm(conv_5x5, use_global_stats=False)
relu_5x5 = L.ReLU(bn_5x5, in_place=True)
pool = L.Pooling(bottom,
kernel_size=3,
stride=1,
pad=1,
pool=P.Pooling.MAX)
pool_proj = L.Convolution(
pool,
kernel_size=1,
num_output=conv_output['pool_proj'],
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=0.2))
bn_proj = L.BatchNorm(pool_proj, use_global_stats=False)
relu_pool_proj = L.ReLU(bn_proj, in_place=True)
concat = L.Concat(bn_1x1, bn_3x3, bn_5x5, bn_proj)
return conv_1x1, bn_1x1, relu_1x1, conv_3x3_reduce, bn_3x3_reduce, relu_3x3_reduce, conv_3x3, bn_3x3, relu_3x3, \
conv_5x5_reduce, bn_5x5_reduce, relu_5x5_reduce, conv_5x5, bn_5x5, relu_5x5, pool, pool_proj, bn_proj, \
relu_pool_proj, concat
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 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_2, 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 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.res256_to_128, 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 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_2, 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 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_2, 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)
## global pool
AVE = caffe_pb2.PoolingParameter.AVE
net.global_pool = L.Pooling(net.res16_2, 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 VGG19Net_Pre10(net, from_layer="data"):
kwargs = {
'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)
}
assert from_layer in net.keys()
# conv1
net.conv1_1 = L.Convolution(net[from_layer],
num_output=64,
pad=1,
kernel_size=3,
**kwargs)
net.relu1_1 = L.ReLU(net.conv1_1, in_place=True)
net.conv1_2 = L.Convolution(net.relu1_1,
num_output=64,
pad=1,
kernel_size=3,
**kwargs)
net.relu1_2 = L.ReLU(net.conv1_2, in_place=True)
# pool1
net.pool1_stage1 = L.Pooling(net.relu1_2,
pool=P.Pooling.MAX,
kernel_size=2,
stride=2)
# conv2
net.conv2_1 = L.Convolution(net.pool1_stage1,
num_output=128,
pad=1,
kernel_size=3,
**kwargs)
net.relu2_1 = L.ReLU(net.conv2_1, in_place=True)
net.conv2_2 = L.Convolution(net.relu2_1,
num_output=128,
pad=1,
kernel_size=3,
**kwargs)
net.relu2_2 = L.ReLU(net.conv2_2, in_place=True)
# pool2
net.pool2_stage1 = L.Pooling(net.relu2_2,
pool=P.Pooling.MAX,
kernel_size=2,
stride=2)
# conv3
net.conv3_1 = L.Convolution(net.pool2_stage1,
num_output=256,
pad=1,
kernel_size=3,
**kwargs)
net.relu3_1 = L.ReLU(net.conv3_1, in_place=True)
net.conv3_2 = L.Convolution(net.relu3_1,
num_output=256,
pad=1,
kernel_size=3,
**kwargs)
net.relu3_2 = L.ReLU(net.conv3_2, in_place=True)
net.conv3_3 = L.Convolution(net.relu3_2,
num_output=256,
pad=1,
kernel_size=3,
**kwargs)
net.relu3_3 = L.ReLU(net.conv3_3, in_place=True)
net.conv3_4 = L.Convolution(net.relu3_3,
num_output=256,
pad=1,
kernel_size=3,
**kwargs)
net.relu3_4 = L.ReLU(net.conv3_4, in_place=True)
# pool3
net.pool3_stage1 = L.Pooling(net.relu3_4,
pool=P.Pooling.MAX,
kernel_size=2,
stride=2)
# conv4
net.conv4_1 = L.Convolution(net.pool3_stage1,
num_output=512,
pad=1,
kernel_size=3,
**kwargs)
net.relu4_1 = L.ReLU(net.conv4_1, in_place=True)
net.conv4_2 = L.Convolution(net.relu4_1,
num_output=512,
pad=1,
kernel_size=3,
**kwargs)
net.relu4_2 = L.ReLU(net.conv4_2, in_place=True)
return net
