def build_network(self, x):
# Building network...
with tf.variable_scope('LeNet'):
x = conv_2d(x,
filter_size=5,
num_filters=6,
name='conv_1',
keep_prob=1)
x = drop_out(x, self.keep_prob_pl)
x = max_pool(x, 2, 2, 'pool_1')
x = conv_2d(x,
filter_size=5,
num_filters=16,
name='conv_2',
keep_prob=1)
x = drop_out(x, self.keep_prob_pl)
x = max_pool(x, 2, 2, 'pool_2')
x = flatten_layer(x)
x = drop_out(x, self.keep_prob_pl)
x = fc_layer(x, 120, name='fc_1', keep_prob=1)
x = drop_out(x, self.keep_prob_pl)
x = fc_layer(x, 84, name='fc_2', keep_prob=1)
x = drop_out(x, self.keep_prob_pl)
self.logits = fc_layer(x,
self.conf.num_cls,
name='fc_3',
use_relu=False,
keep_prob=1)
def build_network(self, x):
# Building network...
with tf.variable_scope('FCNet'):
x = conv_2d(x,
filter_size=3,
stride=1,
num_filters=32,
name='conv_1',
keep_prob=1)
x = tf.contrib.slim.batch_norm(x)
x = conv_2d(x,
filter_size=3,
stride=1,
num_filters=32,
name='conv_2',
keep_prob=1)
x = tf.contrib.slim.batch_norm(x)
x = max_pool(x, 2, 2, 'pool_1')
x = conv_2d(x,
filter_size=3,
stride=1,
num_filters=64,
name='conv_3',
keep_prob=1)
x = tf.contrib.slim.batch_norm(x)
x = conv_2d(x,
filter_size=3,
stride=1,
num_filters=64,
name='conv_4',
keep_prob=1)
x = tf.contrib.slim.batch_norm(x)
x = max_pool(x, 2, 2, 'pool_2')
x = conv_2d(x,
filter_size=3,
stride=1,
num_filters=128,
name='conv_5',
keep_prob=1)
x = tf.contrib.slim.batch_norm(x)
x = conv_2d(x,
filter_size=3,
stride=1,
num_filters=128,
name='conv_6',
keep_prob=1)
x = tf.contrib.slim.batch_norm(x)
x = max_pool(x, 2, 2, 'pool_3')
x = flatten_layer(x)
self.logits = fc_layer(x,
self.conf.num_cls,
name='fc_3',
use_relu=False,
keep_prob=1)
def AlexNet(X, keep_prob, is_train):
net = conv_2d(X, 7, 2, 96, 'CONV1', trainable=True)
net = lrn(net)
net = max_pool(net, 3, 2, 'MaxPool1')
net = conv_2d(net, 5, 2, 256, 'CONV2', trainable=True)
net = lrn(net)
net = max_pool(net, 3, 2, 'MaxPool2')
net = conv_2d(net, 3, 1, 384, 'CONV3', trainable=True)
net = conv_2d(net, 3, 1, 384, 'CONV4', trainable=True)
net = conv_2d(net, 3, 1, 256, 'CONV5', trainable=True)
net = max_pool(net, 3, 2, 'MaxPool3')
layer_flat = flatten_layer(net)
net = fc_layer(layer_flat, 512, 'FC1', trainable=True, use_relu=True)
net = dropout(net, keep_prob)
return net
def AlexNet_target_task(X, keep_prob, num_cls):
net = conv_2d(X, 7, 2, 96, 'CONV1', trainable=False)
net = lrn(net)
net = max_pool(net, 3, 2, 'MaxPool1')
net = conv_2d(net, 5, 2, 256, 'CONV2', trainable=False)
net = lrn(net)
net = max_pool(net, 3, 2, 'MaxPool2')
net = conv_2d(net, 3, 1, 384, 'CONV3', trainable=False)
net = conv_2d(net, 3, 1, 384, 'CONV4', trainable=False)
net = conv_2d(net, 3, 1, 256, 'CONV5', trainable=False)
net = max_pool(net, 3, 2, 'MaxPool3')
layer_flat = flatten_layer(net)
net = fc_layer(layer_flat, 512, 'FC_1', trainable=True, use_relu=True)
net = dropout(net, keep_prob)
net = fc_layer(net, num_cls, 'FC_2', trainable=True, use_relu=False)
return net
def create_network(X, h, keep_prob, numClasses):
num_channels = X.get_shape().as_list()[-1]
res1 = new_conv_layer(inputs=X,
layer_name='res1',
stride=2,
num_inChannel=num_channels,
filter_size=4,
num_filters=32,
batch_norm=True,
use_relu=True)
#res1 = max_pool(res1, ksize=2, stride=2, name='res1_max_pool')
print('---------------------')
print('Res1')
print(res1.get_shape())
print('---------------------')
# Res2
with tf.variable_scope('Res2'):
res2a = bottleneck_block(res1,
32,
block_name='res2a',
s1=1,
k1=1,
nf1=32,
name1='res2a_branch2a',
s2=1,
k2=3,
nf2=32,
name2='res2a_branch2b',
s3=1,
k3=1,
nf3=64,
name3='res2a_branch2c',
s4=1,
k4=1,
name4='res2a_branch1',
first_block=True)
print('Res2a')
print(res2a.get_shape())
print('---------------------')
res2b = bottleneck_block(res2a,
64,
block_name='res2b',
s1=1,
k1=1,
nf1=32,
name1='res2b_branch2a',
s2=1,
k2=3,
nf2=32,
name2='res2b_branch2b',
s3=1,
k3=1,
nf3=64,
name3='res2b_branch2c',
s4=1,
k4=1,
name4='res2b_branch1',
first_block=False)
print('Res2b')
print(res2b.get_shape())
print('---------------------')
res2c = bottleneck_block(res2b,
64,
block_name='res2c',
s1=1,
k1=1,
nf1=32,
name1='res2c_branch2a',
s2=1,
k2=3,
nf2=32,
name2='res2c_branch2b',
s3=1,
k3=1,
nf3=64,
name3='res2c_branch2c',
s4=1,
k4=1,
name4='res2c_branch1',
first_block=False)
print('Res2c')
print(res2c.get_shape())
print('---------------------')
# Res3
with tf.variable_scope('Res3'):
res3a = bottleneck_block(res2c,
64,
block_name='res3a',
s1=2,
k1=1,
nf1=48,
name1='res3a_branch2a',
s2=1,
k2=3,
nf2=48,
name2='res3a_branch2b',
s3=1,
k3=1,
nf3=128,
name3='res3a_branch2c',
s4=2,
k4=1,
name4='res3a_branch1',
first_block=True)
print('Res3a')
print(res3a.get_shape())
print('---------------------')
res3b = bottleneck_block(res3a,
128,
block_name='res3b',
s1=1,
k1=1,
nf1=48,
name1='res3b_branch2a',
s2=1,
k2=3,
nf2=48,
name2='res3b_branch2b',
s3=1,
k3=1,
nf3=128,
name3='res3b_branch2c',
s4=1,
k4=1,
name4='res2b_branch1',
first_block=False)
print('Res3b')
print(res3b.get_shape())
print('---------------------')
res3c = bottleneck_block(res3b,
128,
block_name='res3c',
s1=1,
k1=1,
nf1=48,
name1='res3c_branch2a',
s2=1,
k2=3,
nf2=48,
name2='res3c_branch2b',
s3=1,
k3=1,
nf3=128,
name3='res3c_branch2c',
s4=1,
k4=1,
name4='res3c_branch1',
first_block=False)
print('Res3c')
print(res3c.get_shape())
print('---------------------')
res3d = bottleneck_block(res3c,
128,
block_name='res3d',
s1=1,
k1=1,
nf1=48,
name1='res3d_branch2a',
s2=1,
k2=3,
nf2=48,
name2='res3d_branch2b',
s3=1,
k3=1,
nf3=128,
name3='res3d_branch2c',
s4=1,
k4=1,
name4='res3d_branch1',
first_block=False)
print('Res3d')
print(res3d.get_shape())
print('---------------------')
# Res4
with tf.variable_scope('Res4'):
res4a = bottleneck_block(res3d,
128,
block_name='res4a',
s1=2,
k1=1,
nf1=64,
name1='res4a_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res4a_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res4a_branch2c',
s4=2,
k4=1,
name4='res4a_branch1',
first_block=True)
print('---------------------')
print('Res4a')
print(res4a.get_shape())
print('---------------------')
res4b = bottleneck_block(res4a,
256,
block_name='res4b',
s1=1,
k1=1,
nf1=64,
name1='res4b_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res4b_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res4b_branch2c',
s4=1,
k4=1,
name4='res4b_branch1',
first_block=False)
print('Res4b')
print(res4b.get_shape())
print('---------------------')
res4c = bottleneck_block(res4b,
256,
block_name='res4c',
s1=1,
k1=1,
nf1=64,
name1='res4c_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res4c_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res4c_branch2c',
s4=1,
k4=1,
name4='res4c_branch1',
first_block=False)
print('Res4c')
print(res4c.get_shape())
print('---------------------')
res4d = bottleneck_block(res4c,
256,
block_name='res4d',
s1=1,
k1=1,
nf1=64,
name1='res4d_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res4d_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res4d_branch2c',
s4=1,
k4=1,
name4='res4d_branch1',
first_block=False)
print('Res4d')
print(res4d.get_shape())
print('---------------------')
res4e = bottleneck_block(res4d,
256,
block_name='res4e',
s1=1,
k1=1,
nf1=64,
name1='res4e_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res4e_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res4e_branch2c',
s4=1,
k4=1,
name4='res4e_branch1',
first_block=False)
print('Res4e')
print(res4e.get_shape())
print('---------------------')
res4f = bottleneck_block(res4e,
256,
block_name='res4f',
s1=1,
k1=1,
nf1=64,
name1='res4f_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res4f_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res4f_branch2c',
s4=1,
k4=1,
name4='res4f_branch1',
first_block=False)
print('Res4f')
print(res4f.get_shape())
print('---------------------')
# Res5
with tf.variable_scope('Res5'):
res5a = bottleneck_block(res4f,
256,
block_name='res5a',
s1=1,
k1=1,
nf1=128,
name1='res5a_branch2a',
s2=1,
k2=3,
nf2=128,
name2='res5a_branch2b',
s3=1,
k3=1,
nf3=512,
name3='res5a_branch2c',
s4=1,
k4=1,
name4='res5a_branch1',
first_block=True)
print('---------------------')
print('Res5a')
print(res5a.get_shape())
print('---------------------')
res5b = bottleneck_block(res5a,
512,
block_name='res5b',
s1=1,
k1=1,
nf1=128,
name1='res5b_branch2a',
s2=1,
k2=3,
nf2=128,
name2='res5b_branch2b',
s3=1,
k3=1,
nf3=512,
name3='res5b_branch2c',
s4=1,
k4=1,
name4='res5b_branch1',
first_block=False)
print('Res5b')
print(res5b.get_shape())
print('---------------------')
res5c = bottleneck_block(res5b,
512,
block_name='res5c',
s1=1,
k1=1,
nf1=128,
name1='res5c_branch2a',
s2=1,
k2=3,
nf2=128,
name2='res5c_branch2b',
s3=1,
k3=1,
nf3=512,
name3='res5c_branch2c',
s4=1,
k4=1,
name4='res5c_branch1',
first_block=False)
print('Res5c')
print(res5c.get_shape())
res5c = avg_pool(res5c, ksize=4, stride=1, name='res5_avg_pool')
print('---------------------')
print('Res5c after AVG_POOL')
print(res5c.get_shape())
print('---------------------')
net_flatten, _ = flatten_layer(res5c)
print('---------------------')
print('Matrix dimension to the first FC layer')
print(net_flatten.get_shape())
print('---------------------')
net = fc_layer(net_flatten,
h,
'FC1',
batch_norm=True,
add_reg=True,
use_relu=True)
net = dropout(net, keep_prob)
net = fc_layer(net,
numClasses,
'FC2',
batch_norm=True,
add_reg=True,
use_relu=False)
return net
def create_network(X, numClasses, is_train):
"""
Building the Residual Network with 50 layer
:param X: input
:param h: number of units in the fully connected layer
:param keep_prob: dropout rate
:param numClasses: number of classes
:param is_train: to be used by batch normalization
:return:
"""
res1 = conv_2d(X,
layer_name='res1',
stride=2,
filter_size=7,
num_filters=64,
is_train=is_train,
batch_norm=True,
use_relu=True)
print('---------------------')
print('Res1')
print(res1.get_shape())
print('---------------------')
res1 = max_pool(res1, ksize=3, stride=2, name='res1_max_pool')
print('---------------------')
print('Res1')
print(res1.get_shape())
print('---------------------')
# Res2
with tf.variable_scope('Res2'):
res2a = bottleneck_block(res1,
is_train,
block_name='res2a',
s1=1,
k1=1,
nf1=64,
name1='res2a_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res2a_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res2a_branch2c',
s4=1,
k4=1,
name4='res2a_branch1',
first_block=True)
print('Res2a')
print(res2a.get_shape())
print('---------------------')
res2b = bottleneck_block(res2a,
is_train,
block_name='res2b',
s1=1,
k1=1,
nf1=64,
name1='res2b_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res2b_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res2b_branch2c',
s4=1,
k4=1,
name4='res2b_branch1',
first_block=False)
print('Res2b')
print(res2b.get_shape())
print('---------------------')
res2c = bottleneck_block(res2b,
is_train,
block_name='res2c',
s1=1,
k1=1,
nf1=64,
name1='res2c_branch2a',
s2=1,
k2=3,
nf2=64,
name2='res2c_branch2b',
s3=1,
k3=1,
nf3=256,
name3='res2c_branch2c',
s4=1,
k4=1,
name4='res2c_branch1',
first_block=False)
print('Res2c')
print(res2c.get_shape())
print('---------------------')
# Res3
with tf.variable_scope('Res3'):
res3a = bottleneck_block(res2c,
is_train,
block_name='res3a',
s1=2,
k1=1,
nf1=128,
name1='res3a_branch2a',
s2=1,
k2=3,
nf2=128,
name2='res3a_branch2b',
s3=1,
k3=1,
nf3=512,
name3='res3a_branch2c',
s4=2,
k4=1,
name4='res3a_branch1',
first_block=True)
print('Res3a')
print(res3a.get_shape())
print('---------------------')
res3b = bottleneck_block(res3a,
is_train,
block_name='res3b',
s1=1,
k1=1,
nf1=128,
name1='res3b_branch2a',
s2=1,
k2=3,
nf2=128,
name2='res3b_branch2b',
s3=1,
k3=1,
nf3=512,
name3='res3b_branch2c',
s4=1,
k4=1,
name4='res2b_branch1',
first_block=False)
print('Res3b')
print(res3b.get_shape())
print('---------------------')
res3c = bottleneck_block(res3b,
is_train,
block_name='res3c',
s1=1,
k1=1,
nf1=128,
name1='res3c_branch2a',
s2=1,
k2=3,
nf2=128,
name2='res3c_branch2b',
s3=1,
k3=1,
nf3=512,
name3='res3c_branch2c',
s4=1,
k4=1,
name4='res3c_branch1',
first_block=False)
print('Res3c')
print(res3c.get_shape())
print('---------------------')
res3d = bottleneck_block(res3c,
is_train,
block_name='res3d',
s1=1,
k1=1,
nf1=128,
name1='res3d_branch2a',
s2=1,
k2=3,
nf2=128,
name2='res3d_branch2b',
s3=1,
k3=1,
nf3=512,
name3='res3d_branch2c',
s4=1,
k4=1,
name4='res3d_branch1',
first_block=False)
print('Res3d')
print(res3d.get_shape())
print('---------------------')
# Res4
with tf.variable_scope('Res4'):
res4a = bottleneck_block(res3d,
is_train,
block_name='res4a',
s1=2,
k1=1,
nf1=256,
name1='res4a_branch2a',
s2=1,
k2=3,
nf2=256,
name2='res4a_branch2b',
s3=1,
k3=1,
nf3=1024,
name3='res4a_branch2c',
s4=2,
k4=1,
name4='res4a_branch1',
first_block=True)
print('---------------------')
print('Res4a')
print(res4a.get_shape())
print('---------------------')
res4b = bottleneck_block(res4a,
is_train,
block_name='res4b',
s1=1,
k1=1,
nf1=256,
name1='res4b_branch2a',
s2=1,
k2=3,
nf2=256,
name2='res4b_branch2b',
s3=1,
k3=1,
nf3=1024,
name3='res4b_branch2c',
s4=1,
k4=1,
name4='res4b_branch1',
first_block=False)
print('Res4b')
print(res4b.get_shape())
print('---------------------')
res4c = bottleneck_block(res4b,
is_train,
block_name='res4c',
s1=1,
k1=1,
nf1=256,
name1='res4c_branch2a',
s2=1,
k2=3,
nf2=256,
name2='res4c_branch2b',
s3=1,
k3=1,
nf3=1024,
name3='res4c_branch2c',
s4=1,
k4=1,
name4='res4c_branch1',
first_block=False)
print('Res4c')
print(res4c.get_shape())
print('---------------------')
res4d = bottleneck_block(res4c,
is_train,
block_name='res4d',
s1=1,
k1=1,
nf1=256,
name1='res4d_branch2a',
s2=1,
k2=3,
nf2=256,
name2='res4d_branch2b',
s3=1,
k3=1,
nf3=1024,
name3='res4d_branch2c',
s4=1,
k4=1,
name4='res4d_branch1',
first_block=False)
print('Res4d')
print(res4d.get_shape())
print('---------------------')
res4e = bottleneck_block(res4d,
is_train,
block_name='res4e',
s1=1,
k1=1,
nf1=256,
name1='res4e_branch2a',
s2=1,
k2=3,
nf2=256,
name2='res4e_branch2b',
s3=1,
k3=1,
nf3=1024,
name3='res4e_branch2c',
s4=1,
k4=1,
name4='res4e_branch1',
first_block=False)
print('Res4e')
print(res4e.get_shape())
print('---------------------')
res4f = bottleneck_block(res4e,
is_train,
block_name='res4f',
s1=1,
k1=1,
nf1=256,
name1='res4f_branch2a',
s2=1,
k2=3,
nf2=256,
name2='res4f_branch2b',
s3=1,
k3=1,
nf3=1024,
name3='res4f_branch2c',
s4=1,
k4=1,
name4='res4f_branch1',
first_block=False)
print('Res4f')
print(res4f.get_shape())
print('---------------------')
# Res5
with tf.variable_scope('Res5'):
res5a = bottleneck_block(res4f,
is_train,
block_name='res5a',
s1=2,
k1=1,
nf1=512,
name1='res5a_branch2a',
s2=1,
k2=3,
nf2=512,
name2='res5a_branch2b',
s3=1,
k3=1,
nf3=2048,
name3='res5a_branch2c',
s4=2,
k4=1,
name4='res5a_branch1',
first_block=True)
print('---------------------')
print('Res5a')
print(res5a.get_shape())
print('---------------------')
res5b = bottleneck_block(res5a,
is_train,
block_name='res5b',
s1=1,
k1=1,
nf1=512,
name1='res5b_branch2a',
s2=1,
k2=3,
nf2=512,
name2='res5b_branch2b',
s3=1,
k3=1,
nf3=2048,
name3='res5b_branch2c',
s4=1,
k4=1,
name4='res5b_branch1',
first_block=False)
print('Res5b')
print(res5b.get_shape())
print('---------------------')
res5c = bottleneck_block(res5b,
is_train,
block_name='res5c',
s1=1,
k1=1,
nf1=512,
name1='res5c_branch2a',
s2=1,
k2=3,
nf2=512,
name2='res5c_branch2b',
s3=1,
k3=1,
nf3=2048,
name3='res5c_branch2c',
s4=1,
k4=1,
name4='res5c_branch1',
first_block=False)
# res5c: [batch_size, 8, 8, 2048]
print('Res5c')
print(res5c.get_shape())
k_size = res5c.get_shape().as_list()[1]
num_filters = res5c.get_shape().as_list()[-1]
f_map = tf.reshape(res5c, [-1, k_size * k_size, num_filters],
name='reshape_fmaps')
# [batch_size, 64, 2048]
res5c_gap = avg_pool(res5c,
ksize=k_size,
stride=1,
name='res5_avg_pool')
# [batch_size, 1, 1, 2048]
print('---------------------')
print('Res5c after AVG_POOL')
print(res5c.get_shape())
print('---------------------')
net_flatten = flatten_layer(res5c_gap)
# [batch_size, 2048]
print('---------------------')
print('Matrix dimension to the first FC layer')
print(net_flatten.get_shape())
print('---------------------')
net, W = fc_layer(net_flatten,
numClasses,
'FC1',
is_train=is_train,
batch_norm=True,
add_reg=True,
use_relu=False)
# W: [2048, 14]
W_tiled = tf.tile(tf.expand_dims(W, axis=0), [args.val_batch_size, 1, 1])
# [2048, 14] -> [1, 2048, 14] -> [batch_size, 2048, 14]
heat_map_list = tf.unstack(tf.matmul(f_map, W_tiled), axis=0)
# [batch_size, 64, 14]
# list of heat-maps of length batch_size, each element: [64, 14]
cls_act_map_list = [
tf.nn.softmax(heat_map, dim=0) for heat_map in heat_map_list
]
cls_act_map = tf.stack(cls_act_map_list, axis=0)
# [batch_size, 64, 14]
return net, net_flatten, res5c, cls_act_map
