def decoder(self, input, speaker_id):
net = self.arch['decoder']
c = net['channel']
k = net['kernel']
s = net['stride']
img_h, img_c = net['hc']
featureSize = self.arch['featureSize']
z_emb = tf.layers.dense(input,
img_h * img_c,
bias_initializer=tf.constant_initializer(0.1))
speaker_emb = tf.layers.dense(
speaker_id,
img_h * img_c,
bias_initializer=tf.constant_initializer(0.1))
latent = z_emb + speaker_emb
x = tf.reshape(latent, [-1, img_h, 1, img_c])
for l in range(len(c)):
if (l != len(c)):
x = layer.deconv2d(x,
c[l],
k[l],
s[l],
activation=layer.prelu,
name='decoder-L{}'.format(l))
else:
x = layer.deconv2d(x,
c[l],
k[l],
s[l],
activation=tf.nn.relu,
name='decoder-L{}'.format(l))
x = tf.reshape(x, [-1, featureSize])
return x, speaker_emb
def decoder(self,input_z,name = 'generate_img',is_training = True):
hidden_num = 64
output_dim = 64
with tf.variable_scope(name,reuse = tf.AUTO_REUSE):
x = ly.fc(input_z, hidden_num * 8 * (output_dim // 16) * (output_dim // 16),name = 'gen_fc_0')
x = tf.reshape(x, shape=[self.imle_deep, output_dim // 16, output_dim // 16, hidden_num * 8]) ## 4, 4, 8*64
x = ly.deconv2d(x,hidden_num * 4,name = 'g_deconv2d_0') ### 8,8, 256
x = ly.batch_normal(x,name = 'g_deconv_bn_0',is_training = is_training)
x = ly.relu(x)
x = ly.deconv2d(x,hidden_num * 2,name = 'g_deconv2d_1') ### 16,16, 128
x = ly.batch_normal(x,name = 'g_deconv_bn_1',is_training = is_training)
x = ly.relu(x)
x = ly.deconv2d(x,hidden_num,name = 'g_deconv2d_2') ### 32,32, 64
x = ly.batch_normal(x,name = 'g_deconv_bn_2',is_training = is_training)
x = ly.relu(x)
x = ly.deconv2d(x, 3, name = 'g_deconv2d_3') ### 64,64, 3
x = ly.batch_normal(x,name = 'g_deconv_bn_3',is_training = is_training)
x = tf.nn.tanh(x)
return x
def decoder(z, label):
label = tf.tile(tf.expand_dims(label, 1), [1, tstep, 1])
label = tf.reshape(label, [-1, label.get_shape()[2]])
x = tf.layers.dense(z,
25 * latentSize,
bias_initializer=tf.constant_initializer(0.1))
h = tf.layers.dense(label,
25 * latentSize,
bias_initializer=tf.constant_initializer(0.1))
# x_emb = tf.concat([x, h], axis = 0)
x_emb = x + h
x = tf.reshape(x_emb, [-1, tstep, 25, latentSize])
unit = arch['decoder']
c = unit['channel']
k = unit['kernel']
s = unit['stride']
with tf.variable_scope('decoder', reuse=tf.AUTO_REUSE):
for l in range(len(c)):
x = layer.deconv2d(x,
c[l],
k[l],
s[l],
tf.nn.relu,
name='decoder-L{}'.format(l))
x = tf.reshape(x, [-1, N])
return x, x_emb, h
def deconv_relu_drop(x, kernalshape, samefeture=False, scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernalshape,
n_inputs=kernalshape[0] * kernalshape[1] *
kernalshape[-1],
n_outputs=kernalshape[-2],
activefunction='relu',
variable_name=str(scope) + 'W')
B = bias_variable([kernalshape[-2]], variable_name=str(scope) + 'B')
dconv = tf.nn.relu(deconv2d(x, W, samefeature=samefeture) + B)
return dconv
def generator(self, z, label):
net = self.arch['generator']
c = net['channel']
k = net['kernel']
s = net['stride']
# x = tf.reshape(x, [-1, tstep, N, 1])
label = tf.tile(tf.expand_dims(label, 1), [1, tstep, 1])
label = tf.reshape(label, [-1, 10])
x = tf.layers.dense(z, 25 * self.latentSize, activation=None,
bias_initializer=tf.constant_initializer(0.1))
h = tf.layers.dense(label, 25 * self.latentSize, activation=None,
bias_initializer=tf.constant_initializer(0.1))
z_emb = x + h
x = z_emb
x = tf.reshape(x, [-1, 25, 1, self.latentSize])
for i in range(len(c)):
x = layer.deconv2d(x, c[i], k[i], s[i], tf.nn.relu, padding='SAME', name='generator-L{}'.format(i))
x = tf.reshape(x, [-1, N])
return x
def create_conv_net(x,
keep_prob,
channels,
layers,
features_root=16,
filter_size=3,
pool_size=2,
training=True):
"""
주어진 파라미터를 이용해서 convolution u-net 그래프 생성 함
:param x: input tensor, shape [?,nx,ny,channels]
:param keep_prob: dropout probability tensor
:param channels: number of channels in the input image
:param layers: number of layers in the net
:param features_root: number of features in the first layer
:param filter_size: size of the convolution filter
:param pool_size: size of the max pooling operation
:param summaries: Flag if summaries should be created
"""
logging.info(
"Layers {layers}, features {features}, filter size {filter_size}x{filter_size}, pool size: {pool_size}x{pool_size}"
.format(layers=layers,
features=features_root,
filter_size=filter_size,
pool_size=pool_size))
# Placeholder for the input image
with tf.name_scope("preprocessing"):
nx = tf.shape(x)[1]
ny = tf.shape(x)[2]
x_image = tf.reshape(x, tf.stack([-1, nx, ny, channels]))
in_node = x_image
batch_size = tf.shape(x_image)[0]
weights = []
biases = []
convs = []
pools = OrderedDict()
deconv = OrderedDict()
dw_h_convs = OrderedDict()
up_h_convs = OrderedDict()
in_size = 1000
size = in_size
# down layers
for layer in range(0, layers):
with tf.name_scope("down_conv_{}".format(str(layer))):
features = 2**layer * features_root
stddev = np.sqrt(2 / (filter_size**2 * features))
if layer == 0:
w1 = weight_variable(
[filter_size, filter_size, channels, features],
stddev,
name="w1")
else:
w1 = weight_variable(
[filter_size, filter_size, features // 2, features],
stddev,
name="w1")
w2 = weight_variable(
[filter_size, filter_size, features, features],
stddev,
name="w2")
b1 = bias_variable([features], name="b1")
b2 = bias_variable([features], name="b2")
conv1 = conv2d(in_node, w1, b1, keep_prob)
tmp_h_conv = tf.nn.relu(
tf.layers.batch_normalization(conv1, training=training))
conv2 = conv2d(tmp_h_conv, w2, b2, keep_prob)
dw_h_convs[layer] = tf.nn.relu(
tf.layers.batch_normalization(conv2, training=training))
weights.append((w1, w2))
biases.append((b1, b2))
convs.append((conv1, conv2))
size -= 4
if layer < layers - 1:
pools[layer] = max_pool(dw_h_convs[layer], pool_size)
in_node = pools[layer]
size /= 2
in_node = dw_h_convs[layers - 1]
# up layers
for layer in range(layers - 2, -1, -1):
with tf.name_scope("up_conv_{}".format(str(layer))):
features = 2**(layer + 1) * features_root
stddev = np.sqrt(2 / (filter_size**2 * features))
wd = weight_variable_devonc(
[pool_size, pool_size, features // 2, features],
stddev,
name="wd")
bd = bias_variable([features // 2], name="bd")
h_deconv = tf.nn.relu(deconv2d(in_node, wd, pool_size) + bd)
h_deconv_concat = crop_and_concat(dw_h_convs[layer], h_deconv)
deconv[layer] = h_deconv_concat
w1 = weight_variable(
[filter_size, filter_size, features, features // 2],
stddev,
name="w1")
w2 = weight_variable(
[filter_size, filter_size, features // 2, features // 2],
stddev,
name="w2")
b1 = bias_variable([features // 2], name="b1")
b2 = bias_variable([features // 2], name="b2")
conv1 = conv2d(h_deconv_concat, w1, b1, keep_prob)
h_conv = tf.nn.relu(
tf.layers.batch_normalization(conv1, training=training))
conv2 = conv2d(h_conv, w2, b2, keep_prob)
in_node = tf.nn.relu(
tf.layers.batch_normalization(conv2, training=training))
up_h_convs[layer] = in_node
weights.append((w1, w2))
biases.append((b1, b2))
convs.append((conv1, conv2))
size *= 2
size -= 4
# Output Map
with tf.name_scope("output_map"):
weight = weight_variable(
[1, 1, features_root, 1],
stddev) # 불량 CLASS의 MAP만 Loss함수에 들어가므로 channel은 "1"이 됨.
bias = bias_variable([1], name="bias")
conv = conv2d(in_node, weight, bias, tf.constant(1.0))
output_map = tf.squeeze(tf.nn.sigmoid(conv), axis=-1)
up_h_convs["out"] = output_map
variables = []
for w1, w2 in weights:
variables.append(w1)
variables.append(w2)
for b1, b2 in biases:
variables.append(b1)
variables.append(b2)
return output_map, variables, int(in_size - size)
def __call__(self, input):
with tf.variable_scope(self.name, reuse=self.reuse):
input = ly.conv2d(input,
64,
kernel_size=7,
strides=1,
name='g_conv2d_0')
input = ly.batch_normal(input, name='g_bn_0')
input = tf.nn.relu(input)
input = ly.conv2d(input,
128,
kernel_size=3,
strides=2,
name='g_conv2d_1')
input = ly.batch_normal(input, name='g_bn_1')
input = tf.nn.relu(input)
input = ly.conv2d(input,
256,
kernel_size=3,
strides=2,
name='g_conv2d_2')
input = ly.batch_normal(input, name='g_bn_2')
input = tf.nn.relu(input)
### resnet
for i in range(8):
cell = ly.conv2d(input,
256,
kernel_size=3,
strides=1,
name='g_conv2d_res_%s' % i)
cell = ly.batch_normal(cell, name='g_res_%s' % i)
cell = tf.nn.relu(cell)
input = cell
input = ly.deconv2d(input,
kernel_size=3,
strides=2,
name='g_deconv2d_0')
input = ly.batch_normal(input, name='g_bn_3')
input = tf.nn.relu(input)
input = ly.deconv2d(input,
kernel_size=3,
strides=2,
name='g_deconv2d_1')
input = ly.batch_normal(input, name='g_bn_4')
input = tf.nn.relu(input)
input = ly.conv2d(input,
3,
kernel_size=7,
strides=1,
name='g_conv2d_3')
input = ly.batch_normal(input, name='g_bn_5')
input = tf.nn.tanh(input)
input = tf.image.resize_images(input, (299, 299))
return input ## (-1,28,28,1)
