def __call__(self, inputs, train_phase):
with tf.variable_scope(self.name):
inputs = conv("conv1", inputs, 64, 9)
inputs = prelu("alpha1", inputs)
skip_connection = tf.identity(inputs)
#The paper has 16 residual blocks
for b in range(1, self.B + 1):
inputs = B_residual_blocks("B"+str(b), inputs, train_phase)
# inputs = B_residual_blocks("B2", inputs, train_phase)
# inputs = B_residual_blocks("B3", inputs, train_phase)
# inputs = B_residual_blocks("B4", inputs, train_phase)
# inputs = B_residual_blocks("B5", inputs, train_phase)
inputs = conv("conv2", inputs, 64, 3)
inputs = batchnorm(inputs, train_phase, "BN")
inputs = inputs + skip_connection
inputs = conv("conv3", inputs, 256, 3)
inputs = pixelshuffler(inputs, 2)
inputs = prelu("alpha2", inputs)
inputs = conv("conv4", inputs, 256, 3)
inputs = pixelshuffler(inputs, 2)
inputs = prelu("alpha3", inputs)
inputs = conv("conv5", inputs, 3, 9)
return tf.nn.tanh(inputs)
def encoder(noisy_w, is_ref, scope, z_on=False, do_prelu=False):
#is_ref : Creation phase
skips = []
h_i = noisy_w
# h_i = tf.expand_dims(noisy_w, -1)
for layer_idx, layer_depth in enumerate(g_enc_depths):
print(layer_idx)
bias_init = tf.constant_initializer(0.)
h_i_dwn = downconv(h_i, layer_depth, kwidth=31,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init, name='downconv_{}_{}_{}'.format(layer_idx,
layer_depth, scope))
h_i = h_i_dwn
if layer_idx < len(g_enc_depths) - 1:
skips.append(h_i)
if do_prelu:
h_i = prelu(h_i, name='enc_prelu_{}_{}_{}'.format(layer_idx,layer_depth, scope)) # default: ref=False
else:
h_i = leakyrelu(h_i)
#end_for :)
# Adding z to c
if z_on:
# random code is fused with intermediate representation
z = make_z([batch_size, h_i.get_shape().as_list()[1],g_enc_depths[-1]])
h_i = tf.concat([z, h_i], 2)
return h_i, skips
def decoder(h_i, skips, z=None, z_on=False):
# z+c is called h_i here
g_dec_depths = g_enc_depths[:-1][::-1] + [1]
for layer_idx, layer_depth in enumerate(g_dec_depths):
h_i_dim = h_i.get_shape().as_list()
out_shape = [h_i_dim[0], h_i_dim[1] * 2,
layer_depth] #2 because of skip connections
bias_init = None
#############
if deconv_type == 'deconv':
bias_init = tf.constant_initializer(0.)
h_i_dcv = deconv(h_i,
out_shape,
kwidth=kwidth,
dilation=2,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init)
elif deconv_type == 'nn_deconv':
bias_init = 0.0
h_i_dcv = nn_deconv(
h_i,
kwidth=kwidth,
dilation=2,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init)
h_i = h_i_dcv
if layer_idx < len(g_dec_depths) - 1:
if do_prelu:
h_i = prelu(h_i)
else:
h_i = leakyrelu(h_i)
# fuse skip connection
skip_ = skips[-(layer_idx + 1)]
h_i = tf.concat([h_i, skip_], 2)
else:
h_i = tf.tanh(h_i)
wave = h_i
# Not sure abotu the following
ret_feats = [wave]
if z_on:
ret_feats.append(z)
return ret_feats