def res_blocks(self, inputs, W_name, b_name, scope):
with tf.variable_scope(scope):
net = inputs
for res_id in range(self.num_res_block):
res_net = net
for layer_id in range(self.res_block_size):
filter_name = "{}{}_{}".format(W_name, res_id, layer_id)
bias_name = "{}{}_{}".format(b_name, res_id, layer_id)
curr_filter = self.res_filters[filter_name]
curr_bias = self.res_biases[bias_name]
# convolution
net = ne.conv2d_transpose(
net,
filters=curr_filter,
biases=curr_bias,
strides=self.res_strides[layer_id],
padding=self.res_padding[layer_id])
if self.use_norm == "BATCH":
net = ne.batch_norm(net, self.is_training)
elif self.use_norm == "LAYER":
net = ne.layer_norm(net, self.is_training)
elif self.use_norm == "INSTA":
net = ne.instance_norm(net, self.is_training)
net = ne.leaky_relu(net, self.res_leaky_ratio[layer_id])
#net = ne.leaky_brelu(net, self.res_leaky_ratio[layer_id], self.layer_low_bound, self.output_up_bound) # Nonlinear act
net = ne.drop_out(net, self.res_drop_rate[layer_id],
self.is_training)
net += res_net
net = tf.identity(net, name='res_output')
#import pdb; pdb.set_trace()
return net
def _form_groups(net, start_layer, end_layer):
for layer_id in range(start_layer, end_layer):
#res blocks
W_res_name = "W_g{}_res".format(layer_id)
b_res_name = "b_g{}_res".format(layer_id)
net = self.res_blocks(net, W_res_name, b_res_name, scope="RES_{}".format(layer_id))
# decv
filter_name = "{}{}".format(W_name, layer_id)
bias_name = "{}{}".format(b_name, layer_id)
curr_filter = self.decv_filters[filter_name]
curr_bias = self.decv_biases[bias_name]
# de-convolution
net = ne.conv2d_transpose(net,
filters=curr_filter, biases=curr_bias,
strides=self.decv_strides[layer_id],
padding=self.decv_padding[layer_id])
# batch normalization
if self.use_norm == "BATCH":
net = ne.batch_norm(net, self.is_training)
elif self.use_norm == "LAYER":
net = ne.layer_norm(net, self.is_training)
net = ne.leaky_relu(net, self.decv_leaky_ratio[layer_id])
net = ne.drop_out(net, self.decv_drop_rate[layer_id], self.is_training)
return net
def _form_groups(net, start_layer, end_layer):
for layer_id in range(start_layer, end_layer):
filter_name = "{}{}".format(W_name, layer_id)
bias_name = "{}{}".format(b_name, layer_id)
curr_filter = self.conv_filters[filter_name]
curr_bias = self.conv_biases[bias_name]
# convolution
net = ne.conv2d(net, filters=curr_filter, biases=curr_bias,
strides=self.conv_strides[layer_id],
padding=self.conv_padding[layer_id])
conv_net = net
# batch normalization
if self.use_norm == "BATCH":
net = ne.batch_norm(net, self.is_training)
elif self.use_norm == "LAYER":
net = ne.layer_norm(net, self.is_training)
elif self.use_norm == "INSTA":
net = ne.instance_norm(net, self.is_training)
#net = ne.leaky_brelu(net, self.conv_leaky_ratio[layer_id], self.layer_low_bound, self.output_up_bound) # Nonlinear act
net = ne.leaky_relu(net, self.conv_leaky_ratio[layer_id])
net = ne.drop_out(net, self.conv_drop_rate[layer_id], self.is_training)
# residual for conv
if conv_residual:
net += conv_net
# res blocks
if self.num_res_block != 0:
W_res_name = "W_g{}_res".format(layer_id)
b_res_name = "b_g{}_res".format(layer_id)
net = self.res_blocks(net, W_res_name, b_res_name, scope="RES_{}".format(layer_id))
return net
def _form_groups(net, start_layer, end_layer):
for layer_id in range(start_layer, end_layer):
#res blocks
W_res_name = "W_g{}_res".format(layer_id)
b_res_name = "b_g{}_res".format(layer_id)
net = self.res_blocks(net,
W_res_name,
b_res_name,
scope="RES_{}".format(layer_id))
# decv
filter_name = "{}{}".format(W_name, layer_id)
bias_name = "{}{}".format(b_name, layer_id)
curr_filter = self.decv_filters[filter_name]
curr_bias = self.decv_biases[bias_name]
# de-convolution
net = ne.conv2d_transpose(net,
filters=curr_filter,
biases=curr_bias,
strides=self.decv_strides[layer_id],
padding=self.decv_padding[layer_id])
# batch normalization
if self.use_norm == "BATCH":
net = ne.batch_norm(net, self.is_training)
elif self.use_norm == "LAYER":
net = ne.layer_norm(net, self.is_training)
elif self.use_norm == "INSTA":
net = ne.instance_norm(net, self.is_training)
if layer_id != end_layer - 1:
net = ne.leaky_relu(net, self.decv_leaky_ratio[layer_id])
net = ne.drop_out(net, self.decv_drop_rate[layer_id],
self.is_training)
if layer_id == self.num_decv - 2:
# mask
if FLAGS.USE_LABEL_MASK:
w = net.get_shape().as_list()[1]
h = net.get_shape().as_list()[2]
c = net.get_shape().as_list()[3]
net = tf.reshape(net, [-1, w * h, c])
net = tf.matmul(net, mask_states)
net = tf.reshape(net, [-1, w, h, c])
if self.use_norm == "BATCH":
net = ne.batch_norm(net, self.is_training)
elif self.use_norm == "LAYER":
net = ne.layer_norm(net, self.is_training)
elif self.use_norm == "INSTA":
net = ne.instance_norm(net, self.is_training)
#import pdb; pdb.set_trace()
return net
def _fc_layers(self, inputs, weights_dict, biases_dict, fc_leaky_ratio,
fc_drop_rate, num_fc, W_name, b_name):
net = inputs
for layer_id in range(num_fc):
weight_name = "{}{}".format(W_name, layer_id)
bias_name = "{}{}".format(b_name, layer_id)
curr_weight = weights_dict[weight_name]
curr_bias = biases_dict[bias_name]
net = ne.fully_conn(net, weights=curr_weight, biases=curr_bias)
# batch normalization
if self.use_norm == "BATCH":
net = ne.batch_norm(net, self.is_training, axis=-1)
#net = ne.leaky_brelu(net, self.enfc_leaky_ratio[layer_id], self.enfc_low_bound[layer_id], self.enfc_up_bound[layer_id]) # Nonlinear act
net = ne.leaky_relu(net, fc_leaky_ratio[layer_id])
net = ne.drop_out(net, fc_drop_rate[layer_id], self.is_training)
#net = ne.elu(net)
net = tf.identity(net, name='output')
return net
def defc_layers(self, inputs, W_name="W_defc", b_name="b_defc"):
net = inputs
for layer_id in range(self.num_enfc):
weight_name = "{}{}".format(W_name, layer_id)
bias_name = "{}{}".format(b_name, layer_id)
curr_weight = self.defc_weights[weight_name]
curr_bias = self.defc_biases[bias_name]
net = ne.fully_conn(net, weights=curr_weight, biases=curr_bias)
# batch normalization
if self.use_batch_norm:
net = ne.batch_norm(net, self.is_training, axis=1)
#net = ne.leaky_brelu(net, self.defc_leaky_ratio[layer_id], self.layer_low_bound, self.layer_up_bound) # Nonlinear act
net = ne.leaky_relu(net, self.defc_leaky_ratio[layer_id])
net = ne.drop_out(net, self.defc_drop_rate[layer_id],
self.is_training)
#net = ne.elu(net)
net = tf.identity(net, name='output')
net = tf.reshape(net, [-1] + self.decv_in_shape)
return net
def enfc_layers(self, inputs, W_name="W_enfc", b_name="b_enfc"):
net = tf.reshape(inputs, [
-1, self.conv_out_shape[0] * self.conv_out_shape[1] *
self.conv_out_shape[2]
])
for layer_id in range(self.num_enfc):
weight_name = "{}{}".format(W_name, layer_id)
bias_name = "{}{}".format(b_name, layer_id)
curr_weight = self.enfc_weights[weight_name]
curr_bias = self.enfc_biases[bias_name]
net = ne.fully_conn(net, weights=curr_weight, biases=curr_bias)
# batch normalization
if self.use_norm == "BATCH":
net = ne.batch_norm(net, self.is_training, axis=1)
elif self.use_norm == "LAYER":
net = ne.layer_norm(net, self.is_training)
#net = ne.leaky_brelu(net, self.enfc_leaky_ratio[layer_id], self.enfc_low_bound[layer_id], self.enfc_up_bound[layer_id]) # Nonlinear act
net = ne.leaky_relu(net, self.enfc_leaky_ratio[layer_id])
net = ne.drop_out(net, self.enfc_drop_rate[layer_id],
self.is_training)
#net = ne.elu(net)
net = tf.identity(net, name='output')
return net
