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 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): 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 in_layer(self, inputs, W_name="W_in_", b_name="b_in_"): layer_id = 0 net = inputs filter_name = "{}{}".format(W_name, layer_id) bias_name = "{}{}".format(b_name, layer_id) curr_filter = self.in_filter[filter_name] curr_bias = self.in_bias[bias_name] # 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.in_leaky_ratio) # convolution net = ne.conv2d_transpose(net, filters=curr_filter, biases=curr_bias, strides=self.in_stride, padding=self.in_padding) #net = ne.max_pool_2x2(net) # Pooling net = tf.identity(net, name='in_output') #import pdb; pdb.set_trace() return net
def random_noise_layer(self, inputs, random_mask): net = inputs random_noise = tf.random_normal(tf.shape(net), mean=self.mean, stddev=self.stddev) if random_mask != None: random_noise = tf.multiply(random_mask, random_noise) net += random_noise 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 = tf.identity(net, name='rand_output') return net