generator_discriminator.py

import tensorflow as tf
from layers import *

def generator(inputs, reuse=False, name= "generator") :
    
    inputs = tf.transpose(inputs, perm= [0,2,1])
    
    with tf.compat.v1.variable_scope(name) as scope :
        
        if reuse : 
            scope.reuse_variables()
        else : 
            assert scope.reuse is False
            
        conv1 = conv1d_layer(inputs, 128, 15, 1)
        conv1_gates = conv1d_layer(inputs, 128, 15, 1)
        conv1_glu = gated_linear_unit(conv1, conv1_gates)
    
        down1 = downsample_1d(conv1_glu, 256, 5, 2)
        down2 = downsample_1d(down1, 512, 5, 2)
    
        res_block1 = residual_block(down2, 1024, 3, 1)
        res_block2 = residual_block(res_block1, 1024, 3, 1)
        res_block3 = residual_block(res_block2, 1024, 3, 1)
        res_block4 = residual_block(res_block3, 1024, 3, 1)
        res_block5 = residual_block(res_block4, 1024, 3, 1)
        res_block6 = residual_block(res_block5, 1024, 3, 1)
    
        up1 = upsample_1d(res_block6, 1024, 5, 1)
        up2 = upsample_1d(up1, 512, 5, 1)
    
        conv2 = conv1d_layer(up2, 24, 15, 1)
        outputs = tf.transpose(conv2, perm = [0,2,1])
    
    return outputs


def discriminator(inputs, reuse = False, name = 'discriminator'):

    inputs = tf.transpose(inputs, perm = [0, 2, 1], name = 'input_transpose_dic')

    with tf.compat.v1.variable_scope(name) as scope:

        if reuse:
            scope.reuse_variables()
        else:
            assert scope.reuse is False

        h1 = conv1d_layer(inputs = inputs, filters = 128, kernel_size = 15, strides = 1, activation = None)
        h1_gates = conv1d_layer(inputs = inputs, filters = 128, kernel_size = 15, strides = 1, activation = None)
        h1_glu = gated_linear_layer(inputs = h1, gates = h1_gates)


        d1 = downsample1d_block(inputs = h1_glu, filters = 256, kernel_size = 5, strides = 2, name_prefix = 'downsample1d_block1_')
        d2 = downsample1d_block(inputs = d1, filters = 512, kernel_size = 5, strides = 2, name_prefix = 'downsample1d_block2_')

        r1 = residual1d_block(inputs = d2, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block1_')
        r2 = residual1d_block(inputs = r1, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block2_')
        r3 = residual1d_block(inputs = r2, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block3_')
        r4 = residual1d_block(inputs = r3, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block4_')
        r5 = residual1d_block(inputs = r4, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block5_')
        r6 = residual1d_block(inputs = r5, filters = 1024, kernel_size = 3, strides = 1, name_prefix = 'residual1d_block6_')


        u1 = upsample1d_block(inputs = r6, filters = 1024, kernel_size = 5, strides = 1, shuffle_size = 2, name_prefix = 'upsample1d_block1_')
        u2 = upsample1d_block(inputs = u1, filters = 512, kernel_size = 5, strides = 1, shuffle_size = 2, name_prefix = 'upsample1d_block2_')

        o1 = conv1d_layer(inputs = u2, filters = 24, kernel_size = 15, strides = 1, activation = None, name = 'o1_conv')
        o2 = tf.transpose(o1, perm = [0, 2, 1], name = 'output_transpose')

        return o2