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run.py
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from wavegan import *
import numpy as np
import datetime
import os
import argparse
#tf.config.experimental.set_visible_devices([], 'GPU')
hyperparams = {
'num_channels': 1,
'batch_size': 64,
'model_dim': 64,
'latent_dim': 100,
'phase_shuffle': 2,
'wgan_gp_lambda': 10,
'd_per_g_update': 5,
'adam_alpha': 1e-4,
'adam_beta1': 0.5,
'adam_beta2': 0.9,
'update_losses': 10,
'weights_folder': 'weights_folder/',
'sample_rate': 16000,
'generated_audio_output_dir': "generated_audio"
}
@tf.function
def train_step_disc(x):
z = tf.random.uniform(
shape=[hyperparams['batch_size'], hyperparams['latent_dim']],
minval=-1.,
maxval=1.,
dtype=tf.float32
)
with tf.GradientTape() as disc_tape:
_, disc_loss, _ = wavegan_loss(generator, discriminator, x, z)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
return gradients_of_discriminator
@tf.function
def train_step_gen(x):
z = tf.random.uniform(
shape=[hyperparams['batch_size'], hyperparams['latent_dim']],
minval=-1.,
maxval=1.,
dtype=tf.float32
)
with tf.GradientTape() as gen_tape:
gen_loss, _, _ = wavegan_loss(generator, discriminator, x, z)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
return gradients_of_generator
@tf.function
def wavegan_loss(gen, disc, x, z):
G_z = gen(z)
D_x = disc(x)
D_G_z = disc(G_z)
gen_loss_one = -tf.reduce_mean(D_G_z) #Expected value
gen_loss = tf.reduce_mean(D_x) - tf.reduce_mean(D_G_z)
disc_loss = -gen_loss
# Gradient penalty
epsilon = tf.random.uniform([hyperparams['batch_size'], 1, 1], minval=0., maxval=1.)
x_hat = epsilon * x + (1 - epsilon) * G_z #batch x 16384 x 1
with tf.GradientTape() as tape:
tape.watch(x_hat)
y = disc(x_hat) # 64 x 1
dy_d_x_hat = tape.gradient(y, x_hat) #batch x 16384 x 1
slopes = tf.sqrt(tf.compat.v1.reduce_sum(tf.square(dy_d_x_hat), reduction_indices=[1, 2]))
gp = hyperparams['wgan_gp_lambda'] * tf.reduce_mean((slopes - 1.) ** 2)
return gen_loss, disc_loss + gp, gen_loss_one
def train(dataset, epochs, shuffle=True, initial_log_step=0):
import time
from tqdm import tqdm
update_step = 0
update_log_step = initial_log_step
for epoch in range(epochs):
if shuffle:
np.random.shuffle(dataset)
start = time.time()
offset = 0
iterator = tqdm(range(dataset.shape[0] // hyperparams['batch_size']))
for i in iterator:
batch = dataset[offset:offset + hyperparams['batch_size']]
if i % hyperparams['d_per_g_update'] == 0:
grad_gen = train_step_gen(batch)
grad_disc = train_step_disc(batch)
if update_step % hyperparams['update_losses'] == 0:
z = tf.random.uniform(
shape=[hyperparams['batch_size'], hyperparams['latent_dim']],
minval=-1.,
maxval=1.,
dtype=tf.float32
)
gen_loss, disc_loss, gen_loss_one = wavegan_loss(generator, discriminator, batch, z)
iterator.set_description("\nGen loss: {}, Disc loss: {}".format(gen_loss, disc_loss))
# Write to tensorboard
with train_summary_writer.as_default():
tf.summary.scalar('gen_loss', gen_loss, step=update_log_step)
tf.summary.scalar('disc_loss', disc_loss, step=update_log_step)
tf.summary.scalar('gen_loss_one_term', gen_loss_one, step=update_log_step)
update_log_step += 1
offset += hyperparams['batch_size']
update_step += 1
save_model(generator, discriminator, generator_optimizer, discriminator_optimizer, hyperparams)
write_summaries(grad_gen, grad_disc, hyperparams['generated_audio_output_dir'], epoch)
print('Time for epoch {} is {} sec'.format(epoch + 1, time.time() - start))
def write_summaries(grad_gen, grad_disc, output_dir, epoch):
z = tf.random.uniform(
shape=[hyperparams['batch_size'], hyperparams['latent_dim']],
minval=-1.,
maxval=1.,
dtype=tf.float32
)
generated_audio = generator(z)
sample_dir = os.path.join(output_dir, str(epoch))
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
audi = []
# Generate audio
for i in range(generated_audio.shape[0]):
audi.append(tf.expand_dims(generated_audio[i], 0))
output_path = os.path.join(sample_dir, "{}_{}.wav".format(epoch, i + 1))
string = tf.audio.encode_wav(generated_audio[i], hyperparams['sample_rate'])
tf.io.write_file(output_path, string)
audi = tf.concat(audi, axis=0)
with train_summary_writer.as_default():
tf.summary.audio("audio_samples", audi, hyperparams['sample_rate'], step=epoch, encoding="wav")
for i in range(len(grad_gen)):
tf.summary.histogram("gen_grads_layer_" + str(i), grad_gen[i], step=epoch)
tf.summary.scalar("gen_grad_norm_layer_" + str(i), tf.norm(grad_gen[i], ord=2), step=epoch)
for i in range(len(grad_disc)):
tf.summary.histogram("disc_grads_layer_" + str(i), grad_disc[i], step=epoch)
tf.summary.scalar("disc_grad_norm_layer_" + str(i), tf.norm(grad_disc[i], ord=2), step=epoch)
generator_weights = generator.get_weights()
for i in range(len(generator_weights)):
tf.summary.histogram("gen_weights_layer_" + str(i), generator_weights[i], step=epoch)
discriminator_weights = discriminator.get_weights()
for i in range(len(discriminator_weights)):
tf.summary.histogram("disc_weights_layer_" + str(i), discriminator_weights[i], step=epoch)
def generate_n_samples(output_path, n=50000):
assert n % 10 == 0
c = 0
from tqdm import tqdm
for i in tqdm(range(n // 10)):
z = tf.random.uniform(shape=[10, hyperparams['latent_dim']], minval=-1., maxval=1., dtype=tf.float32)
generated_audio = generator(z)
for j in range(10):
string = tf.audio.encode_wav(generated_audio[j], hyperparams['sample_rate'])
tf.io.write_file(os.path.join(output_path, "{}.wav".format(c)), string)
c += 1
ap = argparse.ArgumentParser()
ap.add_argument("-generate", "--generate", required=False, help="If we want to generate n audio samples", action='store_true')
ap.add_argument("-n", "--n", required=False, default=100, help="Number of samples to generate")
ap.add_argument("-output_path", "--output_path", required=False, help="Output path of the generated samples")
ap.add_argument("-train", "--train", required=False, help="If we want to train", action='store_true')
ap.add_argument("-continue", "--continue", required=False, help="If we want to load the old weights", action='store_true')
ap.add_argument("-epochs", "--epochs", required=False, default=100, type=int, help="The number of epochs to train for")
ap.add_argument("-dataset", "--dataset", required=False, help="The path to the dataset file (.npy)")
ap.add_argument("-initial_log_step", "--initial_log_step", required=False, type=int, help="The step at where we should start logging")
ap.add_argument("-weights", "--weights", required=False, help="The pretrained weights for the different datasets (sc09, piano or cats).")
args = vars(ap.parse_args())
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = 'logs/gradient_tape/' + current_time + '/train'
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
generator = wavegan_generator(hyperparams['model_dim'], hyperparams['num_channels'])
discriminator = wavegan_discriminator(hyperparams['model_dim'], hyperparams['num_channels'])
"""
Define optimizers
"""
generator_optimizer = tf.keras.optimizers.Adam(
learning_rate=hyperparams['adam_alpha'],
beta_1=hyperparams['adam_beta1'],
beta_2=hyperparams['adam_beta2']
)
discriminator_optimizer = tf.keras.optimizers.Adam(
learning_rate=hyperparams['adam_alpha'],
beta_1=hyperparams['adam_beta1'],
beta_2=hyperparams['adam_beta2']
)
if args['weights']:
if args['weights'] == 'sc09':
hyperparams['weights_folder'] = 'weights_folder_sc09/'
elif args['weights'] == 'piano':
hyperparams['weights_folder'] = 'weights_folder_piano/'
elif args['weights'] == 'kittens':
hyperparams['weights_folder'] = 'weights_folder_kittens/'
else:
print('No pretrained weights for the selected dataset. Initializing new weights.')
if args['train']:
initial_log_step = 0
if args['continue']:
load_model(generator, discriminator, generator_optimizer, discriminator_optimizer, hyperparams)
if args['initial_log_step'] is not None:
initial_log_step = args['initial_log_step']
x = np.load(args['dataset'])
train(x, args['epochs'], initial_log_step=initial_log_step)
elif args['generate']:
load_model(generator, discriminator, generator_optimizer, discriminator_optimizer, hyperparams)
if args['output_path'] is not None:
generate_n_samples(args['output_path'], n=args['n'])