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train_multi_gpus.py
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# -*- coding: utf-8 -*-
#/usr/bin/python2
'''
By kyubyong park. kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/tacotron
'''
from __future__ import print_function
import os
import librosa
from tqdm import tqdm
from data_load import get_batch
from hyperparams import Hyperparams as hp
from modules import *
from networks import encode, decode1, decode2
import numpy as np
from prepro import *
import tensorflow as tf
from utils import shift_by_one
class Graph:
def __init__(self, is_training=True):
self.graph = tf.Graph()
with self.graph.as_default():
if is_training:
self.x, self.y, self.z, self.num_batch = get_batch()
self.decoder_inputs = shift_by_one(self.y)
# Make sure that batch size was multiplied by # gpus.
# Now we split the mini-batch data by # gpus.
self.x = tf.split(self.x, hp.num_gpus, 0)
self.y = tf.split(self.y, hp.num_gpus, 0)
self.z = tf.split(self.z, hp.num_gpus, 0)
self.decoder_inputs = tf.split(self.decoder_inputs, hp.num_gpus, 0)
# Sequence lengths for masking
self.x_lengths = tf.to_int32(tf.reduce_sum(tf.sign(tf.abs(self.x)), -1)) # (N,)
self.x_masks = tf.to_float(tf.expand_dims(tf.sign(tf.abs(self.x)), -1)) # (N, T, 1)
# optimizer
self.optimizer = tf.train.AdamOptimizer(learning_rate=hp.lr)
self.losses, self.grads_and_vars_list = [], []
for i in range(hp.num_gpus):
with tf.variable_scope('net', reuse=bool(i)):
with tf.device('/gpu:{}'.format(i)):
with tf.name_scope('gpu_{}'.format(i)):
# Encoder
self.memory = encode(self.x[i], is_training=is_training) # (N, T, E)
# Decoder
self.outputs1 = decode1(self.decoder_inputs[i],
self.memory,
is_training=is_training) # (N, T', hp.n_mels*hp.r)
self.outputs2 = decode2(self.outputs1, is_training=is_training) # (N, T', (1+hp.n_fft//2)*hp.r)
# Loss
if hp.loss_type=="l1": # L1 loss
self.loss1 = tf.abs(self.outputs1 - self.y[i])
self.loss2 = tf.abs(self.outputs2 - self.z[i])
else: # L2 loss
self.loss1 = tf.squared_difference(self.outputs1, self.y[i])
self.loss2 = tf.squared_difference(self.outputs2, self.z[i])
# Target masking
if hp.target_zeros_masking:
self.loss1 *= tf.to_float(tf.not_equal(self.y[i], 0.))
self.loss2 *= tf.to_float(tf.not_equal(self.z[i], 0.))
self.loss1 = tf.reduce_mean(self.loss1)
self.loss2 = tf.reduce_mean(self.loss2)
self.loss = self.loss1 + self.loss2
self.losses.append(self.loss)
self.grads_and_vars = self.optimizer.compute_gradients(self.loss)
self.grads_and_vars_list.append(self.grads_and_vars)
with tf.device('/cpu:0'):
# Aggregate losses, then calculate average loss.
self.mean_loss = tf.add_n(self.losses) / len(self.losses)
#Aggregate gradients, then calculate average gradients.
self.mean_grads_and_vars = []
for grads_and_vars in zip(*self.grads_and_vars_list):
grads = []
for grad, var in grads_and_vars:
if grad is not None:
grads.append(tf.expand_dims(grad, 0))
mean_grad = tf.reduce_mean(tf.concat(grads, 0), 0) #()
self.mean_grads_and_vars.append((mean_grad, var))
# Training Scheme
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self.train_op = self.optimizer.apply_gradients(self.mean_grads_and_vars, self.global_step)
# Summmary
tf.summary.scalar('mean_loss', self.mean_loss)
self.merged = tf.summary.merge_all()
else: # Evaluation
self.x = tf.placeholder(tf.int32, shape=(None, None))
self.y = tf.placeholder(tf.float32, shape=(None, None, hp.n_mels*hp.r))
self.decoder_inputs = shift_by_one(self.y)
with tf.variable_scope('net'):
# Encoder
self.memory = encode(self.x, is_training=is_training) # (N, T, E)
# Decoder
self.outputs1 = decode1(self.decoder_inputs, self.memory, is_training=is_training) # (N, T', hp.n_mels*hp.r)
self.outputs2 = decode2(self.outputs1, is_training=is_training) # (N, T', (1+hp.n_fft//2)*hp.r)
def main():
g = Graph(); print("Training Graph loaded")
with g.graph.as_default():
# Load vocabulary
char2idx, idx2char = load_vocab()
# Training
sv = tf.train.Supervisor(logdir=hp.logdir,
save_model_secs=600)
with sv.managed_session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
for epoch in range(1, hp.num_epochs+1):
if sv.should_stop(): break
for step in tqdm(range(g.num_batch), total=g.num_batch, ncols=70, leave=False, unit='b'):
sess.run(g.train_op)
# Write checkpoint files at every epoch
gs = sess.run(g.global_step)
sv.saver.save(sess, hp.logdir + '/model_epoch_%02d_gs_%d' % (epoch, gs))
if __name__ == '__main__':
main()
print("Done")