def __preprocess_forward_pass(self, x):
self.x_quantized = mu_law(x)
x_scaled = tf.cast(self.x_quantized, tf.float32) / 128.0
# Why are we not expanding dim here? b/c we are defaulting to batch size of 1?
#self.x_scaled = x_scaled
self.input_mean = tf.reduce_mean(x_scaled)
return x_scaled
def __getitem__(self):
"""returns the tensor of the segmented data from the specified part of a track"""
data_and_aug = None
while data_and_aug is None:
data = self.get_random_slice()
if self.augmentation:
data_and_aug = [data, self.augmentation(data)]
else:
data_and_aug = [data, data]
data_and_aug = [mu_law(x / 2 ** 15) for x in data_and_aug]
return torch.tensor(data_and_aug[0]), torch.tensor(data_and_aug[1])
def __getitem__(self, _):
ret = None
while ret is None:
try:
ret = self.try_random_slice()
if self.augmentation:
ret = [ret, self.augmentation(ret)]
else:
ret = [ret, ret]
if self.dataset_name == 'wav':
ret = [mu_law(x / 2**15) for x in ret]
except Exception as e:
logger.info('Exception %s in dataset __getitem__, path %s', e,
self.path)
logger.debug('Exception in H5Dataset', exc_info=True)
return torch.tensor(ret[0]), torch.tensor(ret[1])
def __getitem__(self, _):
# pdb.set_trace()
ret = None
while ret is None:
try:
ret, midi = self.try_random_slice()
if self.augmentation:
ret = [ret, self.augmentation(ret)]
else:
ret = [ret, ret]
if self.dataset_name == 'wav':
ret = [mu_law(x / 2 ** 15) for x in ret]
except Exception as e:
logger.info('Exception %s in dataset __getitem__, path %s', e, self.path)
logger.debug('Exception in H5Dataset', exc_info=True)
# print("getitem shapes: ", ret[0].shape, ret[1].shape, midi.shape)
# if(midi is None):
# return torch.tensor(ret[0]), torch.tensor(ret[1]), None
# return torch.tensor(ret[0]), torch.tensor(ret[1]), torch.LongTensor(midi)
return ret[0], ret[1], midi
def main(args):
print('Starting')
matplotlib.use('agg')
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
checkpoints = args.checkpoint.parent.glob(args.checkpoint.name + '_*.pth')
checkpoints = [c for c in checkpoints if extract_id(c) in args.decoders]
assert len(checkpoints) >= 1, "No checkpoints found."
model_args = torch.load(args.model.parent / 'args.pth')[0]
encoder = wavenet_models.Encoder(model_args)
encoder.load_state_dict(torch.load(checkpoints[0])['encoder_state'])
encoder.eval()
encoder = encoder.cuda()
decoders = []
decoder_ids = []
for checkpoint in checkpoints:
decoder = WaveNet(model_args)
decoder.load_state_dict(torch.load(checkpoint)['decoder_state'])
decoder.eval()
decoder = decoder.cuda()
if args.py:
decoder = WavenetGenerator(decoder,
args.batch_size,
wav_freq=args.rate)
else:
decoder = NVWavenetGenerator(decoder,
args.rate * (args.split_size // 20),
args.batch_size, 3)
decoders += [decoder]
decoder_ids += [extract_id(checkpoint)]
xs = []
assert args.output_next_to_orig ^ (args.output is not None)
if len(args.files) == 1 and args.files[0].is_dir():
top = args.files[0]
file_paths = list(top.glob('**/*.wav')) + list(top.glob('**/*.h5'))
else:
file_paths = args.files
if not args.skip_filter:
file_paths = [f for f in file_paths if not '_' in str(f.name)]
for file_path in file_paths:
if file_path.suffix == '.wav':
data, rate = librosa.load(file_path, sr=16000)
assert rate == 16000
data = utils.mu_law(data)
elif file_path.suffix == '.h5':
data = utils.mu_law(h5py.File(file_path, 'r')['wav'][:] / (2**15))
if data.shape[-1] % args.rate != 0:
data = data[:-(data.shape[-1] % args.rate)]
assert data.shape[-1] % args.rate == 0
else:
raise Exception(f'Unsupported filetype {file_path}')
if args.sample_len:
data = data[:args.sample_len]
else:
args.sample_len = len(data)
xs.append(torch.tensor(data).unsqueeze(0).float().cuda())
xs = torch.stack(xs).contiguous()
print(f'xs size: {xs.size()}')
def save(x, decoder_ix, filepath):
wav = utils.inv_mu_law(x.cpu().numpy())
print(f'X size: {x.shape}')
print(f'X min: {x.min()}, max: {x.max()}')
if args.output_next_to_orig:
save_audio(wav.squeeze(),
filepath.parent / f'{filepath.stem}_{decoder_ix}.wav',
rate=args.rate)
else:
save_audio(wav.squeeze(),
args.output / str(extract_id(args.model)) /
str(args.update) / filepath.with_suffix('.wav').name,
rate=args.rate)
yy = {}
with torch.no_grad():
zz = []
for xs_batch in torch.split(xs, args.batch_size):
zz += [encoder(xs_batch)]
zz = torch.cat(zz, dim=0)
with utils.timeit("Generation timer"):
for i, decoder_id in enumerate(decoder_ids):
yy[decoder_id] = []
decoder = decoders[i]
for zz_batch in torch.split(zz, args.batch_size):
print(zz_batch.shape)
splits = torch.split(zz_batch, args.split_size, -1)
audio_data = []
decoder.reset()
for cond in tqdm.tqdm(splits):
audio_data += [decoder.generate(cond).cpu()]
audio_data = torch.cat(audio_data, -1)
yy[decoder_id] += [audio_data]
yy[decoder_id] = torch.cat(yy[decoder_id], dim=0)
del decoder
for decoder_ix, decoder_result in yy.items():
for sample_result, filepath in zip(decoder_result, file_paths):
save(sample_result, decoder_ix, filepath)
def build(self, inputs):
"""Build the graph for this configuration.
Args:
inputs: A dict of inputs. For training, should contain 'wav'.
Returns:
A dict of outputs that includes the 'predictions',
'init_ops', the 'push_ops', and the 'quantized_input'.
"""
num_stages = 10
num_layers = 30
filter_length = 3
width = 512
skip_width = 256
num_z = 16
# Encode the source with 8-bit Mu-Law.
x = inputs['wav']
batch_size = self.batch_size
x_quantized = utils.mu_law(x)
x_scaled = tf.cast(x_quantized, tf.float32) / 128.0
x_scaled = tf.expand_dims(x_scaled, 2)
encoding = tf.placeholder(name='encoding',
shape=[batch_size, num_z],
dtype=tf.float32)
en = tf.expand_dims(encoding, 1)
init_ops, push_ops = [], []
###
# The WaveNet Decoder.
###
l = x_scaled
l, inits, pushs = utils.causal_linear(x=l,
n_inputs=1,
n_outputs=width,
name='startconv',
rate=1,
batch_size=batch_size,
filter_length=filter_length)
for init in inits:
init_ops.append(init)
for push in pushs:
push_ops.append(push)
# Set up skip connections.
s = utils.linear(l, width, skip_width, name='skip_start')
# Residual blocks with skip connections.
for i in range(num_layers):
dilation = 2**(i % num_stages)
# dilated masked cnn
d, inits, pushs = utils.causal_linear(x=l,
n_inputs=width,
n_outputs=width * 2,
name='dilatedconv_%d' %
(i + 1),
rate=dilation,
batch_size=batch_size,
filter_length=filter_length)
for init in inits:
init_ops.append(init)
for push in pushs:
push_ops.append(push)
# local conditioning
d += utils.linear(en,
num_z,
width * 2,
name='cond_map_%d' % (i + 1))
# gated cnn
assert d.get_shape().as_list()[2] % 2 == 0
m = d.get_shape().as_list()[2] // 2
d = tf.sigmoid(d[:, :, :m]) * tf.tanh(d[:, :, m:])
# residuals
l += utils.linear(d, width, width, name='res_%d' % (i + 1))
# skips
s += utils.linear(d, width, skip_width, name='skip_%d' % (i + 1))
s = tf.nn.relu(s)
s = (utils.linear(s, skip_width, skip_width, name='out1') +
utils.linear(en, num_z, skip_width, name='cond_map_out1'))
s = tf.nn.relu(s)
###
# Compute the logits and get the loss.
###
logits = utils.linear(s, skip_width, 256, name='logits')
logits = tf.reshape(logits, [-1, 256])
probs = tf.nn.softmax(logits, name='softmax')
return {
'init_ops': init_ops,
'push_ops': push_ops,
'predictions': probs,
'encoding': encoding,
'quantized_input': x_quantized,
}
def build(self, inputs, is_training):
"""Build the graph for this configuration.
Args:
inputs: A dict of inputs. For training, should contain 'wav'.
is_training: Whether we are training or not. Not used in this config.
Returns:
A dict of outputs that includes the 'predictions', 'loss', the 'encoding',
the 'quantized_input', and whatever metrics we want to track for eval.
"""
del is_training
num_stages = 10
num_layers = 30
filter_length = 3
width = 512
skip_width = 256
ae_num_stages = 10
ae_num_layers = 30
ae_filter_length = 3
ae_width = 128
# Encode the source with 8-bit Mu-Law.
x = inputs['wav']
x_quantized = utils.mu_law(x)
x_scaled = tf.cast(x_quantized, tf.float32) / 128.0
x_scaled = tf.expand_dims(x_scaled, 2)
###
# The Non-Causal Temporal Encoder.
###
en = masked.conv1d(x_scaled,
causal=False,
num_filters=ae_width,
filter_length=ae_filter_length,
name='ae_startconv')
for num_layer in range(ae_num_layers):
dilation = 2**(num_layer % ae_num_stages)
d = tf.nn.relu(en)
d = masked.conv1d(d,
causal=False,
num_filters=ae_width,
filter_length=ae_filter_length,
dilation=dilation,
name='ae_dilatedconv_%d' % (num_layer + 1))
d = tf.nn.relu(d)
en += masked.conv1d(d,
num_filters=ae_width,
filter_length=1,
name='ae_res_%d' % (num_layer + 1))
en = masked.conv1d(en,
num_filters=self.ae_bottleneck_width,
filter_length=1,
name='ae_bottleneck')
en = masked.pool1d(en, self.ae_hop_length, name='ae_pool', mode='avg')
encoding = en
###
# The WaveNet Decoder.
###
l = masked.shift_right(x_scaled)
l = masked.conv1d(l,
num_filters=width,
filter_length=filter_length,
name='startconv')
# Set up skip connections.
s = masked.conv1d(l,
num_filters=skip_width,
filter_length=1,
name='skip_start')
# Residual blocks with skip connections.
for i in range(num_layers):
dilation = 2**(i % num_stages)
d = masked.conv1d(l,
num_filters=2 * width,
filter_length=filter_length,
dilation=dilation,
name='dilatedconv_%d' % (i + 1))
d = self._condition(
d,
masked.conv1d(en,
num_filters=2 * width,
filter_length=1,
name='cond_map_%d' % (i + 1)))
assert d.get_shape().as_list()[2] % 2 == 0
m = d.get_shape().as_list()[2] // 2
d_sigmoid = tf.sigmoid(d[:, :, :m])
d_tanh = tf.tanh(d[:, :, m:])
d = d_sigmoid * d_tanh
l += masked.conv1d(d,
num_filters=width,
filter_length=1,
name='res_%d' % (i + 1))
s += masked.conv1d(d,
num_filters=skip_width,
filter_length=1,
name='skip_%d' % (i + 1))
s = tf.nn.relu(s)
s = masked.conv1d(s,
num_filters=skip_width,
filter_length=1,
name='out1')
s = self._condition(
s,
masked.conv1d(en,
num_filters=skip_width,
filter_length=1,
name='cond_map_out1'))
s = tf.nn.relu(s)
###
# Compute the logits and get the loss.
###
logits = masked.conv1d(s,
num_filters=256,
filter_length=1,
name='logits')
logits = tf.reshape(logits, [-1, 256])
probs = tf.nn.softmax(logits, name='softmax')
x_indices = tf.cast(tf.reshape(x_quantized, [-1]), tf.int32) + 128
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=x_indices, name='nll'),
0,
name='loss')
return {
'predictions': probs,
'loss': loss,
'eval': {
'nll': loss
},
'quantized_input': x_quantized,
'encoding': encoding,
}
def build(self, inputs, K, D, beta, global_step):
"""Build the graph for this configuration.
Args:
inputs: A dict of inputs. For training, should contain 'wav'.
is_training: Whether we are training or not. Not used in this config.
Returns:
A dict of outputs that includes the 'predictions', 'loss', the 'encoding',
the 'quantized_input', and whatever metrics we want to track for eval.
"""
# Decoder
num_stages = 10
num_layers = 10
filter_length = 3
width = 512
skip_width = 256
# Encoder
ae_num_stages = 10
ae_num_layers = 10
ae_filter_length = 3
ae_width = 128
self.ae_bottleneck_width = D
self.beta = beta
lr = 2e-4
with tf.variable_scope('forward'):
# Encode the source with 8-bit Mu-Law.
#x = inputs['wav']
x = inputs
x_quantized = utils.mu_law(x)
x_scaled = tf.cast(x_quantized, tf.float32) / 128.0
with tf.variable_scope('embed') :
embeds = tf.get_variable('embed', [K, D],
initializer=tf.truncated_normal_initializer(stddev=0.02))
###
# The Non-Causal Temporal Encoder.
###
with tf.variable_scope('enc') as enc_param_scope:
en = masked.conv1d(
x_scaled,
causal=False,
num_filters=ae_width,
filter_length=ae_filter_length,
name='ae_startconv')
for num_layer in range(ae_num_layers):
#dilation = 2**(num_layer % ae_num_stages)
dilation = 1
d = tf.nn.relu(en)
d = masked.conv1d(
d,
causal=False,
num_filters=ae_width,
filter_length=ae_filter_length,
dilation=dilation,
name='ae_dilatedconv_%d' % (num_layer + 1))
d = tf.nn.relu(d)
en += masked.conv1d(
d,
num_filters=ae_width,
filter_length=1,
name='ae_res_%d' % (num_layer + 1))
en = masked.conv1d(
en,
num_filters=self.ae_bottleneck_width,
filter_length=1,
name='ae_bottleneck')
en = masked.pool1d(en, self.ae_hop_length, name='ae_pool', mode='avg')
self.enc_param_scope = enc_param_scope
encoding = en
z_e = encoding
_t = tf.tile(tf.expand_dims(z_e, -2), [1, 1, K, 1]) #[batch,latent_h,latent_w,K,D]
_e = tf.reshape(embeds, [1, 1, K, D])
_t = tf.norm(_t - _e, axis = -1)
k = tf.argmin(_t, axis = -1) # -> [latent_h,latent_w]
self.k = k
z_q = tf.gather(embeds, k)
self.z_e = z_e
self.k = k
self.z_q = z_q
en = self.z_q
###
# The WaveNet Decoder.
###
with tf.variable_scope('dec') as dec_param_scope:
l = masked.shift_right(x_scaled)
l = masked.conv1d(
l, num_filters=width, filter_length=filter_length, name='startconv')
# Set up skip connections.
s = masked.conv1d(
l, num_filters=skip_width, filter_length=1, name='skip_start')
# Residual blocks with skip connections.
for i in range(num_layers):
dilation = 2**(i % num_stages)
d = masked.conv1d(
l,
num_filters=2 * width,
filter_length=filter_length,
dilation=dilation,
name='dilatedconv_%d' % (i + 1))
d = self._condition(d,
masked.conv1d(
en,
num_filters=2 * width,
filter_length=1,
name='cond_map_%d' % (i + 1)))
assert d.get_shape().as_list()[2] % 2 == 0
m = d.get_shape().as_list()[2] // 2
d_sigmoid = tf.sigmoid(d[:, :, :m])
d_tanh = tf.tanh(d[:, :, m:])
d = d_sigmoid * d_tanh
l += masked.conv1d(
d, num_filters=width, filter_length=1, name='res_%d' % (i + 1))
s += masked.conv1d(
d, num_filters=skip_width, filter_length=1, name='skip_%d' % (i + 1))
s = tf.nn.relu(s)
s = masked.conv1d(s, num_filters=skip_width, filter_length=1, name='out1')
s = self._condition(s,
masked.conv1d(
en,
num_filters=skip_width,
filter_length=1,
name='cond_map_out1'))
s = tf.nn.relu(s)
self.dec_param_scope = dec_param_scope
###
# Compute the logits and get the loss.
###
logits = masked.conv1d(s, num_filters=256, filter_length=1, name='logits')
logits = tf.reshape(logits, [-1, 256])
probs = tf.nn.softmax(logits, name='softmax')
x_indices = tf.cast(tf.reshape(x_quantized, [-1]), tf.int32) + 128
self.recon = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=x_indices, name='nll'),
0,
name='recon_loss')
self.vq = tf.reduce_mean(
tf.norm(tf.stop_gradient(self.z_e) - z_q,axis=-1)**2,
axis=[0,1])
self.commit = tf.reduce_mean(
tf.norm(self.z_e - tf.stop_gradient(z_q),axis=-1)**2,
axis=[0,1])
self.loss = self.recon + self.vq + beta * self.commit
#self.loss = self.recon
with tf.variable_scope('backward'):
# Decoder grads
decoder_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.dec_param_scope.name)
decoder_grads = list(zip(
tf.clip_by_global_norm(tf.gradients(self.loss,decoder_vars), 5.0)[0],
decoder_vars))
#decoder_grads = [(tf.clip_by_value(grad, -1.0, 1.0), var) for grad, var in
# decoder_grads if grad is not None]
# Encoder Grads
encoder_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.enc_param_scope.name)
grad_z = tf.gradients(self.recon,self.z_q)
grads = [tf.gradients(self.z_e,var,grad_z)[0] + self.beta*tf.gradients(self.commit,var)[0] for var in encoder_vars]
grads, _ = tf.clip_by_global_norm(grads, 5.0)
encoder_grads = list(zip(grads, encoder_vars))
# Embedding Grads
embed_grads = list(zip(
tf.clip_by_global_norm(tf.gradients(self.vq,embeds), 5.0)[0],
[embeds]))
#ema = tf.train.ExponentialMovingAverage(decay=0.9999,
# num_updates=global_step)
#optimizer = tf.train.SyncReplicasOptimizer(
# tf.train.AdamOptimizer(lr, epsilon=1e-8),
# 1,
# total_num_replicas=1,
# variable_averages=ema,
# variables_to_average=tf.trainable_variables())
optimizer = tf.train.AdamOptimizer(lr, epsilon=1e-8)
self.train_op= optimizer.apply_gradients(
decoder_grads + encoder_grads + embed_grads,
global_step=global_step)
#self.train_op = optimizer.minimize(self.loss, global_step=global_step,
# name="train", colocate_gradients_with_ops=True)
return {
'predictions': probs,
'loss': self.loss,
'train_op': self.train_op,
'eval': {
'nll': self.loss
},
'quantized_input': x_quantized,
'encoding': encoding,
}
import sys
import numpy as np
import librosa
import chainer
from models import VAE
from utils import mu_law
from utils import Preprocess
import opt
model = VAE(opt.d, opt.k, opt.n_loop, opt.n_layer, opt.n_filter, opt.mu,
opt.n_channel1, opt.n_channel2, opt.n_channel3, opt.beta, True)
chainer.serializers.load_npz(sys.argv[1], model, 'updater/model:main/')
n = 1
x = np.expand_dims(
Preprocess(opt.data_format, opt.sr, opt.mu, opt.top_db, opt.sr * 3,
False)(sys.argv[2])[0], 0)
output = model.generate(x)
wave = mu_law(opt.mu).itransform(output)
np.save('result.npy', wave)
librosa.output.write_wav('result.wav', wave, opt.sr)
def __init__(self,
lr,
global_step,
beta,
x,
K,
D,
arch_fn,
sess,
param_scope,
is_training=False):
with tf.variable_scope(param_scope):
enc_spec, enc_param_scope, dec_spec, dec_param_scope = arch_fn(D)
with tf.variable_scope('embed'):
embeds = tf.get_variable(
'embed', [K, D],
initializer=tf.truncated_normal_initializer(stddev=0.02))
with tf.variable_scope('forward') as forward_scope:
# Encoder Pass
x_quantized = mu_law(x)
x_scaled = tf.cast(x_quantized, tf.float32) / 128.0
# Why are we not expanding dim here? b/c we are defaulting to batch size of 1?
#self.x_scaled = x_scaled
self.input_mean = tf.reduce_mean(x_scaled)
_t = x_scaled
for block in enc_spec:
_t = block(_t)
z_e = _t
self.z_e_mean = tf.reduce_mean(z_e)
# Middle Area (Compression or Discretize)
# TODO: Gross.. use brodcast instead!
_t = tf.tile(tf.expand_dims(z_e, -2),
[1, 1, K, 1]) #[batch,latent_h,latent_w,K,D]
_e = tf.reshape(embeds, [1, 1, K, D])
_t = tf.norm(_t - _e, axis=-1)
k = tf.argmin(_t, axis=-1) # -> [latent_h,latent_w]
self.k = k
z_q = tf.gather(embeds, k)
self.z_q_mean = tf.reduce_mean(z_q)
self.z_e = z_e # -> [batch,latent_h,latent_w,D]
self.k = k
self.z_q = z_q # -> [batch,latent_h,latent_w,D]
# End early
#return
# Decoder Pass
# Copy just to be safe...?
_t = tf.identity(z_q)
# THINGS TO DO
# 1. check if x is right dimension, no need to expand dim?
# 2. check if s is same dim as x
# 3. add conditional on speaker id (can do after reconstruction)
num_stages = 10 # Has to do with dilation stages
num_layers = 1 # Could lower the amount of layers
filter_length = 3
width = 512
skip_width = 256
with tf.variable_scope('dec') as dec_param_scope:
# May need to have x be an expanded dim
l = masked.shift_right(x_scaled)
self.l0 = tf.reduce_mean(tf.identity(l))
l, W_mean, b_mean = masked.conv1d_log(
l,
num_filters=width,
filter_length=filter_length,
name='startconv_dec')
self.W_mean = W_mean
self.b_mean = b_mean
self.l1 = tf.reduce_mean(tf.identity(l))
# Skip connection
s = masked.conv1d(l,
num_filters=skip_width,
filter_length=1,
name='skip_start_dec')
self.s0 = tf.reduce_mean(tf.identity(s))
# Residual blocks with skip connection
for i in xrange(num_layers):
dilation = 2**(i % num_stages)
d = masked.conv1d(l,
num_filters=2 * width,
filter_length=filter_length,
dilation=dilation,
name='dilatedconv_%d' % (i + 1))
self.d0 = tf.reduce_mean(tf.identity(d))
# Condition on z_q
d = self._condition(
d,
masked.conv1d(_t,
num_filters=2 * width,
filter_length=1,
name='cond_map_%d' % (i + 1)))
self.d1 = tf.reduce_mean(tf.identity(d))
assert d.get_shape().as_list()[2] % 2 == 0
m = d.get_shape().as_list()[2] // 2
d_sigmoid = tf.sigmoid(d[:, :, :m])
d_tanh = tf.tanh(d[:, :, m:])
d = d_sigmoid * d_tanh
self.d2 = tf.reduce_mean(tf.identity(d))
l += masked.conv1d(d,
num_filters=width,
filter_length=1,
name='res_%d' % (i + 1))
self.l2 = tf.reduce_mean(tf.identity(l))
s += masked.conv1d(d,
num_filters=skip_width,
filter_length=1,
name='skip_%d' % (i + 1))
self.s1 = tf.reduce_mean(tf.identity(s))
s = tf.nn.relu(s)
s = masked.conv1d(s,
num_filters=skip_width,
filter_length=1,
name='out1')
self.s2 = tf.reduce_mean(tf.identity(s))
# Condition on z_q again.
s = self._condition(
s,
masked.conv1d(_t,
num_filters=skip_width,
filter_length=1,
name='cond_map_out1'))
s = tf.nn.relu(s)
self.s3 = tf.reduce_mean(tf.identity(s))
# Should this parameter be trainable...?
logits = masked.conv1d(s,
num_filters=256,
filter_length=1,
name='logits')
self.logits_mean = tf.reduce_mean(tf.identity(logits))
# Losses
# CHECK AXES FOR REDUCE MEAN ON RECON LOSS
logits = tf.reshape(logits, [-1, 256])
#probs = tf.nn.softmax(logits, name='softmax')
x_indices = tf.cast(tf.reshape(x_quantized, [-1]), tf.int32) + 128
self.indices_mean = tf.reduce_mean(x_indices)
self.recon = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=x_indices, name='nll'),
0,
name='recon_loss')
# Reconstruction loss for images.
#self.recon = tf.reduce_mean((self.p_x_z - x) ** 2, axis=[0,1,2,3])
sg_e = tf.stop_gradient(self.z_e)
sg_norm = tf.norm(sg_e - z_q, axis=-1)**2
self.vq = tf.reduce_mean(sg_norm, axis=[0, 1])
self.commit = tf.reduce_mean(tf.norm(self.z_e -
tf.stop_gradient(z_q),
axis=-1)**2,
axis=[0, 1])
self.loss = self.recon + self.vq + beta * self.commit
# NLL
# TODO: is it correct impl?
# it seems tf.reduce_prod(tf.shape(self.z_q)[1:2]) should be multipled
# in front of log(1/K) if we assume uniform prior on z.
#self.nll = -1.*(tf.reduce_mean(tf.log(self.p_x_z),axis=[1,2]) + tf.log(1/tf.cast(K,tf.float32)))/tf.log(2.)
if (is_training):
with tf.variable_scope('backward'):
# Decoder Grads
decoder_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, dec_param_scope.name)
decoder_grads = list(
zip(tf.gradients(self.loss, decoder_vars), decoder_vars))
# Encoder Grads
encoder_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, enc_param_scope.name)
grad_z = tf.gradients(self.recon, z_q)
encoder_grads = [
(tf.gradients(z_e, var, grad_z)[0] +
beta * tf.gradients(self.commit, var)[0], var)
for var in encoder_vars
]
# Embedding Grads
embed_grads = list(zip(tf.gradients(self.vq, embeds),
[embeds]))
optimizer = tf.train.AdamOptimizer(lr)
self.train_op = optimizer.apply_gradients(
decoder_grads + encoder_grads + embed_grads,
global_step=global_step)
else:
# Another decoder pass that we can play with!
self.latent = tf.placeholder(tf.int64, [None, 3, 3])
_t = tf.gather(embeds, self.latent)
for block in dec_spec:
_t = block(_t)
self.gen = _t
all_save_vars = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES,
param_scope.name) + tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, dec_param_scope.name)
save_vars = {
('train/' + '/'.join(var.name.split('/')[1:])).split(':')[0]: var
for var in all_save_vars
}
#for name,var in save_vars.items():
# print(name,var)
self.saver = tf.train.Saver(var_list=save_vars)
