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modules.py
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modules.py
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# -*- coding: utf-8 -*-
#/usr/bin/python2
'''
By kyubyong park. [email protected].
https://www.github.com/kyubyong/tacotron
'''
from __future__ import print_function
import tensorflow as tf
from hyperparams import Hyperparams as hp
def embed(inputs, vocab_size, num_units, zero_pad=True, scope="embedding", reuse=None):
'''Embeds a given tensor.
Args:
inputs: A `Tensor` with type `int32` or `int64` containing the ids
to be looked up in `lookup table`.
vocab_size: An int. Vocabulary size.
num_units: An int. Number of embedding hidden units.
zero_pad: A boolean. If True, all the values of the fist row (id 0)
should be constant zeros.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A `Tensor` with one more rank than inputs's. The last dimesionality
should be `num_units`.
'''
with tf.variable_scope(scope, reuse=reuse):
lookup_table = tf.get_variable('lookup_table',
dtype=tf.float32,
shape=[vocab_size, num_units],
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01))
if zero_pad:
lookup_table = tf.concat((tf.zeros(shape=[1, num_units]),
lookup_table[1:, :]), 0)
return tf.nn.embedding_lookup(lookup_table, inputs)
def normalize(inputs,
type="bn",
decay=.999,
is_training=True,
activation_fn=None,
scope="normalize"):
'''Applies {batch|layer} normalization.
Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has
`batch_size`. If type is `bn`, the normalization is over all but
the last dimension. Or if type is `ln`, the normalization is over
the last dimension. Note that this is different from the native
`tf.contrib.layers.batch_norm`. For this I recommend you change
a line in ``tensorflow/contrib/layers/python/layers/layer.py`
as follows.
Before: mean, variance = nn.moments(inputs, axis, keep_dims=True)
After: mean, variance = nn.moments(inputs, [-1], keep_dims=True)
type: A string. Either "bn" or "ln".
decay: Decay for the moving average. Reasonable values for `decay` are close
to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc.
Lower `decay` value (recommend trying `decay`=0.9) if model experiences
reasonably good training performance but poor validation and/or test
performance.
is_training: Whether or not the layer is in training mode. W
activation_fn: Activation function.
scope: Optional scope for `variable_scope`.
Returns:
A tensor with the same shape and data dtype as `inputs`.
'''
if type=="bn":
inputs_shape = inputs.get_shape()
inputs_rank = inputs_shape.ndims
# use fused batch norm if inputs_rank in [2, 3, 4] as it is much faster.
# pay attention to the fact that fused_batch_norm requires shape to be rank 4 of NHWC.
if inputs_rank in [2, 3, 4]:
if inputs_rank==2:
inputs = tf.expand_dims(inputs, axis=1)
inputs = tf.expand_dims(inputs, axis=2)
elif inputs_rank==3:
inputs = tf.expand_dims(inputs, axis=1)
outputs = tf.contrib.layers.batch_norm(inputs=inputs,
decay=decay,
center=True,
scale=True,
activation_fn=activation_fn,
updates_collections=None,
is_training=is_training,
scope=scope,
zero_debias_moving_mean=True,
fused=True)
# restore original shape
if inputs_rank==2:
outputs = tf.squeeze(outputs, axis=[1, 2])
elif inputs_rank==3:
outputs = tf.squeeze(outputs, axis=1)
else: # fallback to naive batch norm
outputs = tf.contrib.layers.batch_norm(inputs=inputs,
decay=decay,
center=True,
scale=True,
activation_fn=activation_fn,
updates_collections=None,
is_training=is_training,
scope=scope,
fused=False)
elif type=="ln":
outputs = tf.contrib.layers.layer_norm(inputs=inputs,
center=True,
scale=True,
activation_fn=activation_fn,
scope=scope)
else:
raise ValueError("Currently we support `bn` or `ln` only.")
return outputs
def conv1d(inputs,
filters=None,
size=1,
rate=1,
padding="SAME",
use_bias=False,
activation_fn=None,
scope="conv1d",
reuse=None):
'''
Args:
inputs: A 3-D tensor with shape of [batch, time, depth].
filters: An int. Number of outputs (=activation maps)
size: An int. Filter size.
rate: An int. Dilation rate.
padding: Either `same` or `valid` or `causal` (case-insensitive).
use_bias: A boolean.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A masked tensor of the same shape and dtypes as `inputs`.
'''
with tf.variable_scope(scope):
if padding.lower()=="causal":
# pre-padding for causality
pad_len = (size - 1) * rate # padding size
inputs = tf.pad(inputs, [[0, 0], [pad_len, 0], [0, 0]])
padding = "valid"
if filters is None:
filters = inputs.get_shape().as_list[-1]
params = {"inputs":inputs, "filters":filters, "kernel_size":size,
"dilation_rate":rate, "padding":padding, "activation":activation_fn,
"use_bias":use_bias, "reuse":reuse}
outputs = tf.layers.conv1d(**params)
return outputs
def conv1d_banks(inputs, K=16, is_training=True, scope="conv1d_banks", reuse=None):
'''Applies a series of conv1d separately.
Args:
inputs: A 3d tensor with shape of [N, T, C]
K: An int. The size of conv1d banks. That is,
The `inputs` are convolved with K filters: 1, 2, ..., K.
is_training: A boolean. This is passed to an argument of `batch_normalize`.
Returns:
A 3d tensor with shape of [N, T, K*Hp.embed_size//2].
'''
with tf.variable_scope(scope, reuse=reuse):
outputs = conv1d(inputs, hp.embed_size//2, 1) # k=1
#outputs = normalize(outputs, type="bn", is_training=is_training,
# activation_fn=tf.nn.relu)
outputs = ins_normalize(outputs, is_training=is_training )
outputs = tf.nn.relu(outputs)
for k in range(2, K+1): # k = 2...K
with tf.variable_scope("num_{}".format(k)):
#output = conv1d(inputs, hp.embed_size // 2, k, 1)
#output = normalize(output, type="bn", is_training=is_training,
# activation_fn=tf.nn.relu)
output = conv1d(inputs, hp.embed_size//2, k)
output = ins_normalize(output, is_training=is_training)
output = tf.nn.relu(output)
outputs = tf.concat((outputs, output), -1)
return outputs # (N, T, Hp.embed_size//2*K)
def gru(inputs, num_units=None, bidirection=False, scope="gru", reuse=None):
'''Applies a GRU.
Args:
inputs: A 3d tensor with shape of [N, T, C].
num_units: An int. The number of hidden units.
bidirection: A boolean. If True, bidirectional results
are concatenated.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
If bidirection is True, a 3d tensor with shape of [N, T, 2*num_units],
otherwise [N, T, num_units].
'''
with tf.variable_scope(scope, reuse=reuse):
if num_units is None:
num_units = inputs.get_shape().as_list[-1]
cell = tf.contrib.rnn.GRUCell(num_units)
if bidirection:
cell_bw = tf.contrib.rnn.GRUCell(num_units)
outputs, _ = tf.nn.bidirectional_dynamic_rnn(cell, cell_bw, inputs, dtype=tf.float32)
return tf.concat(outputs, 2)
else:
outputs, _ = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
return outputs
def attention_decoder(inputs, memory, num_units=None, scope="attention_decoder", reuse=None):
'''Applies a GRU to `inputs`, while attending `memory`.
Args:
inputs: A 3d tensor with shape of [N, T', C']. Decoder inputs.
num_units: An int. Attention size.
memory: A 3d tensor with shape of [N, T, C]. Outputs of encoder network.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A 3d tensor with shape of [N, T, num_units].
'''
with tf.variable_scope(scope, reuse=reuse):
if num_units is None:
num_units = inputs.get_shape().as_list[-1]
attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(num_units, memory)
decoder_cell = tf.contrib.rnn.GRUCell(num_units)
cell_with_attetion = tf.contrib.seq2seq.DynamicAttentionWrapper(decoder_cell, attention_mechanism, num_units)
outputs, _ = tf.nn.dynamic_rnn(cell_with_attetion, inputs, dtype=tf.float32) #( 1, 6, 16)
return outputs
def prenet(inputs, is_training=True, scope="prenet", reuse=None):
'''Prenet for Encoder and Decoder.
Args:
inputs: A 3D tensor of shape [N, T, hp.embed_size].
is_training: A boolean.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A 3D tensor of shape [N, T, num_units/2].
'''
with tf.variable_scope(scope, reuse=reuse):
#outputs = tf.layers.dense(inputs, units=hp.embed_size, activation=tf.nn.relu, name="dense1")
outputs = dense(inputs, hp.embed_size, act="relu",variable_scope="dense1")
outputs = tf.nn.dropout(outputs, keep_prob=.5 if is_training==True else 1., name="dropout1")
#outputs = tf.layers.dense(outputs, units=hp.embed_size//2, activation=tf.nn.relu, name="dense2")
outputs = dense(outputs, hp.embed_size//2,act="relu",variable_scope="dense2")
outputs = tf.nn.dropout(outputs, keep_prob=.5 if is_training==True else 1., name="dropout2")
return outputs # (N, T, num_units/2)
def dense(inputs, units, bn=True, act=None, is_training=True, variable_scope="dense"):
with tf.variable_scope(variable_scope):
inputs = tf.contrib.layers.fully_connected(inputs, units)
#inputs = batch_normalize(inputs, is_training=is_training)
inputs = ins_normalize(inputs, is_training=is_training)
if act == "relu":
inputs = tf.nn.relu(inputs)
elif act == "sigmoid":
inputs = tf.nn.sigmoid(inputs)
return inputs
def ins_normalize(net, is_training=True):
batch, steps, channels = [i.value for i in net.get_shape()]
var_shape = [channels]
mu, sigma_sq = tf.nn.moments(net, [1], keep_dims=True)
shift = tf.Variable(tf.zeros(var_shape))
scale = tf.Variable(tf.ones(var_shape))
epsilon = 1e-8
normalized = (net-mu)/(sigma_sq + epsilon)**(.5)
return scale * normalized + shift
def highwaynet(inputs, is_training=True, num_units=None, scope="highwaynet", reuse=None):
'''Highway networks, see https://arxiv.org/abs/1505.00387
Args:
inputs: A 3D tensor of shape [N, T, W].
num_units: An int or `None`. Specifies the number of units in the highway layer
or uses the input size if `None`.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A 3D tensor of shape [N, T, W].
'''
if not num_units:
num_units = inputs.get_shape()[-1]
with tf.variable_scope(scope, reuse=reuse):
#H = tf.layers.dense(inputs, units=num_units, activation=tf.nn.relu, name="dense1")
#T = tf.layers.dense(inputs, units=num_units, activation=tf.nn.sigmoid, name="dense2")
H = dense(inputs, num_units, act="relu",variable_scope="dense1")
T = dense(inputs, num_units, act="sigmoid",variable_scope="dense2")
C = 1. - T
outputs = H * T + inputs * C
return outputs