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ceshi.py
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import tensorflow as tf
from modules.sonnet.src import moving_averages
from modules.sonnet.src import initializers
from modules.sonnet.src import types
# from prior import TransformerPrior
'''def emb(input,input_shape):
get_z=TransformerPrior()
input=get_z(input)
return input'''
class VectorQuantizerEMA:
"""Sonnet module representing the VQ-VAE layer.
Implements a slightly modified version of the algorithm presented in
'Neural Discrete Representation Learning' by van den Oord et al.
https://arxiv.org/abs/1711.00937
The difference between VectorQuantizerEMA and VectorQuantizer is that
this module uses exponential moving averages to update the embedding vectors
instead of an auxiliary loss. This has the advantage that the embedding
updates are independent of the choice of optimizer (SGD, RMSProp, Adam, K-Fac,
...) used for the encoder, decoder and other parts of the architecture. For
most experiments the EMA version trains faster than the non-EMA version.
Input any tensor to be quantized. Last dimension will be used as space in
which to quantize. All other dimensions will be flattened and will be seen
as different examples to quantize.
The output tensor will have the same shape as the input.
For example a tensor with shape [16, 32, 32, 64] will be reshaped into
[16384, 64] and all 16384 vectors (each of 64 dimensions) will be quantized
independently.
Attributes:
embedding_dim: integer representing the dimensionality of the tensors in the
quantized space. Inputs to the modules must be in this format as well.
num_embeddings: integer, the number of vectors in the quantized space.
commitment_cost: scalar which controls the weighting of the loss terms (see
equation 4 in the paper).
decay: float, decay for the moving averages.
epsilon: small float constant to avoid numerical instability.
"""
def __init__(self,
embedding_dim,
num_embeddings,
commitment_cost,
decay,
epsilon,
dtype=tf.float32,
name='vector_quantizer_ema'):
"""Initializes a VQ-VAE EMA module.
Args:
embedding_dim: integer representing the dimensionality of the tensors in
the quantized space. Inputs to the modules must be in this format as
well.
num_embeddings: integer, the number of vectors in the quantized space.
commitment_cost: scalar which controls the weighting of the loss terms
(see equation 4 in the paper - this variable is Beta).
decay: float between 0 and 1, controls the speed of the Exponential Moving
Averages.
epsilon: small constant to aid numerical stability, default 1e-5.
dtype: dtype for the embeddings variable, defaults to tf.float32.
name: name of the module.
"""
# super().__init__(name=name)
self.embedding_dim = embedding_dim
self.num_embeddings = num_embeddings
if not 0 <= decay <= 1:
raise ValueError('decay must be in range [0, 1]')
self.decay = decay
self.commitment_cost = commitment_cost
self.epsilon = epsilon
embedding_shape = [embedding_dim, num_embeddings]
initializer = initializers.VarianceScaling(distribution='uniform')
self.embeddings = tf.Variable(
initializer(embedding_shape, dtype), trainable=False, name='embeddings')
self.ema_cluster_size = moving_averages.ExponentialMovingAverage(
decay=self.decay, name='ema_cluster_size')
self.ema_cluster_size.initialize(tf.zeros([num_embeddings], dtype=dtype))
self.ema_dw = moving_averages.ExponentialMovingAverage(
decay=self.decay, name='ema_dw')
self.ema_dw.initialize(self.embeddings)
def __call__(self, inputs, is_training):
"""Connects the module to some inputs.
Args:
inputs: Tensor, final dimension must be equal to embedding_dim. All other
leading dimensions will be flattened and treated as a large batch.
is_training: boolean, whether this connection is to training data. When
this is set to False, the internal moving average statistics will not be
updated.
Returns:
dict containing the following keys and values:
quantize: Tensor containing the quantized version of the input.
loss: Tensor containing the loss to optimize.
perplexity: Tensor containing the perplexity of the encodings.
encodings: Tensor containing the discrete encodings, ie which element
of the quantized space each input element was mapped to.
encoding_indices: Tensor containing the discrete encoding indices, ie
which element of the quantized space each input element was mapped to.
"""
flat_inputs = tf.reshape(inputs, [-1, self.embedding_dim])
distances = (
tf.reduce_sum(flat_inputs ** 2, 1, keepdims=True) -
2 * tf.matmul(flat_inputs, self.embeddings) +
tf.reduce_sum(self.embeddings ** 2, 0, keepdims=True))
encoding_indices = tf.argmax(-distances, 1)
encodings = tf.one_hot(encoding_indices,
self.num_embeddings,
dtype=distances.dtype)
# NB: if your code crashes with a reshape error on the line below about a
# Tensor containing the wrong number of values, then the most likely cause
# is that the input passed in does not have a final dimension equal to
# self.embedding_dim. Ideally we would catch this with an Assert but that
# creates various other problems related to device placement / TPUs.
encoding_indices = tf.reshape(encoding_indices, tf.shape(inputs)[:-1])
quantized = self.quantize(encoding_indices)
e_latent_loss = tf.reduce_mean((tf.stop_gradient(quantized) - inputs) ** 2)
if is_training:
updated_ema_cluster_size = self.ema_cluster_size(
tf.reduce_sum(encodings, axis=0))
dw = tf.matmul(flat_inputs, encodings, transpose_a=True)
updated_ema_dw = self.ema_dw(dw)
n = tf.reduce_sum(updated_ema_cluster_size)
updated_ema_cluster_size = ((updated_ema_cluster_size + self.epsilon) /
(n + self.num_embeddings * self.epsilon) * n)
normalised_updated_ema_w = (
updated_ema_dw / tf.reshape(updated_ema_cluster_size, [1, -1]))
self.embeddings.assign(normalised_updated_ema_w)
loss = self.commitment_cost * e_latent_loss
else:
loss = self.commitment_cost * e_latent_loss
# Straight Through Estimator
quantized = inputs + tf.stop_gradient(quantized - inputs)
avg_probs = tf.reduce_mean(encodings, 0)
perplexity = tf.exp(-tf.reduce_sum(avg_probs *
tf.math.log(avg_probs + 1e-10)))
# print(('量化后的形状为：{}'.format(quantized.shape)))
return loss, quantized
def quantize(self, encoding_indices):
"""Returns embedding tensor for a batch of indices."""
w = tf.transpose(self.embeddings, [1, 0])
return tf.nn.embedding_lookup(w, encoding_indices)