# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Conditional Variational auto-encoder (CVAE)."""
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore._checkparam import Validator
from ....cell import Cell
from ....layer.basic import Dense, OneHot
[docs]class ConditionalVAE(Cell):
r"""
Conditional Variational Auto-Encoder (CVAE).
The difference with VAE is that CVAE uses labels information.
For more details, refer to `Learning Structured Output Representation using Deep Conditional Generative Models
<http://papers.nips.cc/paper/5775-learning-structured-output-representation-using-deep-conditional-
generative-models>`_.
Note:
When encoder and decoder ard defined, the shape of the encoder's output tensor and decoder's input tensor
must be :math:`(N, hidden\_size)`.
The latent_size must be less than or equal to the hidden_size.
Args:
encoder(Cell): The Deep Neural Network (DNN) model defined as encoder.
decoder(Cell): The DNN model defined as decoder.
hidden_size(int): The size of encoder's output tensor.
latent_size(int): The size of the latent space.
num_classes(int): The number of classes.
Inputs:
- **input_x** (Tensor) - The shape of input tensor is :math:`(N, C, H, W)`, which is the same as the input of
encoder.
- **input_y** (Tensor) - The tensor of the target data, the shape is :math:`(N,)`.
Outputs:
- **output** (tuple) - (recon_x(Tensor), x(Tensor), mu(Tensor), std(Tensor)).
Supported Platforms:
``Ascend`` ``GPU``
"""
def __init__(self, encoder, decoder, hidden_size, latent_size, num_classes):
super(ConditionalVAE, self).__init__()
self.encoder = encoder
self.decoder = decoder
if (not isinstance(encoder, Cell)) or (not isinstance(decoder, Cell)):
raise TypeError('The encoder and decoder should be Cell type.')
self.hidden_size = Validator.check_positive_int(hidden_size)
self.latent_size = Validator.check_positive_int(latent_size)
if hidden_size < latent_size:
raise ValueError('The latent_size should be less than or equal to the hidden_size.')
self.num_classes = Validator.check_positive_int(num_classes)
self.normal = C.normal
self.exp = P.Exp()
self.reshape = P.Reshape()
self.shape = P.Shape()
self.concat = P.Concat(axis=1)
self.to_tensor = P.ScalarToArray()
self.one_hot = OneHot(depth=num_classes)
self.dense1 = Dense(self.hidden_size, self.latent_size)
self.dense2 = Dense(self.hidden_size, self.latent_size)
self.dense3 = Dense(self.latent_size + self.num_classes, self.hidden_size)
def _encode(self, x, y):
en_x = self.encoder(x, y)
mu = self.dense1(en_x)
log_var = self.dense2(en_x)
return mu, log_var
def _decode(self, z):
z = self.dense3(z)
recon_x = self.decoder(z)
return recon_x
[docs] def construct(self, x, y):
"""
The input are x and y, so the WithLossCell method needs to be rewritten when using cvae interface.
"""
mu, log_var = self._encode(x, y)
std = self.exp(0.5 * log_var)
z = self.normal(self.shape(mu), mu, std, seed=0)
y = self.one_hot(y)
z_c = self.concat((z, y))
recon_x = self._decode(z_c)
return recon_x, x, mu, std
[docs] def generate_sample(self, sample_y, generate_nums, shape):
"""
Randomly sample from the latent space to generate samples.
Args:
sample_y (Tensor): Define the label of samples. Tensor of shape (generate_nums, ) and type mindspore.int32.
generate_nums (int): The number of samples to generate.
shape(tuple): The shape of sample, which must be the format of (generate_nums, C, H, W) or (-1, C, H, W).
Returns:
Tensor, the generated samples.
"""
generate_nums = Validator.check_positive_int(generate_nums)
if not isinstance(shape, tuple) or len(shape) != 4 or (shape[0] != -1 and shape[0] != generate_nums):
raise ValueError('The shape should be (generate_nums, C, H, W) or (-1, C, H, W).')
sample_z = self.normal((generate_nums, self.latent_size), self.to_tensor(0.0), self.to_tensor(1.0), seed=0)
sample_y = self.one_hot(sample_y)
sample_c = self.concat((sample_z, sample_y))
sample = self._decode(sample_c)
sample = self.reshape(sample, shape)
return sample
[docs] def reconstruct_sample(self, x, y):
"""
Reconstruct samples from original data.
Args:
x (Tensor): The input tensor to be reconstructed, the shape is (N, C, H, W).
y (Tensor): The label of the input tensor, the shape is (N,).
Returns:
Tensor, the reconstructed sample.
"""
mu, log_var = self._encode(x, y)
std = self.exp(0.5 * log_var)
z = self.normal(mu.shape, mu, std, seed=0)
y = self.one_hot(y)
z_c = self.concat((z, y))
recon_x = self._decode(z_c)
return recon_x