# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ============================================================================
"""Variational auto-encoder (VAE)"""
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore._checkparam import check_int_positive
from ....cell import Cell
from ....layer.basic import Dense
[docs]class VAE(Cell):
r"""
Variational Auto-Encoder (VAE).
The VAE defines a generative model, `Z` is sampled from the prior, then used to reconstruct `X` by a decoder.
see more details in `Auto-Encoding Variational Bayes <https://arxiv.org/abs/1312.6114>`_.
Note:
When define the encoder and decoder, the shape of the encoder's output tensor and decoder's input tensor
should be :math:`(N, hidden\_size)`.
The latent_size should be less than or equal to the hidden_size.
Args:
encoder(Cell): The 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.
Inputs:
- **input** (Tensor) - the same shape as the input of encoder, the shape is :math:`(N, C, H, W)`.
Outputs:
- **output** (Tuple) - (recon_x(Tensor), x(Tensor), mu(Tensor), std(Tensor)).
"""
def __init__(self, encoder, decoder, hidden_size, latent_size):
super(VAE, 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 = check_int_positive(hidden_size)
self.latent_size = check_int_positive(latent_size)
if hidden_size < latent_size:
raise ValueError('The latent_size should be less than or equal to the hidden_size.')
self.normal = C.normal
self.exp = P.Exp()
self.reshape = P.Reshape()
self.shape = P.Shape()
self.to_tensor = P.ScalarToArray()
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.hidden_size)
def _encode(self, x):
en_x = self.encoder(x)
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
def construct(self, x):
mu, log_var = self._encode(x)
std = self.exp(0.5 * log_var)
z = self.normal(self.shape(mu), mu, std, seed=0)
recon_x = self._decode(z)
return recon_x, x, mu, std
[docs] def generate_sample(self, generate_nums, shape):
"""
Randomly sample from latent space to generate sample.
Args:
generate_nums (int): The number of samples to generate.
shape(tuple): The shape of sample, it should be (generate_nums, C, H, W) or (-1, C, H, W).
Returns:
Tensor, the generated sample.
"""
generate_nums = check_int_positive(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 = self._decode(sample_z)
sample = self.reshape(sample, shape)
return sample
[docs] def reconstruct_sample(self, x):
"""
Reconstruct sample from original data.
Args:
x (Tensor): The input tensor to be reconstructed, the shape is (N, C, H, W).
Returns:
Tensor, the reconstructed sample.
"""
mu, log_var = self._encode(x)
std = self.exp(0.5 * log_var)
z = self.normal(mu.shape, mu, std, seed=0)
recon_x = self._decode(z)
return recon_x