-
Notifications
You must be signed in to change notification settings - Fork 1.1k
/
joint_vae.py
268 lines (222 loc) · 9.61 KB
/
joint_vae.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
import torch
import numpy as np
from models import BaseVAE
from torch import nn
from torch.nn import functional as F
from .types_ import *
class JointVAE(BaseVAE):
num_iter = 1
def __init__(self,
in_channels: int,
latent_dim: int,
categorical_dim: int,
latent_min_capacity: float =0.,
latent_max_capacity: float = 25.,
latent_gamma: float = 30.,
latent_num_iter: int = 25000,
categorical_min_capacity: float =0.,
categorical_max_capacity: float = 25.,
categorical_gamma: float = 30.,
categorical_num_iter: int = 25000,
hidden_dims: List = None,
temperature: float = 0.5,
anneal_rate: float = 3e-5,
anneal_interval: int = 100, # every 100 batches
alpha: float = 30.,
**kwargs) -> None:
super(JointVAE, self).__init__()
self.latent_dim = latent_dim
self.categorical_dim = categorical_dim
self.temp = temperature
self.min_temp = temperature
self.anneal_rate = anneal_rate
self.anneal_interval = anneal_interval
self.alpha = alpha
self.cont_min = latent_min_capacity
self.cont_max = latent_max_capacity
self.disc_min = categorical_min_capacity
self.disc_max = categorical_max_capacity
self.cont_gamma = latent_gamma
self.disc_gamma = categorical_gamma
self.cont_iter = latent_num_iter
self.disc_iter = categorical_num_iter
modules = []
if hidden_dims is None:
hidden_dims = [32, 64, 128, 256, 512]
# Build Encoder
for h_dim in hidden_dims:
modules.append(
nn.Sequential(
nn.Conv2d(in_channels, out_channels=h_dim,
kernel_size= 3, stride= 2, padding = 1),
nn.BatchNorm2d(h_dim),
nn.LeakyReLU())
)
in_channels = h_dim
self.encoder = nn.Sequential(*modules)
self.fc_mu = nn.Linear(hidden_dims[-1]*4, self.latent_dim)
self.fc_var = nn.Linear(hidden_dims[-1]*4, self.latent_dim)
self.fc_z = nn.Linear(hidden_dims[-1]*4, self.categorical_dim)
# Build Decoder
modules = []
self.decoder_input = nn.Linear(self.latent_dim + self.categorical_dim,
hidden_dims[-1] * 4)
hidden_dims.reverse()
for i in range(len(hidden_dims) - 1):
modules.append(
nn.Sequential(
nn.ConvTranspose2d(hidden_dims[i],
hidden_dims[i + 1],
kernel_size=3,
stride = 2,
padding=1,
output_padding=1),
nn.BatchNorm2d(hidden_dims[i + 1]),
nn.LeakyReLU())
)
self.decoder = nn.Sequential(*modules)
self.final_layer = nn.Sequential(
nn.ConvTranspose2d(hidden_dims[-1],
hidden_dims[-1],
kernel_size=3,
stride=2,
padding=1,
output_padding=1),
nn.BatchNorm2d(hidden_dims[-1]),
nn.LeakyReLU(),
nn.Conv2d(hidden_dims[-1], out_channels= 3,
kernel_size= 3, padding= 1),
nn.Tanh())
self.sampling_dist = torch.distributions.OneHotCategorical(1. / categorical_dim * torch.ones((self.categorical_dim, 1)))
def encode(self, input: Tensor) -> List[Tensor]:
"""
Encodes the input by passing through the encoder network
and returns the latent codes.
:param input: (Tensor) Input tensor to encoder [B x C x H x W]
:return: (Tensor) Latent code [B x D x Q]
"""
result = self.encoder(input)
result = torch.flatten(result, start_dim=1)
# Split the result into mu and var components
# of the latent Gaussian distribution
mu = self.fc_mu(result)
log_var = self.fc_var(result)
z = self.fc_z(result)
z = z.view(-1, self.categorical_dim)
return [mu, log_var, z]
def decode(self, z: Tensor) -> Tensor:
"""
Maps the given latent codes
onto the image space.
:param z: (Tensor) [B x D x Q]
:return: (Tensor) [B x C x H x W]
"""
result = self.decoder_input(z)
result = result.view(-1, 512, 2, 2)
result = self.decoder(result)
result = self.final_layer(result)
return result
def reparameterize(self,
mu: Tensor,
log_var: Tensor,
q: Tensor,
eps:float = 1e-7) -> Tensor:
"""
Gumbel-softmax trick to sample from Categorical Distribution
:param mu: (Tensor) mean of the latent Gaussian [B x D]
:param log_var: (Tensor) Log variance of the latent Gaussian [B x D]
:param q: (Tensor) Categorical latent Codes [B x Q]
:return: (Tensor) [B x (D + Q)]
"""
std = torch.exp(0.5 * log_var)
e = torch.randn_like(std)
z = e * std + mu
# Sample from Gumbel
u = torch.rand_like(q)
g = - torch.log(- torch.log(u + eps) + eps)
# Gumbel-Softmax sample
s = F.softmax((q + g) / self.temp, dim=-1)
s = s.view(-1, self.categorical_dim)
return torch.cat([z, s], dim=1)
def forward(self, input: Tensor, **kwargs) -> List[Tensor]:
mu, log_var, q = self.encode(input)
z = self.reparameterize(mu, log_var, q)
return [self.decode(z), input, q, mu, log_var]
def loss_function(self,
*args,
**kwargs) -> dict:
"""
Computes the VAE loss function.
KL(N(\mu, \sigma), N(0, 1)) = \log \frac{1}{\sigma} + \frac{\sigma^2 + \mu^2}{2} - \frac{1}{2}
:param args:
:param kwargs:
:return:
"""
recons = args[0]
input = args[1]
q = args[2]
mu = args[3]
log_var = args[4]
q_p = F.softmax(q, dim=-1) # Convert the categorical codes into probabilities
kld_weight = kwargs['M_N'] # Account for the minibatch samples from the dataset
batch_idx = kwargs['batch_idx']
# Anneal the temperature at regular intervals
if batch_idx % self.anneal_interval == 0 and self.training:
self.temp = np.maximum(self.temp * np.exp(- self.anneal_rate * batch_idx),
self.min_temp)
recons_loss =F.mse_loss(recons, input, reduction='mean')
# Adaptively increase the discrinimator capacity
disc_curr = (self.disc_max - self.disc_min) * \
self.num_iter/ float(self.disc_iter) + self.disc_min
disc_curr = min(disc_curr, np.log(self.categorical_dim))
# KL divergence between gumbel-softmax distribution
eps = 1e-7
# Entropy of the logits
h1 = q_p * torch.log(q_p + eps)
# Cross entropy with the categorical distribution
h2 = q_p * np.log(1. / self.categorical_dim + eps)
kld_disc_loss = torch.mean(torch.sum(h1 - h2, dim =1), dim=0)
# Compute Continuous loss
# Adaptively increase the continuous capacity
cont_curr = (self.cont_max - self.cont_min) * \
self.num_iter/ float(self.cont_iter) + self.cont_min
cont_curr = min(cont_curr, self.cont_max)
kld_cont_loss = torch.mean(-0.5 * torch.sum(1 + log_var - mu ** 2 - log_var.exp(),
dim=1),
dim=0)
capacity_loss = self.disc_gamma * torch.abs(disc_curr - kld_disc_loss) + \
self.cont_gamma * torch.abs(cont_curr - kld_cont_loss)
# kld_weight = 1.2
loss = self.alpha * recons_loss + kld_weight * capacity_loss
if self.training:
self.num_iter += 1
return {'loss': loss, 'Reconstruction_Loss':recons_loss, 'Capacity_Loss':capacity_loss}
def sample(self,
num_samples:int,
current_device: int, **kwargs) -> Tensor:
"""
Samples from the latent space and return the corresponding
image space map.
:param num_samples: (Int) Number of samples
:param current_device: (Int) Device to run the model
:return: (Tensor)
"""
# [S x D]
z = torch.randn(num_samples,
self.latent_dim)
M = num_samples
np_y = np.zeros((M, self.categorical_dim), dtype=np.float32)
np_y[range(M), np.random.choice(self.categorical_dim, M)] = 1
np_y = np.reshape(np_y, [M , self.categorical_dim])
q = torch.from_numpy(np_y)
# z = self.sampling_dist.sample((num_samples * self.latent_dim, ))
z = torch.cat([z, q], dim = 1).to(current_device)
samples = self.decode(z)
return samples
def generate(self, x: Tensor, **kwargs) -> Tensor:
"""
Given an input image x, returns the reconstructed image
:param x: (Tensor) [B x C x H x W]
:return: (Tensor) [B x C x H x W]
"""
return self.forward(x)[0]