-
Notifications
You must be signed in to change notification settings - Fork 1
/
distributions.py
253 lines (201 loc) · 8.88 KB
/
distributions.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
import math
import torch
import torch.nn as nn
from torch.distributions import Distribution as TorchDistribution
from utils import mean_except_batch, sum_except_batch
class Distribution(nn.Module):
"""Distribution base class."""
def log_prob(self, x, context=None):
"""Calculate log probability under the distribution.
Args:
x: Tensor, shape (batch_size, ...)
Returns:
log_prob: Tensor, shape (batch_size,)
"""
raise NotImplementedError()
def sample(self, num_samples, context=None):
"""Generates samples from the distribution.
Args:
num_samples: int, number of samples to generate.
Returns:
samples: Tensor, shape (num_samples, ...)
"""
raise NotImplementedError()
def sample_with_log_prob(self, num_samples, context=None, n_points=None):
"""Generates samples from the distribution together with their log probability.
Args:
num_samples: int, number of samples to generate.
Returns:
samples: Tensor, shape (num_samples, ...)
log_prob: Tensor, shape (num_samples,)
"""
samples = self.sample(num_samples, context=context, n_points=n_points)
log_prob = self.log_prob(samples, context=context)
return samples, log_prob
def forward(self, *args, mode, **kwargs):
'''
To allow Distribution objects to be wrapped by DataParallelDistribution,
which parallelizes .forward() of replicas on subsets of data.
DataParallelDistribution.log_prob() calls DataParallel.forward().
DataParallel.forward() calls Distribution.forward() for different
data subsets on each device and returns the combined outputs.
'''
if mode == 'log_prob':
return self.log_prob(*args, **kwargs)
else:
raise RuntimeError("Mode {} not supported.".format(mode))
class ConditionalDistribution(Distribution):
"""ConditionalDistribution base class"""
def log_prob(self, x, context):
"""Calculate log probability under the distribution.
Args:
x: Tensor, shape (batch_size, ...).
context: Tensor, shape (batch_size, ...).
Returns:
log_prob: Tensor, shape (batch_size,)
"""
raise NotImplementedError()
def sample(self, context):
"""Generates samples from the distribution.
Args:
context: Tensor, shape (batch_size, ...).
Returns:
samples: Tensor, shape (batch_size, ...).
"""
raise NotImplementedError()
def sample_with_log_prob(self, context):
"""Generates samples from the distribution together with their log probability.
Args:
context: Tensor, shape (batch_size, ...).
Returns::
samples: Tensor, shape (batch_size, ...).
log_prob: Tensor, shape (batch_size,)
"""
raise NotImplementedError()
class ConditionalMeanStdNormal(ConditionalDistribution):
"""A multivariate Normal with conditional mean and learned std."""
def __init__(self, net, scale_shape):
super(ConditionalMeanStdNormal, self).__init__()
self.net = net
self.log_scale = nn.Parameter(torch.zeros(scale_shape))
def cond_dist(self, context):
mean = self.net(context)
return torch.distributions.Normal(loc=mean, scale=self.log_scale.exp())
def log_prob(self, x, context):
dist = self.cond_dist(context)
return sum_except_batch(dist.log_prob(x), num_dims=2)
def sample(self, context):
dist = self.cond_dist(context)
return dist.rsample()
def sample_with_log_prob(self, context):
dist = self.cond_dist(context)
z = dist.rsample()
log_prob = dist.log_prob(z)
log_prob = sum_except_batch(log_prob, num_dims=2)
return z, log_prob
def mean(self, context):
return self.cond_dist(context).mean
class ConditionalNormal(ConditionalDistribution):
"""A multivariate Normal with conditional mean and log_std."""
def __init__(self, net, split_dim=-1, clamp=False):
super().__init__()
self.net = net
self.clamp = clamp
def cond_dist(self, context):
# params = torch.utils.checkpoint.checkpoint(self.net, context, preserve_rng_state=False)
params = self.net(context)
mean, log_std = torch.chunk(params, chunks=2, dim=-1)
scale = log_std.exp()
if self.clamp:
scale = scale.clamp_max(self.clamp)
return torch.distributions.Normal(loc=mean, scale=scale)
def log_prob(self, x, context):
dist = self.cond_dist(context)
return sum_except_batch(dist.log_prob(x), num_dims=2)
def sample(self, context):
dist = self.cond_dist(context)
return dist.rsample()
def sample_with_log_prob(self, context):
dist = self.cond_dist(context)
z = dist.rsample()
log_prob = dist.log_prob(z)
log_prob = sum_except_batch(log_prob, num_dims=2)
return z, log_prob
def mean(self, context):
return self.cond_dist(context).mean
def mean_stddev(self, context):
dist = self.cond_dist(context)
return dist.mean, dist.stddev
class StandardUniform(Distribution):
"""A multivariate Uniform with boundaries (0,1)."""
def __init__(self, shape):
super().__init__()
self.shape = torch.Size(shape)
self.register_buffer('zero', torch.zeros(1))
self.register_buffer('one', torch.ones(1))
def log_prob(self, x, context=None):
lb = mean_except_batch(x.ge(self.zero).type(self.zero.dtype), num_dims=2)
ub = mean_except_batch(x.le(self.one).type(self.one.dtype), num_dims=2)
return torch.log(lb * ub)
def sample(self, num_samples, context=None, n_points=None):
sample_shape = list(self.shape)
sample_shape[-2] = n_points
return torch.rand((num_samples,) + sample_shape, device=self.zero.device, dtype=self.zero.dtype)
class StandardNormal(Distribution):
"""A multivariate Normal with zero mean and unit covariance."""
def __init__(self, shape):
super(StandardNormal, self).__init__()
self.shape = torch.Size(shape)
self.register_buffer('buffer', torch.zeros(1))
def log_prob(self, x, context=None):
log_base = - 0.5 * math.log(2 * math.pi)
log_inner = - 0.5 * x**2
return sum_except_batch(log_base + log_inner, num_dims=2)
def sample(self, num_samples, context=None, n_points=None):
sample_shape = list(self.shape)
sample_shape[-2] = n_points
return torch.randn(num_samples, *sample_shape, device=self.buffer.device, dtype=self.buffer.dtype)
class Normal(Distribution):
def __init__(self, loc, scale, shape):
super().__init__()
self.std_normal = StandardNormal(shape)
self.shape = torch.Size(shape)
self.register_buffer('loc', loc)
self.register_buffer('scale', scale)
def log_prob(self, x, context=None):
x = (x - self.loc) / self.scale
return self.std_normal.log_prob(x, context=None)
def sample(self, num_samples, context=None, n_points=None):
sample_shape = list(self.shape)
sample_shape[-2] = n_points
return (self.std_normal.sample(num_samples=num_samples, n_points=n_points, context=None) * self.scale) + self.loc
class SemanticDistribution(Distribution):
def __init__(self, locs, scale, shape):
super().__init__()
self.std_normal = StandardNormal(shape)
self.shape = torch.Size(shape)
self.register_buffer('locs', locs)
self.register_buffer('scale', scale)
def log_prob(self, x, context=None):
x = (x - self.locs[context]) / self.scale
return self.std_normal.log_prob(x, context=None).sum(axis=1)
def sample(self, num_samples, context=None, n_points=None):
sample_shape = list(self.shape)
sample_shape[-2] = n_points
return (self.std_normal.sample(num_samples=num_samples, n_points=n_points, context=None) * self.scale) + self.locs[context]
class DoubleDistribution(TorchDistribution):
def __init__(self, visual_distribution, semantic_distribution, input_dim, context_dim):
self.input_dim = input_dim
self.context_dim = context_dim
self.split_dim = input_dim - context_dim
self.semantic_distribution = semantic_distribution
self.visual_distribution = visual_distribution
def log_prob(self, x, context):
c_hat, z_hat = x.split([self.split_dim, self.context_dim], dim=1)
return self.semantic_distribution.log_prob(c_hat, context) + self.visual_distribution.log_prob(z_hat)
def sample(self, num_samples, context=None):
raise NotImplementedError
if __name__ == '__main__':
normal = Normal(torch.zeros(2), torch.ones(2), (1, 2))
# std_normal = StandardNormal(torch.zeros(2), torch.ones(2), (1, 2))
normal