-
Notifications
You must be signed in to change notification settings - Fork 16
/
pfrnns.py
187 lines (149 loc) · 7.34 KB
/
pfrnns.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
import torch
from torch import nn
import numpy as np
class PFRNNBaseCell(nn.Module):
"""
This is the base class for the PF-RNNs. We implement the shared functions here, including
1. soft-resampling
2. reparameterization trick
3. obs_extractor o_t(x_t)
4. control_extractor u_t(x_t)
All particles in PF-RNNs are processed in parallel to benefit from GPU parallelization.
"""
def __init__(self, num_particles, input_size, hidden_size, ext_obs, ext_act, resamp_alpha):
"""
:param num_particles: number of particles for a PF-RNN
:param input_size: the size of input x_t
:param hidden_size: the size of the hidden particle h_t^i
:param ext_obs: the size for o_t(x_t)
:param ext_act: the size for u_t(x_t)
:param resamp_alpha: the control parameter \alpha for soft-resampling.
We use the importance sampling with a proposal distribution q(i) = \alpha w_t^i + (1 - \alpha) (1 / K)
"""
super(PFRNNBaseCell, self).__init__()
self.num_particles = num_particles
self.input_size = input_size
self.h_dim = hidden_size
self.ext_obs = ext_obs
self.ext_act = ext_act
self.resamp_alpha = resamp_alpha
self.obs_extractor = nn.Sequential(
nn.Linear(self.input_size, self.ext_obs),
nn.LeakyReLU()
)
self.act_extractor = nn.Sequential(
nn.Linear(self.input_size, self.ext_act),
nn.LeakyReLU()
)
self.fc_obs = nn.Linear(self.ext_obs + self.h_dim, 1)
self.batch_norm = nn.BatchNorm1d(self.num_particles)
def resampling(self, particles, prob):
"""
The implementation of soft-resampling. We implement soft-resampling in a batch-manner.
:param particles: \{(h_t^i, c_t^i)\}_{i=1}^K for PF-LSTM and \{h_t^i\}_{i=1}^K for PF-GRU.
each tensor has a shape: [num_particles * batch_size, h_dim]
:param prob: weights for particles in the log space. Each tensor has a shape: [num_particles * batch_size, 1]
:return: resampled particles and weights according to soft-resampling scheme.
"""
resamp_prob = self.resamp_alpha * torch.exp(prob) + (1 -
self.resamp_alpha) * 1 / self.num_particles
resamp_prob = resamp_prob.view(self.num_particles, -1)
indices = torch.multinomial(resamp_prob.transpose(0, 1),
num_samples=self.num_particles, replacement=True)
batch_size = indices.size(0)
indices = indices.transpose(1, 0).contiguous()
offset = torch.arange(batch_size).type(torch.LongTensor).unsqueeze(0)
if torch.cuda.is_available():
offset = offset.cuda()
indices = offset + indices * batch_size
flatten_indices = indices.view(-1, 1).squeeze()
# PFLSTM
if type(particles) == tuple:
particles_new = (particles[0][flatten_indices],
particles[1][flatten_indices])
# PFGRU
else:
particles_new = particles[flatten_indices]
prob_new = torch.exp(prob.view(-1, 1)[flatten_indices])
prob_new = prob_new / (self.resamp_alpha * prob_new + (1 -
self.resamp_alpha) / self.num_particles)
prob_new = torch.log(prob_new).view(self.num_particles, -1, 1)
prob_new = prob_new - torch.logsumexp(prob_new, dim=0, keepdim=True)
prob_new = prob_new.view(-1, 1)
return particles_new, prob_new
def reparameterize(self, mu, var):
"""
Reparameterization trick
:param mu: mean
:param var: variance
:return: new samples from the Gaussian distribution
"""
std = torch.nn.functional.softplus(var)
if torch.cuda.is_available():
eps = torch.cuda.FloatTensor(std.shape).normal_()
else:
eps = torch.FloatTensor(std.shape).normal_()
return mu + eps * std
class PFLSTMCell(PFRNNBaseCell):
def __init__(self, num_particles, input_size, hidden_size, ext_obs, ext_act, resamp_alpha):
super().__init__(num_particles, input_size,
hidden_size, ext_obs, ext_act, resamp_alpha)
self.fc_ih = nn.Linear(self.ext_act, 5 * self.h_dim)
self.fc_hh = nn.Linear(self.h_dim, 5 * self.h_dim)
def forward(self, input_, hx):
h0, c0, p0 = hx
batch_size = h0.size(0)
wh_b = self.fc_hh(h0)
# by default assume input_ = (obs, control)
obs = self.obs_extractor(input_)
act = self.act_extractor(input_)
wi = self.fc_ih(act)
s = wh_b + wi
f, i, o, mu, var = torch.split(s, split_size_or_sections=self.h_dim,
dim=1)
g_ = self.reparameterize(mu, var).view(
self.num_particles, -1, self.h_dim).transpose(0, 1).contiguous()
g = self.batch_norm(g_).transpose(
0, 1).contiguous().view(-1, self.h_dim)
c1 = torch.sigmoid(f) * c0 + torch.sigmoid(i) * \
nn.functional.leaky_relu(g)
h1 = torch.sigmoid(o) * torch.tanh(c1)
att = torch.cat((obs, h1), dim=1)
logpdf_obs = self.fc_obs(att)
# logpdf_obs = nn.functional.relu6(logpdf_obs).view(self.num_particles, -1, 1) - 3 # hack to shape the range obs logpdf_obs into [-3, 3] for numerical stability
p1 = logpdf_obs.view(self.num_particles, -1, 1) + \
p0.view(self.num_particles, -1, 1)
p1 = p1 - torch.logsumexp(p1, dim=0, keepdim=True)
(h1, c1), p1 = self.resampling((h1, c1), p1)
return h1, c1, p1
class PFGRUCell(PFRNNBaseCell):
def __init__(self, num_particles, input_size, hidden_size, ext_obs, ext_act, resamp_alpha):
super().__init__(num_particles, input_size,
hidden_size, ext_obs, ext_act, resamp_alpha)
self.fc_z = nn.Linear(self.h_dim + self.ext_act, self.h_dim)
self.fc_r = nn.Linear(self.h_dim + self.ext_act, self.h_dim)
self.fc_n = nn.Linear(self.h_dim + self.ext_act, self.h_dim * 2)
def forward(self, input_, hx):
h0, p0 = hx
# by default assume input = (obs, control)
obs = self.obs_extractor(input_)
act = self.act_extractor(input_)
z = torch.sigmoid(self.fc_z(torch.cat((h0, act), dim=1)))
r = torch.sigmoid(self.fc_r(torch.cat((h0, act), dim=1)))
n = self.fc_n(torch.cat((r * h0, act), dim=1))
mu_n, var_n = torch.split(n, split_size_or_sections=self.h_dim, dim=1)
n = self.reparameterize(mu_n, var_n)
n = n.view(self.num_particles, -1, self.h_dim).transpose(0,
1).contiguous()
n = self.batch_norm(n)
n = n.transpose(0, 1).contiguous().view(-1, self.h_dim)
n = nn.functional.leaky_relu(n)
h1 = (1 - z) * n + z * h0
att = torch.cat((h1, obs), dim=1)
logpdf_obs = self.fc_obs(att)
# logpdf_obs = nn.functional.relu6(logpdf_obs) - 3 # hack to shape the range obs logpdf_obs into [-3, 3] for numerical stability
p1 = logpdf_obs + p0
p1 = p1.view(self.num_particles, -1, 1)
p1 = p1 - torch.logsumexp(p1, dim=0, keepdim=True)
h1, p1 = self.resampling(h1, p1)
return h1, p1