-
Notifications
You must be signed in to change notification settings - Fork 15
/
Copy pathdatasets.py
executable file
·209 lines (163 loc) · 6.94 KB
/
datasets.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
import os
from os import path
import numpy as np
import torch
from data import sample_planar, sample_pole
from torch.utils.data import Dataset
torch.set_default_dtype(torch.float64)
class BaseDataset(Dataset):
def __init__(self, data_path, sample_size, noise):
self.sample_size = sample_size
self.noise = noise
self.data_path = data_path
if not os.path.exists(self.data_path):
os.makedirs(self.data_path)
self._process()
self.data_x, self.data_u, self.data_x_next = torch.load(
self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise)
)
def __len__(self):
return len(self.data_x)
def __getitem__(self, index):
return self.data_x[index], self.data_u[index], self.data_x_next[index]
def _process_image(self, img):
pass
def check_exists(self):
return path.exists(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise))
def _process(self):
pass
class PlanarDataset(BaseDataset):
width = 40
height = 40
action_dim = 2
def __init__(self, sample_size, noise):
data_path = "data/planar/"
super(PlanarDataset, self).__init__(data_path, sample_size, noise)
def _process_image(self, img):
return torch.from_numpy(img.flatten()).unsqueeze(0)
def _process(self):
if self.check_exists():
return
else:
(
x_numpy_data,
u_numpy_data,
x_next_numpy_data,
state_numpy_data,
state_next_numpy_data,
) = sample_planar.sample(sample_size=self.sample_size, noise=self.noise)
data_len = len(x_numpy_data)
# place holder for data
data_x = torch.zeros(data_len, self.width * self.height)
data_u = torch.zeros(data_len, self.action_dim)
data_x_next = torch.zeros(data_len, self.width * self.height)
for i in range(data_len):
data_x[i] = self._process_image(x_numpy_data[i])
data_u[i] = torch.from_numpy(u_numpy_data[i])
data_x_next[i] = self._process_image(x_next_numpy_data[i])
data_set = (data_x, data_u, data_x_next)
with open(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise), "wb") as f:
torch.save(data_set, f)
class PendulumDataset(BaseDataset):
width = 48
height = 48 * 2
action_dim = 1
def __init__(self, sample_size, noise):
data_path = "data/pendulum/"
super(PendulumDataset, self).__init__(data_path, sample_size, noise)
def _process_image(self, img):
x = np.vstack((img[:, :, 0], img[:, :, 1])).flatten()
return torch.from_numpy(x).unsqueeze(0)
def _process(self):
if self.check_exists():
return
else:
(
x_numpy_data,
u_numpy_data,
x_next_numpy_data,
state_numpy_data,
state_next_numpy_data,
) = sample_pole.sample(env_name="pendulum", sample_size=self.sample_size, noise=self.noise)
data_len = len(x_numpy_data)
# place holder for data
data_x = torch.zeros(data_len, self.width * self.height)
data_u = torch.zeros(data_len, self.action_dim)
data_x_next = torch.zeros(data_len, self.width * self.height)
for i in range(data_len):
data_x[i] = self._process_image(x_numpy_data[i])
data_u[i] = torch.from_numpy(u_numpy_data[i])
data_x_next[i] = self._process_image(x_next_numpy_data[i])
data_set = (data_x, data_u, data_x_next)
with open(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise), "wb") as f:
torch.save(data_set, f)
class CartPoleDataset(BaseDataset):
width = 80
height = 80 * 2
action_dim = 1
def __init__(self, sample_size, noise):
data_path = "data/cartpole/"
super(CartPoleDataset, self).__init__(data_path, sample_size, noise)
def _process_image(self, img):
x = torch.zeros(size=(2, self.width, self.width))
x[0, :, :] = torch.from_numpy(img[:, :, 0])
x[1, :, :] = torch.from_numpy(img[:, :, 1])
return x.unsqueeze(0)
def _process(self):
if self.check_exists():
return
else:
(
x_numpy_data,
u_numpy_data,
x_next_numpy_data,
state_numpy_data,
state_next_numpy_data,
) = sample_pole.sample(env_name="cartpole", sample_size=self.sample_size, noise=self.noise)
data_len = len(x_numpy_data)
# place holder for data
data_x = torch.zeros(data_len, 2, self.width, self.width)
data_u = torch.zeros(data_len, self.action_dim)
data_x_next = torch.zeros(data_len, 2, self.width, self.width)
for i in range(data_len):
data_x[i] = self._process_image(x_numpy_data[i])
data_u[i] = torch.from_numpy(u_numpy_data[i])
data_x_next[i] = self._process_image(x_next_numpy_data[i])
data_set = (data_x, data_u, data_x_next)
with open(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise), "wb") as f:
torch.save(data_set, f)
class ThreePoleDataset(BaseDataset):
width = 80
height = 80 * 2
action_dim = 3
def __init__(self, sample_size, noise):
data_path = "data/threepole/"
super(ThreePoleDataset, self).__init__(data_path, sample_size, noise)
def _process_image(self, img):
x = torch.zeros(size=(2, self.width, self.width))
x[0, :, :] = torch.from_numpy(img[:, :, 0])
x[1, :, :] = torch.from_numpy(img[:, :, 1])
return x.unsqueeze(0)
def _process(self):
if self.check_exists():
return
else:
(
x_numpy_data,
u_numpy_data,
x_next_numpy_data,
state_numpy_data,
state_next_numpy_data,
) = sample_pole.sample(env_name="threepole", sample_size=self.sample_size, noise=self.noise)
data_len = len(x_numpy_data)
# place holder for data
data_x = torch.zeros(data_len, 2, self.width, self.width)
data_u = torch.zeros(data_len, self.action_dim)
data_x_next = torch.zeros(data_len, 2, self.width, self.width)
for i in range(data_len):
data_x[i] = self._process_image(x_numpy_data[i])
data_u[i] = torch.from_numpy(u_numpy_data[i])
data_x_next[i] = self._process_image(x_next_numpy_data[i])
data_set = (data_x, data_u, data_x_next)
with open(self.data_path + "{:d}_{:.0f}.pt".format(self.sample_size, self.noise), "wb") as f:
torch.save(data_set, f)