-
Notifications
You must be signed in to change notification settings - Fork 23
/
utils.py
154 lines (133 loc) · 6.22 KB
/
utils.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
import os
import numpy as np
import torch
import matplotlib.pyplot as plt
import pycuda.driver as cuda
import pycuda.autoinit # Necessary for using its functions
import fsvae_models.snn_layers as snn_layers
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class aboutCudaDevices():
def __init__(self):
pass
def num_devices(self):
"""Return number of devices connected."""
return cuda.Device.count()
def devices(self):
"""Get info on all devices connected."""
num = cuda.Device.count()
print("%d device(s) found:" % num)
for i in range(num):
print(cuda.Device(i).name(), "(Id: %d)" % i)
def mem_info(self):
"""Get available and total memory of all devices."""
available, total = cuda.mem_get_info()
print("Available: %.2f GB\nTotal: %.2f GB" % (available / 1e9, total / 1e9))
def attributes(self, device_id=0):
"""Get attributes of device with device Id = device_id"""
return cuda.Device(device_id).get_attributes()
def info(self):
"""Class representation as number of devices connected and about them."""
num = cuda.Device.count()
string = ""
string += ("%d device(s) found:\n" % num)
for i in range(num):
string += (" %d) %s (Id: %d)\n" % ((i + 1), cuda.Device(i).name(), i))
string += (" Memory: %.2f GB\n" % (cuda.Device(i).total_memory() / 1e9))
return string
class CountMulAddANN:
def __init__(self) -> None:
self.mul_sum = 0
self.add_sum = 0
def __call__(self, module, module_in, module_out):
if isinstance(module_in, tuple):
module_in = module_in[0]
if isinstance(module_out, tuple):
module_out = module_out[0]
if not module.training:
with torch.no_grad():
if isinstance(module, torch.nn.Conv2d):
s_in = module_in.shape
s_out = module_in.shape
mul = s_in[0]*s_in[1]*s_in[2]*s_in[3] * module.kernel_size[0] * module.kernel_size[1] * module.out_channels / (module.stride[0]*module.stride[1])
add = mul + s_out[0]*s_out[1]*s_out[2]*s_out[3] # 掛け合わせた分だけ足す必要がある + bias
elif isinstance(module, torch.nn.Linear):
s_in = module_in.shape
s_out = module_in.shape
mul = s_in[0]*s_in[1]*s_out[1]
add = mul + s_out[0]*s_out[1]
elif isinstance(module, torch.nn.ConvTranspose2d):
s_in = module_in.shape
s_out = module_in.shape
mul = s_in[0]*s_in[1]*s_in[2]*s_in[3] * module.kernel_size[0] * module.kernel_size[1] * module.out_channels * (module.stride[0]*module.stride[1])
add = mul + s_out[0]*s_out[1]*s_out[2]*s_out[3]
else:
add = 0
mul = 0
self.mul_sum = self.mul_sum + mul
self.add_sum = self.add_sum + add
def clear(self):
self.mul_sum = 0
self.add_sum = 0
class CountMulAddSNN:
def __init__(self) -> None:
self.mul_sum = 0
self.add_sum = 0
def __call__(self, module, module_in, module_out):
if isinstance(module_in, tuple):
module_in = module_in[0]
if isinstance(module_out, tuple):
module_out = module_out[0]
if not module.training:
with torch.no_grad():
if isinstance(module, torch.nn.Conv3d):
if module.is_first_conv:
# real-value images are input to the first conv layer.
s_in = module_in.shape
s_out = module_in.shape
mul = s_in[0]*s_in[1]*s_in[2]*s_in[3]*s_in[4] * module.kernel_size[0] * module.kernel_size[1] * module.out_channels / (module.stride[0]*module.stride[1])
add = mul + s_out[0]*s_out[1]*s_out[2]*s_out[3]*s_out[4] # calc of bias
else:
add = module_in.sum() * module.kernel_size[0] * module.kernel_size[1] * module.out_channels / (module.stride[0]*module.stride[1])
s = module_out.shape # (N,C,H,W,T)
add += s[0] * s[1] * s[2] * s[3] * s[4] # calc of bias
mul = 0
elif isinstance(module, torch.nn.Linear):
add = module_in.sum() * module.out_features
s = module_out.shape # (N,C,T)
add += s[0] * s[1] * s[2]
mul = 0
elif isinstance(module, torch.nn.ConvTranspose3d):
add = module_in.sum() * module.kernel_size[0] * module.kernel_size[1] * module.out_channels * module.stride[0]*module.stride[1]
s = module_out.shape # (N,C,H,W,T)
add += s[0] * s[1] * s[2] * s[3] * s[4]
mul = 0
elif isinstance(module, snn_layers.LIFSpike):
s_in = module_in.shape
if len(s_in) == 5: # conv layer
add = s_in[0] * s_in[1] * s_in[2] * s_in[3] * s_in[4]
elif len(s_in) == 3: # linear layer
add = s_in[0] * s_in[1] * s_in[2]
else:
raise ValueError()
mul = (1-module_out).sum() # event-based activation
else:
add = 0
mul = 0
self.mul_sum = self.mul_sum + mul
self.add_sum = self.add_sum + add
def clear(self):
self.mul_sum = 0
self.add_sum = 0