-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathvgg11.py
269 lines (238 loc) · 13.2 KB
/
vgg11.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
269
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import copy
import math
batch_size=64
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Surrogate_BP_Function(torch.autograd.Function):
@staticmethod
def forward(self, input):
self.save_for_backward(input)
#out = torch.zeros_like(input).cuda()
#out[input > 0] = 1.0
return input.ge(0).type(torch.cuda.FloatTensor)
@staticmethod
def backward(self, grad_output):
input, = self.saved_tensors
grad_input = grad_output.clone()
#grad = torch.exp( -(grad_input - 0.3) **2/(2 * 0.3 ** 2) ) / ((2 * 0.3 * 3.141592653589793) ** 0.5)
#grad = grad_input * grad
#grad = abs(grad_input - threshold) < lens
grad = grad_input * 0.3 * F.threshold(1.0 - torch.abs(input), 0, 0)*2
#grad=F.hardtanh(grad_input)
#grad =grad_input * 0.3*torch.exp(-0.01*torch.abs(input))
#print(grad)
#grad =grad_input * 0.3 * torch.exp(F.threshold(1.0 - torch.abs(input), 0, 0))
return grad#grad
def PoissonGen(inp, rescale_fac=2.0):
rand_inp = torch.rand_like(inp).cuda()
return torch.mul(torch.le(rand_inp , torch.abs(inp)).float(), torch.sign(inp))
class SNN_VGG11_BNTT(nn.Module):
def __init__(self, num_steps, leak_mem=1.0, img_size=32,default_threshold = 1.0,num_cls=100):
super(SNN_VGG11_BNTT, self).__init__()
self.img_size = img_size
self.num_cls = num_cls
self.num_steps = num_steps
self.spike_fn = Surrogate_BP_Function.apply
self.leak_mem = leak_mem
self.batch_num = self.num_steps
#self.threshold=nn.Parameter(torch.tensor(1.0, dtype=torch.float), requires_grad=True)
#self.register_buffer('threshold', torch.tensor([1.]))
#self.threshold = nn.ParameterDict()
print (">>>>>>>>>>>>>>>>>>> VGG 9 >>>>>>>>>>>>>>>>>>>>>>")
print ("***** time step per batchnorm".format(self.batch_num))
print (">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
affine_flag = True
bias_flag = True
self.conv1 = nn.Conv2d(3, 128, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt1 = nn.BatchNorm2d(128, eps=1e-4, momentum=0.8, affine=affine_flag)
self.conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt2 = nn.BatchNorm2d(128, eps=1e-4, momentum=0.8, affine=affine_flag)
#self.pool1 = nn.AvgPool2d(kernel_size=2)
self.conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt3 = nn.BatchNorm2d(128, eps=1e-4, momentum=0.8, affine=affine_flag)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt4 = nn.BatchNorm2d(128, eps=1e-4, momentum=0.8, affine=affine_flag)
#self.pool2 = nn.AvgPool2d(kernel_size=2)
self.conv5 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt5 = nn.BatchNorm2d(128, eps=1e-4, momentum=0.8, affine=affine_flag)
self.conv6 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt6 = nn.BatchNorm2d(128, eps=1e-4, momentum=0.8, affine=affine_flag)
self.conv7 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt7 = nn.BatchNorm2d(128, eps=1e-4, momentum=0.8, affine=affine_flag)
self.conv8 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt8 = nn.BatchNorm2d(128, eps=1e-4, momentum=0.8, affine=affine_flag)
self.conv9 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=bias_flag)
self.bntt9 = nn.BatchNorm2d(256, eps=1e-4, momentum=0.8, affine=affine_flag)
#self.pool3 = nn.AvgPool2d(kernel_size=2)
self.pool3 = nn.MaxPool2d(kernel_size=2)
self.fc1 = nn.Linear((self.img_size//2)*(self.img_size//2)*256, 1024, bias=bias_flag)
self.bntt_fc = nn.BatchNorm1d(1024, eps=1e-4, momentum=0.8, affine=affine_flag)
self.fc2 = nn.Linear(1024, self.num_cls, bias=bias_flag)
self.fc_list=[self.fc1,self.fc2]
self.threshold1=nn.Parameter(torch.tensor(default_threshold))
self.threshold2=nn.Parameter(torch.tensor(default_threshold))
self.threshold3=nn.Parameter(torch.tensor(default_threshold))
self.threshold4=nn.Parameter(torch.tensor(default_threshold))
self.threshold5=nn.Parameter(torch.tensor(default_threshold))
self.threshold6=nn.Parameter(torch.tensor(default_threshold))
self.threshold7=nn.Parameter(torch.tensor(default_threshold))
self.threshold8=nn.Parameter(torch.tensor(default_threshold))
self.threshold9=nn.Parameter(torch.tensor(default_threshold))
self.threshold10=nn.Parameter(torch.tensor(default_threshold))
self.dropout = nn.Dropout(0.20)
# Turn off bias of BNTT
#for bn_list in self.bntt_list:
#for bn_temp in bn_list:
#bn_temp.bias = None
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
if m.bias is not None:
m.bias.data.zero_()
# Initialize the firing thresholds of all the layers
#for m in self.modules():
#if (isinstance(m, nn.Conv2d)):
#m.threshold = 1.0
#torch.nn.init.xavier_uniform_(m.weight, gain=2)
#elif (isinstance(m, nn.Linear)):
#m.threshold = 1.0
#torch.nn.init.xavier_uniform_(m.weight, gain=2)
def forward(self, inp):
batch_size = inp.size(0)
mem_conv1 = torch.zeros(batch_size, 128, self.img_size, self.img_size).cuda()
mem_conv2 = torch.zeros(batch_size, 128, self.img_size, self.img_size).cuda()
mem_conv3 = torch.zeros(batch_size, 128, self.img_size, self.img_size).cuda()
mem_conv4 = torch.zeros(batch_size, 128, self.img_size, self.img_size).cuda()
mem_conv5 = torch.zeros(batch_size, 128, self.img_size, self.img_size).cuda()
mem_conv6 = torch.zeros(batch_size, 128, self.img_size, self.img_size).cuda()
mem_conv7 = torch.zeros(batch_size, 128, self.img_size, self.img_size).cuda()
mem_conv8 = torch.zeros(batch_size, 128, self.img_size, self.img_size).cuda()
mem_conv9 = torch.zeros(batch_size, 256, self.img_size, self.img_size).cuda()
#mem_conv_list = [mem_conv1, mem_conv2, mem_conv3, mem_conv4, mem_conv5, mem_conv6, mem_conv7]
mem_fc1 = torch.zeros(batch_size, 1024).cuda()
mem_fc2 = torch.zeros(batch_size, self.num_cls).cuda()
for t in range(self.num_steps):
#rand_inp = torch.rand_like(inp).cuda()
#spike_inp = torch.mul(torch.le(rand_inp , torch.abs(inp)).float(), torch.sign(inp)) ##le means smaller and equal
#spike_inp = PoissonGen(inp)
#out_prev = spike_inp
mem_conv1 = self.leak_mem* mem_conv1 + self.bntt1(self.conv1(inp))
mem_thr = (mem_conv1 / self.threshold1 - 1.0)
out = self.spike_fn(mem_thr)
#rst = torch.zeros_like(mem_conv1).cuda()
rst = self.threshold1* (mem_thr>0).float() # (mem_thr>0) return 1
mem_conv=mem_conv1.clone()
mem_conv1 = mem_conv - rst
#print("mem_conv1 - rst",mem_conv1.size())
out_prev1 = out.clone()
#print("out_prev1",out_prev1.size())
#self.conv2.weight.data =torch.tanh(self.conv2.weight.data)
mem_conv2 = self.leak_mem * mem_conv2 + self.bntt2(self.conv2(out_prev1))
#print("mem_conv2",mem_conv2.size())
mem_thr = (mem_conv2 / self.threshold2) - 1.0 ###mem_conv2 compare with threshold
out = self.spike_fn(mem_thr)
rst = self.threshold2* (mem_thr>0).float()
#rst = torch.zeros_like(mem_conv2).cuda()
#rst[mem_thr > 0] = self.threshold2 ###soft reset:
mem_conv2 = mem_conv2 - rst ###rst=0 means mem_thr<0,means mem_conv2<threshold,thus:mem_conv2 not change
out_prev2 = out.clone() ###rst=threshold means mem_thr>0,means mem_conv2>threshold,thus:mem_conv2 =0 or mem_conv2 - threshold
#self.conv3.weight.data =torch.tanh(self.conv3.weight.data)
mem_conv3 = self.leak_mem * mem_conv3 + self.bntt3(self.conv3(out_prev2+out_prev1))
mem_thr = (mem_conv3 / self.threshold3) - 1.0
out = self.spike_fn(mem_thr)
rst = self.threshold3* (mem_thr>0).float()
#rst = torch.zeros_like(mem_conv3).cuda()
#rst[mem_thr > 0] = self.threshold3
mem_conv3 = mem_conv3 - rst
out_prev3 = out.clone()
out_prev33=out_prev3+out_prev2
#self.conv4.weight.data =torch.tanh(self.conv4.weight.data)
mem_conv4 = self.leak_mem * mem_conv4 + self.bntt4(self.conv4(out_prev33))
mem_thr = (mem_conv4 / self.threshold4) - 1.0
out = self.spike_fn(mem_thr)
rst = self.threshold4* (mem_thr>0).float()
#rst = torch.zeros_like(mem_conv4).cuda()
#rst[mem_thr > 0] = self.threshold4
mem_conv4 = mem_conv4 - rst
out_prev4 = out.clone()
out_prev44=out_prev4+out_prev3
#self.conv5.weight.data =torch.tanh(self.conv5.weight.data)
mem_conv5 = self.leak_mem * mem_conv5 + self.bntt5(self.conv5(out_prev44))
mem_thr = (mem_conv5 / self.threshold5) - 1.0
out = self.spike_fn(mem_thr)
rst = self.threshold5* (mem_thr>0).float()
#rst = torch.zeros_like(mem_conv5).cuda()
#rst[mem_thr > 0] = self.conv5.threshold5
mem_conv5 = mem_conv5 - rst
out_prev5 = out.clone()
out_prev55=out_prev5+out_prev4
#self.conv6.weight.data =torch.tanh(self.conv6.weight.data)
mem_conv6 = self.leak_mem * mem_conv6 + self.bntt6(self.conv6(out_prev55))
mem_thr = (mem_conv6 / self.threshold6) - 1.0
out = self.spike_fn(mem_thr)
rst = self.threshold6* (mem_thr>0).float()
#rst = torch.zeros_like(mem_conv6).cuda()
#rst[mem_thr > 0] = self.threshold
mem_conv6 = mem_conv6 - rst
out_prev6 = out.clone()
out_prev66=out_prev6+out_prev5
#self.conv7.weight.data =torch.tanh(self.conv7.weight.data)
mem_conv7 = self.leak_mem * mem_conv7 + self.bntt7(self.conv7(out_prev66))
mem_thr = (mem_conv7 / self.threshold7) - 1.0
out = self.spike_fn(mem_thr)
rst = self.threshold7* (mem_thr>0).float()
#rst = torch.zeros_like(mem_conv7).cuda()
#rst[mem_thr > 0] = self.threshold7
mem_conv7 = mem_conv7 - rst
out_prev7 = out.clone()
out_prev77=out_prev7+out_prev6
mem_conv8 = self.leak_mem * mem_conv8 + self.bntt8(self.conv8(out_prev77))
mem_thr = (mem_conv8 / self.threshold8) - 1.0
out = self.spike_fn(mem_thr)
rst = self.threshold8* (mem_thr>0).float()
#rst = torch.zeros_like(mem_conv7).cuda()
#rst[mem_thr > 0] = self.threshold7
mem_conv8 = mem_conv8 - rst
out_prev8 = out.clone()
out_prev88=out_prev8+out_prev7
mem_conv9 = self.leak_mem * mem_conv9 + self.bntt9(self.conv9(out_prev88))
mem_thr = (mem_conv9 / self.threshold9) - 1.0
out = self.spike_fn(mem_thr)
rst = self.threshold9* (mem_thr>0).float()
#rst = torch.zeros_like(mem_conv7).cuda()
#rst[mem_thr > 0] = self.threshold7
mem_conv9 = mem_conv9 - rst
out_prev10 = out.clone()
out_pool3=self.pool3(out_prev10)
out_pool3 = self.dropout(out_pool3)
#out_pool3=Pooling_sNeuron(out_pool3,0.2)
#print(out_prev8.size())
out_pool3 = out_pool3.reshape(batch_size, -1)
#self.fc1.weight.data =torch.tanh(self.fc1.weight.data)
mem_fc1 = self.leak_mem * mem_fc1 + self.bntt_fc(self.fc1(out_pool3)) ### the last layer input
mem_thr = (mem_fc1 / self.threshold10) - 1.0 ###
out = self.spike_fn(mem_thr)
rst = self.threshold10* (mem_thr>0).float()
#rst = torch.zeros_like(mem_fc1).cuda()
#rst[mem_thr > 0] = self.threshold8
mem_fc1 = mem_fc1 - rst
out_prev9 = out.clone()
#self.fc2.weight.data =torch.tanh(self.fc2.weight.data)
# accumulate voltage in the last layer
mem_fc=self.fc2(out_prev9)
mem_fc2 = mem_fc2 + mem_fc
#print("mem_fc2",mem_fc2.size())
out_voltage = mem_fc2 / self.num_steps
return out_voltage