-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcnn_model.py
288 lines (259 loc) · 13.2 KB
/
cnn_model.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
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from torch.nn import init
from torch.autograd import Variable
from collections import OrderedDict
import math
import copy
import torch.nn.functional as F
import sys
class ConvBlock(nn.Module):
def __init__(self, in_size, out_size, kernel, stride):
super(ConvBlock, self).__init__()
pad_size = kernel // 2
self.conv1 = nn.Sequential(nn.Conv2d(in_size, out_size, kernel, stride=stride, padding=pad_size, bias=False),
nn.BatchNorm2d(out_size),
nn.ReLU(inplace=True),)
def forward(self, inputs):
outputs = self.conv1(inputs)
return outputs
class DeConvBlock(nn.Module):
def __init__(self, in_size, out_size, kernel):
super(DeConvBlock, self).__init__()
pad_size = kernel // 2
self.conv1 = nn.Sequential(nn.ConvTranspose2d(in_size, out_size, kernel, stride=2, padding=pad_size, output_padding=1, bias=False),
nn.BatchNorm2d(out_size),
nn.ReLU(inplace=True),)
def forward(self, inputs):
outputs = self.conv1(inputs)
return outputs
class ConvBlock_last(nn.Module):
def __init__(self, in_size, out_size, kernel):
super(ConvBlock_last, self).__init__()
pad_size = kernel // 2
self.conv1 = nn.Sequential(nn.Conv2d(in_size, out_size, kernel, padding=pad_size, bias=False))
# nn.BatchNorm2d(out_size),
# nn.Tanh())
def forward(self, inputs):
outputs = self.conv1(inputs)
return outputs
class DeConvBlock_last(nn.Module):
def __init__(self, in_size, out_size, kernel):
super(DeConvBlock_last, self).__init__()
pad_size = kernel // 2
self.conv1 = nn.Sequential(nn.ConvTranspose2d(in_size, out_size, kernel, padding=pad_size, bias=False))
# nn.BatchNorm2d(out_size),
# nn.Tanh())
def forward(self, inputs):
outputs = self.conv1(inputs)
return outputs
class ConvBlock_s(nn.Module):
def __init__(self, in_size, out_size, kernel, stride):
super(ConvBlock_s, self).__init__()
pad_size = kernel // 2
self.conv1 = nn.Sequential(nn.Conv2d(in_size, out_size, kernel, stride=stride, padding=pad_size, bias=False),
nn.BatchNorm2d(out_size),
nn.ReLU(inplace=True),)
def forward(self, inputs):
outputs = self.conv1(inputs)
return outputs
class ConvBlock_sum(nn.Module):
def __init__(self, in_size, out_size, kernel):
super(ConvBlock_sum, self).__init__()
pad_size = kernel // 2
self.conv1 = nn.Sequential(nn.Conv2d(in_size, out_size, kernel, padding=pad_size, bias=False),
nn.BatchNorm2d(out_size),
nn.ReLU(inplace=True),)
self.relu = nn.ReLU(inplace=True)
def forward(self, inputs1, inputs2):
outputs = self.conv1(inputs1)
in_data = [outputs, inputs2]
# check of the channel size
small_ch_id, large_ch_id = (0, 1) if in_data[0].size(1) < in_data[1].size(1) else (1, 0)
offset = int(in_data[large_ch_id].size()[1] - in_data[small_ch_id].size()[1])
if offset != 0:
tmp = in_data[large_ch_id].data[:, :offset, :, :]
tmp = Variable(tmp).clone()
in_data[small_ch_id] = torch.cat([in_data[small_ch_id], tmp * 0], 1)
out = torch.add(in_data[0], in_data[1])
return self.relu(out)
class ResBlock(nn.Module):
def __init__(self, in_size, out_size, kernel, stride):
super(ResBlock, self).__init__()
pad_size = kernel // 2
self.conv1 = nn.Sequential(nn.Conv2d(in_size, out_size, kernel, stride=stride, padding=pad_size, bias=False),
nn.BatchNorm2d(out_size),
nn.ReLU(inplace=True),
nn.Conv2d(out_size, out_size, kernel, stride=stride, padding=pad_size, bias=False),
nn.BatchNorm2d(out_size))
self.relu = nn.ReLU(inplace=True)
def forward(self, inputs1, inputs2):
x = self.conv1(inputs1)
in_data = [x, inputs2]
# # check of the image size
# if (in_data[0].size(2) - in_data[1].size(2)) != 0:
# small_in_id, large_in_id = (0, 1) if in_data[0].size(2) < in_data[1].size(2) else (1, 0)
# pool_num = math.floor(in_data[large_in_id].size(2) / in_data[small_in_id].size(2))
# for _ in range(pool_num-1):
# in_data[large_in_id] = F.max_pool2d(in_data[large_in_id], 2, 2, 0)
# check of the channel size
small_ch_id, large_ch_id = (0, 1) if in_data[0].size(1) < in_data[1].size(1) else (1, 0)
offset = int(in_data[large_ch_id].size()[1] - in_data[small_ch_id].size()[1])
if offset != 0:
tmp = in_data[large_ch_id].data[:, :offset, :, :]
tmp = Variable(tmp).clone()
in_data[small_ch_id] = torch.cat([in_data[small_ch_id], tmp * 0], 1)
out = torch.add(in_data[0], in_data[1])
return self.relu(out)
class Sum(nn.Module):
def __init__(self):
super(Sum, self).__init__()
def forward(self, inputs1, inputs2):
in_data = [inputs1, inputs2]
# check of the image size
if (in_data[0].size(2) - in_data[1].size(2)) != 0:
small_in_id, large_in_id = (0, 1) if in_data[0].size(2) < in_data[1].size(2) else (1, 0)
pool_num = math.floor(in_data[large_in_id].size(2) / in_data[small_in_id].size(2))
for _ in range(pool_num-1):
in_data[large_in_id] = F.max_pool2d(in_data[large_in_id], 2, 2, 0)
# check of the channel size
small_ch_id, large_ch_id = (0, 1) if in_data[0].size(1) < in_data[1].size(1) else (1, 0)
offset = int(in_data[large_ch_id].size()[1] - in_data[small_ch_id].size()[1])
if offset != 0:
tmp = in_data[large_ch_id].data[:, :offset, :, :]
tmp = Variable(tmp).clone()
in_data[small_ch_id] = torch.cat([in_data[small_ch_id], tmp * 0], 1)
out = torch.add(in_data[0], in_data[1])
return out
class Concat(nn.Module):
def __init__(self):
super(Concat, self).__init__()
def forward(self, inputs1, inputs2):
in_data = [inputs1, inputs2]
# check of the image size
if (in_data[0].size(2) - in_data[1].size(2)) != 0:
small_in_id, large_in_id = (0, 1) if in_data[0].size(2) < in_data[1].size(2) else (1, 0)
pool_num = math.floor(in_data[large_in_id].size(2) / in_data[small_in_id].size(2))
for _ in range(pool_num-1):
in_data[large_in_id] = F.max_pool2d(in_data[large_in_id], 2, 2, 0)
return torch.cat([in_data[0], in_data[1]], 1)
class DeConvBlock_sum(nn.Module):
def __init__(self, in_size, out_size, kernel):
super(DeConvBlock_sum, self).__init__()
pad_size = kernel // 2
self.conv1 = nn.Sequential(nn.ConvTranspose2d(in_size, out_size, kernel, stride=2, padding=pad_size, output_padding=1, bias=False),
nn.BatchNorm2d(out_size),
nn.ReLU(inplace=True),)
self.relu = nn.ReLU(inplace=True)
def forward(self, inputs1, inputs2):
outputs1 = self.conv1(inputs1)
offset = outputs1.size()[2] - inputs2.size()[2]
padding = 2 * [offset // 2, offset // 2]
outputs2 = F.pad(inputs2, padding)
out = torch.add(outputs1, outputs2)
return self.relu(out)
class CGP2CNN(nn.Module):
def __init__(self, cgp, in_channel, n_class, imgSize):
super(CGP2CNN, self).__init__()
self.cgp = cgp
self.pool_size = 2
self.arch = OrderedDict()
self.encode = []
# self.channel_num = [None for _ in range(len(self.cgp))]
# self.size = [None for _ in range(len(self.cgp))]
self.channel_num = [None for _ in range(500)]
self.size = [None for _ in range(500)]
self.channel_num[0] = in_channel
self.size[0] = imgSize
# encoder
i = 0
for name, in1, in2 in self.cgp:
if name == 'input' in name:
i += 1
continue
elif name == 'full':
self.encode.append(nn.Linear(self.channel_num[in1]*self.size[in1]*self.size[in1], n_class))
elif name == 'Max_Pool' or name == 'Avg_Pool':
self.channel_num[i] = self.channel_num[in1]
self.size[i] = int(self.size[in1] / 2)
key = name.split('_')
func = key[0]
if func == 'Max':
self.encode.append(nn.MaxPool2d(2,2))
else:
self.encode.append(nn.AvgPool2d(2,2))
elif name == 'Concat':
self.channel_num[i] = self.channel_num[in1] + self.channel_num[in2]
small_in_id, large_in_id = (in1, in2) if self.size[in1] < self.size[in2] else (in2, in1)
self.size[i] = self.size[small_in_id]
self.encode.append(Concat())
elif name == 'Sum':
small_in_id, large_in_id = (in1, in2) if self.channel_num[in1] < self.channel_num[in2] else (in2, in1)
self.channel_num[i] = self.channel_num[large_in_id]
small_in_id, large_in_id = (in1, in2) if self.size[in1] < self.size[in2] else (in2, in1)
self.size[i] = self.size[small_in_id]
self.encode.append(Sum())
else:
key = name.split('_')
down = key[0]
func = key[1]
out_size = int(key[2])
kernel = int(key[3])
if down == 'S':
if func == 'ConvBlock':
self.channel_num[i] = out_size
self.size[i] = self.size[in1]
self.encode.append(ConvBlock(self.channel_num[in1], out_size, kernel, stride=1))
else:
in_data = [out_size, self.channel_num[in1]]
small_in_id, large_in_id = (0, 1) if in_data[0] < in_data[1] else (1, 0)
self.channel_num[i] = in_data[large_in_id]
# small_in_id, large_in_id = (in1, in2) if self.size[in1] < self.size[in2] else (in2, in1)
# self.size[i] = self.size[small_in_id]
self.size[i] = self.size[in1]
self.encode.append(ResBlock(self.channel_num[in1], out_size, kernel, stride=1))
else:
sys.exit('error')
# if func == 'ConvBlock':
# self.channel_num[i] = out_size
# self.size[i] = int(self.size[in1]/2)
# self.encode.append(ConvBlock(self.channel_num[in1], out_size, kernel, stride=2))
# else:
# in_data = [out_size, self.channel_num[in2]]
# small_in_id, large_in_id = (in1, in2) if self.channel_num[in1] < self.channel_num[in2] else (in2, in1)
# self.channel_num[i] = self.channel_num[large_in_id]
# small_in_id, large_in_id = (in1, in2) if self.size[in1] < self.size[in2] else (in2, in1)
# self.size[i] = self.size[small_in_id]
# self.encode.append(ResBlock(self.channel_num[in1], out_size, kernel, stride=1))
i += 1
self.layer_module = nn.ModuleList(self.encode)
self.outputs = [None for _ in range(len(self.cgp))]
def main(self,x):
outputs = self.outputs
outputs[0] = x # input image
nodeID = 1
for layer in self.layer_module:
if isinstance(layer, ConvBlock):
outputs[nodeID] = layer(outputs[self.cgp[nodeID][1]])
elif isinstance(layer, ResBlock):
outputs[nodeID] = layer(outputs[self.cgp[nodeID][1]], outputs[self.cgp[nodeID][1]])
elif isinstance(layer, torch.nn.modules.linear.Linear):
tmp = outputs[self.cgp[nodeID][1]].view(outputs[self.cgp[nodeID][1]].size(0), -1)
outputs[nodeID] = layer(tmp)
elif isinstance(layer, torch.nn.modules.pooling.MaxPool2d) or isinstance(layer, torch.nn.modules.pooling.AvgPool2d):
if outputs[self.cgp[nodeID][1]].size(2) > 1:
outputs[nodeID] = layer(outputs[self.cgp[nodeID][1]])
else:
outputs[nodeID] = outputs[self.cgp[nodeID][1]]
elif isinstance(layer, Concat):
outputs[nodeID] = layer(outputs[self.cgp[nodeID][1]], outputs[self.cgp[nodeID][2]])
elif isinstance(layer, Sum):
outputs[nodeID] = layer(outputs[self.cgp[nodeID][1]], outputs[self.cgp[nodeID][2]])
else:
sys.exit("Error at CGP2CNN forward")
nodeID += 1
return outputs[nodeID-1]
def forward(self, x, t):
return self.main(x)