-
Notifications
You must be signed in to change notification settings - Fork 12
/
inn_architecture.py
164 lines (127 loc) · 5.84 KB
/
inn_architecture.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
from functools import partial
import pdb
import torch
import torch.nn as nn
from torch.nn.functional import conv2d, interpolate
import numpy as np
import FrEIA.framework as Ff
import FrEIA.modules as Fm
from downsampling_coupling_block import DownsampleCouplingBlock
from all_in_one_block import AIO_Block
from dct_transform import DCTPooling2d
def construct_irevnet(classifier):
inn = constuct_inn(classifier)
projection_layer = nn.Linear(classifier.ndim_tot, classifier.n_classes)
class RevNetWrapper(nn.Module):
def __init__(self, inn, projection_layer):
super().__init__()
self.inn = inn
self.proj = projection_layer
def forward(self, x):
return self.proj(self.inn(x))
return RevNetWrapper(inn, projection_layer)
def constuct_inn(classifier, verbose=False):
fc_width = int(classifier.args['model']['fc_width'])
n_coupling_blocks_fc = int(classifier.args['model']['n_coupling_blocks_fc'])
use_dct = eval(classifier.args['model']['dct_pooling'])
conv_widths = eval(classifier.args['model']['conv_widths'])
n_coupling_blocks_conv = eval(classifier.args['model']['n_coupling_blocks_conv'])
dropouts = eval(classifier.args['model']['dropout_conv'])
dropouts_fc = float(classifier.args['model']['dropout_fc'])
groups = int(classifier.args['model']['n_groups'])
clamp = float(classifier.args['model']['clamp'])
ndim_input = classifier.dims
batchnorm_args = {'track_running_stats':True,
'momentum':0.999,
'eps':1e-4,}
coupling_args = {
'subnet_constructor': None,
'clamp': clamp,
'act_norm': float(classifier.args['model']['act_norm']),
'gin_block': False,
'permute_soft': True,
}
def weights_init(m):
if type(m) == nn.Conv2d:
torch.nn.init.kaiming_normal_(m.weight)
if type(m) == nn.BatchNorm2d:
m.weight.data.fill_(1)
m.bias.data.zero_()
if type(m) == nn.Linear:
torch.nn.init.kaiming_normal_(m.weight)
m.weight.data *= 0.1
def basic_residual_block(width, groups, dropout, relu_first, cin, cout):
width = width * groups
layers = []
if relu_first:
layers = [nn.ReLU()]
else:
layers = []
layers.extend([
nn.Conv2d(cin, width, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(width, **batchnorm_args),
nn.ReLU(inplace=True),
nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False, groups=groups),
nn.BatchNorm2d(width, **batchnorm_args),
nn.ReLU(inplace=True),
nn.Dropout2d(p=dropout),
nn.Conv2d(width, cout, 1, padding=0)
])
layers = nn.Sequential(*layers)
layers.apply(weights_init)
return layers
def strided_residual_block(width, groups, cin, cout):
width = width * groups
layers = nn.Sequential(
nn.Conv2d(cin, width, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(width, **batchnorm_args),
nn.ReLU(),
nn.Conv2d(width, width, kernel_size=3, stride=2, padding=1, bias=False, groups=groups),
nn.BatchNorm2d(width, **batchnorm_args),
nn.ReLU(inplace=True),
nn.Conv2d(width, cout, 1, padding=0)
)
layers.apply(weights_init)
return layers
def fc_constr(c_in, c_out):
net = [nn.Linear(c_in, fc_width),
nn.ReLU(),
nn.Dropout(p=dropouts_fc),
nn.Linear(fc_width, c_out)]
net = nn.Sequential(*net)
net.apply(weights_init)
return net
nodes = [Ff.InputNode(*ndim_input, name='input')]
channels = classifier.input_channels
if classifier.dataset == 'MNIST':
nodes.append(Ff.Node(nodes[-1].out0, Fm.Reshape, {'target_dim':(1, *classifier.dims)}))
nodes.append(Ff.Node(nodes[-1].out0, Fm.HaarDownsampling, {'rebalance':1.}))
channels *= 4
for i, (width, n_blocks) in enumerate(zip(conv_widths, n_coupling_blocks_conv)):
if classifier.dataset == 'MNIST' and i==0:
continue
drop = dropouts[i]
conv_constr = partial(basic_residual_block, width, groups, drop, True)
conv_strided = partial(strided_residual_block, width*2, groups)
conv_lowres = partial(basic_residual_block, width*2, groups, drop, False)
if i == 0:
conv_first = partial(basic_residual_block, width, groups, drop, False)
else:
conv_first = conv_constr
nodes.append(Ff.Node(nodes[-1], AIO_Block, dict(coupling_args, subnet_constructor=conv_first), name=f'CONV_{i}_0'))
for k in range(1, n_blocks):
nodes.append(Ff.Node(nodes[-1], AIO_Block, dict(coupling_args, subnet_constructor=conv_constr), name=f'CONV_{i}_{k}'))
if i < len(conv_widths) - 1:
nodes.append(Ff.Node(nodes[-1], DownsampleCouplingBlock, {'subnet_constructor_low_res':conv_lowres,
'subnet_constructor_strided':conv_strided,
'clamp':clamp}, name=f'DOWN_{i}'))
channels *= 4
if use_dct:
nodes.append(Ff.Node(nodes[-1].out0, DCTPooling2d, {'rebalance':0.5}, name='DCT'))
else:
nodes.append(Ff.Node(nodes[-1].out0, Fm.Flatten, {}, name='Flatten'))
for k in range(n_coupling_blocks_fc):
nodes.append(Ff.Node(nodes[-1], Fm.PermuteRandom, {'seed':k}, name=f'PERM_FC_{k}'))
nodes.append(Ff.Node(nodes[-1].out0, Fm.GLOWCouplingBlock, {'subnet_constructor':fc_constr, 'clamp':2.0}, name=f'FC_{k}'))
nodes.append(Ff.OutputNode(nodes[-1], name='output'))
return Ff.ReversibleGraphNet(nodes, verbose=verbose)