-
Notifications
You must be signed in to change notification settings - Fork 133
/
builder.py
171 lines (139 loc) · 8.72 KB
/
builder.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
import torch.nn as nn
import torch.nn.functional as F
from custom_layers.flatten_layer import FlattenLayer
from custom_layers.se_block import SEBlock
class ConvBuilder(nn.Module):
def __init__(self, base_config):
super(ConvBuilder, self).__init__()
print('ConvBuilder initialized.')
self.BN_eps = 1e-5
self.BN_momentum = 0.1
self.BN_affine = True
self.BN_track_running_stats = True
self.base_config = base_config
self.cur_conv_idx = -1
def set_BN_config(self, eps, momentum, affine, track_running_stats):
self.BN_eps = eps
self.BN_momentum = momentum
self.BN_afine = affine
self.BN_track_running_stats = track_running_stats
def Conv2d(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', use_original_conv=False):
self.cur_conv_idx += 1
return nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
# The running estimates are kept with a default momentum of 0.1.
# By default, the elements of \gammaγ are sampled from \mathcal{U}(0, 1)U(0,1) and the elements of \betaβ are set to 0.
# If track_running_stats is set to False, this layer then does not keep running estimates, and batch statistics are instead used during evaluation time as well.
def BatchNorm2d(self, num_features, eps=None, momentum=None, affine=None, track_running_stats=None):
if eps is None:
eps = self.BN_eps
if momentum is None:
momentum = self.BN_momentum
if affine is None:
affine = self.BN_affine
if track_running_stats is None:
track_running_stats = self.BN_track_running_stats
return nn.BatchNorm2d(num_features=num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats)
def Sequential(self, *args):
return nn.Sequential(*args)
def ReLU(self):
return nn.ReLU()
def Conv2dBN(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', use_original_conv=False):
conv_layer = self.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups,
bias=False, padding_mode=padding_mode, use_original_conv=use_original_conv)
bn_layer = self.BatchNorm2d(num_features=out_channels)
se = self.Sequential()
se.add_module('conv', conv_layer)
se.add_module('bn', bn_layer)
if self.base_config is not None and self.base_config.se_reduce_scale is not None and self.base_config.se_reduce_scale > 0 \
and (self.base_config.se_layers is None or self.cur_conv_idx in self.base_config.se_layers):
print('%%%%%%%%%%% USE SEBLock !')
se.add_module('se', SEBlock(input_channels=out_channels, internal_neurons=out_channels // self.base_config.se_reduce_scale))
return se
def Conv2dBNReLU(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', use_original_conv=False):
conv = self.Conv2dBN(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, padding_mode=padding_mode, use_original_conv=use_original_conv)
conv.add_module('relu', self.ReLU())
return conv
def ReLUConv2dBN(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', use_original_conv=False):
conv_layer = self.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False, padding_mode=padding_mode, use_original_conv=use_original_conv)
bn_layer = self.BatchNorm2d(num_features=out_channels)
se = self.Sequential()
se.add_module('relu', self.ReLU())
se.add_module('conv', conv_layer)
se.add_module('bn', bn_layer)
return se
def Conv2dReLU(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', bias=True, use_original_conv=False):
conv = self.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, padding_mode=padding_mode, bias=bias, use_original_conv=use_original_conv)
result = self.Sequential()
result.add_module('conv', conv)
result.add_module('relu', self.ReLU())
return conv
def BNReLUConv2d(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', use_original_conv=False):
bn_layer = self.BatchNorm2d(num_features=in_channels)
conv_layer = self.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False, padding_mode=padding_mode)
se = self.Sequential()
se.add_module('bn', bn_layer)
se.add_module('relu', self.ReLU())
se.add_module('conv', conv_layer)
return se
def Linear(self, in_features, out_features, bias=True):
return nn.Linear(in_features=in_features, out_features=out_features, bias=bias)
def IntermediateLinear(self, in_features, out_features, bias=True):
return nn.Linear(in_features=in_features, out_features=out_features, bias=bias)
def Identity(self):
return nn.Identity()
def ResIdentity(self, num_channels):
return nn.Identity()
def ResNetAlignOpr(self, channels):
return nn.Identity()
def Dropout(self, keep_prob):
return nn.Dropout(p=1-keep_prob)
def Maxpool2d(self, kernel_size, stride=None, padding=0):
return nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=padding)
def Avgpool2d(self, kernel_size, stride=None, padding=0):
return nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding)
def Flatten(self):
return FlattenLayer()
def GAP(self, kernel_size):
gap = nn.Sequential()
gap.add_module('avg', self.Avgpool2d(kernel_size=kernel_size, stride=kernel_size))
gap.add_module('flatten', self.Flatten())
return gap
def relu(self, in_features):
return F.relu(in_features)
def max_pool2d(self, in_features, kernel_size, stride, padding):
return F.max_pool2d(in_features, kernel_size=kernel_size, stride=stride, padding=padding)
def avg_pool2d(self, in_features, kernel_size, stride, padding):
return F.avg_pool2d(in_features, kernel_size=kernel_size, stride=stride, padding=padding)
def flatten(self, in_features):
result = in_features.view(in_features.size(0), -1)
return result
def add(self, a, b):
return a + b
def GroupNorm(self, num_features, affine=True):
return nn.GroupNorm(num_groups=8, num_channels=num_features, affine=affine)
def Conv2dGroupNorm(self, in_channels, out_channels, kernel_size, stride=1, padding=0,
dilation=1, groups=1, padding_mode='zeros', use_original_conv=True):
assert use_original_conv
conv_layer = self.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups,
bias=False, padding_mode=padding_mode)
gn_layer = self.GroupNorm(out_channels)
se = self.Sequential()
se.add_module('conv', conv_layer)
se.add_module('bn', gn_layer)
return se
def OriginConv2dBN(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros'):
conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups,
bias=False, padding_mode=padding_mode)
bn_layer = self.BatchNorm2d(num_features=out_channels)
se = self.Sequential()
se.add_module('conv', conv_layer)
se.add_module('bn', bn_layer)
return se