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
|
import torch
from torch.nn import Conv1d, Conv2d, Conv3d, ReLU, Linear, BatchNorm1d, BatchNorm2d, BatchNorm3d
class ConvReLU1d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 1d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, relu):
assert type(conv) == Conv1d and type(relu) == ReLU, \
'Incorrect types for input modules{}{}'.format(
type(conv), type(relu))
super(ConvReLU1d, self).__init__(conv, relu)
class ConvReLU2d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 2d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, relu):
assert type(conv) == Conv2d and type(relu) == ReLU, \
'Incorrect types for input modules{}{}'.format(
type(conv), type(relu))
super(ConvReLU2d, self).__init__(conv, relu)
class ConvReLU3d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 3d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, relu):
assert type(conv) == Conv3d and type(relu) == ReLU, \
'Incorrect types for input modules{}{}'.format(
type(conv), type(relu))
super(ConvReLU3d, self).__init__(conv, relu)
class LinearReLU(torch.nn.Sequential):
r"""This is a sequential container which calls the Linear and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, linear, relu):
assert type(linear) == Linear and type(relu) == ReLU, \
'Incorrect types for input modules{}{}'.format(
type(linear), type(relu))
super(LinearReLU, self).__init__(linear, relu)
class ConvBn1d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn):
assert type(conv) == Conv1d and type(bn) == BatchNorm1d, \
'Incorrect types for input modules{}{}'.format(
type(conv), type(bn))
super(ConvBn1d, self).__init__(conv, bn)
class ConvBn2d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn):
assert type(conv) == Conv2d and type(bn) == BatchNorm2d, \
'Incorrect types for input modules{}{}'.format(
type(conv), type(bn))
super(ConvBn2d, self).__init__(conv, bn)
class ConvBnReLU1d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn, relu):
assert type(conv) == Conv1d and type(bn) == BatchNorm1d and \
type(relu) == ReLU, 'Incorrect types for input modules{}{}{}' \
.format(type(conv), type(bn), type(relu))
super(ConvBnReLU1d, self).__init__(conv, bn, relu)
class ConvBnReLU2d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn, relu):
assert type(conv) == Conv2d and type(bn) == BatchNorm2d and \
type(relu) == ReLU, 'Incorrect types for input modules{}{}{}' \
.format(type(conv), type(bn), type(relu))
super(ConvBnReLU2d, self).__init__(conv, bn, relu)
class ConvBn3d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn):
assert type(conv) == Conv3d and type(bn) == BatchNorm3d, \
'Incorrect types for input modules{}{}'.format(
type(conv), type(bn))
super(ConvBn3d, self).__init__(conv, bn)
class ConvBnReLU3d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, conv, bn, relu):
assert type(conv) == Conv3d and type(bn) == BatchNorm3d and \
type(relu) == ReLU, 'Incorrect types for input modules{}{}{}' \
.format(type(conv), type(bn), type(relu))
super(ConvBnReLU3d, self).__init__(conv, bn, relu)
class BNReLU2d(torch.nn.Sequential):
r"""This is a sequential container which calls the BatchNorm 2d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, batch_norm, relu):
assert type(batch_norm) == BatchNorm2d and type(relu) == ReLU, \
'Incorrect types for input modules{}{}'.format(
type(batch_norm), type(relu))
super(BNReLU2d, self).__init__(batch_norm, relu)
class BNReLU3d(torch.nn.Sequential):
r"""This is a sequential container which calls the BatchNorm 3d and ReLU modules.
During quantization this will be replaced with the corresponding fused module."""
def __init__(self, batch_norm, relu):
assert type(batch_norm) == BatchNorm3d and type(relu) == ReLU, \
'Incorrect types for input modules{}{}'.format(
type(batch_norm), type(relu))
super(BNReLU3d, self).__init__(batch_norm, relu)
|
