-
Notifications
You must be signed in to change notification settings - Fork 3
/
direction.py
127 lines (100 loc) · 4.02 KB
/
direction.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
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
class Conv_d11(nn.Module):
def __init__(self):
super(Conv_d11, self).__init__()
kernel = [[-1, 0, 0, 0, 0],
[0, 0, 0,0,0],
[0, 0, 1,0,0],
[0, 0, 0,0,0],
[0,0,0,0,0]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = nn.Parameter(data=kernel, requires_grad=False)
def forward(self, input):
return F.conv2d(input, self.weight, padding=2)
class Conv_d12(nn.Module):
def __init__(self):
super(Conv_d12, self).__init__()
kernel = [[0, 0, -1, 0, 0],
[0, 0, 0,0,0],
[0, 0, 1,0,0],
[0, 0, 0,0,0],
[0,0,0,0,0]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = nn.Parameter(data=kernel, requires_grad=False)
def forward(self, input):
return F.conv2d(input, self.weight, padding=2)
class Conv_d13(nn.Module):
def __init__(self):
super(Conv_d13, self).__init__()
kernel = [[0, 0, 0, 0, -1],
[0, 0, 0,0,0],
[0, 0, 1,0,0],
[0, 0, 0,0,0],
[0,0,0,0,0]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = nn.Parameter(data=kernel, requires_grad=False)
def forward(self, input):
return F.conv2d(input, self.weight, padding=2)
class Conv_d14(nn.Module):
def __init__(self):
super(Conv_d14, self).__init__()
kernel = [[0, 0, 0, 0, 0],
[0, 0, 0,0,0],
[0, 0, 1,0,-1],
[0, 0, 0,0,0],
[0,0,0,0,0]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = nn.Parameter(data=kernel, requires_grad=False)
def forward(self, input):
return F.conv2d(input, self.weight, padding=2)
class Conv_d15(nn.Module):
def __init__(self):
super(Conv_d15, self).__init__()
kernel = [[0, 0, 0, 0, 0],
[0, 0, 0,0,0],
[0, 0, 1,0,0],
[0, 0, 0,0,0],
[0,0,0,0,-1]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = nn.Parameter(data=kernel, requires_grad=False)
def forward(self, input):
return F.conv2d(input, self.weight, padding=2)
class Conv_d16(nn.Module):
def __init__(self):
super(Conv_d16, self).__init__()
kernel = [[0, 0, 0, 0, 0],
[0, 0, 0,0,0],
[0, 0, 1,0,0],
[0, 0, 0,0,0],
[0,0,-1,0,0]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = nn.Parameter(data=kernel, requires_grad=False)
def forward(self, input):
return F.conv2d(input, self.weight, padding=2)
class Conv_d17(nn.Module):
def __init__(self):
super(Conv_d17, self).__init__()
kernel = [[0, 0, 0, 0, 0],
[0, 0, 0,0,0],
[0, 0, 1,0,0],
[0, 0, 0,0,0],
[-1,0,0,0,0]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = nn.Parameter(data=kernel, requires_grad=False)
def forward(self, input):
return F.conv2d(input, self.weight, padding=2)
class Conv_d18(nn.Module):
def __init__(self):
super(Conv_d18, self).__init__()
kernel = [[0, 0, 0, 0, 0],
[0, 0, 0,0,0],
[-1, 0, 1,0,0],
[0, 0, 0,0,0],
[0,0,0,0,0]]
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
self.weight = nn.Parameter(data=kernel, requires_grad=False)
def forward(self, input):
return F.conv2d(input, self.weight, padding=2)