kron积盘算mask类别矩阵

1. 生成类别矩阵如下

2. pytorch 代码

1. import torch
2. import torch.nn as nn
3. import torch.nn.functional as F
5. torch.set_printoptions(precision=3, sci_mode=False)
7. if __name__ == "__main__":
8.     run_code = 0
9.     a_matrix = torch.arange(4).reshape(2, 2) + 1
10.     b_matrix = torch.ones((2, 2))
11.     print(f"a_matrix=\n{a_matrix}")
12.     print(f"b_matrix=\n{b_matrix}")
13.     c_matrix = torch.kron(input=a_matrix, other=b_matrix)
14.     print(f"c_matrix=\n{c_matrix}")
15.     d_matrix = torch.arange(9).reshape(3, 3) + 1
16.     e_matrix = torch.ones((2, 2))
17.     f_matrix = torch.kron(input=d_matrix, other=e_matrix)
18.     print(f"d_matrix=\n{d_matrix}")
19.     print(f"e_matrix=\n{e_matrix}")
20.     print(f"f_matrix=\n{f_matrix}")
21.     g_matrix = f_matrix[1:-1, 1:-1]
22.     print(f"g_matrix=\n{g_matrix}")

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• 效果：
  

1. a_matrix=
2. tensor([[1, 2],
3.         [3, 4]])
4. b_matrix=
5. tensor([[1., 1.],
6.         [1., 1.]])
7. c_matrix=
8. tensor([[1., 1., 2., 2.],
9.         [1., 1., 2., 2.],
10.         [3., 3., 4., 4.],
11.         [3., 3., 4., 4.]])
12. d_matrix=
13. tensor([[1, 2, 3],
14.         [4, 5, 6],
15.         [7, 8, 9]])
16. e_matrix=
17. tensor([[1., 1.],
18.         [1., 1.]])
19. f_matrix=
20. tensor([[1., 1., 2., 2., 3., 3.],
21.         [1., 1., 2., 2., 3., 3.],
22.         [4., 4., 5., 5., 6., 6.],
23.         [4., 4., 5., 5., 6., 6.],
24.         [7., 7., 8., 8., 9., 9.],
25.         [7., 7., 8., 8., 9., 9.]])
26. g_matrix=
27. tensor([[1., 2., 2., 3.],
28.         [4., 5., 5., 6.],
29.         [4., 5., 5., 6.],
30.         [7., 8., 8., 9.]])

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3. 循环移动矩阵

• excel 表现
  
  
  
  
• pytorch 源码
  

1. import torch
2. import torch.nn as nn
3. import torch.nn.functional as F
4. import math
6. torch.set_printoptions(precision=3, sci_mode=False)
9. class WindowMatrix(object):
10.     def __init__(self, num_patch=4, size=2):
11.         self.num_patch = num_patch
12.         self.size = size
13.         self.width = self.num_patch
14.         self.height = self.size * self.size
15.         self._result = torch.zeros((self.width, self.height))
17.     @property
18.     def result(self):
19.         a_size = int(math.sqrt(self.num_patch))
20.         a_matrix = torch.arange(self.num_patch).reshape(a_size, a_size) + 1
21.         b_matrix = torch.ones(self.size, self.size)
22.         self._result = torch.kron(input=a_matrix, other=b_matrix)
23.         return self._result
26. class ShiftedWindowMatrix(object):
27.     def __init__(self, num_patch=9, size=2):
28.         self.num_patch = num_patch
29.         self.size = size
30.         self.width = self.num_patch
31.         self.height = self.size * self.size
32.         self._result = torch.zeros((self.width, self.height))
34.     @property
35.     def result(self):
36.         a_size = int(math.sqrt(self.num_patch))
37.         a_matrix = torch.arange(self.num_patch).reshape(a_size, a_size) + 1
38.         b_matrix = torch.ones(self.size, self.size)
39.         my_result = torch.kron(input=a_matrix, other=b_matrix)
40.         self._result = my_result[1:-1, 1:-1]
41.         return self._result
44. class RollShiftedWindowMatrix(object):
45.     def __init__(self, num_patch=9, size=2):
46.         self.num_patch = num_patch
47.         self.size = size
48.         self.width = self.num_patch
49.         self.height = self.size * self.size
50.         self._result = torch.zeros((self.width, self.height))
52.     @property
53.     def result(self):
54.         a_size = int(math.sqrt(self.num_patch))
55.         a_matrix = torch.arange(self.num_patch).reshape(a_size, a_size) + 1
56.         b_matrix = torch.ones(self.size, self.size)
57.         my_result = torch.kron(input=a_matrix, other=b_matrix)
58.         my_result = my_result[1:-1, 1:-1]
59.         roll_result = torch.roll(input=my_result, shifts=(-1, -1), dims=(-1, -2))
60.         self._result = roll_result
61.         return self._result
64. class BackRollShiftedWindowMatrix(object):
65.     def __init__(self, num_patch=9, size=2):
66.         self.num_patch = num_patch
67.         self.size = size
68.         self.width = self.num_patch
69.         self.height = self.size * self.size
70.         self._result = torch.zeros((self.width, self.height))
72.     @property
73.     def result(self):
74.         a_size = int(math.sqrt(self.num_patch))
75.         a_matrix = torch.arange(self.num_patch).reshape(a_size, a_size) + 1
76.         b_matrix = torch.ones(self.size, self.size)
77.         my_result = torch.kron(input=a_matrix, other=b_matrix)
78.         my_result = my_result[1:-1, 1:-1]
79.         roll_result = torch.roll(input=my_result, shifts=(-1, -1), dims=(-1, -2))
80.         print(f"roll_result=\n{roll_result}")
81.         roll_result = torch.roll(input=roll_result, shifts=(1, 1), dims=(-1, -2))
82.         self._result = roll_result
83.         return self._result
86. if __name__ == "__main__":
87.     run_code = 0
88.     my_window_matrix = WindowMatrix()
89.     my_window_matrix_result = my_window_matrix.result
90.     print(f"my_window_matrix_result=\n{my_window_matrix_result}")
91.     shifted_window_matrix = ShiftedWindowMatrix()
92.     shifed_window_matrix_result = shifted_window_matrix.result
93.     print(f"shifed_window_matrix_result=\n{shifed_window_matrix_result}")
94.     roll_shifted_window_matrix = RollShiftedWindowMatrix()
95.     roll_shifed_window_matrix_result = roll_shifted_window_matrix.result
96.     print(f"roll_shifed_window_matrix_result=\n{roll_shifed_window_matrix_result}")
97.     Back_roll_shifted_window_matrix = BackRollShiftedWindowMatrix()
98.     back_roll_shifed_window_matrix_result = Back_roll_shifted_window_matrix.result
99.     print(f"back_roll_shifed_window_matrix_result=\n{back_roll_shifed_window_matrix_result}")

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• 效果：
  

1. my_window_matrix_result=
2. tensor([[1., 1., 2., 2.],
3.         [1., 1., 2., 2.],
4.         [3., 3., 4., 4.],
5.         [3., 3., 4., 4.]])
6. shifed_window_matrix_result=
7. tensor([[1., 2., 2., 3.],
8.         [4., 5., 5., 6.],
9.         [4., 5., 5., 6.],
10.         [7., 8., 8., 9.]])
11. roll_shifed_window_matrix_result=
12. tensor([[5., 5., 6., 4.],
13.         [5., 5., 6., 4.],
14.         [8., 8., 9., 7.],
15.         [2., 2., 3., 1.]])
16. roll_result=
17. tensor([[5., 5., 6., 4.],
18.         [5., 5., 6., 4.],
19.         [8., 8., 9., 7.],
20.         [2., 2., 3., 1.]])
21. back_roll_shifed_window_matrix_result=
22. tensor([[1., 2., 2., 3.],
23.         [4., 5., 5., 6.],
24.         [4., 5., 5., 6.],
25.         [7., 8., 8., 9.]])

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