torch.meshgrid 的函数原型是

torch.meshgrid(*tensors, indexing=None)

indexing 是 torch.meshgrid 的一个参数。

torch.meshgrid 的功能是生成 “网格数据”，比如生成一张图像的所有像素坐标。

本文，以高度为 4，宽度为 7（即 H=4，W=7）的图像为例子说明。

返回值不同

import torchH = 4
W = 7H_arange = torch.arange(0,H)
W_arange = torch.arange(0,W)print("\nH: 4,    W:7\n")
grid_i, grid_j = torch.meshgrid(W_arange, H_arange ,indexing='ij')
print("grid_i shape: ",grid_i.shape,"      grid_j shape: ",grid_j.shape)
print("grid_i:\n",grid_i,"\n","grid_j:\n",grid_j,'\n')grid_x, grid_y = torch.meshgrid(W_arange, H_arange ,indexing='xy')
print("grid_x shape: ",grid_x.shape,"      grid_y shape: ",grid_y.shape)
print("grid_x:\n",grid_x,"\n","grid_y:\n",grid_y,'\n')

indexing=‘ij’

当设 indexing='ij'
返回的：
grid_i shape: torch.Size([7, 4])
grid_j shape: torch.Size([7, 4])

他们的shape都是 [W,H]
shape 第 0 维是 W，第 1 维是 H
这跟输入 torch.meshgrid 参数 W_arange, H_arange的相对顺序是一致的。

相关代码输出如下：


grid_i:tensor([[0, 0, 0, 0],[1, 1, 1, 1],[2, 2, 2, 2],[3, 3, 3, 3],[4, 4, 4, 4],[5, 5, 5, 5],[6, 6, 6, 6]]) grid_j:tensor([[0, 1, 2, 3],[0, 1, 2, 3],[0, 1, 2, 3],[0, 1, 2, 3],[0, 1, 2, 3],[0, 1, 2, 3],[0, 1, 2, 3]])

indexing=‘xy’

当设 indexing='xy'
grid_x shape: torch.Size([4, 7])
grid_y shape: torch.Size([4, 7])

他们的shape都是 [H,W]

shape 第 1 维是 W，第 0 维是 H
这跟输入 torch.meshgrid 参数 W_arange, H_arange的相对顺序是相反的。

相关代码输出如下：

grid_x shape:  torch.Size([4, 7])       grid_y shape:  torch.Size([4, 7])
grid_x:tensor([[0, 1, 2, 3, 4, 5, 6],[0, 1, 2, 3, 4, 5, 6],[0, 1, 2, 3, 4, 5, 6],[0, 1, 2, 3, 4, 5, 6]]) grid_y:tensor([[0, 0, 0, 0, 0, 0, 0],[1, 1, 1, 1, 1, 1, 1],[2, 2, 2, 2, 2, 2, 2],[3, 3, 3, 3, 3, 3, 3]])

进一步生成像素坐标

indexing=‘ij’

代码

# OpenCV Convention: uv first column then row
coords_ij_col_row = torch.stack([grid_i,grid_j], dim=-1).reshape(-1, 2)
print(coords_ij_col_row)# Matrix Index Convention: first row then column
coords_ij_row_col = torch.stack([grid_j,grid_i], dim=-1).reshape(-1, 2)
print(coords_ij_row_col)

输出：

# OpenCV Convention: uv first column then row
tensor([[0, 0],[0, 1],[0, 2],[0, 3],......[6, 0],[6, 1],[6, 2],[6, 3]])
# Matrix Index Convention: first row then column
tensor([[0, 0],[1, 0],[2, 0],[3, 0],......[0, 6],[1, 6],[2, 6],[3, 6]])Process finished with exit code 0

indexing=‘xy’

# OpenCV Convention: uv first column then row
coords_xy_col_row = torch.stack([grid_x,grid_y], dim=-1).reshape(-1, 2)
print(coords_xy_col_row)# Matrix Index Convention: first row then column
coords_xy_row_col = torch.stack([grid_y,grid_x], dim=-1).reshape(-1, 2)
print(coords_xy_row_col)

输出：

# OpenCV Convention: uv first column then row
tensor([[0, 0],[1, 0],[2, 0],[3, 0],[4, 0],[5, 0],[6, 0],......[0, 3],[1, 3],[2, 3],[3, 3],[4, 3],[5, 3],[6, 3]])# Matrix Index Convention: first row then column
tensor([[0, 0],[0, 1],[0, 2],[0, 3],[0, 4],[0, 5],[0, 6],
......[3, 0],[3, 1],[3, 2],[3, 3],[3, 4],[3, 5],[3, 6]])Process finished with exit code 0

如果不认为设置indexing参数，pytorch 默认 indexing = ‘ij’

如果不设置，pytorch 会报错

UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument.

总结

根据矩阵索引传统，先 row 后 column，那就要选择indexing = ‘xy’

grid_x, grid_y = torch.meshgrid(W_arange, H_arange ,indexing='xy')
# Matrix Index Convention: first row then column
coords_xy_row_col = torch.stack([grid_y,grid_x], dim=-1).reshape(-1, 2)
print(coords_xy_row_col)

根据OpenCV索引传统，先 column 后row ，那就要选择indexing = ‘ij’

grid_i, grid_j = torch.meshgrid(W_arange, H_arange ,indexing='ij')
# OpenCV Convention: uv first column then row
coords_ij_col_row = torch.stack([grid_i,grid_j], dim=-1).reshape(-1, 2)
print(coords_ij_col_row)

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返回值不同

indexing=‘ij’

indexing=‘xy’

进一步生成像素坐标

indexing=‘ij’

indexing=‘xy’

如果不认为设置indexing参数，pytorch 默认 indexing = ‘ij’

总结

根据矩阵索引传统，先 row 后 column，那就要选择indexing = ‘xy’

根据OpenCV索引传统，先 column 后row ，那就要选择indexing = ‘ij’

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