PyTorch中grid_sample的使用及说明

torch.nn.functional.grid_sample(input, grid, mode=‘bilinear', padding_mode=‘zeros')

import torch
from torch.nn import functional as F


inp = torch.ones(1, 1, 4, 4)

# 目的是得到一个 长宽为20的tensor
out_h = 20
out_w = 20
 # grid的生成方式等价于用mesh_grid
new_h = torch.linspace(-1, 1, out_h).view(-1, 1).repeat(1, out_w)
new_w = torch.linspace(-1, 1, out_w).repeat(out_h, 1)
grid = torch.cat((new_h.unsqueeze(2), new_w.unsqueeze(2)), dim=2)
grid = grid.unsqueeze(0)

outp = F.grid_sample(inp, grid=grid, mode='bilinear')
print(outp.shape)  #torch.Size([1, 1, 20, 20])

        real ix = THTensor_fastGet4d(grid, n, h, w, 0);
        real iy = THTensor_fastGet4d(grid, n, h, w, 1);

        // normalize ix, iy from [-1, 1] to [0, IH-1] & [0, IW-1]
        ix = ((ix + 1) / 2) * (IW-1);
        iy = ((iy + 1) / 2) * (IH-1);

F.grid_sample(img, grid, align_corners=True)

dx = torch.linspace(-1,1, 9)
dy = torch.linspace(-1, 1,7)
coords = torch.stack(torch.meshgrid(dy, dx), axis=-1)   #[dy*dx*2]

img.shape : [B,C,H_in,W_in]
grid.shape: [B,H_out,W_out,2]
out: [B,C,H_out,W_out]