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import torch
import torch.nn as nn
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
backwarp_tenGrid = {}
def warp(tenInput, tenFlow):
k = (str(tenFlow.device), str(tenFlow.size()))
if k not in backwarp_tenGrid:
tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(
1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(
1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
backwarp_tenGrid[k] = torch.cat(
[tenHorizontal, tenVertical], 1).to(device)
tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1).to(dtype=tenInput.dtype)
return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)
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