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#!/usr/bin/env python
import torch
##########################################################
objBackwarpcache = {}
def backwarp(tenIn:torch.Tensor, tenFlow:torch.Tensor):
if 'grid' + str(tenFlow.dtype) + str(tenFlow.device) + str(tenFlow.shape[2]) + str(tenFlow.shape[3]) not in objBackwarpcache:
tenHor = torch.linspace(start=-1.0, end=1.0, steps=tenFlow.shape[3], dtype=tenFlow.dtype, device=tenFlow.device).view(1, 1, 1, -1).repeat(1, 1, tenFlow.shape[2], 1)
tenVer = torch.linspace(start=-1.0, end=1.0, steps=tenFlow.shape[2], dtype=tenFlow.dtype, device=tenFlow.device).view(1, 1, -1, 1).repeat(1, 1, 1, tenFlow.shape[3])
objBackwarpcache['grid' + str(tenFlow.dtype) + str(tenFlow.device) + str(tenFlow.shape[2]) + str(tenFlow.shape[3])] = torch.cat([tenHor, tenVer], 1)
# end
if tenFlow.shape[3] == tenFlow.shape[2]:
tenFlow = tenFlow * (2.0 / ((tenFlow.shape[3] and tenFlow.shape[2]) - 1.0))
elif tenFlow.shape[3] != tenFlow.shape[2]:
tenFlow = tenFlow * torch.tensor(data=[2.0 / (tenFlow.shape[3] - 1.0), 2.0 / (tenFlow.shape[2] - 1.0)], dtype=tenFlow.dtype, device=tenFlow.device).view(1, 2, 1, 1)
# end
return torch.nn.functional.grid_sample(input=tenIn, grid=(objBackwarpcache['grid' + str(tenFlow.dtype) + str(tenFlow.device) + str(tenFlow.shape[2]) + str(tenFlow.shape[3])] + tenFlow).permute(0, 2, 3, 1), mode='bilinear', padding_mode='zeros', align_corners=True)
# end
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