| 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) |
| return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True) |
|
|
