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