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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import kornia
class MyHomography(nn.Module):
def __init__(self, init_homo: torch.Tensor) -> None:
super().__init__()
self.homo = nn.Parameter(init_homo.clone().detach())
def forward(self) -> torch.Tensor:
return torch.unsqueeze(self.homo, dim=0)
class TestWarping:
# optimization
lr = 1e-3
num_iterations = 100
def test_smoke(self, device):
img_src_t: torch.Tensor = torch.rand(1, 3, 120, 120).to(device)
img_dst_t: torch.Tensor = torch.rand(1, 3, 120, 120).to(device)
init_homo: torch.Tensor = torch.from_numpy(
np.array([[0.0415, 1.2731, -1.1731], [-0.9094, 0.5072, 0.4272], [0.0762, 1.3981, 1.0646]])
).float()
height, width = img_dst_t.shape[-2:]
warper = kornia.geometry.transform.HomographyWarper(height, width)
dst_homo_src = MyHomography(init_homo=init_homo).to(device)
learning_rate = self.lr
optimizer = optim.Adam(dst_homo_src.parameters(), lr=learning_rate)
for _ in range(self.num_iterations):
# warp the reference image to the destiny with current homography
img_src_to_dst = warper(img_src_t, dst_homo_src())
# compute the photometric loss
loss = F.l1_loss(img_src_to_dst, img_dst_t)
optimizer.zero_grad()
loss.backward()
optimizer.step()
assert not bool(torch.isnan(dst_homo_src.homo.grad).any())
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