def test_local_homography_warper(self):
# generate input data
batch_size = 1
height, width = 16, 32
eye_size = 3 # identity 3x3
# create checkerboard
board = utils.create_checkerboard(height, width, 4)
patch_src = torch.from_numpy(board).view(
1, 1, height, width).expand(batch_size, 1, height, width)
# create local homography
dst_homo_src = utils.create_eye_batch(batch_size, eye_size)
dst_homo_src = dst_homo_src.view(batch_size, -1).unsqueeze(1)
dst_homo_src = dst_homo_src.repeat(1, height * width, 1).view(
1, height, width, 3, 3) # NxHxWx3x3
# warp reference patch
patch_src_to_i = tgm.homography_warp(
patch_src, dst_homo_src, (height, width))
def test_homography_warper(self, batch_size, device_type):
# generate input data
height, width = 128, 64
eye_size = 3 # identity 3x3
device = torch.device(device_type)
# create checkerboard
board = utils.create_checkerboard(height, width, 4)
patch_src = torch.from_numpy(board).view(1, 1, height, width).expand(
batch_size, 1, height, width)
patch_src = patch_src.to(device)
# create base homography
dst_homo_src = utils.create_eye_batch(batch_size, eye_size).to(device)
# instantiate warper
warper = tgm.HomographyWarper(height, width)
for i in range(self.num_tests):
# generate homography noise
homo_delta = torch.zeros_like(dst_homo_src)
homo_delta[:, -1, -1] = 0.0
dst_homo_src_i = dst_homo_src + homo_delta
# transform the points from dst to ref
patch_dst = warper(patch_src, dst_homo_src_i)
patch_dst_to_src = warper(patch_dst, torch.inverse(dst_homo_src_i))
# projected should be equal as initial
error = utils.compute_patch_error(patch_dst, patch_dst_to_src,
height, width)
assert error.item() < self.threshold
# check functional api
patch_dst_to_src_functional = tgm.homography_warp(
patch_dst, torch.inverse(dst_homo_src_i), (height, width))
assert utils.check_equal_torch(patch_dst_to_src,
patch_dst_to_src_functional)