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import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.geometry.epipolar as epi
from kornia.testing import assert_close
class TestSkewSymmetric:
def test_smoke(self, device, dtype):
vec = torch.rand(1, 3, device=device, dtype=dtype)
cross_product_matrix = epi.cross_product_matrix(vec)
assert cross_product_matrix.shape == (1, 3, 3)
@pytest.mark.parametrize("batch_size", [1, 2, 4, 7])
def test_shape(self, batch_size, device, dtype):
B = batch_size
vec = torch.rand(B, 3, device=device, dtype=dtype)
cross_product_matrix = epi.cross_product_matrix(vec)
assert cross_product_matrix.shape == (B, 3, 3)
def test_mean_std(self, device, dtype):
vec = torch.tensor([[1.0, 2.0, 3.0]], device=device, dtype=dtype)
cross_product_matrix = epi.cross_product_matrix(vec)
assert_close(cross_product_matrix[..., 0, 1], -cross_product_matrix[..., 1, 0])
assert_close(cross_product_matrix[..., 0, 2], -cross_product_matrix[..., 2, 0])
assert_close(cross_product_matrix[..., 1, 2], -cross_product_matrix[..., 2, 1])
def test_gradcheck(self, device):
vec = torch.ones(2, 3, device=device, requires_grad=True, dtype=torch.float64)
assert gradcheck(epi.cross_product_matrix, (vec,), raise_exception=True)
class TestEyeLike:
def test_smoke(self, device, dtype):
image = torch.rand(1, 3, 4, 4, device=device, dtype=dtype)
identity = kornia.eye_like(3, image)
assert identity.shape == (1, 3, 3)
assert identity.device == image.device
assert identity.dtype == image.dtype
@pytest.mark.parametrize("batch_size, eye_size", [(1, 2), (2, 3), (3, 3), (2, 4)])
def test_shape(self, batch_size, eye_size, device, dtype):
B, N = batch_size, eye_size
image = torch.rand(B, 3, 4, 4, device=device, dtype=dtype)
identity = kornia.eye_like(N, image)
assert identity.shape == (B, N, N)
assert identity.device == image.device
assert identity.dtype == image.dtype
class TestVecLike:
def test_smoke(self, device, dtype):
image = torch.rand(1, 3, 4, 4, device=device, dtype=dtype)
vec = kornia.vec_like(3, image)
assert vec.shape == (1, 3, 1)
assert vec.device == image.device
assert vec.dtype == image.dtype
@pytest.mark.parametrize("batch_size, eye_size", [(1, 2), (2, 3), (3, 3), (2, 4)])
def test_shape(self, batch_size, eye_size, device, dtype):
B, N = batch_size, eye_size
image = torch.rand(B, 3, 4, 4, device=device, dtype=dtype)
vec = kornia.vec_like(N, image)
assert vec.shape == (B, N, 1)
assert vec.device == image.device
assert vec.dtype == image.dtype
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