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import pytest
import torch
from kornia.testing import assert_close
from kornia.utils import _extract_device_dtype
from kornia.utils.helpers import (
_torch_histc_cast,
_torch_inverse_cast,
_torch_solve_cast,
_torch_svd_cast,
safe_inverse_with_mask,
safe_solve_with_mask,
)
@pytest.mark.parametrize(
"tensor_list,out_device,out_dtype,will_throw_error",
[
([], torch.device('cpu'), torch.get_default_dtype(), False),
([None, None], torch.device('cpu'), torch.get_default_dtype(), False),
([torch.tensor(0, device='cpu', dtype=torch.float16), None], torch.device('cpu'), torch.float16, False),
([torch.tensor(0, device='cpu', dtype=torch.float32), None], torch.device('cpu'), torch.float32, False),
([torch.tensor(0, device='cpu', dtype=torch.float64), None], torch.device('cpu'), torch.float64, False),
([torch.tensor(0, device='cpu', dtype=torch.float16)] * 2, torch.device('cpu'), torch.float16, False),
([torch.tensor(0, device='cpu', dtype=torch.float32)] * 2, torch.device('cpu'), torch.float32, False),
([torch.tensor(0, device='cpu', dtype=torch.float64)] * 2, torch.device('cpu'), torch.float64, False),
(
[torch.tensor(0, device='cpu', dtype=torch.float16), torch.tensor(0, device='cpu', dtype=torch.float64)],
None,
None,
True,
),
(
[torch.tensor(0, device='cpu', dtype=torch.float32), torch.tensor(0, device='cpu', dtype=torch.float64)],
None,
None,
True,
),
(
[torch.tensor(0, device='cpu', dtype=torch.float16), torch.tensor(0, device='cpu', dtype=torch.float32)],
None,
None,
True,
),
],
)
def test_extract_device_dtype(tensor_list, out_device, out_dtype, will_throw_error):
# TODO: include the warning in another way - possibly loggers.
# Add GPU tests when GPU testing available
# if torch.cuda.is_available():
# import warnings
# warnings.warn("Add GPU tests.")
if will_throw_error:
with pytest.raises(ValueError):
_extract_device_dtype(tensor_list)
else:
device, dtype = _extract_device_dtype(tensor_list)
assert device == out_device
assert dtype == out_dtype
class TestInverseCast:
@pytest.mark.parametrize("input_shape", [(1, 3, 4, 4), (2, 4, 5, 5)])
def test_smoke(self, device, dtype, input_shape):
x = torch.rand(input_shape, device=device, dtype=dtype)
y = _torch_inverse_cast(x)
assert y.shape == x.shape
def test_values(self, device, dtype):
x = torch.tensor([[4.0, 7.0], [2.0, 6.0]], device=device, dtype=dtype)
y_expected = torch.tensor([[0.6, -0.7], [-0.2, 0.4]], device=device, dtype=dtype)
y = _torch_inverse_cast(x)
assert_close(y, y_expected)
def test_jit(self, device, dtype):
x = torch.rand(1, 3, 4, 4, device=device, dtype=dtype)
op = _torch_inverse_cast
op_jit = torch.jit.script(op)
assert_close(op(x), op_jit(x))
class TestHistcCast:
def test_smoke(self, device, dtype):
x = torch.tensor([1.0, 2.0, 1.0], device=device, dtype=dtype)
y_expected = torch.tensor([0.0, 2.0, 1.0, 0.0], device=device, dtype=dtype)
y = _torch_histc_cast(x, bins=4, min=0, max=3)
assert_close(y, y_expected)
class TestSvdCast:
def test_smoke(self, device, dtype):
a = torch.randn(5, 3, 3, device=device, dtype=dtype)
u, s, v = _torch_svd_cast(a)
tol_val: float = 1e-1 if dtype == torch.float16 else 1e-3
assert_close(a, u @ torch.diag_embed(s) @ v.transpose(-2, -1), atol=tol_val, rtol=tol_val)
class TestSolveCast:
def test_smoke(self, device, dtype):
A = torch.randn(2, 3, 1, 4, 4, device=device, dtype=dtype)
B = torch.randn(2, 3, 1, 4, 6, device=device, dtype=dtype)
X, _ = _torch_solve_cast(B, A)
error = torch.dist(B, A.matmul(X))
tol_val: float = 1e-1 if dtype == torch.float16 else 1e-4
assert_close(error, torch.zeros_like(error), atol=tol_val, rtol=tol_val)
class TestSolveWithMask:
def test_smoke(self, device, dtype):
A = torch.randn(2, 3, 1, 4, 4, device=device, dtype=dtype)
B = torch.randn(2, 3, 1, 4, 6, device=device, dtype=dtype)
X, _, mask = safe_solve_with_mask(B, A)
X2, _ = _torch_solve_cast(B, A)
tol_val: float = 1e-1 if dtype == torch.float16 else 1e-4
if mask.sum() > 0:
assert_close(X[mask], X2[mask], atol=tol_val, rtol=tol_val)
@pytest.mark.skipif(
(int(torch.__version__.split('.')[0]) == 1) and (int(torch.__version__.split('.')[1]) < 10),
reason='<1.10.0 not supporting',
)
def test_all_bad(self, device, dtype):
A = torch.ones(10, 3, 3, device=device, dtype=dtype)
B = torch.ones(3, 10, device=device, dtype=dtype)
X, _, mask = safe_solve_with_mask(B, A)
assert torch.equal(mask, torch.zeros_like(mask))
class TestInverseWithMask:
def test_smoke(self, device, dtype):
x = torch.tensor([[4.0, 7.0], [2.0, 6.0]], device=device, dtype=dtype)
y_expected = torch.tensor([[0.6, -0.7], [-0.2, 0.4]], device=device, dtype=dtype)
y, mask = safe_inverse_with_mask(x)
assert_close(y, y_expected)
assert torch.equal(mask, torch.ones_like(mask))
@pytest.mark.skipif(
(int(torch.__version__.split('.')[0]) == 1) and (int(torch.__version__.split('.')[1]) < 9),
reason='<1.9.0 not supporting',
)
def test_all_bad(self, device, dtype):
A = torch.ones(10, 3, 3, device=device, dtype=dtype)
X, mask = safe_inverse_with_mask(A)
assert torch.equal(mask, torch.zeros_like(mask))
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