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import pytest
import torch
from torch.autograd import gradcheck
import kornia
from kornia.testing import assert_close
from kornia.utils._compat import torch_version_geq
def _sample_points(batch_size, device, dtype=torch.float32):
src = torch.tensor([[[0.0, 0.0], [0.0, 10.0], [10.0, 0.0], [10.0, 10.0], [5.0, 5.0]]], device=device, dtype=dtype)
src = src.repeat(batch_size, 1, 1)
dst = src + torch.rand_like(src) * 2.5
return src, dst
class TestTransformParameters:
@pytest.mark.parametrize('batch_size', [1, 3])
def test_smoke(self, batch_size, device, dtype):
src = torch.rand(batch_size, 4, 2, device=device)
out = kornia.geometry.transform.get_tps_transform(src, src)
assert len(out) == 2
@pytest.mark.parametrize('batch_size', [1, 3])
def test_no_warp(self, batch_size, device, dtype):
src = torch.rand(batch_size, 5, 2, device=device)
kernel, affine = kornia.geometry.transform.get_tps_transform(src, src)
target_kernel = torch.zeros(batch_size, 5, 2, device=device)
target_affine = torch.zeros(batch_size, 3, 2, device=device)
target_affine[:, [1, 2], [0, 1]] = 1.0
assert_close(kernel, target_kernel, atol=1e-4, rtol=1e-4)
assert_close(affine, target_affine, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize('batch_size', [1, 3])
def test_affine_only(self, batch_size, device, dtype):
src = torch.tensor([[[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0], [0.5, 0.5]]], device=device).repeat(
batch_size, 1, 1
)
dst = src.clone() * 2.0
kernel, _ = kornia.geometry.transform.get_tps_transform(src, dst)
assert_close(kernel, torch.zeros_like(kernel), atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize('batch_size', [1, 3])
def test_exception(self, batch_size, device, dtype):
with pytest.raises(TypeError):
src = torch.rand(batch_size, 5, 2).numpy()
assert kornia.geometry.transform.get_tps_transform(src, src)
with pytest.raises(ValueError):
src = torch.rand(batch_size, 5)
assert kornia.geometry.transform.get_tps_transform(src, src)
@pytest.mark.grad
@pytest.mark.parametrize('batch_size', [1, 3])
@pytest.mark.parametrize('requires_grad', [True, False])
def test_gradcheck(self, batch_size, device, dtype, requires_grad):
opts = dict(device=device, dtype=torch.float64)
src, dst = _sample_points(batch_size, **opts)
src.requires_grad_(requires_grad)
dst.requires_grad_(not requires_grad)
assert gradcheck(kornia.geometry.transform.get_tps_transform, (src, dst), raise_exception=True)
@pytest.mark.jit
@pytest.mark.parametrize('batch_size', [1, 3])
def test_jit(self, batch_size, device, dtype):
src, dst = _sample_points(batch_size, device)
op = kornia.geometry.transform.get_tps_transform
op_jit = torch.jit.script(op)
op_output = op(src, dst)
jit_output = op_jit(src, dst)
assert_close(op_output[0], jit_output[0])
assert_close(op_output[1], jit_output[1])
class TestWarpPoints:
@pytest.mark.parametrize('batch_size', [1, 3])
def test_smoke(self, batch_size, device, dtype):
src, dst = _sample_points(batch_size, device)
kernel, affine = kornia.geometry.transform.get_tps_transform(src, dst)
warp = kornia.geometry.transform.warp_points_tps(src, dst, kernel, affine)
assert warp.shape == src.shape
@pytest.mark.parametrize('batch_size', [1, 3])
def test_warp(self, batch_size, device, dtype):
src, dst = _sample_points(batch_size, device)
kernel, affine = kornia.geometry.transform.get_tps_transform(src, dst)
warp = kornia.geometry.transform.warp_points_tps(src, dst, kernel, affine)
assert_close(warp, dst, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize('batch_size', [1, 3])
def test_exception(self, batch_size, device, dtype):
src = torch.rand(batch_size, 5, 2)
kernel = torch.zeros_like(src)
affine = torch.zeros(batch_size, 3, 2)
with pytest.raises(TypeError):
assert kornia.geometry.transform.warp_points_tps(src.numpy(), src, kernel, affine)
with pytest.raises(TypeError):
assert kornia.geometry.transform.warp_points_tps(src, src.numpy(), kernel, affine)
with pytest.raises(TypeError):
assert kornia.geometry.transform.warp_points_tps(src, src, kernel.numpy(), affine)
with pytest.raises(TypeError):
assert kornia.geometry.transform.warp_points_tps(src, src, kernel, affine.numpy())
with pytest.raises(ValueError):
src_bad = torch.rand(batch_size, 5)
assert kornia.geometry.transform.warp_points_tps(src_bad, src, kernel, affine)
with pytest.raises(ValueError):
src_bad = torch.rand(batch_size, 5)
assert kornia.geometry.transform.warp_points_tps(src, src_bad, kernel, affine)
with pytest.raises(ValueError):
kernel_bad = torch.rand(batch_size, 5)
assert kornia.geometry.transform.warp_points_tps(src, src, kernel_bad, affine)
with pytest.raises(ValueError):
affine_bad = torch.rand(batch_size, 3)
assert kornia.geometry.transform.warp_points_tps(src, src, kernel, affine_bad)
@pytest.mark.grad
@pytest.mark.parametrize('batch_size', [1, 3])
@pytest.mark.parametrize('requires_grad', [True, False])
def test_gradcheck(self, batch_size, device, dtype, requires_grad):
opts = dict(device=device, dtype=torch.float64)
src, dst = _sample_points(batch_size, **opts)
kernel, affine = kornia.geometry.transform.get_tps_transform(src, dst)
kernel.requires_grad_(requires_grad)
affine.requires_grad_(not requires_grad)
assert gradcheck(kornia.geometry.transform.warp_points_tps, (src, dst, kernel, affine), raise_exception=True)
@pytest.mark.jit
@pytest.mark.parametrize('batch_size', [1, 3])
def test_jit(self, batch_size, device, dtype):
src, dst = _sample_points(batch_size, device)
kernel, affine = kornia.geometry.transform.get_tps_transform(src, dst)
op = kornia.geometry.transform.warp_points_tps
op_jit = torch.jit.script(op)
assert_close(op(src, dst, kernel, affine), op_jit(src, dst, kernel, affine))
class TestWarpImage:
@pytest.mark.parametrize('batch_size', [1, 3])
def test_smoke(self, batch_size, device, dtype):
src, dst = _sample_points(batch_size, device)
tensor = torch.rand(batch_size, 3, 32, 32, device=device)
kernel, affine = kornia.geometry.transform.get_tps_transform(src, dst)
warp = kornia.geometry.transform.warp_image_tps(tensor, dst, kernel, affine)
assert warp.shape == tensor.shape
@pytest.mark.skipif(
torch_version_geq(1, 10),
reason="for some reason the solver detects singular matrices in pytorch >=1.10.")
@pytest.mark.parametrize('batch_size', [1, 3])
def test_warp(self, batch_size, device, dtype):
src = torch.tensor([[[-1.0, -1.0], [-1.0, 1.0], [1.0, -1.0], [1.0, -1.0], [0.0, 0.0]]], device=device).repeat(
batch_size, 1, 1
)
# zoom in by a factor of 2
dst = src.clone() * 2.0
tensor = torch.zeros(batch_size, 3, 8, 8, device=device)
tensor[:, :, 2:6, 2:6] = 1.0
expected = torch.ones_like(tensor)
# nn.grid_sample interpolates the at the edges it seems, so the boundaries have values < 1
expected[:, :, [0, -1], :] *= 0.5
expected[:, :, :, [0, -1]] *= 0.5
kernel, affine = kornia.geometry.transform.get_tps_transform(dst, src)
warp = kornia.geometry.transform.warp_image_tps(tensor, src, kernel, affine)
assert_close(warp, expected, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize('batch_size', [1, 3])
def test_exception(self, batch_size, device, dtype):
image = torch.rand(batch_size, 3, 32, 32)
dst = torch.rand(batch_size, 5, 2)
kernel = torch.zeros_like(dst)
affine = torch.zeros(batch_size, 3, 2)
with pytest.raises(TypeError):
assert kornia.geometry.transform.warp_image_tps(image.numpy(), dst, kernel, affine)
with pytest.raises(TypeError):
assert kornia.geometry.transform.warp_image_tps(image, dst.numpy(), kernel, affine)
with pytest.raises(TypeError):
assert kornia.geometry.transform.warp_image_tps(image, dst, kernel.numpy(), affine)
with pytest.raises(TypeError):
assert kornia.geometry.transform.warp_image_tps(image, dst, kernel, affine.numpy())
with pytest.raises(ValueError):
image_bad = torch.rand(batch_size, 32, 32)
assert kornia.geometry.transform.warp_image_tps(image_bad, dst, kernel, affine)
with pytest.raises(ValueError):
dst_bad = torch.rand(batch_size, 5)
assert kornia.geometry.transform.warp_image_tps(image, dst_bad, kernel, affine)
with pytest.raises(ValueError):
kernel_bad = torch.rand(batch_size, 5)
assert kornia.geometry.transform.warp_image_tps(image, dst, kernel_bad, affine)
with pytest.raises(ValueError):
affine_bad = torch.rand(batch_size, 3)
assert kornia.geometry.transform.warp_image_tps(image, dst, kernel, affine_bad)
@pytest.mark.grad
@pytest.mark.parametrize('batch_size', [1, 3])
def test_gradcheck(self, batch_size, device, dtype):
opts = dict(device=device, dtype=torch.float64)
src, dst = _sample_points(batch_size, **opts)
kernel, affine = kornia.geometry.transform.get_tps_transform(src, dst)
image = torch.rand(batch_size, 3, 8, 8, requires_grad=True, **opts)
assert gradcheck(
kornia.geometry.transform.warp_image_tps,
(image, dst, kernel, affine),
raise_exception=True,
atol=1e-4,
rtol=1e-4,
)
@pytest.mark.jit
@pytest.mark.parametrize('batch_size', [1, 3])
def test_jit(self, batch_size, device, dtype):
src, dst = _sample_points(batch_size, device)
kernel, affine = kornia.geometry.transform.get_tps_transform(src, dst)
image = torch.rand(batch_size, 3, 32, 32, device=device)
op = kornia.geometry.transform.warp_image_tps
op_jit = torch.jit.script(op)
assert_close(op(image, dst, kernel, affine), op_jit(image, dst, kernel, affine), rtol=1e-4, atol=1e-4)
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