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248 lines
12 KiB
Python
248 lines
12 KiB
Python
# LICENSE HEADER MANAGED BY add-license-header
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#
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# Copyright 2018 Kornia Team
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import pytest
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import torch
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from kornia.geometry.camera.projection_orthographic import (
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dx_project_points_orthographic,
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project_points_orthographic,
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unproject_points_orthographic,
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)
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from kornia.geometry.camera.projection_z1 import dx_project_points_z1, project_points_z1, unproject_points_z1
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from testing.base import BaseTester
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class TestProjectionZ1(BaseTester):
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def test_smoke(self, device, dtype):
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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assert project_points_z1(points) is not None
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def _test_cardinality_unproject_batch(self, device, dtype, batch_size):
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batch_tuple = (batch_size,) if batch_size is not None else ()
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points = torch.rand(batch_tuple + (3,), device=device, dtype=dtype)
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assert project_points_z1(points).shape == batch_tuple + (2,)
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def _test_cardinality_project_batch(self, device, dtype, batch_size):
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batch_tuple = (batch_size,) if batch_size is not None else ()
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points = torch.rand(batch_tuple + (2,), device=device, dtype=dtype)
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assert unproject_points_z1(points).shape == batch_tuple + (3,)
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@pytest.mark.parametrize("batch_size", [None, 1, 2, 3])
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def test_cardinality(self, device, dtype, batch_size):
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self._test_cardinality_project_batch(device, dtype, batch_size)
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self._test_cardinality_unproject_batch(device, dtype, batch_size)
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def test_project_points_z1(self, device, dtype):
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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expected = torch.tensor([0.3333333432674408, 0.6666666865348816], device=device, dtype=dtype)
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self.assert_close(project_points_z1(points), expected)
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def test_project_points_z1_batch(self, device, dtype):
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points = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], device=device, dtype=dtype)
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expected = torch.tensor(
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[
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[0.3333333432674408, 0.6666666865348816],
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[0.6666666865348816, 0.8333333730697632],
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],
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device=device,
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dtype=dtype,
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)
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self.assert_close(project_points_z1(points), expected)
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def test_project_points_z1_invalid(self, device, dtype):
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# NOTE: this is a corner case where the depth is 0.0 and the point is at infinity
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# the projection is not defined and the function returns inf. The second point
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# is behind the camera which is not a valid point and the user should handle it.
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points = torch.tensor([[1.0, 2.0, 0.0], [4.0, 5.0, -1.0]], device=device, dtype=dtype)
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expected = torch.tensor([[float("inf"), float("inf")], [-4.0, -5.0]], device=device, dtype=dtype)
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self.assert_close(project_points_z1(points), expected)
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def test_unproject_points_z1(self, device, dtype):
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points = torch.tensor([1.0, 2.0], device=device, dtype=dtype)
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expected = torch.tensor([1.0, 2.0, 1.0], device=device, dtype=dtype)
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self.assert_close(unproject_points_z1(points), expected)
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def test_unproject_points_z1_batch(self, device, dtype):
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points = torch.tensor([[1.0, 2.0], [3.0, 4.0]], device=device, dtype=dtype)
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expected = torch.tensor([[1.0, 2.0, 1.0], [3.0, 4.0, 1.0]], device=device, dtype=dtype)
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self.assert_close(unproject_points_z1(points), expected)
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def test_project_unproject(self, device, dtype):
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points = torch.tensor([1.0, 2.0, 2.0], device=device, dtype=dtype)
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extension = torch.tensor([2.0], device=device, dtype=dtype)
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self.assert_close(unproject_points_z1(project_points_z1(points), extension), points)
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def test_unproject_points_z1_extension(self, device, dtype):
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points = torch.tensor([1.0, 2.0], device=device, dtype=dtype)
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extension = torch.tensor([2.0], device=device, dtype=dtype)
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expected = torch.tensor([2.0, 4.0, 2.0], device=device, dtype=dtype)
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self.assert_close(unproject_points_z1(points, extension), expected)
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def test_unproject_points_z1_batch_extension(self, device, dtype):
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points = torch.tensor([[1.0, 2.0], [3.0, 4.0]], device=device, dtype=dtype)
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extension = torch.tensor([2.0, 3.0], device=device, dtype=dtype)
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expected = torch.tensor([[2.0, 4.0, 2.0], [9.0, 12.0, 3.0]], device=device, dtype=dtype)
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self.assert_close(unproject_points_z1(points, extension), expected)
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def test_dx_proj_x(self, device, dtype):
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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expected = torch.tensor(
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[
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[0.3333333432674408, 0.0, -0.1111111119389534],
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[0.0, 0.3333333432674408, -0.2222222238779068],
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],
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device=device,
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dtype=dtype,
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)
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self.assert_close(dx_project_points_z1(points), expected)
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def test_exception(self, device, dtype) -> None:
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from kornia.core.exceptions import ShapeError
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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extension = torch.tensor([2.0], device=device, dtype=dtype)
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with pytest.raises(ShapeError):
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unproject_points_z1(points, extension)
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def _test_gradcheck_unproject(self, device):
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points = torch.tensor([1.0, 2.0], device=device, dtype=torch.float64)
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extension = torch.tensor([2.0], device=device, dtype=torch.float64)
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self.gradcheck(unproject_points_z1, (points, extension))
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def _test_gradcheck_project(self, device):
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=torch.float64)
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self.gradcheck(project_points_z1, (points,))
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def test_gradcheck(self, device) -> None:
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self._test_gradcheck_project(device)
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self._test_gradcheck_unproject(device)
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def _test_jit_unproject(self, device, dtype) -> None:
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points = torch.tensor([1.0, 2.0], device=device, dtype=dtype)
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extension = torch.tensor([2.0], device=device, dtype=dtype)
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op_script = torch.jit.script(unproject_points_z1)
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actual = op_script(points, extension)
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expected = unproject_points_z1(points, extension)
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self.assert_close(actual, expected)
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def _test_jit_project(self, device, dtype) -> None:
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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op_script = torch.jit.script(project_points_z1)
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actual = op_script(points)
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expected = project_points_z1(points)
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self.assert_close(actual, expected)
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def test_jit(self, device, dtype) -> None:
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self._test_jit_project(device, dtype)
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self._test_jit_unproject(device, dtype)
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class TestProjectionOrthographic(BaseTester):
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def test_smoke(self, device, dtype):
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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assert project_points_orthographic(points) is not None
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def _test_cardinality_unproject_batch(self, device, dtype, batch_size):
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batch_tuple = (batch_size,) if batch_size is not None else ()
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points = torch.rand(batch_tuple + (3,), device=device, dtype=dtype)
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assert project_points_orthographic(points).shape == batch_tuple + (2,)
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def _test_cardinality_project_batch(self, device, dtype, batch_size):
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batch_tuple = (batch_size,) if batch_size is not None else ()
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points = torch.rand(batch_tuple + (2,), device=device, dtype=dtype)
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extension = torch.rand(batch_tuple, device=device, dtype=dtype)
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assert unproject_points_orthographic(points, extension).shape == batch_tuple + (3,)
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@pytest.mark.parametrize("batch_size", [None, 1, 2, 3])
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def test_cardinality(self, device, dtype, batch_size):
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self._test_cardinality_project_batch(device, dtype, batch_size)
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self._test_cardinality_unproject_batch(device, dtype, batch_size)
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def test_project_points_orthographic(self, device, dtype):
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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expected = torch.tensor([1.0, 2.0], device=device, dtype=dtype)
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self.assert_close(project_points_orthographic(points), expected)
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def test_project_points_orthographic_batch(self, device, dtype):
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points = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], device=device, dtype=dtype)
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expected = torch.tensor([[1.0, 2.0], [4.0, 5.0]], device=device, dtype=dtype)
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self.assert_close(project_points_orthographic(points), expected)
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def test_unproject_points_orthographic_extension(self, device, dtype):
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points = torch.tensor([1.0, 2.0], device=device, dtype=dtype)
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extension = torch.tensor([2.0], device=device, dtype=dtype)
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expected = torch.tensor([1.0, 2.0, 2.0], device=device, dtype=dtype)
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self.assert_close(unproject_points_orthographic(points, extension), expected)
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def test_unproject_points_orthographic_batch_extension(self, device, dtype):
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points = torch.tensor([[1.0, 2.0], [3.0, 4.0]], device=device, dtype=dtype)
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extension = torch.tensor([2.0, 3.0], device=device, dtype=dtype)
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expected = torch.tensor([[1.0, 2.0, 2.0], [3.0, 4.0, 3.0]], device=device, dtype=dtype)
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self.assert_close(unproject_points_orthographic(points, extension), expected)
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def test_project_unproject(self, device, dtype):
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points = torch.tensor([1.0, 2.0, 2.0], device=device, dtype=dtype)
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extension = torch.tensor([2.0], device=device, dtype=dtype)
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self.assert_close(unproject_points_orthographic(project_points_orthographic(points), extension), points)
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def test_dx_proj_x(self, device, dtype):
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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expected = torch.tensor([1.0], device=device, dtype=dtype)
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self.assert_close(dx_project_points_orthographic(points), expected)
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def test_exception(self, device, dtype) -> None:
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from kornia.core.exceptions import ShapeError
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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extension = torch.tensor([2.0], device=device, dtype=dtype)
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with pytest.raises(ShapeError):
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unproject_points_orthographic(points, extension)
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def _test_gradcheck_project(self, device):
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=torch.float64)
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self.gradcheck(project_points_orthographic, (points,))
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def _test_gradcheck_unproject(self, device):
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points = torch.tensor([1.0, 2.0], device=device, dtype=torch.float64)
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extension = torch.tensor([2.0], device=device, dtype=torch.float64)
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self.gradcheck(unproject_points_orthographic, (points, extension))
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def test_gradcheck(self, device) -> None:
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self._test_gradcheck_project(device)
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self._test_gradcheck_unproject(device)
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def _test_jit_project(self, device, dtype) -> None:
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points = torch.tensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
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op_script = torch.jit.script(project_points_orthographic)
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actual = op_script(points)
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expected = project_points_orthographic(points)
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self.assert_close(actual, expected)
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def _test_jit_unproject(self, device, dtype) -> None:
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points = torch.tensor([1.0, 2.0], device=device, dtype=dtype)
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extension = torch.tensor([2.0], device=device, dtype=dtype)
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op_script = torch.jit.script(unproject_points_orthographic)
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actual = op_script(points, extension)
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expected = unproject_points_orthographic(points, extension)
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self.assert_close(actual, expected)
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def test_jit(self, device, dtype) -> None:
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self._test_jit_project(device, dtype)
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self._test_jit_unproject(device, dtype)
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