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chore (CI): ensure support to pytorch 2.3.0 (kornia#2912)
* chore (CI): ensure support to pytorch 2.3.0 * chore: skip specific dynamo tests for torch 2.3.0 - Report in pytorch/pytorch#126617 * chore: skip specific dynamo tests for torch 2.3.0 - Report in pytorch/pytorch#126619
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‎.github/workflows/pr_test_cpu.yml

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@@ -20,11 +20,11 @@ jobs:
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os: ['Ubuntu-latest', 'Windows-latest']
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pytorch-dtype: ['float32', 'float64']
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uses: kornia/workflows/.github/workflows/tests.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/tests.yml@v1.9.0
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with:
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os: ${{ matrix.os }}
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python-version: '["3.8", "3.11"]'
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pytorch-version: '["1.9.1", "2.2.2"]'
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pytorch-version: '["1.9.1", "2.3.0"]'
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pytorch-dtype: ${{ matrix.pytorch-dtype }}
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tests-cpu-macos:
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coverage:
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uses: kornia/workflows/.github/workflows/coverage.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/coverage.yml@v1.9.0
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typing:
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uses: kornia/workflows/.github/workflows/mypy.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/mypy.yml@v1.9.0
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tutorials:
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uses: kornia/workflows/.github/workflows/tutorials.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/tutorials.yml@v1.9.0
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docs:
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uses: kornia/workflows/.github/workflows/docs.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/docs.yml@v1.9.0
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collector:
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needs: [coverage, tests-cpu, tutorials, typing, docs]
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os: ['Ubuntu-latest', 'Windows-latest'] #, 'MacOS-latest'] add it when https://github.com/pytorch/pytorch/pull/89262 be merged
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pytorch-dtype: ['float32', 'float64']
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uses: kornia/workflows/.github/workflows/tests.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/tests.yml@v1.9.0
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with:
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os: ${{ matrix.os }}
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pytorch-version: '["nightly"]'

‎.github/workflows/scheduled_test_cpu.yml

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# os: ['Ubuntu-latest', 'Windows-latest', 'MacOS-latest']
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pytorch-dtype: ['float32', 'float64']
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uses: kornia/workflows/.github/workflows/tests.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/tests.yml@v1.9.0
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with:
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os: 'Ubuntu-latest'
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python-version: '["3.8", "3.9", "3.10", "3.11"]'
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pytorch-version: '["1.9.1", "1.10.2", "1.11.0", "1.12.1", "1.13.1", "2.0.1", "2.1.2", "2.2.2"]'
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pytorch-version: '["1.9.1", "1.10.2", "1.11.0", "1.12.1", "1.13.1", "2.0.1", "2.1.2", "2.2.2", "2.3.0"]'
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pytorch-dtype: ${{ matrix.pytorch-dtype }}
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pytest-extra: '--runslow'
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matrix:
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pytorch-dtype: ['float32', 'float64']
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uses: kornia/workflows/.github/workflows/tests.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/tests.yml@v1.9.0
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with:
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os: 'Windows-latest'
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python-version: '["3.11"]'
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pytorch-version: '["1.9.1", "2.2.2"]'
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pytorch-version: '["1.9.1", "2.3.0"]'
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pytorch-dtype: ${{ matrix.pytorch-dtype }}
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tests-cpu-mac:
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matrix:
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pytorch-dtype: ['float32', 'float64']
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uses: kornia/workflows/.github/workflows/tests.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/tests.yml@v1.9.0
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with:
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os: 'MacOS-latest'
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pytorch-dtype: ${{ matrix.pytorch-dtype }}
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coverage:
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uses: kornia/workflows/.github/workflows/coverage.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/coverage.yml@v1.9.0
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typing:
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uses: kornia/workflows/.github/workflows/mypy.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/mypy.yml@v1.9.0
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tutorials:
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uses: kornia/workflows/.github/workflows/tutorials.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/tutorials.yml@v1.9.0
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docs:
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uses: kornia/workflows/.github/workflows/docs.yml@v1.8.1
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uses: kornia/workflows/.github/workflows/docs.yml@v1.9.0

‎tests/filters/test_in_range.py

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@@ -4,6 +4,7 @@
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import torch
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from kornia.filters import InRange, in_range
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from kornia.utils._compat import torch_version
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from testing.base import BaseTester, assert_close
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@pytest.mark.parametrize("batch_size", [1, 2])
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def test_dynamo(self, batch_size, device, dtype, torch_optimizer):
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if device == torch.device("cpu") and torch_version() == "2.3.0":
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pytest.skip("Failing to compile on CPU see pytorch/pytorch#126619")
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inpt = torch.rand(batch_size, 3, 5, 5, device=device, dtype=dtype)
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op = InRange(lower=(0.2, 0.2, 0.2), upper=(0.6, 0.6, 0.6), return_mask=True)
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op_optimized = torch_optimizer(op)

‎tests/geometry/transform/test_homography_warper.py

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@@ -262,6 +262,9 @@ def test_gradcheck(self, batch_shape, device):
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@pytest.mark.parametrize("align_corners", [True, False])
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@pytest.mark.parametrize("normalized_coordinates", [True, False])
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def test_dynamo(self, batch_size, align_corners, normalized_coordinates, device, dtype, torch_optimizer):
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if device == torch.device("cpu") and batch_size != 1 and kornia.utils._compat.torch_version() == "2.3.0":
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pytest.skip("Failing to compile batched inputs see pytorch/pytorch#126617")
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# generate input data
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height, width = 128, 64
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eye_size = 3 # identity 3x3
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# create base homography
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dst_homo_src = eye_like(eye_size, patch_src)
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for _ in range(self.num_tests):
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# generate homography noise
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homo_delta = torch.rand_like(dst_homo_src) * 0.3
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# generate homography noise
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homo_delta = torch.rand_like(dst_homo_src) * 0.3
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dst_homo_src_i = dst_homo_src + homo_delta
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dst_homo_src_i = dst_homo_src + homo_delta
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# transform the points with and without jit
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patch_dst = kornia.geometry.transform.homography_warp(
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patch_src,
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dst_homo_src_i,
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(height, width),
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align_corners=align_corners,
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normalized_coordinates=normalized_coordinates,
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)
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patch_dst_optimized = torch_optimizer(kornia.geometry.transform.homography_warp)(
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patch_src,
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dst_homo_src_i,
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(height, width),
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align_corners=align_corners,
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normalized_coordinates=normalized_coordinates,
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)
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# transform the points with and without jit
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patch_dst = kornia.geometry.transform.homography_warp(
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patch_src,
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dst_homo_src_i,
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(height, width),
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align_corners=align_corners,
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normalized_coordinates=normalized_coordinates,
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)
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patch_dst_optimized = torch_optimizer(kornia.geometry.transform.homography_warp)(
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patch_src,
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dst_homo_src_i,
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(height, width),
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align_corners=align_corners,
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normalized_coordinates=normalized_coordinates,
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)
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self.assert_close(patch_dst, patch_dst_optimized, atol=1e-4, rtol=1e-4)
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self.assert_close(patch_dst, patch_dst_optimized, atol=1e-4, rtol=1e-4)
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class TestHomographyNormalTransform(BaseTester):

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