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Merge pull request #66 from Talmaj/talmaj/updates
Fix instance norm tests Update pre-commit config Update tox.ini Fix test_clip Add lint_and_test GitHub action Remove circleci
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‎.circleci/config.yml‎

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‎.github/workflows/lint_and_test.yml‎

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name: Lint and Test
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on: [pull_request]
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [ '3.9', '3.10', '3.11', '3.12' ]
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Cache dependencies
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uses: actions/cache@v4
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with:
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path: ~/.cache/pip
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key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('**/requirements.txt') }}
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restore-keys: |
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${{ runner.os }}-pip-${{ matrix.python-version }}-
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${{ runner.os }}-pip-
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- name: Install dependencies
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run: |
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pip install tox tox-gh-actions
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- name: Run tests
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run: |
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bash download_fixtures.sh
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tox
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- name: Upload coverage to GitHub Artifacts
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uses: actions/upload-artifact@v4
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with:
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name: coverage-${{ matrix.python-version }}
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path: htmlcov/

‎.pre-commit-config.yaml‎

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repos:
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- repo: https://github.com/psf/black
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rev: stable
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rev: 24.8.0
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hooks:
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- id: black
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language_version: python3.8
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language_version: python3.10

‎README.md‎

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# ONNX to PyTorch
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![PyPI - License](https://img.shields.io/pypi/l/onnx2pytorch?color)
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[![CircleCI](https://circleci.com/gh/ToriML/onnx2pytorch.svg?style=shield)](https://app.circleci.com/pipelines/github/ToriML/onnx2pytorch)
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[![Lint and Test](https://github.com/Talmaj/onnx2pytorch/actions/workflows/lint_and_test.yml/badge.svg)](https://github.com/Talmaj/onnx2pytorch/actions/workflows/lint_and_test.yml)
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[![Downloads](https://pepy.tech/badge/onnx2pytorch)](https://pepy.tech/project/onnx2pytorch)
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![PyPI](https://img.shields.io/pypi/v/onnx2pytorch)
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‎download_fixtures.sh‎

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if [[ ! -f shufflenet_v2.onnx ]]; then
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echo Downloading shufflenet_v2
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curl -LJo shufflenet_v2.onnx https://github.com/onnx/models/blob/master/vision/classification/shufflenet/model/shufflenet-v2-10.onnx\?raw\=true
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curl -LJo shufflenet_v2.onnx https://github.com/onnx/models/raw/main/validated/vision/classification/shufflenet/model/shufflenet-v2-10.onnx
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fi
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if [[ $1 == "--all" ]]; then
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if [[ ! -f bertsquad-10.onnx ]]; then
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echo Downloading bertsquad-10
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curl -LJo bertsquad-10.onnx https://github.com/onnx/models/blob/master/text/machine_comprehension/bert-squad/model/bertsquad-10.onnx\?raw\=true
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curl -LJo bertsquad-10.onnx https://github.com/onnx/models/raw/main/validated/text/machine_comprehension/bert-squad/model/bertsquad-10.onnx
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fi
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if [[ ! -f yolo_v4.onnx ]]; then
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echo Downloading yolo_v4
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curl -LJo yolo_v4.onnx https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/yolov4/model/yolov4.onnx\?raw\=true
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curl -LJo yolo_v4.onnx https://github.com/onnx/models/raw/main/validated/vision/object_detection_segmentation/yolov4/model/yolov4.onnx
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fi
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if [[ ! -f super_res.onnx ]]; then
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echo Downloading super_res
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curl -LJo super_res.onnx https://github.com/onnx/models/blob/master/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.onnx\?raw\=true
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curl -LJo super_res.onnx https://github.com/onnx/models/raw/main/validated/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.onnx
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fi
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if [[ ! -f fast_neural_style.onnx ]]; then
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echo Downloading fast_neural_style
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curl -LJo fast_neural_style.onnx https://github.com/onnx/models/blob/master/vision/style_transfer/fast_neural_style/model/rain-princess-9.onnx\?raw\=true
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curl -LJo fast_neural_style.onnx https://github.com/onnx/models/raw/main/validated/vision/style_transfer/fast_neural_style/model/rain-princess-9.onnx
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fi
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if [[ ! -f efficientnet-lite4.onnx ]]; then
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echo Downloading efficientnet-lite4
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curl -LJo efficientnet-lite4.onnx https://github.com/onnx/models/blob/master/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx\?raw\=true
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curl -LJo efficientnet-lite4.onnx https://github.com/onnx/models/raw/main/validated/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx
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fi
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if [[ ! -f mobilenetv2-7.onnx ]]; then
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echo Downloading mobilenetv2-7
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curl -LJo mobilenetv2-7.onnx https://github.com/onnx/models/raw/master/vision/classification/mobilenet/model/mobilenetv2-7.onnx\?raw\=true
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curl -LJo mobilenetv2-7.onnx https://github.com/onnx/models/raw/main/validated/vision/classification/mobilenet/model/mobilenetv2-7.onnx
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fi
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fi

‎onnx2pytorch/operations/instancenorm.py‎

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from torch.nn.modules.batchnorm import _LazyNormBase
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class _LazyInstanceNorm(_LazyNormBase, _InstanceNorm):
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cls_to_become = _InstanceNorm
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except ImportError:
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from torch.nn.modules.lazy import LazyModuleMixin
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from torch.nn.parameter import UninitializedBuffer, UninitializedParameter
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class _LazyInstanceNorm(LazyModuleMixin, _InstanceNorm):
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weight: UninitializedParameter # type: ignore[assignment]
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bias: UninitializedParameter # type: ignore[assignment]
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self.reset_parameters()
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class LazyInstanceNormUnsafe(_LazyInstanceNorm):
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class InstanceNormMixin:
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"""Skips dimension check."""
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def __init__(self, *args, affine=True, **kwargs):
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self.no_batch_dim = None # no_batch_dim has to be set at runtime
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super().__init__(*args, affine=affine, **kwargs)
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def set_no_dim_batch_dim(self, no_batch_dim):
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self.no_batch_dim = no_batch_dim
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def _check_input_dim(self, input):
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return
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def _get_no_batch_dim(self):
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return self.no_batch_dim
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class InstanceNormUnsafe(_InstanceNorm):
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"""Skips dimension check."""
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def __init__(self, *args, affine=True, **kwargs):
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super().__init__(*args, affine=affine, **kwargs)
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class LazyInstanceNormUnsafe(InstanceNormMixin, _LazyInstanceNorm):
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pass
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def _check_input_dim(self, input):
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return
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class InstanceNormUnsafe(InstanceNormMixin, _InstanceNorm):
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pass
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class InstanceNormWrapper(torch.nn.Module):
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if B is not None:
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getattr(self.inu, "bias").data = B
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if self.inu.no_batch_dim is None:
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self.inu.set_no_dim_batch_dim(input.dim() - 1)
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return self.inu.forward(input)

‎tests/onnx2pytorch/convert/test_lstm.py‎

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o2p_lstm = ConvertModel(onnx_lstm, experimental=True)
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with torch.no_grad():
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o2p_output, o2p_h_n, o2p_c_n = o2p_lstm(input, h_0, c_0)
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assert torch.equal(o2p_output, output)
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assert torch.equal(o2p_h_n, h_n)
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assert torch.equal(o2p_c_n, c_n)
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torch.testing.assert_allclose(o2p_output, output, rtol=1e-6, atol=1e-6)
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torch.testing.assert_allclose(o2p_h_n, h_n, rtol=1e-6, atol=1e-6)
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torch.testing.assert_allclose(o2p_c_n, c_n, rtol=1e-6, atol=1e-6)
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onnx_lstm = onnx.ModelProto.FromString(bitstream_data)
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o2p_lstm = ConvertModel(onnx_lstm, experimental=True)
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with torch.no_grad():
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o2p_output, o2p_h_n, o2p_c_n = o2p_lstm(h_0=h_0, input=input, c_0=c_0)
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assert torch.equal(o2p_output, output)
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assert torch.equal(o2p_h_n, h_n)
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assert torch.equal(o2p_c_n, c_n)
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torch.testing.assert_allclose(o2p_output, output, rtol=1e-6, atol=1e-6)
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torch.testing.assert_allclose(o2p_h_n, h_n, rtol=1e-6, atol=1e-6)
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torch.testing.assert_allclose(o2p_c_n, c_n, rtol=1e-6, atol=1e-6)
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with pytest.raises(KeyError):
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o2p_output, o2p_h_n, o2p_c_n = o2p_lstm(h_0=h_0, input=input)
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with pytest.raises(Exception):

‎tests/onnx2pytorch/operations/test_clip.py‎

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assert torch.equal(op(x), exp_y)
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op = Clip(max=0)
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exp_y_np = np.clip(x_np, np.NINF, 0)
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exp_y_np = np.clip(x_np, -np.inf, 0)
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exp_y = torch.from_numpy(exp_y_np)
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assert torch.equal(op(x), exp_y)
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op = Clip()
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exp_y_np = np.clip(x_np, np.NINF, np.inf)
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exp_y_np = np.clip(x_np, -np.inf, np.inf)
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exp_y = torch.from_numpy(exp_y_np)
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assert torch.equal(op(x), exp_y)

‎tox.ini‎

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# and then run "tox" from this directory.
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[tox]
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envlist = clean,py36,py37,py38,py38-torch19,py39
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envlist = clean,py39,py310,py311,py312
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[gh-actions]
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python =
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3.9: py39
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3.10: py310
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3.11: py311
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3.12: py312
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[testenv]
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passenv =

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