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Release 1.9.0 (#964)
* release 1.9.0 Signed-off-by: degaochu <[email protected]> * release 1.9.0 Signed-off-by: degaochu <[email protected]> * release 1.9.0 Signed-off-by: degaochu <[email protected]> * release 1.9.0 Signed-off-by: degaochu <[email protected]> * release 1.9.0 Signed-off-by: degaochu <[email protected]> * trigger CLA check Signed-off-by: degaochu <[email protected]>
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‎.github/workflows/test-backend.yml

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# - use oldest and latest supported python versions
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# - use pinned versions for current onnx-tf release
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os: [ubuntu-latest, windows-latest, macos-latest]
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python-version: [3.6, 3.8]
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onnx-version: ['onnx==1.8.0']
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tensorflow-version: ['tensorflow-cpu==2.4.1 tensorflow-addons==0.12.0']
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python-version: [3.8, 3.9]
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onnx-version: ['onnx==1.9.0']
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tensorflow-version: ['tensorflow-cpu==2.6.0 tensorflow-addons==0.13.0']
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include:
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- # Test previous versions on Linux only (regression)
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# - use pinned versions for previous onnx-tf release
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os: ubuntu-latest
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python-version: 3.6
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onnx-version: 'onnx==1.8.0'
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tensorflow-version: 'tensorflow==2.4.1 tensorflow-addons==0.12.0'
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python-version: 3.8
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onnx-version: 'onnx==1.9.0'
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tensorflow-version: 'tensorflow==2.6.0 tensorflow-addons==0.13.0'
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- # Test development versions on Linux only
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# - latest development versions
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# - allow failure via GitHub branch protection rule
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os: ubuntu-latest
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python-version: 3.8
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python-version: 3.9
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onnx-version: 'development'
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tensorflow-version: 'tensorflow-cpu tensorflow-addons'
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‎.github/workflows/test-modelzoo.yml

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path: wiki
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- uses: actions/setup-python@v2
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with:
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python-version: '3.6'
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python-version: '3.8'
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- name: Install dependencies
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run: |
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pip install onnx==1.8.0 tensorflow==2.4.1 tensorflow-addons==0.12.0
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pip install onnx==1.9.0 tensorflow==2.6.0 tensorflow-addons==0.13.0
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pip install -e .
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- name: Run ModelZoo tests
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run: |

‎ONNX_VERSION_NUMBER

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1.8.0
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1.9.0

‎README.md

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@@ -41,7 +41,7 @@ The specific ONNX release version that we support in the master branch of ONNX-T
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To install the latest version of ONNX-TF via pip, run `pip install onnx-tf`.
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Because users often have their own preferences for which variant of TensorFlow to install (i.e., a GPU version instead of a CPU version), we do not explicitly require tensorflow in the installation script. It is therefore users' responsibility to ensure that the proper variant of TensorFlow is available to ONNX-TF. Moreover, we require TensorFlow version == 2.4.1.
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Because users often have their own preferences for which variant of TensorFlow to install (i.e., a GPU version instead of a CPU version), we do not explicitly require tensorflow in the installation script. It is therefore users' responsibility to ensure that the proper variant of TensorFlow is available to ONNX-TF. Moreover, we require TensorFlow version == 2.6.0.
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## Development
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### Installation
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- Install ONNX master branch from source.
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- Install TensorFlow >= 2.4.1 and tensorflow-addons. (Note TensorFlow 1.x is no longer supported)
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- Install TensorFlow >= 2.6.0 and tensorflow-addons. (Note TensorFlow 1.x is no longer supported)
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- Run `git clone https://github.com/onnx/onnx-tensorflow.git && cd onnx-tensorflow`.
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- Run `pip install -e .`.
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‎VERSION_NUMBER

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1.8.0
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1.9.0

‎Versioning.md

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1.6.0|1.6.0 (opset 11)|2.2.0
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1.7.0|1.7.0 (opset 12)|2.3.1
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1.8.0|1.8.0 (opset 13)|2.4.1
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1.9.0|1.9.0 (opset 14)|2.6.0

‎doc/support_status.md

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‎doc/support_status_v1_9_0.md

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‎test/backend/test_model.py

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@@ -331,14 +331,14 @@ def test_if_with_sequence(self):
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b_in = helper.make_tensor_value_info('b', onnx.TensorProto.FLOAT, [1, 1, 2])
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c_in = helper.make_tensor_value_info('c', onnx.TensorProto.FLOAT, [3, 1, 2])
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cond_in = helper.make_tensor_value_info('cond', TensorProto.BOOL, [])
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s_in = helper.make_tensor_sequence_value_info('S', TensorProto.FLOAT,
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s_in = helper.make_sequence_value_info('S', TensorProto.FLOAT,
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[None, None, None, None])
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sb_out = helper.make_tensor_sequence_value_info('Sb', TensorProto.FLOAT,
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sb_out = helper.make_sequence_value_info('Sb', TensorProto.FLOAT,
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[None, None, None, None])
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sc_out = helper.make_tensor_sequence_value_info('Sc', TensorProto.FLOAT,
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sc_out = helper.make_sequence_value_info('Sc', TensorProto.FLOAT,
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[None, None, None, None])
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s_final_out = helper.make_tensor_sequence_value_info(
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s_final_out = helper.make_sequence_value_info(
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'S_final', TensorProto.FLOAT, [None, None, None, None])
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then_graph = helper.make_graph(nodes=[seq_insert_node1],
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iter_count_in = helper.make_tensor_value_info('iter_count',
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TensorProto.INT64, [])
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cond_in = helper.make_tensor_value_info('cond', TensorProto.BOOL, [])
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s_in = helper.make_tensor_sequence_value_info('S', TensorProto.FLOAT,
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s_in = helper.make_sequence_value_info('S', TensorProto.FLOAT,
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[None, None, None, None])
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cond_out = helper.make_tensor_value_info('cond', TensorProto.BOOL, [])
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s_out = helper.make_tensor_sequence_value_info('Updated_S',
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s_out = helper.make_sequence_value_info('Updated_S',
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TensorProto.FLOAT,
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[None, None, None, None])
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s_final_out = helper.make_tensor_sequence_value_info(
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s_final_out = helper.make_sequence_value_info(
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'S_final', TensorProto.FLOAT, [None, None, None, None])
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body_graph = helper.make_graph(nodes=[seq_insert_node],

‎test/backend/test_onnx_backend.py

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backend_test.exclude(r'test_batchnorm_epsilon_training_mode[a-z,_]*')
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backend_test.exclude(r'test_batchnorm_example_training_mode[a-z,_]*')
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# ONNX 1.9.0 test case does not support sequence
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backend_test.exclude(r'[a-z,_]*identity_sequence_[a-z,_]*')
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# import all test cases at global scope to make them visible to python.unittest
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globals().update(backend_test.enable_report().test_cases)
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