Update onnx version (#80)

.github/workflows/integration-test_onnx_based_torch.yml

@@ -28,14 +28,14 @@ jobs:
         path: |
           ~/.nn_meter
           /home/runner/work/nn-Meter/data/testmodels
-        key: ${{hashFiles('nn_meter/configs/predictors.yaml')}}-${{hashFiles('tests/integration_test/test_latency_predictor.py')}}
+        key: ${{hashFiles('nn_meter/configs/predictors.yaml')}}--${{hashFiles('tests/integration_test/test_latency_predictor.py')}}
 
     - name: Install dependencies
       run: |
         pip install onnx==1.9.0
         pip install torch==1.9.0
         pip install torchvision==0.10.0
-        pip install onnx-simplifier
+        pip install onnx-simplifier==0.3.6
         
     - name: Install nn-Meter
       run: pip install -U .

README.md

@@ -48,7 +48,7 @@ nn-Meter is a latency predictor of models with type of Tensorflow, PyTorch, Onnx
 | Testing Model Type |                                                       Requirements                                                       |
 | :----------------: | :-----------------------------------------------------------------------------------------------------------------------: |
 |     Tensorflow     |                                                  `tensorflow==2.6.0`                                                  |
-|       Torch       | `torch==1.9.0`, `torchvision==0.10.0`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni>=2.4`][1] |
+|       Torch       | `torch==1.9.0`, `torchvision==0.10.0`, (alternative)[`onnx>=1.9.0`, `onnx-simplifier==0.3.6`] or [`nni>=2.4`][1] |
 |        Onnx        |                                                      `onnx==1.9.0`                                                      |
 | nn-Meter IR graph |                                                            ---                                                            |
 |    NNI IR graph    |                                                       `nni>=2.4`                                                       |

nn_meter/ir_converter/utils.py

@@ -29,7 +29,7 @@ def model_file_to_graph(filename: str, model_type: str, input_shape=(1, 3, 224,
     
     apply_nni: switch the torch converter used for torch model parsing. If apply_nni==True, NNI-based converter is used for torch model conversion, which requires 
         nni>=2.4 installation and should use nn interface from NNI `import nni.retiarii.nn.pytorch as nn` to define the PyTorch modules. Otherwise Onnx-based torch 
-        converter is used, which requires onnx installation (well tested version is onnx==1.9.0). NNI-based converter is much faster while the conversion is unstable 
+        converter is used, which requires onnx installation (well tested version is onnx>=1.9.0). NNI-based converter is much faster while the conversion is unstable 
         as it could fail in some case. Onnx-based converter is much slower but stable compared to NNI-based converter. This parameter is only accessed when 
         model_type == 'torch'
     """

nn_meter/predictor/nn_meter_predictor.py

@@ -97,7 +97,7 @@ class nnMeterPredictor:
 
         apply_nni: switch the torch converter used for torch model parsing. If apply_nni==True, NNI-based converter is used for torch model conversion, which requires 
             nni>=2.4 installation and should use nn interface from NNI `import nni.retiarii.nn.pytorch as nn` to define the PyTorch modules. Otherwise Onnx-based torch 
-            converter is used, which requires onnx installation (well tested version is onnx==1.9.0). NNI-based converter is much faster while the conversion is unstable 
+            converter is used, which requires onnx installation (well tested version is onnx>=1.9.0). NNI-based converter is much faster while the conversion is unstable 
             as it could fail in some case. Onnx-based converter is much slower but stable compared to NNI-based converter. This parameter is only accessed when 
             model_type == 'torch'
         """

nn_meter/utils/import_package.py

@@ -5,7 +5,7 @@ from packaging import version
 logging = logging.getLogger("nn-Meter")
 
 
-def try_import_onnx(require_version = ["1.10.0", "1.9.0"]):
+def try_import_onnx(require_version = ["1.12.0", "1.10.0", "1.9.0"]):
     if isinstance(require_version, str):
         require_version = [require_version]
     try: