⌚ Convert and optimize
To convert PyTorch model to ONNX format, use the code below:
PATH = r'models\enet_b0_8\enet_b0_8.pt' # path to your model
feature_extractor_model = torch.load(PATH) # load model
feature_extractor_model.eval() # set the model in evaluation mode
dummy_input = torch.randn(1, 3, 224, 224).cuda() # Create dummy input for the model. It will be used to run the model inside export function.
torch.onnx.export(feature_extractor_model, (dummy_input, ), 'enet_b0_8.onnx') # call the export functionmo --input_model your_ONNX_model -o output_directory_for_IR_model --data_type FP32_or_FP16_or_FP8FP32:
mo --input_model D:\Users\amira\openvino_env\Lib\site-packages\openvino\model_zoo\models\group_project\enet_b0_8\enet_b0_8.onnx -o D:\Users\amira\openvino_env\Lib\site-packages\openvino\model_zoo\models\group_project\enet_b0_8 --data_type FP32FP16:
mo --input_model D:\Users\amira\openvino_env\Lib\site-packages\openvino\model_zoo\models\group_project\enet_b0_8\enet_b0_8.onnx -o D:\Users\amira\openvino_env\Lib\site-packages\openvino\model_zoo\models\group_project\enet_b0_8 --data_type FP16Models to evaluate and the code to run them were borrowed from this repository.
Please be sure that EfficientNet models for PyTorch are based on old timm 0.4.5 package, so that exactly this version should be installed by the following command:
pip install timm==0.4.5
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