Скачать или смотреть How to Convert PyTorch Models to Core ML and TensorFlow

How to Convert PyTorch Models to Core ML and TensorFlow

🔗 Video Link:    • How to Convert PyTorch Models to Core ML a...  

Converting PyTorch models to Core ML and TensorFlow often leverages ONNX (Open Neural Network Exchange) as an intermediate format. First, ensure your PyTorch model is in `eval` mode and provide a dummy input tensor matching your model's expected shape. Then, use `torch.onnx.export()` to convert your PyTorch model into the ONNX format, typically after tracing (`torch.jit.trace`) or scripting it to capture the computational graph. Once you have the ONNX model, the process diverges. For Core ML, utilize the `coremltools` Python library; specifically, `coremltools.converters.onnx.convert()` can directly transform the ONNX model into a `.mlmodel` file, ready for Apple's Core ML framework. For TensorFlow, the `onnx-tf` converter is used. You'll typically use `onnx_tf.backend.prepare()` to create a TensorFlow representation from the ONNX graph, which can then be saved as a TensorFlow SavedModel using `tf.saved_model.save()`, enabling deployment within the TensorFlow ecosystem.

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