Implement Net-to-CoreML Conversion Script #222
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This commit introduces the `net_to_coreml.py` script in the `tf/` directory. The script facilitates the conversion of a neural network file into a TensorFlow model, followed by its transformation into a CoreML model. This process mirrors the TensorFlow model conversion methodology used in `net_to_model.py`.
Key features of the CoreML conversion include:
- Setting the input shape to (1, 112, 8, 8).
- Defining `input_planes` as the input name.
- Specifying output names as `output_policy`, `output_value`, and `output_moves_left`.
- Assigning a concise description to the model, formatted as `Lc0 converted from {net name}`.
The script concludes by saving the CoreML model as `{net name}.mlpackage`. This enhancement enables the conversion of neural networks into CoreML models, which can be executed using Apple's Neural Engine. Future development of the CoreML backend is planned within the `lc0` repository.
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Regarding with the I am running |
This was referenced Dec 17, 2023
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Test 2: 512x19 (PASSED) The |
Modify TFProcess init_net method to accept an argument for including attention weights in the output, and update construct_net method to conditionally include attention weights in the outputs list. This enables flexibility in specifying whether to include attention weights in the model outputs.
Consolidate net-to-model conversion in a separate function to enhance modularity and reduce repeated code.
Modify conversion routines to enable rescaling option for Rule50 inputs, ensuring compatibility with client expectations. This paves the way for improved model adaptation.
Enable specifying compute precision (e.g., FLOAT16) for CoreML model conversion, offering increased flexibility in choosing the precision, thus potentially optimizing model performance.
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Unable to convert the |
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This commit introduces the
net_to_coreml.pyscript in thetf/directory. The script facilitates the conversion of a neural network file into a TensorFlow model, followed by its transformation into a CoreML model. This process mirrors the TensorFlow model conversion methodology used innet_to_model.py.Key features of the CoreML conversion include:
input_planesas the input name.output_policy,output_value, andoutput_moves_left.Lc0 converted from {net name}. The script concludes by saving the CoreML model as{net name}.mlpackage. This enhancement enables the conversion of neural networks into CoreML models, which can be executed using Apple's Neural Engine. Future development of the CoreML backend is planned within thelc0repository.Test 1: 128x10 (PASS)
Test 2: 512x19 (FAILED)
The error message is similar with this issue. apple/coremltools#1768