PyTorch to CoreML: Writing custom layers with Metal
cd Convert
python3 -m pip install -r requirements.txt
python3 convert.py -o /path/to/output/resulting/mlmodel/file
Open TorchCoreMLDemo.xcodeproj in Xcode, build and run the app.
The code of the grid sample Metal-shader is here.
More information about grid sample (warper) algorithm you can find here.
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I have read your work. It's really cool! I'm also working on coremltools recently and have some confusion, hoping you can help me.
According to official guide about Custom Operators, it has not mentioned to set @register_mil_to_nn_mapping. But it seems like this is essential when custom a pytorch layer. I'm little confusing about how to set it, or, how to define it?
Hi, @dneprDroid
I'm trying to implement a 5-D input tensor version of custom grid_sample based on your work. Now i meet two problems hoping you can help me.
First, you said "we should transpose the grid to [n, 2, w, h] shape (for encoding it to a coreml 2-channel texture)". But i found this process may lead to the function always run on CPU in my implementation. The GPU function encode works without this process. I read your code but haven't find a solution. Have you met this problem ever?
Second, since i want implement a 5-D input tensor version of custom grid_sample, how id<MTLTexture> in encode work at this time? I found the data storage is different with ordinary buffer, and i'm not sure how to read data in id<MTLTexture> so that the data can be calculated in later computation process.
Thank you!
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