mirror of
https://github.com/storytold/torchscript-models.git
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Brandon Thomas
48 lines
1.2 KiB
Markdown
48 lines
1.2 KiB
Markdown
jit-models
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==========
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This project exists to turn PyTorch models into Torch JIT, which can be run in
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any language with libtorch bindings.
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How to develop
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--------------
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The best way is to import the *inference* code and work backwards.
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Try to comment out all of the model so `forward` only returns the input, then begin
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opening things up.
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Scripting vs tracing
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--------------------
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Scripting transforms the code. It can contain branches.
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Tracing evaluates the codepath with example data, fixing the branches taken.
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The two techniques can be combined in the various nodes in the module tree.
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Model-specific details
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----------------------
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**glow-tts notes**
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Only two changes!
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`FlowGenerator.forward` (the root module) needs the following changes:
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Hardcode a few constructor args. These come from `inference.py`'s arg set:
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```python
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def forward(self, x, x_lengths, y=None, y_lengths=None, g=None, gen=False, noise_scale=1., length_scale=1.):
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y = None
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y_lengths = None
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noise_scale = .667
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length_scale = 1.0
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gen = True
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```
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Return only the mel output at the end of the `forward` branch used in tracing:
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```python
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#return (y, y_m, y_logs, logdet), attn, logw, logw_, x_m, x_logs
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return y
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```
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