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| import os
| import torch
|
| def export_onnx(model,
| data_in=None,
| quantize: bool = False,
| opset_version: int = 14,
| **kwargs):
| model_scripts = model.export(**kwargs)
| export_dir = kwargs.get("output_dir", os.path.dirname(kwargs.get("init_param")))
| os.makedirs(export_dir, exist_ok=True)
|
| if not isinstance(model_scripts, (list, tuple)):
| model_scripts = (model_scripts,)
| for m in model_scripts:
| m.eval()
| _onnx(m,
| data_in=data_in,
| quantize=quantize,
| opset_version=opset_version,
| export_dir=export_dir,
| **kwargs
| )
| print("output dir: {}".format(export_dir))
|
| return export_dir
|
| def _onnx(model,
| data_in=None,
| quantize: bool = False,
| opset_version: int = 14,
| export_dir:str = None,
| **kwargs):
|
| dummy_input = model.export_dummy_inputs()
|
| verbose = kwargs.get("verbose", False)
|
| export_name = model.export_name() if hasattr(model, "export_name") else "model.onnx"
| model_path = os.path.join(export_dir, export_name)
| torch.onnx.export(
| model,
| dummy_input,
| model_path,
| verbose=verbose,
| opset_version=opset_version,
| input_names=model.export_input_names(),
| output_names=model.export_output_names(),
| dynamic_axes=model.export_dynamic_axes()
| )
|
| if quantize:
| from onnxruntime.quantization import QuantType, quantize_dynamic
| import onnx
| quant_model_path = model_path.replace(".onnx", "_quant.onnx")
| if not os.path.exists(quant_model_path):
| onnx_model = onnx.load(model_path)
| nodes = [n.name for n in onnx_model.graph.node]
| nodes_to_exclude = [m for m in nodes if 'output' in m or 'bias_encoder' in m or 'bias_decoder' in m]
| quantize_dynamic(
| model_input=model_path,
| model_output=quant_model_path,
| op_types_to_quantize=['MatMul'],
| per_channel=True,
| reduce_range=False,
| weight_type=QuantType.QUInt8,
| nodes_to_exclude=nodes_to_exclude,
| )
|
|
