update export

维石

2024-05-28 e7351db81b3bfc4000633eca274c46893d68f64e

 funasr/utils/export_utils.py

@@ -2,7 +2,7 @@
import torch


def export_onnx(model, data_in=None, quantize: bool = False, opset_version: int = 14, **kwargs):
def export(model, data_in=None, quantize: bool = False, opset_version: int = 14, type='onnx', **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)
@@ -11,14 +11,20 @@
        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
        )
        if type == 'onnx':
            _onnx(
                m,
                data_in=data_in,
                quantize=quantize,
                opset_version=opset_version,
                export_dir=export_dir,
                **kwargs
            )
        elif type == 'torchscript':
            _torchscripts(
                m,
                path=export_dir,
            )
        print("output dir: {}".format(export_dir))

    return export_dir
@@ -37,7 +43,7 @@

    verbose = kwargs.get("verbose", False)

    export_name = model.export_name() if hasattr(model, "export_name") else "model.onnx"
    export_name = model.export_name + '.onnx'
    model_path = os.path.join(export_dir, export_name)
    torch.onnx.export(
        model,
@@ -70,3 +76,15 @@
                weight_type=QuantType.QUInt8,
                nodes_to_exclude=nodes_to_exclude,
            )


def _torchscripts(model, path, device='cpu'):
    dummy_input = model.export_dummy_inputs()

    if device == 'cuda':
        model = model.cuda()
        dummy_input = tuple([i.cuda() for i in dummy_input])

    # model_script = torch.jit.script(model)
    model_script = torch.jit.trace(model, dummy_input)
    model_script.save(os.path.join(path, f'{model.export_name}.torchscripts'))