Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR

nichongjia-2007

2023-06-30 012903e42ec890ab5c50137beb365c3d94e731d1

 funasr/export/export_model.py

@@ -1,7 +1,6 @@
import json
from typing import Union, Dict
from pathlib import Path
from typeguard import check_argument_types

import os
import logging
@@ -10,7 +9,7 @@
from funasr.export.models import get_model
import numpy as np
import random
from funasr.utils.types import str2bool
from funasr.utils.types import str2bool, str2triple_str
# torch_version = float(".".join(torch.__version__.split(".")[:2]))
# assert torch_version > 1.9

@@ -24,8 +23,8 @@
        fallback_num: int = 0,
        audio_in: str = None,
        calib_num: int = 200,
        model_revision: str = None,
    ):
        assert check_argument_types()
        self.set_all_random_seed(0)

        self.cache_dir = cache_dir
@@ -41,6 +40,7 @@
        self.frontend = None
        self.audio_in = audio_in
        self.calib_num = calib_num
        self.model_revision = model_revision
        

    def _export(
@@ -171,7 +171,7 @@
        model_dir = tag_name
        if model_dir.startswith('damo'):
            from modelscope.hub.snapshot_download import snapshot_download
            model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir)
            model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir, revision=self.model_revision)
        self.cache_dir = model_dir

        if mode is None:
@@ -192,6 +192,7 @@
                config, model_file, cmvn_file, 'cpu'
            )
            self.frontend = model.frontend
            self.export_config["feats_dim"] = 560
        elif mode.startswith('offline'):
            from funasr.tasks.vad import VADTask
            config = os.path.join(model_dir, 'vad.yaml')
@@ -229,40 +230,42 @@
        # model_script = torch.jit.script(model)
        model_script = model #torch.jit.trace(model)
        model_path = os.path.join(path, f'{model.model_name}.onnx')

        torch.onnx.export(
            model_script,
            dummy_input,
            model_path,
            verbose=verbose,
            opset_version=14,
            input_names=model.get_input_names(),
            output_names=model.get_output_names(),
            dynamic_axes=model.get_dynamic_axes()
        )
        if not os.path.exists(model_path):
            torch.onnx.export(
                model_script,
                dummy_input,
                model_path,
                verbose=verbose,
                opset_version=14,
                input_names=model.get_input_names(),
                output_names=model.get_output_names(),
                dynamic_axes=model.get_dynamic_axes()
            )

        if self.quant:
            from onnxruntime.quantization import QuantType, quantize_dynamic
            import onnx
            quant_model_path = os.path.join(path, f'{model.model_name}_quant.onnx')
            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]
            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,
            )
            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]
                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,
                )


if __name__ == '__main__':
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--model-name', type=str, required=True)
    # parser.add_argument('--model-name', type=str, required=True)
    parser.add_argument('--model-name', type=str, action="append", required=True, default=[])
    parser.add_argument('--export-dir', type=str, required=True)
    parser.add_argument('--type', type=str, default='onnx', help='["onnx", "torch"]')
    parser.add_argument('--device', type=str, default='cpu', help='["cpu", "cuda"]')
@@ -270,6 +273,7 @@
    parser.add_argument('--fallback-num', type=int, default=0, help='amp fallback number')
    parser.add_argument('--audio_in', type=str, default=None, help='["wav", "wav.scp"]')
    parser.add_argument('--calib_num', type=int, default=200, help='calib max num')
    parser.add_argument('--model_revision', type=str, default=None, help='model_revision')
    args = parser.parse_args()

    export_model = ModelExport(
@@ -280,5 +284,8 @@
        fallback_num=args.fallback_num,
        audio_in=args.audio_in,
        calib_num=args.calib_num,
        model_revision=args.model_revision,
    )
    export_model.export(args.model_name)
    for model_name in args.model_name:
        print("export model: {}".format(model_name))
        export_model.export(model_name)