funasr pages

游雁

2023-11-16 4ace5a95b052d338947fc88809a440ccd55cf6b4

 funasr/export/export_model.py

@@ -1,14 +1,11 @@
import json
from typing import Union, Dict
from pathlib import Path

import os
import logging
import torch

from funasr.export.models import get_model
import numpy as np
import random
import logging
import numpy as np
from pathlib import Path
from typing import Union, Dict, List
from funasr.export.models import get_model
from funasr.utils.types import str2bool, str2triple_str
# torch_version = float(".".join(torch.__version__.split(".")[:2]))
# assert torch_version > 1.9
@@ -59,14 +56,22 @@
            model,
            self.export_config,
        )
        model.eval()
        # self._export_onnx(model, verbose, export_dir)
        if self.onnx:
            self._export_onnx(model, verbose, export_dir)
        if isinstance(model, List):
            for m in model:
                m.eval()
                if self.onnx:
                    self._export_onnx(m, verbose, export_dir)
                else:
                    self._export_torchscripts(m, verbose, export_dir)
                print("output dir: {}".format(export_dir))
        else:
            self._export_torchscripts(model, verbose, export_dir)

        print("output dir: {}".format(export_dir))
            model.eval()
            # self._export_onnx(model, verbose, export_dir)
            if self.onnx:
                self._export_onnx(model, verbose, export_dir)
            else:
                self._export_torchscripts(model, verbose, export_dir)
            print("output dir: {}".format(export_dir))


    def _torch_quantize(self, model):
@@ -230,17 +235,17 @@
        # model_script = torch.jit.script(model)
        model_script = model #torch.jit.trace(model)
        model_path = os.path.join(path, f'{model.model_name}.onnx')
        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 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
@@ -249,7 +254,7 @@
            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]
                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,