Merge branch 'main' into dev_aky

aky15

2023-03-15 e33bb15d269bb3e2e41f7a3540d9b92703bb5c50

 funasr/tasks/sv.py

@@ -1,14 +1,18 @@
import argparse
import logging
import os
from pathlib import Path
from typing import Callable
from typing import Collection
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union

import numpy as np
import torch
import yaml
from typeguard import check_argument_types
from typeguard import check_return_type

@@ -21,7 +25,7 @@
from funasr.models.decoder.abs_decoder import AbsDecoder
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.encoder.rnn_encoder import RNNEncoder
from funasr.models.encoder.resnet34_encoder import ResNet34
from funasr.models.encoder.resnet34_encoder import ResNet34, ResNet34_SP_L2Reg
from funasr.models.pooling.statistic_pooling import StatisticPooling
from funasr.models.decoder.sv_decoder import DenseDecoder
from funasr.models.e2e_sv import ESPnetSVModel
@@ -103,6 +107,7 @@
    "encoder",
    classes=dict(
        resnet34=ResNet34,
        resnet34_sp_l2reg=ResNet34_SP_L2Reg,
        rnn=RNNEncoder,
    ),
    type_check=AbsEncoder,
@@ -394,9 +399,16 @@

        # 7. Pooling layer
        pooling_class = pooling_choices.get_class(args.pooling_type)
        pooling_dim = (2, 3)
        eps = 1e-12
        if hasattr(args, "pooling_type_conf"):
            if "pooling_dim" in args.pooling_type_conf:
                pooling_dim = args.pooling_type_conf["pooling_dim"]
            if "eps" in args.pooling_type_conf:
                eps = args.pooling_type_conf["eps"]
        pooling_layer = pooling_class(
            pooling_dim=(2, 3),
            eps=1e-12,
            pooling_dim=pooling_dim,
            eps=eps,
        )
        if args.pooling_type == "statistic":
            encoder_output_size *= 2
@@ -435,3 +447,95 @@

        assert check_return_type(model)
        return model

    # ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
    @classmethod
    def build_model_from_file(
            cls,
            config_file: Union[Path, str] = None,
            model_file: Union[Path, str] = None,
            cmvn_file: Union[Path, str] = None,
            device: str = "cpu",
    ):
        """Build model from the files.

        This method is used for inference or fine-tuning.

        Args:
            config_file: The yaml file saved when training.
            model_file: The model file saved when training.
            cmvn_file: The cmvn file for front-end
            device: Device type, "cpu", "cuda", or "cuda:N".

        """
        assert check_argument_types()
        if config_file is None:
            assert model_file is not None, (
                "The argument 'model_file' must be provided "
                "if the argument 'config_file' is not specified."
            )
            config_file = Path(model_file).parent / "config.yaml"
        else:
            config_file = Path(config_file)

        with config_file.open("r", encoding="utf-8") as f:
            args = yaml.safe_load(f)
        if cmvn_file is not None:
            args["cmvn_file"] = cmvn_file
        args = argparse.Namespace(**args)
        model = cls.build_model(args)
        if not isinstance(model, AbsESPnetModel):
            raise RuntimeError(
                f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
            )
        model.to(device)
        model_dict = dict()
        model_name_pth = None
        if model_file is not None:
            logging.info("model_file is {}".format(model_file))
            if device == "cuda":
                device = f"cuda:{torch.cuda.current_device()}"
            model_dir = os.path.dirname(model_file)
            model_name = os.path.basename(model_file)
            if "model.ckpt-" in model_name or ".bin" in model_name:
                if ".bin" in model_name:
                    model_name_pth = os.path.join(model_dir, model_name.replace('.bin', '.pb'))
                else:
                    model_name_pth = os.path.join(model_dir, "{}.pth".format(model_name))
                if os.path.exists(model_name_pth):
                    logging.info("model_file is load from pth: {}".format(model_name_pth))
                    model_dict = torch.load(model_name_pth, map_location=device)
                else:
                    model_dict = cls.convert_tf2torch(model, model_file)
                model.load_state_dict(model_dict)
            else:
                model_dict = torch.load(model_file, map_location=device)
        model.load_state_dict(model_dict)
        if model_name_pth is not None and not os.path.exists(model_name_pth):
            torch.save(model_dict, model_name_pth)
            logging.info("model_file is saved to pth: {}".format(model_name_pth))

        return model, args

    @classmethod
    def convert_tf2torch(
            cls,
            model,
            ckpt,
    ):
        logging.info("start convert tf model to torch model")
        from funasr.modules.streaming_utils.load_fr_tf import load_tf_dict
        var_dict_tf = load_tf_dict(ckpt)
        var_dict_torch = model.state_dict()
        var_dict_torch_update = dict()
        # speech encoder
        var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        # pooling layer
        var_dict_torch_update_local = model.pooling_layer.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        # decoder
        var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)

        return var_dict_torch_update