# -*- encoding: utf-8 -*-
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import functools
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import logging
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import pickle
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from pathlib import Path
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from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
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import numpy as np
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import yaml
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from onnxruntime import (GraphOptimizationLevel, InferenceSession,
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SessionOptions, get_available_providers, get_device)
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from typeguard import check_argument_types
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import warnings
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root_dir = Path(__file__).resolve().parent
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logger_initialized = {}
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class TokenIDConverter():
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def __init__(self, token_list: Union[List, str],
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):
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check_argument_types()
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self.token_list = token_list
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self.unk_symbol = token_list[-1]
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self.token2id = {v: i for i, v in enumerate(self.token_list)}
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self.unk_id = self.token2id[self.unk_symbol]
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def get_num_vocabulary_size(self) -> int:
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return len(self.token_list)
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def ids2tokens(self,
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integers: Union[np.ndarray, Iterable[int]]) -> List[str]:
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if isinstance(integers, np.ndarray) and integers.ndim != 1:
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raise TokenIDConverterError(
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f"Must be 1 dim ndarray, but got {integers.ndim}")
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return [self.token_list[i] for i in integers]
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def tokens2ids(self, tokens: Iterable[str]) -> List[int]:
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return [self.token2id.get(i, self.unk_id) for i in tokens]
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class CharTokenizer():
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def __init__(
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self,
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symbol_value: Union[Path, str, Iterable[str]] = None,
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space_symbol: str = "<space>",
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remove_non_linguistic_symbols: bool = False,
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):
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check_argument_types()
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self.space_symbol = space_symbol
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self.non_linguistic_symbols = self.load_symbols(symbol_value)
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self.remove_non_linguistic_symbols = remove_non_linguistic_symbols
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@staticmethod
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def load_symbols(value: Union[Path, str, Iterable[str]] = None) -> Set:
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if value is None:
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return set()
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if isinstance(value, Iterable[str]):
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return set(value)
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file_path = Path(value)
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if not file_path.exists():
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logging.warning("%s doesn't exist.", file_path)
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return set()
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with file_path.open("r", encoding="utf-8") as f:
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return set(line.rstrip() for line in f)
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def text2tokens(self, line: Union[str, list]) -> List[str]:
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tokens = []
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while len(line) != 0:
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for w in self.non_linguistic_symbols:
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if line.startswith(w):
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if not self.remove_non_linguistic_symbols:
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tokens.append(line[: len(w)])
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line = line[len(w):]
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break
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else:
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t = line[0]
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if t == " ":
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t = "<space>"
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tokens.append(t)
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line = line[1:]
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return tokens
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def tokens2text(self, tokens: Iterable[str]) -> str:
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tokens = [t if t != self.space_symbol else " " for t in tokens]
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return "".join(tokens)
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def __repr__(self):
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return (
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f"{self.__class__.__name__}("
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f'space_symbol="{self.space_symbol}"'
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f'non_linguistic_symbols="{self.non_linguistic_symbols}"'
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f")"
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)
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class Hypothesis(NamedTuple):
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"""Hypothesis data type."""
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yseq: np.ndarray
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score: Union[float, np.ndarray] = 0
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scores: Dict[str, Union[float, np.ndarray]] = dict()
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states: Dict[str, Any] = dict()
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def asdict(self) -> dict:
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"""Convert data to JSON-friendly dict."""
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return self._replace(
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yseq=self.yseq.tolist(),
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score=float(self.score),
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scores={k: float(v) for k, v in self.scores.items()},
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)._asdict()
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class TokenIDConverterError(Exception):
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pass
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class ONNXRuntimeError(Exception):
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pass
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class OrtInferSession():
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def __init__(self, model_file, device_id=-1, intra_op_num_threads=4):
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device_id = str(device_id)
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sess_opt = SessionOptions()
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sess_opt.intra_op_num_threads = intra_op_num_threads
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sess_opt.log_severity_level = 4
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sess_opt.enable_cpu_mem_arena = False
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sess_opt.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
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cuda_ep = 'CUDAExecutionProvider'
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cuda_provider_options = {
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"device_id": device_id,
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"arena_extend_strategy": "kNextPowerOfTwo",
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"cudnn_conv_algo_search": "EXHAUSTIVE",
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"do_copy_in_default_stream": "true",
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}
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cpu_ep = 'CPUExecutionProvider'
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cpu_provider_options = {
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"arena_extend_strategy": "kSameAsRequested",
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}
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EP_list = []
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if device_id != "-1" and get_device() == 'GPU' \
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and cuda_ep in get_available_providers():
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EP_list = [(cuda_ep, cuda_provider_options)]
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EP_list.append((cpu_ep, cpu_provider_options))
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self._verify_model(model_file)
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self.session = InferenceSession(model_file,
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sess_options=sess_opt,
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providers=EP_list)
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if device_id != "-1" and cuda_ep not in self.session.get_providers():
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warnings.warn(f'{cuda_ep} is not avaiable for current env, the inference part is automatically shifted to be executed under {cpu_ep}.\n'
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'Please ensure the installed onnxruntime-gpu version matches your cuda and cudnn version, '
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'you can check their relations from the offical web site: '
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'https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html',
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RuntimeWarning)
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def __call__(self,
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input_content: List[Union[np.ndarray, np.ndarray]]) -> np.ndarray:
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input_dict = dict(zip(self.get_input_names(), input_content))
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try:
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return self.session.run(self.get_output_names(), input_dict)
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except Exception as e:
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raise ONNXRuntimeError('ONNXRuntime inferece failed.') from e
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def get_input_names(self, ):
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return [v.name for v in self.session.get_inputs()]
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def get_output_names(self,):
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return [v.name for v in self.session.get_outputs()]
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def get_character_list(self, key: str = 'character'):
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return self.meta_dict[key].splitlines()
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def have_key(self, key: str = 'character') -> bool:
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self.meta_dict = self.session.get_modelmeta().custom_metadata_map
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if key in self.meta_dict.keys():
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return True
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return False
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@staticmethod
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def _verify_model(model_path):
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model_path = Path(model_path)
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if not model_path.exists():
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raise FileNotFoundError(f'{model_path} does not exists.')
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if not model_path.is_file():
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raise FileExistsError(f'{model_path} is not a file.')
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def split_to_mini_sentence(words: list, word_limit: int = 20):
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assert word_limit > 1
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if len(words) <= word_limit:
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return [words]
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sentences = []
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length = len(words)
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sentence_len = length // word_limit
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for i in range(sentence_len):
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sentences.append(words[i * word_limit:(i + 1) * word_limit])
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if length % word_limit > 0:
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sentences.append(words[sentence_len * word_limit:])
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return sentences
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def code_mix_split_words(text: str):
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words = []
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segs = text.split()
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for seg in segs:
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# There is no space in seg.
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current_word = ""
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for c in seg:
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if len(c.encode()) == 1:
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# This is an ASCII char.
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current_word += c
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else:
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# This is a Chinese char.
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if len(current_word) > 0:
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words.append(current_word)
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current_word = ""
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words.append(c)
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if len(current_word) > 0:
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words.append(current_word)
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return words
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def read_yaml(yaml_path: Union[str, Path]) -> Dict:
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if not Path(yaml_path).exists():
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raise FileExistsError(f'The {yaml_path} does not exist.')
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with open(str(yaml_path), 'rb') as f:
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data = yaml.load(f, Loader=yaml.Loader)
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return data
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@functools.lru_cache()
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def get_logger(name='funasr_onnx'):
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"""Initialize and get a logger by name.
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If the logger has not been initialized, this method will initialize the
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logger by adding one or two handlers, otherwise the initialized logger will
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be directly returned. During initialization, a StreamHandler will always be
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added.
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Args:
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name (str): Logger name.
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Returns:
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logging.Logger: The expected logger.
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"""
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logger = logging.getLogger(name)
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if name in logger_initialized:
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return logger
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for logger_name in logger_initialized:
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if name.startswith(logger_name):
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return logger
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formatter = logging.Formatter(
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'[%(asctime)s] %(name)s %(levelname)s: %(message)s',
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datefmt="%Y/%m/%d %H:%M:%S")
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sh = logging.StreamHandler()
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sh.setFormatter(formatter)
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logger.addHandler(sh)
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logger_initialized[name] = True
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logger.propagate = False
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return logger
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