Source code for trainer.modelcard

# coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team and 2021 Zilliz.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path
from dataclasses import dataclass
from typing import Union, Dict, Any, Optional, List

# from towhee.utils.log import trainer_log
from towhee.trainer.training_config import TrainingConfig


[docs]@dataclass class ModelCard: """ Utilities to generate and save model card. Recommended attributes from (see papers) Args: model_name (`Optional[str]`): model name model_architecture (`Optional[str]`): model structure model_overview (`Optional[str] = None`): language (`Optional[Union[str, List[str]]]`): language tags (`Optional[Union[str, List[str]]]`): tags tasks (`Optional[Union[str, List[str]]]`): model tasks (eg. classification, prediction, etc.) datasets (`Optional[Union[str, List[str]]]`): datasets used to train/test the model datasets_tags (`Optional[Union[str, List[str]]]`): tags of datasets dataset_args (`Optional[Union[str, List[str]]]`): arguments of dataset eval_results (`Optional[Dict[str, float]]`): evaluation results recorded eval_lines (`Optional[List[str]]`): evaluation baselines training_summary (`Optional[Dict[str, Any]]`): training summary include training information training_config (`Optional[TrainingConfig]`): training configurations source (`Optional[str]`): source of model card (default = "trainer") Example: >>> from towhee.trainer.modelcard import ModelCard >>> model_card = ModelCard(model_name='test') >>> # Print out model name stored in model card >>> model_card.model_name 'test' >>> # Save model card to "path/to/my_dir" as >>> model_card.save_model_card('/path/to/my_dir') >>> # Save model card as "/path/to/my_dir/" >>> model_card.save_model_card('/path/to/my_dir/') """ model_name: Optional[str] = None model_architecture: Optional[str] = None model_overview: Optional[str] = None language: Optional[Union[str, List[str]]] = None # license: Optional[str] = None tags: Optional[Union[str, List[str]]] = None tasks: Optional[Union[str, List[str]]] = None datasets: Optional[Union[str, List[str]]] = None datasets_tags: Optional[Union[str, List[str]]] = None dataset_args: Optional[Union[str, List[str]]] = None eval_results: Optional[Dict[str, float]] = None eval_lines: Optional[List[str]] = None training_summary: Optional[Dict[str, Any]] = None training_config: Optional[TrainingConfig] = None source: Optional[str] = "trainer" def __post_init__(self): pass def __eq__(self, other): return self.__dict__ == other.__dict__ def __repr__(self): return str(self._to_json_string())
[docs] def save_model_card(self, save_directory_or_file): """ Write model card to the given filepath or directory Args: save_directory_or_file (`str`): file path or directory to write and save model card. """ model_card = f"# {self.model_name}" model_card += f"\n\n## Model overview\n{self.model_overview}\n" model_card += f"\n\n### Model architecture\n{self.model_architecture}\n" model_card += "\n\n## Dataset" if self.datasets is None: model_card += "\nDataset unknown.\n" else: if isinstance(self.datasets, str): model_card += f"\nUsing the {self.datasets} as dataset.\n" elif isinstance(self.dataset, (tuple, list)) and len(self.datasets) == 1: model_card += f"\nUsing the {self.datasets} as dataset.\n" else: model_card += ( "\n, ".join( [f"Using the {dataset}" for dataset in self.datasets[:-1]]) + f" and the {self.datasets[-1]} dataset.\n" ) model_card += "\n\n## Training configurations\n" if self.training_config is not None: if self.training_config.optimizer is not None: if isinstance(self.training_config.optimizer, str): model_card += f"\nOptimizer is {self.training_config.optimizer}.\n" else: model_card += "\n".join([f"- {name}: " f"{value}" for name, value in self.training_config.optimizer.items()]) model_card += "\n" if self.training_config.lr_scheduler_type is not None: model_card += f"\nThe scheduler type is {self.training_config.lr_scheduler_type}.\n" if self.training_config.warmup_ratio is not None: model_card += f"\nThe warmup_ratio is {self.training_config.warmup_ratio}.\n" if self.training_config.warmup_steps is not None: model_card += f"\nThe warmup_steps is {self.training_config.warmup_steps}.\n" if is not None: model_card += f"\nLearning rate is {}.\n" else: model_card += "\nLearning rate is needed.\n" # if self.training_config.weight_decay is not None: # model_card += f"\nWeight decay is {self.training_config.weight_decay}." # if self.training_config.adam_beta1 is not None: # model_card += f"\nBeta1 for AdamW optimizer is {self.training_config.adam_beta1}." # if self.training_config.adam_beta2 is not None: # model_card += f"\nBeta2 for AdamW optimizer is {self.training_config.adam_beta2}." # if self.training_config.adam_epsilon is not None: # model_card += f"\nEpsilon for AdamW optimizer is {self.training_config.adam_epsilon}." if self.training_config.metric is not None: model_card += f"\nMetric is {self.training_config.metric}.\n" # # if self.training_config.max_norm_grad is not None: # model_card += f"\nMax gradient norm is {self.training_config.max_norm_grad}." if self.training_config.batch_size is not None: model_card += f"\nBatch size is {self.training_config.batch_size}.\n" if self.training_config.seed is not None: model_card += f"\nRandom seed is {self.training_config.seed}.\n" if self.training_config.epoch_num is not None: model_card += f"\nTraining epochs is {self.training_config.epoch_num}.\n" # if self.training_config.n_gpu is not None: # model_card += f"\nNumber of GPUs is {self.training_config.n_gpu}.\n" else: model_card += "\nTraining configurations is needed.\n" model_card += "\n\n## Training summary\n" if self.training_summary is not None: model_card += "\n" model_card += "\n".join([f"- {name}: {value}" for name, value in self.training_summary.items()]) model_card += "\n" else: model_card += "\nTraining summary is needed.\n" if Path.is_dir(Path(save_directory_or_file)): # If we save using the predefined names, we can load using `from_pretrained` output_model_card_file = Path(save_directory_or_file).joinpath(MODEL_CARD_NAME) else: output_model_card_file = save_directory_or_file with open(output_model_card_file, "w", encoding="utf-8") as f: f.write(model_card)
def _to_json_file(self, json_file_path): """Save this instance to a json file.""" pass def _to_json_string(self): """Serializes this instance to a JSON string.""" pass
[docs] def to_dict(self): """Serializes this instance to a Python dictionary.""" pass
@staticmethod def load_from_file(file_path): pass