mirror of
https://github.com/modelscope/modelscope.git
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c9a6b887a2
1. add tensorboard hook to default config 2. add image visualization support to tensorboard hook and trainer 3. move evaluation logic out of single_gpu_test and multi_gpu_test to make prediction results available for further processing such as result saving and visualization. visualization results are as follows:   Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10894813
106 lines
3.5 KiB
Python
106 lines
3.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import time
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from abc import ABC, abstractmethod
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from typing import Callable, Dict, List, Optional, Tuple, Union
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.trainers.builder import TRAINERS
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from modelscope.utils.config import Config
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from modelscope.utils.constant import Invoke
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from .utils.log_buffer import LogBuffer
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class BaseTrainer(ABC):
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""" Base class for trainer which can not be instantiated.
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BaseTrainer defines necessary interface
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and provide default implementation for basic initialization
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such as parsing config file and parsing commandline args.
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"""
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def __init__(self, cfg_file: str, arg_parse_fn: Optional[Callable] = None):
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""" Trainer basic init, should be called in derived class
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Args:
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cfg_file: Path to configuration file.
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arg_parse_fn: Same as ``parse_fn`` in :obj:`Config.to_args`.
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"""
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self.cfg = Config.from_file(cfg_file)
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if arg_parse_fn:
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self.args = self.cfg.to_args(arg_parse_fn)
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else:
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self.args = None
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self.log_buffer = LogBuffer()
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self.visualization_buffer = LogBuffer()
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self.timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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def get_or_download_model_dir(self, model, model_revision=None):
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if os.path.exists(model):
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model_cache_dir = model if os.path.isdir(
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model) else os.path.dirname(model)
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else:
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model_cache_dir = snapshot_download(
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model,
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revision=model_revision,
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user_agent={Invoke.KEY: Invoke.TRAINER})
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return model_cache_dir
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@abstractmethod
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def train(self, *args, **kwargs):
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""" Train (and evaluate) process
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Train process should be implemented for specific task or
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model, releated paramters have been intialized in
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``BaseTrainer.__init__`` and should be used in this function
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"""
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pass
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@abstractmethod
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def evaluate(self, checkpoint_path: str, *args,
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**kwargs) -> Dict[str, float]:
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""" Evaluation process
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Evaluation process should be implemented for specific task or
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model, releated paramters have been intialized in
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``BaseTrainer.__init__`` and should be used in this function
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"""
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pass
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@TRAINERS.register_module(module_name='dummy')
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class DummyTrainer(BaseTrainer):
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def __init__(self, cfg_file: str, *args, **kwargs):
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""" Dummy Trainer.
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Args:
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cfg_file: Path to configuration file.
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"""
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super().__init__(cfg_file)
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def train(self, *args, **kwargs):
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""" Train (and evaluate) process
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Train process should be implemented for specific task or
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model, releated paramters have been intialized in
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``BaseTrainer.__init__`` and should be used in this function
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"""
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cfg = self.cfg.train
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print(f'train cfg {cfg}')
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def evaluate(self,
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checkpoint_path: str = None,
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*args,
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**kwargs) -> Dict[str, float]:
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""" Evaluation process
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Evaluation process should be implemented for specific task or
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model, releated paramters have been intialized in
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``BaseTrainer.__init__`` and should be used in this function
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"""
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cfg = self.cfg.evaluation
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print(f'eval cfg {cfg}')
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print(f'checkpoint_path {checkpoint_path}')
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