chenxuanhong
122 lines
4.5 KiB
Python
122 lines
4.5 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: trainer_base.py
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# Created Date: Sunday January 16th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Sunday, 6th February 2022 3:06:45 pm
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 Shanghai Jiao Tong University
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#############################################################
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class TrainerBase(object):
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def __init__(self, config, reporter):
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self.config = config
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# logger
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self.reporter = reporter
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#========build evaluation dataloader=============#
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# TODO to modify the key: "your_eval_dataset" to get your evaluation dataset path
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# eval_dataset = config["dataset_paths"][config["eval_dataset_name"]]
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# #================================================#
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# print("Prepare the evaluation dataloader...")
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# dlModulename = config["eval_dataloader"]
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# package = __import__("data_tools.eval_dataloader_%s"%dlModulename, fromlist=True)
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# dataloaderClass = getattr(package, 'EvalDataset')
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# dataloader = dataloaderClass(eval_dataset,
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# config["eval_batch_size"])
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# self.eval_loader= dataloader
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# self.eval_iter = len(dataloader)//config["eval_batch_size"]
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# if len(dataloader)%config["eval_batch_size"]>0:
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# self.eval_iter+=1
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# #==============build tensorboard=================#
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# if self.config["logger"] == "tensorboard":
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# from utilities.utilities import build_tensorboard
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# tensorboard_writer = build_tensorboard(self.config["project_summary"])
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# self.logger = tensorboard_writer
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# elif self.config["logger"] == "wandb":
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# import wandb
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# wandb.init(project="Simswap_HQ", entity="xhchen", notes="512",
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# tags=[self.config["tag"]], name=self.config["version"])
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# wandb.config = {
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# "total_step": self.config["total_step"],
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# "batch_size": self.config["batch_size"]
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# }
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# self.logger = wandb
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# TODO modify this function to build your models
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def __init_framework__(self):
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'''
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This function is designed to define the framework,
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and print the framework information into the log file
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'''
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#===============build models================#
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pass
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# TODO modify this function to configurate the optimizer of your pipeline
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def __setup_optimizers__(self):
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pass
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# TODO modify this function to evaluate your model
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# Evaluate the checkpoint
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def __evaluation__(self,
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step = 0,
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**kwargs
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):
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pass
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def __create_dataloader__(self,
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config,
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cur_gpu
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):
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# Data loader
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#============build train dataloader==============#
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# TODO to modify the key: "your_train_dataset" to get your train dataset path
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dataset = config["dataset_paths"][config["dataset_name"]]
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#================================================#
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print("Prepare the train dataloader...")
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dlModulename = config["dataloader"]
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package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True)
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dataloaderClass = getattr(package, 'GetLoader')
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dataloader_class= dataloaderClass
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dataloader = dataloader_class(dataset,
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cur_gpu,
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config["batch_size"],
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**config["dataset_params"])
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return dataloader
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def train(self):
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#===============build framework================#
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self.init_framework()
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#===============build optimizer================#
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# Optimizer
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# TODO replace below lines to build your optimizer
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print("build the optimizer...")
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self.__setup_optimizers__()
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# set the start point for training loop
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if self.config["phase"] == "finetune":
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self.start = self.config["checkpoint_step"]
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else:
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self.start = 0
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# Start time
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import datetime
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print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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from utilities.logo_class import logo_class
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logo_class.print_start_training() |