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import random
from datetime import datetime
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Union
import torch
import yaml
from jobs.process.BaseProcess import BaseProcess
if TYPE_CHECKING:
from jobs import TrainJob, BaseJob, ExtensionJob
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
class BaseTrainProcess(BaseProcess):
def __init__(
self,
process_id: int,
job,
config: OrderedDict
):
super().__init__(process_id, job, config)
self.process_id: int
self.config: OrderedDict
self.writer: 'SummaryWriter'
self.job: Union['TrainJob', 'BaseJob', 'ExtensionJob']
self.progress_bar: 'tqdm' = None
self.training_seed = self.get_conf('training_seed', self.job.training_seed if hasattr(self.job, 'training_seed') else None)
# if training seed is set, use it
if self.training_seed is not None:
torch.manual_seed(self.training_seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(self.training_seed)
random.seed(self.training_seed)
self.progress_bar = None
self.writer = None
self.training_folder = self.get_conf('training_folder',
self.job.training_folder if hasattr(self.job, 'training_folder') else None)
self.save_root = os.path.join(self.training_folder, self.name)
self.step = 0
self.first_step = 0
self.log_dir = self.get_conf('log_dir', self.job.log_dir if hasattr(self.job, 'log_dir') else None)
self.setup_tensorboard()
self.save_training_config()
def run(self):
super().run()
# implement in child class
# be sure to call super().run() first
pass
# def print(self, message, **kwargs):
def print(self, *args):
if self.progress_bar is not None:
self.progress_bar.write(' '.join(map(str, args)))
self.progress_bar.update()
else:
print(*args)
def setup_tensorboard(self):
if self.log_dir:
from torch.utils.tensorboard import SummaryWriter
now = datetime.now()
time_str = now.strftime('%Y%m%d-%H%M%S')
summary_name = f"{self.name}_{time_str}"
summary_dir = os.path.join(self.log_dir, summary_name)
self.writer = SummaryWriter(summary_dir)
def save_training_config(self):
os.makedirs(self.save_root, exist_ok=True)
save_dif = os.path.join(self.save_root, f'config.yaml')
with open(save_dif, 'w') as f:
yaml.dump(self.job.raw_config, f)
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