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train.py
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train.py
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import argparse
import datetime
import json
import os
import pytorch_lightning as pl
from pytorch_lightning import seed_everything
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.profilers import SimpleProfiler
from model import VitClassifier
from dataset import CIFAR10
def parse_args():
parser = argparse.ArgumentParser(prog='CIFAR10 classifier trainer')
parser.add_argument(
'--device', type=str,
default='cpu',
help='Execution device',
)
parser.add_argument(
'--epochs', type=int,
default=100,
help='Epochs to train',
)
parser.add_argument(
'--logdir', type=str,
default='logs',
help='Path to train logs',
)
parser.add_argument(
'--val_interval', type=int,
default=None,
help='Validation check interval',
)
parser.add_argument(
'--train_batch', type=int,
default=64,
help='Train batch size',
)
parser.add_argument(
'--val_batch', type=int,
default=32,
help='Validation batch size',
)
parser.add_argument(
'--labels_map', type=str,
default='labels.json',
help='Path to JSON with labels map',
)
return parser.parse_args()
def get_session_tstamp():
session_timestamp = str(datetime.datetime.now())
session_timestamp = session_timestamp.replace(' ', '').replace(':', '-').replace('.', '-')
return session_timestamp
def run_training(args):
seed_everything(42, workers=True)
logger = TensorBoardLogger(save_dir=args.logdir, name=get_session_tstamp())
profiler = SimpleProfiler(filename='profiler_report')
trainer = pl.Trainer(
accelerator=args.device,
strategy='auto',
devices='auto',
num_nodes=1,
precision='32-true',
logger=logger,
callbacks=[
ModelCheckpoint(
dirpath=None,
filename='epoch-{epoch:04d}-loss-{loss/val:.6f}-acc-{accuracy/val:.6f}',
monitor='accuracy/val',
verbose=True,
save_last=True,
save_top_k=3,
mode='max',
auto_insert_metric_name=False,
),
LearningRateMonitor()
],
fast_dev_run=False,
max_epochs=args.epochs,
min_epochs=None,
max_steps=-1,
min_steps=None,
max_time=None,
limit_train_batches=None,
limit_val_batches=None,
limit_test_batches=None,
limit_predict_batches=None,
overfit_batches=0.0,
val_check_interval=args.val_interval,
check_val_every_n_epoch=1,
num_sanity_val_steps=None,
log_every_n_steps=50,
enable_checkpointing=None,
enable_progress_bar=True,
enable_model_summary=True,
accumulate_grad_batches=1,
gradient_clip_val=None,
gradient_clip_algorithm='norm',
deterministic=None,
benchmark=None,
inference_mode=True,
use_distributed_sampler=True,
profiler=profiler,
detect_anomaly=False,
barebones=False,
plugins=None,
sync_batchnorm=False,
reload_dataloaders_every_n_epochs=0,
default_root_dir=None,
)
labels = None
if os.path.isfile(args.labels_map):
with open(args.labels_map, 'rt') as f:
labels = json.load(f)
labels = {int(k): v for k, v in labels.items()}
model = VitClassifier(labels_map=labels)
datamodule = CIFAR10(
train_batch=args.train_batch,
val_batch=args.val_batch,
)
trainer.fit(model=model, datamodule=datamodule)
if __name__ == '__main__':
run_training(parse_args())