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train_vit_ood_cifar10.py
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train_vit_ood_cifar10.py
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import os
import argparse
import yaml
import time
from pathlib import Path
import wandb
from ml_collections import config_dict
import torch
from ood_with_vit.trainer import (
ViT_Finetune_CIFAR10_Trainer,
ViT_Finetune_CIFAR100_Trainer,
ViT_Finetune_OOD_CIFAR10_Trainer,
ViT_OOD_CIFAR10_Trainer,
)
datasets = [
'CIFAR10',
'CIFAR100',
'OOD_CIFAR10',
'SVHN',
]
def create_trainer(config):
assert config.dataset.name in datasets, 'Error: unsupported dataset!'
if config.model.pretrained:
if config.dataset.name == 'CIFAR10':
trainer = ViT_Finetune_CIFAR10_Trainer(config)
elif config.dataset.name == 'CIFAR100':
trainer = ViT_Finetune_CIFAR100_Trainer(config)
else:
trainer = ViT_Finetune_OOD_CIFAR10_Trainer(config)
else:
trainer = ViT_OOD_CIFAR10_Trainer(config)
return trainer
def main(config, args):
trainer = create_trainer(config)
# frequently used variables
model_name = config.model.name
base_lr = config.optimizer.base_lr
batch_size = config.train.batch_size
patch_size = config.model.patch_size
log_epoch = config.train.log_epoch
summary = config.summary
# create log directories
log_root = Path(args.log_dir) / model_name / summary
checkpoint_path = log_root / 'checkpoints'
checkpoint_path.mkdir(parents=True, exist_ok=True)
# init wandb
name = f"{summary}_lr{base_lr}_bs{batch_size}"
wandb.init(
project=f"OOD-with-ViT",
name=name,
config=config,
)
wandb.config.scheduler = config.scheduler.name
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
if args.resume:
# load checkpoint
print('resuming from checkpoint...')
assert os.path.isdir(checkpoint_path), 'Error: no checkpoint directory found!'
checkpoint = torch.load(checkpoint_path / args.checkpoint)
start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
trainer.model.load_state_dict(checkpoint['model_state_dict'])
trainer.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
trainer.scaler.load_state_dict(checkpoint['scaler_state_dict'])
wandb.watch(trainer.model)
for epoch in range(start_epoch, config.train.n_epochs):
print(f'\nEpoch: {epoch}')
start = time.time()
train_loss = trainer.train()
val_loss, val_acc = trainer.test()
trainer.step_scheduler(val_loss)
# save checkpoint per every log epoch.
if epoch % log_epoch == 0:
print('Save checkpoint...')
state = {
'epoch': epoch,
'best_acc': best_acc,
'model_state_dict': trainer.model.state_dict(),
'optimizer_state_dict': trainer.optimizer.state_dict(),
'scaler_state_dict': trainer.scaler.state_dict(),
}
torch.save(state, checkpoint_path / f'{summary}_{epoch}.pt')
# save checkpoint if best accuracy achieved.
if val_acc > best_acc:
print('Save best checkpoint...')
best_acc = val_acc
state = {
'epoch': epoch,
'best_acc': best_acc,
'model_state_dict': trainer.model.state_dict(),
'optimizer_state_dict': trainer.optimizer.state_dict(),
'scaler_state_dict': trainer.scaler.state_dict(),
}
torch.save(state, checkpoint_path / f'{summary}_best.pt')
# log training info
content = time.ctime() \
+ f' Epoch {epoch}, lr: {trainer.optimizer.param_groups[0]["lr"]:.7f}' \
+ f' val loss: {val_loss:.5f}, acc: {(val_acc):.5f}, time: {time.time() - start:.3f}'
log_file = log_root / f'{summary}.txt'
with log_file.open(mode='a') as f:
f.write(content + "\n")
print(content)
# log to wandb
wandb.log({
'epoch': epoch,
'train_loss': train_loss,
'val_loss': val_loss,
'val_acc': val_acc,
'lr': trainer.optimizer.param_groups[0]['lr'],
"epoch_time": time.time() - start
})
# writeout wandb
wandb.save(f"wandb_{summary}.h5")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--config', required=True, type=str, help='config filename')
parser.add_argument('--resume', action='store_true', help='resume from checkpoint')
parser.add_argument('--log_dir', default='logs', type=str, help='training log directory')
parser.add_argument('--checkpoint', type=str, help='checkpoint path to resume from')
args = parser.parse_args()
# load yaml config and converts to ConfigDict
with open(args.config) as config_file:
config = yaml.safe_load(config_file)
config = config_dict.ConfigDict(config)
main(config, args)