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train_model.py
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train_model.py
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import argparse
import torchvision.models as models
import torch.nn as nn
from load_data import *
from torch_datasets.configs import (
get_n_classes, get_optimizer, get_lr_scheduler, get_models
)
import time
import torch.backends.cudnn as cudnn
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
def main():
parser = argparse.ArgumentParser(description='Train.')
parser.add_argument('--dataset', default='CIFAR-10', type=str)
parser.add_argument('--data_path', default='./data', type=str)
parser.add_argument('--n_val_samples', default=10000, type=int)
parser.add_argument('--arch', default='resnet18', type=str)
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--lr', default=0.001, type=float)
parser.add_argument('--pretrained', action='store_true', default=False)
parser.add_argument('--train_epoch', default=20, type=int)
parser.add_argument('--eval_interval', default=1, type=int)
parser.add_argument('--save_interval', default=5, type=int)
parser.add_argument('--resume_epoch', default=0, type=int)
parser.add_argument('--dataset_seed', default=1, type=int)
parser.add_argument('--model_seed', default=1, type=int)
args = parser.parse_args()
print(vars(args))
dsname = args.dataset
n_class = get_n_classes(dsname)
if args.pretrained:
save_dir_path = f"./checkpoints/{dsname}/{args.arch}/pretrained"
else:
save_dir_path = f"./checkpoints/{dsname}/{args.arch}/scratch"
if not os.path.exists(save_dir_path):
os.makedirs(save_dir_path)
# setup train/val_iid loaders
trainset = load_train_dataset(dsname=dsname,
iid_path=args.data_path,
n_val_samples=args.n_val_samples,
pretrained=args.pretrained,
seed=args.dataset_seed)
valset = load_val_dataset(dsname=dsname,
iid_path=args.data_path,
n_val_samples=args.n_val_samples,
pretrained=args.pretrained,
seed=args.dataset_seed)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, num_workers=8, shuffle=True, pin_memory=True)
valloader = torch.utils.data.DataLoader(valset, batch_size=args.batch_size, num_workers=8, shuffle=False, pin_memory=True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# init and train base modeltrain_model.py
model = get_models(args.arch, n_class, args.model_seed, args.pretrained).to(device)
n_device = torch.cuda.device_count()
print('available devices:', n_device)
model = torch.nn.DataParallel( model, device_ids=range(n_device) )
cudnn.benchmark = False
optimizer = get_optimizer(args.dataset, model, args.lr, args.pretrained)
scheduler = get_lr_scheduler(args.dataset, optimizer, args.pretrained, T_max=args.train_epoch * len(trainloader))
resume_epoch = args.resume_epoch
if resume_epoch > 0:
ckpt_dir = f"{save_dir_path}/base_model_{args.model_seed}-{resume_epoch}.pt"
ckpt = torch.load(ckpt_dir, map_location=device)
model = ckpt['model']
optimizer = get_optimizer(args.dataset, model, args.lr, args.pretrained)
scheduler = get_lr_scheduler(args.dataset, optimizer, args.pretrained, T_max=args.train_epoch)
optimizer.load_state_dict(ckpt['optimizer_state_dict'])
scheduler.load_state_dict(ckpt['optimizer_state_dict'])
print('begin training...')
train(model, optimizer, scheduler, trainloader, valloader, save_dir_path, args, device)
def train(net, optimizer, scheduler, trainloader, valloader, save_dir, args, device):
net.train()
criterion = nn.CrossEntropyLoss()
scaler = torch.cuda.amp.GradScaler(enabled=True)
for epoch in range(1, args.train_epoch + 1):
train_loss = 0
correct = 0
total = 0
start = time.time()
for batch_idx, items in enumerate(trainloader):
inputs, targets = items[0], items[1]
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
with torch.cuda.amp.autocast():
outputs = net(inputs)
loss = criterion(outputs, targets)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
if batch_idx % 2 == 0:
for param_group in optimizer.param_groups:
current_lr = param_group['lr']
print('Epoch: ', epoch, '(', batch_idx, '/', len(trainloader), ')',
'Loss: %.3f | Acc: %.3f%% (%d/%d)| Lr: %.5f' % (
train_loss / (batch_idx + 1), 100. * correct / total, correct, total, current_lr)
)
if batch_idx % 100 == 0:
print(f"time used: {time.time() - start}s")
if args.dataset == 'RxRx1':
scheduler.step()
if args.dataset != 'RxRx1':
scheduler.step()
end = time.time()
print(f"time used: {end - start}s")
if epoch % args.save_interval == 0:
torch.save({
'model': net,
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict()
},
f"{save_dir}/base_model_{args.model_seed}-{epoch + args.resume_epoch}.pt")
if epoch % args.eval_interval == 0:
net.eval()
val_total = 0
val_correct = 0
with torch.no_grad():
for items in valloader:
inputs, targets = items[0], items[1]
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
_, predicted = outputs.max(1)
val_total += targets.size(0)
val_correct += predicted.eq(targets).sum().item()
net.train()
print(f'Epoch {epoch} Validation Acc: {val_correct / val_total}')
if args.resume_epoch + epoch >= args.train_epoch:
break
net.eval()
torch.save({
'model': net,
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict()
},
f"{save_dir}/base_model_{args.model_seed}-{args.train_epoch}.pt")
print('base model saved to', f"{save_dir}/base_model_{args.model_seed}-{args.train_epoch}.pt")
return net
if __name__ == "__main__":
main()