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parser.add_argument("--num_steps", default=10000, type=int)
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parser.add_argument('--pretrain', default=True, type=bool)
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# learning parameter
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parser.add_argument('--epochs', default=120, type=int)
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parser.add_argument('--learning_rate', default=0.5, type=float) #1e-4 for ViT
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parser.add_argument('--weight_decay', default=5e-4, type=float)
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parser.add_argument('--batch_size', default=128, type=float)
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parser.add_argument('--test_batch_size', default=128, type=float)
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args = parser.parse_args()
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# the number of gpus for multi-process
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gpu_list = list(map(int, args.gpu.split(',')))
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ngpus_per_node = len(gpu_list)
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# cuda visible devices
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
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os.environ['MASTER_ADDR'] = 'localhost'
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os.environ['MASTER_PORT'] = args.port
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# global best_acc
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best_acc = 0
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# Mix Training
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scaler = GradScaler()
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# make checkpoint folder and set checkpoint name for saving
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if not os.path.isdir(f'checkpoint'): os.mkdir(f'checkpoint')
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if not os.path.isdir(f'checkpoint/standard'): os.mkdir(f'checkpoint/standard')
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if not os.path.isdir(f'checkpoint/standard/{args.dataset}'): os.mkdir(f'checkpoint/standard/{args.dataset}')
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if args.network in transformer_list:
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saving_ckpt_name = f'./checkpoint/standard/{args.dataset}/{args.dataset}_{args.network}_{args.tran_type}_patch{args.patch_size}_{args.img_resize}_best.t7'
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else:
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saving_ckpt_name = f'./checkpoint/standard/{args.dataset}/{args.dataset}_{args.network}{args.depth}_best.t7'
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def train(net, trainloader, optimizer, lr_scheduler, scaler):
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net.train()
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train_loss = 0
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correct = 0
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total = 0
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desc = (f'[Train/LR={lr_scheduler.get_lr()[0]:.3f}] Loss: {0:.3f} | Acc: {0:.2f}')
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prog_bar = tqdm(enumerate(trainloader), total=len(trainloader), desc=desc, leave=True)
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for batch_idx, (inputs, targets) in prog_bar:
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inputs, targets = inputs.cuda(), targets.cuda()
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# Accerlating forward propagation
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optimizer.zero_grad()
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with autocast():
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outputs = net(inputs)
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loss = F.cross_entropy(outputs, targets)
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# Accerlating backward propagation
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scaler.scale(loss).backward()
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scaler.step(optimizer)
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scaler.update()
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# scheduling for Cyclic LR
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lr_scheduler.step()
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train_loss += loss.item()
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_, predicted = outputs.max(1)
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total += targets.size(0)
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correct += predicted.eq(targets).sum().item()
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desc = (f'[Train/LR={lr_scheduler.get_lr()[0]:.3f}] Loss: {train_loss / (batch_idx + 1):.3f} | Acc: {100. * correct / total:.2f}')
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prog_bar.set_description(desc, refresh=True)
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def test(net, testloader, rank):
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global best_acc
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net.eval()
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test_loss = 0
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correct = 0
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total = 0
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desc = (f'[Test/Clean] Loss: {test_loss / (0 + 1):.3f} | Acc: {0:.2f}')
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prog_bar = tqdm(enumerate(testloader), total=len(testloader), desc=desc, leave=False)
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for batch_idx, (inputs, targets) in prog_bar:
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inputs, targets = inputs.cuda(), targets.cuda()
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# Accerlating forward propagation
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with autocast():
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outputs = net(inputs)
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loss = F.cross_entropy(outputs, targets)
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test_loss += loss.item()
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_, predicted = outputs.max(1)
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total += targets.size(0)
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correct += predicted.eq(targets).sum().item()
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desc = (f'[Test/Clean] Loss: {test_loss / (batch_idx + 1):.3f} | Acc: {100. * correct / total:.2f}')
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prog_bar.set_description(desc, refresh=True)
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# Save checkpoint.
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acc = 100.*correct/total
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rprint('Current Accuracy is {:.2f}!!'.format(acc), rank)
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