--- a
+++ b/HTNet/image-modality/train.py
@@ -0,0 +1,282 @@
+import datetime
+import os
+import time
+
+import torch
+import torch.utils.data
+from torch import nn
+from torchvision import models, transforms
+import utils
+
+def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, print_freq):
+    model.train()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value}'))
+    metric_logger.add_meter('img/s', utils.SmoothedValue(window_size=10, fmt='{value}'))
+
+    header = 'Epoch: [{}]'.format(epoch)
+    for image, target in metric_logger.log_every(data_loader, print_freq, header):
+        start_time = time.time()
+        image, target = image.to(device), target.to(device)
+        output = model(image)
+        loss = criterion(output, target)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        acc1, acc5 = utils.accuracy(output, target, topk=(1, 2))
+        batch_size = image.shape[0]
+        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])
+        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
+        metric_logger.meters['acc2'].update(acc5.item(), n=batch_size)
+        metric_logger.meters['img/s'].update(batch_size / (time.time() - start_time))
+
+
+def evaluate(model, criterion, data_loader, device, print_freq=100):
+    model.eval()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+    with torch.no_grad():
+        for image, target in metric_logger.log_every(data_loader, print_freq, header):
+            image = image.to(device, non_blocking=True)
+            target = target.to(device, non_blocking=True)
+            output = model(image)
+            loss = criterion(output, target)
+
+            acc1, acc5 = utils.accuracy(output, target, topk=(1, 2))
+            # FIXME need to take into account that the datasets
+            # could have been padded in distributed setup
+            batch_size = image.shape[0]
+            metric_logger.update(loss=loss.item())
+            metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
+            metric_logger.meters['acc2'].update(acc5.item(), n=batch_size)
+    # gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+
+    print(' * Acc@1 {top1.global_avg:.3f} Acc@2 {top5.global_avg:.3f}'
+          .format(top1=metric_logger.acc1, top5=metric_logger.acc2))
+    return metric_logger.acc1.global_avg
+
+
+def _get_cache_path(filepath):
+    import hashlib
+    h = hashlib.sha1(filepath.encode()).hexdigest()
+    cache_path = os.path.join("~", ".torch", "vision", "datasets", "imagefolder", h[:10] + ".pt")
+    cache_path = os.path.expanduser(cache_path)
+    return cache_path
+
+
+def load_data(traindir, valdir, cache_dataset, distributed):
+    # Data loading code
+    print("Loading data")
+    normalize = transforms.Normalize(mean=[0.168, 0.174, 0.182],
+                                     std =[0.159, 0.160, 0.162])
+
+    print("Loading training data")
+    st = time.time()
+    cache_path = _get_cache_path(traindir)
+    if cache_dataset and os.path.exists(cache_path):
+        # Attention, as the transforms are also cached!
+        print("Loading dataset_train from {}".format(cache_path))
+        dataset, _ = torch.load(cache_path)
+    else:
+        dataset = utils.CSVDataset(
+            traindir,
+            transforms.Compose([
+                transforms.RandomResizedCrop(224),
+                transforms.RandomHorizontalFlip(),
+                transforms.RandomRotation(degrees=180),
+                transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.0),
+                transforms.ToTensor(),
+                normalize,
+            ]))
+        if cache_dataset:
+            print("Saving dataset_train to {}".format(cache_path))
+            utils.mkdir(os.path.dirname(cache_path))
+            utils.save_on_master((dataset, traindir), cache_path)
+    print("Took", time.time() - st)
+
+    print("Loading validation data")
+    cache_path = _get_cache_path(valdir)
+    if cache_dataset and os.path.exists(cache_path):
+        # Attention, as the transforms are also cached!
+        print("Loading dataset_test from {}".format(cache_path))
+        dataset_test, _ = torch.load(cache_path)
+    else:
+        dataset_test = utils.CSVDataset(
+            valdir,
+            transforms.Compose([
+                transforms.Resize(256),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                normalize,
+            ]))
+        if cache_dataset:
+            print("Saving dataset_test to {}".format(cache_path))
+            utils.mkdir(os.path.dirname(cache_path))
+            utils.save_on_master((dataset_test, valdir), cache_path)
+
+    print("Creating data loaders")
+    if distributed:
+        train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+        test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test)
+    else:
+        train_sampler = torch.utils.data.RandomSampler(dataset)
+        test_sampler = torch.utils.data.SequentialSampler(dataset_test)
+
+    return dataset, dataset_test, train_sampler, test_sampler
+
+
+def main(args):
+
+    if args.output_dir:
+        utils.mkdir(args.output_dir)
+
+    utils.init_distributed_mode(args)
+    print(args)
+
+    device = torch.device(args.device)
+
+    torch.backends.cudnn.benchmark = True
+
+    train_dir = args.train_file
+    val_dir = args.val_file
+    dataset, dataset_test, train_sampler, test_sampler = load_data(train_dir, val_dir,
+                                                                   args.cache_dataset, args.distributed)
+    data_loader = torch.utils.data.DataLoader(
+        dataset, batch_size=args.batch_size,
+        sampler=train_sampler, num_workers=args.workers, pin_memory=True)
+
+    data_loader_test = torch.utils.data.DataLoader(
+        dataset_test, batch_size=args.batch_size,
+        sampler=test_sampler, num_workers=args.workers, pin_memory=True)
+
+    print("Creating model")
+    model = models.resnet152(num_classes=2)
+    Ca_vs_control_checkpoint = "/media/storage1/project/deep_learning/ultrasound_tjmuch/research2.0/resnet152/model_89.pth"
+    if os.path.exists(Ca_vs_control_checkpoint):
+        checkpoint = torch.load(Ca_vs_control_checkpoint, map_location='cpu')
+        model.load_state_dict(checkpoint['model'])
+        
+    model.to(device)
+    if args.distributed and args.sync_bn:
+        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+    criterion = nn.CrossEntropyLoss()
+
+    optimizer = torch.optim.SGD(
+        model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
+
+    lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)
+
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
+        model_without_ddp = model.module
+
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location='cpu')
+        model_without_ddp.load_state_dict(checkpoint['model'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
+        args.start_epoch = checkpoint['epoch'] + 1
+
+    if args.test_only:
+        evaluate(model, criterion, data_loader_test, device=device)
+        return
+
+    print("Start training")
+    start_time = time.time()
+    for epoch in range(args.start_epoch, args.epochs):
+        if args.distributed:
+            train_sampler.set_epoch(epoch)
+        train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args.print_freq)
+        lr_scheduler.step()
+        evaluate(model, criterion, data_loader_test, device=device)
+        if args.output_dir:
+            checkpoint = {
+                'model': model_without_ddp.state_dict(),
+                'optimizer': optimizer.state_dict(),
+                'lr_scheduler': lr_scheduler.state_dict(),
+                'epoch': epoch,
+                'args': args}
+            utils.save_on_master(
+                checkpoint,
+                os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
+            utils.save_on_master(
+                checkpoint,
+                os.path.join(args.output_dir, 'checkpoint.pth'))
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    print('Training time {}'.format(total_time_str))
+
+
+def parse_args():
+    import argparse
+    parser = argparse.ArgumentParser(description='PyTorch Classification Training')
+
+    parser.add_argument('--train-file', help='training set')
+    parser.add_argument('--val-file', help='validation set')
+    parser.add_argument('--num-classes', help='number of classes for the objective task', type=int)
+    parser.add_argument(
+        "--focal-loss", help="Use focal loss",action="store_true")
+    
+    parser.add_argument('--device', default='cuda', help='device')
+    parser.add_argument('-b', '--batch-size', default=32, type=int)
+    parser.add_argument('--epochs', default=90, type=int, metavar='N',
+                        help='number of total epochs to run')
+    parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
+                        help='number of data loading workers (default: 16)')
+    parser.add_argument('--lr', default=0.1, type=float, help='initial learning rate')
+    parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
+                        help='momentum')
+    parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
+                        metavar='W', help='weight decay (default: 1e-4)',
+                        dest='weight_decay')
+    parser.add_argument('--lr-step-size', default=30, type=int, help='decrease lr every step-size epochs')
+    parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma')
+    parser.add_argument('--print-freq', default=10, type=int, help='print frequency')
+    parser.add_argument('--output-dir', default='.', help='path where to save')
+    parser.add_argument('--resume', default='', help='resume from checkpoint')
+    parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
+                        help='start epoch')
+    parser.add_argument(
+        "--cache-dataset",
+        dest="cache_dataset",
+        help="Cache the datasets for quicker initialization. It also serializes the transforms",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--sync-bn",
+        dest="sync_bn",
+        help="Use sync batch norm",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--test-only",
+        dest="test_only",
+        help="Only test the model",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--pretrained",
+        dest="pretrained",
+        help="Use pre-trained models from the modelzoo",
+        action="store_true",
+    )
+
+    # distributed training parameters
+    parser.add_argument('--world-size', default=1, type=int,
+                        help='number of distributed processes')
+    parser.add_argument('--dist-url', default='env://', help='url used to set up distributed training')
+
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)