validation_size = 0.1
# image_path = 'data/ISIC-2017_Training_Data'
image_path = 'data/processed'
mask_path = 'data/ISIC-2017_Training_Part1_GroundTruth'
label_file = 'data/ISIC-2017_Training_Part3_GroundTruth.csv'
# call class DataManger
mymanager = DataManager(image_path, mask_path, label_file)
X_train, y_train, X_test, y_test, X_val, y_val = load_data()
# Initialize the variables
if torch.cuda.is_available():
X_train = X_train.cuda()
y_train = mymanager.convert_labels(y_train).cuda()
X_test = X_test.cuda()
y_test = mymanager.convert_labels(y_test).cuda()
else:
X_train = X_train
y_train = mymanager.convert_labels(y_train) #.type(torch.LongTensor)
X_test = X_test
y_test = mymanager.convert_labels(y_test) #.type(torch.LongTensor)
# Generate mini-batch for learning
train_dataset = torch.utils.data.TensorDataset(X_train, y_train)
test_dataset = torch.utils.data.TensorDataset(X_test, y_test)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=False)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
transforms.Normalize(mean, std)])
test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
if args.dataset == 'melanoma':
# Path
image_path = './data/processed'
mask_path = './data/ISIC-2017_Training_Part1_GroundTruth'
label_file = './data/ISIC-2017_Training_Part3_GroundTruth.csv'
# Generate a DataManager
mymanager = DataManager(image_path, mask_path, label_file)
# Load the data
X_train, y_train, X_test, y_test, X_val, y_val = load_data()
y_train = mymanager.convert_labels(y_train)
y_test = mymanager.convert_labels(y_test)
train_data = torch.utils.data.TensorDataset(X_train, y_train)
test_data = torch.utils.data.TensorDataset(X_test, y_test)
# Number of labels
nlabels = 3
else:
train_data = dset.CIFAR100(args.data_path, train=True, transform=train_transform, download=True)
test_data = dset.CIFAR100(args.data_path, train=False, transform=test_transform, download=True)
nlabels = 100
train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True,
num_workers=args.prefetch, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.test_bs, shuffle=False,