dataaugmentation

data_aug.py

#data augmentation 
train_transforms = transforms.Compose([
                                       #transforms.ToPILImage(),
                                       transforms.RandomHorizontalFlip(),
                                       transforms.RandomRotation(10),
                                       transforms.RandomResizedCrop(224),
                                       transforms.ToTensor(),
                                       transforms.Normalize([0.485, 0.456, 0.406],
                                                      [0.229, 0.224, 0.225])])

test_transforms = transforms.Compose([
                                      #transforms.ToPILImage(),
                                      transforms.RandomResizedCrop(224),
                                      transforms.ToTensor(),
                                      transforms.Normalize([0.485, 0.456, 0.406],
                                                           [0.229, 0.224, 0.225])])

#load training and test and pass the transforms
train_data = ImageDataset(csv_file=r'gdrive/My Drive/Malaria/train.csv', transform=train_transforms)
test_data = ImageDataset(csv_file=r'gdrive/My Drive/Malaria/test.csv', transform=test_transforms)

#obtaining indices that will be used for validation
val_size = 0.1  # % of validation set to be used

num_train = len(train_data)
indices = list(range(num_train))
np.random.shuffle(indices)
split = int(np.floor(val_size * num_train))
train_idx, val_idx = indices[split:], indices[:split]

#define samplers for obtaining training and validation batches
train_sampler = SubsetRandomSampler(train_idx)
val_sampler = SubsetRandomSampler(val_idx)

#prepare DataLoaders
batch_size = 20  #samples per batch to load
num_workers = 0  #no. of subprocesses to use for data loading

train_loader = DataLoader(train_data, batch_size=batch_size, num_workers=num_workers, sampler=train_sampler)
val_loader = DataLoader(train_data, batch_size=batch_size, num_workers=num_workers, sampler=val_sampler)
test_loader = DataLoader(test_data, batch_size=batch_size, num_workers=num_workers)