def load_dataset(subset: Subset, augment=False) -> batches.BatchGenerator:
dataset = PetsDataset('../data/cifar-10-batches-py', subset)
ops_list = []
if augment:
ops_list += [ops.hflip(), ops.rcrop(32, 12, 'constant')]
ops_list += [
ops.mul(1 / 255),
ops.type_cast(np.float32),
# Imagenet:
# ops.normalize( mean=np.array([0.485, 0.456, 0.406]),
# std=np.array([0.229, 0.224, 0.225])),
# Cifar-10:
ops.normalize(mean=np.array([0.41477802, 0.45935813, 0.49693552]),
std=np.array([0.25241926, 0.24699265, 0.25279155])),
ops.hwc2chw()
]
op = ops.chain(ops_list)
return batches.BatchGenerator(dataset, 128, True, op)
from dlvc.dataset import Subset
import dlvc.ops as ops
np.random.seed(0)
torch.manual_seed(0)
DATA_PATH = "../cifar-10-batches-py/"
MODEL_PATH = "best_model.pt"
train_data = PetsDataset(DATA_PATH, Subset.TRAINING)
val_data = PetsDataset(DATA_PATH, Subset.VALIDATION)
op = ops.chain([
ops.type_cast(np.float32),
ops.add(-127.5),
ops.mul(1 / 127.5),
ops.hflip(),
ops.rcrop(32, 4, 'constant'),
ops.add_noise(),
ops.hwc2chw()
])
train_batches = BatchGenerator(train_data, 128, False, op)
val_batches = BatchGenerator(val_data, 128, False, op)
class Net(nn.Module):
def __init__(self, img_size, num_classes):
super(Net, self).__init__()
self.img_size = img_size
# Instantiate the ReLU nonlinearity