def load_dataset(subset: Subset) -> batches.BatchGenerator:
dataset = PetsDataset('../data/cifar-10-batches-py', subset)
op = ops.chain([
ops.hwc2chw(),
ops.add(-127.5),
ops.mul(1 / 127.5),
ops.type_cast(np.float32)
])
return batches.BatchGenerator(dataset, 128, True, op)
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)
param.requires_grad = False
if USE_TRANSFER_LEARNING:
# there are two networks to use in transfer learning "resnet" and "alexnet"
net = initialize_transfer_learning_model("resnet", NUM_CLASSES,
FREEZE_CNN_PARAMETERS)
net, input_size = net
pad_mode_for_resizing = 'constant'
op_chain = chain([
type_cast(dtype=np.float32),
add(-127.5),
mul(1 / 127.5),
rcrop(25, 2, 'median'),
resize(input_size, pad_mode_for_resizing),
hwc2chw()
])
else:
net = CatDogNet()
op_chain = chain([
type_cast(dtype=np.float32),
add(-127.5),
mul(1 / 127.5),
rcrop(25, 2, 'median'),
hwc2chw()
])
batchGenerator_training = BatchGenerator(pets_training,
BATCH_SIZE,
shuffle=True,
op=op_chain)
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
self.relu = nn.ReLU()
# Instantiate two convolutional blocks
NUM_CLASSES = 2
EPOCHS = 500
lr = 0.001
# weight decay 0 in this configuration, in part 3 this is changed
wd = 0.0
pets_training = PetsDataset(dir, Subset.TRAINING)
pets_validation = PetsDataset(dir, Subset.VALIDATION)
pets_test = PetsDataset(dir, Subset.TEST)
batchGenerator_training = BatchGenerator(pets_training, BATCH_SIZE, shuffle=True,
op=chain([type_cast(dtype=np.float32),
add(-127.5),
mul(1 / 127.5),
hwc2chw()]))
batchGenerator_validation = BatchGenerator(pets_validation, BATCH_SIZE, shuffle=False,
op=chain([type_cast(dtype=np.float32),
add(-127.5),
mul(1 / 127.5),
hwc2chw()]))
batchGenerator_test = BatchGenerator(pets_test, BATCH_SIZE, shuffle=False,
op=chain([type_cast(dtype=np.float32),
add(-127.5),
mul(1 / 127.5),
hwc2chw()]))
class CatDogNet(nn.Module):
def __init__(self):
super(CatDogNet, self).__init__()
