def main():
data = PetsDataset("/home/helmuth/dlvc/cifar-10-batches-py", Subset.TRAINING)
# ops chain
op = ops.chain([
ops.vectorize(),
ops.type_cast(np.float32),
ops.add(-127.5),
ops.mul(1/127.5),
])
# batch generator #1
bg1 = BatchGenerator(data, len(data), False)
assert(len(bg1) == 1)
# batch generator #2
bg2 = BatchGenerator(data, 500, False, op)
assert(len(bg2) == 16)
# first batch
cnt = 0
for batch in bg2:
cnt += 1
if cnt < 16:
assert(batch.data.shape == (500, 3072))
assert(batch.labels.shape == (500,))
assert(batch.data.dtype == np.float32)
assert(np.issubdtype(batch.labels.dtype, np.integer))
if cnt == 1:
print("First batch, first sample, not shuffled")
print(batch.data[0])
# batch generator #3
bg3 = BatchGenerator(data, 500, True, op)
# run 5 times through first sample of shuffled batch generator
for i in range(5):
it = iter(bg3)
print("First batch, first sample, shuffled")
print(next(it).data[0])
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)
def set_parameter(model, freeze_parameters):
if freeze_parameters:
for param in model.parameters():
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,
from dlvc.datasets.pets import PetsDataset
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
import numpy as np
from dlvc.dataset import Subset
from dlvc.datasets.pets import PetsDataset
from dlvc import ops, batches
dataset = PetsDataset('../data/cifar-10-batches-py', Subset.TRAINING)
op = ops.chain([ops.mul(1 / 255), ops.type_cast(np.float32)])
batch_generator = batches.BatchGenerator(dataset, 7959, True, op)
training_images = []
for batch in batch_generator:
training_images.append(batch.data)
training_images = np.array(training_images, dtype=np.float32)
training_images = training_images.reshape(training_images.shape[1:])
train_mean = np.mean(training_images, axis=(0, 1, 2))
train_std = np.std(training_images, axis=(0, 1, 2))
print(train_mean, train_std)
