def main(argv):
"""Main function."""
del argv
dataset = load_data(distorted=True,
data_path=DATA_PATH,
dataset='cifar100')
(x_train_distorted, _), (x_test_distorted, _) = dataset
dataset = load_data(distorted=False,
data_path=DATA_PATH,
dataset='cifar100')
(x_train, _), (x_test, _) = dataset
model = NetworkCifar()
features = model.features(x_train)
str_file = 'features_ll_train.npy'
data_path = os.path.join(PATH_MODEL, str_file)
with tf.gfile.Open(data_path, 'wb') as f:
np.save(f, features)
features = model.features(x_train_distorted)
str_file = 'features_ll_train_distorted.npy'
data_path = os.path.join(PATH_MODEL, str_file)
with tf.gfile.Open(data_path, 'wb') as f:
np.save(f, features)
features = model.features(x_test)
str_file = 'features_ll_test.npy'
data_path = os.path.join(PATH_MODEL, str_file)
with tf.gfile.Open(data_path, 'wb') as f:
np.save(f, features)
features = model.features(x_test_distorted)
str_file = 'features_ll_test_distorted.npy'
data_path = os.path.join(PATH_MODEL, str_file)
with tf.gfile.Open(data_path, 'wb') as f:
np.save(f, features)
def train_cifar100(features_cifar100_train=gin.REQUIRED,
features_cifar100_test=gin.REQUIRED,
data_path_cifar100=gin.REQUIRED,
features_cifar100_train_distorted=gin.REQUIRED,
distorted=gin.REQUIRED,
model_dir=gin.REQUIRED,
dim_input=gin.REQUIRED,
num_classes=gin.REQUIRED):
"""Training function."""
# Define the paths
features_cifar100_train = os.path.join(FLAGS.baseroute,
features_cifar100_train)
features_cifar100_test = os.path.join(FLAGS.baseroute,
features_cifar100_test)
data_path_cifar100 = os.path.join(FLAGS.baseroute, data_path_cifar100)
features_cifar100_train_distorted = os.path.join(
FLAGS.baseroute, features_cifar100_train_distorted)
model_dir = os.path.join(FLAGS.baseroute, model_dir)
# Load the features for cifar100
(_, y_train), (_, y_test) = cifar_input.load_data(distorted,
data_path_cifar100,
'cifar100')
if distorted:
with tf.gfile.Open(features_cifar100_train_distorted, 'r') as f:
x_train = np.load(f)
else:
with tf.gfile.Open(features_cifar100_train, 'r') as f:
x_train = np.load(f)
with tf.gfile.Open(features_cifar100_test, 'r') as f:
x_test = np.load(f)
dataset = (x_train, y_train), (x_test, y_test)
model = build_model(dataset, model_dir, dim_input, num_classes)
# Compute output probabilities on x_test
# Warning: take time, pass over all the saved weights.
model.predict(x_test)
# Postprocessing and metrics
if FLAGS.algorithm in ['simple', 'dropout', 'precond']:
postprocess.postprocess_cifar(FLAGS.workdir, 'cifar100')
path_postprocess = os.path.join(FLAGS.workdir, 'cifar100')
metrics.Metrics(y_test, path_postprocess)
# Write the gin config in the working directory
util.save_gin(os.path.join(FLAGS.workdir, 'gin_configuration.txt'))
util.write_gin(FLAGS.workdir)