if use_gpu:
trained_cnn = trained_cnn.cuda()
print("=CNN state loaded=")
print("Extracting features...")
# Dump the features to then load them
features_folder_name = save_features(trained_cnn, shapes_dataset,
cnn_model_id)
print("crating one hot metadata")
if not shapes_dataset is None:
# Create onehot metadata if not created yet
if not does_shapes_onehot_metadata_exist(shapes_dataset):
create_shapes_onehot_metadata(shapes_dataset)
# Load metadata
train_metadata, valid_metadata, test_metadata, noise_metadata = load_shapes_onehot_metadata(
shapes_dataset)
else:
train_metadata = None
valid_metadata = None
test_metadata = None
noise_metadata = None
print("loaded metadata")
print("loading data")
# Load data
if not shapes_dataset is None:
if not use_symbolic_input:
if should_train_visual:
cnn_dump_id)
# Load data
if should_train_visual:
assert False
_train_data, _valid_data, _test_data = load_images(
'shapes/{}'.format(target_shapes_dataset), BATCH_SIZE, K)
else:
n_pretrained_image_features, _t, _v, test_data = load_pretrained_features_zero_shot(
target_features_folder_name, distractors_features_folder_name,
BATCH_SIZE, K)
assert n_pretrained_image_features == n_image_features
# Create onehot metadata if not created yet - only target is needed
if not does_shapes_onehot_metadata_exist(target_shapes_dataset):
create_shapes_onehot_metadata(target_shapes_dataset)
# Load metadata - only target is needed
_train_metadata, _valid_metadata, target_test_metadata = load_shapes_onehot_metadata(
target_shapes_dataset)
# Settings
dumps_dir = './dumps'
if should_dump and not os.path.exists(dumps_dir):
os.mkdir(dumps_dir)
current_model_dir = '{}/{}_{}_{}'.format(dumps_dir, dump_id, vocab_size,
max_sentence_length)
if should_dump and not os.path.exists(current_model_dir):
os.mkdir(current_model_dir)