def main():
train_data = load_data(current_path + 'data_set/', 'test')
length = len(train_data['video_ids'])
train_data['features'] = train_data['features'][:int(0.7 * length)]
train_data['labels'] = train_data['labels'][:int(0.7 * length)]
train_data['video_ids'] = train_data['video_ids'][:int(0.7 * length)]
train_data['video_filenames'] = train_data['video_filenames'][:int(0.7 * length)]
# train_data = {}
data = {'train_data': train_data}
label_to_idx = load_pickle(current_path + 'data_set/label_to_idx.pkl')
num_images_per_video = 17
model = CaptionGenerator(label_to_idx=label_to_idx, dim_feature=[196, 512],
dim_hidden=1024, n_time_step=num_images_per_video, ctx2out=True,
alpha_c=1.0, selector=True, dropout=False)
solver = CaptioningSolver(model, data, n_epochs=500, batch_size=15, update_rule='adam',
learning_rate=0.0006, print_every=3, save_every=10,
pretrained_model=None, model_path=current_path + 'model/lstm/',
test_model=current_path + 'model/lstm/model-430', log_path=current_path + 'log/',
data_path=current_path + '/data_set/',
test_result_save_path=current_path + 'data_set/test/model_test_result/',
models_val_disp=current_path + 'model/models_accuracy_val.txt')
solver.train()
solver.all_model_val()
def main():
# train_data = load_data(current_path + 'data_set/', 'test')
# length = len(train_data['video_ids'])
# train_data['features'] = train_data['features'][:int(0.7 * length)]
# train_data['labels'] = train_data['labels'][:int(0.7 * length)]
# train_data['video_ids'] = train_data['video_ids'][:int(0.7 * length)]
# train_data['video_filenames'] = train_data['video_filenames'][:int(0.7 * length)]
with open('train_data_vgg.pkl', 'rb') as handle:
train_data = pickle.load(handle)
# length = len(train_data['new_filename'])
train_data['features'] = train_data['features']
train_data['labels'] = train_data['labels']
train_data['video_ids'] = train_data['new_filename']
# train_data['video_filenames'] = train_data['video_filenames'][:int(0.7 * length)]
if train_for_test == 1:
with open('val_data_vgg.pkl', 'rb') as handle:
val_data = pickle.load(handle)
# length = len(train_data['new_filename'])
train_data['features'] = np.concatenate(
(train_data['features'], val_data['features']), axis=0)
train_data['labels'] = np.concatenate(
(train_data['labels'], val_data['labels']), axis=0)
train_data['video_ids'] = np.concatenate(
(train_data['new_filename'], val_data['new_filename']), axis=0)
# train_data = {}
data = {'train_data': train_data}
# label_to_idx = load_pickle(current_path + 'data_set/label_to_idx.pkl')
label_to_idx = load_pickle('labels_to_idx.pkl')
num_images_per_video = 17
model = CaptionGenerator(
label_to_idx=label_to_idx,
# dim_feature=[49, 1280],
dim_feature=[196, 512],
dim_hidden=1024,
n_time_step=num_images_per_video,
ctx2out=True,
alpha_c=1.0,
selector=True,
dropout=False)
solver = CaptioningSolver(model,
data,
n_epochs=300,
batch_size=15,
update_rule='adam',
learning_rate=0.0006,
print_every=3,
save_every=10,
pretrained_model=None,
model_path=current_path + 'model/lstm/',
test_model=current_path + 'model/lstm/model-310',
log_path=current_path + 'log/',
data_path=current_path + '/data_set/',
test_result_save_path=current_path +
'data_set/test/model_test_result/',
models_val_disp=current_path +
'model/lstm/models_accuracy_val.txt')
if train == 1:
solver.train()
if validate == 1:
solver.all_model_val()
if test == 1:
solver.test()
if train_for_test == 1:
solver.train()