def main(argv):
st = time.time()
print 'start loading data'
dataset_train = Dataset(g_.IMAGE_LIST_TRAIN,
subtract_mean=True,
name='train')
dataset_val = Dataset(g_.IMAGE_LIST_VAL, subtract_mean=True, name='val')
print 'done loading data, time=', time.time() - st
train(dataset_train, dataset_val, FLAGS.weights, FLAGS.caffemodel)
def main(argv):
st = time.time()
print 'start loading data'
dataset = Dataset(g_.IMAGE_LIST_TEST, subtract_mean=True, name='test')
print 'done loading data, time=', time.time() - st
test(dataset, FLAGS.weights)
def main(argv):
st = time.time()
print('start loading data')
listfiles_train, labels_train = read_lists(g_.TRAIN_LOL)
listfiles_val, labels_val = read_lists(g_.VAL_LOL)
dataset_train = Dataset(listfiles_train,
labels_train,
subtract_mean=False,
V=g_.NUM_VIEWS)
dataset_val = Dataset(listfiles_val,
labels_val,
subtract_mean=False,
V=g_.NUM_VIEWS)
print('done loading data, time=', time.time() - st)
train(dataset_train, dataset_val, FLAGS.weights, FLAGS.caffemodel)
def main(argv):
st = time.time()
print 'start loading data'
listfiles, labels = read_lists(g_.TEST_LOL)
dataset = Dataset(listfiles, labels, subtract_mean=False, V=g_.NUM_VIEWS)
print 'done loading data, time=', time.time() - st
test(dataset, FLAGS.weights)
data_path = config.data
test = 0
with open(os.path.join(data_path, 'test.txt'), 'r') as f:
for line in f:
if os.path.exists(line.split()[0]):
pass
else:
test += 1
print(line)
print('start loading data')
listfiles_test, labels_test = read_lists(
os.path.join(data_path, 'test.txt'))
dataset_test = Dataset(listfiles_test,
labels_test,
subtract_mean=False,
V=config.num_views)
if not config.test:
LOSS_LOGGER = Logger("{}_loss".format(config.name))
ACC_LOGGER = Logger("{}_acc".format(config.name))
listfiles_train, labels_train = read_lists(
os.path.join(data_path, 'train.txt'))
dataset_train = Dataset(listfiles_train,
labels_train,
subtract_mean=False,
V=config.num_views)
train(dataset_train, dataset_test, config.pretrained_network_file)
if config.weights == -1:
config = add_to_config(config, 'weights', config.max_epoch)