assert os.path.isfile(
params_path), "params.json does not exits at {}".format(params_path)
params = Params(params_path)
params.load(params.update)
#TODO: check and load if there's the best weights so far
# model_dir_has_best_weights = os.path.isdir(os.path.join(args.model_dir, "best_weights"))
#set logger
set_logger(os.path.join(args.model_dir, 'train.log'))
#train/test split
train_fpaths, test_fpaths, train_targets, test_targets = \
get_train_test_split(args.json_path, args.data_dir, train_size=args.train_size)
params.train_size = len(train_fpaths)
params.test_size = len(test_fpaths)
logging.info("Creating the dataset...")
train_inputs = input_fn(True, train_fpaths, train_targets, params)
test_inputs = input_fn(False, test_fpaths, test_targets, params)
logging.info("Creating the model...")
train_model_spec = model_fn(True, train_inputs, params)
test_model_spec = model_fn(False, test_inputs, params, reuse=True)
logging.info("train set predict...")
predict(train_model_spec, args.model_save_dir, params, args.restore_from)
logging.info("test set predict...")
predict(test_model_spec, args.model_save_dir, params, args.restore_from)
os.path.join(train_data_dir, f) for f in os.listdir(train_data_dir)
]
eval_filenames = [
os.path.join(eval_data_dir, f) for f in os.listdir(eval_data_dir)
]
# Get the aligned images list
aligned_images_list_train = glob.glob(train_filenames[1] + '/*.jpg')
aligned_images_list_eval = glob.glob(eval_filenames[1] + '/*.jpg')
# Get the raw images list
raw_images_list_train = glob.glob(train_filenames[0] + '/*.jpg')
raw_images_list_eval = glob.glob(eval_filenames[0] + '/*.jpg')
# Specify the sizes of the dataset we train on and evaluate on
params.train_size = len(aligned_images_list_train)
params.eval_size = len(aligned_images_list_eval)
# Create the two iterators over the two datasets
print('=================================================')
print(
'[INFO] Dataset is built by {0} training images and {1} eval images '.
format(len(aligned_images_list_train), len(aligned_images_list_eval)))
tf.debugging.set_log_device_placement(args.v)
train_dataset = input_fn(True,
raw_images_list_train,
aligned_images_list_train,
params=params)
eval_dataset = input_fn(False,
raw_images_list_eval,