def predict(inp, target, params, restore_from, config=None,\
model_dir='./ie590_project/experiments/ex1', model_save_dir='./ie590_project/experiments/ex1/model_save/1'):
"""predict target values given input file paths
Args:
inp: (list) a string list of image files paths; 2D -> [sample_size, number_of_channels]
model_spec: (dict) model specifications of tf Ops
params: (Params or str) Params object or params.joson path
tar: (list) a float list of target values
restore_from: (str) ckpt or directory name where ckpts are located for restoring
...
Return:
out: (list) a list of precicted target values; have exactly same dimension as target
"""
assert len(inp) == len(target)
iterator_init_op = model_spec['iterator_init_op']
update_metrics_op = model_spec['update_metrics_op']
metrics = model_spec['metrics']
metrics_init_op = model_spec['metrics_init_op']
predictions = model_spec['predictions']
saver = tf.compat.v1.train.Saver()
if type(params) is str:
assert os.path.isfile(
params), "params.json does not exits at {}".format(params)
params = Params(params)
params.load(params.update) # load parameters
params.inp_size = len(inp)
set_logger(os.path.join(model_dir, 'train.log'))
logging.info("Creating the dataset...")
inputs = input_fn(False, inp, target, params)
logging.info("Creating the model...")
model_spec = model_fn(False, inputs, params)
logging.info("Calculating predictions...")
with tf.compat.v1.Session(config=config) as sess:
sess.run(model_spec['variable_init_op'])
save_path = os.path.join(model_save_dir, restore_from)
if os.path.isdir(save_path):
save_path = tf.train.latest_checkpoint(
save_path
) # If restore_from is a directory, get the latest ckpt
saver.restore(sess, save_path)
num_steps = (params.inp_size + params.batch_size -
1) // params.batch_size
sess.run([iterator_init_op, metrics_init_op])
if len(np.shape(target)) == 1:
out = np.empty(np.shape(target))[:, np.newaxis]
else:
out = np.empty(np.shape(target))
for i in tqdm(range(num_steps)):
_, predictions_eval = sess.run([update_metrics_op, predictions])
if i < num_steps - 1:
out[i * params.batch_size:(i + 1) *
params.batch_size, :] = predictions_eval
else:
out[i * params.batch_size:, :] = predictions_eval
return out
for _ in range(num_steps):
_, predictions_eval = sess.run([update_metrics_op, predictions])
if __name__ == '__main__':
start_time = time.time()
#for reproducibility
tf.compat.v1.set_random_seed(123)
args = parser.parse_args()
params_path = os.path.join(args.model_dir, 'params.json')
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...")