def run_command(command, override_path=None, force=False):
# Nondestructive commands that don't require cache.
if command == Command.configs:
config_list = configs.load(override_path=override_path)
configs.print_configs(config_list)
# Destructive commands that require cache but not configs.
elif command == Command.purge:
file_cache = FileCache(configs.set_up_cache_dir())
file_cache.purge()
# Commands that require cache and configs.
else:
config_list = configs.load(override_path=override_path)
file_cache = FileCache(configs.set_up_cache_dir())
if command == Command.publish:
for config in config_list:
file_cache.publish(config, version_for(config), force=force)
elif command == Command.install:
for config in config_list:
file_cache.install(config, version_for(config), force=force)
else:
raise AssertionError("unknown command: " + command)
import tensorflow as tf
import cv2
import time
sys.path.append("../../")
from net import ordinal_3_2
from utils.dataread_utils import ordinal_3_1_reader as ordinal_reader
from utils.preprocess_utils import ordinal_3_2 as preprocessor
from utils.visualize_utils import display_utils
##################### Setting for training ######################
import configs
# t means gt(0) or ord(1)
configs.parse_configs(1)
configs.print_configs()
train_log_dir = os.path.join(configs.log_dir, "train")
valid_log_dir = os.path.join(configs.log_dir, "valid")
if not os.path.exists(configs.model_dir):
os.makedirs(configs.model_dir)
restore_model_iteration = None
#################################################################
if __name__ == "__main__":
################### Initialize the data reader ###################
train_range = np.load(configs.train_range_file)
np.random.shuffle(train_range)
