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)