config.py

import os
import yaml

class Config(object):
    def __init__(self, config_path):
        assert os.path.exists(config_path), "ERROR: Config File doesn't exist"
        with open(config_path, 'r') as f:
            self._yaml = f.read()
            self._dict = yaml.load(self._yaml)

    def __getattr__(self, name):
        if self._dict.get(name) is not None:
            return self._dict[name]

        if DEFAULT_CONFIG.get(name) is not None:
            return DEFAULT_CONFIG[name]

        return None

    def print(self):
        print('Model configurations:')
        print('---------------------------------')
        print(self._yaml)
        print('')
        print('---------------------------------')
        print('')

DEFAULT_CONFIG = {
    'SEED': 10,                     # random seed
    'GPU': [5],                     # list of gpu ids
    'LR': 0.0001,                   # learning rate
    'BETA1': 0.0,                   # adam optimizer beta1
    'BETA2': 0.9,                   # adam optimizer beta2
    'BATCH_SIZE': 8,                # input batch size for training
    'INPUT_SIZE': 256,              # input image size for training 0 for original size
    'SAVE_INTERVAL': 1000,          # how many iterations to wait before saving model (0: never)
    'SAMPLE_INTERVAL': 1000,        # how many iterations to wait before sampling (0: never)
    'SAMPLE_SIZE': 12,              # number of images to sample
    'EVAL_INTERVAL': 0,             # how many iterations to wait before model evaluation (0: never)
    'LOG_INTERVAL': 10,             # how many iterations to wait before logging training status (0: never)
}