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main.py
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main.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
curdir = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, os.path.dirname(curdir))
prodir = '..'
sys.path.insert(0, prodir)
import pickle as pkl
import logging
import argparse
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
from Network import Network
from networks.DAMI import DAMI
from data_loader import Data_loader
from utility import *
CONFIG_ROOT = curdir + '/config'
random_seed = 7
tf.compat.v1.set_random_seed(random_seed)
def main():
start_t = time.time()
# Obtain arguments from system
parser = argparse.ArgumentParser('Tensorflow')
parser.add_argument('--phase', default='train',
help='Phase: Can be train or predict, the default value is train.')
parser.add_argument('--data_name', default='makeup',
help='Data_Name: The data you will use.')
parser.add_argument('--model_name', default='dami',
help='Model_Name: The model you will use.')
parser.add_argument('--model_path', default='none',
help='Model_Path: The model path you will load.')
parser.add_argument('--memory', default='0.',
help='Memory: The gpu memory you will use.')
parser.add_argument('--gpu', default='0',
help='GPU: Which gpu you will use.')
parser.add_argument('--log_path', default='./networks/logs/',
help='path of the log file. If not set, logs are printed to console.')
parser.add_argument('--suffix', default='.128',
help='suffix for differentiate log.')
parser.add_argument('--mode', default='train',
help='mode fold you will try.')
parser.add_argument('--ways', default='dami',
help='whether to use supervised method or position or deal.')
parser.add_argument('--use_pretrain', default='1',
help='whether to use supervised method or position or deal.')
args = parser.parse_args()
logger = logging.getLogger("Tensorflow")
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(message)s')
now_time = '_'.join(time.asctime(time.localtime(time.time())).split(' ')[:3])
# log directory
if not os.path.exists(args.log_path):
os.mkdir(args.log_path)
# log file name setting
log_path = args.log_path + args.model_name + '.' + args.data_name + '.' + args.phase + args.suffix \
+ '.' + args.mode + '.' + args.ways + '.' + now_time + '.log'
if os.path.exists(log_path):
os.remove(log_path)
if args.log_path:
file_handler = logging.FileHandler(log_path)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
else:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
logger.info('Random seed: {}'.format(random_seed))
logger.info('Running with args : {}'.format(args))
# get object named data_loader
data_loader = Data_loader(data_name=args.data_name)
# Get config from file
logger.info('Load data_set and vocab...')
data_config_path = CONFIG_ROOT + '/data/config.' + args.data_name + '.json'
model_config_path = CONFIG_ROOT + '/model/config.' + args.model_name + '.json'
data_config = data_loader.load_config(data_config_path)
model_config = data_loader.load_config(model_config_path)
logger.info('Data config is {}'.format(data_config))
logger.info('Model config is {}'.format(model_config))
# Get config param
model_name = model_config['model_name']
batch_size = model_config['batch_size']
epochs = model_config['epochs']
keep_prob = model_config['keep_prob']
mode = model_config['mode']
is_val = model_config['is_val']
is_test = model_config['is_test']
save_best = model_config['save_best']
shuffle = model_config['shuffle']
data_name = data_config['data_name']
nb_classes = data_config['nb_classes']
mode = args.mode
vocab_path = curdir + '/data/' + data_name + '/vocab.pkl'
memory = float(args.memory)
logger.info("Memory in train %s." % memory)
# Get vocab
with open(vocab_path, 'rb') as fp:
vocab = pkl.load(fp)
# Get Network Framework
if model_name == 'network':
network = Network(memory=memory, vocab=vocab)
elif model_name == 'dami':
network = DAMI(memory=memory, vocab=vocab, config_dict=model_config)
else:
logger.info("We can't find {}: Please check model you want."
.format(model_name))
raise ValueError("We can't find {}: Please check model you want."
.format(model_name))
# Set param for network
network.set_nb_words(min(vocab.size(), data_config['nb_words']) + 1)
network.set_data_name(data_name)
network.set_name(model_name + args.suffix + 'train')
network.set_from_model_config(model_config)
network.set_from_data_config(data_config)
if 'sup' in args.ways:
print('Using data_generator_sup')
data_generator = data_loader.data_generator_sup
elif args.ways == 'crf':
print('Using data_generate_crf')
data_generator = data_loader.data_generator_crf
elif args.ways == 'dami':
print('Using data_generator_m')
data_generator = data_loader.data_generator_m
else:
raise ValueError("Wrong data generator! Please check the 'ways' you input.")
network.build_graph()
logger.info('All values in the Network are {}'.format(network.__dict__))
if args.phase == 'train':
train(network, data_generator, keep_prob, epochs, data_name,
mode=mode, batch_size=batch_size, nb_classes=nb_classes, shuffle=shuffle,
is_val=is_val, is_test=is_test, save_best=save_best, ways=args.ways)
else:
logger.info("{}: Please check phase you want, such as 'train' or 'evaluate'.".format(args.phase))
raise ValueError("{}: Please check phase you want, such as 'train' or 'evaluate'.".format(args.phase))
logger.info('The whole program spends time: {}h: {}m: {}s'.format(int((int(time.time()) - start_t) / 3600),
int((int(time.time()) - start_t) % 3600 / 60),
int((int(time.time()) - start_t) % 3600 % 60)))
print("DONE!")
def train(network, data_generator, keep_prob, epochs, data_name,
mode='train', batch_size=20, nb_classes=2, shuffle=True,
is_val=True, is_test=True, save_best=True, ways='crf'):
if ways == 'crf':
network.train_crf(data_generator=data_generator, keep_prob=keep_prob, epochs=epochs, data_name=data_name,
mode=mode, batch_size=batch_size, nb_classes=nb_classes, shuffle=shuffle,
is_val=is_val, is_test=is_test, save_best=save_best)
elif ways == 'sup':
network.train_sup(data_generator=data_generator, keep_prob=keep_prob, epochs=epochs, data_name=data_name,
mode=mode, batch_size=batch_size, nb_classes=nb_classes, shuffle=shuffle,
is_val=is_val, is_test=is_test, save_best=save_best)
elif ways == 'dami':
network.train(data_generator=data_generator, keep_prob=keep_prob, epochs=epochs, data_name=data_name,
mode=mode, batch_size=batch_size, nb_classes=nb_classes, shuffle=shuffle,
is_val=is_val, is_test=is_test, save_best=save_best)
else:
raise ValueError("Wrong data generator! Please check the 'ways' you input.")
if __name__ == '__main__':
main()