def main(unused_argv):
"""Main entry.
Args:
* unused_argv: unused arguments (after FLAGS is parsed)
"""
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.enbl_multi_gpu:
mgw.init()
trainer = Trainer(data_path=FLAGS.data_path, netcfg=FLAGS.net_cfg)
trainer.build_graph(is_train=True)
trainer.build_graph(is_train=False)
if FLAGS.eval_only:
trainer.eval()
else:
trainer.train()
def main():
_set_random_seeds(RANDOM_SEED)
args = config.get_main_params()
model_name, mode = args.model_name, args.mode
save_dir = args.save_dir
resume, epoch = args.resume, args.epoch
if args.resume:
assert mode == C.TRAIN_TYPE
assert epoch >= 0
params = config.get_model_params(model_name)
params[C.MODEL_NAME] = model_name
# Instantiate saver and a logger in save_dir
# If save_dir is passed in from command line
# params are loaded from the save_dir
# Logger is instantiated in saver
saver = Saver(save_dir, params)
logger = saver.logger
params = saver.params
# if save_dir is passed,
# model_name is used from the model_name in saved params
model_name = params[C.MODEL_NAME]
logger.log('SaveDir: %s' % saver.save_dir)
if mode == C.TRAIN_TYPE:
logger.log('\nLoading dataset..')
dataset = AmazonDataset(params, mode)
logger.log('\n Model: %s, Mode = %s \n' % (model_name, mode))
logger.log('\nLoading dataloader..')
if CACHE_DATASET:
train_loader = pickle.load(open(model_name + 'train.pickle', 'rb'))
dev_loader = pickle.load(open(model_name + 'dev.pickle', 'rb'))
else:
train_loader = AmazonDataLoader(dataset.train, model_name,
params[C.BATCH_SIZE])
dev_loader = AmazonDataLoader(dataset.val, model_name,
params[C.BATCH_SIZE])
pickle.dump(train_loader, open(model_name + 'train.pickle', 'wb'))
pickle.dump(dev_loader, open(model_name + 'dev.pickle', 'wb'))
logger.log('\nInstantiating training..')
trainer = Trainer(train_loader,
params,
dev_loader=dev_loader,
vocab=dataset.vocab,
saver=saver,
resume_training=resume,
resume_epoch=epoch if resume else None)
trainer.train()
elif mode in [C.DEV_TYPE, C.TEST_TYPE]:
logger.log('\nBeginning evaluation ..\n')
# Load saved params and vocabs
# output_file = args.output_file
output_file = "./saved/output_test.json"
logger.log('Loading vocab..')
vocab = saver.load_vocab()
model_name = params[C.MODEL_NAME]
dataset = AmazonDataset(params, mode)
#TODO: next line is a temporary change only.
dataset_typed = dataset.test
#dataset_typed = dataset.val if mode == C.DEV_TYPE else dataset.test
loader = AmazonDataLoader(dataset_typed, model_name,
params[C.BATCH_SIZE])
# Load model
logger.log('Loading saved model..')
model = Seq2Seq(
vocab.get_vocab_size(),
hsizes(params, model_name),
params,
)
saver.load_model(epoch, model)
# Instantiate trainer with saved model
logger.log('Instantiating trainer..')
trainer = Trainer(None,
params,
dev_loader=loader,
saver=saver,
vocab=vocab)
logger.log('Adding model to trainer..')
trainer.model = model
# Evaluation on test set
logger.log('Total number of [%s] batches: %d' %
(mode.upper(), len(list(loader))))
trainer.eval(loader, mode, output_filename=output_file)
logger.log('\nCompleted Evaluation..\n')
else:
raise 'Unimplemented mode: %s' % mode
