def main():
'''
main
'''
# pdb.set_trace() # Debug step
trainer = Trainer()
params = trainer.params
decode_fn = get_decode_fn(params.decode, params.max_decode_len)
trainer.load_data(params.dataset, params.train, params.dev, params.test)
trainer.setup_evalutator()
if params.load and params.load != '0':
if params.load == 'smart':
start_epoch = trainer.smart_load_model(params.model) + 1
else:
start_epoch = trainer.load_model(params.load) + 1
trainer.logger.info('continue training from epoch %d', start_epoch)
trainer.setup_training()
trainer.load_training(params.model)
else: # start from scratch
start_epoch = 0
trainer.build_model()
if params.init:
if os.path.isfile(params.init):
trainer.load_state_dict(params.init)
else:
trainer.dump_state_dict(params.init)
trainer.setup_training()
# pdb.set_trace() # Debug step
trainer.run(start_epoch, decode_fn=decode_fn)
def main():
"""
main
"""
trainer = Trainer()
params = trainer.params
decode_fn = get_decode_fn(params.decode, params.max_decode_len,
params.decode_beam_size)
trainer.load_data(params.dataset, params.train, params.dev, params.test)
trainer.setup_evalutator()
if params.load and params.load != "0":
if params.load == "smart":
start_epoch = trainer.smart_load_model(params.model) + 1
else:
start_epoch = trainer.load_model(params.load) + 1
trainer.logger.info("continue training from epoch %d", start_epoch)
trainer.setup_training()
trainer.load_training(params.model)
else: # start from scratch
start_epoch = 0
trainer.build_model()
if params.init:
if os.path.isfile(params.init):
trainer.load_state_dict(params.init)
else:
trainer.dump_state_dict(params.init)
trainer.setup_training()
trainer.run(start_epoch, decode_fn=decode_fn)
def main():
"""
main
"""
trainer = Trainer()
params = trainer.params
decode_fn = get_decode_fn(params.decode, params.max_decode_len,
params.decode_beam_size)
trainer.load_data(params.dataset, params.train, params.dev, params.test)
trainer.setup_evalutator()
assert params.load
trainer.reload_and_test(params.model, params.load, params.bs, decode_fn)