def load_test_model(opt, dummy_opt):
shared_fields = None
shared_model_opt = None
models = []
for model_path in opt.models:
checkpoint = torch.load(model_path,
map_location=lambda storage, loc: storage)
fields = load_fields_from_vocab(checkpoint['vocab'])
model_opt = checkpoint['opt']
for arg in dummy_opt:
if arg not in model_opt:
model_opt.__dict__[arg] = dummy_opt[arg]
model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint)
model.eval()
model.generator.eval()
if shared_fields is None:
shared_fields = fields
if shared_model_opt is None:
shared_model_opt = model_opt
models.append(model)
ensemble_model = EnsembleModel(models)
return shared_fields, ensemble_model
def load_test_model(opt, dummy_opt, model_path=None):
if model_path is None:
model_path = opt.models[0]
checkpoint = torch.load(model_path,
map_location=lambda storage, loc: storage)
fields = load_fields_from_vocab(checkpoint['vocab'])
model_opt = checkpoint['opt']
for arg in dummy_opt:
if arg not in model_opt:
model_opt.__dict__[arg] = dummy_opt[arg]
model = build_base_model(model_opt, fields, use_gpu(opt), checkpoint)
model.eval()
model.generator.eval()
return fields, model