def override_params(params, args):
params.parse(args.parameters)
src_vocab, src_w2idx, src_idx2w = data.load_vocabulary(args.vocabulary[0])
tgt_vocab, tgt_w2idx, tgt_idx2w = data.load_vocabulary(args.vocabulary[1])
params.vocabulary = {"source": src_vocab, "target": tgt_vocab}
params.lookup = {"source": src_w2idx, "target": tgt_w2idx}
params.mapping = {"source": src_idx2w, "target": tgt_idx2w}
return params
def override_params(params, args):
params.model = args.model or params.model
params.input = args.input or params.input
params.output = args.output or params.output
params.vocab = args.vocabulary or params.vocab
params.parse(args.parameters)
src_vocab, src_w2idx, src_idx2w = data.load_vocabulary(params.vocab[0])
tgt_vocab, tgt_w2idx, tgt_idx2w = data.load_vocabulary(params.vocab[1])
params.vocabulary = {"source": src_vocab, "target": tgt_vocab}
params.lookup = {"source": src_w2idx, "target": tgt_w2idx}
params.mapping = {"source": src_idx2w, "target": tgt_idx2w}
return params
def get_model(args):
model_cls = models.get_model(args.model)
params = default_params()
params = merge_params(params, model_cls.default_params())
params = merge_params(params, predictor.default_params())
params = import_params(args.dir, args.model, params)
params.decode_batch_size = 1
src_vocab, src_w2idx, src_idx2w = data.load_vocabulary(params.vocab[0])
tgt_vocab, tgt_w2idx, tgt_idx2w = data.load_vocabulary(params.vocab[1])
params.vocabulary = {"source": src_vocab, "target": tgt_vocab}
params.lookup = {"source": src_w2idx, "target": tgt_w2idx}
params.mapping = {"source": src_idx2w, "target": tgt_idx2w}
torch.cuda.set_device(0)
torch.set_default_tensor_type(torch.cuda.FloatTensor)
# Create model
model = model_cls(params).cuda()
return model, params