args.gpu,
rnn=rnn,
pre_computed_patterns=None)
if args.gpu:
print("Cuda!")
model.to_cuda(model)
state_dict = torch.load(args.input_model)
else:
state_dict = torch.load(args.input_model,
map_location=lambda storage, loc: storage)
# Loading model
model.load_state_dict(state_dict)
interpret_documents(model, args.batch_size, dev_data, dev_text, args.ofile,
args.max_doc_len)
return 0
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=[soft_pattern_arg_parser(),
general_arg_parser()])
parser.add_argument("--ofile", help="Output file", required=True)
sys.exit(main(parser.parse_args()))
else:
rnn = None
model = SoftPatternClassifier(pattern_specs, mlp_hidden_dim, num_mlp_layers, num_classes, embeddings, vocab,
semiring, args.bias_scale_param, args.gpu, rnn=rnn, pre_computed_patterns=None,
no_sl=args.no_sl, shared_sl=args.shared_sl, no_eps=args.no_eps,
eps_scale=args.eps_scale, self_loop_scale=args.self_loop_scale)
if args.gpu:
state_dict = torch.load(args.input_model)
else:
state_dict = torch.load(args.input_model, map_location=lambda storage, loc: storage)
model.load_state_dict(state_dict)
if args.gpu:
model.to_cuda(model)
visualize_patterns(model, dev_data, dev_text, args.k_best, args.max_doc_len, num_padding_tokens)
return 0
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=[soft_pattern_arg_parser(), general_arg_parser()])
parser.add_argument("-k", "--k_best", help="Number of nearest neighbor phrases", type=int, default=5)
sys.exit(main(parser.parse_args()))