ac1f07e722b1117642774b04d96e7174751e325c,demo.py,,diarization_experiment,#Any#,27

Before Change

  // training
  if args.pretrain == None:
    model = fit(args, train_sequence, train_cluster_id)
    torch.save(model.rnn_model.state_dict(), "rnn_model {}".format(args.dataset))
  else: // use pretrained model
    model = init_model(args, train_sequence, train_cluster_id)
    model.rnn_model.load_state_dict(torch.load("rnn_model {}".format(args.dataset)))

After Change

  test_sequences = test_data["test_sequences"]
  test_cluster_ids = test_data["test_cluster_ids"]

  _ , observation_dim = train_sequence.shape
  input_dim = observation_dim

  model = UISRNN(args, input_dim, observation_dim, .5)

Instances