Ejemplo n.º 1
0
    breaking = args.breaking

    weights = torch.Tensor([weight]).to(device)

    learning_rate = args.learning_rate
    if weighting:
        criterion = nn.BCEWithLogitsLoss(pos_weight=weights, reduction='sum')
    else:
        criterion = nn.BCEWithLogitsLoss(reduction='sum')

    batch_size = 1  #NO PADDING

    load_params = {
        'batch_size': batch_size,
        'shuffle': False,
        'collate_fn': SegmentDataset.get_collate_fn(device)
    }

    load_params_hist = {
        'batch_size': batch_size,
        'shuffle': False,
        'collate_fn': HistoryDataset.get_collate_fn(device)
    }

    p_plot = defaultdict()
    r_plot = defaultdict()

    p_plot_0 = defaultdict()
    r_plot_0 = defaultdict()

    testset = SegmentDataset(data_dir=args.data_path,
Ejemplo n.º 2
0
    parser = argparse.ArgumentParser()
    parser.add_argument("-data_path", type=str, default="../data")
    parser.add_argument("-segment_file", type=str, default="segments.json")
    parser.add_argument("-chains_file", type=str, default="val_chains.json")
    parser.add_argument("-vocab_file", type=str, default="vocab.csv")
    parser.add_argument("-vectors_file", type=str, default="vectors.json")
    parser.add_argument("-split", type=str, default="val")
    args = parser.parse_args()

    print("Loading the vocab...")
    vocab = Vocab(os.path.join(args.data_path, args.vocab_file), 3)

    print("Testing the SegmentDataset class initialization...")

    segment_val_set = SegmentDataset(data_dir=args.data_path,
                                     segment_file=args.segment_file,
                                     vectors_file=args.vectors_file,
                                     split=args.split)

    print("Testing the SegmentDataset class item getter...")
    print("Dataset contains {} segment samples".format(len(segment_val_set)))
    sample_id = 2
    sample = segment_val_set[sample_id]
    print("Segment {}:".format(sample_id))
    print("Image set: {}".format(sample["image_set"]))
    print("Target image index(es): {}".format(sample["targets"]))
    print("Target image Features: {}".format([
        segment_val_set.image_features[sample["image_set"][int(target)]]
        for target in sample["targets"]
    ]))
    print("Encoded segment: {}".format(sample["segment"]))
    print("Decoded segment dialogue: {}".format(vocab.decode(