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,
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(