def collate_fn(xs):
inputs = aggregate_value_by_key(xs, 'src')
inputs = list(zip(*inputs))
inputs = [cuda(pack_tensors(input, voc.pad_id)) for input, voc in zip(inputs, vocabs)]
return {
'src': inputs[0] if len(inputs) == 1 else inputs,
'n_tok': aggregate_value_by_key(xs, 'n_tok', sum),
'n_snt': inputs[0].size(0)
}
def collate_fn(xs):
return {
'src':
cuda(
pack_tensors(aggregate_value_by_key(xs, 'src'),
source_vocab.pad_id)),
'n_tok':
aggregate_value_by_key(xs, 'n_tok', sum),
'refs':
aggregate_value_by_key(xs, 'refs')
}
def collate_fn(xs):
return {
'src':
cuda(
pack_tensors(aggregate_value_by_key(xs, 'src'),
source_vocab.pad_id)),
'trg':
cuda(
pack_tensors(aggregate_value_by_key(xs, 'trg'),
target_vocab.pad_id)),
'n_src_tok':
aggregate_value_by_key(xs, 'n_src_tok', sum),
'n_trg_tok':
aggregate_value_by_key(xs, 'n_trg_tok', sum),
}
def collate_fn(xs):
return {
'src':
cuda(
pack_tensors(aggregate_value_by_key(xs, 'src'),
source_vocab.pad_id)),
'r2l':
cuda(
pack_tensors(aggregate_value_by_key(xs, 'r2l'),
target_vocab.pad_id)),
'l2r':
cuda(
pack_tensors(aggregate_value_by_key(xs, 'l2r'),
target_vocab.pad_id)),
'ntok_src':
aggregate_value_by_key(xs, 'n_src_tok', sum),
'ntok_r2l':
aggregate_value_by_key(xs, 'ntok_r2l', sum),
'ntok_l2r':
aggregate_value_by_key(xs, 'ntok_l2r', sum),
}