c0cb3275d7e2b25e4965763f7f83e7a89ef7c0e4,allennlp/data/iterators/basic_iterator.py,BasicIterator,_create_batches,#BasicIterator#Any#Any#,34

Before Change

    @overrides
    def _create_batches(self, instances: Iterable[Instance], shuffle: bool) -> Iterable[Batch]:
        instances = ensure_list(instances)
        if shuffle:
            random.shuffle(instances)
        grouped_instances = group_by_count(instances, self._batch_size, None)
        // The last group might have not been full, so we check if any of the instances
        // are None, which is how group_by_count pads non-complete batches.
        grouped_instances[-1] = [instance for instance in grouped_instances[-1] if instance is not None]
        return (Batch(batch) for batch in grouped_instances)

    @classmethod
    def from_params(cls, params: Params) -> "BasicIterator":
        batch_size = params.pop_int("batch_size", 32)

After Change

    @overrides
    def _create_batches(self, instances: Iterable[Instance], shuffle: bool) -> Iterable[Batch]:
        // First break the dataset into memory-sized lists:
        for instance_list in self._memory_sized_lists(instances):
            if shuffle:
                random.shuffle(instance_list)
            iterator = iter(instance_list)
            // Then break each memory-sized list into batches.
            for batch_instances in lazy_groups_of(iterator, self._batch_size):
                yield Batch(batch_instances)

    @classmethod
    def from_params(cls, params: Params) -> "BasicIterator":
        batch_size = params.pop_int("batch_size", 32)
        instances_per_epoch = params.pop_int("instances_per_epoch", None)

Instances