def __init__()

in mmf/common/sample.py [0:0]

• 32 lines of code
• 14 McCabe index (conditional complexity)

    def __init__(self, samples=None):
        super().__init__(self)
        if samples is None:
            samples = []

        if len(samples) == 0:
            return

        if self._check_and_load_dict(samples):
            return
        # If passed sample list was in form of key, value pairs of tuples
        # return after loading these
        if self._check_and_load_tuple(samples):
            return

        fields = samples[0].keys()

        for field in fields:
            if isinstance(samples[0][field], torch.Tensor):
                size = (len(samples), *samples[0][field].size())
                self[field] = samples[0][field].new_empty(size)
                if self._get_tensor_field() is None:
                    self._set_tensor_field(field)
            else:
                self[field] = [None for _ in range(len(samples))]

            for idx, sample in enumerate(samples):
                # it should be a tensor but not a 0-d tensor
                if (
                    isinstance(sample[field], torch.Tensor)
                    and len(sample[field].size()) != 0
                    and sample[field].size(0) != samples[0][field].size(0)
                ):
                    raise AssertionError(
                        "Fields for all samples must be equally sized. "
                        "{} is of different sizes".format(field)
                    )

                self[field][idx] = self._get_data_copy(sample[field])

            if isinstance(samples[0][field], collections.abc.Mapping):
                self[field] = SampleList(self[field])