Class TransformList

tensorpack/dataflow/imgaug/transform.py:275–315

Apply a list of transforms sequentially.

class TransformList(BaseTransform):
    """
    Apply a list of transforms sequentially.
    """
    def __init__(self, tfms):
        """
        Args:
            tfms (list[Transform]):
        """
        for t in tfms:
            assert isinstance(t, BaseTransform), t
        self.tfms = tfms

    def _apply(self, x, meth):
        for t in self.tfms:
            x = getattr(t, meth)(x)
        return x

    def __getattr__(self, name):
        if name.startswith("apply_"):
            return lambda x: self._apply(x, name)
        raise AttributeError("TransformList object has no attribute {}".format(name))

    def __str__(self):
        repr_each_tfm = ",\n".join(["  " + repr(x) for x in self.tfms])
        return "imgaug.TransformList([\n{}])".format(repr_each_tfm)

    def __add__(self, other):
        other = other.tfms if isinstance(other, TransformList) else [other]
        return TransformList(self.tfms + other)

    def __iadd__(self, other):
        other = other.tfms if isinstance(other, TransformList) else [other]
        self.tfms.extend(other)
        return self

    def __radd__(self, other):
        other = other.tfms if isinstance(other, TransformList) else [other]
        return TransformList(other + self.tfms)

    __repr__ = __str__


class LazyTransform(BaseTransform):