107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
__author__ = "Alex Rogozhnikov"
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from typing import Any, Dict
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from ..einops import TransformRecipe, _apply_recipe, _prepare_recipes_for_all_dims, get_backend
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from .. import EinopsError
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class RearrangeMixin:
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"""
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Rearrange layer behaves identically to einops.rearrange operation.
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:param pattern: str, rearrangement pattern
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:param axes_lengths: any additional specification of dimensions
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See einops.rearrange for source_examples.
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"""
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def __init__(self, pattern: str, **axes_lengths: Any) -> None:
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super().__init__()
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self.pattern = pattern
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self.axes_lengths = axes_lengths
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# self._recipe = self.recipe() # checking parameters
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self._multirecipe = self.multirecipe()
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self._axes_lengths = tuple(self.axes_lengths.items())
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def __repr__(self) -> str:
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params = repr(self.pattern)
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for axis, length in self.axes_lengths.items():
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params += ", {}={}".format(axis, length)
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return "{}({})".format(self.__class__.__name__, params)
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def multirecipe(self) -> Dict[int, TransformRecipe]:
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try:
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return _prepare_recipes_for_all_dims(
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self.pattern, operation="rearrange", axes_names=tuple(self.axes_lengths)
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)
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except EinopsError as e:
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raise EinopsError(" Error while preparing {!r}\n {}".format(self, e))
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def _apply_recipe(self, x):
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backend = get_backend(x)
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return _apply_recipe(
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backend=backend,
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recipe=self._multirecipe[len(x.shape)],
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tensor=x,
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reduction_type="rearrange",
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axes_lengths=self._axes_lengths,
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)
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def __getstate__(self):
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return {"pattern": self.pattern, "axes_lengths": self.axes_lengths}
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def __setstate__(self, state):
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self.__init__(pattern=state["pattern"], **state["axes_lengths"])
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class ReduceMixin:
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"""
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Reduce layer behaves identically to einops.reduce operation.
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:param pattern: str, rearrangement pattern
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:param reduction: one of available reductions ('min', 'max', 'sum', 'mean', 'prod'), case-sensitive
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:param axes_lengths: any additional specification of dimensions
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See einops.reduce for source_examples.
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"""
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def __init__(self, pattern: str, reduction: str, **axes_lengths: Any):
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super().__init__()
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self.pattern = pattern
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self.reduction = reduction
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self.axes_lengths = axes_lengths
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self._multirecipe = self.multirecipe()
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self._axes_lengths = tuple(self.axes_lengths.items())
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def __repr__(self):
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params = "{!r}, {!r}".format(self.pattern, self.reduction)
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for axis, length in self.axes_lengths.items():
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params += ", {}={}".format(axis, length)
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return "{}({})".format(self.__class__.__name__, params)
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def multirecipe(self) -> Dict[int, TransformRecipe]:
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try:
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return _prepare_recipes_for_all_dims(
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self.pattern, operation=self.reduction, axes_names=tuple(self.axes_lengths)
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)
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except EinopsError as e:
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raise EinopsError(" Error while preparing {!r}\n {}".format(self, e))
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def _apply_recipe(self, x):
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backend = get_backend(x)
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return _apply_recipe(
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backend=backend,
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recipe=self._multirecipe[len(x.shape)],
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tensor=x,
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reduction_type=self.reduction,
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axes_lengths=self._axes_lengths,
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)
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def __getstate__(self):
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return {"pattern": self.pattern, "reduction": self.reduction, "axes_lengths": self.axes_lengths}
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def __setstate__(self, state):
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self.__init__(pattern=state["pattern"], reduction=state["reduction"], **state["axes_lengths"])
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