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mean_unslicer.py
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mean_unslicer.py
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# Copyright 2023 The FastEstimator Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from typing import Iterable, Sequence, Tuple, Union
from fastestimator.backend._zeros_like import zeros_like
from fastestimator.slicer.slicer import Slicer
from fastestimator.types import Tensor
from fastestimator.util.traceability_util import traceable
@traceable()
class MeanUnslicer(Slicer):
"""A slicer which re-combines mini-batches via averaging.
Args:
unslice: The input key(s) which this Slicer un-slices.
axis: The axis along which to cut the data
mode: What mode(s) to invoke this Slicer in. For example, "train", "eval", "test", or "infer". To invoke
regardless of mode, pass None. To invoke in all modes except for a particular one, you can pass an argument
like "!infer" or "!train".
ds_id: What dataset id(s) to invoke this Slicer in. To invoke regardless of ds_id, pass None. To invoke in all
ds_ids except for a particular one, you can pass an argument like "!ds1".
"""
def __init__(self,
unslice: Union[str, Sequence[str]],
mode: Union[None, str, Iterable[str]] = None,
ds_id: Union[None, str, Iterable[str]] = None) -> None:
super().__init__(slice=None, unslice=unslice, mode=mode, ds_id=ds_id)
def _unslice_batch(self, slices: Tuple[Tensor, ...], key: str) -> Tensor:
mean = zeros_like(slices[0])
for minibatch in slices:
mean += minibatch
mean /= len(slices)
return mean