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reshape.py
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reshape.py
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# Copyright 2019 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 Any, Dict, Iterable, List, Tuple, TypeVar, Union
import tensorflow as tf
import torch
from fastestimator.backend._reshape import reshape
from fastestimator.op.tensorop.tensorop import TensorOp
from fastestimator.util.traceability_util import traceable
Tensor = TypeVar('Tensor', tf.Tensor, torch.Tensor)
@traceable()
class Reshape(TensorOp):
"""Reshape a input tensor to conform to a given shape.
Args:
inputs: Key of the input tensor that is to be reshaped.
outputs: Key of the output tensor that has been reshaped.
shape: Target shape.
mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
like "!infer" or "!train".
ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
ds_ids except for a particular one, you can pass an argument like "!ds1".
"""
def __init__(self,
inputs: Union[str, List[str]],
outputs: Union[str, List[str]],
shape: Union[int, Tuple[int, ...]],
mode: Union[None, str, Iterable[str]] = None,
ds_id: Union[None, str, Iterable[str]] = None) -> None:
super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
self.shape = list(shape)
self.in_list, self.out_list = True, True
def forward(self, data: List[Tensor], state: Dict[str, Any]) -> List[Tensor]:
return [reshape(elem, self.shape) for elem in data]