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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, Union
import numpy as np
from fastestimator.op.numpyop.numpyop import NumpyOp
from fastestimator.util.traceability_util import traceable
@traceable()
class Reshape(NumpyOp):
"""An Op which re-shapes data to a target shape.
Args:
shape: The desired output shape. At most one value may be -1 to put all of the leftover elements into that axis.
inputs: Key(s) of the data to be reshaped.
outputs: Key(s) into which to write the converted data.
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,
shape: Union[int, Tuple[int, ...]],
inputs: Union[str, Iterable[str]],
outputs: Union[str, Iterable[str]],
mode: Union[None, str, Iterable[str]] = None,
ds_id: Union[None, str, Iterable[str]] = None):
super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
self.shape = shape
self.in_list, self.out_list = True, True
def forward(self, data: List[np.ndarray], state: Dict[str, Any]) -> List[np.ndarray]:
return [self._apply_reshape(elem) for elem in data]
def _apply_reshape(self, data):
data = np.reshape(data, self.shape)
return data