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_squeeze.py
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_squeeze.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 Optional, TypeVar
import numpy as np
import tensorflow as tf
import torch
Tensor = TypeVar('Tensor', tf.Tensor, torch.Tensor, np.ndarray)
def squeeze(tensor: Tensor, axis: Optional[int] = None) -> Tensor:
"""Remove an `axis` from a `tensor` if that axis has length 1.
This method can be used with Numpy data:
```python
n = np.array([[[[1],[2]]],[[[3],[4]]],[[[5],[6]]]]) # shape == (3, 1, 2, 1)
b = fe.backend.squeeze(n) # [[1, 2], [3, 4], [5, 6]]
b = fe.backend.squeeze(n, axis=1) # [[[1], [2]], [[3], [4]], [[5], [6]]]
b = fe.backend.squeeze(n, axis=3) # [[[1, 2]], [[3, 4]], [[5, 6]]]
```
This method can be used with TensorFlow tensors:
```python
t = tf.constant([[[[1],[2]]],[[[3],[4]]],[[[5],[6]]]]) # shape == (3, 1, 2, 1)
b = fe.backend.squeeze(t) # [[1, 2], [3, 4], [5, 6]]
b = fe.backend.squeeze(t, axis=1) # [[[1], [2]], [[3], [4]], [[5], [6]]]
b = fe.backend.squeeze(t, axis=3) # [[[1, 2]], [[3, 4]], [[5, 6]]]
```
This method can be used with PyTorch tensors:
```python
p = torch.tensor([[[[1],[2]]],[[[3],[4]]],[[[5],[6]]]]) # shape == (3, 1, 2, 1)
b = fe.backend.squeeze(p) # [[1, 2], [3, 4], [5, 6]]
b = fe.backend.squeeze(p, axis=1) # [[[1], [2]], [[3], [4]], [[5], [6]]]
b = fe.backend.squeeze(p, axis=3) # [[[1, 2]], [[3, 4]], [[5, 6]]]
```
Args:
tensor: The input value.
axis: Which axis to squeeze along, which must have length==1 (or pass None to squeeze all length 1 axes).
Returns:
The reshaped `tensor`.
Raises:
ValueError: If `tensor` is an unacceptable data type.
"""
if tf.is_tensor(tensor):
return tf.squeeze(tensor, axis=axis)
elif isinstance(tensor, torch.Tensor):
if axis is None:
return torch.squeeze(tensor)
else:
return torch.squeeze(tensor, dim=axis)
elif isinstance(tensor, np.ndarray):
return np.squeeze(tensor, axis=axis)
else:
raise ValueError("Unrecognized tensor type {}".format(type(tensor)))