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_reduce_max.py
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_reduce_max.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 Sequence, TypeVar, Union
import numpy as np
import tensorflow as tf
import torch
from fastestimator.util.base_util import to_list
Tensor = TypeVar('Tensor', tf.Tensor, torch.Tensor, np.ndarray)
def reduce_max(tensor: Tensor, axis: Union[None, int, Sequence[int]] = None, keepdims: bool = False) -> Tensor:
"""Compute the maximum value along a given `axis` of a `tensor`.
This method can be used with Numpy data:
```python
n = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
b = fe.backend.reduce_max(n) # 8
b = fe.backend.reduce_max(n, axis=0) # [[5, 6], [7, 8]]
b = fe.backend.reduce_max(n, axis=1) # [[3, 4], [7, 8]]
b = fe.backend.reduce_max(n, axis=[0,2]) # [6, 8]
```
This method can be used with TensorFlow tensors:
```python
t = tf.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
b = fe.backend.reduce_max(t) # 8
b = fe.backend.reduce_max(t, axis=0) # [[5, 6], [7, 8]]
b = fe.backend.reduce_max(t, axis=1) # [[3, 4], [7, 8]]
b = fe.backend.reduce_max(t, axis=[0,2]) # [6, 8]
```
This method can be used with PyTorch tensors:
```python
p = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
b = fe.backend.reduce_max(p) # 8
b = fe.backend.reduce_max(p, axis=0) # [[5, 6], [7, 8]]
b = fe.backend.reduce_max(p, axis=1) # [[3, 4], [7, 8]]
b = fe.backend.reduce_max(p, axis=[0,2]) # [6, 8]
```
Args:
tensor: The input value.
axis: Which axis or collection of axes to compute the maximum along.
keepdims: Whether to preserve the number of dimensions during the reduction.
Returns:
The maximum values of `tensor` along `axis`.
Raises:
ValueError: If `tensor` is an unacceptable data type.
"""
if tf.is_tensor(tensor):
return tf.reduce_max(tensor, axis=axis, keepdims=keepdims)
elif isinstance(tensor, torch.Tensor):
if axis is None:
axis = list(range(len(tensor.shape)))
axis = to_list(axis)
axis = reversed(sorted(axis))
for ax in axis:
tensor = tensor.max(dim=ax, keepdim=keepdims)[0]
return tensor
elif isinstance(tensor, np.ndarray):
if isinstance(axis, list):
axis = tuple(axis)
return np.max(tensor, axis=axis, keepdims=keepdims)
else:
raise ValueError("Unrecognized tensor type {}".format(type(tensor)))