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_gather_from_batch.py
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_gather_from_batch.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 TypeVar
import numpy as np
import tensorflow as tf
import torch
from fastestimator.backend._expand_dims import expand_dims
from fastestimator.backend._squeeze import squeeze
from fastestimator.backend._to_tensor import to_tensor
Tensor = TypeVar('Tensor', tf.Tensor, torch.Tensor, np.ndarray)
def gather_from_batch(tensor: Tensor, indices: Tensor) -> Tensor:
"""Gather specific indices from a batch of data.
This method can be useful if you need to compute gradients based on a specific subset of a tensor's output values.
The `indices` will automatically be cast to the correct type (tf, torch, np) based on the type of the `tensor`.
This method can be used with Numpy data:
```python
ind = np.array([1, 0, 1])
n = np.array([[0, 1], [2, 3], [4, 5]])
b = fe.backend.gather_from_batch(n, ind) # [1, 2, 5]
n = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]], [[8, 9], [10, 11]]])
b = fe.backend.gather_from_batch(n, ind) # [[2, 3], [4, 5], [10, 11]]
```
This method can be used with TensorFlow tensors:
```python
ind = tf.constant([1, 0, 1])
t = tf.constant([[0, 1], [2, 3], [4, 5]])
b = fe.backend.gather_from_batch(t, ind) # [1, 2, 5]
t = tf.constant([[[0, 1], [2, 3]], [[4, 5], [6, 7]], [[8, 9], [10, 11]]])
b = fe.backend.gather_from_batch(t, ind) # [[2, 3], [4, 5], [10, 11]]
```
This method can be used with PyTorch tensors:
```python
ind = torch.tensor([1, 0, 1])
p = torch.tensor([[0, 1], [2, 3], [4, 5]])
b = fe.backend.gather_from_batch(p, ind) # [1, 2, 5]
p = torch.tensor([[[0, 1], [2, 3]], [[4, 5], [6, 7]], [[8, 9], [10, 11]]])
b = fe.backend.gather_from_batch(p, ind) # [[2, 3], [4, 5], [10, 11]]
```
Args:
tensor: A tensor of shape (batch, d1, ..., dn).
indices: A tensor of shape (batch, ) or (batch, 1) indicating which indices should be selected.
Returns:
A tensor of shape (batch, d2, ..., dn) containing the elements from `tensor` at the given `indices`.
Raises:
ValueError: If `tensor` is an unacceptable data type.
"""
if tf.is_tensor(tensor):
indices = to_tensor(indices, 'tf')
indices = tf.cast(indices, tf.int64)
if len(indices.shape) == 1: # Indices not batched
indices = expand_dims(indices, 1)
return tf.gather_nd(tensor, indices=indices, batch_dims=1)
elif isinstance(tensor, torch.Tensor):
return tensor[torch.arange(tensor.shape[0]), squeeze(indices)]
elif isinstance(tensor, np.ndarray):
return tensor[np.arange(tensor.shape[0]), squeeze(indices)]
else:
raise ValueError("Unrecognized tensor type {}".format(type(tensor)))