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_gather_from_batch

gather_from_batch

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: 

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: 

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: 

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]]

Parameters:

Name Type Description Default
tensor Tensor

A tensor of shape (batch, d1, ..., dn).

 required
indices Tensor

A tensor of shape (batch, ) or (batch, 1) indicating which indices should be selected.

 required

Returns:

Type Description
Tensor

A tensor of shape (batch, d2, ..., dn) containing the elements from tensor at the given indices.

Raises:

Type Description
ValueError

If tensor is an unacceptable data type.
Source code in fastestimator/fastestimator/backend/_gather_from_batch.py 
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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)))