class nbla::GatherNd

template<typename T>
class GatherNd : public nbla::BaseFunction<>

Gather elements or slices from `data` according to `indices`, which must be at least two-dimensional with the first dimension :math:`M` being less or equal to the :math:`N` dimensions of `data`.

Given `data` with shape :math:`(X_0, X_1, …, X_{N-1})` and indices with shape :math:`(M, Y_0, …, Y_{K-1})` output has shape :math:`(Y_0, …, Y_{K-1}, X_M, …, X_{N-1})`. If :math:`M == N`, output shape is simply :math:`(Y_0, …, Y_{K-1})`.

The forward of :func:`~nnabla.functions.gather_nd` is equivalent to the following Python code:

def gather_nd(data, index):
    import numpy as np
    tmp_index = index.reshape(index.shape[0], -1)
    tmp_index = (idx + (Ellipsis,) for idx in zip(*new_index))
    out_shape = index.shape[1:] + data.shape[index.shape[0]:]
    return np.vstack(data[idx] for idx in tmp_index).reshape(*out_shape)

Inputs:

  • N-D array `data`

  • N-D array `indices`

Outputs:

  • N-D array

Public Functions

inline virtual shared_ptr<Function> copy() const

Copy another instance of Function with the same context.

inline virtual int min_inputs()

Get minimum number of inputs.

This is meant to be used in setup function with in_types which is used to get maximum number of inputs.

inline virtual int min_outputs()

Get minimum number of outputs.

This is meant to be used in setup function with out_types which is used to get max number of outputs.

inline virtual vector<dtypes> in_types()

Get input dtypes.

Last in_type will be used repeatedly if size of in_types is smaller than size of inputs

inline virtual vector<dtypes> out_types()

Get output dtypes.

Last out_type will be used repeatedly if size of out_types is smaller than size of outputs

inline virtual vector<string> allowed_array_classes()

Get array classes that are allowed to be specified by Context.

inline virtual string name()

Get function name in string.

inline virtual bool grad_depends_output_data(int i, int o) const

Dependency flag for checking if in-grad depends on out-data.

Checking if i-th input’ gradient computation requires o-th output’s data or not.

Note

If any of inputs requires an output variable data when computing its gradient, this function must be overridden to return appropriate boolean value. Otherwise, backward computation will be incorrect.

Parameters:

  • i – [in] Input variable index.

  • o – [in] Output variable index.