class nbla::RandomShift

template<typename T>
class RandomShift : public nbla::BaseFunction<const vector<int>&, const string&, float, int, int>

RandomShift randomly shifts the array elements within the specified range.

Inputs:

• N-D array.

Outputs:

• N-D array.

Template Parameters:

T – Data type for computation.

Param shift:

Max absolute amount to shift elements. For example, to shift image data horizontally by +-2 pixels and vertically by +-3 pixels, specify (3,2).

Param border_mode:

Specify how to process the ends of arrays whose values will be undetermined as a result of shifting. nearest: The data at the ends of the original array is copied and used. reflect: Original data reflected at the ends of the original array is used.

Public Functions

inline virtual shared_ptr<Function> copy() const

Copy another instance of Function with the same context.

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 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 string name()

Get function name in string.

inline virtual vector<string> allowed_array_classes()

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

inline virtual bool need_setup_recompute(int o) const

A flag for checking if setup_recompute() is needed.

Checking if o-th output’ data requires setup_recompute().

Note

setup_recompute() will skipped during forward execution if none of outputs requires setup_recompute().

Parameters:

o – [in] Output variable index.

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.