class nbla::ONNXResize

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

Resize an ND array with interpolation.

This function provides a compatible interface to ONNX Resize.

Inputs:

• x: N-D array.

Outputs:

• y: N-D array.

Template Parameters:

T – Data type for computation.

Param roi:

RoIs for tf_crop_and_resize.

Param scales:

Scale factors along axes.

Param sizes:

Output size.

Param mode:

Interpolation mode chosen from (‘nearest’|’linear’|’cubic’).

Param coordinate_transformation_mode:

How to transform coordinates in the resized tensor to coordinates in the original tensor.

Param cubic_coeff_a:

The coefficient for cubic interpolation.

Param exclude_outside:

Whether to set coefficients to zero when a sampling location is outside the input tensor.

Param extrapolation_value:

An extrapolation value used when a sampling location is outside the input tensor at tf_crop_and_resize mode.

Param nearest_mode:

Rounding mode for nearest-neighbor interpolation.

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.