class nbla::DepthwiseDeconvolution

template<typename T>
class DepthwiseDeconvolution : public nbla::BaseFunction<int, const vector<int>&, const vector<int>&, const vector<int>&, int>

1-D and 2-D Depthwise Deconvolution with bias.

Inputs ( \(B\) is base_axis):

• Input \((B + 1 + N)\)-D array ( \(M_1 \times ... \times M_B \times C \times L_1 \times ... \times L_N\)).

• Weight \((1 + N)\)-D array ( \(C \times K_1 \times ... \times K_N\)).

• (optional) Bias vector ( \(C\)).

Outputs:

• \((B + 1 + N)\)-D array ( \( M_1 \times ... \times M_B \times C \times L'_1 \times ... \times L'_N \)).

Template Parameters:

T – Data type for computation.

Param base_axis:

Base axis of Convolution operation. Dimensions up to base_axis is treated as sample dimension.

Param pad:

Padding sizes for dimensions.

Param stride:

Stride sizes for dimensions.

Param dilation:

Dilation sizes for dimensions.

Param divisor:

Number of input feature maps per output feature map.

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 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.