class nbla::Deconvolution

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

N-D Deconvolution with bias operates backward convolution (derivative of output wrt input) plus channel-wise learned bias.

The weights must be given with the same format as in forward convolution, hence the number of input channels (can be seen as output channels of forward convolution) comes to the first dimension, and the second dimension has number of the output channels divided by group.

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 \((2 + N)\)-D array ( \(C' \times C \times K_1 \times ... \times K_N\)).

  • (optional) Bias vector ( \(C'\)).

See also

Convolution for documentation of parameters.

See also

Deconvolution is introduced in Shelhamer et al., Fully Convolutional Networks for Semantic Segmentation. https://arxiv.org/abs/1605.06211

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.