2d pooling More...

Public Member Functions
virtual size_t	workspace ()=0

virtual void	enqueue (Tensor &X, Tensor &Y, ExecutionContext const &e)=0
	when used with kernel based pooling (not global) X and Y dimensions should match at batch and channels and for H/W the dimention for Y should be Y_dim = op((X_dim + 2 * pad[dim] - kernel[dim]) / stride[dim]) + 1 where op is either ceil or floor

Static Public Member Functions
static std::unique_ptr< Pooling2DForward >	create_max_pooling (Context &ctx, int kernel[2], int pad[2], int stride[2], DataType dt=float_data)
	Create max pooling for kernel, pad, stride.

static std::unique_ptr< Pooling2DForward >	create_avg_pooling (Context &ctx, int kernel[2], int pad[2], int stride[2], bool count_include_pad=false, DataType dt=float_data)
	Create max pooling for kernel, pad, stride. More...

static std::unique_ptr< Pooling2DForward >	create_global_max_pooling (Context &ctx, Shape const &in_shape, DataType dt=float_data)
	Max global pooling.

static std::unique_ptr< Pooling2DForward >	create_global_avg_pooling (Context &ctx, Shape const &in_shape, DataType dt=float_data)
	Avergage global pooling.

Detailed Description

2d pooling

Member Function Documentation

static std::unique_ptr<Pooling2DForward> dlprim::core::Pooling2DForward::create_avg_pooling	(	Context &	ctx,
		int	kernel[2],
		int	pad[2],
		int	stride[2],
		bool	count_include_pad = `false`,
		DataType	dt = `float_data`
	)

static

Create max pooling for kernel, pad, stride.

if count_include_pad == true than average is normalized by sizeof kernel otherwise by actual amount of pixel participated

The documentation for this class was generated from the following file: