Compute Library
18.05
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Basic function to simulate a dequantization layer. More...
#include <NEDequantizationLayer.h>
Public Member Functions | |
NEDequantizationLayer () | |
Default constructor. More... | |
void | configure (const ITensor *input, ITensor *output, const ITensor *min_max) |
Configure the kernel. More... | |
void | run () override |
Run the kernels contained in the function. More... | |
Public Member Functions inherited from IFunction | |
virtual | ~IFunction ()=default |
Destructor. More... | |
virtual void | prepare () |
Prepare the function for executing. More... | |
Static Public Member Functions | |
static Status | validate (const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *min_max) |
Static function to check if given info will lead to a valid configuration of NEDequantizationLayer. More... | |
Basic function to simulate a dequantization layer.
This function calls the following NEON kernels:
Definition at line 44 of file NEDequantizationLayer.h.
Default constructor.
Configure the kernel.
[in] | input | Source tensor with at least 3 dimensions. The dimensions over the third will be interpreted as batches. Data types supported: U8. |
[out] | output | Destination tensor with the same dimensions of input. Data type supported: F32. |
[in] | min_max | Pointer to the tensor with shape [2, batches] which stores the minimum and maximum value for each 3D input tensor. The dimensions over the second must match the batched dimensions of the input tensor. Data type supported: F32 |
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overridevirtual |
Run the kernels contained in the function.
For NEON kernels:
For OpenCL kernels:
Implements IFunction.
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static |
Static function to check if given info will lead to a valid configuration of NEDequantizationLayer.
[in] | input | Input tensor info. Data types supported: U8. |
[in] | output | Output tensor info. Data type supported: F32. |
[in] | min_max | Info for the tensor with shape [2, batches] which stores the minimum and maximum value for each 3D input tensor. The dimensions over the second must match the batched dimensions of the input tensor. Data type supported: F32. |