78 lines
4.0 KiB
C
78 lines
4.0 KiB
C
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#ifndef CNN_AVG_POOLING
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#define CNN_AVG_POOLING
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/* os shim includes */
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#include "os_types_api.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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/** \defgroup cnn pool API
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* @brief cnn pool APIs
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* These functions could be used for calculating pooling
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* For any features, every column will occupy a size which is a multiple of 32 bytes. Here column means the last dimension of a tensor
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* For example, if a tensor of type int8_t named A is of size 4 * 30 * 300, it will be saved in the memory of this form so that every column occupies 320 bytes:
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* A[0][0][0], A[0][0][1], ..., A[0][0][299], 0, 0, 0, ..., 0, A[0][1][0], A[0][1][1], ..., A[0][1][299], 0, 0, 0, 0, ..., 0, ..., A[0][29][0], ..., A[0][29][299], 0, ..., 0, A[1][0][0], ...
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* Set out_sep_mode to 1 can accelerate the calculate process, however, in this mode, cpu cannot access the output, and these conditions below must be satisfied:
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* (1) this layer should be either depthwise or pointwise, innerproduct (or fc) could be regarded as pointwise, pooling could be regarded as depthwise
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* (2) if this layer is pointwise, next layer should be depthwise; if this layer is depthwise, next layer should be pointwise
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* (3) width of output plus left padding of next layer should be no larger than 16 bytes
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* (4) stride of next layer should be 1
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* (5) (height + next_pad_up) * channel of output should be no larger than 8192
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* If out_sep_mode of previous layer is set to 1, in_sep_mode of this layer should be 1
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*
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*
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*/
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/** @addtogroup cnn_pool_APIs
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* @{
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*/
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struct cnn_pooling_config_bean
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{
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uint16_t in_channel; // how many channels of input
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uint16_t in_height; // height of input
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uint16_t in_width; // output of input
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uint16_t out_height; // don't need to be setted, this is used to save the height of output when the calculation finishes
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uint16_t out_width; // don't need to be setted, this is used to save the height of output when the calculation finishes
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uint8_t kernel_size_h; // height of the convolving kernel
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uint8_t kernel_size_w; // width of the convolving kernel
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uint8_t stride;
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int8_t padding_to_same; // non-zero: output size will be ceil((input size + 2 * padding)/stride) by padding input. zero : output size will be calculated by config
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uint8_t padding; // zero-padding added to both sides of the input
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uint8_t input_signed; // whether input is signed
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uint8_t mac_8bit; // non_zero: 8bit mode; zero: 16bit mode
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uint8_t count_include_pad; // when non-0, include the zero-padding in the averaging calculation
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uint8_t ceil_mode; // when non-0, use ceil instead of floor to compute the output shape. only valid when padding_to_same is zero
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uint8_t avg; // non-zero: avg pooling, zero: max pooling
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uint8_t out_shift; // the fraction of output minus the fraction of input, you can set this to a positive number for a higher precision, only valid in avgpooling
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uint8_t input_iram; // nonzero - read input from iram, 0 - read input from ddr
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uint8_t output_iram; // nonzero - put output into iram, 0 - put output into ddr
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uint8_t in_sep_mode; // whether read input from iram as separable conv mode
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uint8_t out_sep_mode; // whether put output into iram as separable conv mode
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};
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/* @brief cnn_pool_2d() - do a pooling process using cnn engine, it is necessary that input_addr, output_addr are multiple of 256
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* @parem input_addr: where the input is put
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* @parem output_addr: where the output will be put
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* @parem config: config info of the pooling process
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* @return 0 -- succeed
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* @return 1 -- input channel is 0
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* @return 2 -- input height is 0
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* @return 3 -- input width is 0
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* @return 4 -- input height is so small that output height will be 0
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* @return 5 -- input width is so small that output width will be 0
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* @return 7 -- kernel is 0
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* @return 8 -- kernel is too large
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* @return 9 -- stride is 0
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* @return 10 -- stride is too large
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*/
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uint8_t cnn_pool_2d(uint32_t input_addr, uint32_t output_addr,
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struct cnn_pooling_config_bean *config);
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#ifdef __cplusplus
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}
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#endif
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#endif
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