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💣 Machine learning which might blow up in your face 💣
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123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129#include "im2col.h"
void im2col_cpu(const double* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, double* data_col) {
const int channel_size = height * width;
for (int fitting_height = 0; fitting_height <= (height - kernel_h); fitting_height += stride_h) { for (int fitting_width = 0; fitting_width <= (width - kernel_w); fitting_width += stride_w) { for (int channel = 0; channel < channels; channel++) { for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) { for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { int input_row = fitting_height + kernel_row; int input_col = fitting_width + kernel_col; *(data_col++) = data_im[input_row * width + input_col + channel_size * channel]; } } } } }}
void col2im_cpu(const double* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, double* data_im) {
memset(data_im, 0, height * width * channels * sizeof(double));
const int channel_size = height * width;
for (int fitting_height = 0; fitting_height <= (height - kernel_h); fitting_height += stride_h) { for (int fitting_width = 0; fitting_width <= (width - kernel_w); fitting_width += stride_w) { for (int channel = 0; channel < channels; channel++) { for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) { for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { int input_row = fitting_height + kernel_row; int input_col = fitting_width + kernel_col; data_im[input_row * width + input_col + channel_size * channel] += *(data_col++); } } } } }}
inline double max ( double a, double b ) { return a > b ? a : b; }
void pool_forwards_cpu(const double* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, double* data_pooled) {
const int channel_size = height * width;
for (int channel = 0; channel < channels; channel++) { for (int fitting_height = 0; fitting_height <= (height - kernel_h); fitting_height += stride_h) { for (int fitting_width = 0; fitting_width <= (width - kernel_w); fitting_width += stride_w) { // Start with the value in 0,0 int max_index = fitting_height * width + fitting_width + channel_size * channel; double max_value = data_im[max_index]; // Initial row, skipping the corner we've done for (int kernel_col = 1; kernel_col < kernel_w; kernel_col++) { int input_row = fitting_height; int input_col = fitting_width + kernel_col; int data_index = input_row * width + input_col + channel_size * channel; double data_value = data_im[data_index]; max_value = max ( max_value, data_value ); } // The remaining rows for (int kernel_row = 1; kernel_row < kernel_h; kernel_row++) { for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { int input_row = fitting_height + kernel_row; int input_col = fitting_width + kernel_col; int data_index = input_row * width + input_col + channel_size * channel; double data_value = data_im[data_index]; max_value = max ( max_value, data_value ); } } *(data_pooled++) = max_value; } } }}
void pool_backwards_cpu(const double* data_im, const double* data_pooled, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, double* data_backgrad ) {
memset(data_backgrad, 0, height * width * channels * sizeof(double));
const int channel_size = height * width;
for (int channel = 0; channel < channels; channel++) { for (int fitting_height = 0; fitting_height <= (height - kernel_h); fitting_height += stride_h) { for (int fitting_width = 0; fitting_width <= (width - kernel_w); fitting_width += stride_w) { int max_index = fitting_height * width + fitting_width + channel_size * channel; double max_value = data_im[max_index]; for (int kernel_col = 1; kernel_col < kernel_w; kernel_col++) { int input_row = fitting_height; int input_col = fitting_width + kernel_col; int data_index = input_row * width + input_col + channel_size * channel; double data_value = data_im[data_index]; if ( data_value > max_value ) { max_index = data_index; max_value = data_value; } } for (int kernel_row = 1; kernel_row < kernel_h; kernel_row++) { for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { int input_row = fitting_height + kernel_row; int input_col = fitting_width + kernel_col; int data_index = input_row * width + input_col + channel_size * channel; double data_value = data_im[data_index]; if ( data_value > max_value ) { max_index = data_index; max_value = data_value; } } } data_backgrad[max_index] += *(data_pooled++); } } }}