#include "blas.h"
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#include "utils.h"
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#include <math.h>
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#include <assert.h>
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#include <float.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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void reorg_cpu(float *x, int out_w, int out_h, int out_c, int batch, int stride, int forward, float *out)
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{
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int b,i,j,k;
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int in_c = out_c/(stride*stride);
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//printf("\n out_c = %d, out_w = %d, out_h = %d, stride = %d, forward = %d \n", out_c, out_w, out_h, stride, forward);
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//printf(" in_c = %d, in_w = %d, in_h = %d \n", in_c, out_w*stride, out_h*stride);
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for(b = 0; b < batch; ++b){
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for(k = 0; k < out_c; ++k){
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for(j = 0; j < out_h; ++j){
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for(i = 0; i < out_w; ++i){
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int in_index = i + out_w*(j + out_h*(k + out_c*b));
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int c2 = k % in_c;
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int offset = k / in_c;
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int w2 = i*stride + offset % stride;
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int h2 = j*stride + offset / stride;
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int out_index = w2 + out_w*stride*(h2 + out_h*stride*(c2 + in_c*b));
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if(forward) out[out_index] = x[in_index]; // used by default for forward (i.e. forward = 0)
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else out[in_index] = x[out_index];
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}
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}
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}
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}
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}
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void flatten(float *x, int size, int layers, int batch, int forward)
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{
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float* swap = (float*)xcalloc(size * layers * batch, sizeof(float));
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int i,c,b;
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for(b = 0; b < batch; ++b){
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for(c = 0; c < layers; ++c){
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for(i = 0; i < size; ++i){
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int i1 = b*layers*size + c*size + i;
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int i2 = b*layers*size + i*layers + c;
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if (forward) swap[i2] = x[i1];
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else swap[i1] = x[i2];
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}
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}
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}
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memcpy(x, swap, size*layers*batch*sizeof(float));
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free(swap);
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}
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void weighted_sum_cpu(float *a, float *b, float *s, int n, float *c)
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{
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int i;
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for(i = 0; i < n; ++i){
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c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0);
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}
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}
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void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc)
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{
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int i;
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for(i = 0; i < n; ++i){
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if(da) da[i] += dc[i] * s[i];
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if(db) db[i] += dc[i] * (1-s[i]);
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ds[i] += dc[i] * (a[i] - b[i]);
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}
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}
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static float relu(float src) {
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if (src > 0) return src;
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return 0;
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}
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void shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers, float **layers_output, float *out, float *in, float *weights, int nweights, WEIGHTS_NORMALIZATION_T weights_normalization)
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{
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// nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w)
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const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w)
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int step = 0;
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if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1
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int id;
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#pragma omp parallel for
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for (id = 0; id < size; ++id) {
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int src_id = id;
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const int src_i = src_id % src_outputs;
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src_id /= src_outputs;
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int src_b = src_id;
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float sum = 1, max_val = -FLT_MAX;
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int i;
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if (weights && weights_normalization) {
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if (weights_normalization == SOFTMAX_NORMALIZATION) {
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for (i = 0; i < (n + 1); ++i) {
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const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
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float w = weights[weights_index];
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if (max_val < w) max_val = w;
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}
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}
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const float eps = 0.0001;
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sum = eps;
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for (i = 0; i < (n + 1); ++i) {
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const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
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const float w = weights[weights_index];
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if (weights_normalization == RELU_NORMALIZATION) sum += relu(w);
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else if (weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val);
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}
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}
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if (weights) {
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float w = weights[src_i / step];
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if (weights_normalization == RELU_NORMALIZATION) w = relu(w) / sum;
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else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
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out[id] = in[id] * w; // [0 or c or (c, h ,w)]
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}
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else out[id] = in[id];
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// layers
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for (i = 0; i < n; ++i) {
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int add_outputs = outputs_of_layers[i];
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if (src_i < add_outputs) {
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int add_index = add_outputs*src_b + src_i;
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int out_index = id;
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float *add = layers_output[i];
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if (weights) {
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const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)]
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float w = weights[weights_index];
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if (weights_normalization == RELU_NORMALIZATION) w = relu(w) / sum;
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else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
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out[out_index] += add[add_index] * w; // [0 or c or (c, h ,w)]
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}
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else out[out_index] += add[add_index];
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}
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}
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}
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}
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void backward_shortcut_multilayer_cpu(int size, int src_outputs, int batch, int n, int *outputs_of_layers,
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float **layers_delta, float *delta_out, float *delta_in, float *weights, float *weight_updates, int nweights, float *in, float **layers_output, WEIGHTS_NORMALIZATION_T weights_normalization)
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{
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// nweights - l.n or l.n*l.c or (l.n*l.c*l.h*l.w)
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const int layer_step = nweights / (n + 1); // 1 or l.c or (l.c * l.h * l.w)
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int step = 0;
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if (nweights > 0) step = src_outputs / layer_step; // (l.c * l.h * l.w) or (l.w*l.h) or 1
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int id;
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#pragma omp parallel for
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for (id = 0; id < size; ++id) {
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int src_id = id;
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int src_i = src_id % src_outputs;
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src_id /= src_outputs;
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int src_b = src_id;
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float grad = 1, sum = 1, max_val = -FLT_MAX;;
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int i;
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if (weights && weights_normalization) {
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if (weights_normalization == SOFTMAX_NORMALIZATION) {
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for (i = 0; i < (n + 1); ++i) {
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const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
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float w = weights[weights_index];
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if (max_val < w) max_val = w;
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}
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}
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const float eps = 0.0001;
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sum = eps;
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for (i = 0; i < (n + 1); ++i) {
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const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
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const float w = weights[weights_index];
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if (weights_normalization == RELU_NORMALIZATION) sum += relu(w);
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else if (weights_normalization == SOFTMAX_NORMALIZATION) sum += expf(w - max_val);
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}
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/*
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grad = 0;
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for (i = 0; i < (n + 1); ++i) {
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const int weights_index = src_i / step + i*layer_step; // [0 or c or (c, h ,w)]
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const float delta_w = delta_in[id] * in[id];
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const float w = weights[weights_index];
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if (weights_normalization == RELU_NORMALIZATION) grad += delta_w * relu(w) / sum;
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else if (weights_normalization == SOFTMAX_NORMALIZATION) grad += delta_w * expf(w - max_val) / sum;
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}
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*/
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}
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if (weights) {
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float w = weights[src_i / step];
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if (weights_normalization == RELU_NORMALIZATION) w = relu(w) / sum;
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else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
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delta_out[id] += delta_in[id] * w; // [0 or c or (c, h ,w)]
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weight_updates[src_i / step] += delta_in[id] * in[id] * grad;
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}
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else delta_out[id] += delta_in[id];
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// layers
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for (i = 0; i < n; ++i) {
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int add_outputs = outputs_of_layers[i];
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if (src_i < add_outputs) {
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int add_index = add_outputs*src_b + src_i;
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int out_index = id;
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float *layer_delta = layers_delta[i];
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if (weights) {
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float *add = layers_output[i];
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const int weights_index = src_i / step + (i + 1)*layer_step; // [0 or c or (c, h ,w)]
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float w = weights[weights_index];
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if (weights_normalization == RELU_NORMALIZATION) w = relu(w) / sum;
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else if (weights_normalization == SOFTMAX_NORMALIZATION) w = expf(w - max_val) / sum;
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layer_delta[add_index] += delta_in[id] * w; // [0 or c or (c, h ,w)]
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weight_updates[weights_index] += delta_in[id] * add[add_index] * grad;
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}
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else layer_delta[add_index] += delta_in[id];
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}
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}
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}
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}
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void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float *out)
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{
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int stride = w1/w2;
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int sample = w2/w1;
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assert(stride == h1/h2);
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assert(sample == h2/h1);
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if(stride < 1) stride = 1;
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if(sample < 1) sample = 1;
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int minw = (w1 < w2) ? w1 : w2;
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int minh = (h1 < h2) ? h1 : h2;
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int minc = (c1 < c2) ? c1 : c2;
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int i,j,k,b;
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for(b = 0; b < batch; ++b){
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for(k = 0; k < minc; ++k){
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for(j = 0; j < minh; ++j){
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for(i = 0; i < minw; ++i){
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int out_index = i*sample + w2*(j*sample + h2*(k + c2*b));
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int add_index = i*stride + w1*(j*stride + h1*(k + c1*b));
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out[out_index] += add[add_index];
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}
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}
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}
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}
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}
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void mean_cpu(float *x, int batch, int filters, int spatial, float *mean)
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{
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float scale = 1./(batch * spatial);
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int i,j,k;
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for(i = 0; i < filters; ++i){
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mean[i] = 0;
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for(j = 0; j < batch; ++j){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + i*spatial + k;
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mean[i] += x[index];
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}
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}
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mean[i] *= scale;
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}
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}
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void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance)
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{
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float scale = 1./(batch * spatial - 1);
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int i,j,k;
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for(i = 0; i < filters; ++i){
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variance[i] = 0;
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for(j = 0; j < batch; ++j){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + i*spatial + k;
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variance[i] += pow((x[index] - mean[i]), 2);
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}
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}
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variance[i] *= scale;
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}
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}
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void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial)
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{
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int b, f, i;
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for(b = 0; b < batch; ++b){
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for(f = 0; f < filters; ++f){
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for(i = 0; i < spatial; ++i){
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int index = b*filters*spatial + f*spatial + i;
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x[index] = (x[index] - mean[f])/(sqrt(variance[f] + .00001f));
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}
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}
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}
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}
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void const_cpu(int N, float ALPHA, float *X, int INCX)
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{
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int i;
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for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
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}
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void mul_cpu(int N, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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for(i = 0; i < N; ++i) Y[i*INCY] *= X[i*INCX];
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}
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void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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for(i = 0; i < N; ++i) Y[i*INCY] = pow(X[i*INCX], ALPHA);
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}
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void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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for(i = 0; i < N; ++i) Y[i*INCY] += ALPHA*X[i*INCX];
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}
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void scal_cpu(int N, float ALPHA, float *X, int INCX)
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{
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int i;
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for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA;
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}
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void scal_add_cpu(int N, float ALPHA, float BETA, float *X, int INCX)
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{
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int i;
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for (i = 0; i < N; ++i) X[i*INCX] = X[i*INCX] * ALPHA + BETA;
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}
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void fill_cpu(int N, float ALPHA, float *X, int INCX)
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{
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int i;
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if (INCX == 1 && ALPHA == 0) {
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memset(X, 0, N * sizeof(float));
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}
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else {
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for (i = 0; i < N; ++i) X[i*INCX] = ALPHA;
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}
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}
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void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT)
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{
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int i, j;
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int index = 0;
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for(j = 0; j < B; ++j) {
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for(i = 0; i < NX; ++i){
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if(X) X[j*NX + i] += OUT[index];
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++index;
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}
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for(i = 0; i < NY; ++i){
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if(Y) Y[j*NY + i] += OUT[index];
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++index;
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}
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}
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}
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void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT)
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{
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int i, j;
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int index = 0;
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for(j = 0; j < B; ++j) {
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for(i = 0; i < NX; ++i){
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OUT[index++] = X[j*NX + i];
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}
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for(i = 0; i < NY; ++i){
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OUT[index++] = Y[j*NY + i];
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}
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}
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}
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void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX];
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}
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void mult_add_into_cpu(int N, float *X, float *Y, float *Z)
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{
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int i;
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for(i = 0; i < N; ++i) Z[i] += X[i]*Y[i];
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}
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void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
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{
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int i;
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for(i = 0; i < n; ++i){
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float diff = truth[i] - pred[i];
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float abs_val = fabs(diff);
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if(abs_val < 1) {
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error[i] = diff * diff;
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delta[i] = diff;
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}
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else {
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error[i] = 2*abs_val - 1;
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delta[i] = (diff > 0) ? 1 : -1;
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}
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}
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}
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void l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
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{
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int i;
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for(i = 0; i < n; ++i){
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float diff = truth[i] - pred[i];
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error[i] = fabs(diff);
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delta[i] = diff > 0 ? 1 : -1;
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}
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}
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void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error)
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{
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int i;
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for(i = 0; i < n; ++i){
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float t = truth[i];
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float p = pred[i];
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error[i] = (t) ? -log(p) : 0;
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delta[i] = t-p;
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}
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}
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void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error)
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{
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int i;
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for(i = 0; i < n; ++i){
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float t = truth[i];
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float p = pred[i];
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error[i] = -t*log(p) - (1-t)*log(1-p);
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delta[i] = t-p;
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}
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}
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void l2_cpu(int n, float *pred, float *truth, float *delta, float *error)
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{
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int i;
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for(i = 0; i < n; ++i){
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float diff = truth[i] - pred[i];
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error[i] = diff * diff;
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delta[i] = diff;
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}
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}
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float dot_cpu(int N, float *X, int INCX, float *Y, int INCY)
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{
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int i;
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float dot = 0;
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for(i = 0; i < N; ++i) dot += X[i*INCX] * Y[i*INCY];
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return dot;
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}
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void softmax(float *input, int n, float temp, float *output, int stride)
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{
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int i;
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float sum = 0;
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float largest = -FLT_MAX;
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for(i = 0; i < n; ++i){
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if(input[i*stride] > largest) largest = input[i*stride];
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}
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for(i = 0; i < n; ++i){
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float e = exp(input[i*stride]/temp - largest/temp);
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sum += e;
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output[i*stride] = e;
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}
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for(i = 0; i < n; ++i){
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output[i*stride] /= sum;
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}
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}
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void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)
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{
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int g, b;
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for(b = 0; b < batch; ++b){
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for(g = 0; g < groups; ++g){
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softmax(input + b*batch_offset + g*group_offset, n, temp, output + b*batch_offset + g*group_offset, stride);
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}
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}
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}
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void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out)
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{
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int i, j, k, b;
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for (b = 0; b < batch; ++b) {
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for (k = 0; k < c; ++k) {
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for (j = 0; j < h*stride; ++j) {
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for (i = 0; i < w*stride; ++i) {
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int in_index = b*w*h*c + k*w*h + (j / stride)*w + i / stride;
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int out_index = b*w*h*c*stride*stride + k*w*h*stride*stride + j*w*stride + i;
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if (forward) out[out_index] = scale*in[in_index];
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else in[in_index] += scale*out[out_index];
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}
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}
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}
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}
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}
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void constrain_cpu(int size, float ALPHA, float *X)
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{
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int i;
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for (i = 0; i < size; ++i) {
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X[i] = fminf(ALPHA, fmaxf(-ALPHA, X[i]));
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}
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}
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void fix_nan_and_inf_cpu(float *input, size_t size)
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{
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int i;
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for (i = 0; i < size; ++i) {
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float val = input[i];
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if (isnan(val) || isinf(val))
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input[i] = 1.0f / i; // pseudo random value
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}
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}
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void get_embedding(float *src, int src_w, int src_h, int src_c, int embedding_size, int cur_w, int cur_h, int cur_n, int cur_b, float *dst)
|
{
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int i;
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for (i = 0; i < embedding_size; ++i) {
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const int src_index = cur_b*(src_c*src_h*src_w) + cur_n*(embedding_size*src_h*src_w) + i*src_h*src_w + cur_h*(src_w) + cur_w;
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const float val = src[src_index];
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dst[i] = val;
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//printf(" val = %f, ", val);
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}
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}
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|
// Euclidean_norm
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float math_vector_length(float *A, unsigned int feature_size)
|
{
|
float sum = 0;
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int i;
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for (i = 0; i < feature_size; ++i)
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{
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sum += A[i] * A[i];
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}
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float vector_length = sqrtf(sum);
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return vector_length;
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}
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float cosine_similarity(float *A, float *B, unsigned int feature_size)
|
{
|
float mul = 0.0, d_a = 0.0, d_b = 0.0;
|
|
int i;
|
for(i = 0; i < feature_size; ++i)
|
{
|
mul += A[i] * B[i];
|
d_a += A[i] * A[i];
|
d_b += B[i] * B[i];
|
}
|
float similarity;
|
float divider = sqrtf(d_a) * sqrtf(d_b);
|
if (divider > 0) similarity = mul / divider;
|
else similarity = 0;
|
|
return similarity;
|
}
|
|
int get_sim_P_index(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size)
|
{
|
size_t z;
|
for (z = 0; z < contrast_p_size; ++z) {
|
if (contrast_p[z].i == i && contrast_p[z].j == j) break;
|
}
|
if (z == contrast_p_size) {
|
return -1; // not found
|
}
|
|
return z; // found
|
}
|
|
int check_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size)
|
{
|
size_t z;
|
for (z = 0; z < contrast_p_size; ++z) {
|
if (contrast_p[z].i == i && contrast_p[z].j == j) break;
|
}
|
if (z == contrast_p_size) {
|
return 0; // not found
|
}
|
|
return 1; // found
|
}
|
|
float find_sim(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size)
|
{
|
size_t z;
|
for (z = 0; z < contrast_p_size; ++z) {
|
if (contrast_p[z].i == i && contrast_p[z].j == j) break;
|
}
|
if (z == contrast_p_size) {
|
printf(" Error: find_sim(): sim isn't found: i = %d, j = %d, z = %d \n", i, j, z);
|
getchar();
|
}
|
|
return contrast_p[z].sim;
|
}
|
|
float find_P_constrastive(size_t i, size_t j, contrastive_params *contrast_p, int contrast_p_size)
|
{
|
size_t z;
|
for (z = 0; z < contrast_p_size; ++z) {
|
if (contrast_p[z].i == i && contrast_p[z].j == j) break;
|
}
|
if (z == contrast_p_size) {
|
printf(" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = %d \n", i, j, z);
|
getchar();
|
}
|
|
return contrast_p[z].P;
|
}
|
|
// num_of_samples = 2 * loaded_images = mini_batch_size
|
float P_constrastive_f_det(size_t il, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size)
|
{
|
const float sim = contrast_p[il].sim;
|
const size_t i = contrast_p[il].i;
|
const size_t j = contrast_p[il].j;
|
|
const float numerator = expf(sim / temperature);
|
|
float denominator = 0;
|
int k;
|
for (k = 0; k < contrast_p_size; ++k) {
|
contrastive_params cp = contrast_p[k];
|
//if (k != i && labels[k] != labels[i]) {
|
//if (k != i) {
|
if (cp.i != i && cp.j == j) {
|
//const float sim_den = cp.sim;
|
////const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size);
|
//denominator += expf(sim_den / temperature);
|
denominator += cp.exp_sim;
|
}
|
}
|
|
float result = 0.9999;
|
if (denominator != 0) result = numerator / denominator;
|
if (result > 1) result = 0.9999;
|
return result;
|
}
|
|
// num_of_samples = 2 * loaded_images = mini_batch_size
|
float P_constrastive_f(size_t i, size_t l, int *labels, float **z, unsigned int feature_size, float temperature, contrastive_params *contrast_p, int contrast_p_size)
|
{
|
if (i == l) {
|
fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l);
|
getchar();
|
}
|
|
const float sim = find_sim(i, l, contrast_p, contrast_p_size); // cosine_similarity(z[i], z[l], feature_size);
|
const float numerator = expf(sim / temperature);
|
|
float denominator = 0;
|
int k;
|
for (k = 0; k < contrast_p_size; ++k) {
|
contrastive_params cp = contrast_p[k];
|
//if (k != i && labels[k] != labels[i]) {
|
//if (k != i) {
|
if (cp.i != i && cp.j == l) {
|
//const float sim_den = cp.sim;
|
////const float sim_den = find_sim(k, l, contrast_p, contrast_p_size); // cosine_similarity(z[k], z[l], feature_size);
|
//denominator += expf(sim_den / temperature);
|
denominator += cp.exp_sim;
|
}
|
}
|
|
float result = 0.9999;
|
if (denominator != 0) result = numerator / denominator;
|
if (result > 1) result = 0.9999;
|
return result;
|
}
|
|
void grad_contrastive_loss_positive_f(size_t i, int *class_ids, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size)
|
{
|
const float vec_len = math_vector_length(z[i], feature_size);
|
size_t j;
|
float N = 0;
|
for (j = 0; j < num_of_samples; ++j) {
|
if (labels[i] == labels[j] && labels[i] >= 0) N++;
|
}
|
if (N == 0 || temperature == 0 || vec_len == 0) {
|
fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f, labels[i] = %d \n",
|
N, temperature, vec_len, labels[i]);
|
getchar();
|
return;
|
}
|
const float mult = 1 / ((N - 1) * temperature * vec_len);
|
|
for (j = 0; j < num_of_samples; ++j) {
|
//if (i != j && (i/2) == (j/2)) {
|
if (i != j && labels[i] == labels[j] && labels[i] >= 0) {
|
//printf(" i = %d, j = %d, num_of_samples = %d, labels[i] = %d, labels[j] = %d \n",
|
// i, j, num_of_samples, labels[i], labels[j]);
|
const int sim_P_i = get_sim_P_index(i, j, contrast_p, contrast_p_size);
|
if (sim_P_i < 0) continue;
|
const float sim = contrast_p[sim_P_i].sim;
|
const float P = contrast_p[sim_P_i].P;
|
//if (!check_sim(i, j, contrast_p, contrast_p_size)) continue;
|
//const float sim = find_sim(i, j, contrast_p, contrast_p_size); //cos_sim[i*num_of_samples + j]; // cosine_similarity(z[i], z[j], feature_size);
|
//const float P = find_P_constrastive(i, j, contrast_p, contrast_p_size); //p_constrastive[i*num_of_samples + j]; // P_constrastive(i, j, labels, num_of_samples, z, feature_size, temperature, cos_sim);
|
//const float custom_pos_mult = 1 - sim;
|
|
|
int m;
|
//const float d = mult*(sim * z[i][m] - z[j][m]) * (1 - P); // 1
|
for (m = 0; m < feature_size; ++m) {
|
//const float d = mult*(sim * z[j][m] - z[j][m]) * (1 - P); // my
|
//const float d = mult*(sim * z[i][m] + sim * z[j][m] - z[j][m]) *(1 - P); // 1+2
|
const float d = mult*(sim * z[i][m] - z[j][m]) *(1 - P); // 1 (70%)
|
//const float d = mult*(sim * z[j][m] - z[j][m]) * (1 - P); // 2
|
// printf(" pos: z[j][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[j][m], z[i][m], d, sim);
|
const int out_i = m * wh;
|
delta[out_i] -= d;
|
}
|
}
|
}
|
}
|
|
void grad_contrastive_loss_negative_f(size_t i, int *class_ids, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *delta, int wh, contrastive_params *contrast_p, int contrast_p_size, int neg_max)
|
{
|
const float vec_len = math_vector_length(z[i], feature_size);
|
size_t j;
|
float N = 0;
|
for (j = 0; j < num_of_samples; ++j) {
|
if (labels[i] == labels[j] && labels[i] >= 0) N++;
|
}
|
if (N == 0 || temperature == 0 || vec_len == 0) {
|
fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f, labels[i] = %d \n",
|
N, temperature, vec_len, labels[i]);
|
getchar();
|
return;
|
}
|
const float mult = 1 / ((N - 1) * temperature * vec_len);
|
|
int neg_counter = 0;
|
|
for (j = 0; j < num_of_samples; ++j) {
|
//if (i != j && (i/2) == (j/2)) {
|
if (labels[i] >= 0 && labels[i] == labels[j] && i != j) {
|
|
size_t k;
|
for (k = 0; k < num_of_samples; ++k) {
|
//if (k != i && k != j && labels[k] != labels[i]) {
|
if (k != i && k != j && labels[k] != labels[i] && class_ids[j] == class_ids[k]) {
|
neg_counter++;
|
const int sim_P_i = get_sim_P_index(i, k, contrast_p, contrast_p_size);
|
if (sim_P_i < 0) continue;
|
const float sim = contrast_p[sim_P_i].sim;
|
const float P = contrast_p[sim_P_i].P;
|
//if (!check_sim(i, k, contrast_p, contrast_p_size)) continue;
|
//const float sim = find_sim(i, k, contrast_p, contrast_p_size); //cos_sim[i*num_of_samples + k]; // cosine_similarity(z[i], z[k], feature_size);
|
//const float P = find_P_constrastive(i, k, contrast_p, contrast_p_size); //p_constrastive[i*num_of_samples + k]; // P_constrastive(i, k, labels, num_of_samples, z, feature_size, temperature, cos_sim);
|
//const float custom_pos_mult = 1 + sim;
|
|
int m;
|
//const float d = mult*(z[k][m] + sim * z[i][m]) * P; // my1
|
for (m = 0; m < feature_size; ++m) {
|
//const float d = mult*(z[k][m] + sim * z[i][m]) * P; // 1 (70%)
|
//const float d = mult*(z[k][m] - sim * z[k][m] - sim * z[i][m]) * P; // 1+2
|
const float d = mult*(z[k][m] - sim * z[i][m]) * P; // 1 (70%)
|
//const float d = mult*(z[k][m] - sim * z[k][m]) * P; // 2
|
//printf(" neg: z[k][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[k][m], z[i][m], d, sim);
|
const int out_i = m * wh;
|
delta[out_i] -= d;
|
}
|
|
if (neg_counter >= neg_max) return;
|
}
|
}
|
}
|
}
|
}
|
|
|
|
// num_of_samples = 2 * loaded_images = mini_batch_size
|
float P_constrastive(size_t i, size_t l, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *exp_cos_sim)
|
{
|
if (i == l) {
|
fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l);
|
getchar();
|
}
|
|
//const float sim = cos_sim[i*num_of_samples + l]; // cosine_similarity(z[i], z[l], feature_size);
|
//const float numerator = expf(sim / temperature);
|
const float numerator = exp_cos_sim[i*num_of_samples + l];
|
|
float denominator = 0;
|
int k;
|
for (k = 0; k < num_of_samples; ++k) {
|
//if (k != i && labels[k] != labels[i]) {
|
if (k != i) {
|
//const float sim_den = cos_sim[k*num_of_samples + l]; // cosine_similarity(z[k], z[l], feature_size);
|
//denominator += expf(sim_den / temperature);
|
denominator += exp_cos_sim[k*num_of_samples + l];
|
}
|
}
|
|
float result = numerator / denominator;
|
return result;
|
}
|
|
// i - id of the current sample in mini_batch
|
// labels[num_of_samples] - array with class_id for each sample in the current mini_batch
|
// z[feature_size][num_of_samples] - array of arrays with contrastive features (output of conv-layer, f.e. 128 floats for each sample)
|
// delta[feature_size] - array with deltas for backpropagation
|
// temperature - scalar temperature param (temperature > 0), f.e. temperature = 0.07: Supervised Contrastive Learning
|
void grad_contrastive_loss_positive(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta, int wh)
|
{
|
const float vec_len = math_vector_length(z[i], feature_size);
|
size_t j;
|
float N = 0;
|
for (j = 0; j < num_of_samples; ++j) {
|
if (labels[i] == labels[j]) N++;
|
}
|
if (N == 0 || temperature == 0 || vec_len == 0) {
|
fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f \n", N, temperature, vec_len);
|
getchar();
|
}
|
const float mult = 1 / ((N - 1) * temperature * vec_len);
|
|
for (j = 0; j < num_of_samples; ++j) {
|
//if (i != j && (i/2) == (j/2)) {
|
if (i != j && labels[i] == labels[j]) {
|
//printf(" i = %d, j = %d, num_of_samples = %d, labels[i] = %d, labels[j] = %d \n",
|
// i, j, num_of_samples, labels[i], labels[j]);
|
const float sim = cos_sim[i*num_of_samples + j]; // cosine_similarity(z[i], z[j], feature_size);
|
const float P = p_constrastive[i*num_of_samples + j]; // P_constrastive(i, j, labels, num_of_samples, z, feature_size, temperature, cos_sim);
|
//const float custom_pos_mult = 1 - sim;
|
|
int m;
|
for (m = 0; m < feature_size; ++m) {
|
const float d = mult*(sim * z[i][m] - z[j][m]) * (1 - P); // good
|
//const float d = mult*(sim * z[j][m] - z[j][m]) * (1 - P); // bad
|
// printf(" pos: z[j][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[j][m], z[i][m], d, sim);
|
const int out_i = m * wh;
|
delta[out_i] -= d;
|
}
|
}
|
}
|
}
|
|
// i - id of the current sample in mini_batch
|
// labels[num_of_samples] - array with class_id for each sample in the current mini_batch
|
// z[feature_size][num_of_samples] - array of arrays with contrastive features (output of conv-layer, f.e. 128 floats for each sample)
|
// delta[feature_size] - array with deltas for backpropagation
|
// temperature - scalar temperature param (temperature > 0), f.e. temperature = 0.07: Supervised Contrastive Learning
|
void grad_contrastive_loss_negative(size_t i, int *labels, size_t num_of_samples, float **z, unsigned int feature_size, float temperature, float *cos_sim, float *p_constrastive, float *delta, int wh)
|
{
|
const float vec_len = math_vector_length(z[i], feature_size);
|
size_t j;
|
float N = 0;
|
for (j = 0; j < num_of_samples; ++j) {
|
if (labels[i] == labels[j]) N++;
|
}
|
if (N == 0 || temperature == 0 || vec_len == 0) {
|
fprintf(stderr, " Error: N == 0 || temperature == 0 || vec_len == 0. N=%f, temperature=%f, vec_len=%f \n", N, temperature, vec_len);
|
getchar();
|
}
|
const float mult = 1 / ((N - 1) * temperature * vec_len);
|
|
for (j = 0; j < num_of_samples; ++j) {
|
//if (i != j && (i/2) == (j/2)) {
|
if (i != j && labels[i] == labels[j]) {
|
|
size_t k;
|
for (k = 0; k < num_of_samples; ++k) {
|
//if (k != i && k != j && labels[k] != labels[i]) {
|
if (k != i && k != j && labels[k] >= 0) {
|
const float sim = cos_sim[i*num_of_samples + k]; // cosine_similarity(z[i], z[k], feature_size);
|
const float P = p_constrastive[i*num_of_samples + k]; // P_constrastive(i, k, labels, num_of_samples, z, feature_size, temperature, cos_sim);
|
//const float custom_pos_mult = 1 + sim;
|
|
int m;
|
for (m = 0; m < feature_size; ++m) {
|
const float d = mult*(z[k][m] - sim * z[i][m]) * P; // good
|
//const float d = mult*(z[k][m] - sim * z[k][m]) * P; // bad
|
//printf(" neg: z[k][m] = %f, z[i][m] = %f, d = %f, sim = %f \n", z[k][m], z[i][m], d, sim);
|
const int out_i = m * wh;
|
delta[out_i] -= d;
|
}
|
}
|
}
|
}
|
}
|
}
|
