mirror of
https://github.com/pjreddie/darknet.git
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533 lines
15 KiB
C
533 lines
15 KiB
C
#include "convolutional_layer.h"
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#include "utils.h"
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#include "batchnorm_layer.h"
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#include "im2col.h"
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#include "col2im.h"
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#include "blas.h"
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#include "gemm.h"
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#include <stdio.h>
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#include <time.h>
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void swap_binary(convolutional_layer *l)
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{
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float *swap = l->filters;
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l->filters = l->binary_filters;
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l->binary_filters = swap;
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#ifdef GPU
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swap = l->filters_gpu;
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l->filters_gpu = l->binary_filters_gpu;
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l->binary_filters_gpu = swap;
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#endif
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}
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void binarize_filters2(float *filters, int n, int size, char *binary, float *scales)
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{
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int i, k, f;
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for(f = 0; f < n; ++f){
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float mean = 0;
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for(i = 0; i < size; ++i){
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mean += fabs(filters[f*size + i]);
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}
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mean = mean / size;
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scales[f] = mean;
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for(i = 0; i < size/8; ++i){
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binary[f*size + i] = (filters[f*size + i] > 0) ? 1 : 0;
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for(k = 0; k < 8; ++k){
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}
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}
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}
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}
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void binarize_filters(float *filters, int n, int size, float *binary)
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{
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int i, f;
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for(f = 0; f < n; ++f){
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float mean = 0;
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for(i = 0; i < size; ++i){
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mean += fabs(filters[f*size + i]);
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}
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mean = mean / size;
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for(i = 0; i < size; ++i){
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binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean;
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}
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}
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}
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int convolutional_out_height(convolutional_layer l)
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{
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int h = l.h;
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if (!l.pad) h -= l.size;
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else h -= 1;
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return h/l.stride + 1;
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}
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int convolutional_out_width(convolutional_layer l)
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{
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int w = l.w;
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if (!l.pad) w -= l.size;
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else w -= 1;
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return w/l.stride + 1;
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}
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image get_convolutional_image(convolutional_layer l)
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{
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int h,w,c;
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h = convolutional_out_height(l);
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w = convolutional_out_width(l);
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c = l.n;
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return float_to_image(w,h,c,l.output);
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}
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image get_convolutional_delta(convolutional_layer l)
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{
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int h,w,c;
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h = convolutional_out_height(l);
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w = convolutional_out_width(l);
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c = l.n;
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return float_to_image(w,h,c,l.delta);
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}
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#ifdef CUDNN
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size_t get_workspace_size(layer l){
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size_t most = 0;
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size_t s = 0;
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cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
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l.srcTensorDesc,
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l.filterDesc,
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l.convDesc,
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l.dstTensorDesc,
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l.fw_algo,
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&s);
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if (s > most) most = s;
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cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
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l.srcTensorDesc,
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l.ddstTensorDesc,
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l.convDesc,
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l.dfilterDesc,
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l.bf_algo,
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&s);
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if (s > most) most = s;
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cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
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l.filterDesc,
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l.ddstTensorDesc,
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l.convDesc,
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l.dsrcTensorDesc,
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l.bd_algo,
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&s);
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if (s > most) most = s;
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return most;
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}
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#endif
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convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary, int xnor)
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{
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int i;
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convolutional_layer l = {0};
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l.type = CONVOLUTIONAL;
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l.h = h;
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l.w = w;
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l.c = c;
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l.n = n;
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l.binary = binary;
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l.batch = batch;
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l.stride = stride;
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l.size = size;
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l.pad = pad;
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l.batch_normalize = batch_normalize;
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l.filters = calloc(c*n*size*size, sizeof(float));
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l.filter_updates = calloc(c*n*size*size, sizeof(float));
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l.biases = calloc(n, sizeof(float));
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l.bias_updates = calloc(n, sizeof(float));
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// float scale = 1./sqrt(size*size*c);
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float scale = sqrt(2./(size*size*c));
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for(i = 0; i < c*n*size*size; ++i) l.filters[i] = scale*rand_uniform(-1, 1);
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int out_h = convolutional_out_height(l);
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int out_w = convolutional_out_width(l);
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l.out_h = out_h;
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l.out_w = out_w;
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l.out_c = n;
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l.outputs = l.out_h * l.out_w * l.out_c;
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l.inputs = l.w * l.h * l.c;
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l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
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l.workspace_size = out_h*out_w*size*size*c*sizeof(float);
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l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
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l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
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if(binary){
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l.binary_filters = calloc(c*n*size*size, sizeof(float));
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l.cfilters = calloc(c*n*size*size, sizeof(char));
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l.scales = calloc(n, sizeof(float));
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}
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if(batch_normalize){
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l.scales = calloc(n, sizeof(float));
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l.scale_updates = calloc(n, sizeof(float));
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for(i = 0; i < n; ++i){
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l.scales[i] = 1;
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}
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l.mean = calloc(n, sizeof(float));
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l.variance = calloc(n, sizeof(float));
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l.rolling_mean = calloc(n, sizeof(float));
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l.rolling_variance = calloc(n, sizeof(float));
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}
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#ifdef GPU
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l.filters_gpu = cuda_make_array(l.filters, c*n*size*size);
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l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size);
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l.biases_gpu = cuda_make_array(l.biases, n);
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l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
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l.scales_gpu = cuda_make_array(l.scales, n);
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l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
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l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
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l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
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if(binary){
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l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
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}
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if(xnor){
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l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
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l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
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}
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l.xnor = xnor;
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if(batch_normalize){
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l.mean_gpu = cuda_make_array(l.mean, n);
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l.variance_gpu = cuda_make_array(l.variance, n);
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l.rolling_mean_gpu = cuda_make_array(l.mean, n);
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l.rolling_variance_gpu = cuda_make_array(l.variance, n);
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l.mean_delta_gpu = cuda_make_array(l.mean, n);
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l.variance_delta_gpu = cuda_make_array(l.variance, n);
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l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
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l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
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}
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#ifdef CUDNN
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cudnnCreateTensorDescriptor(&l.srcTensorDesc);
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cudnnCreateTensorDescriptor(&l.dstTensorDesc);
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cudnnCreateFilterDescriptor(&l.filterDesc);
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cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
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cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
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cudnnCreateFilterDescriptor(&l.dfilterDesc);
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cudnnCreateConvolutionDescriptor(&l.convDesc);
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cudnnSetTensor4dDescriptor(l.dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w);
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cudnnSetTensor4dDescriptor(l.ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
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cudnnSetFilter4dDescriptor(l.dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size);
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cudnnSetTensor4dDescriptor(l.srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w);
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cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
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cudnnSetFilter4dDescriptor(l.filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size);
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int padding = l.pad ? l.size/2 : 0;
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cudnnSetConvolution2dDescriptor(l.convDesc, padding, padding, l.stride, l.stride, 1, 1, CUDNN_CROSS_CORRELATION);
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cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
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l.srcTensorDesc,
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l.filterDesc,
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l.convDesc,
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l.dstTensorDesc,
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CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
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0,
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&l.fw_algo);
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cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
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l.filterDesc,
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l.ddstTensorDesc,
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l.convDesc,
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l.dsrcTensorDesc,
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CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
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0,
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&l.bd_algo);
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cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
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l.srcTensorDesc,
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l.ddstTensorDesc,
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l.convDesc,
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l.dfilterDesc,
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CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
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0,
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&l.bf_algo);
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l.workspace_size = get_workspace_size(l);
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#endif
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#endif
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l.activation = activation;
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fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
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return l;
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}
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void denormalize_convolutional_layer(convolutional_layer l)
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{
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int i, j;
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for(i = 0; i < l.n; ++i){
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float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
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for(j = 0; j < l.c*l.size*l.size; ++j){
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l.filters[i*l.c*l.size*l.size + j] *= scale;
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}
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l.biases[i] -= l.rolling_mean[i] * scale;
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}
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}
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void test_convolutional_layer()
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{
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convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0);
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l.batch_normalize = 1;
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float data[] = {1,1,1,1,1,
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1,1,1,1,1,
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1,1,1,1,1,
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1,1,1,1,1,
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1,1,1,1,1,
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2,2,2,2,2,
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2,2,2,2,2,
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2,2,2,2,2,
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2,2,2,2,2,
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2,2,2,2,2,
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3,3,3,3,3,
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3,3,3,3,3,
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3,3,3,3,3,
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3,3,3,3,3,
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3,3,3,3,3};
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network_state state = {0};
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state.input = data;
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forward_convolutional_layer(l, state);
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}
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void resize_convolutional_layer(convolutional_layer *l, int w, int h)
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{
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l->w = w;
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l->h = h;
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int out_w = convolutional_out_width(*l);
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int out_h = convolutional_out_height(*l);
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l->out_w = out_w;
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l->out_h = out_h;
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l->outputs = l->out_h * l->out_w * l->out_c;
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l->inputs = l->w * l->h * l->c;
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l->col_image = realloc(l->col_image,
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out_h*out_w*l->size*l->size*l->c*sizeof(float));
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l->output = realloc(l->output,
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l->batch*out_h * out_w * l->n*sizeof(float));
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l->delta = realloc(l->delta,
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l->batch*out_h * out_w * l->n*sizeof(float));
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#ifdef GPU
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cuda_free(l->delta_gpu);
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cuda_free(l->output_gpu);
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l->delta_gpu = cuda_make_array(l->delta, l->batch*out_h*out_w*l->n);
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l->output_gpu = cuda_make_array(l->output, l->batch*out_h*out_w*l->n);
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#endif
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}
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void add_bias(float *output, float *biases, int batch, int n, int size)
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{
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int i,j,b;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < n; ++i){
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for(j = 0; j < size; ++j){
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output[(b*n + i)*size + j] += biases[i];
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}
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}
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}
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}
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void scale_bias(float *output, float *scales, int batch, int n, int size)
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{
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int i,j,b;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < n; ++i){
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for(j = 0; j < size; ++j){
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output[(b*n + i)*size + j] *= scales[i];
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}
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}
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}
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}
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void backward_bias(float *bias_updates, float *delta, int batch, int n, int size)
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{
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int i,b;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < n; ++i){
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bias_updates[i] += sum_array(delta+size*(i+b*n), size);
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}
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}
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}
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void forward_convolutional_layer(convolutional_layer l, network_state state)
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{
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int out_h = convolutional_out_height(l);
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int out_w = convolutional_out_width(l);
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int i;
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fill_cpu(l.outputs*l.batch, 0, l.output, 1);
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/*
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if(l.binary){
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binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
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binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales);
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swap_binary(&l);
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}
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*/
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if(l.binary){
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int m = l.n;
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int k = l.size*l.size*l.c;
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int n = out_h*out_w;
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char *a = l.cfilters;
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float *b = l.col_image;
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float *c = l.output;
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for(i = 0; i < l.batch; ++i){
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im2col_cpu(state.input, l.c, l.h, l.w,
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l.size, l.stride, l.pad, b);
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gemm_bin(m,n,k,1,a,k,b,n,c,n);
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c += n*m;
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state.input += l.c*l.h*l.w;
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}
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scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
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add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
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activate_array(l.output, m*n*l.batch, l.activation);
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return;
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}
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int m = l.n;
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int k = l.size*l.size*l.c;
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int n = out_h*out_w;
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float *a = l.filters;
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float *b = l.col_image;
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float *c = l.output;
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for(i = 0; i < l.batch; ++i){
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im2col_cpu(state.input, l.c, l.h, l.w,
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l.size, l.stride, l.pad, b);
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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c += n*m;
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state.input += l.c*l.h*l.w;
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}
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if(l.batch_normalize){
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forward_batchnorm_layer(l, state);
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}
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add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
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activate_array(l.output, m*n*l.batch, l.activation);
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}
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void backward_convolutional_layer(convolutional_layer l, network_state state)
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{
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int i;
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int m = l.n;
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int n = l.size*l.size*l.c;
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int k = convolutional_out_height(l)*
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convolutional_out_width(l);
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gradient_array(l.output, m*k*l.batch, l.activation, l.delta);
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backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);
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for(i = 0; i < l.batch; ++i){
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float *a = l.delta + i*m*k;
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float *b = l.col_image;
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float *c = l.filter_updates;
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float *im = state.input+i*l.c*l.h*l.w;
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im2col_cpu(im, l.c, l.h, l.w,
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l.size, l.stride, l.pad, b);
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gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
|
|
|
|
if(state.delta){
|
|
a = l.filters;
|
|
b = l.delta + i*m*k;
|
|
c = l.col_image;
|
|
|
|
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
|
|
|
|
col2im_cpu(l.col_image, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
|
|
}
|
|
}
|
|
}
|
|
|
|
void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay)
|
|
{
|
|
int size = l.size*l.size*l.c*l.n;
|
|
axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
|
|
scal_cpu(l.n, momentum, l.bias_updates, 1);
|
|
|
|
axpy_cpu(size, -decay*batch, l.filters, 1, l.filter_updates, 1);
|
|
axpy_cpu(size, learning_rate/batch, l.filter_updates, 1, l.filters, 1);
|
|
scal_cpu(size, momentum, l.filter_updates, 1);
|
|
}
|
|
|
|
|
|
image get_convolutional_filter(convolutional_layer l, int i)
|
|
{
|
|
int h = l.size;
|
|
int w = l.size;
|
|
int c = l.c;
|
|
return float_to_image(w,h,c,l.filters+i*h*w*c);
|
|
}
|
|
|
|
void rgbgr_filters(convolutional_layer l)
|
|
{
|
|
int i;
|
|
for(i = 0; i < l.n; ++i){
|
|
image im = get_convolutional_filter(l, i);
|
|
if (im.c == 3) {
|
|
rgbgr_image(im);
|
|
}
|
|
}
|
|
}
|
|
|
|
void rescale_filters(convolutional_layer l, float scale, float trans)
|
|
{
|
|
int i;
|
|
for(i = 0; i < l.n; ++i){
|
|
image im = get_convolutional_filter(l, i);
|
|
if (im.c == 3) {
|
|
scale_image(im, scale);
|
|
float sum = sum_array(im.data, im.w*im.h*im.c);
|
|
l.biases[i] += sum*trans;
|
|
}
|
|
}
|
|
}
|
|
|
|
image *get_filters(convolutional_layer l)
|
|
{
|
|
image *filters = calloc(l.n, sizeof(image));
|
|
int i;
|
|
for(i = 0; i < l.n; ++i){
|
|
filters[i] = copy_image(get_convolutional_filter(l, i));
|
|
//normalize_image(filters[i]);
|
|
}
|
|
return filters;
|
|
}
|
|
|
|
image *visualize_convolutional_layer(convolutional_layer l, char *window, image *prev_filters)
|
|
{
|
|
image *single_filters = get_filters(l);
|
|
show_images(single_filters, l.n, window);
|
|
|
|
image delta = get_convolutional_image(l);
|
|
image dc = collapse_image_layers(delta, 1);
|
|
char buff[256];
|
|
sprintf(buff, "%s: Output", window);
|
|
//show_image(dc, buff);
|
|
//save_image(dc, buff);
|
|
free_image(dc);
|
|
return single_filters;
|
|
}
|
|
|