| | |
| | | #include <stdio.h> |
| | | #include <time.h> |
| | | |
| | | void swap_binary(convolutional_layer *l) |
| | | { |
| | | float *swap = l->filters; |
| | | l->filters = l->binary_filters; |
| | | l->binary_filters = swap; |
| | | |
| | | #ifdef GPU |
| | | swap = l->filters_gpu; |
| | | l->filters_gpu = l->binary_filters_gpu; |
| | | l->binary_filters_gpu = swap; |
| | | #endif |
| | | } |
| | | |
| | | void binarize_filters2(float *filters, int n, int size, char *binary, float *scales) |
| | | { |
| | | int i, k, f; |
| | | for(f = 0; f < n; ++f){ |
| | | float mean = 0; |
| | | for(i = 0; i < size; ++i){ |
| | | mean += fabs(filters[f*size + i]); |
| | | } |
| | | mean = mean / size; |
| | | scales[f] = mean; |
| | | for(i = 0; i < size/8; ++i){ |
| | | binary[f*size + i] = (filters[f*size + i] > 0) ? 1 : 0; |
| | | for(k = 0; k < 8; ++k){ |
| | | } |
| | | } |
| | | } |
| | | } |
| | | |
| | | void binarize_filters(float *filters, int n, int size, float *binary) |
| | | { |
| | | int i, f; |
| | | for(f = 0; f < n; ++f){ |
| | | float mean = 0; |
| | | for(i = 0; i < size; ++i){ |
| | | mean += fabs(filters[f*size + i]); |
| | | } |
| | | mean = mean / size; |
| | | for(i = 0; i < size; ++i){ |
| | | binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean; |
| | | } |
| | | } |
| | | } |
| | | |
| | | int convolutional_out_height(convolutional_layer l) |
| | | { |
| | | int h = l.h; |
| | |
| | | |
| | | if(binary){ |
| | | l.binary_filters = calloc(c*n*size*size, sizeof(float)); |
| | | l.cfilters = calloc(c*n*size*size, sizeof(char)); |
| | | l.scales = calloc(n, sizeof(float)); |
| | | } |
| | | |
| | | if(batch_normalize){ |
| | |
| | | } |
| | | } |
| | | |
| | | void forward_convolutional_layer(const convolutional_layer l, network_state state) |
| | | void forward_convolutional_layer(convolutional_layer l, network_state state) |
| | | { |
| | | int out_h = convolutional_out_height(l); |
| | | int out_w = convolutional_out_width(l); |
| | | int i; |
| | | |
| | | fill_cpu(l.outputs*l.batch, 0, l.output, 1); |
| | | /* |
| | | if(l.binary){ |
| | | binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters); |
| | | binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales); |
| | | swap_binary(&l); |
| | | } |
| | | */ |
| | | |
| | | if(l.binary){ |
| | | int m = l.n; |
| | | int k = l.size*l.size*l.c; |
| | | int n = out_h*out_w; |
| | | |
| | | char *a = l.cfilters; |
| | | float *b = l.col_image; |
| | | float *c = l.output; |
| | | |
| | | for(i = 0; i < l.batch; ++i){ |
| | | im2col_cpu(state.input, l.c, l.h, l.w, |
| | | l.size, l.stride, l.pad, b); |
| | | gemm_bin(m,n,k,1,a,k,b,n,c,n); |
| | | c += n*m; |
| | | state.input += l.c*l.h*l.w; |
| | | } |
| | | scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w); |
| | | add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w); |
| | | activate_array(l.output, m*n*l.batch, l.activation); |
| | | return; |
| | | } |
| | | |
| | | int m = l.n; |
| | | int k = l.size*l.size*l.c; |