mirror of https://github.com/AlexeyAB/darknet.git
Joseph Redmon
10 years ago
parent
5f4a5f59b0
commit
fb9e0fe336
17 changed files with 298 additions and 151 deletions
@ -1,72 +1,123 @@ |
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int detection_out_height(detection_layer layer) |
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#include "detection_layer.h" |
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#include "activations.h" |
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#include "softmax_layer.h" |
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#include "blas.h" |
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#include "cuda.h" |
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#include <stdio.h> |
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#include <stdlib.h> |
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int get_detection_layer_locations(detection_layer layer) |
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{ |
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return layer.size + layer.h*layer.stride; |
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return layer.inputs / (layer.classes+layer.coords+layer.rescore); |
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} |
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int detection_out_width(detection_layer layer) |
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int get_detection_layer_output_size(detection_layer layer) |
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{ |
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return layer.size + layer.w*layer.stride; |
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return get_detection_layer_locations(layer)*(layer.classes+layer.coords); |
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} |
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detection_layer *make_detection_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation) |
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detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore) |
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{ |
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int i; |
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size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
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detection_layer *layer = calloc(1, sizeof(detection_layer)); |
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layer->h = h; |
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layer->w = w; |
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layer->c = c; |
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layer->n = n; |
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layer->batch = batch; |
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layer->stride = stride; |
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layer->size = size; |
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assert(c%n == 0); |
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layer->filters = calloc(c*size*size, sizeof(float)); |
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layer->filter_updates = calloc(c*size*size, sizeof(float)); |
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layer->filter_momentum = calloc(c*size*size, sizeof(float)); |
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float scale = 1./(size*size*c); |
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for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform()); |
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int out_h = detection_out_height(*layer); |
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int out_w = detection_out_width(*layer); |
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layer->output = calloc(layer->batch * out_h * out_w * n, sizeof(float)); |
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layer->delta = calloc(layer->batch * out_h * out_w * n, sizeof(float)); |
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layer->activation = activation; |
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layer->batch = batch; |
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layer->inputs = inputs; |
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layer->classes = classes; |
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layer->coords = coords; |
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layer->rescore = rescore; |
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int outputs = get_detection_layer_output_size(*layer); |
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layer->output = calloc(batch*outputs, sizeof(float)); |
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layer->delta = calloc(batch*outputs, sizeof(float)); |
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#ifdef GPU |
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layer->output_gpu = cuda_make_array(0, batch*outputs); |
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layer->delta_gpu = cuda_make_array(0, batch*outputs); |
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#endif |
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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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fprintf(stderr, "Detection Layer\n"); |
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srand(0); |
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return layer; |
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} |
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void forward_detection_layer(const detection_layer layer, float *in) |
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void forward_detection_layer(const detection_layer layer, float *in, float *truth) |
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{ |
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int out_h = detection_out_height(layer); |
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int out_w = detection_out_width(layer); |
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int i,j,fh, fw,c; |
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memset(layer.output, 0, layer->batch*layer->n*out_h*out_w*sizeof(float)); |
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for(c = 0; c < layer.c; ++c){ |
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for(i = 0; i < layer.h; ++i){ |
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for(j = 0; j < layer.w; ++j){ |
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float val = layer->input[j+(i + c*layer.h)*layer.w]; |
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for(fh = 0; fh < layer.size; ++fh){ |
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for(fw = 0; fw < layer.size; ++fw){ |
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int h = i*layer.stride + fh; |
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int w = j*layer.stride + fw; |
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layer.output[w+(h+c/n*out_h)*out_w] += val*layer->filters[fw+(fh+c*layer.size)*layer.size]; |
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} |
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} |
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} |
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int in_i = 0; |
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int out_i = 0; |
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int locations = get_detection_layer_locations(layer); |
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int i,j; |
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for(i = 0; i < layer.batch*locations; ++i){ |
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int mask = (!truth || !truth[out_i + layer.classes - 1]); |
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float scale = 1; |
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if(layer.rescore) scale = in[in_i++]; |
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for(j = 0; j < layer.classes; ++j){ |
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layer.output[out_i++] = scale*in[in_i++]; |
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} |
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softmax_array(layer.output + out_i - layer.classes, layer.classes, layer.output + out_i - layer.classes); |
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activate_array(layer.output+out_i, layer.coords, SIGMOID); |
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for(j = 0; j < layer.coords; ++j){ |
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layer.output[out_i++] = mask*in[in_i++]; |
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} |
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//printf("%d\n", mask);
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//for(j = 0; j < layer.classes+layer.coords; ++j) printf("%f ", layer.output[i*(layer.classes+layer.coords)+j]);
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//printf ("\n");
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} |
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} |
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void backward_detection_layer(const detection_layer layer, float *delta) |
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void backward_detection_layer(const detection_layer layer, float *in, float *delta) |
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{ |
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int locations = get_detection_layer_locations(layer); |
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int i,j; |
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int in_i = 0; |
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int out_i = 0; |
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for(i = 0; i < layer.batch*locations; ++i){ |
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float scale = 1; |
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float latent_delta = 0; |
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if(layer.rescore) scale = in[in_i++]; |
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for(j = 0; j < layer.classes; ++j){ |
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latent_delta += in[in_i]*layer.delta[out_i]; |
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delta[in_i++] = scale*layer.delta[out_i++]; |
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} |
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for(j = 0; j < layer.coords; ++j){ |
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delta[in_i++] = layer.delta[out_i++]; |
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} |
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gradient_array(in + in_i - layer.coords, layer.coords, SIGMOID, layer.delta + out_i - layer.coords);
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if(layer.rescore) delta[in_i-layer.coords-layer.classes-layer.rescore] = latent_delta; |
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} |
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} |
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#ifdef GPU |
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void forward_detection_layer_gpu(const detection_layer layer, float *in, float *truth) |
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{ |
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int outputs = get_detection_layer_output_size(layer); |
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float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float)); |
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float *truth_cpu = 0; |
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if(truth){ |
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truth_cpu = calloc(layer.batch*outputs, sizeof(float)); |
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cuda_pull_array(truth, truth_cpu, layer.batch*outputs); |
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} |
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cuda_pull_array(in, in_cpu, layer.batch*layer.inputs); |
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forward_detection_layer(layer, in_cpu, truth_cpu); |
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cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs); |
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free(in_cpu); |
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if(truth_cpu) free(truth_cpu); |
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} |
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void backward_detection_layer_gpu(detection_layer layer, float *in, float *delta) |
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{ |
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int outputs = get_detection_layer_output_size(layer); |
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float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float)); |
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float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float)); |
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cuda_pull_array(in, in_cpu, layer.batch*layer.inputs); |
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cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs); |
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backward_detection_layer(layer, in_cpu, delta_cpu); |
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cuda_push_array(delta, delta_cpu, layer.batch*layer.inputs); |
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free(in_cpu); |
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free(delta_cpu); |
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} |
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#endif |
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