Sources fixed - Yolo v2 can be used on Windows (MSVS 2015 + CUDA 8.0)

AlexeyAB

2016-12-02 5b6dd3a07c0d933f36f9e2a3aaa95ff855ca2795

 src/detection_layer.c

@@ -2,119 +2,286 @@
#include "activations.h"
#include "softmax_layer.h"
#include "blas.h"
#include "box.h"
#include "cuda.h"
#include "utils.h"
#include <stdio.h>
#include <assert.h>
#include <string.h>
#include <stdlib.h>

int get_detection_layer_locations(detection_layer layer)
detection_layer make_detection_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore)
{
    return layer.inputs / (layer.classes+layer.coords+layer.rescore);
}
    detection_layer l = {0};
    l.type = DETECTION;

int get_detection_layer_output_size(detection_layer layer)
{
    return get_detection_layer_locations(layer)*(layer.classes+layer.coords);
}
    l.n = n;
    l.batch = batch;
    l.inputs = inputs;
    l.classes = classes;
    l.coords = coords;
    l.rescore = rescore;
    l.side = side;
    l.w = side;
    l.h = side;
    assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs);
    l.cost = calloc(1, sizeof(float));
    l.outputs = l.inputs;
    l.truths = l.side*l.side*(1+l.coords+l.classes);
    l.output = calloc(batch*l.outputs, sizeof(float));
    l.delta = calloc(batch*l.outputs, sizeof(float));

detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore)
{
    detection_layer *layer = calloc(1, sizeof(detection_layer));

    layer->batch = batch;
    layer->inputs = inputs;
    layer->classes = classes;
    layer->coords = coords;
    layer->rescore = rescore;
    int outputs = get_detection_layer_output_size(*layer);
    layer->output = calloc(batch*outputs, sizeof(float));
    layer->delta = calloc(batch*outputs, sizeof(float));
    #ifdef GPU
    layer->output_gpu = cuda_make_array(0, batch*outputs);
    layer->delta_gpu = cuda_make_array(0, batch*outputs);
    #endif
    l.forward = forward_detection_layer;
    l.backward = backward_detection_layer;
#ifdef GPU
    l.forward_gpu = forward_detection_layer_gpu;
    l.backward_gpu = backward_detection_layer_gpu;
    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
#endif

    fprintf(stderr, "Detection Layer\n");
    srand(0);

    return layer;
    return l;
}

void forward_detection_layer(const detection_layer layer, float *in, float *truth)
void forward_detection_layer(const detection_layer l, network_state state)
{
    int in_i = 0;
    int out_i = 0;
    int locations = get_detection_layer_locations(layer);
    int locations = l.side*l.side;
    int i,j;
    for(i = 0; i < layer.batch*locations; ++i){
        int mask = (!truth || !truth[out_i + layer.classes - 1]);
        float scale = 1;
        if(layer.rescore) scale = in[in_i++];
        for(j = 0; j < layer.classes; ++j){
            layer.output[out_i++] = scale*in[in_i++];
    memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
    //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1);
    int b;
    if (l.softmax){
        for(b = 0; b < l.batch; ++b){
            int index = b*l.inputs;
            for (i = 0; i < locations; ++i) {
                int offset = i*l.classes;
                softmax(l.output + index + offset, l.classes, 1,
                        l.output + index + offset);
            }
        }
        softmax_array(layer.output + out_i - layer.classes, layer.classes, layer.output + out_i - layer.classes);
        activate_array(in+in_i, layer.coords, SIGMOID);
        for(j = 0; j < layer.coords; ++j){
            layer.output[out_i++] = mask*in[in_i++];
    }
    if(state.train){
        float avg_iou = 0;
        float avg_cat = 0;
        float avg_allcat = 0;
        float avg_obj = 0;
        float avg_anyobj = 0;
        int count = 0;
        *(l.cost) = 0;
        int size = l.inputs * l.batch;
        memset(l.delta, 0, size * sizeof(float));
        for (b = 0; b < l.batch; ++b){
            int index = b*l.inputs;
            for (i = 0; i < locations; ++i) {
                int truth_index = (b*locations + i)*(1+l.coords+l.classes);
                int is_obj = state.truth[truth_index];
                for (j = 0; j < l.n; ++j) {
                    int p_index = index + locations*l.classes + i*l.n + j;
                    l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]);
                    *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2);
                    avg_anyobj += l.output[p_index];
                }

                int best_index = -1;
                float best_iou = 0;
                float best_rmse = 20;

                if (!is_obj){
                    continue;
                }

                int class_index = index + i*l.classes;
                for(j = 0; j < l.classes; ++j) {
                    l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]);
                    *(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2);
                    if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
                    avg_allcat += l.output[class_index+j];
                }

                box truth = float_to_box(state.truth + truth_index + 1 + l.classes);
                truth.x /= l.side;
                truth.y /= l.side;

                for(j = 0; j < l.n; ++j){
                    int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords;
                    box out = float_to_box(l.output + box_index);
                    out.x /= l.side;
                    out.y /= l.side;

                    if (l.sqrt){
                        out.w = out.w*out.w;
                        out.h = out.h*out.h;
                    }

                    float iou  = box_iou(out, truth);
                    //iou = 0;
                    float rmse = box_rmse(out, truth);
                    if(best_iou > 0 || iou > 0){
                        if(iou > best_iou){
                            best_iou = iou;
                            best_index = j;
                        }
                    }else{
                        if(rmse < best_rmse){
                            best_rmse = rmse;
                            best_index = j;
                        }
                    }
                }

                if(l.forced){
                    if(truth.w*truth.h < .1){
                        best_index = 1;
                    }else{
                        best_index = 0;
                    }
                }
                if(l.random && *(state.net.seen) < 64000){
                    best_index = rand()%l.n;
                }

                int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords;
                int tbox_index = truth_index + 1 + l.classes;

                box out = float_to_box(l.output + box_index);
                out.x /= l.side;
                out.y /= l.side;
                if (l.sqrt) {
                    out.w = out.w*out.w;
                    out.h = out.h*out.h;
                }
                float iou  = box_iou(out, truth);

                //printf("%d,", best_index);
                int p_index = index + locations*l.classes + i*l.n + best_index;
                *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2);
                *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2);
                avg_obj += l.output[p_index];
                l.delta[p_index] = l.object_scale * (1.-l.output[p_index]);

                if(l.rescore){
                    l.delta[p_index] = l.object_scale * (iou - l.output[p_index]);
                }

                l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]);
                l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]);
                l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]);
                l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]);
                if(l.sqrt){
                    l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]);
                    l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]);
                }

                *(l.cost) += pow(1-iou, 2);
                avg_iou += iou;
                ++count;
            }
        }

        if(0){
            float *costs = calloc(l.batch*locations*l.n, sizeof(float));
            for (b = 0; b < l.batch; ++b) {
                int index = b*l.inputs;
                for (i = 0; i < locations; ++i) {
                    for (j = 0; j < l.n; ++j) {
                        int p_index = index + locations*l.classes + i*l.n + j;
                        costs[b*locations*l.n + i*l.n + j] = l.delta[p_index]*l.delta[p_index];
                    }
                }
            }
            int indexes[100];
            top_k(costs, l.batch*locations*l.n, 100, indexes);
            float cutoff = costs[indexes[99]];
            for (b = 0; b < l.batch; ++b) {
                int index = b*l.inputs;
                for (i = 0; i < locations; ++i) {
                    for (j = 0; j < l.n; ++j) {
                        int p_index = index + locations*l.classes + i*l.n + j;
                        if (l.delta[p_index]*l.delta[p_index] < cutoff) l.delta[p_index] = 0;
                    }
                }
            }
            free(costs);
        }


        *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);


        printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
        //if(l.reorg) reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);
    }
}

void backward_detection_layer(const detection_layer layer, float *in, float *delta)
void backward_detection_layer(const detection_layer l, network_state state)
{
    int locations = get_detection_layer_locations(layer);
    int i,j;
    int in_i = 0;
    int out_i = 0;
    for(i = 0; i < layer.batch*locations; ++i){
        float scale = 1;
        float latent_delta = 0;
        if(layer.rescore) scale = in[in_i++];
        for(j = 0; j < layer.classes; ++j){
            latent_delta += in[in_i]*layer.delta[out_i];
            delta[in_i++] = scale*layer.delta[out_i++];
    axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
}

void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
{
    int i,j,n;
    float *predictions = l.output;
    //int per_cell = 5*num+classes;
    for (i = 0; i < l.side*l.side; ++i){
        int row = i / l.side;
        int col = i % l.side;
        for(n = 0; n < l.n; ++n){
            int index = i*l.n + n;
            int p_index = l.side*l.side*l.classes + i*l.n + n;
            float scale = predictions[p_index];
            int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4;
            boxes[index].x = (predictions[box_index + 0] + col) / l.side * w;
            boxes[index].y = (predictions[box_index + 1] + row) / l.side * h;
            boxes[index].w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w;
            boxes[index].h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h;
            for(j = 0; j < l.classes; ++j){
                int class_index = i*l.classes;
                float prob = scale*predictions[class_index+j];
                probs[index][j] = (prob > thresh) ? prob : 0;
            }
            if(only_objectness){
                probs[index][0] = scale;
            }
        }
        
        gradient_array(layer.output + out_i, layer.coords, SIGMOID, layer.delta + out_i);
        for(j = 0; j < layer.coords; ++j){
            delta[in_i++] = layer.delta[out_i++];
        }
        if(layer.rescore) delta[in_i-layer.coords-layer.classes-layer.rescore] = latent_delta;
    }
}

#ifdef GPU

void forward_detection_layer_gpu(const detection_layer layer, float *in, float *truth)
void forward_detection_layer_gpu(const detection_layer l, network_state state)
{
    int outputs = get_detection_layer_output_size(layer);
    float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
    float *truth_cpu = 0;
    if(truth){
        truth_cpu = calloc(layer.batch*outputs, sizeof(float));
        cuda_pull_array(truth, truth_cpu, layer.batch*outputs);
    if(!state.train){
        copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
        return;
    }
    cuda_pull_array(in, in_cpu, layer.batch*layer.inputs);
    forward_detection_layer(layer, in_cpu, truth_cpu);
    cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
    free(in_cpu);
    if(truth_cpu) free(truth_cpu);

    float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
    float *truth_cpu = 0;
    if(state.truth){
        int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes);
        truth_cpu = calloc(num_truth, sizeof(float));
        cuda_pull_array(state.truth, truth_cpu, num_truth);
    }
    cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
    network_state cpu_state = state;
    cpu_state.train = state.train;
    cpu_state.truth = truth_cpu;
    cpu_state.input = in_cpu;
    forward_detection_layer(l, cpu_state);
    cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
    cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
    free(cpu_state.input);
    if(cpu_state.truth) free(cpu_state.truth);
}

void backward_detection_layer_gpu(detection_layer layer, float *in, float *delta)
void backward_detection_layer_gpu(detection_layer l, network_state state)
{
    int outputs = get_detection_layer_output_size(layer);

    float *in_cpu =    calloc(layer.batch*layer.inputs, sizeof(float));
    float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float));

    cuda_pull_array(in, in_cpu, layer.batch*layer.inputs);
    cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
    backward_detection_layer(layer, in_cpu, delta_cpu);
    cuda_push_array(delta, delta_cpu, layer.batch*layer.inputs);

    free(in_cpu);
    free(delta_cpu);
    axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1);
    //copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1);
}
#endif