tree stuff

Makefile

@@ -41,10 +41,10 @@ CFLAGS+= -DCUDNN
 LDFLAGS+= -lcudnn
 endif
 
-OBJ=gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o
+OBJ=gemm.o utils.o cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o tree.o
 ifeq ($(GPU), 1) 
 LDFLAGS+= -lstdc++ 
-OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
+OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
 endif
 
 OBJS = $(addprefix $(OBJDIR), $(OBJ))

src/blas.c

@@ -1,6 +1,7 @@
 #include "blas.h"
 #include "math.h"
 #include <assert.h>
+#include <float.h>
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
@@ -179,3 +180,21 @@ float dot_cpu(int N, float *X, int INCX, float *Y, int INCY)
     return dot;
 }
 
+void softmax(float *input, int n, float temp, float *output)
+{
+    int i;
+    float sum = 0;
+    float largest = -FLT_MAX;
+    for(i = 0; i < n; ++i){
+        if(input[i] > largest) largest = input[i];
+    }
+    for(i = 0; i < n; ++i){
+        sum += exp(input[i]/temp-largest/temp);
+    }
+    if(sum) sum = largest/temp+log(sum);
+    else sum = largest-100;
+    for(i = 0; i < n; ++i){
+        output[i] = exp(input[i]/temp-sum);
+    }
+}
+

src/blas.h

@@ -34,7 +34,11 @@ void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error)
 void l2_cpu(int n, float *pred, float *truth, float *delta, float *error);
 void weighted_sum_cpu(float *a, float *b, float *s, int num, float *c);
 
+void softmax(float *input, int n, float temp, float *output);
+
 #ifdef GPU
+#include "cuda.h"
+
 void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY);
 void axpy_ongpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY);
 void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY);
@@ -73,5 +77,7 @@ void mult_add_into_gpu(int num, float *a, float *b, float *c);
 
 void reorg_ongpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out);
 
+void softmax_gpu(float *input, int n, int groups, float temp, float *output, cudaStream_t stream);
+
 #endif
 #endif

src/blas_kernels.cu

@@ -691,3 +691,33 @@ extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c)
     mult_add_into_kernel<<<cuda_gridsize(num), BLOCK>>>(num, a, b, c);
     check_error(cudaPeekAtLastError());
 }
+
+
+__global__ void softmax_kernel(int n, int batch, float *input, float temp, float *output)
+{
+    int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(b >= batch) return;
+
+    int i;
+    float sum = 0;
+    float largest = -INFINITY;
+    for(i = 0; i < n; ++i){
+        int val = input[i+b*n];
+        largest = (val>largest) ? val : largest;
+    }
+    for(i = 0; i < n; ++i){
+        sum += exp(input[i+b*n]/temp-largest/temp);
+    }
+    sum = (sum != 0) ? largest/temp+log(sum) : largest-100;
+    for(i = 0; i < n; ++i){
+        output[i+b*n] = exp(input[i+b*n]/temp-sum);
+    }
+}
+
+extern "C" void softmax_gpu(float *input, int n, int groups, float temp, float *output, cudaStream_t stream)
+{
+    int inputs = n;
+    int batch = groups;
+    softmax_kernel<<<cuda_gridsize(batch), BLOCK, 0, stream>>>(inputs, batch, input, temp, output);
+    check_error(cudaPeekAtLastError());
+}

src/classifier.c

@@ -41,6 +41,20 @@ list *read_data_cfg(char *filename)
     return options;
 }
 
+void hierarchy_predictions(float *predictions, int n, tree *hier)
+{
+    int j;
+    for(j = 0; j < n; ++j){
+        int parent = hier->parent[j];
+        if(parent >= 0){
+            predictions[j] *= predictions[parent]; 
+        }
+    }
+    for(j = 0; j < n; ++j){
+        if(!hier->leaf[j]) predictions[j] = 0;
+    }
+}
+
 float *get_regression_values(char **labels, int n)
 {
     float *v = calloc(n, sizeof(float));
@@ -99,7 +113,8 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
     load_args args = {0};
     args.w = net.w;
     args.h = net.h;
-    args.threads = 16;
+    args.threads = 32;
+    args.hierarchy = net.hierarchy;
 
     args.min = net.min_crop;
     args.max = net.max_crop;
@@ -206,6 +221,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
     args.saturation = net.saturation;
     args.hue = net.hue;
     args.size = net.w;
+    args.hierarchy = net.hierarchy;
 
     args.paths = paths;
     args.classes = classes;
@@ -394,6 +410,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
         float *pred = calloc(classes, sizeof(float));
         for(j = 0; j < 10; ++j){
             float *p = network_predict(net, images[j].data);
+            if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy);
             axpy_cpu(classes, 1, p, 1, pred, 1);
             free_image(images[j]);
         }
@@ -454,6 +471,7 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
         //show_image(crop, "cropped");
         //cvWaitKey(0);
         float *pred = network_predict(net, resized.data);
+        if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy);
 
         free_image(im);
         free_image(resized);
@@ -513,6 +531,7 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
         //show_image(crop, "cropped");
         //cvWaitKey(0);
         float *pred = network_predict(net, crop.data);
+        if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy);
 
         if(resized.data != im.data) free_image(resized);
         free_image(im);
@@ -573,6 +592,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
             image r = resize_min(im, scales[j]);
             resize_network(&net, r.w, r.h);
             float *p = network_predict(net, r.data);
+            if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy);
             axpy_cpu(classes, 1, p, 1, pred, 1);
             flip_image(r);
             p = network_predict(net, r.data);
@@ -672,7 +692,6 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena
     }
 }
 
-
 void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
 {
     network net = parse_network_cfg(cfgfile);
@@ -713,11 +732,13 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
         float *X = r.data;
         time=clock();
         float *predictions = network_predict(net, X);
-        top_predictions(net, top, indexes);
+        if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy);
+        top_k(predictions, net.outputs, top, indexes);
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
         for(i = 0; i < top; ++i){
             int index = indexes[i];
-            printf("%s: %f\n", names[index], predictions[index]);
+            if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root");
+            else printf("%s: %f\n",names[index], predictions[index]);
         }
         if(r.data != im.data) free_image(r);
         free_image(im);
@@ -899,15 +920,15 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
         float curr_threat = 0;
         if(1){
             curr_threat = predictions[0] * 0 + 
-                            predictions[1] * .6 + 
+                predictions[1] * .6 + 
-                            predictions[2];
+                predictions[2];
         } else {
             curr_threat = predictions[218] +
-                        predictions[539] + 
+                predictions[539] + 
-                        predictions[540] + 
+                predictions[540] + 
-                        predictions[368] + 
+                predictions[368] + 
-                        predictions[369] + 
+                predictions[369] + 
-                        predictions[370];
+                predictions[370];
         }
         threat = roll * curr_threat + (1-roll) * threat;
 
@@ -1092,6 +1113,7 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
         show_image(in, "Classifier");
 
         float *predictions = network_predict(net, in_s.data);
+        if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy);
         top_predictions(net, top, indexes);
 
         printf("\033[2J");

src/data.c

@@ -388,12 +388,47 @@ void fill_truth(char *path, char **labels, int k, float *truth)
     if(count != 1) printf("Too many or too few labels: %d, %s\n", count, path);
 }
 
-matrix load_labels_paths(char **paths, int n, char **labels, int k)
+void fill_hierarchy(float *truth, int k, tree *hierarchy)
+{
+    int j;
+    for(j = 0; j < k; ++j){
+        if(truth[j]){
+            int parent = hierarchy->parent[j];
+            while(parent >= 0){
+                truth[parent] = 1;
+                parent = hierarchy->parent[parent];
+            }
+        }
+    }
+    int i;
+    int count = 0;
+    for(j = 0; j < hierarchy->groups; ++j){
+        //printf("%d\n", count);
+        int mask = 1;
+        for(i = 0; i < hierarchy->group_size[j]; ++i){
+            if(truth[count + i]){
+                mask = 0;
+                break;
+            }
+        }
+        if (mask) {
+            for(i = 0; i < hierarchy->group_size[j]; ++i){
+                truth[count + i] = SECRET_NUM;
+            }
+        }
+        count += hierarchy->group_size[j];
+    }
+}
+
+matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy)
 {
     matrix y = make_matrix(n, k);
     int i;
     for(i = 0; i < n && labels; ++i){
         fill_truth(paths[i], labels, k, y.vals[i]);
+        if(hierarchy){
+            fill_hierarchy(y.vals[i], k, hierarchy);
+        }
     }
     return y;
 }
@@ -540,7 +575,7 @@ data load_data_compare(int n, char **paths, int m, int classes, int w, int h)
         while(fscanf(fp2, "%d %f", &id, &iou) == 2){
             if (d.y.vals[i][2*id + 1] < iou) d.y.vals[i][2*id + 1] = iou;
         }
-        
+
         for (j = 0; j < classes; ++j){
             if (d.y.vals[i][2*j] > .5 &&  d.y.vals[i][2*j+1] < .5){
                 d.y.vals[i][2*j] = 1;
@@ -567,7 +602,7 @@ data load_data_swag(char **paths, int n, int classes, float jitter)
 {
     int index = rand()%n;
     char *random_path = paths[index];
-    
+
     image orig = load_image_color(random_path, 0, 0);
     int h = orig.h;
     int w = orig.w;
@@ -680,7 +715,7 @@ void *load_thread(void *ptr)
     if (a.type == OLD_CLASSIFICATION_DATA){
         *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
     } else if (a.type == CLASSIFICATION_DATA){
-        *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
+        *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
     } else if (a.type == SUPER_DATA){
         *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale);
     } else if (a.type == WRITING_DATA){
@@ -771,24 +806,24 @@ data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int
     data d = {0};
     d.shallow = 0;
     d.X = load_image_paths(paths, n, w, h);
-    d.y = load_labels_paths(paths, n, labels, k);
+    d.y = load_labels_paths(paths, n, labels, k, 0);
     if(m) free(paths);
     return d;
 }
 
 /*
-data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
+   data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
-{
+   {
-    data d = {0};
+   data d = {0};
-    d.indexes = calloc(n, sizeof(int));
+   d.indexes = calloc(n, sizeof(int));
-    if(m) paths = get_random_paths_indexes(paths, n, m, d.indexes);
+   if(m) paths = get_random_paths_indexes(paths, n, m, d.indexes);
-    d.shallow = 0;
+   d.shallow = 0;
-    d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
+   d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
-    d.y = load_labels_paths(paths, n, labels, k);
+   d.y = load_labels_paths(paths, n, labels, k);
-    if(m) free(paths);
+   if(m) free(paths);
-    return d;
+   return d;
-}
+   }
-*/
+ */
 
 data load_data_super(char **paths, int n, int m, int w, int h, int scale)
 {
@@ -820,13 +855,13 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale)
     return d;
 }
 
-data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
+data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
 {
     if(m) paths = get_random_paths(paths, n, m);
     data d = {0};
     d.shallow = 0;
     d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
-    d.y = load_labels_paths(paths, n, labels, k);
+    d.y = load_labels_paths(paths, n, labels, k, hierarchy);
     if(m) free(paths);
     return d;
 }

src/data.h

@@ -5,6 +5,7 @@
 #include "matrix.h"
 #include "list.h"
 #include "image.h"
+#include "tree.h"
 
 static inline float distance_from_edge(int x, int max)
 {
@@ -58,6 +59,7 @@ typedef struct load_args{
     image *im;
     image *resized;
     data_type type;
+    tree *hierarchy;
 } load_args;
 
 typedef struct{
@@ -80,7 +82,7 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in
 data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
 matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
 data load_data_super(char **paths, int n, int m, int w, int h, int scale);
-data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
+data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
 data load_go(char *filename);
 
 box_label *read_boxes(char *filename, int *n);

src/detection_layer.c

@@ -58,7 +58,7 @@ void forward_detection_layer(const detection_layer l, network_state state)
             int index = b*l.inputs;
             for (i = 0; i < locations; ++i) {
                 int offset = i*l.classes;
-                softmax_array(l.output + index + offset, l.classes, 1,
+                softmax(l.output + index + offset, l.classes, 1,
                         l.output + index + offset);
             }
         }

src/layer.h

@@ -3,6 +3,7 @@
 
 #include "activations.h"
 #include "stddef.h"
+#include "tree.h"
 
 struct network_state;
 
@@ -93,6 +94,8 @@ struct layer{
     int reorg;
     int log;
 
+    tree *softmax_tree;
+
     float alpha;
     float beta;
     float kappa;

src/network.c

@@ -565,7 +565,6 @@ float *network_accuracies(network net, data d, int n)
     return acc;
 }
 
-
 float network_accuracy_multi(network net, data d, int n)
 {
     matrix guess = network_predict_data_multi(net, d, n);

src/network.h

@@ -5,6 +5,7 @@
 #include "image.h"
 #include "layer.h"
 #include "data.h"
+#include "tree.h"
 
 typedef enum {
     CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
@@ -47,6 +48,7 @@ typedef struct network{
     float hue;
 
     int gpu_index;
+    tree *hierarchy;
 
     #ifdef GPU
     float **input_gpu;

src/parser.c

@@ -221,6 +221,8 @@ softmax_layer parse_softmax(list *options, size_params params)
     int groups = option_find_int_quiet(options, "groups",1);
     softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
     layer.temperature = option_find_float_quiet(options, "temperature", 1);
+    char *tree_file = option_find_str(options, "tree", 0);
+    if (tree_file) layer.softmax_tree = read_tree(tree_file);
     return layer;
 }
 
@@ -598,6 +600,7 @@ network parse_network_cfg(char *filename)
             l = parse_detection(options, params);
         }else if(lt == SOFTMAX){
             l = parse_softmax(options, params);
+            net.hierarchy = l.softmax_tree;
         }else if(lt == NORMALIZATION){
             l = parse_normalization(options, params);
         }else if(lt == BATCHNORM){

src/region_layer.c

@@ -1,6 +1,5 @@
 #include "region_layer.h"
 #include "activations.h"
-#include "softmax_layer.h"
 #include "blas.h"
 #include "box.h"
 #include "cuda.h"
@@ -99,7 +98,7 @@ void forward_region_layer(const region_layer l, network_state state)
             int index = size*i + b*l.outputs;
             l.output[index + 4] = logistic_activate(l.output[index + 4]);
             if(l.softmax){
-                softmax_array(l.output + index + 5, l.classes, 1, l.output + index + 5);
+                softmax(l.output + index + 5, l.classes, 1, l.output + index + 5);
             }
         }
     }

src/softmax_layer.c

@@ -32,31 +32,25 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups)
     return l;
 }
 
-void softmax_array(float *input, int n, float temp, float *output)
-{
-    int i;
-    float sum = 0;
-    float largest = -FLT_MAX;
-    for(i = 0; i < n; ++i){
-        if(input[i] > largest) largest = input[i];
-    }
-    for(i = 0; i < n; ++i){
-        sum += exp(input[i]/temp-largest/temp);
-    }
-    if(sum) sum = largest/temp+log(sum);
-    else sum = largest-100;
-    for(i = 0; i < n; ++i){
-        output[i] = exp(input[i]/temp-sum);
-    }
-}
-
 void forward_softmax_layer(const softmax_layer l, network_state state)
 {
     int b;
     int inputs = l.inputs / l.groups;
     int batch = l.batch * l.groups;
-    for(b = 0; b < batch; ++b){
+    if(l.softmax_tree){
-        softmax_array(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs);
+        for(b = 0; b < batch; ++b){
+            int i;
+            int count = 0;
+            for(i = 0; i < l.softmax_tree->groups; ++i){
+                int group_size = l.softmax_tree->group_size[i];
+                softmax(state.input+b*inputs + count, group_size, l.temperature, l.output+b*inputs + count);
+                count += group_size;
+            }
+        }
+    } else {
+        for(b = 0; b < batch; ++b){
+            softmax(state.input+b*inputs, inputs, l.temperature, l.output+b*inputs);
+        }
     }
 }
 
@@ -68,3 +62,54 @@ void backward_softmax_layer(const softmax_layer l, network_state state)
     }
 }
 
+#ifdef GPU
+
+void pull_softmax_layer_output(const softmax_layer layer)
+{
+    cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
+}
+
+void forward_softmax_layer_gpu(const softmax_layer l, network_state state)
+{
+    int inputs = l.inputs / l.groups;
+    int batch = l.batch * l.groups;
+    int b;
+    if(l.softmax_tree){
+        if(0){
+            float *buff = calloc(inputs * batch, sizeof(float));
+            cuda_pull_array(state.input, buff, batch * inputs);
+            state.input = buff;
+            forward_softmax_layer(l, state);
+            cuda_push_array(l.output_gpu, l.output, batch*inputs);
+            free(buff);
+        } else {
+            int i;
+            const int nstreams = 32;
+            cudaStream_t streams[nstreams];
+            for (i = 0; i < nstreams; ++i) {
+                cudaStreamCreate(&streams[i]);
+            }
+            for (b = 0; b < batch; ++b) {
+                int i;
+                int count = 0;
+                for (i = 0; i < l.softmax_tree->groups; ++i) {
+                    int group_size = l.softmax_tree->group_size[i];
+                    softmax_gpu(state.input+b*inputs + count, group_size, 1, l.temperature, l.output_gpu+b*inputs + count, streams[(b*l.softmax_tree->groups + i) % nstreams]);
+                    count += group_size;
+                }
+            }
+            for(i = 0; i < nstreams; ++i){
+                cudaStreamDestroy(streams[i]);
+            }
+        }
+    } else {
+        softmax_gpu(state.input, inputs, batch, l.temperature, l.output_gpu, 0);
+    }
+}
+
+void backward_softmax_layer_gpu(const softmax_layer layer, network_state state)
+{
+    axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1);
+}
+
+#endif

src/softmax_layer_kernels.cu

@@ -1,70 +0,0 @@
-#include "cuda_runtime.h"
-#include "curand.h"
-#include "cublas_v2.h"
-
-extern "C" {
-#include "softmax_layer.h"
-#include "cuda.h"
-#include "blas.h"
-}
-
-__global__ void forward_softmax_layer_kernel(int n, int batch, float *input, float temp, float *output)
-{
-    int b = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
-    if(b >= batch) return;
-
-    int i;
-    float sum = 0;
-    float largest = -INFINITY;
-    for(i = 0; i < n; ++i){
-        int val = input[i+b*n];
-        largest = (val>largest) ? val : largest;
-    }
-    for(i = 0; i < n; ++i){
-        sum += exp(input[i+b*n]/temp-largest/temp);
-    }
-    sum = (sum != 0) ? largest/temp+log(sum) : largest-100;
-    for(i = 0; i < n; ++i){
-        output[i+b*n] = exp(input[i+b*n]/temp-sum);
-    }
-}
-
-extern "C" void pull_softmax_layer_output(const softmax_layer layer)
-{
-    cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
-}
-
-extern "C" void forward_softmax_layer_gpu(const softmax_layer layer, network_state state)
-{
-    int inputs = layer.inputs / layer.groups;
-    int batch = layer.batch * layer.groups;
-    forward_softmax_layer_kernel<<<cuda_gridsize(batch), BLOCK>>>(inputs, batch, state.input, layer.temperature, layer.output_gpu);
-    check_error(cudaPeekAtLastError());
-}
-
-extern "C" void backward_softmax_layer_gpu(const softmax_layer layer, network_state state)
-{
-    axpy_ongpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, state.delta, 1);
-}
-
-/* This is if you want softmax w/o log-loss classification. You probably don't.
-   int i,j,b;
-   for(b = 0; b < layer.batch; ++b){
-   for(i = 0; i < layer.inputs; ++i){
-   for(j = 0; j < layer.inputs; ++j){
-   int d = (i==j);
-   layer.jacobian[b*layer.inputs*layer.inputs + i*layer.inputs + j] = 
-   layer.output[b*layer.inputs + i] * (d - layer.output[b*layer.inputs + j]);
-   }
-   }
-   }
-   for(b = 0; b < layer.batch; ++b){
-   int M = layer.inputs;
-   int N = 1;
-   int K = layer.inputs;
-   float *A = layer.jacobian + b*layer.inputs*layer.inputs;
-   float *B = layer.delta + b*layer.inputs;
-   float *C = delta + b*layer.inputs;
-   gemm(0,0,M,N,K,1,A,K,B,N,0,C,N);
-   }
- */

src/tree.c

@@ -0,0 +1,49 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include "tree.h"
+#include "utils.h"
+
+tree *read_tree(char *filename)
+{
+    tree t = {0};
+    FILE *fp = fopen(filename, "r");
+    
+    char *line;
+    int last_parent = -1;
+    int group_size = 0;
+    int groups = 0;
+    int n = 0;
+    while((line=fgetl(fp)) != 0){
+        char *id = calloc(256, sizeof(char));
+        int parent = -1;
+        sscanf(line, "%s %d", id, &parent);
+        t.parent = realloc(t.parent, (n+1)*sizeof(int));
+        t.parent[n] = parent;
+        t.name = realloc(t.name, (n+1)*sizeof(char *));
+        t.name[n] = id;
+        if(parent != last_parent){
+            ++groups;
+            t.group_size = realloc(t.group_size, groups * sizeof(int));
+            t.group_size[groups - 1] = group_size;
+            group_size = 0;
+            last_parent = parent;
+        }
+        ++n;
+        ++group_size;
+    }
+    ++groups;
+    t.group_size = realloc(t.group_size, groups * sizeof(int));
+    t.group_size[groups - 1] = group_size;
+    t.n = n;
+    t.groups = groups;
+    t.leaf = calloc(n, sizeof(int));
+    int i;
+    for(i = 0; i < n; ++i) t.leaf[i] = 1;
+    for(i = 0; i < n; ++i) if(t.parent[i] >= 0) t.leaf[t.parent[i]] = 0;
+
+    fclose(fp);
+    tree *tree_ptr = calloc(1, sizeof(tree));
+    *tree_ptr = t;
+    //error(0);
+    return tree_ptr;
+}

src/tree.h

@@ -0,0 +1,16 @@
+#ifndef TREE_H
+#define TREE_H
+
+typedef struct{
+    int *leaf;
+    int n;
+    int *parent;
+    char **name;
+
+    int groups;
+    int *group_size;
+} tree;
+
+tree *read_tree(char *filename);
+
+#endif