fixed dropout ><

src/cnn.c

@@ -294,7 +294,7 @@ void train_asirra()
     while(1){
         i += 1;
         time=clock();
-        data train = load_data_random(imgs*net.batch, paths, m, labels, 2, 256, 256);
+        data train = load_data(paths, imgs*net.batch, m, labels, 2, 256, 256);
         normalize_data_rows(train);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
@@ -404,7 +404,7 @@ void train_imagenet_distributed(char *address)
     printf("%d\n", plist->size);
     clock_t time;
     data train, buffer;
-    pthread_t load_thread = load_data_random_thread(imgs*net.batch, paths, plist->size, labels, 1000, 224, 224, &buffer);
+    pthread_t load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
     while(1){
         i += 1;
 
@@ -416,7 +416,7 @@ void train_imagenet_distributed(char *address)
         pthread_join(load_thread, 0);
         train = buffer;
         normalize_data_rows(train);
-        load_thread = load_data_random_thread(imgs*net.batch, paths, plist->size, labels, 1000, 224, 224, &buffer);
+        load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
 
@@ -434,11 +434,10 @@ void train_imagenet()
     float avg_loss = 1;
     //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
     srand(time(0));
-    network net = parse_network_cfg("cfg/net.cfg");
+    network net = parse_network_cfg("cfg/net.part");
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 1000/net.batch+1;
-    //imgs=1;
+    int i = 9540;
-    int i = 0;
     char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
     list *plist = get_paths("/data/imagenet/cls.train.list");
     char **paths = (char **)list_to_array(plist);
@@ -447,14 +446,14 @@ void train_imagenet()
     pthread_t load_thread;
     data train;
     data buffer;
-    load_thread = load_data_random_thread(imgs*net.batch, paths, plist->size, labels, 1000, 224, 224, &buffer);
+    load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
     while(1){
         i += 1;
         time=clock();
         pthread_join(load_thread, 0);
         train = buffer;
         normalize_data_rows(train);
-        load_thread = load_data_random_thread(imgs*net.batch, paths, plist->size, labels, 1000, 224, 224, &buffer);
+        load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
 #ifdef GPU
@@ -465,7 +464,7 @@ void train_imagenet()
         free_data(train);
         if(i%10==0){
             char buff[256];
-            sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
+            sprintf(buff, "/home/pjreddie/imagenet_backup/net_%d.cfg", i);
             save_network(net, buff);
         }
     }
@@ -473,7 +472,7 @@ void train_imagenet()
 
 void validate_imagenet(char *filename)
 {
-    int i;
+    int i = 0;
     network net = parse_network_cfg(filename);
     srand(time(0));
 
@@ -488,21 +487,28 @@ void validate_imagenet(char *filename)
     float avg_acc = 0;
     float avg_top5 = 0;
     int splits = 50;
+    int num = (i+1)*m/splits - i*m/splits;
 
-    for(i = 0; i < splits; ++i){
+    data val, buffer;
+    pthread_t load_thread = load_data_thread(paths, num, 0, labels, 1000, 224, 224, &buffer);
+    for(i = 1; i <= splits; ++i){
         time=clock();
-        char **part = paths+(i*m/splits);
-        int num = (i+1)*m/splits - i*m/splits;
-        data val = load_data(part, num, labels, 1000, 224, 224);
 
+        pthread_join(load_thread, 0);
+        val = buffer;
         normalize_data_rows(val);
+
+        num = (i+1)*m/splits - i*m/splits;
+        char **part = paths+(i*m/splits);
+        if(i != splits) load_thread = load_data_thread(part, num, 0, labels, 1000, 224, 224, &buffer);
         printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+
         time=clock();
 #ifdef GPU
         float *acc = network_accuracies_gpu(net, val);
         avg_acc += acc[0];
         avg_top5 += acc[1];
-        printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/(i+1), avg_top5/(i+1), sec(clock()-time), val.X.rows);
+        printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/i, avg_top5/i, sec(clock()-time), val.X.rows);
 #endif
         free_data(val);
     }
@@ -895,14 +901,14 @@ void test_correct_alexnet()
     int count = 0;
 
     srand(222222);
-    network net = parse_network_cfg("cfg/alexnet.test");
+    network net = parse_network_cfg("cfg/net.cfg");
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 1000/net.batch+1;
     imgs = 1;
 
     while(++count <= 5){
         time=clock();
-        data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
+        data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 224,224);
         //translate_data_rows(train, -144);
         normalize_data_rows(train);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
@@ -914,10 +920,10 @@ void test_correct_alexnet()
 #ifdef GPU
     count = 0;
     srand(222222);
-    net = parse_network_cfg("cfg/alexnet.test");
+    net = parse_network_cfg("cfg/net.cfg");
     while(++count <= 5){
         time=clock();
-        data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
+        data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224);
         //translate_data_rows(train, -144);
         normalize_data_rows(train);
         printf("Loaded: %lf seconds\n", sec(clock()-time));

src/data.c

@@ -180,16 +180,7 @@ data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh
     return d;
 }
 
-data load_data(char **paths, int n, char **labels, int k, int h, int w)
+char **get_random_paths(char **paths, int n, int m)
-{
-    data d;
-    d.shallow = 0;
-    d.X = load_image_paths(paths, n, h, w);
-    d.y = load_labels_paths(paths, n, labels, k);
-    return d;
-}
-
-data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w)
 {
     char **random_paths = calloc(n, sizeof(char*));
     int i;
@@ -198,14 +189,23 @@ data load_data_random(int n, char **paths, int m, char **labels, int k, int h, i
         random_paths[i] = paths[index];
         if(i == 0) printf("%s\n", paths[index]);
     }
-    data d = load_data(random_paths, n, labels, k, h, w);
+    return random_paths;
-    free(random_paths);
+}
+
+data load_data(char **paths, int n, int m, char **labels, int k, int h, int w)
+{
+    if(m) paths = get_random_paths(paths, n, m);
+    data d;
+    d.shallow = 0;
+    d.X = load_image_paths(paths, n, h, w);
+    d.y = load_labels_paths(paths, n, labels, k);
+    if(m) free(paths);
     return d;
 }
 
 struct load_args{
-    int n;
     char **paths;
+    int n;
     int m;
     char **labels;
     int k;
@@ -217,11 +217,11 @@ struct load_args{
 void *load_in_thread(void *ptr)
 {
     struct load_args a = *(struct load_args*)ptr;
-    *a.d = load_data_random(a.n, a.paths, a.m, a.labels, a.k, a.h, a.w);
+    *a.d = load_data(a.paths, a.n, a.m, a.labels, a.k, a.h, a.w);
     return 0;
 }
 
-pthread_t load_data_random_thread(int n, char **paths, int m, char **labels, int k, int h, int w, data *d)
+pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d)
 {
     pthread_t thread;
     struct load_args *args = calloc(1, sizeof(struct load_args));

src/data.h

@@ -13,9 +13,10 @@ typedef struct{
 
 
 void free_data(data d);
-data load_data(char **paths, int n, char **labels, int k, int h, int w);
+
-pthread_t load_data_random_thread(int n, char **paths, int m, char **labels, int k, int h, int w, data *d);
+data load_data(char **paths, int n, int m, char **labels, int k, int h, int w);
-data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w);
+pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d);
+
 data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw, float scale);
 data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w, int nh, int nw, float scale);
 data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w);

src/dropout_layer.c

@@ -10,8 +10,9 @@ dropout_layer *make_dropout_layer(int batch, int inputs, float probability)
     layer->probability = probability;
     layer->inputs = inputs;
     layer->batch = batch;
-    #ifdef GPU
     layer->rand = calloc(inputs*batch, sizeof(float));
+    layer->scale = 1./(1.-probability);
+    #ifdef GPU
     layer->rand_cl = cl_make_array(layer->rand, inputs*batch);
     #endif
     return layer;
@@ -21,13 +22,21 @@ void forward_dropout_layer(dropout_layer layer, float *input)
 {
     int i;
     for(i = 0; i < layer.batch * layer.inputs; ++i){
-        if(rand_uniform() < layer.probability) input[i] = 0;
+        float r = rand_uniform();
-        else input[i] /= (1-layer.probability);
+        layer.rand[i] = r;
+        if(r < layer.probability) input[i] = 0;
+        else input[i] *= layer.scale;
     }
 }
-void backward_dropout_layer(dropout_layer layer, float *input, float *delta)
+
+void backward_dropout_layer(dropout_layer layer, float *delta)
 {
-    // Don't do shit LULZ
+    int i;
+    for(i = 0; i < layer.batch * layer.inputs; ++i){
+        float r = layer.rand[i];
+        if(r < layer.probability) delta[i] = 0;
+        else delta[i] *= layer.scale;
+    }
 }
 
 #ifdef GPU
@@ -36,7 +45,7 @@ cl_kernel get_dropout_kernel()
     static int init = 0;
     static cl_kernel kernel;
     if(!init){
-        kernel = get_kernel("src/dropout_layer.cl", "forward", 0);
+        kernel = get_kernel("src/dropout_layer.cl", "yoloswag420blazeit360noscope", 0);
         init = 1;
     }
     return kernel;
@@ -56,6 +65,27 @@ void forward_dropout_layer_gpu(dropout_layer layer, cl_mem input)
     cl.error = clSetKernelArg(kernel, i++, sizeof(input), (void*) &input);
     cl.error = clSetKernelArg(kernel, i++, sizeof(layer.rand_cl), (void*) &layer.rand_cl);
     cl.error = clSetKernelArg(kernel, i++, sizeof(layer.probability), (void*) &layer.probability);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.scale), (void*) &layer.scale);
+    check_error(cl);
+
+    const size_t global_size[] = {size};
+
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    check_error(cl);
+}
+
+void backward_dropout_layer_gpu(dropout_layer layer, cl_mem delta)
+{
+    int size = layer.inputs*layer.batch;
+
+    cl_kernel kernel = get_dropout_kernel();
+    cl_command_queue queue = cl.queue;
+
+    cl_uint i = 0;
+    cl.error = clSetKernelArg(kernel, i++, sizeof(delta), (void*) &delta);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.rand_cl), (void*) &layer.rand_cl);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.probability), (void*) &layer.probability);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.scale), (void*) &layer.scale);
     check_error(cl);
 
     const size_t global_size[] = {size};

src/dropout_layer.cl

@@ -1,5 +1,5 @@
-__kernel void forward(__global float *input, __global float *rand, float prob)
+__kernel void yoloswag420blazeit360noscope(__global float *input, __global float *rand, float prob, float scale)
 {
     int id = get_global_id(0);
-    input[id] = (rand[id] < prob) ? 0 : input[id]/(1.-prob);
+    input[id] = (rand[id] < prob) ? 0 : input[id]*scale;
 }

src/dropout_layer.h

@@ -6,8 +6,9 @@ typedef struct{
     int batch;
     int inputs;
     float probability;
-    #ifdef GPU
+    float scale;
     float *rand;
+    #ifdef GPU
     cl_mem rand_cl;
     #endif
 } dropout_layer;
@@ -15,9 +16,11 @@ typedef struct{
 dropout_layer *make_dropout_layer(int batch, int inputs, float probability);
 
 void forward_dropout_layer(dropout_layer layer, float *input);
-void backward_dropout_layer(dropout_layer layer, float *input, float *delta);
+void backward_dropout_layer(dropout_layer layer, float *delta);
-    #ifdef GPU
+
+#ifdef GPU
 void forward_dropout_layer_gpu(dropout_layer layer, cl_mem input);
+void backward_dropout_layer_gpu(dropout_layer layer, cl_mem delta);
 
 #endif
 #endif

src/network.c

@@ -219,6 +219,10 @@ void backward_network(network net, float *input)
             maxpool_layer layer = *(maxpool_layer *)net.layers[i];
             if(i != 0) backward_maxpool_layer(layer, prev_delta);
         }
+        else if(net.types[i] == DROPOUT){
+            dropout_layer layer = *(dropout_layer *)net.layers[i];
+            backward_dropout_layer(layer, prev_delta);
+        }
         else if(net.types[i] == NORMALIZATION){
             normalization_layer layer = *(normalization_layer *)net.layers[i];
             if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);

src/network_gpu.c

@@ -101,6 +101,10 @@ void backward_network_gpu(network net, cl_mem input)
             maxpool_layer layer = *(maxpool_layer *)net.layers[i];
             backward_maxpool_layer_gpu(layer, prev_delta);
         }
+        else if(net.types[i] == DROPOUT){
+            dropout_layer layer = *(dropout_layer *)net.layers[i];
+            backward_dropout_layer_gpu(layer, prev_delta);
+        }
         else if(net.types[i] == SOFTMAX){
             softmax_layer layer = *(softmax_layer *)net.layers[i];
             backward_softmax_layer_gpu(layer, prev_delta);