updates?

src/cnn.c

@@ -429,15 +429,16 @@ void train_imagenet_distributed(char *address)
     }
 }
 
-void train_imagenet()
+void train_imagenet(char *cfgfile)
 {
     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.part");
+    network net = parse_network_cfg(cfgfile);
+    set_learning_network(&net, .000001, .9, .0005);
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 1000/net.batch+1;
-    int i = 9540;
+    int i = 20590;
     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);
@@ -446,14 +447,14 @@ void train_imagenet()
     pthread_t load_thread;
     data train;
     data buffer;
-    load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
+    load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 256, 256, &buffer);
     while(1){
         i += 1;
         time=clock();
         pthread_join(load_thread, 0);
         train = buffer;
         normalize_data_rows(train);
-        load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224, &buffer);
+        load_thread = load_data_thread(paths, imgs*net.batch, plist->size, labels, 1000, 256, 256, &buffer);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
 #ifdef GPU
@@ -490,7 +491,7 @@ void validate_imagenet(char *filename)
     int num = (i+1)*m/splits - i*m/splits;
 
     data val, buffer;
-    pthread_t load_thread = load_data_thread(paths, num, 0, labels, 1000, 224, 224, &buffer);
+    pthread_t load_thread = load_data_thread(paths, num, 0, labels, 1000, 256, 256, &buffer);
     for(i = 1; i <= splits; ++i){
         time=clock();
 
@@ -500,7 +501,7 @@ void validate_imagenet(char *filename)
 
         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);
+        if(i != splits) load_thread = load_data_thread(part, num, 0, labels, 1000, 256, 256, &buffer);
         printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
 
         time=clock();
@@ -514,9 +515,10 @@ void validate_imagenet(char *filename)
     }
 }
 
-void test_detection()
+void test_detection(char *cfgfile)
 {
-    network net = parse_network_cfg("cfg/detnet.test");
+    network net = parse_network_cfg(cfgfile);
+    set_batch_network(&net, 1);
     srand(2222222);
     clock_t time;
     char filename[256];
@@ -618,14 +620,14 @@ void test_cifar10()
 void train_cifar10()
 {
     srand(555555);
-    network net = parse_network_cfg("cfg/cifar10.cfg");
+    network net = parse_network_cfg("cfg/cifar_ramp.part");
     data test = load_cifar10_data("data/cifar10/test_batch.bin");
     int count = 0;
     int iters = 10000/net.batch;
     data train = load_all_cifar10();
     while(++count <= 10000){
         clock_t start = clock(), end;
-        float loss = train_network_sgd(net, train, iters);
+        float loss = train_network_sgd_gpu(net, train, iters);
         end = clock();
         //visualize_network(net);
         //cvWaitKey(5000);
@@ -633,10 +635,10 @@ void train_cifar10()
         //float test_acc = network_accuracy(net, test);
         //printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
         if(count%10 == 0){
-            float test_acc = network_accuracy(net, test);
+            float test_acc = network_accuracy_gpu(net, test);
             printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
             char buff[256];
-            sprintf(buff, "/home/pjreddie/cifar/cifar10_2_%d.cfg", count);
+            sprintf(buff, "/home/pjreddie/cifar/cifar10_%d.cfg", count);
             save_network(net, buff);
         }else{
             printf("%d: Loss: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
@@ -899,40 +901,40 @@ void test_correct_alexnet()
     printf("%d\n", plist->size);
     clock_t time;
     int count = 0;
-
-    srand(222222);
-    network net = parse_network_cfg("cfg/net.cfg");
-    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    network net;
     int imgs = 1000/net.batch+1;
     imgs = 1;
-
+#ifdef GPU
+    count = 0;
+    srand(222222);
+    net = parse_network_cfg("cfg/net.cfg");
     while(++count <= 5){
         time=clock();
-        data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 224,224);
+        data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 256, 256);
         //translate_data_rows(train, -144);
         normalize_data_rows(train);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
-        float loss = train_network_data_cpu(net, train, imgs);
+        float loss = train_network_data_gpu(net, train, imgs);
         printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
         free_data(train);
     }
-#ifdef GPU
+#endif
     count = 0;
     srand(222222);
     net = parse_network_cfg("cfg/net.cfg");
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     while(++count <= 5){
         time=clock();
-        data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 224, 224);
+        data train = load_data(paths, imgs*net.batch, plist->size, labels, 1000, 256,256);
         //translate_data_rows(train, -144);
         normalize_data_rows(train);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
-        float loss = train_network_data_gpu(net, train, imgs);
+        float loss = train_network_data_cpu(net, train, imgs);
         printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
         free_data(train);
     }
-#endif
 }
 
 void run_server()
@@ -972,22 +974,23 @@ int main(int argc, char *argv[])
 #ifdef GPU
     cl_setup(index);
 #endif
-    if(0==strcmp(argv[1], "train")) train_imagenet();
-    else if(0==strcmp(argv[1], "detection")) train_detection_net();
+    if(0==strcmp(argv[1], "detection")) train_detection_net();
     else if(0==strcmp(argv[1], "asirra")) train_asirra();
     else if(0==strcmp(argv[1], "nist")) train_nist();
+    else if(0==strcmp(argv[1], "cifar")) train_cifar10();
     else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
     else if(0==strcmp(argv[1], "test")) test_imagenet();
     else if(0==strcmp(argv[1], "server")) run_server();
-    else if(0==strcmp(argv[1], "detect")) test_detection();
 #ifdef GPU
     else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
 #endif
     else if(argc < 3){
-        fprintf(stderr, "usage: %s <function>\n", argv[0]);
+        fprintf(stderr, "usage: %s <function> <filename>\n", argv[0]);
         return 0;
     }
+    else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2]);
     else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
+    else if(0==strcmp(argv[1], "detect")) test_detection(argv[2]);
     else if(0==strcmp(argv[1], "init")) test_init(argv[2]);
     else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
     else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);

src/crop_layer.c

@@ -21,37 +21,77 @@ crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int
     layer->crop_width = crop_width;
     layer->crop_height = crop_height;
     layer->output = calloc(crop_width*crop_height * c*batch, sizeof(float));
-    layer->delta = calloc(crop_width*crop_height * c*batch, sizeof(float));
+    #ifdef GPU
+    layer->output_cl = cl_make_array(layer->output, crop_width*crop_height*c*batch);
+    #endif
     return layer;
 }
+
 void forward_crop_layer(const crop_layer layer, float *input)
 {
-    int i,j,c,b;
+    int i,j,c,b,row,col;
+    int index;
+    int count = 0;
+    int flip = (layer.flip && rand()%2);
     int dh = rand()%(layer.h - layer.crop_height);
     int dw = rand()%(layer.w - layer.crop_width);
-    int count = 0;
-    if(layer.flip && rand()%2){
-        for(b = 0; b < layer.batch; ++b){
-            for(c = 0; c < layer.c; ++c){
-                for(i = dh; i < dh+layer.crop_height; ++i){
-                    for(j = dw+layer.crop_width-1; j >= dw; --j){
-                        int index = j+layer.w*(i+layer.h*(c + layer.c*b));
-                        layer.output[count++] = input[index];
-                    }
-                }
-            }
-        }
-    }else{
-        for(b = 0; b < layer.batch; ++b){
-            for(c = 0; c < layer.c; ++c){
-                for(i = dh; i < dh+layer.crop_height; ++i){
-                    for(j = dw; j < dw+layer.crop_width; ++j){
-                        int index = j+layer.w*(i+layer.h*(c + layer.c*b));
-                        layer.output[count++] = input[index];
+    for(b = 0; b < layer.batch; ++b){
+        for(c = 0; c < layer.c; ++c){
+            for(i = 0; i < layer.crop_height; ++i){
+                for(j = 0; j < layer.crop_width; ++j){
+                    if(flip){
+                        col = layer.w - dw - j - 1;    
+                    }else{
+                        col = j + dw;
                     }
+                    row = i + dh;
+                    index = col+layer.w*(row+layer.h*(c + layer.c*b)); 
+                    layer.output[count++] = input[index];
                 }
             }
         }
     }
 }
 
+#ifdef GPU
+cl_kernel get_crop_kernel()
+{
+    static int init = 0;
+    static cl_kernel kernel;
+    if(!init){
+        kernel = get_kernel("src/crop_layer.cl", "forward", 0);
+        init = 1;
+    }
+    return kernel;
+}
+
+void forward_crop_layer_gpu(crop_layer layer, cl_mem input)
+{
+    int flip = (layer.flip && rand()%2);
+    int dh = rand()%(layer.h - layer.crop_height);
+    int dw = rand()%(layer.w - layer.crop_width);
+    int size = layer.batch*layer.c*layer.crop_width*layer.crop_height;
+
+    cl_kernel kernel = get_crop_kernel();
+    cl_command_queue queue = cl.queue;
+
+    cl_uint i = 0;
+    cl.error = clSetKernelArg(kernel, i++, sizeof(input), (void*) &input);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.c), (void*) &layer.c);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.h), (void*) &layer.h);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.w), (void*) &layer.w);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.crop_height), (void*) &layer.crop_height);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.crop_width), (void*) &layer.crop_width);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(dh), (void*) &dh);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(dw), (void*) &dw);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(flip), (void*) &flip);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(layer.output_cl), (void*) &layer.output_cl);
+    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);
+}
+
+#endif

src/crop_layer.cl

@@ -0,0 +1,16 @@
+__kernel void forward(__global float *input, int c, int h, int w, int crop_height, int crop_width, int dh, int dw, int flip, __global float *output)
+{
+    int id = get_global_id(0);
+    int count = id;
+    int j = id % crop_width;
+    id /= crop_width;
+    int i = id % crop_height;
+    id /= crop_height;
+    int k = id % c;
+    id /= c;
+    int b = id;
+    int col = (flip) ? w - dw - j - 1 : j + dw;    
+    int row = i + dh;
+    int index = col+w*(row+h*(k + c*b)); 
+    output[count] = input[index];
+}

src/crop_layer.h

@@ -1,6 +1,7 @@
 #ifndef CROP_LAYER_H
 #define CROP_LAYER_H
 
+#include "opencl.h"
 #include "image.h"
 
 typedef struct {
@@ -9,14 +10,19 @@ typedef struct {
     int crop_width;
     int crop_height;
     int flip;
-    float *delta;
     float *output;
+#ifdef GPU
+    cl_mem output_cl;
+#endif
 } crop_layer;
 
 image get_crop_image(crop_layer layer);
 crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip);
 void forward_crop_layer(const crop_layer layer, float *input);
-void backward_crop_layer(const crop_layer layer, float *input, float *delta);
+
+#ifdef GPU
+void forward_crop_layer_gpu(crop_layer layer, cl_mem input);
+#endif
 
 #endif
 

src/network.c

@@ -125,6 +125,9 @@ float *get_network_output_layer(network net, int i)
     } else if(net.types[i] == CONNECTED){
         connected_layer layer = *(connected_layer *)net.layers[i];
         return layer.output;
+    } else if(net.types[i] == CROP){
+        crop_layer layer = *(crop_layer *)net.layers[i];
+        return layer.output;
     } else if(net.types[i] == NORMALIZATION){
         normalization_layer layer = *(normalization_layer *)net.layers[i];
         return layer.output;
@@ -402,6 +405,9 @@ int get_network_input_size_layer(network net, int i)
     } else if(net.types[i] == DROPOUT){
         dropout_layer layer = *(dropout_layer *) net.layers[i];
         return layer.inputs;
+    } else if(net.types[i] == CROP){
+        crop_layer layer = *(crop_layer *) net.layers[i];
+        return layer.c*layer.h*layer.w;
     }
     else if(net.types[i] == FREEWEIGHT){
         freeweight_layer layer = *(freeweight_layer *) net.layers[i];
@@ -411,6 +417,7 @@ int get_network_input_size_layer(network net, int i)
         softmax_layer layer = *(softmax_layer *)net.layers[i];
         return layer.inputs;
     }
+    printf("Can't find input size\n");
     return 0;
 }
 
@@ -425,6 +432,10 @@ int get_network_output_size_layer(network net, int i)
         maxpool_layer layer = *(maxpool_layer *)net.layers[i];
         image output = get_maxpool_image(layer);
         return output.h*output.w*output.c;
+    }
+     else if(net.types[i] == CROP){
+        crop_layer layer = *(crop_layer *) net.layers[i];
+        return layer.c*layer.crop_height*layer.crop_width;
     }
     else if(net.types[i] == CONNECTED){
         connected_layer layer = *(connected_layer *)net.layers[i];
@@ -442,6 +453,7 @@ int get_network_output_size_layer(network net, int i)
         softmax_layer layer = *(softmax_layer *)net.layers[i];
         return layer.inputs;
     }
+    printf("Can't find output size\n");
     return 0;
 }
 

src/network_gpu.c

@@ -55,6 +55,11 @@ void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
             dropout_layer layer = *(dropout_layer *)net.layers[i];
             forward_dropout_layer_gpu(layer, input);
         }
+        else if(net.types[i] == CROP){
+            crop_layer layer = *(crop_layer *)net.layers[i];
+            forward_crop_layer_gpu(layer, input);
+            input = layer.output_cl;
+        }
         //printf("%d %f\n", i, sec(clock()-time));
         /*
            else if(net.types[i] == CROP){
@@ -142,6 +147,10 @@ cl_mem get_network_output_cl_layer(network net, int i)
         maxpool_layer layer = *(maxpool_layer *)net.layers[i];
         return layer.output_cl;
     }
+    else if(net.types[i] == CROP){
+        crop_layer layer = *(crop_layer *)net.layers[i];
+        return layer.output_cl;
+    }
     else if(net.types[i] == SOFTMAX){
         softmax_layer layer = *(softmax_layer *)net.layers[i];
         return layer.output_cl;
@@ -260,7 +269,7 @@ float *get_network_output_gpu(network net)
 
 float *network_predict_gpu(network net, float *input)
 {
-    
+
     int size = get_network_input_size(net) * net.batch;
     cl_mem input_cl = cl_make_array(input, size);
     forward_network_gpu(net, input_cl, 0, 0);