Stable place to commit

src/connected_layer.c

@@ -43,6 +43,7 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVA
 
     for(i = 0; i < outputs; ++i){
         layer->biases[i] = scale;
+       // layer->biases[i] = 1;
     }
 
 #ifdef GPU
@@ -113,9 +114,10 @@ void forward_connected_layer(connected_layer layer, float *input)
 void backward_connected_layer(connected_layer layer, float *input, float *delta)
 {
     int i;
+    float alpha = 1./layer.batch;
     gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
     for(i = 0; i < layer.batch; ++i){
-        axpy_cpu(layer.outputs, 1, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
+        axpy_cpu(layer.outputs, alpha, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
     }
     int m = layer.inputs;
     int k = layer.batch;
@@ -123,7 +125,7 @@ void backward_connected_layer(connected_layer layer, float *input, float *delta)
     float *a = input;
     float *b = layer.delta;
     float *c = layer.weight_updates;
-    gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
+    gemm(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
 
     m = layer.batch;
     k = layer.outputs;
@@ -156,13 +158,18 @@ void push_connected_layer(connected_layer layer)
 
 void update_connected_layer_gpu(connected_layer layer)
 {
+/*
+    cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
+    cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
+    printf("Weights: %f updates: %f\n", mag_array(layer.weights, layer.inputs*layer.outputs), layer.learning_rate*mag_array(layer.weight_updates, layer.inputs*layer.outputs));
+*/
+
     axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
     scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_gpu, 1);
 
     axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
     axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
     scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_gpu, 1);
-    //pull_connected_layer(layer);
 }
 
 void forward_connected_layer_gpu(connected_layer layer, float * input)
@@ -183,10 +190,11 @@ void forward_connected_layer_gpu(connected_layer layer, float * input)
 
 void backward_connected_layer_gpu(connected_layer layer, float * input, float * delta)
 {
+    float alpha = 1./layer.batch;
     int i;
     gradient_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation, layer.delta_gpu);
     for(i = 0; i < layer.batch; ++i){
-        axpy_ongpu_offset(layer.outputs, 1, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
+        axpy_ongpu_offset(layer.outputs, alpha, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
     }
     int m = layer.inputs;
     int k = layer.batch;
@@ -194,7 +202,7 @@ void backward_connected_layer_gpu(connected_layer layer, float * input, float *
     float * a = input;
     float * b = layer.delta_gpu;
     float * c = layer.weight_updates_gpu;
-    gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
+    gemm_ongpu(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
 
     m = layer.batch;
     k = layer.outputs;

src/convolutional_kernels.cu

@@ -28,7 +28,7 @@ extern "C" void bias_output_gpu(const convolutional_layer layer)
     check_error(cudaPeekAtLastError());
 }
 
-__global__ void learn_bias(int batch, int n, int size, float *delta, float *bias_updates)
+__global__ void learn_bias(int batch, int n, int size, float *delta, float *bias_updates, float scale)
 {
     __shared__ float part[BLOCK];
     int i,b;
@@ -44,15 +44,16 @@ __global__ void learn_bias(int batch, int n, int size, float *delta, float *bias
     part[p] = sum;
     __syncthreads();
     if(p == 0){
-        for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
+        for(i = 0; i < BLOCK; ++i) bias_updates[filter] += scale * part[i];
     }
 }
 
 extern "C" void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
 {
     int size = convolutional_out_height(layer)*convolutional_out_width(layer);
+    float alpha = 1./layer.batch;
 
-    learn_bias<<<layer.n, BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu);
+    learn_bias<<<layer.n, BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu, alpha);
     check_error(cudaPeekAtLastError());
 }
 
@@ -99,6 +100,7 @@ extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, float
 
 extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, float *in, float *delta_gpu)
 {
+    float alpha = 1./layer.batch;
     int i;
     int m = layer.n;
     int n = layer.size*layer.size*layer.c;
@@ -115,7 +117,7 @@ extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, floa
         float * c = layer.filter_updates_gpu;
 
         im2col_ongpu(in, i*layer.c*layer.h*layer.w, layer.c,  layer.h,  layer.w,  layer.size,  layer.stride, layer.pad, layer.col_image_gpu);
-        gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
+        gemm_ongpu(0,1,m,n,k,alpha,a + i*m*k,k,b,k,1,c,n);
 
         if(delta_gpu){
 
@@ -151,12 +153,9 @@ extern "C" void update_convolutional_layer_gpu(convolutional_layer layer)
     int size = layer.size*layer.size*layer.c*layer.n;
 
 /*
-    cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
-    cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
     cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, size);
     cuda_pull_array(layer.filters_gpu, layer.filters, size);
-    printf("Bias: %f updates: %f\n", mse_array(layer.biases, layer.n), mse_array(layer.bias_updates, layer.n));
+    printf("Filter: %f updates: %f\n", mag_array(layer.filters, size), layer.learning_rate*mag_array(layer.filter_updates, size));
-    printf("Filter: %f updates: %f\n", mse_array(layer.filters, layer.n), mse_array(layer.filter_updates, layer.n));
     */
 
     axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);

src/convolutional_layer.c

@@ -66,11 +66,12 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
     layer->biases = calloc(n, sizeof(float));
     layer->bias_updates = calloc(n, sizeof(float));
     float scale = 1./sqrt(size*size*c);
-    //scale = .05;
+    //scale = .01;
     for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
     for(i = 0; i < n; ++i){
         //layer->biases[i] = rand_normal()*scale + scale;
         layer->biases[i] = scale;
+        //layer->biases[i] = 1;
     }
     int out_h = convolutional_out_height(*layer);
     int out_w = convolutional_out_width(*layer);
@@ -155,18 +156,20 @@ void forward_convolutional_layer(const convolutional_layer layer, float *in)
 
 void learn_bias_convolutional_layer(convolutional_layer layer)
 {
+    float alpha = 1./layer.batch;
     int i,b;
     int size = convolutional_out_height(layer)
         *convolutional_out_width(layer);
     for(b = 0; b < layer.batch; ++b){
         for(i = 0; i < layer.n; ++i){
-            layer.bias_updates[i] += sum_array(layer.delta+size*(i+b*layer.n), size);
+            layer.bias_updates[i] += alpha * sum_array(layer.delta+size*(i+b*layer.n), size);
         }
     }
 }
 
 void backward_convolutional_layer(convolutional_layer layer, float *in, float *delta)
 {
+    float alpha = 1./layer.batch;
     int i;
     int m = layer.n;
     int n = layer.size*layer.size*layer.c;
@@ -188,7 +191,7 @@ void backward_convolutional_layer(convolutional_layer layer, float *in, float *d
 
         im2col_cpu(im, layer.c, layer.h, layer.w, 
                 layer.size, layer.stride, layer.pad, b);
-        gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+        gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
 
         if(delta){
             a = layer.filters;

src/darknet.c

@@ -206,10 +206,28 @@ void train_imagenet_distributed(char *address)
 }
 */
 
+char *basename(char *cfgfile)
+{
+    char *c = cfgfile;
+    char *next;
+    while((next = strchr(c, '/')))
+    {
+        c = next+1;
+    }
+    c = copy_string(c);
+    next = strchr(c, '_');
+    if (next) *next = 0;
+    next = strchr(c, '.');
+    if (next) *next = 0;
+    return c;
+}
+
 void train_imagenet(char *cfgfile)
 {
-    float avg_loss = 1;
+    float avg_loss = -1;
     srand(time(0));
+    char *base = basename(cfgfile);
+    printf("%s\n", base);
     network net = parse_network_cfg(cfgfile);
     //test_learn_bias(*(convolutional_layer *)net.layers[1]);
     //set_learning_network(&net, net.learning_rate, 0, net.decay);
@@ -235,12 +253,13 @@ void train_imagenet(char *cfgfile)
         time=clock();
         float loss = train_network(net, train);
         net.seen += imgs;
+        if(avg_loss == -1) avg_loss = loss;
         avg_loss = avg_loss*.9 + loss*.1;
         printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
         free_data(train);
         if(i%100==0){
             char buff[256];
-            sprintf(buff, "/home/pjreddie/imagenet_backup/vgg_%d.cfg", i);
+            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.cfg",base, i);
             save_network(net, buff);
         }
     }
@@ -272,7 +291,6 @@ void validate_imagenet(char *filename)
 
         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);
@@ -312,6 +330,7 @@ void test_detection(char *cfgfile)
 
 void test_init(char *cfgfile)
 {
+    gpu_index = -1;
     network net = parse_network_cfg(cfgfile);
     set_batch_network(&net, 1);
     srand(2222222);
@@ -345,7 +364,7 @@ void test_init(char *cfgfile)
 }
 void test_dog(char *cfgfile)
 {
-    image im = load_image_color("data/dog.jpg", 224, 224);
+    image im = load_image_color("data/dog.jpg", 256, 256);
     translate_image(im, -128);
     print_image(im);
     float *X = im.data;
@@ -377,7 +396,7 @@ void test_imagenet(char *cfgfile)
         strtok(filename, "\n");
         image im = load_image_color(filename, 256, 256);
         translate_image(im, -128);
-        //scale_image(im, 1/128.);
+        scale_image(im, 1/128.);
         printf("%d %d %d\n", im.h, im.w, im.c);
         float *X = im.data;
         time=clock();

src/gemm.c

@@ -276,6 +276,7 @@ void test_gpu_accuracy(int TA, int TB, int m, int k, int n)
 
 int test_gpu_blas()
 {
+/*
        test_gpu_accuracy(0,0,10,576,75); 
 
        test_gpu_accuracy(0,0,17,10,10); 
@@ -299,6 +300,15 @@ int test_gpu_blas()
     time_ongpu(0,0,256,196,2304); 
     time_ongpu(0,0,128,4096,12544); 
     time_ongpu(0,0,128,4096,4096); 
+    */
+    time_ongpu(0,0,64,75,12544); 
+    time_ongpu(0,0,64,75,12544); 
+    time_ongpu(0,0,64,75,12544); 
+    time_ongpu(0,0,64,576,12544); 
+    time_ongpu(0,0,256,2304,784); 
+    time_ongpu(1,1,2304,256,784); 
+    time_ongpu(0,0,512,4608,196); 
+    time_ongpu(1,1,4608,512,196); 
 
 return 0;
 }

src/network.c

@@ -133,7 +133,6 @@ void update_network(network net)
         }
         else if(net.types[i] == CONNECTED){
             connected_layer layer = *(connected_layer *)net.layers[i];
-            //secret_update_connected_layer((connected_layer *)net.layers[i]);
             update_connected_layer(layer);
         }
     }

src/network_kernels.cu

@@ -61,6 +61,7 @@ void forward_network_gpu(network net, float * input, float * truth, int train)
             forward_crop_layer_gpu(layer, train, input);
             input = layer.output_gpu;
         }
+        //cudaDeviceSynchronize();
         //printf("Forward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));
     }
 }

src/utils.c

@@ -262,6 +262,16 @@ void translate_array(float *a, int n, float s)
     }
 }
 
+float mag_array(float *a, int n)
+{
+    int i;
+    float sum = 0;
+    for(i = 0; i < n; ++i){
+        sum += a[i]*a[i];   
+    }
+    return sqrt(sum);
+}
+
 void scale_array(float *a, int n, float s)
 {
     int i;

src/utils.h

@@ -28,6 +28,7 @@ float rand_uniform();
 float sum_array(float *a, int n);
 float mean_array(float *a, int n);
 float variance_array(float *a, int n);
+float mag_array(float *a, int n);
 float **one_hot_encode(float *a, int n, int k);
 float sec(clock_t clocks);
 #endif