go updates

Makefile

@@ -1,5 +1,5 @@
-GPU=0
+GPU=1
-OPENCV=0
+OPENCV=1
 DEBUG=0
 
 ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20 

cfg/go.test.cfg

@@ -10,11 +10,11 @@ decay=0.0005
 learning_rate=0.1
 max_batches = 0
 policy=steps
-steps=50000, 90000
+steps=50000
-scales=.1, .1
+scales=.1
 
 [convolutional]
-filters=256
+filters=512
 size=3
 stride=1
 pad=1
@@ -23,6 +23,14 @@ batch_normalize=1
 
 [convolutional]
 filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+batch_normalize=1
+
+[convolutional]
+filters=512
 size=3
 stride=1
 pad=1
@@ -31,6 +39,14 @@ batch_normalize=1
 
 [convolutional]
 filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+batch_normalize=1
+
+[convolutional]
+filters=512
 size=3
 stride=1
 pad=1
@@ -39,6 +55,14 @@ batch_normalize=1
 
 [convolutional]
 filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+batch_normalize=1
+
+[convolutional]
+filters=512
 size=3
 stride=1
 pad=1
@@ -47,12 +71,28 @@ batch_normalize=1
 
 [convolutional]
 filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+batch_normalize=1
+
+[convolutional]
+filters=512
 size=3
 stride=1
 pad=1
 activation=leaky
 batch_normalize=1
 
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+batch_normalize=1
+
 [convolutional]
 filters=1
 size=1

src/classifier.c

@@ -410,7 +410,7 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
 
     char **labels = get_labels(label_list);
     list *plist = get_paths(valid_list);
-    int scales[] = {160, 192, 224, 288, 320, 352, 384};
+    int scales[] = {192, 224, 288, 320, 352};
     int nscales = sizeof(scales)/sizeof(scales[0]);
 
     char **paths = (char **)list_to_array(plist);

src/convolutional_kernels.cu

@@ -65,9 +65,9 @@ __global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, in
     }
 }
 
-void binarize_filters_gpu(float *filters, int n, int size, float *mean)
+void binarize_filters_gpu(float *filters, int n, int size, float *binary)
 {
-    binarize_filters_kernel<<<cuda_gridsize(n), BLOCK>>>(filters, n, size, mean);
+    binarize_filters_kernel<<<cuda_gridsize(n), BLOCK>>>(filters, n, size, binary);
     check_error(cudaPeekAtLastError());
 }
 
@@ -161,13 +161,6 @@ void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int
     check_error(cudaPeekAtLastError());
 }
 
-void swap_binary(convolutional_layer *l)
-{
-    float *swap = l->filters_gpu;
-    l->filters_gpu = l->binary_filters_gpu;
-    l->binary_filters_gpu = swap;
-}
-
 void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
 {
     int i;

src/convolutional_layer.c

@@ -7,6 +7,52 @@
 #include <stdio.h>
 #include <time.h>
 
+void swap_binary(convolutional_layer *l)
+{
+    float *swap = l->filters;
+    l->filters = l->binary_filters;
+    l->binary_filters = swap;
+
+    #ifdef GPU
+    swap = l->filters_gpu;
+    l->filters_gpu = l->binary_filters_gpu;
+    l->binary_filters_gpu = swap;
+    #endif
+}
+
+void binarize_filters2(float *filters, int n, int size, char *binary, float *scales)
+{
+    int i, k, f;
+    for(f = 0; f < n; ++f){
+        float mean = 0;
+        for(i = 0; i < size; ++i){
+            mean += fabs(filters[f*size + i]);
+        }
+        mean = mean / size;
+        scales[f] = mean;
+        for(i = 0; i < size/8; ++i){
+            binary[f*size + i] = (filters[f*size + i] > 0) ? 1 : 0;
+            for(k = 0; k < 8; ++k){
+            }
+        }
+    }
+}
+
+void binarize_filters(float *filters, int n, int size, float *binary)
+{
+    int i, f;
+    for(f = 0; f < n; ++f){
+        float mean = 0;
+        for(i = 0; i < size; ++i){
+            mean += fabs(filters[f*size + i]);
+        }
+        mean = mean / size;
+        for(i = 0; i < size; ++i){
+            binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean;
+        }
+    }
+}
+
 int convolutional_out_height(convolutional_layer l)
 {
     int h = l.h;
@@ -139,6 +185,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
 
     if(binary){
         l.binary_filters = calloc(c*n*size*size, sizeof(float));
+        l.cfilters = calloc(c*n*size*size, sizeof(char));
+        l.scales = calloc(n, sizeof(float));
     }
 
     if(batch_normalize){
@@ -295,13 +343,42 @@ void backward_bias(float *bias_updates, float *delta, int batch, int n, int size
     }
 }
 
-void forward_convolutional_layer(const convolutional_layer l, network_state state)
+void forward_convolutional_layer(convolutional_layer l, network_state state)
 {
     int out_h = convolutional_out_height(l);
     int out_w = convolutional_out_width(l);
     int i;
 
     fill_cpu(l.outputs*l.batch, 0, l.output, 1);
+    /*
+    if(l.binary){
+        binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
+        binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales);
+        swap_binary(&l);
+    }
+    */
+
+    if(l.binary){
+        int m = l.n;
+        int k = l.size*l.size*l.c;
+        int n = out_h*out_w;
+
+        char  *a = l.cfilters;
+        float *b = l.col_image;
+        float *c = l.output;
+
+        for(i = 0; i < l.batch; ++i){
+            im2col_cpu(state.input, l.c, l.h, l.w, 
+                    l.size, l.stride, l.pad, b);
+            gemm_bin(m,n,k,1,a,k,b,n,c,n);
+            c += n*m;
+            state.input += l.c*l.h*l.w;
+        }
+        scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
+        add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
+        activate_array(l.output, m*n*l.batch, l.activation);
+        return;
+    }
 
     int m = l.n;
     int k = l.size*l.size*l.c;

src/convolutional_layer.h

@@ -27,6 +27,9 @@ void resize_convolutional_layer(convolutional_layer *layer, int w, int h);
 void forward_convolutional_layer(const convolutional_layer layer, network_state state);
 void update_convolutional_layer(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay);
 image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters);
+void binarize_filters(float *filters, int n, int size, float *binary);
+void swap_binary(convolutional_layer *l);
+void binarize_filters2(float *filters, int n, int size, char *binary, float *scales);
 
 void backward_convolutional_layer(convolutional_layer layer, network_state state);
 

src/gemm.c

@@ -5,6 +5,28 @@
 #include <stdio.h>
 #include <math.h>
 
+void gemm_bin(int M, int N, int K, float ALPHA, 
+        char  *A, int lda, 
+        float *B, int ldb,
+        float *C, int ldc)
+{
+    int i,j,k;
+    for(i = 0; i < M; ++i){
+        for(k = 0; k < K; ++k){
+            char A_PART = A[i*lda+k];
+            if(A_PART){
+                for(j = 0; j < N; ++j){
+                    C[i*ldc+j] += B[k*ldb+j];
+                }
+            } else {
+                for(j = 0; j < N; ++j){
+                    C[i*ldc+j] -= B[k*ldb+j];
+                }
+            }
+        }
+    }
+}
+
 float *random_matrix(int rows, int cols)
 {
     int i;
@@ -276,7 +298,7 @@ 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); 
@@ -310,7 +332,7 @@ int test_gpu_blas()
     time_ongpu(0,0,512,4608,196); 
     time_ongpu(1,1,4608,512,196); 
 
-return 0;
+    return 0;
 }
 #endif
 

src/gemm.h

@@ -1,6 +1,11 @@
 #ifndef GEMM_H
 #define GEMM_H
 
+void gemm_bin(int M, int N, int K, float ALPHA, 
+        char  *A, int lda, 
+        float *B, int ldb,
+        float *C, int ldc);
+        
 void gemm(int TA, int TB, int M, int N, int K, float ALPHA, 
                     float *A, int lda, 
                     float *B, int ldb,

src/go.c

@@ -10,6 +10,7 @@
 
 int inverted = 1;
 int noi = 1;
+static const int nind = 5;
 
 void train_go(char *cfgfile, char *weightfile)
 {
@@ -147,12 +148,14 @@ void print_board(float *board, int swap, int *indexes)
             int index = j*19 + i;
             if(indexes){
                 int found = 0;
-                for(n = 0; n < 3; ++n){
+                for(n = 0; n < nind; ++n){
                     if(index == indexes[n]){
                         found = 1;
                         if(n == 0) printf("\uff11");
                         else if(n == 1) printf("\uff12");
                         else if(n == 2) printf("\uff13");
+                        else if(n == 3) printf("\uff14");
+                        else if(n == 4) printf("\uff15");
                     }
                 }
                 if(found) continue;
@@ -211,59 +214,56 @@ void test_go(char *filename, char *weightfile)
             if(board[i]) move[i] = 0;
         }
 
-        int indexes[3];
+        int indexes[nind];
         int row, col;
-        top_k(move, 19*19, 3, indexes);
+        top_k(move, 19*19, nind, indexes);
         print_board(board, color, indexes);
-        for(i = 0; i < 3; ++i){
+        for(i = 0; i < nind; ++i){
             int index = indexes[i];
             row = index / 19;
             col = index % 19;
-            printf("Suggested: %c %d, %.2f%%\n", col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100);
+            printf("%d: %c %d, %.2f%%\n", i+1, col + 'A' + 1*(col > 7 && noi), (inverted)?19 - row : row+1, move[index]*100);
         }
-        int index = indexes[0];
-        int rec_row = index / 19;
-        int rec_col = index % 19;
-
         if(color == 1) printf("\u25EF Enter move: ");
         else printf("\u25C9 Enter move: ");
 
         char c;
         char *line = fgetl(stdin);
-        int num = sscanf(line, "%c %d", &c, &row);
+        int picked = 1;
-        if (strlen(line) == 0){
+        int dnum = sscanf(line, "%d", &picked);
-            row = rec_row;
+        int cnum = sscanf(line, "%c", &c);
-            col = rec_col;
+        if (strlen(line) == 0 || dnum) {
+            --picked;
+            if (picked < nind){
+                int index = indexes[picked];
+                row = index / 19;
+                col = index % 19;
                 board[row*19 + col] = 1;
-        }else if (c < 'A' || c > 'T'){
+            }
-            if (c == 'p'){
+        } else if (cnum){
-                flip_board(board);
+            if (c <= 'T' && c >= 'A'){
-                color = -color;
+                int num = sscanf(line, "%c %d", &c, &row);
-                free(line);
+                row = (inverted)?19 - row : row-1;
-                continue;
+                col = c - 'A';
+                if (col > 7 && noi) col -= 1;
+                if (num == 2) board[row*19 + col] = 1;
+            } else if (c == 'p') {
+                // Pass
             } else if(c=='b' || c == 'w'){
                 char g;
-                num = sscanf(line, "%c %c %d", &g, &c, &row);
+                int num = sscanf(line, "%c %c %d", &g, &c, &row);
                 row = (inverted)?19 - row : row-1;
                 col = c - 'A';
                 if (col > 7 && noi) col -= 1;
                 if (num == 3) board[row*19 + col] = (g == 'b') ? color : -color;
-            }else{
+            } else if(c == 'c'){
                 char g;
-                num = sscanf(line, "%c %c %d", &g, &c, &row);
+                int num = sscanf(line, "%c %c %d", &g, &c, &row);
                 row = (inverted)?19 - row : row-1;
                 col = c - 'A';
                 if (col > 7 && noi) col -= 1;
                 if (num == 3) board[row*19 + col] = 0;
             }
-        } else if(num == 2){
-            row = (inverted)?19 - row : row-1;
-            col = c - 'A';
-            if (col > 7 && noi) col -= 1;
-            board[row*19 + col] = 1;
-        }else{
-            free(line);
-            continue;
         }
         free(line);
         update_board(board);

src/image.c

@@ -676,6 +676,17 @@ void show_image_cv(image p, const char *name)
         }
     }
 
+    image binarize_image(image im)
+    {
+        image c = copy_image(im);
+        int i;
+        for(i = 0; i < im.w * im.h * im.c; ++i){
+            if(c.data[i] > .5) c.data[i] = 1;
+            else c.data[i] = 0;
+        }
+        return c;
+    }
+
     void saturate_image(image im, float sat)
     {
         rgb_to_hsv(im);
@@ -798,6 +809,8 @@ void show_image_cv(image p, const char *name)
         image exp5 = copy_image(im);
         exposure_image(exp5, .5);
 
+        image bin = binarize_image(im);
+
 #ifdef GPU
         image r = resize_image(im, im.w, im.h);
         image black = make_image(im.w*2 + 3, im.h*2 + 3, 9);
@@ -818,6 +831,7 @@ void show_image_cv(image p, const char *name)
 #endif
 
         show_image(im,   "Original");
+        show_image(bin,  "Binary");
         show_image(gray, "Gray");
         show_image(sat2, "Saturation-2");
         show_image(sat5, "Saturation-.5");

src/layer.h

@@ -92,6 +92,7 @@ struct layer{
     float *rand;
     float *cost;
     float *filters;
+    char  *cfilters;
     float *filter_updates;
     float *state;
 

src/parser.c

@@ -730,8 +730,44 @@ void save_weights_double(network net, char *filename)
     fclose(fp);
 }
 
+void save_convolutional_weights_binary(layer l, FILE *fp)
+{
+#ifdef GPU
+    if(gpu_index >= 0){
+        pull_convolutional_layer(l);
+    }
+#endif
+    binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
+    int size = l.c*l.size*l.size;
+    int i, j, k;
+    fwrite(l.biases, sizeof(float), l.n, fp);
+    if (l.batch_normalize){
+        fwrite(l.scales, sizeof(float), l.n, fp);
+        fwrite(l.rolling_mean, sizeof(float), l.n, fp);
+        fwrite(l.rolling_variance, sizeof(float), l.n, fp);
+    }
+    for(i = 0; i < l.n; ++i){
+        float mean = l.binary_filters[i*size];
+        if(mean < 0) mean = -mean;
+        fwrite(&mean, sizeof(float), 1, fp);
+        for(j = 0; j < size/8; ++j){
+            int index = i*size + j*8;
+            unsigned char c = 0;
+            for(k = 0; k < 8; ++k){
+                if (j*8 + k >= size) break;
+                if (l.binary_filters[index + k] > 0) c = (c | 1<<k);
+            }
+            fwrite(&c, sizeof(char), 1, fp);
+        }
+    }
+}
+
 void save_convolutional_weights(layer l, FILE *fp)
 {
+    if(l.binary){
+        //save_convolutional_weights_binary(l, fp);
+        //return;
+    }
 #ifdef GPU
     if(gpu_index >= 0){
         pull_convolutional_layer(l);
@@ -843,27 +879,55 @@ void load_connected_weights(layer l, FILE *fp, int transpose)
 #endif
 }
 
-void load_convolutional_weights(layer l, FILE *fp)
+void load_convolutional_weights_binary(layer l, FILE *fp)
 {
-    int num = l.n*l.c*l.size*l.size;
     fread(l.biases, sizeof(float), l.n, fp);
     if (l.batch_normalize && (!l.dontloadscales)){
         fread(l.scales, sizeof(float), l.n, fp);
         fread(l.rolling_mean, sizeof(float), l.n, fp);
         fread(l.rolling_variance, sizeof(float), l.n, fp);
-        /*
+    }
-        int i;
+    int size = l.c*l.size*l.size;
+    int i, j, k;
     for(i = 0; i < l.n; ++i){
-            if(l.rolling_mean[i] > 1 || l.rolling_mean[i] < -1 || l.rolling_variance[i] > 1 || l.rolling_variance[i] < -1)
+        float mean = 0;
-            printf("%f %f\n", l.rolling_mean[i], l.rolling_variance[i]);
+        fread(&mean, sizeof(float), 1, fp);
+        for(j = 0; j < size/8; ++j){
+            int index = i*size + j*8;
+            unsigned char c = 0;
+            fread(&c, sizeof(char), 1, fp);
+            for(k = 0; k < 8; ++k){
+                if (j*8 + k >= size) break;
+                l.filters[index + k] = (c & 1<<k) ? mean : -mean;
             }
-        */
         }
-    fflush(stdout);
+    }
+    binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales);
+#ifdef GPU
+    if(gpu_index >= 0){
+        push_convolutional_layer(l);
+    }
+#endif
+}
+
+void load_convolutional_weights(layer l, FILE *fp)
+{
+    if(l.binary){
+        //load_convolutional_weights_binary(l, fp);
+        //return;
+    }
+    int num = l.n*l.c*l.size*l.size;
+    fread(l.biases, sizeof(float), l.n, fp);
+    if (l.batch_normalize && (!l.dontloadscales)){
+        fread(l.scales, sizeof(float), l.n, fp);
+        fread(l.rolling_mean, sizeof(float), l.n, fp);
+        fread(l.rolling_variance, sizeof(float), l.n, fp);
+    }
     fread(l.filters, sizeof(float), num, fp);
     if (l.flipped) {
         transpose_matrix(l.filters, l.c*l.size*l.size, l.n);
     }
+    if (l.binary) binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.filters);
 #ifdef GPU
     if(gpu_index >= 0){
         push_convolutional_layer(l);