not sure

Makefile

@@ -1,5 +1,5 @@
-GPU=1
-OPENCV=1
+GPU=0
+OPENCV=0
 DEBUG=0
 
 ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20 
@@ -34,7 +34,7 @@ CFLAGS+= -DGPU
 LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
 endif
 
-OBJ=gemm.o utils.o cuda.o deconvolutional_layer.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 imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo2.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 coco_demo.o tag.o cifar.o yolo_demo.o go.o batchnorm_layer.o
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.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 imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.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 coco_demo.o tag.o cifar.o yolo_demo.o go.o batchnorm_layer.o
 ifeq ($(GPU), 1) 
 LDFLAGS+= -lstdc++ 
 OBJ+=convolutional_kernels.o deconvolutional_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

src/network.c

@@ -65,6 +65,8 @@ float get_current_rate(network net)
             return net.learning_rate * pow(net.gamma, batch_num);
         case POLY:
             return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+        case RANDOM:
+            return net.learning_rate * pow(rand_uniform(0,1), net.power);
         case SIG:
             return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
         default:

src/network.h

@@ -7,7 +7,7 @@
 #include "data.h"
 
 typedef enum {
-    CONSTANT, STEP, EXP, POLY, STEPS, SIG
+    CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
 } learning_rate_policy;
 
 typedef struct network{

src/parser.c

@@ -432,6 +432,7 @@ route_layer parse_route(list *options, size_params params, network net)
 
 learning_rate_policy get_policy(char *s)
 {
+    if (strcmp(s, "random")==0) return RANDOM;
     if (strcmp(s, "poly")==0) return POLY;
     if (strcmp(s, "constant")==0) return CONSTANT;
     if (strcmp(s, "step")==0) return STEP;
@@ -497,7 +498,7 @@ void parse_net_options(list *options, network *net)
     } else if (net->policy == SIG){
         net->gamma = option_find_float(options, "gamma", 1);
         net->step = option_find_int(options, "step", 1);
-    } else if (net->policy == POLY){
+    } else if (net->policy == POLY || net->policy == RANDOM){
         net->power = option_find_float(options, "power", 1);
     }
     net->max_batches = option_find_int(options, "max_batches", 0);

src/rnn.c

@@ -13,6 +13,76 @@ typedef struct {
     float *y;
 } float_pair;
 
+int *read_tokenized_data(char *filename, size_t *read)
+{
+    size_t size = 512;
+    size_t count = 0;
+    FILE *fp = fopen(filename, "r");
+    int *d = calloc(size, sizeof(int));
+    int n, one;
+    one = fscanf(fp, "%d", &n);
+    while(one == 1){
+        ++count;
+        if(count > size){
+            size = size*2;
+            d = realloc(d, size*sizeof(int));
+        }
+        d[count-1] = n;
+        one = fscanf(fp, "%d", &n);
+    }
+    fclose(fp);
+    d = realloc(d, count*sizeof(int));
+    *read = count;
+    return d;
+}
+
+char **read_tokens(char *filename, size_t *read)
+{
+    size_t size = 512;
+    size_t count = 0;
+    FILE *fp = fopen(filename, "r");
+    char **d = calloc(size, sizeof(char *));
+    char *line;
+    while((line=fgetl(fp)) != 0){
+        ++count;
+        if(count > size){
+            size = size*2;
+            d = realloc(d, size*sizeof(char *));
+        }
+        d[count-1] = line;
+    }
+    fclose(fp);
+    d = realloc(d, count*sizeof(char *));
+    *read = count;
+    return d;
+}
+
+float_pair get_rnn_token_data(int *tokens, size_t *offsets, int characters, size_t len, int batch, int steps)
+{
+    float *x = calloc(batch * steps * characters, sizeof(float));
+    float *y = calloc(batch * steps * characters, sizeof(float));
+    int i,j;
+    for(i = 0; i < batch; ++i){
+        for(j = 0; j < steps; ++j){
+            int curr = tokens[(offsets[i])%len];
+            int next = tokens[(offsets[i] + 1)%len];
+
+            x[(j*batch + i)*characters + curr] = 1;
+            y[(j*batch + i)*characters + next] = 1;
+
+            offsets[i] = (offsets[i] + 1) % len;
+
+            if(curr >= characters || curr < 0 || next >= characters || next < 0){
+                error("Bad char");
+            }
+        }
+    }
+    float_pair p;
+    p.x = x;
+    p.y = y;
+    return p;
+}
+
 float_pair get_rnn_data(unsigned char *text, size_t *offsets, int characters, size_t len, int batch, int steps)
 {
     float *x = calloc(batch * steps * characters, sizeof(float));
@@ -47,8 +117,8 @@ void reset_rnn_state(network net, int b)
 {
     int i;
     for (i = 0; i < net.n; ++i) {
-        layer l = net.layers[i];
         #ifdef GPU
+        layer l = net.layers[i];
         if(l.state_gpu){
             fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1);
         }
@@ -56,19 +126,26 @@ void reset_rnn_state(network net, int b)
     }
 }
 
-void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear)
+void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear, int tokenized)
 {
     srand(time(0));
     data_seed = time(0);
-    FILE *fp = fopen(filename, "rb");
+    unsigned char *text = 0;
+    int *tokens = 0;
+    size_t size;
+    if(tokenized){
+        tokens = read_tokenized_data(filename, &size);
+    } else {
+        FILE *fp = fopen(filename, "rb");
 
-    fseek(fp, 0, SEEK_END); 
-    size_t size = ftell(fp);
-    fseek(fp, 0, SEEK_SET); 
+        fseek(fp, 0, SEEK_END); 
+        size = ftell(fp);
+        fseek(fp, 0, SEEK_SET); 
 
-    unsigned char *text = calloc(size+1, sizeof(char));
-    fread(text, 1, size, fp);
-    fclose(fp);
+        text = calloc(size+1, sizeof(char));
+        fread(text, 1, size, fp);
+        fclose(fp);
+    }
 
     char *backup_directory = "/home/pjreddie/backup/";
     char *base = basecfg(cfgfile);
@@ -97,7 +174,12 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear)
     while(get_current_batch(net) < net.max_batches){
         i += 1;
         time=clock();
-        float_pair p = get_rnn_data(text, offsets, inputs, size, streams, steps);
+        float_pair p;
+        if(tokenized){
+            p = get_rnn_token_data(tokens, offsets, inputs, size, streams, steps);
+        }else{
+            p = get_rnn_data(text, offsets, inputs, size, streams, steps);
+        }
 
         float loss = train_network_datum(net, p.x, p.y) / (batch);
         free(p.x);
@@ -133,8 +215,22 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear)
     save_weights(net, buff);
 }
 
-void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed)
+void print_symbol(int n, char **tokens){
+    if(tokens){
+        printf("%s ", tokens[n]);
+    } else {
+        printf("%c", n);
+    }
+}
+
+void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float temp, int rseed, char *token_file)
 {
+    char **tokens = 0;
+    if(token_file){
+        size_t n;
+        tokens = read_tokens(token_file, &n);
+    }
+
     srand(rseed);
     char *base = basecfg(cfgfile);
     fprintf(stderr, "%s\n", base);
@@ -147,38 +243,39 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
 
     int i, j;
     for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
-    unsigned char c;
+    int c = 0;
     int len = strlen(seed);
     float *input = calloc(inputs, sizeof(float));
 
-/*
-    fill_cpu(inputs, 0, input, 1);
-    for(i = 0; i < 10; ++i){
-        network_predict(net, input);
-    }
-    fill_cpu(inputs, 0, input, 1);
-    */
+    /*
+       fill_cpu(inputs, 0, input, 1);
+       for(i = 0; i < 10; ++i){
+       network_predict(net, input);
+       }
+       fill_cpu(inputs, 0, input, 1);
+     */
 
     for(i = 0; i < len-1; ++i){
         c = seed[i];
-        input[(int)c] = 1;
+        input[c] = 1;
         network_predict(net, input);
-        input[(int)c] = 0;
-        printf("%c", c);
+        input[c] = 0;
+        print_symbol(c, tokens);
     }
-    c = seed[len-1];
+    if(len) c = seed[len-1];
+    print_symbol(c, tokens);
     for(i = 0; i < num; ++i){
-        printf("%c", c);
-        input[(int)c] = 1;
+        input[c] = 1;
         float *out = network_predict(net, input);
-        input[(int)c] = 0;
+        input[c] = 0;
         for(j = 32; j < 127; ++j){
             //printf("%d %c %f\n",j, j, out[j]);
         }
         for(j = 0; j < inputs; ++j){
-            //if (out[j] < .0001) out[j] = 0;
+            if (out[j] < .0001) out[j] = 0;
         }
         c = sample_array(out, inputs);
+        print_symbol(c, tokens);
     }
     printf("\n");
 }
@@ -195,6 +292,7 @@ void valid_char_rnn(char *cfgfile, char *weightfile, char *seed)
     int inputs = get_network_input_size(net);
 
     int count = 0;
+    int words = 1;
     int c;
     int len = strlen(seed);
     float *input = calloc(inputs, sizeof(float));
@@ -213,12 +311,13 @@ void valid_char_rnn(char *cfgfile, char *weightfile, char *seed)
         if(next == EOF) break;
         if(next < 0 || next >= 255) error("Out of range character");
         ++count;
+        if(next == ' ' || next == '\n' || next == '\t') ++words;
         input[c] = 1;
         float *out = network_predict(net, input);
         input[c] = 0;
         sum += log(out[next])/log2;
         c = next;
-        printf("%d Perplexity: %f\n", count, pow(2, -sum/count));
+        printf("%d Perplexity: %4.4f    Word Perplexity: %4.4f\n", count, pow(2, -sum/count), pow(2, -sum/words));
     }
 }
 
@@ -254,13 +353,15 @@ void vec_char_rnn(char *cfgfile, char *weightfile, char *seed)
             network_predict(net, input);
             input[(int)c] = 0;
         }
-            c = ' ';
-            input[(int)c] = 1;
-            network_predict(net, input);
-            input[(int)c] = 0;
+        c = ' ';
+        input[(int)c] = 1;
+        network_predict(net, input);
+        input[(int)c] = 0;
 
         layer l = net.layers[0];
+        #ifdef GPU
         cuda_pull_array(l.output_gpu, l.output, l.outputs);
+        #endif
         printf("%s", line);
         for(i = 0; i < l.outputs; ++i){
             printf(",%g", l.output[i]);
@@ -281,11 +382,13 @@ void run_char_rnn(int argc, char **argv)
     float temp = find_float_arg(argc, argv, "-temp", .7);
     int rseed = find_int_arg(argc, argv, "-srand", time(0));
     int clear = find_arg(argc, argv, "-clear");
+    int tokenized = find_arg(argc, argv, "-tokenized");
+    char *tokens = find_char_arg(argc, argv, "-tokens", 0);
 
     char *cfg = argv[3];
     char *weights = (argc > 4) ? argv[4] : 0;
-    if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear);
+    if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear, tokenized);
     else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights, seed);
     else if(0==strcmp(argv[2], "vec")) vec_char_rnn(cfg, weights, seed);
-    else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed);
+    else if(0==strcmp(argv[2], "generate")) test_char_rnn(cfg, weights, len, seed, temp, rseed, tokens);
 }