Removing even more conflicts

cfg/combine9k.data

@@ -0,0 +1,10 @@
+classes= 9418
+#train  = /home/pjreddie/data/coco/trainvalno5k.txt
+train  = data/combine9k.train.list
+valid  = /home/pjreddie/data/imagenet/det.val.files
+labels = data/9k.labels
+names  = data/9k.names
+backup = backup/
+map = data/inet9k.map
+eval = imagenet
+results = results

cfg/yolo9000.cfg

@@ -0,0 +1,211 @@
+[net]
+batch=1
+subdivisions=1
+height=416
+width=416
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.00001
+max_batches = 242200
+policy=steps
+steps=500,200000,240000
+scales=10,.1,.1
+
+hue=.1
+saturation=.75
+exposure=.75
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=28269
+size=1
+stride=1
+pad=1
+activation=linear
+
+[region]
+anchors = 0.77871, 1.14074, 3.00525, 4.31277, 9.22725, 9.61974
+bias_match=1
+classes=9418
+coords=4
+num=3
+softmax=1
+jitter=.2
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+thresh = .6
+absolute=1
+random=1
+
+tree=data/9k.tree
+map = data/coco9k.map

data/coco9k.map

@@ -0,0 +1,80 @@
+5177
+3768
+3802
+3800
+4107
+4072
+4071
+3797
+4097
+2645
+5150
+2644
+3257
+2523
+6527
+6866
+6912
+7342
+7255
+7271
+7217
+6858
+7343
+7233
+3704
+4374
+3641
+5001
+3899
+2999
+2631
+5141
+2015
+1133
+1935
+1930
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+2371
+3916
+3745
+3640
+4749
+4736
+4735
+3678
+58
+42
+771
+81
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+141
+786
+700
+218
+791
+2518
+2521
+3637
+2458
+2505
+2519
+3499
+2837
+3503
+2597
+3430
+2080
+5103
+5111
+5102
+3013
+5096
+1102
+3218
+4010
+2266
+1127
+5122
+2360

data/inet9k.map

@@ -0,0 +1,200 @@
+2687
+4107
+8407
+7254
+42
+6797
+127
+2268
+2442
+3704
+260
+1970
+58
+4443
+2661
+2043
+2039
+4858
+4007
+6858
+8408
+166
+2523
+3768
+4347
+6527
+2446
+5005
+3274
+3678
+4918
+709
+4072
+8428
+7223
+2251
+3802
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+7271
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+2849
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+2741
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+2035
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+2664
+5122
+1844
+784
+2223
+7188
+2719
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+4830
+158
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+7228
+1965
+7342
+786
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+8281
+8258
+7406
+3915
+8382
+2437
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+7447
+8285
+5007
+2740
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+5103
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+4910
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+3800
+118
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+2711
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+2226
+7153
+7255
+2631
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+8242
+7445
+3776
+3803
+3690
+2025
+2521
+2316
+7190
+8249
+3352
+2639
+2887
+100
+4219
+3344
+5008
+7224
+3351
+2434
+2074
+2034
+8304
+5004
+6868
+5102
+2645
+4071
+2716
+2717
+7420
+3499
+3763
+5084
+2676
+2046
+5107
+5097
+3944
+4097
+7132
+3956
+7343

src/box.c

@@ -274,6 +274,34 @@ void do_nms_obj(box *boxes, float **probs, int total, int classes, float thresh)
 }
 
 
+void do_nms_sort(box *boxes, float **probs, int total, int classes, float thresh)
+{
+    int i, j, k;
+    sortable_bbox *s = calloc(total, sizeof(sortable_bbox));
+
+    for(i = 0; i < total; ++i){
+        s[i].index = i;       
+        s[i].class = classes;
+        s[i].probs = probs;
+    }
+
+    qsort(s, total, sizeof(sortable_bbox), nms_comparator);
+    for(i = 0; i < total; ++i){
+        if(probs[s[i].index][classes] == 0) continue;
+        box a = boxes[s[i].index];
+        for(j = i+1; j < total; ++j){
+            box b = boxes[s[j].index];
+            if (box_iou(a, b) > thresh){
+                for(k = 0; k < classes+1; ++k){
+                    probs[s[j].index][k] = 0;
+                }
+            }
+        }
+    }
+    free(s);
+}
+
+
 void do_nms_sort(box *boxes, float **probs, int total, int classes, float thresh)
 {
     int i, j, k;

src/cpu_gemm.c

@@ -1,91 +0,0 @@
-#include "mini_blas.h"
-
-void cpu_gemm_nn(int TA, int TB, int M, int N, int K, float ALPHA, 
-        float *A, int lda, 
-        float *B, int ldb,
-        float BETA,
-        float *C, int ldc)
-{
-    int i,j,k;
-    for(i = 0; i < M; ++i){
-        for(k = 0; k < K; ++k){
-            register float A_PART = ALPHA*A[i*lda+k];
-            for(j = 0; j < N; ++j){
-                C[i*ldc+j] += A_PART*B[k*ldb+j];
-            }
-        }
-    }
-}
-
-void cpu_gemm_nt(int TA, int TB, int M, int N, int K, float ALPHA, 
-        float *A, int lda, 
-        float *B, int ldb,
-        float BETA,
-        float *C, int ldc)
-{
-    int i,j,k;
-    for(i = 0; i < M; ++i){
-        for(j = 0; j < N; ++j){
-            register float sum = 0;
-            for(k = 0; k < K; ++k){
-                sum += ALPHA*A[i*lda+k]*B[k+j*ldb];
-            }
-            C[i*ldc+j] += sum;
-        }
-    }
-}
-
-void cpu_gemm_tn(int TA, int TB, int M, int N, int K, float ALPHA, 
-        float *A, int lda, 
-        float *B, int ldb,
-        float BETA,
-        float *C, int ldc)
-{
-    int i,j,k;
-    for(i = 0; i < M; ++i){
-        for(k = 0; k < K; ++k){
-            register float A_PART = ALPHA*A[k*lda+i];
-            for(j = 0; j < N; ++j){
-                C[i*ldc+j] += A_PART*B[k*ldb+j];
-            }
-        }
-    }
-}
-void cpu_gemm_tt(int TA, int TB, int M, int N, int K, float ALPHA, 
-        float *A, int lda, 
-        float *B, int ldb,
-        float BETA,
-        float *C, int ldc)
-{
-    int i,j,k;
-    for(i = 0; i < M; ++i){
-        for(j = 0; j < N; ++j){
-            for(k = 0; k < K; ++k){
-                C[i*ldc+j] += ALPHA*A[i+k*lda]*B[k+j*ldb];
-            }
-        }
-    }
-}
-
-
-void cpu_gemm(int TA, int TB, int M, int N, int K, float ALPHA, 
-        float *A, int lda, 
-        float *B, int ldb,
-        float BETA,
-        float *C, int ldc)
-{
-    int i, j;
-    for(i = 0; i < M; ++i){
-        for(j = 0; j < N; ++j){
-            C[i*ldc + j] *= BETA;
-        }
-    }
-    if(!TA && !TB)
-        cpu_gemm_nn( TA,  TB,  M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
-    else if(TA && !TB)
-        cpu_gemm_tn( TA,  TB,  M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
-    else if(!TA && TB)
-        cpu_gemm_nt( TA,  TB,  M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
-    else
-        cpu_gemm_tt( TA,  TB,  M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc);
-}

src/region_layer.c

@@ -11,7 +11,7 @@
 
 layer make_region_layer(int batch, int w, int h, int n, int classes, int coords)
 {
-    region_layer l = {0};
+    layer l = {0};
     l.type = REGION;
 
     l.n = n;

src/server.c

@@ -1,205 +0,0 @@
-#include <stdio.h> /* needed for sockaddr_in */
-#include <string.h> /* needed for sockaddr_in */
-#include <unistd.h>
-#include <sys/types.h>
-#include <sys/socket.h>
-#include <netinet/in.h> /* needed for sockaddr_in */
-#include <netdb.h>
-#include <pthread.h>
-#include <time.h>
-
-#include "mini_blas.h"
-#include "utils.h"
-#include "parser.h"
-#include "server.h"
-#include "connected_layer.h"
-#include "convolutional_layer.h"
-
-#define SERVER_PORT 9423
-#define STR(x) #x
-
-int socket_setup(int server)
-{
-    int fd = 0;                         /* our socket */
-    struct sockaddr_in me;      /* our address */
-
-    /* create a UDP socket */
-
-    if ((fd = socket(AF_INET, SOCK_STREAM, 0)) < 0) {
-        error("cannot create socket");
-    }
-
-    /* bind the socket to any valid IP address and a specific port */
-    if (server == 1){
-        bzero((char *) &me, sizeof(me));
-        me.sin_family = AF_INET;
-        me.sin_addr.s_addr = htonl(INADDR_ANY);
-        me.sin_port = htons(SERVER_PORT);
-
-        if (bind(fd, (struct sockaddr *)&me, sizeof(me)) < 0) {
-            error("bind failed");
-        }
-    }
-
-    return fd;
-}
-
-typedef struct{
-    int fd;
-    int counter;
-    network net;
-} connection_info;
-
-void read_and_add_into(int fd, float *a, int n)
-{
-    float *buff = calloc(n, sizeof(float));
-    read_all(fd, (char*) buff, n*sizeof(float));
-    axpy_cpu(n, 1, buff, 1, a, 1);
-    free(buff);
-}
-
-void handle_connection(void *pointer)
-{
-    connection_info info = *(connection_info *) pointer;
-    free(pointer);
-    //printf("New Connection\n");
-    if(info.counter%100==0){
-        char buff[256];
-        sprintf(buff, "unikitty/net_%d.part", info.counter);
-        save_network(info.net, buff);
-    }
-    int fd = info.fd;
-    network net = info.net;
-    int i;
-    for(i = 0; i < net.n; ++i){
-        if(net.types[i] == CONVOLUTIONAL){
-            convolutional_layer layer = *(convolutional_layer *) net.layers[i];
-
-            read_and_add_into(fd, layer.bias_updates, layer.n);
-            int num = layer.n*layer.c*layer.size*layer.size;
-            read_and_add_into(fd, layer.filter_updates, num);
-        }
-        if(net.types[i] == CONNECTED){
-            connected_layer layer = *(connected_layer *) net.layers[i];
-
-            read_and_add_into(fd, layer.bias_updates, layer.outputs);
-            read_and_add_into(fd, layer.weight_updates, layer.inputs*layer.outputs);
-        }
-    }
-    for(i = 0; i < net.n; ++i){
-        if(net.types[i] == CONVOLUTIONAL){
-            convolutional_layer layer = *(convolutional_layer *) net.layers[i];
-            update_convolutional_layer(layer);
-
-            write_all(fd, (char*) layer.biases, layer.n*sizeof(float));
-            int num = layer.n*layer.c*layer.size*layer.size;
-            write_all(fd, (char*) layer.filters, num*sizeof(float));
-        }
-        if(net.types[i] == CONNECTED){
-            connected_layer layer = *(connected_layer *) net.layers[i];
-            update_connected_layer(layer);
-            write_all(fd, (char *)layer.biases, layer.outputs*sizeof(float));
-            write_all(fd, (char *)layer.weights, layer.outputs*layer.inputs*sizeof(float));
-        }
-    }
-    //printf("Received updates\n");
-    close(fd);
-}
-
-void server_update(network net)
-{
-    int fd = socket_setup(1);
-    int counter = 18000;
-    listen(fd, 64);
-    struct sockaddr_in client;     /* remote address */
-    socklen_t client_size = sizeof(client);   /* length of addresses */
-    time_t t=0;
-    while(1){
-        connection_info *info = calloc(1, sizeof(connection_info));
-        info->net = net;
-        info->counter = counter;
-        pthread_t worker;
-        int connection = accept(fd, (struct sockaddr *) &client, &client_size);
-        if(!t) t=time(0);
-        info->fd = connection;
-        pthread_create(&worker, NULL, (void *) &handle_connection, info);
-        ++counter;
-        printf("%d\n", counter);
-        //if(counter == 1024) break;
-    }
-    close(fd);
-}
-
-void client_update(network net, char *address)
-{
-    int fd = socket_setup(0);
-
-    struct hostent *hp;     /* host information */
-    struct sockaddr_in server;    /* server address */
-
-    /* fill in the server's address and data */
-    bzero((char*)&server, sizeof(server));
-    server.sin_family = AF_INET;
-    server.sin_port = htons(SERVER_PORT);
-
-    /* look up the address of the server given its name */
-    hp = gethostbyname(address);
-    if (!hp) {
-        perror("no such host");
-        fprintf(stderr, "could not obtain address of %s\n", "localhost");
-    }
-
-    /* put the host's address into the server address structure */
-    memcpy((void *)&server.sin_addr, hp->h_addr_list[0], hp->h_length);
-    if (connect(fd, (struct sockaddr *) &server, sizeof(server)) < 0) {
-        error("error connecting");
-    }
-
-    /* send a message to the server */
-    int i;
-    //printf("Sending\n");
-    for(i = 0; i < net.n; ++i){
-        if(net.types[i] == CONVOLUTIONAL){
-            convolutional_layer layer = *(convolutional_layer *) net.layers[i];
-            write_all(fd, (char*) layer.bias_updates, layer.n*sizeof(float));
-            int num = layer.n*layer.c*layer.size*layer.size;
-            write_all(fd, (char*) layer.filter_updates, num*sizeof(float));
-            memset(layer.bias_updates, 0, layer.n*sizeof(float));
-            memset(layer.filter_updates, 0, num*sizeof(float));
-        }
-        if(net.types[i] == CONNECTED){
-            connected_layer layer = *(connected_layer *) net.layers[i];
-            write_all(fd, (char *)layer.bias_updates, layer.outputs*sizeof(float));
-            write_all(fd, (char *)layer.weight_updates, layer.outputs*layer.inputs*sizeof(float));
-            memset(layer.bias_updates, 0, layer.outputs*sizeof(float));
-            memset(layer.weight_updates, 0, layer.inputs*layer.outputs*sizeof(float));
-        }
-    }
-    //printf("Sent\n");
-
-    for(i = 0; i < net.n; ++i){
-        if(net.types[i] == CONVOLUTIONAL){
-            convolutional_layer layer = *(convolutional_layer *) net.layers[i];
-
-            read_all(fd, (char*) layer.biases, layer.n*sizeof(float));
-            int num = layer.n*layer.c*layer.size*layer.size;
-            read_all(fd, (char*) layer.filters, num*sizeof(float));
-
-#ifdef GPU
-            push_convolutional_layer(layer);
-            #endif
-        }
-        if(net.types[i] == CONNECTED){
-            connected_layer layer = *(connected_layer *) net.layers[i];
-
-            read_all(fd, (char *)layer.biases, layer.outputs*sizeof(float));
-            read_all(fd, (char *)layer.weights, layer.outputs*layer.inputs*sizeof(float));
-
-#ifdef GPU
-            push_connected_layer(layer);
-            #endif
-        }
-    }
-    //printf("Updated\n");
-    close(fd);
-}

src/server.h

@@ -1,4 +0,0 @@
-#include "network.h"
-
-void client_update(network net, char *address);
-void server_update(network net);