From 9942d484122c346650bb5431fd209d9437b5310a Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 11 May 2016 17:45:50 +0000
Subject: [PATCH] stable
---
src/batchnorm_layer.c | 14 +++
Makefile | 6
src/batchnorm_layer.h | 2
src/rnn.c | 61 ++++++++++++++-
src/parser.c | 33 +++++++
src/data.c | 99 ++++++------------------
src/data.h | 6 +
7 files changed, 136 insertions(+), 85 deletions(-)
diff --git a/Makefile b/Makefile
index 1ef1b3b..e689941 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
-GPU=0
-OPENCV=0
+GPU=1
+OPENCV=1
DEBUG=0
ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20
@@ -34,7 +34,7 @@
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 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
+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
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
diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c
index 6ea4040..9b68277 100644
--- a/src/batchnorm_layer.c
+++ b/src/batchnorm_layer.c
@@ -135,6 +135,20 @@
}
#ifdef GPU
+
+void pull_batchnorm_layer(layer l)
+{
+ cuda_pull_array(l.scales_gpu, l.scales, l.c);
+ cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.c);
+ cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.c);
+}
+void push_batchnorm_layer(layer l)
+{
+ cuda_push_array(l.scales_gpu, l.scales, l.c);
+ cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.c);
+ cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c);
+}
+
void forward_batchnorm_layer_gpu(layer l, network_state state)
{
if(l.type == BATCHNORM) copy_ongpu(l.outputs*l.batch, state.input, 1, l.output_gpu, 1);
diff --git a/src/batchnorm_layer.h b/src/batchnorm_layer.h
index 61810b6..99d1d0f 100644
--- a/src/batchnorm_layer.h
+++ b/src/batchnorm_layer.h
@@ -12,6 +12,8 @@
#ifdef GPU
void forward_batchnorm_layer_gpu(layer l, network_state state);
void backward_batchnorm_layer_gpu(layer l, network_state state);
+void pull_batchnorm_layer(layer l);
+void push_batchnorm_layer(layer l);
#endif
#endif
diff --git a/src/data.c b/src/data.c
index b0368ee..fdc4a1d 100644
--- a/src/data.c
+++ b/src/data.c
@@ -271,78 +271,37 @@
free(boxes);
}
-void fill_truth_detection(char *path, float *truth, int classes, int num_boxes, int flip, int background, float dx, float dy, float sx, float sy)
+void fill_truth_detection(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
{
- char *labelpath = find_replace(path, "JPEGImages", "labels");
+ char *labelpath = find_replace(path, "images", "labels");
+ labelpath = find_replace(labelpath, "JPEGImages", "labels");
+
labelpath = find_replace(labelpath, ".jpg", ".txt");
+ labelpath = find_replace(labelpath, ".JPG", ".txt");
labelpath = find_replace(labelpath, ".JPEG", ".txt");
int count = 0;
box_label *boxes = read_boxes(labelpath, &count);
randomize_boxes(boxes, count);
+ correct_boxes(boxes, count, dx, dy, sx, sy, flip);
+ if(count > 17) count = 17;
float x,y,w,h;
- float left, top, right, bot;
int id;
int i;
- if(background){
- for(i = 0; i < num_boxes*num_boxes*(4+classes+background); i += 4+classes+background){
- truth[i] = 1;
- }
- }
- for(i = 0; i < count; ++i){
- left = boxes[i].left * sx - dx;
- right = boxes[i].right * sx - dx;
- top = boxes[i].top * sy - dy;
- bot = boxes[i].bottom* sy - dy;
+
+ for (i = 0; i < count; ++i) {
+ x = boxes[i].x;
+ y = boxes[i].y;
+ w = boxes[i].w;
+ h = boxes[i].h;
id = boxes[i].id;
- if(flip){
- float swap = left;
- left = 1. - right;
- right = 1. - swap;
- }
-
- left = constrain(0, 1, left);
- right = constrain(0, 1, right);
- top = constrain(0, 1, top);
- bot = constrain(0, 1, bot);
-
- x = (left+right)/2;
- y = (top+bot)/2;
- w = (right - left);
- h = (bot - top);
-
- if (x <= 0 || x >= 1 || y <= 0 || y >= 1) continue;
-
- int col = (int)(x*num_boxes);
- int row = (int)(y*num_boxes);
-
- x = x*num_boxes - col;
- y = y*num_boxes - row;
-
- /*
- float maxwidth = distance_from_edge(i, num_boxes);
- float maxheight = distance_from_edge(j, num_boxes);
- w = w/maxwidth;
- h = h/maxheight;
- */
-
- w = constrain(0, 1, w);
- h = constrain(0, 1, h);
if (w < .01 || h < .01) continue;
- if(1){
- w = pow(w, 1./2.);
- h = pow(h, 1./2.);
- }
- int index = (col+row*num_boxes)*(4+classes+background);
- if(truth[index+classes+background+2]) continue;
- if(background) truth[index++] = 0;
- truth[index+id] = 1;
- index += classes;
- truth[index++] = x;
- truth[index++] = y;
- truth[index++] = w;
- truth[index++] = h;
+ truth[i*5] = id;
+ truth[i*5+2] = x;
+ truth[i*5+3] = y;
+ truth[i*5+4] = w;
+ truth[i*5+5] = h;
}
free(boxes);
}
@@ -485,6 +444,7 @@
d.X.vals = calloc(d.X.rows, sizeof(float*));
d.X.cols = h*w*3;
+
int k = size*size*(5+classes);
d.y = make_matrix(n, k);
for(i = 0; i < n; ++i){
@@ -641,7 +601,7 @@
return d;
}
-data load_data_detection(int n, char **paths, int m, int classes, int w, int h, int num_boxes, int background)
+data load_data_detection(int n, int boxes, char **paths, int m, int w, int h, int classes, float jitter)
{
char **random_paths = get_random_paths(paths, n, m);
int i;
@@ -652,16 +612,15 @@
d.X.vals = calloc(d.X.rows, sizeof(float*));
d.X.cols = h*w*3;
- int k = num_boxes*num_boxes*(4+classes+background);
- d.y = make_matrix(n, k);
+ d.y = make_matrix(n, 5*boxes);
for(i = 0; i < n; ++i){
image orig = load_image_color(random_paths[i], 0, 0);
int oh = orig.h;
int ow = orig.w;
- int dw = ow/10;
- int dh = oh/10;
+ int dw = (ow*jitter);
+ int dh = (oh*jitter);
int pleft = rand_uniform(-dw, dw);
int pright = rand_uniform(-dw, dw);
@@ -674,13 +633,6 @@
float sx = (float)swidth / ow;
float sy = (float)sheight / oh;
- /*
- float angle = rand_uniform()*.1 - .05;
- image rot = rotate_image(orig, angle);
- free_image(orig);
- orig = rot;
- */
-
int flip = rand_r(&data_seed)%2;
image cropped = crop_image(orig, pleft, ptop, swidth, sheight);
@@ -691,7 +643,7 @@
if(flip) flip_image(sized);
d.X.vals[i] = sized.data;
- fill_truth_detection(random_paths[i], d.y.vals[i], classes, num_boxes, flip, background, dx, dy, 1./sx, 1./sy);
+ fill_truth_detection(random_paths[i], d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy);
free_image(orig);
free_image(cropped);
@@ -700,6 +652,7 @@
return d;
}
+
void *load_thread(void *ptr)
{
@@ -717,7 +670,7 @@
} else if (a.type == STUDY_DATA){
*a.d = load_data_study(a.paths, a.n, a.m, a.labels, a.classes, a.min, a.max, a.size);
} else if (a.type == DETECTION_DATA){
- *a.d = load_data_detection(a.n, a.paths, a.m, a.classes, a.w, a.h, a.num_boxes, a.background);
+ *a.d = load_data_detection(a.n, a.num_boxes, a.paths, a.m, a.classes, a.w, a.h, a.background);
} else if (a.type == WRITING_DATA){
*a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
} else if (a.type == REGION_DATA){
diff --git a/src/data.h b/src/data.h
index 6befeea..a7347a8 100644
--- a/src/data.h
+++ b/src/data.h
@@ -25,10 +25,12 @@
matrix y;
int *indexes;
int shallow;
+ int *num_boxes;
+ box **boxes;
} data;
typedef enum {
- CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA
+ CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA
} data_type;
typedef struct load_args{
@@ -68,7 +70,7 @@
data load_data_captcha(char **paths, int n, int m, int k, int w, int h);
data load_data_captcha_encode(char **paths, int n, int m, int w, int h);
data load_data(char **paths, int n, int m, char **labels, int k, int w, int h);
-data load_data_detection(int n, char **paths, int m, int classes, int w, int h, int num_boxes, int background);
+data load_data_detection(int n, int boxes, char **paths, int m, int w, int h, int classes, float jitter);
data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size);
data load_data_augment(char **paths, int n, int m, char **labels, int k, int min, int max, int size);
data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size);
diff --git a/src/parser.c b/src/parser.c
index 6c88fd5..b900ad7 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -852,6 +852,18 @@
fwrite(l.filters, sizeof(float), num, fp);
}
+void save_batchnorm_weights(layer l, FILE *fp)
+{
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_batchnorm_layer(l);
+ }
+#endif
+ fwrite(l.scales, sizeof(float), l.c, fp);
+ fwrite(l.rolling_mean, sizeof(float), l.c, fp);
+ fwrite(l.rolling_variance, sizeof(float), l.c, fp);
+}
+
void save_connected_weights(layer l, FILE *fp)
{
#ifdef GPU
@@ -889,6 +901,8 @@
save_convolutional_weights(l, fp);
} if(l.type == CONNECTED){
save_connected_weights(l, fp);
+ } if(l.type == BATCHNORM){
+ save_batchnorm_weights(l, fp);
} if(l.type == RNN){
save_connected_weights(*(l.input_layer), fp);
save_connected_weights(*(l.self_layer), fp);
@@ -943,8 +957,8 @@
if(transpose){
transpose_matrix(l.weights, l.inputs, l.outputs);
}
- //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
- //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
+ //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
+ //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.outputs, fp);
fread(l.rolling_mean, sizeof(float), l.outputs, fp);
@@ -960,6 +974,18 @@
#endif
}
+void load_batchnorm_weights(layer l, FILE *fp)
+{
+ fread(l.scales, sizeof(float), l.c, fp);
+ fread(l.rolling_mean, sizeof(float), l.c, fp);
+ fread(l.rolling_variance, sizeof(float), l.c, fp);
+#ifdef GPU
+ if(gpu_index >= 0){
+ push_batchnorm_layer(l);
+ }
+#endif
+}
+
void load_convolutional_weights_binary(layer l, FILE *fp)
{
fread(l.biases, sizeof(float), l.n, fp);
@@ -1053,6 +1079,9 @@
if(l.type == CONNECTED){
load_connected_weights(l, fp, transpose);
}
+ if(l.type == BATCHNORM){
+ load_batchnorm_weights(l, fp);
+ }
if(l.type == CRNN){
load_convolutional_weights(*(l.input_layer), fp);
load_convolutional_weights(*(l.self_layer), fp);
diff --git a/src/rnn.c b/src/rnn.c
index b72fafc..12f1473 100644
--- a/src/rnn.c
+++ b/src/rnn.c
@@ -183,7 +183,7 @@
printf("\n");
}
-void valid_char_rnn(char *cfgfile, char *weightfile)
+void valid_char_rnn(char *cfgfile, char *weightfile, char *seed)
{
char *base = basecfg(cfgfile);
fprintf(stderr, "%s\n", base);
@@ -196,18 +196,22 @@
int count = 0;
int c;
+ int len = strlen(seed);
float *input = calloc(inputs, sizeof(float));
int i;
- for(i = 0; i < 100; ++i){
+ for(i = 0; i < len; ++i){
+ c = seed[i];
+ input[(int)c] = 1;
network_predict(net, input);
+ input[(int)c] = 0;
}
float sum = 0;
c = getc(stdin);
float log2 = log(2);
while(c != EOF){
int next = getc(stdin);
- if(next < 0 || next >= 255) error("Out of range character");
if(next == EOF) break;
+ if(next < 0 || next >= 255) error("Out of range character");
++count;
input[c] = 1;
float *out = network_predict(net, input);
@@ -218,6 +222,52 @@
}
}
+void vec_char_rnn(char *cfgfile, char *weightfile, char *seed)
+{
+ char *base = basecfg(cfgfile);
+ fprintf(stderr, "%s\n", base);
+
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int inputs = get_network_input_size(net);
+
+ int c;
+ int seed_len = strlen(seed);
+ float *input = calloc(inputs, sizeof(float));
+ int i;
+ char *line;
+ while((line=fgetl(stdin)) != 0){
+ reset_rnn_state(net, 0);
+ for(i = 0; i < seed_len; ++i){
+ c = seed[i];
+ input[(int)c] = 1;
+ network_predict(net, input);
+ input[(int)c] = 0;
+ }
+ strip(line);
+ int str_len = strlen(line);
+ for(i = 0; i < str_len; ++i){
+ c = line[i];
+ 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];
+ cuda_pull_array(l.output_gpu, l.output, l.outputs);
+ printf("%s", line);
+ for(i = 0; i < l.outputs; ++i){
+ printf(",%g", l.output[i]);
+ }
+ printf("\n");
+ }
+}
void run_char_rnn(int argc, char **argv)
{
@@ -226,7 +276,7 @@
return;
}
char *filename = find_char_arg(argc, argv, "-file", "data/shakespeare.txt");
- char *seed = find_char_arg(argc, argv, "-seed", "\n");
+ char *seed = find_char_arg(argc, argv, "-seed", "\n\n");
int len = find_int_arg(argc, argv, "-len", 1000);
float temp = find_float_arg(argc, argv, "-temp", .7);
int rseed = find_int_arg(argc, argv, "-srand", time(0));
@@ -235,6 +285,7 @@
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
if(0==strcmp(argv[2], "train")) train_char_rnn(cfg, weights, filename, clear);
- else if(0==strcmp(argv[2], "valid")) valid_char_rnn(cfg, weights);
+ 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);
}
--
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