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Nist NIN testing multi-crop

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This commit is contained in:
Joseph Redmon 2014-08-11 12:52:07 -07:00
parent 7add111509
commit 176d65b765
11 changed files with 288 additions and 51 deletions
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2
Makefile
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@ -25,7 +25,7 @@ VPATH=./src/
EXEC=cnn
OBJDIR=./obj/
OBJ=network.o image.o cnn.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o gemm.o normalization_layer.o opencl.o im2col.o col2im.o axpy.o dropout_layer.o
OBJ=network.o image.o cnn.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o gemm.o normalization_layer.o opencl.o im2col.o col2im.o axpy.o dropout_layer.o crop_layer.o
OBJS = $(addprefix $(OBJDIR), $(OBJ))
all: $(EXEC)

66
src/cnn.c
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@ -240,9 +240,22 @@ void test_full()
void test_cifar10()
{
srand(222222);
network net = parse_network_cfg("cfg/cifar10_part5.cfg");
data test = load_cifar10_data("data/cifar10/test_batch.bin");
clock_t start = clock(), end;
float test_acc = network_accuracy(net, test);
end = clock();
printf("%f in %f Sec\n", test_acc, (float)(end-start)/CLOCKS_PER_SEC);
visualize_network(net);
cvWaitKey(0);
}
void train_cifar10()
{
srand(555555);
network net = parse_network_cfg("cfg/cifar10.cfg");
//data test = load_cifar10_data("data/cifar10/test_batch.bin");
data test = load_cifar10_data("data/cifar10/test_batch.bin");
int count = 0;
int iters = 10000/net.batch;
data train = load_all_cifar10();
@ -250,12 +263,20 @@ void test_cifar10()
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
end = clock();
//visualize_network(net);
//cvWaitKey(1000);
visualize_network(net);
cvWaitKey(5000);
//float test_acc = network_accuracy(net, test);
//printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
printf("%d: Loss: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
if(count%10 == 0){
float test_acc = network_accuracy(net, test);
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
char buff[256];
sprintf(buff, "/home/pjreddie/cifar/cifar2_%d.cfg", count);
save_network(net, buff);
}else{
printf("%d: Loss: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
}
}
free_data(train);
}
@ -292,13 +313,25 @@ void test_nist_single()
void test_nist()
{
srand(222222);
network net = parse_network_cfg("cfg/nist.cfg");
network net = parse_network_cfg("cfg/nist_final.cfg");
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
translate_data_rows(test, -144);
clock_t start = clock(), end;
float test_acc = network_accuracy_multi(net, test,16);
end = clock();
printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC);
}
void train_nist()
{
srand(222222);
network net = parse_network_cfg("cfg/nist_final.cfg");
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
translate_data_rows(train, -144);
//scale_data_rows(train, 1./128);
translate_data_rows(test, -144);
//scale_data_rows(test, 1./128);
translate_data_rows(train, -144);
//scale_data_rows(train, 1./128);
translate_data_rows(test, -144);
//scale_data_rows(test, 1./128);
//randomize_data(train);
int count = 0;
//clock_t start = clock(), end;
@ -311,12 +344,12 @@ void test_nist()
//float test_acc = 0;
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
/*printf("%f %f %f %f %f\n", mean_array(get_network_output_layer(net,0), 100),
mean_array(get_network_output_layer(net,1), 100),
mean_array(get_network_output_layer(net,2), 100),
mean_array(get_network_output_layer(net,3), 100),
mean_array(get_network_output_layer(net,4), 100));
*/
//save_network(net, "cfg/nist_basic_trained.cfg");
mean_array(get_network_output_layer(net,1), 100),
mean_array(get_network_output_layer(net,2), 100),
mean_array(get_network_output_layer(net,3), 100),
mean_array(get_network_output_layer(net,4), 100));
*/
save_network(net, "cfg/nist_final2.cfg");
//printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
//end = clock();
@ -778,6 +811,7 @@ int main(int argc, char *argv[])
//test_nist_single();
test_nist();
//test_cifar10();
//train_cifar10();
//test_vince();
//test_full();
//tune_VOC();

2
src/convolutional_layer.c
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@ -166,7 +166,7 @@ void learn_bias_convolutional_layer(convolutional_layer layer)
*convolutional_out_width(layer);
for(b = 0; b < layer.batch; ++b){
for(i = 0; i < layer.n; ++i){
layer.bias_updates[i] += mean_array(layer.delta+size*(i+b*layer.n), size);
layer.bias_updates[i] += sum_array(layer.delta+size*(i+b*layer.n), size);
}
}
}

0
src/convolutional_layer_gpu.c
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57
src/crop_layer.c Normal file
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@ -0,0 +1,57 @@
#include "crop_layer.h"
#include <stdio.h>
image get_crop_image(crop_layer layer)
{
int h = layer.crop_height;
int w = layer.crop_width;
int c = layer.c;
return float_to_image(h,w,c,layer.output);
}
crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip)
{
fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c);
crop_layer *layer = calloc(1, sizeof(crop_layer));
layer->batch = batch;
layer->h = h;
layer->w = w;
layer->c = c;
layer->flip = flip;
layer->crop_width = crop_width;
layer->crop_height = crop_height;
layer->output = calloc(crop_width*crop_height * c*batch, sizeof(float));
layer->delta = calloc(crop_width*crop_height * c*batch, sizeof(float));
return layer;
}
void forward_crop_layer(const crop_layer layer, float *input)
{
int i,j,c,b;
int dh = rand()%(layer.h - layer.crop_height);
int dw = rand()%(layer.w - layer.crop_width);
int count = 0;
if(layer.flip && rand()%2){
for(b = 0; b < layer.batch; ++b){
for(c = 0; c < layer.c; ++c){
for(i = dh; i < dh+layer.crop_height; ++i){
for(j = dw+layer.crop_width-1; j >= dw; --j){
int index = j+layer.w*(i+layer.h*(c + layer.c*b));
layer.output[count++] = input[index];
}
}
}
}
}else{
for(b = 0; b < layer.batch; ++b){
for(c = 0; c < layer.c; ++c){
for(i = dh; i < dh+layer.crop_height; ++i){
for(j = dw; j < dw+layer.crop_width; ++j){
int index = j+layer.w*(i+layer.h*(c + layer.c*b));
layer.output[count++] = input[index];
}
}
}
}
}
}

22
src/crop_layer.h Normal file
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@ -0,0 +1,22 @@
#ifndef CROP_LAYER_H
#define CROP_LAYER_H
#include "image.h"
typedef struct {
int batch;
int h,w,c;
int crop_width;
int crop_height;
int flip;
float *delta;
float *output;
} crop_layer;
image get_crop_image(crop_layer layer);
crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip);
void forward_crop_layer(const crop_layer layer, float *input);
void backward_crop_layer(const crop_layer layer, float *input, float *delta);
#endif

60
src/network.c
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@ -4,6 +4,7 @@
#include "data.h"
#include "utils.h"
#include "crop_layer.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
@ -56,6 +57,11 @@ void forward_network(network net, float *input, int train)
forward_softmax_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
forward_crop_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
forward_maxpool_layer(layer, input);
@ -85,6 +91,11 @@ void forward_network(network net, float *input, int train)
forward_connected_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
forward_crop_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
forward_softmax_layer(layer, input);
@ -266,12 +277,14 @@ float train_network_sgd(network net, data d, int n)
int i,j;
float sum = 0;
int index = 0;
for(i = 0; i < n; ++i){
for(j = 0; j < batch; ++j){
int index = rand()%d.X.rows;
index = rand()%d.X.rows;
memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
}
float err = train_network_datum(net, X, y);
sum += err;
//train_network_datum(net, X, y);
@ -300,6 +313,7 @@ float train_network_sgd(network net, data d, int n)
//}
}
//printf("Accuracy: %f\n",(float) correct/n);
//show_image(float_to_image(32,32,3,X), "Orig");
free(X);
free(y);
return (float)sum/(n*batch);
@ -446,6 +460,10 @@ image get_network_image_layer(network net, int i)
normalization_layer layer = *(normalization_layer *)net.layers[i];
return get_normalization_image(layer);
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
return get_crop_image(layer);
}
return make_empty_image(0,0,0);
}
@ -464,6 +482,7 @@ void visualize_network(network net)
image *prev = 0;
int i;
char buff[256];
show_image(get_network_image_layer(net, 0), "Crop");
for(i = 0; i < net.n; ++i){
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
@ -484,6 +503,31 @@ float *network_predict(network net, float *input)
return out;
}
matrix network_predict_data_multi(network net, data test, int n)
{
int i,j,b,m;
int k = get_network_output_size(net);
matrix pred = make_matrix(test.X.rows, k);
float *X = calloc(net.batch*test.X.rows, sizeof(float));
for(i = 0; i < test.X.rows; i += net.batch){
for(b = 0; b < net.batch; ++b){
if(i+b == test.X.rows) break;
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
}
for(m = 0; m < n; ++m){
float *out = network_predict(net, X);
for(b = 0; b < net.batch; ++b){
if(i+b == test.X.rows) break;
for(j = 0; j < k; ++j){
pred.vals[i+b][j] += out[j+b*k]/n;
}
}
}
}
free(X);
return pred;
}
matrix network_predict_data(network net, data test)
{
int i,j,b;
@ -525,6 +569,12 @@ void print_network(network net)
image m = get_maxpool_image(layer);
n = m.h*m.w*m.c;
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
output = layer.output;
image m = get_crop_image(layer);
n = m.h*m.w*m.c;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
output = layer.output;
@ -553,4 +603,12 @@ float network_accuracy(network net, data d)
return acc;
}
float network_accuracy_multi(network net, data d, int n)
{
matrix guess = network_predict_data_multi(net, d, n);
float acc = matrix_accuracy(d.y, guess);
free_matrix(guess);
return acc;
}

4
src/network.h
View File

@ -12,7 +12,8 @@ typedef enum {
MAXPOOL,
SOFTMAX,
NORMALIZATION,
DROPOUT
DROPOUT,
CROP
} LAYER_TYPE;
typedef struct {
@ -41,6 +42,7 @@ float train_network_batch(network net, data d, int n);
void train_network(network net, data d);
matrix network_predict_data(network net, data test);
float network_accuracy(network net, data d);
float network_accuracy_multi(network net, data d, int n);
float *get_network_output(network net);
float *get_network_output_layer(network net, int i);
float *get_network_delta_layer(network net, int i);

116
src/parser.c
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@ -4,6 +4,7 @@
#include "parser.h"
#include "activations.h"
#include "crop_layer.h"
#include "convolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
@ -24,6 +25,7 @@ int is_connected(section *s);
int is_maxpool(section *s);
int is_dropout(section *s);
int is_softmax(section *s);
int is_crop(section *s);
int is_normalization(section *s);
list *read_cfg(char *filename);
@ -43,6 +45,22 @@ void free_section(section *s)
free(s);
}
void parse_data(char *data, float *a, int n)
{
int i;
if(!data) return;
char *curr = data;
char *next = data;
int done = 0;
for(i = 0; i < n && !done; ++i){
while(*++next !='\0' && *next != ',');
if(*next == '\0') done = 1;
*next = '\0';
sscanf(curr, "%g", &a[i]);
curr = next+1;
}
}
convolutional_layer *parse_convolutional(list *options, network *net, int count)
{
int i;
@ -95,30 +113,8 @@ convolutional_layer *parse_convolutional(list *options, network *net, int count)
}
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
if(biases){
char *curr = biases;
char *next = biases;
int done = 0;
for(i = 0; i < n && !done; ++i){
while(*++next !='\0' && *next != ',');
if(*next == '\0') done = 1;
*next = '\0';
sscanf(curr, "%g", &layer->biases[i]);
curr = next+1;
}
}
if(weights){
char *curr = weights;
char *next = weights;
int done = 0;
for(i = 0; i < c*n*size*size && !done; ++i){
while(*++next !='\0' && *next != ',');
if(*next == '\0') done = 1;
*next = '\0';
sscanf(curr, "%g", &layer->filters[i]);
curr = next+1;
}
}
parse_data(biases, layer->biases, n);
parse_data(weights, layer->filters, c*n*size*size);
option_unused(options);
return layer;
}
@ -164,6 +160,10 @@ connected_layer *parse_connected(list *options, network *net, int count)
curr = next+1;
}
}
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(biases, layer->biases, output);
parse_data(weights, layer->weights, input*output);
option_unused(options);
return layer;
}
@ -182,6 +182,36 @@ softmax_layer *parse_softmax(list *options, network *net, int count)
return layer;
}
crop_layer *parse_crop(list *options, network *net, int count)
{
float learning_rate, momentum, decay;
int h,w,c;
int crop_height = option_find_int(options, "crop_height",1);
int crop_width = option_find_int(options, "crop_width",1);
int flip = option_find_int(options, "flip",0);
if(count == 0){
h = option_find_int(options, "height",1);
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
net->batch = option_find_int(options, "batch",1);
learning_rate = option_find_float(options, "learning_rate", .001);
momentum = option_find_float(options, "momentum", .9);
decay = option_find_float(options, "decay", .0001);
net->learning_rate = learning_rate;
net->momentum = momentum;
net->decay = decay;
}else{
image m = get_network_image_layer(*net, count-1);
h = m.h;
w = m.w;
c = m.c;
if(h == 0) error("Layer before crop layer must output image.");
}
crop_layer *layer = make_crop_layer(net->batch,h,w,c,crop_height,crop_width,flip);
option_unused(options);
return layer;
}
maxpool_layer *parse_maxpool(list *options, network *net, int count)
{
int h,w,c;
@ -261,6 +291,10 @@ network parse_network_cfg(char *filename)
connected_layer *layer = parse_connected(options, &net, count);
net.types[count] = CONNECTED;
net.layers[count] = layer;
}else if(is_crop(s)){
crop_layer *layer = parse_crop(options, &net, count);
net.types[count] = CROP;
net.layers[count] = layer;
}else if(is_softmax(s)){
softmax_layer *layer = parse_softmax(options, &net, count);
net.types[count] = SOFTMAX;
@ -290,6 +324,10 @@ network parse_network_cfg(char *filename)
return net;
}
int is_crop(section *s)
{
return (strcmp(s->type, "[crop]")==0);
}
int is_convolutional(section *s)
{
return (strcmp(s->type, "[conv]")==0
@ -389,11 +427,11 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int
l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay);
} else {
if(l->learning_rate != net.learning_rate)
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
if(l->momentum != net.momentum)
fprintf(fp, "momentum=%g\n", l->momentum);
fprintf(fp, "momentum=%g\n", l->momentum);
if(l->decay != net.decay)
fprintf(fp, "decay=%g\n", l->decay);
fprintf(fp, "decay=%g\n", l->decay);
}
fprintf(fp, "filters=%d\n"
"size=%d\n"
@ -432,12 +470,30 @@ void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
"activation=%s\n",
l->outputs,
get_activation_string(l->activation));
fprintf(fp, "data=");
fprintf(fp, "biases=");
for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
fprintf(fp, "\n");
fprintf(fp, "weights=");
for(i = 0; i < l->outputs*l->inputs; ++i) fprintf(fp, "%g,", l->weights[i]);
fprintf(fp, "\n\n");
}
void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count)
{
fprintf(fp, "[crop]\n");
if(count == 0) {
fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n"
"learning_rate=%g\n"
"momentum=%g\n"
"decay=%g\n",
l->batch,l->h, l->w, l->c, net.learning_rate, net.momentum, net.decay);
}
fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip);
}
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, network net, int count)
{
fprintf(fp, "[maxpool]\n");
@ -481,6 +537,8 @@ void save_network(network net, char *filename)
print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], net, i);
else if(net.types[i] == CONNECTED)
print_connected_cfg(fp, (connected_layer *)net.layers[i], net, i);
else if(net.types[i] == CROP)
print_crop_cfg(fp, (crop_layer *)net.layers[i], net, i);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], net, i);
else if(net.types[i] == NORMALIZATION)

9
src/utils.c
View File

@ -143,12 +143,17 @@ float *parse_fields(char *line, int n)
return field;
}
float mean_array(float *a, int n)
float sum_array(float *a, int n)
{
int i;
float sum = 0;
for(i = 0; i < n; ++i) sum += a[i];
return sum/n;
return sum;
}
float mean_array(float *a, int n)
{
return sum_array(a,n)/n;
}
float variance_array(float *a, int n)

1
src/utils.h
View File

@ -21,6 +21,7 @@ int max_index(float *a, int n);
float constrain(float a, float max);
float rand_normal();
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 **one_hot_encode(float *a, int n, int k);