From 0f1a31648c5292fa49b35eac90a2ee676d6c13e6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 31 Jan 2015 06:05:23 +0000
Subject: [PATCH] idk, probably something changed
---
src/cuda.c | 7 +
src/network.c | 5 +
src/image.c | 17 +++
src/crop_layer.c | 11 ++
src/crop_layer.h | 4
src/network_kernels.cu | 2
src/data.c | 2
src/crop_layer_kernels.cu | 11 ++
src/cuda.h | 7 +
src/darknet.c | 126 ++++++++++++++++++-------------
src/utils.c | 1
11 files changed, 121 insertions(+), 72 deletions(-)
diff --git a/src/crop_layer.c b/src/crop_layer.c
index df6eb41..3f0011d 100644
--- a/src/crop_layer.c
+++ b/src/crop_layer.c
@@ -28,14 +28,19 @@
return layer;
}
-void forward_crop_layer(const crop_layer layer, float *input)
+void forward_crop_layer(const crop_layer layer, int train, float *input)
{
int i,j,c,b,row,col;
int index;
int count = 0;
int flip = (layer.flip && rand()%2);
- int dh = rand()%(layer.h - layer.crop_height);
- int dw = rand()%(layer.w - layer.crop_width);
+ int dh = rand()%(layer.h - layer.crop_height + 1);
+ int dw = rand()%(layer.w - layer.crop_width + 1);
+ if(!train){
+ flip = 0;
+ dh = (layer.h - layer.crop_height)/2;
+ dw = (layer.w - layer.crop_width)/2;
+ }
for(b = 0; b < layer.batch; ++b){
for(c = 0; c < layer.c; ++c){
for(i = 0; i < layer.crop_height; ++i){
diff --git a/src/crop_layer.h b/src/crop_layer.h
index 4b4ec87..0d2f03b 100644
--- a/src/crop_layer.h
+++ b/src/crop_layer.h
@@ -17,10 +17,10 @@
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 forward_crop_layer(const crop_layer layer, int train, float *input);
#ifdef GPU
-void forward_crop_layer_gpu(crop_layer layer, float *input);
+void forward_crop_layer_gpu(crop_layer layer, int train, float *input);
#endif
#endif
diff --git a/src/crop_layer_kernels.cu b/src/crop_layer_kernels.cu
index 00ecca5..628c700 100644
--- a/src/crop_layer_kernels.cu
+++ b/src/crop_layer_kernels.cu
@@ -24,11 +24,16 @@
output[count] = input[index];
}
-extern "C" void forward_crop_layer_gpu(crop_layer layer, float *input)
+extern "C" void forward_crop_layer_gpu(crop_layer layer, int train, float *input)
{
int flip = (layer.flip && rand()%2);
- int dh = rand()%(layer.h - layer.crop_height);
- int dw = rand()%(layer.w - layer.crop_width);
+ int dh = rand()%(layer.h - layer.crop_height + 1);
+ int dw = rand()%(layer.w - layer.crop_width + 1);
+ if(!train){
+ flip = 0;
+ dh = (layer.h - layer.crop_height)/2;
+ dw = (layer.w - layer.crop_width)/2;
+ }
int size = layer.batch*layer.c*layer.crop_width*layer.crop_height;
dim3 dimBlock(BLOCK, 1, 1);
diff --git a/src/cuda.c b/src/cuda.c
index 27153ea..8849fb1 100644
--- a/src/cuda.c
+++ b/src/cuda.c
@@ -1,9 +1,12 @@
+int gpu_index = 0;
+
+#ifdef GPU
+
#include "cuda.h"
#include "utils.h"
#include "blas.h"
#include <stdlib.h>
-int gpu_index = 0;
void check_error(cudaError_t status)
{
@@ -96,4 +99,4 @@
check_error(status);
}
-
+#endif
diff --git a/src/cuda.h b/src/cuda.h
index 08c0340..cbe7975 100644
--- a/src/cuda.h
+++ b/src/cuda.h
@@ -1,13 +1,15 @@
#ifndef CUDA_H
#define CUDA_H
+extern int gpu_index;
+
+#ifdef GPU
+
#define BLOCK 256
#include "cuda_runtime.h"
#include "cublas_v2.h"
-extern int gpu_index;
-
void check_error(cudaError_t status);
cublasHandle_t blas_handle();
float *cuda_make_array(float *x, int n);
@@ -19,3 +21,4 @@
dim3 cuda_gridsize(size_t n);
#endif
+#endif
diff --git a/src/darknet.c b/src/darknet.c
index 4f575dc..64012e0 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -209,13 +209,12 @@
void train_imagenet(char *cfgfile)
{
float avg_loss = 1;
- //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
srand(time(0));
network net = parse_network_cfg(cfgfile);
//test_learn_bias(*(convolutional_layer *)net.layers[1]);
//set_learning_network(&net, net.learning_rate, 0, net.decay);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 3072;
+ int imgs = 1024;
int i = net.seen/imgs;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
@@ -231,9 +230,6 @@
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- //normalize_data_rows(train);
- //translate_data_rows(train, -128);
- //scale_data_rows(train, 1./128);
load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -244,7 +240,7 @@
free_data(train);
if(i%100==0){
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/alexnet_%d.cfg", i);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/vgg_%d.cfg", i);
save_network(net, buff);
}
}
@@ -347,10 +343,28 @@
}
free_image(im);
}
-
-void test_imagenet()
+void test_dog(char *cfgfile)
{
- network net = parse_network_cfg("cfg/imagenet_test.cfg");
+ image im = load_image_color("data/dog.jpg", 224, 224);
+ translate_image(im, -128);
+ print_image(im);
+ float *X = im.data;
+ network net = parse_network_cfg(cfgfile);
+ set_batch_network(&net, 1);
+ float *predictions = network_predict(net, X);
+ image crop = get_network_image_layer(net, 0);
+ //show_image(crop, "cropped");
+ // print_image(crop);
+ //show_image(im, "orig");
+ float * inter = get_network_output(net);
+ pm(1000, 1, inter);
+ //cvWaitKey(0);
+}
+
+void test_imagenet(char *cfgfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ set_batch_network(&net, 1);
//imgs=1;
srand(2222222);
int i = 0;
@@ -362,7 +376,8 @@
fgets(filename, 256, stdin);
strtok(filename, "\n");
image im = load_image_color(filename, 256, 256);
- z_normalize_image(im);
+ translate_image(im, -128);
+ //scale_image(im, 1/128.);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = im.data;
time=clock();
@@ -472,28 +487,28 @@
}
/*
-void train_nist_distributed(char *address)
-{
- srand(time(0));
- network net = parse_network_cfg("cfg/nist.client");
- 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);
- normalize_data_rows(train);
- //normalize_data_rows(test);
- int count = 0;
- int iters = 50000/net.batch;
- iters = 1000/net.batch + 1;
- while(++count <= 2000){
- clock_t start = clock(), end;
- float loss = train_network_sgd(net, train, iters);
- client_update(net, address);
- end = clock();
- //float test_acc = network_accuracy_gpu(net, test);
- //float test_acc = 0;
- printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
- }
+ void train_nist_distributed(char *address)
+ {
+ srand(time(0));
+ network net = parse_network_cfg("cfg/nist.client");
+ 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);
+normalize_data_rows(train);
+//normalize_data_rows(test);
+int count = 0;
+int iters = 50000/net.batch;
+iters = 1000/net.batch + 1;
+while(++count <= 2000){
+clock_t start = clock(), end;
+float loss = train_network_sgd(net, train, iters);
+client_update(net, address);
+end = clock();
+//float test_acc = network_accuracy_gpu(net, test);
+//float test_acc = 0;
+printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
}
-*/
+}
+ */
void test_ensemble()
{
@@ -535,7 +550,7 @@
void visualize_cat()
{
network net = parse_network_cfg("cfg/voc_imagenet.cfg");
- image im = load_image("data/cat.png", 0, 0);
+ image im = load_image_color("data/cat.png", 0, 0);
printf("Processing %dx%d image\n", im.h, im.w);
resize_network(net, im.h, im.w, im.c);
forward_network(net, im.data, 0, 0);
@@ -544,6 +559,7 @@
cvWaitKey(0);
}
+#ifdef GPU
void test_convolutional_layer()
{
network net = parse_network_cfg("cfg/nist_conv.cfg");
@@ -561,6 +577,7 @@
bias_output_gpu(layer);
cuda_compare(layer.output_gpu, layer.output, out_size, "biased output");
}
+#endif
void test_correct_nist()
{
@@ -586,7 +603,7 @@
gpu_index = -1;
count = 0;
srand(222222);
- net = parse_network_cfg("cfg/nist_conv.cfg");
+ net = parse_network_cfg("cfg/nist_conv.cfg");
while(++count <= 5){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
@@ -641,27 +658,27 @@
}
/*
-void run_server()
-{
- srand(time(0));
- network net = parse_network_cfg("cfg/net.cfg");
- set_batch_network(&net, 1);
- server_update(net);
-}
+ void run_server()
+ {
+ srand(time(0));
+ network net = parse_network_cfg("cfg/net.cfg");
+ set_batch_network(&net, 1);
+ server_update(net);
+ }
-void test_client()
-{
- network net = parse_network_cfg("cfg/alexnet.client");
- clock_t time=clock();
- client_update(net, "localhost");
- printf("1\n");
- client_update(net, "localhost");
- printf("2\n");
- client_update(net, "localhost");
- printf("3\n");
- printf("Transfered: %lf seconds\n", sec(clock()-time));
-}
-*/
+ void test_client()
+ {
+ network net = parse_network_cfg("cfg/alexnet.client");
+ clock_t time=clock();
+ client_update(net, "localhost");
+ printf("1\n");
+ client_update(net, "localhost");
+ printf("2\n");
+ client_update(net, "localhost");
+ printf("3\n");
+ printf("Transfered: %lf seconds\n", sec(clock()-time));
+ }
+ */
void del_arg(int argc, char **argv, int index)
{
@@ -713,7 +730,6 @@
if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
else if(0==strcmp(argv[1], "test_correct_nist")) test_correct_nist();
- else if(0==strcmp(argv[1], "test")) test_imagenet();
//else if(0==strcmp(argv[1], "server")) run_server();
#ifdef GPU
@@ -725,6 +741,8 @@
return 0;
}
else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2]);
+ else if(0==strcmp(argv[1], "test")) test_imagenet(argv[2]);
+ else if(0==strcmp(argv[1], "dog")) test_dog(argv[2]);
else if(0==strcmp(argv[1], "ctrain")) train_cifar10(argv[2]);
else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
else if(0==strcmp(argv[1], "ctest")) test_cifar10(argv[2]);
diff --git a/src/data.c b/src/data.c
index 87097b6..3a37411 100644
--- a/src/data.c
+++ b/src/data.c
@@ -239,7 +239,7 @@
{
struct load_args a = *(struct load_args*)ptr;
*a.d = load_data(a.paths, a.n, a.m, a.labels, a.k, a.h, a.w);
- translate_data_rows(*a.d, -144);
+ translate_data_rows(*a.d, -128);
scale_data_rows(*a.d, 1./128);
free(ptr);
return 0;
diff --git a/src/image.c b/src/image.c
index ddb5bf5..a686a3e 100644
--- a/src/image.c
+++ b/src/image.c
@@ -484,7 +484,7 @@
exit(0);
}
if(h && w ){
- IplImage *resized = resizeImage(src, h, w, 1);
+ IplImage *resized = resizeImage(src, h, w, 0);
cvReleaseImage(&src);
src = resized;
}
@@ -702,10 +702,21 @@
void print_image(image m)
{
- int i;
- for(i =0 ; i < m.h*m.w*m.c; ++i) printf("%lf, ", m.data[i]);
+ int i, j, k;
+ for(i =0 ; i < m.c; ++i){
+ for(j =0 ; j < m.h; ++j){
+ for(k = 0; k < m.w; ++k){
+ printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]);
+ if(k > 30) break;
+ }
+ printf("\n");
+ if(j > 30) break;
+ }
+ printf("\n");
+ }
printf("\n");
}
+
image collapse_images_vert(image *ims, int n)
{
int color = 1;
diff --git a/src/network.c b/src/network.c
index f554090..b628561 100644
--- a/src/network.c
+++ b/src/network.c
@@ -75,7 +75,7 @@
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer(layer, input);
+ forward_crop_layer(layer, train, input);
input = layer.output;
}
else if(net.types[i] == COST){
@@ -536,6 +536,9 @@
normalization_layer layer = *(normalization_layer *)net.layers[i];
return get_normalization_image(layer);
}
+ else if(net.types[i] == DROPOUT){
+ return get_network_image_layer(net, i-1);
+ }
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
return get_crop_image(layer);
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 7909e46..de8f659 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -58,7 +58,7 @@
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer_gpu(layer, input);
+ forward_crop_layer_gpu(layer, train, input);
input = layer.output_gpu;
}
//printf("Forward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));
diff --git a/src/utils.c b/src/utils.c
index 96062b0..2635494 100644
--- a/src/utils.c
+++ b/src/utils.c
@@ -11,6 +11,7 @@
{
int i,j;
for(i =0 ; i < M; ++i){
+ printf("%d ", i+1);
for(j = 0; j < N; ++j){
printf("%10.6f, ", A[i*N+j]);
}
--
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