From bfffadc75502cadb5d05909435a2167db5204325 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 04 Feb 2015 20:41:20 +0000
Subject: [PATCH] Stable place to commit
---
src/network.c | 1
src/utils.h | 1
src/convolutional_layer.c | 9 +++-
src/network_kernels.cu | 1
src/connected_layer.c | 18 ++++++--
src/gemm.c | 10 +++++
src/convolutional_kernels.cu | 15 +++----
src/darknet.c | 29 ++++++++++++--
src/utils.c | 10 +++++
9 files changed, 72 insertions(+), 22 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 514dff0..1a5fc2b 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -43,6 +43,7 @@
for(i = 0; i < outputs; ++i){
layer->biases[i] = scale;
+ // layer->biases[i] = 1;
}
#ifdef GPU
@@ -113,9 +114,10 @@
void backward_connected_layer(connected_layer layer, float *input, float *delta)
{
int i;
+ float alpha = 1./layer.batch;
gradient_array(layer.output, layer.outputs*layer.batch, layer.activation, layer.delta);
for(i = 0; i < layer.batch; ++i){
- axpy_cpu(layer.outputs, 1, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
+ axpy_cpu(layer.outputs, alpha, layer.delta + i*layer.outputs, 1, layer.bias_updates, 1);
}
int m = layer.inputs;
int k = layer.batch;
@@ -123,7 +125,7 @@
float *a = input;
float *b = layer.delta;
float *c = layer.weight_updates;
- gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
+ gemm(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
m = layer.batch;
k = layer.outputs;
@@ -156,13 +158,18 @@
void update_connected_layer_gpu(connected_layer layer)
{
+/*
+ cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
+ cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
+ printf("Weights: %f updates: %f\n", mag_array(layer.weights, layer.inputs*layer.outputs), layer.learning_rate*mag_array(layer.weight_updates, layer.inputs*layer.outputs));
+*/
+
axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_gpu, 1);
axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_gpu, 1);
- //pull_connected_layer(layer);
}
void forward_connected_layer_gpu(connected_layer layer, float * input)
@@ -183,10 +190,11 @@
void backward_connected_layer_gpu(connected_layer layer, float * input, float * delta)
{
+ float alpha = 1./layer.batch;
int i;
gradient_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation, layer.delta_gpu);
for(i = 0; i < layer.batch; ++i){
- axpy_ongpu_offset(layer.outputs, 1, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
+ axpy_ongpu_offset(layer.outputs, alpha, layer.delta_gpu, i*layer.outputs, 1, layer.bias_updates_gpu, 0, 1);
}
int m = layer.inputs;
int k = layer.batch;
@@ -194,7 +202,7 @@
float * a = input;
float * b = layer.delta_gpu;
float * c = layer.weight_updates_gpu;
- gemm_ongpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
+ gemm_ongpu(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
m = layer.batch;
k = layer.outputs;
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index eaa4161..8645fbf 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -28,7 +28,7 @@
check_error(cudaPeekAtLastError());
}
-__global__ void learn_bias(int batch, int n, int size, float *delta, float *bias_updates)
+__global__ void learn_bias(int batch, int n, int size, float *delta, float *bias_updates, float scale)
{
__shared__ float part[BLOCK];
int i,b;
@@ -44,15 +44,16 @@
part[p] = sum;
__syncthreads();
if(p == 0){
- for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
+ for(i = 0; i < BLOCK; ++i) bias_updates[filter] += scale * part[i];
}
}
extern "C" void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
{
int size = convolutional_out_height(layer)*convolutional_out_width(layer);
+ float alpha = 1./layer.batch;
- learn_bias<<<layer.n, BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu);
+ learn_bias<<<layer.n, BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu, alpha);
check_error(cudaPeekAtLastError());
}
@@ -99,6 +100,7 @@
extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, float *in, float *delta_gpu)
{
+ float alpha = 1./layer.batch;
int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
@@ -115,7 +117,7 @@
float * c = layer.filter_updates_gpu;
im2col_ongpu(in, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
- gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
+ gemm_ongpu(0,1,m,n,k,alpha,a + i*m*k,k,b,k,1,c,n);
if(delta_gpu){
@@ -151,12 +153,9 @@
int size = layer.size*layer.size*layer.c*layer.n;
/*
- cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
- cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, size);
cuda_pull_array(layer.filters_gpu, layer.filters, size);
- printf("Bias: %f updates: %f\n", mse_array(layer.biases, layer.n), mse_array(layer.bias_updates, layer.n));
- printf("Filter: %f updates: %f\n", mse_array(layer.filters, layer.n), mse_array(layer.filter_updates, layer.n));
+ printf("Filter: %f updates: %f\n", mag_array(layer.filters, size), layer.learning_rate*mag_array(layer.filter_updates, size));
*/
axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 6848511..62118e4 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -66,11 +66,12 @@
layer->biases = calloc(n, sizeof(float));
layer->bias_updates = calloc(n, sizeof(float));
float scale = 1./sqrt(size*size*c);
- //scale = .05;
+ //scale = .01;
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
layer->biases[i] = scale;
+ //layer->biases[i] = 1;
}
int out_h = convolutional_out_height(*layer);
int out_w = convolutional_out_width(*layer);
@@ -155,18 +156,20 @@
void learn_bias_convolutional_layer(convolutional_layer layer)
{
+ float alpha = 1./layer.batch;
int i,b;
int size = convolutional_out_height(layer)
*convolutional_out_width(layer);
for(b = 0; b < layer.batch; ++b){
for(i = 0; i < layer.n; ++i){
- layer.bias_updates[i] += sum_array(layer.delta+size*(i+b*layer.n), size);
+ layer.bias_updates[i] += alpha * sum_array(layer.delta+size*(i+b*layer.n), size);
}
}
}
void backward_convolutional_layer(convolutional_layer layer, float *in, float *delta)
{
+ float alpha = 1./layer.batch;
int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
@@ -188,7 +191,7 @@
im2col_cpu(im, layer.c, layer.h, layer.w,
layer.size, layer.stride, layer.pad, b);
- gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+ gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
if(delta){
a = layer.filters;
diff --git a/src/darknet.c b/src/darknet.c
index 64012e0..cc3fc07 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -206,10 +206,28 @@
}
*/
+char *basename(char *cfgfile)
+{
+ char *c = cfgfile;
+ char *next;
+ while((next = strchr(c, '/')))
+ {
+ c = next+1;
+ }
+ c = copy_string(c);
+ next = strchr(c, '_');
+ if (next) *next = 0;
+ next = strchr(c, '.');
+ if (next) *next = 0;
+ return c;
+}
+
void train_imagenet(char *cfgfile)
{
- float avg_loss = 1;
+ float avg_loss = -1;
srand(time(0));
+ char *base = basename(cfgfile);
+ printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
//test_learn_bias(*(convolutional_layer *)net.layers[1]);
//set_learning_network(&net, net.learning_rate, 0, net.decay);
@@ -235,12 +253,13 @@
time=clock();
float loss = train_network(net, train);
net.seen += imgs;
+ if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
free_data(train);
if(i%100==0){
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/vgg_%d.cfg", i);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.cfg",base, i);
save_network(net, buff);
}
}
@@ -272,7 +291,6 @@
pthread_join(load_thread, 0);
val = buffer;
- //normalize_data_rows(val);
num = (i+1)*m/splits - i*m/splits;
char **part = paths+(i*m/splits);
@@ -312,6 +330,7 @@
void test_init(char *cfgfile)
{
+ gpu_index = -1;
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
srand(2222222);
@@ -345,7 +364,7 @@
}
void test_dog(char *cfgfile)
{
- image im = load_image_color("data/dog.jpg", 224, 224);
+ image im = load_image_color("data/dog.jpg", 256, 256);
translate_image(im, -128);
print_image(im);
float *X = im.data;
@@ -377,7 +396,7 @@
strtok(filename, "\n");
image im = load_image_color(filename, 256, 256);
translate_image(im, -128);
- //scale_image(im, 1/128.);
+ scale_image(im, 1/128.);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = im.data;
time=clock();
diff --git a/src/gemm.c b/src/gemm.c
index a923beb..c5e26dc 100644
--- a/src/gemm.c
+++ b/src/gemm.c
@@ -276,6 +276,7 @@
int test_gpu_blas()
{
+/*
test_gpu_accuracy(0,0,10,576,75);
test_gpu_accuracy(0,0,17,10,10);
@@ -299,6 +300,15 @@
time_ongpu(0,0,256,196,2304);
time_ongpu(0,0,128,4096,12544);
time_ongpu(0,0,128,4096,4096);
+ */
+ time_ongpu(0,0,64,75,12544);
+ time_ongpu(0,0,64,75,12544);
+ time_ongpu(0,0,64,75,12544);
+ time_ongpu(0,0,64,576,12544);
+ time_ongpu(0,0,256,2304,784);
+ time_ongpu(1,1,2304,256,784);
+ time_ongpu(0,0,512,4608,196);
+ time_ongpu(1,1,4608,512,196);
return 0;
}
diff --git a/src/network.c b/src/network.c
index b628561..2ec0881 100644
--- a/src/network.c
+++ b/src/network.c
@@ -133,7 +133,6 @@
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- //secret_update_connected_layer((connected_layer *)net.layers[i]);
update_connected_layer(layer);
}
}
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index de8f659..c49f37b 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -61,6 +61,7 @@
forward_crop_layer_gpu(layer, train, input);
input = layer.output_gpu;
}
+ //cudaDeviceSynchronize();
//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 2635494..8261682 100644
--- a/src/utils.c
+++ b/src/utils.c
@@ -262,6 +262,16 @@
}
}
+float mag_array(float *a, int n)
+{
+ int i;
+ float sum = 0;
+ for(i = 0; i < n; ++i){
+ sum += a[i]*a[i];
+ }
+ return sqrt(sum);
+}
+
void scale_array(float *a, int n, float s)
{
int i;
diff --git a/src/utils.h b/src/utils.h
index b1a0587..daf3a41 100644
--- a/src/utils.h
+++ b/src/utils.h
@@ -28,6 +28,7 @@
float sum_array(float *a, int n);
float mean_array(float *a, int n);
float variance_array(float *a, int n);
+float mag_array(float *a, int n);
float **one_hot_encode(float *a, int n, int k);
float sec(clock_t clocks);
#endif
--
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