From 4ab366a805a7678642539465d68ef906b4599aeb Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 22 Dec 2014 14:35:37 -0800
Subject: [PATCH] some fixes, some other experiments
---
Makefile | 2 +-
src/axpy.cl | 2 +-
src/cnn.c | 38 +++++++++++++++++++++-----------------
src/connected_layer.c | 36 ++++++++++++++++++++++++++++++++++--
src/connected_layer.h | 5 +++--
src/data.c | 42 ++++++++++++++++--------------------------
src/dropout_layer.c | 1 +
src/image.c | 1 +
src/network.c | 3 ++-
src/network_gpu.c | 1 +
10 files changed, 81 insertions(+), 50 deletions(-)
diff --git a/Makefile b/Makefile
index a76c532..3247999 100644
--- a/Makefile
+++ b/Makefile
@@ -27,7 +27,7 @@ LDFLAGS+= -lOpenCL
endif
endif
CFLAGS= $(COMMON) $(OPTS)
-CFLAGS= $(COMMON) -O0 -g
+#CFLAGS= $(COMMON) -O0 -g
LDFLAGS+=`pkg-config --libs opencv` -lm -pthread
VPATH=./src/
EXEC=cnn
diff --git a/src/axpy.cl b/src/axpy.cl
index 04eb534..1503e8f 100644
--- a/src/axpy.cl
+++ b/src/axpy.cl
@@ -13,7 +13,7 @@ __kernel void scal(int N, float ALPHA, __global float *X, int INCX)
__kernel void mask(int n, __global float *x, __global float *mask, int mod)
{
int i = get_global_id(0);
- x[i] = (mask[(i/mod)*mod] || i%mod == 0) ? x[i] : 0;
+ x[i] = (i%mod && !mask[(i/mod)*mod]) ? 0 : x[i];
}
__kernel void copy(int N, __global float *X, int OFFX, int INCX, __global float *Y, int OFFY, int INCY)
diff --git a/src/cnn.c b/src/cnn.c
index fd83ee8..59948aa 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -31,21 +31,23 @@ void test_parser()
save_network(net, "cfg/trained_imagenet_smaller.cfg");
}
+#define AMNT 3
void draw_detection(image im, float *box, int side)
{
int j;
int r, c;
- float amount[5] = {0,0,0,0,0};
+ float amount[AMNT] = {0};
for(r = 0; r < side*side; ++r){
- for(j = 0; j < 5; ++j){
- if(box[r*5] > amount[j]) {
- amount[j] = box[r*5];
- break;
+ float val = box[r*5];
+ for(j = 0; j < AMNT; ++j){
+ if(val > amount[j]) {
+ float swap = val;
+ val = amount[j];
+ amount[j] = swap;
}
}
}
- float smallest = amount[0];
- for(j = 1; j < 5; ++j) if(amount[j] < smallest) smallest = amount[j];
+ float smallest = amount[AMNT-1];
for(r = 0; r < side; ++r){
for(c = 0; c < side; ++c){
@@ -57,9 +59,9 @@ void draw_detection(image im, float *box, int side)
int x = c*d+box[j+2]*d;
int h = box[j+3]*256;
int w = box[j+4]*256;
- printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]);
- printf("%d %d %d %d\n", x, y, w, h);
- printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
+ //printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]);
+ //printf("%d %d %d %d\n", x, y, w, h);
+ //printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2);
}
}
@@ -87,9 +89,11 @@ void train_detection_net()
i += 1;
time=clock();
data train = load_data_detection_jitter_random(imgs, paths, plist->size, 256, 256, 7, 7, 256);
- /*
- image im = float_to_image(224, 224, 3, train.X.vals[0]);
- draw_detection(im, train.y.vals[0], 7);
+ //data train = load_data_detection_random(imgs, paths, plist->size, 224, 224, 7, 7, 256);
+
+/*
+ image im = float_to_image(224, 224, 3, train.X.vals[923]);
+ draw_detection(im, train.y.vals[923], 7);
*/
normalize_data_rows(train);
@@ -151,10 +155,10 @@ void train_imagenet(char *cfgfile)
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
srand(time(0));
network net = parse_network_cfg(cfgfile);
- set_learning_network(&net, net.learning_rate, .5, .0005);
+ set_learning_network(&net, net.learning_rate/10., .5, .0005);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
- int i = 23030;
+ int i = 44700;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
char **paths = (char **)list_to_array(plist);
@@ -385,8 +389,8 @@ void train_nist(char *cfgfile)
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
network net = parse_network_cfg(cfgfile);
int count = 0;
- int iters = 60000/net.batch + 1;
- while(++count <= 10){
+ int iters = 6000/net.batch + 1;
+ while(++count <= 100){
clock_t start = clock(), end;
normalize_data_rows(train);
normalize_data_rows(test);
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 96236a3..938b8b8 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -24,15 +24,21 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVA
layer->delta = calloc(batch*outputs, sizeof(float*));
layer->weight_updates = calloc(inputs*outputs, sizeof(float));
+ layer->bias_updates = calloc(outputs, sizeof(float));
+
+ layer->weight_prev = calloc(inputs*outputs, sizeof(float));
+ layer->bias_prev = calloc(outputs, sizeof(float));
+
layer->weights = calloc(inputs*outputs, sizeof(float));
+ layer->biases = calloc(outputs, sizeof(float));
+
+
float scale = 1./sqrt(inputs);
//scale = .01;
for(i = 0; i < inputs*outputs; ++i){
layer->weights[i] = scale*rand_normal();
}
- layer->bias_updates = calloc(outputs, sizeof(float));
- layer->biases = calloc(outputs, sizeof(float));
for(i = 0; i < outputs; ++i){
layer->biases[i] = scale;
}
@@ -52,6 +58,32 @@ connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVA
return layer;
}
+void secret_update_connected_layer(connected_layer *layer)
+{
+ int n = layer->outputs*layer->inputs;
+ float dot = dot_cpu(n, layer->weight_updates, 1, layer->weight_prev, 1);
+ float mag = sqrt(dot_cpu(n, layer->weight_updates, 1, layer->weight_updates, 1))
+ * sqrt(dot_cpu(n, layer->weight_prev, 1, layer->weight_prev, 1));
+ float cos = dot/mag;
+ if(cos > .3) layer->learning_rate *= 1.1;
+ else if (cos < -.3) layer-> learning_rate /= 1.1;
+
+ scal_cpu(n, layer->momentum, layer->weight_prev, 1);
+ axpy_cpu(n, 1, layer->weight_updates, 1, layer->weight_prev, 1);
+ scal_cpu(n, 0, layer->weight_updates, 1);
+
+ scal_cpu(layer->outputs, layer->momentum, layer->bias_prev, 1);
+ axpy_cpu(layer->outputs, 1, layer->bias_updates, 1, layer->bias_prev, 1);
+ scal_cpu(layer->outputs, 0, layer->bias_updates, 1);
+
+ //printf("rate: %f\n", layer->learning_rate);
+
+ axpy_cpu(layer->outputs, layer->learning_rate, layer->bias_prev, 1, layer->biases, 1);
+
+ axpy_cpu(layer->inputs*layer->outputs, -layer->decay, layer->weights, 1, layer->weight_prev, 1);
+ axpy_cpu(layer->inputs*layer->outputs, layer->learning_rate, layer->weight_prev, 1, layer->weights, 1);
+}
+
void update_connected_layer(connected_layer layer)
{
axpy_cpu(layer.outputs, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
diff --git a/src/connected_layer.h b/src/connected_layer.h
index 1e5b4a7..0895728 100644
--- a/src/connected_layer.h
+++ b/src/connected_layer.h
@@ -18,8 +18,8 @@ typedef struct{
float *weight_updates;
float *bias_updates;
- float *weight_adapt;
- float *bias_adapt;
+ float *weight_prev;
+ float *bias_prev;
float *output;
float *delta;
@@ -38,6 +38,7 @@ typedef struct{
} connected_layer;
+void secret_update_connected_layer(connected_layer *layer);
connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, float learning_rate, float momentum, float decay);
void forward_connected_layer(connected_layer layer, float *input);
diff --git a/src/data.c b/src/data.c
index 86e59ef..3f74f6b 100644
--- a/src/data.c
+++ b/src/data.c
@@ -81,6 +81,18 @@ matrix load_image_paths(char **paths, int n, int h, int w)
return X;
}
+char **get_random_paths(char **paths, int n, int m)
+{
+ char **random_paths = calloc(n, sizeof(char*));
+ int i;
+ for(i = 0; i < n; ++i){
+ int index = rand()%m;
+ random_paths[i] = paths[index];
+ if(i == 0) printf("%s\n", paths[index]);
+ }
+ return random_paths;
+}
+
matrix load_labels_paths(char **paths, int n, char **labels, int k)
{
matrix y = make_matrix(n, k);
@@ -138,13 +150,8 @@ void free_data(data d)
data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w, int nh, int nw, float scale)
{
- char **random_paths = calloc(n, sizeof(char*));
+ char **random_paths = get_random_paths(paths, n, m);
int i;
- for(i = 0; i < n; ++i){
- int index = rand()%m;
- random_paths[i] = paths[index];
- if(i == 0) printf("%s\n", paths[index]);
- }
data d;
d.shallow = 0;
d.X = load_image_paths(random_paths, n, h, w);
@@ -154,10 +161,11 @@ data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w,
int dx = rand()%32;
int dy = rand()%32;
fill_truth_detection(random_paths[i], d.y.vals[i], 224, 224, nh, nw, scale, dx, dy);
-
image a = float_to_image(h, w, 3, d.X.vals[i]);
jitter_image(a,224,224,dy,dx);
}
+ d.X.cols = 224*224*3;
+ // print_matrix(d.y);
free(random_paths);
return d;
}
@@ -165,13 +173,7 @@ data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w,
data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw, float scale)
{
- char **random_paths = calloc(n, sizeof(char*));
- int i;
- for(i = 0; i < n; ++i){
- int index = rand()%m;
- random_paths[i] = paths[index];
- if(i == 0) printf("%s\n", paths[index]);
- }
+ char **random_paths = get_random_paths(paths, n, m);
data d;
d.shallow = 0;
d.X = load_image_paths(random_paths, n, h, w);
@@ -180,18 +182,6 @@ data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh
return d;
}
-char **get_random_paths(char **paths, int n, int m)
-{
- char **random_paths = calloc(n, sizeof(char*));
- int i;
- for(i = 0; i < n; ++i){
- int index = rand()%m;
- random_paths[i] = paths[index];
- if(i == 0) printf("%s\n", paths[index]);
- }
- return random_paths;
-}
-
data load_data(char **paths, int n, int m, char **labels, int k, int h, int w)
{
if(m) paths = get_random_paths(paths, n, m);
diff --git a/src/dropout_layer.c b/src/dropout_layer.c
index 8104b56..edcb426 100644
--- a/src/dropout_layer.c
+++ b/src/dropout_layer.c
@@ -80,6 +80,7 @@ void forward_dropout_layer_gpu(dropout_layer layer, cl_mem input)
void backward_dropout_layer_gpu(dropout_layer layer, cl_mem delta)
{
+ if(!delta) return;
int size = layer.inputs*layer.batch;
cl_kernel kernel = get_dropout_kernel();
diff --git a/src/image.c b/src/image.c
index a2664a9..ddb5bf5 100644
--- a/src/image.c
+++ b/src/image.c
@@ -39,6 +39,7 @@ void jitter_image(image a, int h, int w, int dh, int dw)
for(j = 0; j < w; ++j){
int src = j + dw + (i+dh)*a.w + k*a.w*a.h;
int dst = j + i*w + k*w*h;
+ //printf("%d %d\n", src, dst);
a.data[dst] = a.data[src];
}
}
diff --git a/src/network.c b/src/network.c
index 0bf5357..42253dc 100644
--- a/src/network.c
+++ b/src/network.c
@@ -103,7 +103,8 @@ void update_network(network net)
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer(layer);
+ secret_update_connected_layer((connected_layer *)net.layers[i]);
+ //update_connected_layer(layer);
}
}
}
diff --git a/src/network_gpu.c b/src/network_gpu.c
index 6ff95c8..4d2c8d3 100644
--- a/src/network_gpu.c
+++ b/src/network_gpu.c
@@ -195,6 +195,7 @@ float *get_network_output_layer_gpu(network net, int i)
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
+ cl_read_array(layer.output_cl, layer.output, layer.outputs*layer.batch);
return layer.output;
}
else if(net.types[i] == MAXPOOL){
--
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