From 9802287b5890d9b2cc250adba1b9810657a95c9c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 18 Dec 2015 23:55:58 +0000
Subject: [PATCH] some fixes
---
src/convolutional_layer.c | 30 ++++++++++++---
src/blas.c | 6 +++
src/blas.h | 1
src/classifier.c | 26 +++++++------
src/deconvolutional_layer.c | 3 +
src/convolutional_layer.h | 2
6 files changed, 48 insertions(+), 20 deletions(-)
diff --git a/src/blas.c b/src/blas.c
index 941109e..556603c 100644
--- a/src/blas.c
+++ b/src/blas.c
@@ -92,6 +92,12 @@
for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA;
}
+void fill_cpu(int N, float ALPHA, float *X, int INCX)
+{
+ int i;
+ for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
+}
+
void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
{
int i;
diff --git a/src/blas.h b/src/blas.h
index 023024a..208fdaa 100644
--- a/src/blas.h
+++ b/src/blas.h
@@ -13,6 +13,7 @@
void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
void copy_cpu(int N, float *X, int INCX, float *Y, int INCY);
void scal_cpu(int N, float ALPHA, float *X, int INCX);
+void fill_cpu(int N, float ALPHA, float * X, int INCX);
float dot_cpu(int N, float *X, int INCX, float *Y, int INCY);
void test_gpu_blas();
void shortcut_cpu(float *out, int w, int h, int c, int batch, int sample, float *add, int stride, int c2);
diff --git a/src/classifier.c b/src/classifier.c
index 8a3ae5a..ddd88b1 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -143,7 +143,7 @@
clock_t time;
float avg_acc = 0;
float avg_topk = 0;
- int splits = 50;
+ int splits = m/1000;
int num = (i+1)*m/splits - i*m/splits;
data val, buffer;
@@ -201,7 +201,7 @@
int i = 0;
char **names = get_labels(name_list);
clock_t time;
- int indexes[10];
+ int *indexes = calloc(top, sizeof(int));
char buff[256];
char *input = buff;
while(1){
@@ -214,7 +214,7 @@
if(!input) return;
strtok(input, "\n");
}
- image im = load_image_color(input, 256, 256);
+ image im = load_image_color(input, net.w, net.h);
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
@@ -229,10 +229,10 @@
}
}
-void test_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int target_layer)
+void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
{
int curr = 0;
- network net = parse_network_cfg(filename);
+ network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -241,10 +241,8 @@
list *options = read_data_cfg(datacfg);
char *test_list = option_find_str(options, "test", "data/test.list");
- char *label_list = option_find_str(options, "labels", "data/labels.list");
int classes = option_find_int(options, "classes", 2);
- char **labels = get_labels(label_list);
list *plist = get_paths(test_list);
char **paths = (char **)list_to_array(plist);
@@ -262,7 +260,7 @@
args.classes = classes;
args.n = net.batch;
args.m = 0;
- args.labels = labels;
+ args.labels = 0;
args.d = &buffer;
args.type = CLASSIFICATION_DATA;
@@ -283,13 +281,17 @@
time=clock();
matrix pred = network_predict_data(net, val);
- int i;
+ int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];
}
- for(i = 0; i < val.X.rows; ++i){
-
+ for(i = 0; i < pred.rows; ++i){
+ printf("%s", paths[curr-net.batch+i]);
+ for(j = 0; j < pred.cols; ++j){
+ printf("\t%g", pred.vals[i][j]);
+ }
+ printf("\n");
}
free_matrix(pred);
@@ -315,7 +317,7 @@
int layer = layer_s ? atoi(layer_s) : -1;
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights);
- else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights,filename, layer);
+ else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
else if(0==strcmp(argv[2], "valid")) validate_classifier(data, cfg, weights);
}
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index ec571a6..e97b00d 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -194,13 +194,25 @@
#endif
}
-void bias_output(float *output, float *biases, int batch, int n, int size)
+void add_bias(float *output, float *biases, int batch, int n, int size)
{
int i,j,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
- output[(b*n + i)*size + j] = biases[i];
+ output[(b*n + i)*size + j] += biases[i];
+ }
+ }
+ }
+}
+
+void scale_bias(float *output, float *scales, int batch, int n, int size)
+{
+ int i,j,b;
+ for(b = 0; b < batch; ++b){
+ for(i = 0; i < n; ++i){
+ for(j = 0; j < size; ++j){
+ output[(b*n + i)*size + j] *= scales[i];
}
}
}
@@ -222,7 +234,7 @@
int out_w = convolutional_out_width(l);
int i;
- bias_output(l.output, l.biases, l.batch, l.n, out_h*out_w);
+ fill_cpu(l.outputs*l.batch, 0, l.output, 1);
int m = l.n;
int k = l.size*l.size*l.c;
@@ -241,10 +253,16 @@
}
if(l.batch_normalize){
- mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean);
- variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance);
- normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w);
+ if(state.train){
+ mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean);
+ variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance);
+ normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w);
+ } else {
+ normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.n, l.out_h*l.out_w);
+ }
+ scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
}
+ add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
activate_array(l.output, m*n*l.batch, l.activation);
}
diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h
index 436ed7e..e22a396 100644
--- a/src/convolutional_layer.h
+++ b/src/convolutional_layer.h
@@ -31,7 +31,7 @@
void backward_convolutional_layer(convolutional_layer layer, network_state state);
-void bias_output(float *output, float *biases, int batch, int n, int size);
+void add_bias(float *output, float *biases, int batch, int n, int size);
void backward_bias(float *bias_updates, float *delta, int batch, int n, int size);
image get_convolutional_image(convolutional_layer layer);
diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c
index 0f4e1e8..d476957 100644
--- a/src/deconvolutional_layer.c
+++ b/src/deconvolutional_layer.c
@@ -134,7 +134,7 @@
int n = l.h*l.w;
int k = l.c;
- bias_output(l.output, l.biases, l.batch, l.n, size);
+ fill_cpu(l.outputs*l.batch, 0, l.output, 1);
for(i = 0; i < l.batch; ++i){
float *a = l.filters;
@@ -145,6 +145,7 @@
col2im_cpu(c, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size);
}
+ add_bias(l.output, l.biases, l.batch, l.n, size);
activate_array(l.output, l.batch*l.n*size, l.activation);
}
--
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