From cd8d53df21f3ad2810add2a8cff766c745f55a17 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 09 May 2014 22:14:52 +0000
Subject: [PATCH] So there WAS this huge bug. Gone now
---
src/network.c | 166 +++++++++++++++++++++++++++++++++++++------------------
1 files changed, 112 insertions(+), 54 deletions(-)
diff --git a/src/network.c b/src/network.c
index e2c44b0..b75eddf 100644
--- a/src/network.c
+++ b/src/network.c
@@ -6,8 +6,8 @@
#include "connected_layer.h"
#include "convolutional_layer.h"
-//#include "old_conv.h"
#include "maxpool_layer.h"
+#include "normalization_layer.h"
#include "softmax_layer.h"
network make_network(int n, int batch)
@@ -19,6 +19,9 @@
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
net.output = 0;
+ #ifdef GPU
+ net.input_cl = 0;
+ #endif
return net;
}
@@ -48,9 +51,9 @@
fprintf(fp, "[connected]\n");
if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "output=%d\n"
- "activation=%s\n",
- l->outputs,
- get_activation_string(l->activation));
+ "activation=%s\n",
+ l->outputs,
+ get_activation_string(l->activation));
fprintf(fp, "data=");
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]);
@@ -61,13 +64,27 @@
{
fprintf(fp, "[maxpool]\n");
if(first) fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n",
- l->batch,l->h, l->w, l->c);
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ l->batch,l->h, l->w, l->c);
fprintf(fp, "stride=%d\n\n", l->stride);
}
+void print_normalization_cfg(FILE *fp, normalization_layer *l, int first)
+{
+ fprintf(fp, "[localresponsenormalization]\n");
+ if(first) fprintf(fp, "batch=%d\n"
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ l->batch,l->h, l->w, l->c);
+ fprintf(fp, "size=%d\n"
+ "alpha=%g\n"
+ "beta=%g\n"
+ "kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
+}
+
void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
fprintf(fp, "[softmax]\n");
@@ -88,24 +105,42 @@
print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
+ else if(net.types[i] == NORMALIZATION)
+ print_normalization_cfg(fp, (normalization_layer *)net.layers[i], i==0);
else if(net.types[i] == SOFTMAX)
print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
}
fclose(fp);
}
-void forward_network(network net, float *input)
+void forward_network(network net, float *input, int train)
{
int i;
+ #ifdef GPU
+ cl_setup();
+ size_t size = get_network_input_size(net);
+ if(!net.input_cl){
+ net.input_cl = clCreateBuffer(cl.context,
+ CL_MEM_READ_WRITE, size*sizeof(float), 0, &cl.error);
+ check_error(cl);
+ }
+ cl_write_array(net.input_cl, input, size);
+ cl_mem input_cl = net.input_cl;
+ #endif
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ #ifdef GPU
+ forward_convolutional_layer_gpu(layer, input_cl);
+ input_cl = layer.output_cl;
+ #else
forward_convolutional_layer(layer, input);
+ #endif
input = layer.output;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- forward_connected_layer(layer, input);
+ forward_connected_layer(layer, input, train);
input = layer.output;
}
else if(net.types[i] == SOFTMAX){
@@ -118,6 +153,11 @@
forward_maxpool_layer(layer, input);
input = layer.output;
}
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ forward_normalization_layer(layer, input);
+ input = layer.output;
+ }
}
}
@@ -135,6 +175,9 @@
else if(net.types[i] == SOFTMAX){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
+ else if(net.types[i] == NORMALIZATION){
+ //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ }
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
update_connected_layer(layer, step, momentum, decay);
@@ -156,6 +199,9 @@
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
+ } else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ return layer.output;
}
return 0;
}
@@ -225,22 +271,23 @@
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- learn_convolutional_layer(layer);
- //learn_convolutional_layer(layer);
- if(i != 0) backward_convolutional_layer(layer, prev_delta);
+ backward_convolutional_layer(layer, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
if(i != 0) backward_maxpool_layer(layer, prev_input, prev_delta);
}
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- learn_connected_layer(layer, prev_input);
- if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
+ backward_connected_layer(layer, prev_input, prev_delta);
}
}
return error;
@@ -248,7 +295,7 @@
float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
{
- forward_network(net, x);
+ forward_network(net, x, 1);
//int class = get_predicted_class_network(net);
float error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
@@ -272,7 +319,7 @@
error += err;
++pos;
}
-
+
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
//if((i+1)%10 == 0){
@@ -290,7 +337,7 @@
int index = rand()%d.X.rows;
float *x = d.X.vals[index];
float *y = d.y.vals[index];
- forward_network(net, x);
+ forward_network(net, x, 1);
int class = get_predicted_class_network(net);
backward_network(net, x, y);
correct += (y[class]?1:0);
@@ -317,6 +364,27 @@
fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
}
+int get_network_input_size_layer(network net, int i)
+{
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ return layer.h*layer.w*layer.c;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ return layer.h*layer.w*layer.c;
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ return layer.inputs;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.inputs;
+ }
+ return 0;
+}
+
int get_network_output_size_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
@@ -340,36 +408,6 @@
return 0;
}
-/*
-int resize_network(network net, int h, int w, int c)
-{
- int i;
- for (i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer *layer = (convolutional_layer *)net.layers[i];
- layer->h = h;
- layer->w = w;
- layer->c = c;
- image output = get_convolutional_image(*layer);
- h = output.h;
- w = output.w;
- c = output.c;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer *layer = (maxpool_layer *)net.layers[i];
- layer->h = h;
- layer->w = w;
- layer->c = c;
- image output = get_maxpool_image(*layer);
- h = output.h;
- w = output.w;
- c = output.c;
- }
- }
- return 0;
-}
-*/
-
int resize_network(network net, int h, int w, int c)
{
int i;
@@ -381,16 +419,21 @@
h = output.h;
w = output.w;
c = output.c;
- }
- else if(net.types[i] == MAXPOOL){
+ }else if(net.types[i] == MAXPOOL){
maxpool_layer *layer = (maxpool_layer *)net.layers[i];
resize_maxpool_layer(layer, h, w, c);
image output = get_maxpool_image(*layer);
h = output.h;
w = output.w;
c = output.c;
- }
- else{
+ }else if(net.types[i] == NORMALIZATION){
+ normalization_layer *layer = (normalization_layer *)net.layers[i];
+ resize_normalization_layer(layer, h, w, c);
+ image output = get_normalization_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }else{
error("Cannot resize this type of layer");
}
}
@@ -403,6 +446,11 @@
return get_network_output_size_layer(net, i);
}
+int get_network_input_size(network net)
+{
+ return get_network_output_size_layer(net, 0);
+}
+
image get_network_image_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
@@ -413,6 +461,10 @@
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return get_maxpool_image(layer);
}
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ return get_normalization_image(layer);
+ }
return make_empty_image(0,0,0);
}
@@ -428,20 +480,25 @@
void visualize_network(network net)
{
+ image *prev = 0;
int i;
char buff[256];
for(i = 0; i < net.n; ++i){
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- visualize_convolutional_layer(layer, buff);
+ prev = visualize_convolutional_layer(layer, buff, prev);
+ }
+ if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ visualize_normalization_layer(layer, buff);
}
}
}
float *network_predict(network net, float *input)
{
- forward_network(net, input);
+ forward_network(net, input, 0);
float *out = get_network_output(net);
return out;
}
@@ -506,3 +563,4 @@
return acc;
}
+
--
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