From b32a287e38f4c6a41828f18b4669dec9f3af4943 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 17 Jul 2014 17:17:52 +0000
Subject: [PATCH] Merge branch 'master' of pjreddie.com:jnet
---
src/network.c | 225 ++++++++++++++++++++++++++++++++++++--------------------
1 files changed, 144 insertions(+), 81 deletions(-)
diff --git a/src/network.c b/src/network.c
index a77a28e..7088398 100644
--- a/src/network.c
+++ b/src/network.c
@@ -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;
}
@@ -40,17 +43,6 @@
fprintf(fp, "data=");
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
- /*
- int j,k;
- for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
- for(i = 0; i < l->n; ++i){
- for(j = l->c-1; j >= 0; --j){
- for(k = 0; k < l->size*l->size; ++k){
- fprintf(fp, "%g,", l->filters[i*(l->c*l->size*l->size)+j*l->size*l->size+k]);
- }
- }
- }
- */
fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l, int first)
@@ -121,18 +113,29 @@
fclose(fp);
}
-void forward_network(network net, float *input)
+#ifdef GPU
+void forward_network(network net, float *input, int train)
{
+ 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;
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- forward_convolutional_layer(layer, input);
+ forward_convolutional_layer_gpu(layer, input_cl);
+ input_cl = layer.output_cl;
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){
@@ -153,6 +156,41 @@
}
}
+#else
+
+void forward_network(network net, float *input, int train)
+{
+ int i;
+ for(i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ forward_convolutional_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ forward_connected_layer(layer, input, train);
+ input = layer.output;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ forward_softmax_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ 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;
+ }
+ }
+}
+#endif
+
void update_network(network net, float step, float momentum, float decay)
{
int i;
@@ -230,10 +268,13 @@
float sum = 0;
float *delta = get_network_delta(net);
float *out = get_network_output(net);
- int i, k = get_network_output_size(net);
- for(i = 0; i < k; ++i){
- //printf("%f, ", out[i]);
+ int i;
+ for(i = 0; i < get_network_output_size(net)*net.batch; ++i){
+ //if(i %get_network_output_size(net) == 0) printf("\n");
+ //printf("%5.2f %5.2f, ", out[i], truth[i]);
+ //if(i == get_network_output_size(net)) printf("\n");
delta[i] = truth[i] - out[i];
+ //printf("%.10f, ", out[i]);
sum += delta[i]*delta[i];
}
//printf("\n");
@@ -263,9 +304,7 @@
}
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];
@@ -281,8 +320,7 @@
}
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;
@@ -290,7 +328,7 @@
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);
@@ -300,20 +338,38 @@
float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
{
- int i;
- float error = 0;
- int correct = 0;
- int pos = 0;
+ int batch = net.batch;
+ float *X = calloc(batch*d.X.cols, sizeof(float));
+ float *y = calloc(batch*d.y.cols, sizeof(float));
+
+ int i,j;
+ float sum = 0;
for(i = 0; i < n; ++i){
- int index = rand()%d.X.rows;
- float err = train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
+ for(j = 0; j < batch; ++j){
+ int index = rand()%d.X.rows;
+ memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
+ memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
+ }
+ float err = train_network_datum(net, X, y, step, momentum, decay);
+ sum += err;
+ //train_network_datum(net, X, y, step, momentum, decay);
+ /*
float *y = d.y.vals[index];
int class = get_predicted_class_network(net);
correct += (y[class]?1:0);
- if(y[1]){
- error += err;
- ++pos;
+ */
+
+/*
+ for(j = 0; j < d.y.cols*batch; ++j){
+ printf("%6.3f ", y[j]);
}
+ printf("\n");
+ for(j = 0; j < d.y.cols*batch; ++j){
+ printf("%6.3f ", get_network_output(net)[j]);
+ }
+ printf("\n");
+ printf("\n");
+ */
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
@@ -322,24 +378,26 @@
//}
}
//printf("Accuracy: %f\n",(float) correct/n);
- return error/pos;
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
{
- int i;
- int correct = 0;
+ int i,j;
+ float sum = 0;
+ int batch = 2;
for(i = 0; i < n; ++i){
- int index = rand()%d.X.rows;
- float *x = d.X.vals[index];
- float *y = d.y.vals[index];
- forward_network(net, x);
- int class = get_predicted_class_network(net);
- backward_network(net, x, y);
- correct += (y[class]?1:0);
+ for(j = 0; j < batch; ++j){
+ int index = rand()%d.X.rows;
+ float *x = d.X.vals[index];
+ float *y = d.y.vals[index];
+ forward_network(net, x, 1);
+ sum += backward_network(net, x, y);
+ }
+ update_network(net, step, momentum, decay);
}
- update_network(net, step, momentum, decay);
- return (float)correct/n;
-
+ return (float)sum/(n*batch);
}
@@ -359,6 +417,27 @@
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){
@@ -382,36 +461,6 @@
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;
@@ -450,6 +499,11 @@
return get_network_output_size_layer(net, i);
}
+int get_network_input_size(network net)
+{
+ return get_network_input_size_layer(net, 0);
+}
+
image get_network_image_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
@@ -497,22 +551,31 @@
float *network_predict(network net, float *input)
{
- forward_network(net, input);
+ forward_network(net, input, 0);
float *out = get_network_output(net);
return out;
}
matrix network_predict_data(network net, data test)
{
- int i,j;
+ int i,j,b;
int k = get_network_output_size(net);
matrix pred = make_matrix(test.X.rows, k);
- for(i = 0; i < test.X.rows; ++i){
- float *out = network_predict(net, test.X.vals[i]);
- for(j = 0; j < k; ++j){
- pred.vals[i][j] = out[j];
+ float *X = calloc(net.batch*test.X.rows, sizeof(float));
+ for(i = 0; i < test.X.rows; i += net.batch){
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
+ }
+ float *out = network_predict(net, X);
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ for(j = 0; j < k; ++j){
+ pred.vals[i+b][j] = out[j+b*k];
+ }
}
}
+ free(X);
return pred;
}
--
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