From edbccdfcaf46f11e631afe98796f3e6e170da5d0 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 26 Oct 2014 05:04:34 +0000
Subject: [PATCH] Maybe something changed?
---
src/network.c | 183 +++++++++++++++++++++++++++++++++++++--------
1 files changed, 149 insertions(+), 34 deletions(-)
diff --git a/src/network.c b/src/network.c
index 5833166..8167d85 100644
--- a/src/network.c
+++ b/src/network.c
@@ -1,4 +1,5 @@
#include <stdio.h>
+#include <time.h>
#include "network.h"
#include "image.h"
#include "data.h"
@@ -24,16 +25,20 @@
net.outputs = 0;
net.output = 0;
#ifdef GPU
- net.input_cl = 0;
+ net.input_cl = calloc(1, sizeof(cl_mem));
+ net.truth_cl = calloc(1, sizeof(cl_mem));
#endif
return net;
}
#ifdef GPU
+
void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
{
+ //printf("start\n");
int i;
for(i = 0; i < net.n; ++i){
+ //clock_t time = clock();
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
forward_convolutional_layer_gpu(layer, input);
@@ -43,33 +48,34 @@
cost_layer layer = *(cost_layer *)net.layers[i];
forward_cost_layer_gpu(layer, input, truth);
}
- /*
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] == CROP){
- crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer(layer, input);
- input = layer.output;
+ forward_connected_layer_gpu(layer, input);
+ input = layer.output_cl;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- forward_maxpool_layer(layer, input);
- input = layer.output;
+ forward_maxpool_layer_gpu(layer, input);
+ input = layer.output_cl;
}
- else if(net.types[i] == NORMALIZATION){
- normalization_layer layer = *(normalization_layer *)net.layers[i];
- forward_normalization_layer(layer, input);
- input = layer.output;
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ forward_softmax_layer_gpu(layer, input);
+ input = layer.output_cl;
}
- */
+ //printf("%d %f\n", i, sec(clock()-time));
+ /*
+ else if(net.types[i] == CROP){
+ crop_layer layer = *(crop_layer *)net.layers[i];
+ forward_crop_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;
+ }
+ */
}
}
@@ -94,6 +100,18 @@
cost_layer layer = *(cost_layer *)net.layers[i];
backward_cost_layer_gpu(layer, prev_input, prev_delta);
}
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ backward_connected_layer_gpu(layer, prev_input, prev_delta);
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ backward_maxpool_layer_gpu(layer, prev_delta);
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ backward_softmax_layer_gpu(layer, prev_delta);
+ }
}
}
@@ -105,18 +123,9 @@
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
update_convolutional_layer_gpu(layer);
}
- else if(net.types[i] == MAXPOOL){
- //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- }
- 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);
+ update_connected_layer_gpu(layer);
}
}
}
@@ -127,6 +136,18 @@
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.output_cl;
}
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ return layer.output_cl;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ return layer.output_cl;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.output_cl;
+ }
return 0;
}
@@ -136,6 +157,18 @@
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.delta_cl;
}
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ return layer.delta_cl;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ return layer.delta_cl;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.delta_cl;
+ }
return 0;
}
@@ -229,6 +262,8 @@
return layer.output;
} else if(net.types[i] == DROPOUT){
return get_network_output_layer(net, i-1);
+ } else if(net.types[i] == FREEWEIGHT){
+ return get_network_output_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
@@ -258,6 +293,8 @@
return layer.delta;
} else if(net.types[i] == DROPOUT){
return get_network_delta_layer(net, i-1);
+ } else if(net.types[i] == FREEWEIGHT){
+ return get_network_delta_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
@@ -322,7 +359,7 @@
}
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);
+ if(i != 0) backward_maxpool_layer(layer, prev_delta);
}
else if(net.types[i] == NORMALIZATION){
normalization_layer layer = *(normalization_layer *)net.layers[i];
@@ -330,7 +367,7 @@
}
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);
+ if(i != 0) backward_softmax_layer(layer, prev_delta);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
@@ -343,6 +380,55 @@
}
}
+
+#ifdef GPU
+float train_network_datum_gpu(network net, float *x, float *y)
+{
+ int x_size = get_network_input_size(net)*net.batch;
+ int y_size = get_network_output_size(net)*net.batch;
+ clock_t time = clock();
+ if(!*net.input_cl){
+ *net.input_cl = cl_make_array(x, x_size);
+ *net.truth_cl = cl_make_array(y, y_size);
+ }else{
+ cl_write_array(*net.input_cl, x, x_size);
+ cl_write_array(*net.truth_cl, y, y_size);
+ }
+ //printf("trans %f\n", sec(clock()-time));
+ time = clock();
+ forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
+ //printf("forw %f\n", sec(clock()-time));
+ time = clock();
+ backward_network_gpu(net, *net.input_cl);
+ //printf("back %f\n", sec(clock()-time));
+ time = clock();
+ float error = get_network_cost(net);
+ update_network_gpu(net);
+ //printf("updt %f\n", sec(clock()-time));
+ time = clock();
+ return error;
+}
+
+float train_network_sgd_gpu(network net, data d, int n)
+{
+ int batch = net.batch;
+ float *X = calloc(batch*d.X.cols, sizeof(float));
+ float *y = calloc(batch*d.y.cols, sizeof(float));
+
+ int i;
+ float sum = 0;
+ for(i = 0; i < n; ++i){
+ get_batch(d, batch, X, y);
+ float err = train_network_datum_gpu(net, X, y);
+ sum += err;
+ }
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
+}
+#endif
+
+
float train_network_datum(network net, float *x, float *y)
{
forward_network(net, x, y, 1);
@@ -424,6 +510,10 @@
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
+ else if(net.types[i] == FREEWEIGHT){
+ freeweight_layer layer = *(freeweight_layer *) net.layers[i];
+ return layer.inputs;
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
@@ -451,6 +541,10 @@
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
+ else if(net.types[i] == FREEWEIGHT){
+ freeweight_layer layer = *(freeweight_layer *) net.layers[i];
+ return layer.inputs;
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
@@ -538,7 +632,7 @@
image *prev = 0;
int i;
char buff[256];
- show_image(get_network_image_layer(net, 0), "Crop");
+ //show_image(get_network_image_layer(net, 0), "Crop");
for(i = 0; i < net.n; ++i){
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
@@ -552,6 +646,27 @@
}
}
+void top_predictions(network net, int n, int *index)
+{
+ int i,j;
+ int k = get_network_output_size(net);
+ float *out = get_network_output(net);
+ float thresh = FLT_MAX;
+ for(i = 0; i < n; ++i){
+ float max = -FLT_MAX;
+ int max_i = -1;
+ for(j = 0; j < k; ++j){
+ float val = out[j];
+ if(val > max && val < thresh){
+ max = val;
+ max_i = j;
+ }
+ }
+ index[i] = max_i;
+ thresh = max;
+ }
+}
+
float *network_predict(network net, float *input)
{
forward_network(net, input, 0, 0);
--
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