From e36182cd8c5dd5c6d0aa1f77cf5cdca87e8bb1f0 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 21 Nov 2014 23:35:19 +0000
Subject: [PATCH] cleaned up data parsing a lot. probably nothing broken?
---
src/network.c | 188 ++++++++---------------------------------------
1 files changed, 32 insertions(+), 156 deletions(-)
diff --git a/src/network.c b/src/network.c
index f9b4667..339e6eb 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"
@@ -30,120 +31,6 @@
return net;
}
-#ifdef GPU
-void forward_network_gpu(network net, cl_mem input, cl_mem truth, 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_gpu(layer, input);
- input = layer.output_cl;
- }
- else if(net.types[i] == COST){
- 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_gpu(layer, input);
- input = layer.output_cl;
- }
- /*
- 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;
- }
- 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;
- }
- */
- }
-}
-
-void backward_network_gpu(network net, cl_mem input)
-{
- int i;
- cl_mem prev_input;
- cl_mem prev_delta;
- for(i = net.n-1; i >= 0; --i){
- if(i == 0){
- prev_input = input;
- prev_delta = 0;
- }else{
- prev_input = get_network_output_cl_layer(net, i-1);
- prev_delta = get_network_delta_cl_layer(net, i-1);
- }
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- backward_convolutional_layer_gpu(layer, prev_delta);
- }
- else if(net.types[i] == COST){
- 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);
- }
- }
-}
-
-void update_network_gpu(network net)
-{
- int i;
- for(i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer_gpu(layer);
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer_gpu(layer);
- }
- }
-}
-
-cl_mem get_network_output_cl_layer(network net, int i)
-{
- if(net.types[i] == CONVOLUTIONAL){
- 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;
- }
- return 0;
-}
-
-cl_mem get_network_delta_cl_layer(network net, int i)
-{
- if(net.types[i] == CONVOLUTIONAL){
- 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;
- }
- return 0;
-}
-
-#endif
void forward_network(network net, float *input, float *truth, int train)
{
@@ -330,7 +217,7 @@
}
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];
@@ -338,7 +225,7 @@
}
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];
@@ -351,44 +238,7 @@
}
}
-#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;
- 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);
- }
- forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
- //int class = get_predicted_class_network(net);
- backward_network_gpu(net, *net.input_cl);
- float error = get_network_cost(net);
- update_network_gpu(net);
- //return (y[class]?1:0);
- 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)
@@ -411,7 +261,7 @@
int i;
float sum = 0;
for(i = 0; i < n; ++i){
- get_batch(d, batch, X, y);
+ get_random_batch(d, batch, X, y);
float err = train_network_datum(net, X, y);
sum += err;
}
@@ -419,6 +269,7 @@
free(y);
return (float)sum/(n*batch);
}
+
float train_network_batch(network net, data d, int n)
{
int i,j;
@@ -438,6 +289,23 @@
return (float)sum/(n*batch);
}
+float train_network_data_cpu(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_next_batch(d, batch, i*batch, X, y);
+ float err = train_network_datum(net, X, y);
+ sum += err;
+ }
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
+}
void train_network(network net, data d)
{
@@ -594,7 +462,7 @@
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){
@@ -608,6 +476,14 @@
}
}
+void top_predictions(network net, int k, int *index)
+{
+ int size = get_network_output_size(net);
+ float *out = get_network_output(net);
+ top_k(out, size, k, index);
+}
+
+
float *network_predict(network net, float *input)
{
forward_network(net, input, 0, 0);
@@ -645,7 +521,7 @@
int i,j,b;
int k = get_network_output_size(net);
matrix pred = make_matrix(test.X.rows, k);
- float *X = calloc(net.batch*test.X.rows, sizeof(float));
+ float *X = calloc(net.batch*test.X.cols, 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;
--
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