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 |  105 +++++++++++++++++++++++++++++++++++++++++-----------
 1 files changed, 82 insertions(+), 23 deletions(-)

diff --git a/src/network.c b/src/network.c
index f9b4667..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"
@@ -31,10 +32,13 @@
 }
 
 #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);
@@ -49,28 +53,29 @@
             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;
+            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;
+           }
+         */
     }
 }
 
@@ -99,6 +104,14 @@
             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);
+        }
     }
 }
 
@@ -127,6 +140,14 @@
         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;
 }
 
@@ -140,6 +161,14 @@
         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;
 }
 
@@ -330,7 +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];
@@ -338,7 +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];
@@ -351,11 +380,13 @@
     }
 }
 
+
 #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);
@@ -363,14 +394,21 @@
         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);
-    //int class = get_predicted_class_network(net);
+    //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);
-    //return (y[class]?1:0);
+    //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;
@@ -594,7 +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){
@@ -608,6 +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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