From 1c0fd9bb4726f28b5ccf4491b8d108b00c884ec3 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 30 Oct 2014 06:26:41 +0000
Subject: [PATCH] im2col slightly faster

---
 src/network.c |   58 +++++++++++++++++++++++++++++++++++++++++++++++++++++++---
 1 files changed, 55 insertions(+), 3 deletions(-)

diff --git a/src/network.c b/src/network.c
index 6696769..0a72a19 100644
--- a/src/network.c
+++ b/src/network.c
@@ -38,6 +38,7 @@
     //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);
@@ -62,6 +63,7 @@
             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];
@@ -83,6 +85,7 @@
     cl_mem prev_input;
     cl_mem prev_delta;
     for(i = net.n-1; i >= 0; --i){
+        clock_t time = clock();
         if(i == 0){
             prev_input = input;
             prev_delta = 0;
@@ -110,6 +113,7 @@
             softmax_layer layer = *(softmax_layer *)net.layers[i];
             backward_softmax_layer_gpu(layer, prev_delta);
         }
+        printf("back: %d %f\n", i, sec(clock()-time));
     }
 }
 
@@ -384,6 +388,7 @@
 {
     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);
@@ -391,10 +396,18 @@
         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;
 }
 
@@ -407,7 +420,25 @@
     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_gpu(net, X, y);
+        sum += err;
+    }
+    free(X);
+    free(y);
+    return (float)sum/(n*batch);
+}
+
+float train_network_data_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_next_batch(d, batch, i*batch, X, y);
         float err = train_network_datum_gpu(net, X, y);
         sum += err;
     }
@@ -438,7 +469,7 @@
     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;
     }
@@ -621,7 +652,7 @@
     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){
@@ -635,6 +666,27 @@
     } 
 }
 
+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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