From 68213b835b9f15cb449ad2037a8b51c17a3de07b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 14 Mar 2016 22:10:14 +0000
Subject: [PATCH] Makefile

---
 src/network_kernels.cu |   51 +++++++++++++++++++++++++++++++++++++++++++--------
 1 files changed, 43 insertions(+), 8 deletions(-)

diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index cfc6e83..730634e 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -1,3 +1,7 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
 extern "C" {
 #include <stdio.h>
 #include <time.h>
@@ -7,22 +11,25 @@
 #include "image.h"
 #include "data.h"
 #include "utils.h"
-#include "params.h"
 #include "parser.h"
 
 #include "crop_layer.h"
 #include "connected_layer.h"
+#include "rnn_layer.h"
+#include "crnn_layer.h"
 #include "detection_layer.h"
-#include "region_layer.h"
 #include "convolutional_layer.h"
+#include "activation_layer.h"
 #include "deconvolutional_layer.h"
 #include "maxpool_layer.h"
 #include "avgpool_layer.h"
 #include "normalization_layer.h"
 #include "cost_layer.h"
+#include "local_layer.h"
 #include "softmax_layer.h"
 #include "dropout_layer.h"
 #include "route_layer.h"
+#include "shortcut_layer.h"
 #include "blas.h"
 }
 
@@ -34,20 +41,27 @@
 {
     int i;
     for(i = 0; i < net.n; ++i){
+        state.index = i;
         layer l = net.layers[i];
         if(l.delta_gpu){
-            scal_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
+            fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
         }
         if(l.type == CONVOLUTIONAL){
             forward_convolutional_layer_gpu(l, state);
         } else if(l.type == DECONVOLUTIONAL){
             forward_deconvolutional_layer_gpu(l, state);
+        } else if(l.type == ACTIVE){
+            forward_activation_layer_gpu(l, state);
+        } else if(l.type == LOCAL){
+            forward_local_layer_gpu(l, state);
         } else if(l.type == DETECTION){
             forward_detection_layer_gpu(l, state);
-        } else if(l.type == REGION){
-            forward_region_layer_gpu(l, state);
         } else if(l.type == CONNECTED){
             forward_connected_layer_gpu(l, state);
+        } else if(l.type == RNN){
+            forward_rnn_layer_gpu(l, state);
+        } else if(l.type == CRNN){
+            forward_crnn_layer_gpu(l, state);
         } else if(l.type == CROP){
             forward_crop_layer_gpu(l, state);
         } else if(l.type == COST){
@@ -64,6 +78,8 @@
             forward_dropout_layer_gpu(l, state);
         } else if(l.type == ROUTE){
             forward_route_layer_gpu(l, net);
+        } else if(l.type == SHORTCUT){
+            forward_shortcut_layer_gpu(l, state);
         }
         state.input = l.output_gpu;
     }
@@ -75,6 +91,7 @@
     float * original_input = state.input;
     float * original_delta = state.delta;
     for(i = net.n-1; i >= 0; --i){
+        state.index = i;
         layer l = net.layers[i];
         if(i == 0){
             state.input = original_input;
@@ -88,6 +105,10 @@
             backward_convolutional_layer_gpu(l, state);
         } else if(l.type == DECONVOLUTIONAL){
             backward_deconvolutional_layer_gpu(l, state);
+        } else if(l.type == ACTIVE){
+            backward_activation_layer_gpu(l, state);
+        } else if(l.type == LOCAL){
+            backward_local_layer_gpu(l, state);
         } else if(l.type == MAXPOOL){
             if(i != 0) backward_maxpool_layer_gpu(l, state);
         } else if(l.type == AVGPOOL){
@@ -96,18 +117,22 @@
             backward_dropout_layer_gpu(l, state);
         } else if(l.type == DETECTION){
             backward_detection_layer_gpu(l, state);
-        } else if(l.type == REGION){
-            backward_region_layer_gpu(l, state);
         } else if(l.type == NORMALIZATION){
             backward_normalization_layer_gpu(l, state);
         } else if(l.type == SOFTMAX){
             if(i != 0) backward_softmax_layer_gpu(l, state);
         } else if(l.type == CONNECTED){
             backward_connected_layer_gpu(l, state);
+        } else if(l.type == RNN){
+            backward_rnn_layer_gpu(l, state);
+        } else if(l.type == CRNN){
+            backward_crnn_layer_gpu(l, state);
         } else if(l.type == COST){
             backward_cost_layer_gpu(l, state);
         } else if(l.type == ROUTE){
             backward_route_layer_gpu(l, net);
+        } else if(l.type == SHORTCUT){
+            backward_shortcut_layer_gpu(l, state);
         }
     }
 }
@@ -125,6 +150,12 @@
             update_deconvolutional_layer_gpu(l, rate, net.momentum, net.decay);
         } else if(l.type == CONNECTED){
             update_connected_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+        } else if(l.type == RNN){
+            update_rnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+        } else if(l.type == CRNN){
+            update_crnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+        } else if(l.type == LOCAL){
+            update_local_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
         }
     }
 }
@@ -132,9 +163,11 @@
 float train_network_datum_gpu(network net, float *x, float *y)
 {
     network_state state;
+    state.index = 0;
+    state.net = net;
     int x_size = get_network_input_size(net)*net.batch;
     int y_size = get_network_output_size(net)*net.batch;
-    if(net.layers[net.n-1].type == REGION) y_size = net.layers[net.n-1].truths*net.batch;
+    if(net.layers[net.n-1].type == DETECTION) y_size = net.layers[net.n-1].truths*net.batch;
     if(!*net.input_gpu){
         *net.input_gpu = cuda_make_array(x, x_size);
         *net.truth_gpu = cuda_make_array(y, y_size);
@@ -172,6 +205,8 @@
 {
     int size = get_network_input_size(net) * net.batch;
     network_state state;
+    state.index = 0;
+    state.net = net;
     state.input = cuda_make_array(input, size);
     state.truth = 0;
     state.train = 0;

--
Gitblit v1.10.0