From c7c1e0e7b719711ddaf13f128a18e6830d5941e3 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 05 Feb 2016 08:15:12 +0000
Subject: [PATCH] rnn stuff
---
src/network_kernels.cu | 24 +++++++++++++++++++++++-
1 files changed, 23 insertions(+), 1 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 26b8404..ea12819 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -11,13 +11,14 @@
#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 "detection_layer.h"
#include "convolutional_layer.h"
+#include "activation_layer.h"
#include "deconvolutional_layer.h"
#include "maxpool_layer.h"
#include "avgpool_layer.h"
@@ -27,6 +28,7 @@
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "route_layer.h"
+#include "shortcut_layer.h"
#include "blas.h"
}
@@ -38,6 +40,7 @@
{
int i;
for(i = 0; i < net.n; ++i){
+ state.index = i;
layer l = net.layers[i];
if(l.delta_gpu){
fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
@@ -46,12 +49,16 @@
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 == CONNECTED){
forward_connected_layer_gpu(l, state);
+ } else if(l.type == RNN){
+ forward_rnn_layer_gpu(l, state);
} else if(l.type == CROP){
forward_crop_layer_gpu(l, state);
} else if(l.type == COST){
@@ -68,6 +75,8 @@
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;
}
@@ -79,6 +88,7 @@
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;
@@ -92,6 +102,8 @@
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){
@@ -108,10 +120,14 @@
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 == 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);
}
}
}
@@ -129,6 +145,8 @@
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 == LOCAL){
update_local_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
}
@@ -138,6 +156,8 @@
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 == DETECTION) y_size = net.layers[net.n-1].truths*net.batch;
@@ -178,6 +198,8 @@
{
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;
--
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