From ae43c2bc32fbb838bfebeeaf2c2b058ccab5c83c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@burninator.cs.washington.edu>
Date: Thu, 23 Jun 2016 05:31:14 +0000
Subject: [PATCH] hi
---
src/network_kernels.cu | 96 ++++++++++++++++++++++++++++++++++++++---------
1 files changed, 77 insertions(+), 19 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 36f5594..285f72c 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -1,24 +1,38 @@
+#include "cuda_runtime.h"
+#include "curand.h"
+#include "cublas_v2.h"
+
extern "C" {
#include <stdio.h>
#include <time.h>
+#include <assert.h>
#include "network.h"
#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 "gru_layer.h"
+#include "crnn_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"
+#include "normalization_layer.h"
+#include "batchnorm_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"
}
float * get_network_output_gpu_layer(network net, int i);
@@ -27,29 +41,52 @@
void forward_network_gpu(network net, network_state state)
{
+ state.workspace = net.workspace;
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);
+ }
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 == CONNECTED){
forward_connected_layer_gpu(l, state);
+ } else if(l.type == RNN){
+ forward_rnn_layer_gpu(l, state);
+ } else if(l.type == GRU){
+ forward_gru_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){
forward_cost_layer_gpu(l, state);
} else if(l.type == SOFTMAX){
forward_softmax_layer_gpu(l, state);
+ } else if(l.type == NORMALIZATION){
+ forward_normalization_layer_gpu(l, state);
+ } else if(l.type == BATCHNORM){
+ forward_batchnorm_layer_gpu(l, state);
} else if(l.type == MAXPOOL){
forward_maxpool_layer_gpu(l, state);
+ } else if(l.type == AVGPOOL){
+ forward_avgpool_layer_gpu(l, state);
} else if(l.type == DROPOUT){
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;
}
@@ -57,10 +94,12 @@
void backward_network_gpu(network net, network_state state)
{
+ state.workspace = net.workspace;
int i;
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;
@@ -74,20 +113,38 @@
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){
+ if(i != 0) backward_avgpool_layer_gpu(l, state);
} else if(l.type == DROPOUT){
backward_dropout_layer_gpu(l, state);
} else if(l.type == DETECTION){
backward_detection_layer_gpu(l, state);
+ } else if(l.type == NORMALIZATION){
+ backward_normalization_layer_gpu(l, state);
+ } else if(l.type == BATCHNORM){
+ backward_batchnorm_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 == GRU){
+ backward_gru_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);
}
}
}
@@ -96,14 +153,23 @@
{
int i;
int update_batch = net.batch*net.subdivisions;
+ float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
- update_convolutional_layer_gpu(l, update_batch, net.learning_rate, net.momentum, net.decay);
+ update_convolutional_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == DECONVOLUTIONAL){
- update_deconvolutional_layer_gpu(l, net.learning_rate, net.momentum, net.decay);
+ update_deconvolutional_layer_gpu(l, rate, net.momentum, net.decay);
} else if(l.type == CONNECTED){
- update_connected_layer_gpu(l, update_batch, net.learning_rate, net.momentum, net.decay);
+ update_connected_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == GRU){
+ update_gru_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);
}
}
}
@@ -111,8 +177,11 @@
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 == 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);
@@ -127,7 +196,7 @@
forward_network_gpu(net, state);
backward_network_gpu(net, state);
float error = get_network_cost(net);
- if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
+ if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net);
return error;
}
@@ -136,20 +205,7 @@
{
layer l = net.layers[i];
cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
- if(l.type == CONVOLUTIONAL){
- return l.output;
- } else if(l.type == DECONVOLUTIONAL){
- return l.output;
- } else if(l.type == CONNECTED){
- return l.output;
- } else if(l.type == DETECTION){
- return l.output;
- } else if(l.type == MAXPOOL){
- return l.output;
- } else if(l.type == SOFTMAX){
- return l.output;
- }
- return 0;
+ return l.output;
}
float *get_network_output_gpu(network net)
@@ -163,6 +219,8 @@
{
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