From aebe937710ced03d03f73ab23f410f29685655c1 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 11 Aug 2016 18:54:24 +0000
Subject: [PATCH] what do you even write here?
---
src/network_kernels.cu | 100 ++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 97 insertions(+), 3 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 0b50647..3e01019 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -11,17 +11,23 @@
#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 "region_layer.h"
#include "convolutional_layer.h"
+#include "activation_layer.h"
#include "deconvolutional_layer.h"
#include "maxpool_layer.h"
+#include "reorg_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"
@@ -37,6 +43,7 @@
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;
@@ -48,12 +55,22 @@
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 == 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){
@@ -62,8 +79,12 @@
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 == REORG){
+ forward_reorg_layer_gpu(l, state);
} else if(l.type == AVGPOOL){
forward_avgpool_layer_gpu(l, state);
} else if(l.type == DROPOUT){
@@ -79,6 +100,7 @@
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;
@@ -97,22 +119,36 @@
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 == REORG){
+ backward_reorg_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 == REGION){
+ backward_region_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){
@@ -136,20 +172,26 @@
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 == 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);
}
}
}
-float train_network_datum_gpu(network net, float *x, float *y)
+void forward_backward_network_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.layers[net.n-1].truths) 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);
@@ -163,12 +205,64 @@
state.train = 1;
forward_network_gpu(net, state);
backward_network_gpu(net, state);
+}
+
+float train_network_datum_gpu(network net, float *x, float *y)
+{
+ forward_backward_network_gpu(net, x, y);
float error = get_network_cost(net);
if (((*net.seen) / net.batch) % net.subdivisions == 0) update_network_gpu(net);
return error;
}
+typedef struct {
+ network net;
+ float *X;
+ float *y;
+} train_args;
+
+void *train_thread(void *ptr)
+{
+ train_args args = *(train_args*)ptr;
+
+ cudaError_t status = cudaSetDevice(args.net.gpu_index);
+ check_error(status);
+ forward_backward_network_gpu(args.net, args.X, args.y);
+ free(ptr);
+ return 0;
+}
+
+pthread_t train_network_in_thread(train_args args)
+{
+ pthread_t thread;
+ train_args *ptr = (train_args *)calloc(1, sizeof(train_args));
+ *ptr = args;
+ if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed");
+ return thread;
+}
+
+float train_networks(network *nets, int n, data d)
+{
+ int batch = nets[0].batch;
+ float **X = (float **) calloc(n, sizeof(float *));
+ float **y = (float **) calloc(n, sizeof(float *));
+ pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t));
+
+ int i;
+ float sum = 0;
+ for(i = 0; i < n; ++i){
+ X[i] = (float *) calloc(batch*d.X.cols, sizeof(float));
+ y[i] = (float *) calloc(batch*d.y.cols, sizeof(float));
+ get_next_batch(d, batch, i*batch, X[i], y[i]);
+ float err = train_network_datum(nets[i], X[i], y[i]);
+ sum += err;
+ }
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
+}
+
float *get_network_output_layer_gpu(network net, int i)
{
layer l = net.layers[i];
--
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