From 989ab8c38a02fa7ea9c25108151736c62e81c972 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 24 Apr 2015 17:27:50 +0000
Subject: [PATCH] IOU loss function
---
src/network_kernels.cu | 26 +++++++-------------------
1 files changed, 7 insertions(+), 19 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index acc31d7..0b2bb97 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -20,8 +20,8 @@
#include "dropout_layer.h"
}
-extern "C" float * get_network_output_gpu_layer(network net, int i);
-extern "C" float * get_network_delta_gpu_layer(network net, int i);
+float * get_network_output_gpu_layer(network net, int i);
+float * get_network_delta_gpu_layer(network net, int i);
float *get_network_output_gpu(network net);
void forward_network_gpu(network net, network_state state)
@@ -64,7 +64,6 @@
int i;
float * original_input = state.input;
for(i = net.n-1; i >= 0; --i){
- //clock_t time = clock();
if(i == 0){
state.input = original_input;
state.delta = 0;
@@ -72,6 +71,7 @@
state.input = get_network_output_gpu_layer(net, i-1);
state.delta = get_network_delta_gpu_layer(net, i-1);
}
+
if(net.types[i] == CONVOLUTIONAL){
backward_convolutional_layer_gpu(*(convolutional_layer *)net.layers[i], state);
}
@@ -102,10 +102,11 @@
void update_network_gpu(network net)
{
int i;
+ int update_batch = net.batch*net.subdivisions;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer_gpu(layer, net.learning_rate, net.momentum, net.decay);
+ update_convolutional_layer_gpu(layer, update_batch, net.learning_rate, net.momentum, net.decay);
}
else if(net.types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
@@ -113,7 +114,7 @@
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer_gpu(layer, net.learning_rate, net.momentum, net.decay);
+ update_connected_layer_gpu(layer, update_batch, net.learning_rate, net.momentum, net.decay);
}
}
}
@@ -181,7 +182,6 @@
float train_network_datum_gpu(network net, float *x, float *y)
{
- //clock_t time = clock();
network_state state;
int x_size = get_network_input_size(net)*net.batch;
int y_size = get_network_output_size(net)*net.batch;
@@ -195,22 +195,11 @@
state.input = *net.input_gpu;
state.truth = *net.truth_gpu;
state.train = 1;
- //printf("trans %f\n", sec(clock() - time));
- //time = clock();
forward_network_gpu(net, state);
- //printf("forw %f\n", sec(clock() - time));
- //time = clock();
backward_network_gpu(net, state);
- //printf("back %f\n", sec(clock() - time));
- //time = clock();
- update_network_gpu(net);
float error = get_network_cost(net);
+ if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
- //print_letters(y, 50);
- //float *out = get_network_output_gpu(net);
- //print_letters(out, 50);
- //printf("updt %f\n", sec(clock() - time));
- //time = clock();
return error;
}
@@ -256,7 +245,6 @@
float *network_predict_gpu(network net, float *input)
{
-
int size = get_network_input_size(net) * net.batch;
network_state state;
state.input = cuda_make_array(input, size);
--
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