From 7100de0b59892351c03ce60760431b24947e2ca3 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 23 Mar 2015 04:28:45 +0000
Subject: [PATCH] going to break stuff
---
src/dropout_layer_kernels.cu | 4 ++--
src/network.c | 18 ------------------
src/detection.c | 17 +++++++++--------
src/network_kernels.cu | 6 +++---
src/convolutional_kernels.cu | 14 +++++++-------
5 files changed, 21 insertions(+), 38 deletions(-)
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index 9f0a2f8..a9a6837 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -17,7 +17,7 @@
if(offset < size) output[(batch*n+filter)*size + offset] = biases[filter];
}
-extern "C" void bias_output_gpu(float *output, float *biases, int batch, int n, int size)
+void bias_output_gpu(float *output, float *biases, int batch, int n, int size)
{
dim3 dimBlock(BLOCK, 1, 1);
dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
@@ -46,13 +46,13 @@
}
}
-extern "C" void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
+void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
{
backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size, 1);
check_error(cudaPeekAtLastError());
}
-extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
+void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
{
int i;
int m = layer.n;
@@ -71,7 +71,7 @@
activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
}
-extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
+void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
{
int i;
int m = layer.n;
@@ -105,7 +105,7 @@
}
}
-extern "C" void pull_convolutional_layer(convolutional_layer layer)
+void pull_convolutional_layer(convolutional_layer layer)
{
cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
@@ -113,7 +113,7 @@
cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
-extern "C" void push_convolutional_layer(convolutional_layer layer)
+void push_convolutional_layer(convolutional_layer layer)
{
cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
@@ -121,7 +121,7 @@
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
-extern "C" void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
+void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
diff --git a/src/detection.c b/src/detection.c
index 15694c5..522a321 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -3,11 +3,11 @@
#include "parser.h"
-char *class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
+char *class_names[] = {"bg", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
#define AMNT 3
void draw_detection(image im, float *box, int side)
{
- int classes = 20;
+ int classes = 21;
int elems = 4+classes;
int j;
int r, c;
@@ -50,6 +50,7 @@
if(weightfile){
load_weights(&net, weightfile);
}
+ net.seen = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 128;
srand(time(0));
@@ -62,7 +63,7 @@
int im_dim = 512;
int jitter = 64;
int classes = 20;
- int background = 1;
+ int background = 0;
pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
clock_t time;
while(1){
@@ -72,12 +73,12 @@
train = buffer;
load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
- /*
- image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]);
- draw_detection(im, train.y.vals[0], 7);
+/*
+ image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[114]);
+ draw_detection(im, train.y.vals[114], 7);
show_image(im, "truth");
cvWaitKey(0);
- */
+*/
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -108,7 +109,7 @@
char **paths = (char **)list_to_array(plist);
int im_size = 448;
int classes = 20;
- int background = 1;
+ int background = 0;
int num_output = 7*7*(4+classes+background);
int m = plist->size;
diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu
index 4561d89..2638ac5 100644
--- a/src/dropout_layer_kernels.cu
+++ b/src/dropout_layer_kernels.cu
@@ -11,7 +11,7 @@
if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale;
}
-extern "C" void forward_dropout_layer_gpu(dropout_layer layer, network_state state)
+void forward_dropout_layer_gpu(dropout_layer layer, network_state state)
{
if (!state.train) return;
int size = layer.inputs*layer.batch;
@@ -21,7 +21,7 @@
check_error(cudaPeekAtLastError());
}
-extern "C" void backward_dropout_layer_gpu(dropout_layer layer, network_state state)
+void backward_dropout_layer_gpu(dropout_layer layer, network_state state)
{
if(!state.delta) return;
int size = layer.inputs*layer.batch;
diff --git a/src/network.c b/src/network.c
index 61200d3..75c9454 100644
--- a/src/network.c
+++ b/src/network.c
@@ -194,24 +194,6 @@
return get_network_delta_layer(net, net.n-1);
}
-float calculate_error_network(network net, float *truth)
-{
- float sum = 0;
- float *delta = get_network_delta(net);
- float *out = get_network_output(net);
- int i;
- for(i = 0; i < get_network_output_size(net)*net.batch; ++i){
- //if(i %get_network_output_size(net) == 0) printf("\n");
- //printf("%5.2f %5.2f, ", out[i], truth[i]);
- //if(i == get_network_output_size(net)) printf("\n");
- delta[i] = truth[i] - out[i];
- //printf("%.10f, ", out[i]);
- sum += delta[i]*delta[i];
- }
- //printf("\n");
- return sum;
-}
-
int get_predicted_class_network(network net)
{
float *out = get_network_output(net);
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 4fc361d..019f40d 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)
@@ -196,8 +196,8 @@
state.train = 1;
forward_network_gpu(net, state);
backward_network_gpu(net, state);
- if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
float error = get_network_cost(net);
+ if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
return error;
}
--
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