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/convolutional_kernels.cu | 34 +++++++++++++++-------------------
1 files changed, 15 insertions(+), 19 deletions(-)
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index 77304aa..d260a95 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -17,16 +17,16 @@
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);
+ dim3 dimBlock(BLOCK, 1, 1);
bias_output_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
check_error(cudaPeekAtLastError());
}
-__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size, float scale)
+__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size)
{
__shared__ float part[BLOCK];
int i,b;
@@ -42,19 +42,17 @@
part[p] = sum;
__syncthreads();
if(p == 0){
- for(i = 0; i < BLOCK; ++i) bias_updates[filter] += scale * part[i];
+ for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
}
}
-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)
{
- float alpha = 1./batch;
-
- backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size, alpha);
+ backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size);
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;
@@ -63,7 +61,6 @@
convolutional_out_width(layer);
bias_output_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, n);
-
for(i = 0; i < layer.batch; ++i){
im2col_ongpu(state.input + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
float * a = layer.filters_gpu;
@@ -74,9 +71,8 @@
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)
{
- float alpha = 1./layer.batch;
int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
@@ -94,7 +90,7 @@
float * c = layer.filter_updates_gpu;
im2col_ongpu(state.input + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
- gemm_ongpu(0,1,m,n,k,alpha,a + i*m*k,k,b,k,1,c,n);
+ gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(state.delta){
@@ -109,7 +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);
@@ -117,7 +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);
@@ -125,15 +121,15 @@
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
-extern "C" void update_convolutional_layer_gpu(convolutional_layer layer, 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;
- axpy_ongpu(layer.n, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
+ axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1);
- axpy_ongpu(size, -decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
- axpy_ongpu(size, learning_rate, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
+ axpy_ongpu(size, -decay*batch, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
+ axpy_ongpu(size, learning_rate/batch, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
scal_ongpu(size, momentum, layer.filter_updates_gpu, 1);
}
--
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