From dcb000b553d051429a49c8729dc5b1af632e8532 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 12 Mar 2015 05:20:15 +0000
Subject: [PATCH] refactoring and added DARK ZONE
---
src/deconvolutional_kernels.cu | 26 +++++++++++++-------------
1 files changed, 13 insertions(+), 13 deletions(-)
diff --git a/src/deconvolutional_kernels.cu b/src/deconvolutional_kernels.cu
index 1d05a80..aeab2c3 100644
--- a/src/deconvolutional_kernels.cu
+++ b/src/deconvolutional_kernels.cu
@@ -9,7 +9,7 @@
#include "cuda.h"
}
-extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, float *in)
+extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state)
{
int i;
int out_h = deconvolutional_out_height(layer);
@@ -24,7 +24,7 @@
for(i = 0; i < layer.batch; ++i){
float *a = layer.filters_gpu;
- float *b = in + i*layer.c*layer.h*layer.w;
+ float *b = state.input + i*layer.c*layer.h*layer.w;
float *c = layer.col_image_gpu;
gemm_ongpu(1,0,m,n,k,1,a,m,b,n,0,c,n);
@@ -34,7 +34,7 @@
activate_array(layer.output_gpu, layer.batch*layer.n*size, layer.activation);
}
-extern "C" void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, float *in, float *delta_gpu)
+extern "C" void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int out_h = deconvolutional_out_height(layer);
@@ -45,14 +45,14 @@
gradient_array(layer.output_gpu, size*layer.n*layer.batch, layer.activation, layer.delta_gpu);
backward_bias(layer.bias_updates_gpu, layer.delta, layer.batch, layer.n, size);
- if(delta_gpu) memset(delta_gpu, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < layer.batch; ++i){
int m = layer.c;
int n = layer.size*layer.size*layer.n;
int k = layer.h*layer.w;
- float *a = in + i*m*n;
+ float *a = state.input + i*m*n;
float *b = layer.col_image_gpu;
float *c = layer.filter_updates_gpu;
@@ -60,14 +60,14 @@
layer.size, layer.stride, 0, b);
gemm_ongpu(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
- if(delta_gpu){
+ if(state.delta){
int m = layer.c;
int n = layer.h*layer.w;
int k = layer.size*layer.size*layer.n;
float *a = layer.filters_gpu;
float *b = layer.col_image_gpu;
- float *c = delta_gpu + i*n*m;
+ float *c = state.delta + i*n*m;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
@@ -90,15 +90,15 @@
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
-extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer)
+extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
- axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
- scal_ongpu(layer.n,layer.momentum, layer.bias_updates_gpu, 1);
+ axpy_ongpu(layer.n, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
+ scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1);
- axpy_ongpu(size, -layer.decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
- axpy_ongpu(size, layer.learning_rate, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
- scal_ongpu(size, layer.momentum, layer.filter_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);
+ scal_ongpu(size, momentum, layer.filter_updates_gpu, 1);
}
--
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