From 076009ebe308fde0156304e701f36e8bb04e4d6b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 17 Jul 2014 17:14:59 +0000
Subject: [PATCH] Fixed batch stuff in conv layer
---
src/convolutional_layer.c | 47 +++++++++++++++++++++++++++++++----------------
1 files changed, 31 insertions(+), 16 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 7571e7a..44e9244 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -79,7 +79,7 @@
layer->bias_updates_cl = cl_make_array(layer->bias_updates, n);
layer->bias_momentum_cl = cl_make_array(layer->bias_momentum, n);
- layer->col_image_cl = cl_make_array(layer->col_image, layer->batch*out_h*out_w*size*size*c);
+ layer->col_image_cl = cl_make_array(layer->col_image, layer.batch*out_h*out_w*size*size*c);
layer->delta_cl = cl_make_array(layer->delta, layer->batch*out_h*out_w*n);
layer->output_cl = cl_make_array(layer->output, layer->batch*out_h*out_w*n);
#endif
@@ -124,24 +124,32 @@
{
int out_h = convolutional_out_height(layer);
int out_w = convolutional_out_width(layer);
+ int i;
+
+ bias_output(layer);
int m = layer.n;
int k = layer.size*layer.size*layer.c;
- int n = out_h*out_w*layer.batch;
+ int n = out_h*out_w;
float *a = layer.filters;
float *b = layer.col_image;
float *c = layer.output;
- im2col_cpu(in, layer.batch, layer.c, layer.h, layer.w,
- layer.size, layer.stride, layer.pad, b);
- bias_output(layer);
- gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+
+ for(i = 0; i < layer.batch; ++i){
+ im2col_cpu(in, layer.c, layer.h, layer.w,
+ layer.size, layer.stride, layer.pad, b);
+ gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ c += n*m;
+ in += layer.h*layer.w*layer.c;
+ b += k*n;
+ }
/*
int i;
for(i = 0; i < m*n; ++i) printf("%f, ", layer.output[i]);
printf("\n");
*/
- activate_array(layer.output, m*n, layer.activation, 0.);
+ activate_array(layer.output, m*n*layer.batch, layer.activation, 0.);
}
#ifdef GPU
@@ -178,35 +186,42 @@
void backward_convolutional_layer(convolutional_layer layer, float *delta)
{
+ int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
int k = convolutional_out_height(layer)*
- convolutional_out_width(layer)*
- layer.batch;
- gradient_array(layer.output, m*k, layer.activation, layer.delta);
+ convolutional_out_width(layer);
+ gradient_array(layer.output, m*k*layer.batch, layer.activation, layer.delta);
learn_bias_convolutional_layer(layer);
float *a = layer.delta;
float *b = layer.col_image;
float *c = layer.filter_updates;
- gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+ for(i = 0; i < layer.batch; ++i){
+ gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+ a += m*k;
+ b += k*n;
+ }
if(delta){
m = layer.size*layer.size*layer.c;
k = layer.n;
n = convolutional_out_height(layer)*
- convolutional_out_width(layer)*
- layer.batch;
+ convolutional_out_width(layer);
a = layer.filters;
b = layer.delta;
c = layer.col_image;
- gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
-
memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
- col2im_cpu(c, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
+
+ for(i = 0; i < layer.batch; ++i){
+ gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
+ col2im_cpu(c, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
+ c += k*n;
+ delta += layer.h*layer.w*layer.c;
+ }
}
}
--
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