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Fixed batch stuff in conv layer

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This commit is contained in:
Joseph Redmon
2014-07-17 10:14:59 -07:00
parent 1b94df24fd
commit 076009ebe3
6 changed files with 83 additions and 44 deletions
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47
src/convolutional_layer.c
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@ -79,7 +79,7 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
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 @@ void forward_convolutional_layer(const convolutional_layer layer, float *in)
{
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 learn_bias_convolutional_layer(convolutional_layer layer)
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;
}
}
}