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Added batch to col2im, padding option

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This commit is contained in:
Joseph Redmon
2014-07-13 22:07:51 -07:00
parent cd8d53df21
commit 70d622ea54
20 changed files with 428 additions and 134 deletions
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41
src/convolutional_layer.c
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@ -5,12 +5,18 @@
int convolutional_out_height(convolutional_layer layer)
{
return (layer.h-layer.size)/layer.stride + 1;
int h = layer.h;
if (!layer.pad) h -= layer.size;
else h -= 1;
return h/layer.stride + 1;
}
int convolutional_out_width(convolutional_layer layer)
{
return (layer.w-layer.size)/layer.stride + 1;
int w = layer.w;
if (!layer.pad) w -= layer.size;
else w -= 1;
return w/layer.stride + 1;
}
image get_convolutional_image(convolutional_layer layer)
@ -31,7 +37,7 @@ image get_convolutional_delta(convolutional_layer layer)
return float_to_image(h,w,c,layer.delta);
}
convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
{
int i;
size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
@ -43,6 +49,7 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
layer->batch = batch;
layer->stride = stride;
layer->size = size;
layer->pad = pad;
layer->filters = calloc(c*n*size*size, sizeof(float));
layer->filter_updates = calloc(c*n*size*size, sizeof(float));
@ -64,6 +71,17 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
layer->output = calloc(layer->batch*out_h * out_w * n, sizeof(float));
layer->delta = calloc(layer->batch*out_h * out_w * n, sizeof(float));
#ifdef GPU
layer->filters_cl = cl_make_array(layer->filters, c*n*size*size);
layer->filter_updates_cl = cl_make_array(layer->filter_updates, c*n*size*size);
layer->filter_momentum_cl = cl_make_array(layer->filter_momentum, c*n*size*size);
layer->biases_cl = cl_make_array(layer->biases, n);
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->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
layer->activation = activation;
@ -91,12 +109,14 @@ void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c)
void bias_output(const convolutional_layer layer)
{
int i,j;
int i,j,b;
int out_h = convolutional_out_height(layer);
int out_w = convolutional_out_width(layer);
for(i = 0; i < layer.n; ++i){
for(j = 0; j < out_h*out_w; ++j){
layer.output[i*out_h*out_w + j] = layer.biases[i];
for(b = 0; b < layer.batch; ++b){
for(i = 0; i < layer.n; ++i){
for(j = 0; j < out_h*out_w; ++j){
layer.output[(b*layer.n + i)*out_h*out_w + j] = layer.biases[i];
}
}
}
}
@ -114,7 +134,7 @@ void forward_convolutional_layer(const convolutional_layer layer, float *in)
float *b = layer.col_image;
float *c = layer.output;
im2col_cpu(in,layer.batch, layer.c, layer.h, layer.w,
layer.size, layer.stride, b);
layer.size, layer.stride, layer.pad, b);
bias_output(layer);
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
activate_array(layer.output, m*n, layer.activation, 0.);
@ -169,7 +189,6 @@ void backward_convolutional_layer(convolutional_layer layer, float *delta)
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
if(delta){
int i;
m = layer.size*layer.size*layer.c;
k = layer.n;
n = convolutional_out_height(layer)*
@ -183,9 +202,7 @@ void backward_convolutional_layer(convolutional_layer layer, float *delta)
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));
for(i = 0; i < layer.batch; ++i){
col2im_cpu(c+i*n/layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, delta+i*n/layer.batch);
}
col2im_cpu(c, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
}
}