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Detection good, split up col images

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This commit is contained in:
Joseph Redmon
2014-12-03 23:20:29 -08:00
parent ff67f03476
commit 1edcf73a73
16 changed files with 282 additions and 221 deletions
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98
src/convolutional_layer.c
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@@ -65,7 +65,7 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
layer->bias_updates = calloc(n, sizeof(float));
layer->bias_momentum = calloc(n, sizeof(float));
float scale = 1./(size*size*c);
scale = .05;
scale = .01;
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*2*(rand_uniform()-.5);
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
@@ -74,7 +74,7 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
int out_h = convolutional_out_height(*layer);
int out_w = convolutional_out_width(*layer);
layer->col_image = calloc(layer->batch*out_h*out_w*size*size*c, sizeof(float));
layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
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
@@ -86,7 +86,7 @@ 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, 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
@@ -106,7 +106,7 @@ void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c)
int out_w = convolutional_out_width(*layer);
layer->col_image = realloc(layer->col_image,
layer->batch*out_h*out_w*layer->size*layer->size*layer->c*sizeof(float));
out_h*out_w*layer->size*layer->size*layer->c*sizeof(float));
layer->output = realloc(layer->output,
layer->batch*out_h * out_w * layer->n*sizeof(float));
layer->delta = realloc(layer->delta,
@@ -143,13 +143,13 @@ 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, layer.pad, b);
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);
b += k*n;
c += n*m;
in += layer.c*layer.h*layer.w;
}
activate_array(layer.output, m*n*layer.batch, layer.activation);
}
@@ -166,7 +166,7 @@ void learn_bias_convolutional_layer(convolutional_layer layer)
}
}
void backward_convolutional_layer(convolutional_layer layer, float *delta)
void backward_convolutional_layer(convolutional_layer layer, float *in, float *delta)
{
int i;
int m = layer.n;
@@ -176,35 +176,28 @@ void backward_convolutional_layer(convolutional_layer layer, float *delta)
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;
if(delta) memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < layer.batch; ++i){
float *a = layer.delta + i*m*k;
float *b = layer.col_image;
float *c = layer.filter_updates;
float *im = in+i*layer.c*layer.h*layer.w;
im2col_cpu(im, layer.c, layer.h, layer.w,
layer.size, layer.stride, layer.pad, b);
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);
if(delta){
a = layer.filters;
b = layer.delta + i*m*k;
c = layer.col_image;
a = layer.filters;
b = layer.delta;
c = layer.col_image;
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
for(i = 0; i < layer.batch; ++i){
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
b += k*n;
c += m*n;
col2im_cpu(layer.col_image, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta+i*layer.c*layer.h*layer.w);
}
memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
col2im_cpu(layer.col_image, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
}
}
@@ -354,36 +347,17 @@ void forward_convolutional_layer_gpu(convolutional_layer layer, cl_mem in)
bias_output_gpu(layer);
#ifdef TIMEIT
clock_t time = clock();
printf("Forward\n");
#endif
im2col_ongpu(in, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_cl);
#ifdef TIMEIT
clFinish(cl.queue);
printf("Im2col %f\n", sec(clock()-time));
time = clock();
#endif
for(i = 0; i < layer.batch; ++i){
im2col_ongpu(in, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_cl);
cl_mem a = layer.filters_cl;
cl_mem b = layer.col_image_cl;
cl_mem c = layer.output_cl;
gemm_ongpu_offset(0,0,m,n,k,1.,a,0,k,b,i*k*n,n,1.,c,i*m*n,n);
gemm_ongpu_offset(0,0,m,n,k,1.,a,0,k,b,0,n,1.,c,i*m*n,n);
}
#ifdef TIMEIT
clFinish(cl.queue);
printf("Gemm %f\n", sec(clock()-time));
#endif
activate_array_ongpu(layer.output_cl, m*n*layer.batch, layer.activation);
#ifdef TIMEIT
cl_read_array(layer.output_cl, layer.output, m*n*layer.batch);
#endif
}
void backward_convolutional_layer_gpu(convolutional_layer layer, cl_mem delta_cl)
void backward_convolutional_layer_gpu(convolutional_layer layer, cl_mem in, cl_mem delta_cl)
{
int i;
int m = layer.n;
@@ -393,30 +367,26 @@ void backward_convolutional_layer_gpu(convolutional_layer layer, cl_mem delta_cl
gradient_array_ongpu(layer.output_cl, m*k*layer.batch, layer.activation, layer.delta_cl);
learn_bias_convolutional_layer_ongpu(layer);
if(delta_cl) scal_ongpu(layer.batch*layer.h*layer.w*layer.c, 0, delta_cl, 1);
for(i = 0; i < layer.batch; ++i){
cl_mem a = layer.delta_cl;
cl_mem b = layer.col_image_cl;
cl_mem c = layer.filter_updates_cl;
gemm_ongpu_offset(0,1,m,n,k,1,a,i*m*k,k,b,i*k*n,k,1,c,0,n);
}
im2col_ongpu(in, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_cl);
gemm_ongpu_offset(0,1,m,n,k,1,a,i*m*k,k,b,0,k,1,c,0,n);
if(delta_cl){
m = layer.size*layer.size*layer.c;
k = layer.n;
n = convolutional_out_height(layer)*
convolutional_out_width(layer);
if(delta_cl){
for(i = 0; i < layer.batch; ++i){
cl_mem a = layer.filters_cl;
cl_mem b = layer.delta_cl;
cl_mem c = layer.col_image_cl;
gemm_ongpu_offset(1,0,m,n,k,1,a,0,m,b,i*k*n,n,0,c,i*m*n,n);
}
gemm_ongpu_offset(1,0,n,k,m,1,a,0,n,b,i*k*m,k,0,c,0,k);
scal_ongpu(layer.batch*layer.h*layer.w*layer.c,0,delta_cl, 1);
col2im_ongpu(layer.col_image_cl, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta_cl);
col2im_ongpu(layer.col_image_cl, i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta_cl);
}
}
}