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stuff for carlo

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This commit is contained in:
Joseph Redmon
2016-06-06 15:48:52 -07:00
parent d790f21c9a
commit 8a767f1066
12 changed files with 1350 additions and 55 deletions
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127
src/convolutional_layer.c
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@ -8,6 +8,10 @@
#include <stdio.h>
#include <time.h>
#ifndef AI2
#define AI2 0
#endif
void swap_binary(convolutional_layer *l)
{
float *swap = l->filters;
@ -21,24 +25,6 @@ void swap_binary(convolutional_layer *l)
#endif
}
void binarize_filters2(float *filters, int n, int size, char *binary, float *scales)
{
int i, k, f;
for(f = 0; f < n; ++f){
float mean = 0;
for(i = 0; i < size; ++i){
mean += fabs(filters[f*size + i]);
}
mean = mean / size;
scales[f] = mean;
for(i = 0; i < size/8; ++i){
binary[f*size + i] = (filters[f*size + i] > 0) ? 1 : 0;
for(k = 0; k < 8; ++k){
}
}
}
}
void binarize_filters(float *filters, int n, int size, float *binary)
{
int i, f;
@ -54,6 +40,21 @@ void binarize_filters(float *filters, int n, int size, float *binary)
}
}
void binarize_input(float *input, int n, int size, float *binary)
{
int i, s;
for(s = 0; s < size; ++s){
float mean = 0;
for(i = 0; i < n; ++i){
mean += fabs(input[i*size + s]);
}
mean = mean / n;
for(i = 0; i < n; ++i){
binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
}
}
}
int convolutional_out_height(convolutional_layer l)
{
int h = l.h;
@ -89,7 +90,7 @@ image get_convolutional_delta(convolutional_layer l)
}
size_t get_workspace_size(layer l){
#ifdef CUDNN
#ifdef CUDNN
size_t most = 0;
size_t s = 0;
cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
@ -117,9 +118,9 @@ size_t get_workspace_size(layer l){
&s);
if (s > most) most = s;
return most;
#else
#else
return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float);
#endif
#endif
}
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary, int xnor)
@ -133,6 +134,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.c = c;
l.n = n;
l.binary = binary;
l.xnor = xnor;
l.batch = batch;
l.stride = stride;
l.size = size;
@ -164,6 +166,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.cfilters = calloc(c*n*size*size, sizeof(char));
l.scales = calloc(n, sizeof(float));
}
if(xnor){
l.binary_filters = calloc(c*n*size*size, sizeof(float));
l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
}
if(batch_normalize){
l.scales = calloc(n, sizeof(float));
@ -199,7 +205,6 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
}
l.xnor = xnor;
if(batch_normalize){
l.mean_gpu = cuda_make_array(l.mean, n);
@ -325,7 +330,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
l->delta_gpu = cuda_make_array(l->delta, l->batch*out_h*out_w*l->n);
l->output_gpu = cuda_make_array(l->output, l->batch*out_h*out_w*l->n);
#ifdef CUDNN
#ifdef CUDNN
cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w);
cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w);
cudnnSetFilter4dDescriptor(l->dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size);
@ -359,7 +364,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
0,
&l->bf_algo);
#endif
#endif
#endif
l->workspace_size = get_workspace_size(*l);
}
@ -404,7 +409,9 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
int out_w = convolutional_out_width(l);
int i;
fill_cpu(l.outputs*l.batch, 0, l.output, 1);
/*
if(l.binary){
binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
@ -413,44 +420,59 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
}
*/
/*
if(l.binary){
int m = l.n;
int k = l.size*l.size*l.c;
int n = out_h*out_w;
/*
if(l.binary){
int m = l.n;
int k = l.size*l.size*l.c;
int n = out_h*out_w;
char *a = l.cfilters;
char *a = l.cfilters;
float *b = state.workspace;
float *c = l.output;
for(i = 0; i < l.batch; ++i){
im2col_cpu(state.input, l.c, l.h, l.w,
l.size, l.stride, l.pad, b);
gemm_bin(m,n,k,1,a,k,b,n,c,n);
c += n*m;
state.input += l.c*l.h*l.w;
}
scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
activate_array(l.output, m*n*l.batch, l.activation);
return;
}
*/
if(l.xnor && (l.c%32 != 0 || !AI2)){
binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
swap_binary(&l);
for(i = 0; i < l.batch; ++i){
binarize_input(state.input + i*l.inputs, l.c, l.h*l.w, l.binary_input + i*l.inputs);
}
state.input = l.binary_input;
}
int m = l.n;
int k = l.size*l.size*l.c;
int n = out_h*out_w;
if (l.xnor && l.c%32 == 0 && AI2) {
forward_xnor_layer(l, state);
printf("xnor\n");
} else {
float *a = l.filters;
float *b = state.workspace;
float *c = l.output;
for(i = 0; i < l.batch; ++i){
im2col_cpu(state.input, l.c, l.h, l.w,
l.size, l.stride, l.pad, b);
gemm_bin(m,n,k,1,a,k,b,n,c,n);
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
c += n*m;
state.input += l.c*l.h*l.w;
}
scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
activate_array(l.output, m*n*l.batch, l.activation);
return;
}
*/
int m = l.n;
int k = l.size*l.size*l.c;
int n = out_h*out_w;
float *a = l.filters;
float *b = state.workspace;
float *c = l.output;
for(i = 0; i < l.batch; ++i){
im2col_cpu(state.input, l.c, l.h, l.w,
l.size, l.stride, l.pad, b);
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
c += n*m;
state.input += l.c*l.h*l.w;
}
if(l.batch_normalize){
@ -459,6 +481,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
activate_array(l.output, m*n*l.batch, l.activation);
if(l.binary || l.xnor) swap_binary(&l);
}
void backward_convolutional_layer(convolutional_layer l, network_state state)