From 729ce43e6ec45cfdb58e06e227428a0f81c5de0f Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 10 Jun 2016 00:20:31 +0000
Subject: [PATCH] stuff
---
src/convolutional_layer.c | 127 ++++++++++++++++++++++++++----------------
1 files changed, 78 insertions(+), 49 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 5575aac..af867e5 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -8,6 +8,14 @@
#include <stdio.h>
#include <time.h>
+#ifdef AI2
+#include "xnor_layer.h"
+#endif
+
+#ifndef AI2
+#define AI2 0
+#endif
+
void swap_binary(convolutional_layer *l)
{
float *swap = l->filters;
@@ -21,24 +29,6 @@
#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 +44,21 @@
}
}
+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 +94,7 @@
}
size_t get_workspace_size(layer l){
- #ifdef CUDNN
+#ifdef CUDNN
size_t most = 0;
size_t s = 0;
cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
@@ -117,9 +122,9 @@
&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 +138,7 @@
l.c = c;
l.n = n;
l.binary = binary;
+ l.xnor = xnor;
l.batch = batch;
l.stride = stride;
l.size = size;
@@ -164,6 +170,10 @@
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 +209,6 @@
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 +334,7 @@
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 +368,7 @@
CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
0,
&l->bf_algo);
- #endif
+#endif
#endif
l->workspace_size = get_workspace_size(*l);
}
@@ -404,7 +413,9 @@
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);
@@ -413,42 +424,59 @@
}
*/
- 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;
- float *b = state.workspace;
- float *c = l.output;
+ 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){
- 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;
+ binarize_input(state.input + i*l.inputs, l.c, l.h*l.w, l.binary_input + i*l.inputs);
}
- 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;
+ state.input = l.binary_input;
}
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;
+ if (l.xnor && l.c%32 == 0 && AI2) {
+ forward_xnor_layer(l, state);
+ printf("xnor\n");
+ } else {
- 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;
+ 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){
@@ -457,6 +485,7 @@
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)
--
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