From 881d6ee9b6625ee502cb4f27d9b017a3da78caa7 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 13 May 2016 20:46:31 +0000
Subject: [PATCH] fixed
---
src/convolutional_layer.c | 232 ++++++++++++++++++++++++++++++++++++++++-----------------
1 files changed, 162 insertions(+), 70 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 159951d..303f1ef 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,5 +1,6 @@
#include "convolutional_layer.h"
#include "utils.h"
+#include "batchnorm_layer.h"
#include "im2col.h"
#include "col2im.h"
#include "blas.h"
@@ -7,6 +8,52 @@
#include <stdio.h>
#include <time.h>
+void swap_binary(convolutional_layer *l)
+{
+ float *swap = l->filters;
+ l->filters = l->binary_filters;
+ l->binary_filters = swap;
+
+ #ifdef GPU
+ swap = l->filters_gpu;
+ l->filters_gpu = l->binary_filters_gpu;
+ l->binary_filters_gpu = swap;
+ #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;
+ for(f = 0; f < n; ++f){
+ float mean = 0;
+ for(i = 0; i < size; ++i){
+ mean += fabs(filters[f*size + i]);
+ }
+ mean = mean / size;
+ for(i = 0; i < size; ++i){
+ binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean;
+ }
+ }
+}
+
int convolutional_out_height(convolutional_layer l)
{
int h = l.h;
@@ -41,65 +88,39 @@
return float_to_image(w,h,c,l.delta);
}
-void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
-{
- int i,b,f;
- for(f = 0; f < n; ++f){
- float sum = 0;
- for(b = 0; b < batch; ++b){
- for(i = 0; i < size; ++i){
- int index = i + size*(f + n*b);
- sum += delta[index] * x_norm[index];
- }
- }
- scale_updates[f] += sum;
- }
+#ifdef CUDNN
+size_t get_workspace_size(layer l){
+ size_t most = 0;
+ size_t s = 0;
+ cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
+ l.srcTensorDesc,
+ l.filterDesc,
+ l.convDesc,
+ l.dstTensorDesc,
+ l.fw_algo,
+ &s);
+ if (s > most) most = s;
+ cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
+ l.srcTensorDesc,
+ l.ddstTensorDesc,
+ l.convDesc,
+ l.dfilterDesc,
+ l.bf_algo,
+ &s);
+ if (s > most) most = s;
+ cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
+ l.filterDesc,
+ l.ddstTensorDesc,
+ l.convDesc,
+ l.dsrcTensorDesc,
+ l.bd_algo,
+ &s);
+ if (s > most) most = s;
+ return most;
}
+#endif
-void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
-{
-
- int i,j,k;
- for(i = 0; i < filters; ++i){
- mean_delta[i] = 0;
- for (j = 0; j < batch; ++j) {
- for (k = 0; k < spatial; ++k) {
- int index = j*filters*spatial + i*spatial + k;
- mean_delta[i] += delta[index];
- }
- }
- mean_delta[i] *= (-1./sqrt(variance[i] + .00001f));
- }
-}
-void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
-{
-
- int i,j,k;
- for(i = 0; i < filters; ++i){
- variance_delta[i] = 0;
- for(j = 0; j < batch; ++j){
- for(k = 0; k < spatial; ++k){
- int index = j*filters*spatial + i*spatial + k;
- variance_delta[i] += delta[index]*(x[index] - mean[i]);
- }
- }
- variance_delta[i] *= -.5 * pow(variance[i] + .00001f, (float)(-3./2.));
- }
-}
-void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
-{
- int f, j, k;
- for(j = 0; j < batch; ++j){
- for(f = 0; f < filters; ++f){
- for(k = 0; k < spatial; ++k){
- int index = j*filters*spatial + f*spatial + k;
- delta[index] = delta[index] * 1./(sqrt(variance[f]) + .00001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
- }
- }
- }
-}
-
-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)
+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)
{
int i;
convolutional_layer l = {0};
@@ -134,11 +155,14 @@
l.inputs = l.w * l.h * l.c;
l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
+ l.workspace_size = out_h*out_w*size*size*c*sizeof(float);
l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
if(binary){
l.binary_filters = calloc(c*n*size*size, sizeof(float));
+ l.cfilters = calloc(c*n*size*size, sizeof(char));
+ l.scales = calloc(n, sizeof(float));
}
if(batch_normalize){
@@ -165,13 +189,17 @@
l.scales_gpu = cuda_make_array(l.scales, n);
l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
- l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c);
l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
if(binary){
l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
}
+ if(xnor){
+ 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);
@@ -186,6 +214,50 @@
l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
}
+#ifdef CUDNN
+ cudnnCreateTensorDescriptor(&l.srcTensorDesc);
+ cudnnCreateTensorDescriptor(&l.dstTensorDesc);
+ cudnnCreateFilterDescriptor(&l.filterDesc);
+ cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
+ cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
+ cudnnCreateFilterDescriptor(&l.dfilterDesc);
+ cudnnCreateConvolutionDescriptor(&l.convDesc);
+ 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);
+
+ cudnnSetTensor4dDescriptor(l.srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w);
+ cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
+ cudnnSetFilter4dDescriptor(l.filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size);
+ int padding = l.pad ? l.size/2 : 0;
+ cudnnSetConvolution2dDescriptor(l.convDesc, padding, padding, l.stride, l.stride, 1, 1, CUDNN_CROSS_CORRELATION);
+ cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
+ l.srcTensorDesc,
+ l.filterDesc,
+ l.convDesc,
+ l.dstTensorDesc,
+ CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
+ 0,
+ &l.fw_algo);
+ cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
+ l.filterDesc,
+ l.ddstTensorDesc,
+ l.convDesc,
+ l.dsrcTensorDesc,
+ CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
+ 0,
+ &l.bd_algo);
+ cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
+ l.srcTensorDesc,
+ l.ddstTensorDesc,
+ l.convDesc,
+ l.dfilterDesc,
+ CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
+ 0,
+ &l.bf_algo);
+ l.workspace_size = get_workspace_size(l);
+
+#endif
#endif
l.activation = activation;
@@ -208,7 +280,7 @@
void test_convolutional_layer()
{
- convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0);
+ convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0);
l.batch_normalize = 1;
float data[] = {1,1,1,1,1,
1,1,1,1,1,
@@ -251,11 +323,9 @@
l->batch*out_h * out_w * l->n*sizeof(float));
#ifdef GPU
- cuda_free(l->col_image_gpu);
cuda_free(l->delta_gpu);
cuda_free(l->output_gpu);
- l->col_image_gpu = cuda_make_array(l->col_image, out_h*out_w*l->size*l->size*l->c);
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);
#endif
@@ -295,13 +365,42 @@
}
}
-void forward_convolutional_layer(const convolutional_layer l, network_state state)
+void forward_convolutional_layer(convolutional_layer l, network_state state)
{
int out_h = convolutional_out_height(l);
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);
+ binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales);
+ swap_binary(&l);
+ }
+ */
+
+ 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 = l.col_image;
+ 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;
+ }
int m = l.n;
int k = l.size*l.size*l.c;
@@ -320,14 +419,7 @@
}
if(l.batch_normalize){
- if(state.train){
- mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean);
- variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance);
- normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w);
- } else {
- normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.n, l.out_h*l.out_w);
- }
- scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
+ forward_batchnorm_layer(l, state);
}
add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
--
Gitblit v1.10.0