From 52a6c30748c0cc115e77e9301ac631dd5fd8954c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@burninator.cs.washington.edu>
Date: Thu, 23 Jun 2016 07:09:36 +0000
Subject: [PATCH] better demo
---
src/convolutional_layer.c | 409 ++++++++++++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 367 insertions(+), 42 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index b6437d4..4014a24 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 +8,66 @@
#include <stdio.h>
#include <time.h>
+#ifdef AI2
+#include "xnor_layer.h"
+#endif
+
+#ifndef AI2
+#define AI2 0
+void forward_xnor_layer(layer l, network_state state);
+#endif
+
+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_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;
+ }
+ }
+}
+
+void binarize_cpu(float *input, int n, float *binary)
+{
+ int i;
+ for(i = 0; i < n; ++i){
+ binary[i] = (input[i] > 0) ? 1 : -1;
+ }
+}
+
+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;
@@ -41,7 +102,82 @@
return float_to_image(w,h,c,l.delta);
}
-convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
+size_t get_workspace_size(layer l){
+#ifdef CUDNN
+ 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;
+#else
+ return (size_t)l.out_h*l.out_w*l.size*l.size*l.c*sizeof(float);
+#endif
+}
+
+#ifdef GPU
+#ifdef CUDNN
+void cudnn_convolutional_setup(layer *l)
+{
+ 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);
+}
+#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)
{
int i;
convolutional_layer l = {0};
@@ -51,21 +187,23 @@
l.w = w;
l.c = c;
l.n = n;
+ l.binary = binary;
+ l.xnor = xnor;
l.batch = batch;
l.stride = stride;
l.size = size;
l.pad = pad;
+ l.batch_normalize = batch_normalize;
l.filters = calloc(c*n*size*size, sizeof(float));
l.filter_updates = calloc(c*n*size*size, sizeof(float));
l.biases = calloc(n, sizeof(float));
l.bias_updates = calloc(n, sizeof(float));
- float scale = 1./sqrt(size*size*c);
- for(i = 0; i < c*n*size*size; ++i) l.filters[i] = 2*scale*rand_uniform() - scale;
- for(i = 0; i < n; ++i){
- l.biases[i] = scale;
- }
+
+ // float scale = 1./sqrt(size*size*c);
+ float scale = sqrt(2./(size*size*c));
+ for(i = 0; i < c*n*size*size; ++i) l.filters[i] = scale*rand_uniform(-1, 1);
int out_h = convolutional_out_height(l);
int out_w = convolutional_out_width(l);
l.out_h = out_h;
@@ -74,21 +212,79 @@
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = l.w * l.h * l.c;
- l.col_image = calloc(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));
- #ifdef GPU
+ 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(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));
+ l.scale_updates = calloc(n, sizeof(float));
+ for(i = 0; i < n; ++i){
+ l.scales[i] = 1;
+ }
+
+ l.mean = calloc(n, sizeof(float));
+ l.variance = calloc(n, sizeof(float));
+
+ l.rolling_mean = calloc(n, sizeof(float));
+ l.rolling_variance = calloc(n, sizeof(float));
+ }
+
+#ifdef GPU
l.filters_gpu = cuda_make_array(l.filters, c*n*size*size);
l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size);
l.biases_gpu = cuda_make_array(l.biases, n);
l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
- l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c);
+ l.scales_gpu = cuda_make_array(l.scales, n);
+ l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
+
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);
- #endif
+
+ 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);
+ }
+
+ if(batch_normalize){
+ l.mean_gpu = cuda_make_array(l.mean, n);
+ l.variance_gpu = cuda_make_array(l.variance, n);
+
+ l.rolling_mean_gpu = cuda_make_array(l.mean, n);
+ l.rolling_variance_gpu = cuda_make_array(l.variance, n);
+
+ l.mean_delta_gpu = cuda_make_array(l.mean, n);
+ l.variance_delta_gpu = cuda_make_array(l.variance, n);
+
+ 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);
+ cudnn_convolutional_setup(&l);
+#endif
+#endif
+ l.workspace_size = get_workspace_size(l);
l.activation = activation;
fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
@@ -96,38 +292,92 @@
return l;
}
-void resize_convolutional_layer(convolutional_layer *l, int h, int w)
+void denormalize_convolutional_layer(convolutional_layer l)
{
- l->h = h;
+ int i, j;
+ for(i = 0; i < l.n; ++i){
+ float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
+ for(j = 0; j < l.c*l.size*l.size; ++j){
+ l.filters[i*l.c*l.size*l.size + j] *= scale;
+ }
+ l.biases[i] -= l.rolling_mean[i] * scale;
+ }
+}
+
+void test_convolutional_layer()
+{
+ 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,
+ 1,1,1,1,1,
+ 1,1,1,1,1,
+ 1,1,1,1,1,
+ 2,2,2,2,2,
+ 2,2,2,2,2,
+ 2,2,2,2,2,
+ 2,2,2,2,2,
+ 2,2,2,2,2,
+ 3,3,3,3,3,
+ 3,3,3,3,3,
+ 3,3,3,3,3,
+ 3,3,3,3,3,
+ 3,3,3,3,3};
+ network_state state = {0};
+ state.input = data;
+ forward_convolutional_layer(l, state);
+}
+
+void resize_convolutional_layer(convolutional_layer *l, int w, int h)
+{
l->w = w;
- int out_h = convolutional_out_height(*l);
+ l->h = h;
int out_w = convolutional_out_width(*l);
+ int out_h = convolutional_out_height(*l);
- l->col_image = realloc(l->col_image,
- out_h*out_w*l->size*l->size*l->c*sizeof(float));
+ l->out_w = out_w;
+ l->out_h = out_h;
+
+ l->outputs = l->out_h * l->out_w * l->out_c;
+ l->inputs = l->w * l->h * l->c;
+
l->output = realloc(l->output,
- l->batch*out_h * out_w * l->n*sizeof(float));
+ l->batch*out_h * out_w * l->n*sizeof(float));
l->delta = realloc(l->delta,
- l->batch*out_h * out_w * l->n*sizeof(float));
+ l->batch*out_h * out_w * l->n*sizeof(float));
- #ifdef GPU
- cuda_free(l->col_image_gpu);
+#ifdef 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
+ 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
+ cudnn_convolutional_setup(l);
+#endif
+#endif
+ l->workspace_size = get_workspace_size(*l);
}
-void bias_output(float *output, float *biases, int batch, int n, int size)
+void add_bias(float *output, float *biases, int batch, int n, int size)
{
int i,j,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
- output[(b*n + i)*size + j] = biases[i];
+ output[(b*n + i)*size + j] += biases[i];
+ }
+ }
+ }
+}
+
+void scale_bias(float *output, float *scales, int batch, int n, int size)
+{
+ int i,j,b;
+ for(b = 0; b < batch; ++b){
+ for(i = 0; i < n; ++i){
+ for(j = 0; j < size; ++j){
+ output[(b*n + i)*size + j] *= scales[i];
}
}
}
@@ -143,31 +393,83 @@
}
}
-
-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;
- bias_output(l.output, l.biases, l.batch, l.n, out_h*out_w);
+
+ 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 = 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 ){
+ binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
+ swap_binary(&l);
+ binarize_cpu(state.input, l.c*l.h*l.w*l.batch, l.binary_input);
+ 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 = l.col_image;
- 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){
+ forward_batchnorm_layer(l, 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)
@@ -181,11 +483,9 @@
gradient_array(l.output, m*k*l.batch, l.activation, l.delta);
backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);
- if(state.delta) memset(state.delta, 0, l.batch*l.h*l.w*l.c*sizeof(float));
-
for(i = 0; i < l.batch; ++i){
float *a = l.delta + i*m*k;
- float *b = l.col_image;
+ float *b = state.workspace;
float *c = l.filter_updates;
float *im = state.input+i*l.c*l.h*l.w;
@@ -197,11 +497,11 @@
if(state.delta){
a = l.filters;
b = l.delta + i*m*k;
- c = l.col_image;
+ c = state.workspace;
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
- col2im_cpu(l.col_image, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
+ col2im_cpu(state.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
}
}
}
@@ -226,12 +526,37 @@
return float_to_image(w,h,c,l.filters+i*h*w*c);
}
+void rgbgr_filters(convolutional_layer l)
+{
+ int i;
+ for(i = 0; i < l.n; ++i){
+ image im = get_convolutional_filter(l, i);
+ if (im.c == 3) {
+ rgbgr_image(im);
+ }
+ }
+}
+
+void rescale_filters(convolutional_layer l, float scale, float trans)
+{
+ int i;
+ for(i = 0; i < l.n; ++i){
+ image im = get_convolutional_filter(l, i);
+ if (im.c == 3) {
+ scale_image(im, scale);
+ float sum = sum_array(im.data, im.w*im.h*im.c);
+ l.biases[i] += sum*trans;
+ }
+ }
+}
+
image *get_filters(convolutional_layer l)
{
image *filters = calloc(l.n, sizeof(image));
int i;
for(i = 0; i < l.n; ++i){
filters[i] = copy_image(get_convolutional_filter(l, i));
+ //normalize_image(filters[i]);
}
return filters;
}
--
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