From 56b6561ae4f1e238ba1a65701f91b40636037cc2 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 24 Mar 2015 20:20:56 +0000
Subject: [PATCH] stuff changed probably
---
src/convolutional_layer.c | 332 ++++++++++++------------------------------------------
1 files changed, 75 insertions(+), 257 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 77e6483..e20a41c 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,6 +1,9 @@
#include "convolutional_layer.h"
#include "utils.h"
-#include "mini_blas.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "blas.h"
+#include "gemm.h"
#include <stdio.h>
#include <time.h>
@@ -38,16 +41,11 @@
return float_to_image(h,w,c,layer.delta);
}
-convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, float learning_rate, float momentum, float decay)
+convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
{
int i;
- size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
- layer->learning_rate = learning_rate;
- layer->momentum = momentum;
- layer->decay = decay;
-
layer->h = h;
layer->w = w;
layer->c = c;
@@ -59,36 +57,31 @@
layer->filters = calloc(c*n*size*size, sizeof(float));
layer->filter_updates = calloc(c*n*size*size, sizeof(float));
- layer->filter_momentum = calloc(c*n*size*size, sizeof(float));
layer->biases = calloc(n, sizeof(float));
layer->bias_updates = calloc(n, sizeof(float));
- layer->bias_momentum = calloc(n, sizeof(float));
- float scale = 1./(size*size*c);
- scale = .05;
- for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*2*(rand_uniform()-.5);
+ float scale = 1./sqrt(size*size*c);
+ for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
for(i = 0; i < n; ++i){
- //layer->biases[i] = rand_normal()*scale + scale;
- layer->biases[i] = .5;
+ layer->biases[i] = scale;
}
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
- layer->filters_cl = cl_make_array(layer->filters, c*n*size*size);
- layer->filter_updates_cl = cl_make_array(layer->filter_updates, c*n*size*size);
- layer->filter_momentum_cl = cl_make_array(layer->filter_momentum, c*n*size*size);
+ layer->filters_gpu = cuda_make_array(layer->filters, c*n*size*size);
+ layer->filter_updates_gpu = cuda_make_array(layer->filter_updates, c*n*size*size);
- layer->biases_cl = cl_make_array(layer->biases, n);
- layer->bias_updates_cl = cl_make_array(layer->bias_updates, n);
- layer->bias_momentum_cl = cl_make_array(layer->bias_momentum, n);
+ layer->biases_gpu = cuda_make_array(layer->biases, n);
+ layer->bias_updates_gpu = cuda_make_array(layer->bias_updates, n);
- layer->col_image_cl = cl_make_array(layer->col_image, layer->batch*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);
+ layer->col_image_gpu = cuda_make_array(layer->col_image, out_h*out_w*size*size*c);
+ layer->delta_gpu = cuda_make_array(layer->delta, layer->batch*out_h*out_w*n);
+ layer->output_gpu = cuda_make_array(layer->output, layer->batch*out_h*out_w*n);
#endif
layer->activation = activation;
@@ -97,43 +90,61 @@
return layer;
}
-void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c)
+void resize_convolutional_layer(convolutional_layer *layer, int h, int w)
{
layer->h = h;
layer->w = w;
- layer->c = c;
int out_h = convolutional_out_height(*layer);
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,
layer->batch*out_h * out_w * layer->n*sizeof(float));
+
+ #ifdef GPU
+ cuda_free(layer->col_image_gpu);
+ cuda_free(layer->delta_gpu);
+ cuda_free(layer->output_gpu);
+
+ layer->col_image_gpu = cuda_make_array(layer->col_image, out_h*out_w*layer->size*layer->size*layer->c);
+ layer->delta_gpu = cuda_make_array(layer->delta, layer->batch*out_h*out_w*layer->n);
+ layer->output_gpu = cuda_make_array(layer->output, layer->batch*out_h*out_w*layer->n);
+ #endif
}
-void bias_output(const convolutional_layer layer)
+void bias_output(float *output, float *biases, int batch, int n, int size)
{
int i,j,b;
- int out_h = convolutional_out_height(layer);
- int out_w = convolutional_out_width(layer);
- for(b = 0; b < layer.batch; ++b){
- for(i = 0; i < layer.n; ++i){
- for(j = 0; j < out_h*out_w; ++j){
- layer.output[(b*layer.n + i)*out_h*out_w + j] = layer.biases[i];
+ 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];
}
}
}
}
-void forward_convolutional_layer(const convolutional_layer layer, float *in)
+void backward_bias(float *bias_updates, float *delta, int batch, int n, int size)
+{
+ int i,b;
+ for(b = 0; b < batch; ++b){
+ for(i = 0; i < n; ++i){
+ bias_updates[i] += sum_array(delta+size*(i+b*n), size);
+ }
+ }
+}
+
+
+void forward_convolutional_layer(const convolutional_layer layer, network_state state)
{
int out_h = convolutional_out_height(layer);
int out_w = convolutional_out_width(layer);
int i;
- bias_output(layer);
+ bias_output(layer.output, layer.biases, layer.batch, layer.n, out_h*out_w);
int m = layer.n;
int k = layer.size*layer.size*layer.c;
@@ -143,80 +154,61 @@
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(state.input, 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;
+ state.input += layer.c*layer.h*layer.w;
}
activate_array(layer.output, m*n*layer.batch, layer.activation);
}
-void learn_bias_convolutional_layer(convolutional_layer layer)
-{
- int i,b;
- int size = convolutional_out_height(layer)
- *convolutional_out_width(layer);
- for(b = 0; b < layer.batch; ++b){
- for(i = 0; i < layer.n; ++i){
- layer.bias_updates[i] += sum_array(layer.delta+size*(i+b*layer.n), size);
- }
- }
-}
-
-void backward_convolutional_layer(convolutional_layer layer, float *delta)
+void backward_convolutional_layer(convolutional_layer layer, network_state state)
{
int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
int k = convolutional_out_height(layer)*
convolutional_out_width(layer);
- 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;
+ gradient_array(layer.output, m*k*layer.batch, layer.activation, layer.delta);
+ backward_bias(layer.bias_updates, layer.delta, layer.batch, layer.n, k);
+
+ if(state.delta) memset(state.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 = state.input+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(state.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, state.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);
}
}
-void update_convolutional_layer(convolutional_layer layer)
+void update_convolutional_layer(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
- axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
- scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
+ axpy_cpu(layer.n, learning_rate/batch, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.n, momentum, layer.bias_updates, 1);
- scal_cpu(size, 1.-layer.learning_rate*layer.decay, layer.filters, 1);
- axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
- scal_cpu(size, layer.momentum, layer.filter_updates, 1);
+ axpy_cpu(size, -decay*batch, layer.filters, 1, layer.filter_updates, 1);
+ axpy_cpu(size, learning_rate/batch, layer.filter_updates, 1, layer.filters, 1);
+ scal_cpu(size, momentum, layer.filter_updates, 1);
}
@@ -273,177 +265,3 @@
return single_filters;
}
-#ifdef GPU
-
-cl_kernel get_convolutional_learn_bias_kernel()
-{
- static int init = 0;
- static cl_kernel kernel;
- if(!init){
- kernel = get_kernel("src/convolutional_layer.cl", "learn_bias", 0);
- init = 1;
- }
- return kernel;
-}
-
-void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
-{
- int size = convolutional_out_height(layer) * convolutional_out_width(layer);
-
- cl_setup();
- cl_kernel kernel = get_convolutional_learn_bias_kernel();
- cl_command_queue queue = cl.queue;
-
- cl_uint i = 0;
- cl.error = clSetKernelArg(kernel, i++, sizeof(layer.batch), (void*) &layer.batch);
- cl.error = clSetKernelArg(kernel, i++, sizeof(layer.n), (void*) &layer.n);
- cl.error = clSetKernelArg(kernel, i++, sizeof(size), (void*) &size);
- cl.error = clSetKernelArg(kernel, i++, sizeof(layer.delta_cl), (void*) &layer.delta_cl);
- cl.error = clSetKernelArg(kernel, i++, sizeof(layer.bias_updates_cl), (void*) &layer.bias_updates_cl);
- check_error(cl);
-
- const size_t global_size[] = {layer.n};
-
- cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
- check_error(cl);
-}
-
-cl_kernel get_convolutional_bias_kernel()
-{
- static int init = 0;
- static cl_kernel kernel;
- if(!init){
- kernel = get_kernel("src/convolutional_layer.cl", "bias", 0);
- init = 1;
- }
- return kernel;
-}
-
-void bias_output_gpu(const convolutional_layer layer)
-{
- int out_h = convolutional_out_height(layer);
- int out_w = convolutional_out_width(layer);
- int size = out_h*out_w;
-
- cl_setup();
- cl_kernel kernel = get_convolutional_bias_kernel();
- cl_command_queue queue = cl.queue;
-
- cl_uint i = 0;
- cl.error = clSetKernelArg(kernel, i++, sizeof(layer.n), (void*) &layer.n);
- cl.error = clSetKernelArg(kernel, i++, sizeof(size), (void*) &size);
- cl.error = clSetKernelArg(kernel, i++, sizeof(layer.biases_cl), (void*) &layer.biases_cl);
- cl.error = clSetKernelArg(kernel, i++, sizeof(layer.output_cl), (void*) &layer.output_cl);
- check_error(cl);
-
- const size_t global_size[] = {layer.n*size, layer.batch};
-
- cl.error = clEnqueueNDRangeKernel(queue, kernel, 2, 0, global_size, 0, 0, 0, 0);
- check_error(cl);
-}
-
-//#define TIMEIT
-
-void forward_convolutional_layer_gpu(convolutional_layer layer, cl_mem in)
-{
- int i;
- int m = layer.n;
- int k = layer.size*layer.size*layer.c;
- int n = convolutional_out_height(layer)*
- convolutional_out_width(layer);
-
- 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){
- 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);
- }
- #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)
-{
- int i;
- int m = layer.n;
- int n = layer.size*layer.size*layer.c;
- int k = convolutional_out_height(layer)*
- convolutional_out_width(layer);
- gradient_array_ongpu(layer.output_cl, m*k*layer.batch, layer.activation, layer.delta_cl);
- learn_bias_convolutional_layer_ongpu(layer);
-
- 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);
- }
-
- if(delta_cl){
- m = layer.size*layer.size*layer.c;
- k = layer.n;
- n = convolutional_out_height(layer)*
- convolutional_out_width(layer);
-
- 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);
- }
-
- 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);
- }
-}
-
-void pull_convolutional_layer(convolutional_layer layer)
-{
- cl_read_array(layer.filters_cl, layer.filters, layer.c*layer.n*layer.size*layer.size);
- cl_read_array(layer.biases_cl, layer.biases, layer.n);
-}
-
-void push_convolutional_layer(convolutional_layer layer)
-{
- cl_write_array(layer.filters_cl, layer.filters, layer.c*layer.n*layer.size*layer.size);
- cl_write_array(layer.biases_cl, layer.biases, layer.n);
-}
-
-void update_convolutional_layer_gpu(convolutional_layer layer)
-{
- int size = layer.size*layer.size*layer.c*layer.n;
- axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);
- scal_ongpu(layer.n,layer.momentum, layer.bias_updates_cl, 1);
-
- scal_ongpu(size, 1.-layer.learning_rate*layer.decay, layer.filters_cl, 1);
- axpy_ongpu(size, layer.learning_rate, layer.filter_updates_cl, 1, layer.filters_cl, 1);
- scal_ongpu(size, layer.momentum, layer.filter_updates_cl, 1);
- pull_convolutional_layer(layer);
-}
-
-
-#endif
-
--
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