From cd8d53df21f3ad2810add2a8cff766c745f55a17 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 09 May 2014 22:14:52 +0000
Subject: [PATCH] So there WAS this huge bug. Gone now
---
src/convolutional_layer.c | 151 +++++++++++++++++++++++++------------------------
1 files changed, 77 insertions(+), 74 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 31a4af6..5aa76ee 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -55,7 +55,7 @@
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
- layer->biases[i] = 0;
+ layer->biases[i] = .5;
}
int out_h = convolutional_out_height(*layer);
int out_w = convolutional_out_width(*layer);
@@ -63,6 +63,8 @@
layer->col_image = calloc(layer->batch*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
+ #endif
layer->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);
@@ -87,48 +89,76 @@
layer->batch*out_h * out_w * layer->n*sizeof(float));
}
-void forward_convolutional_layer(const convolutional_layer layer, float *in)
+void bias_output(const convolutional_layer layer)
{
- int i;
- int m = layer.n;
- int k = layer.size*layer.size*layer.c;
- int n = convolutional_out_height(layer)*
- convolutional_out_width(layer)*
- layer.batch;
-
- float *a = layer.filters;
- float *b = layer.col_image;
- float *c = layer.output;
- for(i = 0; i < layer.batch; ++i){
- im2col_gpu(in+i*(n/layer.batch), layer.c, layer.h, layer.w, layer.size, layer.stride, b+i*(n/layer.batch));
- }
- gemm(0,0,m,n,k,1,a,k,b,n,0,c,n);
- activate_array(layer.output, m*n, layer.activation);
-}
-
-void learn_bias_convolutional_layer(convolutional_layer layer)
-{
- int i,j,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){
- float sum = 0;
- for(j = 0; j < size; ++j){
- sum += layer.delta[j+size*(i+b*layer.n)];
- }
- layer.bias_updates[i] += sum/size;
+ int i,j;
+ int out_h = convolutional_out_height(layer);
+ int out_w = convolutional_out_width(layer);
+ for(i = 0; i < layer.n; ++i){
+ for(j = 0; j < out_h*out_w; ++j){
+ layer.output[i*out_h*out_w + j] = layer.biases[i];
}
}
}
-void learn_convolutional_layer(convolutional_layer layer)
+void forward_convolutional_layer(const convolutional_layer layer, float *in)
+{
+ int out_h = convolutional_out_height(layer);
+ int out_w = convolutional_out_width(layer);
+
+ int m = layer.n;
+ int k = layer.size*layer.size*layer.c;
+ int n = out_h*out_w*layer.batch;
+
+ float *a = layer.filters;
+ float *b = layer.col_image;
+ float *c = layer.output;
+ im2col_cpu(in,layer.batch, layer.c, layer.h, layer.w,
+ layer.size, layer.stride, b);
+ bias_output(layer);
+ gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ activate_array(layer.output, m*n, layer.activation, 0.);
+}
+
+#ifdef GPU
+void forward_convolutional_layer_gpu(convolutional_layer layer, cl_mem in)
+{
+ int m = layer.n;
+ int k = layer.size*layer.size*layer.c;
+ int n = convolutional_out_height(layer)*
+ convolutional_out_width(layer)*
+ layer.batch;
+
+ cl_write_array(layer.filters_cl, layer.filters, m*k);
+ cl_mem a = layer.filters_cl;
+ cl_mem b = layer.col_image_cl;
+ cl_mem c = layer.output_cl;
+ im2col_ongpu(in, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, b);
+ gemm_ongpu(0,0,m,n,k,1,a,k,b,n,0,c,n);
+ activate_array_ongpu(layer.output_cl, m*n, layer.activation, 0.);
+ cl_read_array(layer.output_cl, layer.output, m*n);
+}
+#endif
+
+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] += mean_array(layer.delta+size*(i+b*layer.n), size);
+ }
+ }
+}
+
+void backward_convolutional_layer(convolutional_layer layer, float *delta)
{
int m = layer.n;
int n = layer.size*layer.size*layer.c;
int k = convolutional_out_height(layer)*
- convolutional_out_width(layer)*
- layer.batch;
+ convolutional_out_width(layer)*
+ layer.batch;
gradient_array(layer.output, m*k, layer.activation, layer.delta);
learn_bias_convolutional_layer(layer);
@@ -137,26 +167,25 @@
float *c = layer.filter_updates;
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
-}
-void backward_convolutional_layer(convolutional_layer layer, float *delta)
-{
- int i;
- int m = layer.size*layer.size*layer.c;
- int k = layer.n;
- int n = convolutional_out_height(layer)*
+ if(delta){
+ int i;
+ m = layer.size*layer.size*layer.c;
+ k = layer.n;
+ n = convolutional_out_height(layer)*
convolutional_out_width(layer)*
layer.batch;
- float *a = layer.filters;
- float *b = layer.delta;
- float *c = layer.col_image;
+ a = layer.filters;
+ b = layer.delta;
+ c = layer.col_image;
- gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
+ gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
- memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
- for(i = 0; i < layer.batch; ++i){
- col2im_cpu(c+i*n/layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, delta+i*n/layer.batch);
+ memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ for(i = 0; i < layer.batch; ++i){
+ col2im_cpu(c+i*n/layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, delta+i*n/layer.batch);
+ }
}
}
@@ -171,32 +200,6 @@
scal_cpu(size, momentum, layer.filter_updates, 1);
}
-void test_convolutional_layer()
-{
- convolutional_layer l = *make_convolutional_layer(1,4,4,1,1,3,1,LINEAR);
- float input[] = {1,2,3,4,
- 5,6,7,8,
- 9,10,11,12,
- 13,14,15,16};
- float filter[] = {.5, 0, .3,
- 0 , 1, 0,
- .2 , 0, 1};
- float delta[] = {1, 2,
- 3, 4};
- float in_delta[] = {.5,1,.3,.6,
- 5,6,7,8,
- 9,10,11,12,
- 13,14,15,16};
- l.filters = filter;
- forward_convolutional_layer(l, input);
- l.delta = delta;
- learn_convolutional_layer(l);
- image filter_updates = float_to_image(3,3,1,l.filter_updates);
- print_image(filter_updates);
- printf("Delta:\n");
- backward_convolutional_layer(l, in_delta);
- pm(4,4,in_delta);
-}
image get_convolutional_filter(convolutional_layer layer, int i)
{
--
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