From d9f1b0b16edeb59281355a855e18a8be343fc33c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 08 Aug 2014 19:04:15 +0000
Subject: [PATCH] probably how maxpool layers should be
---
src/convolutional_layer.c | 302 ++++++++++++++++++++++---------------------------
1 files changed, 135 insertions(+), 167 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 40d5858..6c7f947 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -5,12 +5,18 @@
int convolutional_out_height(convolutional_layer layer)
{
- return (layer.h-layer.size)/layer.stride + 1;
+ int h = layer.h;
+ if (!layer.pad) h -= layer.size;
+ else h -= 1;
+ return h/layer.stride + 1;
}
int convolutional_out_width(convolutional_layer layer)
{
- return (layer.w-layer.size)/layer.stride + 1;
+ int w = layer.w;
+ if (!layer.pad) w -= layer.size;
+ else w -= 1;
+ return w/layer.stride + 1;
}
image get_convolutional_image(convolutional_layer layer)
@@ -31,11 +37,16 @@
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, ACTIVATION activation)
+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)
{
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;
@@ -43,6 +54,7 @@
layer->batch = batch;
layer->stride = stride;
layer->size = size;
+ layer->pad = pad;
layer->filters = calloc(c*n*size*size, sizeof(float));
layer->filter_updates = calloc(c*n*size*size, sizeof(float));
@@ -52,10 +64,11 @@
layer->bias_updates = calloc(n, sizeof(float));
layer->bias_momentum = calloc(n, sizeof(float));
float scale = 1./(size*size*c);
- for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
+ //scale = .0001;
+ for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform()-.5);
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);
@@ -63,10 +76,22 @@
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
+ 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->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->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);
+ #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);
- srand(0);
return layer;
}
@@ -87,196 +112,118 @@
layer->batch*out_h * out_w * layer->n*sizeof(float));
}
-void forward_convolutional_layer(const convolutional_layer layer, float *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)*
- layer.batch;
-
- memset(layer.output, 0, m*n*sizeof(float));
-
- float *a = layer.filters;
- float *b = layer.col_image;
- float *c = layer.output;
- for(i = 0; i < layer.batch; ++i){
- im2col_cpu(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,1,c,n);
-
- for(i = 0; i < m*n; ++i){
- layer.output[i] = activate(layer.output[i], layer.activation);
- }
- //for(i = 0; i < m*n; ++i) if(i%(m*n/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
-
-}
-
-void gradient_delta_convolutional_layer(convolutional_layer layer)
-{
- int i;
- int size = convolutional_out_height(layer)*
- convolutional_out_width(layer)*
- layer.n*
- layer.batch;
- for(i = 0; i < size; ++i){
- layer.delta[i] *= gradient(layer.output[i], layer.activation);
- }
-}
-
-void learn_bias_convolutional_layer(convolutional_layer layer)
+void bias_output(const convolutional_layer layer)
{
int i,j,b;
- int size = convolutional_out_height(layer)
- *convolutional_out_width(layer);
+ 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){
- float sum = 0;
- for(j = 0; j < size; ++j){
- sum += layer.delta[j+size*(i+b*layer.n)];
+ for(j = 0; j < out_h*out_w; ++j){
+ layer.output[(b*layer.n + i)*out_h*out_w + j] = layer.biases[i];
}
- layer.bias_updates[i] += sum/size;
}
}
}
-void learn_convolutional_layer(convolutional_layer layer)
+void forward_convolutional_layer(const convolutional_layer layer, float *in)
{
- gradient_delta_convolutional_layer(layer);
- learn_bias_convolutional_layer(layer);
+ int out_h = convolutional_out_height(layer);
+ int out_w = convolutional_out_width(layer);
+ int i;
+
+ bias_output(layer);
+
int m = layer.n;
- int n = layer.size*layer.size*layer.c;
- int k = convolutional_out_height(layer)*
- convolutional_out_width(layer)*
- layer.batch;
+ int k = layer.size*layer.size*layer.c;
+ int n = out_h*out_w;
- float *a = layer.delta;
+ float *a = layer.filters;
float *b = layer.col_image;
- float *c = layer.filter_updates;
+ float *c = layer.output;
- gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+ 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){
+ gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ c += n*m;
+ in += layer.h*layer.w*layer.c;
+ b += k*n;
+ }
+ /*
+ int i;
+ for(i = 0; i < m*n; ++i) printf("%f, ", layer.output[i]);
+ printf("\n");
+ */
+ 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] += mean_array(layer.delta+size*(i+b*layer.n), size);
+ }
+ }
}
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)*
- convolutional_out_width(layer)*
- layer.batch;
+ 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.filters;
- float *b = layer.delta;
- float *c = layer.col_image;
+ float *a = layer.delta;
+ float *b = layer.col_image;
+ float *c = layer.filter_updates;
-
- memset(c, 0, m*n*sizeof(float));
- gemm(1,0,m,n,k,1,a,m,b,n,1,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);
+ 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);
+
+ a = layer.filters;
+ b = layer.delta;
+ c = layer.col_image;
+
+ memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+
+ for(i = 0; i < layer.batch; ++i){
+ gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
+ col2im_cpu(c, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
+ c += k*n;
+ delta += layer.h*layer.w*layer.c;
+ }
}
}
-void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
+void update_convolutional_layer(convolutional_layer layer)
{
- int i;
int size = layer.size*layer.size*layer.c*layer.n;
- for(i = 0; i < layer.n; ++i){
- layer.biases[i] += step*layer.bias_updates[i];
- layer.bias_updates[i] *= momentum;
- }
- for(i = 0; i < size; ++i){
- layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
- layer.filter_updates[i] *= momentum;
- }
-}
-/*
+ axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.n,layer.momentum, layer.bias_updates, 1);
-void backward_convolutional_layer2(convolutional_layer layer, float *input, float *delta)
-{
- image in_delta = float_to_image(layer.h, layer.w, layer.c, delta);
- image out_delta = get_convolutional_delta(layer);
- int i,j;
- for(i = 0; i < layer.n; ++i){
- rotate_image(layer.kernels[i]);
- }
-
- zero_image(in_delta);
- upsample_image(out_delta, layer.stride, layer.upsampled);
- for(j = 0; j < in_delta.c; ++j){
- for(i = 0; i < layer.n; ++i){
- two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, in_delta, j, layer.edge);
- }
- }
-
- for(i = 0; i < layer.n; ++i){
- rotate_image(layer.kernels[i]);
- }
+ 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);
}
-void learn_convolutional_layer(convolutional_layer layer, float *input)
-{
- int i;
- image in_image = float_to_image(layer.h, layer.w, layer.c, input);
- image out_delta = get_convolutional_delta(layer);
- gradient_delta_convolutional_layer(layer);
- for(i = 0; i < layer.n; ++i){
- kernel_update(in_image, layer.kernel_updates[i], layer.stride, i, out_delta, layer.edge);
- layer.bias_updates[i] += avg_image_layer(out_delta, i);
- }
-}
-
-void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
-{
- int i,j;
- for(i = 0; i < layer.n; ++i){
- layer.bias_momentum[i] = step*(layer.bias_updates[i])
- + momentum*layer.bias_momentum[i];
- layer.biases[i] += layer.bias_momentum[i];
- layer.bias_updates[i] = 0;
- int pixels = layer.kernels[i].h*layer.kernels[i].w*layer.kernels[i].c;
- for(j = 0; j < pixels; ++j){
- layer.kernel_momentum[i].data[j] = step*(layer.kernel_updates[i].data[j] - decay*layer.kernels[i].data[j])
- + momentum*layer.kernel_momentum[i].data[j];
- layer.kernels[i].data[j] += layer.kernel_momentum[i].data[j];
- }
- zero_image(layer.kernel_updates[i]);
- }
-}
-*/
-
-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)
{
int h = layer.size;
@@ -320,12 +267,33 @@
image *single_filters = weighted_sum_filters(layer, 0);
show_images(single_filters, layer.n, window);
- image delta = get_convolutional_delta(layer);
+ image delta = get_convolutional_image(layer);
image dc = collapse_image_layers(delta, 1);
char buff[256];
- sprintf(buff, "%s: Delta", window);
+ sprintf(buff, "%s: Output", window);
//show_image(dc, buff);
+ //save_image(dc, buff);
free_image(dc);
return single_filters;
}
+#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);
+ cl_read_array(layer.output_cl, layer.output, m*n);
+}
+#endif
+
--
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