From e3ee7b7cd64894144a594c0caaa202f7aeac6723 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 15 Mar 2016 02:35:36 +0000
Subject: [PATCH] Hi Harley
---
src/convolutional_layer.c | 460 ++++++++++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 407 insertions(+), 53 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index f83622b..159951d 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,86 +1,440 @@
#include "convolutional_layer.h"
+#include "utils.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "blas.h"
+#include "gemm.h"
+#include <stdio.h>
+#include <time.h>
-double convolution_activation(double x)
+int convolutional_out_height(convolutional_layer l)
{
- return x*(x>0);
+ int h = l.h;
+ if (!l.pad) h -= l.size;
+ else h -= 1;
+ return h/l.stride + 1;
}
-double convolution_gradient(double x)
+int convolutional_out_width(convolutional_layer l)
{
- return (x>=0);
+ int w = l.w;
+ if (!l.pad) w -= l.size;
+ else w -= 1;
+ return w/l.stride + 1;
}
-convolutional_layer make_convolutional_layer(int h, int w, int c, int n, int size, int stride)
+image get_convolutional_image(convolutional_layer l)
{
- int i;
- convolutional_layer layer;
- layer.n = n;
- layer.stride = stride;
- layer.kernels = calloc(n, sizeof(image));
- layer.kernel_updates = calloc(n, sizeof(image));
- for(i = 0; i < n; ++i){
- layer.kernels[i] = make_random_kernel(size, c);
- layer.kernel_updates[i] = make_random_kernel(size, c);
- }
- layer.output = make_image((h-1)/stride+1, (w-1)/stride+1, n);
- layer.upsampled = make_image(h,w,n);
- return layer;
+ int h,w,c;
+ h = convolutional_out_height(l);
+ w = convolutional_out_width(l);
+ c = l.n;
+ return float_to_image(w,h,c,l.output);
}
-void run_convolutional_layer(const image input, const convolutional_layer layer)
+image get_convolutional_delta(convolutional_layer l)
{
- int i;
- for(i = 0; i < layer.n; ++i){
- convolve(input, layer.kernels[i], layer.stride, i, layer.output);
- }
- for(i = 0; i < input.h*input.w*input.c; ++i){
- input.data[i] = convolution_activation(input.data[i]);
+ int h,w,c;
+ h = convolutional_out_height(l);
+ w = convolutional_out_width(l);
+ c = l.n;
+ 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;
}
}
-void backpropagate_layer(image input, convolutional_layer layer)
+void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
{
- int i;
- zero_image(input);
- for(i = 0; i < layer.n; ++i){
- back_convolve(input, layer.kernels[i], layer.stride, i, layer.output);
+
+ 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 backpropagate_layer_convolve(image input, convolutional_layer layer)
+void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
{
- int i,j;
- for(i = 0; i < layer.n; ++i){
- rotate_image(layer.kernels[i]);
- }
- zero_image(input);
- upsample_image(layer.output, layer.stride, layer.upsampled);
- for(j = 0; j < input.c; ++j){
- for(i = 0; i < layer.n; ++i){
- two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, input, j);
+ 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);
+ }
}
}
+}
- for(i = 0; i < layer.n; ++i){
- rotate_image(layer.kernels[i]);
+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 i;
+ convolutional_layer l = {0};
+ l.type = CONVOLUTIONAL;
+
+ l.h = h;
+ l.w = w;
+ l.c = c;
+ l.n = n;
+ l.binary = binary;
+ 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);
+ 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;
+ l.out_w = out_w;
+ l.out_c = n;
+ 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));
+
+ if(binary){
+ l.binary_filters = calloc(c*n*size*size, 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.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(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);
+ }
+#endif
+ 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);
+
+ return l;
+}
+
+void denormalize_convolutional_layer(convolutional_layer l)
+{
+ 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 error_convolutional_layer(image input, convolutional_layer layer)
+void test_convolutional_layer()
+{
+ convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 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;
+ l->h = h;
+ int out_w = convolutional_out_width(*l);
+ int out_h = convolutional_out_height(*l);
+
+ 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->col_image = realloc(l->col_image,
+ out_h*out_w*l->size*l->size*l->c*sizeof(float));
+ l->output = realloc(l->output,
+ 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));
+
+#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
+}
+
+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];
+ }
+ }
+ }
+}
+
+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];
+ }
+ }
+ }
+}
+
+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 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);
+
+ 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;
+
+ 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){
+ 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);
+ }
+ add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
+
+ activate_array(l.output, m*n*l.batch, l.activation);
+}
+
+void backward_convolutional_layer(convolutional_layer l, network_state state)
{
int i;
- for(i = 0; i < layer.n; ++i){
- kernel_update(input, layer.kernel_updates[i], layer.stride, i, layer.output);
+ int m = l.n;
+ int n = l.size*l.size*l.c;
+ int k = convolutional_out_height(l)*
+ convolutional_out_width(l);
+
+ gradient_array(l.output, m*k*l.batch, l.activation, l.delta);
+ backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);
+
+ for(i = 0; i < l.batch; ++i){
+ float *a = l.delta + i*m*k;
+ float *b = l.col_image;
+ float *c = l.filter_updates;
+
+ float *im = state.input+i*l.c*l.h*l.w;
+
+ im2col_cpu(im, l.c, l.h, l.w,
+ l.size, l.stride, l.pad, b);
+ gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
+
+ if(state.delta){
+ a = l.filters;
+ b = l.delta + i*m*k;
+ c = l.col_image;
+
+ 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);
+ }
}
- image old_input = copy_image(input);
- zero_image(input);
- for(i = 0; i < layer.n; ++i){
- back_convolve(input, layer.kernels[i], layer.stride, i, layer.output);
+}
+
+void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay)
+{
+ int size = l.size*l.size*l.c*l.n;
+ axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
+ scal_cpu(l.n, momentum, l.bias_updates, 1);
+
+ axpy_cpu(size, -decay*batch, l.filters, 1, l.filter_updates, 1);
+ axpy_cpu(size, learning_rate/batch, l.filter_updates, 1, l.filters, 1);
+ scal_cpu(size, momentum, l.filter_updates, 1);
+}
+
+
+image get_convolutional_filter(convolutional_layer l, int i)
+{
+ int h = l.size;
+ int w = l.size;
+ int c = l.c;
+ 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);
+ }
}
- for(i = 0; i < input.h*input.w*input.c; ++i){
- input.data[i] = input.data[i]*convolution_gradient(input.data[i]);
+}
+
+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;
+ }
}
- free_image(old_input);
+}
+
+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;
+}
+
+image *visualize_convolutional_layer(convolutional_layer l, char *window, image *prev_filters)
+{
+ image *single_filters = get_filters(l);
+ show_images(single_filters, l.n, window);
+
+ image delta = get_convolutional_image(l);
+ image dc = collapse_image_layers(delta, 1);
+ char buff[256];
+ sprintf(buff, "%s: Output", window);
+ //show_image(dc, buff);
+ //save_image(dc, buff);
+ free_image(dc);
+ return single_filters;
}
--
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