From 989ab8c38a02fa7ea9c25108151736c62e81c972 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 24 Apr 2015 17:27:50 +0000
Subject: [PATCH] IOU loss function
---
src/convolutional_layer.c | 382 ++++++++++++++++++++++++++++-------------------------
1 files changed, 202 insertions(+), 180 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 5accaab..cd357d3 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,225 +1,247 @@
#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>
+
+int convolutional_out_height(convolutional_layer layer)
+{
+ 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)
+{
+ 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)
{
int h,w,c;
- if(layer.edge){
- h = (layer.h-1)/layer.stride + 1;
- w = (layer.w-1)/layer.stride + 1;
- }else{
- h = (layer.h - layer.size)/layer.stride+1;
- w = (layer.h - layer.size)/layer.stride+1;
- }
+ h = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
c = layer.n;
- return double_to_image(h,w,c,layer.output);
+ return float_to_image(w,h,c,layer.output);
}
image get_convolutional_delta(convolutional_layer layer)
{
int h,w,c;
- if(layer.edge){
- h = (layer.h-1)/layer.stride + 1;
- w = (layer.w-1)/layer.stride + 1;
- }else{
- h = (layer.h - layer.size)/layer.stride+1;
- w = (layer.h - layer.size)/layer.stride+1;
- }
+ h = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
c = layer.n;
- return double_to_image(h,w,c,layer.delta);
+ return float_to_image(w,h,c,layer.delta);
}
-convolutional_layer *make_convolutional_layer(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)
{
int i;
- int out_h,out_w;
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
+
layer->h = h;
layer->w = w;
layer->c = c;
layer->n = n;
- layer->edge = 1;
+ layer->batch = batch;
layer->stride = stride;
- layer->kernels = calloc(n, sizeof(image));
- layer->kernel_updates = calloc(n, sizeof(image));
- layer->kernel_momentum = calloc(n, sizeof(image));
- layer->biases = calloc(n, sizeof(double));
- layer->bias_updates = calloc(n, sizeof(double));
- layer->bias_momentum = calloc(n, sizeof(double));
- double scale = 2./(size*size);
+ 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));
+
+ layer->biases = calloc(n, sizeof(float));
+ layer->bias_updates = calloc(n, sizeof(float));
+ float scale = 1./sqrt(size*size*c);
+ for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = 2*scale*rand_uniform() - scale;
for(i = 0; i < n; ++i){
- //layer->biases[i] = rand_normal()*scale + scale;
- layer->biases[i] = 0;
- layer->kernels[i] = make_random_kernel(size, c, scale);
- layer->kernel_updates[i] = make_random_kernel(size, c, 0);
- layer->kernel_momentum[i] = make_random_kernel(size, c, 0);
+ layer->biases[i] = scale;
}
- layer->size = 2*(size/2)+1;
- if(layer->edge){
- out_h = (layer->h-1)/layer->stride + 1;
- out_w = (layer->w-1)/layer->stride + 1;
- }else{
- out_h = (layer->h - layer->size)/layer->stride+1;
- out_w = (layer->h - layer->size)/layer->stride+1;
- }
- 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);
- layer->output = calloc(out_h * out_w * n, sizeof(double));
- layer->delta = calloc(out_h * out_w * n, sizeof(double));
- layer->upsampled = make_image(h,w,n);
+ int out_h = convolutional_out_height(*layer);
+ int out_w = convolutional_out_width(*layer);
+
+ 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_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_gpu = cuda_make_array(layer->biases, n);
+ layer->bias_updates_gpu = cuda_make_array(layer->bias_updates, 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;
+ 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 layer;
}
-void forward_convolutional_layer(const convolutional_layer layer, double *in)
+void resize_convolutional_layer(convolutional_layer *layer, int h, int w)
{
- image input = double_to_image(layer.h, layer.w, layer.c, in);
- image output = get_convolutional_image(layer);
- int i,j;
- for(i = 0; i < layer.n; ++i){
- convolve(input, layer.kernels[i], layer.stride, i, output, layer.edge);
- }
- for(i = 0; i < output.c; ++i){
- for(j = 0; j < output.h*output.w; ++j){
- int index = i*output.h*output.w + j;
- output.data[index] += layer.biases[i];
- output.data[index] = activate(output.data[index], layer.activation);
- }
- }
+ layer->h = h;
+ layer->w = w;
+ int out_h = convolutional_out_height(*layer);
+ int out_w = convolutional_out_width(*layer);
+
+ layer->col_image = realloc(layer->col_image,
+ 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 backward_convolutional_layer(convolutional_layer layer, double *input, double *delta)
+void bias_output(float *output, float *biases, int batch, int n, int size)
{
- int i;
-
- image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
- image out_delta = get_convolutional_delta(layer);
- zero_image(in_delta);
-
- for(i = 0; i < layer.n; ++i){
- back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta, layer.edge);
- }
-}
-
-void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
-{
- image in_delta = double_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]);
- }
-}
-
-void gradient_delta_convolutional_layer(convolutional_layer layer)
-{
- int i;
- image out_delta = get_convolutional_delta(layer);
- image out_image = get_convolutional_image(layer);
- for(i = 0; i < out_image.h*out_image.w*out_image.c; ++i){
- out_delta.data[i] *= gradient(out_image.data[i], layer.activation);
- }
-}
-
-void learn_convolutional_layer(convolutional_layer layer, double *input)
-{
- int i;
- image in_image = double_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, double step, double momentum, double 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 visualize_convolutional_filters(convolutional_layer layer, char *window)
-{
- int color = 1;
- int border = 1;
- int h,w,c;
- int size = layer.size;
- h = size;
- w = (size + border) * layer.n - border;
- c = layer.kernels[0].c;
- if(c != 3 || !color){
- h = (h+border)*c - border;
- c = 1;
- }
-
- image filters = make_image(h,w,c);
- int i,j;
- for(i = 0; i < layer.n; ++i){
- int w_offset = i*(size+border);
- image k = layer.kernels[i];
- image copy = copy_image(k);
- normalize_image(copy);
- for(j = 0; j < k.c; ++j){
- set_pixel(copy,0,0,j,layer.biases[i]);
- }
- if(c == 3 && color){
- embed_image(copy, filters, 0, w_offset);
- }
- else{
- for(j = 0; j < k.c; ++j){
- int h_offset = j*(size+border);
- image layer = get_image_layer(k, j);
- embed_image(layer, filters, h_offset, w_offset);
- free_image(layer);
+ 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];
}
}
- free_image(copy);
}
- image delta = get_convolutional_delta(layer);
- image dc = collapse_image_layers(delta, 1);
- char buff[256];
- sprintf(buff, "%s: Delta", window);
- show_image(dc, buff);
- free_image(dc);
- show_image(filters, window);
- free_image(filters);
}
-void visualize_convolutional_layer(convolutional_layer layer)
+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.output, layer.biases, layer.batch, layer.n, out_h*out_w);
+
+ int m = layer.n;
+ int k = layer.size*layer.size*layer.c;
+ int n = out_h*out_w;
+
+ float *a = layer.filters;
+ float *b = layer.col_image;
+ float *c = layer.output;
+
+ 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);
+ c += n*m;
+ state.input += layer.c*layer.h*layer.w;
+ }
+ activate_array(layer.output, m*n*layer.batch, layer.activation);
+}
+
+void backward_convolutional_layer(convolutional_layer layer, network_state state)
{
int i;
- char buff[256];
- for(i = 0; i < layer.n; ++i){
- image k = layer.kernels[i];
- sprintf(buff, "Kernel %d", i);
- if(k.c <= 3) show_image(k, buff);
- else show_image_layers(k, buff);
+ 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);
+ 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);
+
+ if(state.delta){
+ a = layer.filters;
+ b = layer.delta + i*m*k;
+ c = layer.col_image;
+
+ gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
+
+ 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);
+ }
}
}
+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, learning_rate/batch, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.n, momentum, layer.bias_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);
+}
+
+
+image get_convolutional_filter(convolutional_layer layer, int i)
+{
+ int h = layer.size;
+ int w = layer.size;
+ int c = layer.c;
+ return float_to_image(w,h,c,layer.filters+i*h*w*c);
+}
+
+image *get_filters(convolutional_layer layer)
+{
+ image *filters = calloc(layer.n, sizeof(image));
+ int i;
+ for(i = 0; i < layer.n; ++i){
+ filters[i] = copy_image(get_convolutional_filter(layer, i));
+ }
+ return filters;
+}
+
+image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters)
+{
+ image *single_filters = get_filters(layer);
+ show_images(single_filters, layer.n, window);
+
+ image delta = get_convolutional_image(layer);
+ 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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