From 028696bf15efeca3acb3db8c42a96f7b9e0f55ff Mon Sep 17 00:00:00 2001
From: iovodov <b@ovdv.ru>
Date: Thu, 03 May 2018 13:33:46 +0000
Subject: [PATCH] Output improvements for detector results: When printing detector results, output was done in random order, obfuscating results for interpreting. Now: 1. Text output includes coordinates of rects in (left,right,top,bottom in pixels) along with label and score 2. Text output is sorted by rect lefts to simplify finding appropriate rects on image 3. If several class probs are > thresh for some detection, the most probable is written first and coordinates for others are not repeated 4. Rects are imprinted in image in order by their best class prob, so most probable rects are always on top and not overlayed by less probable ones 5. Most probable label for rect is always written first Also: 6. Message about low GPU memory include required amount
---
src/deconvolutional_layer.c | 216 ++++++++++++++++++++++++++++-------------------------
1 files changed, 113 insertions(+), 103 deletions(-)
diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c
index 532045c..fbef9d5 100644
--- a/src/deconvolutional_layer.c
+++ b/src/deconvolutional_layer.c
@@ -8,172 +8,182 @@
#include <stdio.h>
#include <time.h>
-int deconvolutional_out_height(deconvolutional_layer layer)
+int deconvolutional_out_height(deconvolutional_layer l)
{
- int h = layer.stride*(layer.h - 1) + layer.size;
+ int h = l.stride*(l.h - 1) + l.size;
return h;
}
-int deconvolutional_out_width(deconvolutional_layer layer)
+int deconvolutional_out_width(deconvolutional_layer l)
{
- int w = layer.stride*(layer.w - 1) + layer.size;
+ int w = l.stride*(l.w - 1) + l.size;
return w;
}
-int deconvolutional_out_size(deconvolutional_layer layer)
+int deconvolutional_out_size(deconvolutional_layer l)
{
- return deconvolutional_out_height(layer) * deconvolutional_out_width(layer);
+ return deconvolutional_out_height(l) * deconvolutional_out_width(l);
}
-image get_deconvolutional_image(deconvolutional_layer layer)
+image get_deconvolutional_image(deconvolutional_layer l)
{
int h,w,c;
- h = deconvolutional_out_height(layer);
- w = deconvolutional_out_width(layer);
- c = layer.n;
- return float_to_image(w,h,c,layer.output);
+ h = deconvolutional_out_height(l);
+ w = deconvolutional_out_width(l);
+ c = l.n;
+ return float_to_image(w,h,c,l.output);
}
-image get_deconvolutional_delta(deconvolutional_layer layer)
+image get_deconvolutional_delta(deconvolutional_layer l)
{
int h,w,c;
- h = deconvolutional_out_height(layer);
- w = deconvolutional_out_width(layer);
- c = layer.n;
- return float_to_image(w,h,c,layer.delta);
+ h = deconvolutional_out_height(l);
+ w = deconvolutional_out_width(l);
+ c = l.n;
+ return float_to_image(w,h,c,l.delta);
}
-deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
+deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
{
int i;
- deconvolutional_layer *layer = calloc(1, sizeof(deconvolutional_layer));
+ deconvolutional_layer l = {0};
+ l.type = DECONVOLUTIONAL;
- layer->h = h;
- layer->w = w;
- layer->c = c;
- layer->n = n;
- layer->batch = batch;
- layer->stride = stride;
- layer->size = size;
+ l.h = h;
+ l.w = w;
+ l.c = c;
+ l.n = n;
+ l.batch = batch;
+ l.stride = stride;
+ l.size = size;
- layer->filters = calloc(c*n*size*size, sizeof(float));
- layer->filter_updates = calloc(c*n*size*size, sizeof(float));
+ l.weights = calloc(c*n*size*size, sizeof(float));
+ l.weight_updates = calloc(c*n*size*size, sizeof(float));
- layer->biases = calloc(n, sizeof(float));
- layer->bias_updates = calloc(n, sizeof(float));
+ l.biases = calloc(n, sizeof(float));
+ l.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] = scale*rand_normal();
+ for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal();
for(i = 0; i < n; ++i){
- layer->biases[i] = scale;
+ l.biases[i] = scale;
}
- int out_h = deconvolutional_out_height(*layer);
- int out_w = deconvolutional_out_width(*layer);
+ int out_h = deconvolutional_out_height(l);
+ int out_w = deconvolutional_out_width(l);
- layer->col_image = calloc(h*w*size*size*n, 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));
+ l.out_h = out_h;
+ l.out_w = out_w;
+ l.out_c = n;
+ l.outputs = l.out_w * l.out_h * l.out_c;
+ l.inputs = l.w * l.h * l.c;
+
+ l.col_image = calloc(h*w*size*size*n, 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));
+
+ l.forward = forward_deconvolutional_layer;
+ l.backward = backward_deconvolutional_layer;
+ l.update = update_deconvolutional_layer;
#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);
+ l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);
+ l.weight_updates_gpu = cuda_make_array(l.weight_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);
+ l.biases_gpu = cuda_make_array(l.biases, n);
+ l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
- layer->col_image_gpu = cuda_make_array(layer->col_image, h*w*size*size*n);
- 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);
+ l.col_image_gpu = cuda_make_array(l.col_image, h*w*size*size*n);
+ 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);
#endif
- layer->activation = activation;
+ l.activation = activation;
fprintf(stderr, "Deconvolutional 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;
+ return l;
}
-void resize_deconvolutional_layer(deconvolutional_layer *layer, int h, int w)
+void resize_deconvolutional_layer(deconvolutional_layer *l, int h, int w)
{
- layer->h = h;
- layer->w = w;
- int out_h = deconvolutional_out_height(*layer);
- int out_w = deconvolutional_out_width(*layer);
+ l->h = h;
+ l->w = w;
+ int out_h = deconvolutional_out_height(*l);
+ int out_w = deconvolutional_out_width(*l);
- 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));
+ 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(layer->col_image_gpu);
- cuda_free(layer->delta_gpu);
- cuda_free(layer->output_gpu);
+ cuda_free(l->col_image_gpu);
+ cuda_free(l->delta_gpu);
+ cuda_free(l->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);
+ 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 forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state)
+void forward_deconvolutional_layer(const deconvolutional_layer l, network_state state)
{
int i;
- int out_h = deconvolutional_out_height(layer);
- int out_w = deconvolutional_out_width(layer);
+ int out_h = deconvolutional_out_height(l);
+ int out_w = deconvolutional_out_width(l);
int size = out_h*out_w;
- int m = layer.size*layer.size*layer.n;
- int n = layer.h*layer.w;
- int k = layer.c;
+ int m = l.size*l.size*l.n;
+ int n = l.h*l.w;
+ int k = l.c;
- bias_output(layer.output, layer.biases, layer.batch, layer.n, size);
+ fill_cpu(l.outputs*l.batch, 0, l.output, 1);
- for(i = 0; i < layer.batch; ++i){
- float *a = layer.filters;
- float *b = state.input + i*layer.c*layer.h*layer.w;
- float *c = layer.col_image;
+ for(i = 0; i < l.batch; ++i){
+ float *a = l.weights;
+ float *b = state.input + i*l.c*l.h*l.w;
+ float *c = l.col_image;
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
- col2im_cpu(c, layer.n, out_h, out_w, layer.size, layer.stride, 0, layer.output+i*layer.n*size);
+ col2im_cpu(c, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size);
}
- activate_array(layer.output, layer.batch*layer.n*size, layer.activation);
+ add_bias(l.output, l.biases, l.batch, l.n, size);
+ activate_array(l.output, l.batch*l.n*size, l.activation);
}
-void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state)
+void backward_deconvolutional_layer(deconvolutional_layer l, network_state state)
{
- float alpha = 1./layer.batch;
- int out_h = deconvolutional_out_height(layer);
- int out_w = deconvolutional_out_width(layer);
+ float alpha = 1./l.batch;
+ int out_h = deconvolutional_out_height(l);
+ int out_w = deconvolutional_out_width(l);
int size = out_h*out_w;
int i;
- gradient_array(layer.output, size*layer.n*layer.batch, layer.activation, layer.delta);
- backward_bias(layer.bias_updates, layer.delta, layer.batch, layer.n, size);
+ gradient_array(l.output, size*l.n*l.batch, l.activation, l.delta);
+ backward_bias(l.bias_updates, l.delta, l.batch, l.n, size);
- if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
-
- for(i = 0; i < layer.batch; ++i){
- int m = layer.c;
- int n = layer.size*layer.size*layer.n;
- int k = layer.h*layer.w;
+ for(i = 0; i < l.batch; ++i){
+ int m = l.c;
+ int n = l.size*l.size*l.n;
+ int k = l.h*l.w;
float *a = state.input + i*m*n;
- float *b = layer.col_image;
- float *c = layer.filter_updates;
+ float *b = l.col_image;
+ float *c = l.weight_updates;
- im2col_cpu(layer.delta + i*layer.n*size, layer.n, out_h, out_w,
- layer.size, layer.stride, 0, b);
+ im2col_cpu(l.delta + i*l.n*size, l.n, out_h, out_w,
+ l.size, l.stride, 0, b);
gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
if(state.delta){
- int m = layer.c;
- int n = layer.h*layer.w;
- int k = layer.size*layer.size*layer.n;
+ int m = l.c;
+ int n = l.h*l.w;
+ int k = l.size*l.size*l.n;
- float *a = layer.filters;
- float *b = layer.col_image;
+ float *a = l.weights;
+ float *b = l.col_image;
float *c = state.delta + i*n*m;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
@@ -181,15 +191,15 @@
}
}
-void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay)
+void update_deconvolutional_layer(deconvolutional_layer l, float learning_rate, float momentum, float decay)
{
- int size = layer.size*layer.size*layer.c*layer.n;
- axpy_cpu(layer.n, learning_rate, layer.bias_updates, 1, layer.biases, 1);
- scal_cpu(layer.n, momentum, layer.bias_updates, 1);
+ int size = l.size*l.size*l.c*l.n;
+ axpy_cpu(l.n, learning_rate, l.bias_updates, 1, l.biases, 1);
+ scal_cpu(l.n, momentum, l.bias_updates, 1);
- axpy_cpu(size, -decay, layer.filters, 1, layer.filter_updates, 1);
- axpy_cpu(size, learning_rate, layer.filter_updates, 1, layer.filters, 1);
- scal_cpu(size, momentum, layer.filter_updates, 1);
+ axpy_cpu(size, -decay, l.weights, 1, l.weight_updates, 1);
+ axpy_cpu(size, learning_rate, l.weight_updates, 1, l.weights, 1);
+ scal_cpu(size, momentum, l.weight_updates, 1);
}
--
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