From 1b5afb45838e603fa6780762eb8cc59246dc2d81 Mon Sep 17 00:00:00 2001
From: IlyaOvodov <b@ovdv.ru>
Date: Tue, 08 May 2018 11:09:35 +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.h | 51 ++++++++++-----------------------------------------
1 files changed, 10 insertions(+), 41 deletions(-)
diff --git a/src/deconvolutional_layer.h b/src/deconvolutional_layer.h
index 1da43dc..2d36e02 100644
--- a/src/deconvolutional_layer.h
+++ b/src/deconvolutional_layer.h
@@ -4,55 +4,24 @@
#include "cuda.h"
#include "image.h"
#include "activations.h"
+#include "layer.h"
+#include "network.h"
-typedef struct {
- float learning_rate;
- float momentum;
- float decay;
-
- int batch;
- int h,w,c;
- int n;
- int size;
- int stride;
- float *filters;
- float *filter_updates;
-
- float *biases;
- float *bias_updates;
-
- float *col_image;
- float *delta;
- float *output;
-
- #ifdef GPU
- float * filters_gpu;
- float * filter_updates_gpu;
-
- float * biases_gpu;
- float * bias_updates_gpu;
-
- float * col_image_gpu;
- float * delta_gpu;
- float * output_gpu;
- #endif
-
- ACTIVATION activation;
-} deconvolutional_layer;
+typedef layer deconvolutional_layer;
#ifdef GPU
-void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, float * in);
-void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, float * in, float * delta_gpu);
-void update_deconvolutional_layer_gpu(deconvolutional_layer layer);
+void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state);
+void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state);
+void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay);
void push_deconvolutional_layer(deconvolutional_layer layer);
void pull_deconvolutional_layer(deconvolutional_layer layer);
#endif
-deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation, float learning_rate, float momentum, float decay);
+deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation);
void resize_deconvolutional_layer(deconvolutional_layer *layer, int h, int w);
-void forward_deconvolutional_layer(const deconvolutional_layer layer, float *in);
-void update_deconvolutional_layer(deconvolutional_layer layer);
-void backward_deconvolutional_layer(deconvolutional_layer layer, float *in, float *delta);
+void forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state);
+void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay);
+void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state);
image get_deconvolutional_image(deconvolutional_layer layer);
image get_deconvolutional_delta(deconvolutional_layer layer);
--
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