From 943f6e874b819271a87665cf41199388380989a0 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 05 Apr 2018 20:27:02 +0000
Subject: [PATCH] Added Focal Loss to yolo-layer
---
src/network.c | 4 +-
src/yolo_v2_class.cpp | 1
src/network_kernels.cu | 16 ++++++++
src/parser.c | 1
src/yolo_layer.c | 40 +++++++++++++++----
5 files changed, 51 insertions(+), 11 deletions(-)
diff --git a/src/network.c b/src/network.c
index d532b85..438829a 100644
--- a/src/network.c
+++ b/src/network.c
@@ -757,7 +757,7 @@
layer *l = &net.layers[j];
if (l->type == CONVOLUTIONAL) {
- printf(" Fuse Convolutional layer \t\t l->size = %d \n", l->size);
+ //printf(" Merges Convolutional-%d and batch_norm \n", j);
if (l->batch_normalize) {
int f;
@@ -783,7 +783,7 @@
}
}
else {
- printf(" Skip layer: %d \n", l->type);
+ //printf(" Fusion skip layer type: %d \n", l->type);
}
}
}
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index d6bb294..2e2335d 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -39,6 +39,7 @@
float * get_network_output_gpu_layer(network net, int i);
float * get_network_delta_gpu_layer(network net, int i);
float * get_network_output_gpu(network net);
+#include "opencv2/highgui/highgui_c.h"
void forward_network_gpu(network net, network_state state)
{
@@ -54,6 +55,21 @@
if(net.wait_stream)
cudaStreamSynchronize(get_cuda_stream());
state.input = l.output_gpu;
+/*
+ cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
+ if (l.out_w >= 0 && l.out_h >= 1 && l.c >= 3) {
+ int j;
+ for (j = 0; j < l.out_c; ++j) {
+ image img = make_image(l.out_w, l.out_h, 3);
+ memcpy(img.data, l.output+ l.out_w*l.out_h*j, l.out_w*l.out_h * 1 * sizeof(float));
+ char buff[256];
+ sprintf(buff, "layer-%d slice-%d", i, j);
+ show_image(img, buff);
+ }
+ cvWaitKey(0); // wait press-key in console
+ cvDestroyAllWindows();
+ }
+*/
}
}
diff --git a/src/parser.c b/src/parser.c
index 4de8aeb..651671b 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -274,6 +274,7 @@
//l.max_boxes = option_find_int_quiet(options, "max", 90);
l.jitter = option_find_float(options, "jitter", .2);
+ l.focal_loss = option_find_int_quiet(options, "focal_loss", 0);
l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
l.truth_thresh = option_find_float(options, "truth_thresh", 1);
diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index a735932..ad62426 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -109,18 +109,40 @@
}
-void delta_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat)
+void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss)
{
int n;
if (delta[index]){
- delta[index + stride*class] = 1 - output[index + stride*class];
- if(avg_cat) *avg_cat += output[index + stride*class];
+ delta[index + stride*class_id] = 1 - output[index + stride*class_id];
+ if(avg_cat) *avg_cat += output[index + stride*class_id];
return;
}
- for(n = 0; n < classes; ++n){
- delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n];
- if(n == class && avg_cat) *avg_cat += output[index + stride*n];
- }
+ // Focal loss
+ if (focal_loss) {
+ // Focal Loss
+ float alpha = 0.5; // 0.25 or 0.5
+ //float gamma = 2; // hardcoded in many places of the grad-formula
+
+ int ti = index + stride*class_id;
+ float pt = output[ti] + 0.000000000000001F;
+ //float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832
+ float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss
+
+ for (n = 0; n < classes; ++n) {
+ delta[index + stride*n] = (((n == class_id) ? 1 : 0) - output[index + stride*n]);
+
+ delta[index + stride*n] *= alpha*grad;
+
+ if (n == class_id) *avg_cat += output[index + stride*n];
+ }
+ }
+ else {
+ // default
+ for (n = 0; n < classes; ++n) {
+ delta[index + stride*n] = ((n == class_id) ? 1 : 0) - output[index + stride*n];
+ if (n == class_id && avg_cat) *avg_cat += output[index + stride*n];
+ }
+ }
}
static int entry_index(layer l, int batch, int location, int entry)
@@ -196,7 +218,7 @@
int class = state.truth[best_t*(4 + 1) + b*l.truths + 4];
if (l.map) class = l.map[class];
int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
- delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0);
+ delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0, l.focal_loss);
box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1);
delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h);
}
@@ -236,7 +258,7 @@
int class = state.truth[t*(4 + 1) + b*l.truths + 4];
if (l.map) class = l.map[class];
int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1);
- delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat);
+ delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat, l.focal_loss);
++count;
++class_count;
diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp
index be1b4ee..6cc0252 100644
--- a/src/yolo_v2_class.cpp
+++ b/src/yolo_v2_class.cpp
@@ -69,6 +69,7 @@
}
set_batch_network(&net, 1);
net.gpu_index = cur_gpu_id;
+ fuse_conv_batchnorm(net);
layer l = net.layers[net.n - 1];
int j;
--
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