From ff7e03325a2f36bf4eb13e1f538b78e1549305cc Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 20 May 2015 17:06:42 +0000
Subject: [PATCH] detection exp
---
src/detection.c | 12 +
src/convolutional_layer.c | 3
src/connected_layer.c | 3
cfg/rescore.cfg | 198 ++++++++++++++++++++++
src/data.c | 2
cfg/detection.cfg | 197 +++++++++++++++++++++
src/detection_layer.c | 98 ++++------
7 files changed, 453 insertions(+), 60 deletions(-)
diff --git a/cfg/detection.cfg b/cfg/detection.cfg
new file mode 100644
index 0000000..d08d2af
--- /dev/null
+++ b/cfg/detection.cfg
@@ -0,0 +1,197 @@
+[net]
+batch=64
+subdivisions=4
+height=448
+width=448
+channels=3
+learning_rate=0.01
+momentum=0.9
+decay=0.0005
+seen = 0
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 2
+exposure = 2
+
+[convolutional]
+filters=64
+size=7
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=192
+size=3
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=3
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=1024
+size=3
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=ramp
+
+[convolutional]
+size=3
+stride=2
+pad=1
+filters=1024
+activation=ramp
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=ramp
+
+[connected]
+output=4096
+activation=ramp
+
+[dropout]
+probability=.5
+
+[connected]
+output=1225
+activation=logistic
+
+[detection]
+classes=20
+coords=4
+rescore=0
+nuisance = 1
+background=1
diff --git a/cfg/rescore.cfg b/cfg/rescore.cfg
new file mode 100644
index 0000000..9024d53
--- /dev/null
+++ b/cfg/rescore.cfg
@@ -0,0 +1,198 @@
+[net]
+batch=64
+subdivisions=4
+height=448
+width=448
+channels=3
+learning_rate=0.01
+momentum=0.9
+decay=0.0005
+seen = 0
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 2
+exposure = 2
+
+[convolutional]
+filters=64
+size=7
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=192
+size=3
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=3
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=1024
+size=3
+stride=2
+pad=1
+activation=ramp
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=ramp
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=ramp
+
+[convolutional]
+size=3
+stride=2
+pad=1
+filters=1024
+activation=ramp
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=ramp
+
+[connected]
+output=4096
+activation=ramp
+
+[dropout]
+probability=.5
+
+[connected]
+output=1225
+activation=logistic
+
+[detection]
+classes=20
+coords=4
+rescore=1
+nuisance = 0
+background=0
+
diff --git a/src/connected_layer.c b/src/connected_layer.c
index bff3602..55d84ca 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -29,7 +29,8 @@
l.biases = calloc(outputs, sizeof(float));
- float scale = 1./sqrt(inputs);
+ //float scale = 1./sqrt(inputs);
+ float scale = sqrt(2./inputs);
for(i = 0; i < inputs*outputs; ++i){
l.weights[i] = 2*scale*rand_uniform() - scale;
}
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index b6437d4..67c36c3 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -61,7 +61,8 @@
l.biases = calloc(n, sizeof(float));
l.bias_updates = calloc(n, sizeof(float));
- float scale = 1./sqrt(size*size*c);
+ //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] = 2*scale*rand_uniform() - scale;
for(i = 0; i < n; ++i){
l.biases[i] = scale;
diff --git a/src/data.c b/src/data.c
index 8e290c4..0184984 100644
--- a/src/data.c
+++ b/src/data.c
@@ -174,7 +174,7 @@
}
int index = (i+j*num_boxes)*(4+classes+background);
- //if(truth[index+classes+background+2]) continue;
+ if(truth[index+classes+background+2]) continue;
if(background) truth[index++] = 0;
truth[index+id] = 1;
index += classes;
diff --git a/src/detection.c b/src/detection.c
index 160fa60..c012848 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -47,6 +47,8 @@
int top = (y-h/2)*im.h;
int bot = (y+h/2)*im.h;
draw_box(im, left, top, right, bot, red, green, blue);
+ draw_box(im, left+1, top+1, right+1, bot+1, red, green, blue);
+ draw_box(im, left-1, top-1, right-1, bot-1, red, green, blue);
}
}
}
@@ -116,7 +118,11 @@
float loss = train_network(net, train);
//TODO
+ #ifdef GPU
float *out = get_network_output_gpu(net);
+ #else
+ float *out = get_network_output(net);
+ #endif
image im = float_to_image(net.w, net.h, 3, train.X.vals[127]);
image copy = copy_image(im);
draw_localization(copy, &(out[63*80]));
@@ -213,7 +219,7 @@
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
if(i == 100){
- net.learning_rate *= 10;
+ //net.learning_rate *= 10;
}
if(i%100==0){
char buff[256];
@@ -309,8 +315,8 @@
float y = (pred.vals[j][ci + 1] + row)/num_boxes;
float w = pred.vals[j][ci + 2]; //* distance_from_edge(row, num_boxes);
float h = pred.vals[j][ci + 3]; //* distance_from_edge(col, num_boxes);
- w = pow(w, 1);
- h = pow(h, 1);
+ w = pow(w, 2);
+ h = pow(h, 2);
float prob = scale*pred.vals[j][k+class+background+nuisance];
if(prob < threshold) continue;
printf("%d %d %f %f %f %f %f\n", offset + j, class, prob, x, y, w, h);
diff --git a/src/detection_layer.c b/src/detection_layer.c
index dd68244..af137c6 100644
--- a/src/detection_layer.c
+++ b/src/detection_layer.c
@@ -330,8 +330,9 @@
l.output[out_i++] = mask*state.input[in_i++];
}
}
+ float avg_iou = 0;
+ int count = 0;
if(l.does_cost && state.train){
- int count = 0;
*(l.cost) = 0;
int size = get_detection_layer_output_size(l) * l.batch;
memset(l.delta, 0, size * sizeof(float));
@@ -342,65 +343,54 @@
*(l.cost) += pow(state.truth[j] - l.output[j], 2);
l.delta[j] = state.truth[j] - l.output[j];
}
+
box truth;
- truth.x = state.truth[j+0];
- truth.y = state.truth[j+1];
- truth.w = state.truth[j+2];
- truth.h = state.truth[j+3];
+ truth.x = state.truth[j+0]/7;
+ truth.y = state.truth[j+1]/7;
+ truth.w = pow(state.truth[j+2], 2);
+ truth.h = pow(state.truth[j+3], 2);
box out;
- out.x = l.output[j+0];
- out.y = l.output[j+1];
- out.w = l.output[j+2];
- out.h = l.output[j+3];
+ out.x = l.output[j+0]/7;
+ out.y = l.output[j+1]/7;
+ out.w = pow(l.output[j+2], 2);
+ out.h = pow(l.output[j+3], 2);
+
if(!(truth.w*truth.h)) continue;
- l.delta[j+0] = (truth.x - out.x);
- l.delta[j+1] = (truth.y - out.y);
- l.delta[j+2] = (truth.w - out.w);
- l.delta[j+3] = (truth.h - out.h);
- *(l.cost) += pow((out.x - truth.x), 2);
- *(l.cost) += pow((out.y - truth.y), 2);
- *(l.cost) += pow((out.w - truth.w), 2);
- *(l.cost) += pow((out.h - truth.h), 2);
-
-/*
- l.delta[j+0] = .1 * (truth.x - out.x) / (49 * truth.w * truth.w);
- l.delta[j+1] = .1 * (truth.y - out.y) / (49 * truth.h * truth.h);
- l.delta[j+2] = .1 * (truth.w - out.w) / ( truth.w * truth.w);
- l.delta[j+3] = .1 * (truth.h - out.h) / ( truth.h * truth.h);
-
- *(l.cost) += pow((out.x - truth.x)/truth.w/7., 2);
- *(l.cost) += pow((out.y - truth.y)/truth.h/7., 2);
- *(l.cost) += pow((out.w - truth.w)/truth.w, 2);
- *(l.cost) += pow((out.h - truth.h)/truth.h, 2);
- */
+ float iou = box_iou(out, truth);
+ avg_iou += iou;
++count;
+ dbox delta = diou(out, truth);
+
+ l.delta[j+0] = 10 * delta.dx/7;
+ l.delta[j+1] = 10 * delta.dy/7;
+ l.delta[j+2] = 10 * delta.dw * 2 * sqrt(out.w);
+ l.delta[j+3] = 10 * delta.dh * 2 * sqrt(out.h);
+
+
+ *(l.cost) += pow((1-iou), 2);
+ if(0){
+ l.delta[j+0] = (state.truth[j+0] - l.output[j+0]);
+ l.delta[j+1] = (state.truth[j+1] - l.output[j+1]);
+ l.delta[j+2] = (state.truth[j+2] - l.output[j+2]);
+ l.delta[j+3] = (state.truth[j+3] - l.output[j+3]);
+ }else{
+ l.delta[j+0] = 4 * (state.truth[j+0] - l.output[j+0]) / 7;
+ l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]) / 7;
+ l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
+ l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
+ }
+ if(0){
+ for (j = offset; j < offset+classes; ++j) {
+ if(state.truth[j]) state.truth[j] = iou;
+ l.delta[j] = state.truth[j] - l.output[j];
+ }
+ }
+
+ /*
+ */
}
+ printf("Avg IOU: %f\n", avg_iou/count);
}
- /*
- int count = 0;
- for(i = 0; i < l.batch*locations; ++i){
- for(j = 0; j < l.classes+l.background; ++j){
- printf("%f, ", l.output[count++]);
- }
- printf("\n");
- for(j = 0; j < l.coords; ++j){
- printf("%f, ", l.output[count++]);
- }
- printf("\n");
- }
- */
- /*
- if(l.background || 1){
- for(i = 0; i < l.batch*locations; ++i){
- int index = i*(l.classes+l.coords+l.background);
- for(j= 0; j < l.classes; ++j){
- if(state.truth[index+j+l.background]){
-//dark_zone(l, j, index, state);
-}
-}
-}
-}
- */
}
void backward_detection_layer(const detection_layer l, network_state state)
--
Gitblit v1.10.0