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detection exp

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pull/5299/head
Joseph Redmon 10 years ago
parent 46e1b263e1
commit ff7e03325a
7 changed files with 453 additions and 60 deletions
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  1. 197
      cfg/detection.cfg
  2. 198
      cfg/rescore.cfg
  3. 3
      src/connected_layer.c
  4. 3
      src/convolutional_layer.c
  5. 2
      src/data.c
  6. 12
      src/detection.c
  7. 98
      src/detection_layer.c

197
cfg/detection.cfg
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@ -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

198
cfg/rescore.cfg
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@ -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

3
src/connected_layer.c
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@ -29,7 +29,8 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
l.biases = calloc(outputs, sizeof(float)); 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){ for(i = 0; i < inputs*outputs; ++i){
l.weights[i] = 2*scale*rand_uniform() - scale; l.weights[i] = 2*scale*rand_uniform() - scale;
} }

3
src/convolutional_layer.c
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@ -61,7 +61,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.biases = calloc(n, sizeof(float)); l.biases = calloc(n, sizeof(float));
l.bias_updates = 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 < c*n*size*size; ++i) l.filters[i] = 2*scale*rand_uniform() - scale;
for(i = 0; i < n; ++i){ for(i = 0; i < n; ++i){
l.biases[i] = scale; l.biases[i] = scale;

2
src/data.c
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@ -174,7 +174,7 @@ void fill_truth_detection(char *path, float *truth, int classes, int num_boxes,
} }
int index = (i+j*num_boxes)*(4+classes+background); 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; if(background) truth[index++] = 0;
truth[index+id] = 1; truth[index+id] = 1;
index += classes; index += classes;

12
src/detection.c
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@ -47,6 +47,8 @@ void draw_detection(image im, float *box, int side, char *label)
int top = (y-h/2)*im.h; int top = (y-h/2)*im.h;
int bot = (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, 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 @@ void train_localization(char *cfgfile, char *weightfile)
float loss = train_network(net, train); float loss = train_network(net, train);
//TODO //TODO
#ifdef GPU
float *out = get_network_output_gpu(net); 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 im = float_to_image(net.w, net.h, 3, train.X.vals[127]);
image copy = copy_image(im); image copy = copy_image(im);
draw_localization(copy, &(out[63*80])); draw_localization(copy, &(out[63*80]));
@ -213,7 +219,7 @@ void train_detection_teststuff(char *cfgfile, char *weightfile)
avg_loss = avg_loss*.9 + loss*.1; 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); printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
if(i == 100){ if(i == 100){
net.learning_rate *= 10; //net.learning_rate *= 10;
} }
if(i%100==0){ if(i%100==0){
char buff[256]; char buff[256];
@ -309,8 +315,8 @@ void predict_detections(network net, data d, float threshold, int offset, int cl
float y = (pred.vals[j][ci + 1] + row)/num_boxes; float y = (pred.vals[j][ci + 1] + row)/num_boxes;
float w = pred.vals[j][ci + 2]; //* distance_from_edge(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); float h = pred.vals[j][ci + 3]; //* distance_from_edge(col, num_boxes);
w = pow(w, 1); w = pow(w, 2);
h = pow(h, 1); h = pow(h, 2);
float prob = scale*pred.vals[j][k+class+background+nuisance]; float prob = scale*pred.vals[j][k+class+background+nuisance];
if(prob < threshold) continue; if(prob < threshold) continue;
printf("%d %d %f %f %f %f %f\n", offset + j, class, prob, x, y, w, h); printf("%d %d %f %f %f %f %f\n", offset + j, class, prob, x, y, w, h);

98
src/detection_layer.c
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@ -330,8 +330,9 @@ void forward_detection_layer(const detection_layer l, network_state state)
l.output[out_i++] = mask*state.input[in_i++]; l.output[out_i++] = mask*state.input[in_i++];
} }
} }
float avg_iou = 0;
int count = 0;
if(l.does_cost && state.train){ if(l.does_cost && state.train){
int count = 0;
*(l.cost) = 0; *(l.cost) = 0;
int size = get_detection_layer_output_size(l) * l.batch; int size = get_detection_layer_output_size(l) * l.batch;
memset(l.delta, 0, size * sizeof(float)); memset(l.delta, 0, size * sizeof(float));
@ -342,65 +343,54 @@ void forward_detection_layer(const detection_layer l, network_state state)
*(l.cost) += pow(state.truth[j] - l.output[j], 2); *(l.cost) += pow(state.truth[j] - l.output[j], 2);
l.delta[j] = state.truth[j] - l.output[j]; l.delta[j] = state.truth[j] - l.output[j];
} }
box truth; box truth;
truth.x = state.truth[j+0]; truth.x = state.truth[j+0]/7;
truth.y = state.truth[j+1]; truth.y = state.truth[j+1]/7;
truth.w = state.truth[j+2]; truth.w = pow(state.truth[j+2], 2);
truth.h = state.truth[j+3]; truth.h = pow(state.truth[j+3], 2);
box out; box out;
out.x = l.output[j+0]; out.x = l.output[j+0]/7;
out.y = l.output[j+1]; out.y = l.output[j+1]/7;
out.w = l.output[j+2]; out.w = pow(l.output[j+2], 2);
out.h = l.output[j+3]; out.h = pow(l.output[j+3], 2);
if(!(truth.w*truth.h)) continue; if(!(truth.w*truth.h)) continue;
l.delta[j+0] = (truth.x - out.x); float iou = box_iou(out, truth);
l.delta[j+1] = (truth.y - out.y); avg_iou += iou;
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);
*/
++count; ++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) void backward_detection_layer(const detection_layer l, network_state state)

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