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So I have this new programming paradigm.......
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@ -30,7 +30,12 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int
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l.truths = l.side*l.side*(1+l.coords+l.classes);
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l.output = calloc(batch*l.outputs, sizeof(float));
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l.delta = calloc(batch*l.outputs, sizeof(float));
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l.forward = forward_detection_layer;
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l.backward = backward_detection_layer;
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#ifdef GPU
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l.forward_gpu = forward_detection_layer_gpu;
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l.backward_gpu = backward_detection_layer_gpu;
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l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
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l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
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#endif
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@ -216,6 +221,35 @@ void backward_detection_layer(const detection_layer l, network_state state)
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axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
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}
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void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
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{
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int i,j,n;
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float *predictions = l.output;
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//int per_cell = 5*num+classes;
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for (i = 0; i < l.side*l.side; ++i){
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int row = i / l.side;
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int col = i % l.side;
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for(n = 0; n < l.n; ++n){
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int index = i*l.n + n;
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int p_index = l.side*l.side*l.classes + i*l.n + n;
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float scale = predictions[p_index];
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int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4;
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boxes[index].x = (predictions[box_index + 0] + col) / l.side * w;
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boxes[index].y = (predictions[box_index + 1] + row) / l.side * h;
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boxes[index].w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w;
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boxes[index].h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h;
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for(j = 0; j < l.classes; ++j){
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int class_index = i*l.classes;
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float prob = scale*predictions[class_index+j];
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probs[index][j] = (prob > thresh) ? prob : 0;
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}
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if(only_objectness){
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probs[index][0] = scale;
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}
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}
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}
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}
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#ifdef GPU
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void forward_detection_layer_gpu(const detection_layer l, network_state state)
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