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573 lines
20 KiB
573 lines
20 KiB
#include "region_layer.h" |
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#include "activations.h" |
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#include "blas.h" |
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#include "box.h" |
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#include "cuda.h" |
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#include "utils.h" |
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#include <stdio.h> |
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#include <assert.h> |
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#include <string.h> |
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#include <stdlib.h> |
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#define DOABS 1 |
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region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes) |
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{ |
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region_layer l = {0}; |
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l.type = REGION; |
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l.n = n; |
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l.batch = batch; |
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l.h = h; |
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l.w = w; |
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l.classes = classes; |
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l.coords = coords; |
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l.cost = calloc(1, sizeof(float)); |
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l.biases = calloc(n*2, sizeof(float)); |
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l.bias_updates = calloc(n*2, sizeof(float)); |
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l.outputs = h*w*n*(classes + coords + 1); |
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l.inputs = l.outputs; |
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l.max_boxes = max_boxes; |
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l.truths = max_boxes*(5); |
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l.delta = calloc(batch*l.outputs, sizeof(float)); |
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l.output = calloc(batch*l.outputs, sizeof(float)); |
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int i; |
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for(i = 0; i < n*2; ++i){ |
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l.biases[i] = .5; |
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} |
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l.forward = forward_region_layer; |
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l.backward = backward_region_layer; |
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#ifdef GPU |
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l.forward_gpu = forward_region_layer_gpu; |
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l.backward_gpu = backward_region_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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fprintf(stderr, "detection\n"); |
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srand(0); |
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return l; |
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} |
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void resize_region_layer(layer *l, int w, int h) |
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{ |
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int old_w = l->w; |
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int old_h = l->h; |
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l->w = w; |
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l->h = h; |
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l->outputs = h*w*l->n*(l->classes + l->coords + 1); |
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l->inputs = l->outputs; |
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l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); |
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l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); |
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#ifdef GPU |
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if (old_w < w || old_h < h) { |
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cuda_free(l->delta_gpu); |
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cuda_free(l->output_gpu); |
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l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); |
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l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); |
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} |
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#endif |
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} |
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box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h) |
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{ |
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box b; |
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b.x = (i + logistic_activate(x[index + 0])) / w; |
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b.y = (j + logistic_activate(x[index + 1])) / h; |
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b.w = exp(x[index + 2]) * biases[2*n]; |
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b.h = exp(x[index + 3]) * biases[2*n+1]; |
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if(DOABS){ |
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b.w = exp(x[index + 2]) * biases[2*n] / w; |
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b.h = exp(x[index + 3]) * biases[2*n+1] / h; |
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} |
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return b; |
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} |
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float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale) |
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{ |
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box pred = get_region_box(x, biases, n, index, i, j, w, h); |
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float iou = box_iou(pred, truth); |
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float tx = (truth.x*w - i); |
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float ty = (truth.y*h - j); |
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float tw = log(truth.w / biases[2*n]); |
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float th = log(truth.h / biases[2*n + 1]); |
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if(DOABS){ |
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tw = log(truth.w*w / biases[2*n]); |
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th = log(truth.h*h / biases[2*n + 1]); |
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} |
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delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0])); |
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delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1])); |
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delta[index + 2] = scale * (tw - x[index + 2]); |
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delta[index + 3] = scale * (th - x[index + 3]); |
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return iou; |
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} |
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void delta_region_class(float *output, float *delta, int index, int class_id, int classes, tree *hier, float scale, float *avg_cat, int focal_loss) |
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{ |
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int i, n; |
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if(hier){ |
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float pred = 1; |
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while(class_id >= 0){ |
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pred *= output[index + class_id]; |
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int g = hier->group[class_id]; |
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int offset = hier->group_offset[g]; |
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for(i = 0; i < hier->group_size[g]; ++i){ |
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delta[index + offset + i] = scale * (0 - output[index + offset + i]); |
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} |
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delta[index + class_id] = scale * (1 - output[index + class_id]); |
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class_id = hier->parent[class_id]; |
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} |
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*avg_cat += pred; |
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} else { |
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// Focal loss |
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if (focal_loss) { |
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// Focal Loss |
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float alpha = 0.5; // 0.25 or 0.5 |
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//float gamma = 2; // hardcoded in many places of the grad-formula |
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int ti = index + class_id; |
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float pt = output[ti] + 0.000000000000001F; |
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//float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832 |
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float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss |
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for (n = 0; n < classes; ++n) { |
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delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); |
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delta[index + n] *= alpha*grad; |
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if (n == class_id) *avg_cat += output[index + n]; |
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} |
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} |
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else { |
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// default |
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for (n = 0; n < classes; ++n) { |
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delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); |
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if (n == class_id) *avg_cat += output[index + n]; |
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} |
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} |
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} |
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} |
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float logit(float x) |
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{ |
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return log(x/(1.-x)); |
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} |
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float tisnan(float x) |
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{ |
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return (x != x); |
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} |
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static int entry_index(layer l, int batch, int location, int entry) |
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{ |
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int n = location / (l.w*l.h); |
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int loc = location % (l.w*l.h); |
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return batch*l.outputs + n*l.w*l.h*(l.coords + l.classes + 1) + entry*l.w*l.h + loc; |
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} |
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void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output); |
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void forward_region_layer(const region_layer l, network_state state) |
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{ |
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int i,j,b,t,n; |
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int size = l.coords + l.classes + 1; |
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memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); |
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#ifndef GPU |
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flatten(l.output, l.w*l.h, size*l.n, l.batch, 1); |
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#endif |
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for (b = 0; b < l.batch; ++b){ |
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for(i = 0; i < l.h*l.w*l.n; ++i){ |
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int index = size*i + b*l.outputs; |
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l.output[index + 4] = logistic_activate(l.output[index + 4]); |
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} |
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} |
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#ifndef GPU |
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if (l.softmax_tree){ |
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for (b = 0; b < l.batch; ++b){ |
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for(i = 0; i < l.h*l.w*l.n; ++i){ |
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int index = size*i + b*l.outputs; |
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softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5); |
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} |
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} |
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} else if (l.softmax){ |
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for (b = 0; b < l.batch; ++b){ |
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for(i = 0; i < l.h*l.w*l.n; ++i){ |
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int index = size*i + b*l.outputs; |
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softmax(l.output + index + 5, l.classes, 1, l.output + index + 5, 1); |
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} |
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} |
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} |
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#endif |
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if(!state.train) return; |
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memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); |
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float avg_iou = 0; |
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float recall = 0; |
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float avg_cat = 0; |
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float avg_obj = 0; |
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float avg_anyobj = 0; |
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int count = 0; |
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int class_count = 0; |
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*(l.cost) = 0; |
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for (b = 0; b < l.batch; ++b) { |
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if(l.softmax_tree){ |
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int onlyclass_id = 0; |
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for(t = 0; t < l.max_boxes; ++t){ |
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box truth = float_to_box(state.truth + t*5 + b*l.truths); |
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if(!truth.x) break; |
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int class_id = state.truth[t*5 + b*l.truths + 4]; |
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float maxp = 0; |
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int maxi = 0; |
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if(truth.x > 100000 && truth.y > 100000){ |
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for(n = 0; n < l.n*l.w*l.h; ++n){ |
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int index = size*n + b*l.outputs + 5; |
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float scale = l.output[index-1]; |
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float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class_id); |
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if(p > maxp){ |
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maxp = p; |
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maxi = n; |
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} |
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} |
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int index = size*maxi + b*l.outputs + 5; |
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delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss); |
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++class_count; |
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onlyclass_id = 1; |
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break; |
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} |
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} |
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if(onlyclass_id) continue; |
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} |
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for (j = 0; j < l.h; ++j) { |
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for (i = 0; i < l.w; ++i) { |
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for (n = 0; n < l.n; ++n) { |
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int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs; |
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box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h); |
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float best_iou = 0; |
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int best_class_id = -1; |
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for(t = 0; t < l.max_boxes; ++t){ |
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box truth = float_to_box(state.truth + t*5 + b*l.truths); |
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if(!truth.x) break; |
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float iou = box_iou(pred, truth); |
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if (iou > best_iou) { |
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best_class_id = state.truth[t*5 + b*l.truths + 4]; |
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best_iou = iou; |
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} |
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} |
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avg_anyobj += l.output[index + 4]; |
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l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4])); |
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if(l.classfix == -1) l.delta[index + 4] = l.noobject_scale * ((best_iou - l.output[index + 4]) * logistic_gradient(l.output[index + 4])); |
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else{ |
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if (best_iou > l.thresh) { |
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l.delta[index + 4] = 0; |
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if(l.classfix > 0){ |
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delta_region_class(l.output, l.delta, index + 5, best_class_id, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat, l.focal_loss); |
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++class_count; |
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} |
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} |
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} |
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if(*(state.net.seen) < 12800){ |
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box truth = {0}; |
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truth.x = (i + .5)/l.w; |
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truth.y = (j + .5)/l.h; |
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truth.w = l.biases[2*n]; |
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truth.h = l.biases[2*n+1]; |
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if(DOABS){ |
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truth.w = l.biases[2*n]/l.w; |
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truth.h = l.biases[2*n+1]/l.h; |
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} |
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delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01); |
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} |
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} |
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} |
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} |
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for(t = 0; t < l.max_boxes; ++t){ |
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box truth = float_to_box(state.truth + t*5 + b*l.truths); |
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if(!truth.x) break; |
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float best_iou = 0; |
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int best_index = 0; |
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int best_n = 0; |
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i = (truth.x * l.w); |
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j = (truth.y * l.h); |
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//printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h); |
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box truth_shift = truth; |
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truth_shift.x = 0; |
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truth_shift.y = 0; |
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//printf("index %d %d\n",i, j); |
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for(n = 0; n < l.n; ++n){ |
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int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs; |
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box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h); |
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if(l.bias_match){ |
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pred.w = l.biases[2*n]; |
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pred.h = l.biases[2*n+1]; |
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if(DOABS){ |
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pred.w = l.biases[2*n]/l.w; |
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pred.h = l.biases[2*n+1]/l.h; |
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} |
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} |
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//printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h); |
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pred.x = 0; |
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pred.y = 0; |
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float iou = box_iou(pred, truth_shift); |
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if (iou > best_iou){ |
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best_index = index; |
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best_iou = iou; |
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best_n = n; |
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} |
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} |
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//printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h); |
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float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale); |
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if(iou > .5) recall += 1; |
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avg_iou += iou; |
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//l.delta[best_index + 4] = iou - l.output[best_index + 4]; |
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avg_obj += l.output[best_index + 4]; |
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l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]); |
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if (l.rescore) { |
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l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]); |
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} |
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int class_id = state.truth[t*5 + b*l.truths + 4]; |
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if (l.map) class_id = l.map[class_id]; |
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delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss); |
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++count; |
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++class_count; |
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} |
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} |
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//printf("\n"); |
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#ifndef GPU |
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flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0); |
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#endif |
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*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); |
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printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count); |
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} |
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void backward_region_layer(const region_layer l, network_state state) |
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{ |
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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_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map) |
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{ |
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int i,j,n; |
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float *predictions = l.output; |
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for (i = 0; i < l.w*l.h; ++i){ |
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int row = i / l.w; |
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int col = i % l.w; |
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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 = index * (l.classes + 5) + 4; |
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float scale = predictions[p_index]; |
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if(l.classfix == -1 && scale < .5) scale = 0; |
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int box_index = index * (l.classes + 5); |
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boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h); |
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boxes[index].x *= w; |
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boxes[index].y *= h; |
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boxes[index].w *= w; |
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boxes[index].h *= h; |
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int class_index = index * (l.classes + 5) + 5; |
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if(l.softmax_tree){ |
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hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0); |
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int found = 0; |
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if(map){ |
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for(j = 0; j < 200; ++j){ |
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float prob = scale*predictions[class_index+map[j]]; |
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probs[index][j] = (prob > thresh) ? prob : 0; |
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} |
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} else { |
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for(j = l.classes - 1; j >= 0; --j){ |
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if(!found && predictions[class_index + j] > .5){ |
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found = 1; |
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} else { |
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predictions[class_index + j] = 0; |
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} |
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float prob = predictions[class_index+j]; |
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probs[index][j] = (scale > thresh) ? prob : 0; |
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} |
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} |
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} else { |
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for(j = 0; j < l.classes; ++j){ |
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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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} |
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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_region_layer_gpu(const region_layer l, network_state state) |
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{ |
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/* |
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if(!state.train){ |
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copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1); |
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return; |
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} |
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*/ |
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flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu); |
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if(l.softmax_tree){ |
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int i; |
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int count = 5; |
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for (i = 0; i < l.softmax_tree->groups; ++i) { |
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int group_size = l.softmax_tree->group_size[i]; |
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softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count); |
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count += group_size; |
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} |
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}else if (l.softmax){ |
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softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5); |
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} |
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float *in_cpu = calloc(l.batch*l.inputs, sizeof(float)); |
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float *truth_cpu = 0; |
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if(state.truth){ |
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int num_truth = l.batch*l.truths; |
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truth_cpu = calloc(num_truth, sizeof(float)); |
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cuda_pull_array(state.truth, truth_cpu, num_truth); |
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} |
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cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs); |
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//cudaStreamSynchronize(get_cuda_stream()); |
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network_state cpu_state = state; |
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cpu_state.train = state.train; |
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cpu_state.truth = truth_cpu; |
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cpu_state.input = in_cpu; |
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forward_region_layer(l, cpu_state); |
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//cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); |
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free(cpu_state.input); |
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if(!state.train) return; |
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cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); |
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//cudaStreamSynchronize(get_cuda_stream()); |
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if(cpu_state.truth) free(cpu_state.truth); |
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} |
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void backward_region_layer_gpu(region_layer l, network_state state) |
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{ |
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flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta); |
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} |
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#endif |
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void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) |
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{ |
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int i; |
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int new_w = 0; |
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int new_h = 0; |
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if (((float)netw / w) < ((float)neth / h)) { |
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new_w = netw; |
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new_h = (h * netw) / w; |
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} |
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else { |
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new_h = neth; |
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new_w = (w * neth) / h; |
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} |
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for (i = 0; i < n; ++i) { |
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box b = dets[i].bbox; |
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b.x = (b.x - (netw - new_w) / 2. / netw) / ((float)new_w / netw); |
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b.y = (b.y - (neth - new_h) / 2. / neth) / ((float)new_h / neth); |
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b.w *= (float)netw / new_w; |
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b.h *= (float)neth / new_h; |
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if (!relative) { |
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b.x *= w; |
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b.w *= w; |
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b.y *= h; |
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b.h *= h; |
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} |
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dets[i].bbox = b; |
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} |
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} |
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|
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/* |
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void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets) |
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{ |
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int i, j, n, z; |
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float *predictions = l.output; |
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if (l.batch == 2) { |
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float *flip = l.output + l.outputs; |
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for (j = 0; j < l.h; ++j) { |
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for (i = 0; i < l.w / 2; ++i) { |
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for (n = 0; n < l.n; ++n) { |
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for (z = 0; z < l.classes + l.coords + 1; ++z) { |
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int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; |
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int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); |
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float swap = flip[i1]; |
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flip[i1] = flip[i2]; |
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flip[i2] = swap; |
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if (z == 0) { |
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flip[i1] = -flip[i1]; |
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flip[i2] = -flip[i2]; |
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} |
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} |
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} |
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} |
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} |
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for (i = 0; i < l.outputs; ++i) { |
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l.output[i] = (l.output[i] + flip[i]) / 2.; |
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} |
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} |
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for (i = 0; i < l.w*l.h; ++i) { |
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int row = i / l.w; |
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int col = i % l.w; |
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for (n = 0; n < l.n; ++n) { |
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int index = n*l.w*l.h + i; |
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for (j = 0; j < l.classes; ++j) { |
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dets[index].prob[j] = 0; |
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} |
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int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); |
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int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); |
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int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4); |
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float scale = l.background ? 1 : predictions[obj_index]; |
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dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h); |
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dets[index].objectness = scale > thresh ? scale : 0; |
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if (dets[index].mask) { |
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for (j = 0; j < l.coords - 4; ++j) { |
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dets[index].mask[j] = l.output[mask_index + j*l.w*l.h]; |
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} |
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} |
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|
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int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); |
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if (l.softmax_tree) { |
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|
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hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h); |
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if (map) { |
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for (j = 0; j < 200; ++j) { |
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int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); |
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float prob = scale*predictions[class_index]; |
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dets[index].prob[j] = (prob > thresh) ? prob : 0; |
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} |
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} |
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else { |
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int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h); |
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dets[index].prob[j] = (scale > thresh) ? scale : 0; |
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} |
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} |
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else { |
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if (dets[index].objectness) { |
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for (j = 0; j < l.classes; ++j) { |
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int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j); |
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float prob = scale*predictions[class_index]; |
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dets[index].prob[j] = (prob > thresh) ? prob : 0; |
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} |
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} |
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} |
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} |
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} |
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correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative); |
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} |
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*/ |