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| # Model validation metrics | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def fitness(x): | |
| # Model fitness as a weighted combination of metrics | |
| w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] | |
| return (x[:, :4] * w).sum(1) | |
| def fitness_p(x): | |
| # Model fitness as a weighted combination of metrics | |
| w = [1.0, 0.0, 0.0, 0.0] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] | |
| return (x[:, :4] * w).sum(1) | |
| def fitness_r(x): | |
| # Model fitness as a weighted combination of metrics | |
| w = [0.0, 1.0, 0.0, 0.0] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] | |
| return (x[:, :4] * w).sum(1) | |
| def fitness_ap50(x): | |
| # Model fitness as a weighted combination of metrics | |
| w = [0.0, 0.0, 1.0, 0.0] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] | |
| return (x[:, :4] * w).sum(1) | |
| def fitness_ap(x): | |
| # Model fitness as a weighted combination of metrics | |
| w = [0.0, 0.0, 0.0, 1.0] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] | |
| return (x[:, :4] * w).sum(1) | |
| def fitness_f(x): | |
| # Model fitness as a weighted combination of metrics | |
| #w = [0.0, 0.0, 0.0, 1.0] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] | |
| return ((x[:, 0]*x[:, 1])/(x[:, 0]+x[:, 1])) | |
| def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-recall_curve.png'): | |
| """ Compute the average precision, given the recall and precision curves. | |
| Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. | |
| # Arguments | |
| tp: True positives (nparray, nx1 or nx10). | |
| conf: Objectness value from 0-1 (nparray). | |
| pred_cls: Predicted object classes (nparray). | |
| target_cls: True object classes (nparray). | |
| plot: Plot precision-recall curve at mAP@0.5 | |
| fname: Plot filename | |
| # Returns | |
| The average precision as computed in py-faster-rcnn. | |
| """ | |
| # Sort by objectness | |
| i = np.argsort(-conf) | |
| tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] | |
| # Find unique classes | |
| unique_classes = np.unique(target_cls) | |
| # Create Precision-Recall curve and compute AP for each class | |
| px, py = np.linspace(0, 1, 1000), [] # for plotting | |
| pr_score = 0.1 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898 | |
| s = [unique_classes.shape[0], tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95) | |
| ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s) | |
| for ci, c in enumerate(unique_classes): | |
| i = pred_cls == c | |
| n_l = (target_cls == c).sum() # number of labels | |
| n_p = i.sum() # number of predictions | |
| if n_p == 0 or n_l == 0: | |
| continue | |
| else: | |
| # Accumulate FPs and TPs | |
| fpc = (1 - tp[i]).cumsum(0) | |
| tpc = tp[i].cumsum(0) | |
| # Recall | |
| recall = tpc / (n_l + 1e-16) # recall curve | |
| r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0]) # r at pr_score, negative x, xp because xp decreases | |
| # Precision | |
| precision = tpc / (tpc + fpc) # precision curve | |
| p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0]) # p at pr_score | |
| # AP from recall-precision curve | |
| for j in range(tp.shape[1]): | |
| ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) | |
| if j == 0: | |
| py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 | |
| # Compute F1 score (harmonic mean of precision and recall) | |
| f1 = 2 * p * r / (p + r + 1e-16) | |
| if plot: | |
| py = np.stack(py, axis=1) | |
| fig, ax = plt.subplots(1, 1, figsize=(5, 5)) | |
| ax.plot(px, py, linewidth=0.5, color='grey') # plot(recall, precision) | |
| ax.plot(px, py.mean(1), linewidth=2, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) | |
| ax.set_xlabel('Recall') | |
| ax.set_ylabel('Precision') | |
| ax.set_xlim(0, 1) | |
| ax.set_ylim(0, 1) | |
| plt.legend() | |
| fig.tight_layout() | |
| fig.savefig(fname, dpi=200) | |
| return p, r, ap, f1, unique_classes.astype('int32') | |
| def compute_ap(recall, precision): | |
| """ Compute the average precision, given the recall and precision curves. | |
| Source: https://github.com/rbgirshick/py-faster-rcnn. | |
| # Arguments | |
| recall: The recall curve (list). | |
| precision: The precision curve (list). | |
| # Returns | |
| The average precision as computed in py-faster-rcnn. | |
| """ | |
| # Append sentinel values to beginning and end | |
| mrec = np.concatenate(([0.0], recall, [1.0])) | |
| mpre = np.concatenate(([1.0], precision, [0.0])) | |
| # Compute the precision envelope | |
| mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) | |
| # Integrate area under curve | |
| method = 'interp' # methods: 'continuous', 'interp' | |
| if method == 'interp': | |
| x = np.linspace(0, 1, 101) # 101-point interp (COCO) | |
| ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate | |
| else: # 'continuous' | |
| i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes | |
| ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve | |
| return ap, mpre, mrec | |