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| # Model validation metrics | |
| from pathlib import Path | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import torch | |
| from . import general | |
| 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 ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): | |
| """ 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 | |
| save_dir: Plot save directory | |
| # 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) | |
| nc = unique_classes.shape[0] # number of classes, number of detections | |
| # Create Precision-Recall curve and compute AP for each class | |
| px, py = np.linspace(0, 1, 1000), [] # for plotting | |
| ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) | |
| 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(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases | |
| # Precision | |
| precision = tpc / (tpc + fpc) # precision curve | |
| p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # 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 plot and j == 0: | |
| py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 | |
| # Compute F1 (harmonic mean of precision and recall) | |
| f1 = 2 * p * r / (p + r + 1e-16) | |
| if plot: | |
| plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) | |
| plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') | |
| plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') | |
| plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') | |
| i = f1.mean(0).argmax() # max F1 index | |
| return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') | |
| def compute_ap(recall, precision): | |
| """ Compute the average precision, given the recall and precision curves | |
| # Arguments | |
| recall: The recall curve (list) | |
| precision: The precision curve (list) | |
| # Returns | |
| Average precision, precision curve, recall curve | |
| """ | |
| # Append sentinel values to beginning and end | |
| mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) | |
| mpre = np.concatenate(([1.], precision, [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 | |
| class ConfusionMatrix: | |
| # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix | |
| def __init__(self, nc, conf=0.25, iou_thres=0.45): | |
| self.matrix = np.zeros((nc + 1, nc + 1)) | |
| self.nc = nc # number of classes | |
| self.conf = conf | |
| self.iou_thres = iou_thres | |
| def process_batch(self, detections, labels): | |
| """ | |
| Return intersection-over-union (Jaccard index) of boxes. | |
| Both sets of boxes are expected to be in (x1, y1, x2, y2) format. | |
| Arguments: | |
| detections (Array[N, 6]), x1, y1, x2, y2, conf, class | |
| labels (Array[M, 5]), class, x1, y1, x2, y2 | |
| Returns: | |
| None, updates confusion matrix accordingly | |
| """ | |
| detections = detections[detections[:, 4] > self.conf] | |
| gt_classes = labels[:, 0].int() | |
| detection_classes = detections[:, 5].int() | |
| iou = general.box_iou(labels[:, 1:], detections[:, :4]) | |
| x = torch.where(iou > self.iou_thres) | |
| if x[0].shape[0]: | |
| matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() | |
| if x[0].shape[0] > 1: | |
| matches = matches[matches[:, 2].argsort()[::-1]] | |
| matches = matches[np.unique(matches[:, 1], return_index=True)[1]] | |
| matches = matches[matches[:, 2].argsort()[::-1]] | |
| matches = matches[np.unique(matches[:, 0], return_index=True)[1]] | |
| else: | |
| matches = np.zeros((0, 3)) | |
| n = matches.shape[0] > 0 | |
| m0, m1, _ = matches.transpose().astype(np.int16) | |
| for i, gc in enumerate(gt_classes): | |
| j = m0 == i | |
| if n and sum(j) == 1: | |
| self.matrix[gc, detection_classes[m1[j]]] += 1 # correct | |
| else: | |
| self.matrix[self.nc, gc] += 1 # background FP | |
| if n: | |
| for i, dc in enumerate(detection_classes): | |
| if not any(m1 == i): | |
| self.matrix[dc, self.nc] += 1 # background FN | |
| def matrix(self): | |
| return self.matrix | |
| def plot(self, save_dir='', names=()): | |
| try: | |
| import seaborn as sn | |
| array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize | |
| array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) | |
| fig = plt.figure(figsize=(12, 9), tight_layout=True) | |
| sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size | |
| labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels | |
| sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, | |
| xticklabels=names + ['background FP'] if labels else "auto", | |
| yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) | |
| fig.axes[0].set_xlabel('True') | |
| fig.axes[0].set_ylabel('Predicted') | |
| fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) | |
| except Exception as e: | |
| pass | |
| def print(self): | |
| for i in range(self.nc + 1): | |
| print(' '.join(map(str, self.matrix[i]))) | |
| # Plots ---------------------------------------------------------------------------------------------------------------- | |
| def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): | |
| # Precision-recall curve | |
| fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) | |
| py = np.stack(py, axis=1) | |
| if 0 < len(names) < 21: # display per-class legend if < 21 classes | |
| for i, y in enumerate(py.T): | |
| ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) | |
| else: | |
| ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) | |
| ax.plot(px, py.mean(1), linewidth=3, 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(bbox_to_anchor=(1.04, 1), loc="upper left") | |
| fig.savefig(Path(save_dir), dpi=250) | |
| def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): | |
| # Metric-confidence curve | |
| fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) | |
| if 0 < len(names) < 21: # display per-class legend if < 21 classes | |
| for i, y in enumerate(py): | |
| ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) | |
| else: | |
| ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) | |
| y = py.mean(0) | |
| ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') | |
| ax.set_xlabel(xlabel) | |
| ax.set_ylabel(ylabel) | |
| ax.set_xlim(0, 1) | |
| ax.set_ylim(0, 1) | |
| plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") | |
| fig.savefig(Path(save_dir), dpi=250) | |