def plot_precision_recall()

in util/plot_utils.py [0:0]

• 28 lines of code
• 8 McCabe index (conditional complexity)

def plot_precision_recall(files, naming_scheme='iter'):
    if naming_scheme == 'exp_id':
        # name becomes exp_id
        names = [f.parts[-3] for f in files]
    elif naming_scheme == 'iter':
        names = [f.stem for f in files]
    else:
        raise ValueError(f'not supported {naming_scheme}')
    fig, axs = plt.subplots(ncols=2, figsize=(16, 5))
    for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names):
        data = torch.load(f)
        # precision is n_iou, n_points, n_cat, n_area, max_det
        precision = data['precision']
        recall = data['params'].recThrs
        scores = data['scores']
        # take precision for all classes, all areas and 100 detections
        precision = precision[0, :, :, 0, -1].mean(1)
        scores = scores[0, :, :, 0, -1].mean(1)
        prec = precision.mean()
        rec = data['recall'][0, :, 0, -1].mean()
        print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' +
              f'score={scores.mean():0.3f}, ' +
              f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}'
              )
        axs[0].plot(recall, precision, c=color)
        axs[1].plot(recall, scores, c=color)

    axs[0].set_title('Precision / Recall')
    axs[0].legend(names)
    axs[1].set_title('Scores / Recall')
    axs[1].legend(names)
    return fig, axs