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Add smoothing to curve plots and max F1 index (#7798) 

@sergiossm
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Glenn Jocher 2022-05-13 13:53:21 +02:00 committed by GitHub
parent d95a728f55
commit 8a4175cde0
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1 changed files with 10 additions and 2 deletions
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12
utils/metrics.py
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@ -18,6 +18,14 @@ def fitness(x):
return (x[:, :4] * w).sum(1) return (x[:, :4] * w).sum(1)
def smooth(y, f=0.05):
# Box filter of fraction f
nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
p = np.ones(nf // 2) # ones padding
yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16): def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16):
""" Compute the average precision, given the recall and precision curves. """ Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
@ -79,7 +87,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') 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') plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
i = f1.mean(0).argmax() # max F1 index i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
p, r, f1 = p[:, i], r[:, i], f1[:, i] p, r, f1 = p[:, i], r[:, i], f1[:, i]
tp = (r * nt).round() # true positives tp = (r * nt).round() # true positives
fp = (tp / (p + eps) - tp).round() # false positives fp = (tp / (p + eps) - tp).round() # false positives
@ -337,7 +345,7 @@ def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence'
else: else:
ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric)
y = py.mean(0) y = smooth(py.mean(0), 0.05)
ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') 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_xlabel(xlabel)
ax.set_ylabel(ylabel) ax.set_ylabel(ylabel)