参考：https://blog.csdn.net/liuhao3285/article/details/135233281?spm=1001.2014.3001.5502
fppi=fp/image_num

yolov7中增加FPPI
FPPI实现

yolo7中的评价指标实现位于utils/metrics.py中，我们只需要参照mAP指标在其中增加FPPI的内容即可：

def fppi_per_class(tp, conf, pred_cls, target_cls, image_num, plot=False, save_dir='.', names=(), return_plt=False):""" Compute the false positives per image (FPPW) metric, given the recall and precision curves.Source:# Argumentstp:  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@0.5save_dir:  Plot save directory# ReturnsThe fppi curve """# Sort by objectnessi = np.argsort(-conf)tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]# Find unique classesunique_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 classpx, py = np.linspace(0, 1, 1000), np.linspace(0,100,1000) # for plottingr = np.zeros((nc, 1000))miss_rate = np.zeros((nc, 1000))fppi = np.zeros((nc, 1000))miss_rate_at_fppi = np.zeros((nc, 3)) # missrate at fppi 1, 0.1, 0.01p_miss_rate = np.array([1, 0.1, 0.01])for ci, c in enumerate(unique_classes):i = pred_cls == cn_l = (target_cls == c).sum()  # number of labelsn_p = i.sum()  # number of predictionsif n_p == 0 or n_l == 0:continueelse:# Accumulate FPs and TPsfpc = (1 - tp[i]).cumsum(0)tpc = tp[i].cumsum(0)# Recallrecall = tpc / (n_l + 1e-16)  # recall curver[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreasesmiss_rate[ci] = 1 - r[ci]fp_per_image = fpc/image_numfppi[ci] = np.interp(-px,-conf[i], fp_per_image[:,0], left=0)miss_rate_at_fppi[ci] = np.interp(-p_miss_rate, -fppi[ci], miss_rate[ci])if plot:fig = plot_fppi_curve(fppi, miss_rate, miss_rate_at_fppi, Path(save_dir) / 'fppi_curve.png', names)if return_plt:return fppi, miss_rate, miss_rate_at_fppi, figreturn miss_rate, fppi, miss_rate_at_fppi

将fppi以对数坐标画图：

def plot_fppi_curve(px,py, missrate_at_fppi, save_dir='fppi_curve.png', names=()):fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)py = np.stack(py, axis=1)# semi logfor i, y in enumerate(py.T):ax.semilogx(px[i],y, linewidth=1, label=f'{names[i]} {missrate_at_fppi[i].mean():.3f}')  # plot(recall, precision)ax.semilogx(px.mean(0), py.mean(1), linewidth=3, color='blue', label='all classes %.3f' % missrate_at_fppi.mean())ax.set_xlabel('False Positives Per Image')ax.set_ylabel('Miss Rate')ax.set_xlim(0, 100)ax.set_ylim(0, 1)ax.grid(True)plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")fig.savefig(Path(save_dir), dpi=250)return fig

训练中调用

在test.py中在map计算的下方增加fppi的计算：

p, r, f1, mp, mr, map50, map, t0, t1, mfppi_1 = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
........
........
stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
if len(stats) and stats[0].any():p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)miss_rate, fppi, miss_rate_at_fppi = fppi_per_class(*stats, plot=plots, image_num= image_num, save_dir=save_dir, names=names)mfppi_1 = miss_rate_at_fppi[:,0].mean()ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
else:nt = torch.zeros(1)# Print results
pf = "%20s" + "%12i" * 2 + "%12.3g" * 5  # print format
print(pf % ("all", seen, nt.sum(), mp, mr, map50, map, mfppi_1))
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#返回fppi
return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist(), mfppi_1), maps, t