文章参考自 关于eval.py中MAP，AP计算为0的问题，已经解决！！！（若不是类别名字大小写问题，可尝试参考本文）
YOLOX训练VOC格式数据集出现 AP=0 可查看：解决YOLOX训练时AP为0
一、问题描述：
今天在和一位同学重新学习研究 YOLOX 的过程中，发现所用数据的 AP 为 0，
之前也发过一篇关于 YOLOX AP 为 0 的解决方案，但此次出现该问题主要的原因是 xml标签 不存在 difficult 这一参数导致的 voc_eval 计算 AP 出错
二、解决问题：
那既然已知是 difficult 参数导致的问题，那么就对它进行相应的修改
1. 首先注释掉从xml文件获取 difficult 参数这一操作
for obj in tree.findall("object"):
obj_struct = {
}
obj_struct["name"] = obj.find("name").text
obj_struct["pose"] = obj.find("pose").text
obj_struct["truncated"] = int(obj.find("truncated").text)
# obj_struct["difficult"] = int(obj.find("difficult").text)
bbox = obj.find("bndbox")
2. 修改 difficult 的值
由于不存在 difficult ，所以会导致 difficult = np.array([x["difficult"] for x in R]).astype(bool) 出错，进而导致 npos = npos + sum(~difficult) 出错，再导致 rec = tp / float(npos) 出错，然后 fp、tp 的计算也会出错
if ovmax > ovthresh:
if not R["difficult"][jmax]:
if not R["det"][jmax]:
tp[d] = 1.0
R["det"][jmax] = 1
else:
fp[d] = 1.0
else:
fp[d] = 1.0
因为无 difficult，意味着 difficult 全为 0，那我们只需要将 difficult 赋值为 长度为 R 的全零数组即可，即：
difficult = np.zeros(len(R)).astype(np.bool)
至于 npos 可修改也可不做修改，
# 自增，非difficult样本数量，如果数据集没有 difficult，npos数量 就是 gt数量。
npos = npos + sum(~difficult)
若做修改：
# len(R) 即为 gt数量
npos = npos + len(R)
完成上述修改，再去运行程序，即可获得正确的 AP 值
下面是修改后的完整的 voc_eval.py
#!/usr/bin/env python3
# Code are based on
# https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/datasets/voc_eval.py
# Copyright (c) Bharath Hariharan.
# Copyright (c) Megvii, Inc. and its affiliates.
import os
import pickle
import xml.etree.ElementTree as ET
import numpy as np
def parse_rec(filename):
"""Parse a PASCAL VOC xml file"""
tree = ET.parse(filename)
objects = []
for obj in tree.findall("object"):
obj_struct = {
}
obj_struct["name"] = obj.find("name").text
obj_struct["pose"] = obj.find("pose").text
obj_struct["truncated"] = int(obj.find("truncated").text)
# obj_struct["difficult"] = int(obj.find("difficult").text)
bbox = obj.find("bndbox")
obj_struct["bbox"] = [
int(bbox.find("xmin").text),
int(bbox.find("ymin").text),
int(bbox.find("xmax").text),
int(bbox.find("ymax").text),
]
objects.append(obj_struct)
return objects
def voc_ap(rec, prec, use_07_metric=False):
"""
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.0
for t in np.arange(0.0, 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.0
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.0], rec, [1.0]))
mpre = np.concatenate(([0.0], prec, [0.0]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def voc_eval(
detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False,
):
# first load gt
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, "annots.pkl")
# read list of images
with open(imagesetfile, "r") as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if not os.path.isfile(cachefile):
# load annots
recs = {
}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(annopath.format(imagename))
if i % 100 == 0:
print(f"Reading annotation for {
i + 1}/{
len(imagenames)}")
# save
print(f"Saving cached annotations to {
cachefile}")
with open(cachefile, "wb") as f:
pickle.dump(recs, f)
else:
# load
with open(cachefile, "rb") as f:
recs = pickle.load(f)
# extract gt objects for this class
class_recs = {
}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj["name"] == classname]
bbox = np.array([x["bbox"] for x in R])
# difficult = np.array([x["difficult"] for x in R]).astype(bool)
difficult = np.zeros(len(R)).astype(np.bool)
det = [False] * len(R)
# npos = npos + len(R)
npos = npos + sum(~difficult)
class_recs[imagename] = {
"bbox": bbox, "difficult": difficult, "det": det}
# read dets
detfile = detpath.format(classname)
with open(detfile, "r") as f:
lines = f.readlines()
if len(lines) == 0:
return 0, 0, 0
splitlines = [x.strip().split(" ") for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
# sort by confidence
sorted_ind = np.argsort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R["bbox"].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1.0, 0.0)
ih = np.maximum(iymax - iymin + 1.0, 0.0)
inters = iw * ih
# union
uni = (
(bb[2] - bb[0] + 1.0) * (bb[3] - bb[1] + 1.0)
+ (BBGT[:, 2] - BBGT[:, 0] + 1.0) * (BBGT[:, 3] - BBGT[:, 1] + 1.0) - inters
)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R["difficult"][jmax]:
if not R["det"][jmax]:
tp[d] = 1.0
R["det"][jmax] = 1
else:
fp[d] = 1.0
else:
fp[d] = 1.0
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap