mmaction2 入门教程 02评价指标 每类动作（行为）数量 precision（准确率），recall（召回率）apmAP
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目录
0 前言
这次的博客说说如何在mmaction2中的评价指标，主要是评价每一个动作（行为）的数量和召回率Recall和精准率（Precision）、 AP、mAP。
如果对这些指标不理解的，参考：机器学习：mAP评价指标、mAP (mean Average Precision) for Object Detection
b站：https://www.bilibili.com/video/BV1a14y1475H/
GPU平台：https://cloud.videojj.com/auth/register?inviter=18452&activityChannel=student_invite
1 mmaction2中评价指标
但是在mmaction2中，默认的的评价结果一般如下：
执行：
cd /home/MPCLST/mmaction2_YF/
python tools/test.py configs/detection/ava/my_slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb3.py ./work_dirs/22-8-7ava-lr0005/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/latest.pth --eval mAP
其中
cd /home/MPCLST/mmaction2_YF/ 为mmaction2的路径
my_slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb3.py为配置文件
slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/latest.pth为训练的权重（最后一次epoch）
–eval mAP 评价指标采用mAP
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 77/77, 9.3 task/s, elapsed: 8s, ETA: 0s2022-08-05 18:51:42,986 - mmaction - INFO - Evaluating mAP ...
INFO:mmaction:Evaluating mAP ...
==> 0.0539076 seconds to read file /home/MPCLST/Dataset/annotations/val.csv
==> 0.0548043 seconds to Reading detection results
==> 0.11351 seconds to read file AVA_20220805_185142_result.csv
==> 0.11438 seconds to Reading detection results
==> 0.0137618 seconds to Convert groundtruth
==> 0.678621 seconds to convert detections
==> 0.0614183 seconds to run_evaluator
mAP@0.5IOU= 0.1576104042635473
PerformanceByCategory/AP@0.5IOU/eye Invisible= 0.2180298892002125
PerformanceByCategory/AP@0.5IOU/eye ohter= PerformanceByCategory/AP@0.5IOU/open eyes= PerformanceByCategory/AP@0.5IOU/close eyes= PerformanceByCategory/AP@0.5IOU/lip Invisible= 0.21092473617973181 PerformanceByCategory/AP@0.5IOU/lip ohter= nan PerformanceByCategory/AP@0.5IOU/open mouth= 0.018103458579299812 PerformanceByCategory/AP@0.5IOU/close mouth= 0.4808178582451651 PerformanceByCategory/AP@0.5IOU/body Invisible= 0.0 PerformanceByCategory/AP@0.5IOU/body other= 0.0 PerformanceByCategory/AP@0.5IOU/body sit= 0.3023163122037017 PerformanceByCategory/AP@0.5IOU/body side Sit= 0.05629252504252504 PerformanceByCategory/AP@0.5IOU/body stand= 0.4428815974966386 PerformanceByCategory/AP@0.5IOU/body lying down= nan PerformanceByCategory/AP@0.5IOU/body bend over= 0.011661807580174927 PerformanceByCategory/AP@0.5IOU/body squat= nan PerformanceByCategory/AP@0.5IOU/body rely= nan PerformanceByCategory/AP@0.5IOU/body lie flat= nan PerformanceByCategory/AP@0.5IOU/body lateral= nan PerformanceByCategory/AP@0.5IOU/left hand invisible= PerformanceByCategory/AP@0.5IOU/left hand other= PerformanceByCategory/AP@0.5IOU/left hand palm grip= PerformanceByCategory/AP@0.5IOU/left hand palm spread= 0.3608785801187531 PerformanceByCategory/AP@0.5IOU/left hand palm Point= 0.011808118081180811
PerformanceByCategory/AP@0.5IOU/left hand applause= PerformanceByCategory/AP@0.5IOU/left hand write= nan PerformanceByCategory/AP@0.5IOU/left arm invisible= 0.12179166353235524 PerformanceByCategory/AP@0.5IOU/left arm other= nan PerformanceByCategory/AP@0.5IOU/left arm flat= 0.025848791267662225 PerformanceByCategory/AP@0.5IOU/left arm droop= 0.17178401700149137
nan 0.39957288548832476
0.018996960486322188
PerformanceByCategory/AP@0.5IOU/left arm forward=
PerformanceByCategory/AP@0.5IOU/left arm flexion=
PerformanceByCategory/AP@0.5IOU/left arm raised=
PerformanceByCategory/AP@0.5IOU/left handed behavior object invisible= 0.2202811921971896 PerformanceByCategory/AP@0.5IOU/left handed behavior object other= 0.004090314136125654 PerformanceByCategory/AP@0.5IOU/left handed behavior object book = 0.07097950951517762 PerformanceByCategory/AP@0.5IOU/left handed behavior object exercise book= 0.2699992939947191 PerformanceByCategory/AP@0.5IOU/left handed behavior object spare head= 0.0 PerformanceByCategory/AP@0.5IOU/left handed behavior object electronic equipment= 0.0625 PerformanceByCategory/AP@0.5IOU/left handed behavior object electronic pointing at others= 0.002109704641350211
PerformanceByCategory/AP@0.5IOU/left handed behavior object chalk= nan PerformanceByCategory/AP@0.5IOU/left handed behavior object no interaction= 0.5040241544391736
PerformanceByCategory/AP@0.5IOU/right hand invisible= PerformanceByCategory/AP@0.5IOU/right hand other= PerformanceByCategory/AP@0.5IOU/right hand palm grip= PerformanceByCategory/AP@0.5IOU/right hand palm spread= 0.3032998644432045
0.22222112912575823 nan
0.24973343634144107
nan
0.00720624758668529 0.3526806569267596 0.27237318666844396
0.2581980631539392 0.014660633484162897 0.22999279538304435
PerformanceByCategory/AP@0.5IOU/right hand palm Point= 0.0006038647342995169 PerformanceByCategory/AP@0.5IOU/right hand applause= nan PerformanceByCategory/AP@0.5IOU/right hand write= 0.0 PerformanceByCategory/AP@0.5IOU/right arm invisible= 0.09170294175736904 PerformanceByCategory/AP@0.5IOU/right arm other= nan PerformanceByCategory/AP@0.5IOU/right arm flat= 0.04363534094842507
PerformanceByCategory/AP@0.5IOU/right arm droop= PerformanceByCategory/AP@0.5IOU/right arm forward= PerformanceByCategory/AP@0.5IOU/right arm flexion= PerformanceByCategory/AP@0.5IOU/right arm raised=
0.12056235779316193 0.003629032258064516
0.30850548191847726 0.11344222969091167
PerformanceByCategory/AP@0.5IOU/right handed PerformanceByCategory/AP@0.5IOU/right handed PerformanceByCategory/AP@0.5IOU/right handed PerformanceByCategory/AP@0.5IOU/right handed PerformanceByCategory/AP@0.5IOU/right handed PerformanceByCategory/AP@0.5IOU/right handed 0.021052631578947368
PerformanceByCategory/AP@0.5IOU/right handed PerformanceByCategory/AP@0.5IOU/right handed PerformanceByCategory/AP@0.5IOU/leg invisible= 0.36735369213840974
PerformanceByCategory/AP@0.5IOU/leg other= PerformanceByCategory/AP@0.5IOU/leg stand= PerformanceByCategory/AP@0.5IOU/leg run= PerformanceByCategory/AP@0.5IOU/leg walk= PerformanceByCategory/AP@0.5IOU/leg jump= PerformanceByCategory/AP@0.5IOU/leg Kick= 2022-08-05 18:51:43,917 - mmaction - INFO - mAP@0.5IOU 0.1576
0.28758302075183967 0.21229324685277162
0.488660662884261 nan
0.017013888888888887 nan
behavior object invisible= 0.24774165620121377 behavior object other= 0.037239973118687986 behavior object book = 0.0045173926595858515 behavior object exercise book= 0.09175462925197815 behavior object spare head= 0.0
behavior object electronic equipment=
behavior object electronic pointing at others= nan behavior object chalk= nan
INFO:mmaction:
mAP@0.5IOU 0.1576
2022-08-05 18:51:43,918 - mmaction - INFO - Epoch(val) [50][77] mAP@0.5IOU: 0.1576 INFO:mmaction:Epoch(val) [50][77] mAP@0.5IOU: 0.1576
可以看出，这里有每个类别动作（行为）的AP@0.5IOU，但是我们却没有precision, recall（准确率与召回率）。
其实 precision, recall（准确率与召回率）在mmaction2当中是有处理的，只不过没有输出，那么这篇博客，我就说说，如何把 precision, recall（准确率与召回率）输出。
2 解析评价指标
2.1 默认评价指标
最关键的代码在哪里呢？
那么首先，我们先找出 PerformanceByCategory/AP@0.5IOU/left arm flat= 0.025848791267662225 是从哪里输出的。
首先找到 object_detection_evaluation.py ：
https://github.com/open-mmlab/mmaction2/blob/master/mmaction/core/evaluation/ava_evaluation/object_detection_evaluation.py
for idx in range(per_class_ap.size):
if idx + self._label_id_offset in category_index:
display_name = (
self._metric_prefix +
'PerformanceByCategory/AP@IOU/'.format(
self._matching_iou_threshold,
category_index[idx + self._label_id_offset]['name'],
))
pascal_metrics[display_name] = per_class_ap[idx]
可以看出，这里 pascal_metrics 中存放了每个动作（行为）的分数值，并且输出格式也在其中了。
在哪里输出呢？或者说代码执行到这里后，下一步到哪里去呢？
下一步就到了ava_utils.py：https://github.com/open-mmlab/mmaction2/blob/master/mmaction/core/evaluation/ava_utils.py
for display_name in metrics:
print(f'display_name=\\tmetrics[display_name]')
可以看到，这一步就是输出操作了。
2.2 其他评价指标
现在已经找到了官方默认评价指标的处理和输出位置，那么大家仔细看object_detection_evaluation.py文件，会发现，还有很多评价指标没有输出。
2.3.1 总体评价指标
它们在哪里呢
首先，我们看到 object_detection_evaluation.py ：
https://github.com/open-mmlab/mmaction2/blob/master/mmaction/core/evaluation/ava_evaluation/object_detection_evaluation.py
(per_class_ap, mean_ap, _, _, per_class_corloc,
mean_corloc) = self._evaluation.evaluate()
这两行代码就隐藏了一些信息，一共有6个计算结果：per_class_ap, mean_ap, _, _, per_class_corloc, mean_corloc，并且其中有2个被省略了，那么self._evaluation.evaluate()出自哪里，省略的2个又是什么呢？
答案就在这里 object_detection_evaluation.py ：
https://github.com/open-mmlab/mmaction2/blob/master/mmaction/core/evaluation/ava_evaluation/object_detection_evaluation.py：
return ObjectDetectionEvalMetrics(
self.average_precision_per_class,
mean_ap,
self.precisions_per_class,
self.recalls_per_class,
self.corloc_per_class,
mean_corloc,
)
这样我们就看出了，被省略的原来是self.precisions_per_class，self.recalls_per_class
一个是每个动作（行为）的准确率，一个是每个动作（行为）的召回率，这不就是我们要的东西么。
找到了这6个值之后，我们就将其输出，看看其长啥样。
在object_detection_evaluation.py
https://github.com/open-mmlab/mmaction2/blob/master/mmaction/core/evaluation/ava_evaluation/object_detection_evaluation.py写入：
def evaluate(self):
"""Compute evaluation result.
Returns:
A dictionary of metrics with the following fields -
1. summary_metrics:
'Precision/mAP@<matching_iou_threshold>IOU': mean average
precision at the specified IOU threshold
2. per_category_ap: category specific results with keys of the form
'PerformanceByCategory/mAP@<matching_iou_threshold>IOU/category'
"""
"""
self.average_precision_per_class,
mean_ap,
self.precisions_per_class,
self.recalls_per_class,
self.corloc_per_class,
mean_corloc,
"""
#(per_class_ap, mean_ap, _, _, per_class_corloc,
# mean_corloc) = self._evaluation.evaluate()
(per_class_ap, mean_ap, precisions_per_class, recalls_per_class, per_class_corloc,
mean_corloc) = self._evaluation.evaluate()
metric = f'mAP@self._matching_iou_thresholdIOU'
pascal_metrics = self._metric_prefix + metric: mean_ap
if self._evaluate_corlocs:
pascal_metrics[self._metric_prefix +
'Precision/meanCorLoc@IOU'.format(
self._matching_iou_threshold)] = mean_corloc
category_index = self.create_category_index(self._categories)
# average_precision_per_class --> per_class_ap
print("per_class_ap[0]:",per_class_ap[0])
print("len(per_class_ap)",len(per_class_ap))
# mean_ap
print("mean_ap:",mean_ap)
# precisions_per_class
print("precisions_per_class[0]:",precisions_per_class[0])
print("len(precisions_per_class[0]):",len(precisions_per_class[0]))
print("len(precisions_per_class)",len(precisions_per_class))
# recalls_per_class
print("recalls_per_class[0]:",recalls_per_class[0])
print("len(recalls_per_class[0]):",len(recalls_per_class[0]))
print("len(recalls_per_class)",len(recalls_per_class))
# corloc_per_class --> per_class_corloc
print("per_class_corloc[0]:",per_class_corloc[0])
print("len(per_class_corloc)",len(per_class_corloc))
# mean_corloc
print("mean_corloc",mean_corloc)
input()
for idx in range(per_class_ap.size):
if idx + self._label_id_offset in category_index:
display_name = (
self._metric_prefix +
'PerformanceByCategory/AP@IOU/'.format(
self._matching_iou_threshold,
category_index[idx + self._label_id_offset]['name'],
))
pascal_metrics[display_name] = per_class_ap[idx]
# Optionally add CorLoc metrics.classes
if self._evaluate_corlocs:
display_name = (
self._metric_prefix +
'PerformanceByCategory/CorLoc@IOU/'.format(
self._matching_iou_threshold,
category_index[idx +
self._label_id_offset]['name'],
))
pascal_metrics[display_name] = per_class_corloc[idx]
return pascal_metrics
执行：
cd /home/MPCLST/mmaction2_YF/
python tools/test.py configs/detection/ava/my_slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb3.py ./work_dirs/22-8-7ava-lr0005/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/latest.pth --eval mAP
看看输出结果：
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 44/44, 8.1 task/s, elapsed: 5s, ETA: 0s
Evaluating mAP ...
==> 0.0463362 seconds to read file /home/MPCLST/Dataset/annotations/val.csv
==> 0.0469692 seconds to Reading detection results
==> 0.0420914 seconds to read file AVA_20220809_115528_result.csv
==> 0.0423815 seconds to Reading detection results
==> 0.00971556 seconds to Convert groundtruth
==> 0.41019 seconds to convert detections
per_class_ap[0]: 0.08483126725928619
len(per_class_ap) 71
mean_ap: 0.09254295488519894
precisions_per_class[0]: [1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1.
1. 1. 1. 0.9375 0.88235294 0.83333333
0.84210526 0.85 0.85714286 0.86363636 0.86956522 0.875
0.88 0.88461538 0.88888889 0.89285714 0.89655172 0.9
0.90322581 0.90625 0.90909091 0.91176471 0.91428571 0.91666667
0.91891892 0.92105263 0.8974359 0.9 0.87804878 0.85714286
0.8372093 0.81818182 0.82222222 0.80434783 0.80851064 0.8125
0.79591837 0.8 0.78431373 0.78846154 0.79245283 0.7962963
0.8 0.80357143 0.78947368 0.77586207 0.76271186 0.75
0.73770492 0.72580645 0.71428571 0.703125 0.69230769 0.68181818
0.67164179 0.66176471 0.66666667 0.67142857 0.67605634 0.66666667
0.65753425 0.64864865 0.64 0.63157895 0.62337662 0.61538462
0.62025316 0.6125 0.60493827 0.59756098 0.59036145 0.58333333
0.57647059 0.56976744 0.56321839 0.55681818 0.5505618 0.54444444
0.53846154 0.5326087 0.52688172 0.5212766 0.51578947 0.51041667
0.50515464 0.5 0.49494949 0.49 0.48514851 0.48039216
0.47572816 0.47115385 0.46666667 0.47169811 0.46728972 0.47222222
0.47706422 0.48181818 0.48648649 0.49107143 0.49557522 0.5
0.50434783 0.50862069 0.51282051 0.51694915 0.5210084 0.525
0.52892562 0.53278689 0.53658537 0.54032258 0.544 0.54761905
0.5511811 0.5546875 0.55813953 0.56153846 0.5648855 0.56818182
0.57142857 0.57462687 0.57777778]
len(precisions_per_class[0]): 135
len(precisions_per_class) 45
recalls_per_class[0]: [0.00139082 0.00278164 0.00417246 0.00556328 0.0069541 0.00834492
0.00973574 0.01112656 0.01251739 0.01390821 0.01529903 0.01668985
0.01808067 0.01947149 0.02086231 0.02086231 0.02086231 0.02086231
0.02225313 0.02364395 0.02503477 0.02642559 0.02781641 0.02920723
0.03059805 0.03198887 0.03337969 0.03477051 0.03616134 0.03755216
0.03894298 0.0403338 0.04172462 0.04311544 0.04450626 0.04589708
0.0472879 0.04867872 0.04867872 0.05006954 0.05006954 0.05006954
0.05006954 0.05006954 0.05146036 0.05146036 0.05285118 0.054242
0.054242 0.05563282 0.05563282 0.05702364 0.05841446 0.05980529
0.06119611 0.06258693 0.06258693 0.06258693 0.06258693 0.06258693
0.06258693 0.06258693 0.06258693 0.06258693 0.06258693 0.06258693
0.06258693 0.06258693 0.06397775 0.06536857 0.06675939 0.06675939
0.06675939 0.06675939 0.06675939 0.06675939 0.06675939 0.06675939
0.06815021 0.06815021 0.06815021 0.06815021 0.06815021 0.06815021
0.06815021 0.06815021 0.06815021 0.06815021 0.06815021 0.06815021
0.06815021 0.06815021 0.06815021 0.06815021 0.06815021 0.06815021
0.06815021 0.06815021 0.06815021 0.06815021 0.06815021 0.06815021
0.06815021 0.06815021 0.06815021 0.06954103 0.06954103 0.07093185
0.07232267 0.07371349 0.07510431 0.07649513 0.07788595 0.07927677
0.08066759 0.08205841 0.08344924 0.08484006 0.08623088 0.0876217
0.08901252 0.09040334 0.09179416 0.09318498 0.0945758 0.09596662
0.09735744 0.09874826 0.10013908 0.1015299 0.10292072 0.10431154
0.10570236 0.10709318 0.10848401]
len(recalls_per_class[0]): 135
len(recalls_per_class) 45
per_class_corloc[0]: 0.0
len(per_class_corloc) 71
mean_corloc 0.0
2.3.2 加载的进度条含义
先解释：
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 44/44, 8.1 task/s, elapsed: 5s, ETA: 0s
这里出现了44，它是什么意思呢？
我的测试集有4个视频，每个视频对应11张标注图片，那么总共有44张视频帧被标注。
2.3.3 average_precision_per_class
然后是：average_precision_per_class --> per_class_ap
per_class_ap[0]: 0.08483126725928619
len(per_class_ap) 71
可以看出per_class_ap就是对每个动作（行为）的ap值
2.3.4 mean_ap
mean_ap: 0.09254295488519894
就是最后所有动作（行为）的结果，mAP
2.3.5 precisions_per_class
precisions_per_class[0]: [1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1.
1. 1. 1. 0.9375 0.88235294 0.83333333
0.84210526 0.85 0.85714286 0.86363636 0.86956522 0.875
0.88 0.88461538 0.88888889 0.89285714 0.89655172 0.9
0.90322581 0.90625 0.90909091 0.91176471 0.91428571 0.91666667
0.91891892 0.92105263 0.8974359 0.9 0.87804878 0.85714286
0.8372093 0.81818182 0.82222222 0.80434783 0.80851064 0.8125
0.79591837 0.8 0.78431373 0.78846154 0.79245283 0.7962963
0.8 0.80357143 0.78947368 0.77586207 0.76271186 0.75
0.73770492 0.72580645 0.71428571 0.703125 0.69230769 0.68181818
0.67164179 0.66176471 0.66666667 0.67142857 0.67605634 0.66666667
0.65753425 0.64864865 0.64 0.63157895 0.62337662 0.61538462
0.62025316 0.6125 0.60493827 0.59756098 0.59036145 0.58333333
0.57647059 0.56976744 0.56321839 0.55681818 0.5505618 0.54444444
0.53846154 0.5326087 0.52688172 0.5212766 0.51578947 0.51041667
0.50515464 0.5 0.49494949 0.49 0.48514851 0.48039216
0.47572816 0.47115385 0.46666667 0.47169811 0.46728972 0.47222222
0.47706422 0.48181818 0.48648649 0.49107143 0.49557522 0.5
0.50434783 0.50862069 0.51282051 0.51694915 0.5210084 0.525
0.52892562 0.53278689 0.53658537 0.54032258 0.544 0.54761905
0.5511811 0.5546875 0.55813953 0.56153846 0.5648855 0.56818182
0.57142857 0.57462687 0.57777778]
len(precisions_per_class[0]): 135
len(precisions_per_class) 45
到这里，就出现了一些让人摸不着头脑的数字了，我自定义的数据集有71类动作（行为），测试集4*11=44张
但是这里的135、45，看似和71与44毫无关系，那么135和44代表着什么意思呢？
为了知道135和45的意思，我们需要到object_detection_evaluation.py
https://github.com/open-mmlab/mmaction2/blob/master/mmaction/core/evaluation/ava_evaluation/object_detection_evaluation.py：
def evaluate(self):
"""Compute evaluation result.
Returns:
A named tuple with the following fields -
average_precision: float numpy array of average precision for
each class.
mean_ap: mean average precision of all classes, float scalar
precisions: List of precisions, each precision is a float numpy
array
recalls: List of recalls, each recall is a float numpy array
corloc: numpy float array
mean_corloc: Mean CorLoc score for each class, float scalar
"""
if (self.num_gt_instances_per_class == 0).any():
logging.info(
'The following classes have no ground truth examples: %s',
np.squeeze(np.argwhere(self.num_gt_instances_per_class == 0)) +
self.label_id_offset)
if self.use_weighted_mean_ap:
all_scores = np.array([], dtype=float)
all_tp_fp_labels = np.array([], dtype=bool)
for class_index in range(self.num_class):
if self.num_gt_instances_per_class[class_index] == 0:
continue
if not self.scores_per_class[class_index]:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self.scores_per_class[class_index])
tp_fp_labels = np.concatenate(
self.tp_fp_labels_per_class[class_index])
if self.use_weighted_mean_ap:
all_scores = np.append(all_scores, scores)
all_tp_fp_labels = np.append(all_tp_fp_labels, tp_fp_labels)
precision, recall = metrics.compute_precision_recall(
scores, tp_fp_labels,
self.num_gt_instances_per_class[class_index])
self.precisions_per_class.append(precision)
self.recalls_per_class.append(recall)
average_precision = metrics.compute_average_precision(
precision, recall)
self.average_precision_per_class[class_index] = average_precision
self.corloc_per_class = metrics.compute_cor_loc(
self.num_gt_imgs_per_class,
self.num_images_correctly_detected_per_class)
if self.use_weighted_mean_ap:
num_gt_instances = np.sum(self.num_gt_instances_per_class)
precision, recall = metrics.compute_precision_recall(
all_scores, all_tp_fp_labels, num_gt_instances)
mean_ap = metrics.compute_average_precision(precision, recall)
else:
mean_ap = np.nanmean(self.average_precision_per_class)
mean_corloc = np.nanmean(self.corloc_per_class)
return ObjectDetectionEvalMetrics(
self.average_precision_per_class,
mean_ap,
self.precisions_per_class,
self.recalls_per_class,
self.corloc_per_class,
mean_corloc,
)
上面这段代码包含了非常多的信息
先说135的意思，从precisions_per_class这个名字可以大致知道，135与一个类别的准确率有关，但这一个类别的准确率怎么有135个呢？
看上图，可以知道，precisions_per_class.append由precision组成，precision中又有num_gt_instances_per_class
那么我们只能知道precision与num_gt_instances_per_class有关，并且precision还和scores有关，那么我们就要输出scores与num_gt_instances_per_class的结构与内容
def evaluate(self):
"""Compute evaluation result.
Returns:
A named tuple with the following fields -
average_precision: float numpy array of average precision for
each class.
mean_ap: mean average precision of all classes, float scalar
precisions: List of precisions, each precision is a float numpy
array
recalls: List of recalls, each recall is a float numpy array
corloc: numpy float array
mean_corloc: Mean CorLoc score for each class, float scalar
"""
if (self.num_gt_instances_per_class == 0).any():
logging.info(
'The following classes have no ground truth examples: %s',
np.squeeze(np.argwhere(self.num_gt_instances_per_class == 0)) +
self.label_id_offset)
if self.use_weighted_mean_ap:
all_scores = np.array([], dtype=float)
all_tp_fp_labels = np.array([], dtype=bool)
avg_recalls = np.empty(self.num_class, dtype=float)
avg_precision = np.empty(self.num_class, dtype=float)
for class_index in range(self.num_class):
if self.num_gt_instances_per_class[class_index] == 0:
continue
print("class_index:",class_index)
print("----------------------------------------")
print("self.num_gt_instances_per_class[class_index]:",self.num_gt_instances_per_class[class_index])
print("----------------------------------------")
print("len(self.scores_per_class[class_index]):",len(self.scores_per_class[class_index]))
print("self.scores_per_class[class_index]:",self.scores_per_class[class_index])
print("----------------------------------------")
if not self.scores_per_class[class_index]:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self.scores_per_class[class_index])
tp_fp_labels = np.concatenate(
self.tp_fp_labels_per_class[class_index])
if self.use_weighted_mean_ap:
all_scores = np.append(all_scores, scores)
all_tp_fp_labels = np.append(all_tp_fp_labels, tp_fp_labels)
precision, recall = metrics.compute_precision_recall(
scores, tp_fp_labels,
self.num_gt_instances_per_class[class_index])
print("len(scores):",len(scores))
print("scores:",scores)
print("----------------------------------------")
print("len(tp_fp_labels):",len(tp_fp_labels))
print("tp_fp_labels:",tp_fp_labels)
print("----------------------------------------")
print("len(precision):",len(precision))
print("precision:",precision)
print("----------------------------------------")
print("len(recall):",len(recall))
print("recall:",recall)
print("----------------------------------------")
input()
self.precisions_per_class.append(precision)
self.recalls_per_class.append(recall)
average_precision = metrics.compute_average_precision(
precision, recall)
self.average_precision_per_class[class_index] = average_precision
avg_recalls[class_index] = recall.mean(axis=0)
avg_precision[class_index] = precision.mean(axis=0)
#print("avg_recalls",avg_recalls)
#print("len(avg_recalls)",len(avg_recalls))
#input()
self.corloc_per_class = metrics.compute_cor_loc(
self.num_gt_imgs_per_class,
self.num_images_correctly_detected_per_class)
if self.use_weighted_mean_ap:
num_gt_instances = np.sum(self.num_gt_instances_per_class)
precision, recall = metrics.compute_precision_recall(
all_scores, all_tp_fp_labels, num_gt_instances)
mean_ap = metrics.compute_average_precision(precision, recall)
else:
mean_ap = np.nanmean(self.average_precision_per_class)
mean_corloc = np.nanmean(self.corloc_per_class)
return ObjectDetectionEvalMetrics(
self.average_precision_per_class,
mean_ap,
self.precisions_per_class,
self.recalls_per_class,
self.corloc_per_class,
mean_corloc,
avg_recalls,
avg_precision,
self.num_gt_instances_per_class
)
root@62ef18cf36dde126125c0d60:/home/MPCLST/mmaction2_YF# python tools/test.py configs/detection/ava/my_slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py ./work_dirs/22-8-7ava-lr00125/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/best_mAP&以上是关于mmaction2 入门教程 02评价指标 每类动作（行为）数量 precision（准确率），recall（召回率）apmAP的主要内容，如果未能解决你的问题，请参考以下文章
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