yolo训练日志分析_和美琪一起的日子里的博客-CSDN博客

来自 : CSDN技术社区 发布时间：2021-03-25

yolov3训练日志可视化主要为loss和iou曲线的可视化 这些是我们查看训练效果的重要依据 首先看一个批次的日志输出

说明
一个批次有16*3条信息 每组包含三条信息 分别是
Region 82 Avg IOU:
Region 94 Avg IOU:
Region 106 Avg IOU:

其中每行的参数意义如下
Avg IOU:当前迭代中 预测的box与标注的box的平均交并比 越大越好 期望数值为1
Class: 标注物体的分类准确率 越大越好 期望数值为1
obj: 越大越好 期望数值为1
No obj: 越小越好
.5R: 以IOU 0.5为阈值时候的recall; recall 检出的正样本/实际的正样本
0.75R: 以IOU 0.75为阈值时候的recall;
count:正样本数目。
注 存在nan值说明该子批次没有预测到正样本 在训练开始时候有出现是正常现象。

每个批次最后都会有一个输出行

1: 960.493347, 960.493347 avg, 0.000000 rate, 3.556313 seconds, 64 images 1

各部分代表意义如下
第几批次 总损失 平均损失 当前学习率 当前批次训练时间 目前为止参与训练的图片总数

#coding utf-8import osimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport logginglogging.basicConfig( level logging.DEBUG, format %(asctime)s %(levelname)s: %(message)s , datefmt %Y-%m-%d %H:%M:%S )logger logging.getLogger(__name__)class Yolov3LogVisualization: def __init__(self,log_path,result_dir): self.log_path log_path self.result_dir result_dir def extract_log(self, save_log_path, key_word): with open(self.log_path, r ) as f: with open(save_log_path, w ) as train_log: next_skip False for line in f: if next_skip: next_skip False continue # 去除多gpu的同步log if Syncing in line: continue # 去除除零错误的log if nan in line: continue if Saving weights to in line: next_skip True continue if key_word in line: train_log.write(line) f.close() train_log.close() def parse_loss_log(self,log_path, line_num 2000): result pd.read_csv(log_path, skiprows [x for x in range(line_num) if ((x % 10 ! 9) | (x 1000))],error_bad_lines False, names [ loss , avg , rate , seconds , images ]) result[ loss ] result[ loss ].str.split( ).str.get(1) result[ avg ] result[ avg ].str.split( ).str.get(1) result[ rate ] result[ rate ].str.split( ).str.get(1) result[ seconds ] result[ seconds ].str.split( ).str.get(1) result[ images ] result[ images ].str.split( ).str.get(1) result[ loss ] pd.to_numeric(result[ loss ]) result[ avg ] pd.to_numeric(result[ avg ]) result[ rate ] pd.to_numeric(result[ rate ]) result[ seconds ] pd.to_numeric(result[ seconds ]) result[ images ] pd.to_numeric(result[ images ]) return result def gene_loss_pic(self, pd_loss): fig plt.figure() ax fig.add_subplot(1, 1, 1) ax.plot(pd_loss[ avg ].values, label avg_loss ) ax.legend(loc best ) ax.set_title( The loss curves ) ax.set_xlabel( batches ) fig.savefig(self.result_dir /avg_loss ) logger.info( save iou loss done ) def loss_pic(self): train_log_loss_path os.path.join(self.result_dir, train_log_loss.txt ) self.extract_log(train_log_loss_path, images ) pd_loss self.parse_loss_log(train_log_loss_path) self.gene_loss_pic(pd_loss) def parse_iou_log(self,log_path, line_num 2000): result pd.read_csv(log_path, skiprows [x for x in range(line_num) if (x % 10 0 or x % 10 9)],error_bad_lines False,names [ Region Avg IOU , Class , Obj , No Obj , Avg Recall , count ]) result[ Region Avg IOU ] result[ Region Avg IOU ].str.split( : ).str.get(1) result[ Class ] result[ Class ].str.split( : ).str.get(1) result[ Obj ] result[ Obj ].str.split( : ).str.get(1) result[ No Obj ] result[ No Obj ].str.split( : ).str.get(1) result[ Avg Recall ] result[ Avg Recall ].str.split( : ).str.get(1) result[ count ] result[ count ].str.split( : ).str.get(1) result[ Region Avg IOU ] pd.to_numeric(result[ Region Avg IOU ]) result[ Class ] pd.to_numeric(result[ Class ]) result[ Obj ] pd.to_numeric(result[ Obj ]) result[ No Obj ] pd.to_numeric(result[ No Obj ]) result[ Avg Recall ] pd.to_numeric(result[ Avg Recall ]) result[ count ] pd.to_numeric(result[ count ]) return result def gene_iou_pic(self, pd_loss): fig plt.figure() ax fig.add_subplot(1, 1, 1) ax.plot(pd_loss[ Region Avg IOU ].values, label Region Avg IOU ) # ax.plot(result[ Class ].values,label Class ) # ax.plot(result[ Obj ].values,label Obj ) # ax.plot(result[ No Obj ].values,label No Obj ) # ax.plot(result[ Avg Recall ].values,label Avg Recall ) # ax.plot(result[ count ].values,label count ) ax.legend(loc best ) ax.set_title( The Region Avg IOU curves ) ax.set_xlabel( batches ) fig.savefig(self.result_dir /region_avg_iou ) logger.info( save iou pic done ) def iou_pic(self): train_log_loss_path os.path.join(self.result_dir, train_log_iou.txt ) self.extract_log(train_log_loss_path, IOU ) pd_loss self.parse_iou_log(train_log_loss_path) self.gene_iou_pic(pd_loss)if __name__ __main__ : log_path /Users/songhongwei/Downloads/nohup.out result_dir /Users/songhongwei/PycharmProjects/py2project/hand/data logVis Yolov3LogVisualization(log_path,result_dir) logVis.loss_pic() logVis.iou_pic() 

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