踩坑记录2

pycharm remote interpreter: cannot find declaration

现象:

调试debug 带来不少不便.

解决:

1. 在 project interpreter中的路径修改一下: /bin/python3---> /bin/python.小细节坑爹.

2. 重启 pycharm,下载远程环境的代码,时间视网络环境和数量量决定.由于是远程环境,网络较差,坑爹地大概须要30多分钟,须要耐心等等:

evalue coco dataset error

Traceback (most recent call last):
  File "/root/dxq/question-split-mask-rcnn/doc/evaluater.py", line 15, in <module>
    from pycocotools.coco import COCO
  File "/root/anaconda3/lib/python3.7/site-packages/pycocotools-2.0-py3.7-linux-x86_64.egg/pycocotools/coco.py", line 55, in <module>
    from . import mask as maskUtils
  File "/root/anaconda3/lib/python3.7/site-packages/pycocotools-2.0-py3.7-linux-x86_64.egg/pycocotools/mask.py", line 3, in <module>
    import pycocotools._mask as _mask
  File "__init__.pxd", line 918, in init pycocotools._mask
ValueError: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216 from C header, got 192 from PyObject

复制代码

环境设置错误了...

pip uninstall numpy
pip install numpy==1.16.2
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pycocotools 和numpy的兼容问题. 降级处理. 坑爹玩意,除此以外,还有好多坑,都心塞踩过来了...要不是对 coco格式还算了解,真不是那么容易.

pycocotools evaluate 数据解读.

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DETECTION_MIN_CONFIDENCE = 0.5
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20200522T1052_0601.h5
100%|█████████████████████████████████████| 2346/2346 [3:12:10<00:00,  4.57s/it]

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.384
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.626
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.420
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.148
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.291
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.386
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.184
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.436
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.452
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.183
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.364
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.449
Prediction time: 3965.8749873638153. Average 1.6904837968302708/image
Total time:  11549.180015802383
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20200522T1052_0656.h5
100%|█████████████████████████████████████| 2346/2346 [3:34:07<00:00,  5.06s/it]

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.381
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.621
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.415
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.154
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.290
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.382
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.183
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.432
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.448
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.368
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.443
Prediction time: 4758.105183124542. Average 2.028177827418816/image

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20200211T1100_0789.h5

100%|██████████| 2346/2346 [3:58:21<00:00,  6.23s/it]
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.288
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.494
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.302
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.215
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.288
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.144
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.351
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.364
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.149
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.284
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.361
Prediction time: 5515.610037565231. Average 2.351069922235819/image
Total time:  14321.094527959824

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DETECTION_MIN_CONFIDENCE = 0
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20200522T1052_0601.h5
100%|██████████| 2346/2346 [4:04:43<00:00,  5.23s/it]
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.397
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.653
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.430
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.156
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.302
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.399
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.191
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.454
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.471
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.192
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.381
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.468
Prediction time: 5827.201899766922. Average 2.4838882778205122/image
Total time:  14703.1005589962

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• evaluate 上一次的模型, 本次训练的模型.挑选出上线模型.挑选标准:train loss,val loss, AP,AP50,AP75--->视觉检验
• confidence= 0.5状况: 601, 565, 上一次 789
• confidence= 0 状况: 601, 789. (之后主要用于不一样网络架构的比较. 这个标准比较统一)
• 对比不一样 confidence,相同模型 AP 等状况
  • confidence = 0 > confidence= 0.5
• 对比相同confidence, 不一样模型表现, 本次表现是否好于以前的模型.
  • confidence=0.5. 601 > 656 > 上一次训练的789.
• train loss 比 val loss 更有参考意义.下次训练保存 train loss 最佳便可.