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mxin262 / SwinTextSpotter

Pytorch re-implementation of Paper: SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition (CVPR 2022)
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batch_size #102

Open Huangqqqhhh opened 1 year ago

Huangqqqhhh commented 1 year ago

REC_HEAD: BATCH_SIZE: 128 SOLVER: IMS_PER_BATCH: 8 IMS_PER_BATCH是什么

mxin262 commented 1 year ago

IMS_PER_BATCH就是每个batch里面图片的数量，REC_HEAD下面的BATCH_SIZE是识别的BATCH_SIZE

Huangqqqhhh commented 1 year ago

非常感谢您的回复，我还有一些问题： 1）我的设备是2张3090，在训练中文数据集的时候，IMS_PER_BATCH要调到2（英文时是4），这是因为中文数据集图片更大嘛？

2）在预训练中文数据集的时候，大概每隔一段时间，8万次迭代左右吧，就会出现一个报错，batch size RuntimeError: The size of tensor a (302) must match the size of tensor b (323) at non-singleton dimension 0 后面调整识别头BATCH_SIZE之后，从8万次迭代继续训练，然后在16万、24万多次迭代之后又出现了新的错误，RuntimeError: cannot reshape tensor of 0 elements into shape [0, 256, -1] because the unspecified dimension size -1 can be any value and is ambiguous，为什么每隔8万多次之后会出现错误，而后还可以继续训练？

3）在测试IC15数据集的时候，我发现召回率、准确率等为0，是因为它的注释文件不对嘛？

希望您能给我解答一下，谢谢！  

权是海 @.***

 

------------------ 原始邮件 ------------------ 发件人: @.>; 发送时间: 2023年9月22日(星期五) 上午10:07 收件人: @.>; 抄送: @.>; @.>; 主题: Re: [mxin262/SwinTextSpotter] batch_size (Issue #102)

IMS_PER_BATCH就是每个batch里面图片的数量，REC_HEAD下面的BATCH_SIZE是识别的BATCH_SIZE

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mxin262 commented 1 year ago

1）中文的类别更多，识别分辨率更大了，所以占用显存更多； 2）这是因为最大的文本数量限制了只能300个，调整识别头BATCH_SIZE之后，出现这个报错是因为图片中没有文本实例，可以继续训练的， 3）可以试试把文件先下载好，根据readme来评测

Huangqqqhhh commented 1 year ago

非常感谢您的回复 对于第2个问题，IMS_PER_BATCH和REC_HEAD下面的BATCH_SIZE存在比例关系嘛，因为我调整IMS_PER_BATCH为2这个时候，不知道后面识别的BATCH_SIZE应该设置为多少  

权是海 @.***

 

------------------ 原始邮件 ------------------ 发件人: @.>; 发送时间: 2023年9月22日(星期五) 中午11:03 收件人: @.>; 抄送: @.>; @.>; 主题: Re: [mxin262/SwinTextSpotter] batch_size (Issue #102)

1）中文的类别更多，识别分辨率更大了，所以占用显存更多； 2）这是因为最大的文本数量限制了只能300个，调整识别头BATCH_SIZE之后，出现这个报错是因为图片中没有文本实例，可以继续训练的， 3）可以试试把文件先下载好，根据readme来评测

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mxin262 commented 1 year ago

根据你的显存来设置就好了

Huangqqqhhh commented 1 year ago
[10/19 14:04:32 d2.engine.hooks]: Overall training speed: 19998 iterations in 3:15:31 (0.5866 s / it) [10/19 14:04:32 d2.engine.hooks]: Total training time: 3:16:46 (0:01:14 on hooks) [10/19 14:04:32 d2.data.datasets.coco]: Loaded 300 images in COCO format from datasets/totaltext/totaltext_test.json [10/19 14:04:32 d2.data.build]: Distribution of instances among all 1 categories:   category  #instances   
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[10/19 14:04:32 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(1000, 1000), max_size=1824, sample_style='choice')] [10/19 14:04:32 d2.data.common]: Serializing 300 elements to byte tensors and concatenating them all ... [10/19 14:04:32 d2.data.common]: Serialized dataset takes 0.54 MiB [10/19 14:04:32 d2.evaluation.evaluator]: Start inference on 150 images /home/hhq/data/anaconda3/envs/SWINTS/lib/python3.8/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection   return lib.intersection(a, b, kwargs) /home/hhq/data/anaconda3/envs/SWINTS/lib/python3.8/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection   return lib.intersection(a, b, kwargs) [10/19 14:04:50 d2.evaluation.evaluator]: Inference done 1/150. 6.7070 s / img. ETA=0:44:16 [10/19 14:04:59 d2.evaluation.evaluator]: Inference done 2/150. 6.6461 s / img. 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ETA=0:03:12 [10/19 14:25:47 d2.evaluation.evaluator]: Inference done 131/150. 6.7027 s / img. ETA=0:03:04 [10/19 14:26:06 d2.evaluation.evaluator]: Inference done 132/150. 6.7028 s / img. ETA=0:02:55 [10/19 14:26:24 d2.evaluation.evaluator]: Inference done 133/150. 6.7033 s / img. ETA=0:02:47 [10/19 14:26:35 d2.evaluation.evaluator]: Inference done 134/150. 6.7030 s / img. ETA=0:02:37 [10/19 14:26:49 d2.evaluation.evaluator]: Inference done 135/150. 6.7027 s / img. ETA=0:02:28 [10/19 14:27:00 d2.evaluation.evaluator]: Inference done 136/150. 6.7023 s / img. ETA=0:02:18 [10/19 14:27:15 d2.evaluation.evaluator]: Inference done 137/150. 6.7017 s / img. ETA=0:02:09 [10/19 14:27:24 d2.evaluation.evaluator]: Inference done 138/150. 6.7015 s / img. ETA=0:01:58 [10/19 14:27:38 d2.evaluation.evaluator]: Inference done 139/150. 6.7007 s / img. ETA=0:01:49 [10/19 14:27:48 d2.evaluation.evaluator]: Inference done 140/150. 6.7001 s / img. ETA=0:01:39 [10/19 14:27:59 d2.evaluation.evaluator]: Inference done 141/150. 6.6993 s / img. ETA=0:01:29 [10/19 14:28:07 d2.evaluation.evaluator]: Inference done 142/150. 6.6987 s / img. ETA=0:01:19 [10/19 14:28:15 d2.evaluation.evaluator]: Inference done 143/150. 6.6983 s / img. ETA=0:01:09 [10/19 14:28:24 d2.evaluation.evaluator]: Inference done 144/150. 6.6978 s / img. ETA=0:00:59 [10/19 14:28:32 d2.evaluation.evaluator]: Inference done 145/150. 6.6972 s / img. ETA=0:00:49 [10/19 14:28:43 d2.evaluation.evaluator]: Inference done 146/150. 6.6967 s / img. ETA=0:00:39 [10/19 14:28:54 d2.evaluation.evaluator]: Inference done 147/150. 6.6961 s / img. ETA=0:00:29 [10/19 14:29:02 d2.evaluation.evaluator]: Inference done 148/150. 6.6949 s / img. ETA=0:00:19 [10/19 14:29:13 d2.evaluation.evaluator]: Inference done 149/150. 6.6945 s / img. ETA=0:00:09 [10/19 14:29:21 d2.evaluation.evaluator]: Inference done 150/150. 6.6932 s / img. ETA=0:00:00 [10/19 14:29:21 d2.evaluation.evaluator]: Total inference time: 0:23:56.182427 (9.904706 s / img per device, on 2 devices) [10/19 14:29:21 d2.evaluation.evaluator]: Total inference pure compute time: 0:16:10 (6.693243 s / img per device, on 2 devices) [10/19 14:29:22 d2.evaluation.text_evaluation]: Saving results to ./output/inference/text_results.json An invalid detection in temp_det_results/0000068.txt line 0 is removed ...  An invalid detection in temp_det_results/0000240.txt line 0 is removed ...  Calculated! "E2E_RESULTS: precision: 0.0, recall: 0.0, hmean: 0" "DETECTION_ONLY_RESULTS: precision: 0.6, recall: 0.0040650406504065045, hmean: 0.008075370121130552" Calculated! "E2E_RESULTS: precision: 0.0, recall: 0.0, hmean: 0" "DETECTION_ONLY_RESULTS: precision: 0.5384615384615384, recall: 0.0031616982836495033, hmean: 0.006286484059272564" [10/19 14:29:23 d2.engine.defaults]: Evaluation results for totaltext_test in csv format: [10/19 14:29:23 d2.evaluation.testing]: copypaste: Task: DETECTION_ONLY_RESULTS [10/19 14:29:23 d2.evaluation.testing]: copypaste: precision,recall,hmean [10/19 14:29:23 d2.evaluation.testing]: copypaste: 0.6000,0.0041,0.0081 [10/19 14:29:23 d2.evaluation.testing]: copypaste: Task: None-E2E_RESULTS [10/19 14:29:23 d2.evaluation.testing]: copypaste: precision,recall,hmean [10/19 14:29:23 d2.evaluation.testing]: copypaste: 0.0000,0.0000,0.0000 [10/19 14:29:23 d2.evaluation.testing]: copypaste: Task: Strong-E2E_RESULTS [10/19 14:29:23 d2.evaluation.testing]: copypaste: precision,recall,hmean [10/19 14:29:23 d2.evaluation.testing]: copypaste: 0.0000,0.0000,0.0000

还有个问题，在预训练中文数据时，后面有个验证，显示效果非常差，这是怎么回事呀

权是海 @.***

 

------------------ 原始邮件 ------------------ 发件人: "权是海" @.>; 发送时间: 2023年9月22日(星期五) 中午11:07 @*.**@*.>; @.>; 主题: 回复： [mxin262/SwinTextSpotter] batch_size (Issue #102)

非常感谢您的回复 对于第2个问题，IMS_PER_BATCH和REC_HEAD下面的BATCH_SIZE存在比例关系嘛，因为我调整IMS_PER_BATCH为2这个时候，不知道后面识别的BATCH_SIZE应该设置为多少  

权是海 @.***

 

------------------ 原始邮件 ------------------ 发件人: @.>; 发送时间: 2023年9月22日(星期五) 中午11:03 收件人: @.>; 抄送: @.>; @.>; 主题: Re: [mxin262/SwinTextSpotter] batch_size (Issue #102)

1）中文的类别更多，识别分辨率更大了，所以占用显存更多； 2）这是因为最大的文本数量限制了只能300个，调整识别头BATCH_SIZE之后，出现这个报错是因为图片中没有文本实例，可以继续训练的， 3）可以试试把文件先下载好，根据readme来评测

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mxin262 commented 1 year ago

中文数据只能上传到官网来评估

Huangqqqhhh commented 1 year ago
你好，在测试ic15时，发现，   category  #instances   
    text    0        
                          ，注释文件是从你的项目里下的，这为什么还会这样?

 

权是海 @.***

 

------------------ 原始邮件 ------------------ 发件人: @.>; 发送时间: 2023年10月23日(星期一) 下午2:51 收件人: @.>; 抄送: @.>; @.>; 主题: Re: [mxin262/SwinTextSpotter] batch_size (Issue #102)

中文数据只能上传到官网来评估

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