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ddddocr训练工具

License: Apache License 2.0

Python 100.00%

ddddocr

dddd_trainer's Issues

新模型训练完成后如何应用到目标检测？

新模型训练完成后，用你的示例代码去调用用来目标检测会出现错误，只能用来识别！请问，能把新模型训练的东西，直接用目标检测的方式显示出来吗？

如何增量训练

首先，dddd，yyds
其次，模型训练好后，如何在原有基础上新增数据集继续训练？

StopIteration 問題

只要加入某張圖片就會出現StopIteration，但我完全不知道這圖片有什麼問題，都是用同樣的手法採集的。

：

Traceback (most recent call last):
  File "E:\Daz3D Workshop\Enhance_Queue\_dddd_trainer\utils\train.py", line 124, in start
    test_inputs, test_labels, test_labels_length = next(val_iter)
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__
    data = self._next_data()
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\utils\data\dataloader.py", line 560, in _next_data
    index = self._next_index()  # may raise StopIteration
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\utils\data\dataloader.py", line 512, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
StopIteration

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "E:\Daz3D Workshop\Enhance_Queue\_dddd_trainer\app.py", line 33, in <module>
    fire.Fire(App)
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\fire\core.py", line 141, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\fire\core.py", line 475, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\fire\core.py", line 691, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "E:\Daz3D Workshop\Enhance_Queue\_dddd_trainer\app.py", line 28, in train
    trainer.start()
  File "E:\Daz3D Workshop\Enhance_Queue\_dddd_trainer\utils\train.py", line 128, in start
    test_inputs, test_labels, test_labels_length = next(val_iter)
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__
    data = self._next_data()
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\utils\data\dataloader.py", line 560, in _next_data
    index = self._next_index()  # may raise StopIteration
  File "C:\Users\T1me\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\utils\data\dataloader.py", line 512, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
StopIteration

模型对比

请问有这些模型的对比数据吗？哪种模型收敛较快，哪种模型效果最好，哪种模型速度更快，随带一问，我想把模型转成Tensorflow模型，然后再迁移到移动端平台，哪种模型合适点？

ddddocr
effnetv2_l,
effnetv2_m,
effnetv2_xl,
effnetv2_s,
mobilenetv2,
mobilenetv3_s,
mobilenetv3_l

另外，ddddocr这么模型是原创吗，还是基于其他模型改的？

因磁盘满而中断后,无法自动恢复

RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory

2023-07-26 09:59:13.215 | INFO     | __main__:__init__:12 - 
Hello baby~
2023-07-26 09:59:13.216 | INFO     | __main__:train:26 - 
Start Train ----> images98

2023-07-26 09:59:13.221 | INFO     | utils.train:__init__:41 - 
Taget:
min_Accuracy: 0.97
min_Epoch: 20
max_Loss: 0.05
2023-07-26 09:59:13.221 | INFO     | utils.train:__init__:45 - 
USE GPU ----> 0
2023-07-26 09:59:13.221 | INFO     | utils.train:__init__:52 - 
Search for history checkpoints...
Traceback (most recent call last):
  File "/www/wwwroot/dddd_trainer/app.py", line 33, in <module>
    fire.Fire(App)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/fire/core.py", line 141, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/fire/core.py", line 480, in _Fire
    target=component.__name__)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/fire/core.py", line 691, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/www/wwwroot/dddd_trainer/app.py", line 27, in train
    trainer = train.Train(project_name)
  File "/www/wwwroot/dddd_trainer/utils/train.py", line 63, in __init__
    os.path.join(self.checkpoints_path, newer_checkpoint), self.device)
  File "/www/wwwroot/dddd_trainer/nets/__init__.py", line 223, in load_checkpoint
    param = torch.load(path, map_location=device)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/serialization.py", line 600, in load
    with _open_zipfile_reader(opened_file) as opened_zipfile:
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/serialization.py", line 242, in __init__
    super(_open_zipfile_reader, self).__init__(torch._C.PyTorchFileReader(name_or_buffer))
RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory

请问训练出现LastLoss：nan AvgLoss:nan，是怎么回事，要怎么处理，谢谢

共122个图，配置如下：
Model:
CharSet: [' ', '8', '2', r, v, h, m, b, '5', w, '7', t, k, '6', y, p, '3', l,
q, x, a, e, f, n, s, '4']
ImageChannel: 1
ImageHeight: 64
ImageWidth: -1
Word: false
System:
Allow_Ext: [jpg, jpeg, png, bmp]
GPU: true
GPU_ID: 0
Path: E:\Val\images_login
Project: mlogin
Val: 0.03
Train:
BATCH_SIZE: 32
CNN: {NAME: ddddocr}
DROPOUT: 0.3
LR: 0.01
OPTIMIZER: SGD
SAVE_CHECKPOINTS_STEP: 2000
TARGET: {Accuracy: 0.97, Cost: 0.05, Epoch: 20}
TEST_BATCH_SIZE: 32
TEST_STEP: 1000

2022-07-12 00:38:15.250 | INFO | utils.train:start:108 - [2022-07-12-00_38_15] Epoch: 140500 Step: 421500 LastLoss: nan AvgLoss: nan Lr: 0.00015268545525806817

电脑是2060显卡能搞吗

我是一个小白，想搞个这东西玩玩，弄了3天了，环境一直没搞好，有没有大神心情好能给指导一下，感激不尽，最好能列一个清单，包括python版本，cuda版本和caduu版本，本人电脑是惠普的暗影精灵，显卡3050，8G

可以自己训练的模型也返回每个字符的概率吗

~thx

为啥我的acc通过率一直为0啊，没改过代码

用的是上面的那样的图片，打上标注之后，训练了好久但是通过率一直为0 avgloss 什么的都下降了，就是acc一直不动，看了下tester处的代码，correct_list 这个值一直为空，有没有大佬告诉下我是什么原因呢？

用的是上面的那样的图片，打上标注之后，训练了好久但是通过率一直为0 avgloss 什么的都下降了，就是acc一直不动，看了下tester处的代码，correct_list 这个值一直为空，有没有大佬告诉下我是什么原因呢？

我修改了ImageChannel Acc立马就上来了

我能问一下你是更改了那些配置文件嘛？我比较新手

我和你一样更改了imagechannel 这个参数，但是识别率依旧为0 有没有可能是我图片的问题呢？我看主页中现实的图片按个例子，他没有做任何的处理，所以我也是直接没做处理丢进去了，是不是应该降噪什么的处理一下呢？

大佬有适配m1 gpu的计划吗

请问在已训练出模型的情况下，支持做增量训练吗？

Mac也是按这个步骤来吗，改成CPU训练就可以了嘛

学习素材怎么准备

这些是原始素材，有两个问题，一这些素材的答案怎么准备，只能手动来准备吗；二这个是点选类验证码，应该怎么训练，全部一起训练就好了吗

nets文件夹下的init.py的“export_onnx”函数多出了一个参数

如上两图，如果多出了“_retain_param_name=False”参数在训练完成导出模型时会报错，将其去掉后就成功了。

清晰空心数字无法识别

如下图，空心文字无法识别，我该如何自己训练识别呢？

环境配置完成，但是没有数据集参考

能不能提交一个简单的数据集作为训练的参考，类似YOLO那种。

建议创建项目的时候检查名称

建议执行python app.py create {project_name}命令的时候，检查一下project_name中是否包含下划线，包含下划线的时候抛个错误，不要让创建了

原因：
https://github.com/sml2h3/dddd_trainer/blob/main/utils/train.py#L58

在这个地方加载checkpoints的时候，使用下划线分割文件名，如果自己项目名里面包含下划线，那么这里将加载失败。最终就会像我这里，训练了几天的模型，再次训练的时候加载不成功

请教一下该训练是否支持多GPU

我查看配置文件有个GPU_ID，尝试填写0,1,2,3,4貌似不行，是配置有误，还是本身不支持多GPU并行。

随机哈希值

随机哈希值就是任意值的意思吗？没看懂这个随即哈希值啥意思

对于小数据集小图片，可以使用10系列的显卡吗

数据集都是普通的验证码，并不复杂，可以使用10系列的显卡吗

message print error in cache_data.py

dddd_trainer/utils/cache_data.py

Line 106 in 59e236d

logger.error("val setting vaild!")

Should this be val setting invalid ?

训练过程中，checkpoints有阶段数据，但没有生成onnx的文件

每2000steps 会生成checkpoints的压缩包，
但是没有生成训练后的 onnx文件，请指导一下，该文件如何生成的。

是否支持滑块验证码学习？

抱歉，没搞过python。从网上搜到了这个开源项目确实yyds

这边测试滑块验证码识别率有点低，但是文档上没介绍滑块验证码该怎么归类文件夹

标注？

多通道训练后，调用ddddocr，报错，怎么解决

INVALID_ARGUMENT : Invalid rank for input: input1 Got: 5 Expected: 4 Please fix either the inputs or the model

训练测试集一段时间报错

在训练模型的时候训练一段时间会出现
UserWarning: Exporting a model to ONNX with a batch_size other than 1, with a variable length with LST
M can cause an error when running the ONNX model with a different batch size. Make sure to save the model with a batch size of 1, or define the initial states (h0/c0) as inputs of the mo
del.

按照教程安装的包使用的数据集是测试二的那个数据集。

配置文件如下

Model:
CharSet: [' ', S, '4', F, X, '9', E, Q, V, U, '1', J, R, '5', '7', Z, H, G, P,
A, '2', '6', '8', Y, B, I, L, W, K, T, D, C, '3']
ImageChannel: 1
ImageHeight: 32
ImageWidth: -1
Word: false
System:
Allow_Ext: [jpg, jpeg, png, bmp]
GPU: true
GPU_ID: 0
Path: images
Project: my_test
Val: 0.03
Train:
BATCH_SIZE: 16
CNN: {NAME: ddddocr}
DROPOUT: 0.3
LR: 0.01
OPTIMIZER: SGD
SAVE_CHECKPOINTS_STEP: 2000
TARGET: {Accuracy: 0.97, Cost: 0.05, Epoch: 20}
TEST_BATCH_SIZE: 16
TEST_STEP: 1000

请问这个训练时间怎么预估？

我试了40个图，有快36小时了

将ddddocr换成其他CNN比如mobilenetv2会变成NAN

utils.train:start:110 - [2023-10-07-17_21_35] Epoch: 1 Step: 100 LastLoss: nan AvgLoss: nan Lr: 0.01
2023-10-07 17:21:39.964 | INFO | utils.train:start:110 - [2023-10-07-17_21_39] Epoch: 2 Step: 200 LastLoss: nan AvgLoss: nan Lr: 0.01

训练的数据集可以开源吗

首先非常感谢提供这么优秀的开源库，但是在自己训练的时候效果没有库自带的训练集效果好，所以想问一下训练集可以开源吗？对于识别不是太好的，我们再自己增加样本进行训练

导出时报错：TypeError: export() got an unexpected keyword argument '_retain_param_name'

训练时正常，导出时报错了

环境如下
显卡 Tesla P100
Ubuntu 22.04.1
Python 3.10.6
torch 2.0.0+cu118
torchaudio 2.0.1+cu118
torchvision 0.15.1+cu118
NVIDIA-SMI 525.85.05 Driver Version: 525.85.05 CUDA Version: 12.0

采用ddddocr训练的模型时数据类型报错！

训练的模型，进行识别的时候数据类型报错！
代码如下：
`import ddddocr

ocr = ddddocr.DdddOcr()

ocr = ddddocr.DdddOcr(det=False, ocr=False, import_onnx_path="testocr_1.0_28_19000_2022-03-21-20-44-59.onnx", charsets_path="charsets.json")

with open("0CB7_1644684875.png", 'rb') as f:
image = f.read()

res = ocr.classification(image)
print(res)`

报错输出内容如下：

2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(double)) , expected: (tensor(float))

Traceback (most recent call last):
File "D:\python\ddddocr\1-验证码识别.py", line 10, in
res = ocr.classification(image)
File "C:\Program Files\Python39\lib\site-packages\ddddocr_init_.py", line 1629, in classification
ort_outs = self.__ort_session.run(None, ort_inputs)
File "C:\Users\GCB\AppData\Roaming\Python\Python39\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py", line 195, in run
return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(double)) , expected: (tensor(float))

多通道图片预处理BUG

复现过程：
1、训练配置文件设置为3个通道（彩色图训练）
2、训练完成以后使用DDDDOCR项目运行模型

首先程序报错同：
#2

看了下源码，错误位于
ddddocr/init.py:1629
将这一行改为强制指定类型
ort_inputs = {'input1': np.array([image], dtype=np.float32)}

再次运行程序又报错：

INVALID_ARGUMENT : Invalid rank for input: input1 Got: 5 Expected: 4 Please fix either the inputs or the model.

我在这里加了一行输出了input1的shape

print(ort_inputs['input1'].shape) # 输出： (1, 1, 160, 649, 3)

我猜测模型的输入数据应该是按单通道图片定义的，因此是(1,1,160,649) 但是我使用彩色图训练，图像预处理和单通道图片应该不一致。

But ，我不知道作者是怎么设计彩色图片的预处理的，这里我不知道该怎么改了

彩色图片训练过程24小时仍然acc为0.0

图片数据示例(图片为150高，一共900个数据集)：

配置文件(图片为150高，所以设置高度为144，宽度自动)：

火炬版本：1.10.2，cuda版本：10.2，显卡：2060ti
训练测试数据集时间：24分钟成功，自己的数据长达24小时仍是acc为0.0
运行图(长达24小时，loss一直下降，但acc为0.0，彩色图片) ：

能放出来同花顺客户端训练的权重吗？

我看你想搞量化，前面我都搞完了，就差同花顺验证码识别，大小写头疼

训练了一天英文数字验证码，正确率真的感人，是我电脑不行吗....

有可以提供参考的训练时长吗

一
OS：MacOS Ventura 13.1
Processor: 2GHz Quad-Core Intel Core i5
Memory: 16 GB 3733MHz LPDDR4X
数据集1700+
目前CPU训练了24x3个小时，Acc一直在0.2～0.3之间徘徊

二
OS: ubuntu 22.04.2 LTS 64-bit
Processor: 12th Gen Intel@ Core i5-12400 x 12
Graphic: Nvidia RTX 3060ti 8g
Ram: 32g
CUDA：12.0

数据集1700+
目前训练了11个小时，Acc一直在0.4～0.6之间徘徊

有大佬可以分享下训练时长吗

断点恢复训练问题

貌似本训练工具能够保存断点的训练，是支持断点恢复训练的吗？

请问CPU训练只有阶段数据没有模型数据为何

win11,配置没改过,数据集只有550张很小的图片,100多个小时了还在继续.

Val Data Number is 0

when I run cmd cache ，it works well in cache.train.tmp, but cache.val.tmp is blank,no content.
Continue to run cmd train, There is a log when read cache.val.tmp "Read Cache File End! Caches Num is 0."
Finally result in this error： ValueError: num_samples should be a positive integer value, but got num_samples=0

acc准确率始终是0

    def tester(self, inputs, labels, labels_length):
        predict = self.get_features(inputs)
        pred_decode_labels = []
        labels_list = []
        correct_list = []
        error_list = []
        i = 0
        labels = [int(x) for x in labels.tolist()]
        # labels = labels.tolist()

这里labels都是浮点数，所以后面的比较

            if label_res == pred_res:
                correct_list.append(ids)
            else:
                error_list.append(ids)

基本上都是false。

請問如何告訴模型只可能遇到四個字的驗證碼？

如題，希望知道的人可以幫忙指點一下

export() got an unexpected keyword argument '_retain_param_name'

Traceback (most recent call last):
File "D:\dddd_trainer-main\app.py", line 33, in
fire.Fire(App)
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\fire\core.py", line 141, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\fire\core.py", line 466, in _Fire
component, remaining_args = CallAndUpdateTrace(
File "C:\Users\Administrator\AppData\Local\Programs\Python\Python39\lib\site-packages\fire\core.py", line 681, in_CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "D:\dddd_trainer-main\app.py", line 28, in train
trainer.start()
File "D:\dddd_trainer-main\utils\train.py", line 152, in start
self.net.export_onnx(self.net, dummy_input,
File "D:\dddd_trainer-main\nets_init.py", line 216, in export_onnx
torch.onnx.export(net, dummy_input, graph_path, export_params=True, verbose=False,
TypeError: export() got an unexpected keyword argument '_retain_param_name'

报了这个错误是怎么回事

cpu怎么训练

我修改了config文件中的GPU为false。
执行python app.py train test之后CPU的占用并没有太明显的变化。

这个报错是啥意思

2023-07-26 04:37:11.717 | INFO     | utils.train:start:110 - [2023-07-26-04_37_11]	Epoch: 35952	Step: 1725700	LastLoss: 0.00010419684986118227	AvgLoss: 0.00012866455923358445	Lr: 2.7344957135266526e-10
2023-07-26 04:37:14.401 | INFO     | utils.train:start:110 - [2023-07-26-04_37_14]	Epoch: 35954	Step: 1725800	LastLoss: 0.00011213675315957516	AvgLoss: 0.00012821589938539547	Lr: 2.7344957135266526e-10
2023-07-26 04:37:17.084 | INFO     | utils.train:start:110 - [2023-07-26-04_37_17]	Epoch: 35956	Step: 1725900	LastLoss: 0.00011715076107066125	AvgLoss: 0.00012866744575148914	Lr: 2.7344957135266526e-10
Traceback (most recent call last):
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/serialization.py", line 379, in save
    _save(obj, opened_zipfile, pickle_module, pickle_protocol)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/serialization.py", line 499, in _save
    zip_file.write_record(name, storage.data_ptr(), num_bytes)
OSError: [Errno 28] No space left on device

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/www/wwwroot/dddd_trainer/app.py", line 33, in <module>
    fire.Fire(App)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/fire/core.py", line 141, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/fire/core.py", line 480, in _Fire
    target=component.__name__)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/fire/core.py", line 691, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/www/wwwroot/dddd_trainer/app.py", line 28, in train
    trainer.start()
  File "/www/wwwroot/dddd_trainer/utils/train.py", line 120, in start
    "epoch": self.epoch, "step": self.step, "lr": lr})
  File "/www/wwwroot/dddd_trainer/nets/__init__.py", line 188, in save_model
    torch.save(net, path)
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/serialization.py", line 380, in save
    return
  File "/www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/serialization.py", line 259, in __exit__
    self.file_like.write_end_of_file()
RuntimeError: [enforce fail at inline_container.cc:300] . unexpected pos 17094848 vs 17094736
terminate called after throwing an instance of 'c10::Error'
  what():  [enforce fail at inline_container.cc:300] . unexpected pos 17094848 vs 17094736
frame #0: c10::ThrowEnforceNotMet(char const*, int, char const*, std::string const&, void const*) + 0x47 (0x7fe49ddc3ae7 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x2797840 (0x7fe4e3e5c840 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so)
frame #2: <unknown function> + 0x2792e1c (0x7fe4e3e57e1c in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so)
frame #3: caffe2::serialize::PyTorchStreamWriter::writeRecord(std::string const&, void const*, unsigned long, bool) + 0xb5 (0x7fe4e3e5fa85 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so)
frame #4: caffe2::serialize::PyTorchStreamWriter::writeEndOfFile() + 0x173 (0x7fe4e3e5fd73 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so)
frame #5: caffe2::serialize::PyTorchStreamWriter::~PyTorchStreamWriter() + 0x125 (0x7fe4e3e5ffe5 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so)
frame #6: <unknown function> + 0xb43ee3 (0x7fe56504eee3 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libtorch_python.so)
frame #7: <unknown function> + 0x2a73f8 (0x7fe5647b23f8 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libtorch_python.so)
frame #8: <unknown function> + 0x2a86fe (0x7fe5647b36fe in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/lib/python3.6/site-packages/torch/lib/libtorch_python.so)
frame #9: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x480622]
frame #10: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x434697]
frame #11: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x4346a7]
frame #12: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x4346a7]
frame #13: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x4346a7]
frame #14: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x4346a7]
frame #15: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x4346a7]
frame #16: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x4346a7]
frame #17: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x4346a7]
frame #18: PyDict_SetItemString + 0x3b7 (0x4a2647 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3)
frame #19: PyImport_Cleanup + 0x71 (0x565771 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3)
frame #20: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x421d98]
frame #21: Py_Main + 0x640 (0x43b7d0 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3)
frame #22: main + 0x162 (0x41d982 in /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3)
frame #23: __libc_start_main + 0xf5 (0x7fe57390c555 in /lib64/libc.so.6)
frame #24: /www/wwwroot/dddd_trainer/11ddbaf3386aea1f2974eee984542152_venv/bin/python3() [0x41da40]

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