比PaddleOCR慢太多了，一张图片CPU推理十几秒呀。

想把 paddle 的rec模型转化为pytorch. 遇到点问题

python3 ch_ppocr_v3_rec_converter.py --src_model_path ...../libs/paddle/paddle_ocr_module/ch_PP-OCRv3_rec_infer

Traceback (most recent call last):
  File "ch_ppocr_v3_rec_converter.py", line 93, in <module>
    converter = PPOCRv3RecConverter(cfg, paddle_pretrained_model_path)
  File "ch_ppocr_v3_rec_converter.py", line 14, in __init__
    out_channels = list(para_state_dict.values())[-1].shape[0]
IndexError: list index out of range

看起来像是模型不对，解压后模型文件inference.info, inference.pdiparams, inference.pdmodel 都放在 ch_PP-OCRv3_rec_infer目录下。我这是哪里不对？

File "/home/guanmengnan/miniconda3/envs/TSF/lib/python3.7/tarfile.py", line 1754, in getmember
raise KeyError("filename %r not found" % name)
KeyError: "filename 'storages' not found"

请问检测到的弯曲文字（如圆形徽章），怎么进行水平纠正？相关的纠正代码在哪个文件

I do not have access to baidu pan website , can somebody plz uplaod this models on Google Drive

https://github.com/RapidOCR

这个是我们的team, 致力 于做基于PaddleOCR的生态。 目前解决的是推理问题。 需要你的训练代码加入，诚邀加入我们的Team ，发挥团队的努力 ，做成**最好的开源ocr

可微信我：３６４４７９

大佬什么时候开始更新3.0版本的svtr呢？期待

调用ch_ptocr_v3_rec_infer.pth
https://github.com/frotms/PaddleOCR2Pytorch/blob/main/tools/infer/predict_rec.py#L286
图片resize时高为48，宽被压缩，在识别一些英文和数字时，精度受到影响

按照测试的方法来，报错
python3 ./tools/infer/predict_rec.py --image_dir ./doc/imgs_words --model_path your_rec_pth_path.pth

报错:系统错误找不到指定模块

作者好, 我尝试用 ./tools/infer/predict_system.py 来串联识别英文, 检测模型用的是en_ptocr_v3_det_infer.pth, 识别模型分别试用了en_ptocr_v3_rec_infer.pth 和 en_number_mobile_v2.0_rec_infer.pth, 但是返回的结果都是中文~

dt_boxes num : 2, elapse : 0.0470426082611084
rec_res num  : 2, elapse : 0.06331539154052734
Predict time of ../tess_train/data/1.jpg: 0.112s
房韵, 0.996

rec_res num  : 2, elapse : 0.06833672523498535
Predict time of ../tess_train/data/1.jpg: 0.127s
房韵, 0.755

求助~

按照提示都改成--rec_image_shape='3,32,100' and --rec_char_type='en' 还会报下面的错误，

Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq
If your model has tps module: TPS does not support variable shape.
Please set --rec_image_shape='3,32,100' and --rec_char_type='en'
list index out of range

PPOCR v3.0更新啦，催大佬来转模型了。

Can you share the dataloader (simple_dataset as well as lmdb_dataset) implemented in pytorch in data module and the loss function as well. I was able to reimplemented but can't reproduce the result

使用SRN模型识别时，在rec_postprocess.py文件报错：

看起来是字典不对？我用的是ppocr_keys_v1.txt这个字典。
附rec参数：

params for text recognizer

parser.add_argument("--rec_algorithm", type=str, default='SRN')
parser.add_argument("--rec_model_path", type=str, default='./weights/rec_r50_vd_srn_infer.pth')
parser.add_argument("--rec_image_shape", type=str, default="1, 64, 256")
parser.add_argument("--rec_char_type", type=str, default='en')
parser.add_argument("--rec_batch_num", type=int, default=6)
parser.add_argument("--max_text_length", type=int, default=25)

parser.add_argument("--use_space_char", type=str2bool, default=True)
parser.add_argument("--drop_score", type=float, default=0.5)
parser.add_argument("--limited_max_width", type=int, default=1280)
parser.add_argument("--limited_min_width", type=int, default=16)

parser.add_argument(
    "--vis_font_path", type=str,
    default=os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'doc/fonts/simfang.ttf'))
parser.add_argument(
    "--rec_char_dict_path",
    type=str,
    default=os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
                         'pytorchocr/utils/ppocr_keys_v1.txt'))

params .yaml

parser.add_argument("--det_yaml_path", type=str, default=None)
parser.add_argument("--rec_yaml_path", type=str, default="./configs/rec/rec_r50_fpn_srn.yml")
parser.add_argument("--cls_yaml_path", type=str, default=None)
parser.add_argument("--e2e_yaml_path", type=str, default=None)

执行 ：
python ./tools/infer/predict_system.py --image_dir ./doc/imgs --det_model_path .\models\det_r50_vd_east_v2.0_infer.pth --rec_model_path .\models\rec_r50_vd_srn_i
nfer.pth

报错：
{'model_name': 'large', 'in_channels': 128}
model is loaded: .\models\det_r50_vd_east_v2.0_infer.pth
{'fc_decay': 4e-05, 'in_channels': 96}
Traceback (most recent call last):
File "./tools/infer/predict_system.py", line 171, in
main(utility.parse_args())
File "./tools/infer/predict_system.py", line 128, in main
text_sys = TextSystem(args)
File "./tools/infer/predict_system.py", line 25, in init
self.text_recognizer = predict_rec.TextRecognizer(args, **kwargs)
File "D:\workspace\python\torch11\PaddleOCR2Pytorch-main\tools\infer\predict_rec.py", line 63, in init
self.load_state_dict(weights)
File "D:\workspace\python\torch11\PaddleOCR2Pytorch-main\pytorchocr\base_ocr_v20.py", line 70, in load_state_dict
self.net.load_state_dict(weights)
File "C:\Users\conf.conda\envs\pytorch-gpu-11\lib\site-packages\torch\nn\modules\module.py", line 1497, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for BaseModel:
Missing key(s) in state_dict: "backbone.conv1.conv.weight", "backbone.conv1.bn.weight", "backbone.conv1.bn.bias", "backbone.conv1.bn.running_mean", "backbone.conv1.bn.runnin
g_var", "backbone.blocks.0.expand_conv.conv.weight", "backbone.blocks.0.expand_conv.bn.weight", "backbone.blocks.0.expand_conv.bn.bias", "backbone.blocks.0.expand_conv.bn.running_me
an", "backbone.blocks.0.expand_conv.bn.running_var", "backbone.blocks.0.bottleneck_conv.conv.weight", "backbone.blocks.0.bottleneck_conv.bn.weight", "backbone.blocks.0.bottleneck_co
nv.bn.bias", "backbone.blocks.0.bottleneck_conv.bn.running_mean", "backbone.blocks.0.bottleneck_conv.bn.running_var", "backbone.blocks.0.mid_se.conv1.weight", "backbone.blocks.0.mid
se.conv1.bias", "backbone.blocks.0.mid_se.conv2.weight", "backbone.blocks.0.mid_se.conv2.bias", "backbone.blocks.0.linear_conv.conv.weight", "backbone.blocks.0.linear_conv.bn.weigh
t", "backbone.blocks.0.linear_conv.bn.bias", "backbone.blocks.0.linear_conv.bn.running_mean", "backbone.blocks.0.linear_conv.bn.running_var", "backbone.blocks.1.expand_conv.conv.wei
ght", "backbone.blocks.1.expand_conv.bn.weight", "backbone.blocks.1.expand_conv.bn.bias", "backbone.blocks.1.expand_conv.bn.running_mean", "backbone.blocks.1.expand_conv.bn.running
var", "backbone.blocks.1.bottleneck_conv.conv.weight", "backbone.blocks.1.bottleneck_conv.bn.weight", "backbone.blocks.1.bottleneck_conv.bn.bias", "backbone.blocks.1.bottleneck_conv
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xpand_conv.bn.weight", "backbone.blocks.2.expand_conv.bn.bias", "backbone.blocks.2.expand_conv.bn.running_mean", "backbone.blocks.2.expand_conv.bn.running_var", "backbone.blocks.2.b
ottleneck_conv.conv.weight", "backbone.blocks.2.bottleneck_conv.bn.weight", "backbone.blocks.2.bottleneck_conv.bn.bias", "backbone.blocks.2.bottleneck_conv.bn.running_mean", "backbo
ne.blocks.2.bottleneck_conv.bn.running_var", "backbone.blocks.2.linear_conv.conv.weight", "backbone.blocks.2.linear_conv.bn.weight", "backbone.blocks.2.linear_conv.bn.bias", "backbo
ne.blocks.2.linear_conv.bn.running_mean", "backbone.blocks.2.linear_conv.bn.running_var", "backbone.blocks.3.expand_conv.conv.weight", "backbone.blocks.3.expand_conv.bn.weight", "ba
ckbone.blocks.3.expand_conv.bn.bias", "backbone.blocks.3.expand_conv.bn.running_mean", "backbone.blocks.3.expand_conv.bn.running_var", "backbone.blocks.3.bottleneck_conv.conv.weight
", "backbone.blocks.3.bottleneck_conv.bn.weight", "backbone.blocks.3.bottleneck_conv.bn.bias", "backbone.blocks.3.bottleneck_conv.bn.running_mean", "backbone.blocks.3.bottleneck_con
v.bn.running_var", "backbone.blocks.3.mid_se.conv1.weight", "backbone.blocks.3.mid_se.conv1.bias", "backbone.blocks.3.mid_se.conv2.weight", "backbone.blocks.3.mid_se.conv2.bias", "b
ackbone.blocks.3.linear_conv.conv.weight", "backbone.blocks.3.linear_conv.bn.weight", "backbone.blocks.3.linear_conv.bn.bias", "backbone.blocks.3.linear_conv.bn.running_mean", "back
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.blocks.4.expand_conv.bn.running_mean", "backbone.blocks.4.expand_conv.bn.running_var", "backbone.blocks.4.bottleneck_conv.conv.weight", "backbone.blocks.4.bottleneck_conv.bn.weight
", "backbone.blocks.4.bottleneck_conv.bn.bias", "backbone.blocks.4.bottleneck_conv.bn.running_mean", "backbone.blocks.4.bottleneck_conv.bn.running_var", "backbone.blocks.4.mid_se.co
nv1.weight", "backbone.blocks.4.mid_se.conv1.bias", "backbone.blocks.4.mid_se.conv2.weight", "backbone.blocks.4.mid_se.conv2.bias", "backbone.blocks.4.linear_conv.conv.weight", "bac
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as", "backbone.blocks.5.mid_se.conv2.weight", "backbone.blocks.5.mid_se.conv2.bias", "backbone.blocks.5.linear_conv.conv.weight", "backbone.blocks.5.linear_conv.bn.weight", "backbon
e.blocks.5.linear_conv.bn.bias", "backbone.blocks.5.linear_conv.bn.running_mean", "backbone.blocks.5.linear_conv.bn.running_var", "backbone.blocks.6.expand_conv.conv.weight", "backb
one.blocks.6.expand_conv.bn.weight", "backbone.blocks.6.expand_conv.bn.bias", "backbone.blocks.6.expand_conv.bn.running_mean", "backbone.blocks.6.expand_conv.bn.running_var", "backb
one.blocks.6.bottleneck_conv.conv.weight", "backbone.blocks.6.bottleneck_conv.bn.weight", "backbone.blocks.6.bottleneck_conv.bn.bias", "backbone.blocks.6.bottleneck_conv.bn.running_
mean", "backbone.blocks.6.bottleneck_conv.bn.running_var", "backbone.blocks.6.mid_se.conv1.weight", "backbone.blocks.6.mid_se.conv1.bias", "backbone.blocks.6.mid_se.conv2.weight", "
backbone.blocks.6.mid_se.conv2.bias", "backbone.blocks.6.linear_conv.conv.weight", "backbone.blocks.6.linear_conv.bn.weight", "backbone.blocks.6.linear_conv.bn.bias", "backbone.bloc
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blocks.7.expand_conv.bn.bias", "backbone.blocks.7.expand_conv.bn.running_mean", "backbone.blocks.7.expand_conv.bn.running_var", "backbone.blocks.7.bottleneck_conv.conv.weight", "bac
kbone.blocks.7.bottleneck_conv.bn.weight", "backbone.blocks.7.bottleneck_conv.bn.bias", "backbone.blocks.7.bottleneck_conv.bn.running_mean", "backbone.blocks.7.bottleneck_conv.bn.ru
nning_var", "backbone.blocks.7.mid_se.conv1.weight", "backbone.blocks.7.mid_se.conv1.bias", "backbone.blocks.7.mid_se.conv2.weight", "backbone.blocks.7.mid_se.conv2.bias", "backbone
.blocks.7.linear_conv.conv.weight", "backbone.blocks.7.linear_conv.bn.weight", "backbone.blocks.7.linear_conv.bn.bias", "backbone.blocks.7.linear_conv.bn.running_mean", "backbone.bl
ocks.7.linear_conv.bn.running_var", "backbone.blocks.8.expand_conv.conv.weight", "backbone.blocks.8.expand_conv.bn.weight", "backbone.blocks.8.expand_conv.bn.bias", "backbone.blocks
.8.expand_conv.bn.running_mean", "backbone.blocks.8.expand_conv.bn.running_var", "backbone.blocks.8.bottleneck_conv.conv.weight", "backbone.blocks.8.bottleneck_conv.bn.weight", "bac
kbone.blocks.8.bottleneck_conv.bn.bias", "backbone.blocks.8.bottleneck_conv.bn.running_mean", "backbone.blocks.8.bottleneck_conv.bn.running_var", "backbone.blocks.8.mid_se.conv1.wei
ght", "backbone.blocks.8.mid_se.conv1.bias", "backbone.blocks.8.mid_se.conv2.weight", "backbone.blocks.8.mid_se.conv2.bias", "backbone.blocks.8.linear_conv.conv.weight", "backbone.b
locks.8.linear_conv.bn.weight", "backbone.blocks.8.linear_conv.bn.bias", "backbone.blocks.8.linear_conv.bn.running_mean", "backbone.blocks.8.linear_conv.bn.running_var", "backbone.b
locks.9.expand_conv.conv.weight", "backbone.blocks.9.expand_conv.bn.weight", "backbone.blocks.9.expand_conv.bn.bias", "backbone.blocks.9.expand_conv.bn.running_mean", "backbone.bloc
ks.9.expand_conv.bn.running_var", "backbone.blocks.9.bottleneck_conv.conv.weight", "backbone.blocks.9.bottleneck_conv.bn.weight", "backbone.blocks.9.bottleneck_conv.bn.bias", "backb
one.blocks.9.bottleneck_conv.bn.running_mean", "backbone.blocks.9.bottleneck_conv.bn.running_var", "backbone.blocks.9.mid_se.conv1.weight", "backbone.blocks.9.mid_se.conv1.bias", "b
ackbone.blocks.9.mid_se.conv2.weight", "backbone.blocks.9.mid_se.conv2.bias", "backbone.blocks.9.linear_conv.conv.weight", "backbone.blocks.9.linear_conv.bn.weight", "backbone.block
s.9.linear_conv.bn.bias", "backbone.blocks.9.linear_conv.bn.running_mean", "backbone.blocks.9.linear_conv.bn.running_var", "backbone.blocks.10.expand_conv.conv.weight", "backbone.bl
ocks.10.expand_conv.bn.weight", "backbone.blocks.10.expand_conv.bn.bias", "backbone.blocks.10.expand_conv.bn.running_mean", "backbone.blocks.10.expand_conv.bn.running_var", "backbon
e.blocks.10.bottleneck_conv.conv.weight", "backbone.blocks.10.bottleneck_conv.bn.weight", "backbone.blocks.10.bottleneck_conv.bn.bias", "backbone.blocks.10.bottleneck_conv.bn.runnin
g_mean", "backbone.blocks.10.bottleneck_conv.bn.running_var", "backbone.blocks.10.mid_se.conv1.weight", "backbone.blocks.10.mid_se.conv1.bias", "backbone.blocks.10.mid_se.conv2.weig
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kbone.blocks.10.linear_conv.bn.running_mean", "backbone.blocks.10.linear_conv.bn.running_var", "backbone.conv2.conv.weight", "backbone.conv2.bn.weight", "backbone.conv2.bn.bias", "b
ackbone.conv2.bn.running_mean", "backbone.conv2.bn.running_var", "neck.encoder.lstm.weight_ih_l0", "neck.encoder.lstm.weight_hh_l0", "neck.encoder.lstm.bias_ih_l0", "neck.encoder.ls
tm.bias_hh_l0", "neck.encoder.lstm.weight_ih_l0_reverse", "neck.encoder.lstm.weight_hh_l0_reverse", "neck.encoder.lstm.bias_ih_l0_reverse", "neck.encoder.lstm.bias_hh_l0_reverse", "
neck.encoder.lstm.weight_ih_l1", "neck.encoder.lstm.weight_hh_l1", "neck.encoder.lstm.bias_ih_l1", "neck.encoder.lstm.bias_hh_l1", "neck.encoder.lstm.weight_ih_l1_reverse", "neck.en
coder.lstm.weight_hh_l1_reverse", "neck.encoder.lstm.bias_ih_l1_reverse", "neck.encoder.lstm.bias_hh_l1_reverse", "head.fc.weight", "head.fc.bias".
Unexpected key(s) in state_dict: "backbone.conv.conv.weight", "backbone.conv.bn.weight", "backbone.conv.bn.bias", "backbone.conv.bn.running_mean", "backbone.conv.bn.running_
var", "backbone.conv.bn.num_batches_tracked", "backbone.block_list.bottleneckBlock_0_0.conv0.conv.weight", "backbone.block_list.bottleneckBlock_0_0.conv0.bn.weight", "backbone.block
list.bottleneckBlock_0_0.conv0.bn.bias", "backbone.block_list.bottleneckBlock_0_0.conv0.bn.running_mean", "backbone.block_list.bottleneckBlock_0_0.conv0.bn.running_var", "backbone.
block_list.bottleneckBlock_0_0.conv0.bn.num_batches_tracked", "backbone.block_list.bottleneckBlock_0_0.conv1.conv.weight", "backbone.block_list.bottleneckBlock_0_0.conv1.bn.weight",
"backbone.block_list.bottleneckBlock_0_0.conv1.bn.bias", "backbone.block_list.bottleneckBlock_0_0.conv1.bn.running_mean", "backbone.block_list.bottleneckBlock_0_0.conv1.bn.running
var", "backbone.block_list.bottleneckBlock_0_0.conv1.bn.num_batches_tracked", "backbone.block_list.bottleneckBlock_0_0.conv2.conv.weight", "backbone.block_list.bottleneckBlock_0_0.c
onv2.bn.weight", "backbone.block_list.bottleneckBlock_0_0.conv2.bn.bias", "backbone.block_list.bottleneckBlock_0_0.conv2.bn.running_mean", "backbone.block_list.bottleneckBlock_0_0.c
onv2.bn.running_var", "backbone.block_list.bottleneckBlock_0_0.conv2.bn.num_batches_tracked", "backbone.block_list.bottleneckBlock_0_0.short.conv.conv.weight", "backbone.block_list.
bottleneckBlock_0_0.short.conv.bn.weight", "backbone.block_list.bottleneckBlock_0_0.short.conv.bn.bias", "backbone.block_list.bottleneckBlock_0_0.short.conv.bn.running_mean", "backb
one.block_list.bottleneckBlock_0_0.short.conv.bn.running_var", "backbone.block_list.bottleneckBlock_0_0.short.conv.bn.num_batches_tracked", "backbone.block_list.bottleneckBlock_0_1.
conv0.conv.weight", "backbone.block_list.bottleneckBlock_0_1.conv0.bn.weight", "backbone.block_list.bottleneckBlock_0_1.conv0.bn.bias", "backbone.block_list.bottleneckBlock_0_1.conv
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leneckBlock_0_1.conv1.conv.weight", "backbone.block_list.bottleneckBlock_0_1.conv1.bn.weight", "backbone.block_list.bottleneckBlock_0_1.conv1.bn.bias", "backbone.block_list.bottlene
ckBlock_0_1.conv1.bn.running_mean", "backbone.block_list.bottleneckBlock_0_1.conv1.bn.running_var", "backbone.block_list.bottleneckBlock_0_1.conv1.bn.num_batches_tracked", "backbone
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ck_list.bottleneckBlock_0_1.conv2.bn.running_mean", "backbone.block_list.bottleneckBlock_0_1.conv2.bn.running_var", "backbone.block_list.bottleneckBlock_0_1.conv2.bn.num_batches_tra
cked", "backbone.block_list.bottleneckBlock_0_2.conv0.conv.weight", "backbone.block_list.bottleneckBlock_0_2.conv0.bn.weight", "backbone.block_list.bottleneckBlock_0_2.conv0.bn.bias
", "backbone.block_list.bottleneckBlock_0_2.conv0.bn.running_mean", "backbone.block_list.bottleneckBlock_0_2.conv0.bn.running_var", "backbone.block_list.bottleneckBlock_0_2.conv0.bn
.num_batches_tracked", "backbone.block_list.bottleneckBlock_0_2.conv1.conv.weight", "backbone.block_list.bottleneckBlock_0_2.conv1.bn.weight", "backbone.block_list.bottleneckBlock_0
2.conv1.bn.bias", "backbone.block_list.bottleneckBlock_0_2.conv1.bn.running_mean", "backbone.block_list.bottleneckBlock_0_2.conv1.bn.running_var", "backbone.block_list.bottleneckBl
ock_0_2.conv1.bn.num_batches_tracked", "backbone.block_list.bottleneckBlock_0_2.conv2.conv.weight", "backbone.block_list.bottleneckBlock_0_2.conv2.bn.weight", "backbone.block_list.b
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Hello, thank you for your work.
I trained SRN mudule in PaddleOCR on my dataset. i convert the trained model into inference and onnxt format. How can i convert trained model to pytorch pth file.
Thank you

Pytorch
python3 ./tools/infer/predict_system.py --image_dir ./test.jpeg --det_model_path ch_ptocr_v2_det_infer.pth --rec_model_path ch_ptocr_v2_rec_infer.pth --use_angle_cls true --cls_model_path ch_ptocr_mobile_v2.0_cls_infer.pth

Paddle
python3 tools/infer/predict_system.py --image_dir="./test.jpeg" --det_model_dir="./model/ch_PP-OCRv2_det_infer" --cls_model_dir="./model/ch_ppocr_mobile_v2.0_cls_infer" --rec_model_dir="./model/ch_PP-OCRv2_rec_infer" --use_angle_cls=true

除了输入和参数路径，其他均是默认。对比过代码默认配置两边基本是相同的。从结果看Pytorch要好一些，感觉很奇怪，麻烦懂的大大解答一下

Hi, thanks for releasing this nice converter, it's been very useful! I'm trying to benchmark the performance of PaddleOCR model and the corresponding converted PyTorch model on ICDAR2015 dataset and found there is a big gap between them that I couldn't explain, can you help me?

The model I'm using is det_r50_vd_db_v2.0_train and the corresponding PyTorch model is det_r50_vd_db_v2.0_infer.pth.

The reported performance for DB ResNet50_vd is precision: 86.41%, recall: 78.72%, hmean 82.38%.

However, the best performance I can get from the converted model so far is precision: 66.51%, recall: 69.14%, hmean: 67.80%.
The metric is calculated using the PaddleOCR's DetMetric class and I've matched exactly the same preprocessing and post-processing pipelines. I also excluded those bounding boxes which have ### as labels as done in the PaddleOCR data preparation.

I've also searched issues in PaddleOCR and see if others experienced performance degradation and adjust some inference parameters such as using image_shape as the same as in the training configuration file.

Do you have an idea what's going on in my case? Am I missing something? I would love to have your advice.

Yuhuang.

字符识别infer时，出现错误 ，det 和 cls是正常的
RuntimeError: Error(s) in loading state_dict for BaseModel:
Missing key(s) in state_dict: "backbone.blocks.0.mid_se.conv1.weight", "backbone.blocks.0.mid_se.conv1.bias", "backbone.blocks.0.mid_se.conv2.weight", "backbone.blocks.0.mid_se.conv2.bias", "backbone.blocks.6.mid_se.conv1.weigh

背景：

自己训练了个ppocrv3 rec模型，使用ch_PP-OCRv3_rec.yml 配置文件，训练完成后使用paddleOCR推理精度很高。

问题：

1. 模型在转换过程中报错：KeyError: Student.backbone.conv1._conv.weight

使用的模型结构是 ./configs/det/det_r50_vd_db.yml ，使用的paddle参数文件下载自 https://paddleocr.bj.bcebos.com/pretrained/ResNet50_vd_ssld_pretrained.pdparams
但是在尝试模型转换时出现了如下报错
Traceback (most recent call last):
File "../converter/det_converter.py", line 94, in
converter = DetV20DetConverter(cfg, paddle_pretrained_model_path, **kwargs)
File "../converter/det_converter.py", line 12, in init
self.load_paddle_weights(paddle_pretrained_model_path)
File "../converter/det_converter.py", line 56, in load_paddle_weights
self.net.state_dict()[k].copy_(torch.Tensor(para_state_dict[ppname]))
KeyError: 'neck.in2_conv.weight'
原本猜测是模型不对，想要从给的百度网盘中找合适的模型参数文件，但是本项目中提到的网盘链接（https://pan.baidu.com/s/1getAprT2l_JqwhjwML0g9g 提取码：lmv7）已经失效。

这个还不能工作

请问下我在PaddleOCR那边训练好了一个检测模型后

python3 tools/train.py -c configs/det/det_mv3_db.yml  \
         -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained

然后利用det_converter.py转换为自己的模型后，去预测的时候，就预测不出来了，可能是什么原因呢？

1、训练好的模型，利用PaddleOCR自带的预测命令，都可以预测对。
2、转换后的参数，可以正确的载入检测模型，但是文字一张都预测不出来。

python ./converter/ch_ppocr_v2_rec_converter.py --src_model_path ./ch_PP-OCRv2_rec_train
文件路径：ch_PP-OCRv2_rec_train/best_accuracy/.pdopt、.pdparams、**.states
完整报错如下：
W1223 17:12:03.727869 23403 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.0, Runtime API Version: 10.2
W1223 17:12:03.732515 23403 device_context.cc:422] device: 0, cuDNN Version: 8.0.
Traceback (most recent call last):
File "./converter/ch_ppocr_v2_rec_converter.py", line 80, in
converter = PPOCRv2RecConverter(cfg, paddle_pretrained_model_path)
File "./converter/ch_ppocr_v2_rec_converter.py", line 12, in init
para_state_dict, opti_state_dict = self.read_paddle_weights(paddle_pretrained_model_path)
File "/data/cxchen/PaddleOCR2Pytorch/pytorchocr/base_ocr_v20.py", line 94, in read_paddle_weights
para_state_dict, opti_state_dict = fluid.load_dygraph(weights_path)
File "/data/cxchen/venv/lib/python3.6/site-packages/paddle/fluid/dygraph/checkpoint.py", line 290, in load_dygraph
attrs={'file_path': os.path.join(model_path, name)})
File "/data/cxchen/venv/lib/python3.6/site-packages/paddle/fluid/dygraph/tracer.py", line 45, in trace_op
not stop_gradient)
ValueError: (InvalidArgument) Deserialize to tensor failed, maybe the loaded file is not a paddle model(expected file format: 0, but 1904018048 found).
[Hint: Expected version == 0U, but received version:1904018048 != 0U:0.] (at /paddle/paddle/fluid/framework/lod_tensor.cc:329)
[operator < load > error]

I was able to convert the PP-structure paddle train model (best_accuracy.pdparams, best_accuracy.pdopt, best_accuracy.stated) to pytorch model (model.pth). However I realized that the model output is not the same as the paddle model. I did some investigation and I seems the paddle and the backbone weights are the same however the shape of the attention head in the pytorch model is different than the paddle model. This is a bug in the code. Would you be able to fix it?

Thanks

大佬有想写训练部分的代码吗

I was able to convert the PP-structure paddle train model (best_accuracy.pdparams, best_accuracy.pdopt, best_accuracy.stated) to pytorch model (model.pth). How can I convert the model.pth to onnx model considering that model.pth is training model not inference model?
Should I convert the mode.pth( train) to inference model and then to onnx, or sth?

Thanks

1、转换的pp模型链接：
https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar
2、运行脚本：
python ./converter/ch_ppocr_v3_rec_converter.py
3、报错：

Traceback (most recent call last):
File "./converter/ch_ppocr_v3_rec_converter.py", line 60, in load_paddle_weights
self.net.state_dict()[k].copy_(torch.Tensor(para_state_dict[ppname]))
KeyError: 'Student.backbone.conv1._conv.weight'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "./converter/ch_ppocr_v3_rec_converter.py", line 94, in
converter = PPOCRv3RecConverter(cfg, paddle_pretrained_model_path)
File "./converter/ch_ppocr_v3_rec_converter.py", line 22, in init
self.load_paddle_weights([para_state_dict, opti_state_dict])
File "./converter/ch_ppocr_v3_rec_converter.py", line 64, in load_paddle_weights
print('paddle: {}, {}'.format(ppname, para_state_dict[ppname].shape))
KeyError: 'Student.backbone.conv1._conv.weight'

python tools/infer/predict_e2e.py --e2e_model_path ./en_server_pgnetA_infer.pth --image_dir D:\workspace\python\demo-test\test\OCR\img\20220413114341.jpg --e2e_a
lgorithm PGNet --e2e_pgnet_polygon True --e2e_char_dict_path .\pytorchocr\utils\dict\chinese_cht_dict.txt --vis_font_path doc\fonts\chinese_cht.ttf --e2e_yaml_path ./configs/e2e/e2
e_r50_vd_pg.yml

执行上面的命令
效果很差

请问这个模型能训练自己的数据集吗？

想要基于这些模型做fine-tuning，请问支持吗？

我在视频和图像数据上测试了西班牙语言识别模型，发现无法识别空格，使用Paddle原始模型转换pytorch模型后也不可以识别空格，不知道是因为什么，作者大大能帮忙看一下吗

我想转化成Android可以使用的pt或ptl文件，使用官方提供的转化手段如下：
在predict_det.pt加载好检测的v3模型后添加代码如下：
example = torch.rand(1, 3, 640, 640)
traced_script_module = torch.jit.trace(self.net, example)
traced_script_module_optimized = optimize_for_mobile(traced_script_module)
traced_script_module_optimized._save_for_lite_interpreter("ptocr_v3_det.ptl")
出现如下错误：

Encountering a dict at the output of the tracer might cause the trace to be incorrect, this is only valid if the container structure does not change based on the module's inputs. Consider using a constant container instead (e.g. for list, use a tuple instead. for dict, use a NamedTuple instead). If you absolutely need this and know the side effects, pass strict=False to trace() to allow this behavior.
请问您有什么思路或者解决办法吗？或者后续会提供保存好的pt模型吗？
非常感谢！！！

可以直接训练迁移后的模型吗？

tps.py 中 GridGenerator类中的build_C_paddle和build_P_paddle两个函数生成的张量未添加GPU支持。在模型使用GPU运行时，会导致182与183行出现RuntimeError: Expected object of backend CUDA but got backend CPU for argument #2 'mat2'

@frotms, I tried the Paddle code for conversion of TPS based models, the model gets converted but causes problems during the inference. Can you add support for TPS based models conversion using PyTorch?

请问下对pytorch版本有要求吗？我使用ch_ptocr_v3_rec_infer.pth的识别结果：

python ./tools/infer/predict_rec.py --rec_algorithm SVTR --rec_model_path ./ch_ptocr_v3_rec_infer.pth --rec_image_shape 3,48,320 --image_dir test.jpg

('注室你的以发一夜七发使用4突击步检命中头部海淡了', 0.74806666)

以下是paddleocr的识别结果：

paddleocr --image_dir  test.jpg --det false

('主室你的队友一液七次i使用AKM突击步枪命中头部淘汰了', 0.9387470483779907)

大佬，V2大概多久能搞出来

运行ch_ppocr_v2_rec_converter.py 出现

Traceback (most recent call last):
  File "/home/user/paddle/PaddleOCR2Pytorch/converter/ch_ppocr_v2_rec_converter.py", line 56, in load_paddle_weights
    self.net.state_dict()[k].copy_(torch.Tensor(para_state_dict[ppname]))
KeyError: 'Student.backbone.conv1._conv.weight'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/user/paddle/PaddleOCR2Pytorch/converter/ch_ppocr_v2_rec_converter.py", line 80, in <module>
    converter = PPOCRv2RecConverter(cfg, paddle_pretrained_model_path)
  File "/home/user/paddle/PaddleOCR2Pytorch/converter/ch_ppocr_v2_rec_converter.py", line 19, in __init__
    self.load_paddle_weights([para_state_dict, opti_state_dict])
  File "/home/user/paddle/PaddleOCR2Pytorch/converter/ch_ppocr_v2_rec_converter.py", line 60, in load_paddle_weights
    print('paddle: {}, {}'.format(ppname, para_state_dict[ppname].shape))
KeyError: 'Student.backbone.conv1._conv.weight'

paddle 模型：ch_ppocr_server_v2.0_rec_pre

请问表格识别有吗，谢谢

你好, 感谢您分享的pytorch版ppocr模型.
想说能否提供torchserve的部署方案和文档呢?

• 感谢作者的工作，正好看到一个小问题

• 下面是作者转写的，其中bias=True
  

  PaddleOCR2Pytorch/pytorchocr/modeling/heads/rec_att_head.py

  Lines 76 to 77 in 28ac119

  	self.rnn = nn.GRUCell(
  	input_size=input_size + num_embeddings, hidden_size=hidden_size, bias=True)

• Paddle系列写法源码

        self.rnn = nn.GRUCell(input_size=input_size + num_embeddings, hidden_size=hidden_size)

• 经过查各自API，发现Paddle中的GRUCell，bias默认为None，作者转写对应GRUCell设置为bias=True
• 不知是您笔误，还是有其他用法

大佬，表格识别近期会更新吗

请问实现了ppocr的弯曲文字块的识别了吗