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A pytorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization

Python 65.53% C++ 12.49% Cuda 21.96% Shell 0.01%

dbnet lightweight ocr python3 pytorch textdetection

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👋 Hi, I’m @BADBADBADBOY

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dbnet-lite.pytorch's Issues

When will be model compression ready?

Can you uploaded your pre-trained model to GG Drive?

I am outside of China, so connecting to Baidu services is hard. Can you upload your pre-trained model to GG Drive?

稀疏训练之后，Binary Acc和Binary IoU都增加了几个点，正常吗？

我从头训练完1200个epoch后，Binary Acc为0.89372 ，Binary IoU为0.90966，TotalLoss为0.51959，设置稀疏训练300个epoch后，Binary Acc为0.9376，Binary IoU为0.90966，TotalLoss为0.48383，这个数据正常吗？剪枝才会降低准确率吧

RuntimeError: Error(s) in loading state_dict for DBNet:

when I run train_finetune,there is an error

Resuming from checkpoint.
Traceback (most recent call last):
  File "/home/DISCOVER_summer2022/zhangt/dblite/pruned/train_fintune.py", line 212, in <module>
    train_net(config)
  File "/home/DISCOVER_summer2022/zhangt/dblite/pruned/train_fintune.py", line 90, in train_net
    model.load_state_dict(checkpoint['state_dict'])
  File "/home/DISCOVER_summer2022/zhangt/.conda/envs/dbnet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1625, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for DBNet:
	Missing key(s) in state_dict: "backbone.conv1.weight", "backbone.bn1.weight", "backbone.bn1.bias", "backbone.bn1.running_mean", "backbone.bn1.running_var", "backbone.layer1.0.conv1.weight", "backbone.layer1.0.bn1.weight", "backbone.layer1.0.bn1.bias", "backbone.layer1.0.bn1.running_mean", "backbone.layer1.0.bn1.running_var", "backbone.layer1.0.conv2.weight", "backbone.layer1.0.bn2.weight", "backbone.layer1.0.bn2.bias", "backbone.layer1.0.bn2.running_mean", "backbone.layer1.0.bn2.running_var", "backbone.layer1.1.conv1.weight", "backbone.layer1.1.bn1.weight", "backbone.layer1.1.bn1.bias", "backbone.layer1.1.bn1.running_mean", "backbone.layer1.1.bn1.running_var", "backbone.layer1.1.conv2.weight", "backbone.layer1.1.bn2.weight", "backbone.layer1.1.bn2.bias", "backbone.layer1.1.bn2.running_mean", "backbone.layer1.1.bn2.running_var", "backbone.layer2.0.conv1.weight", "backbone.layer2.0.bn1.weight", "backbone.layer2.0.bn1.bias", "backbone.layer2.0.bn1.running_mean", "backbone.layer2.0.bn1.running_var", "backbone.layer2.0.conv2.weight", "backbone.layer2.0.bn2.weight", "backbone.layer2.0.bn2.bias", "backbone.layer2.0.bn2.running_mean", "backbone.layer2.0.bn2.running_var", "backbone.layer2.0.downsample.0.weight", "backbone.layer2.0.downsample.1.weight", "backbone.layer2.0.downsample.1.bias", "backbone.layer2.0.downsample.1.running_mean", "backbone.layer2.0.downsample.1.running_var", "backbone.layer2.1.conv1.weight", "backbone.layer2.1.bn1.weight", "backbone.layer2.1.bn1.bias", "backbone.layer2.1.bn1.running_mean", "backbone.layer2.1.bn1.running_var", "backbone.layer2.1.conv2.weight", "backbone.layer2.1.bn2.weight", "backbone.layer2.1.bn2.bias", "backbone.layer2.1.bn2.running_mean", "backbone.layer2.1.bn2.running_var", "backbone.layer3.0.conv1.weight", "backbone.layer3.0.bn1.weight", "backbone.layer3.0.bn1.bias", "backbone.layer3.0.bn1.running_mean", "backbone.layer3.0.bn1.running_var", "backbone.layer3.0.conv2.weight", "backbone.layer3.0.bn2.weight", "backbone.layer3.0.bn2.bias", "backbone.layer3.0.bn2.running_mean", "backbone.layer3.0.bn2.running_var", "backbone.layer3.0.downsample.0.weight", "backbone.layer3.0.downsample.1.weight", "backbone.layer3.0.downsample.1.bias", "backbone.layer3.0.downsample.1.running_mean", "backbone.layer3.0.downsample.1.running_var", "backbone.layer3.1.conv1.weight", "backbone.layer3.1.bn1.weight", "backbone.layer3.1.bn1.bias", "backbone.layer3.1.bn1.running_mean", "backbone.layer3.1.bn1.running_var", "backbone.layer3.1.conv2.weight", "backbone.layer3.1.bn2.weight", "backbone.layer3.1.bn2.bias", "backbone.layer3.1.bn2.running_mean", "backbone.layer3.1.bn2.running_var", "backbone.layer4.0.conv1.weight", "backbone.layer4.0.bn1.weight", "backbone.layer4.0.bn1.bias", "backbone.layer4.0.bn1.running_mean", "backbone.layer4.0.bn1.running_var", "backbone.layer4.0.conv2.weight", "backbone.layer4.0.bn2.weight", "backbone.layer4.0.bn2.bias", "backbone.layer4.0.bn2.running_mean", "backbone.layer4.0.bn2.running_var", "backbone.layer4.0.downsample.0.weight", "backbone.layer4.0.downsample.1.weight", "backbone.layer4.0.downsample.1.bias", "backbone.layer4.0.downsample.1.running_mean", "backbone.layer4.0.downsample.1.running_var", "backbone.layer4.1.conv1.weight", "backbone.layer4.1.bn1.weight", "backbone.layer4.1.bn1.bias", "backbone.layer4.1.bn1.running_mean", "backbone.layer4.1.bn1.running_var", "backbone.layer4.1.conv2.weight", "backbone.layer4.1.bn2.weight", "backbone.layer4.1.bn2.bias", "backbone.layer4.1.bn2.running_mean", "backbone.layer4.1.bn2.running_var", "decode.head.in5.conv.weight", "decode.head.in5.bn.weight", "decode.head.in5.bn.bias", "decode.head.in5.bn.running_mean", "decode.head.in5.bn.running_var", "decode.head.in4.conv.weight", "decode.head.in4.bn.weight", "decode.head.in4.bn.bias", "decode.head.in4.bn.running_mean", "decode.head.in4.bn.running_var", "decode.head.in3.conv.weight", "decode.head.in3.bn.weight", "decode.head.in3.bn.bias", "decode.head.in3.bn.running_mean", "decode.head.in3.bn.running_var", "decode.head.in2.conv.weight", "decode.head.in2.bn.weight", "decode.head.in2.bn.bias", "decode.head.in2.bn.running_mean", "decode.head.in2.bn.running_var", "decode.head.out5.0.conv.weight", "decode.head.out5.0.bn.weight", "decode.head.out5.0.bn.bias", "decode.head.out5.0.bn.running_mean", "decode.head.out5.0.bn.running_var", "decode.head.out4.0.conv.weight", "decode.head.out4.0.bn.weight", "decode.head.out4.0.bn.bias", "decode.head.out4.0.bn.running_mean", "decode.head.out4.0.bn.running_var", "decode.head.out3.0.conv.weight", "decode.head.out3.0.bn.weight", "decode.head.out3.0.bn.bias", "decode.head.out3.0.bn.running_mean", "decode.head.out3.0.bn.running_var", "decode.head.out2.conv.weight", "decode.head.out2.bn.weight", "decode.head.out2.bn.bias", "decode.head.out2.bn.running_mean", "decode.head.out2.bn.running_var", "decode.binarize.0.weight", "decode.binarize.1.weight", "decode.binarize.1.bias", "decode.binarize.1.running_mean", "decode.binarize.1.running_var", "decode.binarize.3.weight", "decode.binarize.3.bias", "decode.binarize.4.weight", "decode.binarize.4.bias", "decode.binarize.4.running_mean", "decode.binarize.4.running_var", "decode.binarize.6.weight", "decode.binarize.6.bias", "decode.thresh.0.weight", "decode.thresh.1.weight", "decode.thresh.1.bias", "decode.thresh.1.running_mean", "decode.thresh.1.running_var", "decode.thresh.3.weight", "decode.thresh.3.bias", "decode.thresh.4.weight", "decode.thresh.4.bias", "decode.thresh.4.running_mean", "decode.thresh.4.running_var", "decode.thresh.6.weight", "decode.thresh.6.bias". 
	Unexpected key(s) in state_dict: "module.backbone.conv1.weight", "module.backbone.bn1.weight", "module.backbone.bn1.bias", "module.backbone.bn1.running_mean", "module.backbone.bn1.running_var", "module.backbone.bn1.num_batches_tracked", "module.backbone.layer1.0.conv1.weight", "module.backbone.layer1.0.bn1.weight", "module.backbone.layer1.0.bn1.bias", "module.backbone.layer1.0.bn1.running_mean", "module.backbone.layer1.0.bn1.running_var", "module.backbone.layer1.0.bn1.num_batches_tracked", "module.backbone.layer1.0.conv2.weight", "module.backbone.layer1.0.bn2.weight", "module.backbone.layer1.0.bn2.bias", "module.backbone.layer1.0.bn2.running_mean", "module.backbone.layer1.0.bn2.running_var", "module.backbone.layer1.0.bn2.num_batches_tracked", "module.backbone.layer1.1.conv1.weight", "module.backbone.layer1.1.bn1.weight", "module.backbone.layer1.1.bn1.bias", "module.backbone.layer1.1.bn1.running_mean", "module.backbone.layer1.1.bn1.running_var", "module.backbone.layer1.1.bn1.num_batches_tracked", "module.backbone.layer1.1.conv2.weight", "module.backbone.layer1.1.bn2.weight", "module.backbone.layer1.1.bn2.bias", "module.backbone.layer1.1.bn2.running_mean", "module.backbone.layer1.1.bn2.running_var", "module.backbone.layer1.1.bn2.num_batches_tracked", "module.backbone.layer2.0.conv1.weight", "module.backbone.layer2.0.bn1.weight", "module.backbone.layer2.0.bn1.bias", "module.backbone.layer2.0.bn1.running_mean", "module.backbone.layer2.0.bn1.running_var", "module.backbone.layer2.0.bn1.num_batches_tracked", "module.backbone.layer2.0.conv2.weight", "module.backbone.layer2.0.bn2.weight", "module.backbone.layer2.0.bn2.bias", "module.backbone.layer2.0.bn2.running_mean", "module.backbone.layer2.0.bn2.running_var", "module.backbone.layer2.0.bn2.num_batches_tracked", "module.backbone.layer2.0.downsample.0.weight", "module.backbone.layer2.0.downsample.1.weight", "module.backbone.layer2.0.downsample.1.bias", "module.backbone.layer2.0.downsample.1.running_mean", "module.backbone.layer2.0.downsample.1.running_var", "module.backbone.layer2.0.downsample.1.num_batches_tracked", "module.backbone.layer2.1.conv1.weight", "module.backbone.layer2.1.bn1.weight", "module.backbone.layer2.1.bn1.bias", "module.backbone.layer2.1.bn1.running_mean", "module.backbone.layer2.1.bn1.running_var", "module.backbone.layer2.1.bn1.num_batches_tracked", "module.backbone.layer2.1.conv2.weight", "module.backbone.layer2.1.bn2.weight", "module.backbone.layer2.1.bn2.bias", "module.backbone.layer2.1.bn2.running_mean", "module.backbone.layer2.1.bn2.running_var", "module.backbone.layer2.1.bn2.num_batches_tracked", "module.backbone.layer3.0.conv1.weight", "module.backbone.layer3.0.bn1.weight", "module.backbone.layer3.0.bn1.bias", "module.backbone.layer3.0.bn1.running_mean", "module.backbone.layer3.0.bn1.running_var", "module.backbone.layer3.0.bn1.num_batches_tracked", "module.backbone.layer3.0.conv2.weight", "module.backbone.layer3.0.bn2.weight", "module.backbone.layer3.0.bn2.bias", "module.backbone.layer3.0.bn2.running_mean", "module.backbone.layer3.0.bn2.running_var", "module.backbone.layer3.0.bn2.num_batches_tracked", "module.backbone.layer3.0.downsample.0.weight", "module.backbone.layer3.0.downsample.1.weight", "module.backbone.layer3.0.downsample.1.bias", "module.backbone.layer3.0.downsample.1.running_mean", "module.backbone.layer3.0.downsample.1.running_var", "module.backbone.layer3.0.downsample.1.num_batches_tracked", "module.backbone.layer3.1.conv1.weight", "module.backbone.layer3.1.bn1.weight", "module.backbone.layer3.1.bn1.bias", "module.backbone.layer3.1.bn1.running_mean", "module.backbone.layer3.1.bn1.running_var", "module.backbone.layer3.1.bn1.num_batches_tracked", "module.backbone.layer3.1.conv2.weight", "module.backbone.layer3.1.bn2.weight", "module.backbone.layer3.1.bn2.bias", "module.backbone.layer3.1.bn2.running_mean", "module.backbone.layer3.1.bn2.running_var", "module.backbone.layer3.1.bn2.num_batches_tracked", "module.backbone.layer4.0.conv1.weight", "module.backbone.layer4.0.bn1.weight", "module.backbone.layer4.0.bn1.bias", "module.backbone.layer4.0.bn1.running_mean", "module.backbone.layer4.0.bn1.running_var", "module.backbone.layer4.0.bn1.num_batches_tracked", "module.backbone.layer4.0.conv2.weight", "module.backbone.layer4.0.bn2.weight", "module.backbone.layer4.0.bn2.bias", "module.backbone.layer4.0.bn2.running_mean", "module.backbone.layer4.0.bn2.running_var", "module.backbone.layer4.0.bn2.num_batches_tracked", "module.backbone.layer4.0.downsample.0.weight", "module.backbone.layer4.0.downsample.1.weight", "module.backbone.layer4.0.downsample.1.bias", "module.backbone.layer4.0.downsample.1.running_mean", "module.backbone.layer4.0.downsample.1.running_var", "module.backbone.layer4.0.downsample.1.num_batches_tracked", "module.backbone.layer4.1.conv1.weight", "module.backbone.layer4.1.bn1.weight", "module.backbone.layer4.1.bn1.bias", "module.backbone.layer4.1.bn1.running_mean", "module.backbone.layer4.1.bn1.running_var", "module.backbone.layer4.1.bn1.num_batches_tracked", "module.backbone.layer4.1.conv2.weight", "module.backbone.layer4.1.bn2.weight", "module.backbone.layer4.1.bn2.bias", "module.backbone.layer4.1.bn2.running_mean", "module.backbone.layer4.1.bn2.running_var", "module.backbone.layer4.1.bn2.num_batches_tracked", "module.decode.head.in5.conv.weight", "module.decode.head.in5.bn.weight", "module.decode.head.in5.bn.bias", "module.decode.head.in5.bn.running_mean", "module.decode.head.in5.bn.running_var", "module.decode.head.in5.bn.num_batches_tracked", "module.decode.head.in4.conv.weight", "module.decode.head.in4.bn.weight", "module.decode.head.in4.bn.bias", "module.decode.head.in4.bn.running_mean", "module.decode.head.in4.bn.running_var", "module.decode.head.in4.bn.num_batches_tracked", "module.decode.head.in3.conv.weight", "module.decode.head.in3.bn.weight", "module.decode.head.in3.bn.bias", "module.decode.head.in3.bn.running_mean", "module.decode.head.in3.bn.running_var", "module.decode.head.in3.bn.num_batches_tracked", "module.decode.head.in2.conv.weight", "module.decode.head.in2.bn.weight", "module.decode.head.in2.bn.bias", "module.decode.head.in2.bn.running_mean", "module.decode.head.in2.bn.running_var", "module.decode.head.in2.bn.num_batches_tracked", "module.decode.head.out5.0.conv.weight", "module.decode.head.out5.0.bn.weight", "module.decode.head.out5.0.bn.bias", "module.decode.head.out5.0.bn.running_mean", "module.decode.head.out5.0.bn.running_var", "module.decode.head.out5.0.bn.num_batches_tracked", "module.decode.head.out4.0.conv.weight", "module.decode.head.out4.0.bn.weight", "module.decode.head.out4.0.bn.bias", "module.decode.head.out4.0.bn.running_mean", "module.decode.head.out4.0.bn.running_var", "module.decode.head.out4.0.bn.num_batches_tracked", "module.decode.head.out3.0.conv.weight", "module.decode.head.out3.0.bn.weight", "module.decode.head.out3.0.bn.bias", "module.decode.head.out3.0.bn.running_mean", "module.decode.head.out3.0.bn.running_var", "module.decode.head.out3.0.bn.num_batches_tracked", "module.decode.head.out2.conv.weight", "module.decode.head.out2.bn.weight", "module.decode.head.out2.bn.bias", "module.decode.head.out2.bn.running_mean", "module.decode.head.out2.bn.running_var", "module.decode.head.out2.bn.num_batches_tracked", "module.decode.binarize.0.weight", "module.decode.binarize.1.weight", "module.decode.binarize.1.bias", "module.decode.binarize.1.running_mean", "module.decode.binarize.1.running_var", "module.decode.binarize.1.num_batches_tracked", "module.decode.binarize.3.weight", "module.decode.binarize.3.bias", "module.decode.binarize.4.weight", "module.decode.binarize.4.bias", "module.decode.binarize.4.running_mean", "module.decode.binarize.4.running_var", "module.decode.binarize.4.num_batches_tracked", "module.decode.binarize.6.weight", "module.decode.binarize.6.bias", "module.decode.thresh.0.weight", "module.decode.thresh.1.weight", "module.decode.thresh.1.bias", "module.decode.thresh.1.running_mean", "module.decode.thresh.1.running_var", "module.decode.thresh.1.num_batches_tracked", "module.decode.thresh.3.weight", "module.decode.thresh.3.bias", "module.decode.thresh.4.weight", "module.decode.thresh.4.bias", "module.decode.thresh.4.running_mean", "module.decode.thresh.4.running_var", "module.decode.thresh.4.num_batches_tracked", "module.decode.thresh.6.weight", "module.decode.thresh.6.bias". 

Process finished with exit code 1

跑几个epoch后，评估指标Binary Acc固定在0.5，正常吗？

如题，作者可以加下我QQ吗？有问题想请教你啊
QQ:809969882

关于多边形数据的验证问题

你好，想问下在eval过程中，计算多边形标签的数据集 recall和precision方式是不是没有，只是针对四边形或者(xmin,ymin,xmax,ymax)这种情况

IndexError: list index out of range

Traceback (most recent call last):
File "./pruned/prune.py", line 270, in
prune(config)
File "./pruned/prune.py", line 173, in prune
m.out_channels = prued_mask[index_conv].sum()
IndexError: list index out of range
利用稀疏训练后的模型下标越界了，刚才点错了，重新提一个issue。
train:
gpu_id: '0'
backbone: 'resnet18'
pretrained: True
HeadName: 'DB'
用的默认设置。

DB.pth.tar哪里可以下载？

train中需要config['train']['resume']: ./checkpoints/DB_resnet18_bs_16_ep_1200/DB.pth.tar，DB.pth.tar哪里可以下载呢？

用官方放出的权重测试也得不到正确的结果

我是用的code里放出的FPN Head的权重，经过inference后几乎检测不到文字，所有指标都很低，不知道是哪里出问题了

训练数据集

你好！请问你上传的预训练权重是在icdar2015训练集上得到的吗？

Errors in loading state_dict for DBNet

python3 ./pruned/prune_inference.py
Traceback (most recent call last):
File "./pruned/prune_inference.py", line 134, in
result_dict = test_net(config)
File "./pruned/prune_inference.py", line 47, in test_net
model.load_state_dict(state)
File "/home/jing/.conda/envs/prob/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1045, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for DBNet:
size mismatch for backbone.layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([24, 64, 3, 3]).
size mismatch for backbone.layer1.0.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([24]).
size mismatch for backbone.layer1.0.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([24]).
size mismatch for backbone.layer1.0.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([24]).
......
报错信息如上所示，因为太多只截取了部分。
这个是在剪枝操作完，进行fintune后，测试的时候报错了。我的prune部分配置文件如下：
pruned:
gpu_id: '0'
scale: [73, 77, 81, 85]
base_num: 8
cut_percent: 0.8
pruned_checkpoints: './pruned/checkpoint/pruned_dict.pth.tar'
checkpoints_dict: './pruned/checkpoint/pruned_dict.dict'
save_checkpoints: './pruned/checkpoint'
checkpoints: './checkpoints/DB_resnet18_bs_16_ep_1200/DB.pth.tar'
# checkpoints: './checkpoints/DB_resnet18_bs_16_ep_1500/DB.pth.tar' #
finetune_lr: 0.0005
# resume: './checkpoints/DB_resnet18_bs_16_ep_1200/DB.pth.tar'
resume: './checkpoints/DB_resnet18_bs_16_ep_1500/DB.pth.tar' #
# restore: True
restore: False #
n_epoch: 100
start_val_epoch: 40
报错代码位置为prune_inference.py文件中的47行：model.load_state_dict(state)。

pure is wrong

关于DBhead和FPNhead性能问题？

您好！非常感谢您开源的这个工作！在学习您代码过程中有一些问题，还望可以解答，非常感谢！

在您工程中给出的结果是使用FPN比DB头的精度和召回率都高，为什么会有这样的结果？看原始论文中好像是加入DB起码可以提升2个点，是因为DB模型进行了裁剪吗？还有工程readme给出的结果超参数是如何设置的，比如训练的时候是否加入了二值图的训练？

加载预训练模型

我想要加载官方给的pre-trained-model-synthtext-resnet18预训练模型，请问可以加载吗，是要从resume传入吗

cd models/dcn/ sh make.sh这个必须编译吗？

我没编译程序也能跑通，会有什么问题吗？

能增加DB检测后将对应的目标区域裁剪的代码吗

您好，自定义数据集该如何制作呢？

DB模型收敛的慢

显卡：1080Ti
系统：win10
框架：pytorch1.7
我用的自己的训练集180张图片，验证集20张图片，训练时也收敛，但就是收敛的非常慢，需要1000个epoch左右才能收敛。
没有使用预训练权重，每一张图片中包含的文字相同。
大家在训练DB模型时收敛的快不快呢@BADBADBADBOY

errors occurs when compling dcn module

work@work-Super-Server:~/deep_learning/ocr/DBnet-lite.pytorch/models/dcn$ sh make.sh
running build_ext
building 'deform_conv_cuda' extension
creating /home/work/deep_learning/ocr/DBnet-lite.pytorch/models/dcn/build
creating /home/work/deep_learning/ocr/DBnet-lite.pytorch/models/dcn/build/temp.linux-x86_64-3.6
creating /home/work/deep_learning/ocr/DBnet-lite.pytorch/models/dcn/build/temp.linux-x86_64-3.6/src
Emitting ninja build file /home/work/deep_learning/ocr/DBnet-lite.pytorch/models/dcn/build/temp.linux-x86_64-3.6/build.ninja...
Compiling objects...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
1.10.0
creating build/lib.linux-x86_64-3.6
x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 /home/work/deep_learning/ocr/DBnet-lite.pytorch/models/dcn/build/temp.linux-x86_64-3.6/src/deform_conv_cuda.o /home/work/deep_learning/ocr/DBnet-lite.pytorch/models/dcn/build/temp.linux-x86_64-3.6/src/deform_conv_cuda_kernel.o -L/usr/local/lib/python3.6/dist-packages/torch/lib -L/usr/local/cuda-10.2/lib64 -lc10 -ltorch -ltorch_cpu -ltorch_python -lcudart -lc10_cuda -ltorch_cuda -o build/lib.linux-x86_64-3.6/deform_conv_cuda.cpython-36m-x86_64-linux-gnu.so
x86_64-linux-gnu-g++: error: /home/work/deep_learning/ocr/DBnet-lite.pytorch/models/dcn/build/temp.linux-x86_64-3.6/src/deform_conv_cuda.o: 没有那个文件或目录
x86_64-linux-gnu-g++: error: /home/work/deep_learning/ocr/DBnet-lite.pytorch/models/dcn/build/temp.linux-x86_64-3.6/src/deform_conv_cuda_kernel.o: 没有那个文件或目录
error: command 'x86_64-linux-gnu-g++' failed with exit status 1

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