Official implementations of PSENet, PAN and PAN++.

Hello Author, First of all, I would like to appreciate your work and effort. I have tried your repo. The evaluation code gives me an error of the "The sample 199 not present in GT," but the label text is there. When I tried to see the result via visualizing it on the images, it seems good. Let me know if there is any solution from your side.

more complete log as belows: Epoch: [1 | 600] /data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/torch/nn/functional.py:2941: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead. warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.") /data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/torch/nn/functional.py:3121: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. "See the documentation of nn.Upsample for details.".format(mode)) (1/374) LR: 0.001000 | Batch: 2.668s | Total: 0min | ETA: 17min | Loss: 1.619 | Loss(text/kernel/emb/rec): 0.680/0.193/0.746/0.000 | IoU(text/kernel): 0.324/0.335 | Acc rec: 0.000 Traceback (most recent call last): File "/data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/multiprocessing/queues.py", line 236, in _feed obj = _ForkingPickler.dumps(obj) File "/data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) _pickle.PicklingError: Can't pickle <class 'cPolygon.Error'>: import of module 'cPolygon' failed

the code runs normally when using the CTW1500 datasets. but encounter errors when using my own datasets.

it seems fine in the first run (1/374), what is wrong ? I have no idea.

您好！我现在在自己的数据上进行训练，训练过程是这样的 Epoch: [212 | 600] (1/198) LR: 0.000677 | Batch: 3.934s | Total: 0min | ETA: 13min | Loss: 0.752 | Loss(text/kernel/emb/rec): 0.493/0.199/0.059/0.000 | IoU(text/kernel): 0.055/0.553 | Acc rec: 0.000 (21/198) LR: 0.000677 | Batch: 1.089s | Total: 0min | ETA: 3min | Loss: 0.731 | Loss(text/kernel/emb/rec): 0.478/0.199/0.054/0.000 | IoU(text/kernel): 0.048/0.482 | Acc rec: 0.000 (41/198) LR: 0.000677 | Batch: 1.022s | Total: 1min | ETA: 3min | Loss: 0.732 | Loss(text/kernel/emb/rec): 0.478/0.198/0.056/0.000 | IoU(text/kernel): 0.049/0.476 | Acc rec: 0.000 这个Acc rec一直是0，我终止训练后，在测试数据上进行测试时，output输出的是空的，请问是怎么回事呢，感谢啦！

1. 后处理的代码中当kernel中两个连通域的面积比大于max_rate时，将这两个连通域的flag赋值为1，在扩充时，必须同时满足当前扩充的点所属的连通域的flag值为1且与kernal的similar vector距离大于3时才不扩充该点。请问设flag这步操作的作用是什么，直接判断与Kernel的similar vector的距离可以吗？
2. 论文中扩充的点与kernel相似向量的欧式距离thresh值为6，代码中为3，请问实际应用中这个值跟什么有关系，是数据集的某些特点吗？

Hi,

I try to run your code and figure out that in your last line in pa.pyx

return _pa(kernels[:-1], emb, label, cc, kernel_num, label_num, min_area)

Looks like this should be

return _pa(kernels, emb, label, cc, kernel_num, label_num, min_area)

So that we can scan over all kernels (you skip the last kernel) and there is no crash in this function. Am I correct?

Thanks.

PAN++ic15，An error appears when trying to test the model:

reading type: pil. Traceback (most recent call last): File "test.py", line 155, in main(args) File "test.py", line 138, in main print("No checkpoint found at '{}'".format(args.resume)) AttributeError: 'Namespace' object has no attribute 'resume'

运行 python train.py config/pan/pan_r18_tt.py 后，出现如下情况： Traceback (most recent call last): File "/home/dell2/anaconda3/envs/pannet/lib/python3.6/multiprocessing/queues.py", line 234, in _feed obj = _ForkingPickler.dumps(obj) File "/home/dell2/anaconda3/envs/pannet/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps cls(buf, protocol).dump(obj) _pickle.PicklingError: Can't pickle <class 'cPolygon.Error'>: import of module 'cPolygon' failed 似乎是迭代过程中出现的问题且只出现在训练TT数据集的时候 请问出现这种情况该怎样解决呢？谢谢您

将模型路径和config文件路径配置好了之后，执行python test.py，提示如下： Traceback (most recent call last): File "test.py", line 117, in main(args) File "test.py", line 107, in main test(test_loader, model, cfg) File "test.py", line 56, in test outputs = model(**data) File "/home/ethony/anaconda3/envs/ocr/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in call result = self.forward(*input, **kwargs) File "/media/ethony/C14D581BDA18EBFA/lyg_datas_and_code/OCR_work/pan_pp.pytorch-master/models/pan.py", line 104, in forward det_res = self.det_head.get_results(det_out, img_metas, cfg) File "/media/ethony/C14D581BDA18EBFA/lyg_datas_and_code/OCR_work/pan_pp.pytorch-master/models/head/pa_head.py", line 65, in get_results label = pa(kernels, emb) TypeError: 'module' object is not callable 看提示应该是model/post_processing下的pa没有正确导入，导入为模块了，这应该怎么解决呢

Hi. When I run 'python train.py config/pan/pan_r18_ic15.py' , the errors are as followings: Do you know how to solve the problem? Thank you very much. Traceback (most recent call last): File "train.py", line 234, in main(args) File "train.py", line 216, in main train(train_loader, model, optimizer, epoch, start_iter, cfg) File "train.py", line 41, in train for iter, data in enumerate(train_loader): File "D:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 435, in next data = self._next_data() File "D:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 1085, in _next_data return self._process_data(data) File "D:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 1111, in _process_data data.reraise() File "D:\Anaconda3\lib\site-packages\torch_utils.py", line 428, in reraise raise self.exc_type(msg) TypeError: function takes exactly 5 arguments (1 given)

(zyl_torch16) ubuntu@ubuntu:/data/zhangyl/pan_pp.pytorch-master$ sh ./compile.sh Compiling pa.pyx because it depends on /data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/numpy/init.pxd. [1/1] Cythonizing pa.pyx /data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /data/zhangyl/pan_pp.pytorch-master/models/post_processing/pa/pa.pyx tree = Parsing.p_module(s, pxd, full_module_name) running build_ext building 'pa' extension creating build creating build/temp.linux-x86_64-3.7 gcc -pthread -B /data/tools/anaconda3/envs/zyl_torch16/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/numpy/core/include -I/data/tools/anaconda3/envs/zyl_torch16/include/python3.7m -c pa.cpp -o build/temp.linux-x86_64-3.7/pa.o -O3 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ In file included from /data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0, from /data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12, from /data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4, from pa.cpp:647: /data/tools/anaconda3/envs/zyl_torch16/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp] #warning "Using deprecated NumPy API, disable it with "
^~~~~~~ g++ -pthread -shared -B /data/tools/anaconda3/envs/zyl_torch16/compiler_compat -L/data/tools/anaconda3/envs/zyl_torch16/lib -Wl,-rpath=/data/tools/anaconda3/envs/zyl_torch16/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/pa.o -o /data/zhangyl/pan_pp.pytorch-master/models/post_processing/pa/pa.cpython-37m-x86_64-linux-gnu.so (zyl_torch16) ubuntu@ubuntu:/data/zhangyl/pan_pp.pytorch-master$

this is the compile history, I am not sure whether is successully build or not.

Hi,

Your FAST paper is really amazing! While you already have an implementation of erosion/dilation, let me offer using our set of morphology, implemented in pyre pytorch: https://kornia.readthedocs.io/en/latest/morphology.html

https://kornia-tutorials.readthedocs.io/en/master/morphology_101.html

Best, Dmytro.

Hello Author, First of all, I would like to appreciate your work and effort. I have tried your repo. The evaluation code gives me an error of the "The sample 199 not present in GT," but the label text is there. When I tried to see the result via visualizing it on the images, it seems good. Let me know if there is any solution from your side.

How to predict a new image using the training weight?it doesn't work below.

python test.py config/pan/pan_r18_ic15.py checkpoints/pan_r18_ic15/checkpoint.pth.tar cd eval/ ./eval_ic15.sh

please inform me with [email protected] or wechat SanQian-2012,thanks you so much.

Originally posted by @Devin521314 in https://github.com/whai362/pan_pp.pytorch/issues/91#issuecomment-1233810612

hi: I find there is no 'SOS' in code， I understand SOS should be embedding at the beginning. Please tell me ,thanks! ---------------code----------------------------------------------- class Encoder(nn.Module): def init(self, hidden_dim, voc, char2id, id2char): super(Encoder, self).init() self.hidden_dim = hidden_dim self.vocab_size = len(voc) self.START_TOKEN = char2id['EOS'] self.emb = nn.Embedding(self.vocab_size, self.hidden_dim) self.att = MultiHeadAttentionLayer(self.hidden_dim, 8)

def forward(self, x):
    batch_size, feature_dim, H, W = x.size()
    x_flatten = x.view(batch_size, feature_dim, H * W).permute(0, 2, 1)
    st = x.new_full((batch_size,), self.START_TOKEN, dtype=torch.long)
    emb_st = self.emb(st)
    holistic_feature, _ = self.att(emb_st, x_flatten, x_flatten)
    return