breezedeus / cnocr Goto Github PK

My codes are below:
`# _ coding = utf-8 _

from cnocr import CnOcr
png = 'E:\python\py\Vitaminpic\2018-10-29 维生素价格.png'
ocr = CnOcr()
res = ocr.ocr(png)
print("Predicted Chars:", res)`

However ,I get below with latest version mxnet--1.4.1 :

Traceback (most recent call last):
File "e:\python\exam\exam3.py", line 6, in
res = ocr.ocr(png)
File "C:\Users\NexFord\AppData\Local\Programs\Python\Python37\lib\site-packages\cnocr\cn_ocr.py", line 145, in ocr
img = mx.image.imread(img_fp, 1).asnumpy()
File "C:\Users\NexFord\AppData\Local\Programs\Python\Python37\lib\site-packages\mxnet\image\image.py", line 85, in imread
return _internal._cvimread(filename, *args, **kwargs)
File "", line 35, in _cvimread
File "C:\Users\NexFord\AppData\Local\Programs\Python\Python37\lib\site-packages\mxnet_ctypes\ndarray.py", line 92, in _imperative_invoke
ctypes.byref(out_stypes)))
File "C:\Users\NexFord\AppData\Local\Programs\Python\Python37\lib\site-packages\mxnet\base.py", line 252, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [17:54:53] C:\Jenkins\workspace\mxnet-tag\mxnet\src\io\image_io.cc:222: Check failed: file.is_open() Imread: 'E:\python\py\Vitaminpic\2018-10-29 维生素价格.png' couldn't open file: Invalid argument
So ,Who is Jenkins?

因为对于自然场景的文字识别不好友好，希望能加入CTPN对文本定位

为什么train.txt和test.txt文件名后跟的是一串数字，这些数字和文字之间的对照关系是如何建立的？

是否对mxnet有特定版本支持

英语单词之间空格识别不了

这个项目安装、使用简单，但是在识别的时候速度比较慢。一开始是调用了多行检测的函数，确实是慢，后来改成单行检测，速度有提升，但是比原版的crnn-mxnet还是慢不少。而且没有支持批量识别，在实际使用时会比较慢。

中文识别非常完美，但是还有一点小问题，0和o,1和l，9和g，这种经常识别错，是否可以加一个文本类型的参数呢？

请问dataset cn_ocr能否提供下载，或者能否给出训练集该如何组织呢？谢谢。

使用crnn-mxnet的Synthetic Chinese Dataset样本，执行
python ./cnocr-master/scripts/cnocr_train.py --loss ctc --dataset cn_ocr --data_root ./data/images --train_file ./data/train.txt --test_file ./data/test.txt

cnocr_train/cnocr-master# proc 0 started
proc 1 started
proc 2 started
proc 3 started
proc 0 started
proc 1 started
[16:56:12] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
2019-05-29 16:56:46,983 Epoch[0] Batch [0-50] Speed: 199.47 samples/sec accuracy=0.000000
2019-05-29 16:57:30,919 Epoch[0] Batch [50-100] Speed: 145.66 samples/sec accuracy=0.000000
2019-05-29 16:58:17,541 Epoch[0] Batch [100-150] Speed: 137.27 samples/sec accuracy=0.000000
2019-05-29 16:59:03,438 Epoch[0] Batch [150-200] Speed: 139.44 samples/sec accuracy=0.000000
2019-05-29 16:59:51,143 Epoch[0] Batch [200-250] Speed: 134.16 samples/sec accuracy=0.000000
2019-05-29 17:00:39,957 Epoch[0] Batch [250-300] Speed: 131.11 samples/sec accuracy=0.000000
2019-05-29 17:01:13,673 Epoch[0] Batch [300-350] Speed: 189.82 samples/sec accuracy=0.000000
2019-05-29 17:01:56,561 Epoch[0] Batch [350-400] Speed: 149.23 samples/sec accuracy=0.000000
2019-05-29 17:02:39,406 Epoch[0] Batch [400-450] Speed: 149.37 samples/sec accuracy=0.000000
2019-05-29 17:03:24,134 Epoch[0] Batch [450-500] Speed: 143.09 samples/sec accuracy=0.000000
2019-05-29 17:04:08,365 Epoch[0] Batch [500-550] Speed: 144.69 samples/sec accuracy=0.000000
2019-05-29 17:04:52,112 Epoch[0] Batch [550-600] Speed: 146.30 samples/sec accuracy=0.000000
2019-05-29 17:05:40,416 Epoch[0] Batch [600-650] Speed: 132.49 samples/sec accuracy=0.000000
2019-05-29 17:06:27,831 Epoch[0] Batch [650-700] Speed: 134.98 samples/sec accuracy=0.000000
2019-05-29 17:07:03,921 Epoch[0] Batch [700-750] Speed: 177.34 samples/sec accuracy=0.000000
2019-05-29 17:07:47,872 Epoch[0] Batch [750-800] Speed: 145.62 samples/sec accuracy=0.000000
2019-05-29 17:08:34,820 Epoch[0] Batch [800-850] Speed: 136.32 samples/sec accuracy=0.000000
2019-05-29 17:09:24,825 Epoch[0] Batch [850-900] Speed: 127.99 samples/sec accuracy=0.000000
2019-05-29 17:10:00,173 Epoch[0] Batch [900-950] Speed: 181.06 samples/sec accuracy=0.000000
2019-05-29 17:10:43,914 Epoch[0] Batch [950-1000] Speed: 146.32 samples/sec accuracy=0.000000
2019-05-29 17:11:29,664 Epoch[0] Batch [1000-1050] Speed: 139.89 samples/sec accuracy=0.000000
2019-05-29 17:12:18,869 Epoch[0] Batch [1050-1100] Speed: 130.07 samples/sec accuracy=0.000000
2019-05-29 17:12:54,773 Epoch[0] Batch [1100-1150] Speed: 178.25 samples/sec accuracy=0.000000
2019-05-29 17:13:45,715 Epoch[0] Batch [1150-1200] Speed: 125.63 samples/sec accuracy=0.000000
2019-05-29 17:14:16,195 Epoch[0] Batch [1200-1250] Speed: 209.97 samples/sec accuracy=0.000000
2019-05-29 17:14:58,715 Epoch[0] Batch [1250-1300] Speed: 150.52 samples/sec accuracy=0.000000
2019-05-29 17:15:41,078 Epoch[0] Batch [1300-1350] Speed: 151.08 samples/sec accuracy=0.000000
2019-05-29 17:16:25,221 Epoch[0] Batch [1350-1400] Speed: 144.98 samples/sec accuracy=0.000000
2019-05-29 17:17:09,735 Epoch[0] Batch [1400-1450] Speed: 143.78 samples/sec accuracy=0.000000
2019-05-29 17:17:57,844 Epoch[0] Batch [1450-1500] Speed: 133.03 samples/sec accuracy=0.000000
2019-05-29 17:18:35,149 Epoch[0] Batch [1500-1550] Speed: 171.56 samples/sec accuracy=0.000000
2019-05-29 17:19:22,321 Epoch[0] Batch [1550-1600] Speed: 135.67 samples/sec accuracy=0.000000
2019-05-29 17:19:57,424 Epoch[0] Batch [1600-1650] Speed: 182.32 samples/sec accuracy=0.000000
2019-05-29 17:20:38,650 Epoch[0] Batch [1650-1700] Speed: 155.24 samples/sec accuracy=0.000000
2019-05-29 17:21:23,728 Epoch[0] Batch [1700-1750] Speed: 141.98 samples/sec accuracy=0.000000
2019-05-29 17:22:11,530 Epoch[0] Batch [1750-1800] Speed: 133.89 samples/sec accuracy=0.000000
2019-05-29 17:22:52,154 Epoch[0] Batch [1800-1850] Speed: 157.54 samples/sec accuracy=0.000000
2019-05-29 17:23:38,767 Epoch[0] Batch [1850-1900] Speed: 137.30 samples/sec accuracy=0.000000
2019-05-29 17:24:13,728 Epoch[0] Batch [1900-1950] Speed: 183.07 samples/sec accuracy=0.000000
2019-05-29 17:24:59,449 Epoch[0] Batch [1950-2000] Speed: 139.98 samples/sec accuracy=0.000000
2019-05-29 17:25:45,785 Epoch[0] Batch [2000-2050] Speed: 138.12 samples/sec accuracy=0.000000
2019-05-29 17:26:28,279 Epoch[0] Batch [2050-2100] Speed: 150.61 samples/sec accuracy=0.000000

我发现识别用截图工具截图一小段文字没有问题，如果喂给一个完整的4A扫描件，几乎无法识别。
图片附着如下：

thanks for your great work.
I want to train net on my own data, but I don't know the format of training set. So can you tell me the format of image name and train txt file?
Besides, is there any requirement for image size?
Thanks

百度云盘的模型写着0.1.0版本，程序默认执行的是1.0.0，程序会报错

进一步调小了学习率，好像没有明显效果，要继续生成更多数据吗

您好，请问下非标准书面体无法识别吗，如下图，运行后的结果是空的

多谢作者的分享！
训练图片应该是定长吧？
请问变长图片识别是怎么做到的？切成训练图片长度再塞入batch做inference吗？
我用定长图片训练，感觉图片长度越长，越难训练而且效果越差。
请问你是如何做到长图片也能OCR效果这么好？

2019-08-25 15:34:55,719 Epoch[0] Batch [0-50] Speed: 206.17 samples/sec accuracy=0.000000
2019-08-25 15:35:27,459 Epoch[0] Batch [50-100] Speed: 201.66 samples/sec accuracy=0.000000
2019-08-25 15:35:58,847 Epoch[0] Batch [100-150] Speed: 203.91 samples/sec accuracy=0.000000
2019-08-25 15:36:56,625 Epoch[0] Batch [150-200] Speed: 110.77 samples/sec accuracy=0.000000
2019-08-25 15:38:04,535 Epoch[0] Batch [200-250] Speed: 94.24 samples/sec accuracy=0.000000
2019-08-25 15:39:18,125 Epoch[0] Batch [250-300] Speed: 86.97 samples/sec accuracy=0.000000
2019-08-25 15:40:42,457 Epoch[0] Batch [300-350] Speed: 75.89 samples/sec accuracy=0.000000
2019-08-25 15:42:04,507 Epoch[0] Batch [350-400] Speed: 78.00 samples/sec accuracy=0.000000
2019-08-25 15:43:11,145 Epoch[0] Batch [400-450] Speed: 96.04 samples/sec accuracy=0.000000
2019-08-25 15:44:13,990 Epoch[0] Batch [450-500] Speed: 101.84 samples/sec accuracy=0.000000
2019-08-25 15:45:11,138 Epoch[0] Batch [500-550] Speed: 111.99 samples/sec accuracy=0.000000
2019-08-25 15:47:05,598 Epoch[0] Batch [550-600] Speed: 206.19 samples/sec accuracy=0.000000
2019-08-25 15:47:37,091 Epoch[0] Batch [600-650] Speed: 203.22 samples/sec accuracy=0.000000
2019-08-25 15:48:08,517 Epoch[0] Batch [650-700] Speed: 203.65 samples/sec accuracy=0.000000
如题，是因为训练的epoch太少了么？如果是的话请问大家训练自己模型时，大概运行多少Epoch，accuracy会变化？

我要识别的一句话为“中华人民共和国”，代码为：

from cnocr import CnOcr
ocr = CnOcr()
img = cv2.imread("1.png", 0)
text = ocr.ocr_for_single_line(img)

它识别出的是['严', '这', '吧']，这是怎么回事？

发现截图的识别率接近100%，但是同样的截图拍照之后再识别基本上就识别不出来了，我需要做哪些处理才可以让拍照之后的识别率能够显著提升，谢谢！


上面的可以识别，下面的就识别不了

你好，运行程序的时候默认从dropbox下载模型，但是国内无法下载呀，请问怎么处理啊

大佬，如果自己准备好训练数据（不含360W的训练数据），在已有的模型上继续训练，会不会对模型的整体识别效果产生较大的影响呢？比如，原模型能够正确识别的字，继续训练后，会导致识别错误，会有这种情况发生么？

我看了您的实现，当遇到文件行距特别小，小到上下行偶尔有粘连的时候就无法分割开了。我尝试了用滴水算法去进行分割，效果也不理想，这种情况还有什么好的算法处理吗。还有一个思路是尝试再训练一个模型来专门做分割，但是找了半天也没找到有可以用的训练集

cnocr识别数字比较差，能提高数字识别准确度吗

如何设置使用gpu？

raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [16:16:44] c:\jenkins\workspace\mxnet-tag\mxnet\src\executor../common/exec_utils.h:392: InferShape pass cannot decide shapes for the following arguments (0s means unknown dimensions). Please consider providing them as inputs:
l0_init_h: [], l2_init_h: [], l1_init_h: [], l3_init_h: [],
有知道什么问题吗

python版本：3.7.2
操作系统：windows10
图片中有一个乱码，是这个原因吗？

Traceback (most recent call last):
File "E:/PycharmProjects/mhxy/main.py", line 9, in
ocr = CnOcr()
File "C:\Users\63110\AppData\Local\Programs\Python\Python37\lib\site-packages\cnocr\cn_ocr.py", line 83, in init
self._alphabet, _ = read_charset(os.path.join(self._model_dir, 'label_cn.txt'))
File "C:\Users\63110\AppData\Local\Programs\Python\Python37\lib\site-packages\cnocr\utils.py", line 65, in read_charset
for line in fp:
UnicodeDecodeError: 'gbk' codec can't decode byte 0x8c in position 10: illegal multibyte sequence

My CPU doesn't support AVX. Is there any ways to install lower version or install cnocr from source???

https://www.dropbox.com 网站国内停用了，导致几个包无法下载

hi，I want to add some small dataests to fine—tunning on your models，was your baidu—wangpan model’s final epoach 20？

使用crnn-mnext的样本Synthetic Chinese Dataset，执行cnocr_train.py，accuracy一是0

按照教程，先安装后导入包，结果报如下错误：
1 attempt left
Downloading /home/yqli/.cnocr/cnocr-models-v1.0.0.zip from https://www.dropbox.com/s/7w8l3mk4pvkt34w/cnocr-models-v1.0.0.zip?dl=1...
Traceback (most recent call last):
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/connection.py", line 157, in _new_conn
(self._dns_host, self.port), self.timeout, **extra_kw
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/util/connection.py", line 84, in create_connection
raise err
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/util/connection.py", line 74, in create_connection
sock.connect(sa)
OSError: [Errno 101] Network is unreachable

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/connectionpool.py", line 672, in urlopen
chunked=chunked,
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/connectionpool.py", line 376, in _make_request
self._validate_conn(conn)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/connectionpool.py", line 994, in _validate_conn
conn.connect()
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/connection.py", line 334, in connect
conn = self._new_conn()
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/connection.py", line 169, in _new_conn
self, "Failed to establish a new connection: %s" % e
urllib3.exceptions.NewConnectionError: <urllib3.connection.VerifiedHTTPSConnection object at 0x7faa2f72c9b0>: Failed to establish a new connection: [Errno 101] Network is unreachable

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/requests/adapters.py", line 449, in send
timeout=timeout
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/connectionpool.py", line 720, in urlopen
method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/urllib3/util/retry.py", line 436, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='www.dropbox.com', port=443): Max retries exceeded with url: /s/7w8l3mk4pvkt34w/cnocr-models-v1.0.0.zip?dl=1 (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7faa2f72c9b0>: Failed to establish a new connection: [Errno 101] Network is unreachable',))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "", line 1, in
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/cnocr/cn_ocr.py", line 101, in init
self._assert_and_prepare_model_files(root)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/cnocr/cn_ocr.py", line 126, in _assert_and_prepare_model_files
get_model_file(root)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/cnocr/utils.py", line 69, in get_model_file
download(MODEL_BASE_URL, path=zip_file_path, overwrite=True)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/mxnet/gluon/utils.py", line 342, in download
raise e
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/mxnet/gluon/utils.py", line 309, in download
r = requests.get(url, stream=True, verify=verify_ssl)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/requests/api.py", line 75, in get
return request('get', url, params=params, **kwargs)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/requests/api.py", line 60, in request
return session.request(method=method, url=url, **kwargs)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/requests/sessions.py", line 533, in request
resp = self.send(prep, **send_kwargs)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/requests/sessions.py", line 646, in send
r = adapter.send(request, **kwargs)
File "/home/yqli/anaconda3/envs/practice/lib/python3.6/site-packages/requests/adapters.py", line 516, in send
raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='www.dropbox.com', port=443): Max retries exceeded with url: /s/7w8l3mk4pvkt34w/cnocr-models-v1.0.0.zip?dl=1 (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7faa2f72c9b0>: Failed to establish a new connection: [Errno 101] Network is unreachable',))
在Windows下这一步也是报错

多谢，但是也发现，对于电商网页广告中包含无序文字的图片，识别效果不太好

mark

按照要求，用pip安装了cnocr，pip list看了下也有cnocr库，但是跑脚本的时候，报如下错误，想问一下，该怎么解决。
from cnocr import CnOcr
ImportError: cannot import name 'CnOcr'

cnocr\cn_ocr.py", line 144, in ocr
raise TypeError('Inappropriate argument type.')
TypeError: Inappropriate argument type.

您好！我是一个新手，请教一下如果不用pip安装，直接下载工程文件该怎么调用啊，非常感谢！

我写了一个web服务，对外提供识别接口。代码如下。

import web
import json
from cnocr import CnOcr
urls = ('/upload', 'Upload')

class Upload:
    ocr = CnOcr()
    def GET(self):
        return """<html><head></head><body>
<form method="POST" enctype="multipart/form-data" action="">
<input type="file" name="myfile" />
<br/>
<input type="submit" />
</form>
</body></html>"""

    def POST(self):
        x = web.input(myfile={})
        filedir = './upload_file' # change this to the directory you want to store the file in.
        if 'myfile' in x: # to check if the file-object is created
            filepath=x.myfile.filename.replace('\\','/') # replaces the windows-style slashes with linux ones.
            filename=filepath.split('/')[-1] # splits the and chooses the last part (the filename with extension)
            fout = open(filedir +'/'+ filename,'wb') # creates the file where the uploaded file should be stored
            fout.write(x.myfile.file.read()) # writes the uploaded file to the newly created file.
            fout.close() # closes the file, upload complete.
            resultData = Upload.ocr.ocr( filedir + '/' + filename )
            jsonStr=json.dumps(resultData, cls=NumpyEncoder)
        return jsonStr;


if __name__ == "__main__":
   app = web.application(urls, globals())
   app.run()

class NumpyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,
            np.int16, np.int32, np.int64, np.uint8,
            np.uint16, np.uint32, np.uint64)):
            return int(obj)
        elif isinstance(obj, (np.float_, np.float16, np.float32,
            np.float64)):
            return float(obj)
        elif isinstance(obj,(np.ndarray,)): #### This is the fix
            return obj.tolist()
        return json.JSONEncoder.default(self, obj)

现在的问题是，随着我不断的提交图片进行识别。内存会不断飙升。从最初的1个G到3个G。这还只是识别了10多张图片，是否随着图片识别数量的增多，内存占用会越来越高呢？

是我的web服务的写法有问题，还是什么有问题？不能每次识别完成之后释放调内存吗？
求大神指导~~~

[17:12:04] C:\Jenkins\workspace\mxnet-tag\mxnet\src\nnvm\legacy_json_util.cc:209: Loading symbol saved by previous version v1.3.1. Attempting to upgrade...
[17:12:04] C:\Jenkins\workspace\mxnet-tag\mxnet\src\nnvm\legacy_json_util.cc:217: Symbol successfully upgraded!
Traceback (most recent call last):
File "C:\Program Files\Anaconda3\lib\site-packages\mxnet\symbol\symbol.py", line 1523, in simple_bind
ctypes.byref(exe_handle)))
File "C:\Program Files\Anaconda3\lib\site-packages\mxnet\base.py", line 252, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [17:12:04] c:\jenkins\workspace\mxnet-tag\mxnet\src\executor../common/exec_utils.h:392: InferShape pass cannot decide shapes for the following arguments (0s means unknown dimensions). Please consider providing them as inputs:
l0_init_h: [], l2_init_h: [], l1_init_h: [], l3_init_h: [],

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "D:/文件/OCR项目/新OCR项目/OCR/testocr.py", line 4, in
ocr = CnOcr()
File "D:\文件\OCR项目\新OCR项目\OCR\cnocr2\cnocr\cn_ocr.py", line 107, in init
self._mod = self._get_module(self._hp)
File "D:\文件\OCR项目\新OCR项目\OCR\cnocr2\cnocr\cn_ocr.py", line 134, in _get_module
mod = load_module(prefix, self._model_epoch, data_names, data_shapes, network=network)
File "D:\文件\OCR项目\新OCR项目\OCR\cnocr2\cnocr\cn_ocr.py", line 90, in load_module
mod.bind(for_training=False, data_shapes=data_shapes)
File "C:\Program Files\Anaconda3\lib\site-packages\mxnet\module\module.py", line 429, in bind
state_names=self._state_names)
File "C:\Program Files\Anaconda3\lib\site-packages\mxnet\module\executor_group.py", line 279, in init
self.bind_exec(data_shapes, label_shapes, shared_group)
File "C:\Program Files\Anaconda3\lib\site-packages\mxnet\module\executor_group.py", line 375, in bind_exec
shared_group))
File "C:\Program Files\Anaconda3\lib\site-packages\mxnet\module\executor_group.py", line 662, in _bind_ith_exec
shared_buffer=shared_data_arrays, **input_shapes)
File "C:\Program Files\Anaconda3\lib\site-packages\mxnet\symbol\symbol.py", line 1529, in simple_bind
raise RuntimeError(error_msg)
RuntimeError: simple_bind error. Arguments:
data: (128, 1, 32, 280)
[17:12:04] c:\jenkins\workspace\mxnet-tag\mxnet\src\executor../common/exec_utils.h:392: InferShape pass cannot decide shapes for the following arguments (0s means unknown dimensions). Please consider providing them as inputs:
l0_init_h: [], l2_init_h: [], l1_init_h: [], l3_init_h: [],

Process finished with exit code 1
运行到这个函数的mod.bind(for_training=False, data_shapes=data_shapes)这里出现上述错误
def load_module(prefix, epoch, data_names, data_shapes, network=None):
"""
Loads the model from checkpoint specified by prefix and epoch, binds it
to an executor, and sets its parameters and returns a mx.mod.Module
"""
sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
if network is not None:
sym = network

# We don't need CTC loss for prediction, just a simple softmax will suffice.
# We get the output of the layer just before the loss layer ('pred_fc') and add softmax on top
pred_fc = sym.get_internals()['pred_fc_output']
sym = mx.sym.softmax(data=pred_fc)

mod = mx.mod.Module(symbol=sym, context=mx.cpu(), data_names=data_names, label_names=None)
mod.bind(for_training=False, data_shapes=data_shapes)
mod.set_params(arg_params, aux_params, allow_missing=False)
return mod

如果我把这个模型移植到移动端有可能吗？手机的性能能跑动这个模型吗？

自行生成了一批训练数据，会在训练过程中accuracy会忽然变为0，然后就一直为0，不再发生变化。

目前模型文件windows下存放在C:\Users~\AppData\Roaming\cnocr\下面的，可移植性比较差，import cnocr 后，可以手动指定模型文件所在位置么

我试了从原地址下载，也试了好几个镜像，基本都是这个问题，下面是报错
ERROR: Could not find a version that satisfies the requirement mxnet<1.5.0,>=1.4.1 (from cnocr) (from versions: 0.11.1b20170915, 0.11.1b20170922, 0.11.1b20170929, 0.11.1b20171006, 0.11.1b20171013, 0.12.0b20171020, 0.12.0b20171027, 0.12.0, 0.12.1b20171103, 0.12.1, 1.0.0, 1.0.0.post1, 1.0.0.post3, 1.0.0.post4, 1.0.1b20180114, 1.0.1b20180121, 1.0.1b20180128, 1.0.1b20180202, 1.1.0b20180209, 1.1.0b20180216, 1.1.0.post0, 1.2.0b20180223, 1.2.0b20180302, 1.2.0b20180309, 1.2.0b20180323, 1.2.0b20180330, 1.2.0b20180406, 1.2.0b20180413, 1.2.0b20180420, 1.2.0b20180427, 1.2.0b20180504, 1.2.0, 1.6.0)
ERROR: No matching distribution found for mxnet<1.5.0,>=1.4.1 (from cnocr)

原来的那个项目有load_epoch可以加载现有模型，现在可以在下载的0200之上训练吗？
我试了一下，有报错。
python scripts/cnocr_train.py --dataset cn_ocr --load_epoch 0020
proc 0 started
proc 1 started
proc 2 started
proc 3 started
proc 0 started
proc 1 started
2019-04-25 23:42:32,320 Loaded model ./models/model_0020.params
Process Process-2:
Traceback (most recent call last):
File "/////lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "////python3.6/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "////cnocr/cnocr/data_utils/multiproc_data.py", line 89, in _proc_loop
data = fn()
File "////cnocr/cnocr/data_utils/data_iter.py", line 212, in _gen_sample
labels[idx - 1] = int(img_lst[idx])
IndexError: index 10 is out of bounds for axis 0 with size 10
Process Process-4:

测试了三台win7,两台win10
在win7上代码运行完都不能够结束 一直阻塞

从网盘下载了model,放到cnocr文件夹下，但是运行程序一直要去下载，后来我自己解压了，把models 文件夹放在cnocr文件夹下，这样也不行，后来发现时models下面文件的文件名不同，改了文件名，跳过了下载，后面又出错了，
[21:51:57] C:\Jenkins\workspace\mxnet-tag\mxnet\src\nnvm\legacy_json_util.cc:209: Loading symbol saved by previous version v1.3.1. Attempting to upgrade...
[21:51:57] C:\Jenkins\workspace\mxnet-tag\mxnet\src\nnvm\legacy_json_util.cc:217: Symbol successfully upgraded!
Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\lib\site-packages\mxnet\symbol\symbol.py", line 1523, in simple_bind
ctypes.byref(exe_handle)))
File "C:\ProgramData\Anaconda3\lib\site-packages\mxnet\base.py", line 252, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [21:51:57] c:\jenkins\workspace\mxnet-tag\mxnet\src\executor../common/exec_utils.h:392: InferShape pass cannot decide shapes for the following arguments (0s means unknown dimensions). Please consider providing them as inputs:
l0_init_h: [], l2_init_h: [], l1_init_h: [], l3_init_h: [],

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "D:/OCR/ocrtest.py", line 2, in
ocr = CnOcr()
File "C:\ProgramData\Anaconda3\lib\site-packages\cnocr\cn_ocr.py", line 107, in init
self._mod = self._get_module(self._hp)
File "C:\ProgramData\Anaconda3\lib\site-packages\cnocr\cn_ocr.py", line 134, in _get_module
mod = load_module(prefix, self._model_epoch, data_names, data_shapes, network=network)
File "C:\ProgramData\Anaconda3\lib\site-packages\cnocr\cn_ocr.py", line 90, in load_module
mod.bind(for_training=False, data_shapes=data_shapes)
File "C:\ProgramData\Anaconda3\lib\site-packages\mxnet\module\module.py", line 429, in bind
state_names=self._state_names)
File "C:\ProgramData\Anaconda3\lib\site-packages\mxnet\module\executor_group.py", line 279, in init
self.bind_exec(data_shapes, label_shapes, shared_group)
File "C:\ProgramData\Anaconda3\lib\site-packages\mxnet\module\executor_group.py", line 375, in bind_exec
shared_group))
File "C:\ProgramData\Anaconda3\lib\site-packages\mxnet\module\executor_group.py", line 662, in _bind_ith_exec
shared_buffer=shared_data_arrays, **input_shapes)
File "C:\ProgramData\Anaconda3\lib\site-packages\mxnet\symbol\symbol.py", line 1529, in simple_bind
raise RuntimeError(error_msg)
RuntimeError: simple_bind error. Arguments:
data: (128, 1, 32, 280)
[21:51:57] c:\jenkins\workspace\mxnet-tag\mxnet\src\executor../common/exec_utils.h:392: InferShape pass cannot decide shapes for the following arguments (0s means unknown dimensions). Please consider providing them as inputs:
l0_init_h: [], l2_init_h: [], l1_init_h: [], l3_init_h: [],