Attention_ocr | attention model for chinese_ocr in Pytorch | Computer Vision library

by JimmyHHua Python Version: Current License: No License

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kandi X-RAY | Attention_ocr Summary

Attention_ocr is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Transformer applications. Attention_ocr has no bugs, it has no vulnerabilities and it has low support. However Attention_ocr build file is not available. You can download it from GitHub.

Encoder and decoder with attention model for chinese_ocr in Pytorch 1.0

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Attention_ocr has a low active ecosystem.

It has 31 star(s) with 16 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 1 have been closed. On average issues are closed in 4 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Attention_ocr is current.

Quality

Attention_ocr has 0 bugs and 0 code smells.

Security

Attention_ocr has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Attention_ocr code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Attention_ocr does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Attention_ocr releases are not available. You will need to build from source code and install.

Attention_ocr has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Attention_ocr saves you 160 person hours of effort in developing the same functionality from scratch.

It has 397 lines of code, 28 functions and 7 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Attention_ocr and discovered the below as its top functions. This is intended to give you an instant insight into Attention_ocr implemented functionality, and help decide if they suit your requirements.

Fit the model
Add a value to the sum
Calculate the accuracy score for each test
Reset statistics
Save the checkpoint
Make a directory
The mean and standard deviation
Create a dataset
Create a transformation function for label transformation
Wrapper for cn_transform
Convert an index to a one - hot integer
Read label file
Forward computation
Compute alpha and u

Get all kandi verified functions for this library.

Attention_ocr Key Features

No Key Features are available at this moment for Attention_ocr.

Attention_ocr Examples and Code Snippets

No Code Snippets are available at this moment for Attention_ocr.

Community Discussions

Trending Discussions on Attention_ocr

Error when serving attention_ocr model ("error": "Expected one or two output Tensors, found 17")

QUESTION

Error when serving attention_ocr model ("error": "Expected one or two output Tensors, found 17")

Asked 2020-Sep-18 at 04:18

I'm trying to serve attention_ocr model on docker with tensorflow/serving image.

First, I trained this model with own dataset and get a good result with demo_inference.py

So, I'm export the trained model with export_model.py
python export_model.py --checkpoint=model.ckpt-111111 --export_dir=/tmp/mydir

Then, run docker container for serving the model.
docker run -it --rm -p 8501:8501 -v /tmp/mydir:/models/aocr -e MODEL_NAME=aocr --gpus all tensorflow/serving

And this is my python client script.

...

ANSWER

Answered 2020-Sep-18 at 04:18

Thank you for reporting this issue. I filed a bug (#9264) on Github on your behalf. The issue is that the default signature includes all the endpoints that the model provides. If you want to use the Serving's Classification API, we need to modify the export_model script to export just the 2 tensors expected by the classification API (i.e., predictions and scores). In the meantime, you can use the Predict API, which supports an arbitrary number of output tensors. Please note that when using the predict API via GRPC you can specify output_filter, but the RESTful API does not have that option, so the response is pretty heavy, since it sends back all the attention masks and the raw image. In case somebody else is trying to figure out how to run inference, here are steps that worked for me.

Export the model:

Source https://stackoverflow.com/questions/63931981

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Attention_ocr

You can download it from GitHub.
You can use Attention_ocr like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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