Dummy ResNet Wrapper

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Description

Share any information you would like others to know. For example:

Author: @xxx.

This is an implementation of [XXX].

This project implements a dummy ResNet wrapper, which literally does nothing new but prints "hello world" during initialization.

Usage

For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by conda env export > env.yml.

Prerequisites

Python 3.7
PyTorch 1.6 or higher
MIM
MMOCR

All the commands below rely on the correct configuration of PYTHONPATH, which should point to the project's directory so that Python can locate the module files. In example_project/ root directory, run the following line to add the current directory to PYTHONPATH:

# Linux
export PYTHONPATH=`pwd`:$PYTHONPATH
# Windows PowerShell
$env:PYTHONPATH=Get-Location

Training commands

In MMOCR's root directory, run the following command to train the model:

mim train mmocr configs/dbnet_dummy-resnet_fpnc_1200e_icdar2015.py --work-dir work_dirs/dummy_mae/

To train on multiple GPUs, e.g. 8 GPUs, run the following command:

mim train mmocr configs/dbnet_dummy-resnet_fpnc_1200e_icdar2015.py --work-dir work_dirs/dummy_mae/ --launcher pytorch --gpus 8

Testing commands

In MMOCR's root directory, run the following command to test the model:

mim test mmocr configs/dbnet_dummy-resnet_fpnc_1200e_icdar2015.py --work-dir work_dirs/dummy_mae/ --checkpoint ${CHECKPOINT_PATH}

Results

List the results as usually done in other model's README. Example

You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project.

Method	Backbone	Pretrained Model	Training set	Test set	#epoch	Test size	Precision	Recall	Hmean	Download
DBNet_dummy	DummyResNet	-	ICDAR2015 Train	ICDAR2015 Test	1200	736	0.8853	0.7583	0.8169	model \| log

Citation

You may remove this section if not applicable.

@software{MMOCR_Contributors_OpenMMLab_Text_Detection_2020,
author = {{MMOCR Contributors}},
license = {Apache-2.0},
month = {8},
title = {{OpenMMLab Text Detection, Recognition and Understanding Toolbox}},
url = {https://github.com/open-mmlab/mmocr},
version = {0.3.0},
year = {2020}
}

Checklist

Here is a checklist illustrating a usual development workflow of a successful project, and also serves as an overview of this project's progress.

The PIC (person in charge) or contributors of this project should check all the items that they believe have been finished, which will further be verified by codebase maintainers via a PR.

OpenMMLab's maintainer will review the code to ensure the project's quality. Reaching the first milestone means that this project suffices the minimum requirement of being merged into 'projects/'. But this project is only eligible to become a part of the core package upon attaining the last milestone.

Note that keeping this section up-to-date is crucial not only for this project's developers but the entire community, since there might be some other contributors joining this project and deciding their starting point from this list. It also helps maintainers accurately estimate time and effort on further code polishing, if needed.

A project does not necessarily have to be finished in a single PR, but it's essential for the project to at least reach the first milestone in its very first PR.

Milestone 1: PR-ready, and acceptable to be one of the projects/.
- Finish the code
  
  The code's design shall follow existing interfaces and convention. For example, each model component should be registered into mmocr.registry.MODELS and configurable via a config file.
- Basic docstrings & proper citation
  
  Each major object should contain a docstring, describing its functionality and arguments. If you have adapted the code from other open-source projects, don't forget to cite the source project in docstring and make sure your behavior is not against its license. Typically, we do not accept any code snippet under GPL license. A Short Guide to Open Source Licenses
- Test-time correctness
  
  If you are reproducing the result from a paper, make sure your model's inference-time performance matches that in the original paper. The weights usually could be obtained by simply renaming the keys in the official pre-trained weights. This test could be skipped though, if you are able to prove the training-time correctness and check the second milestone.
- A full README
  
  As this template does.
Milestone 2: Indicates a successful model implementation.
- Training-time correctness
  
  If you are reproducing the result from a paper, checking this item means that you should have trained your model from scratch based on the original paper's specification and verified that the final result matches the report within a minor error range.
Milestone 3: Good to be a part of our core package!
- Type hints and docstrings
  
  Ideally all the methods should have type hints and docstrings. Example
- Unit tests
  
  Unit tests for each module are required. Example
- Code polishing
  
  Refactor your code according to reviewer's comment.
- Metafile.yml
  
  It will be parsed by MIM and Inferencer. Example
Move your modules into the core package following the codebase's file hierarchy structure.

In particular, you may have to refactor this README into a standard one. Example
Refactor your modules into the core package following the codebase's file hierarchy structure.