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Changelog

0.6.0 (05/05/2022)

Highlights

  1. A new recognition algorithm MASTER has been added into MMOCR, which was the championship solution for the “ICDAR 2021 Competition on Scientific Table Image Recognition to Latex”! The model pre-trained on SynthText and MJSynth is available for testing! Credit to @JiaquanYe

  2. DBNet++ has been released now! A new Adaptive Scale Fusion module has been equipped for feature enhancement. Benefiting from this, the new model achieved 2% better h-mean score than its predecessor on the ICDAR2015 dataset.

  3. Three more dataset converters are added: LSVT, RCTW and HierText. Check the dataset zoo (Det & Recog ) to explore further information.

  4. To enhance the data storage efficiency, MMOCR now supports loading both images and labels from .lmdb format annotations for the text recognition task. To enable such a feature, the new lmdb_converter.py is ready for use to pack your cropped images and labels into an lmdb file. For a detailed tutorial, please refer to the following sections and the doc.

  5. Testing models on multiple datasets is a widely used evaluation strategy. MMOCR now supports automatically reporting mean scores when there is more than one dataset to evaluate, which enables a more convenient comparison between checkpoints. Doc

  6. Evaluation is more flexible and customizable now. For text detection tasks, you can set the score threshold range where the best results might come out. (Doc) If too many results are flooding your text recognition train log, you can trim it by specifying a subset of metrics in evaluation config. Check out the Evaluation section for details.

  7. MMOCR provides a script to convert the .json labels obtained by the popular annotation toolkit Labelme to MMOCR-supported data format. @Y-M-Y contributed a log analysis tool that helps users gain a better understanding of the entire training process. Read tutorial docs to get started.

Lmdb Dataset

Reading images or labels from files can be slow when data are excessive, e.g. on a scale of millions. Besides, in academia, most of the scene text recognition datasets are stored in lmdb format, including images and labels. To get closer to the mainstream practice and enhance the data storage efficiency, MMOCR now officially supports loading images and labels from lmdb datasets via a new pipeline LoadImageFromLMDB. This section is intended to serve as a quick walkthrough for you to master this update and apply it to facilitate your research.

Specifications

To better align with the academic community, MMOCR now requires the following specifications for lmdb datasets:

  • The parameter describing the data volume of the dataset is num-samples instead of total_number (deprecated).

  • Images and labels are stored with keys in the form of image-000000001 and label-000000001, respectively.

Usage

  1. Use existing academic lmdb datasets if they meet the specifications; or the tool provided by MMOCR to pack images & annotations into a lmdb dataset.

  • Previously, MMOCR had a function txt2lmdb (deprecated) that only supported converting labels to lmdb format. However, it is quite different from academic lmdb datasets, which usually contain both images and labels. Now MMOCR provides a new utility lmdb_converter to convert recognition datasets with both images and labels to lmdb format.

  • Say that your recognition data in MMOCR’s format are organized as follows. (See an example in ocr_toy_dataset).

    # Directory structure
    
    ├──img_path
    |      |—— img1.jpg
    |      |—— img2.jpg
    |      |—— ...
    |——label.txt (or label.jsonl)
    
    # Annotation format
    
    label.txt:  img1.jpg HELLO
                img2.jpg WORLD
                ...
    
    label.jsonl:    {'filename':'img1.jpg', 'text':'HELLO'}
                    {'filename':'img2.jpg', 'text':'WORLD'}
                    ...
    
  • Then pack these files up:

    python tools/data/utils/lmdb_converter.py  {PATH_TO_LABEL} {OUTPUT_PATH} --i {PATH_TO_IMAGES}
    
  • Check out tools.md for more details.

  1. The second step is to modify the configuration files. For example, to train CRNN on MJ and ST datasets:

  • Set parser as LineJsonParser and file_format as ‘lmdb’ in dataset config

    # configs/_base_/recog_datasets/ST_MJ_train.py
    train1 = dict(
        type='OCRDataset',
        img_prefix=train_img_prefix1,
        ann_file=train_ann_file1,
        loader=dict(
            type='AnnFileLoader',
            repeat=1,
            file_format='lmdb',
            parser=dict(
                type='LineJsonParser',
                keys=['filename', 'text'],
            )),
        pipeline=None,
        test_mode=False)
    
  • Use LoadImageFromLMDB in pipeline:

    # configs/_base_/recog_pipelines/crnn_pipeline.py
    train_pipeline = [
        dict(type='LoadImageFromLMDB', color_type='grayscale'),
        ...
    
  1. You are good to go! Start training and MMOCR will load data from your lmdb dataset.

New Features & Enhancements

  • Add analyze_logs in tools and its description in docs by @Y-M-Y in https://github.com/open-mmlab/mmocr/pull/899

  • Add LSVT Data Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/896

  • Add RCTW dataset converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/914

  • Support computing mean scores in UniformConcatDataset by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/981

  • Support loading images and labels from lmdb file by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/982

  • Add recog2lmdb and new toy dataset files by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/979

  • Add labelme converter for textdet and textrecog by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/972

  • Update CircleCI configs by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/918

  • Update Git Action by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/930

  • More customizable fields in dataloaders by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/933

  • Skip CIs when docs are modified by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/941

  • Rename Github tests, fix ignored paths by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/946

  • Support latest MMCV by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/959

  • Support dynamic threshold range in eval_hmean by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/962

  • Update the version requirement of mmdet in docker by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/966

  • Replace opencv-python-headless with open-python by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/970

  • Update Dataset Configs by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/980

  • Add SynthText dataset config by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/983

  • Automatically report mean scores when applicable by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/995

  • Add DBNet++ by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/973

  • Add MASTER by @JiaquanYe in https://github.com/open-mmlab/mmocr/pull/807

  • Allow choosing metrics to report in text recognition tasks by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/989

  • Add HierText converter by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/948

  • Fix lint_only in CircleCI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/998

Bug Fixes

  • Fix CircleCi Main Branch Accidentally Run PR Stage Test by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/927

  • Fix a deprecate warning about mmdet.datasets.pipelines.formating by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/944

  • Fix a Bug in ResNet plugin by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/967

  • revert a wrong setting in db_r18 cfg by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/978

  • Fix TotalText Anno version issue by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/945

  • Update installation step of albumentations by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/984

  • Fix ImgAug transform by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/949

  • Fix GPG key error in CI and docker by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/988

  • update label.lmdb by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/991

  • correct meta key by @garvan2021 in https://github.com/open-mmlab/mmocr/pull/926

  • Use new image by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/976

  • Fix Data Converter Issues by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/955

Docs

  • Update CONTRIBUTING.md by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/905

  • Fix the misleading description in test.py by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/908

  • Update recog.md for lmdb Generation by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/934

  • Add MMCV by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/954

  • Add wechat QR code to CN readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/960

  • Update CONTRIBUTING.md by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/947

  • Use QR codes from MMCV by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/971

  • Renew dataset_types.md by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/997

New Contributors

  • @Y-M-Y made their first contribution in https://github.com/open-mmlab/mmocr/pull/899

Full Changelog: https://github.com/open-mmlab/mmocr/compare/v0.5.0…v0.6.0

0.5.0 (31/03/2022)

Highlights

  1. MMOCR now supports SPACE recognition! (What a prominent feature!) Users only need to convert the recognition annotations that contain spaces from a plain .txt file to JSON line format .jsonl, and then revise a few configurations to enable the LineJsonParser. For more information, please read our step-by-step tutorial.

  2. Tesseract is now available in MMOCR! While MMOCR is more flexible to support various downstream tasks, users might sometimes not be satisfied with DL models and would like to turn to effective legacy solutions. Therefore, we offer this option in mmocr.utils.ocr by wrapping Tesseract as a detector and/or recognizer. Users can easily create an MMOCR object by MMOCR(det=’Tesseract’, recog=’Tesseract’). Credit to @garvan2021

  3. We release data converters for 16 widely used OCR datasets, including multiple scenarios such as document, handwritten, and scene text. Now it is more convenient to generate annotation files for these datasets. Check the dataset zoo ( Det & Recog ) to explore further information.

  4. Special thanks to @EighteenSprings @BeyondYourself @yangrisheng, who had actively participated in documentation translation!

Migration Guide - ResNet

Some refactoring processes are still going on. For text recognition models, we unified the ResNet-like architectures which are used as backbones. By introducing stage-wise and block-wise plugins, the refactored ResNet is highly flexible to support existing models, like ResNet31 and ResNet45, and other future designs of ResNet variants.

Plugin

  • Plugin is a module category inherited from MMCV’s implementation of PLUGIN_LAYERS, which can be inserted between each stage of ResNet or into a basicblock. You can find a simple implementation of plugin at mmocr/models/textrecog/plugins/common.py, or click the button below.

    Plugin Example
    @PLUGIN_LAYERS.register_module()
    class Maxpool2d(nn.Module):
        """A wrapper around nn.Maxpool2d().
    
        Args:
            kernel_size (int or tuple(int)): Kernel size for max pooling layer
            stride (int or tuple(int)): Stride for max pooling layer
            padding (int or tuple(int)): Padding for pooling layer
        """
    
        def __init__(self, kernel_size, stride, padding=0, **kwargs):
            super(Maxpool2d, self).__init__()
            self.model = nn.MaxPool2d(kernel_size, stride, padding)
    
        def forward(self, x):
            """
            Args:
                x (Tensor): Input feature map
    
            Returns:
                Tensor: The tensor after Maxpooling layer.
            """
            return self.model(x)
    

Stage-wise Plugins

  • ResNet is composed of stages, and each stage is composed of blocks. E.g., ResNet18 is composed of 4 stages, and each stage is composed of basicblocks. For each stage, we provide two ports to insert stage-wise plugins by giving plugins parameters in ResNet.

    [port1: before stage] ---> [stage] ---> [port2: after stage]
    
  • E.g. Using a ResNet with four stages as example. Suppose we want to insert an additional convolution layer before each stage, and an additional convolution layer at stage 1, 2, 4. Then you can define the special ResNet18 like this

    resnet18_speical = ResNet(
            # for simplicity, some required
            # parameters are omitted
            plugins=[
                dict(
                    cfg=dict(
                    type='ConvModule',
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    norm_cfg=dict(type='BN'),
                    act_cfg=dict(type='ReLU')),
                    stages=(True, True, True, True),
                    position='before_stage')
                dict(
                    cfg=dict(
                    type='ConvModule',
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    norm_cfg=dict(type='BN'),
                    act_cfg=dict(type='ReLU')),
                    stages=(True, True, False, True),
                    position='after_stage')
            ])
    
  • You can also insert more than one plugin in each port and those plugins will be executed in order. Let’s take ResNet in MASTER as an example:

    Multiple Plugins Example
    • ResNet in Master is based on ResNet31. And after each stage, a module named GCAModule will be used. The GCAModule is inserted before the stage-wise convolution layer in ResNet31. In conlusion, there will be two plugins at after_stage port in the same time.

      resnet_master = ResNet(
                      # for simplicity, some required
                      # parameters are omitted
                      plugins=[
                          dict(
                              cfg=dict(type='Maxpool2d', kernel_size=2, stride=(2, 2)),
                              stages=(True, True, False, False),
                              position='before_stage'),
                          dict(
                              cfg=dict(type='Maxpool2d', kernel_size=(2, 1), stride=(2, 1)),
                              stages=(False, False, True, False),
                              position='before_stage'),
                          dict(
                              cfg=dict(type='GCAModule', kernel_size=3, stride=1, padding=1),
                              stages=[True, True, True, True],
                              position='after_stage'),
                          dict(
                              cfg=dict(
                                  type='ConvModule',
                                  kernel_size=3,
                                  stride=1,
                                  padding=1,
                                  norm_cfg=dict(type='BN'),
                                  act_cfg=dict(type='ReLU')),
                              stages=(True, True, True, True),
                              position='after_stage')
                      ])
      
      
  • In each plugin, we will pass two parameters (in_channels, out_channels) to support operations that need the information of current channels.

Block-wise Plugin (Experimental)

  • We also refactored the BasicBlock used in ResNet. Now it can be customized with block-wise plugins. Check here for more details.

  • BasicBlock is composed of two convolution layer in the main branch and a shortcut branch. We provide four ports to insert plugins.

        [port1: before_conv1] ---> [conv1] --->
        [port2: after_conv1] ---> [conv2] --->
        [port3: after_conv2] ---> +(shortcut) ---> [port4: after_shortcut]
    
  • In each plugin, we will pass a parameter in_channels to support operations that need the information of current channels.

  • E.g. Build a ResNet with customized BasicBlock with an additional convolution layer before conv1:

    Block-wise Plugin Example
    resnet_31 = ResNet(
            in_channels=3,
            stem_channels=[64, 128],
            block_cfgs=dict(type='BasicBlock'),
            arch_layers=[1, 2, 5, 3],
            arch_channels=[256, 256, 512, 512],
            strides=[1, 1, 1, 1],
            plugins=[
                dict(
                    cfg=dict(type='Maxpool2d',
                    kernel_size=2,
                    stride=(2, 2)),
                    stages=(True, True, False, False),
                    position='before_stage'),
                dict(
                    cfg=dict(type='Maxpool2d',
                    kernel_size=(2, 1),
                    stride=(2, 1)),
                    stages=(False, False, True, False),
                    position='before_stage'),
                dict(
                    cfg=dict(
                    type='ConvModule',
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    norm_cfg=dict(type='BN'),
                    act_cfg=dict(type='ReLU')),
                    stages=(True, True, True, True),
                    position='after_stage')
            ])
    

Full Examples

ResNet without plugins
  • ResNet45 is used in ASTER and ABINet without any plugins.

    resnet45_aster = ResNet(
        in_channels=3,
        stem_channels=[64, 128],
        block_cfgs=dict(type='BasicBlock', use_conv1x1='True'),
        arch_layers=[3, 4, 6, 6, 3],
        arch_channels=[32, 64, 128, 256, 512],
        strides=[(2, 2), (2, 2), (2, 1), (2, 1), (2, 1)])
    
    resnet45_abi = ResNet(
        in_channels=3,
        stem_channels=32,
        block_cfgs=dict(type='BasicBlock', use_conv1x1='True'),
        arch_layers=[3, 4, 6, 6, 3],
        arch_channels=[32, 64, 128, 256, 512],
        strides=[2, 1, 2, 1, 1])
    
ResNet with plugins
  • ResNet31 is a typical architecture to use stage-wise plugins. Before the first three stages, Maxpooling layer is used. After each stage, a convolution layer with BN and ReLU is used.

    resnet_31 = ResNet(
        in_channels=3,
        stem_channels=[64, 128],
        block_cfgs=dict(type='BasicBlock'),
        arch_layers=[1, 2, 5, 3],
        arch_channels=[256, 256, 512, 512],
        strides=[1, 1, 1, 1],
        plugins=[
            dict(
                cfg=dict(type='Maxpool2d',
                kernel_size=2,
                stride=(2, 2)),
                stages=(True, True, False, False),
                position='before_stage'),
            dict(
                cfg=dict(type='Maxpool2d',
                kernel_size=(2, 1),
                stride=(2, 1)),
                stages=(False, False, True, False),
                position='before_stage'),
            dict(
                cfg=dict(
                type='ConvModule',
                kernel_size=3,
                stride=1,
                padding=1,
                norm_cfg=dict(type='BN'),
                act_cfg=dict(type='ReLU')),
                stages=(True, True, True, True),
                position='after_stage')
        ])
    

Migration Guide - Dataset Annotation Loader

The annotation loaders, LmdbLoader and HardDiskLoader, are unified into AnnFileLoader for a more consistent design and wider support on different file formats and storage backends. AnnFileLoader can load the annotations from disk(default), http and petrel backend, and parse the annotation in txt or lmdb format. LmdbLoader and HardDiskLoader are deprecated, and users are recommended to modify their configs to use the new AnnFileLoader. Users can migrate their legacy loader HardDiskLoader referring to the following example:

# Legacy config
train = dict(
    type='OCRDataset',
    ...
    loader=dict(
        type='HardDiskLoader',
        ...))

# Suggested config
train = dict(
    type='OCRDataset',
    ...
    loader=dict(
        type='AnnFileLoader',
        file_storage_backend='disk',
        file_format='txt',
        ...))

Similarly, using AnnFileLoader with file_format='lmdb' instead of LmdbLoader is strongly recommended.

New Features & Enhancements

  • Update mmcv install by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/775

  • Upgrade isort by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/771

  • Automatically infer device for inference if not speicifed by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/781

  • Add open-mmlab precommit hooks by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/787

  • Add windows CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/790

  • Add CurvedSyntext150k Converter by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/719

  • Add FUNSD Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/808

  • Support loading annotation file with petrel/http backend by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/793

  • Support different seeds on different ranks by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/820

  • Support json in recognition converter by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/844

  • Add args and docs for multi-machine training/testing by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/849

  • Add warning info for LineStrParser by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/850

  • Deploy openmmlab-bot by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/876

  • Add Tesserocr Inference by @garvan2021 in https://github.com/open-mmlab/mmocr/pull/814

  • Add LV Dataset Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/871

  • Add SROIE Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/810

  • Add NAF Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/815

  • Add DeText Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/818

  • Add IMGUR Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/825

  • Add ILST Converter by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/833

  • Add KAIST Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/835

  • Add IC11 (Born-digital Images) Data Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/857

  • Add IC13 (Focused Scene Text) Data Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/861

  • Add BID Converter by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/862

  • Add Vintext Converter by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/864

  • Add MTWI Data Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/867

  • Add COCO Text v2 Data Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/872

  • Add ReCTS Data Converter by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/892

  • Refactor ResNets by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/809

Bug Fixes

  • Bump mmdet version to 2.20.0 in Dockerfile by @GPhilo in https://github.com/open-mmlab/mmocr/pull/763

  • Update mmdet version limit by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/773

  • Minimum version requirement of albumentations by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/769

  • Disable worker in the dataloader of gpu unit test by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/780

  • Standardize the type of torch.device in ocr.py by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/800

  • Use RECOGNIZER instead of DETECTORS by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/685

  • Add num_classes to configs of ABINet by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/805

  • Support loading space character from dict file by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/854

  • Description in tools/data/utils/txt2lmdb.py by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/870

  • ignore_index in SARLoss by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/869

  • Fix a bug that may cause inplace operation error by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/884

  • Use hyphen instead of underscores in script args by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/890

Docs

  • Add deprecation message for deploy tools by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/801

  • Reorganizing OpenMMLab projects in readme by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/806

  • Add demo/README_zh.md by @EighteenSprings in https://github.com/open-mmlab/mmocr/pull/802

  • Add detailed version requirement table by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/778

  • Correct misleading section title in training.md by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/819

  • Update README_zh-CN document URL by @BeyondYourself in https://github.com/open-mmlab/mmocr/pull/823

  • translate testing.md. by @yangrisheng in https://github.com/open-mmlab/mmocr/pull/822

  • Fix confused description for load-from and resume-from by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/842

  • Add documents getting_started in docs/zh by @BeyondYourself in https://github.com/open-mmlab/mmocr/pull/841

  • Add the model serving translation document by @BeyondYourself in https://github.com/open-mmlab/mmocr/pull/845

  • Update docs about installation on Windows by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/852

  • Update tutorial notebook by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/853

  • Update Instructions for New Data Converters by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/900

  • Brief installation instruction in README by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/897

  • update doc for ILST, VinText, BID by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/902

  • Fix typos in readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/903

  • Recog dataset doc by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/893

  • Reorganize the directory structure section in det.md by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/894

New Contributors

  • @GPhilo made their first contribution in https://github.com/open-mmlab/mmocr/pull/763

  • @xinke-wang made their first contribution in https://github.com/open-mmlab/mmocr/pull/801

  • @EighteenSprings made their first contribution in https://github.com/open-mmlab/mmocr/pull/802

  • @BeyondYourself made their first contribution in https://github.com/open-mmlab/mmocr/pull/823

  • @yangrisheng made their first contribution in https://github.com/open-mmlab/mmocr/pull/822

  • @Mountchicken made their first contribution in https://github.com/open-mmlab/mmocr/pull/844

  • @garvan2021 made their first contribution in https://github.com/open-mmlab/mmocr/pull/814

Full Changelog: https://github.com/open-mmlab/mmocr/compare/v0.4.1…v0.5.0

v0.4.1 (27/01/2022)

Highlights

  1. Visualizing edge weights in OpenSet KIE is now supported! https://github.com/open-mmlab/mmocr/pull/677

  2. Some configurations have been optimized to significantly speed up the training and testing processes! Don’t worry - you can still tune these parameters in case these modifications do not work. https://github.com/open-mmlab/mmocr/pull/757

  3. Now you can use CPU to train/debug your model! https://github.com/open-mmlab/mmocr/pull/752

  4. We have fixed a severe bug that causes users unable to call mmocr.apis.test with our pre-built wheels. https://github.com/open-mmlab/mmocr/pull/667

New Features & Enhancements

  • Show edge score for openset kie by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/677

  • Download flake8 from github as pre-commit hooks by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/695

  • Deprecate the support for ‘python setup.py test’ by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/722

  • Disable multi-processing feature of cv2 to speed up data loading by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/721

  • Extend ctw1500 converter to support text fields by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/729

  • Extend totaltext converter to support text fields by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/728

  • Speed up training by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/739

  • Add setup multi-processing both in train and test.py by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/757

  • Support CPU training/testing by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/752

  • Support specify gpu for testing and training with gpu-id instead of gpu-ids and gpus by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/756

  • Remove unnecessary custom_import from test.py by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/758

Bug Fixes

  • Fix satrn onnxruntime test by @AllentDan in https://github.com/open-mmlab/mmocr/pull/679

  • Support both ConcatDataset and UniformConcatDataset by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/675

  • Fix bugs of show_results in single_gpu_test by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/667

  • Fix a bug for sar decoder when bi-rnn is used by @MhLiao in https://github.com/open-mmlab/mmocr/pull/690

  • Fix opencv version to avoid some bugs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/694

  • Fix py39 ci error by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/707

  • Update visualize.py by @TommyZihao in https://github.com/open-mmlab/mmocr/pull/715

  • Fix link of config by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/726

  • Use yaml.safe_load instead of load by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/753

  • Add necessary keys to test_pipelines to enable test-time visualization by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/754

Docs

  • Fix recog.md by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/674

  • Add config tutorial by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/683

  • Add MMSelfSup/MMRazor/MMDeploy in readme by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/692

  • Add recog & det model summary by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/693

  • Update docs link by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/710

  • add pull request template.md by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/711

  • Add website links to readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/731

  • update readme according to standard by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/742

New Contributors

  • @MhLiao made their first contribution in https://github.com/open-mmlab/mmocr/pull/690

  • @TommyZihao made their first contribution in https://github.com/open-mmlab/mmocr/pull/715

Full Changelog: https://github.com/open-mmlab/mmocr/compare/v0.4.0…v0.4.1

v0.4.0 (15/12/2021)

Highlights

  1. We release a new text recognition model - ABINet (CVPR 2021, Oral). With it dedicated model design and useful data augmentation transforms, ABINet can achieve the best performance on irregular text recognition tasks. Check it out!

  2. We are also working hard to fulfill the requests from our community. OpenSet KIE is one of the achievement, which extends the application of SDMGR from text node classification to node-pair relation extraction. We also provide a demo script to convert WildReceipt to open set domain, though it cannot take the full advantage of OpenSet format. For more information, please read our tutorial.

  3. APIs of models can be exposed through TorchServe. Docs

Breaking Changes & Migration Guide

Postprocessor

Some refactoring processes are still going on. For all text detection models, we unified their decode implementations into a new module category, POSTPROCESSOR, which is responsible for decoding different raw outputs into boundary instances. In all text detection configs, the text_repr_type argument in bbox_head is deprecated and will be removed in the future release.

Migration Guide: Find a similar line from detection model’s config:

text_repr_type=xxx,

And replace it with

postprocessor=dict(type='{MODEL_NAME}Postprocessor', text_repr_type=xxx)),

Take a snippet of PANet’s config as an example. Before the change, its config for bbox_head looks like:

    bbox_head=dict(
        type='PANHead',
        text_repr_type='poly',
        in_channels=[128, 128, 128, 128],
        out_channels=6,
        loss=dict(type='PANLoss')),

Afterwards:

    bbox_head=dict(
    type='PANHead',
    in_channels=[128, 128, 128, 128],
    out_channels=6,
    loss=dict(type='PANLoss'),
    postprocessor=dict(type='PANPostprocessor', text_repr_type='poly')),

There are other postprocessors and each takes different arguments. Interested users can find their interfaces or implementations in mmocr/models/textdet/postprocess or through our api docs.

New Config Structure

We reorganized the configs/ directory by extracting reusable sections into configs/_base_. Now the directory tree of configs/_base_ is organized as follows:

_base_
├── det_datasets
├── det_models
├── det_pipelines
├── recog_datasets
├── recog_models
├── recog_pipelines
└── schedules

Most of model configs are making full use of base configs now, which makes the overall structural clearer and facilitates fair comparison across models. Despite the seemingly significant hierarchical difference, these changes would not break the backward compatibility as the names of model configs remain the same.

New Features

  • Support openset kie by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/498

  • Add converter for the Open Images v5 text annotations by Krylov et al. by @baudm in https://github.com/open-mmlab/mmocr/pull/497

  • Support Chinese for kie show result by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/464

  • Add TorchServe support for text detection and recognition by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/522

  • Save filename in text detection test results by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/570

  • Add codespell pre-commit hook and fix typos by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/520

  • Avoid duplicate placeholder docs in CN by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/582

  • Save results to json file for kie. by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/589

  • Add SAR_CN to ocr.py by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/579

  • mim extension for windows by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/641

  • Support muitiple pipelines for different datasets by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/657

  • ABINet Framework by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/651

Refactoring

  • Refactor textrecog config structure by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/617

  • Refactor text detection config by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/626

  • refactor transformer modules by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/618

  • refactor textdet postprocess by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/640

Docs

  • C++ example section by @apiaccess21 in https://github.com/open-mmlab/mmocr/pull/593

  • install.md Chinese section by @A465539338 in https://github.com/open-mmlab/mmocr/pull/364

  • Add Chinese Translation of deployment.md. by @fatfishZhao in https://github.com/open-mmlab/mmocr/pull/506

  • Fix a model link and add the metafile for SATRN by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/473

  • Improve docs style by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/474

  • Enhancement & sync Chinese docs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/492

  • TorchServe docs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/539

  • Update docs menu by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/564

  • Docs for KIE CloseSet & OpenSet by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/573

  • Fix broken links by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/576

  • Docstring for text recognition models by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/562

  • Add MMFlow & MIM by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/597

  • Add MMFewShot by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/621

  • Update model readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/604

  • Add input size check to model_inference by @mpena-vina in https://github.com/open-mmlab/mmocr/pull/633

  • Docstring for textdet models by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/561

  • Add MMHuman3D in readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/644

  • Use shared menu from theme instead by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/655

  • Refactor docs structure by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/662

  • Docs fix by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/664

Enhancements

  • Use bounding box around polygon instead of within polygon by @alexander-soare in https://github.com/open-mmlab/mmocr/pull/469

  • Add CITATION.cff by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/476

  • Add py3.9 CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/475

  • update model-index.yml by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/484

  • Use container in CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/502

  • CircleCI Setup by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/611

  • Remove unnecessary custom_import from train.py by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/603

  • Change the upper version of mmcv to 1.5.0 by @zhouzaida in https://github.com/open-mmlab/mmocr/pull/628

  • Update CircleCI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/631

  • Pass custom_hooks to MMCV by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/609

  • Skip CI when some specific files were changed by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/642

  • Add markdown linter in pre-commit hook by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/643

  • Use shape from loaded image by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/652

  • Cancel previous runs that are not completed by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/666

Bug Fixes

  • Modify algorithm “sar” weights path in metafile by @ShoupingShan in https://github.com/open-mmlab/mmocr/pull/581

  • Fix Cuda CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/472

  • Fix image export in test.py for KIE models by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/486

  • Allow invalid polygons in intersection and union by default by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/471

  • Update checkpoints’ links for SATRN by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/518

  • Fix converting to onnx bug because of changing key from img_shape to resize_shape by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/523

  • Fix PyTorch 1.6 incompatible checkpoints by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/540

  • Fix paper field in metafiles by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/550

  • Unify recognition task names in metafiles by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/548

  • Fix py3.9 CI by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/563

  • Always map location to cpu when loading checkpoint by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/567

  • Fix wrong model builder in recog_test_imgs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/574

  • Improve dbnet r50 by fixing img std by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/578

  • Fix resource warning: unclosed file by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/577

  • Fix bug that same start_point for different texts in draw_texts_by_pil by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/587

  • Keep original texts for kie by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/588

  • Fix random seed by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/600

  • Fix DBNet_r50 config by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/625

  • Change SBC case to DBC case by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/632

  • Fix kie demo by @innerlee in https://github.com/open-mmlab/mmocr/pull/610

  • fix type check by @cuhk-hbsun in https://github.com/open-mmlab/mmocr/pull/650

  • Remove depreciated image validator in totaltext converter by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/661

  • Fix change locals() dict by @Fei-Wang in https://github.com/open-mmlab/mmocr/pull/663

  • fix #614: textsnake targets by @HolyCrap96 in https://github.com/open-mmlab/mmocr/pull/660

New Contributors

  • @alexander-soare made their first contribution in https://github.com/open-mmlab/mmocr/pull/469

  • @A465539338 made their first contribution in https://github.com/open-mmlab/mmocr/pull/364

  • @fatfishZhao made their first contribution in https://github.com/open-mmlab/mmocr/pull/506

  • @baudm made their first contribution in https://github.com/open-mmlab/mmocr/pull/497

  • @ShoupingShan made their first contribution in https://github.com/open-mmlab/mmocr/pull/581

  • @apiaccess21 made their first contribution in https://github.com/open-mmlab/mmocr/pull/593

  • @zhouzaida made their first contribution in https://github.com/open-mmlab/mmocr/pull/628

  • @mpena-vina made their first contribution in https://github.com/open-mmlab/mmocr/pull/633

  • @Fei-Wang made their first contribution in https://github.com/open-mmlab/mmocr/pull/663

Full Changelog: https://github.com/open-mmlab/mmocr/compare/v0.3.0…0.4.0

v0.3.0 (25/8/2021)

Highlights

  1. We add a new text recognition model – SATRN! Its pretrained checkpoint achieves the best performance over other provided text recognition models. A lighter version of SATRN is also released which can obtain ~98% of the performance of the original model with only 45 MB in size. (@2793145003) #405

  2. Improve the demo script, ocr.py, which supports applying end-to-end text detection, text recognition and key information extraction models on images with easy-to-use commands. Users can find its full documentation in the demo section. (@samayala22, @manjrekarom) #371, #386, #400, #374, #428

  3. Our documentation is reorganized into a clearer structure. More useful contents are on the way! #409, #454

  4. The requirement of Polygon3 is removed since this project is no longer maintained or distributed. We unified all its references to equivalent substitutions in shapely instead. #448

Breaking Changes & Migration Guide

  1. Upgrade version requirement of MMDetection to 2.14.0 to avoid bugs #382

  2. MMOCR now has its own model and layer registries inherited from MMDetection’s or MMCV’s counterparts. (#436) The modified hierarchical structure of the model registries are now organized as follows.

mmcv.MODELS -> mmdet.BACKBONES -> BACKBONES
mmcv.MODELS -> mmdet.NECKS -> NECKS
mmcv.MODELS -> mmdet.ROI_EXTRACTORS -> ROI_EXTRACTORS
mmcv.MODELS -> mmdet.HEADS -> HEADS
mmcv.MODELS -> mmdet.LOSSES -> LOSSES
mmcv.MODELS -> mmdet.DETECTORS -> DETECTORS
mmcv.ACTIVATION_LAYERS -> ACTIVATION_LAYERS
mmcv.UPSAMPLE_LAYERS -> UPSAMPLE_LAYERS

To migrate your old implementation to our new backend, you need to change the import path of any registries and their corresponding builder functions (including build_detectors) from mmdet.models.builder to mmocr.models.builder. If you have referred to any model or layer of MMDetection or MMCV in your model config, you need to add mmdet. or mmcv. prefix to its name to inform the model builder of the right namespace to work on.

Interested users may check out MMCV’s tutorial on Registry for in-depth explanations on its mechanism.

New Features

  • Automatically replace SyncBN with BN for inference #420, #453

  • Support batch inference for CRNN and SegOCR #407

  • Support exporting documentation in pdf or epub format #406

  • Support persistent_workers option in data loader #459

Bug Fixes

  • Remove depreciated key in kie_test_imgs.py #381

  • Fix dimension mismatch in batch testing/inference of DBNet #383

  • Fix the problem of dice loss which stays at 1 with an empty target given #408

  • Fix a wrong link in ocr.py (@naarkhoo) #417

  • Fix undesired assignment to “pretrained” in test.py #418

  • Fix a problem in polygon generation of DBNet #421, #443

  • Skip invalid annotations in totaltext_converter #438

  • Add zero division handler in poly utils, remove Polygon3 #448

Improvements

  • Replace lanms-proper with lanms-neo to support installation on Windows (with special thanks to @gen-ko who has re-distributed this package!)

  • Support MIM #394

  • Add tests for PyTorch 1.9 in CI #401

  • Enables fullscreen layout in readthedocs #413

  • General documentation enhancement #395

  • Update version checker #427

  • Add copyright info #439

  • Update citation information #440

Contributors

We thank @2793145003, @samayala22, @manjrekarom, @naarkhoo, @gen-ko, @duanjiaqi, @gaotongxiao, @cuhk-hbsun, @innerlee, @wdsd641417025 for their contribution to this release!

v0.2.1 (20/7/2021)

Highlights

  1. Upgrade to use MMCV-full >= 1.3.8 and MMDetection >= 2.13.0 for latest features

  2. Add ONNX and TensorRT export tool, supporting the deployment of DBNet, PSENet, PANet and CRNN (experimental) #278, #291, #300, #328

  3. Unified parameter initialization method which uses init_cfg in config files #365

New Features

  • Support TextOCR dataset #293

  • Support Total-Text dataset #266, #273, #357

  • Support grouping text detection box into lines #290, #304

  • Add benchmark_processing script that benchmarks data loading process #261

  • Add SynthText preprocessor for text recognition models #351, #361

  • Support batch inference during testing #310

  • Add user-friendly OCR inference script #366

Bug Fixes

  • Fix improper class ignorance in SDMGR Loss #221

  • Fix potential numerical zero division error in DRRG #224

  • Fix installing requirements with pip and mim #242

  • Fix dynamic input error of DBNet #269

  • Fix space parsing error in LineStrParser #285

  • Fix textsnake decode error #264

  • Correct isort setup #288

  • Fix a bug in SDMGR config #316

  • Fix kie_test_img for KIE nonvisual #319

  • Fix metafiles #342

  • Fix different device problem in FCENet #334

  • Ignore improper tailing empty characters in annotation files #358

  • Docs fixes #247, #255, #265, #267, #268, #270, #276, #287, #330, #355, #367

  • Fix NRTR config #356, #370

Improvements

  • Add backend for resizeocr #244

  • Skip image processing pipelines in SDMGR novisual #260

  • Speedup DBNet #263

  • Update mmcv installation method in workflow #323

  • Add part of Chinese documentations #353, #362

  • Add support for ConcatDataset with two workflows #348

  • Add list_from_file and list_to_file utils #226

  • Speed up sort_vertex #239

  • Support distributed evaluation of KIE #234

  • Add pretrained FCENet on IC15 #258

  • Support CPU for OCR demo #227

  • Avoid extra image pre-processing steps #375

v0.2.0 (18/5/2021)

Highlights

  1. Add the NER approach Bert-softmax (NAACL’2019)

  2. Add the text detection method DRRG (CVPR’2020)

  3. Add the text detection method FCENet (CVPR’2021)

  4. Increase the ease of use via adding text detection and recognition end-to-end demo, and colab online demo.

  5. Simplify the installation.

New Features

Bug Fixes

  • Fix the duplicated point bug due to transform for textsnake #130

  • Fix CTC loss NaN #159

  • Fix error raised if result is empty in demo #144

  • Fix results missing if one image has a large number of boxes #98

  • Fix package missing in dockerfile #109

Improvements

  • Simplify installation procedure via removing compiling #188

  • Speed up panet post processing so that it can detect dense texts #188

  • Add zh-CN README #70 #95

  • Support windows #89

  • Add Colab #147 #199

  • Add 1-step installation using conda environment #193 #194 #195

v0.1.0 (7/4/2021)

Highlights

  • MMOCR is released.

Main Features

  • Support text detection, text recognition and the corresponding downstream tasks such as key information extraction.

  • For text detection, support both single-step (PSENet, PANet, DBNet, TextSnake) and two-step (MaskRCNN) methods.

  • For text recognition, support CTC-loss based method CRNN; Encoder-decoder (with attention) based methods SAR, Robustscanner; Segmentation based method SegOCR; Transformer based method NRTR.

  • For key information extraction, support GCN based method SDMG-R.

  • Provide checkpoints and log files for all of the methods above.

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