├── README.md ├── images └── framwork.png ├── mmocr ├── .circleci │ ├── config.yml │ ├── docker │ │ └── Dockerfile │ └── test.yml ├── .codespellrc ├── .coveragerc ├── .dev_scripts │ ├── benchmark_full_models.txt │ ├── benchmark_options.py │ ├── benchmark_train_models.txt │ ├── covignore.cfg │ └── diff_coverage_test.sh ├── .gitignore ├── .owners.yml ├── .pre-commit-config.yaml ├── .pylintrc ├── .readthedocs.yml ├── CITATION.cff ├── LICENSE ├── MANIFEST.in ├── README.md ├── configs │ ├── backbone │ │ └── oclip │ │ │ ├── README.md │ │ │ └── metafile.yml │ ├── kie │ │ ├── _base_ │ │ │ ├── datasets │ │ │ │ ├── wildreceipt-openset.py │ │ │ │ └── wildreceipt.py │ │ │ ├── default_runtime.py │ │ │ └── schedules │ │ │ │ └── schedule_adam_60e.py │ │ └── sdmgr │ │ │ ├── README.md │ │ │ ├── _base_sdmgr_novisual.py │ │ │ ├── _base_sdmgr_unet16.py │ │ │ ├── metafile.yml │ │ │ ├── sdmgr_novisual_60e_wildreceipt-openset.py │ │ │ ├── sdmgr_novisual_60e_wildreceipt.py │ │ │ └── sdmgr_unet16_60e_wildreceipt.py │ ├── textdet │ │ ├── _base_ │ │ │ ├── datasets │ │ │ │ ├── ctw1500.py │ │ │ │ ├── icdar2013.py │ │ │ │ ├── icdar2015.py │ │ │ │ ├── icdar2017.py │ │ │ │ ├── synthtext.py │ │ │ │ ├── totaltext.py │ │ │ │ └── toy_data.py │ │ │ ├── default_runtime.py │ │ │ ├── pretrain_runtime.py │ │ │ └── schedules │ │ │ │ ├── schedule_adam_600e.py │ │ │ │ ├── schedule_sgd_100k.py │ │ │ │ ├── schedule_sgd_1200e.py │ │ │ │ └── schedule_sgd_base.py │ │ ├── dbnet │ │ │ ├── README.md │ │ │ ├── _base_dbnet_resnet18_fpnc.py │ │ │ ├── _base_dbnet_resnet50-dcnv2_fpnc.py │ │ │ ├── dbnet_resnet18_fpnc_100k_synthtext.py │ │ │ ├── dbnet_resnet18_fpnc_1200e_icdar2015.py │ │ │ ├── dbnet_resnet18_fpnc_1200e_totaltext.py │ │ │ ├── dbnet_resnet50-dcnv2_fpnc_100k_synthtext.py │ │ │ ├── dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py │ │ │ ├── dbnet_resnet50-oclip_1200e_icdar2015.py │ │ │ ├── dbnet_resnet50_1200e_icdar2015.py │ │ │ ├── dbnet_resnet50_120e_synth_train_ic15_test.py │ │ │ ├── metafile.yml │ │ │ ├── synth_data_train_100k_ic15_test.py │ │ │ ├── synth_finetune_from_pretrain_ctw1500.py │ │ │ └── synth_finetune_from_pretrain_ic15_test.py │ │ ├── dbnetpp │ │ │ ├── README.md │ │ │ ├── _base_dbnetpp_resnet50-dcnv2_fpnc.py │ │ │ ├── dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext.py │ │ │ ├── dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py │ │ │ ├── dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015.py │ │ │ ├── dbnetpp_resnet50_fpnc_1200e_icdar2015.py │ │ │ └── metafile.yml │ │ ├── drrg │ │ │ ├── README.md │ │ │ ├── _base_drrg_resnet50_fpn-unet.py │ │ │ ├── drrg_resnet50-oclip_fpn-unet_1200e_ctw1500.py │ │ │ ├── drrg_resnet50_fpn-unet_1200e_ctw1500.py │ │ │ └── metafile.yml │ │ ├── fcenet │ │ │ ├── README.md │ │ │ ├── _base_fcenet_resnet50-dcnv2_fpn.py │ │ │ ├── _base_fcenet_resnet50_fpn.py │ │ │ ├── fcenet_resnet50-dcnv2_fpn_1500e_ctw1500.py │ │ │ ├── fcenet_resnet50-oclip_fpn_1500e_ctw1500.py │ │ │ ├── fcenet_resnet50-oclip_fpn_1500e_icdar2015.py │ │ │ ├── fcenet_resnet50_fpn_1500e_icdar2015.py │ │ │ ├── fcenet_resnet50_fpn_1500e_totaltext.py │ │ │ ├── finetune.py │ │ │ ├── metafile.yml │ │ │ └── synth_data_train_real_data_test.py │ │ ├── maskrcnn │ │ │ ├── README.md │ │ │ ├── _base_mask-rcnn_resnet50_fpn.py │ │ │ ├── mask-rcnn_resnet50-oclip_fpn_160e_ctw1500.py │ │ │ ├── mask-rcnn_resnet50-oclip_fpn_160e_icdar2015.py │ │ │ ├── mask-rcnn_resnet50_fpn_160e_ctw1500.py │ │ │ ├── mask-rcnn_resnet50_fpn_160e_icdar2015.py │ │ │ ├── mask-rcnn_resnet50_fpn_160e_icdar2017.py │ │ │ └── metafile.yml │ │ ├── panet │ │ │ ├── README.md │ │ │ ├── _base_panet_resnet18_fpem-ffm.py │ │ │ ├── _base_panet_resnet50_fpem-ffm.py │ │ │ ├── metafile.yml │ │ │ ├── panet_resnet18_fpem-ffm_600e_ctw1500.py │ │ │ ├── panet_resnet18_fpem-ffm_600e_icdar2015.py │ │ │ ├── panet_resnet50_fpem-ffm_600e_icdar2017.py │ │ │ └── synth_data_train_real_data_test.py │ │ ├── psenet │ │ │ ├── README.md │ │ │ ├── _base_psenet_resnet50_fpnf.py │ │ │ ├── metafile.yml │ │ │ ├── psenet_resnet50-oclip_fpnf_600e_ctw1500.py │ │ │ ├── psenet_resnet50-oclip_fpnf_600e_icdar2015.py │ │ │ ├── psenet_resnet50_fpnf_600e_ctw1500.py │ │ │ ├── psenet_resnet50_fpnf_600e_icdar2015.py │ │ │ ├── psenet_resnet50_fpnf_600e_icdar2017.py │ │ │ └── psenet_resnet50_synth_train_ic15_test.py │ │ └── textsnake │ │ │ ├── README.md │ │ │ ├── _base_textsnake_resnet50_fpn-unet.py │ │ │ ├── metafile.yml │ │ │ ├── textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500.py │ │ │ └── textsnake_resnet50_fpn-unet_1200e_ctw1500.py │ └── textrecog │ │ ├── _base_ │ │ ├── datasets │ │ │ ├── coco_text_v1.py │ │ │ ├── cute80.py │ │ │ ├── icdar2011.py │ │ │ ├── icdar2013.py │ │ │ ├── icdar2015.py │ │ │ ├── iiit5k.py │ │ │ ├── mjsynth.py │ │ │ ├── svt.py │ │ │ ├── svtp.py │ │ │ ├── synthtext.py │ │ │ ├── synthtext_add.py │ │ │ ├── totaltext.py │ │ │ └── toy_data.py │ │ ├── default_runtime.py │ │ └── schedules │ │ │ ├── schedule_adadelta_5e.py │ │ │ ├── schedule_adam_base.py │ │ │ ├── schedule_adam_step_5e.py │ │ │ └── schedule_adamw_cos_6e.py │ │ ├── abinet │ │ ├── README.md │ │ ├── _base_abinet-vision.py │ │ ├── _base_abinet.py │ │ ├── abinet-vision_20e_st-an_mj.py │ │ ├── abinet_20e_st-an_mj.py │ │ └── metafile.yml │ │ ├── aster │ │ ├── README.md │ │ ├── _base_aster.py │ │ ├── aster_resnet45_6e_st_mj.py │ │ └── metafile.yml │ │ ├── crnn │ │ ├── README.md │ │ ├── _base_crnn_mini-vgg.py │ │ ├── crnn_mini-vgg_5e_mj.py │ │ ├── crnn_mini-vgg_5e_toy.py │ │ └── metafile.yml │ │ ├── master │ │ ├── README.md │ │ ├── _base_master_resnet31.py │ │ ├── master_resnet31_12e_st_mj_sa.py │ │ ├── master_resnet31_12e_toy.py │ │ └── metafile.yml │ │ ├── nrtr │ │ ├── README.md │ │ ├── _base_nrtr_modality-transform.py │ │ ├── _base_nrtr_resnet31.py │ │ ├── metafile.yml │ │ ├── nrtr_modality-transform_6e_st_mj.py │ │ ├── nrtr_modality-transform_6e_toy.py │ │ ├── nrtr_resnet31-1by16-1by8_6e_st_mj.py │ │ └── nrtr_resnet31-1by8-1by4_6e_st_mj.py │ │ ├── robust_scanner │ │ ├── README.md │ │ ├── _base_robustscanner_resnet31.py │ │ ├── metafile.yml │ │ ├── robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py │ │ └── robustscanner_resnet31_5e_toy.py │ │ ├── sar │ │ ├── README.md │ │ ├── _base_sar_resnet31_parallel-decoder.py │ │ ├── metafile.yml │ │ ├── sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real.py │ │ ├── sar_resnet31_parallel-decoder_5e_toy.py │ │ └── sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real.py │ │ ├── satrn │ │ ├── README.md │ │ ├── _base_satrn_shallow.py │ │ ├── metafile.yml │ │ ├── satrn_shallow-small_5e_st_mj.py │ │ └── satrn_shallow_5e_st_mj.py │ │ └── svtr │ │ ├── README.md │ │ ├── _base_svtr-tiny.py │ │ ├── metafile.yml │ │ ├── svtr-base_20e_st_mj.py │ │ ├── svtr-large_20e_st_mj.py │ │ ├── svtr-small_20e_st_mj.py │ │ └── svtr-tiny_20e_st_mj.py ├── dataset_zoo │ ├── cocotextv2 │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── ctw1500 │ │ ├── metafile.yml │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── cute80 │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ └── textrecog.py │ ├── funsd │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── icdar2013 │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── icdar2015 │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── iiit5k │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ └── textrecog.py │ ├── mjsynth │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ └── textrecog.py │ ├── naf │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── sroie │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── svt │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── svtp │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ └── textrecog.py │ ├── synthtext │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── textocr │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ ├── totaltext │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py │ └── wildreceipt │ │ ├── kie.py │ │ ├── metafile.yml │ │ ├── sample_anno.md │ │ ├── textdet.py │ │ ├── textrecog.py │ │ └── textspotting.py ├── demo │ ├── demo_densetext_det.jpg │ ├── demo_kie.jpeg │ ├── demo_text_det.jpg │ ├── demo_text_ocr.jpg │ ├── demo_text_recog.jpg │ └── resources │ │ ├── demo_kie_pred.png │ │ ├── det_vis.png │ │ ├── kie_vis.png │ │ ├── log_analysis_demo.png │ │ └── rec_vis.png ├── dicts │ ├── chinese_english_digits.txt │ ├── english_digits_symbols.txt │ ├── english_digits_symbols_space.txt │ ├── lower_english_digits.txt │ ├── lower_english_digits_space.txt │ └── sdmgr_dict.txt ├── docker │ ├── Dockerfile │ └── serve │ │ ├── Dockerfile │ │ ├── config.properties │ │ └── entrypoint.sh ├── docs │ ├── en │ │ ├── Makefile │ │ ├── _static │ │ │ ├── css │ │ │ │ └── readthedocs.css │ │ │ ├── images │ │ │ │ └── mmocr.png │ │ │ └── js │ │ │ │ ├── collapsed.js │ │ │ │ └── table.js │ │ ├── _templates │ │ │ └── classtemplate.rst │ │ ├── api │ │ │ ├── apis.rst │ │ │ ├── datasets.rst │ │ │ ├── engine.rst │ │ │ ├── evaluation.rst │ │ │ ├── models.rst │ │ │ ├── structures.rst │ │ │ ├── transforms.rst │ │ │ ├── utils.rst │ │ │ └── visualization.rst │ │ ├── basic_concepts │ │ │ ├── convention.md │ │ │ ├── data_flow.md │ │ │ ├── datasets.md │ │ │ ├── engine.md │ │ │ ├── evaluation.md │ │ │ ├── models.md │ │ │ ├── overview.md │ │ │ ├── structures.md │ │ │ ├── transforms.md │ │ │ └── visualizers.md │ │ ├── conf.py │ │ ├── contact.md │ │ ├── dataset_zoo.py │ │ ├── docutils.conf │ │ ├── get_started │ │ │ ├── faq.md │ │ │ ├── install.md │ │ │ ├── overview.md │ │ │ └── quick_run.md │ │ ├── index.rst │ │ ├── make.bat │ │ ├── merge_docs.sh │ │ ├── migration │ │ │ ├── branches.md │ │ │ ├── code.md │ │ │ ├── dataset.md │ │ │ ├── model.md │ │ │ ├── news.md │ │ │ ├── overview.md │ │ │ └── transforms.md │ │ ├── notes │ │ │ ├── branches.md │ │ │ ├── changelog.md │ │ │ ├── changelog_v0.x.md │ │ │ └── contribution_guide.md │ │ ├── project_zoo.py │ │ ├── requirements.txt │ │ ├── stats.py │ │ ├── switch_language.md │ │ ├── user_guides │ │ │ ├── config.md │ │ │ ├── data_prepare │ │ │ │ ├── dataset_preparer.md │ │ │ │ ├── det.md │ │ │ │ ├── kie.md │ │ │ │ └── recog.md │ │ │ ├── dataset_prepare.md │ │ │ ├── inference.md │ │ │ ├── train_test.md │ │ │ ├── useful_tools.md │ │ │ └── visualization.md │ │ └── weight_list.py │ └── zh_cn │ │ ├── Makefile │ │ ├── _static │ │ ├── css │ │ │ └── readthedocs.css │ │ ├── images │ │ │ └── mmocr.png │ │ └── js │ │ │ ├── collapsed.js │ │ │ └── table.js │ │ ├── _templates │ │ └── classtemplate.rst │ │ ├── api │ │ ├── apis.rst │ │ ├── datasets.rst │ │ ├── engine.rst │ │ ├── evaluation.rst │ │ ├── models.rst │ │ ├── structures.rst │ │ ├── transforms.rst │ │ ├── utils.rst │ │ └── visualization.rst │ │ ├── basic_concepts │ │ ├── convention.md │ │ ├── data_flow.md │ │ ├── datasets.md │ │ ├── engine.md │ │ ├── evaluation.md │ │ ├── models.md │ │ ├── overview.md │ │ ├── structures.md │ │ ├── transforms.md │ │ └── visualizers.md │ │ ├── conf.py │ │ ├── contact.md │ │ ├── cp_origin_docs.sh │ │ ├── dataset_zoo.py │ │ ├── docutils.conf │ │ ├── get_started │ │ ├── install.md │ │ ├── overview.md │ │ └── quick_run.md │ │ ├── index.rst │ │ ├── make.bat │ │ ├── merge_docs.sh │ │ ├── migration │ │ ├── branches.md │ │ ├── code.md │ │ ├── dataset.md │ │ ├── model.md │ │ ├── news.md │ │ ├── overview.md │ │ └── transforms.md │ │ ├── notes │ │ ├── branches.md │ │ └── contribution_guide.md │ │ ├── project_zoo.py │ │ ├── stats.py │ │ ├── switch_language.md │ │ ├── user_guides │ │ ├── config.md │ │ ├── data_prepare │ │ │ ├── dataset_preparer.md │ │ │ └── kie.md │ │ ├── dataset_prepare.md │ │ ├── inference.md │ │ ├── train_test.md │ │ ├── useful_tools.md │ │ └── visualization.md │ │ └── weight_list.py ├── mmocr │ ├── __init__.py │ ├── apis │ │ ├── __init__.py │ │ └── inferencers │ │ │ ├── __init__.py │ │ │ ├── base_mmocr_inferencer.py │ │ │ ├── kie_inferencer.py │ │ │ ├── mmocr_inferencer.py │ │ │ ├── textdet_inferencer.py │ │ │ ├── textrec_inferencer.py │ │ │ └── textspot_inferencer.py │ ├── datasets │ │ ├── __init__.py │ │ ├── dataset_wrapper.py │ │ ├── icdar_dataset.py │ │ ├── ocr_dataset.py │ │ ├── preparers │ │ │ ├── __init__.py │ │ │ ├── config_generators │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── textdet_config_generator.py │ │ │ │ ├── textrecog_config_generator.py │ │ │ │ └── textspotting_config_generator.py │ │ │ ├── data_preparer.py │ │ │ ├── dumpers │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── json_dumper.py │ │ │ │ ├── lmdb_dumper.py │ │ │ │ └── wild_receipt_openset_dumper.py │ │ │ ├── gatherers │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── mono_gatherer.py │ │ │ │ ├── naf_gatherer.py │ │ │ │ └── pair_gatherer.py │ │ │ ├── obtainers │ │ │ │ ├── __init__.py │ │ │ │ └── naive_data_obtainer.py │ │ │ ├── packers │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── textdet_packer.py │ │ │ │ ├── textrecog_packer.py │ │ │ │ ├── textspotting_packer.py │ │ │ │ └── wildreceipt_packer.py │ │ │ └── parsers │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── coco_parser.py │ │ │ │ ├── ctw1500_parser.py │ │ │ │ ├── funsd_parser.py │ │ │ │ ├── icdar_txt_parser.py │ │ │ │ ├── mjsynth_parser.py │ │ │ │ ├── naf_parser.py │ │ │ │ ├── sroie_parser.py │ │ │ │ ├── svt_parser.py │ │ │ │ ├── synthtext_parser.py │ │ │ │ ├── totaltext_parser.py │ │ │ │ └── wildreceipt_parser.py │ │ ├── recog_lmdb_dataset.py │ │ ├── recog_text_dataset.py │ │ ├── samplers │ │ │ ├── __init__.py │ │ │ └── batch_aug.py │ │ ├── transforms │ │ │ ├── __init__.py │ │ │ ├── adapters.py │ │ │ ├── formatting.py │ │ │ ├── loading.py │ │ │ ├── ocr_transforms.py │ │ │ ├── textdet_transforms.py │ │ │ ├── textrecog_transforms.py │ │ │ └── wrappers.py │ │ └── wildreceipt_dataset.py │ ├── engine │ │ ├── __init__.py │ │ └── hooks │ │ │ ├── __init__.py │ │ │ └── visualization_hook.py │ ├── evaluation │ │ ├── __init__.py │ │ ├── evaluator │ │ │ ├── __init__.py │ │ │ └── multi_datasets_evaluator.py │ │ ├── functional │ │ │ ├── __init__.py │ │ │ └── hmean.py │ │ └── metrics │ │ │ ├── __init__.py │ │ │ ├── f_metric.py │ │ │ ├── hmean_iou_metric.py │ │ │ └── recog_metric.py │ ├── models │ │ ├── __init__.py │ │ ├── common │ │ │ ├── __init__.py │ │ │ ├── backbones │ │ │ │ ├── __init__.py │ │ │ │ ├── clip_resnet.py │ │ │ │ └── unet.py │ │ │ ├── dictionary │ │ │ │ ├── __init__.py │ │ │ │ └── dictionary.py │ │ │ ├── layers │ │ │ │ ├── __init__.py │ │ │ │ └── transformer_layers.py │ │ │ ├── losses │ │ │ │ ├── __init__.py │ │ │ │ ├── bce_loss.py │ │ │ │ ├── ce_loss.py │ │ │ │ ├── dice_loss.py │ │ │ │ └── l1_loss.py │ │ │ ├── modules │ │ │ │ ├── __init__.py │ │ │ │ └── transformer_module.py │ │ │ └── plugins │ │ │ │ ├── __init__.py │ │ │ │ └── common.py │ │ ├── kie │ │ │ ├── __init__.py │ │ │ ├── extractors │ │ │ │ ├── __init__.py │ │ │ │ └── sdmgr.py │ │ │ ├── heads │ │ │ │ ├── __init__.py │ │ │ │ └── sdmgr_head.py │ │ │ ├── module_losses │ │ │ │ ├── __init__.py │ │ │ │ └── sdmgr_module_loss.py │ │ │ └── postprocessors │ │ │ │ ├── __init__.py │ │ │ │ └── sdmgr_postprocessor.py │ │ ├── textdet │ │ │ ├── __init__.py │ │ │ ├── data_preprocessors │ │ │ │ ├── __init__.py │ │ │ │ └── data_preprocessor.py │ │ │ ├── detectors │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── dbnet.py │ │ │ │ ├── drrg.py │ │ │ │ ├── fcenet.py │ │ │ │ ├── mmdet_wrapper.py │ │ │ │ ├── panet.py │ │ │ │ ├── psenet.py │ │ │ │ ├── single_stage_text_detector.py │ │ │ │ └── textsnake.py │ │ │ ├── heads │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── db_head.py │ │ │ │ ├── drrg_head.py │ │ │ │ ├── fce_head.py │ │ │ │ ├── pan_head.py │ │ │ │ ├── pse_head.py │ │ │ │ └── textsnake_head.py │ │ │ ├── module_losses │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── db_module_loss.py │ │ │ │ ├── drrg_module_loss.py │ │ │ │ ├── fce_module_loss.py │ │ │ │ ├── pan_module_loss.py │ │ │ │ ├── pse_module_loss.py │ │ │ │ ├── seg_based_module_loss.py │ │ │ │ └── textsnake_module_loss.py │ │ │ ├── necks │ │ │ │ ├── __init__.py │ │ │ │ ├── fpem_ffm.py │ │ │ │ ├── fpn_cat.py │ │ │ │ ├── fpn_unet.py │ │ │ │ └── fpnf.py │ │ │ └── postprocessors │ │ │ │ ├── __init__.py │ │ │ │ ├── base.py │ │ │ │ ├── db_postprocessor.py │ │ │ │ ├── drrg_postprocessor.py │ │ │ │ ├── fce_postprocessor.py │ │ │ │ ├── pan_postprocessor.py │ │ │ │ ├── pse_postprocessor.py │ │ │ │ └── textsnake_postprocessor.py │ │ └── textrecog │ │ │ ├── __init__.py │ │ │ ├── backbones │ │ │ ├── __init__.py │ │ │ ├── mini_vgg.py │ │ │ ├── mobilenet_v2.py │ │ │ ├── nrtr_modality_transformer.py │ │ │ ├── resnet.py │ │ │ ├── resnet31_ocr.py │ │ │ ├── resnet_abi.py │ │ │ └── shallow_cnn.py │ │ │ ├── data_preprocessors │ │ │ ├── __init__.py │ │ │ └── data_preprocessor.py │ │ │ ├── decoders │ │ │ ├── __init__.py │ │ │ ├── abi_fuser.py │ │ │ ├── abi_language_decoder.py │ │ │ ├── abi_vision_decoder.py │ │ │ ├── aster_decoder.py │ │ │ ├── base.py │ │ │ ├── crnn_decoder.py │ │ │ ├── master_decoder.py │ │ │ ├── nrtr_decoder.py │ │ │ ├── position_attention_decoder.py │ │ │ ├── robust_scanner_fuser.py │ │ │ ├── sar_decoder.py │ │ │ ├── sar_decoder_with_bs.py │ │ │ ├── sequence_attention_decoder.py │ │ │ └── svtr_decoder.py │ │ │ ├── encoders │ │ │ ├── __init__.py │ │ │ ├── abi_encoder.py │ │ │ ├── aster_encoder.py │ │ │ ├── base.py │ │ │ ├── channel_reduction_encoder.py │ │ │ ├── nrtr_encoder.py │ │ │ ├── sar_encoder.py │ │ │ ├── satrn_encoder.py │ │ │ └── svtr_encoder.py │ │ │ ├── layers │ │ │ ├── __init__.py │ │ │ ├── conv_layer.py │ │ │ ├── dot_product_attention_layer.py │ │ │ ├── lstm_layer.py │ │ │ ├── position_aware_layer.py │ │ │ ├── robust_scanner_fusion_layer.py │ │ │ └── satrn_layers.py │ │ │ ├── module_losses │ │ │ ├── __init__.py │ │ │ ├── abi_module_loss.py │ │ │ ├── base.py │ │ │ ├── ce_module_loss.py │ │ │ └── ctc_module_loss.py │ │ │ ├── plugins │ │ │ ├── __init__.py │ │ │ └── common.py │ │ │ ├── postprocessors │ │ │ ├── __init__.py │ │ │ ├── attn_postprocessor.py │ │ │ ├── base.py │ │ │ └── ctc_postprocessor.py │ │ │ ├── preprocessors │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ └── tps_preprocessor.py │ │ │ └── recognizers │ │ │ ├── __init__.py │ │ │ ├── abinet.py │ │ │ ├── aster.py │ │ │ ├── base.py │ │ │ ├── crnn.py │ │ │ ├── encoder_decoder_recognizer.py │ │ │ ├── encoder_decoder_recognizer_tta.py │ │ │ ├── master.py │ │ │ ├── nrtr.py │ │ │ ├── robust_scanner.py │ │ │ ├── sar.py │ │ │ ├── satrn.py │ │ │ └── svtr.py │ ├── registry.py │ ├── structures │ │ ├── __init__.py │ │ ├── kie_data_sample.py │ │ ├── textdet_data_sample.py │ │ ├── textrecog_data_sample.py │ │ └── textspotting_data_sample.py │ ├── testing │ │ ├── __init__.py │ │ └── data.py │ ├── utils │ │ ├── __init__.py │ │ ├── bbox_utils.py │ │ ├── bezier_utils.py │ │ ├── check_argument.py │ │ ├── collect_env.py │ │ ├── data_converter_utils.py │ │ ├── fileio.py │ │ ├── img_utils.py │ │ ├── mask_utils.py │ │ ├── parsers.py │ │ ├── point_utils.py │ │ ├── polygon_utils.py │ │ ├── processing.py │ │ ├── setup_env.py │ │ ├── string_utils.py │ │ ├── transform_utils.py │ │ └── typing_utils.py │ ├── version.py │ └── visualization │ │ ├── __init__.py │ │ ├── base_visualizer.py │ │ ├── kie_visualizer.py │ │ ├── textdet_visualizer.py │ │ ├── textrecog_visualizer.py │ │ └── textspotting_visualizer.py ├── model-index.yml ├── my_test.sh ├── my_train.sh ├── projects │ ├── ABCNet │ │ ├── README.md │ │ ├── README_V2.md │ │ ├── abcnet │ │ │ ├── __init__.py │ │ │ ├── metric │ │ │ │ ├── __init__.py │ │ │ │ └── e2e_hmean_iou_metric.py │ │ │ ├── model │ │ │ │ ├── __init__.py │ │ │ │ ├── abcnet.py │ │ │ │ ├── abcnet_det_head.py │ │ │ │ ├── abcnet_det_module_loss.py │ │ │ │ ├── abcnet_det_postprocessor.py │ │ │ │ ├── abcnet_postprocessor.py │ │ │ │ ├── abcnet_rec.py │ │ │ │ ├── abcnet_rec_backbone.py │ │ │ │ ├── abcnet_rec_decoder.py │ │ │ │ ├── abcnet_rec_encoder.py │ │ │ │ ├── base_roi_extractor.py │ │ │ │ ├── base_roi_head.py │ │ │ │ ├── bezier_roi_extractor.py │ │ │ │ ├── bifpn.py │ │ │ │ ├── coordinate_head.py │ │ │ │ ├── rec_roi_head.py │ │ │ │ └── two_stage_text_spotting.py │ │ │ └── utils │ │ │ │ ├── __init__.py │ │ │ │ └── bezier_utils.py │ │ ├── config │ │ │ ├── _base_ │ │ │ │ ├── datasets │ │ │ │ │ └── icdar2015.py │ │ │ │ ├── default_runtime.py │ │ │ │ └── schedules │ │ │ │ │ └── schedule_sgd_500e.py │ │ │ ├── abcnet │ │ │ │ ├── _base_abcnet_resnet50_fpn.py │ │ │ │ └── abcnet_resnet50_fpn_500e_icdar2015.py │ │ │ └── abcnet_v2 │ │ │ │ ├── _base_abcnet-v2_resnet50_bifpn.py │ │ │ │ └── abcnet-v2_resnet50_bifpn_500e_icdar2015.py │ │ └── dicts │ │ │ └── abcnet.txt │ ├── README.md │ ├── SPTS │ │ ├── README.md │ │ ├── config │ │ │ ├── _base_ │ │ │ │ ├── datasets │ │ │ │ │ ├── ctw1500-spts.py │ │ │ │ │ ├── icdar2013-spts.py │ │ │ │ │ ├── icdar2013.py │ │ │ │ │ ├── icdar2015-spts.py │ │ │ │ │ ├── icdar2015.py │ │ │ │ │ ├── mlt-spts.py │ │ │ │ │ ├── syntext1-spts.py │ │ │ │ │ ├── syntext2-spts.py │ │ │ │ │ ├── totaltext-spts.py │ │ │ │ │ └── totaltext.py │ │ │ │ └── default_runtime.py │ │ │ └── spts │ │ │ │ ├── _base_spts_resnet50.py │ │ │ │ ├── _base_spts_resnet50_mmocr.py │ │ │ │ ├── spts_resnet50_8xb8-150e_pretrain-spts.py │ │ │ │ ├── spts_resnet50_8xb8-200e_icdar2013.py │ │ │ │ ├── spts_resnet50_8xb8-200e_icdar2015.py │ │ │ │ └── spts_resnet50_8xb8-200e_totaltext.py │ │ ├── dicts │ │ │ └── spts.txt │ │ ├── spts │ │ │ ├── __init__.py │ │ │ ├── datasets │ │ │ │ ├── __init__.py │ │ │ │ ├── adel_dataset.py │ │ │ │ └── transforms │ │ │ │ │ └── spts_transforms.py │ │ │ ├── metric │ │ │ │ ├── __init__.py │ │ │ │ └── e2e_point_metric.py │ │ │ └── model │ │ │ │ ├── __init__.py │ │ │ │ ├── base_text_spotter.py │ │ │ │ ├── encoder_decoder_text_spotter.py │ │ │ │ ├── position_embedding.py │ │ │ │ ├── spts.py │ │ │ │ ├── spts_decoder.py │ │ │ │ ├── spts_dictionary.py │ │ │ │ ├── spts_encoder.py │ │ │ │ ├── spts_module_loss.py │ │ │ │ └── spts_postprocessor.py │ │ └── tools │ │ │ └── ckpt_adapter.py │ ├── example_project │ │ ├── README.md │ │ ├── configs │ │ │ └── dbnet_dummy-resnet_fpnc_1200e_icdar2015.py │ │ └── dummy │ │ │ ├── __init__.py │ │ │ └── dummy_resnet.py │ ├── faq.md │ └── selected.txt ├── requirements.txt ├── requirements │ ├── albu.txt │ ├── build.txt │ ├── docs.txt │ ├── mminstall.txt │ ├── optional.txt │ ├── readthedocs.txt │ ├── runtime.txt │ └── tests.txt ├── resources │ ├── illustration.jpg │ ├── kie.jpg │ ├── mmocr-logo.png │ ├── textdet.jpg │ ├── textrecog.jpg │ └── verification.png ├── setup.cfg ├── setup.py ├── tests │ ├── models │ │ └── textrecog │ │ │ └── test_preprocessors │ │ │ └── test_tps_preprocessor.py │ ├── test_apis │ │ └── test_inferencers │ │ │ ├── test_kie_inferencer.py │ │ │ ├── test_mmocr_inferencer.py │ │ │ ├── test_textdet_inferencer.py │ │ │ └── test_textrec_inferencer.py │ ├── test_datasets │ │ ├── test_dataset_wrapper.py │ │ ├── test_icdar_dataset.py │ │ ├── test_preparers │ │ │ ├── test_config_generators │ │ │ │ ├── test_textdet_config_generator.py │ │ │ │ ├── test_textrecog_config_generator.py │ │ │ │ └── test_textspotting_config_generator.py │ │ │ ├── test_data_preparer.py │ │ │ ├── test_dumpers │ │ │ │ └── test_dumpers.py │ │ │ ├── test_gatherers │ │ │ │ ├── test_mono_gatherer.py │ │ │ │ └── test_pair_gatherer.py │ │ │ ├── test_packers │ │ │ │ ├── test_textdet_packer.py │ │ │ │ ├── test_textrecog_packer.py │ │ │ │ └── test_textspotting_packer.py │ │ │ └── test_parsers │ │ │ │ ├── test_ctw1500_parser.py │ │ │ │ ├── test_funsd_parser.py │ │ │ │ ├── test_icdar_txt_parsers.py │ │ │ │ ├── test_naf_parser.py │ │ │ │ ├── test_sroie_parser.py │ │ │ │ ├── test_svt_parsers.py │ │ │ │ ├── test_tt_parsers.py │ │ │ │ └── test_wildreceipt_parsers.py │ │ ├── test_recog_lmdb_dataset.py │ │ ├── test_recog_text_dataset.py │ │ ├── test_samplers │ │ │ └── test_batch_aug.py │ │ ├── test_transforms │ │ │ ├── test_adapters.py │ │ │ ├── test_formatting.py │ │ │ ├── test_loading.py │ │ │ ├── test_ocr_transforms.py │ │ │ ├── test_textdet_transforms.py │ │ │ ├── test_textrecog_transforms.py │ │ │ └── test_wrappers.py │ │ └── test_wildreceipt_dataset.py │ ├── test_engine │ │ └── test_hooks │ │ │ └── test_visualization_hook.py │ ├── test_evaluation │ │ ├── test_evaluator │ │ │ └── test_multi_datasets_evaluator.py │ │ ├── test_functional │ │ │ └── test_hmean.py │ │ └── test_metrics │ │ │ ├── test_f_metric.py │ │ │ ├── test_hmean_iou_metric.py │ │ │ └── test_recog_metric.py │ ├── test_init.py │ ├── test_models │ │ ├── test_common │ │ │ ├── test_backbones │ │ │ │ └── test_clip_resnet.py │ │ │ ├── test_layers │ │ │ │ └── test_transformer_layers.py │ │ │ ├── test_losses │ │ │ │ ├── test_bce_loss.py │ │ │ │ ├── test_dice_loss.py │ │ │ │ └── test_l1_loss.py │ │ │ ├── test_modules │ │ │ │ └── test_transformer_module.py │ │ │ └── test_plugins │ │ │ │ └── test_avgpool.py │ │ ├── test_kie │ │ │ ├── test_extractors │ │ │ │ └── test_sdmgr.py │ │ │ ├── test_heads │ │ │ │ └── test_sdmgr_head.py │ │ │ ├── test_module_losses │ │ │ │ └── test_sdmgr_module_loss.py │ │ │ └── test_postprocessors │ │ │ │ └── test_sdmgr_postprocessor.py │ │ ├── test_textdet │ │ │ ├── test_data_preprocessors │ │ │ │ └── test_textdet_data_preprocessor.py │ │ │ ├── test_detectors │ │ │ │ └── test_drrg.py │ │ │ ├── test_heads │ │ │ │ ├── test_base_head.py │ │ │ │ ├── test_db_head.py │ │ │ │ ├── test_drrg_head.py │ │ │ │ ├── test_fce_head.py │ │ │ │ ├── test_pan_head.py │ │ │ │ ├── test_pse_head.py │ │ │ │ └── test_textsnake_head.py │ │ │ ├── test_module_losses │ │ │ │ ├── test_db_module_loss.py │ │ │ │ ├── test_drrg_module_loss.py │ │ │ │ ├── test_fce_module_loss.py │ │ │ │ ├── test_pan_module_loss.py │ │ │ │ ├── test_pse_module_loss.py │ │ │ │ └── test_textsnake_module_loss.py │ │ │ ├── test_necks │ │ │ │ ├── test_fpem_ffm.py │ │ │ │ ├── test_fpn_cat.py │ │ │ │ ├── test_fpn_unet.py │ │ │ │ └── test_fpnf.py │ │ │ ├── test_postprocessors │ │ │ │ ├── test_base_postprocessor.py │ │ │ │ ├── test_db_postprocessor.py │ │ │ │ ├── test_drrg_postprocessor.py │ │ │ │ ├── test_fce_postprocessor.py │ │ │ │ ├── test_pan_postprocessor.py │ │ │ │ ├── test_pse_postprocessor.py │ │ │ │ └── test_textsnake_postprocessor.py │ │ │ └── test_wrappers │ │ │ │ └── test_mmdet_wrapper.py │ │ └── test_textrecog │ │ │ ├── test_backbones │ │ │ ├── test_mini_vgg.py │ │ │ ├── test_mobilenet_v2.py │ │ │ ├── test_nrtr_modality_transformer.py │ │ │ ├── test_resnet.py │ │ │ ├── test_resnet31_ocr.py │ │ │ ├── test_resnet_abi.py │ │ │ └── test_shallow_cnn.py │ │ │ ├── test_data_preprocessors │ │ │ └── test_data_preprocessor.py │ │ │ ├── test_decoders │ │ │ ├── test_abi_fuser.py │ │ │ ├── test_abi_language_decoder.py │ │ │ ├── test_abi_vision_decoder.py │ │ │ ├── test_aster_decoder.py │ │ │ ├── test_base_decoder.py │ │ │ ├── test_crnn_decoder.py │ │ │ ├── test_master_decoder.py │ │ │ ├── test_nrtr_decoder.py │ │ │ ├── test_position_attention_decoder.py │ │ │ ├── test_robust_scanner_fuser.py │ │ │ ├── test_sar_decoder.py │ │ │ ├── test_sequence_attention_decoder.py │ │ │ └── test_svtr_decoder.py │ │ │ ├── test_dictionary │ │ │ └── test_dictionary.py │ │ │ ├── test_encoders │ │ │ ├── test_abi_encoder.py │ │ │ ├── test_aster_encoder.py │ │ │ ├── test_channel_reduction_encoder.py │ │ │ ├── test_nrtr_encoder.py │ │ │ ├── test_sar_encoder.py │ │ │ ├── test_satrn_decoder.py │ │ │ └── test_svtr_encoder.py │ │ │ ├── test_layers │ │ │ └── test_conv_layer.py │ │ │ ├── test_module_losses │ │ │ ├── test_abi_module_loss.py │ │ │ ├── test_base_recog_module_loss.py │ │ │ ├── test_ce_module_loss.py │ │ │ └── test_ctc_module_loss.py │ │ │ ├── test_plugins │ │ │ ├── test_gcamodule.py │ │ │ └── test_maxpool.py │ │ │ ├── test_postprocessors │ │ │ ├── test_attn_postprocessor.py │ │ │ ├── test_base_textrecog_postprocessor.py │ │ │ └── test_ctc_postprocessor.py │ │ │ └── test_recognizers │ │ │ ├── test_encoder_decoder_recognizer.py │ │ │ └── test_encoder_decoder_recognizer_tta.py │ ├── test_structures │ │ ├── test_kie_data_sample.py │ │ ├── test_textdet_data_sample.py │ │ ├── test_textrecog_data_sample.py │ │ └── test_textspotting_data_sample.py │ ├── test_utils │ │ ├── test_bbox_utils.py │ │ ├── test_check_argument.py │ │ ├── test_data_converter_utils.py │ │ ├── test_fileio.py │ │ ├── test_img_utils.py │ │ ├── test_mask_utils.py │ │ ├── test_parsers.py │ │ ├── test_point_utils.py │ │ ├── test_polygon_utils.py │ │ ├── test_processing.py │ │ ├── test_string_utils.py │ │ └── test_transform_utils.py │ └── test_visualization │ │ ├── test_base_visualizer.py │ │ ├── test_kie_visualizer.py │ │ ├── test_textdet_visualizer.py │ │ ├── test_textrecog_visualizer.py │ │ └── test_textspotting_visualizer.py └── tools │ ├── analysis_tools │ ├── browse_dataset.py │ ├── get_flops.py │ ├── offline_eval.py │ └── print_config.py │ ├── dataset_converters │ ├── common │ │ ├── curvedsyntext_converter.py │ │ └── extract_kaist.py │ ├── kie │ │ └── closeset_to_openset.py │ ├── prepare_dataset.py │ ├── textdet │ │ ├── art_converter.py │ │ ├── bid_converter.py │ │ ├── coco_to_line_dict.py │ │ ├── cocotext_converter.py │ │ ├── data_migrator.py │ │ ├── detext_converter.py │ │ ├── funsd_converter.py │ │ ├── hiertext_converter.py │ │ ├── ic11_converter.py │ │ ├── ilst_converter.py │ │ ├── imgur_converter.py │ │ ├── kaist_converter.py │ │ ├── lsvt_converter.py │ │ ├── lv_converter.py │ │ ├── mtwi_converter.py │ │ ├── naf_converter.py │ │ ├── rctw_converter.py │ │ ├── rects_converter.py │ │ ├── sroie_converter.py │ │ └── vintext_converter.py │ └── textrecog │ │ ├── art_converter.py │ │ ├── bid_converter.py │ │ ├── cocotext_converter.py │ │ ├── data_migrator.py │ │ ├── detext_converter.py │ │ ├── funsd_converter.py │ │ ├── hiertext_converter.py │ │ ├── ic11_converter.py │ │ ├── ilst_converter.py │ │ ├── imgur_converter.py │ │ ├── kaist_converter.py │ │ ├── lmdb_converter.py │ │ ├── lsvt_converter.py │ │ ├── lv_converter.py │ │ ├── mtwi_converter.py │ │ ├── naf_converter.py │ │ ├── openvino_converter.py │ │ ├── rctw_converter.py │ │ ├── rects_converter.py │ │ ├── sroie_converter.py │ │ └── vintext_converter.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── infer.py │ ├── model_converters │ └── publish_model.py │ ├── slurm_test.sh │ ├── slurm_train.sh │ ├── test.py │ └── train.py └── textfussion ├── CITATION.cff ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── MANIFEST.in ├── Makefile ├── PHILOSOPHY.md ├── README.md ├── _typos.toml ├── build └── lib │ └── diffusers │ ├── __init__.py │ ├── commands │ ├── __init__.py │ ├── diffusers_cli.py │ └── env.py │ ├── configuration_utils.py │ ├── dependency_versions_check.py │ ├── dependency_versions_table.py │ ├── experimental │ ├── __init__.py │ └── rl │ │ ├── __init__.py │ │ └── value_guided_sampling.py │ ├── image_processor.py │ ├── loaders.py │ ├── models │ ├── __init__.py │ ├── attention.py │ ├── attention_flax.py │ ├── attention_processor.py │ ├── autoencoder_kl.py │ ├── controlnet.py │ ├── controlnet_flax.py │ ├── cross_attention.py │ ├── dual_transformer_2d.py │ ├── embeddings.py │ ├── embeddings_flax.py │ ├── modeling_flax_pytorch_utils.py │ ├── modeling_flax_utils.py │ ├── modeling_pytorch_flax_utils.py │ ├── modeling_utils.py │ ├── prior_transformer.py │ ├── resnet.py │ ├── resnet_flax.py │ ├── t5_film_transformer.py │ ├── transformer_2d.py │ ├── transformer_temporal.py │ ├── unet_1d.py │ ├── unet_1d_blocks.py │ ├── unet_2d.py │ ├── unet_2d_blocks.py │ ├── unet_2d_blocks_flax.py │ ├── unet_2d_condition.py │ ├── unet_2d_condition_flax.py │ ├── unet_3d_blocks.py │ ├── unet_3d_condition.py │ ├── vae.py │ ├── vae_flax.py │ └── vq_model.py │ ├── optimization.py │ ├── pipeline_utils.py │ ├── pipelines │ ├── __init__.py │ ├── alt_diffusion │ │ ├── __init__.py │ │ ├── modeling_roberta_series.py │ │ ├── pipeline_alt_diffusion.py │ │ └── pipeline_alt_diffusion_img2img.py │ ├── audio_diffusion │ │ ├── __init__.py │ │ ├── mel.py │ │ └── pipeline_audio_diffusion.py │ ├── audioldm │ │ ├── __init__.py │ │ └── pipeline_audioldm.py │ ├── dance_diffusion │ │ ├── __init__.py │ │ └── pipeline_dance_diffusion.py │ ├── ddim │ │ ├── __init__.py │ │ └── pipeline_ddim.py │ ├── ddpm │ │ ├── __init__.py │ │ └── pipeline_ddpm.py │ ├── dit │ │ ├── __init__.py │ │ └── pipeline_dit.py │ ├── latent_diffusion │ │ ├── __init__.py │ │ ├── pipeline_latent_diffusion.py │ │ └── pipeline_latent_diffusion_superresolution.py │ ├── latent_diffusion_uncond │ │ ├── __init__.py │ │ └── pipeline_latent_diffusion_uncond.py │ ├── onnx_utils.py │ ├── paint_by_example │ │ ├── __init__.py │ │ ├── image_encoder.py │ │ └── pipeline_paint_by_example.py │ ├── pipeline_flax_utils.py │ ├── pipeline_utils.py │ ├── pndm │ │ ├── __init__.py │ │ └── pipeline_pndm.py │ ├── repaint │ │ ├── __init__.py │ │ └── pipeline_repaint.py │ ├── score_sde_ve │ │ ├── __init__.py │ │ └── pipeline_score_sde_ve.py │ ├── semantic_stable_diffusion │ │ ├── __init__.py │ │ └── pipeline_semantic_stable_diffusion.py │ ├── spectrogram_diffusion │ │ ├── __init__.py │ │ ├── continous_encoder.py │ │ ├── midi_utils.py │ │ ├── notes_encoder.py │ │ └── pipeline_spectrogram_diffusion.py │ ├── stable_diffusion │ │ ├── __init__.py │ │ ├── convert_from_ckpt.py │ │ ├── pipeline_cycle_diffusion.py │ │ ├── pipeline_flax_stable_diffusion.py │ │ ├── pipeline_flax_stable_diffusion_controlnet.py │ │ ├── pipeline_flax_stable_diffusion_img2img.py │ │ ├── pipeline_flax_stable_diffusion_inpaint.py │ │ ├── pipeline_onnx_stable_diffusion.py │ │ ├── pipeline_onnx_stable_diffusion_img2img.py │ │ ├── pipeline_onnx_stable_diffusion_inpaint.py │ │ ├── pipeline_onnx_stable_diffusion_inpaint_legacy.py │ │ ├── pipeline_onnx_stable_diffusion_upscale.py │ │ ├── pipeline_stable_diffusion.py │ │ ├── pipeline_stable_diffusion_attend_and_excite.py │ │ ├── pipeline_stable_diffusion_controlnet.py │ │ ├── pipeline_stable_diffusion_depth2img.py │ │ ├── pipeline_stable_diffusion_image_variation.py │ │ ├── pipeline_stable_diffusion_img2img.py │ │ ├── pipeline_stable_diffusion_inpaint.py │ │ ├── pipeline_stable_diffusion_inpaint_legacy.py │ │ ├── pipeline_stable_diffusion_instruct_pix2pix.py │ │ ├── pipeline_stable_diffusion_k_diffusion.py │ │ ├── pipeline_stable_diffusion_latent_upscale.py │ │ ├── pipeline_stable_diffusion_model_editing.py │ │ ├── pipeline_stable_diffusion_panorama.py │ │ ├── pipeline_stable_diffusion_pix2pix_zero.py │ │ ├── pipeline_stable_diffusion_sag.py │ │ ├── pipeline_stable_diffusion_upscale.py │ │ ├── pipeline_stable_unclip.py │ │ ├── pipeline_stable_unclip_img2img.py │ │ ├── safety_checker.py │ │ ├── safety_checker_flax.py │ │ └── stable_unclip_image_normalizer.py │ ├── stable_diffusion_safe │ │ ├── __init__.py │ │ ├── pipeline_stable_diffusion_safe.py │ │ └── safety_checker.py │ ├── stochastic_karras_ve │ │ ├── __init__.py │ │ └── pipeline_stochastic_karras_ve.py │ ├── text_to_video_synthesis │ │ ├── __init__.py │ │ └── pipeline_text_to_video_synth.py │ ├── unclip │ │ ├── __init__.py │ │ ├── pipeline_unclip.py │ │ ├── pipeline_unclip_image_variation.py │ │ └── text_proj.py │ ├── versatile_diffusion │ │ ├── __init__.py │ │ ├── modeling_text_unet.py │ │ ├── pipeline_versatile_diffusion.py │ │ ├── pipeline_versatile_diffusion_dual_guided.py │ │ ├── pipeline_versatile_diffusion_image_variation.py │ │ └── pipeline_versatile_diffusion_text_to_image.py │ └── vq_diffusion │ │ ├── __init__.py │ │ └── pipeline_vq_diffusion.py │ ├── schedulers │ ├── __init__.py │ ├── scheduling_ddim.py │ ├── scheduling_ddim_flax.py │ ├── scheduling_ddim_inverse.py │ ├── scheduling_ddpm.py │ ├── scheduling_ddpm_flax.py │ ├── scheduling_deis_multistep.py │ ├── scheduling_dpmsolver_multistep.py │ ├── scheduling_dpmsolver_multistep_flax.py │ ├── scheduling_dpmsolver_singlestep.py │ ├── scheduling_euler_ancestral_discrete.py │ ├── scheduling_euler_discrete.py │ ├── scheduling_heun_discrete.py │ ├── scheduling_ipndm.py │ ├── scheduling_k_dpm_2_ancestral_discrete.py │ ├── scheduling_k_dpm_2_discrete.py │ ├── scheduling_karras_ve.py │ ├── scheduling_karras_ve_flax.py │ ├── scheduling_lms_discrete.py │ ├── scheduling_lms_discrete_flax.py │ ├── scheduling_pndm.py │ ├── scheduling_pndm_flax.py │ ├── scheduling_repaint.py │ ├── scheduling_sde_ve.py │ ├── scheduling_sde_ve_flax.py │ ├── scheduling_sde_vp.py │ ├── scheduling_unclip.py │ ├── scheduling_unipc_multistep.py │ ├── scheduling_utils.py │ ├── scheduling_utils_flax.py │ └── scheduling_vq_diffusion.py │ ├── training_utils.py │ └── utils │ ├── __init__.py │ ├── accelerate_utils.py │ ├── constants.py │ ├── deprecation_utils.py │ ├── doc_utils.py │ ├── dummy_flax_and_transformers_objects.py │ ├── dummy_flax_objects.py │ ├── dummy_note_seq_objects.py │ ├── dummy_onnx_objects.py │ ├── dummy_pt_objects.py │ ├── dummy_torch_and_librosa_objects.py │ ├── dummy_torch_and_scipy_objects.py │ ├── dummy_torch_and_transformers_and_k_diffusion_objects.py │ ├── dummy_torch_and_transformers_and_onnx_objects.py │ ├── dummy_torch_and_transformers_objects.py │ ├── dummy_transformers_and_torch_and_note_seq_objects.py │ ├── dynamic_modules_utils.py │ ├── hub_utils.py │ ├── import_utils.py │ ├── logging.py │ ├── model_card_template.md │ ├── outputs.py │ ├── pil_utils.py │ ├── testing_utils.py │ └── torch_utils.py ├── docker ├── diffusers-flax-cpu │ └── Dockerfile ├── diffusers-flax-tpu │ └── Dockerfile ├── diffusers-onnxruntime-cpu │ └── Dockerfile ├── diffusers-onnxruntime-cuda │ └── Dockerfile ├── diffusers-pytorch-cpu │ └── Dockerfile └── diffusers-pytorch-cuda │ └── Dockerfile ├── docs ├── README.md ├── TRANSLATING.md └── source │ ├── _config.py │ ├── en │ ├── _toctree.yml │ ├── api │ │ ├── configuration.mdx │ │ ├── diffusion_pipeline.mdx │ │ ├── experimental │ │ │ └── rl.mdx │ │ ├── loaders.mdx │ │ ├── logging.mdx │ │ ├── models.mdx │ │ ├── outputs.mdx │ │ ├── pipelines │ │ │ ├── alt_diffusion.mdx │ │ │ ├── audio_diffusion.mdx │ │ │ ├── audioldm.mdx │ │ │ ├── cycle_diffusion.mdx │ │ │ ├── dance_diffusion.mdx │ │ │ ├── ddim.mdx │ │ │ ├── ddpm.mdx │ │ │ ├── dit.mdx │ │ │ ├── latent_diffusion.mdx │ │ │ ├── latent_diffusion_uncond.mdx │ │ │ ├── overview.mdx │ │ │ ├── paint_by_example.mdx │ │ │ ├── pndm.mdx │ │ │ ├── repaint.mdx │ │ │ ├── score_sde_ve.mdx │ │ │ ├── semantic_stable_diffusion.mdx │ │ │ ├── spectrogram_diffusion.mdx │ │ │ ├── stable_diffusion │ │ │ │ ├── attend_and_excite.mdx │ │ │ │ ├── controlnet.mdx │ │ │ │ ├── depth2img.mdx │ │ │ │ ├── image_variation.mdx │ │ │ │ ├── img2img.mdx │ │ │ │ ├── inpaint.mdx │ │ │ │ ├── latent_upscale.mdx │ │ │ │ ├── model_editing.mdx │ │ │ │ ├── overview.mdx │ │ │ │ ├── panorama.mdx │ │ │ │ ├── pix2pix.mdx │ │ │ │ ├── pix2pix_zero.mdx │ │ │ │ ├── self_attention_guidance.mdx │ │ │ │ ├── text2img.mdx │ │ │ │ └── upscale.mdx │ │ │ ├── stable_diffusion_2.mdx │ │ │ ├── stable_diffusion_safe.mdx │ │ │ ├── stable_unclip.mdx │ │ │ ├── stochastic_karras_ve.mdx │ │ │ ├── text_to_video.mdx │ │ │ ├── unclip.mdx │ │ │ ├── versatile_diffusion.mdx │ │ │ └── vq_diffusion.mdx │ │ └── schedulers │ │ │ ├── ddim.mdx │ │ │ ├── ddim_inverse.mdx │ │ │ ├── ddpm.mdx │ │ │ ├── deis.mdx │ │ │ ├── dpm_discrete.mdx │ │ │ ├── dpm_discrete_ancestral.mdx │ │ │ ├── euler.mdx │ │ │ ├── euler_ancestral.mdx │ │ │ ├── heun.mdx │ │ │ ├── ipndm.mdx │ │ │ ├── lms_discrete.mdx │ │ │ ├── multistep_dpm_solver.mdx │ │ │ ├── overview.mdx │ │ │ ├── pndm.mdx │ │ │ ├── repaint.mdx │ │ │ ├── score_sde_ve.mdx │ │ │ ├── score_sde_vp.mdx │ │ │ ├── singlestep_dpm_solver.mdx │ │ │ ├── stochastic_karras_ve.mdx │ │ │ ├── unipc.mdx │ │ │ └── vq_diffusion.mdx │ ├── conceptual │ │ ├── contribution.mdx │ │ ├── ethical_guidelines.mdx │ │ ├── evaluation.mdx │ │ └── philosophy.mdx │ ├── imgs │ │ ├── access_request.png │ │ └── diffusers_library.jpg │ ├── index.mdx │ ├── installation.mdx │ ├── optimization │ │ ├── fp16.mdx │ │ ├── habana.mdx │ │ ├── mps.mdx │ │ ├── onnx.mdx │ │ ├── open_vino.mdx │ │ ├── opt_overview.mdx │ │ ├── torch2.0.mdx │ │ └── xformers.mdx │ ├── quicktour.mdx │ ├── stable_diffusion.mdx │ ├── training │ │ ├── controlnet.mdx │ │ ├── dreambooth.mdx │ │ ├── instructpix2pix.mdx │ │ ├── lora.mdx │ │ ├── overview.mdx │ │ ├── text2image.mdx │ │ ├── text_inversion.mdx │ │ └── unconditional_training.mdx │ ├── tutorials │ │ ├── basic_training.mdx │ │ └── tutorial_overview.mdx │ └── using-diffusers │ │ ├── audio.mdx │ │ ├── conditional_image_generation.mdx │ │ ├── contribute_pipeline.mdx │ │ ├── controlling_generation.mdx │ │ ├── custom_pipeline_examples.mdx │ │ ├── custom_pipeline_overview.mdx │ │ ├── depth2img.mdx │ │ ├── img2img.mdx │ │ ├── inpaint.mdx │ │ ├── kerascv.mdx │ │ ├── loading.mdx │ │ ├── loading_overview.mdx │ │ ├── other-modalities.mdx │ │ ├── pipeline_overview.mdx │ │ ├── reproducibility.mdx │ │ ├── reusing_seeds.mdx │ │ ├── rl.mdx │ │ ├── schedulers.mdx │ │ ├── stable_diffusion_jax_how_to.mdx │ │ ├── unconditional_image_generation.mdx │ │ ├── using_safetensors │ │ ├── using_safetensors.mdx │ │ ├── weighted_prompts.mdx │ │ └── write_own_pipeline.mdx │ ├── ko │ ├── _toctree.yml │ ├── in_translation.mdx │ ├── index.mdx │ ├── installation.mdx │ └── quicktour.mdx │ └── zh │ ├── _toctree.yml │ ├── index.mdx │ ├── installation.mdx │ └── quicktour.mdx ├── examples ├── README.md ├── community │ ├── README.md │ ├── bit_diffusion.py │ ├── checkpoint_merger.py │ ├── clip_guided_stable_diffusion.py │ ├── clip_guided_stable_diffusion_img2img.py │ ├── composable_stable_diffusion.py │ ├── ddim_noise_comparative_analysis.py │ ├── imagic_stable_diffusion.py │ ├── img2img_inpainting.py │ ├── interpolate_stable_diffusion.py │ ├── lpw_stable_diffusion.py │ ├── lpw_stable_diffusion_onnx.py │ ├── magic_mix.py │ ├── multilingual_stable_diffusion.py │ ├── one_step_unet.py │ ├── sd_text2img_k_diffusion.py │ ├── seed_resize_stable_diffusion.py │ ├── speech_to_image_diffusion.py │ ├── stable_diffusion_comparison.py │ ├── stable_diffusion_controlnet_img2img.py │ ├── stable_diffusion_controlnet_inpaint.py │ ├── stable_diffusion_controlnet_inpaint_img2img.py │ ├── stable_diffusion_mega.py │ ├── stable_unclip.py │ ├── text_inpainting.py │ ├── tiled_upscaling.py │ ├── unclip_image_interpolation.py │ ├── unclip_text_interpolation.py │ └── wildcard_stable_diffusion.py ├── conftest.py ├── controlnet │ ├── README.md │ ├── requirements.txt │ ├── requirements_flax.txt │ ├── train_controlnet.py │ └── train_controlnet_flax.py ├── dreambooth │ ├── README.md │ ├── requirements.txt │ ├── requirements_flax.txt │ ├── train_dreambooth.py │ ├── train_dreambooth_flax.py │ └── train_dreambooth_lora.py ├── inference │ ├── README.md │ ├── image_to_image.py │ └── inpainting.py ├── instruct_pix2pix │ ├── README.md │ ├── requirements.txt │ └── train_instruct_pix2pix.py ├── research_projects │ ├── README.md │ ├── colossalai │ │ ├── README.md │ │ ├── inference.py │ │ ├── requirement.txt │ │ └── train_dreambooth_colossalai.py │ ├── dreambooth_inpaint │ │ ├── README.md │ │ ├── requirements.txt │ │ ├── train_dreambooth_inpaint.py │ │ └── train_dreambooth_inpaint_lora.py │ ├── intel_opts │ │ ├── README.md │ │ ├── inference_bf16.py │ │ └── textual_inversion │ │ │ ├── README.md │ │ │ ├── requirements.txt │ │ │ └── textual_inversion_bf16.py │ ├── lora │ │ ├── README.md │ │ ├── requirements.txt │ │ └── train_text_to_image_lora.py │ ├── mulit_token_textual_inversion │ │ ├── README.md │ │ ├── multi_token_clip.py │ │ ├── requirements.txt │ │ ├── requirements_flax.txt │ │ ├── textual_inversion.py │ │ └── textual_inversion_flax.py │ ├── multi_subject_dreambooth │ │ ├── README.md │ │ ├── requirements.txt │ │ └── train_multi_subject_dreambooth.py │ └── onnxruntime │ │ ├── README.md │ │ ├── text_to_image │ │ ├── README.md │ │ ├── requirements.txt │ │ └── train_text_to_image.py │ │ ├── textual_inversion │ │ ├── README.md │ │ ├── requirements.txt │ │ └── textual_inversion.py │ │ └── unconditional_image_generation │ │ ├── README.md │ │ ├── requirements.txt │ │ └── train_unconditional.py ├── rl │ ├── README.md │ └── run_diffuser_locomotion.py ├── test_examples.py ├── text_to_image │ ├── README.md │ ├── requirements.txt │ ├── requirements_flax.txt │ ├── train_text_to_image.py │ ├── train_text_to_image_flax.py │ └── train_text_to_image_lora.py ├── textual_inversion │ ├── README.md │ ├── requirements.txt │ ├── requirements_flax.txt │ ├── textual_inversion.py │ └── textual_inversion_flax.py └── unconditional_image_generation │ ├── README.md │ ├── requirements.txt │ └── train_unconditional.py ├── my_inpainting ├── label_list.json ├── my_build_synth_data_baseline.py ├── my_build_synth_data_baseline_large_num.py ├── my_test_inpainting_baseline.py ├── my_test_inpainting_baseline_batch_test.py ├── my_test_inpainting_char.py ├── my_test_inpainting_char_multi.py ├── my_test_inpainting_only_pre_prompt_word_multi.py ├── my_test_inpainting_pure_word_prefix_prompt_batch_test.py ├── my_test_inpainting_with_adapter_char_multi.py ├── my_test_inpainting_with_adapter_with_fussion_te_word_multi.py ├── my_test_inpainting_with_adapter_with_pre_prompt_word_multi.py ├── my_test_inpainting_with_adapter_word_multi.py ├── my_test_inpainting_with_adapter_zero_prompt_char_multi.py ├── my_test_inpainting_with_char_adapter_char_multi.py ├── my_test_inpainting_with_controlnet_batch_test.py ├── my_test_inpainting_with_full_controlnet_batch_test.py ├── my_test_inpainting_with_full_controlnet_dual_text_batch_test.py ├── my_train_only_pre_prompt.sh ├── my_train_prefix_prompt.sh ├── my_train_with_adapter_with_pre_prompt.sh ├── my_train_with_char_adapter.sh ├── my_train_with_controlnet.sh ├── my_train_with_full_controlnet.sh ├── my_train_with_full_controlnet_with_dual_text.sh ├── my_train_with_single_adapter.sh ├── my_train_with_single_adapter_with_fussion_te.sh ├── new_paradigm_any_demo.py ├── new_paradigm_build_baseline.py ├── new_paradigm_build_baseline_no_crop.py ├── new_paradigm_build_demo.py ├── new_paradigm_build_dual_text.py ├── new_paradigm_build_with_text_vae.py ├── new_paradigm_dual_text_encoder.sh ├── new_paradigm_train.sh ├── src │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ └── __init__.cpython-39.pyc │ ├── build_synth_data │ │ ├── __pycache__ │ │ │ ├── batch_utils.cpython-310.pyc │ │ │ ├── crop_tools.cpython-310.pyc │ │ │ ├── glyph_utils.cpython-310.pyc │ │ │ └── rec_inferencer.cpython-310.pyc │ │ ├── batch_utils.py │ │ ├── crop_tools.py │ │ ├── glyph_utils.py │ │ └── rec_inferencer.py │ ├── dataset │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── base_text_dataset.cpython-310.pyc │ │ │ ├── batch_utils.cpython-310.pyc │ │ │ ├── crop_image_for_test.cpython-310.pyc │ │ │ ├── new_paradigm_text_dataset.cpython-310.pyc │ │ │ ├── text_dataset.cpython-310.pyc │ │ │ ├── text_dataset.cpython-39.pyc │ │ │ ├── text_mapper.cpython-310.pyc │ │ │ ├── text_mapper.cpython-38.pyc │ │ │ ├── text_mapper.cpython-39.pyc │ │ │ ├── utils.cpython-310.pyc │ │ │ ├── utils.cpython-39.pyc │ │ │ ├── zoom_up_pure_text_dataset.cpython-310.pyc │ │ │ ├── zoom_up_text_dataset.cpython-310.pyc │ │ │ ├── zoom_up_text_dataset.cpython-38.pyc │ │ │ ├── zoom_up_text_dataset.cpython-39.pyc │ │ │ └── zoom_up_with_blank_text_dataset.cpython-310.pyc │ │ ├── base_text_dataset.py │ │ ├── batch_utils.py │ │ ├── crop_image_for_test.py │ │ ├── new_paradigm_text_dataset.py │ │ ├── text_dataset.py │ │ ├── text_mapper.py │ │ ├── utils.py │ │ ├── zoom_up_pure_text_dataset.py │ │ ├── zoom_up_text_dataset.py │ │ └── zoom_up_with_blank_text_dataset.py │ ├── engines │ │ ├── __init__.py │ │ ├── finetune_text_to_image.py │ │ ├── finetune_text_to_image_inpainting.py │ │ ├── finetune_text_to_image_inpainting_with_adapter_with_pre_prompt.py │ │ ├── finetune_text_to_image_inpainting_with_char_adapter.py │ │ ├── finetune_text_to_image_inpainting_with_controlnet.py │ │ ├── finetune_text_to_image_inpainting_with_full_controlnet.py │ │ ├── finetune_text_to_image_inpainting_with_full_controlnet_with_dual_text.py │ │ ├── finetune_text_to_image_inpainting_with_pre_prompt.py │ │ ├── finetune_text_to_image_inpainting_with_prefix_prompt.py │ │ ├── finetune_text_to_image_inpainting_with_single_adapter.py │ │ ├── finetune_text_to_image_inpainting_with_single_adapter_with_fussion_te.py │ │ ├── new_paradigm_inpainting.py │ │ └── new_paradigm_inpainting_dual_text.py │ ├── models │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── adapter.cpython-310.pyc │ │ │ ├── adapter.cpython-39.pyc │ │ │ ├── adapter_with_char_embedding.cpython-39.pyc │ │ │ ├── adapter_with_fussion_TE.cpython-310.pyc │ │ │ ├── adapter_with_fussion_TE.cpython-38.pyc │ │ │ ├── adapter_with_fussion_TE.cpython-39.pyc │ │ │ ├── adapter_with_pre_prompt.cpython-310.pyc │ │ │ ├── attention.cpython-310.pyc │ │ │ ├── char_encoder.cpython-310.pyc │ │ │ ├── controlnet.cpython-310.pyc │ │ │ ├── dual_controlnet.cpython-310.pyc │ │ │ ├── fussion_text_embedding.cpython-39.pyc │ │ │ ├── modules.cpython-310.pyc │ │ │ ├── only_pre_prompt.cpython-310.pyc │ │ │ ├── only_prefix_prompt.cpython-310.pyc │ │ │ ├── openaimodel.cpython-310.pyc │ │ │ ├── transformer_2d_with_controlnet.cpython-310.pyc │ │ │ ├── transformer_2d_with_dual_text_controlnet.cpython-310.pyc │ │ │ ├── unet_2d_blocks_with_adapter.cpython-310.pyc │ │ │ ├── unet_2d_blocks_with_adapter.cpython-38.pyc │ │ │ ├── unet_2d_blocks_with_adapter.cpython-39.pyc │ │ │ ├── unet_2d_blocks_with_controlnet.cpython-310.pyc │ │ │ ├── unet_2d_blocks_with_dual_text_controlnet.cpython-310.pyc │ │ │ ├── unet_2d_with_adapter.cpython-310.pyc │ │ │ ├── unet_2d_with_adapter.cpython-38.pyc │ │ │ ├── unet_2d_with_adapter.cpython-39.pyc │ │ │ ├── unet_2d_with_controlnet.cpython-310.pyc │ │ │ ├── unet_2d_with_dual_text.cpython-310.pyc │ │ │ ├── unet_2d_with_dual_text_controlnet.cpython-310.pyc │ │ │ └── union_net.cpython-310.pyc │ │ ├── adapter.py │ │ ├── adapter_with_char_embedding.py │ │ ├── adapter_with_fussion_TE.py │ │ ├── adapter_with_pre_prompt.py │ │ ├── attention.py │ │ ├── char_encoder.py │ │ ├── controlnet.py │ │ ├── dual_controlnet.py │ │ ├── fussion_text_embedding.py │ │ ├── modules.py │ │ ├── only_pre_prompt.py │ │ ├── only_prefix_prompt.py │ │ ├── openaimodel.py │ │ ├── ori_controlnet.py │ │ ├── transformer_2d_with_dual_text_controlnet.py │ │ ├── unet_2d_blocks_with_adapter.py │ │ ├── unet_2d_blocks_with_controlnet.py │ │ ├── unet_2d_blocks_with_dual_text_controlnet.py │ │ ├── unet_2d_with_adapter.py │ │ ├── unet_2d_with_controlnet.py │ │ ├── unet_2d_with_dual_text.py │ │ ├── unet_2d_with_dual_text_controlnet.py │ │ └── union_net.py │ ├── pipelines │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── new_paradigm_inpainting_dual_text_encoder.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting.cpython-39.pyc │ │ │ ├── stable_diffusion_inpainting_dual_text_full_controlnet.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_full_controlnet.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_mask_controlnet.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_only_controlnet.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_only_pre_prompt.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_only_prefix_prompt.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_text_glyph.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_text_vae_text_glyph.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_with_adapter.cpython-39.pyc │ │ │ ├── stable_diffusion_inpainting_with_adapter_with_fussion_te.cpython-310.pyc │ │ │ ├── stable_diffusion_inpainting_with_adapter_with_fussion_te.cpython-39.pyc │ │ │ ├── stable_diffusion_inpainting_with_adapter_zero_prompt.cpython-39.pyc │ │ │ └── stable_diffusion_inpainting_with_char_adapter.cpython-39.pyc │ │ ├── new_paradigm_inpainting_dual_text_encoder.py │ │ ├── stable_diffusion_inpainting.py │ │ ├── stable_diffusion_inpainting_dual_text_full_controlnet.py │ │ ├── stable_diffusion_inpainting_full_controlnet.py │ │ ├── stable_diffusion_inpainting_mask_controlnet.py │ │ ├── stable_diffusion_inpainting_only_controlnet.py │ │ ├── stable_diffusion_inpainting_only_pre_prompt.py │ │ ├── stable_diffusion_inpainting_only_prefix_prompt.py │ │ ├── stable_diffusion_inpainting_text_glyph.py │ │ ├── stable_diffusion_inpainting_text_vae_text_glyph.py │ │ ├── stable_diffusion_inpainting_with_adapter.py │ │ ├── stable_diffusion_inpainting_with_adapter_with_fussion_te.py │ │ ├── stable_diffusion_inpainting_with_adapter_zero_prompt.py │ │ └── stable_diffusion_inpainting_with_char_adapter.py │ └── utils │ │ ├── ori.png │ │ ├── output.png │ │ ├── res.png │ │ ├── res_area.png │ │ ├── res_trilinear.png │ │ └── vis_mask.py ├── train_vae.py └── train_vae.sh ├── my_pipeline.py ├── pyproject.toml ├── requirements.txt ├── scripts ├── __init__.py ├── change_naming_configs_and_checkpoints.py ├── conversion_ldm_uncond.py ├── convert_dance_diffusion_to_diffusers.py ├── convert_ddpm_original_checkpoint_to_diffusers.py ├── convert_diffusers_to_original_stable_diffusion.py ├── convert_dit_to_diffusers.py ├── convert_k_upscaler_to_diffusers.py ├── convert_kakao_brain_unclip_to_diffusers.py ├── convert_ldm_original_checkpoint_to_diffusers.py ├── convert_lora_safetensor_to_diffusers.py ├── convert_models_diffuser_to_diffusers.py ├── convert_ms_text_to_video_to_diffusers.py ├── convert_music_spectrogram_to_diffusers.py ├── convert_ncsnpp_original_checkpoint_to_diffusers.py ├── convert_original_audioldm_to_diffusers.py ├── convert_original_controlnet_to_diffusers.py ├── convert_original_stable_diffusion_to_diffusers.py ├── convert_stable_diffusion_checkpoint_to_onnx.py ├── convert_unclip_txt2img_to_image_variation.py ├── convert_vae_diff_to_onnx.py ├── convert_vae_pt_to_diffusers.py ├── convert_versatile_diffusion_to_diffusers.py ├── convert_vq_diffusion_to_diffusers.py └── generate_logits.py ├── setup.cfg ├── setup.py ├── src ├── diffusers.egg-info │ ├── PKG-INFO │ ├── SOURCES.txt │ ├── dependency_links.txt │ ├── entry_points.txt │ ├── requires.txt │ └── top_level.txt └── diffusers │ ├── __init__.py │ ├── commands │ ├── __init__.py │ ├── diffusers_cli.py │ └── env.py │ ├── configuration_utils.py │ ├── dependency_versions_check.py │ ├── dependency_versions_table.py │ ├── experimental │ ├── README.md │ ├── __init__.py │ └── rl │ │ ├── __init__.py │ │ └── value_guided_sampling.py │ ├── image_processor.py │ ├── loaders.py │ ├── models │ ├── README.md │ ├── __init__.py │ ├── attention.py │ ├── attention_flax.py │ ├── attention_processor.py │ ├── autoencoder_kl.py │ ├── controlnet.py │ ├── controlnet_flax.py │ ├── cross_attention.py │ ├── dual_transformer_2d.py │ ├── embeddings.py │ ├── embeddings_flax.py │ ├── modeling_flax_pytorch_utils.py │ ├── modeling_flax_utils.py │ ├── modeling_pytorch_flax_utils.py │ ├── modeling_utils.py │ ├── prior_transformer.py │ ├── resnet.py │ ├── resnet_flax.py │ ├── t5_film_transformer.py │ ├── transformer_2d.py │ ├── transformer_temporal.py │ ├── unet_1d.py │ ├── unet_1d_blocks.py │ ├── unet_2d.py │ ├── unet_2d_blocks.py │ ├── unet_2d_blocks_flax.py │ ├── unet_2d_condition.py │ ├── unet_2d_condition_flax.py │ ├── unet_3d_blocks.py │ ├── unet_3d_condition.py │ ├── vae.py │ ├── vae_flax.py │ └── vq_model.py │ ├── optimization.py │ ├── pipeline_utils.py │ ├── pipelines │ ├── README.md │ ├── __init__.py │ ├── alt_diffusion │ │ ├── __init__.py │ │ ├── modeling_roberta_series.py │ │ ├── pipeline_alt_diffusion.py │ │ └── pipeline_alt_diffusion_img2img.py │ ├── audio_diffusion │ │ ├── __init__.py │ │ ├── mel.py │ │ └── pipeline_audio_diffusion.py │ ├── audioldm │ │ ├── __init__.py │ │ └── pipeline_audioldm.py │ ├── dance_diffusion │ │ ├── __init__.py │ │ └── pipeline_dance_diffusion.py │ ├── ddim │ │ ├── __init__.py │ │ └── pipeline_ddim.py │ ├── ddpm │ │ ├── __init__.py │ │ └── pipeline_ddpm.py │ ├── dit │ │ ├── __init__.py │ │ └── pipeline_dit.py │ ├── latent_diffusion │ │ ├── __init__.py │ │ ├── pipeline_latent_diffusion.py │ │ └── pipeline_latent_diffusion_superresolution.py │ ├── latent_diffusion_uncond │ │ ├── __init__.py │ │ └── pipeline_latent_diffusion_uncond.py │ ├── onnx_utils.py │ ├── paint_by_example │ │ ├── __init__.py │ │ ├── image_encoder.py │ │ └── pipeline_paint_by_example.py │ ├── pipeline_flax_utils.py │ ├── pipeline_utils.py │ ├── pndm │ │ ├── __init__.py │ │ └── pipeline_pndm.py │ ├── repaint │ │ ├── __init__.py │ │ └── pipeline_repaint.py │ ├── score_sde_ve │ │ ├── __init__.py │ │ └── pipeline_score_sde_ve.py │ ├── semantic_stable_diffusion │ │ ├── __init__.py │ │ └── pipeline_semantic_stable_diffusion.py │ ├── spectrogram_diffusion │ │ ├── __init__.py │ │ ├── continous_encoder.py │ │ ├── midi_utils.py │ │ ├── notes_encoder.py │ │ └── pipeline_spectrogram_diffusion.py │ ├── stable_diffusion │ │ ├── README.md │ │ ├── __init__.py │ │ ├── convert_from_ckpt.py │ │ ├── pipeline_cycle_diffusion.py │ │ ├── pipeline_flax_stable_diffusion.py │ │ ├── pipeline_flax_stable_diffusion_controlnet.py │ │ ├── pipeline_flax_stable_diffusion_img2img.py │ │ ├── pipeline_flax_stable_diffusion_inpaint.py │ │ ├── pipeline_onnx_stable_diffusion.py │ │ ├── pipeline_onnx_stable_diffusion_img2img.py │ │ ├── pipeline_onnx_stable_diffusion_inpaint.py │ │ ├── pipeline_onnx_stable_diffusion_inpaint_legacy.py │ │ ├── pipeline_onnx_stable_diffusion_upscale.py │ │ ├── pipeline_stable_diffusion.py │ │ ├── pipeline_stable_diffusion_attend_and_excite.py │ │ ├── pipeline_stable_diffusion_controlnet.py │ │ ├── pipeline_stable_diffusion_depth2img.py │ │ ├── pipeline_stable_diffusion_image_variation.py │ │ ├── pipeline_stable_diffusion_img2img.py │ │ ├── pipeline_stable_diffusion_inpaint.py │ │ ├── pipeline_stable_diffusion_inpaint_legacy.py │ │ ├── pipeline_stable_diffusion_instruct_pix2pix.py │ │ ├── pipeline_stable_diffusion_k_diffusion.py │ │ ├── pipeline_stable_diffusion_latent_upscale.py │ │ ├── pipeline_stable_diffusion_model_editing.py │ │ ├── pipeline_stable_diffusion_panorama.py │ │ ├── pipeline_stable_diffusion_pix2pix_zero.py │ │ ├── pipeline_stable_diffusion_sag.py │ │ ├── pipeline_stable_diffusion_upscale.py │ │ ├── pipeline_stable_unclip.py │ │ ├── pipeline_stable_unclip_img2img.py │ │ ├── safety_checker.py │ │ ├── safety_checker_flax.py │ │ └── stable_unclip_image_normalizer.py │ ├── stable_diffusion_safe │ │ ├── __init__.py │ │ ├── pipeline_stable_diffusion_safe.py │ │ └── safety_checker.py │ ├── stochastic_karras_ve │ │ ├── __init__.py │ │ └── pipeline_stochastic_karras_ve.py │ ├── text_to_video_synthesis │ │ ├── __init__.py │ │ └── pipeline_text_to_video_synth.py │ ├── unclip │ │ ├── __init__.py │ │ ├── pipeline_unclip.py │ │ ├── pipeline_unclip_image_variation.py │ │ └── text_proj.py │ ├── versatile_diffusion │ │ ├── __init__.py │ │ ├── modeling_text_unet.py │ │ ├── pipeline_versatile_diffusion.py │ │ ├── pipeline_versatile_diffusion_dual_guided.py │ │ ├── pipeline_versatile_diffusion_image_variation.py │ │ └── pipeline_versatile_diffusion_text_to_image.py │ └── vq_diffusion │ │ ├── __init__.py │ │ └── pipeline_vq_diffusion.py │ ├── schedulers │ ├── README.md │ ├── __init__.py │ ├── scheduling_ddim.py │ ├── scheduling_ddim_flax.py │ ├── scheduling_ddim_inverse.py │ ├── scheduling_ddpm.py │ ├── scheduling_ddpm_flax.py │ ├── scheduling_deis_multistep.py │ ├── scheduling_dpmsolver_multistep.py │ ├── scheduling_dpmsolver_multistep_flax.py │ ├── scheduling_dpmsolver_singlestep.py │ ├── scheduling_euler_ancestral_discrete.py │ ├── scheduling_euler_discrete.py │ ├── scheduling_heun_discrete.py │ ├── scheduling_ipndm.py │ ├── scheduling_k_dpm_2_ancestral_discrete.py │ ├── scheduling_k_dpm_2_discrete.py │ ├── scheduling_karras_ve.py │ ├── scheduling_karras_ve_flax.py │ ├── scheduling_lms_discrete.py │ ├── scheduling_lms_discrete_flax.py │ ├── scheduling_pndm.py │ ├── scheduling_pndm_flax.py │ ├── scheduling_repaint.py │ ├── scheduling_sde_ve.py │ ├── scheduling_sde_ve_flax.py │ ├── scheduling_sde_vp.py │ ├── scheduling_unclip.py │ ├── scheduling_unipc_multistep.py │ ├── scheduling_utils.py │ ├── scheduling_utils_flax.py │ └── scheduling_vq_diffusion.py │ ├── training_utils.py │ └── utils │ ├── __init__.py │ ├── accelerate_utils.py │ ├── constants.py │ ├── deprecation_utils.py │ ├── doc_utils.py │ ├── dummy_flax_and_transformers_objects.py │ ├── dummy_flax_objects.py │ ├── dummy_note_seq_objects.py │ ├── dummy_onnx_objects.py │ ├── dummy_pt_objects.py │ ├── dummy_torch_and_librosa_objects.py │ ├── dummy_torch_and_scipy_objects.py │ ├── dummy_torch_and_transformers_and_k_diffusion_objects.py │ ├── dummy_torch_and_transformers_and_onnx_objects.py │ ├── dummy_torch_and_transformers_objects.py │ ├── dummy_transformers_and_torch_and_note_seq_objects.py │ ├── dynamic_modules_utils.py │ ├── hub_utils.py │ ├── import_utils.py │ ├── logging.py │ ├── model_card_template.md │ ├── outputs.py │ ├── pil_utils.py │ ├── testing_utils.py │ └── torch_utils.py ├── tests └── utils ├── check_config_docstrings.py ├── check_copies.py ├── check_doc_toc.py ├── check_dummies.py ├── check_inits.py ├── check_repo.py ├── check_table.py ├── custom_init_isort.py ├── get_modified_files.py ├── overwrite_expected_slice.py ├── print_env.py ├── release.py └── stale.py /images/framwork.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/images/framwork.png -------------------------------------------------------------------------------- /mmocr/.circleci/docker/Dockerfile: -------------------------------------------------------------------------------- 1 | ARG PYTORCH="1.8.1" 2 | ARG CUDA="10.2" 3 | ARG CUDNN="7" 4 | 5 | FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel 6 | 7 | # To fix GPG key error when running apt-get update 8 | RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub 9 | RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub 10 | 11 | RUN apt-get update && apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx 12 | -------------------------------------------------------------------------------- /mmocr/.codespellrc: -------------------------------------------------------------------------------- 1 | [codespell] 2 | skip = *.ipynb 3 | count = 4 | quiet-level = 3 5 | ignore-words-list = convertor,convertors,formating,nin,wan,datas,hist,ned 6 | -------------------------------------------------------------------------------- /mmocr/.coveragerc: -------------------------------------------------------------------------------- 1 | [run] 2 | omit = 3 | */__init__.py 4 | -------------------------------------------------------------------------------- /mmocr/.dev_scripts/benchmark_options.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | 3 | third_part_libs = [ 4 | 'pip install -r ../requirements/albu.txt', 5 | ] 6 | 7 | default_floating_range = 0.5 8 | -------------------------------------------------------------------------------- /mmocr/.dev_scripts/benchmark_train_models.txt: -------------------------------------------------------------------------------- 1 | textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py 2 | textdet/fcenet/fcenet_resnet50_fpn_1500e_icdar2015.py 3 | textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015.py 4 | textrecog/abinet/abinet-vision_20e_st-an_mj.py 5 | textrecog/crnn/crnn_mini-vgg_5e_mj.py 6 | textrecog/aster/aster_resnet45_6e_st_mj.py 7 | textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py 8 | textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real.py 9 | textrecog/svtr/svtr-small_20e_st_mj.py 10 | -------------------------------------------------------------------------------- /mmocr/.dev_scripts/covignore.cfg: -------------------------------------------------------------------------------- 1 | # Each line should be the relative path to the root directory 2 | # of this repo. Support regular expression as well. 3 | # For example: 4 | # mmocr/models/textdet/postprocess/utils.py 5 | # .*/utils.py 6 | .*/__init__.py 7 | 8 | # It will be removed after all models have been refactored 9 | mmocr/utils/bbox_utils.py 10 | 11 | # Major part is covered, however, it's hard to cover model's output. 12 | mmocr/models/textdet/detectors/mmdet_wrapper.py 13 | 14 | # It will be removed after KieVisualizer and TextSpotterVisualizer 15 | mmocr/visualization/visualize.py 16 | 17 | # Add tests for data preparers later 18 | mmocr/datasets/preparers 19 | -------------------------------------------------------------------------------- /mmocr/.owners.yml: -------------------------------------------------------------------------------- 1 | assign: 2 | strategy: 3 | random 4 | # daily-shift-based 5 | scedule: 6 | '*/1 * * * *' 7 | assignees: 8 | - gaotongxiao 9 | - Harold-lkk 10 | -------------------------------------------------------------------------------- /mmocr/.readthedocs.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | 3 | formats: all 4 | 5 | python: 6 | version: 3.7 7 | install: 8 | - requirements: requirements/docs.txt 9 | - requirements: requirements/readthedocs.txt 10 | -------------------------------------------------------------------------------- /mmocr/CITATION.cff: -------------------------------------------------------------------------------- 1 | cff-version: 1.2.0 2 | message: "If you use this software, please cite it as below." 3 | title: "OpenMMLab Text Detection, Recognition and Understanding Toolbox" 4 | authors: 5 | - name: "MMOCR Contributors" 6 | version: 0.3.0 7 | date-released: 2020-08-15 8 | repository-code: "https://github.com/open-mmlab/mmocr" 9 | license: Apache-2.0 10 | -------------------------------------------------------------------------------- /mmocr/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include requirements/*.txt 2 | include mmocr/.mim/model-index.yml 3 | include mmocr/.mim/dicts/*.txt 4 | recursive-include mmocr/.mim/configs *.py *.yml 5 | recursive-include mmocr/.mim/tools *.sh *.py 6 | -------------------------------------------------------------------------------- /mmocr/README.md: -------------------------------------------------------------------------------- 1 | 该项目基于mmocr框架，请依据requirements.txt搭建环境 2 | 3 | 在configs/textdet中配置好文本图像数据集的路径后，使用./my_train.sh脚本进行检测实验 4 | -------------------------------------------------------------------------------- /mmocr/configs/backbone/oclip/metafile.yml: -------------------------------------------------------------------------------- 1 | Collections: 2 | - Name: oCLIP 3 | Metadata: 4 | Training Data: SynthText 5 | Architecture: 6 | - CLIPResNet 7 | Paper: 8 | URL: https://arxiv.org/abs/2203.03911 9 | Title: 'Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting' 10 | README: configs/backbone/oclip/README.md 11 | 12 | Models: 13 | Weights: https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth 14 | -------------------------------------------------------------------------------- /mmocr/configs/kie/_base_/datasets/wildreceipt.py: -------------------------------------------------------------------------------- 1 | wildreceipt_data_root = 'data/wildreceipt/' 2 | 3 | wildreceipt_train = dict( 4 | type='WildReceiptDataset', 5 | data_root=wildreceipt_data_root, 6 | metainfo=wildreceipt_data_root + 'class_list.txt', 7 | ann_file='train.txt', 8 | pipeline=None) 9 | 10 | wildreceipt_test = dict( 11 | type='WildReceiptDataset', 12 | data_root=wildreceipt_data_root, 13 | metainfo=wildreceipt_data_root + 'class_list.txt', 14 | ann_file='test.txt', 15 | test_mode=True, 16 | pipeline=None) 17 | -------------------------------------------------------------------------------- /mmocr/configs/kie/_base_/schedules/schedule_adam_60e.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optim_wrapper = dict( 3 | type='OptimWrapper', optimizer=dict(type='Adam', weight_decay=0.0001)) 4 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=60, val_interval=1) 5 | val_cfg = dict(type='ValLoop') 6 | test_cfg = dict(type='TestLoop') 7 | # learning rate 8 | param_scheduler = [ 9 | dict(type='MultiStepLR', milestones=[40, 50], end=60), 10 | ] 11 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/datasets/ctw1500.py: -------------------------------------------------------------------------------- 1 | ctw1500_textdet_data_root = 'data/ctw1500' 2 | 3 | ctw1500_textdet_train = dict( 4 | type='OCRDataset', 5 | data_root=ctw1500_textdet_data_root, 6 | ann_file='textdet_train.json', 7 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 8 | pipeline=None) 9 | 10 | ctw1500_textdet_test = dict( 11 | type='OCRDataset', 12 | data_root=ctw1500_textdet_data_root, 13 | ann_file='textdet_test.json', 14 | test_mode=True, 15 | pipeline=None) 16 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/datasets/icdar2013.py: -------------------------------------------------------------------------------- 1 | icdar2013_textdet_data_root = 'data/icdar2013' 2 | 3 | icdar2013_textdet_train = dict( 4 | type='OCRDataset', 5 | data_root=icdar2013_textdet_data_root, 6 | ann_file='textdet_train.json', 7 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 8 | pipeline=None) 9 | 10 | icdar2013_textdet_test = dict( 11 | type='OCRDataset', 12 | data_root=icdar2013_textdet_data_root, 13 | ann_file='textdet_test.json', 14 | test_mode=True, 15 | pipeline=None) 16 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/datasets/icdar2017.py: -------------------------------------------------------------------------------- 1 | icdar2017_textdet_data_root = 'data/mlt2017' 2 | 3 | icdar2017_textdet_train = dict( 4 | type='OCRDataset', 5 | data_root=icdar2017_textdet_data_root, 6 | ann_file='textdet_test.json', 7 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 8 | pipeline=None) 9 | 10 | icdar2017_textdet_test = dict( 11 | type='OCRDataset', 12 | data_root=icdar2017_textdet_data_root, 13 | ann_file='textdet_test.json', 14 | test_mode=True, 15 | pipeline=None) 16 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/datasets/synthtext.py: -------------------------------------------------------------------------------- 1 | synthtext_textdet_data_root = 'data/synthtext' 2 | 3 | synthtext_textdet_train = dict( 4 | type='OCRDataset', 5 | data_root=synthtext_textdet_data_root, 6 | ann_file='textdet_train.json', 7 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 8 | pipeline=None) 9 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/datasets/totaltext.py: -------------------------------------------------------------------------------- 1 | totaltext_textdet_data_root = 'data/totaltext' 2 | 3 | totaltext_textdet_train = dict( 4 | type='OCRDataset', 5 | data_root=totaltext_textdet_data_root, 6 | ann_file='textdet_train.json', 7 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 8 | pipeline=None) 9 | 10 | totaltext_textdet_test = dict( 11 | type='OCRDataset', 12 | data_root=totaltext_textdet_data_root, 13 | ann_file='textdet_test.json', 14 | test_mode=True, 15 | pipeline=None) 16 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/datasets/toy_data.py: -------------------------------------------------------------------------------- 1 | toy_det_data_root = 'tests/data/det_toy_dataset' 2 | 3 | toy_det_train = dict( 4 | type='OCRDataset', 5 | data_root=toy_det_data_root, 6 | ann_file='instances_training.json', 7 | data_prefix=dict(img_path='imgs/'), 8 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 9 | pipeline=None) 10 | 11 | toy_det_test = dict( 12 | type='OCRDataset', 13 | data_root=toy_det_data_root, 14 | ann_file='instances_test.json', 15 | data_prefix=dict(img_path='imgs/'), 16 | test_mode=True, 17 | pipeline=None) 18 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/pretrain_runtime.py: -------------------------------------------------------------------------------- 1 | _base_ = 'default_runtime.py' 2 | 3 | default_hooks = dict( 4 | logger=dict(type='LoggerHook', interval=1000), 5 | checkpoint=dict( 6 | type='CheckpointHook', 7 | interval=10000, 8 | by_epoch=False, 9 | max_keep_ckpts=1), 10 | ) 11 | 12 | # Evaluation 13 | val_evaluator = None 14 | test_evaluator = None 15 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/schedules/schedule_adam_600e.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-3)) 3 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=60, val_interval=10) 4 | val_cfg = dict(type='ValLoop') 5 | test_cfg = dict(type='TestLoop') 6 | # learning rate 7 | param_scheduler = [ 8 | dict(type='PolyLR', power=0.9, end=60), 9 | ] 10 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/schedules/schedule_sgd_100k.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optim_wrapper = dict( 3 | type='OptimWrapper', 4 | optimizer=dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001)) 5 | 6 | train_cfg = dict(type='IterBasedTrainLoop', max_iters=100000) 7 | # test_cfg = None 8 | # val_cfg = None 9 | val_cfg = dict(type='ValLoop') 10 | test_cfg = dict(type='TestLoop') 11 | # learning policy 12 | param_scheduler = [ 13 | dict(type='PolyLR', power=0.9, eta_min=1e-7, by_epoch=False, end=100000), 14 | ] 15 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/schedules/schedule_sgd_1200e.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optim_wrapper = dict( 3 | type='OptimWrapper', 4 | optimizer=dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001)) 5 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1200, val_interval=100) 6 | val_cfg = dict(type='ValLoop') 7 | test_cfg = dict(type='TestLoop') 8 | # learning policy 9 | param_scheduler = [ 10 | dict(type='PolyLR', power=0.9, eta_min=1e-7, end=1200), 11 | ] 12 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/_base_/schedules/schedule_sgd_base.py: -------------------------------------------------------------------------------- 1 | # Note: This schedule config serves as a base config for other schedules. 2 | # Users would have to at least fill in "max_epochs" and "val_interval" 3 | # in order to use this config in their experiments. 4 | 5 | # optimizer 6 | optim_wrapper = dict( 7 | type='OptimWrapper', 8 | optimizer=dict(type='SGD', lr=0.007, momentum=0.9, weight_decay=0.0001)) 9 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=None, val_interval=20) 10 | val_cfg = dict(type='ValLoop') 11 | test_cfg = dict(type='TestLoop') 12 | # learning policy 13 | param_scheduler = [ 14 | dict(type='ConstantLR', factor=1.0), 15 | ] 16 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/dbnet/dbnet_resnet50-oclip_1200e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py', 3 | ] 4 | 5 | load_from = None 6 | 7 | _base_.model.backbone = dict( 8 | type='CLIPResNet', 9 | init_cfg=dict( 10 | type='Pretrained', 11 | checkpoint='https://download.openmmlab.com/' 12 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 13 | 14 | _base_.train_dataloader.num_workers = 24 15 | _base_.optim_wrapper.optimizer.lr = 0.002 16 | 17 | param_scheduler = [ 18 | dict(type='LinearLR', end=100, start_factor=0.001), 19 | dict(type='PolyLR', power=0.9, eta_min=1e-7, begin=100, end=1200), 20 | ] 21 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/dbnet/dbnet_resnet50_1200e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py', 3 | ] 4 | 5 | load_from = None 6 | 7 | _base_.model.backbone = dict( 8 | type='mmdet.ResNet', 9 | depth=50, 10 | num_stages=4, 11 | out_indices=(0, 1, 2, 3), 12 | frozen_stages=-1, 13 | norm_cfg=dict(type='BN', requires_grad=True), 14 | norm_eval=True, 15 | style='pytorch', 16 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')) 17 | 18 | _base_.train_dataloader.num_workers = 24 19 | _base_.optim_wrapper.optimizer.lr = 0.002 20 | 21 | param_scheduler = [ 22 | dict(type='LinearLR', end=100, start_factor=0.001), 23 | dict(type='PolyLR', power=0.9, eta_min=1e-7, begin=100, end=1200), 24 | ] 25 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py', 3 | ] 4 | 5 | load_from = None 6 | 7 | _base_.model.backbone = dict( 8 | type='CLIPResNet', 9 | init_cfg=dict( 10 | type='Pretrained', 11 | checkpoint='https://download.openmmlab.com/' 12 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 13 | 14 | _base_.train_dataloader.num_workers = 24 15 | _base_.optim_wrapper.optimizer.lr = 0.002 16 | 17 | param_scheduler = [ 18 | dict(type='LinearLR', end=200, start_factor=0.001), 19 | dict(type='PolyLR', power=0.9, eta_min=1e-7, begin=200, end=1200), 20 | ] 21 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/dbnetpp/dbnetpp_resnet50_fpnc_1200e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py', 3 | ] 4 | 5 | load_from = None 6 | 7 | _base_.model.backbone = dict( 8 | type='mmdet.ResNet', 9 | depth=50, 10 | num_stages=4, 11 | out_indices=(0, 1, 2, 3), 12 | frozen_stages=-1, 13 | norm_cfg=dict(type='BN', requires_grad=True), 14 | norm_eval=True, 15 | style='pytorch', 16 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')) 17 | 18 | _base_.train_dataloader.num_workers = 24 19 | _base_.optim_wrapper.optimizer.lr = 0.003 20 | 21 | param_scheduler = [ 22 | dict(type='LinearLR', end=200, start_factor=0.001), 23 | dict(type='PolyLR', power=0.9, eta_min=1e-7, begin=200, end=1200), 24 | ] 25 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/drrg/drrg_resnet50-oclip_fpn-unet_1200e_ctw1500.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'drrg_resnet50_fpn-unet_1200e_ctw1500.py', 3 | ] 4 | 5 | load_from = None 6 | 7 | _base_.model.backbone = dict( 8 | type='CLIPResNet', 9 | init_cfg=dict( 10 | type='Pretrained', 11 | checkpoint='https://download.openmmlab.com/' 12 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 13 | 14 | param_scheduler = [ 15 | dict(type='LinearLR', end=100, start_factor=0.001), 16 | dict(type='PolyLR', power=0.9, eta_min=1e-7, begin=100, end=1200), 17 | ] 18 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/fcenet/_base_fcenet_resnet50-dcnv2_fpn.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '_base_fcenet_resnet50_fpn.py', 3 | ] 4 | 5 | model = dict( 6 | backbone=dict( 7 | norm_eval=True, 8 | style='pytorch', 9 | dcn=dict(type='DCNv2', deform_groups=2, fallback_on_stride=False), 10 | stage_with_dcn=(False, True, True, True)), 11 | det_head=dict( 12 | module_loss=dict( 13 | type='FCEModuleLoss', 14 | num_sample=50, 15 | level_proportion_range=((0, 0.25), (0.2, 0.65), (0.55, 1.0))), 16 | postprocessor=dict(text_repr_type='poly', alpha=1.0, beta=2.0))) 17 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_ctw1500.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'fcenet_resnet50-dcnv2_fpn_1500e_ctw1500.py', 3 | ] 4 | 5 | load_from = None 6 | 7 | _base_.model.backbone = dict( 8 | type='CLIPResNet', 9 | out_indices=(1, 2, 3), 10 | init_cfg=dict( 11 | type='Pretrained', 12 | checkpoint='https://download.openmmlab.com/' 13 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 14 | 15 | _base_.train_dataloader.num_workers = 24 16 | _base_.optim_wrapper.optimizer.lr = 0.0005 17 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'fcenet_resnet50_fpn_1500e_icdar2015.py', 3 | ] 4 | load_from = None 5 | 6 | _base_.model.backbone = dict( 7 | type='CLIPResNet', 8 | out_indices=(1, 2, 3), 9 | init_cfg=dict( 10 | type='Pretrained', 11 | checkpoint='https://download.openmmlab.com/' 12 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 13 | 14 | _base_.train_dataloader.batch_size = 16 15 | _base_.train_dataloader.num_workers = 24 16 | _base_.optim_wrapper.optimizer.lr = 0.0005 17 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'mask-rcnn_resnet50_fpn_160e_ctw1500.py', 3 | ] 4 | 5 | load_from = None 6 | 7 | _base_.model.cfg.backbone = dict( 8 | _scope_='mmocr', 9 | type='CLIPResNet', 10 | init_cfg=dict( 11 | type='Pretrained', 12 | checkpoint='https://download.openmmlab.com/' 13 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 14 | 15 | _base_.optim_wrapper.optimizer.lr = 0.02 16 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'mask-rcnn_resnet50_fpn_160e_icdar2015.py', 3 | ] 4 | 5 | load_from = None 6 | 7 | _base_.model.cfg.backbone = dict( 8 | _scope_='mmocr', 9 | type='CLIPResNet', 10 | init_cfg=dict( 11 | type='Pretrained', 12 | checkpoint='https://download.openmmlab.com/' 13 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 14 | 15 | _base_.optim_wrapper.optimizer.lr = 0.02 16 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2017.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'mask-rcnn_resnet50_fpn_160e_icdar2015.py', 3 | '../_base_/datasets/icdar2017.py', 4 | ] 5 | 6 | icdar2017_textdet_train = _base_.icdar2017_textdet_train 7 | icdar2017_textdet_test = _base_.icdar2017_textdet_test 8 | # use the same pipeline as icdar2015 9 | icdar2017_textdet_train.pipeline = _base_.train_pipeline 10 | icdar2017_textdet_test.pipeline = _base_.test_pipeline 11 | 12 | train_dataloader = dict(dataset=icdar2017_textdet_train) 13 | val_dataloader = dict(dataset=icdar2017_textdet_test) 14 | test_dataloader = val_dataloader 15 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/panet/_base_panet_resnet50_fpem-ffm.py: -------------------------------------------------------------------------------- 1 | _base_ = '_base_panet_resnet18_fpem-ffm.py' 2 | 3 | model = dict( 4 | type='PANet', 5 | backbone=dict( 6 | _delete_=True, 7 | type='mmdet.ResNet', 8 | depth=50, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='caffe', 15 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), 16 | ), 17 | neck=dict(in_channels=[256, 512, 1024, 2048]), 18 | det_head=dict(postprocessor=dict(text_repr_type='poly'))) 19 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_ctw1500.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'psenet_resnet50_fpnf_600e_ctw1500.py', 3 | ] 4 | 5 | _base_.model.backbone = dict( 6 | type='CLIPResNet', 7 | init_cfg=dict( 8 | type='Pretrained', 9 | checkpoint='https://download.openmmlab.com/' 10 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 11 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'psenet_resnet50_fpnf_600e_icdar2015.py', 3 | ] 4 | 5 | _base_.model.backbone = dict( 6 | type='CLIPResNet', 7 | init_cfg=dict( 8 | type='Pretrained', 9 | checkpoint='https://download.openmmlab.com/' 10 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 11 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2017.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'psenet_resnet50_fpnf_600e_icdar2015.py', 3 | '../_base_/datasets/icdar2017.py', 4 | ] 5 | 6 | icdar2017_textdet_train = _base_.icdar2017_textdet_train 7 | icdar2017_textdet_test = _base_.icdar2017_textdet_test 8 | # use the same pipeline as icdar2015 9 | icdar2017_textdet_train.pipeline = _base_.train_pipeline 10 | icdar2017_textdet_test.pipeline = _base_.test_pipeline 11 | 12 | train_dataloader = dict(dataset=icdar2017_textdet_train) 13 | val_dataloader = dict(dataset=icdar2017_textdet_test) 14 | test_dataloader = val_dataloader 15 | 16 | auto_scale_lr = dict(base_batch_size=64 * 4) 17 | -------------------------------------------------------------------------------- /mmocr/configs/textdet/textsnake/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'textsnake_resnet50_fpn-unet_1200e_ctw1500.py', 3 | ] 4 | 5 | _base_.model.backbone = dict( 6 | type='CLIPResNet', 7 | init_cfg=dict( 8 | type='Pretrained', 9 | checkpoint='https://download.openmmlab.com/' 10 | 'mmocr/backbone/resnet50-oclip-7ba0c533.pth')) 11 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/coco_text_v1.py: -------------------------------------------------------------------------------- 1 | cocotextv1_textrecog_data_root = 'data/rec/coco_text_v1' 2 | 3 | cocotextv1_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=cocotextv1_textrecog_data_root, 6 | ann_file='train_labels.json', 7 | test_mode=False, 8 | pipeline=None) 9 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/cute80.py: -------------------------------------------------------------------------------- 1 | cute80_textrecog_data_root = 'data/cute80' 2 | 3 | cute80_textrecog_test = dict( 4 | type='OCRDataset', 5 | data_root=cute80_textrecog_data_root, 6 | ann_file='textrecog_test.json', 7 | test_mode=True, 8 | pipeline=None) 9 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/icdar2011.py: -------------------------------------------------------------------------------- 1 | icdar2011_textrecog_data_root = 'data/rec/icdar_2011/' 2 | 3 | icdar2011_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=icdar2011_textrecog_data_root, 6 | ann_file='train_labels.json', 7 | test_mode=False, 8 | pipeline=None) 9 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/icdar2013.py: -------------------------------------------------------------------------------- 1 | icdar2013_textrecog_data_root = 'data/icdar2013' 2 | 3 | icdar2013_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=icdar2013_textrecog_data_root, 6 | ann_file='textrecog_train.json', 7 | pipeline=None) 8 | 9 | icdar2013_textrecog_test = dict( 10 | type='OCRDataset', 11 | data_root=icdar2013_textrecog_data_root, 12 | ann_file='textrecog_test.json', 13 | test_mode=True, 14 | pipeline=None) 15 | 16 | icdar2013_857_textrecog_test = dict( 17 | type='OCRDataset', 18 | data_root=icdar2013_textrecog_data_root, 19 | ann_file='textrecog_test_857.json', 20 | test_mode=True, 21 | pipeline=None) 22 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/icdar2015.py: -------------------------------------------------------------------------------- 1 | icdar2015_textrecog_data_root = 'data/icdar2015' 2 | 3 | icdar2015_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=icdar2015_textrecog_data_root, 6 | ann_file='textrecog_train.json', 7 | pipeline=None) 8 | 9 | icdar2015_textrecog_test = dict( 10 | type='OCRDataset', 11 | data_root=icdar2015_textrecog_data_root, 12 | ann_file='textrecog_test.json', 13 | test_mode=True, 14 | pipeline=None) 15 | 16 | icdar2015_1811_textrecog_test = dict( 17 | type='OCRDataset', 18 | data_root=icdar2015_textrecog_data_root, 19 | ann_file='textrecog_test_1811.json', 20 | test_mode=True, 21 | pipeline=None) 22 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/iiit5k.py: -------------------------------------------------------------------------------- 1 | iiit5k_textrecog_data_root = 'data/iiit5k' 2 | 3 | iiit5k_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=iiit5k_textrecog_data_root, 6 | ann_file='textrecog_train.json', 7 | pipeline=None) 8 | 9 | iiit5k_textrecog_test = dict( 10 | type='OCRDataset', 11 | data_root=iiit5k_textrecog_data_root, 12 | ann_file='textrecog_test.json', 13 | test_mode=True, 14 | pipeline=None) 15 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/mjsynth.py: -------------------------------------------------------------------------------- 1 | mjsynth_textrecog_data_root = 'data/mjsynth' 2 | 3 | mjsynth_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=mjsynth_textrecog_data_root, 6 | ann_file='textrecog_train.json', 7 | pipeline=None) 8 | 9 | mjsynth_sub_textrecog_train = dict( 10 | type='OCRDataset', 11 | data_root=mjsynth_textrecog_data_root, 12 | ann_file='subset_textrecog_train.json', 13 | pipeline=None) 14 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/svt.py: -------------------------------------------------------------------------------- 1 | svt_textrecog_data_root = 'data/svt' 2 | 3 | svt_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=svt_textrecog_data_root, 6 | ann_file='textrecog_train.json', 7 | pipeline=None) 8 | 9 | svt_textrecog_test = dict( 10 | type='OCRDataset', 11 | data_root=svt_textrecog_data_root, 12 | ann_file='textrecog_test.json', 13 | test_mode=True, 14 | pipeline=None) 15 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/svtp.py: -------------------------------------------------------------------------------- 1 | svtp_textrecog_data_root = 'data/svtp' 2 | 3 | svtp_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=svtp_textrecog_data_root, 6 | ann_file='textrecog_train.json', 7 | pipeline=None) 8 | 9 | svtp_textrecog_test = dict( 10 | type='OCRDataset', 11 | data_root=svtp_textrecog_data_root, 12 | ann_file='textrecog_test.json', 13 | test_mode=True, 14 | pipeline=None) 15 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/synthtext.py: -------------------------------------------------------------------------------- 1 | synthtext_textrecog_data_root = 'data/synthtext' 2 | 3 | synthtext_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=synthtext_textrecog_data_root, 6 | ann_file='textrecog_train.json', 7 | pipeline=None) 8 | 9 | synthtext_sub_textrecog_train = dict( 10 | type='OCRDataset', 11 | data_root=synthtext_textrecog_data_root, 12 | ann_file='subset_textrecog_train.json', 13 | pipeline=None) 14 | 15 | synthtext_an_textrecog_train = dict( 16 | type='OCRDataset', 17 | data_root=synthtext_textrecog_data_root, 18 | ann_file='alphanumeric_textrecog_train.json', 19 | pipeline=None) 20 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/synthtext_add.py: -------------------------------------------------------------------------------- 1 | synthtext_add_textrecog_data_root = 'data/rec/synthtext_add/' 2 | 3 | synthtext_add_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=synthtext_add_textrecog_data_root, 6 | ann_file='train_labels.json', 7 | test_mode=False, 8 | pipeline=None) 9 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/totaltext.py: -------------------------------------------------------------------------------- 1 | totaltext_textrecog_data_root = 'data/totaltext/' 2 | 3 | totaltext_textrecog_train = dict( 4 | type='OCRDataset', 5 | data_root=totaltext_textrecog_data_root, 6 | ann_file='textrecog_train.json', 7 | test_mode=False, 8 | pipeline=None) 9 | 10 | totaltext_textrecog_test = dict( 11 | type='OCRDataset', 12 | data_root=totaltext_textrecog_data_root, 13 | ann_file='textrecog_test.json', 14 | test_mode=True, 15 | pipeline=None) 16 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/datasets/toy_data.py: -------------------------------------------------------------------------------- 1 | toy_data_root = 'tests/data/rec_toy_dataset/' 2 | 3 | toy_rec_train = dict( 4 | type='OCRDataset', 5 | data_root=toy_data_root, 6 | data_prefix=dict(img_path='imgs/'), 7 | ann_file='labels.json', 8 | pipeline=None, 9 | test_mode=False) 10 | 11 | toy_rec_test = dict( 12 | type='OCRDataset', 13 | data_root=toy_data_root, 14 | data_prefix=dict(img_path='imgs/'), 15 | ann_file='labels.json', 16 | pipeline=None, 17 | test_mode=True) 18 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/schedules/schedule_adadelta_5e.py: -------------------------------------------------------------------------------- 1 | optim_wrapper = dict( 2 | type='OptimWrapper', optimizer=dict(type='Adadelta', lr=1.0)) 3 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=5, val_interval=1) 4 | val_cfg = dict(type='ValLoop') 5 | test_cfg = dict(type='TestLoop') 6 | # learning rate 7 | param_scheduler = [ 8 | dict(type='ConstantLR', factor=1.0), 9 | ] 10 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/schedules/schedule_adam_base.py: -------------------------------------------------------------------------------- 1 | # Note: This schedule config serves as a base config for other schedules. 2 | # Users would have to at least fill in "max_epochs" and "val_interval" 3 | # in order to use this config in their experiments. 4 | 5 | # optimizer 6 | optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=3e-4)) 7 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=None, val_interval=1) 8 | val_cfg = dict(type='ValLoop') 9 | test_cfg = dict(type='TestLoop') 10 | # learning policy 11 | param_scheduler = [ 12 | dict(type='ConstantLR', factor=1.0), 13 | ] 14 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/schedules/schedule_adam_step_5e.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=1e-3)) 3 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=5, val_interval=1) 4 | val_cfg = dict(type='ValLoop') 5 | test_cfg = dict(type='TestLoop') 6 | # learning policy 7 | param_scheduler = [ 8 | dict(type='MultiStepLR', milestones=[3, 4], end=5), 9 | ] 10 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/_base_/schedules/schedule_adamw_cos_6e.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optim_wrapper = dict( 3 | type='OptimWrapper', 4 | optimizer=dict( 5 | type='AdamW', 6 | lr=4e-4, 7 | betas=(0.9, 0.999), 8 | eps=1e-08, 9 | weight_decay=0.05)) 10 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=6, val_interval=1) 11 | val_cfg = dict(type='ValLoop') 12 | test_cfg = dict(type='TestLoop') 13 | 14 | # learning policy 15 | param_scheduler = [ 16 | dict( 17 | type='CosineAnnealingLR', 18 | T_max=6, 19 | eta_min=4e-6, 20 | convert_to_iter_based=True) 21 | ] 22 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/abinet/_base_abinet.py: -------------------------------------------------------------------------------- 1 | _base_ = '_base_abinet-vision.py' 2 | 3 | model = dict( 4 | decoder=dict( 5 | d_model=512, 6 | num_iters=3, 7 | language_decoder=dict( 8 | type='ABILanguageDecoder', 9 | d_model=512, 10 | n_head=8, 11 | d_inner=2048, 12 | n_layers=4, 13 | dropout=0.1, 14 | detach_tokens=True, 15 | use_self_attn=False, 16 | )), ) 17 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'nrtr_resnet31-1by16-1by8_6e_st_mj.py', 3 | ] 4 | 5 | model = dict(backbone=dict(last_stage_pool=False)) 6 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real.py', 3 | ] 4 | 5 | model = dict(decoder=dict(type='SequentialSARDecoder')) 6 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py: -------------------------------------------------------------------------------- 1 | _base_ = ['satrn_shallow_5e_st_mj.py'] 2 | 3 | model = dict( 4 | backbone=dict(type='ShallowCNN', input_channels=3, hidden_dim=256), 5 | encoder=dict( 6 | type='SATRNEncoder', 7 | n_layers=6, 8 | n_head=8, 9 | d_k=256 // 8, 10 | d_v=256 // 8, 11 | d_model=256, 12 | n_position=100, 13 | d_inner=256 * 4, 14 | dropout=0.1), 15 | decoder=dict( 16 | type='NRTRDecoder', 17 | n_layers=6, 18 | d_embedding=256, 19 | n_head=8, 20 | d_model=256, 21 | d_inner=256 * 4, 22 | d_k=256 // 8, 23 | d_v=256 // 8)) 24 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/svtr/svtr-base_20e_st_mj.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'svtr-tiny_20e_st_mj.py', 3 | ] 4 | 5 | model = dict( 6 | preprocessor=dict(output_image_size=(48, 160), ), 7 | encoder=dict( 8 | img_size=[48, 160], 9 | max_seq_len=40, 10 | out_channels=256, 11 | embed_dims=[128, 256, 384], 12 | depth=[3, 6, 9], 13 | num_heads=[4, 8, 12], 14 | mixer_types=['Local'] * 8 + ['Global'] * 10), 15 | decoder=dict(in_channels=256)) 16 | 17 | train_dataloader = dict(batch_size=256, ) 18 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/svtr/svtr-large_20e_st_mj.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'svtr-tiny_20e_st_mj.py', 3 | ] 4 | 5 | model = dict( 6 | preprocessor=dict(output_image_size=(48, 160), ), 7 | encoder=dict( 8 | img_size=[48, 160], 9 | max_seq_len=40, 10 | out_channels=384, 11 | embed_dims=[192, 256, 512], 12 | depth=[3, 9, 9], 13 | num_heads=[6, 8, 16], 14 | mixer_types=['Local'] * 10 + ['Global'] * 11), 15 | decoder=dict(in_channels=384)) 16 | 17 | train_dataloader = dict(batch_size=128, ) 18 | 19 | optim_wrapper = dict(optimizer=dict(lr=2.5 / (10**4))) 20 | -------------------------------------------------------------------------------- /mmocr/configs/textrecog/svtr/svtr-small_20e_st_mj.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | 'svtr-tiny_20e_st_mj.py', 3 | ] 4 | 5 | model = dict( 6 | encoder=dict( 7 | embed_dims=[96, 192, 256], 8 | depth=[3, 6, 6], 9 | num_heads=[3, 6, 8], 10 | mixer_types=['Local'] * 8 + ['Global'] * 7)) 11 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/cocotextv2/textrecog.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.packer.type = 'TextRecogCropPacker' 4 | _base_.val_preparer.packer.type = 'TextRecogCropPacker' 5 | 6 | config_generator = dict(type='TextRecogConfigGenerator') 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/cocotextv2/textspotting.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.packer.type = 'TextSpottingPacker' 4 | _base_.test_preparer.packer.type = 'TextSpottingPacker' 5 | 6 | config_generator = dict(type='TextSpottingConfigGenerator') 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/ctw1500/textrecog.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train' 4 | _base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test' 5 | 6 | _base_.train_preparer.packer.type = 'TextRecogCropPacker' 7 | _base_.test_preparer.packer.type = 'TextRecogCropPacker' 8 | 9 | config_generator = dict(type='TextRecogConfigGenerator') 10 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/ctw1500/textspotting.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train' 4 | _base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test' 5 | 6 | _base_.train_preparer.packer.type = 'TextSpottingPacker' 7 | _base_.test_preparer.packer.type = 'TextSpottingPacker' 8 | 9 | _base_.test_preparer.obtainer.files.append( 10 | dict( 11 | url='https://download.openmmlab.com/mmocr/data/1.x/textspotting/' 12 | 'ctw1500/lexicons.zip', 13 | save_name='ctw1500_lexicons.zip', 14 | md5='168150ca45da161917bf35a20e45b8d6', 15 | content=['lexicons'], 16 | mapping=[['ctw1500_lexicons/lexicons', 'lexicons']])) 17 | 18 | _base_.delete.append('ctw1500_lexicons') 19 | config_generator = dict(type='TextSpottingConfigGenerator') 20 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/cute80/sample_anno.md: -------------------------------------------------------------------------------- 1 | **Text Recognition** 2 | 3 | ```text 4 | # timage/img_name text 1 text 5 | 6 | timage/001.jpg RONALDO 1 RONALDO 7 | timage/002.jpg 7 1 7 8 | timage/003.jpg SEACREST 1 SEACREST 9 | timage/004.jpg BEACH 1 BEACH 10 | ``` 11 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/funsd/textrecog.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train' 4 | _base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test' 5 | 6 | _base_.train_preparer.packer.type = 'TextRecogCropPacker' 7 | _base_.test_preparer.packer.type = 'TextRecogCropPacker' 8 | 9 | config_generator = dict(type='TextRecogConfigGenerator') 10 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/funsd/textspotting.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | _base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train' 3 | _base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test' 4 | 5 | _base_.train_preparer.packer.type = 'TextSpottingPacker' 6 | _base_.test_preparer.packer.type = 'TextSpottingPacker' 7 | 8 | config_generator = dict(type='TextSpottingConfigGenerator') 9 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/icdar2013/sample_anno.md: -------------------------------------------------------------------------------- 1 | **Text Detection** 2 | 3 | ```text 4 | # train split 5 | # x1 y1 x2 y2 "transcript" 6 | 7 | 158 128 411 181 "Footpath" 8 | 443 128 501 169 "To" 9 | 64 200 363 243 "Colchester" 10 | 11 | # test split 12 | # x1, y1, x2, y2, "transcript" 13 | 14 | 38, 43, 920, 215, "Tiredness" 15 | 275, 264, 665, 450, "kills" 16 | 0, 699, 77, 830, "A" 17 | ``` 18 | 19 | **Text Recognition** 20 | 21 | ```text 22 | # img_name, "text" 23 | 24 | word_1.png, "PROPER" 25 | word_2.png, "FOOD" 26 | word_3.png, "PRONTO" 27 | ``` 28 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/icdar2015/sample_anno.md: -------------------------------------------------------------------------------- 1 | **Text Detection** 2 | 3 | ```text 4 | # x1,y1,x2,y2,x3,y3,x4,y4,trans 5 | 6 | 377,117,463,117,465,130,378,130,Genaxis Theatre 7 | 493,115,519,115,519,131,493,131,[06] 8 | 374,155,409,155,409,170,374,170,### 9 | ``` 10 | 11 | **Text Recognition** 12 | 13 | ```text 14 | # img_name, "text" 15 | 16 | word_1.png, "Genaxis Theatre" 17 | word_2.png, "[06]" 18 | word_3.png, "62-03" 19 | ``` 20 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/iiit5k/metafile.yml: -------------------------------------------------------------------------------- 1 | Name: 'IIIT5K' 2 | Paper: 3 | Title: Scene Text Recognition using Higher Order Language Priors 4 | URL: http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/Home/mishraBMVC12.pdf 5 | Venue: BMVC 6 | Year: '2012' 7 | BibTeX: '@InProceedings{MishraBMVC12, 8 | author = "Mishra, A. and Alahari, K. and Jawahar, C.~V.", 9 | title = "Scene Text Recognition using Higher Order Language Priors", 10 | booktitle = "BMVC", 11 | year = "2012"}' 12 | Data: 13 | Website: http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html 14 | Language: 15 | - English 16 | Scene: 17 | - Natural Scene 18 | Granularity: 19 | - Word 20 | Tasks: 21 | - textrecog 22 | License: 23 | Type: N/A 24 | Link: N/A 25 | Format: .txt 26 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/iiit5k/sample_anno.md: -------------------------------------------------------------------------------- 1 | **Text Recognition** 2 | 3 | ```text 4 | # img_name, "text" 5 | 6 | train/1009_2.png You 7 | train/1017_1.png Rescue 8 | train/1017_2.png mission 9 | ``` 10 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/mjsynth/sample_anno.md: -------------------------------------------------------------------------------- 1 | **Text Recognition** 2 | 3 | ```txt 4 | ./3000/7/182_slinking_71711.jpg 71711 5 | ./3000/7/182_REMODELERS_64541.jpg 64541 6 | ``` 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/sroie/sample_anno.md: -------------------------------------------------------------------------------- 1 | **Text Detection, Text Recognition and Text Spotting** 2 | 3 | ```text 4 | # x1,y1,x2,y2,x3,y3,x4,y4,trans 5 | 6 | 72,25,326,25,326,64,72,64,TAN WOON YANN 7 | 50,82,440,82,440,121,50,121,BOOK TA .K(TAMAN DAYA) SDN BND 8 | 205,121,285,121,285,139,205,139,789417-W 9 | ``` 10 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/sroie/textrecog.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train' 4 | _base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test' 5 | _base_.train_preparer.packer.type = 'TextRecogCropPacker' 6 | _base_.test_preparer.packer.type = 'TextRecogCropPacker' 7 | 8 | config_generator = dict(type='TextRecogConfigGenerator') 9 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/sroie/textspotting.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train' 4 | _base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test' 5 | _base_.train_preparer.packer.type = 'TextSpottingPacker' 6 | _base_.test_preparer.packer.type = 'TextSpottingPacker' 7 | 8 | config_generator = dict(type='TextSpottingConfigGenerator') 9 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/svt/metafile.yml: -------------------------------------------------------------------------------- 1 | Name: 'Street View Text Dataset (SVT)' 2 | Paper: 3 | Title: Word Spotting in the Wild 4 | URL: https://link.springer.com/content/pdf/10.1007/978-3-642-15549-9_43.pdf 5 | Venue: ECCV 6 | Year: '2010' 7 | BibTeX: '@inproceedings{wang2010word, 8 | title={Word spotting in the wild}, 9 | author={Wang, Kai and Belongie, Serge}, 10 | booktitle={European conference on computer vision}, 11 | pages={591--604}, 12 | year={2010}, 13 | organization={Springer}}' 14 | Data: 15 | Website: http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset 16 | Language: 17 | - English 18 | Scene: 19 | - Natural Scene 20 | Granularity: 21 | - Word 22 | Tasks: 23 | - textdet 24 | - textrecog 25 | - textspotting 26 | License: 27 | Type: N/A 28 | Link: N/A 29 | Format: .xml 30 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/svt/textrecog.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.packer.type = 'TextRecogCropPacker' 4 | _base_.test_preparer.packer.type = 'TextRecogCropPacker' 5 | 6 | config_generator = dict(type='TextRecogConfigGenerator') 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/svt/textspotting.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.packer.type = 'TextSpottingPacker' 4 | _base_.test_preparer.packer.type = 'TextSpottingPacker' 5 | 6 | config_generator = dict(type='TextSpottingConfigGenerator') 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/svtp/sample_anno.md: -------------------------------------------------------------------------------- 1 | **Text Recognition** 2 | 3 | ```txt 4 | 13_15_0_par.jpg WYNDHAM 5 | 13_15_1_par.jpg HOTEL 6 | 12_16_0_par.jpg UNITED 7 | ``` 8 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/synthtext/textspotting.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.packer.type = 'TextSpottingPacker' 4 | _base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train' 5 | 6 | config_generator = dict(type='TextSpottingConfigGenerator') 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/textocr/textrecog.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.packer.type = 'TextRecogCropPacker' 4 | _base_.val_preparer.packer.type = 'TextRecogCropPacker' 5 | 6 | config_generator = dict(type='TextRecogConfigGenerator') 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/textocr/textspotting.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.packer.type = 'TextSpottingPacker' 4 | _base_.val_preparer.packer.type = 'TextSpottingPacker' 5 | 6 | config_generator = dict(type='TextSpottingConfigGenerator') 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/totaltext/sample_anno.md: -------------------------------------------------------------------------------- 1 | **Text Detection/Spotting** 2 | 3 | ```text 4 | x: [[259 313 389 427 354 302]], y: [[542 462 417 459 507 582]], ornt: [u'c'], transcriptions: [u'PAUL'] 5 | x: [[400 478 494 436]], y: [[398 380 448 465]], ornt: [u'#'], transcriptions: [u'#'] 6 | ``` 7 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/totaltext/textrecog.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train' 4 | _base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test' 5 | _base_.train_preparer.packer.type = 'TextRecogCropPacker' 6 | _base_.test_preparer.packer.type = 'TextRecogCropPacker' 7 | 8 | config_generator = dict(type='TextRecogConfigGenerator') 9 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/wildreceipt/textdet.py: -------------------------------------------------------------------------------- 1 | _base_ = ['kie.py'] 2 | 3 | _base_.train_preparer.update( 4 | dict( 5 | parser=dict(type='WildreceiptTextDetAnnParser'), 6 | packer=dict(type='TextDetPacker'), 7 | dumper=dict(type='JsonDumper'))) 8 | _base_.test_preparer.update( 9 | dict( 10 | parser=dict(type='WildreceiptTextDetAnnParser'), 11 | packer=dict(type='TextDetPacker'), 12 | dumper=dict(type='JsonDumper'))) 13 | 14 | config_generator = dict(type='TextDetConfigGenerator') 15 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/wildreceipt/textrecog.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.update( 4 | dict( 5 | parser=dict(type='WildreceiptTextDetAnnParser'), 6 | packer=dict(type='TextRecogCropPacker'), 7 | dumper=dict(type='JsonDumper'))) 8 | 9 | _base_.test_preparer.update( 10 | dict( 11 | parser=dict(type='WildreceiptTextDetAnnParser'), 12 | packer=dict(type='TextRecogCropPacker'), 13 | dumper=dict(type='JsonDumper'))) 14 | 15 | config_generator = dict(type='TextRecogConfigGenerator') 16 | -------------------------------------------------------------------------------- /mmocr/dataset_zoo/wildreceipt/textspotting.py: -------------------------------------------------------------------------------- 1 | _base_ = ['textdet.py'] 2 | 3 | _base_.train_preparer.packer.type = 'TextSpottingPacker' 4 | _base_.test_preparer.packer.type = 'TextSpottingPacker' 5 | 6 | config_generator = dict(type='TextSpottingConfigGenerator') 7 | -------------------------------------------------------------------------------- /mmocr/demo/demo_densetext_det.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/demo/demo_densetext_det.jpg -------------------------------------------------------------------------------- /mmocr/demo/demo_kie.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/demo/demo_kie.jpeg 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| j 21 | k 22 | l 23 | m 24 | n 25 | o 26 | p 27 | q 28 | r 29 | s 30 | t 31 | u 32 | v 33 | w 34 | x 35 | y 36 | z 37 | A 38 | B 39 | C 40 | D 41 | E 42 | F 43 | G 44 | H 45 | I 46 | J 47 | K 48 | L 49 | M 50 | N 51 | O 52 | P 53 | Q 54 | R 55 | S 56 | T 57 | U 58 | V 59 | W 60 | X 61 | Y 62 | Z 63 | ! 64 | " 65 | # 66 | $ 67 | % 68 | & 69 | ' 70 | ( 71 | ) 72 | * 73 | + 74 | , 75 | - 76 | . 77 | / 78 | : 79 | ; 80 | < 81 | = 82 | > 83 | ? 84 | @ 85 | [ 86 | \ 87 | ] 88 | _ 89 | ` 90 | ~ -------------------------------------------------------------------------------- /mmocr/dicts/english_digits_symbols_space.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 2 4 | 3 5 | 4 6 | 5 7 | 6 8 | 7 9 | 8 10 | 9 11 | a 12 | b 13 | c 14 | d 15 | e 16 | f 17 | g 18 | h 19 | i 20 | j 21 | k 22 | l 23 | m 24 | n 25 | o 26 | p 27 | q 28 | r 29 | s 30 | t 31 | u 32 | v 33 | w 34 | x 35 | y 36 | z 37 | A 38 | B 39 | C 40 | D 41 | E 42 | F 43 | G 44 | H 45 | I 46 | J 47 | K 48 | L 49 | M 50 | N 51 | O 52 | P 53 | Q 54 | R 55 | S 56 | T 57 | U 58 | V 59 | W 60 | X 61 | Y 62 | Z 63 | ! 64 | " 65 | # 66 | $ 67 | % 68 | & 69 | ' 70 | ( 71 | ) 72 | * 73 | + 74 | , 75 | - 76 | . 77 | / 78 | : 79 | ; 80 | < 81 | = 82 | > 83 | ? 84 | @ 85 | [ 86 | \ 87 | ] 88 | _ 89 | ` 90 | ~ 91 | -------------------------------------------------------------------------------- /mmocr/dicts/lower_english_digits.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 2 4 | 3 5 | 4 6 | 5 7 | 6 8 | 7 9 | 8 10 | 9 11 | a 12 | b 13 | c 14 | d 15 | e 16 | f 17 | g 18 | h 19 | i 20 | j 21 | k 22 | l 23 | m 24 | n 25 | o 26 | p 27 | q 28 | r 29 | s 30 | t 31 | u 32 | v 33 | w 34 | x 35 | y 36 | z -------------------------------------------------------------------------------- /mmocr/dicts/lower_english_digits_space.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 1 3 | 2 4 | 3 5 | 4 6 | 5 7 | 6 8 | 7 9 | 8 10 | 9 11 | a 12 | b 13 | c 14 | d 15 | e 16 | f 17 | g 18 | h 19 | i 20 | j 21 | k 22 | l 23 | m 24 | n 25 | o 26 | p 27 | q 28 | r 29 | s 30 | t 31 | u 32 | v 33 | w 34 | x 35 | y 36 | z 37 | -------------------------------------------------------------------------------- /mmocr/dicts/sdmgr_dict.txt: -------------------------------------------------------------------------------- 1 | / 2 | \ 3 | . 4 | $ 5 | £ 6 | € 7 | ¥ 8 | : 9 | - 10 | , 11 | * 12 | # 13 | ( 14 | ) 15 | % 16 | @ 17 | ! 18 | ' 19 | & 20 | = 21 | > 22 | + 23 | " 24 | × 25 | ? 26 | < 27 | [ 28 | ] 29 | _ 30 | 0 31 | 1 32 | 2 33 | 3 34 | 4 35 | 5 36 | 6 37 | 7 38 | 8 39 | 9 40 | a 41 | b 42 | c 43 | d 44 | e 45 | f 46 | g 47 | h 48 | i 49 | j 50 | k 51 | l 52 | m 53 | n 54 | o 55 | p 56 | q 57 | r 58 | s 59 | t 60 | u 61 | v 62 | w 63 | x 64 | y 65 | z 66 | A 67 | B 68 | C 69 | D 70 | E 71 | F 72 | G 73 | H 74 | I 75 | J 76 | K 77 | L 78 | M 79 | N 80 | O 81 | P 82 | Q 83 | R 84 | S 85 | T 86 | U 87 | V 88 | W 89 | X 90 | Y 91 | Z -------------------------------------------------------------------------------- /mmocr/docker/serve/config.properties: -------------------------------------------------------------------------------- 1 | inference_address=http://0.0.0.0:8080 2 | management_address=http://0.0.0.0:8081 3 | metrics_address=http://0.0.0.0:8082 4 | model_store=/home/model-server/model-store 5 | load_models=all 6 | -------------------------------------------------------------------------------- /mmocr/docker/serve/entrypoint.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | if [[ "$1" = "serve" ]]; then 5 | shift 1 6 | torchserve --start --ts-config /home/model-server/config.properties 7 | else 8 | eval "$@" 9 | fi 10 | 11 | # prevent docker exit 12 | tail -f /dev/null 13 | -------------------------------------------------------------------------------- /mmocr/docs/en/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /mmocr/docs/en/_static/css/readthedocs.css: -------------------------------------------------------------------------------- 1 | .header-logo { 2 | background-image: url("../images/mmocr.png"); 3 | background-size: 110px 40px; 4 | height: 40px; 5 | width: 110px; 6 | } 7 | -------------------------------------------------------------------------------- /mmocr/docs/en/_static/images/mmocr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/docs/en/_static/images/mmocr.png -------------------------------------------------------------------------------- /mmocr/docs/en/_static/js/collapsed.js: -------------------------------------------------------------------------------- 1 | var collapsedSections = ['Migration Guides', 'API Reference'] 2 | -------------------------------------------------------------------------------- /mmocr/docs/en/_templates/classtemplate.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | .. currentmodule:: {{ module }} 4 | 5 | 6 | {{ name | underline}} 7 | 8 | .. autoclass:: {{ name }} 9 | :members: 10 | 11 | 12 | .. 13 | autogenerated from source/_templates/classtemplate.rst 14 | note it does not have :inherited-members: 15 | -------------------------------------------------------------------------------- /mmocr/docs/en/api/apis.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | mmocr.apis 5 | =================================== 6 | 7 | .. contents:: mmocr.apis 8 | :depth: 2 9 | :local: 10 | :backlinks: top 11 | 12 | .. currentmodule:: mmocr.apis.inferencers 13 | 14 | Inferencers 15 | --------------------------------------------- 16 | 17 | .. autosummary:: 18 | :toctree: generated 19 | :nosignatures: 20 | :template: classtemplate.rst 21 | 22 | MMOCRInferencer 23 | TextDetInferencer 24 | TextRecInferencer 25 | TextSpotInferencer 26 | KIEInferencer 27 | -------------------------------------------------------------------------------- /mmocr/docs/en/api/engine.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | mmocr.engine 5 | =================================== 6 | 7 | .. contents:: mmocr.engine 8 | :depth: 2 9 | :local: 10 | :backlinks: top 11 | 12 | .. currentmodule:: mmocr.engine.hooks 13 | 14 | Hooks 15 | --------------------------------------------- 16 | 17 | .. autosummary:: 18 | :toctree: generated 19 | :nosignatures: 20 | :template: classtemplate.rst 21 | 22 | VisualizationHook 23 | -------------------------------------------------------------------------------- /mmocr/docs/en/api/structures.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | mmocr.structures 5 | =================================== 6 | 7 | .. currentmodule:: mmocr.structures 8 | .. autosummary:: 9 | :toctree: generated 10 | :nosignatures: 11 | :template: classtemplate.rst 12 | 13 | TextDetDataSample 14 | TextRecogDataSample 15 | KIEDataSample 16 | -------------------------------------------------------------------------------- /mmocr/docs/en/api/visualization.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | mmocr.visualization 5 | =================================== 6 | 7 | .. currentmodule:: mmocr.visualization 8 | 9 | .. autosummary:: 10 | :toctree: generated 11 | :nosignatures: 12 | :template: classtemplate.rst 13 | 14 | BaseLocalVisualizer 15 | TextDetLocalVisualizer 16 | TextRecogLocalVisualizer 17 | TextSpottingLocalVisualizer 18 | KIELocalVisualizer 19 | -------------------------------------------------------------------------------- /mmocr/docs/en/basic_concepts/convention.md: -------------------------------------------------------------------------------- 1 | # Convention\[coming soon\] 2 | 3 | Coming Soon! 4 | -------------------------------------------------------------------------------- /mmocr/docs/en/basic_concepts/data_flow.md: -------------------------------------------------------------------------------- 1 | # Data Flow\[coming soon\] 2 | 3 | Coming Soon! 4 | -------------------------------------------------------------------------------- /mmocr/docs/en/basic_concepts/engine.md: -------------------------------------------------------------------------------- 1 | # Engine\[coming soon\] 2 | 3 | Coming Soon! 4 | -------------------------------------------------------------------------------- /mmocr/docs/en/basic_concepts/models.md: -------------------------------------------------------------------------------- 1 | # Models\[coming soon\] 2 | 3 | Coming Soon! 4 | -------------------------------------------------------------------------------- /mmocr/docs/en/basic_concepts/overview.md: -------------------------------------------------------------------------------- 1 | # Overview & Features\[coming soon\] 2 | 3 | Coming Soon! 4 | -------------------------------------------------------------------------------- /mmocr/docs/en/basic_concepts/visualizers.md: -------------------------------------------------------------------------------- 1 | # Visualizers\[coming soon\] 2 | 3 | Coming Soon! 4 | -------------------------------------------------------------------------------- /mmocr/docs/en/docutils.conf: -------------------------------------------------------------------------------- 1 | [html writers] 2 | table_style: colwidths-auto 3 | -------------------------------------------------------------------------------- /mmocr/docs/en/migration/model.md: -------------------------------------------------------------------------------- 1 | # Pretrained Model Migration 2 | 3 | Due to the extensive refactoring and fixing of the model structure in the new version, MMOCR 1.x does not support load weights trained by the old version. We have updated the pre-training weights and logs of all models on our website. 4 | 5 | In addition, we are working on the development of a weight migration tool for text detection tasks and plan to release it in the near future. Since the text recognition and key information extraction models are too much modified and the migration is lossy, we do not plan to support them accordingly for the time being. If you have specific requirements, please feel free to raise an [Issue](https://github.com/open-mmlab/mmocr/issues). 6 | -------------------------------------------------------------------------------- /mmocr/docs/en/requirements.txt: -------------------------------------------------------------------------------- 1 | recommonmark 2 | sphinx 3 | sphinx_markdown_tables 4 | sphinx_rtd_theme 5 | -------------------------------------------------------------------------------- /mmocr/docs/en/switch_language.md: -------------------------------------------------------------------------------- 1 | ## English 2 | 3 | ## 简体中文 4 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = . 9 | BUILDDIR = _build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/_static/css/readthedocs.css: -------------------------------------------------------------------------------- 1 | .header-logo { 2 | background-image: url("../images/mmocr.png"); 3 | background-size: 110px 40px; 4 | height: 40px; 5 | width: 110px; 6 | } 7 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/_static/images/mmocr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/docs/zh_cn/_static/images/mmocr.png -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/_static/js/collapsed.js: -------------------------------------------------------------------------------- 1 | var collapsedSections = ['MMOCR 0.x 迁移指南', 'API 文档'] 2 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/_templates/classtemplate.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | .. currentmodule:: {{ module }} 4 | 5 | 6 | {{ name | underline}} 7 | 8 | .. autoclass:: {{ name }} 9 | :members: 10 | 11 | 12 | .. 13 | autogenerated from source/_templates/classtemplate.rst 14 | note it does not have :inherited-members: 15 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/api/apis.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | mmocr.apis 5 | =================================== 6 | 7 | .. contents:: mmocr.apis 8 | :depth: 2 9 | :local: 10 | :backlinks: top 11 | 12 | .. currentmodule:: mmocr.apis.inferencers 13 | 14 | Inferencers 15 | --------------------------------------------- 16 | 17 | .. autosummary:: 18 | :toctree: generated 19 | :nosignatures: 20 | :template: classtemplate.rst 21 | 22 | MMOCRInferencer 23 | TextDetInferencer 24 | TextRecInferencer 25 | TextSpotInferencer 26 | KIEInferencer 27 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/api/engine.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | mmocr.engine 5 | =================================== 6 | 7 | .. contents:: mmocr.engine 8 | :depth: 2 9 | :local: 10 | :backlinks: top 11 | 12 | .. currentmodule:: mmocr.engine.hooks 13 | 14 | Hooks 15 | --------------------------------------------- 16 | 17 | .. autosummary:: 18 | :toctree: generated 19 | :nosignatures: 20 | :template: classtemplate.rst 21 | 22 | VisualizationHook 23 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/api/structures.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | mmocr.structures 5 | =================================== 6 | 7 | .. currentmodule:: mmocr.structures 8 | .. autosummary:: 9 | :toctree: generated 10 | :nosignatures: 11 | :template: classtemplate.rst 12 | 13 | TextDetDataSample 14 | TextRecogDataSample 15 | KIEDataSample 16 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/api/visualization.rst: -------------------------------------------------------------------------------- 1 | .. role:: hidden 2 | :class: hidden-section 3 | 4 | mmocr.visualization 5 | =================================== 6 | 7 | .. currentmodule:: mmocr.visualization 8 | 9 | .. autosummary:: 10 | :toctree: generated 11 | :nosignatures: 12 | :template: classtemplate.rst 13 | 14 | BaseLocalVisualizer 15 | TextDetLocalVisualizer 16 | TextRecogLocalVisualizer 17 | TextSpottingLocalVisualizer 18 | KIELocalVisualizer 19 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/basic_concepts/convention.md: -------------------------------------------------------------------------------- 1 | # 开发默认约定\[待更新\] 2 | 3 | 待更新 4 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/basic_concepts/data_flow.md: -------------------------------------------------------------------------------- 1 | # 数据流\[待更新\] 2 | 3 | 待更新 4 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/basic_concepts/engine.md: -------------------------------------------------------------------------------- 1 | # 引擎\[待更新\] 2 | 3 | 待更新 4 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/basic_concepts/models.md: -------------------------------------------------------------------------------- 1 | # 模型\[待更新\] 2 | 3 | 待更新 4 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/basic_concepts/overview.md: -------------------------------------------------------------------------------- 1 | # 设计理念与特性\[待更新\] 2 | 3 | 待更新 4 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/basic_concepts/visualizers.md: -------------------------------------------------------------------------------- 1 | # 可视化组件\[待更新\] 2 | 3 | 待更新 4 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/cp_origin_docs.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | # Copy *.md files from docs/ if it doesn't have a Chinese translation 4 | 5 | for filename in $(find ../en/ -name '*.md' -printf "%P\n"); 6 | do 7 | mkdir -p $(dirname $filename) 8 | cp -n ../en/$filename ./$filename 9 | done 10 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/docutils.conf: -------------------------------------------------------------------------------- 1 | [html writers] 2 | table_style: colwidths-auto 3 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/migration/model.md: -------------------------------------------------------------------------------- 1 | # 预训练模型迁移指南 2 | 3 | 由于在新版本中我们对模型的结构进行了大量的重构和修复，MMOCR 1.x 并不能直接读入旧版的预训练权重。我们在网站上同步更新了所有模型的预训练权重和log，供有需要的用户使用。 4 | 5 | 此外，我们正在进行针对文本检测任务的权重迁移工具的开发，并计划于近期版本内发布。由于文本识别和关键信息提取模型改动过大，且迁移是有损的，我们暂时不计划作相应支持。如果您有具体的需求，欢迎通过 [Issue](https://github.com/open-mmlab/mmocr/issues) 向我们提问。 6 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/notes/branches.md: -------------------------------------------------------------------------------- 1 | # 分支 2 | 3 | 本文档旨在全面解释 MMOCR 中每个分支的目的和功能。 4 | 5 | ## 分支概述 6 | 7 | ### 1. `main` 8 | 9 | `main` 分支是 MMOCR 项目的默认分支。它包含了 MMOCR 的最新稳定版本，目前包含了 MMOCR 1.x（例如 v1.0.0）的代码。`main` 分支确保用户能够使用最新和最可靠的软件版本。 10 | 11 | ### 2. `dev-1.x` 12 | 13 | `dev-1.x` 分支用于开发 MMOCR 的下一个版本。此分支将在发版前进行依赖性测试，通过的提交将会合成到新版本中，并被发布到 `main` 分支。通过设置单独的开发分支，项目可以在不影响 `main` 分支稳定性的情况下继续发展。**所有 PR 应合并到 `dev-1.x` 分支。** 14 | 15 | ### 3. `0.x` 16 | 17 | `0.x` 分支用作 MMOCR 0.x（例如 v0.6.3）的存档。此分支将不再积极接受更新或改进，但它仍可作为历史参考，或供尚未升级到 MMOCR 1.x 的用户使用。 18 | 19 | ### 4. `1.x` 20 | 21 | 它是 `main` 分支的别名，旨在实现从兼容性时期平稳过渡。它将在 2023 年的年中删除。 22 | 23 | ```{note} 24 | 分支映射在 2023.04.06 发生了变化。有关旧分支映射和迁移指南，请参阅[分支迁移指南](../migration/branches.md)。 25 | ``` 26 | -------------------------------------------------------------------------------- /mmocr/docs/zh_cn/switch_language.md: -------------------------------------------------------------------------------- 1 | ## English 2 | 3 | ## 简体中文 4 | -------------------------------------------------------------------------------- /mmocr/mmocr/apis/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .inferencers import * # NOQA 3 | -------------------------------------------------------------------------------- /mmocr/mmocr/apis/inferencers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .kie_inferencer import KIEInferencer 3 | from .mmocr_inferencer import MMOCRInferencer 4 | from .textdet_inferencer import TextDetInferencer 5 | from .textrec_inferencer import TextRecInferencer 6 | from .textspot_inferencer import TextSpotInferencer 7 | 8 | __all__ = [ 9 | 'TextDetInferencer', 'TextRecInferencer', 'KIEInferencer', 10 | 'MMOCRInferencer', 'TextSpotInferencer' 11 | ] 12 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .dataset_wrapper import ConcatDataset 3 | from .icdar_dataset import IcdarDataset 4 | from .ocr_dataset import OCRDataset 5 | from .recog_lmdb_dataset import RecogLMDBDataset 6 | from .recog_text_dataset import RecogTextDataset 7 | from .samplers import * # NOQA 8 | from .transforms import * # NOQA 9 | from .wildreceipt_dataset import WildReceiptDataset 10 | 11 | __all__ = [ 12 | 'IcdarDataset', 'OCRDataset', 'RecogLMDBDataset', 'RecogTextDataset', 13 | 'WildReceiptDataset', 'ConcatDataset' 14 | ] 15 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/preparers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .config_generators import * # noqa 3 | from .data_preparer import DatasetPreparer 4 | from .dumpers import * # noqa 5 | from .gatherers import * # noqa 6 | from .obtainers import * # noqa 7 | from .packers import * # noqa 8 | from .parsers import * # noqa 9 | 10 | __all__ = ['DatasetPreparer'] 11 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/preparers/config_generators/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseDatasetConfigGenerator 3 | from .textdet_config_generator import TextDetConfigGenerator 4 | from .textrecog_config_generator import TextRecogConfigGenerator 5 | from .textspotting_config_generator import TextSpottingConfigGenerator 6 | 7 | __all__ = [ 8 | 'BaseDatasetConfigGenerator', 'TextDetConfigGenerator', 9 | 'TextRecogConfigGenerator', 'TextSpottingConfigGenerator' 10 | ] 11 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/preparers/dumpers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseDumper 3 | from .json_dumper import JsonDumper 4 | from .lmdb_dumper import TextRecogLMDBDumper 5 | from .wild_receipt_openset_dumper import WildreceiptOpensetDumper 6 | 7 | __all__ = [ 8 | 'BaseDumper', 'JsonDumper', 'WildreceiptOpensetDumper', 9 | 'TextRecogLMDBDumper' 10 | ] 11 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/preparers/dumpers/json_dumper.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import os.path as osp 3 | from typing import Dict 4 | 5 | import mmengine 6 | 7 | from mmocr.registry import DATA_DUMPERS 8 | from .base import BaseDumper 9 | 10 | 11 | @DATA_DUMPERS.register_module() 12 | class JsonDumper(BaseDumper): 13 | """Dumper for json file.""" 14 | 15 | def dump(self, data: Dict) -> None: 16 | """Dump data to json file. 17 | 18 | Args: 19 | data (Dict): Data to be dumped. 20 | """ 21 | 22 | filename = f'{self.task}_{self.split}.json' 23 | dst_file = osp.join(self.data_root, filename) 24 | mmengine.dump(data, dst_file) 25 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/preparers/dumpers/wild_receipt_openset_dumper.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import os.path as osp 3 | from typing import List 4 | 5 | from mmocr.registry import DATA_DUMPERS 6 | from mmocr.utils import list_to_file 7 | from .base import BaseDumper 8 | 9 | 10 | @DATA_DUMPERS.register_module() 11 | class WildreceiptOpensetDumper(BaseDumper): 12 | 13 | def dump(self, data: List): 14 | """Dump data to txt file. 15 | 16 | Args: 17 | data (List): Data to be dumped. 18 | """ 19 | 20 | filename = f'openset_{self.split}.txt' 21 | dst_file = osp.join(self.data_root, filename) 22 | list_to_file(dst_file, data) 23 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/preparers/gatherers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | 3 | from .base import BaseGatherer 4 | from .mono_gatherer import MonoGatherer 5 | from .naf_gatherer import NAFGatherer 6 | from .pair_gatherer import PairGatherer 7 | 8 | __all__ = ['BaseGatherer', 'MonoGatherer', 'PairGatherer', 'NAFGatherer'] 9 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/preparers/obtainers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .naive_data_obtainer import NaiveDataObtainer 3 | 4 | __all__ = ['NaiveDataObtainer'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/preparers/packers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BasePacker 3 | from .textdet_packer import TextDetPacker 4 | from .textrecog_packer import TextRecogCropPacker, TextRecogPacker 5 | from .textspotting_packer import TextSpottingPacker 6 | from .wildreceipt_packer import WildReceiptPacker 7 | 8 | __all__ = [ 9 | 'BasePacker', 'TextDetPacker', 'TextRecogPacker', 'TextRecogCropPacker', 10 | 'TextSpottingPacker', 'WildReceiptPacker' 11 | ] 12 | -------------------------------------------------------------------------------- /mmocr/mmocr/datasets/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .batch_aug import BatchAugSampler 3 | 4 | __all__ = ['BatchAugSampler'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/engine/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .hooks import * # NOQA 3 | -------------------------------------------------------------------------------- /mmocr/mmocr/engine/hooks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .visualization_hook import VisualizationHook 3 | 4 | __all__ = ['VisualizationHook'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .evaluator import * # NOQA 3 | from .metrics import * # NOQA 4 | -------------------------------------------------------------------------------- /mmocr/mmocr/evaluation/evaluator/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .multi_datasets_evaluator import MultiDatasetsEvaluator 3 | 4 | __all__ = ['MultiDatasetsEvaluator'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/evaluation/functional/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .hmean import compute_hmean 3 | 4 | __all__ = ['compute_hmean'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/evaluation/metrics/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .f_metric import F1Metric 3 | from .hmean_iou_metric import HmeanIOUMetric 4 | from .recog_metric import CharMetric, OneMinusNEDMetric, WordMetric 5 | 6 | __all__ = [ 7 | 'WordMetric', 'CharMetric', 'OneMinusNEDMetric', 'HmeanIOUMetric', 8 | 'F1Metric' 9 | ] 10 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .common import * # NOQA 3 | from .kie import * # NOQA 4 | from .textdet import * # NOQA 5 | from .textrecog import * # NOQA 6 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/common/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .backbones import * # NOQA 3 | from .dictionary import * # NOQA 4 | from .layers import * # NOQA 5 | from .losses import * # NOQA 6 | from .modules import * # NOQA 7 | from .plugins import * # NOQA 8 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/common/backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .clip_resnet import CLIPResNet 3 | from .unet import UNet 4 | 5 | __all__ = ['UNet', 'CLIPResNet'] 6 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/common/dictionary/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | 3 | from .dictionary import Dictionary 4 | 5 | __all__ = ['Dictionary'] 6 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/common/layers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .transformer_layers import TFDecoderLayer, TFEncoderLayer 3 | 4 | __all__ = ['TFEncoderLayer', 'TFDecoderLayer'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/common/losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .bce_loss import (MaskedBalancedBCELoss, MaskedBalancedBCEWithLogitsLoss, 3 | MaskedBCELoss, MaskedBCEWithLogitsLoss) 4 | from .ce_loss import CrossEntropyLoss 5 | from .dice_loss import MaskedDiceLoss, MaskedSquareDiceLoss 6 | from .l1_loss import MaskedSmoothL1Loss, SmoothL1Loss 7 | 8 | __all__ = [ 9 | 'MaskedBalancedBCEWithLogitsLoss', 'MaskedDiceLoss', 'MaskedSmoothL1Loss', 10 | 'MaskedSquareDiceLoss', 'MaskedBCEWithLogitsLoss', 'SmoothL1Loss', 11 | 'CrossEntropyLoss', 'MaskedBalancedBCELoss', 'MaskedBCELoss' 12 | ] 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/common/losses/ce_loss.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch.nn as nn 3 | 4 | from mmocr.registry import MODELS 5 | 6 | 7 | @MODELS.register_module() 8 | class CrossEntropyLoss(nn.CrossEntropyLoss): 9 | """Cross entropy loss.""" 10 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/common/modules/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .transformer_module import (MultiHeadAttention, PositionalEncoding, 3 | PositionwiseFeedForward, 4 | ScaledDotProductAttention) 5 | 6 | __all__ = [ 7 | 'ScaledDotProductAttention', 'MultiHeadAttention', 8 | 'PositionwiseFeedForward', 'PositionalEncoding' 9 | ] 10 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/common/plugins/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .common import AvgPool2d 3 | 4 | __all__ = ['AvgPool2d'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/kie/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .extractors import * # NOQA 3 | from .heads import * # NOQA 4 | from .module_losses import * # NOQA 5 | from .postprocessors import * # NOQA 6 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/kie/extractors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .sdmgr import SDMGR 3 | 4 | __all__ = ['SDMGR'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/kie/heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .sdmgr_head import SDMGRHead 3 | 4 | __all__ = ['SDMGRHead'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/kie/module_losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .sdmgr_module_loss import SDMGRModuleLoss 3 | 4 | __all__ = ['SDMGRModuleLoss'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/kie/postprocessors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .sdmgr_postprocessor import SDMGRPostProcessor 3 | 4 | __all__ = ['SDMGRPostProcessor'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .data_preprocessors import * # NOQA 3 | from .detectors import * # NOQA 4 | from .heads import * # NOQA 5 | from .module_losses import * # NOQA 6 | from .necks import * # NOQA 7 | from .postprocessors import * # NOQA 8 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/data_preprocessors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .data_preprocessor import TextDetDataPreprocessor 3 | 4 | __all__ = ['TextDetDataPreprocessor'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/detectors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .dbnet import DBNet 3 | from .drrg import DRRG 4 | from .fcenet import FCENet 5 | from .mmdet_wrapper import MMDetWrapper 6 | from .panet import PANet 7 | from .psenet import PSENet 8 | from .single_stage_text_detector import SingleStageTextDetector 9 | from .textsnake import TextSnake 10 | 11 | __all__ = [ 12 | 'SingleStageTextDetector', 'DBNet', 'PANet', 'PSENet', 'TextSnake', 13 | 'FCENet', 'DRRG', 'MMDetWrapper' 14 | ] 15 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/detectors/dbnet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .single_stage_text_detector import SingleStageTextDetector 4 | 5 | 6 | @MODELS.register_module() 7 | class DBNet(SingleStageTextDetector): 8 | """The class for implementing DBNet text detector: Real-time Scene Text 9 | Detection with Differentiable Binarization. 10 | 11 | [https://arxiv.org/abs/1911.08947]. 12 | """ 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/detectors/drrg.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .single_stage_text_detector import SingleStageTextDetector 4 | 5 | 6 | @MODELS.register_module() 7 | class DRRG(SingleStageTextDetector): 8 | """The class for implementing DRRG text detector. Deep Relational Reasoning 9 | Graph Network for Arbitrary Shape Text Detection. 10 | 11 | [https://arxiv.org/abs/2003.07493] 12 | """ 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/detectors/fcenet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .single_stage_text_detector import SingleStageTextDetector 4 | 5 | 6 | @MODELS.register_module() 7 | class FCENet(SingleStageTextDetector): 8 | """The class for implementing FCENet text detector 9 | FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped Text 10 | Detection 11 | 12 | [https://arxiv.org/abs/2104.10442] 13 | """ 14 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/detectors/panet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .single_stage_text_detector import SingleStageTextDetector 4 | 5 | 6 | @MODELS.register_module() 7 | class PANet(SingleStageTextDetector): 8 | """The class for implementing PANet text detector: 9 | 10 | Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel 11 | Aggregation Network [https://arxiv.org/abs/1908.05900]. 12 | """ 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/detectors/psenet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .single_stage_text_detector import SingleStageTextDetector 4 | 5 | 6 | @MODELS.register_module() 7 | class PSENet(SingleStageTextDetector): 8 | """The class for implementing PSENet text detector: Shape Robust Text 9 | Detection with Progressive Scale Expansion Network. 10 | 11 | [https://arxiv.org/abs/1806.02559]. 12 | """ 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/detectors/textsnake.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .single_stage_text_detector import SingleStageTextDetector 4 | 5 | 6 | @MODELS.register_module() 7 | class TextSnake(SingleStageTextDetector): 8 | """The class for implementing TextSnake text detector: TextSnake: A 9 | Flexible Representation for Detecting Text of Arbitrary Shapes. 10 | 11 | [https://arxiv.org/abs/1807.01544] 12 | """ 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseTextDetHead 3 | from .db_head import DBHead 4 | from .drrg_head import DRRGHead 5 | from .fce_head import FCEHead 6 | from .pan_head import PANHead 7 | from .pse_head import PSEHead 8 | from .textsnake_head import TextSnakeHead 9 | 10 | __all__ = [ 11 | 'PSEHead', 'PANHead', 'DBHead', 'FCEHead', 'TextSnakeHead', 'DRRGHead', 12 | 'BaseTextDetHead' 13 | ] 14 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/module_losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .db_module_loss import DBModuleLoss 3 | from .drrg_module_loss import DRRGModuleLoss 4 | from .fce_module_loss import FCEModuleLoss 5 | from .pan_module_loss import PANModuleLoss 6 | from .pse_module_loss import PSEModuleLoss 7 | from .seg_based_module_loss import SegBasedModuleLoss 8 | from .textsnake_module_loss import TextSnakeModuleLoss 9 | 10 | __all__ = [ 11 | 'PANModuleLoss', 'PSEModuleLoss', 'DBModuleLoss', 'TextSnakeModuleLoss', 12 | 'FCEModuleLoss', 'DRRGModuleLoss', 'SegBasedModuleLoss' 13 | ] 14 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/necks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .fpem_ffm import FPEM_FFM 3 | from .fpn_cat import FPNC 4 | from .fpn_unet import FPN_UNet 5 | from .fpnf import FPNF 6 | 7 | __all__ = ['FPEM_FFM', 'FPNF', 'FPNC', 'FPN_UNet'] 8 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textdet/postprocessors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseTextDetPostProcessor 3 | from .db_postprocessor import DBPostprocessor 4 | from .drrg_postprocessor import DRRGPostprocessor 5 | from .fce_postprocessor import FCEPostprocessor 6 | from .pan_postprocessor import PANPostprocessor 7 | from .pse_postprocessor import PSEPostprocessor 8 | from .textsnake_postprocessor import TextSnakePostprocessor 9 | 10 | __all__ = [ 11 | 'PSEPostprocessor', 'PANPostprocessor', 'DBPostprocessor', 12 | 'DRRGPostprocessor', 'FCEPostprocessor', 'TextSnakePostprocessor', 13 | 'BaseTextDetPostProcessor' 14 | ] 15 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .backbones import * # NOQA 3 | from .data_preprocessors import * # NOQA 4 | from .decoders import * # NOQA 5 | from .encoders import * # NOQA 6 | from .layers import * # NOQA 7 | from .module_losses import * # NOQA 8 | from .plugins import * # NOQA 9 | from .postprocessors import * # NOQA 10 | from .preprocessors import * # NOQA 11 | from .recognizers import * # NOQA 12 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .mini_vgg import MiniVGG 3 | from .mobilenet_v2 import MobileNetV2 4 | from .nrtr_modality_transformer import NRTRModalityTransform 5 | from .resnet import ResNet 6 | from .resnet31_ocr import ResNet31OCR 7 | from .resnet_abi import ResNetABI 8 | from .shallow_cnn import ShallowCNN 9 | 10 | __all__ = [ 11 | 'ResNet31OCR', 'MiniVGG', 'NRTRModalityTransform', 'ShallowCNN', 12 | 'ResNetABI', 'ResNet', 'MobileNetV2' 13 | ] 14 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/data_preprocessors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .data_preprocessor import TextRecogDataPreprocessor 3 | 4 | __all__ = ['TextRecogDataPreprocessor'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/encoders/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .abi_encoder import ABIEncoder 3 | from .aster_encoder import ASTEREncoder 4 | from .base import BaseEncoder 5 | from .channel_reduction_encoder import ChannelReductionEncoder 6 | from .nrtr_encoder import NRTREncoder 7 | from .sar_encoder import SAREncoder 8 | from .satrn_encoder import SATRNEncoder 9 | from .svtr_encoder import SVTREncoder 10 | 11 | __all__ = [ 12 | 'SAREncoder', 'NRTREncoder', 'BaseEncoder', 'ChannelReductionEncoder', 13 | 'SATRNEncoder', 'ABIEncoder', 'SVTREncoder', 'ASTEREncoder' 14 | ] 15 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/encoders/base.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmengine.model import BaseModule 3 | 4 | from mmocr.registry import MODELS 5 | 6 | 7 | @MODELS.register_module() 8 | class BaseEncoder(BaseModule): 9 | """Base Encoder class for text recognition.""" 10 | 11 | def forward(self, feat, **kwargs): 12 | return feat 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/layers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .conv_layer import BasicBlock, Bottleneck 3 | from .dot_product_attention_layer import DotProductAttentionLayer 4 | from .lstm_layer import BidirectionalLSTM 5 | from .position_aware_layer import PositionAwareLayer 6 | from .robust_scanner_fusion_layer import RobustScannerFusionLayer 7 | from .satrn_layers import Adaptive2DPositionalEncoding, SATRNEncoderLayer 8 | 9 | __all__ = [ 10 | 'BidirectionalLSTM', 'Adaptive2DPositionalEncoding', 'BasicBlock', 11 | 'Bottleneck', 'RobustScannerFusionLayer', 'DotProductAttentionLayer', 12 | 'PositionAwareLayer', 'SATRNEncoderLayer' 13 | ] 14 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/layers/lstm_layer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch.nn as nn 3 | 4 | 5 | class BidirectionalLSTM(nn.Module): 6 | 7 | def __init__(self, nIn, nHidden, nOut): 8 | super().__init__() 9 | 10 | self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True) 11 | self.embedding = nn.Linear(nHidden * 2, nOut) 12 | 13 | def forward(self, input): 14 | recurrent, _ = self.rnn(input) 15 | T, b, h = recurrent.size() 16 | t_rec = recurrent.view(T * b, h) 17 | 18 | output = self.embedding(t_rec) # [T * b, nOut] 19 | output = output.view(T, b, -1) 20 | 21 | return output 22 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/layers/robust_scanner_fusion_layer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import torch 3 | import torch.nn as nn 4 | from mmengine.model import BaseModule 5 | 6 | 7 | class RobustScannerFusionLayer(BaseModule): 8 | 9 | def __init__(self, dim_model, dim=-1, init_cfg=None): 10 | super().__init__(init_cfg=init_cfg) 11 | 12 | self.dim_model = dim_model 13 | self.dim = dim 14 | 15 | self.linear_layer = nn.Linear(dim_model * 2, dim_model * 2) 16 | self.glu_layer = nn.GLU(dim=dim) 17 | 18 | def forward(self, x0, x1): 19 | assert x0.size() == x1.size() 20 | fusion_input = torch.cat([x0, x1], self.dim) 21 | output = self.linear_layer(fusion_input) 22 | output = self.glu_layer(output) 23 | 24 | return output 25 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/module_losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .abi_module_loss import ABIModuleLoss 3 | from .base import BaseTextRecogModuleLoss 4 | from .ce_module_loss import CEModuleLoss 5 | from .ctc_module_loss import CTCModuleLoss 6 | 7 | __all__ = [ 8 | 'BaseTextRecogModuleLoss', 'CEModuleLoss', 'CTCModuleLoss', 'ABIModuleLoss' 9 | ] 10 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/plugins/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .common import GCAModule, Maxpool2d 3 | 4 | __all__ = ['Maxpool2d', 'GCAModule'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/postprocessors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .attn_postprocessor import AttentionPostprocessor 3 | from .base import BaseTextRecogPostprocessor 4 | from .ctc_postprocessor import CTCPostProcessor 5 | 6 | __all__ = [ 7 | 'BaseTextRecogPostprocessor', 'AttentionPostprocessor', 'CTCPostProcessor' 8 | ] 9 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/preprocessors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .tps_preprocessor import STN, TPStransform 3 | 4 | __all__ = ['TPStransform', 'STN'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/preprocessors/base.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmengine.model import BaseModule 3 | 4 | from mmocr.registry import MODELS 5 | 6 | 7 | @MODELS.register_module() 8 | class BasePreprocessor(BaseModule): 9 | """Base Preprocessor class for text recognition.""" 10 | 11 | def forward(self, x, **kwargs): 12 | return x 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/recognizers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .abinet import ABINet 3 | from .aster import ASTER 4 | from .base import BaseRecognizer 5 | from .crnn import CRNN 6 | from .encoder_decoder_recognizer import EncoderDecoderRecognizer 7 | from .encoder_decoder_recognizer_tta import EncoderDecoderRecognizerTTAModel 8 | from .master import MASTER 9 | from .nrtr import NRTR 10 | from .robust_scanner import RobustScanner 11 | from .sar import SARNet 12 | from .satrn import SATRN 13 | from .svtr import SVTR 14 | 15 | __all__ = [ 16 | 'BaseRecognizer', 'EncoderDecoderRecognizer', 'CRNN', 'SARNet', 'NRTR', 17 | 'RobustScanner', 'SATRN', 'ABINet', 'MASTER', 'SVTR', 'ASTER', 18 | 'EncoderDecoderRecognizerTTAModel' 19 | ] 20 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/recognizers/abinet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .encoder_decoder_recognizer import EncoderDecoderRecognizer 4 | 5 | 6 | @MODELS.register_module() 7 | class ABINet(EncoderDecoderRecognizer): 8 | """Implementation of `Read Like Humans: Autonomous, Bidirectional and 9 | Iterative LanguageModeling for Scene Text Recognition. 10 | 11 | `_ 12 | """ 13 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/recognizers/aster.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .encoder_decoder_recognizer import EncoderDecoderRecognizer 4 | 5 | 6 | @MODELS.register_module() 7 | class ASTER(EncoderDecoderRecognizer): 8 | """Implement `ASTER: An Attentional Scene Text Recognizer with Flexible 9 | Rectification. 10 | 11 | `_""" 9 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/recognizers/nrtr.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .encoder_decoder_recognizer import EncoderDecoderRecognizer 4 | 5 | 6 | @MODELS.register_module() 7 | class NRTR(EncoderDecoderRecognizer): 8 | """Implementation of `NRTR `_""" 9 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/recognizers/robust_scanner.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .encoder_decoder_recognizer import EncoderDecoderRecognizer 4 | 5 | 6 | @MODELS.register_module() 7 | class RobustScanner(EncoderDecoderRecognizer): 8 | """Implementation of `RobustScanner. 9 | 10 | 11 | """ 12 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/recognizers/sar.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .encoder_decoder_recognizer import EncoderDecoderRecognizer 4 | 5 | 6 | @MODELS.register_module() 7 | class SARNet(EncoderDecoderRecognizer): 8 | """Implementation of `SAR `_""" 9 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/recognizers/satrn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .encoder_decoder_recognizer import EncoderDecoderRecognizer 4 | 5 | 6 | @MODELS.register_module() 7 | class SATRN(EncoderDecoderRecognizer): 8 | """Implementation of `SATRN `_""" 9 | -------------------------------------------------------------------------------- /mmocr/mmocr/models/textrecog/recognizers/svtr.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .encoder_decoder_recognizer import EncoderDecoderRecognizer 4 | 5 | 6 | @MODELS.register_module() 7 | class SVTR(EncoderDecoderRecognizer): 8 | """A PyTorch implementation of : `SVTR: Scene Text Recognition with a 9 | Single Visual Model `_""" 10 | -------------------------------------------------------------------------------- /mmocr/mmocr/structures/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .kie_data_sample import KIEDataSample 3 | from .textdet_data_sample import TextDetDataSample 4 | from .textrecog_data_sample import TextRecogDataSample 5 | from .textspotting_data_sample import TextSpottingDataSample 6 | 7 | __all__ = [ 8 | 'TextDetDataSample', 'TextRecogDataSample', 'KIEDataSample', 9 | 'TextSpottingDataSample' 10 | ] 11 | -------------------------------------------------------------------------------- /mmocr/mmocr/testing/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .data import create_dummy_dict_file, create_dummy_textdet_inputs 3 | 4 | __all__ = ['create_dummy_dict_file', 'create_dummy_textdet_inputs'] 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/utils/collect_env.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmengine.utils import get_git_hash 3 | from mmengine.utils.dl_utils import collect_env as collect_base_env 4 | 5 | import mmocr 6 | 7 | 8 | def collect_env(): 9 | """Collect the information of the running environments.""" 10 | env_info = collect_base_env() 11 | env_info['MMOCR'] = mmocr.__version__ + '+' + get_git_hash()[:7] 12 | return env_info 13 | 14 | 15 | if __name__ == '__main__': 16 | for name, val in collect_env().items(): 17 | print(f'{name}: {val}') 18 | -------------------------------------------------------------------------------- /mmocr/mmocr/version.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Open-MMLab. All rights reserved. 2 | 3 | __version__ = '1.0.0' 4 | short_version = __version__ 5 | -------------------------------------------------------------------------------- /mmocr/mmocr/visualization/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base_visualizer import BaseLocalVisualizer 3 | from .kie_visualizer import KIELocalVisualizer 4 | from .textdet_visualizer import TextDetLocalVisualizer 5 | from .textrecog_visualizer import TextRecogLocalVisualizer 6 | from .textspotting_visualizer import TextSpottingLocalVisualizer 7 | 8 | __all__ = [ 9 | 'BaseLocalVisualizer', 'KIELocalVisualizer', 'TextDetLocalVisualizer', 10 | 'TextRecogLocalVisualizer', 'TextSpottingLocalVisualizer' 11 | ] 12 | -------------------------------------------------------------------------------- /mmocr/model-index.yml: -------------------------------------------------------------------------------- 1 | Import: 2 | - configs/textdet/dbnet/metafile.yml 3 | - configs/textdet/dbnetpp/metafile.yml 4 | - configs/textdet/maskrcnn/metafile.yml 5 | - configs/textdet/drrg/metafile.yml 6 | - configs/textdet/fcenet/metafile.yml 7 | - configs/textdet/panet/metafile.yml 8 | - configs/textdet/psenet/metafile.yml 9 | - configs/textdet/textsnake/metafile.yml 10 | - configs/textrecog/abinet/metafile.yml 11 | - configs/textrecog/aster/metafile.yml 12 | - configs/textrecog/crnn/metafile.yml 13 | - configs/textrecog/master/metafile.yml 14 | - configs/textrecog/nrtr/metafile.yml 15 | - configs/textrecog/svtr/metafile.yml 16 | - configs/textrecog/robust_scanner/metafile.yml 17 | - configs/textrecog/sar/metafile.yml 18 | - configs/textrecog/satrn/metafile.yml 19 | - configs/kie/sdmgr/metafile.yml 20 | -------------------------------------------------------------------------------- /mmocr/my_test.sh: -------------------------------------------------------------------------------- 1 | CUDA_VISIBLE_DEVICES=0 python tools/test.py configs/textdet/dbnet/synth_data_train_100k_ic15_test.py output/new_10k_synthtext/epoch_1.pth --save-preds 2 | -------------------------------------------------------------------------------- /mmocr/my_train.sh: -------------------------------------------------------------------------------- 1 | CUDA_VISIBLE_DEVICES=0 python tools/train.py configs/textdet/dbnet/synth_data_train_100k_ic15_test.py --work-dir output/new_SD_base_10000_curve --amp 2 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/abcnet/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | # Copyright (c) OpenMMLab. All rights reserved. 3 | from .metric import * # NOQA 4 | from .model import * # NOQA 5 | from .utils import * # NOQA 6 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/abcnet/metric/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .e2e_hmean_iou_metric import E2EHmeanIOUMetric 3 | 4 | __all__ = ['E2EHmeanIOUMetric'] 5 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/abcnet/model/abcnet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .two_stage_text_spotting import TwoStageTextSpotter 4 | 5 | 6 | @MODELS.register_module() 7 | class ABCNet(TwoStageTextSpotter): 8 | """CTC-loss based recognizer.""" 9 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/abcnet/model/abcnet_rec.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.models.textrecog import EncoderDecoderRecognizer 3 | from mmocr.registry import MODELS 4 | 5 | 6 | @MODELS.register_module() 7 | class ABCNetRec(EncoderDecoderRecognizer): 8 | """CTC-loss based recognizer.""" 9 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/abcnet/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .bezier_utils import bezier2poly, poly2bezier 3 | 4 | __all__ = ['poly2bezier', 'bezier2poly'] 5 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/config/_base_/datasets/icdar2015.py: -------------------------------------------------------------------------------- 1 | icdar2015_textspotting_data_root = 'data/icdar2015' 2 | 3 | icdar2015_textspotting_train = dict( 4 | type='OCRDataset', 5 | data_root=icdar2015_textspotting_data_root, 6 | ann_file='textspotting_train.json', 7 | pipeline=None) 8 | 9 | icdar2015_textspotting_test = dict( 10 | type='OCRDataset', 11 | data_root=icdar2015_textspotting_data_root, 12 | ann_file='textspotting_test.json', 13 | test_mode=True, 14 | # indices=50, 15 | pipeline=None) 16 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/config/_base_/schedules/schedule_sgd_500e.py: -------------------------------------------------------------------------------- 1 | # optimizer 2 | optim_wrapper = dict( 3 | type='OptimWrapper', 4 | optimizer=dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001), 5 | clip_grad=dict(type='value', clip_value=1)) 6 | train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=500, val_interval=20) 7 | val_cfg = dict(type='ValLoop') 8 | test_cfg = dict(type='TestLoop') 9 | # learning policy 10 | param_scheduler = [ 11 | dict(type='LinearLR', end=1000, start_factor=0.001, by_epoch=False), 12 | ] 13 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/config/abcnet_v2/abcnet-v2_resnet50_bifpn_500e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '_base_abcnet-v2_resnet50_bifpn.py', 3 | '../_base_/datasets/icdar2015.py', 4 | '../_base_/default_runtime.py', 5 | ] 6 | 7 | # dataset settings 8 | icdar2015_textspotting_test = _base_.icdar2015_textspotting_test 9 | icdar2015_textspotting_test.pipeline = _base_.test_pipeline 10 | 11 | val_dataloader = dict( 12 | batch_size=1, 13 | num_workers=4, 14 | persistent_workers=True, 15 | sampler=dict(type='DefaultSampler', shuffle=False), 16 | dataset=icdar2015_textspotting_test) 17 | 18 | test_dataloader = val_dataloader 19 | 20 | val_cfg = dict(type='ValLoop') 21 | test_cfg = dict(type='TestLoop') 22 | 23 | custom_imports = dict(imports=['abcnet'], allow_failed_imports=False) 24 | -------------------------------------------------------------------------------- /mmocr/projects/ABCNet/dicts/abcnet.txt: -------------------------------------------------------------------------------- 1 | 2 | ! 3 | " 4 | # 5 | $ 6 | % 7 | & 8 | ' 9 | ( 10 | ) 11 | * 12 | + 13 | , 14 | - 15 | . 16 | / 17 | 0 18 | 1 19 | 2 20 | 3 21 | 4 22 | 5 23 | 6 24 | 7 25 | 8 26 | 9 27 | : 28 | ; 29 | < 30 | = 31 | > 32 | ? 33 | @ 34 | A 35 | B 36 | C 37 | D 38 | E 39 | F 40 | G 41 | H 42 | I 43 | J 44 | K 45 | L 46 | M 47 | N 48 | O 49 | P 50 | Q 51 | R 52 | S 53 | T 54 | U 55 | V 56 | W 57 | X 58 | Y 59 | Z 60 | [ 61 | \ 62 | ] 63 | ^ 64 | _ 65 | ` 66 | a 67 | b 68 | c 69 | d 70 | e 71 | f 72 | g 73 | h 74 | i 75 | j 76 | k 77 | l 78 | m 79 | n 80 | o 81 | p 82 | q 83 | r 84 | s 85 | t 86 | u 87 | v 88 | w 89 | x 90 | y 91 | z 92 | { 93 | | 94 | } 95 | ~ -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/ctw1500-spts.py: -------------------------------------------------------------------------------- 1 | ctw1500_textspotting_data_root = 'data/CTW1500' 2 | 3 | ctw1500_textspotting_train = dict( 4 | type='AdelDataset', 5 | data_root=ctw1500_textspotting_data_root, 6 | ann_file='annotations/train_ctw1500_maxlen25_v2.json', 7 | data_prefix=dict(img_path='ctwtrain_text_image/'), 8 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 9 | pipeline=None) 10 | 11 | ctw1500_textspotting_test = dict( 12 | type='AdelDataset', 13 | data_root=ctw1500_textspotting_data_root, 14 | ann_file='annotations/test_ctw1500_maxlen25.json', 15 | data_prefix=dict(img_path='ctwtest_text_image/'), 16 | test_mode=True, 17 | pipeline=None) 18 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/icdar2013-spts.py: -------------------------------------------------------------------------------- 1 | icdar2013_textspotting_data_root = 'spts-data/icdar2013' 2 | 3 | icdar2013_textspotting_train = dict( 4 | type='AdelDataset', 5 | data_root=icdar2013_textspotting_data_root, 6 | ann_file='ic13_train.json', 7 | data_prefix=dict(img_path='train_images/'), 8 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 9 | pipeline=None) 10 | 11 | icdar2013_textspotting_test = dict( 12 | type='AdelDataset', 13 | data_root=icdar2013_textspotting_data_root, 14 | data_prefix=dict(img_path='test_images/'), 15 | ann_file='ic13_test.json', 16 | test_mode=True, 17 | pipeline=None) 18 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/icdar2013.py: -------------------------------------------------------------------------------- 1 | icdar2013_textspotting_data_root = 'data/icdar2013' 2 | 3 | icdar2013_textspotting_train = dict( 4 | type='OCRDataset', 5 | data_root=icdar2013_textspotting_data_root, 6 | ann_file='textspotting_train.json', 7 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 8 | pipeline=None) 9 | 10 | icdar2013_textspotting_test = dict( 11 | type='OCRDataset', 12 | data_root=icdar2013_textspotting_data_root, 13 | ann_file='textspotting_test.json', 14 | test_mode=True, 15 | pipeline=None) 16 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/icdar2015-spts.py: -------------------------------------------------------------------------------- 1 | icdar2015_textspotting_data_root = 'spts-data/icdar2015' 2 | 3 | icdar2015_textspotting_train = dict( 4 | type='AdelDataset', 5 | data_root=icdar2015_textspotting_data_root, 6 | ann_file='ic15_train.json', 7 | data_prefix=dict(img_path='train_images/'), 8 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 9 | pipeline=None) 10 | 11 | icdar2015_textspotting_test = dict( 12 | type='AdelDataset', 13 | data_root=icdar2015_textspotting_data_root, 14 | data_prefix=dict(img_path='test_images/'), 15 | ann_file='ic15_test.json', 16 | test_mode=True, 17 | pipeline=None) 18 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/icdar2015.py: -------------------------------------------------------------------------------- 1 | icdar2015_textspotting_data_root = 'data/icdar2015' 2 | 3 | icdar2015_textspotting_train = dict( 4 | type='OCRDataset', 5 | data_root=icdar2015_textspotting_data_root, 6 | ann_file='textspotting_train.json', 7 | pipeline=None) 8 | 9 | icdar2015_textspotting_test = dict( 10 | type='OCRDataset', 11 | data_root=icdar2015_textspotting_data_root, 12 | ann_file='textspotting_test.json', 13 | test_mode=True, 14 | pipeline=None) 15 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/mlt-spts.py: -------------------------------------------------------------------------------- 1 | mlt_textspotting_data_root = 'spts-data/mlt2017' 2 | 3 | mlt_textspotting_train = dict( 4 | type='AdelDataset', 5 | data_root=mlt_textspotting_data_root, 6 | ann_file='train.json', 7 | data_prefix=dict(img_path='MLT_train_images/'), 8 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 9 | pipeline=None) 10 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/syntext1-spts.py: -------------------------------------------------------------------------------- 1 | syntext1_textspotting_data_root = 'spts-data/syntext1' 2 | 3 | syntext1_textspotting_train = dict( 4 | type='AdelDataset', 5 | data_root=syntext1_textspotting_data_root, 6 | ann_file='train.json', 7 | data_prefix=dict(img_path='syntext_word_eng/'), 8 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 9 | pipeline=None) 10 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/syntext2-spts.py: -------------------------------------------------------------------------------- 1 | syntext2_textspotting_data_root = 'spts-data/syntext2' 2 | 3 | syntext2_textspotting_train = dict( 4 | type='AdelDataset', 5 | data_root=syntext2_textspotting_data_root, 6 | ann_file='train.json', 7 | data_prefix=dict(img_path='emcs_imgs/'), 8 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 9 | pipeline=None) 10 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/totaltext-spts.py: -------------------------------------------------------------------------------- 1 | totaltext_textspotting_data_root = 'spts-data/totaltext' 2 | 3 | totaltext_textspotting_train = dict( 4 | type='AdelDataset', 5 | data_root=totaltext_textspotting_data_root, 6 | ann_file='train.json', 7 | data_prefix=dict(img_path='train_images/'), 8 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 9 | pipeline=None) 10 | 11 | totaltext_textspotting_test = dict( 12 | type='AdelDataset', 13 | data_root=totaltext_textspotting_data_root, 14 | ann_file='test.json', 15 | data_prefix=dict(img_path='test_images/'), 16 | test_mode=True, 17 | pipeline=None) 18 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/config/_base_/datasets/totaltext.py: -------------------------------------------------------------------------------- 1 | totaltext_textspotting_data_root = 'data/totaltext' 2 | 3 | totaltext_textspotting_train = dict( 4 | type='OCRDataset', 5 | data_root=totaltext_textspotting_data_root, 6 | ann_file='textspotting_train.json', 7 | filter_cfg=dict(filter_empty_gt=True, min_size=32), 8 | pipeline=None) 9 | 10 | totaltext_textspotting_test = dict( 11 | type='OCRDataset', 12 | data_root=totaltext_textspotting_data_root, 13 | ann_file='textspotting_test.json', 14 | test_mode=True, 15 | pipeline=None) 16 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/dicts/spts.txt: -------------------------------------------------------------------------------- 1 | 2 | ! 3 | " 4 | # 5 | $ 6 | % 7 | & 8 | ' 9 | ( 10 | ) 11 | * 12 | + 13 | , 14 | - 15 | . 16 | / 17 | 0 18 | 1 19 | 2 20 | 3 21 | 4 22 | 5 23 | 6 24 | 7 25 | 8 26 | 9 27 | : 28 | ; 29 | < 30 | = 31 | > 32 | ? 33 | @ 34 | A 35 | B 36 | C 37 | D 38 | E 39 | F 40 | G 41 | H 42 | I 43 | J 44 | K 45 | L 46 | M 47 | N 48 | O 49 | P 50 | Q 51 | R 52 | S 53 | T 54 | U 55 | V 56 | W 57 | X 58 | Y 59 | Z 60 | [ 61 | \ 62 | ] 63 | ^ 64 | _ 65 | ` 66 | a 67 | b 68 | c 69 | d 70 | e 71 | f 72 | g 73 | h 74 | i 75 | j 76 | k 77 | l 78 | m 79 | n 80 | o 81 | p 82 | q 83 | r 84 | s 85 | t 86 | u 87 | v 88 | w 89 | x 90 | y 91 | z 92 | { 93 | | 94 | } 95 | ~ -------------------------------------------------------------------------------- /mmocr/projects/SPTS/spts/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | # Copyright (c) OpenMMLab. All rights reserved. 3 | from .datasets import * # NOQA 4 | from .metric import * # NOQA 5 | from .model import * # NOQA 6 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/spts/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .adel_dataset import AdelDataset 3 | from .transforms.spts_transforms import (Bezier2Polygon, ConvertText, 4 | LoadOCRAnnotationsWithBezier, 5 | Polygon2Bezier, RescaleToShortSide) 6 | 7 | __all__ = [ 8 | 'AdelDataset', 'LoadOCRAnnotationsWithBezier', 'Bezier2Polygon', 9 | 'Polygon2Bezier', 'ConvertText', 'RescaleToShortSide' 10 | ] 11 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/spts/metric/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .e2e_point_metric import E2EPointMetric 3 | 4 | __all__ = ['E2EPointMetric'] 5 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/spts/model/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .spts import SPTS 3 | from .spts_decoder import SPTSDecoder 4 | from .spts_dictionary import SPTSDictionary 5 | from .spts_encoder import SPTSEncoder 6 | from .spts_module_loss import SPTSModuleLoss 7 | from .spts_postprocessor import SPTSPostprocessor 8 | 9 | __all__ = [ 10 | 'SPTSEncoder', 'SPTSDecoder', 'SPTSPostprocessor', 'SPTS', 11 | 'SPTSDictionary', 'SPTSModuleLoss' 12 | ] 13 | -------------------------------------------------------------------------------- /mmocr/projects/SPTS/spts/model/spts.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmocr.registry import MODELS 3 | from .encoder_decoder_text_spotter import EncoderDecoderTextSpotter 4 | 5 | 6 | @MODELS.register_module() 7 | class SPTS(EncoderDecoderTextSpotter): 8 | """SPTS.""" 9 | -------------------------------------------------------------------------------- /mmocr/projects/example_project/configs/dbnet_dummy-resnet_fpnc_1200e_icdar2015.py: -------------------------------------------------------------------------------- 1 | _base_ = ['mmocr::textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py'] 2 | 3 | custom_imports = dict(imports=['dummy']) 4 | 5 | _base_.model.backbone.type = 'DummyResNet' 6 | -------------------------------------------------------------------------------- /mmocr/projects/example_project/dummy/__init__.py: -------------------------------------------------------------------------------- 1 | from .dummy_resnet import DummyResNet 2 | 3 | __all__ = ['DummyResNet'] 4 | -------------------------------------------------------------------------------- /mmocr/projects/example_project/dummy/dummy_resnet.py: -------------------------------------------------------------------------------- 1 | from mmdet.models.backbones import ResNet 2 | 3 | from mmocr.registry import MODELS 4 | 5 | 6 | @MODELS.register_module() 7 | class DummyResNet(ResNet): 8 | """Implements a dummy ResNet wrapper for demonstration purpose. 9 | 10 | Args: 11 | **kwargs: All the arguments are passed to the parent class. 12 | """ 13 | 14 | def __init__(self, **kwargs) -> None: 15 | print('Hello world!') 16 | super().__init__(**kwargs) 17 | -------------------------------------------------------------------------------- /mmocr/projects/selected.txt: -------------------------------------------------------------------------------- 1 | projects/ABCNet/README.md 2 | projects/ABCNet/README_V2.md 3 | projects/SPTS/README.md 4 | -------------------------------------------------------------------------------- /mmocr/requirements/albu.txt: -------------------------------------------------------------------------------- 1 | albumentations>=1.1.0 --no-binary qudida,albumentations 2 | -------------------------------------------------------------------------------- /mmocr/requirements/build.txt: -------------------------------------------------------------------------------- 1 | # These must be installed before building mmocr 2 | numpy 3 | pyclipper 4 | torch>=1.1 5 | -------------------------------------------------------------------------------- /mmocr/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | docutils==0.16.0 2 | markdown>=3.4.0 3 | myst-parser 4 | -e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme 5 | sphinx==4.0.2 6 | sphinx-tabs 7 | sphinx_copybutton 8 | sphinx_markdown_tables>=0.0.16 9 | tabulate 10 | -------------------------------------------------------------------------------- /mmocr/requirements/mminstall.txt: -------------------------------------------------------------------------------- 1 | mmcv>=2.0.0rc4,<2.1.0 2 | mmdet>=3.0.0rc5,<3.1.0 3 | mmengine>=0.7.0, <1.0.0 4 | -------------------------------------------------------------------------------- /mmocr/requirements/optional.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/requirements/optional.txt -------------------------------------------------------------------------------- /mmocr/requirements/readthedocs.txt: -------------------------------------------------------------------------------- 1 | imgaug 2 | kwarray 3 | lmdb 4 | matplotlib 5 | mmcv>=2.0.0rc1 6 | mmdet>=3.0.0rc0 7 | mmengine>=0.1.0 8 | pyclipper 9 | rapidfuzz>=2.0.0 10 | regex 11 | scikit-image 12 | scipy 13 | shapely 14 | titlecase 15 | torch 16 | torchvision 17 | -------------------------------------------------------------------------------- /mmocr/requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | imgaug 2 | lmdb 3 | matplotlib 4 | numpy 5 | opencv-python >=4.2.0.32, != 4.5.5.* # avoid Github security alert 6 | pyclipper 7 | pycocotools 8 | rapidfuzz>=2.0.0 9 | scikit-image 10 | -------------------------------------------------------------------------------- /mmocr/requirements/tests.txt: -------------------------------------------------------------------------------- 1 | asynctest 2 | codecov 3 | flake8 4 | interrogate 5 | isort 6 | # Note: used for kwarray.group_items, this may be ported to mmcv in the future. 7 | kwarray 8 | lanms-neo==1.0.2 9 | parameterized 10 | pytest 11 | pytest-cov 12 | pytest-runner 13 | ubelt 14 | xdoctest >= 0.10.0 15 | yapf 16 | -------------------------------------------------------------------------------- /mmocr/resources/illustration.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/resources/illustration.jpg -------------------------------------------------------------------------------- /mmocr/resources/kie.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/resources/kie.jpg -------------------------------------------------------------------------------- /mmocr/resources/mmocr-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/resources/mmocr-logo.png -------------------------------------------------------------------------------- /mmocr/resources/textdet.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/resources/textdet.jpg -------------------------------------------------------------------------------- /mmocr/resources/textrecog.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/resources/textrecog.jpg -------------------------------------------------------------------------------- /mmocr/resources/verification.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/mmocr/resources/verification.png -------------------------------------------------------------------------------- /mmocr/setup.cfg: -------------------------------------------------------------------------------- 1 | [bdist_wheel] 2 | universal=1 3 | 4 | [yapf] 5 | based_on_style = pep8 6 | blank_line_before_nested_class_or_def = true 7 | split_before_expression_after_opening_paren = true 8 | split_penalty_import_names=0 9 | SPLIT_PENALTY_AFTER_OPENING_BRACKET=800 10 | 11 | [isort] 12 | line_length = 79 13 | multi_line_output = 0 14 | extra_standard_library = setuptools 15 | known_first_party = mmocr 16 | known_third_party = PIL,cv2,imgaug,lanms,lmdb,matplotlib,mmcv,mmdet,numpy,packaging,pyclipper,pytest,pytorch_sphinx_theme,rapidfuzz,requests,scipy,shapely,skimage,titlecase,torch,torchvision,ts,yaml,mmengine 17 | no_lines_before = STDLIB,LOCALFOLDER 18 | default_section = THIRDPARTY 19 | 20 | [style] 21 | BASED_ON_STYLE = pep8 22 | BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true 23 | SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true 24 | -------------------------------------------------------------------------------- /mmocr/tests/test_evaluation/test_functional/test_hmean.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | from mmocr.evaluation.functional import compute_hmean 5 | 6 | 7 | class TestHmean(TestCase): 8 | 9 | def test_compute_hmean(self): 10 | with self.assertRaises(AssertionError): 11 | compute_hmean(0, 0, 0.0, 0) 12 | with self.assertRaises(AssertionError): 13 | compute_hmean(0, 0, 0, 0.0) 14 | with self.assertRaises(AssertionError): 15 | compute_hmean([1], 0, 0, 0) 16 | with self.assertRaises(AssertionError): 17 | compute_hmean(0, [1], 0, 0) 18 | 19 | _, _, hmean = compute_hmean(2, 2, 2, 2) 20 | self.assertEqual(hmean, 1) 21 | 22 | _, _, hmean = compute_hmean(0, 0, 2, 2) 23 | self.assertEqual(hmean, 0) 24 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_common/test_modules/test_transformer_module.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | import torch 5 | 6 | from mmocr.models.common.modules import PositionalEncoding 7 | 8 | 9 | class TestPositionalEncoding(TestCase): 10 | 11 | def test_forward(self): 12 | pos_encoder = PositionalEncoding() 13 | x = torch.rand(1, 30, 512) 14 | out = pos_encoder(x) 15 | assert out.size() == x.size() 16 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_common/test_plugins/test_avgpool.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | import torch 5 | 6 | from mmocr.models.common.plugins import AvgPool2d 7 | 8 | 9 | class TestAvgPool2d(TestCase): 10 | 11 | def setUp(self) -> None: 12 | self.img = torch.rand(1, 3, 32, 100) 13 | 14 | def test_avgpool2d(self): 15 | avgpool2d = AvgPool2d(kernel_size=2, stride=2) 16 | self.assertEqual(avgpool2d(self.img).shape, torch.Size([1, 3, 16, 50])) 17 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textdet/test_heads/test_pse_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | import torch 5 | 6 | from mmocr.models.textdet.heads import PSEHead 7 | 8 | 9 | class TestPSEHead(TestCase): 10 | 11 | def setUp(self): 12 | self.feature = torch.randn((2, 10, 40, 50)) 13 | 14 | def test_init(self): 15 | with self.assertRaises(TypeError): 16 | PSEHead(in_channels=1) 17 | 18 | with self.assertRaises(TypeError): 19 | PSEHead(out_channels='out') 20 | 21 | def test_forward(self): 22 | pse_head = PSEHead(in_channels=[10], hidden_dim=128, out_channel=7) 23 | results = pse_head(self.feature) 24 | self.assertEqual(results.shape, (2, 7, 40, 50)) 25 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textdet/test_heads/test_textsnake_head.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | import torch 5 | 6 | from mmocr.models.textdet.heads import TextSnakeHead 7 | 8 | 9 | class TestTextSnakeHead(TestCase): 10 | 11 | def test_init(self): 12 | with self.assertRaises(AssertionError): 13 | TextSnakeHead(in_channels='test') 14 | 15 | def test_forward(self): 16 | ts_head = TextSnakeHead(in_channels=10) 17 | data = torch.randn((2, 10, 40, 50)) 18 | results = ts_head(data, None) 19 | self.assertEqual(results.shape, (2, 5, 40, 50)) 20 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textdet/test_necks/test_fpnf.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import unittest 3 | 4 | import torch 5 | from parameterized import parameterized 6 | 7 | from mmocr.models.textdet.necks import FPNF 8 | 9 | 10 | class TestFPNF(unittest.TestCase): 11 | 12 | def setUp(self): 13 | in_channels = [256, 512, 1024, 2048] 14 | size = [112, 56, 28, 14] 15 | inputs = [] 16 | for i in range(4): 17 | inputs.append(torch.rand(1, in_channels[i], size[i], size[i])) 18 | self.inputs = inputs 19 | 20 | @parameterized.expand([('concat'), ('add')]) 21 | def test_forward(self, fusion_type): 22 | fpnf = FPNF(fusion_type=fusion_type) 23 | outputs = fpnf.forward(self.inputs) 24 | self.assertListEqual(list(outputs.size()), [1, 256, 112, 112]) 25 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textrecog/test_backbones/test_mini_vgg.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | import torch 5 | 6 | from mmocr.models.textrecog.backbones import MiniVGG 7 | 8 | 9 | class TestMiniVGG(TestCase): 10 | 11 | def test_forward(self): 12 | 13 | model = MiniVGG() 14 | model.init_weights() 15 | model.train() 16 | 17 | imgs = torch.randn(1, 3, 32, 160) 18 | feats = model(imgs) 19 | self.assertEqual(feats.shape, torch.Size([1, 512, 1, 41])) 20 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textrecog/test_backbones/test_mobilenet_v2.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | import torch 5 | 6 | from mmocr.models.textrecog.backbones import MobileNetV2 7 | 8 | 9 | class TestMobileNetV2(TestCase): 10 | 11 | def setUp(self) -> None: 12 | self.img = torch.rand(1, 3, 32, 160) 13 | 14 | def test_mobilenetv2(self): 15 | mobilenet_v2 = MobileNetV2() 16 | self.assertEqual( 17 | mobilenet_v2(self.img).shape, torch.Size([1, 1280, 1, 43])) 18 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textrecog/test_backbones/test_nrtr_modality_transformer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import unittest 3 | 4 | import torch 5 | 6 | from mmocr.models.textrecog.backbones import NRTRModalityTransform 7 | 8 | 9 | class TestNRTRBackbone(unittest.TestCase): 10 | 11 | def setUp(self): 12 | self.img = torch.randn(2, 3, 32, 100) 13 | 14 | def test_encoder(self): 15 | nrtr_backbone = NRTRModalityTransform() 16 | nrtr_backbone.init_weights() 17 | nrtr_backbone.train() 18 | out_enc = nrtr_backbone(self.img) 19 | self.assertEqual(out_enc.shape, torch.Size([2, 512, 1, 25])) 20 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textrecog/test_backbones/test_shallow_cnn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import unittest 3 | 4 | import torch 5 | 6 | from mmocr.models.textrecog.backbones import ShallowCNN 7 | 8 | 9 | class TestShallowCNN(unittest.TestCase): 10 | 11 | def setUp(self): 12 | self.imgs = torch.randn(1, 1, 32, 100) 13 | 14 | def test_shallow_cnn(self): 15 | 16 | model = ShallowCNN() 17 | model.init_weights() 18 | model.train() 19 | 20 | feat = model(self.imgs) 21 | self.assertEqual(feat.shape, torch.Size([1, 512, 8, 25])) 22 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textrecog/test_encoders/test_abi_encoder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | import torch 5 | 6 | from mmocr.models.textrecog.encoders.abi_encoder import ABIEncoder 7 | 8 | 9 | class TestABIEncoder(TestCase): 10 | 11 | def test_init(self): 12 | with self.assertRaises(AssertionError): 13 | ABIEncoder(d_model=512, n_head=10) 14 | 15 | def test_forward(self): 16 | model = ABIEncoder() 17 | x = torch.randn(10, 512, 8, 32) 18 | self.assertEqual(model(x, None).shape, torch.Size([10, 512, 8, 32])) 19 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textrecog/test_encoders/test_aster_encoder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | import unittest 3 | 4 | import torch 5 | 6 | from mmocr.models.textrecog.encoders import ASTEREncoder 7 | 8 | 9 | class TestASTEREncoder(unittest.TestCase): 10 | 11 | def test_encoder(self): 12 | encoder = ASTEREncoder(10) 13 | feat = torch.randn(2, 10, 1, 25) 14 | out = encoder(feat) 15 | self.assertEqual(out.shape, torch.Size([2, 25, 10])) 16 | -------------------------------------------------------------------------------- /mmocr/tests/test_models/test_textrecog/test_plugins/test_maxpool.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from unittest import TestCase 3 | 4 | import torch 5 | 6 | from mmocr.models.textrecog.plugins import Maxpool2d 7 | 8 | 9 | class TestMaxpool2d(TestCase): 10 | 11 | def setUp(self) -> None: 12 | self.img = torch.rand(1, 3, 32, 100) 13 | 14 | def test_maxpool2d(self): 15 | maxpool2d = Maxpool2d(kernel_size=2, stride=2) 16 | self.assertEqual(maxpool2d(self.img).shape, torch.Size([1, 3, 16, 50])) 17 | -------------------------------------------------------------------------------- /mmocr/tools/dist_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | CONFIG=$1 4 | CHECKPOINT=$2 5 | GPUS=$3 6 | NNODES=${NNODES:-1} 7 | NODE_RANK=${NODE_RANK:-0} 8 | PORT=${PORT:-29500} 9 | MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} 10 | 11 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 12 | python -m torch.distributed.launch \ 13 | --nnodes=$NNODES \ 14 | --node_rank=$NODE_RANK \ 15 | --master_addr=$MASTER_ADDR \ 16 | --nproc_per_node=$GPUS \ 17 | --master_port=$PORT \ 18 | $(dirname "$0")/test.py \ 19 | $CONFIG \ 20 | $CHECKPOINT \ 21 | --launcher pytorch \ 22 | ${@:4} 23 | -------------------------------------------------------------------------------- /mmocr/tools/dist_train.sh: -------------------------------------------------------------------------------- 1 | 2 | #!/usr/bin/env bash 3 | 4 | CONFIG=$1 5 | GPUS=$2 6 | NNODES=${NNODES:-1} 7 | NODE_RANK=${NODE_RANK:-0} 8 | PORT=${PORT:-29500} 9 | MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} 10 | 11 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 12 | python -m torch.distributed.launch \ 13 | --nnodes=$NNODES \ 14 | --node_rank=$NODE_RANK \ 15 | --master_addr=$MASTER_ADDR \ 16 | --nproc_per_node=$GPUS \ 17 | --master_port=$PORT \ 18 | $(dirname "$0")/train.py \ 19 | $CONFIG \ 20 | --launcher pytorch ${@:3} 21 | -------------------------------------------------------------------------------- /mmocr/tools/slurm_test.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -x 4 | export PYTHONPATH=`pwd`:$PYTHONPATH 5 | 6 | PARTITION=$1 7 | JOB_NAME=$2 8 | CONFIG=$3 9 | CHECKPOINT=$4 10 | GPUS=${GPUS:-8} 11 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 12 | PY_ARGS=${@:5} 13 | SRUN_ARGS=${SRUN_ARGS:-""} 14 | 15 | srun -p ${PARTITION} \ 16 | --job-name=${JOB_NAME} \ 17 | --gres=gpu:${GPUS_PER_NODE} \ 18 | --ntasks=${GPUS} \ 19 | --ntasks-per-node=${GPUS_PER_NODE} \ 20 | --kill-on-bad-exit=1 \ 21 | ${SRUN_ARGS} \ 22 | python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} 23 | -------------------------------------------------------------------------------- /mmocr/tools/slurm_train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | export MASTER_PORT=$((12000 + $RANDOM % 20000)) 3 | 4 | set -x 5 | 6 | PARTITION=$1 7 | JOB_NAME=$2 8 | CONFIG=$3 9 | WORK_DIR=$4 10 | GPUS=${GPUS:-8} 11 | GPUS_PER_NODE=${GPUS_PER_NODE:-8} 12 | CPUS_PER_TASK=${CPUS_PER_TASK:-5} 13 | PY_ARGS=${@:5} 14 | SRUN_ARGS=${SRUN_ARGS:-""} 15 | 16 | PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ 17 | srun -p ${PARTITION} \ 18 | --job-name=${JOB_NAME} \ 19 | --gres=gpu:${GPUS_PER_NODE} \ 20 | --ntasks=${GPUS} \ 21 | --ntasks-per-node=${GPUS_PER_NODE} \ 22 | --cpus-per-task=${CPUS_PER_TASK} \ 23 | --kill-on-bad-exit=1 \ 24 | ${SRUN_ARGS} \ 25 | python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS} 26 | -------------------------------------------------------------------------------- /textfussion/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include LICENSE 2 | include src/diffusers/utils/model_card_template.md 3 | -------------------------------------------------------------------------------- /textfussion/README.md: -------------------------------------------------------------------------------- 1 | 该项目主要基于diffusers==0.15.0.dev0框架，请依照requirements.txt进行环境的搭建 2 | 3 | 准备好文本图像训练集后，通过./my_inpainting/new_paradigm_train.sh脚本进行生成模型的训练 4 | 5 | 完成模型训练后，通过./my_inpainting/my_build_synth_data_baseline.py脚本，制作合成数据集 6 | -------------------------------------------------------------------------------- /textfussion/_typos.toml: -------------------------------------------------------------------------------- 1 | # Files for typos 2 | # Instruction: https://github.com/marketplace/actions/typos-action#getting-started 3 | 4 | [default.extend-identifiers] 5 | 6 | [default.extend-words] 7 | NIN="NIN" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py 8 | nd="np" # nd may be np (numpy) 9 | parms="parms" # parms is used in scripts/convert_original_stable_diffusion_to_diffusers.py 10 | 11 | 12 | [files] 13 | extend-exclude = ["_typos.toml"] 14 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/experimental/__init__.py: -------------------------------------------------------------------------------- 1 | from .rl import ValueGuidedRLPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/experimental/rl/__init__.py: -------------------------------------------------------------------------------- 1 | from .value_guided_sampling import ValueGuidedRLPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/audio_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from .mel import Mel 2 | from .pipeline_audio_diffusion import AudioDiffusionPipeline 3 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/audioldm/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import ( 2 | OptionalDependencyNotAvailable, 3 | is_torch_available, 4 | is_transformers_available, 5 | is_transformers_version, 6 | ) 7 | 8 | 9 | try: 10 | if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")): 11 | raise OptionalDependencyNotAvailable() 12 | except OptionalDependencyNotAvailable: 13 | from ...utils.dummy_torch_and_transformers_objects import ( 14 | AudioLDMPipeline, 15 | ) 16 | else: 17 | from .pipeline_audioldm import AudioLDMPipeline 18 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/dance_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_dance_diffusion import DanceDiffusionPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/ddim/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_ddim import DDIMPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/ddpm/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_ddpm import DDPMPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/dit/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_dit import DiTPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/latent_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import is_transformers_available 2 | from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline 3 | 4 | 5 | if is_transformers_available(): 6 | from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline 7 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/latent_diffusion_uncond/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_latent_diffusion_uncond import LDMPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/paint_by_example/__init__.py: -------------------------------------------------------------------------------- 1 | from dataclasses import dataclass 2 | from typing import List, Optional, Union 3 | 4 | import numpy as np 5 | import PIL 6 | from PIL import Image 7 | 8 | from ...utils import is_torch_available, is_transformers_available 9 | 10 | 11 | if is_transformers_available() and is_torch_available(): 12 | from .image_encoder import PaintByExampleImageEncoder 13 | from .pipeline_paint_by_example import PaintByExamplePipeline 14 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/pndm/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_pndm import PNDMPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/repaint/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_repaint import RePaintPipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/score_sde_ve/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_score_sde_ve import ScoreSdeVePipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/stochastic_karras_ve/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_stochastic_karras_ve import KarrasVePipeline 2 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/unclip/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import ( 2 | OptionalDependencyNotAvailable, 3 | is_torch_available, 4 | is_transformers_available, 5 | is_transformers_version, 6 | ) 7 | 8 | 9 | try: 10 | if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")): 11 | raise OptionalDependencyNotAvailable() 12 | except OptionalDependencyNotAvailable: 13 | from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline 14 | else: 15 | from .pipeline_unclip import UnCLIPPipeline 16 | from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline 17 | from .text_proj import UnCLIPTextProjModel 18 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/pipelines/vq_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import is_torch_available, is_transformers_available 2 | 3 | 4 | if is_transformers_available() and is_torch_available(): 5 | from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline 6 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/utils/dummy_note_seq_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class MidiProcessor(metaclass=DummyObject): 6 | _backends = ["note_seq"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["note_seq"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["note_seq"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["note_seq"]) 18 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/utils/dummy_onnx_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class OnnxRuntimeModel(metaclass=DummyObject): 6 | _backends = ["onnx"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["onnx"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["onnx"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["onnx"]) 18 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/utils/dummy_torch_and_scipy_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class LMSDiscreteScheduler(metaclass=DummyObject): 6 | _backends = ["torch", "scipy"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["torch", "scipy"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["torch", "scipy"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["torch", "scipy"]) 18 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/utils/dummy_torch_and_transformers_and_k_diffusion_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class StableDiffusionKDiffusionPipeline(metaclass=DummyObject): 6 | _backends = ["torch", "transformers", "k_diffusion"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["torch", "transformers", "k_diffusion"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["torch", "transformers", "k_diffusion"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["torch", "transformers", "k_diffusion"]) 18 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class SpectrogramDiffusionPipeline(metaclass=DummyObject): 6 | _backends = ["transformers", "torch", "note_seq"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["transformers", "torch", "note_seq"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["transformers", "torch", "note_seq"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["transformers", "torch", "note_seq"]) 18 | -------------------------------------------------------------------------------- /textfussion/build/lib/diffusers/utils/pil_utils.py: -------------------------------------------------------------------------------- 1 | import PIL.Image 2 | import PIL.ImageOps 3 | from packaging import version 4 | 5 | 6 | if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): 7 | PIL_INTERPOLATION = { 8 | "linear": PIL.Image.Resampling.BILINEAR, 9 | "bilinear": PIL.Image.Resampling.BILINEAR, 10 | "bicubic": PIL.Image.Resampling.BICUBIC, 11 | "lanczos": PIL.Image.Resampling.LANCZOS, 12 | "nearest": PIL.Image.Resampling.NEAREST, 13 | } 14 | else: 15 | PIL_INTERPOLATION = { 16 | "linear": PIL.Image.LINEAR, 17 | "bilinear": PIL.Image.BILINEAR, 18 | "bicubic": PIL.Image.BICUBIC, 19 | "lanczos": PIL.Image.LANCZOS, 20 | "nearest": PIL.Image.NEAREST, 21 | } 22 | -------------------------------------------------------------------------------- /textfussion/docs/source/_config.py: -------------------------------------------------------------------------------- 1 | # docstyle-ignore 2 | INSTALL_CONTENT = """ 3 | # Diffusers installation 4 | ! pip install diffusers transformers datasets accelerate 5 | # To install from source instead of the last release, comment the command above and uncomment the following one. 6 | # ! pip install git+https://github.com/huggingface/diffusers.git 7 | """ 8 | 9 | notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] -------------------------------------------------------------------------------- /textfussion/docs/source/en/api/experimental/rl.mdx: -------------------------------------------------------------------------------- 1 | 12 | 13 | # TODO 14 | 15 | Coming soon! -------------------------------------------------------------------------------- /textfussion/docs/source/en/imgs/access_request.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/textfussion/docs/source/en/imgs/access_request.png -------------------------------------------------------------------------------- /textfussion/docs/source/en/imgs/diffusers_library.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/textfussion/docs/source/en/imgs/diffusers_library.jpg -------------------------------------------------------------------------------- /textfussion/docs/source/en/using-diffusers/audio.mdx: -------------------------------------------------------------------------------- 1 | 12 | 13 | # Using Diffusers for audio 14 | 15 | [`DanceDiffusionPipeline`] and [`AudioDiffusionPipeline`] can be used to generate 16 | audio rapidly! More coming soon! -------------------------------------------------------------------------------- /textfussion/docs/source/ko/in_translation.mdx: -------------------------------------------------------------------------------- 1 | 12 | 13 | # 번역중 14 | 15 | 열심히 번역을 진행중입니다. 조금만 기다려주세요. 16 | 감사합니다! -------------------------------------------------------------------------------- /textfussion/examples/community/one_step_unet.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import torch 3 | 4 | from diffusers import DiffusionPipeline 5 | 6 | 7 | class UnetSchedulerOneForwardPipeline(DiffusionPipeline): 8 | def __init__(self, unet, scheduler): 9 | super().__init__() 10 | 11 | self.register_modules(unet=unet, scheduler=scheduler) 12 | 13 | def __call__(self): 14 | image = torch.randn( 15 | (1, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size), 16 | ) 17 | timestep = 1 18 | 19 | model_output = self.unet(image, timestep).sample 20 | scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample 21 | 22 | result = scheduler_output - scheduler_output + torch.ones_like(scheduler_output) 23 | 24 | return result 25 | -------------------------------------------------------------------------------- /textfussion/examples/controlnet/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | datasets 7 | -------------------------------------------------------------------------------- /textfussion/examples/controlnet/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | datasets 3 | flax 4 | optax 5 | torch 6 | torchvision 7 | ftfy 8 | tensorboard 9 | Jinja2 10 | -------------------------------------------------------------------------------- /textfussion/examples/dreambooth/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | -------------------------------------------------------------------------------- /textfussion/examples/dreambooth/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | flax 3 | optax 4 | torch 5 | torchvision 6 | ftfy 7 | tensorboard 8 | Jinja2 9 | -------------------------------------------------------------------------------- /textfussion/examples/inference/image_to_image.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | from diffusers import StableDiffusionImg2ImgPipeline # noqa F401 4 | 5 | 6 | warnings.warn( 7 | "The `image_to_image.py` script is outdated. Please use directly `from diffusers import" 8 | " StableDiffusionImg2ImgPipeline` instead." 9 | ) 10 | -------------------------------------------------------------------------------- /textfussion/examples/inference/inpainting.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401 4 | 5 | 6 | warnings.warn( 7 | "The `inpainting.py` script is outdated. Please use directly `from diffusers import" 8 | " StableDiffusionInpaintPipeline` instead." 9 | ) 10 | -------------------------------------------------------------------------------- /textfussion/examples/instruct_pix2pix/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | datasets 5 | ftfy 6 | tensorboard -------------------------------------------------------------------------------- /textfussion/examples/research_projects/README.md: -------------------------------------------------------------------------------- 1 | # Research projects 2 | 3 | This folder contains various research projects using 🧨 Diffusers. 4 | They are not really maintained by the core maintainers of this library and often require a specific version of Diffusers that is indicated in the requirements file of each folder. 5 | Updating them to the most recent version of the library will require some work. 6 | 7 | To use any of them, just run the command 8 | 9 | ``` 10 | pip install -r requirements.txt 11 | ``` 12 | inside the folder of your choice. 13 | 14 | If you need help with any of those, please open an issue where you directly ping the author(s), as indicated at the top of the README of each folder. 15 | -------------------------------------------------------------------------------- /textfussion/examples/research_projects/colossalai/inference.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from diffusers import StableDiffusionPipeline 4 | 5 | 6 | model_id = "path-to-your-trained-model" 7 | pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") 8 | 9 | prompt = "A photo of sks dog in a bucket" 10 | image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] 11 | 12 | image.save("dog-bucket.png") 13 | -------------------------------------------------------------------------------- /textfussion/examples/research_projects/colossalai/requirement.txt: -------------------------------------------------------------------------------- 1 | diffusers 2 | torch 3 | torchvision 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | transformers -------------------------------------------------------------------------------- /textfussion/examples/research_projects/dreambooth_inpaint/requirements.txt: -------------------------------------------------------------------------------- 1 | diffusers==0.9.0 2 | accelerate 3 | torchvision 4 | transformers>=4.21.0 5 | ftfy 6 | tensorboard 7 | Jinja2 8 | -------------------------------------------------------------------------------- /textfussion/examples/research_projects/intel_opts/textual_inversion/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.21.0 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | intel_extension_for_pytorch>=1.13 8 | -------------------------------------------------------------------------------- /textfussion/examples/research_projects/lora/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | datasets 5 | ftfy 6 | tensorboard 7 | Jinja2 8 | git+https://github.com/huggingface/peft.git -------------------------------------------------------------------------------- /textfussion/examples/research_projects/mulit_token_textual_inversion/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | -------------------------------------------------------------------------------- /textfussion/examples/research_projects/mulit_token_textual_inversion/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | flax 3 | optax 4 | torch 5 | torchvision 6 | ftfy 7 | tensorboard 8 | Jinja2 9 | -------------------------------------------------------------------------------- /textfussion/examples/research_projects/multi_subject_dreambooth/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 -------------------------------------------------------------------------------- /textfussion/examples/research_projects/onnxruntime/README.md: -------------------------------------------------------------------------------- 1 | ## Diffusers examples with ONNXRuntime optimizations 2 | 3 | **This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.** 4 | 5 | This aims to provide diffusers examples with ONNXRuntime optimizations for training/fine-tuning unconditional image generation, text to image, and textual inversion. Please see individual directories for more details on how to run each task using ONNXRuntime. -------------------------------------------------------------------------------- /textfussion/examples/research_projects/onnxruntime/text_to_image/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | datasets 5 | ftfy 6 | tensorboard 7 | modelcards 8 | -------------------------------------------------------------------------------- /textfussion/examples/research_projects/onnxruntime/textual_inversion/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | modelcards 7 | -------------------------------------------------------------------------------- /textfussion/examples/research_projects/onnxruntime/unconditional_image_generation/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | datasets 4 | -------------------------------------------------------------------------------- /textfussion/examples/text_to_image/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | transformers>=4.25.1 3 | datasets 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | -------------------------------------------------------------------------------- /textfussion/examples/text_to_image/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | datasets 3 | flax 4 | optax 5 | torch 6 | torchvision 7 | ftfy 8 | tensorboard 9 | Jinja2 10 | -------------------------------------------------------------------------------- /textfussion/examples/textual_inversion/requirements.txt: 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-------------------------------------------------------------------------------- 1 | export MODEL_NAME="stabilityai/stable-diffusion-2-inpainting" 2 | export OUTPUT_DIR="output/pretrain_8702_text_vae" 3 | 4 | NCCL_P2P_DISABLE=1 accelerate launch train_vae.py \ 5 | --pretrained_model_name_or_path=$MODEL_NAME \ 6 | --output_dir=$OUTPUT_DIR \ 7 | --resolution=512 \ 8 | --train_batch_size=4 \ 9 | --gradient_accumulation_steps=1 \ 10 | --gradient_checkpointing \ 11 | --learning_rate=5e-6 \ 12 | --num_train_epochs=3 \ 13 | --lr_scheduler="constant" \ 14 | --lr_warmup_steps=3000 \ 15 | --dataloader_num_workers=8 \ 16 | --mixed_precision=fp16 \ 17 | -------------------------------------------------------------------------------- /textfussion/pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.black] 2 | line-length = 119 3 | target-version = ['py37'] 4 | 5 | [tool.ruff] 6 | # Never enforce `E501` (line length violations). 7 | ignore = ["C901", "E501", "E741", "W605"] 8 | select = ["C", "E", "F", "I", "W"] 9 | line-length = 119 10 | 11 | # Ignore import violations in all `__init__.py` files. 12 | [tool.ruff.per-file-ignores] 13 | "__init__.py" = ["E402", "F401", "F403", "F811"] 14 | "src/diffusers/utils/dummy_*.py" = ["F401"] 15 | 16 | [tool.ruff.isort] 17 | lines-after-imports = 2 18 | known-first-party = ["diffusers"] 19 | -------------------------------------------------------------------------------- /textfussion/scripts/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/99Franklin/DiffText/6c16119cf08b1d23e71da55e93efa69e4a61384d/textfussion/scripts/__init__.py -------------------------------------------------------------------------------- /textfussion/setup.cfg: -------------------------------------------------------------------------------- 1 | [isort] 2 | default_section = FIRSTPARTY 3 | ensure_newline_before_comments = True 4 | force_grid_wrap = 0 5 | include_trailing_comma = True 6 | known_first_party = accelerate 7 | known_third_party = 8 | numpy 9 | torch 10 | torch_xla 11 | 12 | line_length = 119 13 | lines_after_imports = 2 14 | multi_line_output = 3 15 | use_parentheses = True 16 | 17 | [flake8] 18 | ignore = E203, E722, E501, E741, W503, W605 19 | max-line-length = 119 20 | per-file-ignores = __init__.py:F401 21 | -------------------------------------------------------------------------------- /textfussion/src/diffusers.egg-info/dependency_links.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers.egg-info/entry_points.txt: -------------------------------------------------------------------------------- 1 | [console_scripts] 2 | diffusers-cli = diffusers.commands.diffusers_cli:main 3 | -------------------------------------------------------------------------------- /textfussion/src/diffusers.egg-info/top_level.txt: -------------------------------------------------------------------------------- 1 | diffusers 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/experimental/README.md: -------------------------------------------------------------------------------- 1 | # 🧨 Diffusers Experimental 2 | 3 | We are adding experimental code to support novel applications and usages of the Diffusers library. 4 | Currently, the following experiments are supported: 5 | * Reinforcement learning via an implementation of the [Diffuser](https://arxiv.org/abs/2205.09991) model. -------------------------------------------------------------------------------- /textfussion/src/diffusers/experimental/__init__.py: -------------------------------------------------------------------------------- 1 | from .rl import ValueGuidedRLPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/experimental/rl/__init__.py: -------------------------------------------------------------------------------- 1 | from .value_guided_sampling import ValueGuidedRLPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/models/README.md: -------------------------------------------------------------------------------- 1 | # Models 2 | 3 | For more detail on the models, please refer to the [docs](https://huggingface.co/docs/diffusers/api/models). -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/audio_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from .mel import Mel 2 | from .pipeline_audio_diffusion import AudioDiffusionPipeline 3 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/audioldm/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import ( 2 | OptionalDependencyNotAvailable, 3 | is_torch_available, 4 | is_transformers_available, 5 | is_transformers_version, 6 | ) 7 | 8 | 9 | try: 10 | if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")): 11 | raise OptionalDependencyNotAvailable() 12 | except OptionalDependencyNotAvailable: 13 | from ...utils.dummy_torch_and_transformers_objects import ( 14 | AudioLDMPipeline, 15 | ) 16 | else: 17 | from .pipeline_audioldm import AudioLDMPipeline 18 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/dance_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_dance_diffusion import DanceDiffusionPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/ddim/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_ddim import DDIMPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/ddpm/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_ddpm import DDPMPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/dit/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_dit import DiTPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/latent_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import is_transformers_available 2 | from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline 3 | 4 | 5 | if is_transformers_available(): 6 | from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline 7 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/latent_diffusion_uncond/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_latent_diffusion_uncond import LDMPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/paint_by_example/__init__.py: -------------------------------------------------------------------------------- 1 | from dataclasses import dataclass 2 | from typing import List, Optional, Union 3 | 4 | import numpy as np 5 | import PIL 6 | from PIL import Image 7 | 8 | from ...utils import is_torch_available, is_transformers_available 9 | 10 | 11 | if is_transformers_available() and is_torch_available(): 12 | from .image_encoder import PaintByExampleImageEncoder 13 | from .pipeline_paint_by_example import PaintByExamplePipeline 14 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/pndm/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_pndm import PNDMPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/repaint/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_repaint import RePaintPipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/score_sde_ve/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_score_sde_ve import ScoreSdeVePipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/stochastic_karras_ve/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline_stochastic_karras_ve import KarrasVePipeline 2 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/unclip/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import ( 2 | OptionalDependencyNotAvailable, 3 | is_torch_available, 4 | is_transformers_available, 5 | is_transformers_version, 6 | ) 7 | 8 | 9 | try: 10 | if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")): 11 | raise OptionalDependencyNotAvailable() 12 | except OptionalDependencyNotAvailable: 13 | from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline 14 | else: 15 | from .pipeline_unclip import UnCLIPPipeline 16 | from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline 17 | from .text_proj import UnCLIPTextProjModel 18 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/pipelines/vq_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import is_torch_available, is_transformers_available 2 | 3 | 4 | if is_transformers_available() and is_torch_available(): 5 | from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline 6 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/schedulers/README.md: -------------------------------------------------------------------------------- 1 | # Schedulers 2 | 3 | For more information on the schedulers, please refer to the [docs](https://huggingface.co/docs/diffusers/api/schedulers/overview). -------------------------------------------------------------------------------- /textfussion/src/diffusers/utils/dummy_note_seq_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class MidiProcessor(metaclass=DummyObject): 6 | _backends = ["note_seq"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["note_seq"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["note_seq"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["note_seq"]) 18 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/utils/dummy_onnx_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class OnnxRuntimeModel(metaclass=DummyObject): 6 | _backends = ["onnx"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["onnx"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["onnx"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["onnx"]) 18 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/utils/dummy_torch_and_scipy_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class LMSDiscreteScheduler(metaclass=DummyObject): 6 | _backends = ["torch", "scipy"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["torch", "scipy"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["torch", "scipy"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["torch", "scipy"]) 18 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/utils/dummy_torch_and_transformers_and_k_diffusion_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class StableDiffusionKDiffusionPipeline(metaclass=DummyObject): 6 | _backends = ["torch", "transformers", "k_diffusion"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["torch", "transformers", "k_diffusion"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["torch", "transformers", "k_diffusion"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["torch", "transformers", "k_diffusion"]) 18 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class SpectrogramDiffusionPipeline(metaclass=DummyObject): 6 | _backends = ["transformers", "torch", "note_seq"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["transformers", "torch", "note_seq"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["transformers", "torch", "note_seq"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["transformers", "torch", "note_seq"]) 18 | -------------------------------------------------------------------------------- /textfussion/src/diffusers/utils/pil_utils.py: -------------------------------------------------------------------------------- 1 | import PIL.Image 2 | import PIL.ImageOps 3 | from packaging import version 4 | 5 | 6 | if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): 7 | PIL_INTERPOLATION = { 8 | "linear": PIL.Image.Resampling.BILINEAR, 9 | "bilinear": PIL.Image.Resampling.BILINEAR, 10 | "bicubic": PIL.Image.Resampling.BICUBIC, 11 | "lanczos": PIL.Image.Resampling.LANCZOS, 12 | "nearest": PIL.Image.Resampling.NEAREST, 13 | } 14 | else: 15 | PIL_INTERPOLATION = { 16 | "linear": PIL.Image.LINEAR, 17 | "bilinear": PIL.Image.BILINEAR, 18 | "bicubic": PIL.Image.BICUBIC, 19 | "lanczos": PIL.Image.LANCZOS, 20 | "nearest": PIL.Image.NEAREST, 21 | } 22 | -------------------------------------------------------------------------------- /textfussion/tests: -------------------------------------------------------------------------------- 1 | 2 | --------------------------------------------------------------------------------