├── .copyright.hook ├── .gitattributes ├── .github └── ISSUE_TEMPLATE │ ├── ---bug-report---bug--.md │ ├── ---feature-request--------.md │ └── ---general-issue-------.md ├── .gitignore ├── .pre-commit-config.yaml ├── .style.yapf ├── .travis.yml ├── EISeg ├── LICENSE ├── MANIFEST.in ├── README.md ├── README_AR.md ├── README_EN.md ├── docs │ ├── image.md │ ├── image_en.md │ ├── install.md │ ├── install_en.md │ ├── medical.md │ ├── medical_en.md │ ├── remote_sensing.md │ ├── remote_sensing_en.md │ ├── tools.md │ ├── video.md │ └── video_en.md ├── eiseg │ ├── __init__.py │ ├── __main__.py │ ├── app.py │ ├── config │ │ ├── colormap.txt │ │ └── config.yaml │ ├── controller.py │ ├── exe.py │ ├── inference │ │ ├── __init__.py │ │ ├── clicker.py │ │ ├── predictor │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ └── ops.py │ │ └── transforms │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ ├── crops.py │ │ │ ├── flip.py │ │ │ ├── limit_longest_side.py │ │ │ └── zoom_in.py │ ├── models.py │ ├── plugin │ │ ├── __init__.py │ │ ├── medical │ │ │ ├── __init__.py │ │ │ └── med.py │ │ ├── n2grid │ │ │ ├── __init__.py │ │ │ ├── grid.py │ │ │ └── rs_grid.py │ │ ├── remotesensing │ │ │ ├── __init__.py │ │ │ ├── imgtools.py │ │ │ ├── raster.py │ │ │ └── shape.py │ │ └── video │ │ │ ├── __init__.py │ │ │ ├── inference_core.py │ │ │ ├── load_model.py │ │ │ ├── util │ │ │ ├── __init__.py │ │ │ ├── range_transform.py │ │ │ └── tensor_util.py │ │ │ └── video_tools.py │ ├── resource │ │ ├── 3D.png │ │ ├── About.png │ │ ├── AutoSave.png │ │ ├── ChangeOutputDir.png │ │ ├── Clear.png │ │ ├── ClearLabel.png │ │ ├── ClearRecent.png │ │ ├── Close.png │ │ ├── Data.png │ │ ├── DeleteAllPolygon.png │ │ ├── DeletePolygon.png │ │ ├── Egypt.png │ │ ├── English.png │ │ ├── ExportLabel.png │ │ ├── File.png │ │ ├── ImportLabel.png │ │ ├── Label.png │ │ ├── Language.png │ │ ├── Log.png │ │ ├── MedicalImaging.png │ │ ├── Model.png │ │ ├── N2.png │ │ ├── Net.png │ │ ├── Next.png │ │ ├── Ok.png │ │ ├── OpenFolder.png │ │ ├── OpenImage.png │ │ ├── Paddle.png │ │ ├── Play.png │ │ ├── Prev.png │ │ ├── Propagate.png │ │ ├── Qt.png │ │ ├── Quit.png │ │ ├── Redo.png │ │ ├── RemoteSensing.png │ │ ├── ReportBug.png │ │ ├── Same.png │ │ ├── Save.png │ │ ├── SaveAs.png │ │ ├── SaveCOCO.png │ │ ├── SaveCutout.png │ │ ├── SaveGrayScale.png │ │ ├── SaveJson.png │ │ ├── SaveLargestCC.png │ │ ├── SavePseudoColor.png │ │ ├── Setting.png │ │ ├── Shortcut.png │ │ ├── Show.png │ │ ├── ShowRSPoly.png │ │ ├── Stop.png │ │ ├── Undo.png │ │ ├── Use.png │ │ ├── Video.png │ │ ├── VideoAnno.png │ │ ├── loading.gif │ │ └── 中文.png │ ├── run.py │ ├── ui.py │ ├── util │ │ ├── __init__.py │ │ ├── coco.py.bk │ │ ├── coco │ │ │ ├── __init__.py │ │ │ ├── _mask.pyx │ │ │ ├── coco.py │ │ │ ├── cocoeval.py │ │ │ ├── common │ │ │ │ ├── gason.cpp │ │ │ │ ├── gason.h │ │ │ │ ├── maskApi.c │ │ │ │ └── maskApi.h │ │ │ └── mask.py │ │ ├── colormap.py │ │ ├── config.py │ │ ├── exp_imports │ │ │ └── default.py │ │ ├── label.py │ │ ├── language.py │ │ ├── manager.py │ │ ├── misc.py │ │ ├── opath.py │ │ ├── palette.py │ │ ├── polygon.py │ │ ├── qt.py │ │ ├── regularization │ │ │ ├── __init__.py │ │ │ ├── cal_line.py │ │ │ ├── cal_point.py │ │ │ ├── rdp_alg.py │ │ │ ├── rotate_ang.py │ │ │ └── rs_regularization.py │ │ ├── serialization.py │ │ ├── translate │ │ │ ├── Arabic.qm │ │ │ └── English.qm │ │ └── vis.py │ └── widget │ │ ├── __init__.py │ │ ├── bbox.py │ │ ├── create.py │ │ ├── grip.py │ │ ├── line.py │ │ ├── loading.py │ │ ├── polygon.py │ │ ├── scene.py │ │ ├── shortcut.py │ │ ├── table.py │ │ ├── view.py │ │ └── vtk.py ├── init.sh ├── requirements-med.txt ├── requirements-rs.txt ├── requirements-video.txt ├── requirements.txt ├── setup.py └── tool │ ├── baidu_translate.py │ ├── cut_video.py │ ├── medical2video.py │ ├── pypi.sh │ ├── semantic2instance.py │ ├── translate.pro │ ├── translateUI.py │ ├── ts │ ├── Arabic.ts │ └── English.ts │ └── update_md5.py ├── LICENSE ├── Matting ├── README.md ├── README_CN.md ├── configs │ ├── benchmarks │ │ ├── Composition-1k │ │ │ └── closeform_composition1k.yml │ │ ├── Distinctions-646 │ │ │ └── closeform_distinctions646.yml │ │ └── PPM │ │ │ ├── README.md │ │ │ ├── closeform.yml │ │ │ ├── fast.yml │ │ │ ├── knn.yml │ │ │ ├── learningbased.yml │ │ │ └── randomwalks.yml │ ├── dim │ │ └── dim-vgg16.yml │ ├── human_matting │ │ └── human_matting-resnet34_vd.yml │ ├── modnet │ │ ├── modnet-hrnet_w18.yml │ │ ├── modnet-mobilenetv2.yml │ │ └── modnet-resnet50_vd.yml │ ├── ppmatting │ │ ├── README.md │ │ ├── ppmatting-hrnet_w18-human_1024.yml │ │ ├── ppmatting-hrnet_w18-human_512.yml │ │ ├── ppmatting-hrnet_w48-composition.yml │ │ └── ppmatting-hrnet_w48-distinctions.yml │ └── quick_start │ │ └── modnet-mobilenetv2.yml ├── deploy │ ├── human_matting_android_demo │ │ ├── .gitignore │ │ ├── LICENSE │ │ ├── README.md │ │ ├── README_CN.md │ │ ├── app │ │ │ ├── .gitignore │ │ │ ├── build.gradle │ │ │ ├── gradlew │ │ │ ├── gradlew.bat │ │ │ ├── local.properties │ │ │ ├── proguard-rules.pro │ │ │ └── src │ │ │ │ ├── androidTest │ │ │ │ └── java │ │ │ │ │ └── com │ │ │ │ │ └── baidu │ │ │ │ │ └── paddle │ │ │ │ │ └── lite │ │ │ │ │ └── demo │ │ │ │ │ └── ExampleInstrumentedTest.java │ │ │ │ ├── main │ │ │ │ ├── AndroidManifest.xml │ │ │ │ ├── assets │ │ │ │ │ └── image_matting │ │ │ │ │ │ ├── images │ │ │ │ │ │ ├── bg.jpg │ │ │ │ │ │ └── human.jpg │ │ │ │ │ │ └── labels │ │ │ │ │ │ └── label_list │ │ │ │ ├── java │ │ │ │ │ └── com │ │ │ │ │ │ └── paddle │ │ │ │ │ │ └── demo │ │ │ │ │ │ └── matting │ │ │ │ │ │ ├── AppCompatPreferenceActivity.java │ │ │ │ │ │ ├── MainActivity.java │ │ │ │ │ │ ├── Predictor.java │ │ │ │ │ │ ├── SettingsActivity.java │ │ │ │ │ │ ├── Utils.java │ │ │ │ │ │ ├── config │ │ │ │ │ │ └── Config.java │ │ │ │ │ │ ├── preprocess │ │ │ │ │ │ └── Preprocess.java │ │ │ │ │ │ └── visual │ │ │ │ │ │ └── Visualize.java │ │ │ │ └── res │ │ │ │ │ ├── drawable-v24 │ │ │ │ │ └── ic_launcher_foreground.xml │ │ │ │ │ ├── drawable │ │ │ │ │ ├── ic_launcher_background.xml │ │ │ │ │ └── paddle_logo.png │ │ │ │ │ ├── layout │ │ │ │ │ └── activity_main.xml │ │ │ │ │ ├── menu │ │ │ │ │ └── menu_action_options.xml │ │ │ │ │ ├── mipmap-anydpi-v26 │ │ │ │ │ ├── ic_launcher.xml │ │ │ │ │ └── ic_launcher_round.xml │ │ │ │ │ ├── mipmap-hdpi │ │ │ │ │ ├── ic_launcher.png │ │ │ │ │ └── ic_launcher_round.png │ │ │ │ │ ├── mipmap-mdpi │ │ │ │ │ ├── ic_launcher.png │ │ │ │ │ └── ic_launcher_round.png │ │ │ │ │ ├── mipmap-xhdpi │ │ │ │ │ ├── ic_launcher.png │ │ │ │ │ └── ic_launcher_round.png │ │ │ │ │ ├── mipmap-xxhdpi │ │ │ │ │ ├── ic_launcher.png │ │ │ │ │ └── ic_launcher_round.png │ │ │ │ │ ├── mipmap-xxxhdpi │ │ │ │ │ ├── ic_launcher.png │ │ │ │ │ └── ic_launcher_round.png │ │ │ │ │ ├── values │ │ │ │ │ ├── arrays.xml │ │ │ │ │ ├── colors.xml │ │ │ │ │ ├── strings.xml │ │ │ │ │ └── styles.xml │ │ │ │ │ └── xml │ │ │ │ │ └── settings.xml │ │ │ │ └── test │ │ │ │ └── java │ │ │ │ └── com │ │ │ │ └── baidu │ │ │ │ └── paddle │ │ │ │ └── lite │ │ │ │ └── demo │ │ │ │ └── ExampleUnitTest.java │ │ ├── build.gradle │ │ ├── gradle.properties │ │ ├── gradle │ │ │ └── wrapper │ │ │ │ ├── gradle-wrapper.jar │ │ │ │ └── gradle-wrapper.properties │ │ ├── gradlew │ │ ├── gradlew.bat │ │ └── settings.gradle │ └── python │ │ └── infer.py ├── ppmatting │ ├── __init__.py │ ├── core │ │ ├── __init__.py │ │ ├── predict.py │ │ ├── train.py │ │ ├── val.py │ │ └── val_ml.py │ ├── datasets │ │ ├── __init__.py │ │ ├── composition_1k.py │ │ ├── distinctions_646.py │ │ └── matting_dataset.py │ ├── metrics │ │ ├── __init__.py │ │ └── metric.py │ ├── ml │ │ ├── __init__.py │ │ └── methods.py │ ├── models │ │ ├── __init__.py │ │ ├── backbone │ │ │ ├── __init__.py │ │ │ ├── gca_enc.py │ │ │ ├── hrnet.py │ │ │ ├── mobilenet_v2.py │ │ │ ├── resnet_vd.py │ │ │ └── vgg.py │ │ ├── dim.py │ │ ├── gca.py │ │ ├── human_matting.py │ │ ├── layers │ │ │ ├── __init__.py │ │ │ └── gca_module.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ └── loss.py │ │ ├── modnet.py │ │ └── ppmatting.py │ ├── transforms │ │ ├── __init__.py │ │ └── transforms.py │ └── utils │ │ ├── __init__.py │ │ ├── estimate_foreground_ml.py │ │ └── utils.py ├── requirements.txt └── tools │ ├── bg_replace.py │ ├── export.py │ ├── predict.py │ ├── train.py │ ├── update_vgg16_params.py │ └── val.py ├── README.md ├── benchmark ├── README.md ├── README_CN.md ├── configs │ ├── cityscapes_30imgs.yml │ ├── fastscnn.yml │ ├── ocrnet_hrnetw48.yml │ └── segformer_b0.yml ├── deeplabv3p.yml ├── hrnet.yml ├── hrnet48.yml ├── run_all.sh ├── run_benchmark.sh ├── run_fp16.sh └── run_fp32.sh ├── configs ├── README.md ├── README_cn.md ├── _base_ │ ├── ade20k.yml │ ├── autonue.yml │ ├── chase_db1.yml │ ├── cityscapes.yml │ ├── cityscapes_1024x1024.yml │ ├── cityscapes_769x769.yml │ ├── cityscapes_769x769_setr.yml │ ├── coco_stuff.yml │ ├── drive.yml │ ├── hrf.yml │ ├── pascal_context.yml │ ├── pascal_voc12.yml │ ├── pascal_voc12aug.yml │ └── stare.yml ├── ann │ ├── README.md │ ├── ann_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── ann_resnet101_os8_voc12aug_512x512_40k.yml │ ├── ann_resnet50_os8_cityscapes_1024x512_80k.yml │ └── ann_resnet50_os8_voc12aug_512x512_40k.yml ├── attention_unet │ ├── README.md │ └── attention_unet_cityscapes_1024x512_80k.yml ├── bisenet │ ├── README.md │ └── bisenet_cityscapes_1024x1024_160k.yml ├── bisenetv1 │ ├── README.md │ └── bisenetv1_resnet18_os8_cityscapes_1024x512_160k.yml ├── ccnet │ ├── README.md │ └── ccnet_resnet101_os8_cityscapes_769x769_60k.yml ├── danet │ ├── README.md │ ├── danet_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── danet_resnet50_os8_cityscapes_1024x512_80k.yml │ └── danet_resnet50_os8_voc12aug_512x512_40k.yml ├── ddrnet │ ├── README.md │ └── ddrnet23_cityscapes_1024x1024_120k.yml ├── decoupled_segnet │ ├── README.md │ ├── decoupledsegnet_resnet50_os8_cityscapes_1024x512_80k.yml │ └── decoupledsegnet_resnet50_os8_cityscapes_832x832_80k.yml ├── deeplabv3 │ ├── README.md │ ├── deeplabv3_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── deeplabv3_resnet101_os8_voc12aug_512x512_40k.yml │ ├── deeplabv3_resnet50_os8_cityscapes_1024x512_80k.yml │ └── deeplabv3_resnet50_os8_voc12aug_512x512_40k.yml ├── deeplabv3p │ ├── README.md │ ├── deeplabv3p_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── deeplabv3p_resnet101_os8_cityscapes_769x769_80k.yml │ ├── deeplabv3p_resnet101_os8_voc12aug_512x512_40k.yml │ ├── deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml │ ├── deeplabv3p_resnet50_os8_cityscapes_1024x512_80k_rmiloss.yml │ └── deeplabv3p_resnet50_os8_voc12aug_512x512_40k.yml ├── dmnet │ ├── README.md │ └── dmnet_resnet101_os8_cityscapes_1024x512_80k.yml ├── dnlnet │ ├── README.md │ ├── dnlnet_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── dnlnet_resnet101_os8_voc12aug_512x512_40k.yml │ ├── dnlnet_resnet50_os8_cityscapes_1024x512_80k.yml │ └── dnlnet_resnet50_os8_voc12aug_512x512_40k.yml ├── emanet │ ├── README.md │ ├── emanet_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── emanet_resnet101_os8_voc12aug_512x512_40k.yml │ ├── emanet_resnet50_os8_cityscapes_1024x512_80k.yml │ └── emanet_resnet50_os8_voc12aug_512x512_40k.yml ├── encnet │ ├── README.md │ └── encnet_resnet101_os8_cityscapes_1024x512_80k.yml ├── enet │ ├── README.md │ └── enet_cityscapes_1024x512_80k.yml ├── espnet │ ├── README.md │ └── espnet_cityscapes_1024x512_120k.yml ├── espnetv1 │ ├── README.md │ └── espnetv1_cityscapes_1024x512_120k.yml ├── fastfcn │ ├── README.md │ └── fastfcn_resnet50_os8_ade20k_480x480_120k.yml ├── fastscnn │ ├── README.md │ ├── fastscnn_cityscapes_1024x1024_160k.yml │ ├── fastscnn_cityscapes_1024x1024_40k.yml │ └── fastscnn_cityscapes_1024x1024_40k_SCL.yml ├── fcn │ ├── README.md │ ├── fcn_hrnetw18_cityscapes_1024x512_80k.yml │ ├── fcn_hrnetw18_cityscapes_1024x512_80k_bs4.yml │ ├── fcn_hrnetw18_cityscapes_1024x512_80k_bs4_SCL.yml │ ├── fcn_hrnetw18_pphumanseg14k.yml │ ├── fcn_hrnetw18_voc12aug_512x512_40k.yml │ ├── fcn_hrnetw48_cityscapes_1024x512_80k.yml │ └── fcn_hrnetw48_voc12aug_512x512_40k.yml ├── gcnet │ ├── README.md │ ├── gcnet_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── gcnet_resnet101_os8_voc12aug_512x512_40k.yml │ ├── gcnet_resnet50_os8_cityscapes_1024x512_80k.yml │ └── gcnet_resnet50_os8_voc12aug_512x512_40k.yml ├── ginet │ ├── README.md │ ├── ginet_resnet101_os8_ade20k_520x520_150k.yml │ ├── ginet_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── ginet_resnet101_os8_voc12aug_512x512_40k.yml │ ├── ginet_resnet50_os8_ade20k_520x520_150k.yml │ ├── ginet_resnet50_os8_cityscapes_1024x512_80k.yml │ └── ginet_resnet50_os8_voc12aug_512x512_40k.yml ├── glore │ ├── README.md │ ├── glore_resnet50_os8_cityscapes_1024x512_80k.yml │ └── glore_resnet50_os8_voc12aug_512x512_40k.yml ├── gscnn │ ├── README.md │ └── gscnn_resnet50_os8_cityscapes_1024x512_80k.yml ├── hardnet │ ├── README.md │ └── hardnet_cityscapes_1024x1024_160k.yml ├── hrnet_w48_contrast │ ├── HRNet_W48_contrast_cityscapes_1024x512_60k.yml │ └── README.md ├── isanet │ ├── README.md │ ├── isanet_resnet101_os8_cityscapes_769x769_80k.yml │ ├── isanet_resnet101_os8_voc12aug_512x512_40k.yml │ ├── isanet_resnet50_os8_cityscapes_769x769_80k.yml │ └── isanet_resnet50_os8_voc12aug_512x512_40k.yml ├── lraspp │ ├── README.md │ ├── lraspp_mobilenetv3_cityscapes_1024x512_80k.yml │ ├── lraspp_mobilenetv3_cityscapes_1024x512_80k_large_kernel.yml │ └── lraspp_mobilenetv3_cityscapes_1024x512_80k_os32.yml ├── mobileseg │ ├── README.md │ ├── README_cn.md │ ├── mobileseg_ghostnet_cityscapes_1024x512_80k.yml │ ├── mobileseg_litehrnet18_cityscapes_1024x512_80k.yml │ ├── mobileseg_mobilenetv2_cityscapes_1024x512_80k.yml │ ├── mobileseg_mobilenetv3_cityscapes_1024x512_80k.yml │ └── mobileseg_shufflenetv2_cityscapes_1024x512_80k.yml ├── ocrnet │ ├── README.md │ ├── ocrnet_hrnetw18_cityscapes_1024x512_160k.yml │ ├── ocrnet_hrnetw18_cityscapes_1024x512_160k_lovasz_softmax.yml │ ├── ocrnet_hrnetw18_road_extraction_768x768_15k.yml │ ├── ocrnet_hrnetw18_road_extraction_768x768_15k_lovasz_hinge.yml │ ├── ocrnet_hrnetw18_voc12aug_512x512_40k.yml │ ├── ocrnet_hrnetw48_cityscapes_1024x512_160k.yml │ ├── ocrnet_hrnetw48_cityscapes_1024x512_40k.yml │ ├── ocrnet_hrnetw48_cityscapes_1024x512_40k_SCL.yml │ └── ocrnet_hrnetw48_voc12aug_512x512_40k.yml ├── pfpn │ ├── README.md │ └── pfpn_resnet101_os8_cityscapes_512x1024_40k.yml ├── pointrend │ ├── README.md │ ├── pointrend_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── pointrend_resnet101_os8_voc12aug_512x512_40k.yml │ ├── pointrend_resnet50_os8_cityscapes_1024x512_80k.yml │ └── pointrend_resnet50_os8_voc12aug_512x512_40k.yml ├── portraitnet │ ├── README.md │ ├── portraitnet_eg1800_224x224_46k.yml │ └── portraitnet_supervisely_224x224_60k.yml ├── pp_humanseg_lite │ ├── README.md │ ├── pp_humanseg_lite_export_398x224.yml │ ├── pp_humanseg_lite_mini_supervisely.yml │ └── pphumanseg_lite.png ├── pp_liteseg │ ├── README.md │ ├── pp_liteseg_stdc1_camvid_960x720_10k.yml │ ├── pp_liteseg_stdc1_camvid_960x720_10k_for_test.yml │ ├── pp_liteseg_stdc1_cityscapes_1024x512_scale0.5_160k.yml │ ├── pp_liteseg_stdc1_cityscapes_1024x512_scale0.75_160k.yml │ ├── pp_liteseg_stdc1_cityscapes_1024x512_scale1.0_160k.yml │ ├── pp_liteseg_stdc2_camvid_960x720_10k.yml │ ├── pp_liteseg_stdc2_camvid_960x720_10k_for_test.yml │ ├── pp_liteseg_stdc2_cityscapes_1024x512_scale0.5_160k.yml │ ├── pp_liteseg_stdc2_cityscapes_1024x512_scale0.75_160k.yml │ └── pp_liteseg_stdc2_cityscapes_1024x512_scale1.0_160k.yml ├── pspnet │ ├── README.md │ ├── pspnet_resnet101_os8_cityscapes_1024x512_80k.yml │ ├── pspnet_resnet101_os8_voc12aug_512x512_40k.yml │ ├── pspnet_resnet50_os8_cityscapes_1024x512_80k.yml │ └── pspnet_resnet50_os8_voc12aug_512x512_40k.yml ├── pssl │ ├── README.md │ ├── pp_liteseg_stdc1_pssl.yml │ ├── pp_liteseg_stdc2_cityscapes_1024x512_scale1.0_160k_pssl.yml │ ├── pp_liteseg_stdc2_pssl.yml │ ├── stdc1_seg_pssl.yml │ └── stdc2_seg_pssl.yml ├── quick_start │ ├── bisenet_optic_disc_512x512_1k.yml │ ├── deeplabv3p_resnet18_os8_optic_disc_512x512_1k_student.yml │ ├── deeplabv3p_resnet50_os8_optic_disc_512x512_1k_teacher.yml │ └── pp_liteseg_optic_disc_512x512_1k.yml ├── segformer │ ├── README.md │ ├── segformer_b0_cityscapes_1024x1024_160k.yml │ ├── segformer_b0_cityscapes_1024x512_160k.yml │ ├── segformer_b1_cityscapes_1024x1024_160k.yml │ ├── segformer_b1_cityscapes_1024x512_160k.yml │ ├── segformer_b2_cityscapes_1024x1024_160k.yml │ ├── segformer_b2_cityscapes_1024x512_160k.yml │ ├── segformer_b3_cityscapes_1024x1024_160k.yml │ ├── segformer_b3_cityscapes_1024x512_160k.yml │ ├── segformer_b4_cityscapes_1024x1024_160k.yml │ ├── segformer_b4_cityscapes_1024x512_160k.yml │ ├── segformer_b5_cityscapes_1024x1024_160k.yml │ └── segformer_b5_cityscapes_1024x512_160k.yml ├── segmenter │ ├── README.md │ ├── segmenter_vit_base_linear_ade20k_512x512_160k.yml │ ├── segmenter_vit_base_mask_ade20k_512x512_160k.yml │ ├── segmenter_vit_small_linear_ade20k_512x512_160k.yml │ └── segmenter_vit_small_mask_ade20k_512x512_160k.yml ├── segnet │ ├── README.md │ └── segnet_cityscapes_1024x512_80k.yml ├── setr │ ├── README.md │ ├── setr_mla_large_cityscapes_769x769_40k.yml │ ├── setr_naive_large_cityscapes_769x769_40k.yml │ └── setr_pup_large_cityscapes_769x769_40k.yml ├── sfnet │ ├── README.md │ ├── sfnet_resnet18_os8_cityscapes_1024x1024_80k.yml │ └── sfnet_resnet50_os8_cityscapes_1024x1024_80k.yml ├── smrt │ ├── README.md │ ├── base_cfg.yml │ ├── bisenetv2.yml │ ├── deeplabv3p_resnet50_os8.yml │ ├── ocrnet_hrnetw18.yml │ ├── pp_liteseg_stdc1.yml │ ├── pp_liteseg_stdc2.yml │ └── sfnet_resnet18_os8.yml ├── stdcseg │ ├── README.md │ ├── stdc1_seg_cityscapes_1024x512_80k.yml │ ├── stdc1_seg_voc12aug_512x512_40k.yml │ ├── stdc2_seg_cityscapes_1024x512_80k.yml │ └── stdc2_seg_voc12aug_512x512_40k.yml ├── u2net │ ├── README.md │ ├── u2net_cityscapes_1024x512_160k.yml │ └── u2netp_cityscapes_1024x512_160k.yml ├── unet │ ├── README.md │ ├── unet_chasedb1_128x128_40k.yml │ ├── unet_cityscapes_1024x512_160k.yml │ ├── unet_drive_128x128_40k.yml │ ├── unet_hrf_256x256_40k.yml │ └── unet_stare_128x128_40k.yml ├── unet_3plus │ ├── README.md │ └── unet_3plus_cityscapes_1024x512_160k.yml ├── unet_plusplus │ ├── README.md │ └── unet_plusplus_cityscapes_1024x512_160k.yml └── upernet │ ├── README.md │ └── upernet_resnet101_os8_cityscapes_512x1024_40k.yml ├── contrib ├── AutoNUE │ ├── README.md │ ├── configs │ │ ├── auto_nue_auto_label.yml │ │ ├── auto_nue_map+city_crop.yml │ │ ├── mscale_auto_nue_map+city@1920.yml │ │ ├── sscale_auto_nue_map+city@1920.yml │ │ └── swin_transformer_mla_base_patch4_window7_160k_autonue.yml │ ├── core │ │ ├── __init__.py │ │ ├── infer.py │ │ ├── infer_crop.py │ │ ├── infer_ensemble.py │ │ ├── infer_ensemble_three.py │ │ ├── infer_generate_autolabel.py │ │ ├── predict_ensemble.py │ │ ├── predict_ensemble_three.py │ │ ├── predict_generate_autolabel.py │ │ ├── val.py │ │ └── val_crop.py │ ├── datasets │ │ ├── __init__.py │ │ ├── auto_nue.py │ │ ├── auto_nue_autolabel.py │ │ └── auto_nue_crop.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ └── hrnet_nv.py │ │ ├── mscale_ocrnet.py │ │ └── ocrnet_nv.py │ ├── predict.py │ ├── predict_ensemble.py │ ├── predict_ensemble_three.py │ ├── scripts │ │ └── train.py │ ├── tools │ │ └── IDD_labeling.py │ ├── train.py │ └── val.py ├── CityscapesSOTA │ ├── README.md │ ├── configs │ │ ├── README.md │ │ ├── mscale_ocr_cityscapes_autolabel_mapillary.yml │ │ └── mscale_ocr_cityscapes_autolabel_mapillary_ms_val.yml │ ├── datasets │ │ ├── __init__.py │ │ └── cityscapes_autolabeling.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ └── hrnet_nv.py │ │ ├── mscale_ocrnet.py │ │ └── ocrnet_nv.py │ ├── predict.py │ ├── scripts │ │ └── train.py │ ├── tools │ │ ├── cityscapes_labels.py │ │ └── convert_cityscapes_autolabeling.py │ ├── train.py │ └── val.py ├── DomainAdaptation │ ├── README.md │ ├── configs │ │ └── deeplabv2 │ │ │ ├── deeplabv2_resnet101_os8_gta5cityscapes_1280x640_160k_newds_edge_rec.yml │ │ │ ├── deeplabv2_resnet101_os8_gta5cityscapes_1280x640_160k_newds_edgestream.yml │ │ │ ├── deeplabv2_resnet101_os8_gta5cityscapes_1280x640_160k_newds_featpullin.yml │ │ │ ├── deeplabv2_resnet101_os8_gta5cityscapes_1280x640_160k_newds_gta5src.yml │ │ │ └── deeplabv2_resnet101_os8_gta5cityscapes_1280x640_160k_newds_sfnet.yml │ ├── cvlibs │ │ ├── __init__.py │ │ └── config.py │ ├── datasets │ │ ├── __init__.py │ │ ├── cityscapes_noconfig.py │ │ ├── gta5_noconfig.py │ │ └── synthia.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ ├── __init__.py │ │ │ └── resnet.py │ │ ├── deeplabv2.py │ │ ├── ema.py │ │ └── gscnn.py │ ├── requirements.txt │ ├── run-DA_src.sh │ ├── script │ │ ├── __init__.py │ │ ├── train.py │ │ └── val.py │ ├── train.py │ ├── utils │ │ ├── __init__.py │ │ ├── augmentation.py │ │ ├── config_check.py │ │ └── utils.py │ └── val.py ├── LaneSeg │ ├── README.md │ ├── README_CN.md │ ├── configs │ │ ├── bisenetV2_tusimple_640x368_300k.yml │ │ └── fastscnn_tusimple_640x368_300k.yml │ ├── core │ │ ├── __init__.py │ │ ├── infer.py │ │ ├── predict.py │ │ ├── train.py │ │ └── val.py │ ├── data │ │ ├── images │ │ │ ├── added_prediction │ │ │ │ └── 3.jpg │ │ │ ├── points │ │ │ │ └── 3.jpg │ │ │ └── pseudo_color_prediction │ │ │ │ └── 3.png │ │ └── test_images │ │ │ └── 3.jpg │ ├── datasets │ │ ├── __init__.py │ │ └── tusimple.py │ ├── deploy │ │ ├── cpp │ │ │ ├── CMakeLists.txt │ │ │ ├── README.md │ │ │ ├── README_CN.md │ │ │ ├── run_seg_cpu.sh │ │ │ ├── run_seg_gpu.sh │ │ │ └── src │ │ │ │ ├── lane_postprocess.cpp │ │ │ │ ├── lane_postprocess.hpp │ │ │ │ └── test_seg.cc │ │ └── python │ │ │ └── infer.py │ ├── export.py │ ├── losses │ │ ├── __init__.py │ │ └── lane_cross_entropy_loss.py │ ├── predict.py │ ├── third_party │ │ ├── __init__.py │ │ ├── generate_tusimple_dataset.py │ │ ├── get_lane_coords.py │ │ ├── lane.py │ │ └── tusimple_processor.py │ ├── train.py │ ├── transforms │ │ ├── __init__.py │ │ └── lane_transforms.py │ └── val.py ├── MedicalSeg │ ├── .gitignore │ ├── .pre-commit-config.yaml │ ├── LICENSE │ ├── README.md │ ├── README_CN.md │ ├── configs │ │ ├── _base_ │ │ │ └── global_configs.yml │ │ ├── lung_coronavirus │ │ │ ├── README.md │ │ │ ├── lung_coronavirus.yml │ │ │ └── vnet_lung_coronavirus_128_128_128_15k.yml │ │ ├── mri_spine_seg │ │ │ ├── README.md │ │ │ ├── mri_spine_seg_1e-1_big_rmresizecrop.yml │ │ │ ├── mri_spine_seg_1e-1_big_rmresizecrop_class20.yml │ │ │ ├── vnet_mri_spine_seg_512_512_12_15k.yml │ │ │ └── vnetdeepsup_mri_spine_seg_512_512_12_15k.yml │ │ ├── msd_brain_seg │ │ │ ├── README.md │ │ │ ├── msd_brain_seg_1e-4.yml │ │ │ └── unetr_msd_brain_seg_1e-4.yml │ │ └── schedulers │ │ │ └── two_stage_coarseseg_fineseg.yml │ ├── deploy │ │ └── python │ │ │ ├── README.md │ │ │ └── infer.py │ ├── documentation │ │ ├── tutorial.md │ │ └── tutorial_cn.md │ ├── export.py │ ├── medicalseg │ │ ├── __init__.py │ │ ├── core │ │ │ ├── __init__.py │ │ │ ├── infer.py │ │ │ ├── train.py │ │ │ └── val.py │ │ ├── cvlibs │ │ │ ├── __init__.py │ │ │ ├── config.py │ │ │ └── manager.py │ │ ├── datasets │ │ │ ├── __init__.py │ │ │ ├── dataset.py │ │ │ ├── lung_coronavirus.py │ │ │ ├── mri_spine_seg.py │ │ │ └── msd_brain_seg.py │ │ ├── models │ │ │ ├── __init__.py │ │ │ ├── losses │ │ │ │ ├── __init__.py │ │ │ │ ├── binary_cross_entropy_loss.py │ │ │ │ ├── cross_entropy_loss.py │ │ │ │ ├── dice_loss.py │ │ │ │ ├── loss_utils.py │ │ │ │ └── mixes_losses.py │ │ │ ├── unetr.py │ │ │ ├── vnet.py │ │ │ └── vnet_deepsup.py │ │ ├── transforms │ │ │ ├── __init__.py │ │ │ ├── functional.py │ │ │ └── transform.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── config_check.py │ │ │ ├── download.py │ │ │ ├── env_util │ │ │ ├── __init__.py │ │ │ ├── seg_env.py │ │ │ └── sys_env.py │ │ │ ├── logger.py │ │ │ ├── loss_utils.py │ │ │ ├── metric.py │ │ │ ├── op_flops_run.py │ │ │ ├── progbar.py │ │ │ ├── timer.py │ │ │ ├── train_profiler.py │ │ │ ├── utils.py │ │ │ └── visualize.py │ ├── requirements.txt │ ├── run-vnet-mri.sh │ ├── run-vnet.sh │ ├── test.py │ ├── test_tipc │ │ ├── README.md │ │ ├── common_func.sh │ │ ├── configs │ │ │ └── unetr │ │ │ │ ├── msd_brain_test.yml │ │ │ │ └── train_infer_python.txt │ │ ├── prepare.sh │ │ └── test_train_inference_python.sh │ ├── tools │ │ ├── __init__.py │ │ ├── prepare.py │ │ ├── prepare_lung_coronavirus.py │ │ ├── prepare_mri_spine_seg.py │ │ ├── prepare_msd.py │ │ ├── prepare_msd_brain_seg.py │ │ ├── prepare_prostate.py │ │ ├── preprocess_globals.yml │ │ └── preprocess_utils │ │ │ ├── __init__.py │ │ │ ├── dataset_json.py │ │ │ ├── geometry.py │ │ │ ├── global_var.py │ │ │ ├── load_image.py │ │ │ ├── uncompress.py │ │ │ └── values.py │ ├── train.py │ ├── val.py │ └── visualize.ipynb ├── PP-HumanSeg │ ├── README.md │ ├── README_cn.md │ ├── configs │ │ ├── human_pp_humansegv1_lite.yml │ │ ├── human_pp_humansegv1_mobile.yml │ │ ├── human_pp_humansegv1_server.yml │ │ ├── human_pp_humansegv2_lite.yml │ │ ├── human_pp_humansegv2_mobile.yml │ │ ├── portrait_pp_humansegv1_lite.yml │ │ └── portrait_pp_humansegv2_lite.yml │ ├── paper.md │ └── src │ │ ├── __init__.py │ │ ├── download_data.py │ │ ├── download_inference_models.py │ │ ├── download_pretrained_models.py │ │ ├── infer.py │ │ ├── optic_flow_process.py │ │ └── seg_demo.py └── PanopticDeepLab │ ├── README.md │ ├── README_CN.md │ ├── configs │ ├── _base_ │ │ └── cityscapes_panoptic.yml │ └── panoptic_deeplab │ │ ├── panoptic_deeplab_resnet50_os32_cityscapes_1025x513_bs8_90k_lr00005.yml │ │ └── panoptic_deeplab_resnet50_os32_cityscapes_2049x1025_bs1_90k_lr00005.yml │ ├── core │ ├── __init__.py │ ├── infer.py │ ├── predict.py │ ├── train.py │ └── val.py │ ├── datasets │ ├── __init__.py │ └── cityscapes_panoptic.py │ ├── docs │ ├── panoptic_deeplab.jpg │ ├── visualization_instance.png │ ├── visualization_panoptic.png │ └── visualization_semantic.png │ ├── models │ ├── __init__.py │ └── panoptic_deeplab.py │ ├── predict.py │ ├── train.py │ ├── transforms │ ├── __init__.py │ └── target_transforms.py │ ├── utils │ ├── __init__.py │ ├── evaluation │ │ ├── __init__.py │ │ ├── instance.py │ │ ├── panoptic.py │ │ └── semantic.py │ └── visualize.py │ └── val.py ├── deploy ├── cpp │ ├── CMakeLists.txt │ ├── README.md │ ├── README_cn.md │ ├── run_seg_cpu.sh │ ├── run_seg_gpu.sh │ ├── run_seg_gpu_trt.sh │ ├── run_seg_gpu_trt_dynamic_shape.sh │ └── src │ │ └── test_seg.cc ├── lite │ ├── README.md │ ├── example │ │ ├── human_1.png │ │ ├── human_2.png │ │ └── human_3.png │ └── human_segmentation_demo │ │ ├── .gitignore │ │ ├── app │ │ ├── .gitignore │ │ ├── build.gradle │ │ ├── gradlew │ │ ├── gradlew.bat │ │ ├── local.properties │ │ ├── proguard-rules.pro │ │ └── src │ │ │ ├── androidTest │ │ │ └── java │ │ │ │ └── com │ │ │ │ └── baidu │ │ │ │ └── paddle │ │ │ │ └── lite │ │ │ │ └── demo │ │ │ │ └── ExampleInstrumentedTest.java │ │ │ ├── main │ │ │ ├── AndroidManifest.xml │ │ │ ├── assets │ │ │ │ └── image_segmentation │ │ │ │ │ ├── images │ │ │ │ │ └── human.jpg │ │ │ │ │ └── labels │ │ │ │ │ └── label_list │ │ │ ├── java │ │ │ │ └── com │ │ │ │ │ └── baidu │ │ │ │ │ └── paddle │ │ │ │ │ └── lite │ │ │ │ │ └── demo │ │ │ │ │ └── segmentation │ │ │ │ │ ├── AppCompatPreferenceActivity.java │ │ │ │ │ ├── MainActivity.java │ │ │ │ │ ├── Predictor.java │ │ │ │ │ ├── SettingsActivity.java │ │ │ │ │ ├── Utils.java │ │ │ │ │ ├── config │ │ │ │ │ └── Config.java │ │ │ │ │ ├── preprocess │ │ │ │ │ └── Preprocess.java │ │ │ │ │ └── visual │ │ │ │ │ └── Visualize.java │ │ │ └── res │ │ │ │ ├── drawable-v24 │ │ │ │ └── ic_launcher_foreground.xml │ │ │ │ ├── drawable │ │ │ │ └── ic_launcher_background.xml │ │ │ │ ├── layout │ │ │ │ └── activity_main.xml │ │ │ │ ├── menu │ │ │ │ └── menu_action_options.xml │ │ │ │ ├── mipmap-anydpi-v26 │ │ │ │ ├── ic_launcher.xml │ │ │ │ └── ic_launcher_round.xml │ │ │ │ ├── mipmap-hdpi │ │ │ │ ├── ic_launcher.png │ │ │ │ └── ic_launcher_round.png │ │ │ │ ├── mipmap-mdpi │ │ │ │ ├── ic_launcher.png │ │ │ │ └── ic_launcher_round.png │ │ │ │ ├── mipmap-xhdpi │ │ │ │ ├── ic_launcher.png │ │ │ │ └── ic_launcher_round.png │ │ │ │ ├── mipmap-xxhdpi │ │ │ │ ├── ic_launcher.png │ │ │ │ └── ic_launcher_round.png │ │ │ │ ├── mipmap-xxxhdpi │ │ │ │ ├── ic_launcher.png │ │ │ │ └── ic_launcher_round.png │ │ │ │ ├── values │ │ │ │ ├── arrays.xml │ │ │ │ ├── colors.xml │ │ │ │ ├── strings.xml │ │ │ │ └── styles.xml │ │ │ │ └── xml │ │ │ │ └── settings.xml │ │ │ └── test │ │ │ └── java │ │ │ └── com │ │ │ └── baidu │ │ │ └── paddle │ │ │ └── lite │ │ │ └── demo │ │ │ └── ExampleUnitTest.java │ │ ├── build.gradle │ │ ├── gradle.properties │ │ ├── gradle │ │ └── wrapper │ │ │ ├── gradle-wrapper.jar │ │ │ └── gradle-wrapper.properties │ │ ├── gradlew │ │ ├── gradlew.bat │ │ └── settings.gradle ├── onnxruntime_cpp │ ├── CMakeLists.txt │ ├── README.md │ └── src │ │ ├── ort_session_handler.cpp │ │ ├── ort_session_handler.hpp │ │ └── test_seg.cpp ├── python │ ├── README.md │ ├── collect_dynamic_shape.py │ ├── infer.py │ ├── infer_benchmark.py │ ├── infer_dataset.py │ ├── infer_onnx.py │ └── infer_onnx_trt.py ├── serving │ ├── README.md │ └── test_serving.py └── web │ ├── README.md │ └── example │ ├── bg │ └── bg.jpg │ ├── index.html │ ├── index.ts │ ├── package.json │ ├── tsconfig.json │ └── webpack.config.js ├── docs ├── Makefile ├── README.md ├── add_new_model.md ├── api_example.md ├── api_example_cn.md ├── apis │ ├── README.md │ ├── README_CN.md │ ├── backbones.md │ ├── backbones │ │ ├── backbones.md │ │ ├── backbones_cn.md │ │ └── index.rst │ ├── core.md │ ├── core │ │ ├── core.md │ │ ├── core_cn.md │ │ └── index.rst │ ├── cvlibs.md │ ├── cvlibs │ │ ├── cvlibs.md │ │ ├── cvlibs_cn.md │ │ └── index.rst │ ├── datasets.md │ ├── datasets │ │ ├── datasets.md │ │ └── datasets_cn.md │ ├── index.rst │ ├── losses.md │ ├── losses │ │ ├── index.rst │ │ ├── losses.md │ │ └── losses_cn.md │ ├── models.md │ ├── models │ │ ├── index.rst │ │ ├── models.md │ │ └── models_cn.md │ ├── transforms.md │ └── transforms │ │ ├── index.rst │ │ ├── transforms.md │ │ └── transforms_cn.md ├── conf.py ├── data │ ├── README.md │ ├── custom │ │ ├── data_prepare.md │ │ ├── data_prepare_cn.md │ │ └── index.rst │ ├── image │ │ ├── ITK-SNAP.png │ │ ├── LabelMeing.png │ │ ├── file_list.png │ │ ├── file_list2.png │ │ ├── image-1.png │ │ ├── image-10.jpg │ │ ├── image-11.png │ │ ├── image-2.png │ │ ├── image-3.png │ │ ├── image-4-1.png │ │ ├── image-4-2.png │ │ ├── image-5.png │ │ ├── image-6-2.png │ │ ├── image-6.png │ │ ├── image-7.png │ │ ├── jingling-1.png │ │ ├── jingling-2.png │ │ ├── jingling-3.png │ │ ├── jingling-4.png │ │ ├── jingling-5.png │ │ └── labelme_polygons.jpg │ ├── marker │ │ ├── LabelMe.md │ │ ├── LabelMe_cn.md │ │ ├── index.rst │ │ ├── marker.md │ │ └── marker_cn.md │ ├── pre_data.md │ ├── pre_data_cn.md │ └── transform │ │ ├── index.rst │ │ ├── transform.md │ │ └── transform_cn.md ├── deployment │ ├── inference │ │ ├── cpp_inference.md │ │ ├── cpp_inference_cn.md │ │ ├── index.rst │ │ ├── infer_benchmark.md │ │ ├── infer_benchmark_cn.md │ │ ├── inference.md │ │ ├── inference_cn.md │ │ ├── python_inference.md │ │ └── python_inference_cn.md │ ├── lite │ │ ├── example │ │ │ ├── human.png │ │ │ ├── human_1.png │ │ │ ├── human_2.png │ │ │ └── human_3.png │ │ ├── index.rst │ │ ├── lite.md │ │ └── lite_cn.md │ ├── serving │ │ ├── index.rst │ │ ├── serving.md │ │ └── serving_cn.md │ └── web │ │ ├── image │ │ └── figure1.png │ │ ├── index.rst │ │ ├── web.md │ │ └── web_cn.md ├── design │ ├── create │ │ ├── add_new_model.md │ │ ├── add_new_model_cn.md │ │ └── index.rst │ └── use │ │ ├── index.rst │ │ ├── use.md │ │ └── use_cn.md ├── evaluation │ ├── evaluate.md │ ├── evaluate_cn.md │ └── index.rst ├── faq │ └── faq │ │ ├── faq.md │ │ ├── faq_cn.md │ │ ├── faq_imgs │ │ └── ann_config.png │ │ └── index.rst ├── images │ ├── Lovasz_Hinge_Evaluate_mIoU.png │ ├── Lovasz_Softmax_Evaluate_mIoU.png │ ├── QQ_chat.png │ ├── activate.png │ ├── anli.png │ ├── api_fig1.png │ ├── api_fig2.png │ ├── chat.png │ ├── cityscapes_predict_demo.png │ ├── deepglobe.png │ ├── eiseg_demo.gif │ ├── f1.png │ ├── f2.png │ ├── f3.png │ ├── feature.png │ ├── fig1.png │ ├── fig2.png │ ├── fig3.png │ ├── fig4.png │ ├── fig5.png │ ├── human.png │ ├── interactive.gif │ ├── love.png │ ├── model.png │ ├── optic_test_image.jpg │ ├── paddleseg_logo.png │ ├── quick_start_predict.jpg │ ├── quick_start_vdl.jpg │ ├── readme │ │ ├── 二次元.gif │ │ ├── 人体解析.gif │ │ ├── 人像分割-0.gif │ │ └── 人像分割.gif │ ├── seg_news_icon.png │ ├── teach.png │ └── yinyong.png ├── index.rst ├── install.md ├── install_cn.md ├── loss_usage.md ├── make.bat ├── model_export.md ├── model_export_cn.md ├── model_export_onnx.md ├── model_export_onnx_cn.md ├── model_zoo_overview.md ├── model_zoo_overview_cn.md ├── models │ ├── deeplabv3.md │ ├── deeplabv3_cn.md │ ├── fascnn.md │ ├── fascnn_cn.md │ ├── images │ │ ├── Fast-SCNN.png │ │ ├── OCRNet.png │ │ ├── UNet.png │ │ ├── convolution.png │ │ └── deeplabv3+.png │ ├── index.rst │ ├── ocrnet.md │ ├── ocrnet_cn.md │ ├── unet.md │ └── unet_cn.md ├── module │ ├── data │ │ ├── data.md │ │ ├── data_cn.md │ │ └── index.rst │ ├── images │ │ ├── Lovasz_Hinge_Evaluate_mIoU.png │ │ ├── Lovasz_Softmax_Evaluate_mIoU.png │ │ ├── VOC2012.png │ │ ├── annotation │ │ │ ├── image-1.png │ │ │ ├── image-10.jpg │ │ │ ├── image-11.png │ │ │ ├── image-2.png │ │ │ ├── image-3.png │ │ │ ├── image-4-1.png │ │ │ ├── image-4-2.png │ │ │ ├── image-5.png │ │ │ ├── image-6-2.png │ │ │ ├── image-6.png │ │ │ ├── image-7.png │ │ │ ├── jingling-1.png │ │ │ ├── jingling-2.png │ │ │ ├── jingling-3.png │ │ │ ├── jingling-4.png │ │ │ └── jingling-5.png │ │ ├── aug_method.png │ │ ├── cityscapes.png │ │ ├── cityscapes_predict_demo.png │ │ ├── cosine_decay_example.png │ │ ├── data_aug_example.png │ │ ├── data_aug_flip_mirror.png │ │ ├── data_aug_flow.png │ │ ├── deepglobe.png │ │ ├── deeplabv3p.png │ │ ├── dice.png │ │ ├── dice2.png │ │ ├── dice3.png │ │ ├── fast-scnn.png │ │ ├── file_list.png │ │ ├── file_list2.png │ │ ├── gn.png │ │ ├── hrnet.png │ │ ├── icnet.png │ │ ├── image-10.jpg │ │ ├── loss_comparison.png │ │ ├── lovasz-hinge-vis.png │ │ ├── lovasz-hinge.png │ │ ├── lovasz-softmax.png │ │ ├── optic_test_image.jpg │ │ ├── piecewise_decay_example.png │ │ ├── poly_decay_example.png │ │ ├── pspnet.png │ │ ├── pspnet2.png │ │ ├── qq_group2.png │ │ ├── quick_start_predict.jpg │ │ ├── quick_start_vdl.jpg │ │ ├── rangescale.png │ │ ├── seg_news_icon.png │ │ ├── softmax_loss.png │ │ ├── unet.png │ │ ├── usage_vis_demo.jpg │ │ ├── visualdl_image.png │ │ ├── visualdl_scalar.png │ │ └── warmup_with_poly_decay_example.png │ ├── index.rst │ ├── loss │ │ ├── BCELoss_cn.md │ │ ├── BCELoss_en.md │ │ ├── BootstrappedCrossEntropyLoss_cn.md │ │ ├── BootstrappedCrossEntropyLoss_en.md │ │ ├── CrossEntropyLoss_cn.md │ │ ├── CrossEntropyLoss_en.md │ │ ├── DiceLoss_cn.md │ │ ├── DiceLoss_en.md │ │ ├── DualTaskLoss_cn.md │ │ ├── DualTaskLoss_en.md │ │ ├── EdgeAttentionLoss_cn.md │ │ ├── EdgeAttentionLoss_en.md │ │ ├── L1Loss_cn.md │ │ ├── L1Loss_en.md │ │ ├── LovaszHingeLoss_cn.md │ │ ├── LovaszHingeLoss_en.md │ │ ├── LovaszSoftmaxLoss_cn.md │ │ ├── LovaszSoftmaxLoss_en.md │ │ ├── MSELoss_cn.md │ │ ├── MSELoss_en.md │ │ ├── MixedLoss_cn.md │ │ ├── MixedLoss_en.md │ │ ├── OhemCrossEntropyLoss_cn.md │ │ ├── OhemCrossEntropyLoss_en.md │ │ ├── OhemEdgeAttentionLoss_cn.md │ │ ├── OhemEdgeAttentionLoss_en.md │ │ ├── RelaxBoundaryLoss_cn.md │ │ ├── RelaxBoundaryLoss_en.md │ │ ├── SemanticConnectivityLoss_cn.md │ │ ├── SemanticConnectivityLoss_en.md │ │ ├── index.rst │ │ ├── losses_cn.md │ │ ├── losses_en.md │ │ ├── lovasz_loss_cn.md │ │ └── lovasz_loss_en.md │ └── tricks │ │ ├── index.rst │ │ └── tricks.md ├── paddleseg.png ├── pr │ ├── images │ │ ├── 001_fork.png │ │ ├── 002_clone.png │ │ ├── 003_precommit_pass.png │ │ └── 004_create_pr.png │ └── pr │ │ ├── index.rst │ │ ├── pr.md │ │ ├── pr_cn.md │ │ ├── style.md │ │ └── style_cn.md ├── predict │ ├── color_map │ │ ├── after_mapped.jpeg │ │ └── before_mapped.jpeg │ ├── predict.md │ └── predict_cn.md ├── quick_start.md ├── quick_start_cn.md ├── release_notes.md ├── release_notes_cn.md ├── requirements.txt ├── slim │ ├── distill │ │ ├── distill.md │ │ ├── distill_cn.md │ │ └── index.rst │ ├── prune │ │ ├── index.rst │ │ ├── prune.md │ │ └── prune_cn.md │ └── quant │ │ ├── index.rst │ │ ├── quant.md │ │ └── quant_cn.md ├── static │ ├── static.md │ └── static_cn.md ├── train │ ├── index.rst │ ├── train.md │ └── train_cn.md ├── whole_process.md └── whole_process_cn.md ├── export.py ├── myconfig ├── rs.yml └── segformer-b2-rs.yml ├── paddleseg ├── __init__.py ├── core │ ├── __init__.py │ ├── infer.py │ ├── predict.py │ ├── train.py │ └── val.py ├── cvlibs │ ├── __init__.py │ ├── callbacks.py │ ├── config.py │ ├── manager.py │ └── param_init.py ├── datasets │ ├── __init__.py │ ├── ade.py │ ├── chase_db1.py │ ├── cityscapes.py │ ├── cocostuff.py │ ├── dataset.py │ ├── drive.py │ ├── eg1800.py │ ├── hrf.py │ ├── mini_deep_globe_road_extraction.py │ ├── mydataset.py │ ├── optic_disc_seg.py │ ├── pascal_context.py │ ├── pp_humanseg14k.py │ ├── pssl.py │ ├── stare.py │ ├── supervisely.py │ └── voc.py ├── models │ ├── __init__.py │ ├── ann.py │ ├── attention_unet.py │ ├── backbones │ │ ├── __init__.py │ │ ├── ghostnet.py │ │ ├── hrnet.py │ │ ├── lite_hrnet.py │ │ ├── mix_transformer.py │ │ ├── mobilenetv2.py │ │ ├── mobilenetv3.py │ │ ├── resnet_vd.py │ │ ├── shufflenetv2.py │ │ ├── stdcnet.py │ │ ├── swin_transformer.py │ │ ├── transformer_utils.py │ │ ├── vision_transformer.py │ │ └── xception_deeplab.py │ ├── bisenet.py │ ├── bisenetv1.py │ ├── ccnet.py │ ├── danet.py │ ├── ddrnet.py │ ├── decoupled_segnet.py │ ├── deeplab.py │ ├── dmnet.py │ ├── dnlnet.py │ ├── emanet.py │ ├── encnet.py │ ├── enet.py │ ├── espnet.py │ ├── espnetv1.py │ ├── fast_scnn.py │ ├── fastfcn.py │ ├── fcn.py │ ├── gcnet.py │ ├── ginet.py │ ├── glore.py │ ├── gscnn.py │ ├── hardnet.py │ ├── hrnet_contrast.py │ ├── isanet.py │ ├── layers │ │ ├── __init__.py │ │ ├── activation.py │ │ ├── attention.py │ │ ├── layer_libs.py │ │ ├── nonlocal2d.py │ │ ├── pyramid_pool.py │ │ ├── tensor_fusion.py │ │ ├── tensor_fusion_helper.py │ │ └── wrap_functions.py │ ├── losses │ │ ├── __init__.py │ │ ├── binary_cross_entropy_loss.py │ │ ├── bootstrapped_cross_entropy.py │ │ ├── cross_entropy_loss.py │ │ ├── decoupledsegnet_relax_boundary_loss.py │ │ ├── detail_aggregate_loss.py │ │ ├── dice_loss.py │ │ ├── edge_attention_loss.py │ │ ├── focal_loss.py │ │ ├── gscnn_dual_task_loss.py │ │ ├── kl_loss.py │ │ ├── l1_loss.py │ │ ├── lovasz_loss.py │ │ ├── mean_square_error_loss.py │ │ ├── mixed_loss.py │ │ ├── ohem_cross_entropy_loss.py │ │ ├── ohem_edge_attention_loss.py │ │ ├── pixel_contrast_cross_entropy_loss.py │ │ ├── point_cross_entropy_loss.py │ │ ├── rmi_loss.py │ │ ├── semantic_connectivity_loss.py │ │ └── semantic_encode_cross_entropy_loss.py │ ├── lraspp.py │ ├── mla_transformer.py │ ├── mobileseg.py │ ├── ocrnet.py │ ├── pfpnnet.py │ ├── pointrend.py │ ├── portraitnet.py │ ├── pp_liteseg.py │ ├── pphumanseg_lite.py │ ├── pspnet.py │ ├── segformer.py │ ├── segmenter.py │ ├── segnet.py │ ├── setr.py │ ├── sfnet.py │ ├── sinet.py │ ├── stdcseg.py │ ├── u2net.py │ ├── unet.py │ ├── unet_3plus.py │ ├── unet_plusplus.py │ └── upernet.py ├── transforms │ ├── __init__.py │ ├── functional.py │ └── transforms.py └── utils │ ├── __init__.py │ ├── config_check.py │ ├── download.py │ ├── ema.py │ ├── logger.py │ ├── metrics.py │ ├── op_flops_funs.py │ ├── progbar.py │ ├── timer.py │ ├── train_profiler.py │ ├── utils.py │ └── visualize.py ├── predict.py ├── requirements.txt ├── setup.py ├── slim ├── distill │ ├── README.md │ ├── distill_config.py │ ├── distill_train.py │ └── distill_utils.py ├── prune │ ├── README.md │ └── prune.py └── quant │ ├── README.md │ ├── ptq.py │ ├── qat_config.py │ ├── qat_export.py │ ├── qat_train.py │ └── qat_val.py ├── test_tipc ├── README.md ├── benchmark_train.sh ├── common_func.sh ├── compare_results.py ├── configs │ ├── _base_ │ │ ├── ade20k.yml │ │ ├── autonue.yml │ │ ├── cityscapes.yml │ │ ├── cityscapes_1024x1024.yml │ │ ├── cityscapes_769x769.yml │ │ ├── cityscapes_769x769_setr.yml │ │ ├── coco_stuff.yml │ │ ├── pascal_context.yml │ │ ├── pascal_voc12.yml │ │ └── pascal_voc12aug.yml │ ├── bisenetv1 │ │ ├── bisenetv1_resnet18_os8_cityscapes_1024x512_160k.yml │ │ └── train_infer_python.txt │ ├── bisenetv2 │ │ ├── bisenet_cityscapes_1024x1024_160k.yml │ │ └── train_infer_python.txt │ ├── ccnet │ │ ├── ccnet_resnet101_os8_cityscapes_769x769_60k.yml │ │ └── train_infer_python.txt │ ├── ddrnet │ │ ├── ddrnet23_cityscapes_1024x1024_120k.yml │ │ └── train_infer_python.txt │ ├── deeplabv3p_resnet50 │ │ ├── deeplabv3p_resnet50_humanseg_512x512_mini_supervisely.yml │ │ ├── deeplabv3p_resnet50_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt │ │ ├── deeplabv3p_resnet50_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── train_infer_python.txt │ │ ├── train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt │ │ └── train_ptq_infer_python.txt │ ├── deeplabv3p_resnet50_cityscapes │ │ ├── deeplabv3p_resnet50_1024x512_cityscapes.yml │ │ └── train_infer_python.txt │ ├── encnet │ │ ├── encnet_resnet101_os8_cityscapes_1024x512_80k.yml │ │ └── train_infer_python.txt │ ├── enet │ │ ├── enet_cityscapes_1024x512_adam_0.002_80k.yml │ │ └── train_infer_python.txt │ ├── espnetv2 │ │ ├── espnet_cityscapes_1024x512_120k.yml │ │ └── train_infer_python.txt │ ├── fastscnn │ │ ├── fastscnn_cityscapes.yml │ │ └── train_infer_python.txt │ ├── fcn_hrnetw18 │ │ ├── fcn_hrnetw18_1024x512_cityscapes.yml │ │ ├── fcn_hrnetw18_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt │ │ ├── fcn_hrnetw18_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── train_infer_python.txt │ │ ├── train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt │ │ └── train_ptq_infer_python.txt │ ├── fcn_hrnetw18_small │ │ ├── fcn_hrnetw18_small_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt │ │ ├── fcn_hrnetw18_small_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt │ │ ├── fcn_hrnetw18_small_v1_humanseg_192x192_mini_supervisely.yml │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── train_infer_python.txt │ │ ├── train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt │ │ └── train_ptq_infer_python.txt │ ├── glore │ │ ├── glore_resnet50_os8_cityscapes_1024x512_80k.yml │ │ └── train_infer_python.txt │ ├── hrnet_w48_contrast │ │ ├── HRNet_W48_contrast_cityscapes_1024x512_60k.yml │ │ └── train_infer_python.txt │ ├── ocrnet_hrnetw18 │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── ocrnet_hrnetw18_cityscapes_1024x512_160k.yml │ │ ├── ocrnet_hrnetw18_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt │ │ ├── ocrnet_hrnetw18_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt │ │ ├── train_infer_python.txt │ │ ├── train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt │ │ └── train_ptq_infer_python.txt │ ├── ocrnet_hrnetw48 │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── ocrnet_hrnetw48_cityscapes_1024x512.yml │ │ ├── ocrnet_hrnetw48_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt │ │ ├── ocrnet_hrnetw48_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt │ │ ├── train_infer_python.txt │ │ ├── train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt │ │ └── train_ptq_infer_python.txt │ ├── pfpnnet │ │ ├── pfpn_resnet101_os8_cityscapes_512x1024_40k.yml │ │ └── train_infer_python.txt │ ├── pp_liteseg_stdc1 │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── pp_liteseg_stdc1_cityscapes_1024x512_160k.yml │ │ ├── pp_liteseg_stdc1_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt │ │ ├── pp_liteseg_stdc1_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt │ │ ├── train_infer_python.txt │ │ ├── train_linux_gpu_fleet_normal_infer_python_linux_gpu_cpu.txt │ │ ├── train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt │ │ └── train_ptq_infer_python.txt │ ├── pp_liteseg_stdc2 │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── pp_liteseg_stdc2_cityscapes_1024x512_160k.yml │ │ ├── pp_liteseg_stdc2_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt │ │ ├── pp_liteseg_stdc2_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt │ │ ├── train_infer_python.txt │ │ ├── train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt │ │ └── train_ptq_infer_python.txt │ ├── pphumanseg_lite │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── pphumanseg_lite_mini_supervisely.yml │ │ ├── pphumanseg_lite_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt │ │ ├── pphumanseg_lite_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt │ │ ├── train_infer_python.txt │ │ ├── train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt │ │ └── train_ptq_infer_python.txt │ ├── ppmatting │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── modnet_mobilenetv2.yml │ │ └── train_infer_python.txt │ ├── segformer_b0 │ │ ├── segformer_b0_cityscapes_1024x1024_160k.yml │ │ └── train_infer_python.txt │ ├── stdc_stdc1 │ │ ├── inference_cpp.txt │ │ ├── model_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt │ │ ├── model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt │ │ ├── stdc1_seg_cityscapes_1024x512_80k.yml │ │ └── train_infer_python.txt │ └── upernet │ │ ├── train_infer_python.txt │ │ └── upernet_resnet101_os8_cityscapes_512x1024_40k.yml ├── cpp │ ├── CMakeLists.txt │ ├── build.sh │ ├── cityscapes_demo.png │ ├── humanseg_demo.jpg │ ├── include │ │ ├── config.h │ │ ├── preprocess_op.h │ │ ├── seg.h │ │ └── utility.h │ └── src │ │ ├── config.cpp │ │ ├── main.cpp │ │ ├── preprocess_op.cpp │ │ ├── seg.cpp │ │ └── utility.cpp ├── docs │ ├── benchmark_train.md │ ├── cityscapes_demo.jpg │ ├── cityscapes_val_5.list │ ├── compare_right.png │ ├── compare_wrong.png │ ├── guide.png │ ├── install.md │ ├── test.png │ ├── test_infer_js.md │ ├── test_inference_cpp.md │ ├── test_paddle2onnx.md │ ├── test_serving_infer_cpp.md │ ├── test_serving_infer_python.md │ ├── test_train_amp_inference_python.md │ ├── test_train_fleet_inference_python.md │ └── test_train_inference_python.md ├── prepare.sh ├── prepare_js.sh ├── requirements.txt ├── results │ ├── python_fcn_hrnetw18_small_results_fp16.txt │ └── python_fcn_hrnetw18_small_results_fp32.txt ├── scripts │ └── analysis.py ├── serving_cpp │ ├── general_seg_op.cpp │ ├── general_seg_op.h │ ├── modify_serving_client_conf.py │ ├── prepare_server.sh │ └── serving_client.py ├── serving_python │ ├── config.yml │ ├── pipeline_http_client.py │ ├── preprocess_ops.py │ └── web_service.py ├── test_infer_js.sh ├── test_inference_cpp.sh ├── test_paddle2onnx.sh ├── test_ptq_inference_python.sh ├── test_serving_infer_cpp.sh ├── test_serving_infer_python.sh ├── test_train_inference_python.sh ├── val.py └── web │ ├── imgs │ ├── human.jpg │ └── seg.png │ ├── index.html │ ├── index.test.js │ ├── jest-puppeteer.config.js │ └── jest.config.js ├── tests ├── analyze_infer_log.py ├── run_check_install.sh ├── test_infer_benchmark.sh └── test_infer_dataset.sh ├── tools ├── analyze_model.py ├── convert_cityscapes.py ├── convert_cocostuff.py ├── convert_voc2010.py ├── create_dataset_list.py ├── gray2pseudo_color.py ├── labelme2seg.py ├── plot_model_performance.py ├── split_dataset_list.py ├── visualize_annotation.py └── voc_augment.py ├── train.py └── val.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/---feature-request--------.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: "\U0001F680 Feature request / 新功能需求" 3 | about: Suggest an idea for this project / 提出一个新的功能需求或改进建议 4 | title: "[Feature Request]" 5 | labels: enhancement 6 | assignees: '' 7 | 8 | --- 9 | 10 | Welcome to propose a new feature! To help us understand your great feature, please provide following information: 11 | 1. A clear and concise description of the proposed feature. 12 | 2. Tell us why the feature is useful. 13 | 3. If possible, please show related codes . 14 | 15 | --- 16 | 17 | 欢迎提出一个新功能需求，为了帮助我们更好理解您的需求，辛苦提供下面信息： 18 | 1. 清晰简洁的语言提出新功能需求。 19 | 2. 请描述这个需求的必要性。 20 | 3. 如果可以，辛苦您提供相关代码实现效果。 21 | -------------------------------------------------------------------------------- /.style.yapf: -------------------------------------------------------------------------------- 1 | [style] 2 | based_on_style = pep8 3 | column_limit = 80 4 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: python 2 | 3 | python: 4 | - '3.6' 5 | 6 | env: 7 | - PYTHONPATH=${PWD} 8 | 9 | install: 10 | - pip install --upgrade paddlepaddle 11 | - pip install -r requirements.txt 12 | 13 | script: 14 | - /bin/bash legacy/test/ci/check_code_style.sh 15 | 16 | notifications: 17 | email: 18 | on_success: change 19 | on_failure: always 20 | -------------------------------------------------------------------------------- /EISeg/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include eiseg/config/* 2 | include eiseg/resource/* 3 | include eiseg/util/translate/* -------------------------------------------------------------------------------- /EISeg/eiseg/config/colormap.txt: -------------------------------------------------------------------------------- 1 | 53,119,181 2 | 245,128,6 3 | 67,159,36 4 | 204,43,41 5 | 145,104,190 6 | 135,86,75 7 | 219,120,195 8 | 127,127,127 9 | 187,189,18 10 | 72,190,207 11 | 178,199,233 12 | 248,187,118 13 | 160,222,135 14 | 247,153,150 15 | 195,176,214 16 | 192,156,148 17 | 241,183,211 18 | 199,199,199 19 | 218,219,139 20 | 166,218,229 -------------------------------------------------------------------------------- /EISeg/eiseg/inference/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/EISeg/eiseg/inference/__init__.py -------------------------------------------------------------------------------- /EISeg/eiseg/plugin/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | -------------------------------------------------------------------------------- /EISeg/eiseg/plugin/video/__init__.py: -------------------------------------------------------------------------------- 1 | # The video propagation and fusion code was heavily based on https://github.com/hkchengrex/MiVOS # Users should be careful about adopting these functions in any commercial matters. # https://github.com/hkchengrex/MiVOS/blob/main/LICENSE from .inference_core import InferenceCore from .video_tools import overlay_davis -------------------------------------------------------------------------------- /EISeg/eiseg/plugin/video/util/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/EISeg/eiseg/plugin/video/util/__init__.py -------------------------------------------------------------------------------- /EISeg/eiseg/plugin/video/util/range_transform.py: -------------------------------------------------------------------------------- 1 | # The video propagation and fusion code was heavily based on https://github.com/hkchengrex/MiVOS 2 | # Users should be careful about adopting these functions in any commercial matters. 3 | # https://github.com/hkchengrex/MiVOS/blob/main/LICENSE 4 | 5 | from paddle.vision import transforms 6 | 7 | im_mean = (124, 116, 104) 8 | 9 | im_normalization = transforms.Normalize( 10 | mean=[0.485, 0.456, 0.406], 11 | std=[0.229, 0.224, 0.225], ) 12 | 13 | inv_im_trans = transforms.Normalize( 14 | mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225], 15 | std=[1 / 0.229, 1 / 0.224, 1 / 0.225], ) 16 | -------------------------------------------------------------------------------- /EISeg/eiseg/resource/3D.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/EISeg/eiseg/resource/3D.png -------------------------------------------------------------------------------- /EISeg/eiseg/resource/About.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/EISeg/eiseg/resource/About.png 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-------------------------------------------------------------------------------- 1 | __author__ = 'tylin' 2 | -------------------------------------------------------------------------------- /EISeg/eiseg/util/exp_imports/default.py: -------------------------------------------------------------------------------- 1 | import paddle 2 | from functools import partial 3 | from easydict import EasyDict as edict 4 | from albumentations import * 5 | 6 | from data.datasets import * 7 | from model.losses import * 8 | from data.transforms import * 9 | #from isegm.engine.trainer import ISTrainer 10 | from model.metrics import AdaptiveIoU 11 | from data.points_sampler import MultiPointSampler 12 | from model.initializer import XavierGluon 13 | 14 | from model.is_hrnet_model import HRNetModel 15 | from model.is_deeplab_model import DeeplabModel -------------------------------------------------------------------------------- /EISeg/eiseg/util/opath.py: -------------------------------------------------------------------------------- 1 | import re 2 | 3 | 4 | # 检查中文 5 | def check_cn(path): 6 | zh_model = re.compile(u'[\u4e00-\u9fa5]') 7 | return zh_model.search(path) 8 | 9 | 10 | # 替换斜杠 11 | def normcase(path): 12 | return eval(repr(path).replace('\\\\', '/')) 13 | -------------------------------------------------------------------------------- /EISeg/eiseg/util/palette.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def get_color_map(N=256): 5 | def bitget(byteval, idx): 6 | return ((byteval & (1 << idx)) != 0) 7 | 8 | cmap = np.zeros((N, 3), dtype=np.uint8) 9 | for i in range(N): 10 | r = g = b = 0 11 | c = i 12 | for j in range(8): 13 | r = r | (bitget(c, 0) << 7 - j) 14 | g = g | (bitget(c, 1) << 7 - j) 15 | b = b | (bitget(c, 2) << 7 - j) 16 | c = c >> 3 17 | 18 | cmap[i] = np.array([r, g, b]) 19 | 20 | return cmap 21 | 22 | 23 | color_map = get_color_map() 24 | 25 | 26 | def pal_color_map(): 27 | return color_map 28 | -------------------------------------------------------------------------------- /EISeg/eiseg/util/translate/Arabic.qm: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/EISeg/eiseg/util/translate/Arabic.qm -------------------------------------------------------------------------------- /EISeg/eiseg/util/translate/English.qm: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/EISeg/eiseg/util/translate/English.qm -------------------------------------------------------------------------------- /EISeg/eiseg/widget/__init__.py: -------------------------------------------------------------------------------- 1 | from .shortcut import ShortcutWidget 2 | from .loading import LoadingWidget 3 | from .line import LineItem 4 | from .grip import GripItem 5 | from .bbox import BBoxAnnotation 6 | from .polygon import PolygonAnnotation 7 | from .scene import AnnotationScene 8 | from .view import AnnotationView 9 | from .create import (create_text, create_button, create_slider, DockWidget, 10 | creat_dock) 11 | from .table import TableWidget 12 | -------------------------------------------------------------------------------- /EISeg/init.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | ROOT=`cd "$(dirname ${BASH_SOURCE[0]})" && pwd` 4 | 5 | echo "ROOT : $ROOT" 6 | 7 | export PYTHONPATH=$PYTHONPATH:$ROOT/eiseg 8 | -------------------------------------------------------------------------------- /EISeg/requirements-med.txt: -------------------------------------------------------------------------------- 1 | SimpleITK 2 | -------------------------------------------------------------------------------- /EISeg/requirements-rs.txt: -------------------------------------------------------------------------------- 1 | GDAL>=3.3.0 2 | rasterio>=1.2.4 3 | -------------------------------------------------------------------------------- /EISeg/requirements-video.txt: -------------------------------------------------------------------------------- 1 | vtk -------------------------------------------------------------------------------- /EISeg/requirements.txt: -------------------------------------------------------------------------------- 1 | pyqt5 2 | qtpy 3 | opencv-python 4 | scipy 5 | paddleseg 6 | albumentations 7 | cython 8 | pyyaml 9 | wget 10 | requests 11 | easydict 12 | scikit-image 13 | protobuf==3.20.0 14 | -------------------------------------------------------------------------------- /EISeg/tool/pypi.sh: -------------------------------------------------------------------------------- 1 | rm dist/* 2 | python setup.py sdist bdist_wheel 3 | twine upload --repository-url https://test.pypi.org/legacy/ dist/* --verbose 4 | # https://upload.pypi.org/legacy/ 5 | 6 | conda create -n test python=3.9 7 | conda activate test 8 | pip install --upgrade eiseg 9 | pip install paddlepaddle 10 | eiseg 11 | -------------------------------------------------------------------------------- /EISeg/tool/translate.pro: -------------------------------------------------------------------------------- 1 | CODECFORTR = UTF-8 2 | SOURCES = ../eiseg/app.py ../eiseg/ui.py ../eiseg/widget/shortcut.py 3 | TRANSLATIONS = ./ts/out.ts 4 | -------------------------------------------------------------------------------- /Matting/configs/benchmarks/Composition-1k/closeform_composition1k.yml: -------------------------------------------------------------------------------- 1 | 2 | 3 | val_dataset: 4 | type: Composition1K 5 | dataset_root: data/Composition-1k 6 | val_file: val.txt 7 | separator: '|' 8 | transforms: 9 | - type: LoadImages 10 | - type: ResizeByShort 11 | short_size: 512 12 | - type: ResizeToIntMult 13 | mult_int: 32 14 | - type: Normalize 15 | mode: val 16 | get_trimap: True 17 | 18 | model: 19 | type: CloseFormMatting 20 | 21 | -------------------------------------------------------------------------------- /Matting/configs/benchmarks/Distinctions-646/closeform_distinctions646.yml: -------------------------------------------------------------------------------- 1 | 2 | 3 | val_dataset: 4 | type: Distinctions646 5 | dataset_root: data/Distinctions-646 6 | val_file: val.txt 7 | separator: '|' 8 | transforms: 9 | - type: LoadImages 10 | - type: ResizeByShort 11 | short_size: 512 12 | - type: ResizeToIntMult 13 | mult_int: 32 14 | - type: Normalize 15 | mode: val 16 | get_trimap: True 17 | 18 | model: 19 | type: CloseFormMatting 20 | 21 | -------------------------------------------------------------------------------- /Matting/configs/benchmarks/PPM/README.md: -------------------------------------------------------------------------------- 1 | ### PPM 2 | 3 | | Method | SAD | MSE | Grad | Conn | 4 | |-|-|-|-|-| 5 | |ClosedFormMatting|40.6251|0.0782|55.5716|40.6646| 6 | |KNNMatting|41.5604|0.0681|52.5200|42.1784| 7 | |FastMatting|35.8735|0.0492|48.9267|35.6183| 8 | |LearningBasedMatting|40.5506|0.0776|55.3923|40.5690| 9 | |RandomWalksMatting|54.6315|0.0962|69.8779|54.0870| 10 | -------------------------------------------------------------------------------- /Matting/configs/benchmarks/PPM/closeform.yml: -------------------------------------------------------------------------------- 1 | 2 | 3 | val_dataset: 4 | type: MattingDataset 5 | dataset_root: data/PPM-100 6 | val_file: val.txt 7 | transforms: 8 | - type: LoadImages 9 | - type: ResizeByShort 10 | short_size: 512 11 | - type: ResizeToIntMult 12 | mult_int: 32 13 | - type: Normalize 14 | mode: val 15 | get_trimap: True 16 | 17 | model: 18 | type: CloseFormMatting 19 | 20 | -------------------------------------------------------------------------------- /Matting/configs/benchmarks/PPM/fast.yml: -------------------------------------------------------------------------------- 1 | 2 | _base_: closeform.yml 3 | 4 | model: 5 | type: FastMatting 6 | 7 | -------------------------------------------------------------------------------- /Matting/configs/benchmarks/PPM/knn.yml: -------------------------------------------------------------------------------- 1 | 2 | _base_: closeform.yml 3 | 4 | model: 5 | type: KNNMatting 6 | 7 | -------------------------------------------------------------------------------- /Matting/configs/benchmarks/PPM/learningbased.yml: -------------------------------------------------------------------------------- 1 | 2 | _base_: closeform.yml 3 | 4 | model: 5 | type: LearningBasedMatting 6 | 7 | -------------------------------------------------------------------------------- /Matting/configs/benchmarks/PPM/randomwalks.yml: -------------------------------------------------------------------------------- 1 | 2 | _base_: closeform.yml 3 | 4 | model: 5 | type: RandomWalksMatting 6 | 7 | -------------------------------------------------------------------------------- /Matting/configs/modnet/modnet-hrnet_w18.yml: -------------------------------------------------------------------------------- 1 | _base_: modnet-mobilenetv2.yml 2 | model: 3 | backbone: 4 | type: HRNet_W18 5 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 6 | -------------------------------------------------------------------------------- /Matting/configs/modnet/modnet-resnet50_vd.yml: -------------------------------------------------------------------------------- 1 | _base_: modnet-mobilenetv2.yml 2 | model: 3 | backbone: 4 | type: ResNet50_vd 5 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 6 | -------------------------------------------------------------------------------- /Matting/configs/ppmatting/ppmatting-hrnet_w48-composition.yml: -------------------------------------------------------------------------------- 1 | _base_: 'ppmatting-hrnet_w48-distinctions.yml' 2 | 3 | train_dataset: 4 | dataset_root: data/matting/Composition-1k 5 | 6 | val_dataset: 7 | dataset_root: data/matting/Composition-1k -------------------------------------------------------------------------------- /Matting/deploy/human_matting_android_demo/.gitignore: -------------------------------------------------------------------------------- 1 | *.iml 2 | .gradle 3 | /local.properties 4 | /.idea/caches 5 | /.idea/libraries 6 | /.idea/modules.xml 7 | /.idea/workspace.xml 8 | /.idea/navEditor.xml 9 | /.idea/assetWizardSettings.xml 10 | .DS_Store 11 | /build 12 | /captures 13 | .externalNativeBuild 14 | -------------------------------------------------------------------------------- /Matting/deploy/human_matting_android_demo/app/.gitignore: 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This is only used by Gradle. 5 | # For customization when using a Version Control System, please read the 6 | # header note. 7 | #Mon Nov 25 17:01:52 CST 2019 8 | sdk.dir=/Users/chenlingchi/Library/Android/sdk 9 | -------------------------------------------------------------------------------- /Matting/deploy/human_matting_android_demo/app/src/main/assets/image_matting/images/bg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/Matting/deploy/human_matting_android_demo/app/src/main/assets/image_matting/images/bg.jpg -------------------------------------------------------------------------------- /Matting/deploy/human_matting_android_demo/app/src/main/assets/image_matting/images/human.jpg: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /Matting/deploy/human_matting_android_demo/app/src/test/java/com/baidu/paddle/lite/demo/ExampleUnitTest.java: -------------------------------------------------------------------------------- 1 | package com.baidu.paddle.lite.demo; 2 | 3 | import org.junit.Test; 4 | 5 | import static org.junit.Assert.*; 6 | 7 | /** 8 | * Example local unit test, which will execute on the development machine (host). 9 | * 10 | * @see Testing documentation 11 | */ 12 | public class ExampleUnitTest { 13 | @Test 14 | public void addition_isCorrect() { 15 | assertEquals(4, 2 + 2); 16 | } 17 | } -------------------------------------------------------------------------------- /Matting/deploy/human_matting_android_demo/gradle/wrapper/gradle-wrapper.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/Matting/deploy/human_matting_android_demo/gradle/wrapper/gradle-wrapper.jar -------------------------------------------------------------------------------- /Matting/deploy/human_matting_android_demo/gradle/wrapper/gradle-wrapper.properties: -------------------------------------------------------------------------------- 1 | #Thu Aug 22 15:05:37 CST 2019 2 | distributionBase=GRADLE_USER_HOME 3 | distributionPath=wrapper/dists 4 | zipStoreBase=GRADLE_USER_HOME 5 | zipStorePath=wrapper/dists 6 | distributionUrl=https\://services.gradle.org/distributions/gradle-5.1.1-all.zip 7 | -------------------------------------------------------------------------------- /Matting/deploy/human_matting_android_demo/settings.gradle: -------------------------------------------------------------------------------- 1 | include ':app' 2 | -------------------------------------------------------------------------------- /Matting/ppmatting/__init__.py: -------------------------------------------------------------------------------- 1 | from . import ml, metrics, transforms, datasets, models 2 | -------------------------------------------------------------------------------- /Matting/ppmatting/core/__init__.py: -------------------------------------------------------------------------------- 1 | from .val import evaluate 2 | from .val_ml import evaluate_ml 3 | from .train import train 4 | from .predict import predict -------------------------------------------------------------------------------- /Matting/ppmatting/metrics/__init__.py: -------------------------------------------------------------------------------- 1 | from .metric import MSE, SAD, Grad, Conn 2 | 3 | metrics_class_dict = {'sad': SAD, 'mse': MSE, 'grad': Grad, 'conn': Conn} 4 | -------------------------------------------------------------------------------- /Matting/ppmatting/ml/__init__.py: -------------------------------------------------------------------------------- 1 | from .methods import CloseFormMatting, KNNMatting, LearningBasedMatting, FastMatting, RandomWalksMatting 2 | -------------------------------------------------------------------------------- /Matting/ppmatting/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .backbone import * 2 | from .losses import * 3 | from .modnet import MODNet 4 | from .human_matting import HumanMatting 5 | from .dim import DIM 6 | from .ppmatting import PPMatting 7 | from .gca import GCABaseline, GCA 8 | -------------------------------------------------------------------------------- /Matting/ppmatting/models/backbone/__init__.py: -------------------------------------------------------------------------------- 1 | from .mobilenet_v2 import * 2 | from .hrnet import * 3 | from .resnet_vd import * 4 | from .vgg import * 5 | from .gca_enc import * -------------------------------------------------------------------------------- /Matting/ppmatting/models/losses/__init__.py: -------------------------------------------------------------------------------- 1 | from .loss import * 2 | -------------------------------------------------------------------------------- /Matting/ppmatting/transforms/__init__.py: -------------------------------------------------------------------------------- 1 | from .transforms import * 2 | -------------------------------------------------------------------------------- /Matting/ppmatting/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .estimate_foreground_ml import estimate_foreground_ml 2 | from .utils import get_files, get_image_list, mkdir 3 | -------------------------------------------------------------------------------- /Matting/requirements.txt: -------------------------------------------------------------------------------- 1 | paddleseg >= 2.5 2 | pymatting 3 | scikit-image 4 | numba 5 | opencv-python==4.5.4.60 6 | -------------------------------------------------------------------------------- /benchmark/configs/ocrnet_hrnetw48.yml: -------------------------------------------------------------------------------- 1 | # The ocrnet_hrnetw48 config for train benchmark 2 | _base_: './cityscapes_30imgs.yml' 3 | 4 | batch_size: 2 5 | iters: 500 6 | 7 | model: 8 | type: OCRNet 9 | backbone: 10 | type: HRNet_W48 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz 12 | num_classes: 19 13 | backbone_indices: [0] 14 | 15 | optimizer: 16 | type: sgd 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.01 21 | power: 0.9 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | - type: CrossEntropyLoss 27 | coef: [1, 0.4] 28 | -------------------------------------------------------------------------------- /benchmark/run_fp16.sh: -------------------------------------------------------------------------------- 1 | export FLAGS_conv_workspace_size_limit=2000 #MB 2 | export FLAGS_cudnn_exhaustive_search=1 3 | export FLAGS_cudnn_batchnorm_spatial_persistent=1 4 | 5 | python train.py --config benchmark/deeplabv3p.yml \ 6 | --iters=500 \ 7 | --batch_size 4 \ 8 | --learning_rate 0.01 \ 9 | --num_workers 8 \ 10 | --log_iters 20 \ 11 | --data_format NHWC \ 12 | --precision fp16 13 | -------------------------------------------------------------------------------- /benchmark/run_fp32.sh: -------------------------------------------------------------------------------- 1 | export FLAGS_conv_workspace_size_limit=2000 #MB 2 | export FLAGS_cudnn_exhaustive_search=1 3 | export FLAGS_cudnn_batchnorm_spatial_persistent=1 4 | 5 | python train.py --config benchmark/deeplabv3p.yml \ 6 | --iters=500 \ 7 | --batch_size 2 \ 8 | --learning_rate 0.01 \ 9 | --num_workers 8 \ 10 | --log_iters 20 \ 11 | --data_format NCHW \ 12 | -------------------------------------------------------------------------------- /configs/_base_/cityscapes_1024x1024.yml: -------------------------------------------------------------------------------- 1 | _base_: './cityscapes.yml' 2 | 3 | train_dataset: 4 | transforms: 5 | - type: ResizeStepScaling 6 | min_scale_factor: 0.5 7 | max_scale_factor: 2.0 8 | scale_step_size: 0.25 9 | - type: RandomPaddingCrop 10 | crop_size: [1024, 1024] 11 | - type: RandomHorizontalFlip 12 | - type: RandomDistort 13 | brightness_range: 0.4 14 | contrast_range: 0.4 15 | saturation_range: 0.4 16 | - type: Normalize 17 | 18 | val_dataset: 19 | transforms: 20 | - type: Normalize 21 | -------------------------------------------------------------------------------- /configs/_base_/cityscapes_769x769.yml: -------------------------------------------------------------------------------- 1 | _base_: './cityscapes.yml' 2 | 3 | train_dataset: 4 | transforms: 5 | - type: ResizeStepScaling 6 | min_scale_factor: 0.5 7 | max_scale_factor: 2.0 8 | scale_step_size: 0.25 9 | - type: RandomPaddingCrop 10 | crop_size: [769, 769] 11 | - type: RandomHorizontalFlip 12 | - type: RandomDistort 13 | brightness_range: 0.4 14 | contrast_range: 0.4 15 | saturation_range: 0.4 16 | - type: Normalize 17 | 18 | val_dataset: 19 | transforms: 20 | - type: Padding 21 | target_size: [2049, 1025] 22 | - type: Normalize 23 | -------------------------------------------------------------------------------- /configs/_base_/cityscapes_769x769_setr.yml: -------------------------------------------------------------------------------- 1 | _base_: './cityscapes.yml' 2 | 3 | train_dataset: 4 | transforms: 5 | - type: ResizeStepScaling 6 | min_scale_factor: 0.25 7 | max_scale_factor: 2.0 8 | scale_step_size: 0.25 9 | - type: RandomPaddingCrop 10 | crop_size: [769, 769] 11 | - type: RandomHorizontalFlip 12 | - type: RandomDistort 13 | brightness_range: 0.5 14 | contrast_range: 0.5 15 | saturation_range: 0.5 16 | - type: Normalize 17 | 18 | val_dataset: 19 | transforms: 20 | - type: Padding 21 | target_size: [2048, 1024] 22 | - type: Normalize 23 | -------------------------------------------------------------------------------- /configs/_base_/pascal_voc12aug.yml: -------------------------------------------------------------------------------- 1 | _base_: './pascal_voc12.yml' 2 | 3 | train_dataset: 4 | mode: trainaug 5 | -------------------------------------------------------------------------------- /configs/ann/ann_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'ann_resnet50_os8_cityscapes_1024x512_80k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/ann/ann_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'ann_resnet50_os8_voc12aug_512x512_40k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/ann/ann_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | loss: 4 | types: 5 | - type: CrossEntropyLoss 6 | coef: [1, 0.4] 7 | 8 | model: 9 | type: ANN 10 | backbone: 11 | type: ResNet50_vd 12 | output_stride: 8 13 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 14 | backbone_indices: [2, 3] 15 | key_value_channels: 256 16 | inter_channels: 512 17 | psp_size: [1, 3, 6, 8] 18 | enable_auxiliary_loss: True 19 | align_corners: False 20 | pretrained: null 21 | -------------------------------------------------------------------------------- /configs/attention_unet/README.md: -------------------------------------------------------------------------------- 1 | # Attention U-Net: Learning Where to Look for the Pancreas 2 | 3 | ## Reference 4 | 5 | > Oktay, Ozan, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori et al. "Attention u-net: Learning where to look for the pancreas." arXiv preprint arXiv:1804.03999 (2018). 6 | -------------------------------------------------------------------------------- /configs/attention_unet/attention_unet_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | lr_scheduler: 7 | type: PolynomialDecay 8 | learning_rate: 0.05 9 | end_lr: 0.0 10 | power: 0.9 11 | 12 | model: 13 | type: AttentionUNet 14 | pretrained: Null 15 | -------------------------------------------------------------------------------- /configs/bisenet/bisenet_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | model: 4 | type: BiSeNetV2 5 | num_classes: 19 6 | 7 | optimizer: 8 | type: sgd 9 | weight_decay: 0.0005 10 | 11 | loss: 12 | types: 13 | - type: CrossEntropyLoss 14 | - type: CrossEntropyLoss 15 | - type: CrossEntropyLoss 16 | - type: CrossEntropyLoss 17 | - type: CrossEntropyLoss 18 | coef: [1, 1, 1, 1, 1] 19 | 20 | batch_size: 4 21 | iters: 160000 22 | 23 | lr_scheduler: 24 | type: PolynomialDecay 25 | learning_rate: 0.05 26 | end_lr: 0.0 27 | power: 0.9 28 | -------------------------------------------------------------------------------- /configs/bisenetv1/bisenetv1_resnet18_os8_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | model: 7 | type: BiseNetV1 8 | backbone: 9 | type: ResNet18_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet18_vd_ssld_v2.tar.gz 12 | 13 | optimizer: 14 | type: sgd 15 | weight_decay: 0.0005 16 | 17 | loss: 18 | types: 19 | - type: OhemCrossEntropyLoss 20 | - type: OhemCrossEntropyLoss 21 | - type: OhemCrossEntropyLoss 22 | coef: [1, 1, 1] 23 | 24 | lr_scheduler: 25 | type: PolynomialDecay 26 | learning_rate: 0.01 27 | end_lr: 0.0 28 | power: 0.9 29 | -------------------------------------------------------------------------------- /configs/ccnet/ccnet_resnet101_os8_cityscapes_769x769_60k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_769x769.yml' 2 | 3 | batch_size: 2 4 | iters: 60000 5 | 6 | model: 7 | type: CCNet 8 | backbone: 9 | type: ResNet101_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 12 | backbone_indices: [2, 3] 13 | enable_auxiliary_loss: True 14 | dropout_prob: 0.1 15 | recurrence: 2 16 | 17 | loss: 18 | types: 19 | - type: OhemCrossEntropyLoss 20 | - type: CrossEntropyLoss 21 | coef: [1, 0.4] 22 | 23 | lr_scheduler: 24 | type: PolynomialDecay 25 | learning_rate: 0.01 26 | power: 0.9 27 | end_lr: 1.0e-4 28 | -------------------------------------------------------------------------------- /configs/danet/danet_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 1 4 | iters: 80000 5 | 6 | model: 7 | type: DANet 8 | backbone: 9 | type: ResNet101_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 12 | num_classes: 19 13 | backbone_indices: [2, 3] 14 | 15 | optimizer: 16 | type: sgd 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.01 21 | power: 0.9 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | - type: CrossEntropyLoss 27 | - type: CrossEntropyLoss 28 | - type: CrossEntropyLoss 29 | coef: [1, 1, 1, 0.4] 30 | -------------------------------------------------------------------------------- /configs/danet/danet_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: DANet 8 | backbone: 9 | type: ResNet50_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 12 | num_classes: 19 13 | backbone_indices: [2, 3] 14 | 15 | optimizer: 16 | type: sgd 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.01 21 | power: 0.9 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | - type: CrossEntropyLoss 27 | - type: CrossEntropyLoss 28 | - type: CrossEntropyLoss 29 | coef: [1, 1, 1, 0.4] 30 | -------------------------------------------------------------------------------- /configs/danet/danet_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: DANet 5 | backbone: 6 | type: ResNet50_vd 7 | output_stride: 8 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 9 | backbone_indices: [2, 3] 10 | 11 | loss: 12 | types: 13 | - type: CrossEntropyLoss 14 | - type: CrossEntropyLoss 15 | - type: CrossEntropyLoss 16 | - type: CrossEntropyLoss 17 | coef: [1, 1, 1, 0.4] 18 | -------------------------------------------------------------------------------- /configs/ddrnet/ddrnet23_cityscapes_1024x1024_120k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 3 4 | iters: 120000 5 | 6 | model: 7 | type: DDRNet_23 8 | enable_auxiliary_loss: False 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/cityscapes/ddrnet23_cityscapes_1024x1024_120k/pretrain/model.pdparams 10 | 11 | optimizer: 12 | type: sgd 13 | weight_decay: 0.0005 14 | 15 | loss: 16 | types: 17 | - type: OhemCrossEntropyLoss 18 | coef: [1] 19 | 20 | lr_scheduler: 21 | type: PolynomialDecay 22 | learning_rate: 0.01 23 | end_lr: 0.0 24 | power: 0.9 25 | -------------------------------------------------------------------------------- /configs/deeplabv3/deeplabv3_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'deeplabv3_resnet50_os8_cityscapes_1024x512_80k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/deeplabv3/deeplabv3_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'deeplabv3_resnet50_os8_voc12aug_512x512_40k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/deeplabv3/deeplabv3_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: DeepLabV3 8 | backbone: 9 | type: ResNet50_vd 10 | output_stride: 8 11 | multi_grid: [1, 2, 4] 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 13 | backbone_indices: [3] 14 | aspp_ratios: [1, 12, 24, 36] 15 | aspp_out_channels: 256 16 | align_corners: False 17 | pretrained: null 18 | -------------------------------------------------------------------------------- /configs/deeplabv3/deeplabv3_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: DeepLabV3 5 | backbone: 6 | type: ResNet50_vd 7 | output_stride: 8 8 | multi_grid: [1, 2, 4] 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | backbone_indices: [3] 11 | aspp_ratios: [1, 12, 24, 36] 12 | aspp_out_channels: 256 13 | align_corners: False 14 | pretrained: null 15 | -------------------------------------------------------------------------------- /configs/deeplabv3p/deeplabv3p_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml' 2 | 3 | 4 | model: 5 | backbone: 6 | type: ResNet101_vd 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 8 | -------------------------------------------------------------------------------- /configs/deeplabv3p/deeplabv3p_resnet101_os8_cityscapes_769x769_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_769x769.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: DeepLabV3P 8 | backbone: 9 | type: ResNet101_vd 10 | output_stride: 8 11 | multi_grid: [1, 2, 4] 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 13 | num_classes: 19 14 | backbone_indices: [0, 3] 15 | aspp_ratios: [1, 12, 24, 36] 16 | aspp_out_channels: 256 17 | align_corners: True 18 | pretrained: null 19 | -------------------------------------------------------------------------------- /configs/deeplabv3p/deeplabv3p_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'deeplabv3p_resnet50_os8_voc12aug_512x512_40k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: DeepLabV3P 8 | backbone: 9 | type: ResNet50_vd 10 | output_stride: 8 11 | multi_grid: [1, 2, 4] 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 13 | num_classes: 19 14 | backbone_indices: [0, 3] 15 | aspp_ratios: [1, 12, 24, 36] 16 | aspp_out_channels: 256 17 | align_corners: False 18 | pretrained: null 19 | -------------------------------------------------------------------------------- /configs/deeplabv3p/deeplabv3p_resnet50_os8_cityscapes_1024x512_80k_rmiloss.yml: -------------------------------------------------------------------------------- 1 | _base_: 'deeplabv3p_resnet50_os8_cityscapes_1024x512_80k.yml' 2 | 3 | loss: 4 | types: 5 | - type: MixedLoss 6 | losses: 7 | - type: CrossEntropyLoss 8 | - type: RMILoss 9 | coef: [0.5, 0.5] 10 | -------------------------------------------------------------------------------- /configs/deeplabv3p/deeplabv3p_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: DeepLabV3P 5 | backbone: 6 | type: ResNet50_vd 7 | output_stride: 8 8 | multi_grid: [1, 2, 4] 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | backbone_indices: [0, 3] 11 | aspp_ratios: [1, 12, 24, 36] 12 | aspp_out_channels: 256 13 | align_corners: False 14 | pretrained: null 15 | -------------------------------------------------------------------------------- /configs/dmnet/dmnet_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: DMNet 8 | backbone: 9 | type: ResNet101_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 12 | 13 | optimizer: 14 | type: sgd 15 | weight_decay: 0.0005 16 | 17 | loss: 18 | types: 19 | - type: CrossEntropyLoss 20 | - type: CrossEntropyLoss 21 | coef: [1, 0.4] 22 | 23 | lr_scheduler: 24 | type: PolynomialDecay 25 | learning_rate: 0.01 26 | end_lr: 0.0 27 | power: 0.9 28 | -------------------------------------------------------------------------------- /configs/dnlnet/dnlnet_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: DNLNet 8 | backbone: 9 | type: ResNet101_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 12 | num_classes: 19 13 | 14 | optimizer: 15 | type: sgd 16 | momentum: 0.9 17 | weight_decay: 0.00004 18 | 19 | lr_scheduler: 20 | type: PolynomialDecay 21 | learning_rate: 0.01 22 | power: 0.9 23 | 24 | 25 | loss: 26 | types: 27 | - type: CrossEntropyLoss 28 | - type: CrossEntropyLoss 29 | coef: [1, 0.4] 30 | -------------------------------------------------------------------------------- /configs/dnlnet/dnlnet_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: DNLNet 5 | backbone: 6 | type: ResNet101_vd 7 | output_stride: 8 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 9 | 10 | optimizer: 11 | type: sgd 12 | momentum: 0.9 13 | weight_decay: 4.0e-05 14 | 15 | lr_scheduler: 16 | type: PolynomialDecay 17 | learning_rate: 0.01 18 | power: 0.9 19 | 20 | loss: 21 | types: 22 | - type: CrossEntropyLoss 23 | - type: CrossEntropyLoss 24 | coef: [1, 0.4] 25 | -------------------------------------------------------------------------------- /configs/dnlnet/dnlnet_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: DNLNet 8 | backbone: 9 | type: ResNet50_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 12 | num_classes: 19 13 | 14 | optimizer: 15 | type: sgd 16 | momentum: 0.9 17 | weight_decay: 0.00004 18 | 19 | lr_scheduler: 20 | type: PolynomialDecay 21 | learning_rate: 0.01 22 | power: 0.9 23 | 24 | 25 | loss: 26 | types: 27 | - type: CrossEntropyLoss 28 | - type: CrossEntropyLoss 29 | coef: [1, 0.4] 30 | -------------------------------------------------------------------------------- /configs/dnlnet/dnlnet_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: DNLNet 5 | backbone: 6 | type: ResNet50_vd 7 | output_stride: 8 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 9 | 10 | optimizer: 11 | type: sgd 12 | momentum: 0.9 13 | weight_decay: 4.0e-05 14 | 15 | lr_scheduler: 16 | type: PolynomialDecay 17 | learning_rate: 0.01 18 | power: 0.9 19 | 20 | loss: 21 | types: 22 | - type: CrossEntropyLoss 23 | - type: CrossEntropyLoss 24 | coef: [1, 0.4] 25 | -------------------------------------------------------------------------------- /configs/emanet/emanet_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: EMANet 5 | backbone: 6 | type: ResNet101_vd 7 | output_stride: 8 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 9 | ema_channels: 512 10 | gc_channels: 256 11 | num_bases: 64 12 | stage_num: 3 13 | momentum: 0.1 14 | concat_input: True 15 | enable_auxiliary_loss: True 16 | align_corners: True 17 | 18 | optimizer: 19 | type: sgd 20 | momentum: 0.9 21 | weight_decay: 0.0005 22 | 23 | 24 | loss: 25 | types: 26 | - type: CrossEntropyLoss 27 | - type: CrossEntropyLoss 28 | coef: [1, 0.4] 29 | -------------------------------------------------------------------------------- /configs/emanet/emanet_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | 4 | model: 5 | type: EMANet 6 | backbone: 7 | type: ResNet50_vd 8 | output_stride: 8 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | ema_channels: 512 11 | gc_channels: 256 12 | num_bases: 64 13 | stage_num: 3 14 | momentum: 0.1 15 | concat_input: True 16 | enable_auxiliary_loss: True 17 | align_corners: True 18 | 19 | optimizer: 20 | type: sgd 21 | momentum: 0.9 22 | weight_decay: 0.0005 23 | 24 | 25 | loss: 26 | types: 27 | - type: CrossEntropyLoss 28 | - type: CrossEntropyLoss 29 | coef: [1, 0.4] 30 | -------------------------------------------------------------------------------- /configs/fastscnn/fastscnn_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | loss: 7 | types: 8 | - type: CrossEntropyLoss 9 | coef: [1.0, 0.4] 10 | 11 | lr_scheduler: 12 | type: PolynomialDecay 13 | learning_rate: 0.05 14 | end_lr: 1.0e-4 15 | power: 0.9 16 | 17 | model: 18 | type: FastSCNN 19 | num_classes: 19 20 | enable_auxiliary_loss: True 21 | pretrained: null 22 | -------------------------------------------------------------------------------- /configs/fastscnn/fastscnn_cityscapes_1024x1024_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 4 4 | iters: 40000 5 | 6 | loss: 7 | types: 8 | - type: CrossEntropyLoss 9 | coef: [1.0, 0.4] 10 | 11 | lr_scheduler: 12 | type: PolynomialDecay 13 | learning_rate: 0.025 14 | end_lr: 1.0e-4 15 | power: 0.9 16 | 17 | model: 18 | type: FastSCNN 19 | num_classes: 19 20 | enable_auxiliary_loss: True 21 | pretrained: null 22 | -------------------------------------------------------------------------------- /configs/fastscnn/fastscnn_cityscapes_1024x1024_40k_SCL.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 4 4 | iters: 40000 5 | 6 | loss: 7 | types: 8 | - type: MixedLoss 9 | losses: 10 | - type: CrossEntropyLoss 11 | - type: SemanticConnectivityLoss 12 | coef: [1, 0.01] 13 | - type: CrossEntropyLoss 14 | coef: [1.0, 0.4] 15 | 16 | lr_scheduler: 17 | type: PolynomialDecay 18 | learning_rate: 0.025 19 | end_lr: 1.0e-4 20 | power: 0.9 21 | 22 | model: 23 | type: FastSCNN 24 | num_classes: 19 25 | enable_auxiliary_loss: True 26 | pretrained: null 27 | -------------------------------------------------------------------------------- /configs/fcn/fcn_hrnetw18_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | model: 4 | type: FCN 5 | backbone: 6 | type: HRNet_W18 7 | align_corners: False 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 9 | num_classes: 19 10 | pretrained: Null 11 | backbone_indices: [-1] 12 | 13 | optimizer: 14 | weight_decay: 0.0005 15 | 16 | iters: 80000 17 | -------------------------------------------------------------------------------- /configs/fcn/fcn_hrnetw18_cityscapes_1024x512_80k_bs4.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | model: 4 | type: FCN 5 | backbone: 6 | type: HRNet_W18 7 | align_corners: False 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 9 | num_classes: 19 10 | pretrained: Null 11 | backbone_indices: [-1] 12 | 13 | optimizer: 14 | weight_decay: 0.0005 15 | 16 | iters: 80000 17 | batch_size: 4 18 | -------------------------------------------------------------------------------- /configs/fcn/fcn_hrnetw18_cityscapes_1024x512_80k_bs4_SCL.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | model: 4 | type: FCN 5 | backbone: 6 | type: HRNet_W18 7 | align_corners: False 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 9 | num_classes: 19 10 | pretrained: Null 11 | backbone_indices: [-1] 12 | 13 | optimizer: 14 | weight_decay: 0.0005 15 | 16 | iters: 80000 17 | batch_size: 4 18 | 19 | loss: 20 | types: 21 | - type: MixedLoss 22 | losses: 23 | - type: CrossEntropyLoss 24 | - type: SemanticConnectivityLoss 25 | coef: [1, 0.05] 26 | coef: [1] 27 | -------------------------------------------------------------------------------- /configs/fcn/fcn_hrnetw18_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: FCN 5 | backbone: 6 | type: HRNet_W18 7 | align_corners: False 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 9 | num_classes: 21 10 | pretrained: Null 11 | backbone_indices: [-1] 12 | 13 | optimizer: 14 | weight_decay: 0.0005 15 | -------------------------------------------------------------------------------- /configs/fcn/fcn_hrnetw48_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: './fcn_hrnetw18_cityscapes_1024x512_80k.yml' 2 | 3 | model: 4 | backbone: 5 | type: HRNet_W48 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/fcn/fcn_hrnetw48_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: './fcn_hrnetw18_voc12aug_512x512_40k.yml' 2 | 3 | model: 4 | backbone: 5 | type: HRNet_W48 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/gcnet/gcnet_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'gcnet_resnet50_os8_cityscapes_1024x512_80k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/gcnet/gcnet_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: './gcnet_resnet50_os8_voc12aug_512x512_40k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/gcnet/gcnet_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | lr_scheduler: 7 | type: PolynomialDecay 8 | learning_rate: 0.01 9 | power: 0.9 10 | end_lr: 1.0e-5 11 | 12 | loss: 13 | types: 14 | - type: CrossEntropyLoss 15 | coef: [1, 0.4] 16 | 17 | model: 18 | type: GCNet 19 | backbone: 20 | type: ResNet50_vd 21 | output_stride: 8 22 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 23 | gc_channels: 512 24 | ratio: 0.25 25 | enable_auxiliary_loss: True 26 | align_corners: False 27 | pretrained: null 28 | -------------------------------------------------------------------------------- /configs/gcnet/gcnet_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | lr_scheduler: 4 | type: PolynomialDecay 5 | learning_rate: 0.01 6 | power: 0.9 7 | end_lr: 1.0e-5 8 | 9 | loss: 10 | types: 11 | - type: CrossEntropyLoss 12 | coef: [1, 0.4] 13 | 14 | model: 15 | type: GCNet 16 | backbone: 17 | type: ResNet50_vd 18 | output_stride: 8 19 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 20 | gc_channels: 512 21 | ratio: 0.25 22 | enable_auxiliary_loss: True 23 | align_corners: False 24 | pretrained: null 25 | -------------------------------------------------------------------------------- /configs/ginet/ginet_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'ginet_resnet50_os8_cityscapes_1024x512_80k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/ginet/ginet_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'ginet_resnet50_os8_voc12aug_512x512_40k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/ginet/ginet_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | 7 | model: 8 | type: GINet 9 | backbone: 10 | type: ResNet50_vd 11 | output_stride: 8 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 13 | backbone_indices: [0, 1, 2, 3] 14 | enable_auxiliary_loss: True 15 | jpu: True 16 | align_corners: True 17 | pretrained: null 18 | 19 | 20 | loss: 21 | types: 22 | - type: CrossEntropyLoss 23 | coef: [1, 0.4] 24 | -------------------------------------------------------------------------------- /configs/ginet/ginet_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | 4 | model: 5 | type: GINet 6 | backbone: 7 | type: ResNet50_vd 8 | output_stride: 8 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | backbone_indices: [0, 1, 2, 3] 11 | enable_auxiliary_loss: True 12 | jpu: True 13 | align_corners: True 14 | pretrained: null 15 | 16 | loss: 17 | types: 18 | - type: CrossEntropyLoss 19 | coef: [1, 0.4] 20 | -------------------------------------------------------------------------------- /configs/glore/glore_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | learning_rate: 7 | decay: 8 | end_lr: 1.0e-5 9 | 10 | loss: 11 | types: 12 | - type: CrossEntropyLoss 13 | coef: [1, 0.4] 14 | 15 | model: 16 | type: GloRe 17 | backbone: 18 | type: ResNet50_vd 19 | output_stride: 8 20 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 21 | enable_auxiliary_loss: True 22 | align_corners: False 23 | pretrained: null 24 | -------------------------------------------------------------------------------- /configs/glore/glore_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | 4 | model: 5 | type: GloRe 6 | backbone: 7 | type: ResNet50_vd 8 | output_stride: 8 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | enable_auxiliary_loss: True 11 | align_corners: False 12 | pretrained: null 13 | 14 | loss: 15 | types: 16 | - type: CrossEntropyLoss 17 | coef: [1, 0.4] 18 | -------------------------------------------------------------------------------- /configs/hardnet/hardnet_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | lr_scheduler: 7 | type: PolynomialDecay 8 | learning_rate: 0.02 9 | 10 | optimizer: 11 | type: sgd 12 | momentum: 0.9 13 | weight_decay: 5.0e-4 14 | 15 | model: 16 | type: HarDNet 17 | pretrained: null 18 | 19 | loss: 20 | types: 21 | - type: BootstrappedCrossEntropyLoss 22 | min_K: 4096 23 | loss_th: 0.3 24 | coef: [1] 25 | -------------------------------------------------------------------------------- /configs/isanet/isanet_resnet101_os8_cityscapes_769x769_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_769x769.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: ISANet 8 | isa_channels: 256 9 | backbone: 10 | type: ResNet101_vd 11 | output_stride: 8 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 13 | num_classes: 19 14 | 15 | optimizer: 16 | type: sgd 17 | momentum: 0.9 18 | weight_decay: 0.00001 19 | 20 | lr_scheduler: 21 | type: PolynomialDecay 22 | learning_rate: 0.01 23 | power: 0.9 24 | 25 | loss: 26 | types: 27 | - type: CrossEntropyLoss 28 | - type: CrossEntropyLoss 29 | coef: [1, 0.4] 30 | -------------------------------------------------------------------------------- /configs/isanet/isanet_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: ISANet 5 | isa_channels: 256 6 | backbone: 7 | type: ResNet101_vd 8 | output_stride: 8 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 10 | align_corners: True 11 | 12 | optimizer: 13 | type: sgd 14 | momentum: 0.9 15 | weight_decay: 4.0e-05 16 | 17 | lr_scheduler: 18 | type: PolynomialDecay 19 | learning_rate: 0.01 20 | power: 0.9 21 | 22 | loss: 23 | types: 24 | - type: CrossEntropyLoss 25 | - type: CrossEntropyLoss 26 | coef: [1, 0.4] 27 | -------------------------------------------------------------------------------- /configs/isanet/isanet_resnet50_os8_cityscapes_769x769_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_769x769.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: ISANet 8 | isa_channels: 256 9 | backbone: 10 | type: ResNet50_vd 11 | output_stride: 8 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 13 | num_classes: 19 14 | 15 | optimizer: 16 | type: sgd 17 | momentum: 0.9 18 | weight_decay: 0.00001 19 | 20 | lr_scheduler: 21 | type: PolynomialDecay 22 | learning_rate: 0.01 23 | power: 0.9 24 | 25 | 26 | loss: 27 | types: 28 | - type: CrossEntropyLoss 29 | - type: CrossEntropyLoss 30 | coef: [1, 0.4] 31 | -------------------------------------------------------------------------------- /configs/isanet/isanet_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: ISANet 5 | isa_channels: 256 6 | backbone: 7 | type: ResNet50_vd 8 | output_stride: 8 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | align_corners: True 11 | 12 | optimizer: 13 | type: sgd 14 | momentum: 0.9 15 | weight_decay: 0.00001 16 | 17 | lr_scheduler: 18 | type: PolynomialDecay 19 | learning_rate: 0.01 20 | power: 0.9 21 | 22 | loss: 23 | types: 24 | - type: CrossEntropyLoss 25 | - type: CrossEntropyLoss 26 | coef: [1, 0.4] 27 | -------------------------------------------------------------------------------- /configs/ocrnet/ocrnet_hrnetw18_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 160000 5 | 6 | model: 7 | type: OCRNet 8 | backbone: 9 | type: HRNet_W18 10 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 11 | num_classes: 19 12 | backbone_indices: [0] 13 | 14 | optimizer: 15 | type: sgd 16 | 17 | lr_scheduler: 18 | type: PolynomialDecay 19 | learning_rate: 0.01 20 | power: 0.9 21 | 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | - type: CrossEntropyLoss 27 | coef: [1, 0.4] 28 | -------------------------------------------------------------------------------- /configs/ocrnet/ocrnet_hrnetw18_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | model: 4 | type: OCRNet 5 | backbone: 6 | type: HRNet_W18 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 8 | backbone_indices: [0] 9 | 10 | optimizer: 11 | type: sgd 12 | 13 | lr_scheduler: 14 | type: PolynomialDecay 15 | learning_rate: 0.01 16 | power: 0.9 17 | 18 | 19 | loss: 20 | types: 21 | - type: CrossEntropyLoss 22 | - type: CrossEntropyLoss 23 | coef: [1, 1] 24 | -------------------------------------------------------------------------------- /configs/ocrnet/ocrnet_hrnetw48_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | 2 | _base_: '../_base_/cityscapes.yml' 3 | 4 | batch_size: 2 5 | iters: 160000 6 | 7 | model: 8 | type: OCRNet 9 | backbone: 10 | type: HRNet_W48 11 | 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz 13 | num_classes: 19 14 | backbone_indices: [0] 15 | 16 | optimizer: 17 | type: sgd 18 | 19 | lr_scheduler: 20 | type: PolynomialDecay 21 | learning_rate: 0.01 22 | power: 0.9 23 | 24 | 25 | 26 | loss: 27 | types: 28 | - type: CrossEntropyLoss 29 | - type: CrossEntropyLoss 30 | coef: [1, 0.4] 31 | -------------------------------------------------------------------------------- /configs/ocrnet/ocrnet_hrnetw48_cityscapes_1024x512_40k.yml: -------------------------------------------------------------------------------- 1 | 2 | _base_: '../_base_/cityscapes.yml' 3 | 4 | batch_size: 2 5 | iters: 40000 6 | 7 | model: 8 | type: OCRNet 9 | backbone: 10 | type: HRNet_W48 11 | 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz 13 | num_classes: 19 14 | backbone_indices: [0] 15 | 16 | optimizer: 17 | type: sgd 18 | 19 | lr_scheduler: 20 | type: PolynomialDecay 21 | learning_rate: 0.01 22 | power: 0.9 23 | 24 | loss: 25 | types: 26 | - type: CrossEntropyLoss 27 | - type: CrossEntropyLoss 28 | coef: [1, 0.4] 29 | -------------------------------------------------------------------------------- /configs/ocrnet/ocrnet_hrnetw48_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: './ocrnet_hrnetw18_voc12aug_512x512_40k.yml' 2 | 3 | model: 4 | backbone: 5 | type: HRNet_W48 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/pointrend/pointrend_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'pointrend_resnet50_os8_cityscapes_1024x512_80k.yml' 2 | 3 | 4 | model: 5 | backbone: 6 | type: ResNet101_vd 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 8 | -------------------------------------------------------------------------------- /configs/pointrend/pointrend_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'pointrend_resnet50_os8_voc12aug_512x512_40k.yml' 2 | 3 | 4 | model: 5 | backbone: 6 | type: ResNet101_vd 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 8 | -------------------------------------------------------------------------------- /configs/pointrend/pointrend_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | 4 | model: 5 | type: PointRend 6 | backbone: 7 | type: ResNet50_vd 8 | output_stride: 8 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | backbone_indices: [0, 1, 2, 3] 11 | 12 | 13 | loss: 14 | types: 15 | - type: CrossEntropyLoss 16 | - type: PointCrossEntropyLoss 17 | coef: [1, 1] 18 | 19 | 20 | optimizer: 21 | type: sgd 22 | momentum: 0.9 23 | weight_decay: 0.0005 24 | -------------------------------------------------------------------------------- /configs/pointrend/pointrend_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | 4 | model: 5 | type: PointRend 6 | backbone: 7 | type: ResNet50_vd 8 | output_stride: 8 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | backbone_indices: [0, 1, 2, 3] 11 | 12 | 13 | loss: 14 | types: 15 | - type: CrossEntropyLoss 16 | - type: PointCrossEntropyLoss 17 | coef: [1, 1] 18 | 19 | 20 | optimizer: 21 | type: sgd 22 | momentum: 0.9 23 | weight_decay: 0.0005 24 | -------------------------------------------------------------------------------- /configs/pp_humanseg_lite/README.md: -------------------------------------------------------------------------------- 1 | # PP-HumanSeg-Lite 2 | 3 | A self-developed ultra lightweight model ConnectNet, is suitable for real-time segmentation scenarios on the web or mobile. See [paper](https://arxiv.org/abs/2112.07146) for more information. 4 | 5 | ## Network Structure 6 | ![](pphumanseg_lite.png) 7 | 8 | ## Performance 9 | Refer to [PP-HumanSeg](../../contrib/PP-HumanSeg). 10 | -------------------------------------------------------------------------------- /configs/pp_humanseg_lite/pp_humanseg_lite_export_398x224.yml: -------------------------------------------------------------------------------- 1 | 2 | model: 3 | type: PPHumanSegLite 4 | align_corners: False 5 | num_classes: 2 6 | 7 | export: 8 | transforms: 9 | - type: Resize 10 | target_size: [398, 224] 11 | - type: Normalize 12 | 13 | val_dataset: 14 | type: Dataset 15 | dataset_root: data/mini_supervisely 16 | val_path: data/mini_supervisely/val.txt 17 | num_classes: 2 18 | transforms: 19 | - type: Resize 20 | target_size: [398, 224] 21 | - type: Normalize 22 | mode: val 23 | -------------------------------------------------------------------------------- /configs/pp_humanseg_lite/pphumanseg_lite.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/configs/pp_humanseg_lite/pphumanseg_lite.png -------------------------------------------------------------------------------- /configs/pp_liteseg/pp_liteseg_stdc1_camvid_960x720_10k_for_test.yml: -------------------------------------------------------------------------------- 1 | _base_: './pp_liteseg_stdc1_camvid_960x720_10k.yml' 2 | 3 | val_dataset: 4 | val_path: data/camvid/test.txt 5 | -------------------------------------------------------------------------------- /configs/pp_liteseg/pp_liteseg_stdc1_cityscapes_1024x512_scale0.75_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: './pp_liteseg_stdc1_cityscapes_1024x512_scale0.5_160k.yml' 2 | 3 | test_config: 4 | aug_eval: True 5 | scales: 0.75 6 | -------------------------------------------------------------------------------- /configs/pp_liteseg/pp_liteseg_stdc1_cityscapes_1024x512_scale1.0_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: './pp_liteseg_stdc1_cityscapes_1024x512_scale0.5_160k.yml' 2 | 3 | train_dataset: 4 | transforms: 5 | - type: ResizeStepScaling 6 | min_scale_factor: 0.5 7 | max_scale_factor: 2.0 8 | scale_step_size: 0.25 9 | - type: RandomPaddingCrop 10 | crop_size: [1024, 512] 11 | - type: RandomHorizontalFlip 12 | - type: RandomDistort 13 | brightness_range: 0.5 14 | contrast_range: 0.5 15 | saturation_range: 0.5 16 | - type: Normalize 17 | mode: train 18 | 19 | test_config: 20 | aug_eval: True 21 | scales: 1.0 22 | -------------------------------------------------------------------------------- /configs/pp_liteseg/pp_liteseg_stdc2_camvid_960x720_10k.yml: -------------------------------------------------------------------------------- 1 | _base_: './pp_liteseg_stdc1_camvid_960x720_10k.yml' 2 | 3 | model: 4 | _inherited_: False # not inherit the model params from the base yaml 5 | type: PPLiteSeg 6 | backbone: 7 | type: STDC2 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz 9 | -------------------------------------------------------------------------------- /configs/pp_liteseg/pp_liteseg_stdc2_camvid_960x720_10k_for_test.yml: -------------------------------------------------------------------------------- 1 | _base_: './pp_liteseg_stdc1_camvid_960x720_10k.yml' 2 | 3 | val_dataset: 4 | val_path: data/camvid/test.txt 5 | 6 | model: 7 | _inherited_: False 8 | type: PPLiteSeg 9 | backbone: 10 | type: STDC2 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz 12 | -------------------------------------------------------------------------------- /configs/pp_liteseg/pp_liteseg_stdc2_cityscapes_1024x512_scale0.5_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: './pp_liteseg_stdc1_cityscapes_1024x512_scale0.5_160k.yml' 2 | 3 | test_config: 4 | aug_eval: True 5 | scales: 0.5 6 | 7 | model: 8 | _inherited_: False # not inherit the model params from the base yaml 9 | type: PPLiteSeg 10 | backbone: 11 | type: STDC2 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz 13 | -------------------------------------------------------------------------------- /configs/pp_liteseg/pp_liteseg_stdc2_cityscapes_1024x512_scale0.75_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: './pp_liteseg_stdc1_cityscapes_1024x512_scale0.5_160k.yml' 2 | 3 | test_config: 4 | aug_eval: True 5 | scales: 0.75 6 | 7 | model: 8 | _inherited_: False 9 | type: PPLiteSeg 10 | backbone: 11 | type: STDC2 12 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz 13 | -------------------------------------------------------------------------------- /configs/pspnet/pspnet_resnet101_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'pspnet_resnet50_os8_cityscapes_1024x512_80k.yml' 2 | 3 | model: 4 | backbone: 5 | type: ResNet101_vd 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 7 | -------------------------------------------------------------------------------- /configs/pspnet/pspnet_resnet101_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'pspnet_resnet50_os8_voc12aug_512x512_40k.yml' 2 | 3 | train_dataset: 4 | transforms: 5 | - type: RandomPaddingCrop 6 | crop_size: [512, 512] 7 | - type: RandomHorizontalFlip 8 | - type: RandomDistort 9 | brightness_range: 0.4 10 | contrast_range: 0.4 11 | saturation_range: 0.4 12 | - type: Normalize 13 | 14 | model: 15 | backbone: 16 | type: ResNet101_vd 17 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 18 | -------------------------------------------------------------------------------- /configs/pspnet/pspnet_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | lr_scheduler: 7 | type: PolynomialDecay 8 | learning_rate: 0.01 9 | power: 0.9 10 | end_lr: 1.0e-5 11 | 12 | loss: 13 | types: 14 | - type: CrossEntropyLoss 15 | coef: [1, 0.4] 16 | 17 | model: 18 | type: PSPNet 19 | backbone: 20 | type: ResNet50_vd 21 | output_stride: 8 22 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 23 | enable_auxiliary_loss: True 24 | align_corners: False 25 | pretrained: null 26 | -------------------------------------------------------------------------------- /configs/pspnet/pspnet_resnet50_os8_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | loss: 4 | types: 5 | - type: CrossEntropyLoss 6 | coef: [1, 0.4] 7 | 8 | model: 9 | type: PSPNet 10 | backbone: 11 | type: ResNet50_vd 12 | output_stride: 8 13 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 14 | enable_auxiliary_loss: True 15 | align_corners: False 16 | pretrained: null 17 | -------------------------------------------------------------------------------- /configs/pssl/pp_liteseg_stdc2_cityscapes_1024x512_scale1.0_160k_pssl.yml: -------------------------------------------------------------------------------- 1 | _base_: '../pp_liteseg/pp_liteseg_stdc2_cityscapes_1024x512_scale1.0_160k.yml' 2 | 3 | model: 4 | _inherited_: False 5 | type: PPLiteSeg 6 | backbone: 7 | type: STDC2 8 | pretrained: null 9 | pretrained: /root/codespace/PaddleSeg-release-2.5/work_dirs_pp_liteseg_stdc2_pssl/snapshot/iter_66725/model.pdparams 10 | -------------------------------------------------------------------------------- /configs/pssl/pp_liteseg_stdc2_pssl.yml: -------------------------------------------------------------------------------- 1 | _base_: 'pp_liteseg_stdc1_pssl.yml' 2 | 3 | model: 4 | _inherited_: False # not inherit the model params from the base yaml 5 | type: PPLiteSeg 6 | backbone: 7 | type: STDC2 8 | relative_lr: 0.1 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz 10 | pretrained: null 11 | num_classes: 1001 12 | -------------------------------------------------------------------------------- /configs/pssl/stdc2_seg_pssl.yml: -------------------------------------------------------------------------------- 1 | _base_: 'stdc1_seg_pssl.yml' 2 | 3 | model: 4 | backbone: 5 | type: STDC2 6 | relative_lr: 0.1 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/STDCNet2.tar.gz 8 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b0_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 2 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B0 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b0.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | 28 | test_config: 29 | is_slide: True 30 | crop_size: [1024, 1024] 31 | stride: [768, 768] 32 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b0_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 1 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B0 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b0.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b1_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 2 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B1 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b1.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | 28 | test_config: 29 | is_slide: True 30 | crop_size: [1024, 1024] 31 | stride: [768, 768] 32 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b1_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 1 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B1 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b1.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b2_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 2 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B2 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b2.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | 28 | test_config: 29 | is_slide: True 30 | crop_size: [1024, 1024] 31 | stride: [768, 768] 32 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b2_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 1 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B2 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b2.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b3_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 2 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B3 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b3.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | 28 | test_config: 29 | is_slide: True 30 | crop_size: [1024, 1024] 31 | stride: [768, 768] 32 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b3_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 1 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B3 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b3.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b4_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 2 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B4 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b4.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | 28 | test_config: 29 | is_slide: True 30 | crop_size: [1024, 1024] 31 | stride: [768, 768] 32 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b4_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 1 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B4 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b4.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b5_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | batch_size: 1 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B5 8 | num_classes: 19 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b5.tar.gz 10 | 11 | optimizer: 12 | _inherited_: False 13 | type: AdamW 14 | beta1: 0.9 15 | beta2: 0.999 16 | weight_decay: 0.01 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.00006 21 | power: 1 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | 28 | test_config: 29 | is_slide: True 30 | crop_size: [1024, 1024] 31 | stride: [768, 768] 32 | -------------------------------------------------------------------------------- /configs/segformer/segformer_b5_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 1 4 | iters: 160000 5 | 6 | model: 7 | type: SegFormer_B5 8 | num_classes: 19 9 | 10 | optimizer: 11 | _inherited_: False 12 | type: AdamW 13 | beta1: 0.9 14 | beta2: 0.999 15 | weight_decay: 0.01 16 | 17 | lr_scheduler: 18 | type: PolynomialDecay 19 | learning_rate: 0.00006 20 | power: 1 21 | 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | coef: [1] 27 | -------------------------------------------------------------------------------- /configs/segmenter/segmenter_vit_base_mask_ade20k_512x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: './segmenter_vit_base_linear_ade20k_512x512_160k.yml' 2 | 3 | model: 4 | type: MaskSegmenter 5 | h_embed_dim: 768 6 | h_depth: 2 7 | h_num_heads: 12 8 | h_mlp_ratio: 4 9 | h_drop_rate: 0.0 10 | h_drop_path_rate: 0.1 11 | -------------------------------------------------------------------------------- /configs/segmenter/segmenter_vit_small_linear_ade20k_512x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: './segmenter_vit_base_linear_ade20k_512x512_160k.yml' 2 | 3 | model: 4 | type: LinearSegmenter 5 | backbone: 6 | type: VisionTransformer 7 | img_size: 512 8 | patch_size: 16 9 | embed_dim: 384 10 | depth: 12 11 | num_heads: 6 12 | mlp_ratio: 4 13 | qkv_bias: True 14 | drop_rate: 0.0 15 | drop_path_rate: 0.1 16 | final_norm: True 17 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/pretrained_models/vit_small_patch16_384_augreg.tar.gz 18 | -------------------------------------------------------------------------------- /configs/segmenter/segmenter_vit_small_mask_ade20k_512x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: './segmenter_vit_small_linear_ade20k_512x512_160k.yml' 2 | 3 | model: 4 | type: MaskSegmenter 5 | h_embed_dim: 384 6 | h_depth: 2 7 | h_num_heads: 6 8 | h_mlp_ratio: 4 9 | h_drop_rate: 0.0 10 | h_drop_path_rate: 0.1 11 | -------------------------------------------------------------------------------- /configs/segnet/segnet_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: SegNet 8 | pretrained: Null 9 | -------------------------------------------------------------------------------- /configs/sfnet/sfnet_resnet50_os8_cityscapes_1024x1024_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'sfnet_resnet18_os8_cityscapes_1024x1024_80k.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | model: 7 | type: SFNet 8 | backbone: 9 | type: ResNet50_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 12 | backbone_indices: [0, 1, 2, 3] 13 | -------------------------------------------------------------------------------- /configs/smrt/bisenetv2.yml: -------------------------------------------------------------------------------- 1 | _base_: './base_cfg.yml' 2 | 3 | model: 4 | type: BiSeNetV2 5 | 6 | loss: 7 | types: 8 | - type: MixedLoss 9 | losses: 10 | - type: OhemCrossEntropyLoss 11 | min_kept: 65000 12 | - type: LovaszSoftmaxLoss 13 | coef: [0.8, 0.2] 14 | coef: [1, 1, 1, 1, 1] -------------------------------------------------------------------------------- /configs/smrt/deeplabv3p_resnet50_os8.yml: -------------------------------------------------------------------------------- 1 | _base_: './base_cfg.yml' 2 | 3 | model: 4 | type: DeepLabV3P 5 | backbone: 6 | type: ResNet50_vd 7 | output_stride: 8 8 | multi_grid: [1, 2, 4] 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 10 | backbone_indices: [0, 3] 11 | aspp_ratios: [1, 12, 24, 36] 12 | aspp_out_channels: 256 13 | align_corners: False 14 | pretrained: null 15 | 16 | 17 | loss: 18 | types: 19 | - type: MixedLoss 20 | losses: 21 | - type: OhemCrossEntropyLoss 22 | min_kept: 65000 23 | - type: LovaszSoftmaxLoss 24 | coef: [0.8, 0.2] 25 | coef: [1] -------------------------------------------------------------------------------- /configs/smrt/ocrnet_hrnetw18.yml: -------------------------------------------------------------------------------- 1 | _base_: './base_cfg.yml' 2 | 3 | model: 4 | type: OCRNet 5 | backbone: 6 | type: HRNet_W18 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 8 | backbone_indices: [0] 9 | 10 | loss: 11 | types: 12 | - type: MixedLoss 13 | losses: 14 | - type: OhemCrossEntropyLoss 15 | min_kept: 65000 16 | - type: LovaszSoftmaxLoss 17 | coef: [0.8, 0.2] 18 | coef: [1, 0.4] -------------------------------------------------------------------------------- /configs/smrt/pp_liteseg_stdc1.yml: -------------------------------------------------------------------------------- 1 | _base_: './base_cfg.yml' 2 | 3 | model: 4 | type: PPLiteSeg 5 | backbone: 6 | type: STDC1 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet1.tar.gz 8 | arm_out_chs: [32, 64, 128] 9 | seg_head_inter_chs: [32, 64, 64] 10 | 11 | loss: 12 | types: 13 | - type: MixedLoss 14 | losses: 15 | - type: OhemCrossEntropyLoss 16 | min_kept: 65000 17 | - type: LovaszSoftmaxLoss 18 | coef: [0.8, 0.2] 19 | coef: [1, 1, 1] -------------------------------------------------------------------------------- /configs/smrt/pp_liteseg_stdc2.yml: -------------------------------------------------------------------------------- 1 | _base_: './base_cfg.yml' 2 | 3 | model: 4 | type: PPLiteSeg 5 | backbone: 6 | type: STDC2 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz 8 | 9 | loss: 10 | types: 11 | - type: MixedLoss 12 | losses: 13 | - type: OhemCrossEntropyLoss 14 | min_kept: 65000 15 | - type: LovaszSoftmaxLoss 16 | coef: [0.8, 0.2] 17 | coef: [1, 1, 1] -------------------------------------------------------------------------------- /configs/smrt/sfnet_resnet18_os8.yml: -------------------------------------------------------------------------------- 1 | _base_: './base_cfg.yml' 2 | 3 | model: 4 | type: SFNet 5 | backbone: 6 | type: ResNet18_vd 7 | output_stride: 8 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet18_vd_ssld_v2.tar.gz 9 | backbone_indices: [0, 1, 2, 3] 10 | 11 | loss: 12 | types: 13 | - type: MixedLoss 14 | losses: 15 | - type: OhemCrossEntropyLoss 16 | min_kept: 65000 17 | - type: LovaszSoftmaxLoss 18 | coef: [0.8, 0.2] 19 | coef: [1] -------------------------------------------------------------------------------- /configs/stdcseg/stdc1_seg_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 12 4 | iters: 80000 5 | 6 | model: 7 | type: STDCSeg 8 | backbone: 9 | type: STDC1 10 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/STDCNet1.tar.gz 11 | pretrained: null 12 | 13 | loss: 14 | types: 15 | - type: OhemCrossEntropyLoss 16 | - type: OhemCrossEntropyLoss 17 | - type: OhemCrossEntropyLoss 18 | - type: DetailAggregateLoss 19 | coef: [1, 1, 1, 1] 20 | -------------------------------------------------------------------------------- /configs/stdcseg/stdc1_seg_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/pascal_voc12aug.yml' 2 | 3 | 4 | model: 5 | type: STDCSeg 6 | backbone: 7 | type: STDC1 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/STDCNet1.tar.gz 9 | pretrained: null 10 | 11 | loss: 12 | types: 13 | - type: OhemCrossEntropyLoss 14 | - type: OhemCrossEntropyLoss 15 | - type: OhemCrossEntropyLoss 16 | - type: DetailAggregateLoss 17 | coef: [1, 1, 1, 1] 18 | -------------------------------------------------------------------------------- /configs/stdcseg/stdc2_seg_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'stdc1_seg_cityscapes_1024x512_80k.yml' 2 | 3 | 4 | model: 5 | backbone: 6 | type: STDC2 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/STDCNet2.tar.gz 8 | -------------------------------------------------------------------------------- /configs/stdcseg/stdc2_seg_voc12aug_512x512_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'stdc1_seg_voc12aug_512x512_40k.yml' 2 | 3 | 4 | model: 5 | backbone: 6 | type: STDC2 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/STDCNet2.tar.gz 8 | -------------------------------------------------------------------------------- /configs/u2net/README.md: -------------------------------------------------------------------------------- 1 | # U2-Net: Going deeper with nested U-structure for salient object detection 2 | 3 | ## Reference 4 | > Qin, Xuebin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R. Zaiane, and Martin Jagersand. "U2-Net: Going deeper with nested U-structure for salient object detection." Pattern Recognition 106 (2020): 107404. 5 | -------------------------------------------------------------------------------- /configs/u2net/u2net_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | model: 7 | type: U2Net 8 | num_classes: 19 9 | pretrained: Null 10 | 11 | loss: 12 | coef: [1, 1, 1, 1, 1, 1, 1] 13 | -------------------------------------------------------------------------------- /configs/u2net/u2netp_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | model: 7 | type: U2Netp 8 | num_classes: 19 9 | pretrained: Null 10 | 11 | loss: 12 | coef: [1, 1, 1, 1, 1, 1, 1] 13 | -------------------------------------------------------------------------------- /configs/unet/unet_chasedb1_128x128_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/chase_db1.yml' 2 | 3 | batch_size: 4 4 | iters: 40000 5 | 6 | model: 7 | type: UNet 8 | num_classes: 2 9 | use_deconv: False 10 | pretrained: Null 11 | -------------------------------------------------------------------------------- /configs/unet/unet_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | model: 7 | type: UNet 8 | num_classes: 19 9 | use_deconv: False 10 | pretrained: Null 11 | -------------------------------------------------------------------------------- /configs/unet/unet_drive_128x128_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/drive.yml' 2 | 3 | batch_size: 4 4 | iters: 40000 5 | 6 | model: 7 | type: UNet 8 | num_classes: 2 9 | use_deconv: False 10 | pretrained: Null 11 | -------------------------------------------------------------------------------- /configs/unet/unet_hrf_256x256_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/hrf.yml' 2 | 3 | batch_size: 4 4 | iters: 40000 5 | 6 | model: 7 | type: UNet 8 | num_classes: 2 9 | use_deconv: False 10 | pretrained: Null 11 | -------------------------------------------------------------------------------- /configs/unet/unet_stare_128x128_40k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/stare.yml' 2 | 3 | batch_size: 4 4 | iters: 40000 5 | 6 | model: 7 | type: UNet 8 | num_classes: 2 9 | use_deconv: False 10 | pretrained: Null 11 | -------------------------------------------------------------------------------- /configs/unet_3plus/README.md: -------------------------------------------------------------------------------- 1 | # UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation 2 | 3 | ## Reference 4 | 5 | > Huang H , Lin L , Tong R , et al. UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation[J]. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020. 6 | -------------------------------------------------------------------------------- /configs/unet_3plus/unet_3plus_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | model: 7 | type: UNet3Plus 8 | in_channels: 3 9 | num_classes: 19 10 | is_batchnorm: True 11 | is_deepsup: False 12 | is_CGM: False 13 | -------------------------------------------------------------------------------- /configs/unet_plusplus/README.md: -------------------------------------------------------------------------------- 1 | # A Nested U-Net Architecture for Medical Image Segmentation 2 | 3 | ## Reference 4 | 5 | > Zhou, Zongwei, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. "Unet++: A nested u-net architecture for medical image segmentation." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3-11. Springer, Cham, 2018. 6 | -------------------------------------------------------------------------------- /configs/unet_plusplus/unet_plusplus_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | model: 7 | type: UNetPlusPlus 8 | in_channels: 3 9 | num_classes: 19 10 | use_deconv: False 11 | align_corners: False 12 | pretrained: Null 13 | is_ds: True 14 | -------------------------------------------------------------------------------- /contrib/CityscapesSOTA/configs/README.md: -------------------------------------------------------------------------------- 1 | # Hierarchical multi-scale attention for semantic segmentation 2 | 3 | ## Reference 4 | > Tao, Andrew, Karan Sapra, and Bryan Catanzaro. "Hierarchical multi-scale attention for semantic segmentation." arXiv preprint arXiv:2005.10821 (2020). 5 | -------------------------------------------------------------------------------- /contrib/DomainAdaptation/requirements.txt: -------------------------------------------------------------------------------- 1 | albumentations 2 | paddleseg==2.3.0 3 | -------------------------------------------------------------------------------- /contrib/DomainAdaptation/run-DA_src.sh: -------------------------------------------------------------------------------- 1 | export CUDA_VISIBLE_DEVICES=2 2 | 3 | yml=deeplabv2_resnet101_os8_gta5cityscapes_1280x640_160k_newds_gta5src 4 | save_dir=saved_model_develop/${yml}_test 5 | mkdir -p ${save_dir} 6 | 7 | python train.py \ 8 | --config configs/deeplabv2/${yml}.yml --use_vdl --save_dir $save_dir \ 9 | --save_interval 1000 --log_iters 30 \ 10 | --num_workers 4 --do_eval \ 11 | --keep_checkpoint_max 10 --seed 1234 \ 12 | 2>&1 | tee ${save_dir}/log \ 13 | -------------------------------------------------------------------------------- /contrib/LaneSeg/data/images/added_prediction/3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/LaneSeg/data/images/added_prediction/3.jpg -------------------------------------------------------------------------------- /contrib/LaneSeg/data/images/points/3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/LaneSeg/data/images/points/3.jpg -------------------------------------------------------------------------------- /contrib/LaneSeg/data/images/pseudo_color_prediction/3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/LaneSeg/data/images/pseudo_color_prediction/3.png -------------------------------------------------------------------------------- /contrib/LaneSeg/data/test_images/3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/LaneSeg/data/test_images/3.jpg -------------------------------------------------------------------------------- /contrib/LaneSeg/third_party/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/configs/_base_/global_configs.yml: -------------------------------------------------------------------------------- 1 | data_root: data/ 2 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/configs/lung_coronavirus/vnet_lung_coronavirus_128_128_128_15k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'lung_coronavirus.yml' 2 | 3 | model: 4 | type: VNet 5 | elu: False 6 | in_channels: 1 7 | num_classes: 3 8 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/lung_coronavirus/vnet_lung_coronavirus_128_128_128_15k/pretrain/model.pdparams 9 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/configs/mri_spine_seg/vnet_mri_spine_seg_512_512_12_15k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'mri_spine_seg_1e-2_big_rmresizecrop_class20.yml' 2 | 3 | model: 4 | type: VNet 5 | elu: False 6 | in_channels: 1 7 | num_classes: 20 8 | pretrained: null 9 | kernel_size: [[2,2,4], [2,2,2], [2,2,2], [2,2,2]] 10 | stride_size: [[2,2,1], [2,2,1], [2,2,2], [2,2,2]] 11 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/configs/mri_spine_seg/vnetdeepsup_mri_spine_seg_512_512_12_15k.yml: -------------------------------------------------------------------------------- 1 | _base_: 'mri_spine_seg_1e-2_big_rmresizecrop_class20.yml' 2 | 3 | model: 4 | type: VNetDeepSup 5 | elu: False 6 | in_channels: 1 7 | num_classes: 20 8 | pretrained: null 9 | kernel_size: [[2,2,4], [2,2,2], [2,2,2], [2,2,2]] 10 | stride_size: [[2,2,1], [2,2,1], [2,2,2], [2,2,2]] 11 | 12 | loss: 13 | types: 14 | - type: MixedLoss 15 | losses: 16 | - type: CrossEntropyLoss 17 | weight: Null 18 | - type: DiceLoss 19 | coef: [1, 1] 20 | coef: [0.25, 0.25, 0.25, 0.25] 21 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/configs/msd_brain_seg/unetr_msd_brain_seg_1e-4.yml: -------------------------------------------------------------------------------- 1 | _base_: 'msd_brain_seg_1e-4.yml' 2 | 3 | model: 4 | type: UNETR 5 | img_shape: (128, 128, 128) 6 | in_channels: 4 7 | num_classes: 4 8 | embed_dim: 768 9 | patch_size: 16 10 | num_heads: 12 11 | dropout: 0.1 -------------------------------------------------------------------------------- /contrib/MedicalSeg/configs/schedulers/two_stage_coarseseg_fineseg.yml: -------------------------------------------------------------------------------- 1 | configs: 2 | config1: a.yml 3 | config2: b.yml 4 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/requirements.txt: -------------------------------------------------------------------------------- 1 | scikit-image 2 | numpy 3 | paddlepaddle-gpu>=2.2.0 4 | SimpleITK>=2.1.1 5 | PyYAML 6 | pynrrd 7 | tqdm 8 | visualdl 9 | sklearn 10 | filelock 11 | nibabel 12 | pydicom 13 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/tools/__init__.py: -------------------------------------------------------------------------------- 1 | from .prepare import Prep 2 | from .preprocess_utils import * 3 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/tools/preprocess_globals.yml: -------------------------------------------------------------------------------- 1 | use_gpu: False 2 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/tools/preprocess_utils/__init__.py: -------------------------------------------------------------------------------- 1 | import yaml 2 | import codecs 3 | from . import global_var 4 | # Import global_val then everywhere else can change/use the global dict 5 | with codecs.open('tools/preprocess_globals.yml', 'r', 'utf-8') as file: 6 | dic = yaml.load(file, Loader=yaml.FullLoader) 7 | global_var.init() 8 | if dic['use_gpu']: 9 | global_var.set_value('USE_GPU', True) 10 | else: 11 | global_var.set_value('USE_GPU', False) 12 | 13 | from .values import * 14 | from .uncompress import uncompressor 15 | from .geometry import * 16 | from .load_image import * 17 | from .dataset_json import parse_msd_basic_info 18 | -------------------------------------------------------------------------------- /contrib/MedicalSeg/tools/preprocess_utils/dataset_json.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | 4 | def parse_msd_basic_info(json_path): 5 | """ 6 | get dataset basic info from msd dataset.json 7 | """ 8 | dict = json.loads(open(json_path, "r").read()) 9 | info = {} 10 | info["modalities"] = tuple(dict["modality"].values()) 11 | info["labels"] = dict["labels"] 12 | info["dataset_name"] = dict["name"] 13 | info["dataset_description"] = dict["description"] 14 | info["license_desc"] = dict["licence"] 15 | info["dataset_reference"] = dict["reference"] 16 | return info 17 | -------------------------------------------------------------------------------- /contrib/PP-HumanSeg/src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/PP-HumanSeg/src/__init__.py -------------------------------------------------------------------------------- /contrib/PanopticDeepLab/configs/panoptic_deeplab/panoptic_deeplab_resnet50_os32_cityscapes_1025x513_bs8_90k_lr00005.yml: -------------------------------------------------------------------------------- 1 | _base_: ./panoptic_deeplab_resnet50_os32_cityscapes_2049x1025_bs1_90k_lr00005.yml 2 | 3 | batch_size: 8 4 | 5 | train_dataset: 6 | transforms: 7 | - type: ResizeStepScaling 8 | min_scale_factor: 0.5 9 | max_scale_factor: 2.0 10 | scale_step_size: 0.25 11 | - type: RandomPaddingCrop 12 | crop_size: [1025, 513] 13 | label_padding_value: [0, 0, 0] 14 | - type: RandomHorizontalFlip 15 | - type: RandomDistort 16 | brightness_range: 0.4 17 | contrast_range: 0.4 18 | saturation_range: 0.4 19 | - type: Normalize 20 | -------------------------------------------------------------------------------- /contrib/PanopticDeepLab/docs/panoptic_deeplab.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/PanopticDeepLab/docs/panoptic_deeplab.jpg -------------------------------------------------------------------------------- /contrib/PanopticDeepLab/docs/visualization_instance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/PanopticDeepLab/docs/visualization_instance.png -------------------------------------------------------------------------------- /contrib/PanopticDeepLab/docs/visualization_panoptic.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/PanopticDeepLab/docs/visualization_panoptic.png -------------------------------------------------------------------------------- /contrib/PanopticDeepLab/docs/visualization_semantic.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/contrib/PanopticDeepLab/docs/visualization_semantic.png -------------------------------------------------------------------------------- /deploy/cpp/README.md: -------------------------------------------------------------------------------- 1 | # PaddleSeg C++ Predictive Deployment Scenario 2 | 3 | ## Deploy the PaddleSeg model using Paddle Inference C++ 4 | 5 | To deploy PaddleSeg model using Paddle Inference C++, please refer to [Tutorial](../../docs/deployment/inference/cpp_inference.md). 6 | 7 | ## Deploy the PaddleSeg model using PaddleX 8 | 9 | Currently, PaddleSeg model C++ deployment can be performed based on PaddleX ([Deployment Tutorial](https://github.com/PaddlePaddle/PaddleX/tree/develop/deploy/cpp)). 10 | 11 | Hardware support: 12 | * CPU(linux/windows) 13 | * GPU(linux/windows) 14 | * Jetson(TX2/Nano/Xavier) 15 | -------------------------------------------------------------------------------- /deploy/cpp/README_cn.md: -------------------------------------------------------------------------------- 1 | # PaddleSeg C++ 预测部署方案 2 | 3 | ## 使用Paddle Inference C++部署PaddleSeg模型 4 | 5 | 使用Paddle Inference C++部署PaddleSeg模型，请参考[教程](../../docs/deployment/inference/cpp_inference.md)。 6 | 7 | ## 使用PaddleX部署PaddleSeg模型 8 | 9 | 目前可基于PaddleX进行PaddleSeg模型C++部署（[部署教程](https://github.com/PaddlePaddle/PaddleX/tree/develop/deploy/cpp)）。 10 | 11 | 硬件支持 12 | * CPU(linux/windows) 13 | * GPU(linux/windows) 14 | * Jetson(TX2/Nano/Xavier) 15 | -------------------------------------------------------------------------------- /deploy/cpp/run_seg_gpu.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set +x 3 | set -e 4 | 5 | WITH_MKL=ON 6 | WITH_GPU=ON 7 | USE_TENSORRT=OFF 8 | DEMO_NAME=test_seg 9 | 10 | work_path=$(dirname $(readlink -f $0)) 11 | LIB_DIR="${work_path}/paddle_inference" 12 | 13 | # compile 14 | mkdir -p build 15 | cd build 16 | rm -rf * 17 | 18 | cmake .. \ 19 | -DDEMO_NAME=${DEMO_NAME} \ 20 | -DWITH_MKL=${WITH_MKL} \ 21 | -DWITH_GPU=${WITH_GPU} \ 22 | -DUSE_TENSORRT=${USE_TENSORRT} \ 23 | -DWITH_STATIC_LIB=OFF \ 24 | -DPADDLE_LIB=${LIB_DIR} 25 | 26 | make -j 27 | 28 | # run 29 | cd .. 30 | 31 | ./build/test_seg \ 32 | --model_dir=./stdc1seg_infer_model \ 33 | --img_path=./cityscapes_demo.png \ 34 | --devices=GPU 35 | -------------------------------------------------------------------------------- /deploy/lite/README.md: -------------------------------------------------------------------------------- 1 | Use Paddle Lite to deploy inference model on Android mobile phone, please refer to [document](../../docs/deployment/lite/lite.md). 2 | 3 | 使用Paddle Lite在安卓手机上部署预测模型，请参考[文档](../../docs/deployment/lite/lite_cn.md). 4 | -------------------------------------------------------------------------------- /deploy/lite/example/human_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/deploy/lite/example/human_1.png -------------------------------------------------------------------------------- /deploy/lite/example/human_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/deploy/lite/example/human_2.png -------------------------------------------------------------------------------- /deploy/lite/example/human_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/deploy/lite/example/human_3.png -------------------------------------------------------------------------------- /deploy/lite/human_segmentation_demo/.gitignore: -------------------------------------------------------------------------------- 1 | *.iml 2 | .gradle 3 | /local.properties 4 | /.idea/caches 5 | /.idea/libraries 6 | /.idea/modules.xml 7 | /.idea/workspace.xml 8 | /.idea/navEditor.xml 9 | /.idea/assetWizardSettings.xml 10 | .DS_Store 11 | /build 12 | /captures 13 | .externalNativeBuild 14 | -------------------------------------------------------------------------------- /deploy/lite/human_segmentation_demo/app/.gitignore: -------------------------------------------------------------------------------- 1 | /build 2 | -------------------------------------------------------------------------------- /deploy/lite/human_segmentation_demo/app/local.properties: -------------------------------------------------------------------------------- 1 | ## This file must *NOT* be checked into Version Control Systems, 2 | # as it contains information specific to your local configuration. 3 | # 4 | # Location of the SDK. This is only used by Gradle. 5 | # For customization when using a Version Control System, please read the 6 | # header note. 7 | #Mon Nov 25 17:01:52 CST 2019 8 | sdk.dir=/Users/chenlingchi/Library/Android/sdk 9 | -------------------------------------------------------------------------------- /deploy/lite/human_segmentation_demo/app/src/main/assets/image_segmentation/images/human.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/deploy/lite/human_segmentation_demo/app/src/main/assets/image_segmentation/images/human.jpg -------------------------------------------------------------------------------- /deploy/lite/human_segmentation_demo/app/src/main/assets/image_segmentation/labels/label_list: -------------------------------------------------------------------------------- 1 | background 2 | human 3 | 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11 | */ 12 | public class ExampleUnitTest { 13 | @Test 14 | public void addition_isCorrect() { 15 | assertEquals(4, 2 + 2); 16 | } 17 | } -------------------------------------------------------------------------------- /deploy/lite/human_segmentation_demo/gradle/wrapper/gradle-wrapper.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/deploy/lite/human_segmentation_demo/gradle/wrapper/gradle-wrapper.jar -------------------------------------------------------------------------------- /deploy/lite/human_segmentation_demo/gradle/wrapper/gradle-wrapper.properties: -------------------------------------------------------------------------------- 1 | #Thu Aug 22 15:05:37 CST 2019 2 | distributionBase=GRADLE_USER_HOME 3 | distributionPath=wrapper/dists 4 | zipStoreBase=GRADLE_USER_HOME 5 | zipStorePath=wrapper/dists 6 | distributionUrl=https\://services.gradle.org/distributions/gradle-5.1.1-all.zip 7 | -------------------------------------------------------------------------------- /deploy/lite/human_segmentation_demo/settings.gradle: -------------------------------------------------------------------------------- 1 | include ':app' 2 | -------------------------------------------------------------------------------- /deploy/python/README.md: -------------------------------------------------------------------------------- 1 | Please refer to the [tutorial](../../docs/deployment/inference/python_inference.md) for Python deployment using Paddle Inference. 2 | -------------------------------------------------------------------------------- /deploy/serving/README.md: -------------------------------------------------------------------------------- 1 | Please refer to the [tutorial](../../docs/deployment/serving/serving.md) for deployment using Paddle Serving. 2 | -------------------------------------------------------------------------------- /deploy/web/README.md: -------------------------------------------------------------------------------- 1 | Use Paddle.js to deploy inference model on web, please refer to [document](../../docs/deployment/web/web.md). 2 | 3 | 使用Paddle.js在网页上部署预测模型，请参考[文档](../../docs/deployment/web/web_cn.md). 4 | -------------------------------------------------------------------------------- /deploy/web/example/bg/bg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/deploy/web/example/bg/bg.jpg -------------------------------------------------------------------------------- /docs/README.md: -------------------------------------------------------------------------------- 1 | # PaddleSeg Document 2 | All PaddleSeg tutorials are organized as the format of Read the Docs. 3 | -------------------------------------------------------------------------------- /docs/apis/README.md: -------------------------------------------------------------------------------- 1 | English | [简体中文](README_CN.md) 2 | ## Data Transformation (Data Augmentation) 3 | [paddleseg.transforms](./transforms/transforms.md) 4 | 5 | ## Dataset Processing 6 | [paddleseg.datasets](./datasets/datasets.md) 7 | 8 | ## Semantic Segmentation Model Set 9 | [paddleseg.models](./models/models.md) 10 | 11 | ## Backbone Networks 12 | [paddleseg.models.backbone](./backbones/backbones.md) 13 | 14 | ## Training,Evaluating and Predicting 15 | [paddleseg.core](./core/core.md) 16 | 17 | ## Computer Vision Library 18 | [paddleseg.cvlibs](./cvlibs/cvlibs.md) 19 | -------------------------------------------------------------------------------- /docs/apis/README_CN.md: -------------------------------------------------------------------------------- 1 | 简体中文 | [English](README.md) 2 | ## 数据变换（数据增强） 3 | [paddleseg.transforms](./transforms/transforms_cn.md) 4 | 5 | ## 数据集处理 6 | [paddleseg.datasets](./datasets/datasets_cn.md) 7 | 8 | ## 语义分割模型集 9 | [paddleseg.models](./models/models_cn.md) 10 | 11 | ## 骨干网络 12 | [paddleseg.models.backbone](./backbones/backbones_cn.md) 13 | 14 | ## 训练、评估和预测 15 | [paddleseg.core](./core/core_cn.md) 16 | 17 | ## 计算机视觉库 18 | [paddleseg.cvlibs](./cvlibs/cvlibs_cn.md) 19 | -------------------------------------------------------------------------------- /docs/apis/backbones/index.rst: -------------------------------------------------------------------------------- 1 | 骨干网络 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | backbones.md 8 | -------------------------------------------------------------------------------- /docs/apis/core/index.rst: -------------------------------------------------------------------------------- 1 | 训练、评估和预测 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | core.md 8 | -------------------------------------------------------------------------------- /docs/apis/cvlibs/index.rst: -------------------------------------------------------------------------------- 1 | 视觉通用工具类 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | cvlibs.md 8 | -------------------------------------------------------------------------------- /docs/apis/index.rst: -------------------------------------------------------------------------------- 1 | API接口说明 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | transforms/index.rst 8 | models/index.rst 9 | backbones/index.rst 10 | core/index.rst 11 | cvlibs/index.rst 12 | -------------------------------------------------------------------------------- /docs/apis/losses/index.rst: -------------------------------------------------------------------------------- 1 | 损失函数 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | losses.md 8 | -------------------------------------------------------------------------------- /docs/apis/models/index.rst: -------------------------------------------------------------------------------- 1 | 视觉模型集 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | models.md 8 | -------------------------------------------------------------------------------- /docs/apis/transforms/index.rst: -------------------------------------------------------------------------------- 1 | 数据处理与增强 2 | ============================ 3 | 4 | transforms为PaddleSeg的模型训练提供了数据的预处理和数据增强接口。 5 | 6 | .. toctree:: 7 | :maxdepth: 1 8 | 9 | transforms.md 10 | datasets.md 11 | -------------------------------------------------------------------------------- /docs/data/README.md: -------------------------------------------------------------------------------- 1 | Coming 2 | -------------------------------------------------------------------------------- /docs/data/custom/index.rst: 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Fast-SCNN结构图
8 | 9 | 具体原理细节请参考[Fast-SCNN: Fast Semantic Segmentation Network](https://arxiv.org/abs/1902.04502) 10 | -------------------------------------------------------------------------------- /docs/models/images/Fast-SCNN.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/docs/models/images/Fast-SCNN.png -------------------------------------------------------------------------------- /docs/models/images/OCRNet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/docs/models/images/OCRNet.png -------------------------------------------------------------------------------- /docs/models/images/UNet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/docs/models/images/UNet.png -------------------------------------------------------------------------------- /docs/models/images/convolution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/docs/models/images/convolution.png -------------------------------------------------------------------------------- /docs/models/images/deeplabv3+.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/docs/models/images/deeplabv3+.png -------------------------------------------------------------------------------- /docs/models/index.rst: -------------------------------------------------------------------------------- 1 | 经典模型 2 | ======================================= 3 | 4 | 常见的四种经典模型 5 | 6 | 7 | .. toctree:: 8 | :maxdepth: 1 9 | :caption: 文档目录: 10 | 11 | deeplabv3.md 12 | fascnn.md 13 | ocrnet.md 14 | unet.md 15 | -------------------------------------------------------------------------------- /docs/models/unet_cn.md: -------------------------------------------------------------------------------- 1 | # U-Net 2 | 3 | U-Net [1] 起源于医疗图像分割，具有参数少、计算快、应用性强的特点，对于一般场景适应度很高。U-Net最早于2015年提出，并在ISBI 2015 Cell Tracking Challenge取得了第一。经过发展，目前有多个变形和应用。 4 | 原始U-Net的结构是标准的编码器-解码器结构。如下图所示，左侧可视为一个编码器，右侧可视为一个解码器。编码器由四个子模块组成，每个子模块包含两个卷积层，每个子模块之后又通过max pool进行下采样。编码器整体呈现逐渐缩小的结构，不断减少池化层的空间维度，缩小特征图的分辨率，以捕获上下文信息。 5 | 解码器呈现与编码器对称的扩张结构，逐步修复分割对象的细节和空间维度，实现精准的定位。解码器同样也包含四个子模块，分辨率通过上采样操作依次增大，直到与输入图像的分辨率基本一致。 6 | 该网络还使用了跳跃连接，即解码器每上采样一次，就以拼接的方式将解码器和编码器中对应相同分辨率的特征图进行特征融合，帮助解码器更好地恢复目标的细节。由于网络整体结构类似于大写的英文字母U，故得名U-Net。 7 | 8 | ![](./images/UNet.png) 9 | 10 |
U-Net结构图
11 | 12 | 具体原理细节请参考[U-Net:Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)。 13 | -------------------------------------------------------------------------------- /docs/module/data/index.rst: -------------------------------------------------------------------------------- 1 | 数据增强 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | data.md 8 | -------------------------------------------------------------------------------- /docs/module/images/Lovasz_Hinge_Evaluate_mIoU.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/docs/module/images/Lovasz_Hinge_Evaluate_mIoU.png -------------------------------------------------------------------------------- /docs/module/images/Lovasz_Softmax_Evaluate_mIoU.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /docs/module/loss/DualTaskLoss_cn.md: -------------------------------------------------------------------------------- 1 | 简体中文 | [English](DualTaskLoss_en.md) 2 | ## [DualTaskLoss](../../../paddleseg/models/losses/gscnn_dual_task_loss.py) 3 | 用于为半监督学习的 Dual-task 一致性以对模型进行约束。DualTaskLoss 旨在强化多个任务之间的一致性。 4 | 5 | ```python 6 | class paddleseg.models.losses.DualTaskLoss( 7 | ignore_index = 255, 8 | tau = 0.5 9 | ) 10 | ``` 11 | 12 | ## Dual task loss 使用指南 13 | 14 | ### 参数 15 | * **ignore_index** (int64): 指定一个在标注图中要忽略的像素值，其对输入梯度不产生贡献。当标注图中存在无法标注（或很难标注）的像素时，可以将其标注为某特定灰度值。在计算损失值时，其与原图像对应位置的像素将不作为损失函数的自变量。 *默认:``255``* 16 | * **tau** (float): Gumbel softmax 样本的tau。 17 | -------------------------------------------------------------------------------- /docs/module/loss/EdgeAttentionLoss_cn.md: -------------------------------------------------------------------------------- 1 | 简体中文 | [English](EdgeAttentionLoss_en.md) 2 | ## [EdgeAttentionLoss](../../../paddleseg/models/losses/edge_attention_loss.py) 3 | 适合以 encoder 提取edge，以 decoder 进行加权聚合的多任务训练场景。是一种融合边缘检测与注意力机制进行多 loss 的组合输出的方法。 4 | 5 | ```python 6 | class paddleseg.models.losses.EdgeAttentionLoss( 7 | edge_threshold = 0.8, 8 | ignore_index = 255 9 | ) 10 | ``` 11 | 12 | ## Edge attention loss 使用指南 13 | 14 | ### 参数 15 | * **edge_threshold** (float): 值大于 edge_threshold 的像素被视为边缘。 16 | * **ignore_index** (int64): 指定一个在标注图中要忽略的像素值，其对输入梯度不产生贡献。当标注图中存在无法标注（或很难标注）的像素时，可以将其标注为某特定灰度值。在计算损失值时，其与原图像对应位置的像素将不作为损失函数的自变量。 *默认:``255``* 17 | -------------------------------------------------------------------------------- /docs/module/loss/LovaszHingeLoss_cn.md: -------------------------------------------------------------------------------- 1 | 简体中文 | [English](LovaszHingeLoss_en.md) 2 | ## [LovaszHingeLoss](../../../paddleseg/models/losses/lovasz_loss.py) 3 | Hinge Loss是在不连续、不平滑的简单阶梯损失函数上改进的一种损失函数。对于正样本，Hinge Loss的输出应大于等于1；对于正样本，Hinge Loss的输出应小于等于-1。 4 | 5 | ```python 6 | class paddleseg.models.losses.LovaszHingeLoss(ignore_index = 255) 7 | ``` 8 | 9 | ## Binary Lovasz hinge loss使用指南 10 | 11 | ### 参数 12 | * **ignore_index** (int64): 指定一个在标注图中要忽略的像素值，其对输入梯度不产生贡献。当标注图中存在无法标注（或很难标注）的像素时，可以将其标注为某特定灰度值。在计算损失值时，其与原图像对应位置的像素将不作为损失函数的自变量。 *默认:``255``* 13 | -------------------------------------------------------------------------------- /docs/module/loss/MixedLoss_cn.md: -------------------------------------------------------------------------------- 1 | 简体中文 | [English](MixedLoss_en.md) 2 | ## [MixedLoss](../../../paddleseg/models/losses/mixed_loss.py) 3 | 4 | 实现混合loss训练。PaddleSeg每一种损失函数对应网络的一个logit 输出，如果要某个网络输出应用多种损失函数需要修改网络代码。MixedLoss 将允许网络对多个损失函数结果进行加权计算，只需以模块化的形式装入，就可以实现混合loss训练。 5 | 6 | ```python 7 | class paddleseg.models.losses.MixedLoss(losses, coef) 8 | ``` 9 | 10 | 11 | ## Mixed loss使用指南 12 | 13 | ### 参数 14 | * **losses** (list of nn.Layer): 由多个损失函数类所组成的列表。 15 | * **coef** (float|int): 每个损失函数类的权重比。 16 | 17 | ### 返回值 18 | * MixedLoss 类的可调用对象。 19 | -------------------------------------------------------------------------------- /docs/module/loss/index.rst: -------------------------------------------------------------------------------- 1 | loss说明 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | lovasz_loss.md 8 | -------------------------------------------------------------------------------- /docs/module/tricks/index.rst: -------------------------------------------------------------------------------- 1 | tricks 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | tricks.md 8 | -------------------------------------------------------------------------------- /docs/module/tricks/tricks.md: -------------------------------------------------------------------------------- 1 | # tricks 2 | 3 | coming soon! 4 | -------------------------------------------------------------------------------- /docs/paddleseg.png: 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-------------------------------------------------------------------------------- 1 | 模型蒸馏 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | distill.md 8 | -------------------------------------------------------------------------------- /docs/slim/prune/index.rst: -------------------------------------------------------------------------------- 1 | 模型裁剪 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | prune.md 8 | -------------------------------------------------------------------------------- /docs/slim/quant/index.rst: -------------------------------------------------------------------------------- 1 | 模型量化 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | quant.md 8 | -------------------------------------------------------------------------------- /docs/static/static.md: -------------------------------------------------------------------------------- 1 | English | [简体中文](static_cn.md) 2 | 3 | # PaddleSeg Static Graph 4 | 5 | After release/2.3, PaddleSeg will not keep the ```legacy``` directory that is for static graph. Please find it in [release/2.2](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.2/legacy) or before, if you need. 6 | -------------------------------------------------------------------------------- /docs/static/static_cn.md: -------------------------------------------------------------------------------- 1 | 简体中文 | [English](static.md) 2 | 3 | # PaddleSeg静态图 4 | 5 | 为了提供更好的动态图开发功能，从release/2.3起，PaddleSeg将不再维护静态图版本。静态图版本在[release/2.2](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.2/legacy)以及之前的分支继续保留。 6 | -------------------------------------------------------------------------------- /docs/train/index.rst: -------------------------------------------------------------------------------- 1 | 模型训练 2 | ============================ 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | train.md -------------------------------------------------------------------------------- /myconfig/segformer-b2-rs.yml: -------------------------------------------------------------------------------- 1 | _base_: 'rs.yml' 2 | 3 | batch_size: 16 4 | iters: 480000 5 | 6 | model: 7 | type: SegFormer_B2 8 | num_classes: 4 9 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/mix_vision_transformer_b2.tar.gz 10 | 11 | optimizer: 12 | type: AdamW 13 | beta1: 0.9 14 | beta2: 0.999 15 | weight_decay: 0.01 16 | 17 | lr_scheduler: 18 | type: PolynomialDecay 19 | learning_rate: 0.00006 20 | power: 1 21 | 22 | loss: 23 | types: 24 | - type: MixedLoss 25 | losses: 26 | - type: CrossEntropyLoss 27 | - type: LovaszSoftmaxLoss 28 | coef: [0.8, 0.2] 29 | coef: [1] -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pyyaml >= 5.1 2 | visualdl >= 2.0.0 3 | opencv-python 4 | tqdm 5 | filelock 6 | scipy 7 | prettytable 8 | sklearn 9 | -------------------------------------------------------------------------------- /slim/distill/README.md: -------------------------------------------------------------------------------- 1 | Please refer to the [tutorial](../../docs/slim/distill/distill.md) for model distillation. 2 | -------------------------------------------------------------------------------- /slim/prune/README.md: -------------------------------------------------------------------------------- 1 | Please refer to the [tutorial](../../docs/slim/prune/prune.md) for model pruning. 2 | -------------------------------------------------------------------------------- /slim/quant/README.md: -------------------------------------------------------------------------------- 1 | Please refer to the [tutorial](../../docs/slim/quant/quant.md) for model quantization. 2 | -------------------------------------------------------------------------------- /test_tipc/configs/_base_/cityscapes_1024x1024.yml: -------------------------------------------------------------------------------- 1 | _base_: './cityscapes.yml' 2 | 3 | train_dataset: 4 | transforms: 5 | - type: ResizeStepScaling 6 | min_scale_factor: 0.5 7 | max_scale_factor: 2.0 8 | scale_step_size: 0.25 9 | - type: RandomPaddingCrop 10 | crop_size: [1024, 1024] 11 | - type: RandomHorizontalFlip 12 | - type: RandomDistort 13 | brightness_range: 0.4 14 | contrast_range: 0.4 15 | saturation_range: 0.4 16 | - type: Normalize 17 | 18 | val_dataset: 19 | transforms: 20 | - type: Normalize 21 | 22 | export: 23 | transforms: 24 | - type: Resize 25 | target_size: [512, 512] 26 | - type: Normalize 27 | -------------------------------------------------------------------------------- /test_tipc/configs/_base_/cityscapes_769x769_setr.yml: -------------------------------------------------------------------------------- 1 | _base_: './cityscapes.yml' 2 | 3 | train_dataset: 4 | transforms: 5 | - type: ResizeStepScaling 6 | min_scale_factor: 0.25 7 | max_scale_factor: 2.0 8 | scale_step_size: 0.25 9 | - type: RandomPaddingCrop 10 | crop_size: [769, 769] 11 | - type: RandomHorizontalFlip 12 | - type: RandomDistort 13 | brightness_range: 0.5 14 | contrast_range: 0.5 15 | saturation_range: 0.5 16 | - type: Normalize 17 | 18 | val_dataset: 19 | transforms: 20 | - type: Padding 21 | target_size: [2048, 1024] 22 | - type: Normalize 23 | -------------------------------------------------------------------------------- /test_tipc/configs/_base_/pascal_voc12aug.yml: -------------------------------------------------------------------------------- 1 | _base_: './pascal_voc12.yml' 2 | 3 | train_dataset: 4 | mode: trainaug 5 | -------------------------------------------------------------------------------- /test_tipc/configs/bisenetv1/bisenetv1_resnet18_os8_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 4 4 | iters: 160000 5 | 6 | model: 7 | type: BiseNetV1 8 | backbone: 9 | type: ResNet18_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet18_vd_ssld_v2.tar.gz 12 | 13 | optimizer: 14 | type: sgd 15 | weight_decay: 0.0005 16 | 17 | loss: 18 | types: 19 | - type: OhemCrossEntropyLoss 20 | - type: OhemCrossEntropyLoss 21 | - type: OhemCrossEntropyLoss 22 | coef: [1, 1, 1] 23 | 24 | lr_scheduler: 25 | type: PolynomialDecay 26 | learning_rate: 0.01 27 | end_lr: 0.0 28 | power: 0.9 29 | -------------------------------------------------------------------------------- /test_tipc/configs/bisenetv2/bisenet_cityscapes_1024x1024_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | model: 4 | type: BiSeNetV2 5 | num_classes: 19 6 | 7 | optimizer: 8 | type: sgd 9 | weight_decay: 0.0005 10 | 11 | loss: 12 | types: 13 | - type: CrossEntropyLoss 14 | - type: CrossEntropyLoss 15 | - type: CrossEntropyLoss 16 | - type: CrossEntropyLoss 17 | - type: CrossEntropyLoss 18 | coef: [1, 1, 1, 1, 1] 19 | 20 | batch_size: 4 21 | iters: 160000 22 | 23 | lr_scheduler: 24 | type: PolynomialDecay 25 | learning_rate: 0.05 26 | end_lr: 0.0 27 | power: 0.9 28 | -------------------------------------------------------------------------------- /test_tipc/configs/ccnet/ccnet_resnet101_os8_cityscapes_769x769_60k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_769x769.yml' 2 | 3 | batch_size: 2 4 | iters: 60000 5 | 6 | model: 7 | type: CCNet 8 | backbone: 9 | type: ResNet101_vd 10 | output_stride: 8 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet101_vd_ssld.tar.gz 12 | backbone_indices: [2, 3] 13 | enable_auxiliary_loss: True 14 | dropout_prob: 0.1 15 | recurrence: 2 16 | 17 | loss: 18 | types: 19 | - type: OhemCrossEntropyLoss 20 | - type: CrossEntropyLoss 21 | coef: [1, 0.4] 22 | 23 | lr_scheduler: 24 | type: PolynomialDecay 25 | learning_rate: 0.01 26 | power: 0.9 27 | end_lr: 1.0e-4 28 | -------------------------------------------------------------------------------- /test_tipc/configs/ddrnet/ddrnet23_cityscapes_1024x1024_120k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes_1024x1024.yml' 2 | 3 | model: 4 | type: DDRNet_23 5 | enable_auxiliary_loss: False 6 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/cityscapes/ddrnet23_cityscapes_1024x1024_120k/pretrain/model.pdparams 7 | 8 | optimizer: 9 | type: sgd 10 | weight_decay: 0.0005 11 | 12 | loss: 13 | types: 14 | - type: OhemCrossEntropyLoss 15 | coef: [1] 16 | 17 | batch_size: 3 18 | iters: 120000 19 | 20 | lr_scheduler: 21 | type: PolynomialDecay 22 | learning_rate: 0.01 23 | end_lr: 0.0 24 | power: 0.9 25 | -------------------------------------------------------------------------------- /test_tipc/configs/deeplabv3p_resnet50/deeplabv3p_resnet50_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name deeplabv3p_resnet50 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/pp_humanseg_server_export_512x512/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/pp_humanseg_server_export_512x512/model.pdiparams 14 | is_normalize 1 15 | is_resize 1 16 | resize_width 512 17 | resize_height 512 18 | 19 | -------------------------------------------------------------------------------- /test_tipc/configs/fcn_hrnetw18/fcn_hrnetw18_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name fcn_hrnetw18 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/fcn_hrnetw18_cityscapes_1024x512_80k/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/fcn_hrnetw18_cityscapes_1024x512_80k/model.pdiparams 14 | is_normalize 1 15 | is_resize 0 16 | 17 | -------------------------------------------------------------------------------- /test_tipc/configs/fcn_hrnetw18/fcn_hrnetw18_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt: -------------------------------------------------------------------------------- 1 | ===========================paddle2onnx_params=========================== 2 | model_name:fcn_hrnetw18 3 | python:python3.7 4 | 2onnx: paddle2onnx 5 | --model_dir:./test_tipc/infer_models/fcn_hrnetw18_cityscapes_1024x512_80k/ 6 | --model_filename:model.pdmodel 7 | --params_filename:model.pdiparams 8 | --save_file:./test_tipc/infer_models/fcn_hrnetw18_cityscapes_1024x512_80k/model.onnx 9 | --opset_version:11 10 | --enable_onnx_checker:True 11 | inference:deploy/python/infer_onnx.py 12 | --onnx_file:./test_tipc/infer_models/fcn_hrnetw18_cityscapes_1024x512_80k/model.onnx 13 | --img_path:test_tipc/cpp/cityscapes_demo.png -------------------------------------------------------------------------------- /test_tipc/configs/fcn_hrnetw18/train_ptq_infer_python.txt: -------------------------------------------------------------------------------- 1 | ===========================ptq_params=========================== 2 | model_name:fcn_hrnetw18_KL 3 | python:python3.7 4 | ## 5 | --model_dir:test_tipc/output/fcn_hrnetw18_KL/fcn_hrnetw18_cityscapes_1024x512_80k 6 | ## 7 | --config:test_tipc/configs/fcn_hrnetw18/fcn_hrnetw18_1024x512_cityscapes.yml 8 | --batch_num:1 9 | --batch_size:1 10 | ## 11 | trainer:PTQ 12 | PTQ:slim/quant/ptq.py 13 | ## 14 | ===========================infer_params=========================== 15 | inference:deploy/python/infer.py 16 | --device:cpu|gpu 17 | --batch_size:1 18 | --config:quant_model/deploy.yaml 19 | --image_path:test_tipc/cpp/cityscapes_demo.png 20 | --benchmark:True -------------------------------------------------------------------------------- /test_tipc/configs/fcn_hrnetw18_small/fcn_hrnetw18_small_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name fcn_hrnetw18_small 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/pp_humanseg_mobile_export_192x192/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/pp_humanseg_mobile_export_192x192/model.pdiparams 14 | is_normalize 1 15 | is_resize 1 16 | resize_width 192 17 | resize_height 192 18 | 19 | -------------------------------------------------------------------------------- /test_tipc/configs/glore/glore_resnet50_os8_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 80000 5 | 6 | learning_rate: 7 | decay: 8 | end_lr: 1.0e-5 9 | 10 | loss: 11 | types: 12 | - type: CrossEntropyLoss 13 | coef: [1, 0.4] 14 | 15 | model: 16 | type: GloRe 17 | backbone: 18 | type: ResNet50_vd 19 | output_stride: 8 20 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 21 | enable_auxiliary_loss: True 22 | align_corners: False 23 | pretrained: null 24 | -------------------------------------------------------------------------------- /test_tipc/configs/ocrnet_hrnetw18/ocrnet_hrnetw18_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 2 4 | iters: 160000 5 | 6 | model: 7 | type: OCRNet 8 | backbone: 9 | type: HRNet_W18 10 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w18_ssld.tar.gz 11 | num_classes: 19 12 | backbone_indices: [0] 13 | 14 | optimizer: 15 | type: sgd 16 | 17 | lr_scheduler: 18 | type: PolynomialDecay 19 | learning_rate: 0.01 20 | power: 0.9 21 | 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | - type: CrossEntropyLoss 27 | coef: [1, 0.4] 28 | -------------------------------------------------------------------------------- /test_tipc/configs/ocrnet_hrnetw18/ocrnet_hrnetw18_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name ocrnet_hrnetw18 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/ocrnet_hrnetw18_cityscapes_1024x512_160k/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/ocrnet_hrnetw18_cityscapes_1024x512_160k/model.pdiparams 14 | is_normalize 1 15 | is_resize 0 16 | 17 | -------------------------------------------------------------------------------- /test_tipc/configs/ocrnet_hrnetw48/ocrnet_hrnetw48_cityscapes_1024x512.yml: -------------------------------------------------------------------------------- 1 | # The ocrnet_hrnetw48 config for train benchmark 2 | _base_: '../_base_/cityscapes.yml' 3 | 4 | batch_size: 2 5 | iters: 500 6 | 7 | model: 8 | type: OCRNet 9 | backbone: 10 | type: HRNet_W48 11 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz 12 | num_classes: 19 13 | backbone_indices: [0] 14 | 15 | optimizer: 16 | type: sgd 17 | 18 | lr_scheduler: 19 | type: PolynomialDecay 20 | learning_rate: 0.01 21 | power: 0.9 22 | 23 | loss: 24 | types: 25 | - type: CrossEntropyLoss 26 | - type: CrossEntropyLoss 27 | coef: [1, 0.4] 28 | -------------------------------------------------------------------------------- /test_tipc/configs/ocrnet_hrnetw48/ocrnet_hrnetw48_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name ocrnet_hrnetw48 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/ocrnet_hrnetw48_cityscapes_1024x512_160k/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/ocrnet_hrnetw48_cityscapes_1024x512_160k/model.pdiparams 14 | is_normalize 1 15 | is_resize 0 16 | 17 | -------------------------------------------------------------------------------- /test_tipc/configs/pp_liteseg_stdc1/pp_liteseg_stdc1_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name pp_liteseg_stdc1 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/pp_liteseg_infer_model/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/pp_liteseg_infer_model/model.pdiparams 14 | is_normalize 1 15 | is_resize 0 16 | 17 | -------------------------------------------------------------------------------- /test_tipc/configs/pp_liteseg_stdc1/pp_liteseg_stdc1_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt: -------------------------------------------------------------------------------- 1 | ===========================paddle2onnx_params=========================== 2 | model_name:pp_liteseg_stdc1 3 | python:python3.7 4 | 2onnx: paddle2onnx 5 | --model_dir:./test_tipc/infer_models/pp_liteseg_stdc1_fix_shape/ 6 | --model_filename:model.pdmodel 7 | --params_filename:model.pdiparams 8 | --save_file:./test_tipc/infer_models/pp_liteseg_stdc1_fix_shape/model.onnx 9 | --opset_version:11 10 | --enable_onnx_checker:True 11 | inference:deploy/python/infer_onnx.py 12 | --onnx_file:./test_tipc/infer_models/pp_liteseg_stdc1_fix_shape/model.onnx 13 | --img_path:test_tipc/cpp/cityscapes_demo.png -------------------------------------------------------------------------------- /test_tipc/configs/pp_liteseg_stdc2/pp_liteseg_stdc2_cityscapes_1024x512_160k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | model: 4 | type: PPLiteSeg 5 | backbone: 6 | type: STDC2 7 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz 8 | 9 | optimizer: 10 | type: sgd 11 | weight_decay: 0.0005 12 | 13 | loss: 14 | types: 15 | - type: OhemCrossEntropyLoss 16 | min_kept: 130000 # batch_size * 1024 * 512 // 16 17 | - type: OhemCrossEntropyLoss 18 | min_kept: 130000 19 | - type: OhemCrossEntropyLoss 20 | min_kept: 130000 21 | coef: [1, 1, 1] 22 | 23 | batch_size: 4 24 | iters: 160000 25 | 26 | lr_scheduler: 27 | learning_rate: 0.005 28 | -------------------------------------------------------------------------------- /test_tipc/configs/pp_liteseg_stdc2/pp_liteseg_stdc2_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name pp_liteseg_stdc2 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/pp_liteseg_stdc2_cityscapes_1024x512_scale1.0_160k/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/pp_liteseg_stdc2_cityscapes_1024x512_scale1.0_160k/model.pdiparams 14 | is_normalize 1 15 | is_resize 0 16 | 17 | -------------------------------------------------------------------------------- /test_tipc/configs/pp_liteseg_stdc2/pp_liteseg_stdc2_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt: -------------------------------------------------------------------------------- 1 | ===========================paddle2onnx_params=========================== 2 | model_name:pp_liteseg_stdc2 3 | python:python3.7 4 | 2onnx: paddle2onnx 5 | --model_dir:./test_tipc/infer_models/pp_liteseg_stdc2_fix_shape/ 6 | --model_filename:model.pdmodel 7 | --params_filename:model.pdiparams 8 | --save_file:./test_tipc/infer_models/pp_liteseg_stdc2_fix_shape/model.onnx 9 | --opset_version:11 10 | --enable_onnx_checker:True 11 | inference:deploy/python/infer_onnx.py 12 | --onnx_file:./test_tipc/infer_models/pp_liteseg_stdc2_fix_shape/model.onnx 13 | --img_path:test_tipc/cpp/cityscapes_demo.png -------------------------------------------------------------------------------- /test_tipc/configs/pphumanseg_lite/pphumanseg_lite_model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name pphumanseg_lite 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/pp_humanseg_lite_export_398x224/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/pp_humanseg_lite_export_398x224/model.pdiparams 14 | is_normalize 1 15 | is_resize 1 16 | resize_width 398 17 | resize_height 224 18 | 19 | -------------------------------------------------------------------------------- /test_tipc/configs/pphumanseg_lite/pphumanseg_lite_model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt: -------------------------------------------------------------------------------- 1 | ===========================paddle2onnx_params=========================== 2 | model_name:pp_humanseg_lite 3 | python:python3.7 4 | 2onnx: paddle2onnx 5 | --model_dir:./test_tipc/infer_models/pp_humanseg_lite_export_398x224/ 6 | --model_filename:model.pdmodel 7 | --params_filename:model.pdiparams 8 | --save_file:./test_tipc/infer_models/pp_humanseg_lite_export_398x224/model.onnx 9 | --opset_version:11 10 | --enable_onnx_checker:True 11 | inference:deploy/python/infer_onnx.py 12 | --onnx_file:./test_tipc/infer_models/pp_humanseg_lite_export_398x224/model.onnx 13 | --img_path:test_tipc/cpp/humanseg_demo.jpg -------------------------------------------------------------------------------- /test_tipc/configs/pphumanseg_lite/train_ptq_infer_python.txt: -------------------------------------------------------------------------------- 1 | ===========================ptq_params=========================== 2 | model_name:pp_humanseg_lite_KL 3 | python:python3.7 4 | ## 5 | --model_dir:test_tipc/output/pp_humanseg_lite_KL/pp_humanseg_lite_export_398x224 6 | ## 7 | --config:test_tipc/configs/pphumanseg_lite/pphumanseg_lite_mini_supervisely.yml 8 | --batch_num:1 9 | --batch_size:1 10 | ## 11 | trainer:PTQ 12 | PTQ:slim/quant/ptq.py 13 | ## 14 | ===========================infer_params=========================== 15 | inference:deploy/python/infer.py 16 | --device:cpu|gpu 17 | --batch_size:1 18 | --config:quant_model/deploy.yaml 19 | --image_path:test_tipc/cpp/humanseg_demo.jpg 20 | --benchmark:True -------------------------------------------------------------------------------- /test_tipc/configs/stdc_stdc1/inference_cpp.txt: -------------------------------------------------------------------------------- 1 | # model load config 2 | model_name stdc_stdc1 3 | use_gpu 0 4 | gpu_id 0 5 | gpu_mem 4000 6 | cpu_math_library_num_threads 10 7 | use_mkldnn 1 8 | use_tensorrt 0 9 | use_fp16 0 10 | 11 | # config 12 | model_path ./test_tipc/cpp/inference_models/stdc1seg_infer_model/model.pdmodel 13 | params_path ./test_tipc/cpp/inference_models/stdc1seg_infer_model/model.pdiparams 14 | is_normalize 1 15 | is_resize 0 16 | 17 | -------------------------------------------------------------------------------- /test_tipc/configs/stdc_stdc1/stdc1_seg_cityscapes_1024x512_80k.yml: -------------------------------------------------------------------------------- 1 | _base_: '../_base_/cityscapes.yml' 2 | 3 | batch_size: 12 4 | iters: 80000 5 | 6 | model: 7 | type: STDCSeg 8 | backbone: 9 | type: STDC1 10 | pretrained: https://bj.bcebos.com/paddleseg/dygraph/STDCNet1.tar.gz 11 | pretrained: null 12 | 13 | loss: 14 | types: 15 | - type: OhemCrossEntropyLoss 16 | - type: OhemCrossEntropyLoss 17 | - type: OhemCrossEntropyLoss 18 | - type: DetailAggregateLoss 19 | coef: [1, 1, 1, 1] 20 | -------------------------------------------------------------------------------- /test_tipc/cpp/cityscapes_demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/cpp/cityscapes_demo.png -------------------------------------------------------------------------------- /test_tipc/cpp/humanseg_demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/cpp/humanseg_demo.jpg -------------------------------------------------------------------------------- /test_tipc/docs/cityscapes_demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/docs/cityscapes_demo.jpg -------------------------------------------------------------------------------- /test_tipc/docs/compare_right.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/docs/compare_right.png -------------------------------------------------------------------------------- /test_tipc/docs/compare_wrong.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/docs/compare_wrong.png -------------------------------------------------------------------------------- /test_tipc/docs/guide.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/docs/guide.png -------------------------------------------------------------------------------- /test_tipc/docs/test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/docs/test.png -------------------------------------------------------------------------------- /test_tipc/requirements.txt: -------------------------------------------------------------------------------- 1 | pre-commit 2 | yapf == 0.26.0 3 | flake8 4 | pyyaml >= 5.1 5 | visualdl >= 2.0.0 6 | opencv-python == 4.5.2.54 7 | tqdm 8 | filelock 9 | scipy 10 | prettytable 11 | paddleseg 12 | scikit-image 13 | numba 14 | pymatting 15 | -------------------------------------------------------------------------------- /test_tipc/results/python_fcn_hrnetw18_small_results_fp16.txt: -------------------------------------------------------------------------------- 1 | #Images: 50 2 | mIoU: 0.8725311260007227 3 | Acc: 0.9448749852040087 4 | Kappa: 0.9448749490413363 5 | Class IoU: [0.92665219 0.81841007] 6 | Class Acc: [0.97443367 0.87082671] 7 | -------------------------------------------------------------------------------- /test_tipc/results/python_fcn_hrnetw18_small_results_fp32.txt: -------------------------------------------------------------------------------- 1 | #Images: 50 2 | mIoU: 0.8725311260007227 3 | Acc: 0.9448749852040087 4 | Kappa: 0.9448749490413363 5 | Class IoU: [0.92665219 0.81841007] 6 | Class Acc: [0.97443367 0.87082671] 7 | -------------------------------------------------------------------------------- /test_tipc/test_infer_js.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -o errexit 4 | set -o nounset 5 | 6 | cd test_tipc/web 7 | # run humanseg test in chrome 8 | ./node_modules/.bin/jest --config ./jest.config.js -------------------------------------------------------------------------------- /test_tipc/web/imgs/human.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/web/imgs/human.jpg -------------------------------------------------------------------------------- /test_tipc/web/imgs/seg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lzzzzzm/Classification-RSImage/9cf360956d2cefb28e3507984a3f3a38f50de916/test_tipc/web/imgs/seg.png -------------------------------------------------------------------------------- /test_tipc/web/jest-puppeteer.config.js: -------------------------------------------------------------------------------- 1 | // jest-puppeteer.config.js 2 | module.exports = { 3 | launch: { 4 | headless: false, 5 | product: 'chrome' 6 | }, 7 | browserContext: 'default', 8 | server: { 9 | command: 'python3 -m http.server 9811', 10 | port: 9811, 11 | launchTimeout: 10000, 12 | debug: true 13 | } 14 | }; --------------------------------------------------------------------------------