├── mmsegmentation ├── mmsegmentation.egg-info │ ├── not-zip-safe │ ├── dependency_links.txt │ └── top_level.txt ├── docs │ ├── zh_cn │ │ ├── user_guides │ │ │ ├── deployment.md │ │ │ └── index.rst │ │ ├── imgs │ │ │ ├── zhihu_qrcode.jpg │ │ │ ├── qq_group_qrcode.jpg │ │ │ └── seggroup_qrcode.jpg │ │ ├── migration │ │ │ └── index.rst │ │ ├── _static │ │ │ ├── images │ │ │ │ └── mmsegmentation.png │ │ │ └── css │ │ │ │ └── readthedocs.css │ │ └── switch_language.md │ └── en │ │ ├── _static │ │ ├── images │ │ │ └── mmsegmentation.png │ │ └── css │ │ │ └── readthedocs.css │ │ ├── migration │ │ └── index.rst │ │ ├── switch_language.md │ │ └── user_guides │ │ └── index.rst ├── tests │ ├── data │ │ ├── pseudo_dataset │ │ │ ├── splits │ │ │ │ ├── val.txt │ │ │ │ └── train.txt │ │ │ ├── gts │ │ │ │ ├── 00000_gt.png │ │ │ │ ├── 00001_gt.png │ │ │ │ ├── 00002_gt.png │ │ │ │ ├── 00003_gt.png │ │ │ │ └── 00004_gt.png │ │ │ └── imgs │ │ │ │ ├── 00000_img.jpg │ │ │ │ ├── 00001_img.jpg │ │ │ │ ├── 00002_img.jpg │ │ │ │ ├── 00003_img.jpg │ │ │ │ └── 00004_img.jpg │ │ ├── pseudo_isaid_dataset │ │ │ ├── img_dir │ │ │ │ ├── P0000_0_896_1024_1920.png │ │ │ │ └── P0000_0_896_1536_2432.png │ │ │ ├── splits │ │ │ │ ├── val.txt │ │ │ │ └── train.txt │ │ │ └── ann_dir │ │ │ │ ├── P0000_0_896_1024_1920_instance_color_RGB.png │ │ │ │ └── P0000_0_896_1536_2432_instance_color_RGB.png │ │ ├── seg.png │ │ ├── color.jpg │ │ ├── gray.jpg │ │ ├── biomedical.npy │ │ ├── biomedical.pkl │ │ ├── biomedical.nii.gz │ │ ├── biomedical_ann.nii.gz │ │ ├── pseudo_loveda_dataset │ │ │ ├── ann_dir │ │ │ │ ├── 0.png │ │ │ │ ├── 1.png │ │ │ │ └── 2.png │ │ │ └── img_dir │ │ │ │ ├── 0.png │ │ │ │ ├── 1.png │ │ │ │ └── 2.png │ │ ├── pseudo_lip_dataset │ │ │ ├── val_images │ │ │ │ └── 86_185913.jpg │ │ │ ├── train_images │ │ │ │ └── 684_2150041.jpg │ │ │ ├── val_segmentations │ │ │ │ └── 86_185913.png │ │ │ └── train_segmentations │ │ │ │ └── 684_2150041.png │ │ ├── pseudo_refuge_dataset │ │ │ ├── ann_dir │ │ │ │ └── pseudo_g0001.png │ │ │ └── img_dir │ │ │ │ └── pseudo_g0001.png │ │ ├── pseudo_potsdam_dataset │ │ │ ├── ann_dir │ │ │ │ └── 2_10_0_0_512_512.png │ │ │ └── img_dir │ │ │ │ └── 2_10_0_0_512_512.png │ │ ├── pseudo_synapse_dataset │ │ │ ├── ann_dir │ │ │ │ ├── case0005_slice000.png │ │ │ │ └── case0005_slice001.png │ │ │ └── img_dir │ │ │ │ ├── case0005_slice000.jpg │ │ │ │ └── case0005_slice001.jpg │ │ ├── pseudo_vaihingen_dataset │ │ │ ├── ann_dir │ │ │ │ └── area1_0_0_512_512.png │ │ │ └── img_dir │ │ │ │ └── area1_0_0_512_512.png │ │ ├── pseudo_mapillary_dataset │ │ │ ├── v1.2 │ │ │ │ └── __CRyFzoDOXn6unQ6a3DnQ.png │ │ │ ├── v2.0 │ │ │ │ └── __CRyFzoDOXn6unQ6a3DnQ.png │ │ │ └── images │ │ │ │ └── __CRyFzoDOXn6unQ6a3DnQ.jpg │ │ └── pseudo_cityscapes_dataset │ │ │ ├── gtFine │ │ │ └── val │ │ │ │ └── frankfurt │ │ │ │ ├── frankfurt_000000_000294_gtFine_labelIds.png │ │ │ │ ├── frankfurt_000000_000294_gtFine_instanceIds.png │ │ │ │ └── frankfurt_000000_000294_gtFine_labelTrainIds.png │ │ │ └── leftImg8bit │ │ │ └── val │ │ │ └── frankfurt │ │ │ └── frankfurt_000000_000294_leftImg8bit.png │ ├── __init__.py │ └── test_models │ │ ├── __init__.py │ │ ├── test_heads │ │ └── __init__.py │ │ ├── test_necks │ │ └── __init__.py │ │ ├── test_utils │ │ └── __init__.py │ │ ├── test_backbones │ │ └── __init__.py │ │ └── test_segmentors │ │ └── __init__.py ├── requirements │ ├── optional.txt │ ├── mminstall.txt │ ├── runtime.txt │ ├── tests.txt │ ├── readthedocs.txt │ └── docs.txt ├── demo │ └── demo.png ├── resources │ ├── 3dogs.jpg │ ├── seg_demo.gif │ ├── test_step.png │ ├── 3dogs_mask.png │ ├── mmseg-logo.png │ ├── train_step.png │ ├── encoder_decoder_dataflow.png │ └── cascade_encoder_decoder_dataflow.png ├── projects │ ├── isnet │ │ └── decode_heads │ │ │ └── __init__.py │ ├── sam_inference_demo │ │ └── sam │ │ │ ├── __init__.py │ │ │ └── utils │ │ │ └── __init__.py │ ├── example_project │ │ ├── dummy │ │ │ └── __init__.py │ │ └── configs │ │ │ └── fcn_dummy-r50-d8_4xb2-40k_cityscapes-512x1024.py │ └── hssn │ │ ├── losses │ │ └── __init__.py │ │ └── decode_head │ │ └── __init__.py ├── requirements.txt ├── mmseg │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ └── version.cpython-38.pyc │ ├── utils │ │ └── __pycache__ │ │ │ ├── io.cpython-38.pyc │ │ │ ├── misc.cpython-38.pyc │ │ │ ├── set_env.cpython-38.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── class_names.cpython-38.pyc │ │ │ ├── collect_env.cpython-38.pyc │ │ │ └── typing_utils.cpython-38.pyc │ ├── datasets │ │ ├── __pycache__ │ │ │ ├── ade.cpython-38.pyc │ │ │ ├── hrf.cpython-38.pyc │ │ │ ├── lip.cpython-38.pyc │ │ │ ├── voc.cpython-38.pyc │ │ │ ├── drive.cpython-38.pyc │ │ │ ├── isaid.cpython-38.pyc │ │ │ ├── isprs.cpython-38.pyc │ │ │ ├── loveda.cpython-38.pyc │ │ │ ├── refuge.cpython-38.pyc │ │ │ ├── stare.cpython-38.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── chase_db1.cpython-38.pyc │ │ │ ├── decathlon.cpython-38.pyc │ │ │ ├── mapillary.cpython-38.pyc │ │ │ ├── potsdam.cpython-38.pyc │ │ │ ├── synapse.cpython-38.pyc │ │ │ ├── cityscapes.cpython-38.pyc │ │ │ ├── coco_stuff.cpython-38.pyc │ │ │ ├── dark_zurich.cpython-38.pyc │ │ │ ├── basesegdataset.cpython-38.pyc │ │ │ ├── night_driving.cpython-38.pyc │ │ │ ├── pascal_context.cpython-38.pyc │ │ │ └── dataset_wrappers.cpython-38.pyc │ │ └── transforms │ │ │ └── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── loading.cpython-38.pyc │ │ │ ├── formatting.cpython-38.pyc │ │ │ └── transforms.cpython-38.pyc │ ├── engine │ │ ├── __pycache__ │ │ │ └── __init__.cpython-38.pyc │ │ ├── hooks │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ └── visualization_hook.cpython-38.pyc │ │ │ └── __init__.py │ │ ├── optimizers │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ └── layer_decay_optimizer_constructor.cpython-38.pyc │ │ │ └── __init__.py │ │ └── __init__.py │ ├── models │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── builder.cpython-38.pyc │ │ │ └── data_preprocessor.cpython-38.pyc │ │ ├── necks │ │ │ ├── __pycache__ │ │ │ │ ├── fpn.cpython-38.pyc │ │ │ │ ├── jpu.cpython-38.pyc │ │ │ │ ├── ic_neck.cpython-38.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── mla_neck.cpython-38.pyc │ │ │ │ ├── featurepyramid.cpython-38.pyc │ │ │ │ └── multilevel_neck.cpython-38.pyc │ │ │ └── __init__.py │ │ ├── utils │ │ │ └── __pycache__ │ │ │ │ ├── embed.cpython-38.pyc │ │ │ │ ├── ppm.cpython-38.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── encoding.cpython-38.pyc │ │ │ │ ├── res_layer.cpython-38.pyc │ │ │ │ ├── se_layer.cpython-38.pyc │ │ │ │ ├── wrappers.cpython-38.pyc │ │ │ │ ├── basic_block.cpython-38.pyc │ │ │ │ ├── make_divisible.cpython-38.pyc │ │ │ │ ├── shape_convert.cpython-38.pyc │ │ │ │ ├── up_conv_block.cpython-38.pyc │ │ │ │ ├── inverted_residual.cpython-38.pyc │ │ │ │ └── self_attention_block.cpython-38.pyc │ │ ├── backbones │ │ │ └── __pycache__ │ │ │ │ ├── mae.cpython-38.pyc │ │ │ │ ├── mit.cpython-38.pyc │ │ │ │ ├── vit.cpython-38.pyc │ │ │ │ ├── beit.cpython-38.pyc │ │ │ │ ├── cgnet.cpython-38.pyc │ │ │ │ ├── erfnet.cpython-38.pyc │ │ │ │ ├── hrnet.cpython-38.pyc │ │ │ │ ├── icnet.cpython-38.pyc │ │ │ │ ├── mscan.cpython-38.pyc │ │ │ │ ├── pidnet.cpython-38.pyc │ │ │ │ ├── resnet.cpython-38.pyc │ │ │ │ ├── stdc.cpython-38.pyc │ │ │ │ ├── swin.cpython-38.pyc │ │ │ │ ├── twins.cpython-38.pyc │ │ │ │ ├── unet.cpython-38.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── resnest.cpython-38.pyc │ │ │ │ ├── resnext.cpython-38.pyc │ │ │ │ ├── bisenetv1.cpython-38.pyc │ │ │ │ ├── bisenetv2.cpython-38.pyc │ │ │ │ ├── fast_scnn.cpython-38.pyc │ │ │ │ ├── mobilenet_v2.cpython-38.pyc │ │ │ │ ├── mobilenet_v3.cpython-38.pyc │ │ │ │ └── timm_backbone.cpython-38.pyc │ │ ├── losses │ │ │ └── __pycache__ │ │ │ │ ├── utils.cpython-38.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── accuracy.cpython-38.pyc │ │ │ │ ├── dice_loss.cpython-38.pyc │ │ │ │ ├── focal_loss.cpython-38.pyc │ │ │ │ ├── lovasz_loss.cpython-38.pyc │ │ │ │ ├── boundary_loss.cpython-38.pyc │ │ │ │ ├── tversky_loss.cpython-38.pyc │ │ │ │ ├── cross_entropy_loss.cpython-38.pyc │ │ │ │ └── ohem_cross_entropy_loss.cpython-38.pyc │ │ ├── segmentors │ │ │ ├── __pycache__ │ │ │ │ ├── base.cpython-38.pyc │ │ │ │ ├── seg_tta.cpython-38.pyc │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── encoder_decoder.cpython-38.pyc │ │ │ │ └── cascade_encoder_decoder.cpython-38.pyc │ │ │ └── __init__.py │ │ └── decode_heads │ │ │ └── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── ann_head.cpython-38.pyc │ │ │ ├── apc_head.cpython-38.pyc │ │ │ ├── cc_head.cpython-38.pyc │ │ │ ├── da_head.cpython-38.pyc │ │ │ ├── dm_head.cpython-38.pyc │ │ │ ├── dnl_head.cpython-38.pyc │ │ │ ├── dpt_head.cpython-38.pyc │ │ │ ├── ema_head.cpython-38.pyc │ │ │ ├── enc_head.cpython-38.pyc │ │ │ ├── fcn_head.cpython-38.pyc │ │ │ ├── fpn_head.cpython-38.pyc │ │ │ ├── gc_head.cpython-38.pyc │ │ │ ├── ham_head.cpython-38.pyc │ │ │ ├── isa_head.cpython-38.pyc │ │ │ ├── nl_head.cpython-38.pyc │ │ │ ├── ocr_head.cpython-38.pyc │ │ │ ├── pid_head.cpython-38.pyc │ │ │ ├── psa_head.cpython-38.pyc │ │ │ ├── psp_head.cpython-38.pyc │ │ │ ├── aspp_head.cpython-38.pyc │ │ │ ├── knet_head.cpython-38.pyc │ │ │ ├── point_head.cpython-38.pyc │ │ │ ├── stdc_head.cpython-38.pyc │ │ │ ├── uper_head.cpython-38.pyc │ │ │ ├── decode_head.cpython-38.pyc │ │ │ ├── lraspp_head.cpython-38.pyc │ │ │ ├── sep_aspp_head.cpython-38.pyc │ │ │ ├── sep_fcn_head.cpython-38.pyc │ │ │ ├── setr_mla_head.cpython-38.pyc │ │ │ ├── setr_up_head.cpython-38.pyc │ │ │ ├── maskformer_head.cpython-38.pyc │ │ │ ├── segformer_head.cpython-38.pyc │ │ │ ├── mask2former_head.cpython-38.pyc │ │ │ ├── cascade_decode_head.cpython-38.pyc │ │ │ └── segmenter_mask_head.cpython-38.pyc │ ├── registry │ │ └── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── registry.cpython-38.pyc │ ├── evaluation │ │ ├── __pycache__ │ │ │ └── __init__.cpython-38.pyc │ │ ├── __init__.py │ │ └── metrics │ │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── iou_metric.cpython-38.pyc │ │ │ └── citys_metric.cpython-38.pyc │ │ │ └── __init__.py │ ├── structures │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── seg_data_sample.cpython-38.pyc │ │ ├── sampler │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-38.pyc │ │ │ │ ├── builder.cpython-38.pyc │ │ │ │ ├── base_pixel_sampler.cpython-38.pyc │ │ │ │ └── ohem_pixel_sampler.cpython-38.pyc │ │ │ └── __init__.py │ │ └── __init__.py │ ├── visualization │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ └── local_visualizer.cpython-38.pyc │ └── apis │ │ └── __init__.py ├── MANIFEST.in ├── configs │ ├── icnet │ │ ├── icnet_r101-d8_4xb2-160k_cityscapes-832x832.py │ │ ├── icnet_r101-d8_4xb2-80k_cityscapes-832x832.py │ │ ├── icnet_r18-d8_4xb2-160k_cityscapes-832x832.py │ │ ├── icnet_r18-d8_4xb2-80k_cityscapes-832x832.py │ │ ├── icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py │ │ ├── icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py │ │ ├── icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py │ │ ├── icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py │ │ ├── icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py │ │ ├── icnet_r50-d8_4xb2-80k_cityscapes-832x832.py │ │ ├── icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py │ │ └── icnet_r50-d8_4xb2-160k_cityscapes-832x832.py │ ├── stdc │ │ ├── stdc2_4xb12-80k_cityscapes-512x1024.py │ │ ├── stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py │ │ └── stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py │ ├── ann │ │ ├── ann_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── ann_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── ann_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── ann_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── ann_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── ann_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── ann_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── ann_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── ann_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── ann_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── dnlnet │ │ ├── dnl_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── dnl_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── dnl_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── dnl_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── fcn │ │ ├── fcn_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── fcn_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── fcn_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── fcn_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── fcn_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── fcn_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py │ │ ├── fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py │ │ ├── fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py │ │ ├── fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py │ │ ├── fcn_r101-d8_4xb4-40k_pascal-context-480x480.py │ │ ├── fcn_r101-d8_4xb4-80k_pascal-context-480x480.py │ │ ├── fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py │ │ ├── fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py │ │ ├── fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py │ │ ├── fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py │ │ ├── fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn_r18-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py │ │ └── fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py │ ├── apcnet │ │ ├── apcnet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── apcnet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── ccnet │ │ ├── ccnet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── ccnet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── danet │ │ ├── danet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── danet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── danet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── danet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── danet_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── danet_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── danet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── danet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── danet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── danet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── dmnet │ │ ├── dmnet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── dmnet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── encnet │ │ ├── encnet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── encnet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── encnet_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── encnet_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── encnet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── encnet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── gcnet │ │ ├── gcnet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── gcnet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── isanet │ │ ├── isanet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── isanet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── isanet_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── isanet_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── isanet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── isanet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── psanet │ │ ├── psanet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── psanet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── psanet_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── psanet_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── psanet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── psanet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── pspnet │ │ ├── pspnet_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── pspnet_r101-d8_4xb4-80k_loveda-512x512.py │ │ ├── pspnet_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── pspnet_r101-d8_4xb4-80k_potsdam-512x512.py │ │ ├── pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py │ │ ├── pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py │ │ ├── pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py │ │ ├── pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py │ │ ├── pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py │ │ ├── pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py │ │ ├── pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py │ │ ├── pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py │ │ ├── pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py │ │ ├── pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py │ │ ├── pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py │ │ ├── pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py │ │ ├── pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py │ │ ├── pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py │ │ ├── pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py │ │ ├── pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── pspnet_r18-d8_4xb4-80k_isaid-896x896.py │ │ ├── pspnet_r18-d8_4xb4-80k_loveda-512x512.py │ │ ├── pspnet_r18-d8_4xb4-80k_potsdam-512x512.py │ │ ├── pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py │ │ ├── pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py │ │ └── pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py │ ├── sem_fpn │ │ ├── fpn_r101_4xb2-80k_cityscapes-512x1024.py │ │ ├── fpn_r101_4xb4-160k_ade20k-512x512.py │ │ ├── fpn_r50_4xb2-80k_cityscapes-512x1024.py │ │ └── fpn_r50_4xb4-160k_ade20k-512x512.py │ ├── upernet │ │ ├── upernet_r101_4xb4-160k_ade20k-512x512.py │ │ ├── upernet_r101_4xb4-80k_ade20k-512x512.py │ │ ├── upernet_r101_4xb2-40k_cityscapes-769x769.py │ │ ├── upernet_r101_4xb2-80k_cityscapes-769x769.py │ │ ├── upernet_r101_4xb4-20k_voc12aug-512x512.py │ │ ├── upernet_r101_4xb4-40k_voc12aug-512x512.py │ │ ├── upernet_r101_4xb2-40k_cityscapes-512x1024.py │ │ ├── upernet_r101_4xb2-80k_cityscapes-512x1024.py │ │ ├── upernet_r18_4xb2-40k_cityscapes-512x1024.py │ │ ├── upernet_r18_4xb2-80k_cityscapes-512x1024.py │ │ ├── upernet_r50_4xb2-40k_cityscapes-512x1024.py │ │ └── upernet_r50_4xb2-80k_cityscapes-512x1024.py │ ├── deeplabv3 │ │ ├── deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py │ │ ├── deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py │ │ ├── deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py │ │ ├── deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py │ │ ├── deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py │ │ ├── deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py │ │ ├── deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py │ │ ├── deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py │ │ ├── deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py │ │ ├── deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py │ │ ├── deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py │ │ └── deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py │ ├── emanet │ │ ├── emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── emanet_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ └── emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── nonlocal_net │ │ ├── nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── point_rend │ │ ├── pointrend_r101_4xb4-160k_ade20k-512x512.py │ │ └── pointrend_r101_4xb2-80k_cityscapes-512x1024.py │ ├── deeplabv3plus │ │ ├── deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py │ │ ├── deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py │ │ ├── deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py │ │ ├── deeplabv3plus_r101-d8_4xb4-80k_ade20k-512x512.py │ │ ├── deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py │ │ ├── deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py │ │ ├── deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py │ │ ├── deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py │ │ ├── deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py │ │ ├── deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── deeplabv3plus_r101-d8_4xb4-40k_pascal-context-480x480.py │ │ ├── deeplabv3plus_r101-d8_4xb4-80k_pascal-context-480x480.py │ │ ├── deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py │ │ ├── deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py │ │ ├── deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py │ │ ├── deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py │ │ ├── deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py │ │ ├── deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py │ │ └── deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py │ ├── vit │ │ ├── vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py │ │ ├── vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py │ │ ├── vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py │ │ └── vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py │ ├── setr │ │ └── setr_vit-l_mla_8xb2-160k_ade20k-512x512.py │ ├── bisenetv1 │ │ ├── bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py │ │ ├── bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py │ │ ├── bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py │ │ └── bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py │ ├── mask2former │ │ ├── mask2former_r101_8xb2-160k_ade20k-512x512.py │ │ ├── mask2former_r101_8xb2-90k_cityscapes-512x1024.py │ │ └── mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py │ ├── maskformer │ │ └── maskformer_r101-d32_8xb2-160k_ade20k-512x512.py │ ├── fastfcn │ │ ├── fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py │ │ ├── fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py │ │ └── fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py │ ├── bisenetv2 │ │ └── bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py │ ├── segformer │ │ ├── segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py │ │ ├── segformer_mit-b1_8xb2-160k_ade20k-512x512.py │ │ ├── segformer_mit-b2_8xb2-160k_ade20k-512x512.py │ │ ├── segformer_mit-b3_8xb2-160k_ade20k-512x512.py │ │ ├── segformer_mit-b4_8xb2-160k_ade20k-512x512.py │ │ ├── segformer_mit-b5_8xb2-160k_ade20k-512x512.py │ │ ├── segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py │ │ ├── segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py │ │ ├── segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py │ │ └── segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py │ ├── unet │ │ ├── unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py │ │ ├── unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py │ │ ├── unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py │ │ ├── unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py │ │ ├── unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py │ │ ├── unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py │ │ ├── unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py │ │ ├── unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py │ │ ├── unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py │ │ ├── unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py │ │ ├── unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py │ │ ├── unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py │ │ ├── unet-s5-d16_fcn_4xb4-40k_drive-64x64.py │ │ ├── unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py │ │ └── unet-s5-d16_fcn_4xb4-40k_stare-128x128.py │ ├── hrnet │ │ ├── fcn_hr18_4xb2-160k_cityscapes-512x1024.py │ │ ├── fcn_hr18_4xb2-40k_cityscapes-512x1024.py │ │ ├── fcn_hr18_4xb2-80k_cityscapes-512x1024.py │ │ ├── fcn_hr18_4xb4-80k_isaid-896x896.py │ │ ├── fcn_hr18_4xb4-80k_loveda-512x512.py │ │ ├── fcn_hr18_4xb4-160k_ade20k-512x512.py │ │ ├── fcn_hr18_4xb4-80k_ade20k-512x512.py │ │ ├── fcn_hr18_4xb4-80k_potsdam-512x512.py │ │ ├── fcn_hr18_4xb4-80k_vaihingen-512x512.py │ │ ├── fcn_hr18_4xb4-20k_voc12aug-512x512.py │ │ └── fcn_hr18_4xb4-40k_voc12aug-512x512.py │ ├── ocrnet │ │ ├── ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py │ │ ├── ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py │ │ └── ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py │ ├── resnest │ │ ├── resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py │ │ ├── resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py │ │ ├── resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py │ │ ├── resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py │ │ ├── resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py │ │ ├── resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py │ │ ├── resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py │ │ └── resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py │ ├── pidnet │ │ └── pidnet-m_2xb6-120k_1024x1024-cityscapes.py │ └── twins │ │ ├── twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py │ │ └── twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py ├── docker │ └── serve │ │ ├── config.properties │ │ └── entrypoint.sh └── CITATION.cff ├── figures └── 0.png ├── test.py └── train.py /mmsegmentation/mmsegmentation.egg-info/not-zip-safe: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /mmsegmentation/docs/zh_cn/user_guides/deployment.md: -------------------------------------------------------------------------------- 1 | # 模型部署 2 | -------------------------------------------------------------------------------- /mmsegmentation/mmsegmentation.egg-info/dependency_links.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_dataset/splits/val.txt: -------------------------------------------------------------------------------- 1 | 00004 2 | -------------------------------------------------------------------------------- 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All rights reserved. 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_isaid_dataset/splits/val.txt: -------------------------------------------------------------------------------- 1 | P0000_0_896_1024_1920 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_isaid_dataset/splits/train.txt: -------------------------------------------------------------------------------- 1 | P0000_0_896_1536_2432 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_dataset/splits/train.txt: -------------------------------------------------------------------------------- 1 | 00000 2 | 00001 3 | 00002 4 | 00003 5 | -------------------------------------------------------------------------------- /mmsegmentation/tests/test_models/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_isaid_dataset/ann_dir/P0000_0_896_1024_1920_instance_color_RGB.png: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_isaid_dataset/ann_dir/P0000_0_896_1536_2432_instance_color_RGB.png: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /mmsegmentation/demo/demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/demo/demo.png -------------------------------------------------------------------------------- /mmsegmentation/requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | numpy 3 | packaging 4 | prettytable 5 | scipy 6 | -------------------------------------------------------------------------------- /mmsegmentation/tests/test_models/test_heads/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/test_models/test_necks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/test_models/test_utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/test_models/test_backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmsegmentation/tests/test_models/test_segmentors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /mmsegmentation/requirements/tests.txt: -------------------------------------------------------------------------------- 1 | codecov 2 | flake8 3 | interrogate 4 | pytest 5 | xdoctest>=0.10.0 6 | yapf 7 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/seg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/seg.png -------------------------------------------------------------------------------- /mmsegmentation/resources/3dogs.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/resources/3dogs.jpg -------------------------------------------------------------------------------- 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All rights reserved. 2 | from .local_visualizer import SegLocalVisualizer 3 | 4 | __all__ = ['SegLocalVisualizer'] 5 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/visualization/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/visualization/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_lip_dataset/val_images/86_185913.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_lip_dataset/val_images/86_185913.jpg -------------------------------------------------------------------------------- /mmsegmentation/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include requirements/*.txt 2 | include mmseg/.mim/model-index.yml 3 | recursive-include mmseg/.mim/configs *.py *.yaml 4 | recursive-include mmseg/.mim/tools *.py *.sh 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' 2 | model = dict(backbone=dict(backbone_cfg=dict(depth=101))) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' 2 | model = dict(backbone=dict(backbone_cfg=dict(depth=101))) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './stdc1_4xb12-80k_cityscapes-512x1024.py' 2 | model = dict(backbone=dict(backbone_cfg=dict(stdc_type='STDCNet2'))) 3 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/datasets/__pycache__/dataset_wrappers.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/datasets/__pycache__/dataset_wrappers.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/engine/hooks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .visualization_hook import SegVisualizationHook 3 | 4 | __all__ = ['SegVisualizationHook'] 5 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/__pycache__/data_preprocessor.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/__pycache__/data_preprocessor.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/backbones/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/backbones/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/backbones/__pycache__/resnest.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/backbones/__pycache__/resnest.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/backbones/__pycache__/resnext.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/backbones/__pycache__/resnext.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/losses/__pycache__/focal_loss.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/losses/__pycache__/focal_loss.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/losses/__pycache__/lovasz_loss.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/losses/__pycache__/lovasz_loss.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/segmentors/__pycache__/seg_tta.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/segmentors/__pycache__/seg_tta.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/utils/__pycache__/basic_block.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/utils/__pycache__/basic_block.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_lip_dataset/train_images/684_2150041.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_lip_dataset/train_images/684_2150041.jpg -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_refuge_dataset/ann_dir/pseudo_g0001.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_refuge_dataset/ann_dir/pseudo_g0001.png -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_refuge_dataset/img_dir/pseudo_g0001.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_refuge_dataset/img_dir/pseudo_g0001.png -------------------------------------------------------------------------------- /mmsegmentation/docs/en/switch_language.md: -------------------------------------------------------------------------------- 1 | ## English 2 | 3 | ## 简体中文 4 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/datasets/transforms/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/datasets/transforms/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/datasets/transforms/__pycache__/loading.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/datasets/transforms/__pycache__/loading.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/engine/optimizers/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/engine/optimizers/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. 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/mmsegmentation/configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | 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/mmsegmentation/tests/data/pseudo_synapse_dataset/img_dir/case0005_slice000.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_synapse_dataset/img_dir/case0005_slice000.jpg -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_synapse_dataset/img_dir/case0005_slice001.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_synapse_dataset/img_dir/case0005_slice001.jpg -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | # Single gpu 4 | # os.system("CUDA_VISIBLE_DEVICES=0 python ./mmsegmentation/tools/test.py rdrnet-s-simple_2xb6-120k_cityscapes-1024x1024.py ./weight/seg/rdrnet_weight.pth") 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './isanet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_loveda-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fpn_r50_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_vaihingen_dataset/ann_dir/area1_0_0_512_512.png -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_vaihingen_dataset/img_dir/area1_0_0_512_512.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_vaihingen_dataset/img_dir/area1_0_0_512_512.png -------------------------------------------------------------------------------- /mmsegmentation/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './isanet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './isanet_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './isanet_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_potsdam-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/decode_heads/__pycache__/maskformer_head.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/decode_heads/__pycache__/maskformer_head.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/decode_heads/__pycache__/segformer_head.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/decode_heads/__pycache__/segformer_head.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_mapillary_dataset/v1.2/__CRyFzoDOXn6unQ6a3DnQ.png: 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-------------------------------------------------------------------------------- /mmsegmentation/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './emanet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb4-40k_pascal-context-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb4-40k_pascal-context-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './isanet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './isanet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pointrend_r50_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/docs/en/_static/css/readthedocs.css: -------------------------------------------------------------------------------- 1 | .header-logo { 2 | background-image: url("../images/mmsegmentation.png"); 3 | background-size: 201px 40px; 4 | height: 40px; 5 | width: 201px; 6 | } 7 | -------------------------------------------------------------------------------- /mmsegmentation/docs/zh_cn/_static/css/readthedocs.css: -------------------------------------------------------------------------------- 1 | .header-logo { 2 | background-image: url("../images/mmsegmentation.png"); 3 | background-size: 201px 40px; 4 | height: 40px; 5 | width: 201px; 6 | } 7 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/decode_heads/__pycache__/mask2former_head.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/decode_heads/__pycache__/mask2former_head.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/losses/__pycache__/ohem_cross_entropy_loss.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/losses/__pycache__/ohem_cross_entropy_loss.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/structures/sampler/__pycache__/base_pixel_sampler.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/structures/sampler/__pycache__/base_pixel_sampler.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/structures/sampler/__pycache__/ohem_pixel_sampler.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/structures/sampler/__pycache__/ohem_pixel_sampler.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/projects/hssn/losses/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .hiera_triplet_loss_cityscape import HieraTripletLossCityscape 3 | 4 | __all__ = ['HieraTripletLossCityscape'] 5 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_mapillary_dataset/images/__CRyFzoDOXn6unQ6a3DnQ.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_mapillary_dataset/images/__CRyFzoDOXn6unQ6a3DnQ.jpg -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb4-40k_pascal-context-59-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb4-80k_pascal-context-59-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './pointrend_r50_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/decode_heads/__pycache__/cascade_decode_head.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/decode_heads/__pycache__/cascade_decode_head.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/decode_heads/__pycache__/segmenter_mask_head.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/decode_heads/__pycache__/segmenter_mask_head.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' 2 | model = dict( 3 | backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18))) 4 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' 2 | model = dict( 3 | backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18))) 4 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-40k_pascal-context-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_pascal-context-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/segmentors/__pycache__/cascade_encoder_decoder.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/models/segmentors/__pycache__/cascade_encoder_decoder.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | docutils==0.16.0 2 | myst-parser 3 | -e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme 4 | sphinx==4.0.2 5 | sphinx_copybutton 6 | sphinx_markdown_tables 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-80k_pascal-context-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-40k_pascal-context-59-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/docker/serve/config.properties: -------------------------------------------------------------------------------- 1 | inference_address=http://0.0.0.0:8080 2 | management_address=http://0.0.0.0:8081 3 | metrics_address=http://0.0.0.0:8082 4 | model_store=/home/model-server/model-store 5 | load_models=all 6 | -------------------------------------------------------------------------------- /mmsegmentation/projects/hssn/decode_head/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .sep_aspp_contrast_head import DepthwiseSeparableASPPContrastHead 3 | 4 | __all__ = ['DepthwiseSeparableASPPContrastHead'] 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/evaluation/metrics/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .citys_metric import CityscapesMetric 3 | from .iou_metric import IoUMetric 4 | 5 | __all__ = ['IoUMetric', 'CityscapesMetric'] 6 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/engine/optimizers/__pycache__/layer_decay_optimizer_constructor.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/mmseg/engine/optimizers/__pycache__/layer_decay_optimizer_constructor.cpython-38.pyc -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-40k_pascal-context-59-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_pascal-context-59-480x480.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py' # noqa 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py' # noqa 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_cityscapes_dataset/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_labelIds.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_cityscapes_dataset/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_labelIds.png -------------------------------------------------------------------------------- /mmsegmentation/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' 2 | 3 | model = dict( 4 | pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', 5 | backbone=dict(drop_path_rate=0.1), 6 | ) 7 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_cityscapes_dataset/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_cityscapes_dataset/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png -------------------------------------------------------------------------------- /mmsegmentation/configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./setr_vit-l-mla_8xb1-160k_ade20k-512x512.py'] 2 | 3 | # num_gpus: 8 -> batch_size: 16 4 | train_dataloader = dict(batch_size=2) 5 | val_dataloader = dict(batch_size=1) 6 | test_dataloader = val_dataloader 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py' 2 | 3 | model = dict( 4 | pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', 5 | backbone=dict(drop_path_rate=0.1), 6 | neck=None) 7 | -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_cityscapes_dataset/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_instanceIds.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_cityscapes_dataset/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_instanceIds.png -------------------------------------------------------------------------------- /mmsegmentation/tests/data/pseudo_cityscapes_dataset/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_labelTrainIds.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gyyang23/RDRNet/HEAD/mmsegmentation/tests/data/pseudo_cityscapes_dataset/gtFine/val/frankfurt/frankfurt_000000_000294_gtFine_labelTrainIds.png -------------------------------------------------------------------------------- /mmsegmentation/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' 2 | 3 | model = dict( 4 | pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', 5 | backbone=dict(drop_path_rate=0.1), 6 | neck=None) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' 2 | 3 | model = dict( 4 | pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', 5 | backbone=dict(drop_path_rate=0.1, final_norm=True)) 6 | -------------------------------------------------------------------------------- /mmsegmentation/docker/serve/entrypoint.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | if [[ "$1" = "serve" ]]; then 5 | shift 1 6 | torchserve --start --ts-config /home/model-server/config.properties 7 | else 8 | eval "$@" 9 | fi 10 | 11 | # prevent docker exit 12 | tail -f /dev/null 13 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py' # noqa 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py' # noqa 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py' 2 | train_dataloader = dict(batch_size=8, num_workers=4) 3 | val_dataloader = dict(batch_size=1, num_workers=4) 4 | test_dataloader = val_dataloader 5 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py' 2 | optim_wrapper = dict( 3 | _delete_=True, 4 | type='AmpOptimWrapper', 5 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005), 6 | loss_scale=512.) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./mask2former_r50_8xb2-160k_ade20k-512x512.py'] 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/apis/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .inference import inference_model, init_model, show_result_pyplot 3 | from .mmseg_inferencer import MMSegInferencer 4 | 5 | __all__ = [ 6 | 'init_model', 'inference_model', 'show_result_pyplot', 'MMSegInferencer' 7 | ] 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' 2 | model = dict( 3 | backbone=dict( 4 | backbone_cfg=dict( 5 | init_cfg=dict( 6 | type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './maskformer_r50-d32_8xb2-160k_ade20k-512x512.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | # model settings 2 | _base_ = './fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py' 3 | train_dataloader = dict(batch_size=4, num_workers=4) 4 | val_dataloader = dict(batch_size=1, num_workers=4) 5 | test_dataloader = val_dataloader 6 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | # model settings 2 | _base_ = './fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py' 3 | train_dataloader = dict(batch_size=4, num_workers=4) 4 | val_dataloader = dict(batch_size=1, num_workers=4) 5 | test_dataloader = val_dataloader 6 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' 2 | model = dict( 3 | backbone=dict( 4 | backbone_cfg=dict( 5 | init_cfg=dict( 6 | type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./mask2former_r50_8xb2-90k_cityscapes-512x1024.py'] 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py' 2 | optim_wrapper = dict( 3 | _delete_=True, 4 | type='AmpOptimWrapper', 5 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005), 6 | loss_scale=512.) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './fpn_r50_4xb4-160k_ade20k-512x512.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | crop_size = (512, 512) 4 | data_preprocessor = dict(size=crop_size) 5 | model = dict(data_preprocessor=data_preprocessor) 6 | -------------------------------------------------------------------------------- /mmsegmentation/configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py' 2 | optim_wrapper = dict( 3 | _delete_=True, 4 | type='AmpOptimWrapper', 5 | optimizer=dict(type='SGD', lr=0.05, momentum=0.9, weight_decay=0.0005), 6 | loss_scale=512.) 7 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/structures/sampler/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base_pixel_sampler import BasePixelSampler 3 | from .builder import build_pixel_sampler 4 | from .ohem_pixel_sampler import OHEMPixelSampler 5 | 6 | __all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler'] 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r18_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb2-40k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict(in_channels=[64, 128, 256, 512]), 6 | auxiliary_head=dict(in_channels=256)) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r18_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict(in_channels=[64, 128, 256, 512]), 6 | auxiliary_head=dict(in_channels=256)) 7 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/engine/optimizers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .layer_decay_optimizer_constructor import ( 3 | LayerDecayOptimizerConstructor, LearningRateDecayOptimizerConstructor) 4 | 5 | __all__ = [ 6 | 'LearningRateDecayOptimizerConstructor', 'LayerDecayOptimizerConstructor' 7 | ] 8 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/structures/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .sampler import BasePixelSampler, OHEMPixelSampler, build_pixel_sampler 3 | from .seg_data_sample import SegDataSample 4 | 5 | __all__ = [ 6 | 'SegDataSample', 'BasePixelSampler', 'OHEMPixelSampler', 7 | 'build_pixel_sampler' 8 | ] 9 | -------------------------------------------------------------------------------- /mmsegmentation/CITATION.cff: -------------------------------------------------------------------------------- 1 | cff-version: 1.2.0 2 | message: "If you use this software, please cite it as below." 3 | authors: 4 | - name: "MMSegmentation Contributors" 5 | title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark" 6 | date-released: 2020-07-10 7 | url: "https://github.com/open-mmlab/mmsegmentation" 8 | license: Apache-2.0 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './bisenetv1_r101-d32_4xb4-160k_coco-stuff164k-512x512.py' 2 | model = dict( 3 | backbone=dict( 4 | backbone_cfg=dict( 5 | init_cfg=dict( 6 | type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | backbone_cfg=dict( 6 | init_cfg=dict( 7 | type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' 2 | model = dict( 3 | backbone=dict( 4 | backbone_cfg=dict( 5 | depth=101, 6 | init_cfg=dict( 7 | type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' 2 | model = dict( 3 | backbone=dict( 4 | backbone_cfg=dict( 5 | depth=101, 6 | init_cfg=dict( 7 | type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py' 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optim_wrapper = dict( 4 | _delete_=True, 5 | type='AmpOptimWrapper', 6 | optimizer=optimizer, 7 | loss_scale=512.) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] 2 | 3 | model = dict( 4 | backbone=dict( 5 | init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b1.pth'), 6 | embed_dims=64), 7 | decode_head=dict(in_channels=[64, 128, 320, 512])) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_fcn_4xb4-40k_drive-64x64.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_fcn_4xb4-40k_stare-128x128.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ann_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ann/ann_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ann_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fpn_r50.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py' 2 | model = dict( 3 | type='EncoderDecoder', 4 | backbone=dict( 5 | backbone_cfg=dict( 6 | init_cfg=dict( 7 | type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py' 2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) 3 | optim_wrapper = dict( 4 | _delete_=True, 5 | type='AmpOptimWrapper', 6 | optimizer=optimizer, 7 | loss_scale=512.) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/dnl_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/dnl_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/danet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/danet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/emanet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/psanet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/psanet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/upernet_r50.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/upernet_r50.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_isaid-896x896.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_loveda-512x512.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = '../fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict( 5 | type='ResNeSt', 6 | stem_channels=128, 7 | radix=2, 8 | reduction_factor=4, 9 | avg_down_stride=True)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] 2 | 3 | # model settings 4 | model = dict( 5 | pretrained='pretrain/mit_b1.pth', 6 | backbone=dict( 7 | embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[2, 2, 2, 2]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] 2 | 3 | # model settings 4 | model = dict( 5 | pretrained='pretrain/mit_b2.pth', 6 | backbone=dict( 7 | embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 4, 6, 3]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] 2 | 3 | # model settings 4 | model = dict( 5 | pretrained='pretrain/mit_b3.pth', 6 | backbone=dict( 7 | embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 4, 18, 3]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] 2 | 3 | # model settings 4 | model = dict( 5 | pretrained='pretrain/mit_b4.pth', 6 | backbone=dict( 7 | embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 8, 27, 3]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] 2 | 3 | # model settings 4 | model = dict( 5 | pretrained='pretrain/mit_b5.pth', 6 | backbone=dict( 7 | embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 6, 40, 3]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py: -------------------------------------------------------------------------------- 1 | _base_ = './unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py' 2 | model = dict( 3 | decode_head=dict(loss_decode=[ 4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), 5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) 6 | ])) 7 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | # Multiple gpus 4 | os.system("CUDA_VISIBLE_DEVICES=0,1 bash ./mmsegmentation/tools/dist_train.sh rdrnet-s-simple_2xb6-120k_cityscapes-1024x1024.py 2 --work-dir ./weight/seg") 5 | 6 | # Single gpu 7 | # os.system("CUDA_VISIBLE_DEVICES=0 python ./mmsegmentation/tools/train.py rdrnet-s-simple_2xb6-120k_cityscapes-1024x1024.py --work-dir ./weight/seg") 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' 2 | model = dict( 3 | backbone=dict( 4 | layer_channels=(128, 512), 5 | backbone_cfg=dict( 6 | depth=18, 7 | init_cfg=dict( 8 | type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')))) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/icnet_r50-d8.py', 3 | '../_base_/datasets/cityscapes_832x832.py', '../_base_/default_runtime.py', 4 | '../_base_/schedules/schedule_80k.py' 5 | ] 6 | crop_size = (832, 832) 7 | data_preprocessor = dict(size=crop_size) 8 | model = dict(data_preprocessor=data_preprocessor) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict(data_preprocessor=data_preprocessor) 8 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_potsdam-512x512.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = '../fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict( 5 | type='ResNeSt', 6 | stem_channels=128, 7 | radix=2, 8 | reduction_factor=4, 9 | avg_down_stride=True)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = '../pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict( 5 | type='ResNeSt', 6 | stem_channels=128, 7 | radix=2, 8 | reduction_factor=4, 9 | avg_down_stride=True)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/projects/example_project/configs/fcn_dummy-r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = ['mmseg::fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py'] 2 | 3 | custom_imports = dict(imports=['dummy']) 4 | 5 | crop_size = (512, 1024) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, backbone=dict(type='DummyResNet')) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' 2 | model = dict( 3 | backbone=dict( 4 | layer_channels=(128, 512), 5 | backbone_cfg=dict( 6 | depth=18, 7 | init_cfg=dict( 8 | type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')))) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/icnet_r50-d8.py', 3 | '../_base_/datasets/cityscapes_832x832.py', '../_base_/default_runtime.py', 4 | '../_base_/schedules/schedule_160k.py' 5 | ] 6 | crop_size = (832, 832) 7 | data_preprocessor = dict(size=crop_size) 8 | model = dict(data_preprocessor=data_preprocessor) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc1_20220308-5368626c.pth' # noqa 2 | _base_ = './stdc1_4xb12-80k_cityscapes-512x1024.py' 3 | model = dict( 4 | backbone=dict( 5 | backbone_cfg=dict( 6 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint)))) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc2_20220308-7dbd9127.pth' # noqa 2 | _base_ = './stdc2_4xb12-80k_cityscapes-512x1024.py' 3 | model = dict( 4 | backbone=dict( 5 | backbone_cfg=dict( 6 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint)))) 7 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='torchvision://resnet18', 4 | backbone=dict(type='ResNet', depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='torchvision://resnet18', 4 | backbone=dict(type='ResNet', depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/isaid.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (896, 896) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=16)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/loveda.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 512) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=7)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = '../deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict( 5 | type='ResNeSt', 6 | stem_channels=128, 7 | radix=2, 8 | reduction_factor=4, 9 | avg_down_stride=True)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = '../pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict( 5 | type='ResNeSt', 6 | stem_channels=128, 7 | radix=2, 8 | reduction_factor=4, 9 | avg_down_stride=True)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] 2 | 3 | model = dict( 4 | backbone=dict( 5 | init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b2.pth'), 6 | embed_dims=64, 7 | num_layers=[3, 4, 6, 3]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnet18_v1c', 4 | backbone=dict(depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/ade20k.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' 4 | ] 5 | crop_size = (512, 512) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=150)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/ade20k.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 512) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=150)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/potsdam.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 512) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=6)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] 2 | 3 | model = dict( 4 | backbone=dict( 5 | init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b3.pth'), 6 | embed_dims=64, 7 | num_layers=[3, 4, 18, 3]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] 2 | 3 | model = dict( 4 | backbone=dict( 5 | init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b4.pth'), 6 | embed_dims=64, 7 | num_layers=[3, 8, 27, 3]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] 2 | 3 | model = dict( 4 | backbone=dict( 5 | init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b5.pth'), 6 | embed_dims=64, 7 | num_layers=[3, 6, 40, 3]), 8 | decode_head=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fpn_r50.py', '../_base_/datasets/ade20k.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' 4 | ] 5 | crop_size = (512, 512) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=150)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/segmentors/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseSegmentor 3 | from .cascade_encoder_decoder import CascadeEncoderDecoder 4 | from .encoder_decoder import EncoderDecoder 5 | from .seg_tta import SegTTAModel 6 | 7 | __all__ = [ 8 | 'BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder', 'SegTTAModel' 9 | ] 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/deeplabv3plus_r50-d8.py', 3 | '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py', 4 | '../_base_/schedules/schedule_40k.py' 5 | ] 6 | crop_size = (512, 1024) 7 | data_preprocessor = dict(size=crop_size) 8 | model = dict(data_preprocessor=data_preprocessor) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/deeplabv3plus_r50-d8.py', 3 | '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py', 4 | '../_base_/schedules/schedule_80k.py' 5 | ] 6 | crop_size = (512, 1024) 7 | data_preprocessor = dict(size=crop_size) 8 | model = dict(data_preprocessor=data_preprocessor) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fastfcn_r50-d32_jpu_psp.py', 3 | '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py', 4 | '../_base_/schedules/schedule_80k.py' 5 | ] 6 | crop_size = (512, 1024) 7 | data_preprocessor = dict(size=crop_size) 8 | model = dict(data_preprocessor=data_preprocessor) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/vaihingen.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | crop_size = (512, 512) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=6)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pidnet-s_2xb6-120k_1024x1024-cityscapes.py' 2 | checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-m_imagenet1k_20230306-39893c52.pth' # noqa 3 | model = dict( 4 | backbone=dict(channels=64, init_cfg=dict(checkpoint=checkpoint_file)), 5 | decode_head=dict(in_channels=256)) 6 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='torchvision://resnet18', 4 | backbone=dict(type='ResNet', depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='torchvision://resnet18', 4 | backbone=dict(type='ResNet', depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = '../deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict( 5 | type='ResNeSt', 6 | stem_channels=128, 7 | radix=2, 8 | reduction_factor=4, 9 | avg_down_stride=True)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py' 2 | model = dict( 3 | pretrained='torchvision://resnet18', 4 | backbone=dict(type='ResNet', depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_voc12_aug.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' 4 | ] 5 | crop_size = (512, 512) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=21)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_voc12_aug.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (512, 512) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=21)) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict( 5 | type='ResNeSt', 6 | stem_channels=128, 7 | radix=2, 8 | reduction_factor=4, 9 | avg_down_stride=True)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/engine/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .hooks import SegVisualizationHook 3 | from .optimizers import (LayerDecayOptimizerConstructor, 4 | LearningRateDecayOptimizerConstructor) 5 | 6 | __all__ = [ 7 | 'LearningRateDecayOptimizerConstructor', 'LayerDecayOptimizerConstructor', 8 | 'SegVisualizationHook' 9 | ] 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' 2 | model = dict( 3 | pretrained='torchvision://resnet18', 4 | backbone=dict(type='ResNet', depth=18), 5 | decode_head=dict( 6 | in_channels=512, 7 | channels=128, 8 | ), 9 | auxiliary_head=dict(in_channels=256, channels=64)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/docs/zh_cn/user_guides/index.rst: -------------------------------------------------------------------------------- 1 | 训练 & 测试 2 | ************** 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | 1_config.md 8 | 2_dataset_prepare.md 9 | 3_inference.md 10 | 4_train_test.md 11 | 12 | 实用工具 13 | ************* 14 | 15 | .. toctree:: 16 | :maxdepth: 2 17 | 18 | visualization.md 19 | useful_tools.md 20 | deployment.md 21 | visualization_feature_map.md 22 | -------------------------------------------------------------------------------- /mmsegmentation/mmseg/models/necks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .featurepyramid import Feature2Pyramid 3 | from .fpn import FPN 4 | from .ic_neck import ICNeck 5 | from .jpu import JPU 6 | from .mla_neck import MLANeck 7 | from .multilevel_neck import MultiLevelNeck 8 | 9 | __all__ = [ 10 | 'FPN', 'MultiLevelNeck', 'MLANeck', 'ICNeck', 'JPU', 'Feature2Pyramid' 11 | ] 12 | -------------------------------------------------------------------------------- /mmsegmentation/configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py'] 2 | 3 | pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_22k_20220317-e5c09f74.pth' # noqa 4 | model = dict( 5 | backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained))) 6 | -------------------------------------------------------------------------------- /mmsegmentation/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py: -------------------------------------------------------------------------------- 1 | _base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py' # noqa 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict( 5 | type='ResNeSt', 6 | stem_channels=128, 7 | radix=2, 8 | reduction_factor=4, 9 | avg_down_stride=True)) 10 | -------------------------------------------------------------------------------- /mmsegmentation/docs/en/user_guides/index.rst: -------------------------------------------------------------------------------- 1 | Train & Test 2 | ************** 3 | 4 | .. toctree:: 5 | :maxdepth: 1 6 | 7 | 1_config.md 8 | 2_dataset_prepare.md 9 | 3_inference.md 10 | 4_train_test.md 11 | 12 | Useful Tools 13 | ************* 14 | 15 | .. toctree:: 16 | :maxdepth: 2 17 | 18 | visualization.md 19 | useful_tools.md 20 | deployment.md 21 | visualization_feature_map.md 22 | -------------------------------------------------------------------------------- /mmsegmentation/configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py'] 2 | 3 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth' # noqa 4 | 5 | model = dict( 6 | backbone=dict( 7 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint), 8 | depths=[3, 4, 18, 3]), ) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py'] 2 | 3 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth' # noqa 4 | 5 | model = dict( 6 | backbone=dict( 7 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint), 8 | depths=[3, 8, 27, 3])) 9 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/drive.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (64, 64) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, 9 | test_cfg=dict(crop_size=(64, 64), stride=(42, 42))) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/hrf.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (256, 256) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, 9 | test_cfg=dict(crop_size=(256, 256), stride=(170, 170))) 10 | -------------------------------------------------------------------------------- /mmsegmentation/configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/stare.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | crop_size = (128, 128) 6 | data_preprocessor = dict(size=crop_size) 7 | model = dict( 8 | data_preprocessor=data_preprocessor, 9 | test_cfg=dict(crop_size=(128, 128), stride=(85, 85))) 10 | --------------------------------------------------------------------------------