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Repository: zhu-xlab/SSL4EO-S12
Branch: main
Commit: 2156913c5d8e
Files: 319
Total size: 4.1 MB
Directory structure:
gitextract_cmyl0w8x/
├── .gitignore
├── LICENSE
├── README.md
├── data/
│ ├── 50k_ids_random.csv
│ └── void_ids.csv
└── src/
├── benchmark/
│ ├── pretrain_data2vec/
│ │ └── readme.md
│ ├── pretrain_ssl/
│ │ ├── datasets/
│ │ │ └── SSL4EO/
│ │ │ ├── ssl4eo_dataset.py
│ │ │ └── ssl4eo_dataset_lmdb.py
│ │ ├── models/
│ │ │ ├── data2vec/
│ │ │ │ ├── README.md
│ │ │ │ ├── README_Original.md
│ │ │ │ ├── dall_e/
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── decoder.py
│ │ │ │ │ ├── encoder.py
│ │ │ │ │ └── utils.py
│ │ │ │ ├── dataset_folder.py
│ │ │ │ ├── datasets.py
│ │ │ │ ├── engine_for_cyclical.py
│ │ │ │ ├── engine_for_cyclical_joint.py
│ │ │ │ ├── engine_for_finetuning.py
│ │ │ │ ├── engine_for_pretraining.py
│ │ │ │ ├── get_started_for_image_classification.md
│ │ │ │ ├── masking_generator.py
│ │ │ │ ├── modeling_cyclical.py
│ │ │ │ ├── modeling_cyclical_joint.py
│ │ │ │ ├── modeling_discrete_vae.py
│ │ │ │ ├── modeling_finetune.py
│ │ │ │ ├── modeling_pretrain.py
│ │ │ │ ├── models.py
│ │ │ │ ├── optim_factory.py
│ │ │ │ ├── requirements.txt
│ │ │ │ ├── run_beit_pretraining.py
│ │ │ │ ├── run_class_finetuning.py
│ │ │ │ ├── run_cyclical.py
│ │ │ │ ├── run_cyclical_joint.py
│ │ │ │ ├── semantic_segmentation/
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── backbone/
│ │ │ │ │ │ └── beit.py
│ │ │ │ │ ├── configs/
│ │ │ │ │ │ ├── _base_/
│ │ │ │ │ │ │ ├── datasets/
│ │ │ │ │ │ │ │ ├── ade20k.py
│ │ │ │ │ │ │ │ └── ade20k_640x640.py
│ │ │ │ │ │ │ ├── default_runtime.py
│ │ │ │ │ │ │ ├── models/
│ │ │ │ │ │ │ │ └── upernet_beit.py
│ │ │ │ │ │ │ └── schedules/
│ │ │ │ │ │ │ ├── schedule_160k.py
│ │ │ │ │ │ │ └── schedule_320k.py
│ │ │ │ │ │ └── beit/
│ │ │ │ │ │ └── upernet/
│ │ │ │ │ │ ├── upernet_beit_base_12_512_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_base_12_512_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_base_12_640_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_base_12_640_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_large_24_512_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_large_24_512_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_large_24_640_slide_160k_ade20k.py
│ │ │ │ │ │ └── upernet_beit_large_24_640_slide_160k_ade20k_ms.py
│ │ │ │ │ ├── mmcv_custom/
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── apex_runner/
│ │ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ │ ├── apex_iter_based_runner.py
│ │ │ │ │ │ │ ├── checkpoint.py
│ │ │ │ │ │ │ └── optimizer.py
│ │ │ │ │ │ ├── checkpoint.py
│ │ │ │ │ │ ├── layer_decay_optimizer_constructor.py
│ │ │ │ │ │ ├── resize_transform.py
│ │ │ │ │ │ └── train_api.py
│ │ │ │ │ └── tools/
│ │ │ │ │ ├── dist_test.sh
│ │ │ │ │ ├── dist_train.sh
│ │ │ │ │ ├── test.py
│ │ │ │ │ └── train.py
│ │ │ │ ├── transforms.py
│ │ │ │ └── utils.py
│ │ │ ├── dino/
│ │ │ │ ├── utils.py
│ │ │ │ └── vision_transformer.py
│ │ │ ├── mae/
│ │ │ │ ├── engine_finetune.py
│ │ │ │ ├── engine_finetune_BE.py
│ │ │ │ ├── engine_finetune_EU.py
│ │ │ │ ├── engine_finetune_SS.py
│ │ │ │ ├── engine_pretrain.py
│ │ │ │ ├── main_finetune.py
│ │ │ │ ├── main_linprobe.py
│ │ │ │ ├── main_pretrain.py
│ │ │ │ ├── models_mae.py
│ │ │ │ ├── models_vit.py
│ │ │ │ ├── submitit_finetune.py
│ │ │ │ ├── submitit_linprobe.py
│ │ │ │ ├── submitit_pretrain.py
│ │ │ │ └── util/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── crop.py
│ │ │ │ ├── datasets.py
│ │ │ │ ├── lars.py
│ │ │ │ ├── lr_decay.py
│ │ │ │ ├── lr_sched.py
│ │ │ │ ├── misc.py
│ │ │ │ └── pos_embed.py
│ │ │ ├── moco/
│ │ │ │ ├── builder.py
│ │ │ │ └── loader.py
│ │ │ ├── moco_v2/
│ │ │ │ ├── README.md
│ │ │ │ ├── __init__.py
│ │ │ │ ├── builder.py
│ │ │ │ ├── detection/
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── configs/
│ │ │ │ │ │ ├── Base-RCNN-C4-BN.yaml
│ │ │ │ │ │ ├── coco_R_50_C4_2x.yaml
│ │ │ │ │ │ ├── coco_R_50_C4_2x_moco.yaml
│ │ │ │ │ │ ├── pascal_voc_R_50_C4_24k.yaml
│ │ │ │ │ │ └── pascal_voc_R_50_C4_24k_moco.yaml
│ │ │ │ │ ├── convert-pretrain-to-detectron2.py
│ │ │ │ │ └── train_net.py
│ │ │ │ ├── loader.py
│ │ │ │ └── main_lincls.py
│ │ │ ├── moco_v3/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── builder.py
│ │ │ │ ├── loader.py
│ │ │ │ ├── optimizer.py
│ │ │ │ └── vits.py
│ │ │ ├── rs_transforms_float32.py
│ │ │ └── rs_transforms_uint8.py
│ │ ├── pretrain_data2vec.py
│ │ ├── pretrain_dino_s2c.py
│ │ ├── pretrain_mae_s2c.py
│ │ ├── pretrain_moco_v2_s2c.py
│ │ ├── pretrain_moco_v2_seco_ms.py
│ │ ├── pretrain_moco_v2_sen12ms_ms.py
│ │ ├── pretrain_moco_v3_s2c.py
│ │ └── scripts/
│ │ └── pretrain/
│ │ ├── srun_train_SEN12MS_moco_rn50_rgb.sh
│ │ ├── srun_train_SEN12MS_moco_rn50_s2c.sh
│ │ ├── srun_train_SeCo_moco_rn50_s2a.sh
│ │ ├── srun_train_dino_rn50_s2c.sh
│ │ ├── srun_train_dino_vits16_s2c.sh
│ │ ├── srun_train_mae_vits16_s2c.sh
│ │ ├── srun_train_moco_rn50_s2c.sh
│ │ ├── srun_train_moco_vits16_s2c.sh
│ │ ├── submit_pretrain_data2vec.sh
│ │ └── train_data2vec_vits16_s2c.sh
│ ├── transfer_change_detection/
│ │ ├── datasets/
│ │ │ ├── __init__.py
│ │ │ ├── oscd_datamodule.py
│ │ │ └── oscd_dataset.py
│ │ ├── main_oscd.py
│ │ ├── models/
│ │ │ ├── __init__.py
│ │ │ └── segmentation.py
│ │ ├── readme.md
│ │ ├── test.sh
│ │ ├── train.sh
│ │ ├── utils/
│ │ │ ├── __init__.py
│ │ │ ├── data.py
│ │ │ └── transforms.py
│ │ └── validate_oscd.py
│ ├── transfer_classification/
│ │ ├── datasets/
│ │ │ ├── BigEarthNet/
│ │ │ │ ├── bigearthnet_dataset_seco.py
│ │ │ │ └── bigearthnet_dataset_seco_lmdb_s2_uint8.py
│ │ │ ├── EuroSat/
│ │ │ │ └── eurosat_dataset.py
│ │ │ └── So2Sat/
│ │ │ └── so2sat_lcz42_dataset.py
│ │ ├── linear_BE_data2vec.py
│ │ ├── linear_BE_dino.py
│ │ ├── linear_BE_mae.py
│ │ ├── linear_BE_moco.py
│ │ ├── linear_BE_moco_v3.py
│ │ ├── linear_BE_sup.py
│ │ ├── linear_EU_data2vec.py
│ │ ├── linear_EU_dino.py
│ │ ├── linear_EU_mae.py
│ │ ├── linear_EU_moco.py
│ │ ├── linear_EU_moco_v3.py
│ │ ├── linear_SS_data2vec.py
│ │ ├── linear_SS_dino.py
│ │ ├── linear_SS_mae.py
│ │ ├── linear_SS_moco.py
│ │ ├── linear_SS_moco_v3.py
│ │ ├── models/
│ │ │ ├── data2vec/
│ │ │ │ ├── README.md
│ │ │ │ ├── README_Original.md
│ │ │ │ ├── dall_e/
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── decoder.py
│ │ │ │ │ ├── encoder.py
│ │ │ │ │ └── utils.py
│ │ │ │ ├── dataset_folder.py
│ │ │ │ ├── datasets.py
│ │ │ │ ├── engine_for_cyclical.py
│ │ │ │ ├── engine_for_cyclical_joint.py
│ │ │ │ ├── engine_for_finetuning.py
│ │ │ │ ├── engine_for_pretraining.py
│ │ │ │ ├── get_started_for_image_classification.md
│ │ │ │ ├── masking_generator.py
│ │ │ │ ├── modeling_cyclical.py
│ │ │ │ ├── modeling_cyclical_joint.py
│ │ │ │ ├── modeling_discrete_vae.py
│ │ │ │ ├── modeling_finetune.py
│ │ │ │ ├── modeling_pretrain.py
│ │ │ │ ├── models.py
│ │ │ │ ├── optim_factory.py
│ │ │ │ ├── requirements.txt
│ │ │ │ ├── run_beit_pretraining.py
│ │ │ │ ├── run_class_finetuning.py
│ │ │ │ ├── run_cyclical.py
│ │ │ │ ├── run_cyclical_joint.py
│ │ │ │ ├── semantic_segmentation/
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── backbone/
│ │ │ │ │ │ └── beit.py
│ │ │ │ │ ├── configs/
│ │ │ │ │ │ ├── _base_/
│ │ │ │ │ │ │ ├── datasets/
│ │ │ │ │ │ │ │ ├── ade20k.py
│ │ │ │ │ │ │ │ └── ade20k_640x640.py
│ │ │ │ │ │ │ ├── default_runtime.py
│ │ │ │ │ │ │ ├── models/
│ │ │ │ │ │ │ │ └── upernet_beit.py
│ │ │ │ │ │ │ └── schedules/
│ │ │ │ │ │ │ ├── schedule_160k.py
│ │ │ │ │ │ │ └── schedule_320k.py
│ │ │ │ │ │ └── beit/
│ │ │ │ │ │ └── upernet/
│ │ │ │ │ │ ├── upernet_beit_base_12_512_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_base_12_512_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_base_12_640_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_base_12_640_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_large_24_512_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_large_24_512_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_large_24_640_slide_160k_ade20k.py
│ │ │ │ │ │ └── upernet_beit_large_24_640_slide_160k_ade20k_ms.py
│ │ │ │ │ ├── mmcv_custom/
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── apex_runner/
│ │ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ │ ├── apex_iter_based_runner.py
│ │ │ │ │ │ │ ├── checkpoint.py
│ │ │ │ │ │ │ └── optimizer.py
│ │ │ │ │ │ ├── checkpoint.py
│ │ │ │ │ │ ├── layer_decay_optimizer_constructor.py
│ │ │ │ │ │ ├── resize_transform.py
│ │ │ │ │ │ └── train_api.py
│ │ │ │ │ └── tools/
│ │ │ │ │ ├── dist_test.sh
│ │ │ │ │ ├── dist_train.sh
│ │ │ │ │ ├── test.py
│ │ │ │ │ └── train.py
│ │ │ │ ├── transforms.py
│ │ │ │ └── utils.py
│ │ │ ├── dino/
│ │ │ │ ├── utils.py
│ │ │ │ └── vision_transformer.py
│ │ │ ├── mae/
│ │ │ │ ├── engine_finetune.py
│ │ │ │ ├── engine_finetune_BE.py
│ │ │ │ ├── engine_finetune_EU.py
│ │ │ │ ├── engine_finetune_SS.py
│ │ │ │ ├── engine_pretrain.py
│ │ │ │ ├── main_finetune.py
│ │ │ │ ├── main_linprobe.py
│ │ │ │ ├── main_pretrain.py
│ │ │ │ ├── models_mae.py
│ │ │ │ ├── models_vit.py
│ │ │ │ ├── submitit_finetune.py
│ │ │ │ ├── submitit_linprobe.py
│ │ │ │ ├── submitit_pretrain.py
│ │ │ │ └── util/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── crop.py
│ │ │ │ ├── datasets.py
│ │ │ │ ├── lars.py
│ │ │ │ ├── lr_decay.py
│ │ │ │ ├── lr_sched.py
│ │ │ │ ├── misc.py
│ │ │ │ └── pos_embed.py
│ │ │ ├── moco/
│ │ │ │ ├── builder.py
│ │ │ │ └── loader.py
│ │ │ ├── moco_v2/
│ │ │ │ ├── README.md
│ │ │ │ ├── __init__.py
│ │ │ │ ├── builder.py
│ │ │ │ ├── detection/
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── configs/
│ │ │ │ │ │ ├── Base-RCNN-C4-BN.yaml
│ │ │ │ │ │ ├── coco_R_50_C4_2x.yaml
│ │ │ │ │ │ ├── coco_R_50_C4_2x_moco.yaml
│ │ │ │ │ │ ├── pascal_voc_R_50_C4_24k.yaml
│ │ │ │ │ │ └── pascal_voc_R_50_C4_24k_moco.yaml
│ │ │ │ │ ├── convert-pretrain-to-detectron2.py
│ │ │ │ │ └── train_net.py
│ │ │ │ ├── loader.py
│ │ │ │ └── main_lincls.py
│ │ │ ├── moco_v3/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── builder.py
│ │ │ │ ├── loader.py
│ │ │ │ ├── optimizer.py
│ │ │ │ └── vits.py
│ │ │ ├── rs_transforms_float32.py
│ │ │ └── rs_transforms_uint8.py
│ │ └── scripts/
│ │ ├── ablation/
│ │ │ ├── srun_ImageNet_ft_moco-v2-ep800_pad_rn50_s2c_BE_10.sh
│ │ │ ├── srun_ImageNet_lc_moco-v2-ep200_pad_rn50_s2c_BE_10.sh
│ │ │ ├── srun_ImageNet_lc_moco-v2-ep800_pad_rn50_s2c_BE_10.sh
│ │ │ ├── srun_ImageNet_lc_sup_pad_rn50_s2c_BE_10.sh
│ │ │ ├── srun_ImageNet_lc_sup_reinit_rn50_s2c_BE_10.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_rgb_BE_10.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_rgb_BE_100.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_rgb_EU.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_s2c_BE_10.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_s2c_BE_100.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_s2c_EU.sh
│ │ │ ├── srun_SeCo_lc_moco_rn50_s2c_BE_10.sh
│ │ │ ├── srun_SeCo_lc_moco_rn50_s2c_BE_100.sh
│ │ │ └── srun_SeCo_lc_moco_rn50_s2c_EU.sh
│ │ └── benchmark/
│ │ ├── ft_data2vec_vit16_s2c_BE_100.sh
│ │ ├── ft_data2vec_vits16_s2c_EU_100.sh
│ │ ├── ft_data2vec_vits16_s2c_SS_100.sh
│ │ ├── lc_data2vec_vit16_s2c_BE_100.sh
│ │ ├── lc_data2vec_vits16_s2c_EU_100.sh
│ │ ├── lc_data2vec_vits16_s2c_SS_100.sh
│ │ ├── srun_ft_dino_rn50_s2c_BE.sh
│ │ ├── srun_ft_dino_rn50_s2c_EU.sh
│ │ ├── srun_ft_dino_rn50_s2c_SS.sh
│ │ ├── srun_ft_dino_vits16_s2_BE.sh
│ │ ├── srun_ft_dino_vits16_s2c_EU.sh
│ │ ├── srun_ft_dino_vits16_s2c_SS.sh
│ │ ├── srun_ft_mae_vits16_s2c_BE.sh
│ │ ├── srun_ft_mae_vits16_s2c_EU.sh
│ │ ├── srun_ft_mae_vits16_s2c_SS.sh
│ │ ├── srun_ft_moco_rn50_s2c_BE.sh
│ │ ├── srun_ft_moco_rn50_s2c_EU.sh
│ │ ├── srun_ft_moco_rn50_s2c_SS.sh
│ │ ├── srun_ft_moco_vits16_s2c_BE.sh
│ │ ├── srun_ft_moco_vits16_s2c_EU.sh
│ │ ├── srun_ft_moco_vits16_s2c_SS.sh
│ │ ├── srun_lc_dino_rn50_s2c_BE.sh
│ │ ├── srun_lc_dino_rn50_s2c_EU.sh
│ │ ├── srun_lc_dino_rn50_s2c_SS.sh
│ │ ├── srun_lc_dino_vits16_s2c_BE.sh
│ │ ├── srun_lc_dino_vits16_s2c_EU.sh
│ │ ├── srun_lc_dino_vits16_s2c_SS.sh
│ │ ├── srun_lc_mae_vits16_s2c_BE.sh
│ │ ├── srun_lc_mae_vits16_s2c_EU.sh
│ │ ├── srun_lc_mae_vits16_s2c_SS.sh
│ │ ├── srun_lc_moco_rn50_s2c_BE.sh
│ │ ├── srun_lc_moco_rn50_s2c_EU.sh
│ │ ├── srun_lc_moco_rn50_s2c_SS.sh
│ │ ├── srun_lc_moco_vits16_s2c_BE.sh
│ │ ├── srun_lc_moco_vits16_s2c_EU.sh
│ │ └── srun_lc_moco_vits16_s2c_SS.sh
│ ├── transfer_segmentation/
│ │ └── readme.md
│ └── utils/
│ ├── convert_model_torchvision.py
│ └── vis_tsne.ipynb
└── download_data/
├── convert_rgb.py
├── readme.md
├── ssl4eo_downloader.py
└── ssl4eo_s12_downloader.py
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FILE CONTENTS
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FILE: .gitignore
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**/.ipynb_checkpoints/*
**/.vscode/*
**/__pycache__/*
**/srun_outputs/*
**/checkpoints/*
**/lightning_logs/
**/results/*
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FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# SSL4EO-S12
The [SSL4EO-S12 dataset](https://arxiv.org/abs/2211.07044) is a large-scale multimodal multitemporal dataset for unsupervised/self-supervised pre-training in Earth observation. The dataset consists of unlabeled patch triplets (Sentinel-1 dual-pol SAR, Sentinel-2 top-of-atmosphere multispectral, Sentinel-2 surface reflectance multispectral) from 251079 locations across the globe, each patch covering 2640mx2640m and including four seasonal time stamps.
### Access the dataset
- [x] **Raw dataset**: The full SSL4EO-S12 dataset (1.5TB, 500GB for each modality) is accessible at [mediaTUM](https://mediatum.ub.tum.de/1660427). There are some void IDs (gaps in folder names), see `data/void_ids.csv`. Center coordinates of all locations are available [here](https://drive.google.com/file/d/1RyJnGznSbMparS88BhHkXxETf0K-qYqI/view?usp=sharing).
- [x] **Example subset**: An example 100-patch subset (600MB) is available at [Google Drive](https://drive.google.com/file/d/1sRWcYbaWs-efXza6kw03GlJQdZHq5iRN/view?usp=sharing).
- [x] **Compressed dataset**: A compressed 8-bit version (20-50GB for each modality, including an RGB version) is available at [mediaTUM](https://mediatum.ub.tum.de/1702379). The raw 16/32-bit values are normalized by mean and std and converted to uint8, plus a default geotiff JPEG compression with quality 75. *Note: in our experiments, 8-bit input (without JPEG compression) performs comparably well as 16-bit.*
- [ ] A 50k (random) RGB subset (18GB) is available here (link broken). Sample IDs see `data/50k_ids_random.csv`.
**Updates**
- For faster access in some regions, we have hosted a copy of the data in [HuggingFace](https://huggingface.co/datasets/wangyi111/SSL4EO-S12). Note that only the original data in mediaTUM has a proper DOI.
- We've got some feedback that the compressed dataset (with JPEG compression) has a performance drop compared to the raw data, which could be because of the lossy compression. We plan to update it with a lossless version (yet the file size will increase). Also, do you have INode (number of single files) limit on your server? We could consider updating one resampled GeoTiff for all bands (as in [SSL4EO-L](https://arxiv.org/abs/2306.09424)). If you have any issues or wish for updates, let us know!
### Collect your own data
Check [`src/download_data`](src/download_data) for instructions to download sentinel or other products from Google Earth Engine.
### Pre-trained models
The pre-trained models with different SSL methods are provided as follows (13 bands of S2-L1C, 100 epochs, input clip to [0,1] by dividing 10000).
| SSL method | Arch | BigEarthNet* | EuroSAT | So2Sat-LCZ42 | Download | | | Usage |
|:----------:|:--------:|:-----------:|:-------:|:------------:|:-----------------------------------------------------------------------------------------------------------:|:--------:|:----:|:----:|
| [MoCo](https://github.com/facebookresearch/moco) | ResNet50 | [91.8%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_moco_rn50_s2c_BE.sh) | [99.1%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_moco_rn50_s2c_EU.sh) | [60.9%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_moco_vits16_s2c_SS.sh) | [full ckpt](https://drive.google.com/file/d/1OrtPfG2wkO05bimstQ_T9Dza8z3zp8i-/view?usp=sharing) | [backbone](https://drive.google.com/file/d/1MAe3dCW4hPasSaBMZAVkJVX80LONkrLY/view?usp=sharing) | [logs](https://drive.google.com/file/d/1G66pdvJmeD6Rc-OZdOKA1h2Vnvq_0nnt/view?usp=sharing) | [define model](https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L228-L236), [load weights](https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L248-L276) |
| [MoCo](https://github.com/facebookresearch/moco-v3) | ViT-S/16 | [89.9%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_moco_vits16_s2c_BE.sh) | [98.6%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_moco_vits16_s2c_EU.sh) | [61.6%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_moco_vits16_s2c_SS.sh) | [full ckpt](https://drive.google.com/file/d/1Tx07L6OilkfcgE2HWiSXHRmRepCPdn6V/view?usp=sharing) | [backbone](https://drive.google.com/file/d/1LREGuI6w7Gq6Xm0jFQdxxtp8QkmLvJWk/view?usp=sharing) | [logs](https://drive.google.com/file/d/1f05B85T4Y2-RntfAw42uICKm9mwilHXF/view?usp=sharing) | [define model](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/linear_BE_moco_v3.py#L182-L184), [load weights](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/linear_BE_moco_v3.py#L199-L220) |
| [DINO](https://github.com/facebookresearch/dino) | ResNet50 | [90.7%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_dino_rn50_s2c_BE.sh) | [99.1%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_dino_rn50_s2c_EU.sh) | [63.6%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_dino_rn50_s2c_SS.sh) | [full ckpt](https://drive.google.com/file/d/1iSHHp_cudPjZlshqWXVZj5TK74P32a2q/view?usp=sharing) | [backbone](https://drive.google.com/file/d/1B4o_NvY7O6fJrvsOUR-7QzLYNpRL1ieA/view?usp=sharing) | [logs](https://drive.google.com/file/d/1VxjT-3n1ckbvnlsF81jZwmm9Wvb3YX0H/view?usp=sharing) | [define model](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/linear_BE_dino.py#L57-L61), [load weights](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/models/dino/utils.py#L92-L103) |
| [DINO](https://github.com/facebookresearch/dino) | ViT-S/16 | [90.5%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_dino_vits16_s2_BE.sh) | [99.0%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_dino_vits16_s2c_EU.sh) | [62.2%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_dino_vits16_s2c_SS.sh) | [full ckpt](https://drive.google.com/file/d/1CseO5vvMReGlAulm5o4ZgbjUgj8VlAH7/view?usp=sharing) | [backbone](https://drive.google.com/file/d/1kjQWfPRI5z43EmRkw5fzgHU01hB7E_4H/view?usp=sharing) | [logs](https://drive.google.com/file/d/1eeKrKFMa6akGyXugBRF6-rJ7oTIeZAno/view?usp=sharing) | [define model](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/linear_BE_dino.py#L53-L55), [load weights](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/models/dino/utils.py#L92-L103) |
| [MAE](https://github.com/facebookresearch/mae) | ViT-S/16 | [88.9%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_mae_vits16_s2c_BE.sh) | [98.7%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_mae_vits16_s2c_EU.sh) | [63.9%](src/benchmark/transfer_classification/scripts/benchmark/srun_ft_mae_vits16_s2c_SS.sh) | [full ckpt](https://drive.google.com/file/d/1QTBKl1asxgQCNd6bO2azXZNPfoQ3Sazv/view?usp=sharing) | [backbone](https://drive.google.com/file/d/1hdie-7orFnj5Q1E1C2BudqwQCvMk3Fza/view?usp=sharing) | [logs](https://drive.google.com/file/d/1uJojq9q_fKMdD6cO1YXCPguZYEmfj35s/view?usp=sharing) | [define model](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/linear_BE_mae.py#L232-L236), [load weights](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/linear_BE_mae.py#L238-L259) |
| [Data2vec](https://github.com/facebookresearch/fairseq/tree/main/examples/data2vec) | ViT-S/16 | [90.3%](src/benchmark/transfer_classification/scripts/benchmark/ft_data2vec_vit16_s2c_BE_100.sh) | [99.1%](src/benchmark/transfer_classification/scripts/benchmark/ft_data2vec_vits16_s2c_EU_100.sh) | [64.8%](src/benchmark/transfer_classification/scripts/benchmark/lc_data2vec_vits16_s2c_SS_100.sh) | [full ckpt](https://drive.google.com/file/d/1VbIGBwzZYndv4v1vx9FiD6IP-YwsHEns/view?usp=sharing) | [backbone](https://drive.google.com/file/d/1YecuYPAxl1NIzLmsmdbUROjCb5g0t80l/view?usp=sharing) | logs | [define model](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/linear_BE_data2vec.py#L372-L390), [load weights](https://github.com/zhu-xlab/SSL4EO-S12/blob/1a668f76fd46762a19780293675a6e23e5204e72/src/benchmark/transfer_classification/linear_BE_data2vec.py#L406-L553) |
\* Note the results for BigEarthNet are based on the train/val split following [SeCo](https://github.com/ServiceNow/seasonal-contrast/blob/8285173ec205b64bc3e53b880344dd6c3f79fa7a/datasets/bigearthnet_dataset.py#L119) and [In-domain representation learning for RS](https://github.com/google-research/google-research/tree/master/remote_sensing_representations).
Other pre-trained models:
| SSL method | Arch | Input | Download | | |
|:----------:|:--------:|:----------------:|:----------------------------------------------------------------------------------------------------------------------------:|:--------:|:----:|
| MoCo | ResNet18 | S2-L1C 13 bands | [full ckpt](https://drive.google.com/file/d/1iWLm7ljQ6tKZiVp47pJUPDe3Un0BUd9o/view?usp=sharing) | backbone | logs |
| | ResNet18 | S2-L1C RGB | [full ckpt](https://drive.google.com/file/d/1HfgXS5VpQA39k8mFrWMbHvYwuT_j6Mbi/view?usp=sharing), [full ckpt ep200](https://drive.google.com/file/d/1U_m39Owahk15Vg1uL1MYbPAmAyUWBKfI/view?usp=sharing) | backbone | logs |
| | ResNet50 | S2-L1C RGB | [full ckpt](https://drive.google.com/file/d/1UEpA9sOcA47W0cmwQhkSeXfQxrL-EcJB/view?usp=sharing) | backbone | logs |
| | ResNet50 | S1 SAR 2 bands | [full ckpt](https://drive.google.com/file/d/1gjTTWikf1qORJyFifWD1ksk9HzezqQ0b/view?usp=sharing) | [backbone](https://drive.google.com/file/d/1E5MvVI1SnQneQXe37QAWx_B6aoTiSN24/view?usp=sharing) | logs |
| MAE | ViT-S/16 | S1 SAR 2 bands | [full ckpt](https://huggingface.co/wangyi111/SSL4EO-S12/resolve/main/B2_vits16_mae_ep99.pth) | backbone |
| | ViT-B/16 | S1 SAR 2 bands | [full ckpt](https://huggingface.co/wangyi111/SSL4EO-S12/resolve/main/B2_vitb16_mae_ep99.pth) | backbone |
| | ViT-L/16 | S1 SAR 2 bands | [full ckpt](https://huggingface.co/wangyi111/SSL4EO-S12/resolve/main/B2_vitl16_mae_ep99.pth) | backbone |
| | ViT-H/14 | S1 SAR 2 bands | [full ckpt](https://huggingface.co/wangyi111/SSL4EO-S12/resolve/main/B2_vith14_mae_ep199.pth) | backbone |
| | ViT-B/16 | S2-L1C 13 bands | [full ckpt](https://huggingface.co/wangyi111/SSL4EO-S12/resolve/main/B13_vitb16_mae_ep99.pth) | backbone |
| | ViT-L/16 | S2-L1C 13 bands | [full ckpt](https://huggingface.co/wangyi111/SSL4EO-S12/resolve/main/B13_vitl16_mae_ep99.pth) | backbone |
| | ViT-H/14 | S2-L1C 13 bands | [full ckpt](https://huggingface.co/wangyi111/SSL4EO-S12/resolve/main/B13_vith14_mae_ep199.pth) | backbone |
**\* The pretrained models are also available in [TorchGeo](https://github.com/microsoft/torchgeo).**
### License
This repository is released under the Apache 2.0 license. The dataset and pretrained model weights are released under the CC-BY-4.0 license.
### Citation
```BibTeX
@article{wang2022ssl4eo,
title={SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation},
author={Wang, Yi and Braham, Nassim Ait Ali and Xiong, Zhitong and Liu, Chenying and Albrecht, Conrad M and Zhu, Xiao Xiang},
journal={arXiv preprint arXiv:2211.07044},
year={2022}
}
```
================================================
FILE: data/50k_ids_random.csv
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gitextract_cmyl0w8x/
├── .gitignore
├── LICENSE
├── README.md
├── data/
│ ├── 50k_ids_random.csv
│ └── void_ids.csv
└── src/
├── benchmark/
│ ├── pretrain_data2vec/
│ │ └── readme.md
│ ├── pretrain_ssl/
│ │ ├── datasets/
│ │ │ └── SSL4EO/
│ │ │ ├── ssl4eo_dataset.py
│ │ │ └── ssl4eo_dataset_lmdb.py
│ │ ├── models/
│ │ │ ├── data2vec/
│ │ │ │ ├── README.md
│ │ │ │ ├── README_Original.md
│ │ │ │ ├── dall_e/
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── decoder.py
│ │ │ │ │ ├── encoder.py
│ │ │ │ │ └── utils.py
│ │ │ │ ├── dataset_folder.py
│ │ │ │ ├── datasets.py
│ │ │ │ ├── engine_for_cyclical.py
│ │ │ │ ├── engine_for_cyclical_joint.py
│ │ │ │ ├── engine_for_finetuning.py
│ │ │ │ ├── engine_for_pretraining.py
│ │ │ │ ├── get_started_for_image_classification.md
│ │ │ │ ├── masking_generator.py
│ │ │ │ ├── modeling_cyclical.py
│ │ │ │ ├── modeling_cyclical_joint.py
│ │ │ │ ├── modeling_discrete_vae.py
│ │ │ │ ├── modeling_finetune.py
│ │ │ │ ├── modeling_pretrain.py
│ │ │ │ ├── models.py
│ │ │ │ ├── optim_factory.py
│ │ │ │ ├── requirements.txt
│ │ │ │ ├── run_beit_pretraining.py
│ │ │ │ ├── run_class_finetuning.py
│ │ │ │ ├── run_cyclical.py
│ │ │ │ ├── run_cyclical_joint.py
│ │ │ │ ├── semantic_segmentation/
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── backbone/
│ │ │ │ │ │ └── beit.py
│ │ │ │ │ ├── configs/
│ │ │ │ │ │ ├── _base_/
│ │ │ │ │ │ │ ├── datasets/
│ │ │ │ │ │ │ │ ├── ade20k.py
│ │ │ │ │ │ │ │ └── ade20k_640x640.py
│ │ │ │ │ │ │ ├── default_runtime.py
│ │ │ │ │ │ │ ├── models/
│ │ │ │ │ │ │ │ └── upernet_beit.py
│ │ │ │ │ │ │ └── schedules/
│ │ │ │ │ │ │ ├── schedule_160k.py
│ │ │ │ │ │ │ └── schedule_320k.py
│ │ │ │ │ │ └── beit/
│ │ │ │ │ │ └── upernet/
│ │ │ │ │ │ ├── upernet_beit_base_12_512_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_base_12_512_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_base_12_640_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_base_12_640_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_large_24_512_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_large_24_512_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_large_24_640_slide_160k_ade20k.py
│ │ │ │ │ │ └── upernet_beit_large_24_640_slide_160k_ade20k_ms.py
│ │ │ │ │ ├── mmcv_custom/
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── apex_runner/
│ │ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ │ ├── apex_iter_based_runner.py
│ │ │ │ │ │ │ ├── checkpoint.py
│ │ │ │ │ │ │ └── optimizer.py
│ │ │ │ │ │ ├── checkpoint.py
│ │ │ │ │ │ ├── layer_decay_optimizer_constructor.py
│ │ │ │ │ │ ├── resize_transform.py
│ │ │ │ │ │ └── train_api.py
│ │ │ │ │ └── tools/
│ │ │ │ │ ├── dist_test.sh
│ │ │ │ │ ├── dist_train.sh
│ │ │ │ │ ├── test.py
│ │ │ │ │ └── train.py
│ │ │ │ ├── transforms.py
│ │ │ │ └── utils.py
│ │ │ ├── dino/
│ │ │ │ ├── utils.py
│ │ │ │ └── vision_transformer.py
│ │ │ ├── mae/
│ │ │ │ ├── engine_finetune.py
│ │ │ │ ├── engine_finetune_BE.py
│ │ │ │ ├── engine_finetune_EU.py
│ │ │ │ ├── engine_finetune_SS.py
│ │ │ │ ├── engine_pretrain.py
│ │ │ │ ├── main_finetune.py
│ │ │ │ ├── main_linprobe.py
│ │ │ │ ├── main_pretrain.py
│ │ │ │ ├── models_mae.py
│ │ │ │ ├── models_vit.py
│ │ │ │ ├── submitit_finetune.py
│ │ │ │ ├── submitit_linprobe.py
│ │ │ │ ├── submitit_pretrain.py
│ │ │ │ └── util/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── crop.py
│ │ │ │ ├── datasets.py
│ │ │ │ ├── lars.py
│ │ │ │ ├── lr_decay.py
│ │ │ │ ├── lr_sched.py
│ │ │ │ ├── misc.py
│ │ │ │ └── pos_embed.py
│ │ │ ├── moco/
│ │ │ │ ├── builder.py
│ │ │ │ └── loader.py
│ │ │ ├── moco_v2/
│ │ │ │ ├── README.md
│ │ │ │ ├── __init__.py
│ │ │ │ ├── builder.py
│ │ │ │ ├── detection/
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── configs/
│ │ │ │ │ │ ├── Base-RCNN-C4-BN.yaml
│ │ │ │ │ │ ├── coco_R_50_C4_2x.yaml
│ │ │ │ │ │ ├── coco_R_50_C4_2x_moco.yaml
│ │ │ │ │ │ ├── pascal_voc_R_50_C4_24k.yaml
│ │ │ │ │ │ └── pascal_voc_R_50_C4_24k_moco.yaml
│ │ │ │ │ ├── convert-pretrain-to-detectron2.py
│ │ │ │ │ └── train_net.py
│ │ │ │ ├── loader.py
│ │ │ │ └── main_lincls.py
│ │ │ ├── moco_v3/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── builder.py
│ │ │ │ ├── loader.py
│ │ │ │ ├── optimizer.py
│ │ │ │ └── vits.py
│ │ │ ├── rs_transforms_float32.py
│ │ │ └── rs_transforms_uint8.py
│ │ ├── pretrain_data2vec.py
│ │ ├── pretrain_dino_s2c.py
│ │ ├── pretrain_mae_s2c.py
│ │ ├── pretrain_moco_v2_s2c.py
│ │ ├── pretrain_moco_v2_seco_ms.py
│ │ ├── pretrain_moco_v2_sen12ms_ms.py
│ │ ├── pretrain_moco_v3_s2c.py
│ │ └── scripts/
│ │ └── pretrain/
│ │ ├── srun_train_SEN12MS_moco_rn50_rgb.sh
│ │ ├── srun_train_SEN12MS_moco_rn50_s2c.sh
│ │ ├── srun_train_SeCo_moco_rn50_s2a.sh
│ │ ├── srun_train_dino_rn50_s2c.sh
│ │ ├── srun_train_dino_vits16_s2c.sh
│ │ ├── srun_train_mae_vits16_s2c.sh
│ │ ├── srun_train_moco_rn50_s2c.sh
│ │ ├── srun_train_moco_vits16_s2c.sh
│ │ ├── submit_pretrain_data2vec.sh
│ │ └── train_data2vec_vits16_s2c.sh
│ ├── transfer_change_detection/
│ │ ├── datasets/
│ │ │ ├── __init__.py
│ │ │ ├── oscd_datamodule.py
│ │ │ └── oscd_dataset.py
│ │ ├── main_oscd.py
│ │ ├── models/
│ │ │ ├── __init__.py
│ │ │ └── segmentation.py
│ │ ├── readme.md
│ │ ├── test.sh
│ │ ├── train.sh
│ │ ├── utils/
│ │ │ ├── __init__.py
│ │ │ ├── data.py
│ │ │ └── transforms.py
│ │ └── validate_oscd.py
│ ├── transfer_classification/
│ │ ├── datasets/
│ │ │ ├── BigEarthNet/
│ │ │ │ ├── bigearthnet_dataset_seco.py
│ │ │ │ └── bigearthnet_dataset_seco_lmdb_s2_uint8.py
│ │ │ ├── EuroSat/
│ │ │ │ └── eurosat_dataset.py
│ │ │ └── So2Sat/
│ │ │ └── so2sat_lcz42_dataset.py
│ │ ├── linear_BE_data2vec.py
│ │ ├── linear_BE_dino.py
│ │ ├── linear_BE_mae.py
│ │ ├── linear_BE_moco.py
│ │ ├── linear_BE_moco_v3.py
│ │ ├── linear_BE_sup.py
│ │ ├── linear_EU_data2vec.py
│ │ ├── linear_EU_dino.py
│ │ ├── linear_EU_mae.py
│ │ ├── linear_EU_moco.py
│ │ ├── linear_EU_moco_v3.py
│ │ ├── linear_SS_data2vec.py
│ │ ├── linear_SS_dino.py
│ │ ├── linear_SS_mae.py
│ │ ├── linear_SS_moco.py
│ │ ├── linear_SS_moco_v3.py
│ │ ├── models/
│ │ │ ├── data2vec/
│ │ │ │ ├── README.md
│ │ │ │ ├── README_Original.md
│ │ │ │ ├── dall_e/
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── decoder.py
│ │ │ │ │ ├── encoder.py
│ │ │ │ │ └── utils.py
│ │ │ │ ├── dataset_folder.py
│ │ │ │ ├── datasets.py
│ │ │ │ ├── engine_for_cyclical.py
│ │ │ │ ├── engine_for_cyclical_joint.py
│ │ │ │ ├── engine_for_finetuning.py
│ │ │ │ ├── engine_for_pretraining.py
│ │ │ │ ├── get_started_for_image_classification.md
│ │ │ │ ├── masking_generator.py
│ │ │ │ ├── modeling_cyclical.py
│ │ │ │ ├── modeling_cyclical_joint.py
│ │ │ │ ├── modeling_discrete_vae.py
│ │ │ │ ├── modeling_finetune.py
│ │ │ │ ├── modeling_pretrain.py
│ │ │ │ ├── models.py
│ │ │ │ ├── optim_factory.py
│ │ │ │ ├── requirements.txt
│ │ │ │ ├── run_beit_pretraining.py
│ │ │ │ ├── run_class_finetuning.py
│ │ │ │ ├── run_cyclical.py
│ │ │ │ ├── run_cyclical_joint.py
│ │ │ │ ├── semantic_segmentation/
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── backbone/
│ │ │ │ │ │ └── beit.py
│ │ │ │ │ ├── configs/
│ │ │ │ │ │ ├── _base_/
│ │ │ │ │ │ │ ├── datasets/
│ │ │ │ │ │ │ │ ├── ade20k.py
│ │ │ │ │ │ │ │ └── ade20k_640x640.py
│ │ │ │ │ │ │ ├── default_runtime.py
│ │ │ │ │ │ │ ├── models/
│ │ │ │ │ │ │ │ └── upernet_beit.py
│ │ │ │ │ │ │ └── schedules/
│ │ │ │ │ │ │ ├── schedule_160k.py
│ │ │ │ │ │ │ └── schedule_320k.py
│ │ │ │ │ │ └── beit/
│ │ │ │ │ │ └── upernet/
│ │ │ │ │ │ ├── upernet_beit_base_12_512_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_base_12_512_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_base_12_640_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_base_12_640_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_large_24_512_slide_160k_ade20k.py
│ │ │ │ │ │ ├── upernet_beit_large_24_512_slide_160k_ade20k_ms.py
│ │ │ │ │ │ ├── upernet_beit_large_24_640_slide_160k_ade20k.py
│ │ │ │ │ │ └── upernet_beit_large_24_640_slide_160k_ade20k_ms.py
│ │ │ │ │ ├── mmcv_custom/
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── apex_runner/
│ │ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ │ ├── apex_iter_based_runner.py
│ │ │ │ │ │ │ ├── checkpoint.py
│ │ │ │ │ │ │ └── optimizer.py
│ │ │ │ │ │ ├── checkpoint.py
│ │ │ │ │ │ ├── layer_decay_optimizer_constructor.py
│ │ │ │ │ │ ├── resize_transform.py
│ │ │ │ │ │ └── train_api.py
│ │ │ │ │ └── tools/
│ │ │ │ │ ├── dist_test.sh
│ │ │ │ │ ├── dist_train.sh
│ │ │ │ │ ├── test.py
│ │ │ │ │ └── train.py
│ │ │ │ ├── transforms.py
│ │ │ │ └── utils.py
│ │ │ ├── dino/
│ │ │ │ ├── utils.py
│ │ │ │ └── vision_transformer.py
│ │ │ ├── mae/
│ │ │ │ ├── engine_finetune.py
│ │ │ │ ├── engine_finetune_BE.py
│ │ │ │ ├── engine_finetune_EU.py
│ │ │ │ ├── engine_finetune_SS.py
│ │ │ │ ├── engine_pretrain.py
│ │ │ │ ├── main_finetune.py
│ │ │ │ ├── main_linprobe.py
│ │ │ │ ├── main_pretrain.py
│ │ │ │ ├── models_mae.py
│ │ │ │ ├── models_vit.py
│ │ │ │ ├── submitit_finetune.py
│ │ │ │ ├── submitit_linprobe.py
│ │ │ │ ├── submitit_pretrain.py
│ │ │ │ └── util/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── crop.py
│ │ │ │ ├── datasets.py
│ │ │ │ ├── lars.py
│ │ │ │ ├── lr_decay.py
│ │ │ │ ├── lr_sched.py
│ │ │ │ ├── misc.py
│ │ │ │ └── pos_embed.py
│ │ │ ├── moco/
│ │ │ │ ├── builder.py
│ │ │ │ └── loader.py
│ │ │ ├── moco_v2/
│ │ │ │ ├── README.md
│ │ │ │ ├── __init__.py
│ │ │ │ ├── builder.py
│ │ │ │ ├── detection/
│ │ │ │ │ ├── README.md
│ │ │ │ │ ├── configs/
│ │ │ │ │ │ ├── Base-RCNN-C4-BN.yaml
│ │ │ │ │ │ ├── coco_R_50_C4_2x.yaml
│ │ │ │ │ │ ├── coco_R_50_C4_2x_moco.yaml
│ │ │ │ │ │ ├── pascal_voc_R_50_C4_24k.yaml
│ │ │ │ │ │ └── pascal_voc_R_50_C4_24k_moco.yaml
│ │ │ │ │ ├── convert-pretrain-to-detectron2.py
│ │ │ │ │ └── train_net.py
│ │ │ │ ├── loader.py
│ │ │ │ └── main_lincls.py
│ │ │ ├── moco_v3/
│ │ │ │ ├── __init__.py
│ │ │ │ ├── builder.py
│ │ │ │ ├── loader.py
│ │ │ │ ├── optimizer.py
│ │ │ │ └── vits.py
│ │ │ ├── rs_transforms_float32.py
│ │ │ └── rs_transforms_uint8.py
│ │ └── scripts/
│ │ ├── ablation/
│ │ │ ├── srun_ImageNet_ft_moco-v2-ep800_pad_rn50_s2c_BE_10.sh
│ │ │ ├── srun_ImageNet_lc_moco-v2-ep200_pad_rn50_s2c_BE_10.sh
│ │ │ ├── srun_ImageNet_lc_moco-v2-ep800_pad_rn50_s2c_BE_10.sh
│ │ │ ├── srun_ImageNet_lc_sup_pad_rn50_s2c_BE_10.sh
│ │ │ ├── srun_ImageNet_lc_sup_reinit_rn50_s2c_BE_10.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_rgb_BE_10.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_rgb_BE_100.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_rgb_EU.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_s2c_BE_10.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_s2c_BE_100.sh
│ │ │ ├── srun_SEN12MS_lc_moco_rn50_s2c_EU.sh
│ │ │ ├── srun_SeCo_lc_moco_rn50_s2c_BE_10.sh
│ │ │ ├── srun_SeCo_lc_moco_rn50_s2c_BE_100.sh
│ │ │ └── srun_SeCo_lc_moco_rn50_s2c_EU.sh
│ │ └── benchmark/
│ │ ├── ft_data2vec_vit16_s2c_BE_100.sh
│ │ ├── ft_data2vec_vits16_s2c_EU_100.sh
│ │ ├── ft_data2vec_vits16_s2c_SS_100.sh
│ │ ├── lc_data2vec_vit16_s2c_BE_100.sh
│ │ ├── lc_data2vec_vits16_s2c_EU_100.sh
│ │ ├── lc_data2vec_vits16_s2c_SS_100.sh
│ │ ├── srun_ft_dino_rn50_s2c_BE.sh
│ │ ├── srun_ft_dino_rn50_s2c_EU.sh
│ │ ├── srun_ft_dino_rn50_s2c_SS.sh
│ │ ├── srun_ft_dino_vits16_s2_BE.sh
│ │ ├── srun_ft_dino_vits16_s2c_EU.sh
│ │ ├── srun_ft_dino_vits16_s2c_SS.sh
│ │ ├── srun_ft_mae_vits16_s2c_BE.sh
│ │ ├── srun_ft_mae_vits16_s2c_EU.sh
│ │ ├── srun_ft_mae_vits16_s2c_SS.sh
│ │ ├── srun_ft_moco_rn50_s2c_BE.sh
│ │ ├── srun_ft_moco_rn50_s2c_EU.sh
│ │ ├── srun_ft_moco_rn50_s2c_SS.sh
│ │ ├── srun_ft_moco_vits16_s2c_BE.sh
│ │ ├── srun_ft_moco_vits16_s2c_EU.sh
│ │ ├── srun_ft_moco_vits16_s2c_SS.sh
│ │ ├── srun_lc_dino_rn50_s2c_BE.sh
│ │ ├── srun_lc_dino_rn50_s2c_EU.sh
│ │ ├── srun_lc_dino_rn50_s2c_SS.sh
│ │ ├── srun_lc_dino_vits16_s2c_BE.sh
│ │ ├── srun_lc_dino_vits16_s2c_EU.sh
│ │ ├── srun_lc_dino_vits16_s2c_SS.sh
│ │ ├── srun_lc_mae_vits16_s2c_BE.sh
│ │ ├── srun_lc_mae_vits16_s2c_EU.sh
│ │ ├── srun_lc_mae_vits16_s2c_SS.sh
│ │ ├── srun_lc_moco_rn50_s2c_BE.sh
│ │ ├── srun_lc_moco_rn50_s2c_EU.sh
│ │ ├── srun_lc_moco_rn50_s2c_SS.sh
│ │ ├── srun_lc_moco_vits16_s2c_BE.sh
│ │ ├── srun_lc_moco_vits16_s2c_EU.sh
│ │ └── srun_lc_moco_vits16_s2c_SS.sh
│ ├── transfer_segmentation/
│ │ └── readme.md
│ └── utils/
│ ├── convert_model_torchvision.py
│ └── vis_tsne.ipynb
└── download_data/
├── convert_rgb.py
├── readme.md
├── ssl4eo_downloader.py
└── ssl4eo_s12_downloader.py
SYMBOL INDEX (1795 symbols across 175 files)
FILE: src/benchmark/pretrain_ssl/datasets/SSL4EO/ssl4eo_dataset.py
function normalize (line 36) | def normalize(img, mean, std):
class SSL4EO (line 45) | class SSL4EO(torch.utils.data.Dataset):
method __init__ (line 47) | def __init__(self,root, normalize=False, mode=['s1','s2a','s2c'], dtyp...
method __getitem__ (line 56) | def __getitem__(self,index):
method __len__ (line 75) | def __len__(self):
method get_array (line 78) | def get_array(self, patch_id, mode):
class Subset (line 127) | class Subset(Dataset):
method __init__ (line 129) | def __init__(self, dataset, indices):
method __getitem__ (line 133) | def __getitem__(self, idx):
method __len__ (line 136) | def __len__(self):
method __getattr__ (line 139) | def __getattr__(self, name):
function random_subset (line 143) | def random_subset(dataset, frac, seed=None):
class _RepeatSampler (line 149) | class _RepeatSampler(object):
method __init__ (line 156) | def __init__(self, sampler):
method __iter__ (line 159) | def __iter__(self):
class InfiniteDataLoader (line 164) | class InfiniteDataLoader(DataLoader):
method __init__ (line 170) | def __init__(self, *args, **kwargs):
method __len__ (line 175) | def __len__(self):
method __iter__ (line 178) | def __iter__(self):
function make_lmdb (line 183) | def make_lmdb(dataset, lmdb_file, num_workers=6,mode=['s1','s2a','s2c']):
FILE: src/benchmark/pretrain_ssl/datasets/SSL4EO/ssl4eo_dataset_lmdb.py
function normalize (line 23) | def normalize(img, mean, std):
class Subset (line 32) | class Subset(Dataset):
method __init__ (line 34) | def __init__(self, dataset, indices):
method __getitem__ (line 38) | def __getitem__(self, idx):
method __len__ (line 41) | def __len__(self):
method __getattr__ (line 44) | def __getattr__(self, name):
function random_subset (line 48) | def random_subset(dataset, frac, seed=None):
class _RepeatSampler (line 54) | class _RepeatSampler(object):
method __init__ (line 61) | def __init__(self, sampler):
method __iter__ (line 64) | def __iter__(self):
class InfiniteDataLoader (line 69) | class InfiniteDataLoader(DataLoader):
method __init__ (line 75) | def __init__(self, *args, **kwargs):
method __len__ (line 80) | def __len__(self):
method __iter__ (line 83) | def __iter__(self):
function make_lmdb (line 88) | def make_lmdb(dataset, lmdb_file, num_workers=6,mode=['s1','s2a','s2c']):
class LMDBDataset (line 121) | class LMDBDataset(Dataset):
method __init__ (line 123) | def __init__(self, lmdb_file, is_slurm_job=False, s1_transform=None, s...
method _init_db (line 146) | def _init_db(self):
method __getitem__ (line 152) | def __getitem__(self, index):
method __len__ (line 228) | def __len__(self):
class TwoCropsTransform (line 247) | class TwoCropsTransform:
method __init__ (line 250) | def __init__(self, base_transform, season='fixed'):
method __call__ (line 254) | def __call__(self, x):
class GaussianBlur (line 276) | class GaussianBlur(object):
method __init__ (line 279) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 282) | def __call__(self, x):
class RandomBrightness (line 289) | class RandomBrightness(object):
method __init__ (line 292) | def __init__(self, brightness=0.4):
method __call__ (line 295) | def __call__(self, sample):
class RandomContrast (line 301) | class RandomContrast(object):
method __init__ (line 304) | def __init__(self, contrast=0.4):
method __call__ (line 307) | def __call__(self, sample):
class ToGray (line 313) | class ToGray(object):
method __init__ (line 314) | def __init__(self, out_channels):
method __call__ (line 316) | def __call__(self,sample):
class RandomChannelDrop (line 323) | class RandomChannelDrop(object):
method __init__ (line 326) | def __init__(self, min_n_drop=1, max_n_drop=8):
method __call__ (line 330) | def __call__(self, sample):
FILE: src/benchmark/pretrain_ssl/models/data2vec/dall_e/__init__.py
function load_model (line 9) | def load_model(path: str, device: torch.device = None) -> nn.Module:
FILE: src/benchmark/pretrain_ssl/models/data2vec/dall_e/decoder.py
class DecoderBlock (line 13) | class DecoderBlock(nn.Module):
method __attrs_post_init__ (line 21) | def __attrs_post_init__(self) -> None:
method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
class Decoder (line 42) | class Decoder(nn.Module):
method __attrs_post_init__ (line 54) | def __attrs_post_init__(self) -> None:
method forward (line 86) | def forward(self, x: torch.Tensor) -> torch.Tensor:
FILE: src/benchmark/pretrain_ssl/models/data2vec/dall_e/encoder.py
class EncoderBlock (line 13) | class EncoderBlock(nn.Module):
method __attrs_post_init__ (line 21) | def __attrs_post_init__(self) -> None:
method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
class Encoder (line 42) | class Encoder(nn.Module):
method __attrs_post_init__ (line 53) | def __attrs_post_init__(self) -> None:
method forward (line 85) | def forward(self, x: torch.Tensor) -> torch.Tensor:
FILE: src/benchmark/pretrain_ssl/models/data2vec/dall_e/utils.py
class Conv2d (line 11) | class Conv2d(nn.Module):
method __attrs_post_init__ (line 20) | def __attrs_post_init__(self) -> None:
method forward (line 31) | def forward(self, x: torch.Tensor) -> torch.Tensor:
function map_pixels (line 45) | def map_pixels(x: torch.Tensor) -> torch.Tensor:
function unmap_pixels (line 51) | def unmap_pixels(x: torch.Tensor) -> torch.Tensor:
FILE: src/benchmark/pretrain_ssl/models/data2vec/dataset_folder.py
function has_file_allowed_extension (line 21) | def has_file_allowed_extension(filename: str, extensions: Tuple[str, ......
function is_image_file (line 34) | def is_image_file(filename: str) -> bool:
function make_dataset (line 46) | def make_dataset(
class DatasetFolder (line 76) | class DatasetFolder(VisionDataset):
method __init__ (line 108) | def __init__(
method _find_classes (line 138) | def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
method __getitem__ (line 156) | def __getitem__(self, index: int) -> Tuple[Any, Any]:
method __len__ (line 180) | def __len__(self) -> int:
function pil_loader (line 187) | def pil_loader(path: str) -> Image.Image:
function accimage_loader (line 195) | def accimage_loader(path: str) -> Any:
function default_loader (line 204) | def default_loader(path: str) -> Any:
class ImageFolder (line 234) | class ImageFolder(DatasetFolder):
method __init__ (line 261) | def __init__(
FILE: src/benchmark/pretrain_ssl/models/data2vec/datasets.py
class SeasonTransform (line 35) | class SeasonTransform:
method __init__ (line 37) | def __init__(self, base_transform, season='fixed'):
method __call__ (line 41) | def __call__(self, x):
class DataAugmentations (line 64) | class DataAugmentations(object):
method __init__ (line 65) | def __init__(self, args):
method __call__ (line 130) | def __call__(self, image):
method __repr__ (line 141) | def __repr__(self):
function build_beit_pretraining_dataset (line 152) | def build_beit_pretraining_dataset(args):
function build_transform (line 167) | def build_transform(is_train, args):
FILE: src/benchmark/pretrain_ssl/models/data2vec/engine_for_cyclical.py
function train_one_epoch (line 23) | def train_one_epoch(model: torch.nn.Module, model_ema: torch.nn.Module, ...
FILE: src/benchmark/pretrain_ssl/models/data2vec/engine_for_cyclical_joint.py
function train_one_epoch (line 24) | def train_one_epoch(model: torch.nn.Module, model_ema: torch.nn.Module, ...
FILE: src/benchmark/pretrain_ssl/models/data2vec/engine_for_finetuning.py
function train_class_batch (line 27) | def train_class_batch(model, samples, target, criterion, bool_masked_pos...
function get_loss_scale_for_deepspeed (line 33) | def get_loss_scale_for_deepspeed(model):
function train_one_epoch (line 38) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 173) | def evaluate(data_loader, model, device, metric='acc', padd=False, oneho...
FILE: src/benchmark/pretrain_ssl/models/data2vec/engine_for_pretraining.py
function train_one_epoch (line 22) | def train_one_epoch(model: torch.nn.Module, d_vae: torch.nn.Module,
FILE: src/benchmark/pretrain_ssl/models/data2vec/masking_generator.py
class MaskingGenerator (line 29) | class MaskingGenerator:
method __init__ (line 30) | def __init__(
method __repr__ (line 46) | def __repr__(self):
method get_shape (line 52) | def get_shape(self):
method _mask (line 55) | def _mask(self, mask, max_mask_patches):
method __call__ (line 79) | def __call__(self):
FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_cyclical.py
function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0.0, std=1.0):
class VisionTransformerForCyclicalTraining (line 32) | class VisionTransformerForCyclicalTraining(nn.Module):
method __init__ (line 33) | def __init__(
method fix_init_weight (line 127) | def fix_init_weight(self):
method _init_weights (line 135) | def _init_weights(self, m):
method no_weight_decay (line 149) | def no_weight_decay(self):
method get_num_layers (line 152) | def get_num_layers(self):
method forward_features (line 155) | def forward_features(self, x, bool_masked_pos, layer_results):
method forward (line 194) | def forward(self, x, bool_masked_pos, return_all_tokens=False, layer_r...
function beit_base_patch16_224 (line 214) | def beit_base_patch16_224(pretrained=False, **kwargs):
function beit_small_patch16_224 (line 234) | def beit_small_patch16_224(pretrained=False, **kwargs):
function beit_large_patch16_224 (line 255) | def beit_large_patch16_224(pretrained=False, **kwargs):
function beit_huge_patch16_224 (line 275) | def beit_huge_patch16_224(pretrained=False, **kwargs):
FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_cyclical_joint.py
function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0.0, std=1.0):
class VisionTransformerForCyclicalJointTraining (line 32) | class VisionTransformerForCyclicalJointTraining(nn.Module):
method __init__ (line 33) | def __init__(
method fix_init_weight (line 126) | def fix_init_weight(self):
method _init_weights (line 134) | def _init_weights(self, m):
method no_weight_decay (line 148) | def no_weight_decay(self):
method get_num_layers (line 151) | def get_num_layers(self):
method forward_features (line 154) | def forward_features(self, x, bool_masked_pos, layer_results):
method forward (line 183) | def forward(self, x, bool_masked_pos, return_all_tokens=False, layer_r...
function beit_base_joint_patch16_224 (line 203) | def beit_base_joint_patch16_224(pretrained=False, **kwargs):
FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_discrete_vae.py
function top_k (line 19) | def top_k(logits, thres = 0.5):
function exists (line 28) | def exists(val):
function default (line 32) | def default(val, d):
function eval_decorator (line 36) | def eval_decorator(fn):
class BasicVAE (line 46) | class BasicVAE(nn.Module):
method get_codebook_indices (line 48) | def get_codebook_indices(self, images):
method decode (line 51) | def decode(self, img_seq):
method get_codebook_probs (line 54) | def get_codebook_probs(self, img_seq):
method get_image_tokens_size (line 57) | def get_image_tokens_size(self):
method get_image_size (line 60) | def get_image_size(self):
class ResBlock (line 64) | class ResBlock(nn.Module):
method __init__ (line 65) | def __init__(self, chan_in, hidden_size, chan_out):
method forward (line 75) | def forward(self, x):
class DiscreteVAE (line 79) | class DiscreteVAE(BasicVAE):
method __init__ (line 80) | def __init__(
method get_image_size (line 127) | def get_image_size(self):
method get_image_tokens_size (line 130) | def get_image_tokens_size(self):
method get_codebook_indices (line 135) | def get_codebook_indices(self, images):
method get_codebook_probs (line 142) | def get_codebook_probs(self, images):
method decode (line 146) | def decode(
method forward (line 158) | def forward(
class Dalle_VAE (line 206) | class Dalle_VAE(BasicVAE):
method __init__ (line 207) | def __init__(self, image_size):
method load_model (line 213) | def load_model(self, model_dir, device):
method decode (line 217) | def decode(self, img_seq):
method get_codebook_indices (line 223) | def get_codebook_indices(self, images):
method get_codebook_probs (line 227) | def get_codebook_probs(self, images):
method forward (line 231) | def forward(self, img_seq_prob, no_process=False):
FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_finetune.py
function _cfg (line 23) | def _cfg(url='', **kwargs):
class DropPath (line 33) | class DropPath(nn.Module):
method __init__ (line 36) | def __init__(self, drop_prob=None):
method forward (line 40) | def forward(self, x):
method extra_repr (line 43) | def extra_repr(self) -> str:
class Mlp (line 47) | class Mlp(nn.Module):
method __init__ (line 48) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 57) | def forward(self, x):
class Attention (line 67) | class Attention(nn.Module):
method __init__ (line 68) | def __init__(
method forward (line 121) | def forward(self, x, rel_pos_bias=None):
class Block (line 154) | class Block(nn.Module):
method __init__ (line 156) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
method forward (line 177) | def forward(self, x, rel_pos_bias=None):
class PatchEmbed (line 189) | class PatchEmbed(nn.Module):
method __init__ (line 192) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 204) | def forward(self, x, **kwargs):
class RelativePositionBias (line 213) | class RelativePositionBias(nn.Module):
method __init__ (line 215) | def __init__(self, window_size, num_heads):
method forward (line 244) | def forward(self):
class VisionTransformer (line 252) | class VisionTransformer(nn.Module):
method __init__ (line 255) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
method fix_init_weight (line 315) | def fix_init_weight(self):
method _init_weights (line 323) | def _init_weights(self, m):
method get_num_layers (line 334) | def get_num_layers(self):
method no_weight_decay (line 338) | def no_weight_decay(self):
method get_classifier (line 341) | def get_classifier(self):
method reset_classifier (line 344) | def reset_classifier(self, num_classes, global_pool=''):
method forward_features (line 348) | def forward_features(self, x, bool_masked_pos=None):
method forward (line 391) | def forward(self, x, bool_masked_pos=None):
function beit_small_patch16_224 (line 398) | def beit_small_patch16_224(pretrained=False, **kwargs):
function beit_base_patch16_224 (line 407) | def beit_base_patch16_224(pretrained=False, **kwargs):
function beit_base_patch16_384 (line 416) | def beit_base_patch16_384(pretrained=False, **kwargs):
function beit_large_patch16_224 (line 425) | def beit_large_patch16_224(pretrained=False, **kwargs):
function beit_large_patch16_384 (line 434) | def beit_large_patch16_384(pretrained=False, **kwargs):
function beit_large_patch16_512 (line 443) | def beit_large_patch16_512(pretrained=False, **kwargs):
FILE: src/benchmark/pretrain_ssl/models/data2vec/modeling_pretrain.py
function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0., std=1.):
class VisionTransformerForMaskedImageModeling (line 32) | class VisionTransformerForMaskedImageModeling(nn.Module):
method __init__ (line 33) | def __init__(self, img_size=224, patch_size=16, in_chans=3, vocab_size...
method fix_init_weight (line 79) | def fix_init_weight(self):
method _init_weights (line 87) | def _init_weights(self, m):
method no_weight_decay (line 101) | def no_weight_decay(self):
method get_num_layers (line 104) | def get_num_layers(self):
method forward_features (line 107) | def forward_features(self, x, bool_masked_pos):
method forward (line 129) | def forward(self, x, bool_masked_pos, return_all_tokens=False):
function beit_base_patch16_224_8k_vocab (line 140) | def beit_base_patch16_224_8k_vocab(pretrained=False, **kwargs):
function beit_large_patch16_224_8k_vocab (line 155) | def beit_large_patch16_224_8k_vocab(pretrained=False, **kwargs):
FILE: src/benchmark/pretrain_ssl/models/data2vec/models.py
function _cfg (line 32) | def _cfg(url='', **kwargs):
class Attention (line 71) | class Attention(nn.Module):
method __init__ (line 72) | def __init__(
method forward (line 127) | def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
class Block (line 157) | class Block(nn.Module):
method __init__ (line 159) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=...
method forward (line 179) | def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
class RelativePositionBias (line 189) | class RelativePositionBias(nn.Module):
method __init__ (line 191) | def __init__(self, window_size, num_heads):
method forward (line 220) | def forward(self):
class Beit (line 228) | class Beit(nn.Module):
method __init__ (line 232) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
method fix_init_weight (line 281) | def fix_init_weight(self):
method _init_weights (line 289) | def _init_weights(self, m):
method get_num_layers (line 298) | def get_num_layers(self):
method no_weight_decay (line 302) | def no_weight_decay(self):
method get_classifier (line 305) | def get_classifier(self):
method reset_classifier (line 308) | def reset_classifier(self, num_classes, global_pool=''):
method forward_features (line 312) | def forward_features(self, x):
method forward (line 333) | def forward(self, x):
function _create_beit (line 339) | def _create_beit(variant, pretrained=False, default_cfg=None, **kwargs):
function beit_base_patch16_224 (line 354) | def beit_base_patch16_224(pretrained=False, **kwargs):
function beit_base_patch16_384 (line 366) | def beit_base_patch16_384(pretrained=False, **kwargs):
function beit_base_patch16_224_in22k (line 375) | def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
function beit_large_patch16_224 (line 384) | def beit_large_patch16_224(pretrained=False, **kwargs):
function beit_large_patch16_384 (line 393) | def beit_large_patch16_384(pretrained=False, **kwargs):
function beit_large_patch16_512 (line 402) | def beit_large_patch16_512(pretrained=False, **kwargs):
function beit_large_patch16_224_in22k (line 411) | def beit_large_patch16_224_in22k(pretrained=False, **kwargs):
FILE: src/benchmark/pretrain_ssl/models/data2vec/optim_factory.py
function get_num_layer_for_vit (line 33) | def get_num_layer_for_vit(var_name, num_max_layer):
class LayerDecayValueAssigner (line 47) | class LayerDecayValueAssigner(object):
method __init__ (line 48) | def __init__(self, values):
method get_scale (line 51) | def get_scale(self, layer_id):
method get_layer_id (line 54) | def get_layer_id(self, var_name):
function get_parameter_groups (line 58) | def get_parameter_groups(model, weight_decay=1e-5, skip_list=(), get_num...
function create_optimizer (line 100) | def create_optimizer(args, model, get_num_layer=None, get_layer_scale=No...
FILE: src/benchmark/pretrain_ssl/models/data2vec/run_beit_pretraining.py
function get_args (line 34) | def get_args():
function get_model (line 139) | def get_model(args):
function main (line 154) | def main(args):
FILE: src/benchmark/pretrain_ssl/models/data2vec/run_class_finetuning.py
function get_args (line 39) | def get_args():
function main (line 241) | def main(args, ds_init):
FILE: src/benchmark/pretrain_ssl/models/data2vec/run_cyclical.py
function get_args (line 37) | def get_args():
function get_model (line 280) | def get_model(args):
function main (line 304) | def main(args):
FILE: src/benchmark/pretrain_ssl/models/data2vec/run_cyclical_joint.py
function get_args (line 36) | def get_args():
function get_model (line 264) | def get_model(args):
function main (line 279) | def main(args):
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/backbone/beit.py
class DropPath (line 26) | class DropPath(nn.Module):
method __init__ (line 29) | def __init__(self, drop_prob=None):
method forward (line 33) | def forward(self, x):
method extra_repr (line 36) | def extra_repr(self) -> str:
class Mlp (line 40) | class Mlp(nn.Module):
method __init__ (line 41) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 50) | def forward(self, x):
class Attention (line 60) | class Attention(nn.Module):
method __init__ (line 61) | def __init__(
method forward (line 117) | def forward(self, x, rel_pos_bias=None):
class Block (line 150) | class Block(nn.Module):
method __init__ (line 152) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
method forward (line 172) | def forward(self, x, rel_pos_bias=None):
class PatchEmbed (line 182) | class PatchEmbed(nn.Module):
method __init__ (line 185) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 197) | def forward(self, x, **kwargs):
class HybridEmbed (line 209) | class HybridEmbed(nn.Module):
method __init__ (line 213) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
method forward (line 237) | def forward(self, x):
class RelativePositionBias (line 244) | class RelativePositionBias(nn.Module):
method __init__ (line 246) | def __init__(self, window_size, num_heads):
method forward (line 275) | def forward(self):
class BEiT (line 284) | class BEiT(nn.Module):
method __init__ (line 287) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
method fix_init_weight (line 366) | def fix_init_weight(self):
method _init_weights (line 374) | def _init_weights(self, m):
method init_weights (line 383) | def init_weights(self, pretrained=None):
method get_num_layers (line 409) | def get_num_layers(self):
method no_weight_decay (line 413) | def no_weight_decay(self):
method forward_features (line 416) | def forward_features(self, x):
method forward (line 441) | def forward(self, x):
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/apex_iter_based_runner.py
class IterBasedRunnerAmp (line 20) | class IterBasedRunnerAmp(IterBasedRunner):
method save_checkpoint (line 26) | def save_checkpoint(self,
method resume (line 68) | def resume(self,
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/checkpoint.py
function save_checkpoint (line 19) | def save_checkpoint(model, filename, optimizer=None, meta=None):
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/optimizer.py
class DistOptimizerHook (line 9) | class DistOptimizerHook(OptimizerHook):
method __init__ (line 12) | def __init__(self, update_interval=1, grad_clip=None, coalesce=True, b...
method before_run (line 19) | def before_run(self, runner):
method after_train_iter (line 22) | def after_train_iter(self, runner):
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/checkpoint.py
function _get_mmcv_home (line 34) | def _get_mmcv_home():
function load_state_dict (line 45) | def load_state_dict(module, state_dict, strict=False, logger=None):
function load_url_dist (line 113) | def load_url_dist(url, model_dir=None, map_location="cpu"):
function load_pavimodel_dist (line 127) | def load_pavimodel_dist(model_path, map_location=None):
function load_fileclient_dist (line 155) | def load_fileclient_dist(filename, backend, map_location):
function get_torchvision_models (line 176) | def get_torchvision_models():
function get_external_models (line 188) | def get_external_models():
function get_mmcls_models (line 202) | def get_mmcls_models():
function get_deprecated_model_names (line 209) | def get_deprecated_model_names():
function _process_mmcls_checkpoint (line 218) | def _process_mmcls_checkpoint(checkpoint):
function _load_checkpoint (line 229) | def _load_checkpoint(filename, map_location=None):
function cosine_scheduler (line 290) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
function load_checkpoint (line 310) | def load_checkpoint(model,
function weights_to_cpu (line 476) | def weights_to_cpu(state_dict):
function _save_to_state_dict (line 491) | def _save_to_state_dict(module, destination, prefix, keep_vars):
function get_state_dict (line 511) | def get_state_dict(module, destination=None, prefix='', keep_vars=False):
function save_checkpoint (line 555) | def save_checkpoint(model, filename, optimizer=None, meta=None):
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/layer_decay_optimizer_constructor.py
function get_num_layer_for_vit (line 6) | def get_num_layer_for_vit(var_name, num_max_layer):
class LayerDecayOptimizerConstructor (line 19) | class LayerDecayOptimizerConstructor(DefaultOptimizerConstructor):
method add_params (line 20) | def add_params(self, params, module, prefix='', is_dcn_module=None):
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/resize_transform.py
class SETR_Resize (line 8) | class SETR_Resize(object):
method __init__ (line 35) | def __init__(self,
method random_select (line 66) | def random_select(img_scales):
method random_sample (line 84) | def random_sample(img_scales):
method random_sample_ratio (line 111) | def random_sample_ratio(img_scale, ratio_range):
method _random_scale (line 137) | def _random_scale(self, results):
method _resize_img (line 170) | def _resize_img(self, results):
method _resize_seg (line 206) | def _resize_seg(self, results):
method __call__ (line 217) | def __call__(self, results):
method __repr__ (line 235) | def __repr__(self):
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/train_api.py
function set_random_seed (line 18) | def set_random_seed(seed, deterministic=False):
function train_segmentor (line 37) | def train_segmentor(model,
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/tools/test.py
function parse_args (line 17) | def parse_args():
function main (line 67) | def main():
FILE: src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/tools/train.py
function parse_args (line 23) | def parse_args():
function main (line 67) | def main():
FILE: src/benchmark/pretrain_ssl/models/data2vec/transforms.py
class ToNumpy (line 20) | class ToNumpy:
method __call__ (line 22) | def __call__(self, pil_img):
class ToTensor (line 30) | class ToTensor:
method __init__ (line 32) | def __init__(self, dtype=torch.float32):
method __call__ (line 35) | def __call__(self, pil_img):
function _pil_interp (line 53) | def _pil_interp(method):
class RandomResizedCropAndInterpolationWithTwoPic (line 68) | class RandomResizedCropAndInterpolationWithTwoPic:
method __init__ (line 83) | def __init__(self, size, second_size=None, scale=(0.08, 1.0), ratio=(3...
method get_params (line 109) | def get_params(img, scale, ratio):
method __call__ (line 151) | def __call__(self, img):
method __repr__ (line 170) | def __repr__(self):
FILE: src/benchmark/pretrain_ssl/models/data2vec/utils.py
class SmoothedValue (line 33) | class SmoothedValue(object):
method __init__ (line 38) | def __init__(self, window_size=20, fmt=None):
method update (line 46) | def update(self, value, n=1):
method synchronize_between_processes (line 51) | def synchronize_between_processes(self):
method median (line 65) | def median(self):
method avg (line 70) | def avg(self):
method global_avg (line 75) | def global_avg(self):
method max (line 79) | def max(self):
method value (line 83) | def value(self):
method __str__ (line 86) | def __str__(self):
class MetricLogger (line 95) | class MetricLogger(object):
method __init__ (line 96) | def __init__(self, delimiter="\t"):
method update (line 100) | def update(self, **kwargs):
method __getattr__ (line 109) | def __getattr__(self, attr):
method __str__ (line 117) | def __str__(self):
method synchronize_between_processes (line 125) | def synchronize_between_processes(self):
method add_meter (line 129) | def add_meter(self, name, meter):
method log_every (line 132) | def log_every(self, iterable, print_freq, header=None):
class TensorboardLogger (line 179) | class TensorboardLogger(object):
method __init__ (line 180) | def __init__(self, log_dir):
method set_step (line 184) | def set_step(self, step=None):
method update (line 190) | def update(self, head='scalar', step=None, **kwargs):
method flush (line 199) | def flush(self):
function _load_checkpoint_for_ema (line 203) | def _load_checkpoint_for_ema(model_ema, checkpoint):
function setup_for_distributed (line 217) | def setup_for_distributed(is_master):
function is_dist_avail_and_initialized (line 232) | def is_dist_avail_and_initialized():
function get_world_size (line 240) | def get_world_size():
function get_rank (line 246) | def get_rank():
function is_main_process (line 252) | def is_main_process():
function save_on_master (line 256) | def save_on_master(*args, **kwargs):
function init_distributed_mode (line 261) | def init_distributed_mode(args):
function load_state_dict (line 314) | def load_state_dict(model, state_dict, prefix='', ignore_missing="relati...
class NativeScalerWithGradNormCount (line 363) | class NativeScalerWithGradNormCount:
method __init__ (line 366) | def __init__(self):
method __call__ (line 369) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
method state_dict (line 385) | def state_dict(self):
method load_state_dict (line 388) | def load_state_dict(self, state_dict):
function get_grad_norm_ (line 392) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
function cosine_scheduler (line 407) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
function tri_phase_scheduler (line 426) | def tri_phase_scheduler(base_value, final_value, epochs, niter_per_ep, w...
function save_model (line 461) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
function auto_load_model (line 486) | def auto_load_model(args, model, model_without_ddp, optimizer, loss_scal...
function create_d_vae (line 541) | def create_d_vae(weight_path, d_vae_type, image_size, device):
function get_dalle_vae (line 550) | def get_dalle_vae(weight_path, image_size, device):
function get_d_vae (line 556) | def get_d_vae(weight_path, image_size, device):
function create_ds_config (line 576) | def create_ds_config(args):
FILE: src/benchmark/pretrain_ssl/models/dino/utils.py
class GaussianBlur (line 37) | class GaussianBlur(object):
method __init__ (line 41) | def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
method __call__ (line 46) | def __call__(self, img):
class cvGaussianBlur (line 58) | class cvGaussianBlur(object):
method __init__ (line 61) | def __init__(self, p=0.5, sigma=[.1, 2.]):
method __call__ (line 65) | def __call__(self, x):
class Solarization (line 78) | class Solarization(object):
method __init__ (line 82) | def __init__(self, p):
method __call__ (line 85) | def __call__(self, img):
function load_pretrained_weights (line 92) | def load_pretrained_weights(model, pretrained_weights, checkpoint_key, m...
function load_pretrained_linear_weights (line 133) | def load_pretrained_linear_weights(linear_classifier, model_name, patch_...
function clip_gradients (line 153) | def clip_gradients(model, clip):
function cancel_gradients_last_layer (line 165) | def cancel_gradients_last_layer(epoch, model, freeze_last_layer):
function restart_from_checkpoint (line 173) | def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):
function cosine_scheduler (line 208) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
function bool_flag (line 222) | def bool_flag(s):
function fix_random_seeds (line 236) | def fix_random_seeds(seed=31):
class SmoothedValue (line 245) | class SmoothedValue(object):
method __init__ (line 250) | def __init__(self, window_size=20, fmt=None):
method update (line 258) | def update(self, value, n=1):
method synchronize_between_processes (line 263) | def synchronize_between_processes(self):
method median (line 277) | def median(self):
method avg (line 282) | def avg(self):
method global_avg (line 287) | def global_avg(self):
method max (line 291) | def max(self):
method value (line 295) | def value(self):
method __str__ (line 298) | def __str__(self):
function reduce_dict (line 307) | def reduce_dict(input_dict, average=True):
class MetricLogger (line 334) | class MetricLogger(object):
method __init__ (line 335) | def __init__(self, delimiter="\t"):
method update (line 339) | def update(self, **kwargs):
method __getattr__ (line 346) | def __getattr__(self, attr):
method __str__ (line 354) | def __str__(self):
method synchronize_between_processes (line 362) | def synchronize_between_processes(self):
method add_meter (line 366) | def add_meter(self, name, meter):
method log_every (line 369) | def log_every(self, iterable, print_freq, header=None):
function get_sha (line 424) | def get_sha():
function is_dist_avail_and_initialized (line 444) | def is_dist_avail_and_initialized():
function get_world_size (line 452) | def get_world_size():
function get_rank (line 458) | def get_rank():
function is_main_process (line 464) | def is_main_process():
function save_on_master (line 468) | def save_on_master(*args, **kwargs):
function setup_for_distributed (line 473) | def setup_for_distributed(is_master):
function init_distributed_mode (line 488) | def init_distributed_mode(args):
function accuracy (line 526) | def accuracy(output, target, topk=(1,)):
function _no_grad_trunc_normal_ (line 536) | def _no_grad_trunc_normal_(tensor, mean, std, a, b):
function trunc_normal_ (line 572) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
class LARS (line 577) | class LARS(torch.optim.Optimizer):
method __init__ (line 581) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001,
method step (line 589) | def step(self):
class MultiCropWrapper (line 618) | class MultiCropWrapper(nn.Module):
method __init__ (line 627) | def __init__(self, backbone, head):
method forward (line 634) | def forward(self, x):
function get_params_groups (line 656) | def get_params_groups(model):
function has_batchnorms (line 670) | def has_batchnorms(model):
class PCA (line 678) | class PCA():
method __init__ (line 682) | def __init__(self, dim=256, whit=0.5):
method train_pca (line 687) | def train_pca(self, cov):
method apply (line 713) | def apply(self, x):
function compute_ap (line 732) | def compute_ap(ranks, nres):
function compute_map (line 767) | def compute_map(ranks, gnd, kappas=[]):
function multi_scale (line 839) | def multi_scale(samples, model):
FILE: src/benchmark/pretrain_ssl/models/dino/vision_transformer.py
function drop_path (line 27) | def drop_path(x, drop_prob: float = 0., training: bool = False):
class DropPath (line 38) | class DropPath(nn.Module):
method __init__ (line 41) | def __init__(self, drop_prob=None):
method forward (line 45) | def forward(self, x):
class Mlp (line 49) | class Mlp(nn.Module):
method __init__ (line 50) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 59) | def forward(self, x):
class Attention (line 68) | class Attention(nn.Module):
method __init__ (line 69) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
method forward (line 80) | def forward(self, x):
class Block (line 95) | class Block(nn.Module):
method __init__ (line 96) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
method forward (line 107) | def forward(self, x, return_attention=False):
class PatchEmbed (line 116) | class PatchEmbed(nn.Module):
method __init__ (line 119) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 128) | def forward(self, x):
class VisionTransformer (line 134) | class VisionTransformer(nn.Module):
method __init__ (line 136) | def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_clas...
method _init_weights (line 165) | def _init_weights(self, m):
method interpolate_pos_encoding (line 174) | def interpolate_pos_encoding(self, x, w, h):
method prepare_tokens (line 196) | def prepare_tokens(self, x):
method forward (line 209) | def forward(self, x):
method get_last_selfattention (line 216) | def get_last_selfattention(self, x):
method get_intermediate_layers (line 225) | def get_intermediate_layers(self, x, n=1):
function vit_tiny (line 236) | def vit_tiny(patch_size=16, **kwargs):
function vit_small (line 243) | def vit_small(patch_size=16, **kwargs):
function vit_base (line 250) | def vit_base(patch_size=16, **kwargs):
class DINOHead (line 257) | class DINOHead(nn.Module):
method __init__ (line 258) | def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True...
method _init_weights (line 281) | def _init_weights(self, m):
method forward (line 287) | def forward(self, x):
FILE: src/benchmark/pretrain_ssl/models/mae/engine_finetune.py
function train_one_epoch (line 25) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 99) | def evaluate(data_loader, model, device):
FILE: src/benchmark/pretrain_ssl/models/mae/engine_finetune_BE.py
function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 107) | def evaluate(data_loader, model, device, criterion):
FILE: src/benchmark/pretrain_ssl/models/mae/engine_finetune_EU.py
function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 104) | def evaluate(data_loader, model, device, criterion):
FILE: src/benchmark/pretrain_ssl/models/mae/engine_finetune_SS.py
function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 104) | def evaluate(data_loader, model, device, criterion):
FILE: src/benchmark/pretrain_ssl/models/mae/engine_pretrain.py
function train_one_epoch (line 23) | def train_one_epoch(model: torch.nn.Module,
FILE: src/benchmark/pretrain_ssl/models/mae/main_finetune.py
function get_args_parser (line 42) | def get_args_parser():
function main (line 158) | def main(args):
FILE: src/benchmark/pretrain_ssl/models/mae/main_linprobe.py
function get_args_parser (line 47) | def get_args_parser():
function main (line 121) | def main(args):
FILE: src/benchmark/pretrain_ssl/models/mae/main_pretrain.py
class SeasonTransform (line 41) | class SeasonTransform:
method __init__ (line 43) | def __init__(self, base_transform, season='fixed'):
method __call__ (line 47) | def __call__(self, x):
function get_args_parser (line 72) | def get_args_parser():
function main (line 151) | def main(args):
FILE: src/benchmark/pretrain_ssl/models/mae/models_mae.py
class MaskedAutoencoderViT (line 22) | class MaskedAutoencoderViT(nn.Module):
method __init__ (line 25) | def __init__(self, img_size=224, patch_size=16, in_chans=3,
method initialize_weights (line 67) | def initialize_weights(self):
method _init_weights (line 87) | def _init_weights(self, m):
method patchify (line 97) | def patchify(self, imgs):
method unpatchify (line 111) | def unpatchify(self, x):
method random_masking (line 125) | def random_masking(self, x, mask_ratio):
method forward_encoder (line 152) | def forward_encoder(self, x, mask_ratio):
method forward_decoder (line 174) | def forward_decoder(self, x, ids_restore):
method forward_loss (line 200) | def forward_loss(self, imgs, pred, mask):
method forward (line 218) | def forward(self, imgs, mask_ratio=0.75):
function mae_vit_small_patch16_dec512d8b (line 225) | def mae_vit_small_patch16_dec512d8b(**kwargs):
function mae_vit_base_patch16_dec512d8b (line 232) | def mae_vit_base_patch16_dec512d8b(**kwargs):
function mae_vit_large_patch16_dec512d8b (line 240) | def mae_vit_large_patch16_dec512d8b(**kwargs):
function mae_vit_huge_patch14_dec512d8b (line 248) | def mae_vit_huge_patch14_dec512d8b(**kwargs):
FILE: src/benchmark/pretrain_ssl/models/mae/models_vit.py
class VisionTransformer (line 20) | class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
method __init__ (line 23) | def __init__(self, global_pool=False, **kwargs):
method forward_features (line 34) | def forward_features(self, x):
function vit_small_patch16 (line 55) | def vit_small_patch16(**kwargs):
function vit_base_patch16 (line 61) | def vit_base_patch16(**kwargs):
function vit_large_patch16 (line 68) | def vit_large_patch16(**kwargs):
function vit_huge_patch14 (line 75) | def vit_huge_patch14(**kwargs):
FILE: src/benchmark/pretrain_ssl/models/mae/submitit_finetune.py
function parse_args (line 19) | def parse_args():
function get_shared_folder (line 33) | def get_shared_folder() -> Path:
function get_init_file (line 42) | def get_init_file():
class Trainer (line 51) | class Trainer(object):
method __init__ (line 52) | def __init__(self, args):
method __call__ (line 55) | def __call__(self):
method checkpoint (line 61) | def checkpoint(self):
method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
function main (line 86) | def main():
FILE: src/benchmark/pretrain_ssl/models/mae/submitit_linprobe.py
function parse_args (line 19) | def parse_args():
function get_shared_folder (line 33) | def get_shared_folder() -> Path:
function get_init_file (line 42) | def get_init_file():
class Trainer (line 51) | class Trainer(object):
method __init__ (line 52) | def __init__(self, args):
method __call__ (line 55) | def __call__(self):
method checkpoint (line 61) | def checkpoint(self):
method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
function main (line 86) | def main():
FILE: src/benchmark/pretrain_ssl/models/mae/submitit_pretrain.py
function parse_args (line 19) | def parse_args():
function get_shared_folder (line 33) | def get_shared_folder() -> Path:
function get_init_file (line 42) | def get_init_file():
class Trainer (line 51) | class Trainer(object):
method __init__ (line 52) | def __init__(self, args):
method __call__ (line 55) | def __call__(self):
method checkpoint (line 61) | def checkpoint(self):
method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
function main (line 86) | def main():
FILE: src/benchmark/pretrain_ssl/models/mae/util/crop.py
class RandomResizedCrop (line 15) | class RandomResizedCrop(transforms.RandomResizedCrop):
method get_params (line 23) | def get_params(img, scale, ratio):
FILE: src/benchmark/pretrain_ssl/models/mae/util/datasets.py
function build_dataset (line 20) | def build_dataset(is_train, args):
function build_transform (line 31) | def build_transform(is_train, args):
FILE: src/benchmark/pretrain_ssl/models/mae/util/lars.py
class LARS (line 14) | class LARS(torch.optim.Optimizer):
method __init__ (line 18) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
method step (line 23) | def step(self):
FILE: src/benchmark/pretrain_ssl/models/mae/util/lr_decay.py
function param_groups_lrd (line 15) | def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], ...
function get_layer_id_for_vit (line 64) | def get_layer_id_for_vit(name, num_layers):
FILE: src/benchmark/pretrain_ssl/models/mae/util/lr_sched.py
function adjust_learning_rate (line 9) | def adjust_learning_rate(optimizer, epoch, args):
FILE: src/benchmark/pretrain_ssl/models/mae/util/misc.py
class SmoothedValue (line 24) | class SmoothedValue(object):
method __init__ (line 29) | def __init__(self, window_size=20, fmt=None):
method update (line 37) | def update(self, value, n=1):
method synchronize_between_processes (line 42) | def synchronize_between_processes(self):
method median (line 56) | def median(self):
method avg (line 61) | def avg(self):
method global_avg (line 66) | def global_avg(self):
method max (line 70) | def max(self):
method value (line 74) | def value(self):
method __str__ (line 77) | def __str__(self):
class MetricLogger (line 86) | class MetricLogger(object):
method __init__ (line 87) | def __init__(self, delimiter="\t"):
method update (line 91) | def update(self, **kwargs):
method __getattr__ (line 100) | def __getattr__(self, attr):
method __str__ (line 108) | def __str__(self):
method synchronize_between_processes (line 116) | def synchronize_between_processes(self):
method add_meter (line 120) | def add_meter(self, name, meter):
method log_every (line 123) | def log_every(self, iterable, print_freq, header=None):
function setup_for_distributed (line 170) | def setup_for_distributed(is_master):
function is_dist_avail_and_initialized (line 187) | def is_dist_avail_and_initialized():
function get_world_size (line 195) | def get_world_size():
function get_rank (line 201) | def get_rank():
function is_main_process (line 207) | def is_main_process():
function save_on_master (line 211) | def save_on_master(*args, **kwargs):
function init_distributed_mode (line 216) | def init_distributed_mode(args):
class NativeScalerWithGradNormCount (line 253) | class NativeScalerWithGradNormCount:
method __init__ (line 256) | def __init__(self):
method __call__ (line 259) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
method state_dict (line 275) | def state_dict(self):
method load_state_dict (line 278) | def load_state_dict(self, state_dict):
function get_grad_norm_ (line 282) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
function save_model (line 297) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
function load_model (line 317) | def load_model(args, model_without_ddp, optimizer, loss_scaler):
function all_reduce_mean (line 334) | def all_reduce_mean(x):
FILE: src/benchmark/pretrain_ssl/models/mae/util/pos_embed.py
function get_2d_sincos_pos_embed (line 20) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
function get_2d_sincos_pos_embed_from_grid (line 38) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
function get_1d_sincos_pos_embed_from_grid (line 49) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
function interpolate_pos_embed (line 75) | def interpolate_pos_embed(model, checkpoint_model):
FILE: src/benchmark/pretrain_ssl/models/moco/builder.py
class MoCo (line 6) | class MoCo(nn.Module):
method __init__ (line 11) | def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, ml...
method _momentum_update_key_encoder (line 68) | def _momentum_update_key_encoder(self):
method _dequeue_and_enqueue (line 76) | def _dequeue_and_enqueue(self, keys):
method _batch_shuffle_ddp (line 92) | def _batch_shuffle_ddp(self, x):
method _batch_unshuffle_ddp (line 120) | def _batch_unshuffle_ddp(self, x, idx_unshuffle):
method forward (line 138) | def forward(self, im_q, im_k):
function concat_all_gather (line 188) | def concat_all_gather(tensor):
FILE: src/benchmark/pretrain_ssl/models/moco/loader.py
class TwoCropsTransform (line 11) | class TwoCropsTransform:
method __init__ (line 14) | def __init__(self, base_transform):
method __call__ (line 17) | def __call__(self, x):
class GaussianBlur (line 23) | class GaussianBlur(object):
method __init__ (line 26) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 29) | def __call__(self, x):
FILE: src/benchmark/pretrain_ssl/models/moco_v2/builder.py
class MoCo (line 6) | class MoCo(nn.Module):
method __init__ (line 11) | def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, ml...
method _momentum_update_key_encoder (line 57) | def _momentum_update_key_encoder(self):
method _dequeue_and_enqueue (line 65) | def _dequeue_and_enqueue(self, keys):
method _batch_shuffle_ddp (line 81) | def _batch_shuffle_ddp(self, x):
method _batch_unshuffle_ddp (line 109) | def _batch_unshuffle_ddp(self, x, idx_unshuffle):
method forward (line 127) | def forward(self, im_q, im_k):
function concat_all_gather (line 177) | def concat_all_gather(tensor):
FILE: src/benchmark/pretrain_ssl/models/moco_v2/detection/train_net.py
class Res5ROIHeadsExtraNorm (line 15) | class Res5ROIHeadsExtraNorm(Res5ROIHeads):
method _build_res5_block (line 20) | def _build_res5_block(self, cfg):
class Trainer (line 28) | class Trainer(DefaultTrainer):
method build_evaluator (line 30) | def build_evaluator(cls, cfg, dataset_name, output_folder=None):
function setup (line 40) | def setup(args):
function main (line 49) | def main(args):
FILE: src/benchmark/pretrain_ssl/models/moco_v2/loader.py
class TwoCropsTransform (line 11) | class TwoCropsTransform:
method __init__ (line 14) | def __init__(self, base_transform):
method __call__ (line 17) | def __call__(self, x):
class GaussianBlur (line 23) | class GaussianBlur(object):
method __init__ (line 26) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 29) | def __call__(self, x):
FILE: src/benchmark/pretrain_ssl/models/moco_v2/main_lincls.py
function main (line 86) | def main():
function main_worker (line 121) | def main_worker(gpu, ngpus_per_node, args):
function train (line 308) | def train(train_loader, model, criterion, optimizer, epoch, args):
function validate (line 360) | def validate(val_loader, model, criterion, args):
function save_checkpoint (line 404) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
function sanity_check (line 410) | def sanity_check(state_dict, pretrained_weights):
class AverageMeter (line 434) | class AverageMeter(object):
method __init__ (line 436) | def __init__(self, name, fmt=':f'):
method reset (line 441) | def reset(self):
method update (line 447) | def update(self, val, n=1):
method __str__ (line 453) | def __str__(self):
class ProgressMeter (line 458) | class ProgressMeter(object):
method __init__ (line 459) | def __init__(self, num_batches, meters, prefix=""):
method display (line 464) | def display(self, batch):
method _get_batch_fmtstr (line 469) | def _get_batch_fmtstr(self, num_batches):
function adjust_learning_rate (line 475) | def adjust_learning_rate(optimizer, epoch, args):
function accuracy (line 484) | def accuracy(output, target, topk=(1,)):
FILE: src/benchmark/pretrain_ssl/models/moco_v3/builder.py
class MoCo (line 11) | class MoCo(nn.Module):
method __init__ (line 16) | def __init__(self, base_encoder, dim=256, mlp_dim=4096, T=1.0):
method _build_mlp (line 36) | def _build_mlp(self, num_layers, input_dim, mlp_dim, output_dim, last_...
method _build_projector_and_predictor_mlps (line 54) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
method _update_momentum_encoder (line 58) | def _update_momentum_encoder(self, m):
method contrastive_loss (line 63) | def contrastive_loss(self, q, k):
method forward (line 75) | def forward(self, x1, x2, m):
class MoCo_ResNet (line 99) | class MoCo_ResNet(MoCo):
method _build_projector_and_predictor_mlps (line 100) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
class MoCo_ViT (line 112) | class MoCo_ViT(MoCo):
method _build_projector_and_predictor_mlps (line 113) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
function concat_all_gather (line 127) | def concat_all_gather(tensor):
FILE: src/benchmark/pretrain_ssl/models/moco_v3/loader.py
class TwoCropsTransform (line 14) | class TwoCropsTransform:
method __init__ (line 17) | def __init__(self, base_transform1, base_transform2):
method __call__ (line 21) | def __call__(self, x):
class GaussianBlur (line 27) | class GaussianBlur(object):
method __init__ (line 30) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 33) | def __call__(self, x):
class Solarize (line 40) | class Solarize(object):
method __call__ (line 43) | def __call__(self, x):
class cvGaussianBlur (line 50) | class cvGaussianBlur(object):
method __init__ (line 53) | def __init__(self, p=0.5, sigma=[.1, 2.]):
method __call__ (line 57) | def __call__(self, x):
FILE: src/benchmark/pretrain_ssl/models/moco_v3/optimizer.py
class LARS (line 10) | class LARS(torch.optim.Optimizer):
method __init__ (line 14) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
method step (line 19) | def step(self):
FILE: src/benchmark/pretrain_ssl/models/moco_v3/vits.py
class VisionTransformerMoCo (line 25) | class VisionTransformerMoCo(VisionTransformer):
method __init__ (line 26) | def __init__(self, stop_grad_conv1=False, **kwargs):
method build_2d_sincos_position_embedding (line 53) | def build_2d_sincos_position_embedding(self, temperature=10000.):
class ConvStem (line 72) | class ConvStem(nn.Module):
method __init__ (line 76) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 104) | def forward(self, x):
function vit_small (line 115) | def vit_small(**kwargs):
function vit_base (line 122) | def vit_base(**kwargs):
function vit_conv_small (line 129) | def vit_conv_small(**kwargs):
function vit_conv_base (line 137) | def vit_conv_base(**kwargs):
FILE: src/benchmark/pretrain_ssl/models/rs_transforms_float32.py
class RandomBrightness (line 7) | class RandomBrightness(object):
method __init__ (line 10) | def __init__(self, brightness=0.4):
method __call__ (line 13) | def __call__(self, sample):
class RandomContrast (line 19) | class RandomContrast(object):
method __init__ (line 22) | def __init__(self, contrast=0.4):
method __call__ (line 25) | def __call__(self, sample):
class ToGray (line 31) | class ToGray(object):
method __init__ (line 32) | def __init__(self, out_channels):
method __call__ (line 34) | def __call__(self,sample):
class RandomChannelDrop (line 41) | class RandomChannelDrop(object):
method __init__ (line 44) | def __init__(self, min_n_drop=1, max_n_drop=8):
method __call__ (line 48) | def __call__(self, sample):
FILE: src/benchmark/pretrain_ssl/models/rs_transforms_uint8.py
class RandomBrightness (line 7) | class RandomBrightness(object):
method __init__ (line 10) | def __init__(self, brightness=0.4):
method __call__ (line 13) | def __call__(self, sample):
class RandomContrast (line 19) | class RandomContrast(object):
method __init__ (line 22) | def __init__(self, contrast=0.4):
method __call__ (line 25) | def __call__(self, sample):
class ToGray (line 31) | class ToGray(object):
method __init__ (line 32) | def __init__(self, out_channels):
method __call__ (line 34) | def __call__(self,sample):
class RandomChannelDrop (line 41) | class RandomChannelDrop(object):
method __init__ (line 44) | def __init__(self, min_n_drop=1, max_n_drop=8):
method __call__ (line 48) | def __call__(self, sample):
class GaussianBlur (line 57) | class GaussianBlur(object):
method __init__ (line 60) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 63) | def __call__(self, x):
class Solarize (line 70) | class Solarize(object):
method __init__ (line 72) | def __init__(self, threshold=0.5):
method __call__ (line 75) | def __call__(self, x):
class RandomSensorDrop_S1S2 (line 82) | class RandomSensorDrop_S1S2(object):
method __init__ (line 85) | def __init__(self):
method __call__ (line 88) | def __call__(self, sample):
class SensorDrop_S1S2 (line 98) | class SensorDrop_S1S2(object):
method __init__ (line 99) | def __init__(self, sensor):
method __call__ (line 101) | def __call__(self,sample):
class RandomSensorDrop_RGBD (line 109) | class RandomSensorDrop_RGBD(object):
method __init__ (line 112) | def __init__(self):
method __call__ (line 115) | def __call__(self, sample):
class SensorDrop_RGBD (line 125) | class SensorDrop_RGBD(object):
method __init__ (line 126) | def __init__(self, sensor):
method __call__ (line 128) | def __call__(self,sample):
FILE: src/benchmark/pretrain_ssl/pretrain_data2vec.py
function get_args (line 38) | def get_args():
function get_model (line 293) | def get_model(args):
function main (line 317) | def main(args):
FILE: src/benchmark/pretrain_ssl/pretrain_dino_s2c.py
function get_args_parser (line 54) | def get_args_parser():
function train_dino (line 158) | def train_dino(args):
function train_one_epoch (line 369) | def train_one_epoch(student, teacher, teacher_without_ddp, dino_loss, da...
class DINOLoss (line 434) | class DINOLoss(nn.Module):
method __init__ (line 435) | def __init__(self, out_dim, ncrops, warmup_teacher_temp, teacher_temp,
method forward (line 451) | def forward(self, student_output, teacher_output, epoch):
method update_center (line 478) | def update_center(self, teacher_output):
class DataAugmentationDINO (line 491) | class DataAugmentationDINO(object):
method __init__ (line 492) | def __init__(self, global_crops_scale, local_crops_scale, local_crops_...
method __call__ (line 534) | def __call__(self, image):
class DataAugmentationDINO_S2 (line 549) | class DataAugmentationDINO_S2(object):
method __init__ (line 550) | def __init__(self, global_crops_scale, local_crops_scale, local_crops_...
method __call__ (line 591) | def __call__(self, image):
class DataAugmentationDINO_S1 (line 618) | class DataAugmentationDINO_S1(object):
method __init__ (line 619) | def __init__(self, global_crops_scale, local_crops_scale, local_crops_...
method __call__ (line 657) | def __call__(self, image):
FILE: src/benchmark/pretrain_ssl/pretrain_mae_s2c.py
class SeasonTransform (line 41) | class SeasonTransform:
method __init__ (line 43) | def __init__(self, base_transform, season='fixed'):
method __call__ (line 47) | def __call__(self, x):
function get_args_parser (line 72) | def get_args_parser():
function main (line 153) | def main(args):
FILE: src/benchmark/pretrain_ssl/pretrain_moco_v2_s2c.py
class TwoCropsTransform (line 128) | class TwoCropsTransform:
method __init__ (line 131) | def __init__(self, base_transform, season='fixed'):
method __call__ (line 135) | def __call__(self, x):
function main (line 157) | def main():
function main_worker (line 213) | def main_worker(gpu, ngpus_per_node, args):
function train (line 388) | def train(train_loader, model, criterion, optimizer, epoch, args):
function save_checkpoint (line 442) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
class AverageMeter (line 448) | class AverageMeter(object):
method __init__ (line 450) | def __init__(self, name, fmt=':f'):
method reset (line 455) | def reset(self):
method update (line 461) | def update(self, val, n=1):
method __str__ (line 467) | def __str__(self):
class ProgressMeter (line 472) | class ProgressMeter(object):
method __init__ (line 473) | def __init__(self, num_batches, meters, prefix=""):
method display (line 478) | def display(self, batch):
method _get_batch_fmtstr (line 483) | def _get_batch_fmtstr(self, num_batches):
function adjust_learning_rate (line 489) | def adjust_learning_rate(optimizer, epoch, args):
function accuracy (line 501) | def accuracy(output, target, topk=(1,)):
FILE: src/benchmark/pretrain_ssl/pretrain_moco_v2_seco_ms.py
class SeCoDataset (line 135) | class SeCoDataset(torch.utils.data.Dataset):
method __init__ (line 137) | def __init__(self,root, normalize=False, dtype='uint8'):
method __getitem__ (line 145) | def __getitem__(self,index):
method __len__ (line 152) | def __len__(self):
method get_array (line 155) | def get_array(self, patch_id, season):
class LMDBDataset (line 173) | class LMDBDataset(torch.utils.data.Dataset):
method __init__ (line 175) | def __init__(self, lmdb_file, is_slurm_job=False, s2a_transform=None):
method _init_db (line 189) | def _init_db(self):
method __getitem__ (line 195) | def __getitem__(self, index):
method __len__ (line 212) | def __len__(self):
class TwoCropsTransform (line 217) | class TwoCropsTransform:
method __init__ (line 220) | def __init__(self, base_transform):
method __call__ (line 223) | def __call__(self, x):
function main (line 234) | def main():
function main_worker (line 290) | def main_worker(gpu, ngpus_per_node, args):
function train (line 463) | def train(train_loader, model, criterion, optimizer, epoch, args):
function save_checkpoint (line 517) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
class AverageMeter (line 523) | class AverageMeter(object):
method __init__ (line 525) | def __init__(self, name, fmt=':f'):
method reset (line 530) | def reset(self):
method update (line 536) | def update(self, val, n=1):
method __str__ (line 542) | def __str__(self):
class ProgressMeter (line 547) | class ProgressMeter(object):
method __init__ (line 548) | def __init__(self, num_batches, meters, prefix=""):
method display (line 553) | def display(self, batch):
method _get_batch_fmtstr (line 558) | def _get_batch_fmtstr(self, num_batches):
function adjust_learning_rate (line 564) | def adjust_learning_rate(optimizer, epoch, args):
function accuracy (line 576) | def accuracy(output, target, topk=(1,)):
FILE: src/benchmark/pretrain_ssl/pretrain_moco_v2_sen12ms_ms.py
class SEN12MSDataset (line 134) | class SEN12MSDataset(torch.utils.data.Dataset):
method __init__ (line 135) | def __init__(self, root_dir, mode, transform):
method __getitem__ (line 141) | def __getitem__(self,index):
method __len__ (line 157) | def __len__(self):
class LMDBDataset (line 161) | class LMDBDataset(torch.utils.data.Dataset):
method __init__ (line 163) | def __init__(self, lmdb_file, is_slurm_job=False, s2c_transform=None):
method _init_db (line 177) | def _init_db(self):
method __getitem__ (line 183) | def __getitem__(self, index):
method __len__ (line 200) | def __len__(self):
class TwoCropsTransform (line 205) | class TwoCropsTransform:
method __init__ (line 208) | def __init__(self, base_transform):
method __call__ (line 211) | def __call__(self, x):
function main (line 222) | def main():
function main_worker (line 278) | def main_worker(gpu, ngpus_per_node, args):
function train (line 451) | def train(train_loader, model, criterion, optimizer, epoch, args):
function save_checkpoint (line 505) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
class AverageMeter (line 511) | class AverageMeter(object):
method __init__ (line 513) | def __init__(self, name, fmt=':f'):
method reset (line 518) | def reset(self):
method update (line 524) | def update(self, val, n=1):
method __str__ (line 530) | def __str__(self):
class ProgressMeter (line 535) | class ProgressMeter(object):
method __init__ (line 536) | def __init__(self, num_batches, meters, prefix=""):
method display (line 541) | def display(self, batch):
method _get_batch_fmtstr (line 546) | def _get_batch_fmtstr(self, num_batches):
function adjust_learning_rate (line 552) | def adjust_learning_rate(optimizer, epoch, args):
function accuracy (line 564) | def accuracy(output, target, topk=(1,)):
FILE: src/benchmark/pretrain_ssl/pretrain_moco_v3_s2c.py
class TwoCropsTransform (line 141) | class TwoCropsTransform:
method __init__ (line 144) | def __init__(self, base_transform1, base_transform2, season='fixed'):
method __call__ (line 149) | def __call__(self, x):
function main (line 172) | def main():
function main_worker (line 216) | def main_worker(gpu, ngpus_per_node, args):
function train (line 447) | def train(train_loader, model, optimizer, scaler, summary_writer, epoch,...
function save_checkpoint (line 498) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
class AverageMeter (line 504) | class AverageMeter(object):
method __init__ (line 506) | def __init__(self, name, fmt=':f'):
method reset (line 511) | def reset(self):
method update (line 517) | def update(self, val, n=1):
method __str__ (line 523) | def __str__(self):
class ProgressMeter (line 528) | class ProgressMeter(object):
method __init__ (line 529) | def __init__(self, num_batches, meters, prefix=""):
method display (line 534) | def display(self, batch):
method _get_batch_fmtstr (line 539) | def _get_batch_fmtstr(self, num_batches):
function adjust_learning_rate (line 545) | def adjust_learning_rate(optimizer, epoch, args):
function adjust_moco_momentum (line 556) | def adjust_moco_momentum(epoch, args):
FILE: src/benchmark/transfer_change_detection/datasets/oscd_datamodule.py
class RandomFlip (line 15) | class RandomFlip:
method __call__ (line 17) | def __call__(self, *xs):
class RandomRotation (line 23) | class RandomRotation:
method __init__ (line 25) | def __init__(self):
method __call__ (line 28) | def __call__(self, *xs):
class RandomSwap (line 33) | class RandomSwap:
method __call__ (line 35) | def __call__(self, x1, x2, y):
class ToTensor (line 42) | class ToTensor:
method __call__ (line 44) | def __call__(self, *xs):
class Compose (line 48) | class Compose:
method __init__ (line 50) | def __init__(self, transforms):
method __call__ (line 53) | def __call__(self, *xs):
class ChangeDetectionDataModule (line 59) | class ChangeDetectionDataModule(LightningDataModule):
method __init__ (line 61) | def __init__(self, data_dir, RGB_bands=True, BGR_bands=False, S2A_band...
method setup (line 79) | def setup(self, stage=None):
method train_dataloader (line 97) | def train_dataloader(self):
method val_dataloader (line 107) | def val_dataloader(self):
FILE: src/benchmark/transfer_change_detection/datasets/oscd_dataset.py
function read_image (line 48) | def read_image(path, bands, normalize=True, value_discard=True):
class ChangeDetectionDataset (line 81) | class ChangeDetectionDataset(Dataset):
method __init__ (line 83) | def __init__(self, root, split='all', bands=None, transform=None,
method __getitem__ (line 104) | def __getitem__(self, index):
method __len__ (line 128) | def __len__(self):
FILE: src/benchmark/transfer_change_detection/main_oscd.py
function get_args (line 21) | def get_args():
function dice_loss (line 43) | def dice_loss(out,mask,epsilon=1e-5):
class SiamSegment (line 49) | class SiamSegment(LightningModule):
method __init__ (line 51) | def __init__(self, backbone, feature_indices, feature_channels):
method forward (line 60) | def forward(self, x1, x2):
method training_step (line 63) | def training_step(self, batch, batch_idx):
method validation_step (line 83) | def validation_step(self, batch, batch_idx):
method shared_step (line 103) | def shared_step(self, batch):
method configure_optimizers (line 114) | def configure_optimizers(self):
function load_ssl_resnet_encoder (line 122) | def load_ssl_resnet_encoder(net, ckp_path, pf_sdict='module.encoder_q.'):
FILE: src/benchmark/transfer_change_detection/models/segmentation.py
function get_segmentation_model (line 6) | def get_segmentation_model(backbone, feature_indices, feature_channels):
class SegmentationEncoder (line 23) | class SegmentationEncoder(torch.nn.Module):
method __init__ (line 24) | def __init__(self, backbone, feature_indices, diff=False):
method forward (line 39) | def forward(self, x1, x2):
method concatenate (line 64) | def concatenate(self, x1, x2):
class DoubleConv (line 71) | class DoubleConv(nn.Module):
method __init__ (line 72) | def __init__(self, in_channels, out_channels, mid_channels=None):
method forward (line 85) | def forward(self, x):
class Down (line 89) | class Down(nn.Module):
method __init__ (line 92) | def __init__(self, in_channels, out_channels):
method forward (line 99) | def forward(self, x):
class Up (line 103) | class Up(nn.Module):
method __init__ (line 106) | def __init__(self, in_channels, out_channels, bilinear=True):
method forward (line 117) | def forward(self, x1, x2):
class OutConv (line 132) | class OutConv(nn.Module):
method __init__ (line 133) | def __init__(self, in_channels, out_channels):
method forward (line 137) | def forward(self, x):
class UNet (line 165) | class UNet(nn.Module):
method __init__ (line 166) | def __init__(self, encoder, feature_channels, n_classes, concat_mult=2...
method forward (line 196) | def forward(self, *in_x):
class UNetSmall (line 220) | class UNetSmall(nn.Module):
method __init__ (line 221) | def __init__(self, encoder, feature_channels, n_classes, concat_mult=2...
method forward (line 245) | def forward(self, *in_x):
FILE: src/benchmark/transfer_change_detection/utils/data.py
class Subset (line 10) | class Subset(Dataset):
method __init__ (line 12) | def __init__(self, dataset, indices):
method __getitem__ (line 16) | def __getitem__(self, idx):
method __len__ (line 19) | def __len__(self):
method __getattr__ (line 22) | def __getattr__(self, name):
function random_subset (line 26) | def random_subset(dataset, frac, seed=None):
class _RepeatSampler (line 32) | class _RepeatSampler(object):
method __init__ (line 39) | def __init__(self, sampler):
method __iter__ (line 42) | def __iter__(self):
class InfiniteDataLoader (line 47) | class InfiniteDataLoader(DataLoader):
method __init__ (line 53) | def __init__(self, *args, **kwargs):
method __len__ (line 58) | def __len__(self):
method __iter__ (line 61) | def __iter__(self):
function make_lmdb (line 66) | def make_lmdb(dataset, lmdb_file, num_workers=8):
class LMDBDataset (line 84) | class LMDBDataset(Dataset):
method __init__ (line 86) | def __init__(self, lmdb_file, transform=None, target_transform=None):
method __getitem__ (line 95) | def __getitem__(self, index):
method __len__ (line 111) | def __len__(self):
FILE: src/benchmark/transfer_change_detection/utils/transforms.py
class Map (line 2) | class Map:
method __init__ (line 4) | def __init__(self, transform):
method __call__ (line 7) | def __call__(self, inputs):
class ApplyN (line 11) | class ApplyN:
method __init__ (line 13) | def __init__(self, transform, n):
method __call__ (line 17) | def __call__(self, input):
class LeaveOneOut (line 21) | class LeaveOneOut:
method __init__ (line 23) | def __init__(self, transforms):
method __call__ (line 26) | def __call__(self, input):
class AddOne (line 37) | class AddOne:
method __init__ (line 39) | def __init__(self, transforms):
method __call__ (line 42) | def __call__(self, input):
FILE: src/benchmark/transfer_change_detection/validate_oscd.py
function get_args (line 21) | def get_args():
class SiamSegment (line 44) | class SiamSegment(LightningModule):
method __init__ (line 46) | def __init__(self, backbone, feature_indices, feature_channels):
method forward (line 54) | def forward(self, x1, x2):
method training_step (line 57) | def training_step(self, batch, batch_idx):
method validation_step (line 77) | def validation_step(self, batch, batch_idx):
method shared_step (line 97) | def shared_step(self, batch):
method configure_optimizers (line 107) | def configure_optimizers(self):
function load_ssl_resnet_encoder (line 115) | def load_ssl_resnet_encoder(net, ckp_path, pf_sdict='module.encoder_q.'):
FILE: src/benchmark/transfer_classification/datasets/BigEarthNet/bigearthnet_dataset_seco.py
function normalize (line 117) | def normalize(img, mean, std):
class Bigearthnet (line 125) | class Bigearthnet(Dataset):
method __init__ (line 138) | def __init__(self, root, split, bands=None, transform=None, target_tra...
method __getitem__ (line 165) | def __getitem__(self, index):
method __len__ (line 192) | def __len__(self):
method get_multihot_old (line 196) | def get_multihot_old(labels):
method get_multihot_new (line 203) | def get_multihot_new(labels):
FILE: src/benchmark/transfer_classification/datasets/BigEarthNet/bigearthnet_dataset_seco_lmdb_s2_uint8.py
function normalize (line 17) | def normalize(img, mean, std):
class Subset (line 24) | class Subset(Dataset):
method __init__ (line 26) | def __init__(self, dataset, indices):
method __getitem__ (line 30) | def __getitem__(self, idx):
method __len__ (line 33) | def __len__(self):
method __getattr__ (line 36) | def __getattr__(self, name):
function random_subset (line 40) | def random_subset(dataset, frac, seed=None):
class _RepeatSampler (line 46) | class _RepeatSampler(object):
method __init__ (line 53) | def __init__(self, sampler):
method __iter__ (line 56) | def __iter__(self):
class InfiniteDataLoader (line 61) | class InfiniteDataLoader(DataLoader):
method __init__ (line 67) | def __init__(self, *args, **kwargs):
method __len__ (line 72) | def __len__(self):
method __iter__ (line 75) | def __iter__(self):
function make_lmdb (line 80) | def make_lmdb(dataset, lmdb_file, num_workers=6):
class LMDBDataset (line 98) | class LMDBDataset(Dataset):
method __init__ (line 100) | def __init__(self, lmdb_file, is_slurm_job=False, transform=None, norm...
method _init_db (line 122) | def _init_db(self):
method __getitem__ (line 128) | def __getitem__(self, index):
method __len__ (line 149) | def __len__(self):
FILE: src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py
function is_valid_file (line 51) | def is_valid_file(filename):
function normalize (line 54) | def normalize(img, mean, std):
class EurosatDataset (line 61) | class EurosatDataset(Dataset):
method __init__ (line 63) | def __init__(self, root, bands='B13', transform=None, normalize=False):
method __getitem__ (line 89) | def __getitem__(self, index):
method __len__ (line 123) | def __len__(self):
class Subset (line 127) | class Subset(Dataset):
method __init__ (line 135) | def __init__(self, dataset, indices, transform=None):
method __getitem__ (line 140) | def __getitem__(self, idx):
method __len__ (line 146) | def __len__(self):
FILE: src/benchmark/transfer_classification/datasets/So2Sat/so2sat_lcz42_dataset.py
class Subset (line 52) | class Subset(Dataset):
method __init__ (line 54) | def __init__(self, dataset, indices):
method __getitem__ (line 58) | def __getitem__(self, idx):
method __len__ (line 61) | def __len__(self):
method __getattr__ (line 64) | def __getattr__(self, name):
function random_subset (line 68) | def random_subset(dataset, frac, seed=None):
function normalize (line 90) | def normalize(img, mean, std):
class So2SatDataset (line 100) | class So2SatDataset(Dataset):
method __init__ (line 101) | def __init__(self,path,bands=None,quantile=None,transform=None, normal...
method __getitem__ (line 113) | def __getitem__(self, index):
method __len__ (line 161) | def __len__(self):
FILE: src/benchmark/transfer_classification/linear_BE_data2vec.py
function build_dataset_be (line 41) | def build_dataset_be(is_train, args):
function get_args (line 83) | def get_args():
function main (line 292) | def main(args, ds_init):
FILE: src/benchmark/transfer_classification/linear_BE_dino.py
function eval_linear (line 41) | def eval_linear(args):
function train (line 212) | def train(model, linear_classifier, optimizer, loader, epoch, n, avgpool):
function validate_network (line 259) | def validate_network(val_loader, model, linear_classifier, n, avgpool):
class LinearClassifier (line 311) | class LinearClassifier(nn.Module):
method __init__ (line 313) | def __init__(self, dim, num_labels=1000):
method forward (line 320) | def forward(self, x):
FILE: src/benchmark/transfer_classification/linear_BE_mae.py
function get_args_parser (line 47) | def get_args_parser():
function main (line 127) | def main(args):
FILE: src/benchmark/transfer_classification/linear_BE_moco.py
function init_distributed_mode (line 69) | def init_distributed_mode(args):
function fix_random_seeds (line 98) | def fix_random_seeds(seed=42):
function adjust_learning_rate (line 106) | def adjust_learning_rate(optimizer, epoch, args):
function main (line 118) | def main():
FILE: src/benchmark/transfer_classification/linear_BE_moco_v3.py
function main (line 113) | def main():
function main_worker (line 157) | def main_worker(gpu, ngpus_per_node, args):
function train (line 407) | def train(train_loader, model, criterion, optimizer, epoch, args):
function validate (line 471) | def validate(val_loader, model, criterion, args):
function save_checkpoint (line 526) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
function sanity_check (line 532) | def sanity_check(state_dict, pretrained_weights, linear_keyword):
class AverageMeter (line 556) | class AverageMeter(object):
method __init__ (line 558) | def __init__(self, name, fmt=':f'):
method reset (line 563) | def reset(self):
method update (line 569) | def update(self, val, n=1):
method __str__ (line 575) | def __str__(self):
class ProgressMeter (line 580) | class ProgressMeter(object):
method __init__ (line 581) | def __init__(self, num_batches, meters, prefix=""):
method display (line 586) | def display(self, batch):
method _get_batch_fmtstr (line 591) | def _get_batch_fmtstr(self, num_batches):
function adjust_learning_rate (line 597) | def adjust_learning_rate(optimizer, epoch, args):
function accuracy (line 610) | def accuracy(output, target, topk=(1,)):
FILE: src/benchmark/transfer_classification/linear_BE_sup.py
function init_distributed_mode (line 71) | def init_distributed_mode(args):
function fix_random_seeds (line 100) | def fix_random_seeds(seed=42):
function adjust_learning_rate (line 108) | def adjust_learning_rate(optimizer, epoch, args):
function main (line 120) | def main():
FILE: src/benchmark/transfer_classification/linear_EU_data2vec.py
function build_dataset_eu (line 44) | def build_dataset_eu(is_train, args):
function get_args (line 76) | def get_args():
function main (line 285) | def main(args, ds_init):
FILE: src/benchmark/transfer_classification/linear_EU_dino.py
function eval_linear (line 43) | def eval_linear(args):
function train (line 199) | def train(model, linear_classifier, optimizer, loader, epoch, n, avgpool):
function validate_network (line 243) | def validate_network(val_loader, model, linear_classifier, n, avgpool):
class LinearClassifier (line 293) | class LinearClassifier(nn.Module):
method __init__ (line 295) | def __init__(self, dim, num_labels=1000):
method forward (line 302) | def forward(self, x):
FILE: src/benchmark/transfer_classification/linear_EU_mae.py
function get_args_parser (line 48) | def get_args_parser():
function main (line 127) | def main(args):
FILE: src/benchmark/transfer_classification/linear_EU_moco.py
function init_distributed_mode (line 57) | def init_distributed_mode(args):
function fix_random_seeds (line 86) | def fix_random_seeds(seed=42):
function adjust_learning_rate (line 94) | def adjust_learning_rate(optimizer, epoch, args):
function main (line 106) | def main():
FILE: src/benchmark/transfer_classification/linear_EU_moco_v3.py
function init_distributed_mode (line 60) | def init_distributed_mode(args):
function fix_random_seeds (line 89) | def fix_random_seeds(seed=42):
function adjust_learning_rate (line 97) | def adjust_learning_rate(optimizer, epoch, args):
function main (line 109) | def main():
FILE: src/benchmark/transfer_classification/linear_SS_data2vec.py
function build_dataset_ss (line 42) | def build_dataset_ss(is_train, args):
function get_args (line 76) | def get_args():
function main (line 287) | def main(args, ds_init):
FILE: src/benchmark/transfer_classification/linear_SS_dino.py
function eval_linear (line 39) | def eval_linear(args):
function train (line 201) | def train(model, linear_classifier, optimizer, loader, epoch, n, avgpool):
function validate_network (line 245) | def validate_network(val_loader, model, linear_classifier, n, avgpool):
class LinearClassifier (line 295) | class LinearClassifier(nn.Module):
method __init__ (line 297) | def __init__(self, dim, num_labels=1000):
method forward (line 304) | def forward(self, x):
FILE: src/benchmark/transfer_classification/linear_SS_mae.py
function get_args_parser (line 46) | def get_args_parser():
function main (line 125) | def main(args):
FILE: src/benchmark/transfer_classification/linear_SS_moco.py
function init_distributed_mode (line 53) | def init_distributed_mode(args):
function fix_random_seeds (line 82) | def fix_random_seeds(seed=42):
function adjust_learning_rate (line 90) | def adjust_learning_rate(optimizer, epoch, args):
function main (line 102) | def main():
FILE: src/benchmark/transfer_classification/linear_SS_moco_v3.py
function init_distributed_mode (line 60) | def init_distributed_mode(args):
function fix_random_seeds (line 89) | def fix_random_seeds(seed=42):
function adjust_learning_rate (line 97) | def adjust_learning_rate(optimizer, epoch, args):
function main (line 109) | def main():
FILE: src/benchmark/transfer_classification/models/data2vec/dall_e/__init__.py
function load_model (line 9) | def load_model(path: str, device: torch.device = None) -> nn.Module:
FILE: src/benchmark/transfer_classification/models/data2vec/dall_e/decoder.py
class DecoderBlock (line 13) | class DecoderBlock(nn.Module):
method __attrs_post_init__ (line 21) | def __attrs_post_init__(self) -> None:
method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
class Decoder (line 42) | class Decoder(nn.Module):
method __attrs_post_init__ (line 54) | def __attrs_post_init__(self) -> None:
method forward (line 86) | def forward(self, x: torch.Tensor) -> torch.Tensor:
FILE: src/benchmark/transfer_classification/models/data2vec/dall_e/encoder.py
class EncoderBlock (line 13) | class EncoderBlock(nn.Module):
method __attrs_post_init__ (line 21) | def __attrs_post_init__(self) -> None:
method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
class Encoder (line 42) | class Encoder(nn.Module):
method __attrs_post_init__ (line 53) | def __attrs_post_init__(self) -> None:
method forward (line 85) | def forward(self, x: torch.Tensor) -> torch.Tensor:
FILE: src/benchmark/transfer_classification/models/data2vec/dall_e/utils.py
class Conv2d (line 11) | class Conv2d(nn.Module):
method __attrs_post_init__ (line 20) | def __attrs_post_init__(self) -> None:
method forward (line 31) | def forward(self, x: torch.Tensor) -> torch.Tensor:
function map_pixels (line 45) | def map_pixels(x: torch.Tensor) -> torch.Tensor:
function unmap_pixels (line 51) | def unmap_pixels(x: torch.Tensor) -> torch.Tensor:
FILE: src/benchmark/transfer_classification/models/data2vec/dataset_folder.py
function has_file_allowed_extension (line 21) | def has_file_allowed_extension(filename: str, extensions: Tuple[str, ......
function is_image_file (line 34) | def is_image_file(filename: str) -> bool:
function make_dataset (line 46) | def make_dataset(
class DatasetFolder (line 76) | class DatasetFolder(VisionDataset):
method __init__ (line 108) | def __init__(
method _find_classes (line 138) | def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
method __getitem__ (line 156) | def __getitem__(self, index: int) -> Tuple[Any, Any]:
method __len__ (line 180) | def __len__(self) -> int:
function pil_loader (line 187) | def pil_loader(path: str) -> Image.Image:
function accimage_loader (line 195) | def accimage_loader(path: str) -> Any:
function default_loader (line 204) | def default_loader(path: str) -> Any:
class ImageFolder (line 234) | class ImageFolder(DatasetFolder):
method __init__ (line 261) | def __init__(
FILE: src/benchmark/transfer_classification/models/data2vec/datasets.py
class SeasonTransform (line 35) | class SeasonTransform:
method __init__ (line 37) | def __init__(self, base_transform, season='fixed'):
method __call__ (line 41) | def __call__(self, x):
class DataAugmentations (line 64) | class DataAugmentations(object):
method __init__ (line 65) | def __init__(self, args):
method __call__ (line 130) | def __call__(self, image):
method __repr__ (line 141) | def __repr__(self):
function build_beit_pretraining_dataset (line 152) | def build_beit_pretraining_dataset(args):
function build_transform (line 167) | def build_transform(is_train, args):
FILE: src/benchmark/transfer_classification/models/data2vec/engine_for_cyclical.py
function train_one_epoch (line 23) | def train_one_epoch(model: torch.nn.Module, model_ema: torch.nn.Module, ...
FILE: src/benchmark/transfer_classification/models/data2vec/engine_for_cyclical_joint.py
function train_one_epoch (line 24) | def train_one_epoch(model: torch.nn.Module, model_ema: torch.nn.Module, ...
FILE: src/benchmark/transfer_classification/models/data2vec/engine_for_finetuning.py
function train_class_batch (line 27) | def train_class_batch(model, samples, target, criterion, bool_masked_pos...
function get_loss_scale_for_deepspeed (line 33) | def get_loss_scale_for_deepspeed(model):
function train_one_epoch (line 38) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 173) | def evaluate(data_loader, model, device, metric='acc', padd=False, oneho...
FILE: src/benchmark/transfer_classification/models/data2vec/engine_for_pretraining.py
function train_one_epoch (line 22) | def train_one_epoch(model: torch.nn.Module, d_vae: torch.nn.Module,
FILE: src/benchmark/transfer_classification/models/data2vec/masking_generator.py
class MaskingGenerator (line 29) | class MaskingGenerator:
method __init__ (line 30) | def __init__(
method __repr__ (line 46) | def __repr__(self):
method get_shape (line 52) | def get_shape(self):
method _mask (line 55) | def _mask(self, mask, max_mask_patches):
method __call__ (line 79) | def __call__(self):
FILE: src/benchmark/transfer_classification/models/data2vec/modeling_cyclical.py
function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0.0, std=1.0):
class VisionTransformerForCyclicalTraining (line 32) | class VisionTransformerForCyclicalTraining(nn.Module):
method __init__ (line 33) | def __init__(
method fix_init_weight (line 127) | def fix_init_weight(self):
method _init_weights (line 135) | def _init_weights(self, m):
method no_weight_decay (line 149) | def no_weight_decay(self):
method get_num_layers (line 152) | def get_num_layers(self):
method forward_features (line 155) | def forward_features(self, x, bool_masked_pos, layer_results):
method forward (line 194) | def forward(self, x, bool_masked_pos, return_all_tokens=False, layer_r...
function beit_base_patch16_224 (line 214) | def beit_base_patch16_224(pretrained=False, **kwargs):
function beit_small_patch16_224 (line 234) | def beit_small_patch16_224(pretrained=False, **kwargs):
function beit_large_patch16_224 (line 255) | def beit_large_patch16_224(pretrained=False, **kwargs):
function beit_huge_patch16_224 (line 275) | def beit_huge_patch16_224(pretrained=False, **kwargs):
FILE: src/benchmark/transfer_classification/models/data2vec/modeling_cyclical_joint.py
function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0.0, std=1.0):
class VisionTransformerForCyclicalJointTraining (line 32) | class VisionTransformerForCyclicalJointTraining(nn.Module):
method __init__ (line 33) | def __init__(
method fix_init_weight (line 126) | def fix_init_weight(self):
method _init_weights (line 134) | def _init_weights(self, m):
method no_weight_decay (line 148) | def no_weight_decay(self):
method get_num_layers (line 151) | def get_num_layers(self):
method forward_features (line 154) | def forward_features(self, x, bool_masked_pos, layer_results):
method forward (line 183) | def forward(self, x, bool_masked_pos, return_all_tokens=False, layer_r...
function beit_base_joint_patch16_224 (line 203) | def beit_base_joint_patch16_224(pretrained=False, **kwargs):
FILE: src/benchmark/transfer_classification/models/data2vec/modeling_discrete_vae.py
function top_k (line 19) | def top_k(logits, thres = 0.5):
function exists (line 28) | def exists(val):
function default (line 32) | def default(val, d):
function eval_decorator (line 36) | def eval_decorator(fn):
class BasicVAE (line 46) | class BasicVAE(nn.Module):
method get_codebook_indices (line 48) | def get_codebook_indices(self, images):
method decode (line 51) | def decode(self, img_seq):
method get_codebook_probs (line 54) | def get_codebook_probs(self, img_seq):
method get_image_tokens_size (line 57) | def get_image_tokens_size(self):
method get_image_size (line 60) | def get_image_size(self):
class ResBlock (line 64) | class ResBlock(nn.Module):
method __init__ (line 65) | def __init__(self, chan_in, hidden_size, chan_out):
method forward (line 75) | def forward(self, x):
class DiscreteVAE (line 79) | class DiscreteVAE(BasicVAE):
method __init__ (line 80) | def __init__(
method get_image_size (line 127) | def get_image_size(self):
method get_image_tokens_size (line 130) | def get_image_tokens_size(self):
method get_codebook_indices (line 135) | def get_codebook_indices(self, images):
method get_codebook_probs (line 142) | def get_codebook_probs(self, images):
method decode (line 146) | def decode(
method forward (line 158) | def forward(
class Dalle_VAE (line 206) | class Dalle_VAE(BasicVAE):
method __init__ (line 207) | def __init__(self, image_size):
method load_model (line 213) | def load_model(self, model_dir, device):
method decode (line 217) | def decode(self, img_seq):
method get_codebook_indices (line 223) | def get_codebook_indices(self, images):
method get_codebook_probs (line 227) | def get_codebook_probs(self, images):
method forward (line 231) | def forward(self, img_seq_prob, no_process=False):
FILE: src/benchmark/transfer_classification/models/data2vec/modeling_finetune.py
function _cfg (line 23) | def _cfg(url='', **kwargs):
class DropPath (line 33) | class DropPath(nn.Module):
method __init__ (line 36) | def __init__(self, drop_prob=None):
method forward (line 40) | def forward(self, x):
method extra_repr (line 43) | def extra_repr(self) -> str:
class Mlp (line 47) | class Mlp(nn.Module):
method __init__ (line 48) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 57) | def forward(self, x):
class Attention (line 67) | class Attention(nn.Module):
method __init__ (line 68) | def __init__(
method forward (line 121) | def forward(self, x, rel_pos_bias=None):
class Block (line 154) | class Block(nn.Module):
method __init__ (line 156) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
method forward (line 177) | def forward(self, x, rel_pos_bias=None):
class PatchEmbed (line 189) | class PatchEmbed(nn.Module):
method __init__ (line 192) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 204) | def forward(self, x, **kwargs):
class RelativePositionBias (line 213) | class RelativePositionBias(nn.Module):
method __init__ (line 215) | def __init__(self, window_size, num_heads):
method forward (line 244) | def forward(self):
class VisionTransformer (line 252) | class VisionTransformer(nn.Module):
method __init__ (line 255) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
method fix_init_weight (line 315) | def fix_init_weight(self):
method _init_weights (line 323) | def _init_weights(self, m):
method get_num_layers (line 334) | def get_num_layers(self):
method no_weight_decay (line 338) | def no_weight_decay(self):
method get_classifier (line 341) | def get_classifier(self):
method reset_classifier (line 344) | def reset_classifier(self, num_classes, global_pool=''):
method forward_features (line 348) | def forward_features(self, x, bool_masked_pos=None):
method forward (line 391) | def forward(self, x, bool_masked_pos=None):
function beit_small_patch16_224 (line 398) | def beit_small_patch16_224(pretrained=False, **kwargs):
function beit_base_patch16_224 (line 407) | def beit_base_patch16_224(pretrained=False, **kwargs):
function beit_base_patch16_384 (line 416) | def beit_base_patch16_384(pretrained=False, **kwargs):
function beit_large_patch16_224 (line 425) | def beit_large_patch16_224(pretrained=False, **kwargs):
function beit_large_patch16_384 (line 434) | def beit_large_patch16_384(pretrained=False, **kwargs):
function beit_large_patch16_512 (line 443) | def beit_large_patch16_512(pretrained=False, **kwargs):
FILE: src/benchmark/transfer_classification/models/data2vec/modeling_pretrain.py
function trunc_normal_ (line 22) | def trunc_normal_(tensor, mean=0., std=1.):
class VisionTransformerForMaskedImageModeling (line 32) | class VisionTransformerForMaskedImageModeling(nn.Module):
method __init__ (line 33) | def __init__(self, img_size=224, patch_size=16, in_chans=3, vocab_size...
method fix_init_weight (line 79) | def fix_init_weight(self):
method _init_weights (line 87) | def _init_weights(self, m):
method no_weight_decay (line 101) | def no_weight_decay(self):
method get_num_layers (line 104) | def get_num_layers(self):
method forward_features (line 107) | def forward_features(self, x, bool_masked_pos):
method forward (line 129) | def forward(self, x, bool_masked_pos, return_all_tokens=False):
function beit_base_patch16_224_8k_vocab (line 140) | def beit_base_patch16_224_8k_vocab(pretrained=False, **kwargs):
function beit_large_patch16_224_8k_vocab (line 155) | def beit_large_patch16_224_8k_vocab(pretrained=False, **kwargs):
FILE: src/benchmark/transfer_classification/models/data2vec/models.py
function _cfg (line 32) | def _cfg(url='', **kwargs):
class Attention (line 71) | class Attention(nn.Module):
method __init__ (line 72) | def __init__(
method forward (line 127) | def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
class Block (line 157) | class Block(nn.Module):
method __init__ (line 159) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=...
method forward (line 179) | def forward(self, x, rel_pos_bias: Optional[torch.Tensor] = None):
class RelativePositionBias (line 189) | class RelativePositionBias(nn.Module):
method __init__ (line 191) | def __init__(self, window_size, num_heads):
method forward (line 220) | def forward(self):
class Beit (line 228) | class Beit(nn.Module):
method __init__ (line 232) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
method fix_init_weight (line 281) | def fix_init_weight(self):
method _init_weights (line 289) | def _init_weights(self, m):
method get_num_layers (line 298) | def get_num_layers(self):
method no_weight_decay (line 302) | def no_weight_decay(self):
method get_classifier (line 305) | def get_classifier(self):
method reset_classifier (line 308) | def reset_classifier(self, num_classes, global_pool=''):
method forward_features (line 312) | def forward_features(self, x):
method forward (line 333) | def forward(self, x):
function _create_beit (line 339) | def _create_beit(variant, pretrained=False, default_cfg=None, **kwargs):
function beit_base_patch16_224 (line 354) | def beit_base_patch16_224(pretrained=False, **kwargs):
function beit_base_patch16_384 (line 366) | def beit_base_patch16_384(pretrained=False, **kwargs):
function beit_base_patch16_224_in22k (line 375) | def beit_base_patch16_224_in22k(pretrained=False, **kwargs):
function beit_large_patch16_224 (line 384) | def beit_large_patch16_224(pretrained=False, **kwargs):
function beit_large_patch16_384 (line 393) | def beit_large_patch16_384(pretrained=False, **kwargs):
function beit_large_patch16_512 (line 402) | def beit_large_patch16_512(pretrained=False, **kwargs):
function beit_large_patch16_224_in22k (line 411) | def beit_large_patch16_224_in22k(pretrained=False, **kwargs):
FILE: src/benchmark/transfer_classification/models/data2vec/optim_factory.py
function get_num_layer_for_vit (line 33) | def get_num_layer_for_vit(var_name, num_max_layer):
class LayerDecayValueAssigner (line 47) | class LayerDecayValueAssigner(object):
method __init__ (line 48) | def __init__(self, values):
method get_scale (line 51) | def get_scale(self, layer_id):
method get_layer_id (line 54) | def get_layer_id(self, var_name):
function get_parameter_groups (line 58) | def get_parameter_groups(model, weight_decay=1e-5, skip_list=(), get_num...
function create_optimizer (line 100) | def create_optimizer(args, model, get_num_layer=None, get_layer_scale=No...
FILE: src/benchmark/transfer_classification/models/data2vec/run_beit_pretraining.py
function get_args (line 34) | def get_args():
function get_model (line 139) | def get_model(args):
function main (line 154) | def main(args):
FILE: src/benchmark/transfer_classification/models/data2vec/run_class_finetuning.py
function get_args (line 39) | def get_args():
function main (line 241) | def main(args, ds_init):
FILE: src/benchmark/transfer_classification/models/data2vec/run_cyclical.py
function get_args (line 37) | def get_args():
function get_model (line 280) | def get_model(args):
function main (line 304) | def main(args):
FILE: src/benchmark/transfer_classification/models/data2vec/run_cyclical_joint.py
function get_args (line 36) | def get_args():
function get_model (line 264) | def get_model(args):
function main (line 279) | def main(args):
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/backbone/beit.py
class DropPath (line 26) | class DropPath(nn.Module):
method __init__ (line 29) | def __init__(self, drop_prob=None):
method forward (line 33) | def forward(self, x):
method extra_repr (line 36) | def extra_repr(self) -> str:
class Mlp (line 40) | class Mlp(nn.Module):
method __init__ (line 41) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 50) | def forward(self, x):
class Attention (line 60) | class Attention(nn.Module):
method __init__ (line 61) | def __init__(
method forward (line 117) | def forward(self, x, rel_pos_bias=None):
class Block (line 150) | class Block(nn.Module):
method __init__ (line 152) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
method forward (line 172) | def forward(self, x, rel_pos_bias=None):
class PatchEmbed (line 182) | class PatchEmbed(nn.Module):
method __init__ (line 185) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 197) | def forward(self, x, **kwargs):
class HybridEmbed (line 209) | class HybridEmbed(nn.Module):
method __init__ (line 213) | def __init__(self, backbone, img_size=224, feature_size=None, in_chans...
method forward (line 237) | def forward(self, x):
class RelativePositionBias (line 244) | class RelativePositionBias(nn.Module):
method __init__ (line 246) | def __init__(self, window_size, num_heads):
method forward (line 275) | def forward(self):
class BEiT (line 284) | class BEiT(nn.Module):
method __init__ (line 287) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe...
method fix_init_weight (line 366) | def fix_init_weight(self):
method _init_weights (line 374) | def _init_weights(self, m):
method init_weights (line 383) | def init_weights(self, pretrained=None):
method get_num_layers (line 409) | def get_num_layers(self):
method no_weight_decay (line 413) | def no_weight_decay(self):
method forward_features (line 416) | def forward_features(self, x):
method forward (line 441) | def forward(self, x):
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/apex_iter_based_runner.py
class IterBasedRunnerAmp (line 20) | class IterBasedRunnerAmp(IterBasedRunner):
method save_checkpoint (line 26) | def save_checkpoint(self,
method resume (line 68) | def resume(self,
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/checkpoint.py
function save_checkpoint (line 19) | def save_checkpoint(model, filename, optimizer=None, meta=None):
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/optimizer.py
class DistOptimizerHook (line 9) | class DistOptimizerHook(OptimizerHook):
method __init__ (line 12) | def __init__(self, update_interval=1, grad_clip=None, coalesce=True, b...
method before_run (line 19) | def before_run(self, runner):
method after_train_iter (line 22) | def after_train_iter(self, runner):
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/mmcv_custom/checkpoint.py
function _get_mmcv_home (line 34) | def _get_mmcv_home():
function load_state_dict (line 45) | def load_state_dict(module, state_dict, strict=False, logger=None):
function load_url_dist (line 113) | def load_url_dist(url, model_dir=None, map_location="cpu"):
function load_pavimodel_dist (line 127) | def load_pavimodel_dist(model_path, map_location=None):
function load_fileclient_dist (line 155) | def load_fileclient_dist(filename, backend, map_location):
function get_torchvision_models (line 176) | def get_torchvision_models():
function get_external_models (line 188) | def get_external_models():
function get_mmcls_models (line 202) | def get_mmcls_models():
function get_deprecated_model_names (line 209) | def get_deprecated_model_names():
function _process_mmcls_checkpoint (line 218) | def _process_mmcls_checkpoint(checkpoint):
function _load_checkpoint (line 229) | def _load_checkpoint(filename, map_location=None):
function cosine_scheduler (line 290) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
function load_checkpoint (line 310) | def load_checkpoint(model,
function weights_to_cpu (line 476) | def weights_to_cpu(state_dict):
function _save_to_state_dict (line 491) | def _save_to_state_dict(module, destination, prefix, keep_vars):
function get_state_dict (line 511) | def get_state_dict(module, destination=None, prefix='', keep_vars=False):
function save_checkpoint (line 555) | def save_checkpoint(model, filename, optimizer=None, meta=None):
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/mmcv_custom/layer_decay_optimizer_constructor.py
function get_num_layer_for_vit (line 6) | def get_num_layer_for_vit(var_name, num_max_layer):
class LayerDecayOptimizerConstructor (line 19) | class LayerDecayOptimizerConstructor(DefaultOptimizerConstructor):
method add_params (line 20) | def add_params(self, params, module, prefix='', is_dcn_module=None):
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/mmcv_custom/resize_transform.py
class SETR_Resize (line 8) | class SETR_Resize(object):
method __init__ (line 35) | def __init__(self,
method random_select (line 66) | def random_select(img_scales):
method random_sample (line 84) | def random_sample(img_scales):
method random_sample_ratio (line 111) | def random_sample_ratio(img_scale, ratio_range):
method _random_scale (line 137) | def _random_scale(self, results):
method _resize_img (line 170) | def _resize_img(self, results):
method _resize_seg (line 206) | def _resize_seg(self, results):
method __call__ (line 217) | def __call__(self, results):
method __repr__ (line 235) | def __repr__(self):
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/mmcv_custom/train_api.py
function set_random_seed (line 18) | def set_random_seed(seed, deterministic=False):
function train_segmentor (line 37) | def train_segmentor(model,
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/tools/test.py
function parse_args (line 17) | def parse_args():
function main (line 67) | def main():
FILE: src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/tools/train.py
function parse_args (line 23) | def parse_args():
function main (line 67) | def main():
FILE: src/benchmark/transfer_classification/models/data2vec/transforms.py
class ToNumpy (line 20) | class ToNumpy:
method __call__ (line 22) | def __call__(self, pil_img):
class ToTensor (line 30) | class ToTensor:
method __init__ (line 32) | def __init__(self, dtype=torch.float32):
method __call__ (line 35) | def __call__(self, pil_img):
function _pil_interp (line 53) | def _pil_interp(method):
class RandomResizedCropAndInterpolationWithTwoPic (line 68) | class RandomResizedCropAndInterpolationWithTwoPic:
method __init__ (line 83) | def __init__(self, size, second_size=None, scale=(0.08, 1.0), ratio=(3...
method get_params (line 109) | def get_params(img, scale, ratio):
method __call__ (line 151) | def __call__(self, img):
method __repr__ (line 170) | def __repr__(self):
FILE: src/benchmark/transfer_classification/models/data2vec/utils.py
class SmoothedValue (line 33) | class SmoothedValue(object):
method __init__ (line 38) | def __init__(self, window_size=20, fmt=None):
method update (line 46) | def update(self, value, n=1):
method synchronize_between_processes (line 51) | def synchronize_between_processes(self):
method median (line 65) | def median(self):
method avg (line 70) | def avg(self):
method global_avg (line 75) | def global_avg(self):
method max (line 79) | def max(self):
method value (line 83) | def value(self):
method __str__ (line 86) | def __str__(self):
class MetricLogger (line 95) | class MetricLogger(object):
method __init__ (line 96) | def __init__(self, delimiter="\t"):
method update (line 100) | def update(self, **kwargs):
method __getattr__ (line 109) | def __getattr__(self, attr):
method __str__ (line 117) | def __str__(self):
method synchronize_between_processes (line 125) | def synchronize_between_processes(self):
method add_meter (line 129) | def add_meter(self, name, meter):
method log_every (line 132) | def log_every(self, iterable, print_freq, header=None):
class TensorboardLogger (line 179) | class TensorboardLogger(object):
method __init__ (line 180) | def __init__(self, log_dir):
method set_step (line 184) | def set_step(self, step=None):
method update (line 190) | def update(self, head='scalar', step=None, **kwargs):
method flush (line 199) | def flush(self):
function _load_checkpoint_for_ema (line 203) | def _load_checkpoint_for_ema(model_ema, checkpoint):
function setup_for_distributed (line 217) | def setup_for_distributed(is_master):
function is_dist_avail_and_initialized (line 232) | def is_dist_avail_and_initialized():
function get_world_size (line 240) | def get_world_size():
function get_rank (line 246) | def get_rank():
function is_main_process (line 252) | def is_main_process():
function save_on_master (line 256) | def save_on_master(*args, **kwargs):
function init_distributed_mode (line 261) | def init_distributed_mode(args):
function load_state_dict (line 314) | def load_state_dict(model, state_dict, prefix='', ignore_missing="relati...
class NativeScalerWithGradNormCount (line 363) | class NativeScalerWithGradNormCount:
method __init__ (line 366) | def __init__(self):
method __call__ (line 369) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
method state_dict (line 385) | def state_dict(self):
method load_state_dict (line 388) | def load_state_dict(self, state_dict):
function get_grad_norm_ (line 392) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
function cosine_scheduler (line 407) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
function tri_phase_scheduler (line 426) | def tri_phase_scheduler(base_value, final_value, epochs, niter_per_ep, w...
function save_model (line 461) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
function auto_load_model (line 486) | def auto_load_model(args, model, model_without_ddp, optimizer, loss_scal...
function create_d_vae (line 541) | def create_d_vae(weight_path, d_vae_type, image_size, device):
function get_dalle_vae (line 550) | def get_dalle_vae(weight_path, image_size, device):
function get_d_vae (line 556) | def get_d_vae(weight_path, image_size, device):
function create_ds_config (line 576) | def create_ds_config(args):
FILE: src/benchmark/transfer_classification/models/dino/utils.py
class GaussianBlur (line 37) | class GaussianBlur(object):
method __init__ (line 41) | def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
method __call__ (line 46) | def __call__(self, img):
class cvGaussianBlur (line 58) | class cvGaussianBlur(object):
method __init__ (line 61) | def __init__(self, p=0.5, sigma=[.1, 2.]):
method __call__ (line 65) | def __call__(self, x):
class Solarization (line 78) | class Solarization(object):
method __init__ (line 82) | def __init__(self, p):
method __call__ (line 85) | def __call__(self, img):
function load_pretrained_weights (line 92) | def load_pretrained_weights(model, pretrained_weights, checkpoint_key, m...
function load_pretrained_linear_weights (line 133) | def load_pretrained_linear_weights(linear_classifier, model_name, patch_...
function clip_gradients (line 153) | def clip_gradients(model, clip):
function cancel_gradients_last_layer (line 165) | def cancel_gradients_last_layer(epoch, model, freeze_last_layer):
function restart_from_checkpoint (line 173) | def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):
function cosine_scheduler (line 208) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
function bool_flag (line 222) | def bool_flag(s):
function fix_random_seeds (line 236) | def fix_random_seeds(seed=31):
class SmoothedValue (line 245) | class SmoothedValue(object):
method __init__ (line 250) | def __init__(self, window_size=20, fmt=None):
method update (line 258) | def update(self, value, n=1):
method synchronize_between_processes (line 263) | def synchronize_between_processes(self):
method median (line 277) | def median(self):
method avg (line 282) | def avg(self):
method global_avg (line 287) | def global_avg(self):
method max (line 291) | def max(self):
method value (line 295) | def value(self):
method __str__ (line 298) | def __str__(self):
function reduce_dict (line 307) | def reduce_dict(input_dict, average=True):
class MetricLogger (line 334) | class MetricLogger(object):
method __init__ (line 335) | def __init__(self, delimiter="\t"):
method update (line 339) | def update(self, **kwargs):
method __getattr__ (line 346) | def __getattr__(self, attr):
method __str__ (line 354) | def __str__(self):
method synchronize_between_processes (line 362) | def synchronize_between_processes(self):
method add_meter (line 366) | def add_meter(self, name, meter):
method log_every (line 369) | def log_every(self, iterable, print_freq, header=None):
function get_sha (line 424) | def get_sha():
function is_dist_avail_and_initialized (line 444) | def is_dist_avail_and_initialized():
function get_world_size (line 452) | def get_world_size():
function get_rank (line 458) | def get_rank():
function is_main_process (line 464) | def is_main_process():
function save_on_master (line 468) | def save_on_master(*args, **kwargs):
function setup_for_distributed (line 473) | def setup_for_distributed(is_master):
function init_distributed_mode (line 488) | def init_distributed_mode(args):
function accuracy (line 526) | def accuracy(output, target, topk=(1,)):
function _no_grad_trunc_normal_ (line 536) | def _no_grad_trunc_normal_(tensor, mean, std, a, b):
function trunc_normal_ (line 572) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
class LARS (line 577) | class LARS(torch.optim.Optimizer):
method __init__ (line 581) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001,
method step (line 589) | def step(self):
class MultiCropWrapper (line 618) | class MultiCropWrapper(nn.Module):
method __init__ (line 627) | def __init__(self, backbone, head):
method forward (line 634) | def forward(self, x):
function get_params_groups (line 656) | def get_params_groups(model):
function has_batchnorms (line 670) | def has_batchnorms(model):
class PCA (line 678) | class PCA():
method __init__ (line 682) | def __init__(self, dim=256, whit=0.5):
method train_pca (line 687) | def train_pca(self, cov):
method apply (line 713) | def apply(self, x):
function compute_ap (line 732) | def compute_ap(ranks, nres):
function compute_map (line 767) | def compute_map(ranks, gnd, kappas=[]):
function multi_scale (line 839) | def multi_scale(samples, model):
FILE: src/benchmark/transfer_classification/models/dino/vision_transformer.py
function drop_path (line 27) | def drop_path(x, drop_prob: float = 0., training: bool = False):
class DropPath (line 38) | class DropPath(nn.Module):
method __init__ (line 41) | def __init__(self, drop_prob=None):
method forward (line 45) | def forward(self, x):
class Mlp (line 49) | class Mlp(nn.Module):
method __init__ (line 50) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 59) | def forward(self, x):
class Attention (line 68) | class Attention(nn.Module):
method __init__ (line 69) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
method forward (line 80) | def forward(self, x):
class Block (line 95) | class Block(nn.Module):
method __init__ (line 96) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
method forward (line 107) | def forward(self, x, return_attention=False):
class PatchEmbed (line 116) | class PatchEmbed(nn.Module):
method __init__ (line 119) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 128) | def forward(self, x):
class VisionTransformer (line 134) | class VisionTransformer(nn.Module):
method __init__ (line 136) | def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_clas...
method _init_weights (line 165) | def _init_weights(self, m):
method interpolate_pos_encoding (line 174) | def interpolate_pos_encoding(self, x, w, h):
method prepare_tokens (line 196) | def prepare_tokens(self, x):
method forward (line 209) | def forward(self, x):
method get_last_selfattention (line 216) | def get_last_selfattention(self, x):
method get_intermediate_layers (line 225) | def get_intermediate_layers(self, x, n=1):
function vit_tiny (line 236) | def vit_tiny(patch_size=16, **kwargs):
function vit_small (line 243) | def vit_small(patch_size=16, **kwargs):
function vit_base (line 250) | def vit_base(patch_size=16, **kwargs):
class DINOHead (line 257) | class DINOHead(nn.Module):
method __init__ (line 258) | def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True...
method _init_weights (line 281) | def _init_weights(self, m):
method forward (line 287) | def forward(self, x):
FILE: src/benchmark/transfer_classification/models/mae/engine_finetune.py
function train_one_epoch (line 25) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 99) | def evaluate(data_loader, model, device):
FILE: src/benchmark/transfer_classification/models/mae/engine_finetune_BE.py
function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 107) | def evaluate(data_loader, model, device, criterion):
FILE: src/benchmark/transfer_classification/models/mae/engine_finetune_EU.py
function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 104) | def evaluate(data_loader, model, device, criterion):
FILE: src/benchmark/transfer_classification/models/mae/engine_finetune_SS.py
function train_one_epoch (line 29) | def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
function evaluate (line 104) | def evaluate(data_loader, model, device, criterion):
FILE: src/benchmark/transfer_classification/models/mae/engine_pretrain.py
function train_one_epoch (line 23) | def train_one_epoch(model: torch.nn.Module,
FILE: src/benchmark/transfer_classification/models/mae/main_finetune.py
function get_args_parser (line 42) | def get_args_parser():
function main (line 158) | def main(args):
FILE: src/benchmark/transfer_classification/models/mae/main_linprobe.py
function get_args_parser (line 47) | def get_args_parser():
function main (line 121) | def main(args):
FILE: src/benchmark/transfer_classification/models/mae/main_pretrain.py
class SeasonTransform (line 41) | class SeasonTransform:
method __init__ (line 43) | def __init__(self, base_transform, season='fixed'):
method __call__ (line 47) | def __call__(self, x):
function get_args_parser (line 72) | def get_args_parser():
function main (line 151) | def main(args):
FILE: src/benchmark/transfer_classification/models/mae/models_mae.py
class MaskedAutoencoderViT (line 22) | class MaskedAutoencoderViT(nn.Module):
method __init__ (line 25) | def __init__(self, img_size=224, patch_size=16, in_chans=3,
method initialize_weights (line 67) | def initialize_weights(self):
method _init_weights (line 87) | def _init_weights(self, m):
method patchify (line 97) | def patchify(self, imgs):
method unpatchify (line 111) | def unpatchify(self, x):
method random_masking (line 125) | def random_masking(self, x, mask_ratio):
method forward_encoder (line 152) | def forward_encoder(self, x, mask_ratio):
method forward_decoder (line 174) | def forward_decoder(self, x, ids_restore):
method forward_loss (line 200) | def forward_loss(self, imgs, pred, mask):
method forward (line 218) | def forward(self, imgs, mask_ratio=0.75):
function mae_vit_small_patch16_dec512d8b (line 225) | def mae_vit_small_patch16_dec512d8b(**kwargs):
function mae_vit_base_patch16_dec512d8b (line 232) | def mae_vit_base_patch16_dec512d8b(**kwargs):
function mae_vit_large_patch16_dec512d8b (line 240) | def mae_vit_large_patch16_dec512d8b(**kwargs):
function mae_vit_huge_patch14_dec512d8b (line 248) | def mae_vit_huge_patch14_dec512d8b(**kwargs):
FILE: src/benchmark/transfer_classification/models/mae/models_vit.py
class VisionTransformer (line 20) | class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
method __init__ (line 23) | def __init__(self, global_pool=False, **kwargs):
method forward_features (line 34) | def forward_features(self, x):
function vit_small_patch16 (line 55) | def vit_small_patch16(**kwargs):
function vit_base_patch16 (line 61) | def vit_base_patch16(**kwargs):
function vit_large_patch16 (line 68) | def vit_large_patch16(**kwargs):
function vit_huge_patch14 (line 75) | def vit_huge_patch14(**kwargs):
FILE: src/benchmark/transfer_classification/models/mae/submitit_finetune.py
function parse_args (line 19) | def parse_args():
function get_shared_folder (line 33) | def get_shared_folder() -> Path:
function get_init_file (line 42) | def get_init_file():
class Trainer (line 51) | class Trainer(object):
method __init__ (line 52) | def __init__(self, args):
method __call__ (line 55) | def __call__(self):
method checkpoint (line 61) | def checkpoint(self):
method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
function main (line 86) | def main():
FILE: src/benchmark/transfer_classification/models/mae/submitit_linprobe.py
function parse_args (line 19) | def parse_args():
function get_shared_folder (line 33) | def get_shared_folder() -> Path:
function get_init_file (line 42) | def get_init_file():
class Trainer (line 51) | class Trainer(object):
method __init__ (line 52) | def __init__(self, args):
method __call__ (line 55) | def __call__(self):
method checkpoint (line 61) | def checkpoint(self):
method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
function main (line 86) | def main():
FILE: src/benchmark/transfer_classification/models/mae/submitit_pretrain.py
function parse_args (line 19) | def parse_args():
function get_shared_folder (line 33) | def get_shared_folder() -> Path:
function get_init_file (line 42) | def get_init_file():
class Trainer (line 51) | class Trainer(object):
method __init__ (line 52) | def __init__(self, args):
method __call__ (line 55) | def __call__(self):
method checkpoint (line 61) | def checkpoint(self):
method _setup_gpu_args (line 73) | def _setup_gpu_args(self):
function main (line 86) | def main():
FILE: src/benchmark/transfer_classification/models/mae/util/crop.py
class RandomResizedCrop (line 15) | class RandomResizedCrop(transforms.RandomResizedCrop):
method get_params (line 23) | def get_params(img, scale, ratio):
FILE: src/benchmark/transfer_classification/models/mae/util/datasets.py
function build_dataset (line 20) | def build_dataset(is_train, args):
function build_transform (line 31) | def build_transform(is_train, args):
FILE: src/benchmark/transfer_classification/models/mae/util/lars.py
class LARS (line 14) | class LARS(torch.optim.Optimizer):
method __init__ (line 18) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
method step (line 23) | def step(self):
FILE: src/benchmark/transfer_classification/models/mae/util/lr_decay.py
function param_groups_lrd (line 15) | def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], ...
function get_layer_id_for_vit (line 64) | def get_layer_id_for_vit(name, num_layers):
FILE: src/benchmark/transfer_classification/models/mae/util/lr_sched.py
function adjust_learning_rate (line 9) | def adjust_learning_rate(optimizer, epoch, args):
FILE: src/benchmark/transfer_classification/models/mae/util/misc.py
class SmoothedValue (line 24) | class SmoothedValue(object):
method __init__ (line 29) | def __init__(self, window_size=20, fmt=None):
method update (line 37) | def update(self, value, n=1):
method synchronize_between_processes (line 42) | def synchronize_between_processes(self):
method median (line 56) | def median(self):
method avg (line 61) | def avg(self):
method global_avg (line 66) | def global_avg(self):
method max (line 70) | def max(self):
method value (line 74) | def value(self):
method __str__ (line 77) | def __str__(self):
class MetricLogger (line 86) | class MetricLogger(object):
method __init__ (line 87) | def __init__(self, delimiter="\t"):
method update (line 91) | def update(self, **kwargs):
method __getattr__ (line 100) | def __getattr__(self, attr):
method __str__ (line 108) | def __str__(self):
method synchronize_between_processes (line 116) | def synchronize_between_processes(self):
method add_meter (line 120) | def add_meter(self, name, meter):
method log_every (line 123) | def log_every(self, iterable, print_freq, header=None):
function setup_for_distributed (line 170) | def setup_for_distributed(is_master):
function is_dist_avail_and_initialized (line 187) | def is_dist_avail_and_initialized():
function get_world_size (line 195) | def get_world_size():
function get_rank (line 201) | def get_rank():
function is_main_process (line 207) | def is_main_process():
function save_on_master (line 211) | def save_on_master(*args, **kwargs):
function init_distributed_mode (line 216) | def init_distributed_mode(args):
class NativeScalerWithGradNormCount (line 253) | class NativeScalerWithGradNormCount:
method __init__ (line 256) | def __init__(self):
method __call__ (line 259) | def __call__(self, loss, optimizer, clip_grad=None, parameters=None, c...
method state_dict (line 275) | def state_dict(self):
method load_state_dict (line 278) | def load_state_dict(self, state_dict):
function get_grad_norm_ (line 282) | def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
function save_model (line 297) | def save_model(args, epoch, model, model_without_ddp, optimizer, loss_sc...
function load_model (line 317) | def load_model(args, model_without_ddp, optimizer, loss_scaler):
function all_reduce_mean (line 334) | def all_reduce_mean(x):
FILE: src/benchmark/transfer_classification/models/mae/util/pos_embed.py
function get_2d_sincos_pos_embed (line 20) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
function get_2d_sincos_pos_embed_from_grid (line 38) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
function get_1d_sincos_pos_embed_from_grid (line 49) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
function interpolate_pos_embed (line 75) | def interpolate_pos_embed(model, checkpoint_model):
FILE: src/benchmark/transfer_classification/models/moco/builder.py
class MoCo (line 6) | class MoCo(nn.Module):
method __init__ (line 11) | def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, ml...
method _momentum_update_key_encoder (line 68) | def _momentum_update_key_encoder(self):
method _dequeue_and_enqueue (line 76) | def _dequeue_and_enqueue(self, keys):
method _batch_shuffle_ddp (line 92) | def _batch_shuffle_ddp(self, x):
method _batch_unshuffle_ddp (line 120) | def _batch_unshuffle_ddp(self, x, idx_unshuffle):
method forward (line 138) | def forward(self, im_q, im_k):
function concat_all_gather (line 188) | def concat_all_gather(tensor):
FILE: src/benchmark/transfer_classification/models/moco/loader.py
class TwoCropsTransform (line 11) | class TwoCropsTransform:
method __init__ (line 14) | def __init__(self, base_transform):
method __call__ (line 17) | def __call__(self, x):
class GaussianBlur (line 23) | class GaussianBlur(object):
method __init__ (line 26) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 29) | def __call__(self, x):
FILE: src/benchmark/transfer_classification/models/moco_v2/builder.py
class MoCo (line 6) | class MoCo(nn.Module):
method __init__ (line 11) | def __init__(self, base_encoder, dim=128, K=65536, m=0.999, T=0.07, ml...
method _momentum_update_key_encoder (line 57) | def _momentum_update_key_encoder(self):
method _dequeue_and_enqueue (line 65) | def _dequeue_and_enqueue(self, keys):
method _batch_shuffle_ddp (line 81) | def _batch_shuffle_ddp(self, x):
method _batch_unshuffle_ddp (line 109) | def _batch_unshuffle_ddp(self, x, idx_unshuffle):
method forward (line 127) | def forward(self, im_q, im_k):
function concat_all_gather (line 177) | def concat_all_gather(tensor):
FILE: src/benchmark/transfer_classification/models/moco_v2/detection/train_net.py
class Res5ROIHeadsExtraNorm (line 15) | class Res5ROIHeadsExtraNorm(Res5ROIHeads):
method _build_res5_block (line 20) | def _build_res5_block(self, cfg):
class Trainer (line 28) | class Trainer(DefaultTrainer):
method build_evaluator (line 30) | def build_evaluator(cls, cfg, dataset_name, output_folder=None):
function setup (line 40) | def setup(args):
function main (line 49) | def main(args):
FILE: src/benchmark/transfer_classification/models/moco_v2/loader.py
class TwoCropsTransform (line 11) | class TwoCropsTransform:
method __init__ (line 14) | def __init__(self, base_transform):
method __call__ (line 17) | def __call__(self, x):
class GaussianBlur (line 23) | class GaussianBlur(object):
method __init__ (line 26) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 29) | def __call__(self, x):
FILE: src/benchmark/transfer_classification/models/moco_v2/main_lincls.py
function main (line 86) | def main():
function main_worker (line 121) | def main_worker(gpu, ngpus_per_node, args):
function train (line 308) | def train(train_loader, model, criterion, optimizer, epoch, args):
function validate (line 360) | def validate(val_loader, model, criterion, args):
function save_checkpoint (line 404) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
function sanity_check (line 410) | def sanity_check(state_dict, pretrained_weights):
class AverageMeter (line 434) | class AverageMeter(object):
method __init__ (line 436) | def __init__(self, name, fmt=':f'):
method reset (line 441) | def reset(self):
method update (line 447) | def update(self, val, n=1):
method __str__ (line 453) | def __str__(self):
class ProgressMeter (line 458) | class ProgressMeter(object):
method __init__ (line 459) | def __init__(self, num_batches, meters, prefix=""):
method display (line 464) | def display(self, batch):
method _get_batch_fmtstr (line 469) | def _get_batch_fmtstr(self, num_batches):
function adjust_learning_rate (line 475) | def adjust_learning_rate(optimizer, epoch, args):
function accuracy (line 484) | def accuracy(output, target, topk=(1,)):
FILE: src/benchmark/transfer_classification/models/moco_v3/builder.py
class MoCo (line 11) | class MoCo(nn.Module):
method __init__ (line 16) | def __init__(self, base_encoder, dim=256, mlp_dim=4096, T=1.0):
method _build_mlp (line 36) | def _build_mlp(self, num_layers, input_dim, mlp_dim, output_dim, last_...
method _build_projector_and_predictor_mlps (line 54) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
method _update_momentum_encoder (line 58) | def _update_momentum_encoder(self, m):
method contrastive_loss (line 63) | def contrastive_loss(self, q, k):
method forward (line 75) | def forward(self, x1, x2, m):
class MoCo_ResNet (line 99) | class MoCo_ResNet(MoCo):
method _build_projector_and_predictor_mlps (line 100) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
class MoCo_ViT (line 112) | class MoCo_ViT(MoCo):
method _build_projector_and_predictor_mlps (line 113) | def _build_projector_and_predictor_mlps(self, dim, mlp_dim):
function concat_all_gather (line 127) | def concat_all_gather(tensor):
FILE: src/benchmark/transfer_classification/models/moco_v3/loader.py
class TwoCropsTransform (line 14) | class TwoCropsTransform:
method __init__ (line 17) | def __init__(self, base_transform1, base_transform2):
method __call__ (line 21) | def __call__(self, x):
class GaussianBlur (line 27) | class GaussianBlur(object):
method __init__ (line 30) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 33) | def __call__(self, x):
class Solarize (line 40) | class Solarize(object):
method __call__ (line 43) | def __call__(self, x):
class cvGaussianBlur (line 50) | class cvGaussianBlur(object):
method __init__ (line 53) | def __init__(self, p=0.5, sigma=[.1, 2.]):
method __call__ (line 57) | def __call__(self, x):
FILE: src/benchmark/transfer_classification/models/moco_v3/optimizer.py
class LARS (line 10) | class LARS(torch.optim.Optimizer):
method __init__ (line 14) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_c...
method step (line 19) | def step(self):
FILE: src/benchmark/transfer_classification/models/moco_v3/vits.py
class VisionTransformerMoCo (line 25) | class VisionTransformerMoCo(VisionTransformer):
method __init__ (line 26) | def __init__(self, stop_grad_conv1=False, **kwargs):
method build_2d_sincos_position_embedding (line 53) | def build_2d_sincos_position_embedding(self, temperature=10000.):
class ConvStem (line 72) | class ConvStem(nn.Module):
method __init__ (line 76) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
method forward (line 104) | def forward(self, x):
function vit_small (line 115) | def vit_small(**kwargs):
function vit_base (line 122) | def vit_base(**kwargs):
function vit_conv_small (line 129) | def vit_conv_small(**kwargs):
function vit_conv_base (line 137) | def vit_conv_base(**kwargs):
FILE: src/benchmark/transfer_classification/models/rs_transforms_float32.py
class RandomBrightness (line 7) | class RandomBrightness(object):
method __init__ (line 10) | def __init__(self, brightness=0.4):
method __call__ (line 13) | def __call__(self, sample):
class RandomContrast (line 19) | class RandomContrast(object):
method __init__ (line 22) | def __init__(self, contrast=0.4):
method __call__ (line 25) | def __call__(self, sample):
class ToGray (line 31) | class ToGray(object):
method __init__ (line 32) | def __init__(self, out_channels):
method __call__ (line 34) | def __call__(self,sample):
class RandomChannelDrop (line 41) | class RandomChannelDrop(object):
method __init__ (line 44) | def __init__(self, min_n_drop=1, max_n_drop=8):
method __call__ (line 48) | def __call__(self, sample):
FILE: src/benchmark/transfer_classification/models/rs_transforms_uint8.py
class RandomBrightness (line 7) | class RandomBrightness(object):
method __init__ (line 10) | def __init__(self, brightness=0.4):
method __call__ (line 13) | def __call__(self, sample):
class RandomContrast (line 19) | class RandomContrast(object):
method __init__ (line 22) | def __init__(self, contrast=0.4):
method __call__ (line 25) | def __call__(self, sample):
class ToGray (line 31) | class ToGray(object):
method __init__ (line 32) | def __init__(self, out_channels):
method __call__ (line 34) | def __call__(self,sample):
class RandomChannelDrop (line 41) | class RandomChannelDrop(object):
method __init__ (line 44) | def __init__(self, min_n_drop=1, max_n_drop=8):
method __call__ (line 48) | def __call__(self, sample):
class GaussianBlur (line 57) | class GaussianBlur(object):
method __init__ (line 60) | def __init__(self, sigma=[.1, 2.]):
method __call__ (line 63) | def __call__(self, x):
class Solarize (line 70) | class Solarize(object):
method __init__ (line 72) | def __init__(self, threshold=0.5):
method __call__ (line 75) | def __call__(self, x):
class RandomSensorDrop_S1S2 (line 82) | class RandomSensorDrop_S1S2(object):
method __init__ (line 85) | def __init__(self):
method __call__ (line 88) | def __call__(self, sample):
class SensorDrop_S1S2 (line 98) | class SensorDrop_S1S2(object):
method __init__ (line 99) | def __init__(self, sensor):
method __call__ (line 101) | def __call__(self,sample):
class RandomSensorDrop_RGBD (line 109) | class RandomSensorDrop_RGBD(object):
method __init__ (line 112) | def __init__(self):
method __call__ (line 115) | def __call__(self, sample):
class SensorDrop_RGBD (line 125) | class SensorDrop_RGBD(object):
method __init__ (line 126) | def __init__(self, sensor):
method __call__ (line 128) | def __call__(self,sample):
FILE: src/download_data/convert_rgb.py
function normalize (line 73) | def normalize(img,mean,std):
function get_array (line 82) | def get_array(patch_id, mode, RGB=False, norm=False):
FILE: src/download_data/ssl4eo_downloader.py
class UniformSampler (line 98) | class UniformSampler:
method sample_point (line 99) | def sample_point(self) -> List[float]:
class GaussianSampler (line 105) | class GaussianSampler:
method __init__ (line 106) | def __init__(
method sample_point (line 119) | def sample_point(self) -> List[float]:
method get_world_cities (line 127) | def get_world_cities(download_root: str = "world_cities") -> List[Dict...
method get_interest_points (line 143) | def get_interest_points(
method km2deg (line 151) | def km2deg(kms: float, radius: float = 6371) -> float:
method deg2km (line 155) | def deg2km(deg: float, radius: float = 6371) -> float:
class BoundedUniformSampler (line 159) | class BoundedUniformSampler:
method __init__ (line 160) | def __init__(self, boundaries: shape = None) -> None:
method sample_point (line 166) | def sample_point(self) -> List[float]:
method get_country_boundaries (line 177) | def get_country_boundaries(
class OverlapError (line 188) | class OverlapError(Exception):
function date2str (line 192) | def date2str(date: datetime) -> str:
function get_period (line 196) | def get_period(date: datetime, days: int = 5) -> Tuple[str, str, str, str]:
function maskS2clouds (line 212) | def maskS2clouds(args: Any, image: ee.Image) -> ee.Image:
function get_collection (line 220) | def get_collection(
function filter_collection (line 230) | def filter_collection(
function center_crop (line 254) | def center_crop(
function adjust_coords (line 264) | def adjust_coords(
function get_properties (line 280) | def get_properties(image: ee.Image) -> Any:
function get_patch (line 284) | def get_patch(
function get_random_patches_match (line 329) | def get_random_patches_match(
function get_random_patches_rtree (line 366) | def get_random_patches_rtree(
function get_random_patches_grid (line 447) | def get_random_patches_grid(
function save_geotiff (line 526) | def save_geotiff(
function save_patch (line 550) | def save_patch(
class Counter (line 567) | class Counter:
method __init__ (line 568) | def __init__(self, start: int = 0) -> None:
method update (line 572) | def update(self, delta: int = 1) -> int:
function fix_random_seeds (line 578) | def fix_random_seeds(seed: int = 42) -> None:
function worker (line 756) | def worker(idx: int) -> None:
FILE: src/download_data/ssl4eo_s12_downloader.py
class GeoSampler (line 59) | class GeoSampler:
method sample_point (line 61) | def sample_point(self):
class UniformSampler (line 65) | class UniformSampler(GeoSampler):
method sample_point (line 67) | def sample_point(self):
class GaussianSampler (line 74) | class GaussianSampler(GeoSampler):
method __init__ (line 76) | def __init__(self, interest_points=None, num_cities=1000, std=20):
method sample_point (line 84) | def sample_point(self,idx):
method get_world_cities (line 96) | def get_world_cities(download_root=os.path.expanduser('./world_cities/...
method get_interest_points (line 110) | def get_interest_points(cities, size=10000):
method km2deg (line 116) | def km2deg(kms, radius=6371):
method deg2km (line 119) | def deg2km(deg, radius=6371):
class BoundedUniformSampler (line 123) | class BoundedUniformSampler(GeoSampler):
method __init__ (line 125) | def __init__(self, boundaries=None):
method sample_point (line 131) | def sample_point(self):
method get_country_boundaries (line 143) | def get_country_boundaries(download_root=os.path.expanduser('~/.cache/...
class OverlapError (line 152) | class OverlapError(Exception):
function date2str (line 156) | def date2str(date):
function get_period (line 160) | def get_period(date, days=5):
function maskS2clouds (line 168) | def maskS2clouds(image):
function get_collection_s2a (line 179) | def get_collection_s2a(cloud_pct=20):
function get_collection_s2c (line 185) | def get_collection_s2c(cloud_pct=20):
function get_collection_s1 (line 191) | def get_collection_s1():
function filter_collection (line 195) | def filter_collection(collection, coords, period=None):
function filter_collection_s1 (line 208) | def filter_collection_s1(collection, coords, period=None):
function center_crop (line 226) | def center_crop(img, out_size):
function adjust_coords (line 234) | def adjust_coords(coords, old_size, new_size):
function get_properties (line 245) | def get_properties(image):
function get_patch_s1 (line 252) | def get_patch_s1(collection, coords, radius, bands=None, crop=None):
function get_patch_s2 (line 286) | def get_patch_s2(collection, coords, radius, bands=None, crop=None):
function get_random_patches_grid (line 324) | def get_random_patches_grid(idx, collections, bands, crops, sampler, dat...
function get_random_patches_rtree (line 382) | def get_random_patches_rtree(idx, collections, bands, crops, sampler, da...
function get_random_patches_match (line 433) | def get_random_patches_match(idx, collections, bands, crops, sampler, da...
function save_geotiff (line 471) | def save_geotiff(img, coords, filename):
function save_patch (line 491) | def save_patch(raster, coords, metadata, path, preview=False):
class Counter (line 509) | class Counter:
method __init__ (line 511) | def __init__(self, start=0):
method update (line 515) | def update(self, delta=1):
function fix_random_seeds (line 521) | def fix_random_seeds(seed=42):
function worker (line 639) | def worker(idx):
Condensed preview — 319 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (4,461K chars).
[
{
"path": ".gitignore",
"chars": 126,
"preview": "**/.ipynb_checkpoints/*\r\n**/.vscode/*\r\n**/__pycache__/*\r\n**/srun_outputs/*\r\n**/checkpoints/*\r\n**/lightning_logs/\r\n**/res"
},
{
"path": "LICENSE",
"chars": 11324,
"preview": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licens"
},
{
"path": "README.md",
"chars": 12402,
"preview": "# SSL4EO-S12\nThe [SSL4EO-S12 dataset](https://arxiv.org/abs/2211.07044) is a large-scale multimodal multitemporal datase"
},
{
"path": "data/50k_ids_random.csv",
"chars": 327969,
"preview": "2\n9\n13\n15\n22\n24\n34\n44\n48\n51\n55\n64\n70\n75\n84\n89\n92\n106\n111\n121\n123\n129\n132\n138\n140\n142\n147\n148\n149\n150\n156\n165\n168\n170\n171"
},
{
"path": "data/void_ids.csv",
"chars": 679,
"preview": "124990\n124991\n124992\n124993\n124994\n124995\n124996\n124997\n124998\n124999\n125000\n125001\n125002\n125003\n125004\n125005\n125006\n1"
},
{
"path": "src/benchmark/pretrain_data2vec/readme.md",
"chars": 81,
"preview": "Refer to https://github.com/facebookresearch/fairseq/tree/main/examples/data2vec."
},
{
"path": "src/benchmark/pretrain_ssl/datasets/SSL4EO/ssl4eo_dataset.py",
"chars": 8701,
"preview": "import numpy as np\nimport rasterio\nimport torch\nimport os\nimport cv2\nimport csv\nimport pickle\nimport torch\nimport numpy "
},
{
"path": "src/benchmark/pretrain_ssl/datasets/SSL4EO/ssl4eo_dataset_lmdb.py",
"chars": 14420,
"preview": "import pickle\nimport torch\nimport numpy as np\nfrom torch.utils.data import Dataset, DataLoader\nimport lmdb\nfrom tqdm imp"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/README.md",
"chars": 6517,
"preview": "# data2vec\n\ndata2vec is a framework for self-supervised representation learning for images, speech, and text as describe"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/README_Original.md",
"chars": 12418,
"preview": "# [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)\n\nOfficial PyTorch implementation and"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/dall_e/__init__.py",
"chars": 577,
"preview": "import io, requests\nimport torch\nimport torch.nn as nn\n\nfrom .encoder import Encoder\nfrom .decoder import Decoder\nfrom ."
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/dall_e/decoder.py",
"chars": 3930,
"preview": "import attr\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom collections im"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/dall_e/encoder.py",
"chars": 3769,
"preview": "import attr\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom collections im"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/dall_e/utils.py",
"chars": 1771,
"preview": "import attr\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nlogit_laplace_eps: float = "
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/dataset_folder.py",
"chars": 10048,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/datasets.py",
"chars": 7744,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/engine_for_cyclical.py",
"chars": 9001,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/engine_for_cyclical_joint.py",
"chars": 6757,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/engine_for_finetuning.py",
"chars": 9308,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/engine_for_pretraining.py",
"chars": 4956,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/get_started_for_image_classification.md",
"chars": 5651,
"preview": "# Fine-tuning BEiT on ImageNet-1k (image classification)\n\n## Setup\n\n1. [Setup environment](README.md#setup).\n2. Download"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/masking_generator.py",
"chars": 3664,
"preview": "\"\"\"\nOriginally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0\nCopyright Zhun Zhong & Lian"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/modeling_cyclical.py",
"chars": 10171,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/modeling_cyclical_joint.py",
"chars": 8084,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/modeling_discrete_vae.py",
"chars": 7531,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/modeling_finetune.py",
"chars": 19298,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/modeling_pretrain.py",
"chars": 6746,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/models.py",
"chars": 18627,
"preview": "\"\"\" BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)\nModel from official source: https:/"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/optim_factory.py",
"chars": 7032,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/requirements.txt",
"chars": 130,
"preview": "torch==1.7.1\ntorchvision==0.8.2\ntimm==0.3.2\nPillow\nblobfile\nmypy\nnumpy\npytest\nrequests\neinops\ntensorboardX\ndeepspeed==0."
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/run_beit_pretraining.py",
"chars": 12795,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/run_class_finetuning.py",
"chars": 31719,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/run_cyclical.py",
"chars": 23577,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/run_cyclical_joint.py",
"chars": 21342,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/README.md",
"chars": 2694,
"preview": "# ADE20k Semantic segmentation with BEiT\n\n## Getting started \n\n1. Install the [mmsegmentation](https://github.com/open-m"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/backbone/beit.py",
"chars": 19195,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/_base_/datasets/ade20k.py",
"chars": 1844,
"preview": "# dataset settings\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n m"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/_base_/datasets/ade20k_640x640.py",
"chars": 1844,
"preview": "# dataset settings\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n m"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/_base_/default_runtime.py",
"chars": 321,
"preview": "# yapf:disable\nlog_config = dict(\n interval=50,\n hooks=[\n dict(type='TextLoggerHook', by_epoch=False),\n "
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/_base_/models/upernet_beit.py",
"chars": 1834,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/_base_/schedules/schedule_160k.py",
"chars": 382,
"preview": "# optimizer\noptimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)\noptimizer_config = dict()\n# learnin"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/_base_/schedules/schedule_320k.py",
"chars": 382,
"preview": "# optimizer\noptimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)\noptimizer_config = dict()\n# learnin"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_512_slide_160k_ade20k.py",
"chars": 2712,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_512_slide_160k_ade20k_ms.py",
"chars": 3325,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k.py",
"chars": 2720,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k_ms.py",
"chars": 3511,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_512_slide_160k_ade20k.py",
"chars": 2724,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_512_slide_160k_ade20k_ms.py",
"chars": 3585,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_640_slide_160k_ade20k.py",
"chars": 2944,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_640_slide_160k_ade20k_ms.py",
"chars": 3591,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/__init__.py",
"chars": 397,
"preview": "# -*- coding: utf-8 -*-\n\nfrom .checkpoint import load_checkpoint\nfrom .layer_decay_optimizer_constructor import LayerDec"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/__init__.py",
"chars": 206,
"preview": "# Copyright (c) Open-MMLab. All rights reserved.\nfrom .checkpoint import save_checkpoint\nfrom .apex_iter_based_runner im"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/apex_iter_based_runner.py",
"chars": 3884,
"preview": "# Copyright (c) Open-MMLab. All rights reserved.\nimport os.path as osp\nimport platform\nimport shutil\n\nimport torch\nfrom "
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/checkpoint.py",
"chars": 2883,
"preview": "# Copyright (c) Open-MMLab. All rights reserved.\nimport os.path as osp\nimport time\nfrom tempfile import TemporaryDirecto"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/apex_runner/optimizer.py",
"chars": 1158,
"preview": "from mmcv.runner import OptimizerHook, HOOKS\ntry:\n import apex\nexcept:\n print('apex is not installed')\n\n\n@HOOKS.re"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/checkpoint.py",
"chars": 24160,
"preview": "# Copyright (c) Open-MMLab. All rights reserved.\nimport io\nimport os\nimport os.path as osp\nimport pkgutil\nimport time\nim"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/layer_decay_optimizer_constructor.py",
"chars": 3653,
"preview": "import json\nfrom mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor\nfrom mmcv.runner import get_dist_inf"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/resize_transform.py",
"chars": 9164,
"preview": "import mmcv\nimport numpy as np\n\nfrom mmseg.datasets.builder import PIPELINES\n\n\n@PIPELINES.register_module()\nclass SETR_R"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/mmcv_custom/train_api.py",
"chars": 4416,
"preview": "import random\nimport warnings\n\nimport numpy as np\nimport torch\nfrom mmcv.parallel import MMDataParallel, MMDistributedDa"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/tools/dist_test.sh",
"chars": 271,
"preview": "#!/usr/bin/env bash\n\nCONFIG=$1\nCHECKPOINT=$2\nGPUS=$3\nPORT=${PORT:-29500}\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH \\\npyt"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/tools/dist_train.sh",
"chars": 247,
"preview": "#!/usr/bin/env bash\n\nCONFIG=$1\nGPUS=$2\nPORT=${PORT:-29500}\n\nPYTHONPATH=\"$(dirname $0)/..\":$PYTHONPATH \\\npython -m torch."
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/tools/test.py",
"chars": 5467,
"preview": "import argparse\nimport os\n\nimport mmcv\nimport torch\nfrom mmcv.parallel import MMDataParallel, MMDistributedDataParallel\n"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/semantic_segmentation/tools/train.py",
"chars": 5775,
"preview": "import argparse\nimport copy\nimport os\nimport os.path as osp\nimport time\n\nimport mmcv\nimport mmcv_custom\nimport torch\nfro"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/transforms.py",
"chars": 6781,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/data2vec/utils.py",
"chars": 21527,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/models/dino/utils.py",
"chars": 28719,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n"
},
{
"path": "src/benchmark/pretrain_ssl/models/dino/vision_transformer.py",
"chars": 11162,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/engine_finetune.py",
"chars": 4778,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/engine_finetune_BE.py",
"chars": 5614,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/engine_finetune_EU.py",
"chars": 5217,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/engine_finetune_SS.py",
"chars": 5287,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/engine_pretrain.py",
"chars": 3047,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/main_finetune.py",
"chars": 15633,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/main_linprobe.py",
"chars": 13333,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/main_pretrain.py",
"chars": 11054,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/models_mae.py",
"chars": 10199,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/models_vit.py",
"chars": 2615,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/submitit_finetune.py",
"chars": 4350,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/submitit_linprobe.py",
"chars": 4354,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/submitit_pretrain.py",
"chars": 4327,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/util/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/util/crop.py",
"chars": 1361,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/util/datasets.py",
"chars": 1902,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/util/lars.py",
"chars": 1851,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/util/lr_decay.py",
"chars": 2461,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/util/lr_sched.py",
"chars": 802,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/util/misc.py",
"chars": 11587,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/mae/util/pos_embed.py",
"chars": 4047,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco/builder.py",
"chars": 7573,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport torch\nimport torch.nn as nn\n\n\nclass MoCo(n"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco/loader.py",
"chars": 941,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n\n# This source code is licensed under the lice"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/README.md",
"chars": 5596,
"preview": "## MoCo: Momentum Contrast for Unsupervised Visual Representation Learning\n\n<p align=\"center\">\n <img src=\"https://user-"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/__init__.py",
"chars": 71,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/builder.py",
"chars": 6593,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport torch\nimport torch.nn as nn\n\n\nclass MoCo(n"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/detection/README.md",
"chars": 1898,
"preview": "\n## MoCo: Transferring to Detection\n\nThe `train_net.py` script reproduces the object detection experiments on Pascal VOC"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/detection/configs/Base-RCNN-C4-BN.yaml",
"chars": 298,
"preview": "MODEL:\n META_ARCHITECTURE: \"GeneralizedRCNN\"\n RPN:\n PRE_NMS_TOPK_TEST: 6000\n POST_NMS_TOPK_TEST: 1000\n ROI_HEAD"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/detection/configs/coco_R_50_C4_2x.yaml",
"chars": 310,
"preview": "_BASE_: \"Base-RCNN-C4-BN.yaml\"\nMODEL:\n MASK_ON: True\n WEIGHTS: \"detectron2://ImageNetPretrained/MSRA/R-50.pkl\"\nINPUT:\n"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/detection/configs/coco_R_50_C4_2x_moco.yaml",
"chars": 207,
"preview": "_BASE_: \"coco_R_50_C4_2x.yaml\"\nMODEL:\n PIXEL_MEAN: [123.675, 116.280, 103.530]\n PIXEL_STD: [58.395, 57.120, 57.375]\n "
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/detection/configs/pascal_voc_R_50_C4_24k.yaml",
"chars": 408,
"preview": "_BASE_: \"Base-RCNN-C4-BN.yaml\"\nMODEL:\n MASK_ON: False\n WEIGHTS: \"detectron2://ImageNetPretrained/MSRA/R-50.pkl\"\n ROI_"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/detection/configs/pascal_voc_R_50_C4_24k_moco.yaml",
"chars": 214,
"preview": "_BASE_: \"pascal_voc_R_50_C4_24k.yaml\"\nMODEL:\n PIXEL_MEAN: [123.675, 116.280, 103.530]\n PIXEL_STD: [58.395, 57.120, 57."
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/detection/convert-pretrain-to-detectron2.py",
"chars": 1021,
"preview": "#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport pickle as pkl\nimpor"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/detection/train_net.py",
"chars": 2250,
"preview": "#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport os\n\nfrom detectron2"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/loader.py",
"chars": 941,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n\n# This source code is licensed under the lice"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v2/main_lincls.py",
"chars": 19571,
"preview": "#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport argparse\nimport buil"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v3/__init__.py",
"chars": 71,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v3/builder.py",
"chars": 4926,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n\n# This source code is licensed under the lice"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v3/loader.py",
"chars": 1811,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n\n# This source code is licensed under the lice"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v3/optimizer.py",
"chars": 1639,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n\n# This source code is licensed under the lice"
},
{
"path": "src/benchmark/pretrain_ssl/models/moco_v3/vits.py",
"chars": 5882,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n\n# This source code is licensed under the lice"
},
{
"path": "src/benchmark/pretrain_ssl/models/rs_transforms_float32.py",
"chars": 1535,
"preview": "import numpy as np\nimport torch\nimport random\n\n\n\nclass RandomBrightness(object):\n \"\"\" Random Brightness \"\"\"\n \n "
},
{
"path": "src/benchmark/pretrain_ssl/models/rs_transforms_uint8.py",
"chars": 3604,
"preview": "import numpy as np\nimport torch\nimport random\nimport cv2\n\n\nclass RandomBrightness(object):\n \"\"\" Random Brightness \"\"\""
},
{
"path": "src/benchmark/pretrain_ssl/pretrain_data2vec.py",
"chars": 24271,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/pretrain_ssl/pretrain_dino_s2c.py",
"chars": 30828,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n"
},
{
"path": "src/benchmark/pretrain_ssl/pretrain_mae_s2c.py",
"chars": 11271,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/pretrain_ssl/pretrain_moco_v2_s2c.py",
"chars": 20753,
"preview": "#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport argparse\nimport buil"
},
{
"path": "src/benchmark/pretrain_ssl/pretrain_moco_v2_seco_ms.py",
"chars": 23424,
"preview": "#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport argparse\nimport buil"
},
{
"path": "src/benchmark/pretrain_ssl/pretrain_moco_v2_sen12ms_ms.py",
"chars": 22701,
"preview": "#!/usr/bin/env python\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport argparse\nimport buil"
},
{
"path": "src/benchmark/pretrain_ssl/pretrain_moco_v3_s2c.py",
"chars": 22695,
"preview": "#!/usr/bin/env python\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n\n# This source code is "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/srun_train_SEN12MS_moco_rn50_rgb.sh",
"chars": 1481,
"preview": "#!/usr/bin/env bash\n\n# slurm job configuration\n#SBATCH --nodes=1\n#SBATCH --ntasks=4\n#SBATCH --ntasks-per-node=4\n#SBATCH "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/srun_train_SEN12MS_moco_rn50_s2c.sh",
"chars": 1483,
"preview": "#!/usr/bin/env bash\n\n# slurm job configuration\n#SBATCH --nodes=1\n#SBATCH --ntasks=4\n#SBATCH --ntasks-per-node=4\n#SBATCH "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/srun_train_SeCo_moco_rn50_s2a.sh",
"chars": 1465,
"preview": "#!/usr/bin/env bash\n\n# slurm job configuration\n#SBATCH --nodes=1\n#SBATCH --ntasks=4\n#SBATCH --ntasks-per-node=4\n#SBATCH "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/srun_train_dino_rn50_s2c.sh",
"chars": 1525,
"preview": "#!/usr/bin/env bash\n\n# slurm job configuration\n#SBATCH --nodes=1\n#SBATCH --ntasks=4\n#SBATCH --ntasks-per-node=4\n#SBATCH "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/srun_train_dino_vits16_s2c.sh",
"chars": 1296,
"preview": "#!/usr/bin/env bash\n\n# slurm job configuration\n#SBATCH --nodes=1\n#SBATCH --ntasks=4\n#SBATCH --ntasks-per-node=4\n#SBATCH "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/srun_train_mae_vits16_s2c.sh",
"chars": 1635,
"preview": "#!/usr/bin/env bash\n\n# slurm job configuration\n#SBATCH --nodes=1\n#SBATCH --ntasks=4\n#SBATCH --ntasks-per-node=4\n#SBATCH "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/srun_train_moco_rn50_s2c.sh",
"chars": 1410,
"preview": "#!/usr/bin/env bash\n\n# slurm job configuration\n#SBATCH --nodes=1\n#SBATCH --ntasks=4\n#SBATCH --ntasks-per-node=4\n#SBATCH "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/srun_train_moco_vits16_s2c.sh",
"chars": 1473,
"preview": "#!/usr/bin/env bash\n\n# slurm job configuration\n#SBATCH --nodes=1\n#SBATCH --ntasks=4\n#SBATCH --ntasks-per-node=4\n#SBATCH "
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/submit_pretrain_data2vec.sh",
"chars": 229,
"preview": "#!/bin/bash -x\n#SBATCH --account=hai_ssl4eo\n#SBATCH --nodes=1\n#SBATCH --output=mpi-out.%j\n#SBATCH --error=mpi-err.%j\n#SB"
},
{
"path": "src/benchmark/pretrain_ssl/scripts/pretrain/train_data2vec_vits16_s2c.sh",
"chars": 1137,
"preview": "OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=4 pretrain_data2vec.py \\\n --data_path '/p/s"
},
{
"path": "src/benchmark/transfer_change_detection/datasets/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/benchmark/transfer_change_detection/datasets/oscd_datamodule.py",
"chars": 3165,
"preview": "import random\n\nfrom torch.utils.data import DataLoader\nfrom torchvision.transforms import functional as TF\nfrom pytorch_"
},
{
"path": "src/benchmark/transfer_change_detection/datasets/oscd_dataset.py",
"chars": 4384,
"preview": "from pathlib import Path\nfrom itertools import product\n\nfrom torch.utils.data import Dataset\nimport rasterio\nimport nump"
},
{
"path": "src/benchmark/transfer_change_detection/main_oscd.py",
"chars": 9200,
"preview": "from pathlib import Path\n# from copy import deepcopy\nfrom argparse import ArgumentParser\n\nimport torch\nfrom torch.nn imp"
},
{
"path": "src/benchmark/transfer_change_detection/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/benchmark/transfer_change_detection/models/segmentation.py",
"chars": 10639,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef get_segmentation_model(backbone, feature_indice"
},
{
"path": "src/benchmark/transfer_change_detection/readme.md",
"chars": 56,
"preview": "Refer to https://github.com/ElementAI/seasonal-contrast."
},
{
"path": "src/benchmark/transfer_change_detection/test.sh",
"chars": 775,
"preview": "python validate_oscd.py \\\n--data_dir /p/project/hai_ssl4eo/wang_yi/data/oscd \\\n--resnet_type 50 \\\n--init_type random \\\n-"
},
{
"path": "src/benchmark/transfer_change_detection/train.sh",
"chars": 705,
"preview": "python main_oscd.py \\\n--data_dir /p/project/hai_ssl4eo/wang_yi/data/oscd \\\n--resnet_type 50 \\\n--init_type random \\\n--n_c"
},
{
"path": "src/benchmark/transfer_change_detection/utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/benchmark/transfer_change_detection/utils/data.py",
"chars": 3188,
"preview": "import pickle\n\nimport numpy as np\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader\nimport lmdb\nfro"
},
{
"path": "src/benchmark/transfer_change_detection/utils/transforms.py",
"chars": 1076,
"preview": "\nclass Map:\n\n def __init__(self, transform):\n self.transform = transform\n\n def __call__(self, inputs):\n "
},
{
"path": "src/benchmark/transfer_change_detection/validate_oscd.py",
"chars": 8853,
"preview": "from pathlib import Path\n# from copy import deepcopy\nfrom argparse import ArgumentParser\n\nimport torch\nfrom torch.nn imp"
},
{
"path": "src/benchmark/transfer_classification/datasets/BigEarthNet/bigearthnet_dataset_seco.py",
"chars": 10091,
"preview": "'''\nread data from geotiff files.\nmodified from SeCo, to be final checked.\n'''\n\nimport json\nfrom pathlib import Path\n\nim"
},
{
"path": "src/benchmark/transfer_classification/datasets/BigEarthNet/bigearthnet_dataset_seco_lmdb_s2_uint8.py",
"chars": 6094,
"preview": "import pickle\n\nimport numpy as np\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader\nimport lmdb\nfro"
},
{
"path": "src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py",
"chars": 5834,
"preview": "import numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom cvtorchvision import cvtra"
},
{
"path": "src/benchmark/transfer_classification/datasets/So2Sat/so2sat_lcz42_dataset.py",
"chars": 7492,
"preview": "'''\n1st band: B2\n2nd band: B3\n3rd band: B4\n4th band: B5\n5th band: B6\n6th band: B7\n7th band: B8\n8th band: B8A\n9th band: B"
},
{
"path": "src/benchmark/transfer_classification/linear_BE_data2vec.py",
"chars": 33782,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/linear_BE_dino.py",
"chars": 16287,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n"
},
{
"path": "src/benchmark/transfer_classification/linear_BE_mae.py",
"chars": 15189,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/transfer_classification/linear_BE_moco.py",
"chars": 17134,
"preview": "\"\"\"\r\nsupervised training of BigEarthNet (all bands) with resnet18/50\r\n\r\nTODO:\r\n -- optimize and reduce RAM usage\r\n -- "
},
{
"path": "src/benchmark/transfer_classification/linear_BE_moco_v3.py",
"chars": 24861,
"preview": "#!/usr/bin/env python\n\n# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n\n# This source code is "
},
{
"path": "src/benchmark/transfer_classification/linear_BE_sup.py",
"chars": 17130,
"preview": "\"\"\"\r\nsupervised training of BigEarthNet (all bands) with resnet18/50\r\n\r\nTODO:\r\n -- optimize and reduce RAM usage\r\n -- "
},
{
"path": "src/benchmark/transfer_classification/linear_EU_data2vec.py",
"chars": 33706,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/linear_EU_dino.py",
"chars": 15820,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n"
},
{
"path": "src/benchmark/transfer_classification/linear_EU_mae.py",
"chars": 14374,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/transfer_classification/linear_EU_moco.py",
"chars": 14362,
"preview": "\n\nimport torch\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import models\n\n##"
},
{
"path": "src/benchmark/transfer_classification/linear_EU_moco_v3.py",
"chars": 14225,
"preview": "\n\nimport torch\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import models\n\n##"
},
{
"path": "src/benchmark/transfer_classification/linear_SS_data2vec.py",
"chars": 33593,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/linear_SS_dino.py",
"chars": 15631,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n"
},
{
"path": "src/benchmark/transfer_classification/linear_SS_mae.py",
"chars": 14060,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "src/benchmark/transfer_classification/linear_SS_moco.py",
"chars": 14071,
"preview": "\nimport torch\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import models\n\n## "
},
{
"path": "src/benchmark/transfer_classification/linear_SS_moco_v3.py",
"chars": 13898,
"preview": "\nimport torch\nfrom PIL import Image\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import models\n\n## "
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/README.md",
"chars": 6517,
"preview": "# data2vec\n\ndata2vec is a framework for self-supervised representation learning for images, speech, and text as describe"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/README_Original.md",
"chars": 12418,
"preview": "# [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)\n\nOfficial PyTorch implementation and"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/dall_e/__init__.py",
"chars": 577,
"preview": "import io, requests\nimport torch\nimport torch.nn as nn\n\nfrom .encoder import Encoder\nfrom .decoder import Decoder\nfrom ."
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/dall_e/decoder.py",
"chars": 3930,
"preview": "import attr\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom collections im"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/dall_e/encoder.py",
"chars": 3769,
"preview": "import attr\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom collections im"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/dall_e/utils.py",
"chars": 1771,
"preview": "import attr\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nlogit_laplace_eps: float = "
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/dataset_folder.py",
"chars": 10048,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/datasets.py",
"chars": 7744,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/engine_for_cyclical.py",
"chars": 9001,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/engine_for_cyclical_joint.py",
"chars": 6757,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/engine_for_finetuning.py",
"chars": 9308,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/engine_for_pretraining.py",
"chars": 4956,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/get_started_for_image_classification.md",
"chars": 5651,
"preview": "# Fine-tuning BEiT on ImageNet-1k (image classification)\n\n## Setup\n\n1. [Setup environment](README.md#setup).\n2. Download"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/masking_generator.py",
"chars": 3664,
"preview": "\"\"\"\nOriginally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0\nCopyright Zhun Zhong & Lian"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/modeling_cyclical.py",
"chars": 10171,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/modeling_cyclical_joint.py",
"chars": 8084,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/modeling_discrete_vae.py",
"chars": 7531,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/modeling_finetune.py",
"chars": 19298,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/modeling_pretrain.py",
"chars": 6746,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/models.py",
"chars": 18627,
"preview": "\"\"\" BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)\nModel from official source: https:/"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/optim_factory.py",
"chars": 7032,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/requirements.txt",
"chars": 130,
"preview": "torch==1.7.1\ntorchvision==0.8.2\ntimm==0.3.2\nPillow\nblobfile\nmypy\nnumpy\npytest\nrequests\neinops\ntensorboardX\ndeepspeed==0."
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/run_beit_pretraining.py",
"chars": 12795,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/run_class_finetuning.py",
"chars": 31719,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/run_cyclical.py",
"chars": 23577,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/run_cyclical_joint.py",
"chars": 21342,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/README.md",
"chars": 2694,
"preview": "# ADE20k Semantic segmentation with BEiT\n\n## Getting started \n\n1. Install the [mmsegmentation](https://github.com/open-m"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/backbone/beit.py",
"chars": 19195,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/_base_/datasets/ade20k.py",
"chars": 1844,
"preview": "# dataset settings\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n m"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/_base_/datasets/ade20k_640x640.py",
"chars": 1844,
"preview": "# dataset settings\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n m"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/_base_/default_runtime.py",
"chars": 321,
"preview": "# yapf:disable\nlog_config = dict(\n interval=50,\n hooks=[\n dict(type='TextLoggerHook', by_epoch=False),\n "
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/_base_/models/upernet_beit.py",
"chars": 1834,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/_base_/schedules/schedule_160k.py",
"chars": 382,
"preview": "# optimizer\noptimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)\noptimizer_config = dict()\n# learnin"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/_base_/schedules/schedule_320k.py",
"chars": 382,
"preview": "# optimizer\noptimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)\noptimizer_config = dict()\n# learnin"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_512_slide_160k_ade20k.py",
"chars": 2712,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_512_slide_160k_ade20k_ms.py",
"chars": 3325,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k.py",
"chars": 2720,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k_ms.py",
"chars": 3511,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_512_slide_160k_ade20k.py",
"chars": 2724,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
},
{
"path": "src/benchmark/transfer_classification/models/data2vec/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_512_slide_160k_ade20k_ms.py",
"chars": 3585,
"preview": "# --------------------------------------------------------\n# BEIT: BERT Pre-Training of Image Transformers (https://arxi"
}
]
// ... and 119 more files (download for full content)
About this extraction
This page contains the full source code of the zhu-xlab/SSL4EO-S12 GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 319 files (4.1 MB), approximately 1.1M tokens, and a symbol index with 1795 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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