├── Examples └── visulization.png ├── License ├── Readme.md ├── detector_codes ├── AutoGAN-master │ ├── .DS_Store │ ├── .gitmodules │ ├── README.md │ ├── autogan │ ├── code │ │ ├── GAN_Detection_Test.py │ │ ├── GAN_Detection_Train.py │ │ ├── Loggers.py │ │ ├── Utils.py │ │ ├── cycleGAN_dataset.py │ │ ├── pggan_dnet.py │ │ ├── run_test.py │ │ └── run_training.py │ ├── conda │ │ └── AutoGAN.yml │ ├── data │ │ ├── fake │ │ └── real │ └── fig │ │ ├── AutoGAN.png │ │ ├── AutoGAN_Image.png │ │ └── checkerboard.png ├── CNNDetection-master │ ├── LICENSE.txt │ ├── README.md │ ├── data │ │ ├── __init__.py │ │ └── datasets.py │ ├── dataset │ │ ├── test │ │ │ └── download_testset.sh │ │ ├── train │ │ │ └── download_trainset.sh │ │ └── val │ │ │ └── download_valset.sh │ ├── demo.py │ ├── demo_dir.py │ ├── earlystop.py │ ├── eval.py │ ├── eval_config.py │ ├── examples │ │ ├── fake.png │ │ ├── real.png │ │ └── realfakedir │ │ │ ├── 0_real │ │ │ └── real.png │ │ │ └── 1_fake │ │ │ └── fake.png │ ├── networks │ │ ├── __init__.py │ │ ├── base_model.py │ │ ├── lpf.py │ │ ├── resnet.py │ │ ├── resnet_lpf.py │ │ └── trainer.py │ ├── options │ │ ├── __init__.py │ │ ├── base_options.py │ │ ├── test_options.py │ │ └── train_options.py │ ├── requirements.txt │ ├── train.py │ ├── util.py │ ├── validate.py │ └── weights │ │ └── download_weights.sh ├── F3Net-main │ ├── README.md │ └── f3net.py ├── Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master │ ├── .fuse_hidden0000b11f00000019 │ ├── README.md │ ├── face.PNG │ ├── pggan-celeba │ │ ├── __pycache__ │ │ │ └── resnet18_gram.cpython-37.pyc │ │ ├── demo.py │ │ ├── gene.py │ │ ├── list │ │ ├── main.py │ │ ├── resnet18_gram.py │ │ ├── result.txt │ │ ├── test.py │ │ ├── test2.py │ │ └── test3.py │ ├── pngdata │ │ └── data │ │ │ └── style-ffhq │ │ │ └── .fuse_hidden0000b34e00000018 │ ├── stylegan-celeba │ │ ├── .fuse_hidden0000b13200000005 │ │ ├── .fuse_hidden0000b13600000004 │ │ ├── .fuse_hidden0000b13800000003 │ │ ├── .fuse_hidden0000b30800000008 │ │ ├── .fuse_hidden0000b30900000007 │ │ ├── .fuse_hidden0000b55f00000006 │ │ ├── .fuse_hidden0000b5a300000009 │ │ ├── .fuse_hidden0000b5a400000010 │ │ ├── .fuse_hidden0000b5a50000000f │ │ ├── .fuse_hidden0000b5a800000011 │ │ ├── .fuse_hidden0000b5aa00000013 │ │ ├── .fuse_hidden0000b5ab00000012 │ │ ├── __pycache__ │ │ │ └── resnet18_gram.cpython-37.pyc │ │ ├── demo.py │ │ ├── demo.txt │ │ ├── gene.py │ │ ├── list │ │ ├── main.py │ │ ├── resnet18_gram.py │ │ ├── result.txt │ │ ├── test.py │ │ ├── test2.py │ │ └── test3.py │ └── stylegan-ffhq │ │ ├── __pycache__ │ │ └── resnet18_gram.cpython-37.pyc │ │ ├── demo.py │ │ ├── gene.py │ │ ├── list │ │ ├── main.py │ │ ├── resnet18_gram.py │ │ ├── result.txt │ │ ├── test.py │ │ ├── test2.py │ │ ├── test3.py │ │ └── test4.py ├── Readme.md ├── Swin-Transformer-main │ ├── .gitignore │ ├── CODE_OF_CONDUCT.md │ ├── LICENSE │ ├── MODELHUB.md │ ├── README.md │ ├── SECURITY.md │ ├── SUPPORT.md │ ├── config.py │ ├── configs │ │ ├── simmim │ │ │ ├── simmim_finetune__swin_base__img224_window7__800ep.yaml │ │ │ ├── simmim_finetune__swinv2_base__img224_window14__800ep.yaml │ │ │ ├── simmim_pretrain__swin_base__img192_window6__800ep.yaml │ │ │ └── simmim_pretrain__swinv2_base__img192_window12__800ep.yaml │ │ ├── swin │ │ │ ├── swin_base_patch4_window12_384_22kto1k_finetune.yaml │ │ │ ├── swin_base_patch4_window12_384_finetune.yaml │ │ │ ├── swin_base_patch4_window7_224.yaml │ │ │ ├── swin_base_patch4_window7_224_22k.yaml │ │ │ ├── swin_base_patch4_window7_224_22kto1k_finetune.yaml │ │ │ ├── swin_large_patch4_window12_384_22kto1k_finetune.yaml │ │ │ ├── swin_large_patch4_window7_224_22k.yaml │ │ │ ├── swin_large_patch4_window7_224_22kto1k_finetune.yaml │ │ │ ├── swin_small_patch4_window7_224.yaml │ │ │ ├── swin_small_patch4_window7_224_22k.yaml │ │ │ ├── swin_small_patch4_window7_224_22kto1k_finetune.yaml │ │ │ ├── swin_tiny_c24_patch4_window8_256.yaml │ │ │ ├── swin_tiny_patch4_window7_224.yaml │ │ │ ├── swin_tiny_patch4_window7_224_22k.yaml │ │ │ └── swin_tiny_patch4_window7_224_22kto1k_finetune.yaml │ │ ├── swinmlp │ │ │ ├── swin_mlp_base_patch4_window7_224.yaml │ │ │ ├── swin_mlp_tiny_c12_patch4_window8_256.yaml │ │ │ ├── swin_mlp_tiny_c24_patch4_window8_256.yaml │ │ │ └── swin_mlp_tiny_c6_patch4_window8_256.yaml │ │ ├── swinmoe │ │ │ ├── swin_moe_base_patch4_window12_192_16expert_32gpu_22k.yaml │ │ │ ├── swin_moe_base_patch4_window12_192_32expert_32gpu_22k.yaml │ │ │ ├── swin_moe_base_patch4_window12_192_8expert_32gpu_22k.yaml │ │ │ ├── swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml │ │ │ ├── swin_moe_base_patch4_window12_192_densebaseline_22k.yaml │ │ │ ├── swin_moe_small_patch4_window12_192_16expert_32gpu_22k.yaml │ │ │ ├── swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml │ │ │ ├── swin_moe_small_patch4_window12_192_64expert_64gpu_22k.yaml │ │ │ ├── swin_moe_small_patch4_window12_192_8expert_32gpu_22k.yaml │ │ │ ├── swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml │ │ │ └── swin_moe_small_patch4_window12_192_densebaseline_22k.yaml │ │ └── swinv2 │ │ │ ├── swinv2_base_patch4_window12_192_22k.yaml │ │ │ ├── swinv2_base_patch4_window12to16_192to256_22kto1k_ft.yaml │ │ │ ├── swinv2_base_patch4_window12to24_192to384_22kto1k_ft.yaml │ │ │ ├── swinv2_base_patch4_window16_256.yaml │ │ │ ├── swinv2_base_patch4_window8_256.yaml │ │ │ ├── swinv2_large_patch4_window12_192_22k.yaml │ │ │ ├── swinv2_large_patch4_window12to16_192to256_22kto1k_ft.yaml │ │ │ ├── swinv2_large_patch4_window12to24_192to384_22kto1k_ft.yaml │ │ │ ├── swinv2_small_patch4_window16_256.yaml │ │ │ ├── swinv2_small_patch4_window8_256.yaml │ │ │ ├── swinv2_tiny_patch4_window16_256.yaml │ │ │ └── swinv2_tiny_patch4_window8_256.yaml │ ├── data │ │ ├── __init__.py │ │ ├── build.py │ │ ├── cached_image_folder.py │ │ ├── data_simmim_ft.py │ │ ├── data_simmim_pt.py │ │ ├── imagenet22k_dataset.py │ │ ├── map22kto1k.txt │ │ ├── samplers.py │ │ └── zipreader.py │ ├── figures │ │ └── teaser.png │ ├── get_started.md │ ├── kernels │ │ └── window_process │ │ │ ├── setup.py │ │ │ ├── swin_window_process.cpp │ │ │ ├── swin_window_process_kernel.cu │ │ │ ├── unit_test.py │ │ │ └── window_process.py │ ├── logger.py │ ├── lr_scheduler.py │ ├── main.py │ ├── main_moe.py │ ├── main_simmim_ft.py │ ├── main_simmim_pt.py │ ├── models │ │ ├── __init__.py │ │ ├── build.py │ │ ├── simmim.py │ │ ├── swin_mlp.py │ │ ├── swin_transformer.py │ │ ├── swin_transformer_moe.py │ │ └── swin_transformer_v2.py │ ├── optimizer.py │ ├── utils.py │ ├── utils_moe.py │ └── utils_simmim.py ├── deit-main │ ├── .circleci │ │ └── config.yml │ ├── .github │ │ ├── CODE_OF_CONDUCT.md │ │ ├── CONTRIBUTING.md │ │ ├── attn.png │ │ ├── cait.png │ │ ├── cosub.png │ │ ├── deit.png │ │ ├── hmlp.png │ │ ├── paral.png │ │ ├── patch_convnet.png │ │ ├── resmlp.png │ │ ├── revenge.png │ │ └── revenge_da.png │ ├── .gitignore │ ├── LICENSE │ ├── README.md │ ├── README_3things.md │ ├── README_cait.md │ ├── README_cosub.md │ ├── README_deit.md │ ├── README_patchconvnet.md │ ├── README_resmlp.md │ ├── README_revenge.md │ ├── augment.py │ ├── cait_models.py │ ├── datasets.py │ ├── engine.py │ ├── hubconf.py │ ├── losses.py │ ├── main.py │ ├── models.py │ ├── models_v2.py │ ├── patchconvnet_models.py │ ├── requirements.txt │ ├── resmlp_models.py │ ├── run_with_submitit.py │ ├── samplers.py │ ├── tox.ini │ └── utils.py └── pytorch-image-models-0.6.12 │ ├── .gitattributes │ ├── .github │ ├── FUNDING.yml │ ├── ISSUE_TEMPLATE │ │ ├── bug_report.md │ │ ├── config.yml │ │ └── feature_request.md │ └── workflows │ │ ├── build_documentation.yml │ │ ├── build_pr_documentation.yml │ │ ├── delete_doc_comment.yml │ │ └── tests.yml │ ├── .gitignore │ ├── LICENSE │ ├── MANIFEST.in │ ├── README.md │ ├── avg_checkpoints.py │ ├── benchmark.py │ ├── bulk_runner.py │ ├── clean_checkpoint.py │ ├── convert │ ├── convert_from_mxnet.py │ └── convert_nest_flax.py │ ├── distributed_train.sh │ ├── docs │ ├── archived_changes.md │ ├── changes.md │ ├── feature_extraction.md │ ├── index.md │ ├── javascripts │ │ └── tables.js │ ├── models.md │ ├── models │ │ ├── .pages │ │ ├── .templates │ │ │ ├── code_snippets.md │ │ │ ├── generate_readmes.py │ │ │ └── models │ │ │ │ ├── adversarial-inception-v3.md │ │ │ │ ├── advprop.md │ │ │ │ ├── big-transfer.md │ │ │ │ ├── csp-darknet.md │ │ │ │ ├── csp-resnet.md │ │ │ │ ├── csp-resnext.md │ │ │ │ ├── densenet.md │ │ │ │ ├── dla.md │ │ │ │ ├── dpn.md │ │ │ │ ├── ecaresnet.md │ │ │ │ ├── efficientnet-pruned.md │ │ │ │ ├── efficientnet.md │ │ │ │ ├── ensemble-adversarial.md │ │ │ │ ├── ese-vovnet.md │ │ │ │ ├── fbnet.md │ │ │ │ ├── gloun-inception-v3.md │ │ │ │ ├── gloun-resnet.md │ │ │ │ ├── gloun-resnext.md │ │ │ │ ├── gloun-senet.md │ │ │ │ ├── gloun-seresnext.md │ │ │ │ ├── gloun-xception.md │ │ │ │ ├── hrnet.md │ │ │ │ ├── ig-resnext.md │ │ │ │ ├── inception-resnet-v2.md │ │ │ │ ├── inception-v3.md │ │ │ │ ├── inception-v4.md │ │ │ │ ├── legacy-se-resnet.md │ │ │ │ ├── legacy-se-resnext.md │ │ │ │ ├── legacy-senet.md │ │ │ │ ├── mixnet.md │ │ │ │ ├── mnasnet.md │ │ │ │ ├── mobilenet-v2.md │ │ │ │ ├── mobilenet-v3.md │ │ │ │ ├── nasnet.md │ │ │ │ ├── noisy-student.md │ │ │ │ ├── pnasnet.md │ │ │ │ ├── regnetx.md │ │ │ │ ├── regnety.md │ │ │ │ ├── res2net.md │ │ │ │ ├── res2next.md │ │ │ │ ├── resnest.md │ │ │ │ ├── resnet-d.md │ │ │ │ ├── resnet.md │ │ │ │ ├── resnext.md │ │ │ │ ├── rexnet.md │ │ │ │ ├── se-resnet.md │ │ │ │ ├── selecsls.md │ │ │ │ ├── seresnext.md │ │ │ │ ├── skresnet.md │ │ │ │ ├── skresnext.md │ │ │ │ ├── spnasnet.md │ │ │ │ ├── ssl-resnet.md │ │ │ │ ├── ssl-resnext.md │ │ │ │ ├── swsl-resnet.md │ │ │ │ ├── swsl-resnext.md │ │ │ │ ├── tf-efficientnet-condconv.md │ │ │ │ ├── tf-efficientnet-lite.md │ │ │ │ ├── tf-efficientnet.md │ │ │ │ ├── tf-inception-v3.md │ │ │ │ ├── tf-mixnet.md │ │ │ │ ├── tf-mobilenet-v3.md │ │ │ │ ├── tresnet.md │ │ │ │ ├── vision-transformer.md │ │ │ │ ├── wide-resnet.md │ │ │ │ └── xception.md │ │ ├── adversarial-inception-v3.md │ │ ├── advprop.md │ │ ├── big-transfer.md │ │ ├── csp-darknet.md │ │ ├── csp-resnet.md │ │ ├── csp-resnext.md │ │ ├── densenet.md │ │ ├── dla.md │ │ ├── dpn.md │ │ ├── ecaresnet.md │ │ ├── efficientnet-pruned.md │ │ ├── efficientnet.md │ │ ├── ensemble-adversarial.md │ │ ├── ese-vovnet.md │ │ ├── fbnet.md │ │ ├── gloun-inception-v3.md │ │ ├── gloun-resnet.md │ │ ├── gloun-resnext.md │ │ ├── gloun-senet.md │ │ ├── gloun-seresnext.md │ │ ├── gloun-xception.md │ │ ├── hrnet.md │ │ ├── ig-resnext.md │ │ ├── inception-resnet-v2.md │ │ ├── inception-v3.md │ │ ├── inception-v4.md │ │ ├── legacy-se-resnet.md │ │ ├── legacy-se-resnext.md │ │ ├── legacy-senet.md │ │ ├── mixnet.md │ │ ├── mnasnet.md │ │ ├── mobilenet-v2.md │ │ ├── mobilenet-v3.md │ │ ├── nasnet.md │ │ ├── noisy-student.md │ │ ├── pnasnet.md │ │ ├── regnetx.md │ │ ├── regnety.md │ │ ├── res2net.md │ │ ├── res2next.md │ │ ├── resnest.md │ │ ├── resnet-d.md │ │ ├── resnet.md │ │ ├── resnext.md │ │ ├── rexnet.md │ │ ├── se-resnet.md │ │ ├── selecsls.md │ │ ├── seresnext.md │ │ ├── skresnet.md │ │ ├── skresnext.md │ │ ├── spnasnet.md │ │ ├── ssl-resnet.md │ │ ├── ssl-resnext.md │ │ ├── swsl-resnet.md │ │ ├── swsl-resnext.md │ │ ├── tf-efficientnet-condconv.md │ │ ├── tf-efficientnet-lite.md │ │ ├── tf-efficientnet.md │ │ ├── tf-inception-v3.md │ │ ├── tf-mixnet.md │ │ ├── tf-mobilenet-v3.md │ │ ├── tresnet.md │ │ ├── vision-transformer.md │ │ ├── wide-resnet.md │ │ └── xception.md │ ├── results.md │ ├── scripts.md │ └── training_hparam_examples.md │ ├── hfdocs │ └── source │ │ ├── _config.py │ │ ├── _toctree.yml │ │ ├── archived_changes.mdx │ │ ├── changes.mdx │ │ ├── feature_extraction.mdx │ │ ├── index.mdx │ │ ├── model_pages.mdx │ │ ├── models.mdx │ │ ├── models │ │ ├── adversarial-inception-v3.mdx │ │ ├── advprop.mdx │ │ ├── big-transfer.mdx │ │ ├── csp-darknet.mdx │ │ ├── csp-resnet.mdx │ │ ├── csp-resnext.mdx │ │ ├── densenet.mdx │ │ ├── dla.mdx │ │ ├── dpn.mdx │ │ ├── ecaresnet.mdx │ │ ├── efficientnet-pruned.mdx │ │ ├── efficientnet.mdx │ │ ├── ensemble-adversarial.mdx │ │ ├── ese-vovnet.mdx │ │ ├── fbnet.mdx │ │ ├── gloun-inception-v3.mdx │ │ ├── gloun-resnet.mdx │ │ ├── gloun-resnext.mdx │ │ ├── gloun-senet.mdx │ │ ├── gloun-seresnext.mdx │ │ ├── gloun-xception.mdx │ │ ├── hrnet.mdx │ │ ├── ig-resnext.mdx │ │ ├── inception-resnet-v2.mdx │ │ ├── inception-v3.mdx │ │ ├── inception-v4.mdx │ │ ├── legacy-se-resnet.mdx │ │ ├── legacy-se-resnext.mdx │ │ ├── legacy-senet.mdx │ │ ├── mixnet.mdx │ │ ├── mnasnet.mdx │ │ ├── mobilenet-v2.mdx │ │ ├── mobilenet-v3.mdx │ │ ├── nasnet.mdx │ │ ├── noisy-student.mdx │ │ ├── pnasnet.mdx │ │ ├── regnetx.mdx │ │ ├── regnety.mdx │ │ ├── res2net.mdx │ │ ├── res2next.mdx │ │ ├── resnest.mdx │ │ ├── resnet-d.mdx │ │ ├── resnet.mdx │ │ ├── resnext.mdx │ │ ├── rexnet.mdx │ │ ├── se-resnet.mdx │ │ ├── selecsls.mdx │ │ ├── seresnext.mdx │ │ ├── skresnet.mdx │ │ ├── skresnext.mdx │ │ ├── spnasnet.mdx │ │ ├── ssl-resnet.mdx │ │ ├── swsl-resnet.mdx │ │ ├── swsl-resnext.mdx │ │ ├── tf-efficientnet-condconv.mdx │ │ ├── tf-efficientnet-lite.mdx │ │ ├── tf-efficientnet.mdx │ │ ├── tf-inception-v3.mdx │ │ ├── tf-mixnet.mdx │ │ ├── tf-mobilenet-v3.mdx │ │ ├── tresnet.mdx │ │ ├── wide-resnet.mdx │ │ └── xception.mdx │ │ ├── results.mdx │ │ ├── scripts.mdx │ │ └── training_hparam_examples.mdx │ ├── hubconf.py │ ├── inference.py │ ├── mkdocs.yml │ ├── model-index.yml │ ├── requirements-docs.txt │ ├── requirements-modelindex.txt │ ├── requirements.txt │ ├── results │ ├── README.md │ ├── benchmark-infer-amp-nchw-pt111-cu113-rtx3090.csv │ ├── benchmark-infer-amp-nchw-pt112-cu113-rtx3090.csv │ ├── benchmark-infer-amp-nhwc-pt111-cu113-rtx3090.csv │ ├── benchmark-infer-amp-nhwc-pt112-cu113-rtx3090.csv │ ├── benchmark-train-amp-nchw-pt111-cu113-rtx3090.csv │ ├── benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv │ ├── benchmark-train-amp-nhwc-pt111-cu113-rtx3090.csv │ ├── benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv │ ├── generate_csv_results.py │ ├── imagenet21k_goog_synsets.txt │ ├── imagenet_a_indices.txt │ ├── imagenet_a_synsets.txt │ ├── imagenet_r_indices.txt │ ├── imagenet_r_synsets.txt │ ├── imagenet_real_labels.json │ ├── imagenet_synsets.txt │ ├── model_metadata-in1k.csv │ ├── results-imagenet-a-clean.csv │ ├── results-imagenet-a.csv │ ├── results-imagenet-r-clean.csv │ ├── results-imagenet-r.csv │ ├── results-imagenet-real.csv │ ├── results-imagenet.csv │ ├── results-imagenetv2-matched-frequency.csv │ └── results-sketch.csv │ ├── setup.cfg │ ├── setup.py │ ├── tests │ ├── __init__.py │ ├── test_layers.py │ ├── test_models.py │ ├── test_optim.py │ └── test_utils.py │ ├── timm │ ├── __init__.py │ ├── data │ │ ├── __init__.py │ │ ├── auto_augment.py │ │ ├── config.py │ │ ├── constants.py │ │ ├── dataset.py │ │ ├── dataset_factory.py │ │ ├── distributed_sampler.py │ │ ├── loader.py │ │ ├── mixup.py │ │ ├── parsers │ │ │ ├── __init__.py │ │ │ ├── class_map.py │ │ │ ├── img_extensions.py │ │ │ ├── parser.py │ │ │ ├── parser_factory.py │ │ │ ├── parser_image_folder.py │ │ │ ├── parser_image_in_tar.py │ │ │ ├── parser_image_tar.py │ │ │ └── parser_tfds.py │ │ ├── random_erasing.py │ │ ├── real_labels.py │ │ ├── tf_preprocessing.py │ │ ├── transforms.py │ │ └── transforms_factory.py │ ├── loss │ │ ├── __init__.py │ │ ├── asymmetric_loss.py │ │ ├── binary_cross_entropy.py │ │ ├── cross_entropy.py │ │ └── jsd.py │ ├── models │ │ ├── __init__.py │ │ ├── beit.py │ │ ├── byoanet.py │ │ ├── byobnet.py │ │ ├── cait.py │ │ ├── coat.py │ │ ├── convit.py │ │ ├── convmixer.py │ │ ├── convnext.py │ │ ├── crossvit.py │ │ ├── cspnet.py │ │ ├── deit.py │ │ ├── densenet.py │ │ ├── dla.py │ │ ├── dpn.py │ │ ├── edgenext.py │ │ ├── efficientformer.py │ │ ├── efficientnet.py │ │ ├── efficientnet_blocks.py │ │ ├── efficientnet_builder.py │ │ ├── factory.py │ │ ├── features.py │ │ ├── fx_features.py │ │ ├── gcvit.py │ │ ├── ghostnet.py │ │ ├── gluon_resnet.py │ │ ├── gluon_xception.py │ │ ├── hardcorenas.py │ │ ├── helpers.py │ │ ├── hrnet.py │ │ ├── hub.py │ │ ├── inception_resnet_v2.py │ │ ├── inception_v3.py │ │ ├── inception_v4.py │ │ ├── layers │ │ │ ├── __init__.py │ │ │ ├── activations.py │ │ │ ├── activations_jit.py │ │ │ ├── activations_me.py │ │ │ ├── adaptive_avgmax_pool.py │ │ │ ├── attention_pool2d.py │ │ │ ├── blur_pool.py │ │ │ ├── bottleneck_attn.py │ │ │ ├── cbam.py │ │ │ ├── classifier.py │ │ │ ├── cond_conv2d.py │ │ │ ├── config.py │ │ │ ├── conv2d_same.py │ │ │ ├── conv_bn_act.py │ │ │ ├── create_act.py │ │ │ ├── create_attn.py │ │ │ ├── create_conv2d.py │ │ │ ├── create_norm.py │ │ │ ├── create_norm_act.py │ │ │ ├── drop.py │ │ │ ├── eca.py │ │ │ ├── evo_norm.py │ │ │ ├── fast_norm.py │ │ │ ├── filter_response_norm.py │ │ │ ├── gather_excite.py │ │ │ ├── global_context.py │ │ │ ├── halo_attn.py │ │ │ ├── helpers.py │ │ │ ├── inplace_abn.py │ │ │ ├── lambda_layer.py │ │ │ ├── linear.py │ │ │ ├── median_pool.py │ │ │ ├── mixed_conv2d.py │ │ │ ├── ml_decoder.py │ │ │ ├── mlp.py │ │ │ ├── non_local_attn.py │ │ │ ├── norm.py │ │ │ ├── norm_act.py │ │ │ ├── padding.py │ │ │ ├── patch_embed.py │ │ │ ├── pool2d_same.py │ │ │ ├── pos_embed.py │ │ │ ├── selective_kernel.py │ │ │ ├── separable_conv.py │ │ │ ├── space_to_depth.py │ │ │ ├── split_attn.py │ │ │ ├── split_batchnorm.py │ │ │ ├── squeeze_excite.py │ │ │ ├── std_conv.py │ │ │ ├── test_time_pool.py │ │ │ ├── trace_utils.py │ │ │ └── weight_init.py │ │ ├── levit.py │ │ ├── maxxvit.py │ │ ├── mlp_mixer.py │ │ ├── mobilenetv3.py │ │ ├── mobilevit.py │ │ ├── mvitv2.py │ │ ├── nasnet.py │ │ ├── nest.py │ │ ├── nfnet.py │ │ ├── pit.py │ │ ├── pnasnet.py │ │ ├── poolformer.py │ │ ├── pruned │ │ │ ├── ecaresnet101d_pruned.txt │ │ │ ├── ecaresnet50d_pruned.txt │ │ │ ├── efficientnet_b1_pruned.txt │ │ │ ├── efficientnet_b2_pruned.txt │ │ │ └── efficientnet_b3_pruned.txt │ │ ├── pvt_v2.py │ │ ├── registry.py │ │ ├── regnet.py │ │ ├── res2net.py │ │ ├── resnest.py │ │ ├── resnet.py │ │ ├── resnetv2.py │ │ ├── rexnet.py │ │ ├── selecsls.py │ │ ├── senet.py │ │ ├── sequencer.py │ │ ├── sknet.py │ │ ├── swin_transformer.py │ │ ├── swin_transformer_v2.py │ │ ├── swin_transformer_v2_cr.py │ │ ├── tnt.py │ │ ├── tresnet.py │ │ ├── twins.py │ │ ├── vgg.py │ │ ├── visformer.py │ │ ├── vision_transformer.py │ │ ├── vision_transformer_hybrid.py │ │ ├── vision_transformer_relpos.py │ │ ├── volo.py │ │ ├── vovnet.py │ │ ├── xception.py │ │ ├── xception_aligned.py │ │ └── xcit.py │ ├── optim │ │ ├── __init__.py │ │ ├── adabelief.py │ │ ├── adafactor.py │ │ ├── adahessian.py │ │ ├── adamp.py │ │ ├── adamw.py │ │ ├── lamb.py │ │ ├── lars.py │ │ ├── lookahead.py │ │ ├── madgrad.py │ │ ├── nadam.py │ │ ├── nvnovograd.py │ │ ├── optim_factory.py │ │ ├── radam.py │ │ ├── rmsprop_tf.py │ │ └── sgdp.py │ ├── scheduler │ │ ├── __init__.py │ │ ├── cosine_lr.py │ │ ├── multistep_lr.py │ │ ├── plateau_lr.py │ │ ├── poly_lr.py │ │ ├── scheduler.py │ │ ├── scheduler_factory.py │ │ ├── step_lr.py │ │ └── tanh_lr.py │ ├── utils │ │ ├── __init__.py │ │ ├── agc.py │ │ ├── checkpoint_saver.py │ │ ├── clip_grad.py │ │ ├── cuda.py │ │ ├── decay_batch.py │ │ ├── distributed.py │ │ ├── jit.py │ │ ├── log.py │ │ ├── metrics.py │ │ ├── misc.py │ │ ├── model.py │ │ ├── model_ema.py │ │ ├── random.py │ │ └── summary.py │ └── version.py │ ├── train.py │ └── validate.py ├── generator_codes ├── BigGAN-PyTorch-master │ ├── .gitignore │ ├── BigGAN.py │ ├── BigGANdeep.py │ ├── LICENSE │ ├── README.md │ ├── TFHub │ │ ├── README.md │ │ ├── biggan_v1.py │ │ └── converter.py │ ├── animal_hash.py │ ├── calculate_inception_moments.py │ ├── datasets.py │ ├── imgs │ │ ├── D Singular Values.png │ │ ├── DeepSamples.png │ │ ├── DogBall.png │ │ ├── G Singular Values.png │ │ ├── IS_FID.png │ │ ├── Losses.png │ │ ├── header_image.jpg │ │ └── interp_sample.jpg │ ├── inception_tf13.py │ ├── inception_utils.py │ ├── layers.py │ ├── logs │ │ ├── BigGAN_ch96_bs256x8.jsonl │ │ ├── compare_IS.m │ │ ├── metalog.txt │ │ ├── process_inception_log.m │ │ └── process_training.m │ ├── losses.py │ ├── make_hdf5.py │ ├── sample.py │ ├── scripts │ │ ├── launch_BigGAN_bs256x8.sh │ │ ├── launch_BigGAN_bs512x4.sh │ │ ├── launch_BigGAN_ch64_bs256x8.sh │ │ ├── launch_BigGAN_deep.sh │ │ ├── launch_SAGAN_bs128x2_ema.sh │ │ ├── launch_SNGAN.sh │ │ ├── launch_cifar_ema.sh │ │ ├── sample_BigGAN_bs256x8.sh │ │ ├── sample_cifar_ema.sh │ │ └── utils │ │ │ ├── duplicate.sh │ │ │ └── prepare_data.sh │ ├── sync_batchnorm │ │ ├── __init__.py │ │ ├── batchnorm.py │ │ ├── batchnorm_reimpl.py │ │ ├── comm.py │ │ ├── replicate.py │ │ └── unittest.py │ ├── train.py │ ├── train_fns.py │ └── utils.py ├── Readme.md ├── VQ-Diffusion-main │ ├── LICENSE │ ├── OUTPUT │ │ └── pretrained_model │ │ │ ├── config_imagenet.yaml │ │ │ ├── config_text.yaml │ │ │ └── taming_dvae │ │ │ ├── config.yaml │ │ │ ├── taming_f8_8192_openimages.yaml │ │ │ ├── vqgan_ffhq_f16_1024.yaml │ │ │ └── vqgan_imagenet_f16_16384.yaml │ ├── SECURITY.md │ ├── configs │ │ ├── coco.yaml │ │ ├── coco_tune.yaml │ │ ├── cub200.yaml │ │ ├── ffhq.yaml │ │ ├── imagenet.yaml │ │ └── ithq.yaml │ ├── figures │ │ └── framework.png │ ├── help_folder │ │ ├── readme.md │ │ └── statistics │ │ │ ├── taming_vqvae_2887.pt │ │ │ └── taming_vqvae_974.pt │ ├── image_synthesis │ │ ├── data │ │ │ ├── build.py │ │ │ ├── cub200_dataset.py │ │ │ ├── ffhq_dataset.py │ │ │ ├── imagenet_class_index.json │ │ │ ├── imagenet_dataset.py │ │ │ ├── mscoco_dataset.py │ │ │ └── utils │ │ │ │ ├── comm.py │ │ │ │ ├── image_preprocessor.py │ │ │ │ └── manage.py │ │ ├── distributed │ │ │ ├── distributed.py │ │ │ └── launch.py │ │ ├── engine │ │ │ ├── clip_grad_norm.py │ │ │ ├── ema.py │ │ │ ├── logger.py │ │ │ ├── lr_scheduler.py │ │ │ └── solver.py │ │ ├── modeling │ │ │ ├── build.py │ │ │ ├── codecs │ │ │ │ ├── base_codec.py │ │ │ │ ├── image_codec │ │ │ │ │ ├── ema_vqvae.py │ │ │ │ │ ├── patch_vqgan.py │ │ │ │ │ └── taming_gumbel_vqvae.py │ │ │ │ └── text_codec │ │ │ │ │ └── tokenize.py │ │ │ ├── embeddings │ │ │ │ ├── base_embedding.py │ │ │ │ ├── class_embedding.py │ │ │ │ ├── clip_text_embedding.py │ │ │ │ └── dalle_mask_image_embedding.py │ │ │ ├── models │ │ │ │ ├── conditional_dalle.py │ │ │ │ ├── dalle.py │ │ │ │ └── unconditional_dalle.py │ │ │ ├── modules │ │ │ │ └── clip │ │ │ │ │ ├── README.md │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── bpe_simple_vocab_16e6.txt.gz │ │ │ │ │ ├── clip.py │ │ │ │ │ ├── clip_tokenizer.py │ │ │ │ │ ├── model.py │ │ │ │ │ └── simple_tokenizer.py │ │ │ ├── transformers │ │ │ │ ├── diffusion_transformer.py │ │ │ │ └── transformer_utils.py │ │ │ └── utils │ │ │ │ └── misc.py │ │ ├── taming │ │ │ ├── lr_scheduler.py │ │ │ ├── models │ │ │ │ ├── cond_transformer.py │ │ │ │ └── vqgan.py │ │ │ ├── modules │ │ │ │ ├── diffusionmodules │ │ │ │ │ └── model.py │ │ │ │ ├── discriminator │ │ │ │ │ └── model.py │ │ │ │ ├── losses │ │ │ │ │ ├── __init__.py │ │ │ │ │ ├── lpips.py │ │ │ │ │ ├── segmentation.py │ │ │ │ │ └── vqperceptual.py │ │ │ │ ├── misc │ │ │ │ │ └── coord.py │ │ │ │ ├── transformer │ │ │ │ │ ├── mingpt.py │ │ │ │ │ └── permuter.py │ │ │ │ ├── util.py │ │ │ │ └── vqvae │ │ │ │ │ └── quantize.py │ │ │ └── util.py │ │ └── utils │ │ │ ├── io.py │ │ │ └── misc.py │ ├── inference_VQ_Diffusion.py │ ├── install_req.sh │ ├── readme.md │ ├── running_command │ │ ├── run_train_coco.py │ │ ├── run_train_cub.py │ │ ├── run_train_ffhq.py │ │ ├── run_train_imagenet.py │ │ └── run_tune_coco.py │ └── train.py ├── glide-text2im-main │ ├── .gitignore │ ├── LICENSE │ ├── README.md │ ├── glide_text2im │ │ ├── __init__.py │ │ ├── clip │ │ │ ├── __init__.py │ │ │ ├── attention.py │ │ │ ├── config.yaml │ │ │ ├── encoders.py │ │ │ ├── model_creation.py │ │ │ └── utils.py │ │ ├── download.py │ │ ├── fp16_util.py │ │ ├── gaussian_diffusion.py │ │ ├── model_creation.py │ │ ├── nn.py │ │ ├── respace.py │ │ ├── text2im_model.py │ │ ├── tokenizer │ │ │ ├── __init__.py │ │ │ ├── bpe.py │ │ │ ├── bpe_simple_vocab_16e6.txt.gz │ │ │ ├── encoder.json.gz │ │ │ ├── simple_tokenizer.py │ │ │ └── vocab.bpe.gz │ │ ├── unet.py │ │ └── xf.py │ ├── model-card.md │ ├── notebooks │ │ ├── clip_guided.ipynb │ │ ├── grass.png │ │ ├── inpaint.ipynb │ │ └── text2im.ipynb │ └── setup.py ├── guided-diffusion-main │ ├── .gitignore │ ├── LICENSE │ ├── README.md │ ├── datasets │ │ ├── README.md │ │ └── lsun_bedroom.py │ ├── evaluations │ │ ├── README.md │ │ ├── evaluator.py │ │ └── requirements.txt │ ├── guided_diffusion │ │ ├── __init__.py │ │ ├── dist_util.py │ │ ├── fp16_util.py │ │ ├── gaussian_diffusion.py │ │ ├── image_datasets.py │ │ ├── logger.py │ │ ├── losses.py │ │ ├── nn.py │ │ ├── resample.py │ │ ├── respace.py │ │ ├── script_util.py │ │ ├── train_util.py │ │ └── unet.py │ ├── model-card.md │ ├── scripts │ │ ├── classifier_sample.py │ │ ├── classifier_train.py │ │ ├── image_nll.py │ │ ├── image_sample.py │ │ ├── image_train.py │ │ ├── super_res_sample.py │ │ └── super_res_train.py │ └── setup.py └── stable-diffusion-main │ ├── LICENSE │ ├── README.md │ ├── Stable_Diffusion_v1_Model_Card.md │ ├── assets │ ├── a-painting-of-a-fire.png │ ├── a-photograph-of-a-fire.png │ ├── a-shirt-with-a-fire-printed-on-it.png │ ├── a-shirt-with-the-inscription-'fire'.png │ ├── a-watercolor-painting-of-a-fire.png │ ├── birdhouse.png │ ├── fire.png │ ├── inpainting.png │ ├── modelfigure.png │ ├── rdm-preview.jpg │ ├── reconstruction1.png │ ├── reconstruction2.png │ ├── results.gif │ ├── rick.jpeg │ ├── stable-samples │ │ ├── img2img │ │ │ ├── mountains-1.png │ │ │ ├── mountains-2.png │ │ │ ├── mountains-3.png │ │ │ ├── sketch-mountains-input.jpg │ │ │ ├── upscaling-in.png │ │ │ └── upscaling-out.png │ │ └── txt2img │ │ │ ├── 000002025.png │ │ │ ├── 000002035.png │ │ │ ├── merged-0005.png │ │ │ ├── merged-0006.png │ │ │ └── merged-0007.png │ ├── the-earth-is-on-fire,-oil-on-canvas.png │ ├── txt2img-convsample.png │ ├── txt2img-preview.png │ └── v1-variants-scores.jpg │ ├── configs │ ├── autoencoder │ │ ├── autoencoder_kl_16x16x16.yaml │ │ ├── autoencoder_kl_32x32x4.yaml │ │ ├── autoencoder_kl_64x64x3.yaml │ │ └── autoencoder_kl_8x8x64.yaml │ ├── latent-diffusion │ │ ├── celebahq-ldm-vq-4.yaml │ │ ├── cin-ldm-vq-f8.yaml │ │ ├── cin256-v2.yaml │ │ ├── ffhq-ldm-vq-4.yaml │ │ ├── lsun_bedrooms-ldm-vq-4.yaml │ │ ├── lsun_churches-ldm-kl-8.yaml │ │ └── txt2img-1p4B-eval.yaml │ ├── retrieval-augmented-diffusion │ │ └── 768x768.yaml │ └── stable-diffusion │ │ └── v1-inference.yaml │ ├── data │ ├── DejaVuSans.ttf │ ├── example_conditioning │ │ ├── superresolution │ │ │ └── sample_0.jpg │ │ └── text_conditional │ │ │ └── sample_0.txt │ ├── imagenet_clsidx_to_label.txt │ ├── imagenet_train_hr_indices.p │ ├── imagenet_val_hr_indices.p │ ├── index_synset.yaml │ └── inpainting_examples │ │ ├── 6458524847_2f4c361183_k.png │ │ ├── 6458524847_2f4c361183_k_mask.png │ │ ├── 8399166846_f6fb4e4b8e_k.png │ │ ├── 8399166846_f6fb4e4b8e_k_mask.png │ │ ├── alex-iby-G_Pk4D9rMLs.png │ │ ├── alex-iby-G_Pk4D9rMLs_mask.png │ │ ├── bench2.png │ │ ├── bench2_mask.png │ │ ├── bertrand-gabioud-CpuFzIsHYJ0.png │ │ ├── bertrand-gabioud-CpuFzIsHYJ0_mask.png │ │ ├── billow926-12-Wc-Zgx6Y.png │ │ ├── billow926-12-Wc-Zgx6Y_mask.png │ │ ├── overture-creations-5sI6fQgYIuo.png │ │ ├── overture-creations-5sI6fQgYIuo_mask.png │ │ ├── photo-1583445095369-9c651e7e5d34.png │ │ └── photo-1583445095369-9c651e7e5d34_mask.png │ ├── environment.yaml │ ├── ldm │ ├── data │ │ ├── __init__.py │ │ ├── base.py │ │ ├── imagenet.py │ │ └── lsun.py │ ├── lr_scheduler.py │ ├── models │ │ ├── autoencoder.py │ │ └── diffusion │ │ │ ├── __init__.py │ │ │ ├── classifier.py │ │ │ ├── ddim.py │ │ │ ├── ddpm.py │ │ │ ├── dpm_solver │ │ │ ├── __init__.py │ │ │ ├── dpm_solver.py │ │ │ └── sampler.py │ │ │ └── plms.py │ ├── modules │ │ ├── attention.py │ │ ├── diffusionmodules │ │ │ ├── __init__.py │ │ │ ├── model.py │ │ │ ├── openaimodel.py │ │ │ └── util.py │ │ ├── distributions │ │ │ ├── __init__.py │ │ │ └── distributions.py │ │ ├── ema.py │ │ ├── encoders │ │ │ ├── __init__.py │ │ │ └── modules.py │ │ ├── image_degradation │ │ │ ├── __init__.py │ │ │ ├── bsrgan.py │ │ │ ├── bsrgan_light.py │ │ │ ├── utils │ │ │ │ └── test.png │ │ │ └── utils_image.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── contperceptual.py │ │ │ └── vqperceptual.py │ │ └── x_transformer.py │ └── util.py │ ├── main.py │ ├── models │ ├── first_stage_models │ │ ├── kl-f16 │ │ │ └── config.yaml │ │ ├── kl-f32 │ │ │ └── config.yaml │ │ ├── kl-f4 │ │ │ └── config.yaml │ │ ├── kl-f8 │ │ │ └── config.yaml │ │ ├── vq-f16 │ │ │ └── config.yaml │ │ ├── vq-f4-noattn │ │ │ └── config.yaml │ │ ├── vq-f4 │ │ │ └── config.yaml │ │ ├── vq-f8-n256 │ │ │ └── config.yaml │ │ └── vq-f8 │ │ │ └── config.yaml │ └── ldm │ │ ├── bsr_sr │ │ └── config.yaml │ │ ├── celeba256 │ │ └── config.yaml │ │ ├── cin256 │ │ └── config.yaml │ │ ├── ffhq256 │ │ └── config.yaml │ │ ├── inpainting_big │ │ └── config.yaml │ │ ├── layout2img-openimages256 │ │ └── config.yaml │ │ ├── lsun_beds256 │ │ └── config.yaml │ │ ├── lsun_churches256 │ │ └── config.yaml │ │ ├── semantic_synthesis256 │ │ └── config.yaml │ │ ├── semantic_synthesis512 │ │ └── config.yaml │ │ └── text2img256 │ │ └── config.yaml │ ├── notebook_helpers.py │ ├── scripts │ ├── download_first_stages.sh │ ├── download_models.sh │ ├── img2img.py │ ├── inpaint.py │ ├── knn2img.py │ ├── latent_imagenet_diffusion.ipynb │ ├── sample_diffusion.py │ ├── tests │ │ └── test_watermark.py │ ├── train_searcher.py │ └── txt2img.py │ └── setup.py └── index.html /Examples/visulization.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/Examples/visulization.png -------------------------------------------------------------------------------- /License: -------------------------------------------------------------------------------- 1 | Licenses 2 | 3 | Unless specifically labeled otherwise, these Datasets are provided to You under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (“CC BY-NC-SA 4.0”), with the additional terms included herein. 4 | The CC BY-NC-SA 4.0 may be accessed at https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode. When You download or use the Datasets from the Website or elsewhere, You are agreeing to comply with the terms of CC BY-NC-SA 4.0, and also agreeing to the Dataset Terms. 5 | Where these Dataset Terms conflict with the terms of CC BY-NC-SA 4.0, these Dataset Terms shall prevail. We reiterate once again that this dataset is used only for non-commercial purposes such as academic research, teaching, or scientific publications. 6 | We prohibits You from using the dataset or any derivative works for commercial purposes, such as selling data or using it for commercial gain. 7 | -------------------------------------------------------------------------------- /detector_codes/AutoGAN-master/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/AutoGAN-master/.DS_Store -------------------------------------------------------------------------------- /detector_codes/AutoGAN-master/.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "pytorch-CycleGAN-and-pix2pix"] 2 | path = pytorch-CycleGAN-and-pix2pix 3 | url = https://github.com/spongezhang/pytorch-CycleGAN-and-pix2pix.git 4 | branch = auto_gan 5 | -------------------------------------------------------------------------------- /detector_codes/AutoGAN-master/autogan: -------------------------------------------------------------------------------- 1 | pytorch-CycleGAN-and-pix2pix -------------------------------------------------------------------------------- /detector_codes/AutoGAN-master/data/fake: -------------------------------------------------------------------------------- 1 | ../autogan/generated/ -------------------------------------------------------------------------------- /detector_codes/AutoGAN-master/data/real: -------------------------------------------------------------------------------- 1 | ../autogan/datasets/ -------------------------------------------------------------------------------- /detector_codes/AutoGAN-master/fig/AutoGAN.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/AutoGAN-master/fig/AutoGAN.png -------------------------------------------------------------------------------- /detector_codes/AutoGAN-master/fig/AutoGAN_Image.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/AutoGAN-master/fig/AutoGAN_Image.png -------------------------------------------------------------------------------- /detector_codes/AutoGAN-master/fig/checkerboard.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/AutoGAN-master/fig/checkerboard.png -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/data/__init__.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | from torch.utils.data.sampler import WeightedRandomSampler 4 | 5 | from .datasets import dataset_folder 6 | 7 | 8 | def get_dataset(opt): 9 | dset_lst = [] 10 | for cls in opt.classes: 11 | root = opt.dataroot + '/' + cls 12 | dset = dataset_folder(opt, root) 13 | dset_lst.append(dset) 14 | return torch.utils.data.ConcatDataset(dset_lst) 15 | 16 | 17 | def get_bal_sampler(dataset): 18 | targets = [] 19 | for d in dataset.datasets: 20 | targets.extend(d.targets) 21 | 22 | ratio = np.bincount(targets) 23 | w = 1. / torch.tensor(ratio, dtype=torch.float) 24 | sample_weights = w[targets] 25 | sampler = WeightedRandomSampler(weights=sample_weights, 26 | num_samples=len(sample_weights)) 27 | return sampler 28 | 29 | 30 | def create_dataloader(opt): 31 | shuffle = not opt.serial_batches if (opt.isTrain and not opt.class_bal) else False 32 | dataset = get_dataset(opt) 33 | sampler = get_bal_sampler(dataset) if opt.class_bal else None 34 | 35 | data_loader = torch.utils.data.DataLoader(dataset, 36 | batch_size=opt.batch_size, 37 | shuffle=shuffle, 38 | sampler=sampler, 39 | num_workers=int(opt.num_threads)) 40 | return data_loader 41 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/dataset/test/download_testset.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | DOCUMENT_ID="1z_fD3UKgWQyOTZIBbYSaQ-hz4AzUrLC1" 3 | FINAL_DOWNLOADED_FILENAME="testset.zip" 4 | 5 | curl -c /tmp/cookies "https://drive.google.com/uc?export=download&id=$DOCUMENT_ID" > /tmp/intermezzo.html 6 | curl -L -b /tmp/cookies "https://drive.google.com$(cat /tmp/intermezzo.html | grep -Po 'uc-download-link" [^>]* href="\K[^"]*' | sed 's/\&/\&/g')" > $FINAL_DOWNLOADED_FILENAME 7 | unzip testset.zip 8 | rm testset.zip 9 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/dataset/train/download_trainset.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | DOCUMENT_ID="1iVNBV0glknyTYGA9bCxT_d0CVTOgGcKh" 3 | FINAL_DOWNLOADED_FILENAME="trainset.zip" 4 | 5 | curl -c /tmp/cookies "https://drive.google.com/uc?export=download&id=$DOCUMENT_ID" > /tmp/intermezzo.html 6 | curl -L -b /tmp/cookies "https://drive.google.com$(cat /tmp/intermezzo.html | grep -Po 'uc-download-link" [^>]* href="\K[^"]*' | sed 's/\&/\&/g')" > $FINAL_DOWNLOADED_FILENAME 7 | unzip trainset.zip 8 | rm trainset.zip 9 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/dataset/val/download_valset.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | DOCUMENT_ID="1FU7xF8Wl_F8b0tgL0529qg2nZ_RpdVNL" 3 | FINAL_DOWNLOADED_FILENAME="valset.zip" 4 | 5 | curl -c /tmp/cookies "https://drive.google.com/uc?export=download&id=$DOCUMENT_ID" > /tmp/intermezzo.html 6 | curl -L -b /tmp/cookies "https://drive.google.com$(cat /tmp/intermezzo.html | grep -Po 'uc-download-link" [^>]* href="\K[^"]*' | sed 's/\&/\&/g')" > $FINAL_DOWNLOADED_FILENAME 7 | unzip valset.zip 8 | rm valset.zip 9 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/demo.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import torch 4 | import torch.nn 5 | import argparse 6 | import numpy as np 7 | import torchvision.transforms as transforms 8 | import torchvision.datasets as datasets 9 | from PIL import Image 10 | from networks.resnet import resnet50 11 | 12 | parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) 13 | parser.add_argument('-f','--file', default='examples_realfakedir') 14 | parser.add_argument('-m','--model_path', type=str, default='weights/blur_jpg_prob0.5.pth') 15 | parser.add_argument('-c','--crop', type=int, default=None, help='by default, do not crop. specify crop size') 16 | parser.add_argument('--use_cpu', action='store_true', help='uses gpu by default, turn on to use cpu') 17 | 18 | opt = parser.parse_args() 19 | 20 | model = resnet50(num_classes=1) 21 | state_dict = torch.load(opt.model_path, map_location='cpu') 22 | model.load_state_dict(state_dict['model']) 23 | if(not opt.use_cpu): 24 | model.cuda() 25 | model.eval() 26 | 27 | # Transform 28 | trans_init = [] 29 | if(opt.crop is not None): 30 | trans_init = [transforms.CenterCrop(opt.crop),] 31 | print('Cropping to [%i]'%opt.crop) 32 | else: 33 | print('Not cropping') 34 | trans = transforms.Compose(trans_init + [ 35 | transforms.ToTensor(), 36 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 37 | ]) 38 | 39 | img = trans(Image.open(opt.file).convert('RGB')) 40 | 41 | with torch.no_grad(): 42 | in_tens = img.unsqueeze(0) 43 | if(not opt.use_cpu): 44 | in_tens = in_tens.cuda() 45 | prob = model(in_tens).sigmoid().item() 46 | 47 | print('probability of being synthetic: {:.2f}%'.format(prob * 100)) 48 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/eval.py: -------------------------------------------------------------------------------- 1 | import os 2 | import csv 3 | import torch 4 | 5 | from validate import validate 6 | from networks.resnet import resnet50 7 | from options.test_options import TestOptions 8 | from eval_config import * 9 | 10 | 11 | # Running tests 12 | opt = TestOptions().parse(print_options=False) 13 | model_name = os.path.basename(model_path).replace('.pth', '') 14 | rows = [["{} model testing on...".format(model_name)], 15 | ['testset', 'accuracy', 'avg precision']] 16 | 17 | print("{} model testing on...".format(model_name)) 18 | for v_id, val in enumerate(vals): 19 | opt.dataroot = '{}/{}'.format(dataroot, val) 20 | opt.classes = os.listdir(opt.dataroot) if multiclass[v_id] else [''] 21 | opt.no_resize = True # testing without resizing by default 22 | 23 | model = resnet50(num_classes=1) 24 | state_dict = torch.load(model_path, map_location='cpu') 25 | model.load_state_dict(state_dict['model']) 26 | model.cuda() 27 | model.eval() 28 | 29 | acc, ap, _, _, _, _ = validate(model, opt) 30 | rows.append([val, acc, ap]) 31 | print("({}) acc: {}; ap: {}".format(val, acc, ap)) 32 | 33 | csv_name = results_dir + '/{}.csv'.format(model_name) 34 | with open(csv_name, 'w') as f: 35 | csv_writer = csv.writer(f, delimiter=',') 36 | csv_writer.writerows(rows) 37 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/eval_config.py: -------------------------------------------------------------------------------- 1 | from util import mkdir 2 | 3 | 4 | # directory to store the results 5 | results_dir = './results/' 6 | mkdir(results_dir) 7 | 8 | # root to the testsets 9 | dataroot = './dataset/test/' 10 | 11 | # list of synthesis algorithms 12 | vals = ['progan', 'stylegan', 'biggan', 'cyclegan', 'stargan', 'gaugan', 13 | 'crn', 'imle', 'seeingdark', 'san', 'deepfake', 'stylegan2', 'whichfaceisreal'] 14 | 15 | # indicates if corresponding testset has multiple classes 16 | multiclass = [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0] 17 | 18 | # model 19 | model_path = 'weights/blur_jpg_prob0.5.pth' 20 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/examples/fake.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/CNNDetection-master/examples/fake.png -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/examples/real.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/CNNDetection-master/examples/real.png -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/examples/realfakedir/0_real/real.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/CNNDetection-master/examples/realfakedir/0_real/real.png 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/CNNDetection-master/options/__init__.py -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/options/test_options.py: -------------------------------------------------------------------------------- 1 | from .base_options import BaseOptions 2 | 3 | 4 | class TestOptions(BaseOptions): 5 | def initialize(self, parser): 6 | parser = BaseOptions.initialize(self, parser) 7 | parser.add_argument('--model_path') 8 | parser.add_argument('--no_resize', action='store_true') 9 | parser.add_argument('--no_crop', action='store_true') 10 | parser.add_argument('--eval', action='store_true', help='use eval mode during test time.') 11 | 12 | self.isTrain = False 13 | return parser 14 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/requirements.txt: -------------------------------------------------------------------------------- 1 | scipy 2 | scikit-learn 3 | numpy 4 | opencv_python 5 | Pillow 6 | torch>=1.2.0 7 | torchvision 8 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/util.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | 4 | 5 | def mkdirs(paths): 6 | if isinstance(paths, list) and not isinstance(paths, str): 7 | for path in paths: 8 | mkdir(path) 9 | else: 10 | mkdir(paths) 11 | 12 | 13 | def mkdir(path): 14 | if not os.path.exists(path): 15 | os.makedirs(path) 16 | 17 | 18 | def unnormalize(tens, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): 19 | # assume tensor of shape NxCxHxW 20 | return tens * torch.Tensor(std)[None, :, None, None] + torch.Tensor( 21 | mean)[None, :, None, None] 22 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/validate.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | from networks.resnet import resnet50 4 | from sklearn.metrics import average_precision_score, precision_recall_curve, accuracy_score 5 | from options.test_options import TestOptions 6 | from data import create_dataloader 7 | 8 | 9 | def validate(model, opt): 10 | data_loader = create_dataloader(opt) 11 | 12 | with torch.no_grad(): 13 | y_true, y_pred = [], [] 14 | for img, label in data_loader: 15 | in_tens = img.cuda() 16 | y_pred.extend(model(in_tens).sigmoid().flatten().tolist()) 17 | y_true.extend(label.flatten().tolist()) 18 | 19 | y_true, y_pred = np.array(y_true), np.array(y_pred) 20 | r_acc = accuracy_score(y_true[y_true==0], y_pred[y_true==0] > 0.5) 21 | f_acc = accuracy_score(y_true[y_true==1], y_pred[y_true==1] > 0.5) 22 | acc = accuracy_score(y_true, y_pred > 0.5) 23 | ap = average_precision_score(y_true, y_pred) 24 | return acc, ap, r_acc, f_acc, y_true, y_pred 25 | 26 | 27 | if __name__ == '__main__': 28 | opt = TestOptions().parse(print_options=False) 29 | 30 | model = resnet50(num_classes=1) 31 | state_dict = torch.load(opt.model_path, map_location='cpu') 32 | model.load_state_dict(state_dict['model']) 33 | model.cuda() 34 | model.eval() 35 | 36 | acc, avg_precision, r_acc, f_acc, y_true, y_pred = validate(model, opt) 37 | 38 | print("accuracy:", acc) 39 | print("average precision:", avg_precision) 40 | 41 | print("accuracy of real images:", r_acc) 42 | print("accuracy of fake images:", f_acc) 43 | -------------------------------------------------------------------------------- /detector_codes/CNNDetection-master/weights/download_weights.sh: -------------------------------------------------------------------------------- 1 | wget https://www.dropbox.com/s/2g2jagq2jn1fd0i/blur_jpg_prob0.5.pth?dl=0 -O ./weights/blur_jpg_prob0.5.pth 2 | wget https://www.dropbox.com/s/h7tkpcgiwuftb6g/blur_jpg_prob0.1.pth?dl=0 -O ./weights/blur_jpg_prob0.1.pth 3 | 4 | -------------------------------------------------------------------------------- /detector_codes/F3Net-main/README.md: -------------------------------------------------------------------------------- 1 | # Thinking in frequency: Face forgery detection by mining frequency-aware clues 2 | 3 | GitHub top languageGitHub last commitGitHub repo size 4 |
5 | *European Conference on Computer Vision 2020*
6 | 7 | 8 | This implementation is mainly based on the desciption in the [paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570086.pdf) on top of XceptionNet. The inplementation has three models: 9 | * **MixBlock**: Two-stream Collaborative Framework (@ block 7 and block 12). If you change the input image size, revise the ```width``` and ```height``` parameters in lines ```49``` and ```50```. 10 | * **FAD**: Frequency-aware Decomposition 11 | * **LFS**: Local Frequency Statistics 12 | 13 | 14 | Source code is mainly referenced from this [repo](https://github.com/yyk-wew/F3Net).
15 | Download pre-trained XceptionNet from [here](http://data.lip6.fr/cadene/pretrainedmodels/xception-b5690688.pth). 16 | # 17 | *Star if you find it useful.* ⭐ 18 | -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/.fuse_hidden0000b11f00000019: -------------------------------------------------------------------------------- 1 | # 2 | ## Global Texture Enhancement for Fake Face Detection in the Wild (CVPR2020) 3 | 4 | Code for the paper [Global Texture Enhancement for Fake Face Detection in the Wild](https://arxiv.org/abs/2002.00133), CVPR 2020. 5 | 6 | **Authors**: Zhengzhe Liu, Xiaojuan Qi, Philip H.S. Torr 7 | 8 | 9 | 10 | 11 | ## Demo 12 | 13 | Download [the model and the demo data](https://drive.google.com/) for StyleGAN-FFHQ, StyleGAN-CelebA and PGGAN-CelebA, respectively. In each folder, 14 | 15 | ``` 16 | python demo.py 17 | ``` 18 | 19 | It will print the file name, processing (resize 8x, JPEG or original image) and prediction (0 for fake and 1 for real). 20 | 21 | ## Data Preparation 22 | 23 | Download the 10k images from FFHQ, CelebA, and generate 10k images using StyleGAN, PGGAN on the datasets. Please save the 1024*1024 resolution images with PNG format, not JPG. 24 | 25 | Optionally, to evaluate the low-resolution GANs, download images from CelebA dataset, and generate images using DCGAN, DRAGAN and StarGAN. 26 | 27 | 28 | ## Training 29 | 30 | Generate the filelist for training. 31 | 32 | ``` 33 | python gene.py 34 | ``` 35 | 36 | Put graminit.pth to the training folder as initialization, and start training, while evaluate the model on the validation set regularly to choose the best model. 37 | 38 | ``` 39 | python main.py 40 | ``` 41 | 42 | ## Evaluation 43 | 44 | 45 | Modify "root" folder and image path in test.py, and then test the images on all the datasets. 46 | 47 | ``` 48 | python test.py 49 | python test2.py 50 | python test3.py 51 | ``` 52 | 53 | ## Contact 54 | If you have any questions or suggestions about this repo, please feel free to contact me (liuzhengzhelzz@gmail.com). 55 | 56 | -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/README.md: -------------------------------------------------------------------------------- 1 | # 2 | ## Global Texture Enhancement for Fake Face Detection in the Wild (CVPR2020) 3 | 4 | Code for the paper [Global Texture Enhancement for Fake Face Detection in the Wild](https://arxiv.org/abs/2002.00133), CVPR 2020. 5 | 6 | **Authors**: Zhengzhe Liu, Xiaojuan Qi, Philip H.S. Torr 7 | 8 | 9 | 10 | 11 | ## Demo 12 | 13 | Download [the model and the demo data](https://drive.google.com/drive/folders/10fSAR8lh94FDGNoMMo5cfDCKQqkZ2Nsm?usp=sharing) for StyleGAN-FFHQ, StyleGAN-CelebA and PGGAN-CelebA, respectively. In each folder, 14 | 15 | ``` 16 | python demo.py 17 | ``` 18 | 19 | It will print the file name, processing (resize 8x, JPEG or original image) and prediction (0 for fake and 1 for real). 20 | 21 | ## Data Preparation 22 | 23 | Download the 10k images from FFHQ, CelebA, and generate 10k images using StyleGAN, PGGAN on the datasets. Please save the 1024*1024 resolution images with PNG format, not JPG. 24 | 25 | Optionally, to evaluate the low-resolution GANs, download images from CelebA dataset, and generate images using DCGAN, DRAGAN and StarGAN. 26 | 27 | 28 | ## Training 29 | 30 | Generate the filelist for training. 31 | 32 | ``` 33 | python gene.py 34 | ``` 35 | 36 | Put graminit.pth to the training folder as initialization, and start training, while evaluate the model on the validation set regularly to choose the best model. 37 | 38 | ``` 39 | python main.py 40 | ``` 41 | 42 | ## Evaluation 43 | 44 | 45 | Modify "root" folder and image path in test.py, and then test the images on all the datasets. 46 | 47 | ``` 48 | python test.py 49 | python test2.py 50 | python test3.py 51 | ``` 52 | 53 | ## Contact 54 | If you have any questions or suggestions about this repo, please feel free to contact me (liuzhengzhelzz@gmail.com). 55 | 56 | -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/face.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/face.PNG -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/pggan-celeba/__pycache__/resnet18_gram.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/pggan-celeba/__pycache__/resnet18_gram.cpython-37.pyc -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/pggan-celeba/gene.py: -------------------------------------------------------------------------------- 1 | import glob,random 2 | 3 | root='./' 4 | lines=[] 5 | paths1=glob.glob(root+'pngdata/prog-gan-cele/*.png') 6 | for path in paths1: 7 | line=path+' 0\n' 8 | lines.append(line) 9 | 10 | paths2=glob.glob(root+'pngdata/celeba-1024/*.png') 11 | for path in paths2: 12 | line=path+' 1\n' 13 | lines.append(line) 14 | 15 | f=open('list.txt', 'w') 16 | random.shuffle(lines) 17 | for line in lines: 18 | f.write(line) 19 | f.close() 20 | 21 | 22 | -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/pggan-celeba/result.txt: -------------------------------------------------------------------------------- 1 | [0.7][0.][0.8][0.][0.8][0.][0.][0.81818182][0.][0.90909091][0.][0.90909091][0.90909091][0.][0.90909091][0.][0.90909091][0.][0.81818182][0.][0.72727273][0.][0.71428571][0.][0.][0.81818182][0.][0.90909091][0.][0.] -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/pngdata/data/style-ffhq/.fuse_hidden0000b34e00000018: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/pngdata/data/style-ffhq/.fuse_hidden0000b34e00000018 -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-celeba/.fuse_hidden0000b30800000008: -------------------------------------------------------------------------------- 1 | [0.][0.][0.][0.][0.][0.][0.][0.][0.][0.][0.][0.][0.][0.][0.][0.][0.][0.] -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-celeba/.fuse_hidden0000b55f00000006: -------------------------------------------------------------------------------- 1 | [0.8][0.][0.9][0.][0.9][0.][0.][0.90909091][0.][0.90909091][0.][0.90909091][0.54545455][0.][0.81818182][0.][0.90909091][0.][0.85714286][0.][0.72727273][0.][0.80952381][0.][0.][0.81818182][0.][0.90909091][0.27272727][0.] -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-celeba/.fuse_hidden0000b5a400000010: -------------------------------------------------------------------------------- 1 | import sys 2 | import numpy as np 3 | import torch,os,random,glob 4 | from torch import nn 5 | from torch import optim 6 | import torch.nn.functional as F 7 | from torch.autograd import Variable 8 | from torchvision import datasets, transforms#, models 9 | from torch.utils.data import Dataset, DataLoader 10 | import cv2 11 | import torch.utils.model_zoo as model_zoo 12 | import resnet18_gram as resnet 13 | import os 14 | #import scipy.io 15 | from torchvision.transforms import transforms 16 | 17 | 18 | for idx in range(115,116): 19 | model=torch.load('aerialmodel'+str(idx)+'.pth') 20 | model.eval() 21 | corr=0.0 22 | wrong=0.0 23 | 24 | corrs = np.zeros((2,1)) 25 | wrongs = np.zeros((2,1)) 26 | 27 | 28 | gt=1 29 | cnt=0 30 | paths=glob.glob('/media/lzz/LENOVO_USB_HDD1/pngdata/fang/*') 31 | for path in paths: 32 | name=path.split('/')[-1].split('.')[0] 33 | if int(cnt)>10000: 34 | continue 35 | cnt+=1 36 | im=cv2.imread(path) 37 | h=im.shape[0] 38 | ims = np.zeros((1, 3, h, h)) 39 | ims[0, 0, :, :] = im[:, :, 0] 40 | ims[0, 1, :, :] = im[:, :, 1] 41 | ims[0, 2, :, :] = im[:, :, 2] 42 | 43 | image_tensor =torch.tensor(ims).float() 44 | inputs = Variable(image_tensor).float().cuda() 45 | output = model(inputs) 46 | output=output.detach().cpu().numpy() 47 | 48 | pred=np.argmax(output) 49 | print (gt,pred) 50 | 51 | if int(gt)==int(pred): 52 | corr+=1 53 | corrs[int(gt)] = corrs[int(gt)]+1 54 | else: 55 | wrong+=1 56 | wrongs[int(gt)] = wrongs[int(gt)] + 1 57 | 58 | print(corrs[0]/(corrs[0]+wrongs[0]+1)) 59 | print(corrs[1]/(corrs[1]+wrongs[1]+1),idx) 60 | 61 | -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-celeba/__pycache__/resnet18_gram.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-celeba/__pycache__/resnet18_gram.cpython-37.pyc -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-celeba/demo.txt: -------------------------------------------------------------------------------- 1 | [0.72727273][0.][0.72727273][0.][0.80952381][0.][0.][0.81818182][0.][0.90909091][0.45454545][0.][0.7][0.][0.][0.90909091][0.27272727][0.] -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-celeba/gene.py: -------------------------------------------------------------------------------- 1 | import glob,random 2 | 3 | root='./' 4 | lines=[] 5 | paths1=glob.glob(root+'pngdata/style-cele/*.png') 6 | for path in paths1: 7 | line=path+' 0\n' 8 | lines.append(line) 9 | 10 | paths2=glob.glob(root+'pngdata/celeba-1024/*.png') 11 | for path in paths2: 12 | line=path+' 1\n' 13 | lines.append(line) 14 | 15 | f=open('list.txt', 'w') 16 | random.shuffle(lines) 17 | for line in lines: 18 | f.write(line) 19 | f.close() 20 | 21 | 22 | -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-celeba/result.txt: -------------------------------------------------------------------------------- 1 | [0.8][0.][0.9][0.][0.9][0.][0.][0.90909091][0.][0.90909091][0.][0.90909091][0.54545455][0.][0.81818182][0.][0.90909091][0.][0.85714286][0.][0.72727273][0.][0.80952381][0.][0.][0.81818182][0.][0.90909091][0.27272727][0.] -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-ffhq/__pycache__/resnet18_gram.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-ffhq/__pycache__/resnet18_gram.cpython-37.pyc -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-ffhq/gene.py: -------------------------------------------------------------------------------- 1 | import glob,random 2 | 3 | root='./' 4 | lines=[] 5 | paths1=glob.glob(root+'pngdata/style-ffhq/*.png') 6 | for path in paths1: 7 | line=path+' 0\n' 8 | lines.append(line) 9 | 10 | paths2=glob.glob(root+'pngdata/style-ffhq/*.png') 11 | for path in paths2: 12 | line=path+' 1\n' 13 | lines.append(line) 14 | 15 | f=open('list.txt', 'w') 16 | random.shuffle(lines) 17 | for line in lines: 18 | f.write(line) 19 | f.close() 20 | 21 | 22 | -------------------------------------------------------------------------------- /detector_codes/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master/stylegan-ffhq/result.txt: -------------------------------------------------------------------------------- 1 | [0.90909091][0.][0.90909091][0.][0.90909091][0.][0.][0.81818182][0.][0.90909091][0.][0.90909091][0.][0.90909091][0.5][0.] -------------------------------------------------------------------------------- /detector_codes/Readme.md: -------------------------------------------------------------------------------- 1 | # Codes of Detectors 2 | 3 | These are the codes of the detectors used in GenImage benchmark. 4 | 5 | ResNet50: pytorch-image-models-0.6.12 6 | 7 | DeiT-s:deit-main 8 | 9 | Swin-T:Swin-Transformer-main 10 | 11 | CNNSpot: CNNDetection-master 12 | 13 | Spec:AutoGAN 14 | 15 | F3Net:F3Net-main 16 | 17 | GramNet:Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild-master 18 | 19 | 20 | -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Microsoft Open Source Code of Conduct 2 | 3 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 4 | 5 | Resources: 6 | 7 | - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) 8 | - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 9 | - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns 10 | -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Microsoft Corporation. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE 22 | -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/SUPPORT.md: -------------------------------------------------------------------------------- 1 | # TODO: The maintainer of this repo has not yet edited this file 2 | 3 | **REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project? 4 | 5 | - **No CSS support:** Fill out this template with information about how to file issues and get help. 6 | - **Yes CSS support:** Fill out an intake form at [aka.ms/spot](https://aka.ms/spot). CSS will work with/help you to determine next steps. More details also available at [aka.ms/onboardsupport](https://aka.ms/onboardsupport). 7 | - **Not sure?** Fill out a SPOT intake as though the answer were "Yes". CSS will help you decide. 8 | 9 | *Then remove this first heading from this SUPPORT.MD file before publishing your repo.* 10 | 11 | # Support 12 | 13 | ## How to file issues and get help 14 | 15 | This project uses GitHub Issues to track bugs and feature requests. Please search the existing 16 | issues before filing new issues to avoid duplicates. For new issues, file your bug or 17 | feature request as a new Issue. 18 | 19 | For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE 20 | FOR HOW TO ENGAGE REPO OWNERS OR COMMUNITY FOR HELP. COULD BE A STACK OVERFLOW TAG OR OTHER 21 | CHANNEL. WHERE WILL YOU HELP PEOPLE?**. 22 | 23 | ## Microsoft Support Policy 24 | 25 | Support for this **PROJECT or PRODUCT** is limited to the resources listed above. 26 | -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/simmim/simmim_finetune__swin_base__img224_window7__800ep.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: simmim_finetune 4 | DROP_PATH_RATE: 0.1 5 | SWIN: 6 | EMBED_DIM: 128 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 4, 8, 16, 32 ] 9 | WINDOW_SIZE: 7 10 | DATA: 11 | IMG_SIZE: 224 12 | TRAIN: 13 | EPOCHS: 100 14 | WARMUP_EPOCHS: 20 15 | BASE_LR: 1.25e-3 16 | WARMUP_LR: 2.5e-7 17 | MIN_LR: 2.5e-7 18 | WEIGHT_DECAY: 0.05 19 | LAYER_DECAY: 0.8 20 | PRINT_FREQ: 100 21 | SAVE_FREQ: 5 22 | TAG: simmim_finetune__swin_base__img224_window7__800ep -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/simmim/simmim_finetune__swinv2_base__img224_window14__800ep.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swinv2 3 | NAME: simmim_finetune 4 | DROP_PATH_RATE: 0.1 5 | SWINV2: 6 | EMBED_DIM: 128 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 4, 8, 16, 32 ] 9 | WINDOW_SIZE: 14 10 | PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ] 11 | DATA: 12 | IMG_SIZE: 224 13 | TRAIN: 14 | EPOCHS: 100 15 | WARMUP_EPOCHS: 20 16 | BASE_LR: 1.25e-3 17 | WARMUP_LR: 2.5e-7 18 | MIN_LR: 2.5e-7 19 | WEIGHT_DECAY: 0.05 20 | LAYER_DECAY: 0.75 21 | PRINT_FREQ: 100 22 | SAVE_FREQ: 5 23 | TAG: simmim_finetune__swinv2_base__img224_window14__800ep -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/simmim/simmim_pretrain__swin_base__img192_window6__800ep.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: simmim_pretrain 4 | DROP_PATH_RATE: 0.0 5 | SWIN: 6 | EMBED_DIM: 128 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 4, 8, 16, 32 ] 9 | WINDOW_SIZE: 6 10 | DATA: 11 | IMG_SIZE: 192 12 | MASK_PATCH_SIZE: 32 13 | MASK_RATIO: 0.6 14 | TRAIN: 15 | EPOCHS: 800 16 | WARMUP_EPOCHS: 10 17 | BASE_LR: 1e-4 18 | WARMUP_LR: 5e-7 19 | WEIGHT_DECAY: 0.05 20 | LR_SCHEDULER: 21 | NAME: 'multistep' 22 | GAMMA: 0.1 23 | MULTISTEPS: [700,] 24 | PRINT_FREQ: 100 25 | SAVE_FREQ: 5 26 | TAG: simmim_pretrain__swin_base__img192_window6__800ep -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/simmim/simmim_pretrain__swinv2_base__img192_window12__800ep.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swinv2 3 | NAME: simmim_pretrain 4 | DROP_PATH_RATE: 0.1 5 | SIMMIM: 6 | NORM_TARGET: 7 | ENABLE: True 8 | PATCH_SIZE: 47 9 | SWINV2: 10 | EMBED_DIM: 128 11 | DEPTHS: [ 2, 2, 18, 2 ] 12 | NUM_HEADS: [ 4, 8, 16, 32 ] 13 | WINDOW_SIZE: 12 14 | DATA: 15 | IMG_SIZE: 192 16 | MASK_PATCH_SIZE: 32 17 | MASK_RATIO: 0.6 18 | TRAIN: 19 | EPOCHS: 800 20 | WARMUP_EPOCHS: 10 21 | BASE_LR: 1e-4 22 | WARMUP_LR: 5e-7 23 | WEIGHT_DECAY: 0.05 24 | LR_SCHEDULER: 25 | NAME: 'multistep' 26 | GAMMA: 0.1 27 | MULTISTEPS: [700,] 28 | PRINT_FREQ: 100 29 | SAVE_FREQ: 5 30 | TAG: simmim_pretrain__swinv2_base__img192_window12__800ep -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_base_patch4_window12_384_22kto1k_finetune.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 384 3 | MODEL: 4 | TYPE: swin 5 | NAME: swin_base_patch4_window12_384_22kto1k_finetune 6 | DROP_PATH_RATE: 0.2 7 | SWIN: 8 | EMBED_DIM: 128 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 12 12 | TRAIN: 13 | EPOCHS: 30 14 | WARMUP_EPOCHS: 5 15 | WEIGHT_DECAY: 1e-8 16 | BASE_LR: 2e-05 17 | WARMUP_LR: 2e-08 18 | MIN_LR: 2e-07 19 | TEST: 20 | CROP: False -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_base_patch4_window12_384_finetune.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 384 3 | MODEL: 4 | TYPE: swin 5 | NAME: swin_base_patch4_window12_384_finetune 6 | DROP_PATH_RATE: 0.5 7 | SWIN: 8 | EMBED_DIM: 128 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 12 12 | TRAIN: 13 | EPOCHS: 30 14 | WARMUP_EPOCHS: 5 15 | WEIGHT_DECAY: 1e-8 16 | BASE_LR: 2e-05 17 | WARMUP_LR: 2e-08 18 | MIN_LR: 2e-07 19 | TEST: 20 | CROP: False -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_base_patch4_window7_224.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: swin_base_patch4_window7_224 4 | DROP_PATH_RATE: 0.5 5 | SWIN: 6 | EMBED_DIM: 128 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 4, 8, 16, 32 ] 9 | WINDOW_SIZE: 7 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_base_patch4_window7_224_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | MODEL: 4 | TYPE: swin 5 | NAME: swin_base_patch4_window7_224_22k 6 | DROP_PATH_RATE: 0.2 7 | SWIN: 8 | EMBED_DIM: 128 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 7 12 | TRAIN: 13 | EPOCHS: 90 14 | WARMUP_EPOCHS: 5 15 | WEIGHT_DECAY: 0.05 16 | BASE_LR: 1.25e-4 # 4096 batch-size 17 | WARMUP_LR: 1.25e-7 18 | MIN_LR: 1.25e-6 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_base_patch4_window7_224_22kto1k_finetune.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: swin_base_patch4_window7_224_22kto1k_finetune 4 | DROP_PATH_RATE: 0.2 5 | SWIN: 6 | EMBED_DIM: 128 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 4, 8, 16, 32 ] 9 | WINDOW_SIZE: 7 10 | TRAIN: 11 | EPOCHS: 30 12 | WARMUP_EPOCHS: 5 13 | WEIGHT_DECAY: 1e-8 14 | BASE_LR: 2e-05 15 | WARMUP_LR: 2e-08 16 | MIN_LR: 2e-07 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_large_patch4_window12_384_22kto1k_finetune.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 384 3 | MODEL: 4 | TYPE: swin 5 | NAME: swin_large_patch4_window12_384_22kto1k_finetune 6 | DROP_PATH_RATE: 0.2 7 | SWIN: 8 | EMBED_DIM: 192 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 6, 12, 24, 48 ] 11 | WINDOW_SIZE: 12 12 | TRAIN: 13 | EPOCHS: 30 14 | WARMUP_EPOCHS: 5 15 | WEIGHT_DECAY: 1e-8 16 | BASE_LR: 2e-05 17 | WARMUP_LR: 2e-08 18 | MIN_LR: 2e-07 19 | TEST: 20 | CROP: False -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_large_patch4_window7_224_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | MODEL: 4 | TYPE: swin 5 | NAME: swin_large_patch4_window7_224_22k 6 | DROP_PATH_RATE: 0.2 7 | SWIN: 8 | EMBED_DIM: 192 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 6, 12, 24, 48 ] 11 | WINDOW_SIZE: 7 12 | TRAIN: 13 | EPOCHS: 90 14 | WARMUP_EPOCHS: 5 15 | WEIGHT_DECAY: 0.05 16 | BASE_LR: 1.25e-4 # 4096 batch-size 17 | WARMUP_LR: 1.25e-7 18 | MIN_LR: 1.25e-6 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_large_patch4_window7_224_22kto1k_finetune.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: swin_large_patch4_window7_224_22kto1k_finetune 4 | DROP_PATH_RATE: 0.2 5 | SWIN: 6 | EMBED_DIM: 192 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 6, 12, 24, 48 ] 9 | WINDOW_SIZE: 7 10 | TRAIN: 11 | EPOCHS: 30 12 | WARMUP_EPOCHS: 5 13 | WEIGHT_DECAY: 1e-8 14 | BASE_LR: 2e-05 15 | WARMUP_LR: 2e-08 16 | MIN_LR: 2e-07 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_small_patch4_window7_224.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: swin_small_patch4_window7_224 4 | DROP_PATH_RATE: 0.3 5 | SWIN: 6 | EMBED_DIM: 96 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 3, 6, 12, 24 ] 9 | WINDOW_SIZE: 7 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_small_patch4_window7_224_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | MODEL: 4 | TYPE: swin 5 | NAME: swin_small_patch4_window7_224_22k 6 | DROP_PATH_RATE: 0.2 7 | SWIN: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 3, 6, 12, 24 ] 11 | WINDOW_SIZE: 7 12 | TRAIN: 13 | EPOCHS: 90 14 | WARMUP_EPOCHS: 5 15 | WEIGHT_DECAY: 0.05 16 | BASE_LR: 1.25e-4 # 4096 batch-size 17 | WARMUP_LR: 1.25e-7 18 | MIN_LR: 1.25e-6 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_small_patch4_window7_224_22kto1k_finetune.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: swin_small_patch4_window7_224_22kto1k_finetune 4 | DROP_PATH_RATE: 0.2 5 | SWIN: 6 | EMBED_DIM: 96 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 3, 6, 12, 24 ] 9 | WINDOW_SIZE: 7 10 | TRAIN: 11 | EPOCHS: 30 12 | WARMUP_EPOCHS: 5 13 | WEIGHT_DECAY: 1e-8 14 | BASE_LR: 2e-05 15 | WARMUP_LR: 2e-08 16 | MIN_LR: 2e-07 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_tiny_c24_patch4_window8_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swin 5 | NAME: swin_tiny_c24_patch4_window8_256 6 | DROP_PATH_RATE: 0.2 7 | SWIN: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 6, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 8 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_tiny_patch4_window7_224.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: swin_tiny_patch4_window7_224 4 | DROP_PATH_RATE: 0.2 5 | SWIN: 6 | EMBED_DIM: 96 7 | DEPTHS: [ 2, 2, 6, 2 ] 8 | NUM_HEADS: [ 3, 6, 12, 24 ] 9 | WINDOW_SIZE: 7 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_tiny_patch4_window7_224_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | MODEL: 4 | TYPE: swin 5 | NAME: swin_tiny_patch4_window7_224_22k 6 | DROP_PATH_RATE: 0.1 7 | SWIN: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 6, 2 ] 10 | NUM_HEADS: [ 3, 6, 12, 24 ] 11 | WINDOW_SIZE: 7 12 | TRAIN: 13 | EPOCHS: 90 14 | WARMUP_EPOCHS: 5 15 | WEIGHT_DECAY: 0.05 16 | BASE_LR: 1.25e-4 # 4096 batch-size 17 | WARMUP_LR: 1.25e-7 18 | MIN_LR: 1.25e-6 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swin/swin_tiny_patch4_window7_224_22kto1k_finetune.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin 3 | NAME: swin_tiny_patch4_window7_224_22kto1k_finetune 4 | DROP_PATH_RATE: 0.1 5 | SWIN: 6 | EMBED_DIM: 96 7 | DEPTHS: [ 2, 2, 6, 2 ] 8 | NUM_HEADS: [ 3, 6, 12, 24 ] 9 | WINDOW_SIZE: 7 10 | TRAIN: 11 | EPOCHS: 30 12 | WARMUP_EPOCHS: 5 13 | WEIGHT_DECAY: 1e-8 14 | BASE_LR: 2e-05 15 | WARMUP_LR: 2e-08 16 | MIN_LR: 2e-07 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmlp/swin_mlp_base_patch4_window7_224.yaml: -------------------------------------------------------------------------------- 1 | MODEL: 2 | TYPE: swin_mlp 3 | NAME: swin_mlp_base_patch4_window7_224 4 | DROP_PATH_RATE: 0.5 5 | SWIN_MLP: 6 | EMBED_DIM: 128 7 | DEPTHS: [ 2, 2, 18, 2 ] 8 | NUM_HEADS: [ 4, 8, 16, 32 ] 9 | WINDOW_SIZE: 7 10 | -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmlp/swin_mlp_tiny_c12_patch4_window8_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swin_mlp 5 | NAME: swin_mlp_tiny_c12_patch4_window8_256 6 | DROP_PATH_RATE: 0.2 7 | SWIN_MLP: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 6, 2 ] 10 | NUM_HEADS: [ 8, 16, 32, 64 ] 11 | WINDOW_SIZE: 8 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmlp/swin_mlp_tiny_c24_patch4_window8_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swin_mlp 5 | NAME: swin_mlp_tiny_c24_patch4_window8_256 6 | DROP_PATH_RATE: 0.2 7 | SWIN_MLP: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 6, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 8 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmlp/swin_mlp_tiny_c6_patch4_window8_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swin_mlp 5 | NAME: swin_mlp_tiny_c6_patch4_window8_256 6 | DROP_PATH_RATE: 0.2 7 | SWIN_MLP: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 6, 2 ] 10 | NUM_HEADS: [ 16, 32, 64, 128 ] 11 | WINDOW_SIZE: 8 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_base_patch4_window12_192_16expert_32gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_base_patch4_window12_192_16expert_32gpu_22k 7 | DROP_PATH_RATE: 0.3 8 | SWIN_MOE: 9 | EMBED_DIM: 128 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 4, 8, 16, 32 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: -2 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | IS_GSHARD_LOSS: False 20 | MOE_DROP: 0.1 21 | AUX_LOSS_WEIGHT: 0.01 22 | TRAIN: 23 | EPOCHS: 90 24 | WARMUP_EPOCHS: 10 25 | WEIGHT_DECAY: 0.1 26 | BASE_LR: 1.25e-4 # 4096 batch-size 27 | WARMUP_LR: 1.25e-7 28 | MIN_LR: 1.25e-6 29 | CLIP_GRAD: 3.0 30 | TEST: 31 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_base_patch4_window12_192_32expert_32gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_base_patch4_window12_192_32expert_32gpu_22k 7 | DROP_PATH_RATE: 0.3 8 | SWIN_MOE: 9 | EMBED_DIM: 128 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 4, 8, 16, 32 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: 1 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | IS_GSHARD_LOSS: False 20 | MOE_DROP: 0.1 21 | AUX_LOSS_WEIGHT: 0.01 22 | TRAIN: 23 | EPOCHS: 90 24 | WARMUP_EPOCHS: 10 25 | WEIGHT_DECAY: 0.1 26 | BASE_LR: 1.25e-4 # 4096 batch-size 27 | WARMUP_LR: 1.25e-7 28 | MIN_LR: 1.25e-6 29 | CLIP_GRAD: 3.0 30 | TEST: 31 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_base_patch4_window12_192_8expert_32gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_base_patch4_window12_192_8expert_32gpu_22k 7 | DROP_PATH_RATE: 0.3 8 | SWIN_MOE: 9 | EMBED_DIM: 128 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 4, 8, 16, 32 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: -4 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | IS_GSHARD_LOSS: False 20 | MOE_DROP: 0.1 21 | AUX_LOSS_WEIGHT: 0.01 22 | TRAIN: 23 | EPOCHS: 90 24 | WARMUP_EPOCHS: 10 25 | WEIGHT_DECAY: 0.1 26 | BASE_LR: 1.25e-4 # 4096 batch-size 27 | WARMUP_LR: 1.25e-7 28 | MIN_LR: 1.25e-6 29 | CLIP_GRAD: 3.0 30 | TEST: 31 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_base_patch4_window12_192_cosine_router_32expert_32gpu_22k 7 | DROP_PATH_RATE: 0.3 8 | SWIN_MOE: 9 | EMBED_DIM: 128 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 4, 8, 16, 32 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: 1 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | COSINE_ROUTER: True 20 | IS_GSHARD_LOSS: False 21 | MOE_DROP: 0.1 22 | AUX_LOSS_WEIGHT: 0.01 23 | TRAIN: 24 | EPOCHS: 90 25 | WARMUP_EPOCHS: 10 26 | WEIGHT_DECAY: 0.1 27 | BASE_LR: 1.25e-4 # 4096 batch-size 28 | WARMUP_LR: 1.25e-7 29 | MIN_LR: 1.25e-6 30 | CLIP_GRAD: 3.0 31 | TEST: 32 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_base_patch4_window12_192_densebaseline_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_base_patch4_window12_192_densebaseline_22k 7 | DROP_PATH_RATE: 0.2 8 | SWIN_MOE: 9 | EMBED_DIM: 128 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 4, 8, 16, 32 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ -1 ], [ -1 ] ] 15 | TRAIN: 16 | EPOCHS: 90 17 | WARMUP_EPOCHS: 10 18 | WEIGHT_DECAY: 0.1 19 | BASE_LR: 1.25e-4 # 4096 batch-size 20 | WARMUP_LR: 1.25e-7 21 | MIN_LR: 1.25e-6 22 | CLIP_GRAD: 3.0 23 | MOE: 24 | SAVE_MASTER: True 25 | TEST: 26 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_small_patch4_window12_192_16expert_32gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_small_patch4_window12_192_16expert_32gpu_22k 7 | DROP_PATH_RATE: 0.2 8 | SWIN_MOE: 9 | EMBED_DIM: 96 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 3, 6, 12, 24 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: -2 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | IS_GSHARD_LOSS: False 20 | MOE_DROP: 0.1 21 | AUX_LOSS_WEIGHT: 0.01 22 | TRAIN: 23 | EPOCHS: 90 24 | WARMUP_EPOCHS: 10 25 | WEIGHT_DECAY: 0.1 26 | BASE_LR: 1.25e-4 # 4096 batch-size 27 | WARMUP_LR: 1.25e-7 28 | MIN_LR: 1.25e-6 29 | CLIP_GRAD: 3.0 30 | TEST: 31 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_small_patch4_window12_192_32expert_32gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_small_patch4_window12_192_32expert_32gpu_22k 7 | DROP_PATH_RATE: 0.2 8 | SWIN_MOE: 9 | EMBED_DIM: 96 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 3, 6, 12, 24 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: 1 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | IS_GSHARD_LOSS: False 20 | MOE_DROP: 0.1 21 | AUX_LOSS_WEIGHT: 0.01 22 | TRAIN: 23 | EPOCHS: 90 24 | WARMUP_EPOCHS: 10 25 | WEIGHT_DECAY: 0.1 26 | BASE_LR: 1.25e-4 # 4096 batch-size 27 | WARMUP_LR: 1.25e-7 28 | MIN_LR: 1.25e-6 29 | CLIP_GRAD: 3.0 30 | TEST: 31 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_small_patch4_window12_192_64expert_64gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_small_patch4_window12_192_64expert_64gpu_22k 7 | DROP_PATH_RATE: 0.2 8 | SWIN_MOE: 9 | EMBED_DIM: 96 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 3, 6, 12, 24 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: 1 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | IS_GSHARD_LOSS: False 20 | MOE_DROP: 0.1 21 | AUX_LOSS_WEIGHT: 0.01 22 | TRAIN: 23 | EPOCHS: 90 24 | WARMUP_EPOCHS: 10 25 | WEIGHT_DECAY: 0.1 26 | BASE_LR: 1.25e-4 # 4096 batch-size 27 | WARMUP_LR: 1.25e-7 28 | MIN_LR: 1.25e-6 29 | CLIP_GRAD: 3.0 30 | TEST: 31 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_small_patch4_window12_192_8expert_32gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_small_patch4_window12_192_8expert_32gpu_22k 7 | DROP_PATH_RATE: 0.2 8 | SWIN_MOE: 9 | EMBED_DIM: 96 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 3, 6, 12, 24 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: -4 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | IS_GSHARD_LOSS: False 20 | MOE_DROP: 0.1 21 | AUX_LOSS_WEIGHT: 0.01 22 | TRAIN: 23 | EPOCHS: 90 24 | WARMUP_EPOCHS: 10 25 | WEIGHT_DECAY: 0.1 26 | BASE_LR: 1.25e-4 # 4096 batch-size 27 | WARMUP_LR: 1.25e-7 28 | MIN_LR: 1.25e-6 29 | CLIP_GRAD: 3.0 30 | TEST: 31 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_small_patch4_window12_192_cosine_router_32expert_32gpu_22k 7 | DROP_PATH_RATE: 0.2 8 | SWIN_MOE: 9 | EMBED_DIM: 96 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 3, 6, 12, 24 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | INIT_STD: 0.005 15 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ 1, 3, 5, 7, 9, 11, 13, 15, 17 ], [ 1 ] ] 16 | NUM_LOCAL_EXPERTS: 1 17 | TOP_VALUE: 1 18 | CAPACITY_FACTOR: 1.25 19 | COSINE_ROUTER: True 20 | IS_GSHARD_LOSS: False 21 | MOE_DROP: 0.1 22 | AUX_LOSS_WEIGHT: 0.01 23 | TRAIN: 24 | EPOCHS: 90 25 | WARMUP_EPOCHS: 10 26 | WEIGHT_DECAY: 0.1 27 | BASE_LR: 1.25e-4 # 4096 batch-size 28 | WARMUP_LR: 1.25e-7 29 | MIN_LR: 1.25e-6 30 | CLIP_GRAD: 3.0 31 | TEST: 32 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinmoe/swin_moe_small_patch4_window12_192_densebaseline_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swin_moe 6 | NAME: swin_moe_small_patch4_window12_192_densebaseline_22k 7 | DROP_PATH_RATE: 0.2 8 | SWIN_MOE: 9 | EMBED_DIM: 96 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 3, 6, 12, 24 ] 12 | WINDOW_SIZE: 12 13 | MLP_FC2_BIAS: False 14 | MOE_BLOCKS: [ [ -1 ], [ -1 ], [ -1 ], [ -1 ] ] 15 | TRAIN: 16 | EPOCHS: 90 17 | WARMUP_EPOCHS: 10 18 | WEIGHT_DECAY: 0.1 19 | BASE_LR: 1.25e-4 # 4096 batch-size 20 | WARMUP_LR: 1.25e-7 21 | MIN_LR: 1.25e-6 22 | CLIP_GRAD: 3.0 23 | MOE: 24 | SAVE_MASTER: True 25 | TEST: 26 | SHUFFLE: True -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_base_patch4_window12_192_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swinv2 6 | NAME: swinv2_base_patch4_window12_192_22k 7 | DROP_PATH_RATE: 0.2 8 | SWINV2: 9 | EMBED_DIM: 128 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 4, 8, 16, 32 ] 12 | WINDOW_SIZE: 12 13 | TRAIN: 14 | EPOCHS: 90 15 | WARMUP_EPOCHS: 5 16 | WEIGHT_DECAY: 0.1 17 | BASE_LR: 1.25e-4 # 4096 batch-size 18 | WARMUP_LR: 1.25e-7 19 | MIN_LR: 1.25e-6 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_base_patch4_window12to16_192to256_22kto1k_ft 6 | DROP_PATH_RATE: 0.2 7 | SWINV2: 8 | EMBED_DIM: 128 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 16 12 | PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ] 13 | TRAIN: 14 | EPOCHS: 30 15 | WARMUP_EPOCHS: 5 16 | WEIGHT_DECAY: 1e-8 17 | BASE_LR: 2e-05 18 | WARMUP_LR: 2e-08 19 | MIN_LR: 2e-07 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 384 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_base_patch4_window12to24_192to384_22kto1k_ft 6 | DROP_PATH_RATE: 0.2 7 | SWINV2: 8 | EMBED_DIM: 128 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 24 12 | PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ] 13 | TRAIN: 14 | EPOCHS: 30 15 | WARMUP_EPOCHS: 5 16 | WEIGHT_DECAY: 1e-8 17 | BASE_LR: 2e-05 18 | WARMUP_LR: 2e-08 19 | MIN_LR: 2e-07 20 | TEST: 21 | CROP: False -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_base_patch4_window16_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_base_patch4_window16_256 6 | DROP_PATH_RATE: 0.5 7 | SWINV2: 8 | EMBED_DIM: 128 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 16 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_base_patch4_window8_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_base_patch4_window8_256 6 | DROP_PATH_RATE: 0.5 7 | SWINV2: 8 | EMBED_DIM: 128 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 4, 8, 16, 32 ] 11 | WINDOW_SIZE: 8 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_large_patch4_window12_192_22k.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | DATASET: imagenet22K 3 | IMG_SIZE: 192 4 | MODEL: 5 | TYPE: swinv2 6 | NAME: swinv2_large_patch4_window12_192_22k 7 | DROP_PATH_RATE: 0.2 8 | SWINV2: 9 | EMBED_DIM: 192 10 | DEPTHS: [ 2, 2, 18, 2 ] 11 | NUM_HEADS: [ 6, 12, 24, 48 ] 12 | WINDOW_SIZE: 12 13 | TRAIN: 14 | EPOCHS: 90 15 | WARMUP_EPOCHS: 5 16 | WEIGHT_DECAY: 0.1 17 | BASE_LR: 1.25e-4 # 4096 batch-size 18 | WARMUP_LR: 1.25e-7 19 | MIN_LR: 1.25e-6 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_base_patch4_window12to16_192to256_22kto1k_ft 6 | DROP_PATH_RATE: 0.2 7 | SWINV2: 8 | EMBED_DIM: 192 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 6, 12, 24, 48 ] 11 | WINDOW_SIZE: 16 12 | PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ] 13 | TRAIN: 14 | EPOCHS: 30 15 | WARMUP_EPOCHS: 5 16 | WEIGHT_DECAY: 1e-8 17 | BASE_LR: 2e-05 18 | WARMUP_LR: 2e-08 19 | MIN_LR: 2e-07 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 384 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_large_patch4_window12to24_192to384_22kto1k_ft 6 | DROP_PATH_RATE: 0.2 7 | SWINV2: 8 | EMBED_DIM: 192 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 6, 12, 24, 48 ] 11 | WINDOW_SIZE: 24 12 | PRETRAINED_WINDOW_SIZES: [ 12, 12, 12, 6 ] 13 | TRAIN: 14 | EPOCHS: 30 15 | WARMUP_EPOCHS: 5 16 | WEIGHT_DECAY: 1e-8 17 | BASE_LR: 2e-05 18 | WARMUP_LR: 2e-08 19 | MIN_LR: 2e-07 20 | TEST: 21 | CROP: False -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_small_patch4_window16_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_small_patch4_window16_256 6 | DROP_PATH_RATE: 0.3 7 | SWINV2: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 3, 6, 12, 24 ] 11 | WINDOW_SIZE: 16 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_small_patch4_window8_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_small_patch4_window8_256 6 | DROP_PATH_RATE: 0.3 7 | SWINV2: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 18, 2 ] 10 | NUM_HEADS: [ 3, 6, 12, 24 ] 11 | WINDOW_SIZE: 8 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_tiny_patch4_window16_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_tiny_patch4_window16_256 6 | DROP_PATH_RATE: 0.2 7 | SWINV2: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 6, 2 ] 10 | NUM_HEADS: [ 3, 6, 12, 24 ] 11 | WINDOW_SIZE: 16 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/configs/swinv2/swinv2_tiny_patch4_window8_256.yaml: -------------------------------------------------------------------------------- 1 | DATA: 2 | IMG_SIZE: 256 3 | MODEL: 4 | TYPE: swinv2 5 | NAME: swinv2_tiny_patch4_window8_256 6 | DROP_PATH_RATE: 0.2 7 | SWINV2: 8 | EMBED_DIM: 96 9 | DEPTHS: [ 2, 2, 6, 2 ] 10 | NUM_HEADS: [ 3, 6, 12, 24 ] 11 | WINDOW_SIZE: 8 -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/data/__init__.py: -------------------------------------------------------------------------------- 1 | from .build import build_loader as _build_loader 2 | from .data_simmim_pt import build_loader_simmim 3 | from .data_simmim_ft import build_loader_finetune 4 | 5 | 6 | def build_loader(config, simmim=False, is_pretrain=False): 7 | if not simmim: 8 | return _build_loader(config) 9 | if is_pretrain: 10 | return build_loader_simmim(config) 11 | else: 12 | return build_loader_finetune(config) 13 | -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/data/samplers.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Swin Transformer 3 | # Copyright (c) 2021 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ze Liu 6 | # -------------------------------------------------------- 7 | 8 | import torch 9 | 10 | 11 | class SubsetRandomSampler(torch.utils.data.Sampler): 12 | r"""Samples elements randomly from a given list of indices, without replacement. 13 | 14 | Arguments: 15 | indices (sequence): a sequence of indices 16 | """ 17 | 18 | def __init__(self, indices): 19 | self.epoch = 0 20 | self.indices = indices 21 | 22 | def __iter__(self): 23 | return (self.indices[i] for i in torch.randperm(len(self.indices))) 24 | 25 | def __len__(self): 26 | return len(self.indices) 27 | 28 | def set_epoch(self, epoch): 29 | self.epoch = epoch 30 | -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/figures/teaser.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/Swin-Transformer-main/figures/teaser.png -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/kernels/window_process/setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | from torch.utils.cpp_extension import BuildExtension, CUDAExtension 3 | 4 | 5 | setup(name='swin_window_process', 6 | ext_modules=[ 7 | CUDAExtension('swin_window_process', [ 8 | 'swin_window_process.cpp', 9 | 'swin_window_process_kernel.cu', 10 | ]) 11 | ], 12 | cmdclass={'build_ext': BuildExtension}) -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/logger.py: -------------------------------------------------------------------------------- 1 | # -------------------------------------------------------- 2 | # Swin Transformer 3 | # Copyright (c) 2021 Microsoft 4 | # Licensed under The MIT License [see LICENSE for details] 5 | # Written by Ze Liu 6 | # -------------------------------------------------------- 7 | 8 | import os 9 | import sys 10 | import logging 11 | import functools 12 | from termcolor import colored 13 | 14 | 15 | @functools.lru_cache() 16 | def create_logger(output_dir, dist_rank=0, name=''): 17 | # create logger 18 | logger = logging.getLogger(name) 19 | logger.setLevel(logging.DEBUG) 20 | logger.propagate = False 21 | 22 | # create formatter 23 | fmt = '[%(asctime)s %(name)s] (%(filename)s %(lineno)d): %(levelname)s %(message)s' 24 | color_fmt = colored('[%(asctime)s %(name)s]', 'green') + \ 25 | colored('(%(filename)s %(lineno)d)', 'yellow') + ': %(levelname)s %(message)s' 26 | 27 | # create console handlers for master process 28 | if dist_rank == 0: 29 | console_handler = logging.StreamHandler(sys.stdout) 30 | console_handler.setLevel(logging.DEBUG) 31 | console_handler.setFormatter( 32 | logging.Formatter(fmt=color_fmt, datefmt='%Y-%m-%d %H:%M:%S')) 33 | logger.addHandler(console_handler) 34 | 35 | # create file handlers 36 | file_handler = logging.FileHandler(os.path.join(output_dir, f'log_rank{dist_rank}.txt'), mode='a') 37 | file_handler.setLevel(logging.DEBUG) 38 | file_handler.setFormatter(logging.Formatter(fmt=fmt, datefmt='%Y-%m-%d %H:%M:%S')) 39 | logger.addHandler(file_handler) 40 | 41 | return logger 42 | -------------------------------------------------------------------------------- /detector_codes/Swin-Transformer-main/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .build import build_model -------------------------------------------------------------------------------- /detector_codes/deit-main/.circleci/config.yml: -------------------------------------------------------------------------------- 1 | version: 2.1 2 | 3 | jobs: 4 | python_lint: 5 | docker: 6 | - image: circleci/python:3.7 7 | steps: 8 | - checkout 9 | - run: 10 | command: | 11 | pip install --user --progress-bar off flake8 typing 12 | flake8 . 13 | test: 14 | docker: 15 | - image: circleci/python:3.7 16 | steps: 17 | - checkout 18 | - run: 19 | command: | 20 | pip install --user --progress-bar off pytest 21 | pip install --user --progress-bar off torch torchvision 22 | pip install --user --progress-bar off timm==0.3.2 23 | pytest . 24 | 25 | workflows: 26 | build: 27 | jobs: 28 | - python_lint 29 | -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Code of Conduct 2 | 3 | Facebook has adopted a Code of Conduct that we expect project participants to adhere to. 4 | Please read the [full text](https://code.fb.com/codeofconduct/) 5 | so that you can understand what actions will and will not be tolerated. 6 | -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to DeiT 2 | We want to make contributing to this project as easy and transparent as 3 | possible. 4 | 5 | ## Our Development Process 6 | Minor changes and improvements will be released on an ongoing basis. Larger changes (e.g., changesets implementing a new paper) will be released on a more periodic basis. 7 | 8 | ## Pull Requests 9 | We actively welcome your pull requests. 10 | 11 | 1. Fork the repo and create your branch from `main`. 12 | 2. If you've added code that should be tested, add tests. 13 | 3. If you've changed APIs, update the documentation. 14 | 4. Ensure the test suite passes. 15 | 5. Make sure your code lints. 16 | 6. If you haven't already, complete the Contributor License Agreement ("CLA"). 17 | 18 | ## Contributor License Agreement ("CLA") 19 | In order to accept your pull request, we need you to submit a CLA. You only need 20 | to do this once to work on any of Facebook's open source projects. 21 | 22 | Complete your CLA here: 23 | 24 | ## Issues 25 | We use GitHub issues to track public bugs. Please ensure your description is 26 | clear and has sufficient instructions to be able to reproduce the issue. 27 | 28 | Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe 29 | disclosure of security bugs. In those cases, please go through the process 30 | outlined on that page and do not file a public issue. 31 | 32 | ## Coding Style 33 | * 4 spaces for indentation rather than tabs 34 | * 80 character line length 35 | * PEP8 formatting following [Black](https://black.readthedocs.io/en/stable/) 36 | 37 | ## License 38 | By contributing to DeiT, you agree that your contributions will be licensed 39 | under the LICENSE file in the root directory of this source tree. 40 | -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/attn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/attn.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/cait.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/cait.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/cosub.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/cosub.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/deit.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/deit.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/hmlp.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/hmlp.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/paral.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/paral.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/patch_convnet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/patch_convnet.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/resmlp.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/resmlp.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/revenge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/revenge.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.github/revenge_da.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/deit-main/.github/revenge_da.png -------------------------------------------------------------------------------- /detector_codes/deit-main/.gitignore: -------------------------------------------------------------------------------- 1 | *.swp 2 | **/__pycache__/** 3 | imnet_resnet50_scratch/timm_temp/ 4 | .dumbo.json 5 | checkpoints/ 6 | -------------------------------------------------------------------------------- /detector_codes/deit-main/README_cosub.md: -------------------------------------------------------------------------------- 1 | # Co-training 2L Submodels for Visual Recognition 2 | 3 | This repository contains PyTorch evaluation code, training code and pretrained models for the following projects: 4 | * [DeiT](README_deit.md) (Data-Efficient Image Transformers), ICML 2021 5 | * [CaiT](README_cait.md) (Going deeper with Image Transformers), ICCV 2021 (Oral) 6 | * [ResMLP](README_resmlp.md) (ResMLP: Feedforward networks for image classification with data-efficient training) 7 | * [PatchConvnet](README_patchconvnet.md) (Augmenting Convolutional networks with attention-based aggregation) 8 | * [3Things](README_3things.md) (Three things everyone should know about Vision Transformers) 9 | * [DeiT III](README_revenge.md) (DeiT III: Revenge of the ViT) 10 | * Cosub (Co-training 2L Submodels for Visual Recognition) 11 | 12 | This new training recipes improve previous training strategy with different architectures: 13 | 14 | ![Cosub](.github/cosub.png) 15 | 16 | For details see [Co-training 2L Submodels for Visual Recognition](https://arxiv.org/pdf/2212.04884.pdf) by Hugo Touvron, Matthieu Cord, Maxime Oquab, Piotr Bojanowski, Jakob Verbeek and Hervé Jégou. 17 | 18 | If you use this code for a paper please cite: 19 | 20 | ``` 21 | @article{Touvron2022Cotraining2S, 22 | title={Co-training 2L Submodels for Visual Recognition}, 23 | author={Hugo Touvron and Matthieu Cord and Maxime Oquab and Piotr Bojanowski and Jakob Verbeek and Herv'e J'egou}, 24 | journal={arXiv preprint arXiv:2212.04884}, 25 | year={2022}, 26 | } 27 | ``` 28 | -------------------------------------------------------------------------------- /detector_codes/deit-main/hubconf.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2015-present, Facebook, Inc. 2 | # All rights reserved. 3 | from models import * 4 | from cait_models import * 5 | from resmlp_models import * 6 | #from patchconvnet_models import * 7 | 8 | dependencies = ["torch", "torchvision", "timm"] 9 | -------------------------------------------------------------------------------- /detector_codes/deit-main/requirements.txt: -------------------------------------------------------------------------------- 1 | torch==1.13.1 2 | torchvision==0.8.1 3 | timm==0.3.2 4 | -------------------------------------------------------------------------------- /detector_codes/deit-main/tox.ini: -------------------------------------------------------------------------------- 1 | [flake8] 2 | max-line-length = 120 3 | ignore = F401,E402,F403,W503,W504 4 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/.gitattributes: -------------------------------------------------------------------------------- 1 | *.ipynb linguist-documentation 2 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | github: rwightman 3 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a bug report to help us improve. Issues are for reporting bugs or requesting 4 | features, the discussion forum is available for asking questions or seeking help 5 | from the community. 6 | title: "[BUG] Issue title..." 7 | labels: bug 8 | assignees: rwightman 9 | 10 | --- 11 | 12 | **Describe the bug** 13 | A clear and concise description of what the bug is. 14 | 15 | **To Reproduce** 16 | Steps to reproduce the behavior: 17 | 1. 18 | 2. 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS: [e.g. Windows 10, Ubuntu 18.04] 28 | - This repository version [e.g. pip 0.3.1 or commit ref] 29 | - PyTorch version w/ CUDA/cuDNN [e.g. from `conda list`, 1.7.0 py3.8_cuda11.0.221_cudnn8.0.3_0] 30 | 31 | **Additional context** 32 | Add any other context about the problem here. 33 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/.github/ISSUE_TEMPLATE/config.yml: -------------------------------------------------------------------------------- 1 | blank_issues_enabled: false 2 | contact_links: 3 | - name: Community Discussions 4 | url: https://github.com/rwightman/pytorch-image-models/discussions 5 | about: Hparam request in issues will be ignored! Issues are for features and bugs. Questions can be asked in Discussions. 6 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project. Hparam requests, training help are not feature requests. 4 | The discussion forum is available for asking questions or seeking help from the community. 5 | title: "[FEATURE] Feature title..." 6 | labels: enhancement 7 | assignees: '' 8 | 9 | --- 10 | 11 | **Is your feature request related to a problem? Please describe.** 12 | A clear and concise description of what the problem is. 13 | 14 | **Describe the solution you'd like** 15 | A clear and concise description of what you want to happen. 16 | 17 | **Describe alternatives you've considered** 18 | A clear and concise description of any alternative solutions or features you've considered. 19 | 20 | **Additional context** 21 | Add any other context or screenshots about the feature request here. 22 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/.github/workflows/build_documentation.yml: -------------------------------------------------------------------------------- 1 | name: Build documentation 2 | 3 | on: 4 | push: 5 | branches: 6 | - master 7 | - doc-builder* 8 | - v*-release 9 | 10 | jobs: 11 | build: 12 | uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main 13 | with: 14 | commit_sha: ${{ github.sha }} 15 | package: pytorch-image-models 16 | package_name: timm 17 | repo_owner: rwightman 18 | path_to_docs: pytorch-image-models/hfdocs/source 19 | secrets: 20 | token: ${{ secrets.HUGGINGFACE_PUSH }} -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/.github/workflows/build_pr_documentation.yml: -------------------------------------------------------------------------------- 1 | name: Build PR Documentation 2 | 3 | on: 4 | pull_request: 5 | 6 | concurrency: 7 | group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} 8 | cancel-in-progress: true 9 | 10 | jobs: 11 | build: 12 | uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main 13 | with: 14 | commit_sha: ${{ github.event.pull_request.head.sha }} 15 | pr_number: ${{ github.event.number }} 16 | package: pytorch-image-models 17 | package_name: timm 18 | repo_owner: rwightman 19 | path_to_docs: pytorch-image-models/hfdocs/source 20 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/.github/workflows/delete_doc_comment.yml: -------------------------------------------------------------------------------- 1 | name: Delete dev documentation 2 | 3 | on: 4 | pull_request: 5 | types: [ closed ] 6 | 7 | 8 | jobs: 9 | delete: 10 | uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main 11 | with: 12 | pr_number: ${{ github.event.number }} 13 | package: timm -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include timm/models/pruned/*.txt 2 | 3 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/distributed_train.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | NUM_PROC=$1 3 | shift 4 | python3 -m torch.distributed.launch --nproc_per_node=$NUM_PROC train.py "$@" 5 | 6 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/docs/javascripts/tables.js: -------------------------------------------------------------------------------- 1 | app.location$.subscribe(function() { 2 | var tables = document.querySelectorAll("article table") 3 | tables.forEach(function(table) { 4 | new Tablesort(table) 5 | }) 6 | }) -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/docs/models/.pages: -------------------------------------------------------------------------------- 1 | title: Model Pages -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/hfdocs/source/_config.py: -------------------------------------------------------------------------------- 1 | default_branch_name = "master" -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/hfdocs/source/model_pages.mdx: -------------------------------------------------------------------------------- 1 | # Available Models 2 | 3 | `timm` comes bundled with a number of model architectures and corresponding pretrained models. 4 | 5 | In these pages, you will find the models available in the `timm` library, as well as information on how to use them. -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/hubconf.py: -------------------------------------------------------------------------------- 1 | dependencies = ['torch'] 2 | from timm.models import registry 3 | 4 | globals().update(registry._model_entrypoints) 5 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/mkdocs.yml: -------------------------------------------------------------------------------- 1 | site_name: 'Pytorch Image Models' 2 | site_description: 'Pretained Image Recognition Models' 3 | repo_name: 'rwightman/pytorch-image-models' 4 | repo_url: 'https://github.com/rwightman/pytorch-image-models' 5 | nav: 6 | - index.md 7 | - models.md 8 | - ... | models/*.md 9 | - results.md 10 | - scripts.md 11 | - training_hparam_examples.md 12 | - feature_extraction.md 13 | - changes.md 14 | - archived_changes.md 15 | theme: 16 | name: 'material' 17 | feature: 18 | tabs: false 19 | extra_javascript: 20 | - 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML' 21 | - https://cdnjs.cloudflare.com/ajax/libs/tablesort/5.2.1/tablesort.min.js 22 | - javascripts/tables.js 23 | markdown_extensions: 24 | - codehilite: 25 | linenums: true 26 | - admonition 27 | - pymdownx.arithmatex 28 | - pymdownx.betterem: 29 | smart_enable: all 30 | - pymdownx.caret 31 | - pymdownx.critic 32 | - pymdownx.details 33 | - pymdownx.emoji: 34 | emoji_generator: !!python/name:pymdownx.emoji.to_svg 35 | - pymdownx.inlinehilite 36 | - pymdownx.magiclink 37 | - pymdownx.mark 38 | - pymdownx.smartsymbols 39 | - pymdownx.superfences 40 | - pymdownx.tasklist: 41 | custom_checkbox: true 42 | - pymdownx.tilde 43 | - mdx_truly_sane_lists 44 | plugins: 45 | - search 46 | - awesome-pages 47 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/model-index.yml: -------------------------------------------------------------------------------- 1 | Import: 2 | - ./docs/models/*.md 3 | Library: 4 | Name: PyTorch Image Models 5 | Headline: PyTorch image models, scripts, pretrained weights 6 | Website: https://rwightman.github.io/pytorch-image-models/ 7 | Repository: https://github.com/rwightman/pytorch-image-models 8 | Docs: https://rwightman.github.io/pytorch-image-models/ 9 | README: "# PyTorch Image Models\r\n\r\nPyTorch Image Models (TIMM) is a library\ 10 | \ for state-of-the-art image classification. With this library you can:\r\n\r\n\ 11 | - Choose from 300+ pre-trained state-of-the-art image classification models.\r\ 12 | \n- Train models afresh on research datasets such as ImageNet using provided scripts.\r\ 13 | \n- Finetune pre-trained models on your own datasets, including the latest cutting\ 14 | \ edge models." 15 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/requirements-docs.txt: -------------------------------------------------------------------------------- 1 | mkdocs 2 | mkdocs-material 3 | mdx_truly_sane_lists 4 | mkdocs-awesome-pages-plugin -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/requirements-modelindex.txt: -------------------------------------------------------------------------------- 1 | model-index==0.1.10 2 | jinja2==2.11.3 3 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/requirements.txt: -------------------------------------------------------------------------------- 1 | torch>=1.7 2 | torchvision 3 | pyyaml 4 | huggingface_hub 5 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/setup.cfg: -------------------------------------------------------------------------------- 1 | [dist_conda] 2 | 3 | conda_name_differences = 'torch:pytorch' 4 | channels = pytorch 5 | noarch = True 6 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/detector_codes/pytorch-image-models-0.6.12/tests/__init__.py -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/__init__.py: -------------------------------------------------------------------------------- 1 | from .version import __version__ 2 | from .models import create_model, list_models, is_model, list_modules, model_entrypoint, \ 3 | is_scriptable, is_exportable, set_scriptable, set_exportable, has_pretrained_cfg_key, is_pretrained_cfg_key, \ 4 | get_pretrained_cfg_value, is_model_pretrained 5 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/data/__init__.py: -------------------------------------------------------------------------------- 1 | from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\ 2 | rand_augment_transform, auto_augment_transform 3 | from .config import resolve_data_config 4 | from .constants import * 5 | from .dataset import ImageDataset, IterableImageDataset, AugMixDataset 6 | from .dataset_factory import create_dataset 7 | from .loader import create_loader 8 | from .mixup import Mixup, FastCollateMixup 9 | from .parsers import create_parser,\ 10 | get_img_extensions, is_img_extension, set_img_extensions, add_img_extensions, del_img_extensions 11 | from .real_labels import RealLabelsImagenet 12 | from .transforms import * 13 | from .transforms_factory import create_transform 14 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/data/constants.py: -------------------------------------------------------------------------------- 1 | DEFAULT_CROP_PCT = 0.875 2 | IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) 3 | IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) 4 | IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5) 5 | IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5) 6 | IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255) 7 | IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3) 8 | OPENAI_CLIP_MEAN = (0.48145466, 0.4578275, 0.40821073) 9 | OPENAI_CLIP_STD = (0.26862954, 0.26130258, 0.27577711) 10 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/data/parsers/__init__.py: -------------------------------------------------------------------------------- 1 | from .parser_factory import create_parser 2 | from .img_extensions import * 3 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/data/parsers/class_map.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pickle 3 | 4 | def load_class_map(map_or_filename, root=''): 5 | if isinstance(map_or_filename, dict): 6 | assert dict, 'class_map dict must be non-empty' 7 | return map_or_filename 8 | class_map_path = map_or_filename 9 | if not os.path.exists(class_map_path): 10 | class_map_path = os.path.join(root, class_map_path) 11 | assert os.path.exists(class_map_path), 'Cannot locate specified class map file (%s)' % map_or_filename 12 | class_map_ext = os.path.splitext(map_or_filename)[-1].lower() 13 | if class_map_ext == '.txt': 14 | with open(class_map_path) as f: 15 | class_to_idx = {v.strip(): k for k, v in enumerate(f)} 16 | elif class_map_ext == '.pkl': 17 | with open(class_map_path,'rb') as f: 18 | class_to_idx = pickle.load(f) 19 | else: 20 | assert False, f'Unsupported class map file extension ({class_map_ext}).' 21 | return class_to_idx 22 | 23 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/data/parsers/img_extensions.py: -------------------------------------------------------------------------------- 1 | from copy import deepcopy 2 | 3 | __all__ = ['get_img_extensions', 'is_img_extension', 'set_img_extensions', 'add_img_extensions', 'del_img_extensions'] 4 | 5 | 6 | IMG_EXTENSIONS = ('.png', '.jpg', '.jpeg') # singleton, kept public for bwd compat use 7 | _IMG_EXTENSIONS_SET = set(IMG_EXTENSIONS) # set version, private, kept in sync 8 | 9 | 10 | def _set_extensions(extensions): 11 | global IMG_EXTENSIONS 12 | global _IMG_EXTENSIONS_SET 13 | dedupe = set() # NOTE de-duping tuple while keeping original order 14 | IMG_EXTENSIONS = tuple(x for x in extensions if x not in dedupe and not dedupe.add(x)) 15 | _IMG_EXTENSIONS_SET = set(extensions) 16 | 17 | 18 | def _valid_extension(x: str): 19 | return x and isinstance(x, str) and len(x) >= 2 and x.startswith('.') 20 | 21 | 22 | def is_img_extension(ext): 23 | return ext in _IMG_EXTENSIONS_SET 24 | 25 | 26 | def get_img_extensions(as_set=False): 27 | return deepcopy(_IMG_EXTENSIONS_SET if as_set else IMG_EXTENSIONS) 28 | 29 | 30 | def set_img_extensions(extensions): 31 | assert len(extensions) 32 | for x in extensions: 33 | assert _valid_extension(x) 34 | _set_extensions(extensions) 35 | 36 | 37 | def add_img_extensions(ext): 38 | if not isinstance(ext, (list, tuple, set)): 39 | ext = (ext,) 40 | for x in ext: 41 | assert _valid_extension(x) 42 | extensions = IMG_EXTENSIONS + tuple(ext) 43 | _set_extensions(extensions) 44 | 45 | 46 | def del_img_extensions(ext): 47 | if not isinstance(ext, (list, tuple, set)): 48 | ext = (ext,) 49 | extensions = tuple(x for x in IMG_EXTENSIONS if x not in ext) 50 | _set_extensions(extensions) 51 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/data/parsers/parser.py: -------------------------------------------------------------------------------- 1 | from abc import abstractmethod 2 | 3 | 4 | class Parser: 5 | def __init__(self): 6 | pass 7 | 8 | @abstractmethod 9 | def _filename(self, index, basename=False, absolute=False): 10 | pass 11 | 12 | def filename(self, index, basename=False, absolute=False): 13 | return self._filename(index, basename=basename, absolute=absolute) 14 | 15 | def filenames(self, basename=False, absolute=False): 16 | return [self._filename(index, basename=basename, absolute=absolute) for index in range(len(self))] 17 | 18 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/data/parsers/parser_factory.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from .parser_image_folder import ParserImageFolder 4 | from .parser_image_in_tar import ParserImageInTar 5 | 6 | 7 | def create_parser(name, root, split='train', **kwargs): 8 | name = name.lower() 9 | name = name.split('/', 2) 10 | prefix = '' 11 | if len(name) > 1: 12 | prefix = name[0] 13 | name = name[-1] 14 | 15 | # FIXME improve the selection right now just tfds prefix or fallback path, will need options to 16 | # explicitly select other options shortly 17 | if prefix == 'tfds': 18 | from .parser_tfds import ParserTfds # defer tensorflow import 19 | parser = ParserTfds(root, name, split=split, **kwargs) 20 | else: 21 | assert os.path.exists(root) 22 | # default fallback path (backwards compat), use image tar if root is a .tar file, otherwise image folder 23 | # FIXME support split here, in parser? 24 | if os.path.isfile(root) and os.path.splitext(root)[1] == '.tar': 25 | parser = ParserImageInTar(root, **kwargs) 26 | else: 27 | parser = ParserImageFolder(root, **kwargs) 28 | return parser 29 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/data/real_labels.py: -------------------------------------------------------------------------------- 1 | """ Real labels evaluator for ImageNet 2 | Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159 3 | Based on Numpy example at https://github.com/google-research/reassessed-imagenet 4 | 5 | Hacked together by / Copyright 2020 Ross Wightman 6 | """ 7 | import os 8 | import json 9 | import numpy as np 10 | 11 | 12 | class RealLabelsImagenet: 13 | 14 | def __init__(self, filenames, real_json='real.json', topk=(1, 5)): 15 | with open(real_json) as real_labels: 16 | real_labels = json.load(real_labels) 17 | real_labels = {f'ILSVRC2012_val_{i + 1:08d}.JPEG': labels for i, labels in enumerate(real_labels)} 18 | self.real_labels = real_labels 19 | self.filenames = filenames 20 | assert len(self.filenames) == len(self.real_labels) 21 | self.topk = topk 22 | self.is_correct = {k: [] for k in topk} 23 | self.sample_idx = 0 24 | 25 | def add_result(self, output): 26 | maxk = max(self.topk) 27 | _, pred_batch = output.topk(maxk, 1, True, True) 28 | pred_batch = pred_batch.cpu().numpy() 29 | for pred in pred_batch: 30 | filename = self.filenames[self.sample_idx] 31 | filename = os.path.basename(filename) 32 | if self.real_labels[filename]: 33 | for k in self.topk: 34 | self.is_correct[k].append( 35 | any([p in self.real_labels[filename] for p in pred[:k]])) 36 | self.sample_idx += 1 37 | 38 | def get_accuracy(self, k=None): 39 | if k is None: 40 | return {k: float(np.mean(self.is_correct[k])) * 100 for k in self.topk} 41 | else: 42 | return float(np.mean(self.is_correct[k])) * 100 43 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/loss/__init__.py: -------------------------------------------------------------------------------- 1 | from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel 2 | from .binary_cross_entropy import BinaryCrossEntropy 3 | from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy 4 | from .jsd import JsdCrossEntropy 5 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/loss/cross_entropy.py: -------------------------------------------------------------------------------- 1 | """ Cross Entropy w/ smoothing or soft targets 2 | 3 | Hacked together by / Copyright 2021 Ross Wightman 4 | """ 5 | 6 | import torch 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | 10 | 11 | class LabelSmoothingCrossEntropy(nn.Module): 12 | """ NLL loss with label smoothing. 13 | """ 14 | def __init__(self, smoothing=0.1): 15 | super(LabelSmoothingCrossEntropy, self).__init__() 16 | assert smoothing < 1.0 17 | self.smoothing = smoothing 18 | self.confidence = 1. - smoothing 19 | 20 | def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: 21 | logprobs = F.log_softmax(x, dim=-1) 22 | nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) 23 | nll_loss = nll_loss.squeeze(1) 24 | smooth_loss = -logprobs.mean(dim=-1) 25 | loss = self.confidence * nll_loss + self.smoothing * smooth_loss 26 | return loss.mean() 27 | 28 | 29 | class SoftTargetCrossEntropy(nn.Module): 30 | 31 | def __init__(self): 32 | super(SoftTargetCrossEntropy, self).__init__() 33 | 34 | def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: 35 | loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1) 36 | return loss.mean() 37 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/loss/jsd.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | from .cross_entropy import LabelSmoothingCrossEntropy 6 | 7 | 8 | class JsdCrossEntropy(nn.Module): 9 | """ Jensen-Shannon Divergence + Cross-Entropy Loss 10 | 11 | Based on impl here: https://github.com/google-research/augmix/blob/master/imagenet.py 12 | From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - 13 | https://arxiv.org/abs/1912.02781 14 | 15 | Hacked together by / Copyright 2020 Ross Wightman 16 | """ 17 | def __init__(self, num_splits=3, alpha=12, smoothing=0.1): 18 | super().__init__() 19 | self.num_splits = num_splits 20 | self.alpha = alpha 21 | if smoothing is not None and smoothing > 0: 22 | self.cross_entropy_loss = LabelSmoothingCrossEntropy(smoothing) 23 | else: 24 | self.cross_entropy_loss = torch.nn.CrossEntropyLoss() 25 | 26 | def __call__(self, output, target): 27 | split_size = output.shape[0] // self.num_splits 28 | assert split_size * self.num_splits == output.shape[0] 29 | logits_split = torch.split(output, split_size) 30 | 31 | # Cross-entropy is only computed on clean images 32 | loss = self.cross_entropy_loss(logits_split[0], target[:split_size]) 33 | probs = [F.softmax(logits, dim=1) for logits in logits_split] 34 | 35 | # Clamp mixture distribution to avoid exploding KL divergence 36 | logp_mixture = torch.clamp(torch.stack(probs).mean(axis=0), 1e-7, 1).log() 37 | loss += self.alpha * sum([F.kl_div( 38 | logp_mixture, p_split, reduction='batchmean') for p_split in probs]) / len(probs) 39 | return loss 40 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/models/layers/blur_pool.py: -------------------------------------------------------------------------------- 1 | """ 2 | BlurPool layer inspired by 3 | - Kornia's Max_BlurPool2d 4 | - Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar` 5 | 6 | Hacked together by Chris Ha and Ross Wightman 7 | """ 8 | 9 | import torch 10 | import torch.nn as nn 11 | import torch.nn.functional as F 12 | import numpy as np 13 | from .padding import get_padding 14 | 15 | 16 | class BlurPool2d(nn.Module): 17 | r"""Creates a module that computes blurs and downsample a given feature map. 18 | See :cite:`zhang2019shiftinvar` for more details. 19 | Corresponds to the Downsample class, which does blurring and subsampling 20 | 21 | Args: 22 | channels = Number of input channels 23 | filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5. 24 | stride (int): downsampling filter stride 25 | 26 | Returns: 27 | torch.Tensor: the transformed tensor. 28 | """ 29 | def __init__(self, channels, filt_size=3, stride=2) -> None: 30 | super(BlurPool2d, self).__init__() 31 | assert filt_size > 1 32 | self.channels = channels 33 | self.filt_size = filt_size 34 | self.stride = stride 35 | self.padding = [get_padding(filt_size, stride, dilation=1)] * 4 36 | coeffs = torch.tensor((np.poly1d((0.5, 0.5)) ** (self.filt_size - 1)).coeffs.astype(np.float32)) 37 | blur_filter = (coeffs[:, None] * coeffs[None, :])[None, None, :, :].repeat(self.channels, 1, 1, 1) 38 | self.register_buffer('filt', blur_filter, persistent=False) 39 | 40 | def forward(self, x: torch.Tensor) -> torch.Tensor: 41 | x = F.pad(x, self.padding, 'reflect') 42 | return F.conv2d(x, self.filt, stride=self.stride, groups=self.channels) 43 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/models/layers/conv2d_same.py: -------------------------------------------------------------------------------- 1 | """ Conv2d w/ Same Padding 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import torch 6 | import torch.nn as nn 7 | import torch.nn.functional as F 8 | from typing import Tuple, Optional 9 | 10 | from .padding import pad_same, get_padding_value 11 | 12 | 13 | def conv2d_same( 14 | x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: Tuple[int, int] = (1, 1), 15 | padding: Tuple[int, int] = (0, 0), dilation: Tuple[int, int] = (1, 1), groups: int = 1): 16 | x = pad_same(x, weight.shape[-2:], stride, dilation) 17 | return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups) 18 | 19 | 20 | class Conv2dSame(nn.Conv2d): 21 | """ Tensorflow like 'SAME' convolution wrapper for 2D convolutions 22 | """ 23 | 24 | def __init__(self, in_channels, out_channels, kernel_size, stride=1, 25 | padding=0, dilation=1, groups=1, bias=True): 26 | super(Conv2dSame, self).__init__( 27 | in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias) 28 | 29 | def forward(self, x): 30 | return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) 31 | 32 | 33 | def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs): 34 | padding = kwargs.pop('padding', '') 35 | kwargs.setdefault('bias', False) 36 | padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs) 37 | if is_dynamic: 38 | return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs) 39 | else: 40 | return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs) 41 | 42 | 43 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/models/layers/create_conv2d.py: -------------------------------------------------------------------------------- 1 | """ Create Conv2d Factory Method 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | 6 | from .mixed_conv2d import MixedConv2d 7 | from .cond_conv2d import CondConv2d 8 | from .conv2d_same import create_conv2d_pad 9 | 10 | 11 | def create_conv2d(in_channels, out_channels, kernel_size, **kwargs): 12 | """ Select a 2d convolution implementation based on arguments 13 | Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d. 14 | 15 | Used extensively by EfficientNet, MobileNetv3 and related networks. 16 | """ 17 | if isinstance(kernel_size, list): 18 | assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently 19 | if 'groups' in kwargs: 20 | groups = kwargs.pop('groups') 21 | if groups == in_channels: 22 | kwargs['depthwise'] = True 23 | else: 24 | assert groups == 1 25 | # We're going to use only lists for defining the MixedConv2d kernel groups, 26 | # ints, tuples, other iterables will continue to pass to normal conv and specify h, w. 27 | m = MixedConv2d(in_channels, out_channels, kernel_size, **kwargs) 28 | else: 29 | depthwise = kwargs.pop('depthwise', False) 30 | # for DW out_channels must be multiple of in_channels as must have out_channels % groups == 0 31 | groups = in_channels if depthwise else kwargs.pop('groups', 1) 32 | if 'num_experts' in kwargs and kwargs['num_experts'] > 0: 33 | m = CondConv2d(in_channels, out_channels, kernel_size, groups=groups, **kwargs) 34 | else: 35 | m = create_conv2d_pad(in_channels, out_channels, kernel_size, groups=groups, **kwargs) 36 | return m 37 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/models/layers/helpers.py: -------------------------------------------------------------------------------- 1 | """ Layer/Module Helpers 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | from itertools import repeat 6 | import collections.abc 7 | 8 | 9 | # From PyTorch internals 10 | def _ntuple(n): 11 | def parse(x): 12 | if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): 13 | return x 14 | return tuple(repeat(x, n)) 15 | return parse 16 | 17 | 18 | to_1tuple = _ntuple(1) 19 | to_2tuple = _ntuple(2) 20 | to_3tuple = _ntuple(3) 21 | to_4tuple = _ntuple(4) 22 | to_ntuple = _ntuple 23 | 24 | 25 | def make_divisible(v, divisor=8, min_value=None, round_limit=.9): 26 | min_value = min_value or divisor 27 | new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) 28 | # Make sure that round down does not go down by more than 10%. 29 | if new_v < round_limit * v: 30 | new_v += divisor 31 | return new_v 32 | 33 | 34 | def extend_tuple(x, n): 35 | # pdas a tuple to specified n by padding with last value 36 | if not isinstance(x, (tuple, list)): 37 | x = (x,) 38 | else: 39 | x = tuple(x) 40 | pad_n = n - len(x) 41 | if pad_n <= 0: 42 | return x[:n] 43 | return x + (x[-1],) * pad_n 44 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/models/layers/linear.py: -------------------------------------------------------------------------------- 1 | """ Linear layer (alternate definition) 2 | """ 3 | import torch 4 | import torch.nn.functional as F 5 | from torch import nn as nn 6 | 7 | 8 | class Linear(nn.Linear): 9 | r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b` 10 | 11 | Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting 12 | weight & bias to input.dtype to work around an issue w/ torch.addmm in this use case. 13 | """ 14 | def forward(self, input: torch.Tensor) -> torch.Tensor: 15 | if torch.jit.is_scripting(): 16 | bias = self.bias.to(dtype=input.dtype) if self.bias is not None else None 17 | return F.linear(input, self.weight.to(dtype=input.dtype), bias=bias) 18 | else: 19 | return F.linear(input, self.weight, self.bias) 20 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/models/layers/patch_embed.py: -------------------------------------------------------------------------------- 1 | """ Image to Patch Embedding using Conv2d 2 | 3 | A convolution based approach to patchifying a 2D image w/ embedding projection. 4 | 5 | Based on the impl in https://github.com/google-research/vision_transformer 6 | 7 | Hacked together by / Copyright 2020 Ross Wightman 8 | """ 9 | from torch import nn as nn 10 | 11 | from .helpers import to_2tuple 12 | from .trace_utils import _assert 13 | 14 | 15 | class PatchEmbed(nn.Module): 16 | """ 2D Image to Patch Embedding 17 | """ 18 | def __init__( 19 | self, 20 | img_size=224, 21 | patch_size=16, 22 | in_chans=3, 23 | embed_dim=768, 24 | norm_layer=None, 25 | flatten=True, 26 | bias=True, 27 | ): 28 | super().__init__() 29 | img_size = to_2tuple(img_size) 30 | patch_size = to_2tuple(patch_size) 31 | self.img_size = img_size 32 | self.patch_size = patch_size 33 | self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) 34 | self.num_patches = self.grid_size[0] * self.grid_size[1] 35 | self.flatten = flatten 36 | 37 | self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) 38 | self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() 39 | 40 | def forward(self, x): 41 | B, C, H, W = x.shape 42 | _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") 43 | _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") 44 | x = self.proj(x) 45 | if self.flatten: 46 | x = x.flatten(2).transpose(1, 2) # BCHW -> BNC 47 | x = self.norm(x) 48 | return x 49 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/models/layers/trace_utils.py: -------------------------------------------------------------------------------- 1 | try: 2 | from torch import _assert 3 | except ImportError: 4 | def _assert(condition: bool, message: str): 5 | assert condition, message 6 | 7 | 8 | def _float_to_int(x: float) -> int: 9 | """ 10 | Symbolic tracing helper to substitute for inbuilt `int`. 11 | Hint: Inbuilt `int` can't accept an argument of type `Proxy` 12 | """ 13 | return int(x) 14 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/optim/__init__.py: -------------------------------------------------------------------------------- 1 | from .adabelief import AdaBelief 2 | from .adafactor import Adafactor 3 | from .adahessian import Adahessian 4 | from .adamp import AdamP 5 | from .adamw import AdamW 6 | from .lamb import Lamb 7 | from .lars import Lars 8 | from .lookahead import Lookahead 9 | from .madgrad import MADGRAD 10 | from .nadam import Nadam 11 | from .nvnovograd import NvNovoGrad 12 | from .radam import RAdam 13 | from .rmsprop_tf import RMSpropTF 14 | from .sgdp import SGDP 15 | from .optim_factory import create_optimizer, create_optimizer_v2, optimizer_kwargs 16 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/scheduler/__init__.py: -------------------------------------------------------------------------------- 1 | from .cosine_lr import CosineLRScheduler 2 | from .multistep_lr import MultiStepLRScheduler 3 | from .plateau_lr import PlateauLRScheduler 4 | from .poly_lr import PolyLRScheduler 5 | from .step_lr import StepLRScheduler 6 | from .tanh_lr import TanhLRScheduler 7 | 8 | from .scheduler_factory import create_scheduler 9 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .agc import adaptive_clip_grad 2 | from .checkpoint_saver import CheckpointSaver 3 | from .clip_grad import dispatch_clip_grad 4 | from .cuda import ApexScaler, NativeScaler 5 | from .decay_batch import decay_batch_step, check_batch_size_retry 6 | from .distributed import distribute_bn, reduce_tensor 7 | from .jit import set_jit_legacy, set_jit_fuser 8 | from .log import setup_default_logging, FormatterNoInfo 9 | from .metrics import AverageMeter, accuracy 10 | from .misc import natural_key, add_bool_arg 11 | from .model import unwrap_model, get_state_dict, freeze, unfreeze 12 | from .model_ema import ModelEma, ModelEmaV2 13 | from .random import random_seed 14 | from .summary import update_summary, get_outdir 15 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/agc.py: -------------------------------------------------------------------------------- 1 | """ Adaptive Gradient Clipping 2 | 3 | An impl of AGC, as per (https://arxiv.org/abs/2102.06171): 4 | 5 | @article{brock2021high, 6 | author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan}, 7 | title={High-Performance Large-Scale Image Recognition Without Normalization}, 8 | journal={arXiv preprint arXiv:}, 9 | year={2021} 10 | } 11 | 12 | Code references: 13 | * Official JAX impl (paper authors): https://github.com/deepmind/deepmind-research/tree/master/nfnets 14 | * Phil Wang's PyTorch gist: https://gist.github.com/lucidrains/0d6560077edac419ab5d3aa29e674d5c 15 | 16 | Hacked together by / Copyright 2021 Ross Wightman 17 | """ 18 | import torch 19 | 20 | 21 | def unitwise_norm(x, norm_type=2.0): 22 | if x.ndim <= 1: 23 | return x.norm(norm_type) 24 | else: 25 | # works for nn.ConvNd and nn,Linear where output dim is first in the kernel/weight tensor 26 | # might need special cases for other weights (possibly MHA) where this may not be true 27 | return x.norm(norm_type, dim=tuple(range(1, x.ndim)), keepdim=True) 28 | 29 | 30 | def adaptive_clip_grad(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0): 31 | if isinstance(parameters, torch.Tensor): 32 | parameters = [parameters] 33 | for p in parameters: 34 | if p.grad is None: 35 | continue 36 | p_data = p.detach() 37 | g_data = p.grad.detach() 38 | max_norm = unitwise_norm(p_data, norm_type=norm_type).clamp_(min=eps).mul_(clip_factor) 39 | grad_norm = unitwise_norm(g_data, norm_type=norm_type) 40 | clipped_grad = g_data * (max_norm / grad_norm.clamp(min=1e-6)) 41 | new_grads = torch.where(grad_norm < max_norm, g_data, clipped_grad) 42 | p.grad.detach().copy_(new_grads) 43 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/clip_grad.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from timm.utils.agc import adaptive_clip_grad 4 | 5 | 6 | def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0): 7 | """ Dispatch to gradient clipping method 8 | 9 | Args: 10 | parameters (Iterable): model parameters to clip 11 | value (float): clipping value/factor/norm, mode dependant 12 | mode (str): clipping mode, one of 'norm', 'value', 'agc' 13 | norm_type (float): p-norm, default 2.0 14 | """ 15 | if mode == 'norm': 16 | torch.nn.utils.clip_grad_norm_(parameters, value, norm_type=norm_type) 17 | elif mode == 'value': 18 | torch.nn.utils.clip_grad_value_(parameters, value) 19 | elif mode == 'agc': 20 | adaptive_clip_grad(parameters, value, norm_type=norm_type) 21 | else: 22 | assert False, f"Unknown clip mode ({mode})." 23 | 24 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/distributed.py: -------------------------------------------------------------------------------- 1 | """ Distributed training/validation utils 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import torch 6 | from torch import distributed as dist 7 | 8 | from .model import unwrap_model 9 | 10 | 11 | def reduce_tensor(tensor, n): 12 | rt = tensor.clone() 13 | dist.all_reduce(rt, op=dist.ReduceOp.SUM) 14 | rt /= n 15 | return rt 16 | 17 | 18 | def distribute_bn(model, world_size, reduce=False): 19 | # ensure every node has the same running bn stats 20 | for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True): 21 | if ('running_mean' in bn_name) or ('running_var' in bn_name): 22 | if reduce: 23 | # average bn stats across whole group 24 | torch.distributed.all_reduce(bn_buf, op=dist.ReduceOp.SUM) 25 | bn_buf /= float(world_size) 26 | else: 27 | # broadcast bn stats from rank 0 to whole group 28 | torch.distributed.broadcast(bn_buf, 0) 29 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/log.py: -------------------------------------------------------------------------------- 1 | """ Logging helpers 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import logging 6 | import logging.handlers 7 | 8 | 9 | class FormatterNoInfo(logging.Formatter): 10 | def __init__(self, fmt='%(levelname)s: %(message)s'): 11 | logging.Formatter.__init__(self, fmt) 12 | 13 | def format(self, record): 14 | if record.levelno == logging.INFO: 15 | return str(record.getMessage()) 16 | return logging.Formatter.format(self, record) 17 | 18 | 19 | def setup_default_logging(default_level=logging.INFO, log_path=''): 20 | console_handler = logging.StreamHandler() 21 | console_handler.setFormatter(FormatterNoInfo()) 22 | logging.root.addHandler(console_handler) 23 | logging.root.setLevel(default_level) 24 | if log_path: 25 | file_handler = logging.handlers.RotatingFileHandler(log_path, maxBytes=(1024 ** 2 * 2), backupCount=3) 26 | file_formatter = logging.Formatter("%(asctime)s - %(name)20s: [%(levelname)8s] - %(message)s") 27 | file_handler.setFormatter(file_formatter) 28 | logging.root.addHandler(file_handler) 29 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/metrics.py: -------------------------------------------------------------------------------- 1 | """ Eval metrics and related 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | 6 | 7 | class AverageMeter: 8 | """Computes and stores the average and current value""" 9 | def __init__(self): 10 | self.reset() 11 | 12 | def reset(self): 13 | self.val = 0 14 | self.avg = 0 15 | self.sum = 0 16 | self.count = 0 17 | 18 | def update(self, val, n=1): 19 | self.val = val 20 | self.sum += val * n 21 | self.count += n 22 | self.avg = self.sum / self.count 23 | 24 | 25 | def accuracy(output, target, topk=(1,)): 26 | """Computes the accuracy over the k top predictions for the specified values of k""" 27 | maxk = min(max(topk), output.size()[1]) 28 | batch_size = target.size(0) 29 | _, pred = output.topk(maxk, 1, True, True) 30 | pred = pred.t() 31 | correct = pred.eq(target.reshape(1, -1).expand_as(pred)) 32 | return [correct[:min(k, maxk)].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] 33 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/misc.py: -------------------------------------------------------------------------------- 1 | """ Misc utils 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import re 6 | 7 | 8 | def natural_key(string_): 9 | """See http://www.codinghorror.com/blog/archives/001018.html""" 10 | return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] 11 | 12 | 13 | def add_bool_arg(parser, name, default=False, help=''): 14 | dest_name = name.replace('-', '_') 15 | group = parser.add_mutually_exclusive_group(required=False) 16 | group.add_argument('--' + name, dest=dest_name, action='store_true', help=help) 17 | group.add_argument('--no-' + name, dest=dest_name, action='store_false', help=help) 18 | parser.set_defaults(**{dest_name: default}) 19 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/random.py: -------------------------------------------------------------------------------- 1 | import random 2 | import numpy as np 3 | import torch 4 | 5 | 6 | def random_seed(seed=42, rank=0): 7 | torch.manual_seed(seed + rank) 8 | np.random.seed(seed + rank) 9 | random.seed(seed + rank) 10 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/utils/summary.py: -------------------------------------------------------------------------------- 1 | """ Summary utilities 2 | 3 | Hacked together by / Copyright 2020 Ross Wightman 4 | """ 5 | import csv 6 | import os 7 | from collections import OrderedDict 8 | try: 9 | import wandb 10 | except ImportError: 11 | pass 12 | 13 | def get_outdir(path, *paths, inc=False): 14 | outdir = os.path.join(path, *paths) 15 | if not os.path.exists(outdir): 16 | os.makedirs(outdir) 17 | elif inc: 18 | count = 1 19 | outdir_inc = outdir + '-' + str(count) 20 | while os.path.exists(outdir_inc): 21 | count = count + 1 22 | outdir_inc = outdir + '-' + str(count) 23 | assert count < 100 24 | outdir = outdir_inc 25 | os.makedirs(outdir) 26 | return outdir 27 | 28 | 29 | def update_summary(epoch, train_metrics, eval_metrics, filename, write_header=False, log_wandb=False): 30 | rowd = OrderedDict(epoch=epoch) 31 | rowd.update([('train_' + k, v) for k, v in train_metrics.items()]) 32 | rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()]) 33 | if log_wandb: 34 | wandb.log(rowd) 35 | with open(filename, mode='a') as cf: 36 | dw = csv.DictWriter(cf, fieldnames=rowd.keys()) 37 | if write_header: # first iteration (epoch == 1 can't be used) 38 | dw.writeheader() 39 | dw.writerow(rowd) 40 | -------------------------------------------------------------------------------- /detector_codes/pytorch-image-models-0.6.12/timm/version.py: -------------------------------------------------------------------------------- 1 | __version__ = '0.6.12' 2 | -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/.gitignore: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Andy Brock 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/TFHub/README.md: -------------------------------------------------------------------------------- 1 | # BigGAN-PyTorch TFHub converter 2 | This dir contains scripts for taking the [pre-trained generator weights from TFHub](https://tfhub.dev/s?q=biggan) and porting them to BigGAN-Pytorch. 3 | 4 | In addition to the base libraries for BigGAN-PyTorch, to run this code you will need: 5 | 6 | TensorFlow 7 | TFHub 8 | parse 9 | 10 | Note that this code is only presently set up to run the ported models without truncation--you'll need to accumulate standing stats at each truncation level yourself if you wish to employ it. 11 | 12 | To port the 128x128 model from tfhub, produce a pretrained weights .pth file, and generate samples using all your GPUs, run 13 | 14 | `python converter.py -r 128 --generate_samples --parallel` -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/imgs/D Singular Values.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/BigGAN-PyTorch-master/imgs/D Singular Values.png -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/imgs/DeepSamples.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/BigGAN-PyTorch-master/imgs/DeepSamples.png -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/imgs/DogBall.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/BigGAN-PyTorch-master/imgs/DogBall.png -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/imgs/G Singular Values.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/BigGAN-PyTorch-master/imgs/G Singular Values.png -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/imgs/IS_FID.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/BigGAN-PyTorch-master/imgs/IS_FID.png -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/imgs/Losses.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/BigGAN-PyTorch-master/imgs/Losses.png -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/imgs/header_image.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/BigGAN-PyTorch-master/imgs/header_image.jpg -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/imgs/interp_sample.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/BigGAN-PyTorch-master/imgs/interp_sample.jpg -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/logs/process_inception_log.m: -------------------------------------------------------------------------------- 1 | function [itr, IS, FID, t] = process_inception_log(fname, which_log) 2 | f = sprintf('%s_%s',fname, which_log);%'G_loss.log'); 3 | fid = fopen(f,'r'); 4 | itr = []; 5 | IS = []; 6 | FID = []; 7 | t = []; 8 | i = 1; 9 | while ~feof(fid); 10 | s = fgets(fid); 11 | parsed = sscanf(s,'{"itr": %d, "IS_mean": %f, "IS_std": %f, "FID": %f, "_stamp": %f}'); 12 | itr(i) = parsed(1); 13 | IS(i) = parsed(2); 14 | FID(i) = parsed(4); 15 | t(i) = parsed(5); 16 | i = i + 1; 17 | end 18 | fclose(fid); 19 | end -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/losses.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | 4 | # DCGAN loss 5 | def loss_dcgan_dis(dis_fake, dis_real): 6 | L1 = torch.mean(F.softplus(-dis_real)) 7 | L2 = torch.mean(F.softplus(dis_fake)) 8 | return L1, L2 9 | 10 | 11 | def loss_dcgan_gen(dis_fake): 12 | loss = torch.mean(F.softplus(-dis_fake)) 13 | return loss 14 | 15 | 16 | # Hinge Loss 17 | def loss_hinge_dis(dis_fake, dis_real): 18 | loss_real = torch.mean(F.relu(1. - dis_real)) 19 | loss_fake = torch.mean(F.relu(1. + dis_fake)) 20 | return loss_real, loss_fake 21 | # def loss_hinge_dis(dis_fake, dis_real): # This version returns a single loss 22 | # loss = torch.mean(F.relu(1. - dis_real)) 23 | # loss += torch.mean(F.relu(1. + dis_fake)) 24 | # return loss 25 | 26 | 27 | def loss_hinge_gen(dis_fake): 28 | loss = -torch.mean(dis_fake) 29 | return loss 30 | 31 | # Default to hinge loss 32 | generator_loss = loss_hinge_gen 33 | discriminator_loss = loss_hinge_dis -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/launch_BigGAN_bs256x8.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | python train.py \ 3 | --dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 256 --load_in_mem \ 4 | --num_G_accumulations 8 --num_D_accumulations 8 \ 5 | --num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ 6 | --G_attn 64 --D_attn 64 \ 7 | --G_nl inplace_relu --D_nl inplace_relu \ 8 | --SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ 9 | --G_ortho 0.0 \ 10 | --G_shared \ 11 | --G_init ortho --D_init ortho \ 12 | --hier --dim_z 120 --shared_dim 128 \ 13 | --G_eval_mode \ 14 | --G_ch 96 --D_ch 96 \ 15 | --ema --use_ema --ema_start 20000 \ 16 | --test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ 17 | --use_multiepoch_sampler \ -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/launch_BigGAN_bs512x4.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | python train.py \ 3 | --dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 512 --load_in_mem \ 4 | --num_G_accumulations 4 --num_D_accumulations 4 \ 5 | --num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ 6 | --G_attn 64 --D_attn 64 \ 7 | --G_nl inplace_relu --D_nl inplace_relu \ 8 | --SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ 9 | --G_ortho 0.0 \ 10 | --G_shared \ 11 | --G_init ortho --D_init ortho \ 12 | --hier --dim_z 120 --shared_dim 128 \ 13 | --G_eval_mode \ 14 | --G_ch 96 --D_ch 96 \ 15 | --ema --use_ema --ema_start 20000 \ 16 | --test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ 17 | --use_multiepoch_sampler \ -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/launch_BigGAN_ch64_bs256x8.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | python train.py \ 3 | --dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 256 --load_in_mem \ 4 | --num_G_accumulations 8 --num_D_accumulations 8 \ 5 | --num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ 6 | --G_attn 64 --D_attn 64 \ 7 | --G_nl inplace_relu --D_nl inplace_relu \ 8 | --SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ 9 | --G_ortho 0.0 \ 10 | --G_shared \ 11 | --G_init ortho --D_init ortho \ 12 | --hier --dim_z 120 --shared_dim 128 \ 13 | --G_eval_mode \ 14 | --G_ch 64 --G_ch 64 \ 15 | --ema --use_ema --ema_start 20000 \ 16 | --test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ 17 | --use_multiepoch_sampler -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/launch_BigGAN_deep.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | python train.py \ 3 | --model BigGANdeep \ 4 | --dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 256 \ 5 | --num_G_accumulations 8 --num_D_accumulations 8 \ 6 | --num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ 7 | --G_attn 64 --D_attn 64 \ 8 | --G_ch 128 --D_ch 128 \ 9 | --G_depth 2 --D_depth 2 \ 10 | --G_nl inplace_relu --D_nl inplace_relu \ 11 | --SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ 12 | --G_ortho 0.0 \ 13 | --G_shared \ 14 | --G_init ortho --D_init ortho \ 15 | --hier --dim_z 128 --shared_dim 128 \ 16 | --ema --use_ema --ema_start 20000 --G_eval_mode \ 17 | --test_every 2000 --save_every 500 --num_best_copies 5 --num_save_copies 2 --seed 0 \ 18 | --use_multiepoch_sampler \ -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/launch_SAGAN_bs128x2_ema.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | python train.py \ 3 | --dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 128 \ 4 | --num_G_accumulations 2 --num_D_accumulations 2 \ 5 | --num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ 6 | --G_attn 64 --D_attn 64 \ 7 | --G_nl relu --D_nl relu \ 8 | --SN_eps 1e-8 --BN_eps 1e-5 --adam_eps 1e-8 \ 9 | --G_ortho 0.0 \ 10 | --G_init xavier --D_init xavier \ 11 | --ema --use_ema --ema_start 2000 --G_eval_mode \ 12 | --test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ 13 | --name_suffix SAGAN_ema \ -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/launch_SNGAN.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | python train.py \ 3 | --dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 64 \ 4 | --num_G_accumulations 1 --num_D_accumulations 1 \ 5 | --num_D_steps 5 --G_lr 2e-4 --D_lr 2e-4 --D_B2 0.900 --G_B2 0.900 \ 6 | --G_attn 0 --D_attn 0 \ 7 | --G_nl relu --D_nl relu \ 8 | --SN_eps 1e-8 --BN_eps 1e-5 --adam_eps 1e-8 \ 9 | --G_ortho 0.0 \ 10 | --D_thin \ 11 | --G_init xavier --D_init xavier \ 12 | --G_eval_mode \ 13 | --test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ 14 | --name_suffix SNGAN \ -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/launch_cifar_ema.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | CUDA_VISIBLE_DEVICES=0,1 python train.py \ 3 | --shuffle --batch_size 50 --parallel \ 4 | --num_G_accumulations 1 --num_D_accumulations 1 --num_epochs 500 \ 5 | --num_D_steps 4 --G_lr 2e-4 --D_lr 2e-4 \ 6 | --dataset C10 \ 7 | --G_ortho 0.0 \ 8 | --G_attn 0 --D_attn 0 \ 9 | --G_init N02 --D_init N02 \ 10 | --ema --use_ema --ema_start 1000 \ 11 | --test_every 5000 --save_every 2000 --num_best_copies 5 --num_save_copies 2 --seed 0 -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/sample_BigGAN_bs256x8.sh: -------------------------------------------------------------------------------- 1 | # use z_var to change the variance of z for all the sampling 2 | # use --mybn --accumulate_stats --num_standing_accumulations 32 to 3 | # use running stats 4 | python sample.py \ 5 | --dataset I128_hdf5 --parallel --shuffle --num_workers 8 --batch_size 256 \ 6 | --num_G_accumulations 8 --num_D_accumulations 8 \ 7 | --num_D_steps 1 --G_lr 1e-4 --D_lr 4e-4 --D_B2 0.999 --G_B2 0.999 \ 8 | --G_attn 64 --D_attn 64 \ 9 | --G_ch 96 --D_ch 96 \ 10 | --G_nl inplace_relu --D_nl inplace_relu \ 11 | --SN_eps 1e-6 --BN_eps 1e-5 --adam_eps 1e-6 \ 12 | --G_ortho 0.0 \ 13 | --G_shared \ 14 | --G_init ortho --D_init ortho --skip_init \ 15 | --hier --dim_z 120 --shared_dim 128 \ 16 | --ema --ema_start 20000 \ 17 | --use_multiepoch_sampler \ 18 | --test_every 2000 --save_every 1000 --num_best_copies 5 --num_save_copies 2 --seed 0 \ 19 | --skip_init --G_batch_size 512 --use_ema --G_eval_mode --sample_trunc_curves 0.05_0.05_1.0 \ 20 | --sample_inception_metrics --sample_npz --sample_random --sample_sheets --sample_interps 21 | -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/sample_cifar_ema.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | CUDA_VISIBLE_DEVICES=0,1 python sample.py \ 3 | --shuffle --batch_size 50 --G_batch_size 256 --parallel \ 4 | --num_G_accumulations 1 --num_D_accumulations 1 --num_epochs 500 \ 5 | --num_D_steps 4 --G_lr 2e-4 --D_lr 2e-4 \ 6 | --dataset C10 \ 7 | --G_ortho 0.0 \ 8 | --G_attn 0 --D_attn 0 \ 9 | --G_init N02 --D_init N02 \ 10 | --ema --use_ema --ema_start 1000 \ 11 | --test_every 5000 --save_every 2000 --num_best_copies 5 --num_save_copies 2 --seed 0 -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/utils/duplicate.sh: -------------------------------------------------------------------------------- 1 | #duplicate.sh 2 | source=BigGAN_I128_hdf5_seed0_Gch64_Dch64_bs256_Glr1.0e-04_Dlr4.0e-04_Gnlinplace_relu_Dnlinplace_relu_Ginitxavier_Dinitxavier_Gshared_alex0 3 | target=BigGAN_I128_hdf5_seed0_Gch64_Dch64_bs256_Glr1.0e-04_Dlr4.0e-04_Gnlinplace_relu_Dnlinplace_relu_Ginitxavier_Dinitxavier_Gshared_alex0A 4 | logs_root=logs 5 | weights_root=weights 6 | echo "copying ${source} to ${target}" 7 | cp -r ${logs_root}/${source} ${logs_root}/${target} 8 | cp ${logs_root}/${source}_log.jsonl ${logs_root}/${target}_log.jsonl 9 | cp ${weights_root}/${source}_G.pth ${weights_root}/${target}_G.pth 10 | cp ${weights_root}/${source}_G_ema.pth ${weights_root}/${target}_G_ema.pth 11 | cp ${weights_root}/${source}_D.pth ${weights_root}/${target}_D.pth 12 | cp ${weights_root}/${source}_G_optim.pth ${weights_root}/${target}_G_optim.pth 13 | cp ${weights_root}/${source}_D_optim.pth ${weights_root}/${target}_D_optim.pth 14 | cp ${weights_root}/${source}_state_dict.pth ${weights_root}/${target}_state_dict.pth -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/scripts/utils/prepare_data.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | python make_hdf5.py --dataset I128 --batch_size 256 --data_root data 3 | python calculate_inception_moments.py --dataset I128_hdf5 --data_root data -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/sync_batchnorm/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # File : __init__.py 3 | # Author : Jiayuan Mao 4 | # Email : maojiayuan@gmail.com 5 | # Date : 27/01/2018 6 | # 7 | # This file is part of Synchronized-BatchNorm-PyTorch. 8 | # https://github.com/vacancy/Synchronized-BatchNorm-PyTorch 9 | # Distributed under MIT License. 10 | 11 | from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d 12 | from .replicate import DataParallelWithCallback, patch_replication_callback 13 | -------------------------------------------------------------------------------- /generator_codes/BigGAN-PyTorch-master/sync_batchnorm/unittest.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # File : unittest.py 3 | # Author : Jiayuan Mao 4 | # Email : maojiayuan@gmail.com 5 | # Date : 27/01/2018 6 | # 7 | # This file is part of Synchronized-BatchNorm-PyTorch. 8 | # https://github.com/vacancy/Synchronized-BatchNorm-PyTorch 9 | # Distributed under MIT License. 10 | 11 | import unittest 12 | import torch 13 | 14 | 15 | class TorchTestCase(unittest.TestCase): 16 | def assertTensorClose(self, x, y): 17 | adiff = float((x - y).abs().max()) 18 | if (y == 0).all(): 19 | rdiff = 'NaN' 20 | else: 21 | rdiff = float((adiff / y).abs().max()) 22 | 23 | message = ( 24 | 'Tensor close check failed\n' 25 | 'adiff={}\n' 26 | 'rdiff={}\n' 27 | ).format(adiff, rdiff) 28 | self.assertTrue(torch.allclose(x, y), message) 29 | 30 | -------------------------------------------------------------------------------- /generator_codes/Readme.md: -------------------------------------------------------------------------------- 1 | # Codes of Detectors 2 | 3 | These are the codes of the detectors used in GenImage benchmark. 4 | 5 | Stable Diffusion: stable-diffusion-main (https://github.com/CompVis/stable-diffusion) 6 | 7 | GLIDE: guided-diffusion-main (https://github.com/openai/glide-text2im) 8 | 9 | VDQM: VQ-Diffusion-main (https://github.com/microsoft/VQ-Diffusion) 10 | 11 | BigGAN: BigGAN-PyTorch-master (https://github.com/ajbrock/BigGAN-PyTorch) 12 | 13 | ADM: guided-diffusion-main (https://github.com/openai/guided-diffusion) 14 | 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Microsoft Corporation. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE 22 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/OUTPUT/pretrained_model/taming_dvae/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | params: 3 | decoder_config: 4 | params: 5 | attn_resolutions: 6 | - - 32 7 | - 32 8 | ch: 64 9 | ch_mult: 10 | - 1 11 | - 2 12 | - 4 13 | - 8 14 | dropout: 0.0 15 | num_res_blocks: 2 16 | out_ch: 3 17 | resolution: 18 | - 256 19 | - 256 20 | z_channels: 256 21 | target: image_synthesis.modeling.codecs.image_codec.patch_vqgan.Decoder 22 | embed_dim: 128 23 | encoder_config: 24 | params: 25 | activate_output: false 26 | attn_resolutions: 27 | - - 32 28 | - 32 29 | ch: 64 30 | ch_mult: 31 | - 1 32 | - 2 33 | - 4 34 | - 8 35 | dropout: 0.0 36 | in_channels: 3 37 | num_res_blocks: 2 38 | out_ch: 3 39 | resolution: 40 | - 256 41 | - 256 42 | z_channels: 256 43 | target: image_synthesis.modeling.codecs.image_codec.patch_vqgan.Encoder 44 | n_embed: 4096 45 | quantizer_type: EMAVQ 46 | trainable: true 47 | target: image_synthesis.modeling.codecs.image_codec.patch_vqgan.PatchVQGAN 48 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/OUTPUT/pretrained_model/taming_dvae/taming_f8_8192_openimages.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: image_synthesis.taming.models.vqgan.GumbelVQ 4 | params: 5 | kl_weight: 1.0e-08 6 | embed_dim: 256 7 | n_embed: 8192 8 | monitor: val/rec_loss 9 | temperature_scheduler_config: 10 | target: image_synthesis.taming.lr_scheduler.LambdaWarmUpCosineScheduler 11 | params: 12 | warm_up_steps: 0 13 | max_decay_steps: 1000001 14 | lr_start: 0.9 15 | lr_max: 0.9 16 | lr_min: 1.0e-06 17 | ddconfig: 18 | double_z: false 19 | z_channels: 256 20 | resolution: 256 21 | in_channels: 3 22 | out_ch: 3 23 | ch: 128 24 | ch_mult: 25 | - 1 26 | - 1 27 | - 2 28 | - 4 29 | num_res_blocks: 2 30 | attn_resolutions: 31 | - 32 32 | dropout: 0.0 33 | lossconfig: 34 | target: image_synthesis.taming.modules.losses.vqperceptual.DummyLoss 35 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/OUTPUT/pretrained_model/taming_dvae/vqgan_ffhq_f16_1024.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 0.0625 3 | target: image_synthesis.taming.models.cond_transformer.Net2NetTransformer 4 | params: 5 | cond_stage_config: __is_unconditional__ 6 | first_stage_key: image 7 | transformer_config: 8 | target: image_synthesis.taming.modules.transformer.mingpt.GPT 9 | params: 10 | vocab_size: 1024 11 | block_size: 256 12 | n_layer: 24 13 | n_head: 16 14 | n_embd: 1664 15 | first_stage_config: 16 | target: image_synthesis.taming.models.vqgan.VQModel 17 | params: 18 | embed_dim: 256 19 | n_embed: 1024 20 | ddconfig: 21 | double_z: false 22 | z_channels: 256 23 | resolution: 256 24 | in_channels: 3 25 | out_ch: 3 26 | ch: 128 27 | ch_mult: 28 | - 1 29 | - 1 30 | - 2 31 | - 2 32 | - 4 33 | num_res_blocks: 2 34 | attn_resolutions: 35 | - 16 36 | dropout: 0.0 37 | lossconfig: 38 | target: image_synthesis.taming.modules.losses.vqperceptual.DummyLoss 39 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/OUTPUT/pretrained_model/taming_dvae/vqgan_imagenet_f16_16384.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: image_synthesis.taming.models.vqgan.VQModel 4 | params: 5 | embed_dim: 256 6 | n_embed: 16384 7 | monitor: val/rec_loss 8 | ddconfig: 9 | double_z: false 10 | z_channels: 256 11 | resolution: 256 12 | in_channels: 3 13 | out_ch: 3 14 | ch: 128 15 | ch_mult: 16 | - 1 17 | - 1 18 | - 2 19 | - 2 20 | - 4 21 | num_res_blocks: 2 22 | attn_resolutions: 23 | - 16 24 | dropout: 0.0 25 | lossconfig: 26 | target: image_synthesis.taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 27 | params: 28 | disc_conditional: false 29 | disc_in_channels: 3 30 | disc_start: 0 31 | disc_weight: 0.75 32 | disc_num_layers: 2 33 | codebook_weight: 1.0 34 | 35 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/figures/framework.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/VQ-Diffusion-main/figures/framework.png -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/help_folder/readme.md: -------------------------------------------------------------------------------- 1 | Statistics folder contains some dictionary to shrink the codebook from [Taming Transformer](https://github.com/CompVis/taming-transformers). -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/help_folder/statistics/taming_vqvae_2887.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/VQ-Diffusion-main/help_folder/statistics/taming_vqvae_2887.pt -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/help_folder/statistics/taming_vqvae_974.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/VQ-Diffusion-main/help_folder/statistics/taming_vqvae_974.pt -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/data/ffhq_dataset.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data import Dataset 2 | import numpy as np 3 | import io 4 | from PIL import Image 5 | import os 6 | import json 7 | import random 8 | from image_synthesis.utils.misc import instantiate_from_config 9 | import torchvision.datasets as datasets 10 | 11 | class FFHQDataset(datasets.ImageFolder): 12 | def __init__(self, data_root, im_preprocessor_config): 13 | self.img_preprocessor = instantiate_from_config(im_preprocessor_config) 14 | super(FFHQDataset, self).__init__(root=data_root) 15 | 16 | def __getitem__(self, index): 17 | # image_name = self.imgs[index][0].split('/')[-1] 18 | image = super(FFHQDataset, self).__getitem__(index)[0] 19 | image = self.img_preprocessor(image=np.array(image).astype(np.uint8))['image'] 20 | data = { 21 | 'image': np.transpose(image.astype(np.float32), (2, 0, 1)), 22 | } 23 | return data 24 | 25 | 26 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/engine/clip_grad_norm.py: -------------------------------------------------------------------------------- 1 | from torch.nn.utils import clip_grad_norm_ 2 | 3 | 4 | class ClipGradNorm(object): 5 | def __init__(self, 6 | start_iteration=0, 7 | end_iteration=-1, # if negative, the norm will be always clipped 8 | max_norm=0.5): 9 | self.start_iteration = start_iteration 10 | self.end_iteration = end_iteration 11 | self.max_norm = max_norm 12 | 13 | self.last_epoch = -1 14 | 15 | 16 | def __call__(self, parameters): 17 | self.last_epoch += 1 18 | clip = False 19 | if self.last_epoch >= self.start_iteration: 20 | clip = True 21 | if self.end_iteration > 0 and self.last_epoch < self.end_iteration: 22 | clip = True 23 | if clip: 24 | clip_grad_norm_(parameters, max_norm=self.max_norm) 25 | 26 | def state_dict(self): 27 | return {key: value for key, value in self.__dict__.items()} 28 | 29 | 30 | def load_state_dict(self, state_dict): 31 | self.__dict__.update(state_dict) -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/modeling/build.py: -------------------------------------------------------------------------------- 1 | from image_synthesis.utils.misc import instantiate_from_config 2 | 3 | 4 | def build_model(config, args=None): 5 | return instantiate_from_config(config['model']) 6 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/modeling/codecs/base_codec.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | 4 | 5 | class BaseCodec(nn.Module): 6 | 7 | def get_tokens(self, x, **kwargs): 8 | """ 9 | Input: 10 | x: input data 11 | Return: 12 | indices: B x L, the codebook indices, where L is the length 13 | of flattened feature map size 14 | """ 15 | raise NotImplementedError 16 | 17 | def get_number_of_tokens(self): 18 | """ 19 | Return: int, the number of tokens 20 | """ 21 | raise NotImplementedError 22 | 23 | def encode(self, img): 24 | raise NotImplementedError 25 | 26 | def decode(self, img_seq): 27 | raise NotImplementedError 28 | 29 | def forward(self, **kwargs): 30 | raise NotImplementedError 31 | 32 | def train(self, mode=True): 33 | self.training = mode 34 | if self.trainable and mode: 35 | return super().train(True) 36 | else: 37 | return super().train(False) 38 | 39 | def _set_trainable(self): 40 | if not self.trainable: 41 | for pn, p in self.named_parameters(): 42 | p.requires_grad = False 43 | self.eval() -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/modeling/embeddings/base_embedding.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | 4 | 5 | class BaseEmbedding(nn.Module): 6 | 7 | def get_loss(self): 8 | return None 9 | 10 | def forward(self, **kwargs): 11 | raise NotImplementedError 12 | 13 | def train(self, mode=True): 14 | self.training = mode 15 | if self.trainable and mode: 16 | super().train() 17 | return self 18 | 19 | def _set_trainable(self): 20 | if not self.trainable: 21 | for pn, p in self.named_parameters(): 22 | p.requires_grad = False 23 | self.eval() 24 | 25 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/modeling/embeddings/class_embedding.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from .base_embedding import BaseEmbedding 4 | 5 | class ClassEmbedding(BaseEmbedding): 6 | def __init__(self, 7 | num_embed=1000, 8 | embed_dim=512, 9 | identity=False, 10 | trainable=True, 11 | ): 12 | super().__init__() 13 | self.identity = identity 14 | self.trainable = trainable 15 | self.num_embed = num_embed 16 | self.embed_dim = embed_dim 17 | if self.identity == False: 18 | 19 | self.emb = nn.Embedding(self.num_embed, embed_dim) 20 | self._set_trainable() 21 | 22 | def forward(self, index, **kwargs): 23 | """ 24 | index: B x L, index 25 | mask: B x L, bool type. The value of False indicating padded index 26 | """ 27 | if self.identity == True: 28 | return index 29 | else: 30 | emb = self.emb(index).unsqueeze(1) 31 | return emb 32 | 33 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/modeling/modules/clip/README.md: -------------------------------------------------------------------------------- 1 | https://github.com/openai/CLIP -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/modeling/modules/clip/__init__.py: -------------------------------------------------------------------------------- 1 | from .clip import * 2 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/modeling/modules/clip/bpe_simple_vocab_16e6.txt.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/VQ-Diffusion-main/image_synthesis/modeling/modules/clip/bpe_simple_vocab_16e6.txt.gz -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/taming/lr_scheduler.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | class LambdaWarmUpCosineScheduler: 5 | """ 6 | note: use with a base_lr of 1.0 7 | """ 8 | def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): 9 | self.lr_warm_up_steps = warm_up_steps 10 | self.lr_start = lr_start 11 | self.lr_min = lr_min 12 | self.lr_max = lr_max 13 | self.lr_max_decay_steps = max_decay_steps 14 | self.last_lr = 0. 15 | self.verbosity_interval = verbosity_interval 16 | 17 | def schedule(self, n): 18 | if self.verbosity_interval > 0: 19 | if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") 20 | if n < self.lr_warm_up_steps: 21 | lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start 22 | self.last_lr = lr 23 | return lr 24 | else: 25 | t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) 26 | t = min(t, 1.0) 27 | lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( 28 | 1 + np.cos(t * np.pi)) 29 | self.last_lr = lr 30 | return lr 31 | 32 | def __call__(self, n): 33 | return self.schedule(n) 34 | 35 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/taming/modules/losses/__init__.py: -------------------------------------------------------------------------------- 1 | from image_synthesis.taming.modules.losses.vqperceptual import DummyLoss 2 | 3 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/taming/modules/losses/segmentation.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | 4 | 5 | class BCELoss(nn.Module): 6 | def forward(self, prediction, target): 7 | loss = F.binary_cross_entropy_with_logits(prediction,target) 8 | return loss, {} 9 | 10 | 11 | class BCELossWithQuant(nn.Module): 12 | def __init__(self, codebook_weight=1.): 13 | super().__init__() 14 | self.codebook_weight = codebook_weight 15 | 16 | def forward(self, qloss, target, prediction, split): 17 | bce_loss = F.binary_cross_entropy_with_logits(prediction,target) 18 | loss = bce_loss + self.codebook_weight*qloss 19 | return loss, {"{}/total_loss".format(split): loss.clone().detach().mean(), 20 | "{}/bce_loss".format(split): bce_loss.detach().mean(), 21 | "{}/quant_loss".format(split): qloss.detach().mean() 22 | } 23 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/taming/modules/misc/coord.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | class CoordStage(object): 4 | def __init__(self, n_embed, down_factor): 5 | self.n_embed = n_embed 6 | self.down_factor = down_factor 7 | 8 | def eval(self): 9 | return self 10 | 11 | def encode(self, c): 12 | """fake vqmodel interface""" 13 | assert 0.0 <= c.min() and c.max() <= 1.0 14 | b,ch,h,w = c.shape 15 | assert ch == 1 16 | 17 | c = torch.nn.functional.interpolate(c, scale_factor=1/self.down_factor, 18 | mode="area") 19 | c = c.clamp(0.0, 1.0) 20 | c = self.n_embed*c 21 | c_quant = c.round() 22 | c_ind = c_quant.to(dtype=torch.long) 23 | 24 | info = None, None, c_ind 25 | return c_quant, None, info 26 | 27 | def decode(self, c): 28 | c = c/self.n_embed 29 | c = torch.nn.functional.interpolate(c, scale_factor=self.down_factor, 30 | mode="nearest") 31 | return c 32 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/image_synthesis/utils/io.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import yaml 3 | import torch 4 | import json 5 | 6 | def load_yaml_config(path): 7 | with open(path) as f: 8 | config = yaml.full_load(f) 9 | return config 10 | 11 | 12 | def save_config_to_yaml(config, path): 13 | assert path.endswith('.yaml') 14 | with open(path, 'w') as f: 15 | f.write(yaml.dump(config)) 16 | f.close() 17 | 18 | def save_dict_to_json(d, path, indent=None): 19 | json.dump(d, open(path, 'w'), indent=indent) 20 | 21 | 22 | def load_dict_from_json(path): 23 | return json.load(open(path, 'r')) 24 | 25 | 26 | def write_args(args, path): 27 | args_dict = dict((name, getattr(args, name)) for name in dir(args)if not name.startswith('_')) 28 | with open(path, 'a') as args_file: 29 | args_file.write('==> torch version: {}\n'.format(torch.__version__)) 30 | args_file.write('==> cudnn version: {}\n'.format(torch.backends.cudnn.version())) 31 | args_file.write('==> Cmd:\n') 32 | args_file.write(str(sys.argv)) 33 | args_file.write('\n==> args:\n') 34 | for k, v in sorted(args_dict.items()): 35 | args_file.write(' %s: %s\n' % (str(k), str(v))) 36 | args_file.close() -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/install_req.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | pip install torch==1.9.0 torchvision --no-cache-dir -U | cat 3 | pip install omegaconf pytorch-lightning --no-cache-dir -U | cat 4 | pip install timm==0.3.4 --no-cache-dir -U | cat 5 | pip install tensorboard==1.15.0 --no-cache-dir -U | cat 6 | pip install lmdb tqdm --no-cache-dir -U | cat 7 | pip install einops ftfy --no-cache-dir -U | cat 8 | pip install git+https://github.com/openai/DALL-E.git --no-cache-dir -U | cat -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/running_command/run_train_coco.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | string = "python train.py --name coco_train --config_file configs/coco.yaml --num_node 1 --tensorboard --load_path OUTPUT/pretrained_model/CC_pretrained.pth" 4 | 5 | os.system(string) 6 | 7 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/running_command/run_train_cub.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | string = "python train.py --name cub200_train --config_file configs/cub200.yaml --num_node 1 --tensorboard --load_path OUTPUT/pretrained_model/CC_pretrained.pth" 4 | 5 | os.system(string) 6 | 7 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/running_command/run_train_ffhq.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | string = "python train.py --name ffhq_train --config_file configs/ffhq.yaml --num_node 1 --tensorboard" 4 | 5 | os.system(string) 6 | 7 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/running_command/run_train_imagenet.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | string = "python train.py --name imagenet_train --config_file configs/imagenet.yaml --num_node 1 --tensorboard" 4 | 5 | os.system(string) 6 | 7 | -------------------------------------------------------------------------------- /generator_codes/VQ-Diffusion-main/running_command/run_tune_coco.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | string = "python train.py --name coco_tune --config_file configs/coco_tune.yaml --num_node 1 --tensorboard --load_path OUTPUT/pretrained_model/COCO_pretrained.pth" 4 | 5 | os.system(string) 6 | 7 | -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | *.egg-info/ 3 | .DS_Store 4 | -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 OpenAI 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/README.md: -------------------------------------------------------------------------------- 1 | # GLIDE 2 | 3 | This is the official codebase for running the small, filtered-data GLIDE model from [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/abs/2112.10741). 4 | 5 | For details on the pre-trained models in this repository, see the [Model Card](model-card.md). 6 | 7 | # Usage 8 | 9 | To install this package, clone this repository and then run: 10 | 11 | ``` 12 | pip install -e . 13 | ``` 14 | 15 | For detailed usage examples, see the [notebooks](notebooks) directory. 16 | 17 | * The [text2im](notebooks/text2im.ipynb) [![][colab]][colab-text2im] notebook shows how to use GLIDE (filtered) with classifier-free guidance to produce images conditioned on text prompts. 18 | * The [inpaint](notebooks/inpaint.ipynb) [![][colab]][colab-inpaint] notebook shows how to use GLIDE (filtered) to fill in a masked region of an image, conditioned on a text prompt. 19 | * The [clip_guided](notebooks/clip_guided.ipynb) [![][colab]][colab-guided] notebook shows how to use GLIDE (filtered) + a filtered noise-aware CLIP model to produce images conditioned on text prompts. 20 | 21 | [colab]: 22 | [colab-text2im]: 23 | [colab-inpaint]: 24 | [colab-guided]: 25 | -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/glide_text2im/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | A codebase for performing model inference with a text-conditional diffusion model. 3 | """ 4 | -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/glide_text2im/clip/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/glide-text2im-main/glide_text2im/clip/__init__.py -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/glide_text2im/clip/config.yaml: -------------------------------------------------------------------------------- 1 | logit_scale: 100.0 2 | 3 | # Diffusion settings 4 | beta_schedule: "squaredcos_cap_v2" 5 | n_timesteps: 1000 6 | 7 | # Architecture settings 8 | image_size: 64 9 | patch_size: 4 10 | n_vocab: 65536 11 | max_text_len: 77 12 | n_embd: 512 13 | n_head_state_text: 64 14 | n_head_text: 8 15 | n_xf_blocks_text: 12 16 | n_head_state_image: 64 17 | n_head_image: 12 18 | n_xf_blocks_image: 12 19 | -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/glide_text2im/fp16_util.py: -------------------------------------------------------------------------------- 1 | """ 2 | Helpers to inference with 16-bit precision. 3 | """ 4 | 5 | import torch.nn as nn 6 | 7 | 8 | def convert_module_to_f16(l): 9 | """ 10 | Convert primitive modules to float16. 11 | """ 12 | if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)): 13 | l.weight.data = l.weight.data.half() 14 | if l.bias is not None: 15 | l.bias.data = l.bias.data.half() 16 | 17 | 18 | def convert_module_to_f32(l): 19 | """ 20 | Convert primitive modules to float32, undoing convert_module_to_f16(). 21 | """ 22 | if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)): 23 | l.weight.data = l.weight.data.float() 24 | if l.bias is not None: 25 | l.bias.data = l.bias.data.float() 26 | -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/glide_text2im/tokenizer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/glide-text2im-main/glide_text2im/tokenizer/__init__.py -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/glide_text2im/tokenizer/bpe_simple_vocab_16e6.txt.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/glide-text2im-main/glide_text2im/tokenizer/bpe_simple_vocab_16e6.txt.gz -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/glide_text2im/tokenizer/encoder.json.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/glide-text2im-main/glide_text2im/tokenizer/encoder.json.gz -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/glide_text2im/tokenizer/vocab.bpe.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/glide-text2im-main/glide_text2im/tokenizer/vocab.bpe.gz -------------------------------------------------------------------------------- /generator_codes/glide-text2im-main/notebooks/grass.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /generator_codes/guided-diffusion-main/.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | __pycache__/ 3 | classify_image_graph_def.pb 4 | -------------------------------------------------------------------------------- /generator_codes/guided-diffusion-main/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 OpenAI 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /generator_codes/guided-diffusion-main/evaluations/requirements.txt: -------------------------------------------------------------------------------- 1 | tensorflow-gpu>=2.0 2 | scipy 3 | requests 4 | tqdm -------------------------------------------------------------------------------- /generator_codes/guided-diffusion-main/guided_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Codebase for "Improved Denoising Diffusion Probabilistic Models". 3 | """ 4 | -------------------------------------------------------------------------------- /generator_codes/guided-diffusion-main/setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup( 4 | name="guided-diffusion", 5 | py_modules=["guided_diffusion"], 6 | install_requires=["blobfile>=1.0.5", "torch", "tqdm"], 7 | ) 8 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/assets/a-painting-of-a-fire.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/stable-diffusion-main/assets/a-painting-of-a-fire.png -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/assets/a-photograph-of-a-fire.png: -------------------------------------------------------------------------------- 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z_channels: 16 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 128 21 | ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [16] 24 | dropout: 0.0 25 | 26 | 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 12 31 | wrap: True 32 | train: 33 | target: ldm.data.imagenet.ImageNetSRTrain 34 | params: 35 | size: 256 36 | degradation: pil_nearest 37 | validation: 38 | target: ldm.data.imagenet.ImageNetSRValidation 39 | params: 40 | size: 256 41 | degradation: pil_nearest 42 | 43 | lightning: 44 | callbacks: 45 | image_logger: 46 | target: main.ImageLogger 47 | params: 48 | batch_frequency: 1000 49 | max_images: 8 50 | increase_log_steps: True 51 | 52 | trainer: 53 | benchmark: True 54 | accumulate_grad_batches: 2 55 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/configs/autoencoder/autoencoder_kl_32x32x4.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 4 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 0.000001 12 | disc_weight: 0.5 13 | 14 | ddconfig: 15 | double_z: True 16 | z_channels: 4 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 128 21 | ch_mult: [ 1,2,4,4 ] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [ ] 24 | dropout: 0.0 25 | 26 | data: 27 | target: main.DataModuleFromConfig 28 | params: 29 | batch_size: 12 30 | wrap: True 31 | train: 32 | target: ldm.data.imagenet.ImageNetSRTrain 33 | params: 34 | size: 256 35 | degradation: pil_nearest 36 | validation: 37 | target: ldm.data.imagenet.ImageNetSRValidation 38 | params: 39 | size: 256 40 | degradation: pil_nearest 41 | 42 | lightning: 43 | callbacks: 44 | image_logger: 45 | target: main.ImageLogger 46 | params: 47 | batch_frequency: 1000 48 | max_images: 8 49 | increase_log_steps: True 50 | 51 | trainer: 52 | benchmark: True 53 | accumulate_grad_batches: 2 54 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/configs/autoencoder/autoencoder_kl_64x64x3.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 3 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 0.000001 12 | disc_weight: 0.5 13 | 14 | ddconfig: 15 | double_z: True 16 | z_channels: 3 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 128 21 | ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [ ] 24 | dropout: 0.0 25 | 26 | 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 12 31 | wrap: True 32 | train: 33 | target: ldm.data.imagenet.ImageNetSRTrain 34 | params: 35 | size: 256 36 | degradation: pil_nearest 37 | validation: 38 | target: ldm.data.imagenet.ImageNetSRValidation 39 | params: 40 | size: 256 41 | degradation: pil_nearest 42 | 43 | lightning: 44 | callbacks: 45 | image_logger: 46 | target: main.ImageLogger 47 | params: 48 | batch_frequency: 1000 49 | max_images: 8 50 | increase_log_steps: True 51 | 52 | trainer: 53 | benchmark: True 54 | accumulate_grad_batches: 2 55 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/configs/autoencoder/autoencoder_kl_8x8x64.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-6 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: "val/rec_loss" 6 | embed_dim: 64 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 0.000001 12 | disc_weight: 0.5 13 | 14 | ddconfig: 15 | double_z: True 16 | z_channels: 64 17 | resolution: 256 18 | in_channels: 3 19 | out_ch: 3 20 | ch: 128 21 | ch_mult: [ 1,1,2,2,4,4] # num_down = len(ch_mult)-1 22 | num_res_blocks: 2 23 | attn_resolutions: [16,8] 24 | dropout: 0.0 25 | 26 | data: 27 | target: main.DataModuleFromConfig 28 | params: 29 | batch_size: 12 30 | wrap: True 31 | train: 32 | target: ldm.data.imagenet.ImageNetSRTrain 33 | params: 34 | size: 256 35 | degradation: pil_nearest 36 | validation: 37 | target: ldm.data.imagenet.ImageNetSRValidation 38 | params: 39 | size: 256 40 | degradation: pil_nearest 41 | 42 | lightning: 43 | callbacks: 44 | image_logger: 45 | target: main.ImageLogger 46 | params: 47 | batch_frequency: 1000 48 | max_images: 8 49 | increase_log_steps: True 50 | 51 | trainer: 52 | 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torchmetrics==0.6.0 28 | - kornia==0.6 29 | - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers 30 | - -e git+https://github.com/openai/CLIP.git@main#egg=clip 31 | - -e . 32 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/stable-diffusion-main/ldm/data/__init__.py -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/data/base.py: -------------------------------------------------------------------------------- 1 | from abc import abstractmethod 2 | from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset 3 | 4 | 5 | class Txt2ImgIterableBaseDataset(IterableDataset): 6 | ''' 7 | Define an interface to make the IterableDatasets for text2img data chainable 8 | ''' 9 | def __init__(self, num_records=0, valid_ids=None, size=256): 10 | super().__init__() 11 | self.num_records = num_records 12 | self.valid_ids = valid_ids 13 | self.sample_ids = valid_ids 14 | self.size = size 15 | 16 | print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.') 17 | 18 | def __len__(self): 19 | return self.num_records 20 | 21 | @abstractmethod 22 | def __iter__(self): 23 | pass -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/models/diffusion/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/stable-diffusion-main/ldm/models/diffusion/__init__.py -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/models/diffusion/dpm_solver/__init__.py: -------------------------------------------------------------------------------- 1 | from .sampler import DPMSolverSampler -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/modules/diffusionmodules/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/stable-diffusion-main/ldm/modules/diffusionmodules/__init__.py -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/modules/distributions/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/stable-diffusion-main/ldm/modules/distributions/__init__.py -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/modules/encoders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/stable-diffusion-main/ldm/modules/encoders/__init__.py -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/modules/image_degradation/__init__.py: -------------------------------------------------------------------------------- 1 | from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr 2 | from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light 3 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/modules/image_degradation/utils/test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GenImage-Dataset/GenImage/746781bfa446619e1a4629726eb98d5e69c18240/generator_codes/stable-diffusion-main/ldm/modules/image_degradation/utils/test.png -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/ldm/modules/losses/__init__.py: -------------------------------------------------------------------------------- 1 | from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/kl-f16/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: val/rec_loss 6 | embed_dim: 16 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 1.0e-06 12 | disc_weight: 0.5 13 | ddconfig: 14 | double_z: true 15 | z_channels: 16 16 | resolution: 256 17 | in_channels: 3 18 | out_ch: 3 19 | ch: 128 20 | ch_mult: 21 | - 1 22 | - 1 23 | - 2 24 | - 2 25 | - 4 26 | num_res_blocks: 2 27 | attn_resolutions: 28 | - 16 29 | dropout: 0.0 30 | data: 31 | target: main.DataModuleFromConfig 32 | params: 33 | batch_size: 6 34 | wrap: true 35 | train: 36 | target: ldm.data.openimages.FullOpenImagesTrain 37 | params: 38 | size: 384 39 | crop_size: 256 40 | validation: 41 | target: ldm.data.openimages.FullOpenImagesValidation 42 | params: 43 | size: 384 44 | crop_size: 256 45 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/kl-f32/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: val/rec_loss 6 | embed_dim: 64 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 1.0e-06 12 | disc_weight: 0.5 13 | ddconfig: 14 | double_z: true 15 | z_channels: 64 16 | resolution: 256 17 | in_channels: 3 18 | out_ch: 3 19 | ch: 128 20 | ch_mult: 21 | - 1 22 | - 1 23 | - 2 24 | - 2 25 | - 4 26 | - 4 27 | num_res_blocks: 2 28 | attn_resolutions: 29 | - 16 30 | - 8 31 | dropout: 0.0 32 | data: 33 | target: main.DataModuleFromConfig 34 | params: 35 | batch_size: 6 36 | wrap: true 37 | train: 38 | target: ldm.data.openimages.FullOpenImagesTrain 39 | params: 40 | size: 384 41 | crop_size: 256 42 | validation: 43 | target: ldm.data.openimages.FullOpenImagesValidation 44 | params: 45 | size: 384 46 | crop_size: 256 47 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/kl-f4/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: val/rec_loss 6 | embed_dim: 3 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 1.0e-06 12 | disc_weight: 0.5 13 | ddconfig: 14 | double_z: true 15 | z_channels: 3 16 | resolution: 256 17 | in_channels: 3 18 | out_ch: 3 19 | ch: 128 20 | ch_mult: 21 | - 1 22 | - 2 23 | - 4 24 | num_res_blocks: 2 25 | attn_resolutions: [] 26 | dropout: 0.0 27 | data: 28 | target: main.DataModuleFromConfig 29 | params: 30 | batch_size: 10 31 | wrap: true 32 | train: 33 | target: ldm.data.openimages.FullOpenImagesTrain 34 | params: 35 | size: 384 36 | crop_size: 256 37 | validation: 38 | target: ldm.data.openimages.FullOpenImagesValidation 39 | params: 40 | size: 384 41 | crop_size: 256 42 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/kl-f8/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.AutoencoderKL 4 | params: 5 | monitor: val/rec_loss 6 | embed_dim: 4 7 | lossconfig: 8 | target: ldm.modules.losses.LPIPSWithDiscriminator 9 | params: 10 | disc_start: 50001 11 | kl_weight: 1.0e-06 12 | disc_weight: 0.5 13 | ddconfig: 14 | double_z: true 15 | z_channels: 4 16 | resolution: 256 17 | in_channels: 3 18 | out_ch: 3 19 | ch: 128 20 | ch_mult: 21 | - 1 22 | - 2 23 | - 4 24 | - 4 25 | num_res_blocks: 2 26 | attn_resolutions: [] 27 | dropout: 0.0 28 | data: 29 | target: main.DataModuleFromConfig 30 | params: 31 | batch_size: 4 32 | wrap: true 33 | train: 34 | target: ldm.data.openimages.FullOpenImagesTrain 35 | params: 36 | size: 384 37 | crop_size: 256 38 | validation: 39 | target: ldm.data.openimages.FullOpenImagesValidation 40 | params: 41 | size: 384 42 | crop_size: 256 43 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/vq-f16/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 8 6 | n_embed: 16384 7 | ddconfig: 8 | double_z: false 9 | z_channels: 8 10 | resolution: 256 11 | in_channels: 3 12 | out_ch: 3 13 | ch: 128 14 | ch_mult: 15 | - 1 16 | - 1 17 | - 2 18 | - 2 19 | - 4 20 | num_res_blocks: 2 21 | attn_resolutions: 22 | - 16 23 | dropout: 0.0 24 | lossconfig: 25 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 26 | params: 27 | disc_conditional: false 28 | disc_in_channels: 3 29 | disc_start: 250001 30 | disc_weight: 0.75 31 | disc_num_layers: 2 32 | codebook_weight: 1.0 33 | 34 | data: 35 | target: main.DataModuleFromConfig 36 | params: 37 | batch_size: 14 38 | num_workers: 20 39 | wrap: true 40 | train: 41 | target: ldm.data.openimages.FullOpenImagesTrain 42 | params: 43 | size: 384 44 | crop_size: 256 45 | validation: 46 | target: ldm.data.openimages.FullOpenImagesValidation 47 | params: 48 | size: 384 49 | crop_size: 256 50 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/vq-f4-noattn/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 3 6 | n_embed: 8192 7 | monitor: val/rec_loss 8 | 9 | ddconfig: 10 | attn_type: none 11 | double_z: false 12 | z_channels: 3 13 | resolution: 256 14 | in_channels: 3 15 | out_ch: 3 16 | ch: 128 17 | ch_mult: 18 | - 1 19 | - 2 20 | - 4 21 | num_res_blocks: 2 22 | attn_resolutions: [] 23 | dropout: 0.0 24 | lossconfig: 25 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 26 | params: 27 | disc_conditional: false 28 | disc_in_channels: 3 29 | disc_start: 11 30 | disc_weight: 0.75 31 | codebook_weight: 1.0 32 | 33 | data: 34 | target: main.DataModuleFromConfig 35 | params: 36 | batch_size: 8 37 | num_workers: 12 38 | wrap: true 39 | train: 40 | target: ldm.data.openimages.FullOpenImagesTrain 41 | params: 42 | crop_size: 256 43 | validation: 44 | target: ldm.data.openimages.FullOpenImagesValidation 45 | params: 46 | crop_size: 256 47 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/vq-f4/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 3 6 | n_embed: 8192 7 | monitor: val/rec_loss 8 | 9 | ddconfig: 10 | double_z: false 11 | z_channels: 3 12 | resolution: 256 13 | in_channels: 3 14 | out_ch: 3 15 | ch: 128 16 | ch_mult: 17 | - 1 18 | - 2 19 | - 4 20 | num_res_blocks: 2 21 | attn_resolutions: [] 22 | dropout: 0.0 23 | lossconfig: 24 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 25 | params: 26 | disc_conditional: false 27 | disc_in_channels: 3 28 | disc_start: 0 29 | disc_weight: 0.75 30 | codebook_weight: 1.0 31 | 32 | data: 33 | target: main.DataModuleFromConfig 34 | params: 35 | batch_size: 8 36 | num_workers: 16 37 | wrap: true 38 | train: 39 | target: ldm.data.openimages.FullOpenImagesTrain 40 | params: 41 | crop_size: 256 42 | validation: 43 | target: ldm.data.openimages.FullOpenImagesValidation 44 | params: 45 | crop_size: 256 46 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/vq-f8-n256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 4 6 | n_embed: 256 7 | monitor: val/rec_loss 8 | ddconfig: 9 | double_z: false 10 | z_channels: 4 11 | resolution: 256 12 | in_channels: 3 13 | out_ch: 3 14 | ch: 128 15 | ch_mult: 16 | - 1 17 | - 2 18 | - 2 19 | - 4 20 | num_res_blocks: 2 21 | attn_resolutions: 22 | - 32 23 | dropout: 0.0 24 | lossconfig: 25 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 26 | params: 27 | disc_conditional: false 28 | disc_in_channels: 3 29 | disc_start: 250001 30 | disc_weight: 0.75 31 | codebook_weight: 1.0 32 | 33 | data: 34 | target: main.DataModuleFromConfig 35 | params: 36 | batch_size: 10 37 | num_workers: 20 38 | wrap: true 39 | train: 40 | target: ldm.data.openimages.FullOpenImagesTrain 41 | params: 42 | size: 384 43 | crop_size: 256 44 | validation: 45 | target: ldm.data.openimages.FullOpenImagesValidation 46 | params: 47 | size: 384 48 | crop_size: 256 49 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/first_stage_models/vq-f8/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 4.5e-06 3 | target: ldm.models.autoencoder.VQModel 4 | params: 5 | embed_dim: 4 6 | n_embed: 16384 7 | monitor: val/rec_loss 8 | ddconfig: 9 | double_z: false 10 | z_channels: 4 11 | resolution: 256 12 | in_channels: 3 13 | out_ch: 3 14 | ch: 128 15 | ch_mult: 16 | - 1 17 | - 2 18 | - 2 19 | - 4 20 | num_res_blocks: 2 21 | attn_resolutions: 22 | - 32 23 | dropout: 0.0 24 | lossconfig: 25 | target: taming.modules.losses.vqperceptual.VQLPIPSWithDiscriminator 26 | params: 27 | disc_conditional: false 28 | disc_in_channels: 3 29 | disc_num_layers: 2 30 | disc_start: 1 31 | disc_weight: 0.6 32 | codebook_weight: 1.0 33 | data: 34 | target: main.DataModuleFromConfig 35 | params: 36 | batch_size: 10 37 | num_workers: 20 38 | wrap: true 39 | train: 40 | target: ldm.data.openimages.FullOpenImagesTrain 41 | params: 42 | size: 384 43 | crop_size: 256 44 | validation: 45 | target: ldm.data.openimages.FullOpenImagesValidation 46 | params: 47 | size: 384 48 | crop_size: 256 49 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/models/ldm/semantic_synthesis256/config.yaml: -------------------------------------------------------------------------------- 1 | model: 2 | base_learning_rate: 1.0e-06 3 | target: ldm.models.diffusion.ddpm.LatentDiffusion 4 | params: 5 | linear_start: 0.0015 6 | linear_end: 0.0205 7 | log_every_t: 100 8 | timesteps: 1000 9 | loss_type: l1 10 | first_stage_key: image 11 | cond_stage_key: segmentation 12 | image_size: 64 13 | channels: 3 14 | concat_mode: true 15 | cond_stage_trainable: true 16 | unet_config: 17 | target: ldm.modules.diffusionmodules.openaimodel.UNetModel 18 | params: 19 | image_size: 64 20 | in_channels: 6 21 | out_channels: 3 22 | model_channels: 128 23 | attention_resolutions: 24 | - 32 25 | - 16 26 | - 8 27 | num_res_blocks: 2 28 | channel_mult: 29 | - 1 30 | - 4 31 | - 8 32 | num_heads: 8 33 | first_stage_config: 34 | target: ldm.models.autoencoder.VQModelInterface 35 | params: 36 | embed_dim: 3 37 | n_embed: 8192 38 | ddconfig: 39 | double_z: false 40 | z_channels: 3 41 | resolution: 256 42 | in_channels: 3 43 | out_ch: 3 44 | ch: 128 45 | ch_mult: 46 | - 1 47 | - 2 48 | - 4 49 | num_res_blocks: 2 50 | attn_resolutions: [] 51 | dropout: 0.0 52 | lossconfig: 53 | target: torch.nn.Identity 54 | cond_stage_config: 55 | target: ldm.modules.encoders.modules.SpatialRescaler 56 | params: 57 | n_stages: 2 58 | in_channels: 182 59 | out_channels: 3 60 | -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/scripts/download_first_stages.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | wget -O models/first_stage_models/kl-f4/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f4.zip 3 | wget -O models/first_stage_models/kl-f8/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f8.zip 4 | wget -O models/first_stage_models/kl-f16/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f16.zip 5 | wget -O models/first_stage_models/kl-f32/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f32.zip 6 | wget -O models/first_stage_models/vq-f4/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4.zip 7 | wget -O models/first_stage_models/vq-f4-noattn/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4-noattn.zip 8 | wget -O models/first_stage_models/vq-f8/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8.zip 9 | wget -O models/first_stage_models/vq-f8-n256/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip 10 | wget -O models/first_stage_models/vq-f16/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f16.zip 11 | 12 | 13 | 14 | cd models/first_stage_models/kl-f4 15 | unzip -o model.zip 16 | 17 | cd ../kl-f8 18 | unzip -o model.zip 19 | 20 | cd ../kl-f16 21 | unzip -o model.zip 22 | 23 | cd ../kl-f32 24 | unzip -o model.zip 25 | 26 | cd ../vq-f4 27 | unzip -o model.zip 28 | 29 | cd ../vq-f4-noattn 30 | unzip -o model.zip 31 | 32 | cd ../vq-f8 33 | unzip -o model.zip 34 | 35 | cd ../vq-f8-n256 36 | unzip -o model.zip 37 | 38 | cd ../vq-f16 39 | unzip -o model.zip 40 | 41 | cd ../.. -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/scripts/tests/test_watermark.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import fire 3 | from imwatermark import WatermarkDecoder 4 | 5 | 6 | def testit(img_path): 7 | bgr = cv2.imread(img_path) 8 | decoder = WatermarkDecoder('bytes', 136) 9 | watermark = decoder.decode(bgr, 'dwtDct') 10 | try: 11 | dec = watermark.decode('utf-8') 12 | except: 13 | dec = "null" 14 | print(dec) 15 | 16 | 17 | if __name__ == "__main__": 18 | fire.Fire(testit) -------------------------------------------------------------------------------- /generator_codes/stable-diffusion-main/setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | name='latent-diffusion', 5 | version='0.0.1', 6 | description='', 7 | packages=find_packages(), 8 | install_requires=[ 9 | 'torch', 10 | 'numpy', 11 | 'tqdm', 12 | ], 13 | ) --------------------------------------------------------------------------------