├── .gitignore ├── torch_logs ├── test_vae_grf_mvtec_tile_check │ └── test_vae_grf_mvtec_tile_check_test_values.txt ├── test_vae_grf_mvtec_tile_check_loss_values.txt ├── test_vit_vae_mvtec_grid │ ├── test_vit_vae_mvtec_grid_loss_5.png │ ├── test_vit_vae_mvtec_grid_test_5.png │ ├── test_vit_vae_mvtec_grid_loss_10.png │ ├── test_vit_vae_mvtec_grid_loss_100.png │ ├── test_vit_vae_mvtec_grid_loss_25.png │ ├── test_vit_vae_mvtec_grid_loss_50.png │ ├── test_vit_vae_mvtec_grid_test_10.png │ ├── test_vit_vae_mvtec_grid_test_100.png │ ├── test_vit_vae_mvtec_grid_test_25.png │ ├── test_vit_vae_mvtec_grid_test_50.png │ └── test_vit_vae_mvtec_grid_test_values.txt ├── test_vit_vae_mvtec_tile │ ├── test_vit_vae_mvtec_tile_loss_5.png │ ├── test_vit_vae_mvtec_tile_test_5.png │ ├── test_vit_vae_mvtec_tile_loss_10.png │ ├── test_vit_vae_mvtec_tile_loss_100.png │ ├── test_vit_vae_mvtec_tile_loss_25.png │ ├── test_vit_vae_mvtec_tile_loss_50.png │ ├── test_vit_vae_mvtec_tile_test_10.png │ ├── test_vit_vae_mvtec_tile_test_100.png │ ├── test_vit_vae_mvtec_tile_test_25.png │ ├── test_vit_vae_mvtec_tile_test_50.png │ └── test_vit_vae_mvtec_tile_test_values.txt ├── test_vit_vae_mvtec_cable │ ├── test_vit_vae_mvtec_cable_loss_10.png │ ├── test_vit_vae_mvtec_cable_loss_25.png │ ├── test_vit_vae_mvtec_cable_loss_5.png │ ├── test_vit_vae_mvtec_cable_loss_50.png │ ├── test_vit_vae_mvtec_cable_test_10.png │ ├── test_vit_vae_mvtec_cable_test_25.png │ ├── test_vit_vae_mvtec_cable_test_5.png │ ├── test_vit_vae_mvtec_cable_test_50.png │ ├── test_vit_vae_mvtec_cable_loss_100.png │ ├── test_vit_vae_mvtec_cable_test_100.png │ └── test_vit_vae_mvtec_cable_test_values.txt ├── test_vit_vae_mvtec_screw │ ├── test_vit_vae_mvtec_screw_loss_10.png │ ├── test_vit_vae_mvtec_screw_loss_25.png │ ├── test_vit_vae_mvtec_screw_loss_5.png │ ├── test_vit_vae_mvtec_screw_loss_50.png │ ├── test_vit_vae_mvtec_screw_test_10.png │ ├── test_vit_vae_mvtec_screw_test_25.png │ ├── test_vit_vae_mvtec_screw_test_5.png │ ├── test_vit_vae_mvtec_screw_test_50.png │ ├── test_vit_vae_mvtec_screw_loss_100.png │ ├── test_vit_vae_mvtec_screw_test_100.png │ └── test_vit_vae_mvtec_screw_test_values.txt ├── test_vit_vae_mvtec_bottle │ ├── test_vit_vae_mvtec_bottle_loss_10.png │ ├── test_vit_vae_mvtec_bottle_loss_25.png │ ├── test_vit_vae_mvtec_bottle_loss_5.png │ ├── test_vit_vae_mvtec_bottle_loss_50.png │ ├── test_vit_vae_mvtec_bottle_test_10.png │ ├── test_vit_vae_mvtec_bottle_test_25.png │ ├── test_vit_vae_mvtec_bottle_test_5.png │ ├── test_vit_vae_mvtec_bottle_test_50.png │ ├── test_vit_vae_mvtec_bottle_loss_100.png │ ├── test_vit_vae_mvtec_bottle_test_100.png │ └── test_vit_vae_mvtec_bottle_test_values.txt ├── test_vit_vae_mvtec_carpet │ ├── test_vit_vae_mvtec_carpet_loss_10.png │ ├── test_vit_vae_mvtec_carpet_loss_25.png │ ├── test_vit_vae_mvtec_carpet_loss_5.png │ ├── test_vit_vae_mvtec_carpet_loss_50.png │ ├── test_vit_vae_mvtec_carpet_test_10.png │ ├── test_vit_vae_mvtec_carpet_test_25.png │ ├── test_vit_vae_mvtec_carpet_test_5.png │ ├── test_vit_vae_mvtec_carpet_test_50.png │ ├── test_vit_vae_mvtec_carpet_loss_100.png │ ├── test_vit_vae_mvtec_carpet_test_100.png │ └── test_vit_vae_mvtec_carpet_test_values.txt ├── test_vit_vae_mvtec_zipper │ ├── test_vit_vae_mvtec_zipper_loss_10.png │ ├── test_vit_vae_mvtec_zipper_loss_25.png │ ├── test_vit_vae_mvtec_zipper_loss_5.png │ ├── test_vit_vae_mvtec_zipper_loss_50.png │ ├── test_vit_vae_mvtec_zipper_test_10.png │ ├── test_vit_vae_mvtec_zipper_test_25.png │ ├── test_vit_vae_mvtec_zipper_test_5.png │ ├── test_vit_vae_mvtec_zipper_test_50.png │ ├── test_vit_vae_mvtec_zipper_loss_100.png │ ├── test_vit_vae_mvtec_zipper_test_100.png │ └── test_vit_vae_mvtec_zipper_test_values.txt ├── test_vit_vae_mvtec_capsule │ ├── test_vit_vae_mvtec_capsule_loss_10.png │ ├── test_vit_vae_mvtec_capsule_loss_100.png │ ├── test_vit_vae_mvtec_capsule_loss_25.png │ ├── test_vit_vae_mvtec_capsule_loss_5.png │ ├── test_vit_vae_mvtec_capsule_loss_50.png │ ├── test_vit_vae_mvtec_capsule_test_10.png │ ├── test_vit_vae_mvtec_capsule_test_100.png │ ├── test_vit_vae_mvtec_capsule_test_25.png │ ├── test_vit_vae_mvtec_capsule_test_5.png │ ├── test_vit_vae_mvtec_capsule_test_50.png │ └── test_vit_vae_mvtec_capsule_test_values.txt ├── test_vit_vae_mvtec_hazelnut │ ├── test_vit_vae_mvtec_hazelnut_loss_5.png │ ├── test_vit_vae_mvtec_hazelnut_test_5.png │ ├── test_vit_vae_mvtec_hazelnut_loss_10.png │ ├── test_vit_vae_mvtec_hazelnut_loss_100.png │ ├── test_vit_vae_mvtec_hazelnut_loss_25.png │ ├── test_vit_vae_mvtec_hazelnut_loss_50.png │ ├── test_vit_vae_mvtec_hazelnut_test_10.png │ ├── test_vit_vae_mvtec_hazelnut_test_100.png │ ├── test_vit_vae_mvtec_hazelnut_test_25.png │ ├── test_vit_vae_mvtec_hazelnut_test_50.png │ └── test_vit_vae_mvtec_hazelnut_test_values.txt ├── test_vit_vae_mvtec_leather │ ├── test_vit_vae_mvtec_leather_loss_10.png │ ├── test_vit_vae_mvtec_leather_loss_100.png │ ├── test_vit_vae_mvtec_leather_loss_25.png │ ├── test_vit_vae_mvtec_leather_loss_5.png │ ├── test_vit_vae_mvtec_leather_loss_50.png │ ├── test_vit_vae_mvtec_leather_test_10.png │ ├── test_vit_vae_mvtec_leather_test_100.png │ ├── test_vit_vae_mvtec_leather_test_25.png │ ├── test_vit_vae_mvtec_leather_test_5.png │ ├── test_vit_vae_mvtec_leather_test_50.png │ └── test_vit_vae_mvtec_leather_test_values.txt ├── test_vit_vae_mvtec_toothbrush │ ├── test_vit_vae_mvtec_toothbrush_loss_10.png │ ├── test_vit_vae_mvtec_toothbrush_loss_25.png │ ├── test_vit_vae_mvtec_toothbrush_loss_5.png │ ├── test_vit_vae_mvtec_toothbrush_loss_50.png │ ├── test_vit_vae_mvtec_toothbrush_test_10.png │ ├── test_vit_vae_mvtec_toothbrush_test_25.png │ ├── test_vit_vae_mvtec_toothbrush_test_5.png │ ├── test_vit_vae_mvtec_toothbrush_test_50.png │ ├── test_vit_vae_mvtec_toothbrush_loss_100.png │ ├── test_vit_vae_mvtec_toothbrush_test_100.png │ └── test_vit_vae_mvtec_toothbrush_test_values.txt ├── test_vit_vae_freeze_mvtec_screw │ ├── test_vit_vae_freeze_mvtec_screw_loss_10.png │ ├── test_vit_vae_freeze_mvtec_screw_loss_100.png │ ├── test_vit_vae_freeze_mvtec_screw_loss_25.png │ ├── test_vit_vae_freeze_mvtec_screw_loss_5.png │ ├── test_vit_vae_freeze_mvtec_screw_loss_50.png │ ├── test_vit_vae_freeze_mvtec_screw_test_10.png │ ├── test_vit_vae_freeze_mvtec_screw_test_100.png │ ├── test_vit_vae_freeze_mvtec_screw_test_25.png │ ├── test_vit_vae_freeze_mvtec_screw_test_5.png │ ├── test_vit_vae_freeze_mvtec_screw_test_50.png │ └── test_vit_vae_freeze_mvtec_screw_test_values.txt ├── test_vit_vae_freeze_mvtec_zipper │ ├── test_vit_vae_freeze_mvtec_zipper_loss_5.png │ ├── test_vit_vae_freeze_mvtec_zipper_test_5.png │ ├── test_vit_vae_freeze_mvtec_zipper_loss_10.png │ ├── test_vit_vae_freeze_mvtec_zipper_loss_25.png │ ├── test_vit_vae_freeze_mvtec_zipper_loss_50.png │ ├── test_vit_vae_freeze_mvtec_zipper_test_10.png │ ├── test_vit_vae_freeze_mvtec_zipper_test_25.png │ ├── test_vit_vae_freeze_mvtec_zipper_test_50.png │ └── test_vit_vae_freeze_mvtec_zipper_test_values.txt ├── test_vit_vae_mvtec_carpet_freeze │ ├── test_vit_vae_mvtec_carpet_freeze_loss_5.png │ ├── test_vit_vae_mvtec_carpet_freeze_test_5.png │ ├── test_vit_vae_mvtec_carpet_freeze_loss_10.png │ ├── test_vit_vae_mvtec_carpet_freeze_loss_25.png │ ├── test_vit_vae_mvtec_carpet_freeze_test_10.png │ ├── test_vit_vae_mvtec_carpet_freeze_test_25.png │ └── test_vit_vae_mvtec_carpet_freeze_test_values.txt ├── test_vit_vae_mvtec_leather_freeze │ ├── test_vit_vae_mvtec_leather_freeze_loss_5.png │ ├── test_vit_vae_mvtec_leather_freeze_test_5.png │ ├── test_vit_vae_mvtec_leather_freeze_loss_10.png │ ├── test_vit_vae_mvtec_leather_freeze_loss_25.png │ ├── test_vit_vae_mvtec_leather_freeze_test_10.png │ ├── test_vit_vae_mvtec_leather_freeze_test_25.png │ └── test_vit_vae_mvtec_leather_freeze_test_values.txt ├── test_vit_vae_miad_electrical_insulator │ ├── test_vit_vae_miad_electrical_insulator_loss_5.png │ ├── test_vit_vae_miad_electrical_insulator_test_5.png │ ├── test_vit_vae_miad_electrical_insulator_loss_10.png │ ├── test_vit_vae_miad_electrical_insulator_loss_100.png │ ├── test_vit_vae_miad_electrical_insulator_loss_25.png │ ├── test_vit_vae_miad_electrical_insulator_loss_50.png │ ├── test_vit_vae_miad_electrical_insulator_test_10.png │ ├── test_vit_vae_miad_electrical_insulator_test_100.png │ ├── test_vit_vae_miad_electrical_insulator_test_25.png │ ├── test_vit_vae_miad_electrical_insulator_test_50.png │ └── test_vit_vae_miad_electrical_insulator_test_values.txt ├── test_vit_vae_mvtec_carpet_freeze_loss_values.txt ├── test_vit_vae_mvtec_leather_freeze_loss_values.txt ├── test_vit_vae_freeze_mvtec_zipper_loss_values.txt ├── test_vit_vae_mvtec_zipper_loss_values.txt ├── test_vit_vae_mvtec_bottle_loss_values.txt ├── test_vit_vae_mvtec_toothbrush_loss_values.txt ├── test_vit_vae_miad_electrical_insulator_loss_values.txt ├── test_vit_vae_mvtec_screw_loss_values.txt ├── test_vit_vae_freeze_mvtec_screw_loss_values.txt ├── test_vit_vae_mvtec_capsule_loss_values.txt ├── test_vit_vae_mvtec_cable_loss_values.txt ├── test_vit_vae_mvtec_hazelnut_loss_values.txt ├── test_vit_vae_mvtec_leather_loss_values.txt ├── test_vit_vae_mvtec_carpet_loss_values.txt ├── test_vit_vae_mvtec_grid_loss_values.txt └── test_vit_vae_mvtec_tile_loss_values.txt ├── mvtec_predictions ├── gt_test_vae_grf_mvtec_zipper_new.png ├── ori_test_vae_grf_mvtec_zipper_new.png ├── rec_test_vae_grf_mvtec_zipper_new.png └── final_amap_test_vae_grf_mvtec_zipper_new.png ├── torch_results └── test_vae_grf_mvtec_tile_check_img_train_1.png ├── vae_test.sh ├── vae_train.sh ├── README.md ├── requirements.txt ├── vit_vae.py ├── vae.py ├── vae_train.py ├── utils.py └── datasets.py /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | 3 | *.pth 4 | 5 | __pycache__ -------------------------------------------------------------------------------- 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8 | --num_epochs=600\ 9 | --img_size=256\ 10 | --batch_size=16\ 11 | --batch_size_test=8\ 12 | --latent_img_size=32\ 13 | --z_dim=256\ 14 | --beta=1\ 15 | --nb_channels=3\ 16 | --model=vae_grf\ 17 | --corr_type=corr_id\ 18 | --force_train\ 19 | --intest\ 20 | 21 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_carpet_freeze/test_vit_vae_mvtec_carpet_freeze_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.49609676383077966, 3 | 2,0.49437619660517085, 4 | 3,0.49331616484250274, 5 | 4,0.49258168849553396, 6 | 5,0.49360188918698467, 7 | 6,0.49541196796629794, 8 | 7,0.5017910327503603, 9 | 8,0.5047659793087277, 10 | 9,0.49890723047184055, 11 | 10,0.4979808263295904, 12 | 11,0.4985777558043661, 13 | 12,0.497458911712524, 14 | 13,0.4992444556240745, 15 | 14,0.5002145275499726, 16 | 15,0.5006784844603996, 17 | 16,0.500552340852578, 18 | 17,0.5021404079606218, 19 | 18,0.4995361526328637, 20 | 19,0.4992499138381508, 21 | 20,0.5003600913793407, 22 | 21,0.5001268383271463, 23 | 22,0.49964565107079895, 24 | 23,0.4995750284955365, 25 | 24,0.49880328755840475, 26 | 25,0.4974380917853686, 27 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_leather_freeze/test_vit_vae_mvtec_leather_freeze_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.4788839327649198, 3 | 2,0.47682300824392043, 4 | 3,0.47502691339060166, 5 | 4,0.4702291492037225, 6 | 5,0.46605971286055825, 7 | 6,0.4623207671083194, 8 | 7,0.46231262757180364, 9 | 8,0.46490951627183935, 10 | 9,0.4665478048811461, 11 | 10,0.4687230817362313, 12 | 11,0.4682384526114286, 13 | 12,0.4713438119060074, 14 | 13,0.4686958192718843, 15 | 14,0.4685295560260468, 16 | 15,0.46868952681797404, 17 | 16,0.4706806048588539, 18 | 17,0.47347029087143144, 19 | 18,0.47267859744326063, 20 | 19,0.47226280502729, 21 | 20,0.46997846781436803, 22 | 21,0.47466536904982465, 23 | 22,0.47466310404171197, 24 | 23,0.4734754180454853, 25 | 24,0.4740386820490826, 26 | 25,0.47346102052334543, 27 | 26,0.47667756302018505, 28 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_carpet_freeze_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,14006.2060546875,14108.6416015625,-102.4346694946289, 3 | 2,19035.455078125,19100.603515625,-65.14662170410156, 4 | 3,19114.841796875,19155.583984375,-40.74776077270508, 5 | 4,19152.630859375,19178.02734375,-25.39870262145996, 6 | 5,19176.1328125,19192.09765625,-15.972946166992188, 7 | 6,19186.609375,19196.896484375,-10.28135871887207, 8 | 7,19203.1015625,19210.005859375,-6.907489776611328, 9 | 8,19243.732421875,19248.759765625,-5.032741069793701, 10 | 9,19439.75,19444.271484375,-4.527265548706055, 11 | 10,20065.53515625,20070.25,-4.715066432952881, 12 | 11,19144.65234375,19149.369140625,-4.710750579833984, 13 | 12,20407.9765625,20412.529296875,-4.550267219543457, 14 | 13,21030.98046875,21035.578125,-4.593255996704102, 15 | 14,21419.1484375,21423.7265625,-4.584375858306885, 16 | 15,21638.92578125,21643.478515625,-4.545839786529541, 17 | 16,19069.2890625,19073.76953125,-4.47454833984375, 18 | 17,21091.8359375,21096.1953125,-4.359729290008545, 19 | 18,21765.03515625,21769.408203125,-4.363614559173584, 20 | 19,22120.732421875,22125.0390625,-4.300405502319336, 21 | 20,22711.9375,22716.203125,-4.260283946990967, 22 | 21,18924.033203125,18928.26953125,-4.233668327331543, 23 | 22,22170.74609375,22174.875,-4.136912822723389, 24 | 23,23456.279296875,23460.4296875,-4.151424884796143, 25 | 24,23990.51171875,23994.671875,-4.1567583084106445, 26 | 25,24279.97265625,24284.09375,-4.124305725097656, 27 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_leather_freeze_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,30726.796875,30831.40234375,-104.60252380371094, 3 | 2,48294.1796875,48367.6015625,-73.4164810180664, 4 | 3,48545.109375,48595.171875,-50.058807373046875, 5 | 4,48625.13671875,48659.10546875,-33.944793701171875, 6 | 5,48691.21484375,48715.30859375,-24.074670791625977, 7 | 6,48666.20703125,48684.8984375,-18.69244384765625, 8 | 7,48779.90625,48795.19921875,-15.297392845153809, 9 | 8,48872.89453125,48886.4765625,-13.569165229797363, 10 | 9,48963.77734375,48975.89453125,-12.113576889038086, 11 | 10,49021.75390625,49032.578125,-10.83712387084961, 12 | 11,48678.88671875,48688.84375,-9.973886489868164, 13 | 12,49002.19921875,49011.296875,-9.079880714416504, 14 | 13,49081.62890625,49090.0,-8.37072467803955, 15 | 14,49133.8359375,49141.44140625,-7.593382358551025, 16 | 15,49151.94140625,49158.80859375,-6.882568836212158, 17 | 16,48684.53125,48691.08984375,-6.557276248931885, 18 | 17,49068.8515625,49075.0546875,-6.201365947723389, 19 | 18,49142.359375,49148.2265625,-5.875649452209473, 20 | 19,49171.05859375,49176.41796875,-5.371474742889404, 21 | 20,49174.77734375,49179.7265625,-4.939423561096191, 22 | 21,48694.41015625,48699.28515625,-4.879964828491211, 23 | 22,49093.24609375,49098.0390625,-4.793321132659912, 24 | 23,49164.96875,49169.5234375,-4.551514148712158, 25 | 24,49189.703125,49193.90234375,-4.201690196990967, 26 | 25,49193.5546875,49197.44921875,-3.8996400833129883, 27 | 26,48702.05859375,48706.03515625,-3.987543821334839, 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Variational Autoencoder for Anomaly Detection: A Comparative Study 3 | 4 | This repository contains the implementation of the paper titled "Variational Autoencoder for Anomaly Detection: A Comparative Study". This paper code base is developed based on the code from [Hugo's original paper](https://github.com/HGangloff/vae_grf). 5 | 6 | ## Getting Started 7 | 8 | Before running the experiments, ensure you have downloaded the [MVTec](https://www.mvtec.com/company/research/datasets/mvtec-ad/downloads) and [MiAD](https://miad-2022.github.io/) datasets. Modify the dataset links in `datasets.py` accordingly to link these datasets to your experiment. 9 | 10 | ### Prerequisites 11 | 12 | The required libraries are listed in `requirements.txt`. Install them using the following command: 13 | 14 | ```bash 15 | pip install -r requirements.txt 16 | ``` 17 | 18 | It is recommended to run the installation in a Conda environment, especially on Windows. 19 | 20 | ## Training 21 | 22 | To train the model, execute the following command: 23 | 24 | ```bash 25 | sh vae_train.sh 26 | ``` 27 | 28 | For training Variational Autoencoder (VAE) and VAE-GRF as per our experiments, ensure the following parameters: 29 | 30 | - `batch_size`: 8 31 | - `latent_image_size`: 32 32 | - `latent_dim`: 256 33 | - `image_size`: 256 34 | 35 | In `vae_test.py`, modify `mad = mad.repeat(16, axis=0).repeat(16, axis=1)` to `mad = mad.repeat(8, axis=0).repeat(8, axis=1)` to run VAE and VAE-GRF. 36 | 37 | For training ViT-VAE as per our experiments, ensure the following parameters: 38 | 39 | - `batch_size`: 8 40 | - `latent_image_size`: 14 41 | - `latent_dim`: 384 42 | - `image_size`: 224 43 | 44 | In `vae_test.py`, modify `mad = mad.repeat(8, axis=0).repeat(8, axis=1)` to `mad = mad.repeat(16, axis=0).repeat(16, axis=1)` to run ViT-VAE. 45 | 46 | ## Testing 47 | 48 | To test the model, use the following command: 49 | 50 | ```bash 51 | sh vae_test.sh 52 | ``` 53 | 54 | Ensure to provide the appropriate parameters as mentioned above. 55 | 56 | ## Built With 57 | 58 | The code is built using PyTorch and other standard libraries. 59 | 60 | ## More Information 61 | 62 | For more details, please refer to the publication. 63 | 64 | --- 65 | 66 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_freeze_mvtec_zipper/test_vit_vae_freeze_mvtec_zipper_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.4642716237406691, 3 | 2,0.45471645479565354, 4 | 3,0.44033377632669546, 5 | 4,0.4242211500749789, 6 | 5,0.41323553326689155, 7 | 6,0.39520369832759183, 8 | 7,0.4065823856169156, 9 | 8,0.41142818420286476, 10 | 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-------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | brotlipy==0.7.0 2 | certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1700303426725/work/certifi 3 | cffi @ file:///C:/b/abs_49n3v2hyhr/croot/cffi_1670423218144/work 4 | charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work 5 | colorama==0.4.6 6 | contourpy==1.1.1 7 | cryptography @ file:///C:/b/abs_f4do8t8jfs/croot/cryptography_1694444424531/work 8 | cycler==0.11.0 9 | einops==0.7.0 10 | filelock @ file:///C:/b/abs_c7yrhs9uz2/croot/filelock_1672387617533/work 11 | fonttools==4.42.1 12 | fsspec==2023.12.2 13 | huggingface-hub==0.20.2 14 | idna @ file:///C:/b/abs_bdhbebrioa/croot/idna_1666125572046/work 15 | imageio==2.31.3 16 | importlib-resources==6.0.1 17 | Jinja2 @ file:///C:/b/abs_7cdis66kl9/croot/jinja2_1666908141852/work 18 | joblib==1.3.2 19 | kiwisolver==1.4.5 20 | lazy_loader==0.3 21 | lxml==5.0.1 22 | MarkupSafe @ file:///C:/ci/markupsafe_1654508077284/work 23 | matplotlib==3.8.0 24 | mkl-fft==1.3.6 25 | mkl-random @ file:///C:/Users/dev-admin/mkl/mkl_random_1682977971003/work 26 | mkl-service==2.4.0 27 | mpmath @ file:///C:/b/abs_7833jrbiox/croot/mpmath_1690848321154/work 28 | networkx @ file:///C:/b/abs_e6gi1go5op/croot/networkx_1690562046966/work 29 | numpy @ file:///C:/b/abs_f6napi3n6e/croot/numpy_and_numpy_base_1691091651337/work 30 | opencv-python==4.8.1.78 31 | packaging==23.1 32 | pandas==2.1.4 33 | Pillow==9.3.0 34 | pip-date==1.0.5 35 | pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work 36 | pyOpenSSL @ file:///C:/b/abs_08f38zyck4/croot/pyopenssl_1690225407403/work 37 | pyparsing==3.1.1 38 | PySocks @ file:///C:/ci/pysocks_1605307512533/work 39 | python-dateutil==2.8.2 40 | pytz==2023.3.post1 41 | PyWavelets==1.4.1 42 | PyYAML==6.0.1 43 | regex==2023.12.25 44 | requests @ file:///C:/b/abs_316c2inijk/croot/requests_1690400295842/work 45 | safetensors==0.4.1 46 | scikit-image==0.21.0 47 | scikit-learn==1.3.0 48 | scipy==1.11.2 49 | six==1.16.0 50 | sympy @ file:///C:/b/abs_95fbf1z7n6/croot/sympy_1668202411612/work 51 | threadpoolctl==3.2.0 52 | tifffile==2023.8.30 53 | tokenizers==0.15.0 54 | torch==2.0.0 55 | torchaudio==2.0.0 56 | torchvision==0.15.0 57 | tqdm==4.66.1 58 | transformers==4.36.2 59 | typing_extensions @ file:///C:/b/abs_213vg2cd1l/croot/typing_extensions_1690297804941/work 60 | tzdata==2023.4 61 | urllib3 @ file:///C:/b/abs_889_loyqv4/croot/urllib3_1686163174463/work 62 | win-inet-pton @ file:///C:/ci/win_inet_pton_1605306162074/work 63 | zipp==3.16.2 64 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_bottle/test_vit_vae_mvtec_bottle_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.5400001044663957, 3 | 2,0.5856544452501932, 4 | 3,0.6024726339699453, 5 | 4,0.6087657872805106, 6 | 5,0.605703840086601, 7 | 6,0.6081238501005831, 8 | 7,0.6070222615927745, 9 | 8,0.6059796450108141, 10 | 9,0.6041969946053101, 11 | 10,0.6171801716870016, 12 | 11,0.6327884739754939, 13 | 12,0.6063734422743089, 14 | 13,0.6042600384381493, 15 | 14,0.613369079660161, 16 | 15,0.5587094583736064, 17 | 16,0.588690543723252, 18 | 17,0.6116194571974389, 19 | 18,0.6174567107313045, 20 | 19,0.6292463293295363, 21 | 20,0.6240310078330964, 22 | 21,0.5896822770184362, 23 | 22,0.6380540938461958, 24 | 23,0.629702774751141, 25 | 24,0.6309851781398335, 26 | 25,0.6152280778742125, 27 | 26,0.574474992683113, 28 | 27,0.6450055256691991, 29 | 28,0.6408275545340789, 30 | 29,0.641696336340331, 31 | 30,0.6394872814104392, 32 | 31,0.5720417008221493, 33 | 32,0.6385996373679329, 34 | 33,0.6345851881009168, 35 | 34,0.6310646744711355, 36 | 35,0.6362899826768065, 37 | 36,0.6338970096294084, 38 | 37,0.6282179458797564, 39 | 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1 | epoch,aucroc, 2 | 1,0.45694753521329845, 3 | 2,0.4898184763371901, 4 | 3,0.48447268476402205, 5 | 4,0.6186686880557452, 6 | 5,0.6216470169256898, 7 | 6,0.6278955226875853, 8 | 7,0.6281250670500538, 9 | 8,0.6280428726025886, 10 | 9,0.6315335750509108, 11 | 10,0.6315793381880405, 12 | 11,0.5978334112018743, 13 | 12,0.6342138648395651, 14 | 13,0.6406407718199443, 15 | 14,0.6327000697886824, 16 | 15,0.6235661497841913, 17 | 16,0.5576733858610834, 18 | 17,0.6214342088075107, 19 | 18,0.6139317876169759, 20 | 19,0.6101714885729528, 21 | 20,0.6003181449193938, 22 | 21,0.5254819013664973, 23 | 22,0.603985861246843, 24 | 23,0.6019344101382631, 25 | 24,0.5947442203498075, 26 | 25,0.59644813840838, 27 | 26,0.5098853135187549, 28 | 27,0.6016431644545575, 29 | 28,0.6039638431345731, 30 | 29,0.5994021710342852, 31 | 30,0.6073596564734449, 32 | 31,0.5112198209731855, 33 | 32,0.6005558443544854, 34 | 33,0.6035179601767737, 35 | 34,0.600774081718557, 36 | 35,0.5998301413136707, 37 | 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/torch_logs/test_vit_vae_mvtec_screw/test_vit_vae_mvtec_screw_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.6487898309380205, 3 | 2,0.6642986412439242, 4 | 3,0.6083831997176414, 5 | 4,0.6567659242565886, 6 | 5,0.6738077847897995, 7 | 6,0.641752103979762, 8 | 7,0.6860906456567141, 9 | 8,0.6692397616957617, 10 | 9,0.6708057870253883, 11 | 10,0.7044610775378733, 12 | 11,0.7077774442072597, 13 | 12,0.7031112380107964, 14 | 13,0.6901719901890087, 15 | 14,0.6710962900608229, 16 | 15,0.7124078820054092, 17 | 16,0.7025045690538779, 18 | 17,0.7238497544195432, 19 | 18,0.7183991092039616, 20 | 19,0.7195131408031546, 21 | 20,0.7109873064242026, 22 | 21,0.7166630831456109, 23 | 22,0.7065047130849178, 24 | 23,0.7005772883092256, 25 | 24,0.7203868521384162, 26 | 25,0.7130231384794254, 27 | 26,0.7261769016163109, 28 | 27,0.7085901978394764, 29 | 28,0.717274899271082, 30 | 29,0.7091456681555893, 31 | 30,0.7185496773186046, 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-------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_zipper/test_vit_vae_mvtec_zipper_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.4592490192826169, 3 | 2,0.45626537107652787, 4 | 3,0.46039864401935826, 5 | 4,0.624961229793742, 6 | 5,0.624337721412373, 7 | 6,0.6786750648970026, 8 | 7,0.6582039130644929, 9 | 8,0.6133105061965719, 10 | 9,0.6132759777767759, 11 | 10,0.6030515736585826, 12 | 11,0.6575109810713736, 13 | 12,0.623037833255893, 14 | 13,0.5910966691370442, 15 | 14,0.6261825805173329, 16 | 15,0.6268367155226131, 17 | 16,0.6628831688484844, 18 | 17,0.6231642562810602, 19 | 18,0.6321792294473898, 20 | 19,0.6458183389910837, 21 | 20,0.65391295095965, 22 | 21,0.7102813152342972, 23 | 22,0.622466380258978, 24 | 23,0.6378113490442939, 25 | 24,0.6356775818643502, 26 | 25,0.6426369152155081, 27 | 26,0.729118701870816, 28 | 27,0.5953550756598118, 29 | 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99,0.6961124687844272, 101 | 100,0.6985521255255172, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_freeze_mvtec_screw/test_vit_vae_freeze_mvtec_screw_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.5417430090778078, 3 | 2,0.5396031125651853, 4 | 3,0.5337073804378225, 5 | 4,0.5307033217791972, 6 | 5,0.5287265226237546, 7 | 6,0.5282137725642307, 8 | 7,0.5272860614738353, 9 | 8,0.5257566822973376, 10 | 9,0.5260783299880951, 11 | 10,0.5253009280416349, 12 | 11,0.5252650661635527, 13 | 12,0.5247872164113725, 14 | 13,0.5247883616192274, 15 | 14,0.5242428525507421, 16 | 15,0.5240646622834364, 17 | 16,0.5249031868022194, 18 | 17,0.5248553423876754, 19 | 18,0.5241633370097949, 20 | 19,0.5240067296062572, 21 | 20,0.5238454375310886, 22 | 21,0.5236533251675117, 23 | 22,0.5241058609764866, 24 | 23,0.5243815474190334, 25 | 24,0.523863295628126, 26 | 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96,0.5211216940487252, 98 | 97,0.5211514152745884, 99 | 98,0.5211304514880486, 100 | 99,0.5207129148961162, 101 | 100,0.5210540074954224, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_tile/test_vit_vae_mvtec_tile_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.5178255295896319, 3 | 2,0.4745919050458216, 4 | 3,0.4922616532932562, 5 | 4,0.5095506977808408, 6 | 5,0.496338941054014, 7 | 6,0.458283337448072, 8 | 7,0.45100992020489156, 9 | 8,0.46083267489691626, 10 | 9,0.4597052492532079, 11 | 10,0.5034034501794558, 12 | 11,0.4994118150978286, 13 | 12,0.4847315286283731, 14 | 13,0.4790964454673138, 15 | 14,0.49631158774593565, 16 | 15,0.4926135356483369, 17 | 16,0.5114173031602922, 18 | 17,0.47796350122317977, 19 | 18,0.4632602362775814, 20 | 19,0.4785970416810248, 21 | 20,0.46610821236099303, 22 | 21,0.48460597200094224, 23 | 22,0.4657240774565875, 24 | 23,0.46022590361902815, 25 | 24,0.4653155526909984, 26 | 25,0.4666832538834005, 27 | 26,0.4774270248489221, 28 | 27,0.45755995631287305, 29 | 28,0.45649129287577733, 30 | 29,0.4563985780395491, 31 | 30,0.44176760765854306, 32 | 31,0.4513233681645299, 33 | 32,0.4452911720902857, 34 | 33,0.4489045042206054, 35 | 34,0.4523923923576856, 36 | 35,0.45253003859030205, 37 | 36,0.4485955527627382, 38 | 37,0.44518682892217454, 39 | 38,0.4525256998392564, 40 | 39,0.45038244279076134, 41 | 40,0.4471291205471173, 42 | 41,0.4445987106901245, 43 | 42,0.4495221445883718, 44 | 43,0.45487513476536645, 45 | 44,0.4541236833011235, 46 | 45,0.4439222829235018, 47 | 46,0.44120300096916865, 48 | 47,0.43857479575472336, 49 | 48,0.4352173704996528, 50 | 49,0.4345888966614659, 51 | 50,0.4329885021536924, 52 | 51,0.4311361285906413, 53 | 52,0.430326105679176, 54 | 53,0.4335272290568278, 55 | 54,0.4364966984328286, 56 | 55,0.4372956792384804, 57 | 56,0.4370035220760986, 58 | 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53,0.3282040647008303, 55 | 54,0.3357537571109348, 56 | 55,0.3273625976612884, 57 | 56,0.3313980679862282, 58 | 57,0.33034968235791967, 59 | 58,0.3265702119102396, 60 | 59,0.3361986300668417, 61 | 60,0.328895255032995, 62 | 61,0.3227578248314305, 63 | 62,0.31752402581875844, 64 | 63,0.32029230454997404, 65 | 64,0.3309628641695209, 66 | 65,0.323398121041208, 67 | 66,0.31973178883374437, 68 | 67,0.3250734763214499, 69 | 68,0.3304419856824199, 70 | 69,0.32344118158610424, 71 | 70,0.3118707217878536, 72 | 71,0.3228470997292837, 73 | 72,0.32602767841576147, 74 | 73,0.3244718106379461, 75 | 74,0.31418003452267573, 76 | 75,0.32361628851978796, 77 | 76,0.3236336559793487, 78 | 77,0.31676206272010154, 79 | 78,0.3155309134321856, 80 | 79,0.3120710328313358, 81 | 80,0.3083872372208275, 82 | 81,0.3147043734321917, 83 | 82,0.3052890766475201, 84 | 83,0.30838223235934065, 85 | 84,0.30666633964925, 86 | 85,0.30742560652259676, 87 | 86,0.3068089254773779, 88 | 87,0.3094093568568189, 89 | 88,0.30157506828772496, 90 | 89,0.30373512006722003, 91 | 90,0.2987568671774621, 92 | 91,0.29399375992984067, 93 | 92,0.29355869928643685, 94 | 93,0.3039102363959534, 95 | 94,0.2928703326763077, 96 | 95,0.29624095225465474, 97 | 96,0.29766540933925134, 98 | 97,0.29677240398292043, 99 | 98,0.2949935165003226, 100 | 99,0.2948100403219654, 101 | 100,0.2925477836119712, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_grid/test_vit_vae_mvtec_grid_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.41800914188493454, 3 | 2,0.5247620303053275, 4 | 3,0.42953469143152684, 5 | 4,0.4140488263904015, 6 | 5,0.4550749425660098, 7 | 6,0.4743256899727163, 8 | 7,0.46678660533932415, 9 | 8,0.47626705653784235, 10 | 9,0.45055595231098716, 11 | 10,0.4548812550552837, 12 | 11,0.4994640852670709, 13 | 12,0.45602356260283894, 14 | 13,0.45429323891672696, 15 | 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5,0.33175468581632744, 7 | 6,0.3429174367550325, 8 | 7,0.3750319343244665, 9 | 8,0.3611801298735662, 10 | 9,0.3814604987038447, 11 | 10,0.34598369068686396, 12 | 11,0.465653393495649, 13 | 12,0.3823179225389699, 14 | 13,0.34695099697791665, 15 | 14,0.36640738940103623, 16 | 15,0.36574432071240404, 17 | 16,0.3092561009942376, 18 | 17,0.3661264904679324, 19 | 18,0.34434682107666037, 20 | 19,0.34660883381852403, 21 | 20,0.34152126120373794, 22 | 21,0.3304922544235694, 23 | 22,0.3410424131913734, 24 | 23,0.3377112441460402, 25 | 24,0.32160037646090145, 26 | 25,0.3115823515221603, 27 | 26,0.2772496699259078, 28 | 27,0.34818843171643893, 29 | 28,0.3273571788458582, 30 | 29,0.3448558406070237, 31 | 30,0.3451005605732717, 32 | 31,0.28088494049644697, 33 | 32,0.35976847306075166, 34 | 33,0.3496838952909964, 35 | 34,0.35877909156136845, 36 | 35,0.3337862800134451, 37 | 36,0.3253575355469006, 38 | 37,0.3399716149604907, 39 | 38,0.35522819102729924, 40 | 39,0.35519811314136057, 41 | 40,0.3501417118486118, 42 | 41,0.32254931124634706, 43 | 42,0.3378270162937934, 44 | 43,0.35145856789676594, 45 | 44,0.3467904383585067, 46 | 45,0.33127041455867456, 47 | 46,0.338526823339253, 48 | 47,0.33866918738319135, 49 | 48,0.3493142013772116, 50 | 49,0.36293788564139584, 51 | 50,0.3537632677030131, 52 | 51,0.32167328327356737, 53 | 52,0.3522557640437052, 54 | 53,0.3328241420382011, 55 | 54,0.3416582415070071, 56 | 55,0.34315859936663984, 57 | 56,0.35171620098043244, 58 | 57,0.3483642743168971, 59 | 58,0.3392335397629651, 60 | 59,0.3038135323304315, 61 | 60,0.3020068789571237, 62 | 61,0.3326761483309525, 63 | 62,0.3279290597281228, 64 | 63,0.3206245292406646, 65 | 64,0.3389130687900964, 66 | 65,0.32408806819511854, 67 | 66,0.3267227919209696, 68 | 67,0.31839551332255844, 69 | 68,0.33262606278522744, 70 | 69,0.32634874828949223, 71 | 70,0.3342617821810906, 72 | 71,0.33623185820978463, 73 | 72,0.3496461677213528, 74 | 73,0.3285644623279857, 75 | 74,0.33490485907096257, 76 | 75,0.3363516357444307, 77 | 76,0.31880299101951415, 78 | 77,0.32323361209345447, 79 | 78,0.3193811625703664, 80 | 79,0.3254034107349283, 81 | 80,0.32475665384874697, 82 | 81,0.33966365276256305, 83 | 82,0.33592122571318106, 84 | 83,0.34044746211444127, 85 | 84,0.3299189322316005, 86 | 85,0.33086577526574124, 87 | 86,0.33298117948560585, 88 | 87,0.32574654017306104, 89 | 88,0.3351921560985269, 90 | 89,0.32986843007417566, 91 | 90,0.3216364169980789, 92 | 91,0.3184856464097222, 93 | 92,0.3228065063764446, 94 | 93,0.3256200447158727, 95 | 94,0.3319442235862672, 96 | 95,0.3330825943420316, 97 | 96,0.3329165023327988, 98 | 97,0.3228845604642055, 99 | 98,0.31659118869852243, 100 | 99,0.31694048553680837, 101 | 100,0.32142203735015507, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_toothbrush/test_vit_vae_mvtec_toothbrush_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.4624468097384046, 3 | 2,0.4910107353030068, 4 | 3,0.4850529907390845, 5 | 4,0.4154365391876747, 6 | 5,0.4055801745611836, 7 | 6,0.40027624995155603, 8 | 7,0.4079390908563283, 9 | 8,0.44053414270870384, 10 | 9,0.4421573304141868, 11 | 10,0.42273375836451665, 12 | 11,0.4362927924940956, 13 | 12,0.41410694874542714, 14 | 13,0.41221814215696984, 15 | 14,0.4089802809781248, 16 | 15,0.4141594451816565, 17 | 16,0.4357689062759709, 18 | 17,0.40606752378391303, 19 | 18,0.4156995261922621, 20 | 19,0.4049371878794302, 21 | 20,0.4192552996002351, 22 | 21,0.4435556775203635, 23 | 22,0.41512030294897156, 24 | 23,0.4141911361949233, 25 | 24,0.42102236065059934, 26 | 25,0.4159852019914617, 27 | 26,0.4396012762514617, 28 | 27,0.42519309703401476, 29 | 28,0.4232242881281982, 30 | 29,0.41649818747893386, 31 | 30,0.42121830868261434, 32 | 31,0.4343002915883819, 33 | 32,0.4277585434706007, 34 | 33,0.4223470074537443, 35 | 34,0.4186284354175084, 36 | 35,0.418280084697895, 37 | 36,0.4186272812879207, 38 | 37,0.4211949977561534, 39 | 38,0.42825429285140915, 40 | 39,0.42184578427343655, 41 | 40,0.42226380097406097, 42 | 41,0.4155733712599482, 43 | 42,0.41658524783144346, 44 | 43,0.4167463497668936, 45 | 44,0.42494714559944746, 46 | 45,0.42538818690479197, 47 | 46,0.42418134741176455, 48 | 47,0.4216774803688483, 49 | 48,0.42502184925569586, 50 | 49,0.42092037987374886, 51 | 50,0.4389191183858465, 52 | 51,0.43719671041356745, 53 | 52,0.42555991743570537, 54 | 53,0.4315784746428829, 55 | 54,0.4280315968382807, 56 | 55,0.42496170644181597, 57 | 56,0.43037471283088113, 58 | 57,0.42026363311750836, 59 | 58,0.42460880039084764, 60 | 59,0.41815881298427154, 61 | 60,0.43850045145177574, 62 | 61,0.4347587005137458, 63 | 62,0.43710718592262954, 64 | 63,0.4362554800867129, 65 | 64,0.4359878990455643, 66 | 65,0.4328163017163381, 67 | 66,0.4289477339419838, 68 | 67,0.43599238262221846, 69 | 68,0.42837206834849273, 70 | 69,0.43338119061756075, 71 | 70,0.4372515735404776, 72 | 71,0.4355353721921113, 73 | 72,0.4316601115353469, 74 | 73,0.4383064016029098, 75 | 74,0.4295442038861984, 76 | 75,0.4318385375581817, 77 | 76,0.43874461302787254, 78 | 77,0.43543817296256404, 79 | 78,0.43289332024158417, 80 | 79,0.4354766180487691, 81 | 80,0.4281407779505314, 82 | 81,0.4306692495768617, 83 | 82,0.4331576210058955, 84 | 83,0.4372383753751132, 85 | 84,0.43446562186858667, 86 | 85,0.43432124460760846, 87 | 86,0.43713334527810854, 88 | 87,0.440097869738563, 89 | 88,0.44276344813744284, 90 | 89,0.4404598518242747, 91 | 90,0.4394837586622387, 92 | 91,0.4447805743051705, 93 | 92,0.44349627953833337, 94 | 93,0.44473403908536957, 95 | 94,0.44482234941716037, 96 | 95,0.4473002277260749, 97 | 96,0.4410798644520213, 98 | 97,0.45141788876882527, 99 | 98,0.4435934683473075, 100 | 99,0.44435384341430834, 101 | 100,0.44861555246975393, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_miad_electrical_insulator/test_vit_vae_miad_electrical_insulator_test_values.txt: -------------------------------------------------------------------------------- 1 | epoch,aucroc, 2 | 1,0.34160631555547954, 3 | 2,0.41659810451169577, 4 | 3,0.42319396215559624, 5 | 4,0.37431258211808593, 6 | 5,0.37893465016716876, 7 | 6,0.3856874009824647, 8 | 7,0.3998442506378511, 9 | 8,0.3818759390235339, 10 | 9,0.3800564277662586, 11 | 10,0.38548093640731906, 12 | 11,0.3699479047673415, 13 | 12,0.38249819613307073, 14 | 13,0.3979274958719504, 15 | 14,0.3979286197782354, 16 | 15,0.39174499071445945, 17 | 16,0.3838474237892727, 18 | 17,0.38306486539010887, 19 | 18,0.3777427184407245, 20 | 19,0.38485187695833334, 21 | 20,0.39110855616264384, 22 | 21,0.3507433256051739, 23 | 22,0.4009856371569077, 24 | 23,0.406243317639943, 25 | 24,0.4171189446434437, 26 | 25,0.41654203509783655, 27 | 26,0.4221035705487358, 28 | 27,0.4076933083076325, 29 | 28,0.40624748364897934, 30 | 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99,0.40722516350587157, 101 | 100,0.4065063356269336, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_freeze_mvtec_zipper_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,72032.1640625,72131.578125,-99.43710327148438, 3 | 2,130327.296875,130387.5078125,-60.209129333496094, 4 | 3,136026.15625,136064.5625,-38.43333435058594, 5 | 4,136347.6875,136374.125,-26.46321678161621, 6 | 5,136518.6875,136540.203125,-21.514551162719727, 7 | 6,136568.15625,136590.609375,-22.443098068237305, 8 | 7,137099.625,137123.3125,-23.714826583862305, 9 | 8,137769.421875,137792.890625,-23.481731414794922, 10 | 9,138494.40625,138517.796875,-23.410205841064453, 11 | 10,138744.015625,138766.953125,-22.91396141052246, 12 | 11,136731.515625,136754.890625,-23.362123489379883, 13 | 12,138902.28125,138925.90625,-23.60040283203125, 14 | 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-------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_screw_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,12454.6728515625,12591.0556640625,-136.38070678710938, 3 | 2,28187.328125,28317.728515625,-130.3988494873047, 4 | 3,39931.2265625,40054.765625,-123.53621673583984, 5 | 4,48418.890625,48537.28515625,-118.39344787597656, 6 | 5,54474.99609375,54589.19921875,-114.21439361572266, 7 | 6,58562.5390625,58673.48046875,-110.95756530761719, 8 | 7,61391.08203125,61498.41796875,-107.35618591308594, 9 | 8,63176.66796875,63280.41796875,-103.77424621582031, 10 | 9,64295.5859375,64395.89453125,-100.28990936279297, 11 | 10,64996.8046875,65093.81640625,-97.01517486572266, 12 | 11,65357.41015625,65451.6171875,-94.21316528320312, 13 | 12,65655.96875,65747.28125,-91.2991943359375, 14 | 13,65824.65625,65913.1015625,-88.43528747558594, 15 | 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32,66427.6484375,66476.46875,-48.821876525878906, 34 | 33,66449.953125,66497.140625,-47.17557144165039, 35 | 34,66460.6875,66506.375,-45.71190643310547, 36 | 35,66468.1953125,66512.515625,-44.326393127441406, 37 | 36,66479.015625,66522.2265625,-43.18940734863281, 38 | 37,66496.1015625,66538.109375,-41.97109603881836, 39 | 38,66521.9921875,66562.84375,-40.837486267089844, 40 | 39,66539.9765625,66579.7265625,-39.7653923034668, 41 | 40,66566.5390625,66605.28125,-38.74103927612305, 42 | 41,66595.046875,66632.7265625,-37.677764892578125, 43 | 42,66613.1796875,66649.859375,-36.6898193359375, 44 | 43,66639.0546875,66674.84375,-35.782386779785156, 45 | 44,66661.40625,66696.296875,-34.88981246948242, 46 | 45,66694.546875,66728.6015625,-34.075008392333984, 47 | 46,66726.046875,66759.2890625,-33.240177154541016, 48 | 47,66562.40625,66594.625,-32.216217041015625, 49 | 48,66650.7421875,66682.921875,-32.18785095214844, 50 | 49,66772.4921875,66804.09375,-31.623563766479492, 51 | 50,66786.421875,66817.296875,-30.87550163269043, 52 | 51,66740.421875,66771.109375,-30.6677188873291, 53 | 52,66843.9296875,66874.46875,-30.53769302368164, 54 | 53,66881.5234375,66911.4140625,-29.897960662841797, 55 | 54,66904.6328125,66933.90625,-29.24945640563965, 56 | 55,66923.0703125,66951.671875,-28.618877410888672, 57 | 56,66938.0625,66966.09375,-28.01304054260254, 58 | 57,66943.3828125,66970.8515625,-27.470245361328125, 59 | 58,66939.8125,66966.8203125,-27.024866104125977, 60 | 59,66813.25,66839.828125,-26.583282470703125, 61 | 60,66848.1015625,66874.5546875,-26.47233009338379, 62 | 61,66978.3046875,67004.515625,-26.209318161010742, 63 | 62,67010.125,67035.9453125,-25.835079193115234, 64 | 63,67030.7421875,67056.1796875,-25.430322647094727, 65 | 64,67050.4609375,67075.4609375,-25.005538940429688, 66 | 65,67072.671875,67097.25,-24.577964782714844, 67 | 66,67089.9375,67114.1171875,-24.166736602783203, 68 | 67,67103.6796875,67127.421875,-23.763151168823242, 69 | 68,67120.671875,67144.078125,-23.386526107788086, 70 | 69,67137.3984375,67160.4609375,-23.044336318969727, 71 | 70,67153.03125,67175.7578125,-22.726533889770508, 72 | 71,67168.0078125,67190.4453125,-22.427156448364258, 73 | 72,67185.7578125,67207.8828125,-22.11954689025879, 74 | 73,67200.4296875,67222.2421875,-21.833139419555664, 75 | 74,67213.125,67234.7109375,-21.574996948242188, 76 | 75,67220.9453125,67242.3046875,-21.350521087646484, 77 | 76,67229.5546875,67250.6953125,-21.169601440429688, 78 | 77,67228.6953125,67249.71875,-21.03179931640625, 79 | 78,67248.7265625,67269.65625,-20.928428649902344, 80 | 79,67272.265625,67293.03125,-20.785980224609375, 81 | 80,67298.234375,67318.8359375,-20.596092224121094, 82 | 81,67326.125,67346.4609375,-20.34481430053711, 83 | 82,67345.296875,67365.3671875,-20.06620979309082, 84 | 83,67359.1328125,67378.9375,-19.794572830200195, 85 | 84,67372.0859375,67391.640625,-19.547122955322266, 86 | 85,67382.25,67401.578125,-19.32785987854004, 87 | 86,67394.109375,67413.21875,-19.12327766418457, 88 | 87,67400.4140625,67419.3671875,-18.95303726196289, 89 | 88,67404.0078125,67422.8515625,-18.821327209472656, 90 | 89,67399.4296875,67418.15625,-18.717140197753906, 91 | 90,67408.484375,67427.1796875,-18.68222999572754, 92 | 91,67433.4140625,67452.0703125,-18.63944435119629, 93 | 92,67457.6484375,67476.1796875,-18.52459144592285, 94 | 93,67479.9921875,67498.390625,-18.386993408203125, 95 | 94,67497.6796875,67515.9296875,-18.240869522094727, 96 | 95,67511.9296875,67530.03125,-18.103532791137695, 97 | 96,67526.578125,67544.53125,-17.96139144897461, 98 | 97,67541.0625,67558.8828125,-17.827192306518555, 99 | 98,67550.6640625,67568.375,-17.68490219116211, 100 | 99,67564.1171875,67581.6953125,-17.5705623626709, 101 | 100,67575.921875,67593.3828125,-17.479934692382812, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_freeze_mvtec_screw_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,41958.296875,42065.74609375,-107.44868469238281, 3 | 2,54504.5703125,54593.91796875,-89.35201263427734, 4 | 3,55512.828125,55599.58203125,-86.75811767578125, 5 | 4,57837.07421875,57927.7578125,-90.70346069335938, 6 | 5,59941.171875,60034.65234375,-93.47239685058594, 7 | 6,59174.51171875,59272.44140625,-97.92489624023438, 8 | 7,61701.76953125,61803.39453125,-101.62061309814453, 9 | 8,62561.81640625,62662.28125,-100.46514129638672, 10 | 9,62673.73046875,62773.20703125,-99.50293731689453, 11 | 10,63408.8671875,63507.91796875,-99.06893157958984, 12 | 11,61372.87109375,61475.578125,-102.71791076660156, 13 | 12,64037.15625,64143.69140625,-106.5207290649414, 14 | 13,64574.44921875,64678.9921875,-104.54511260986328, 15 | 14,64645.421875,64748.30078125,-102.87272644042969, 16 | 15,64589.578125,64690.7265625,-101.13565826416016, 17 | 16,62720.8984375,62825.45703125,-104.56060028076172, 18 | 17,64811.2578125,64919.421875,-108.16339111328125, 19 | 18,65062.453125,65168.4453125,-106.0218505859375, 20 | 19,65291.578125,65395.95703125,-104.38157653808594, 21 | 20,65561.6484375,65664.4921875,-102.83977508544922, 22 | 21,63952.6015625,64057.9609375,-105.37081146240234, 23 | 22,65597.453125,65706.0390625,-108.57427215576172, 24 | 23,65905.96875,66012.359375,-106.39209747314453, 25 | 24,66037.453125,66141.640625,-104.17127227783203, 26 | 25,66183.765625,66285.9140625,-102.12378692626953, 27 | 26,64835.125,64938.86328125,-103.73914337158203, 28 | 27,66023.125,66129.9609375,-106.83836364746094, 29 | 28,66374.046875,66478.5703125,-104.51490783691406, 30 | 29,66519.140625,66621.1640625,-102.02071380615234, 31 | 30,66624.0859375,66723.890625,-99.79866790771484, 32 | 31,65272.3203125,65373.359375,-101.03355407714844, 33 | 32,66280.3125,66384.4375,-104.13582611083984, 34 | 33,66721.015625,66822.8828125,-101.85942077636719, 35 | 34,66835.109375,66934.3671875,-99.2568588256836, 36 | 35,66851.0546875,66947.8125,-96.79147338867188, 37 | 36,66799.9140625,66894.3046875,-94.39595794677734, 38 | 37,66797.09375,66889.3515625,-92.24346160888672, 39 | 38,66865.875,66956.2578125,-90.37883758544922, 40 | 39,67001.0546875,67089.59375,-88.55987548828125, 41 | 40,67123.09375,67209.4609375,-86.39411163330078, 42 | 41,67174.8046875,67258.984375,-84.18651580810547, 43 | 42,67162.3828125,67244.4453125,-82.07296752929688, 44 | 43,67067.296875,67147.609375,-80.29803466796875, 45 | 44,67001.2890625,67080.2890625,-78.98774719238281, 46 | 45,67061.671875,67139.296875,-77.62909698486328, 47 | 46,67192.8359375,67269.1171875,-76.2852783203125, 48 | 47,67328.953125,67403.703125,-74.75192260742188, 49 | 48,67383.1953125,67456.265625,-73.09053802490234, 50 | 49,67374.03125,67445.5546875,-71.51914978027344, 51 | 50,67354.234375,67424.578125,-70.32975006103516, 52 | 51,64775.7734375,64856.671875,-80.90982818603516, 53 | 52,66883.828125,66973.453125,-89.63328552246094, 54 | 53,67439.078125,67525.4921875,-86.41996002197266, 55 | 54,67593.6015625,67676.7421875,-83.14328002929688, 56 | 55,67649.421875,67729.640625,-80.23626708984375, 57 | 56,67629.2890625,67706.921875,-77.63127136230469, 58 | 57,67546.015625,67621.2109375,-75.20640563964844, 59 | 58,67563.9765625,67636.9609375,-73.000732421875, 60 | 59,67624.0,67694.8984375,-70.93540954589844, 61 | 60,67652.953125,67722.1171875,-69.14952087402344, 62 | 61,67658.3203125,67725.796875,-67.48693084716797, 63 | 62,67654.4375,67720.265625,-65.82492065429688, 64 | 63,67597.2578125,67661.6328125,-64.36991882324219, 65 | 64,67614.2421875,67677.6171875,-63.37668991088867, 66 | 65,67653.6875,67716.1328125,-62.45603942871094, 67 | 66,67658.4453125,67719.9140625,-61.46857833862305, 68 | 67,67708.8046875,67769.1796875,-60.36673355102539, 69 | 68,67763.3671875,67822.546875,-59.197898864746094, 70 | 69,67785.875,67843.8203125,-57.96517562866211, 71 | 70,67790.6796875,67847.265625,-56.58973693847656, 72 | 71,67797.765625,67853.328125,-55.55824279785156, 73 | 72,67762.3984375,67817.2421875,-54.841339111328125, 74 | 73,67769.5234375,67823.875,-54.3309211730957, 75 | 74,67878.8515625,67932.3828125,-53.497955322265625, 76 | 75,67950.046875,68002.390625,-52.351802825927734, 77 | 76,67985.5078125,68036.7578125,-51.236602783203125, 78 | 77,67998.1015625,68048.140625,-50.033782958984375, 79 | 78,67982.328125,68031.3671875,-49.02375411987305, 80 | 79,67941.53125,67989.734375,-48.226409912109375, 81 | 80,67911.9296875,67959.8828125,-47.9372444152832, 82 | 81,67953.0625,68000.546875,-47.49530029296875, 83 | 82,68005.59375,68052.375,-46.76441192626953, 84 | 83,68058.609375,68104.28125,-45.65907669067383, 85 | 84,68077.125,68121.65625,-44.55458068847656, 86 | 85,68049.96875,68093.6640625,-43.681236267089844, 87 | 86,68009.828125,68052.953125,-43.1464958190918, 88 | 87,67988.0078125,68030.984375,-42.967891693115234, 89 | 88,68004.1171875,68046.953125,-42.85206604003906, 90 | 89,68041.296875,68083.734375,-42.44502258300781, 91 | 90,68126.2265625,68168.0234375,-41.79047775268555, 92 | 91,68199.984375,68240.7421875,-40.74610137939453, 93 | 92,68229.1953125,68268.859375,-39.647525787353516, 94 | 93,68224.65625,68263.3125,-38.654056549072266, 95 | 94,68180.6171875,68218.59375,-38.0032844543457, 96 | 95,68093.75,68131.734375,-37.98884201049805, 97 | 96,68098.4765625,68136.609375,-38.12714385986328, 98 | 97,68196.8125,68234.34375,-37.52915954589844, 99 | 98,68230.2734375,68266.984375,-36.72789764404297, 100 | 99,68220.71875,68256.828125,-36.11322784423828, 101 | 100,68195.359375,68230.859375,-35.5120849609375, 102 | -------------------------------------------------------------------------------- /vit_vae.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | import torch 4 | from torch import nn 5 | from torchvision.models.resnet import resnet18 6 | 7 | from transformers import ViTModel 8 | import torch.nn.functional as F 9 | import copy 10 | 11 | class ViTVAE(nn.Module): 12 | 13 | def __init__(self, img_size, nb_channels, latent_img_size, z_dim, rec_loss="xent", beta=1, delta=1): 14 | ''' 15 | ''' 16 | super(ViTVAE, self).__init__() 17 | 18 | self.img_size = img_size 19 | self.nb_channels = nb_channels 20 | self.latent_img_size = latent_img_size 21 | self.z_dim = z_dim 22 | self.beta = beta 23 | self.rec_loss = rec_loss 24 | self.delta = delta 25 | # self.nb_conv = int(np.log2((img_size+32) // latent_img_size)) 26 | self.nb_conv = 3 27 | self.max_depth_conv = 384 28 | 29 | 30 | self.vit = ViTModel.from_pretrained('google/vit-base-patch16-224') 31 | 32 | 33 | self.out_en = nn.Sequential( 34 | nn.Conv2d(3, 512, 35 | kernel_size=1, stride=1, padding=0), 36 | nn.BatchNorm2d(512), 37 | nn.LeakyReLU() 38 | ) 39 | 40 | 41 | 42 | self.decoder_layers = [] 43 | 44 | for i in reversed(range(1, 5)): 45 | depth_in = 3*2 ** (2+ i + 1) 46 | depth_out = 3*2 ** (2 + i) 47 | # print("depth out: ", depth_out) 48 | if i == 1: 49 | depth_out = self.nb_channels 50 | self.decoder_layers.append(nn.Sequential( 51 | 52 | nn.ConvTranspose2d(depth_in, depth_out, 4, 2, 1), 53 | )) 54 | else: 55 | self.decoder_layers.append(nn.Sequential( 56 | nn.ConvTranspose2d(depth_in, depth_out, 4, 2, 1), 57 | nn.BatchNorm2d(depth_out), 58 | nn.ReLU() 59 | )) 60 | self.conv_decoder = nn.Sequential( 61 | *self.decoder_layers 62 | ) 63 | 64 | def encoder(self, x): 65 | 66 | x = self.vit(x)[0][:,1:,:] 67 | x = x.permute(0, 2, 1) 68 | x = x.reshape(x.shape[0], x.shape[1], 14, 14) 69 | print("vit output: ", x.shape) 70 | 71 | return x[:, :self.z_dim], x[:, self.z_dim :] 72 | 73 | def reparameterize(self, mu, logvar): 74 | if self.training: 75 | std = torch.exp(torch.mul(logvar, 0.5)) 76 | eps = torch.randn_like(std) 77 | return eps * std + mu 78 | else: 79 | return mu 80 | 81 | def decoder(self, z): 82 | 83 | x = self.conv_decoder(z) 84 | 85 | # print("decoder output shape: ", x.shape) 86 | x = nn.Sigmoid()(x) 87 | return x 88 | 89 | def forward(self, x): 90 | mu, logvar = self.encoder(x) 91 | z = self.reparameterize(mu, logvar) 92 | self.mu = mu 93 | self.logvar = logvar 94 | return self.decoder(z), (mu, logvar) 95 | def xent_continuous_ber(self, recon_x, x, pixelwise=False): 96 | ''' p(x_i|z_i) a continuous bernoulli ''' 97 | eps = 1e-6 98 | def log_norm_const(x): 99 | # numerically stable computation 100 | x = torch.clamp(x, eps, 1 - eps) 101 | x = torch.where((x < 0.49) | (x > 0.51), x, 0.49 * 102 | torch.ones_like(x)) 103 | return torch.log((2 * self.tarctanh(1 - 2 * x)) / 104 | (1 - 2 * x) + eps) 105 | if pixelwise: 106 | return (x * torch.log(recon_x + eps) + 107 | (1 - x) * torch.log(1 - recon_x + eps) + 108 | log_norm_const(recon_x)) 109 | else: 110 | return torch.sum(x * torch.log(recon_x + eps) + 111 | (1 - x) * torch.log(1 - recon_x + eps) + 112 | log_norm_const(recon_x), dim=(1, 2, 3)) 113 | 114 | def mean_from_lambda(self, l): 115 | ''' because the mean of a continuous bernoulli is not its lambda ''' 116 | l = torch.clamp(l, 10e-6, 1 - 10e-6) 117 | l = torch.where((l < 0.49) | (l > 0.51), l, 0.49 * 118 | torch.ones_like(l)) 119 | return l / (2 * l - 1) + 1 / (2 * self.tarctanh(1 - 2 * l)) 120 | 121 | def kld(self): 122 | # NOTE -kld actually 123 | return 0.5 * torch.sum( 124 | 1 + self.logvar - self.mu.pow(2) - self.logvar.exp(), 125 | dim=(1) 126 | ) 127 | 128 | def loss_function(self, recon_x, x): 129 | rec_term = self.xent_continuous_ber(recon_x, x) 130 | rec_term = torch.mean(rec_term) 131 | 132 | kld = torch.mean(self.kld()) 133 | 134 | L = (rec_term + self.beta * kld) 135 | 136 | loss = L 137 | 138 | loss_dict = { 139 | 'loss': loss, 140 | 'rec_term': rec_term, 141 | '-beta*kld': self.beta * kld 142 | } 143 | 144 | return loss, loss_dict 145 | 146 | def step(self, input_mb): 147 | recon_mb, _ = self.forward(input_mb) 148 | 149 | loss, loss_dict = self.loss_function(recon_mb, input_mb) 150 | 151 | recon_mb = self.mean_from_lambda(recon_mb) 152 | 153 | return loss, recon_mb, loss_dict 154 | 155 | def tarctanh(self, x): 156 | return 0.5 * torch.log((1+x)/(1-x)) 157 | 158 | 159 | 160 | 161 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_capsule_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,57333.390625,57439.8515625,-106.47393035888672, 3 | 2,79185.0078125,79250.5234375,-65.53148651123047, 4 | 3,80743.96875,80795.3359375,-51.339969635009766, 5 | 4,81572.4453125,81615.9453125,-43.51609802246094, 6 | 5,82731.953125,82768.125,-36.16767120361328, 7 | 6,83586.5,83617.390625,-30.89835548400879, 8 | 7,84480.6171875,84507.1953125,-26.583045959472656, 9 | 8,85340.7890625,85364.6640625,-23.856342315673828, 10 | 9,86254.3125,86276.0,-21.692432403564453, 11 | 10,87174.0859375,87193.6328125,-19.581632614135742, 12 | 11,87335.1171875,87353.8359375,-18.70386505126953, 13 | 12,88628.2734375,88645.421875,-17.12936782836914, 14 | 13,89881.015625,89896.9609375,-15.937423706054688, 15 | 14,96946.625,96961.953125,-15.350759506225586, 16 | 15,103126.3359375,103141.46875,-15.143768310546875, 17 | 16,102923.96875,102939.25,-15.257322311401367, 18 | 17,103741.90625,103756.375,-14.486616134643555, 19 | 18,103979.328125,103992.9296875,-13.548652648925781, 20 | 19,104115.4765625,104128.25,-12.809814453125, 21 | 20,104170.421875,104182.484375,-12.035271644592285, 22 | 21,103177.25,103189.7265625,-12.496316909790039, 23 | 22,103995.046875,104006.84375,-11.810676574707031, 24 | 23,104214.78125,104225.3125,-10.553813934326172, 25 | 24,104291.8515625,104301.828125,-9.952325820922852, 26 | 25,104310.5234375,104320.171875,-9.64566421508789, 27 | 26,103226.734375,103237.296875,-10.573355674743652, 28 | 27,104117.9453125,104127.640625,-9.725154876708984, 29 | 28,104289.15625,104297.9375,-8.80718994140625, 30 | 29,104363.4609375,104371.9453125,-8.508641242980957, 31 | 30,104423.6015625,104431.8828125,-8.262117385864258, 32 | 31,103253.421875,103263.046875,-9.649145126342773, 33 | 32,104217.875,104226.34375,-8.478670120239258, 34 | 33,104462.015625,104469.671875,-7.690188407897949, 35 | 34,104512.5,104519.875,-7.387361526489258, 36 | 35,104537.0546875,104544.2890625,-7.230698585510254, 37 | 36,104536.9296875,104544.0078125,-7.091273784637451, 38 | 37,104563.796875,104570.703125,-6.882047653198242, 39 | 38,104601.3359375,104608.078125,-6.714791774749756, 40 | 39,104610.5859375,104617.1171875,-6.514689922332764, 41 | 40,104625.65625,104632.1484375,-6.465232849121094, 42 | 41,104633.234375,104639.609375,-6.354729652404785, 43 | 42,104638.796875,104645.03125,-6.260558128356934, 44 | 43,104642.40625,104648.53125,-6.157606601715088, 45 | 44,104669.125,104675.15625,-6.026417255401611, 46 | 45,104692.8515625,104698.7734375,-5.882482051849365, 47 | 46,104714.109375,104719.8515625,-5.764097690582275, 48 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64,104879.890625,104884.34375,-4.476861476898193, 66 | 65,104865.8359375,104870.3125,-4.48384952545166, 67 | 66,104866.421875,104870.8359375,-4.397346496582031, 68 | 67,104878.5078125,104882.8125,-4.339612007141113, 69 | 68,104874.890625,104879.1640625,-4.292916297912598, 70 | 69,104898.515625,104902.828125,-4.304200649261475, 71 | 70,104908.0703125,104912.3125,-4.243791580200195, 72 | 71,104923.609375,104927.7734375,-4.163493633270264, 73 | 72,104923.625,104927.71875,-4.086935520172119, 74 | 73,104924.671875,104928.71875,-4.032575607299805, 75 | 74,104923.8125,104927.8671875,-4.068153381347656, 76 | 75,104923.5546875,104927.53125,-3.9911813735961914, 77 | 76,104913.703125,104917.6640625,-3.961742639541626, 78 | 77,104927.515625,104931.4140625,-3.9082448482513428, 79 | 78,104943.1171875,104946.9765625,-3.8614604473114014, 80 | 79,104949.7109375,104953.5625,-3.8604280948638916, 81 | 80,104953.390625,104957.15625,-3.8017756938934326, 82 | 81,104957.8125,104961.515625,-3.7513604164123535, 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99,104981.8984375,104985.1171875,-3.2431704998016357, 101 | 100,104975.53125,104978.7734375,-3.2529468536376953, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_cable_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,23665.953125,23789.521484375,-123.5557861328125, 3 | 2,37361.98046875,37468.1796875,-106.19994354248047, 4 | 3,38123.25390625,38215.85546875,-92.60417938232422, 5 | 4,37891.421875,37981.4140625,-89.98763275146484, 6 | 5,38407.84765625,38490.25390625,-82.40739440917969, 7 | 6,38436.51953125,38511.37109375,-74.82958221435547, 8 | 7,38588.15234375,38655.96484375,-67.82028198242188, 9 | 8,38730.15234375,38790.8671875,-60.71840286254883, 10 | 9,39022.921875,39076.875,-53.94705581665039, 11 | 10,39717.58203125,39765.73828125,-48.1646614074707, 12 | 11,38441.6953125,38491.55078125,-49.842472076416016, 13 | 12,40335.203125,40379.51953125,-44.30678939819336, 14 | 13,41655.296875,41694.80859375,-39.5223274230957, 15 | 14,42887.01171875,42923.96484375,-36.96237564086914, 16 | 15,43714.09765625,43749.265625,-35.15961837768555, 17 | 16,38172.12109375,38213.75,-41.62757110595703, 18 | 17,43223.89453125,43259.9375,-36.0399055480957, 19 | 18,45395.16796875,45426.9453125,-31.78525161743164, 20 | 19,46230.25390625,46260.74609375,-30.507545471191406, 21 | 20,46731.7890625,46761.34375,-29.572248458862305, 22 | 21,38048.38671875,38085.0390625,-36.64076614379883, 23 | 22,44924.98046875,44956.25390625,-31.267370223999023, 24 | 23,47684.25,47711.63671875,-27.38671875, 25 | 24,48379.26171875,48405.84375,-26.578227996826172, 26 | 25,48567.4296875,48593.3984375,-25.981016159057617, 27 | 26,37916.296875,37949.35546875,-33.054786682128906, 28 | 27,46024.88671875,46052.80078125,-27.945552825927734, 29 | 28,49055.421875,49079.86328125,-24.442211151123047, 30 | 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63,53985.78125,54001.69140625,-15.891814231872559, 65 | 64,54060.4296875,54076.21875,-15.791098594665527, 66 | 65,54107.0078125,54122.7265625,-15.72581672668457, 67 | 66,54160.47265625,54176.0859375,-15.619061470031738, 68 | 67,54211.57421875,54227.06640625,-15.503527641296387, 69 | 68,54258.96484375,54274.40625,-15.429764747619629, 70 | 69,54336.03125,54351.35546875,-15.310962677001953, 71 | 70,54368.1015625,54383.29296875,-15.194890975952148, 72 | 71,54372.1328125,54387.2578125,-15.108302116394043, 73 | 72,54406.87890625,54421.90234375,-15.012788772583008, 74 | 73,54441.87890625,54456.8359375,-14.929190635681152, 75 | 74,54472.9765625,54487.84375,-14.85902214050293, 76 | 75,54527.59375,54542.41796875,-14.815475463867188, 77 | 76,54618.40625,54633.13671875,-14.72769832611084, 78 | 77,54727.171875,54741.8515625,-14.675111770629883, 79 | 78,54832.40234375,54847.05078125,-14.637508392333984, 80 | 79,54909.98828125,54924.58984375,-14.600902557373047, 81 | 80,54941.578125,54956.0390625,-14.467124938964844, 82 | 81,54927.32421875,54941.7109375,-14.395965576171875, 83 | 82,54935.08984375,54949.49609375,-14.404417991638184, 84 | 83,54946.84375,54961.23828125,-14.371377944946289, 85 | 84,54923.76953125,54938.08203125,-14.297368049621582, 86 | 85,54907.5234375,54921.75390625,-14.220307350158691, 87 | 86,55003.24609375,55017.40625,-14.171613693237305, 88 | 87,55125.75390625,55139.9609375,-14.192099571228027, 89 | 88,53078.8828125,53091.51171875,-12.632488250732422, 90 | 89,53462.16015625,53471.0390625,-8.870773315429688, 91 | 90,54546.7734375,54555.5625,-8.781289100646973, 92 | 91,54817.96484375,54826.84765625,-8.877996444702148, 93 | 92,54816.7109375,54825.74609375,-9.027688980102539, 94 | 93,54752.6796875,54761.82421875,-9.130064964294434, 95 | 94,54834.1171875,54843.28125,-9.18272590637207, 96 | 95,55038.28125,55047.51171875,-9.235821723937988, 97 | 96,55225.89453125,55235.2109375,-9.307015419006348, 98 | 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10,114600.0078125,114622.203125,-22.177684783935547, 12 | 11,113447.171875,113470.3984375,-23.255979537963867, 13 | 12,114240.375,114260.171875,-19.795482635498047, 14 | 13,114749.1171875,114766.3984375,-17.286855697631836, 15 | 14,114921.9609375,114938.3984375,-16.450931549072266, 16 | 15,114956.359375,114972.0546875,-15.758979797363281, 17 | 16,113463.0859375,113478.6484375,-15.555798530578613, 18 | 17,114564.09375,114578.453125,-14.333565711975098, 19 | 18,115026.5234375,115040.1328125,-13.53559398651123, 20 | 19,115111.765625,115124.875,-13.09748363494873, 21 | 20,115197.7578125,115210.5390625,-12.79898738861084, 22 | 21,113451.46875,113464.5078125,-13.022626876831055, 23 | 22,114789.484375,114801.4140625,-11.957857131958008, 24 | 23,115238.578125,115249.9453125,-11.37261962890625, 25 | 24,115304.1328125,115315.125,-11.022425651550293, 26 | 25,115327.4140625,115338.1484375,-10.749424934387207, 27 | 26,113479.1015625,113490.4921875,-11.376007080078125, 28 | 27,114892.078125,114902.421875,-10.371967315673828, 29 | 28,115338.6640625,115348.5234375,-9.81988525390625, 30 | 29,115448.0234375,115457.609375,-9.605940818786621, 31 | 30,115515.203125,115524.546875,-9.350250244140625, 32 | 31,113498.0625,113508.1796875,-10.081061363220215, 33 | 32,115101.59375,115110.78125,-9.156499862670898, 34 | 33,115493.734375,115502.2890625,-8.517082214355469, 35 | 34,115554.1953125,115562.53125,-8.322857856750488, 36 | 35,115599.6015625,115607.7265625,-8.134735107421875, 37 | 36,115660.6796875,115668.703125,-8.040152549743652, 38 | 37,115707.4609375,115715.390625,-7.915395259857178, 39 | 38,115762.1171875,115769.8203125,-7.704136848449707, 40 | 39,115801.015625,115808.546875,-7.542995929718018, 41 | 40,115824.546875,115831.984375,-7.447357654571533, 42 | 41,115826.796875,115834.1015625,-7.354088306427002, 43 | 42,115838.34375,115845.546875,-7.254711151123047, 44 | 43,115850.078125,115857.2421875,-7.14811372756958, 45 | 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92,27781.31640625,27783.798828125,-2.477428913116455, 94 | 93,27797.169921875,27799.642578125,-2.465561628341675, 95 | 94,27809.818359375,27812.275390625,-2.4603612422943115, 96 | 95,27819.880859375,27822.333984375,-2.4576914310455322, 97 | 96,27839.208984375,27841.67578125,-2.4680206775665283, 98 | 97,27851.2421875,27853.69921875,-2.4594013690948486, 99 | 98,27867.443359375,27869.90234375,-2.4569900035858154, 100 | 99,27877.630859375,27880.08984375,-2.457322835922241, 101 | 100,27893.20703125,27895.685546875,-2.4714882373809814, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_grid_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,1879.248779296875,1968.688720703125,-89.43944549560547, 3 | 2,2395.16259765625,2433.791259765625,-38.628562927246094, 4 | 3,2428.53466796875,2450.069091796875,-21.533620834350586, 5 | 4,2448.989990234375,2458.178466796875,-9.18725872039795, 6 | 5,2471.96630859375,2480.208984375,-8.242996215820312, 7 | 6,2450.579833984375,2455.9765625,-5.397418975830078, 8 | 7,2566.91162109375,2572.709228515625,-5.797798156738281, 9 | 8,2823.552490234375,2830.6376953125,-7.085775852203369, 10 | 9,3339.728271484375,3347.230224609375,-7.501388072967529, 11 | 10,4174.46240234375,4181.7646484375,-7.302094459533691, 12 | 11,2384.89404296875,2392.29443359375,-7.400285720825195, 13 | 12,4907.076171875,4913.962890625,-6.888442516326904, 14 | 13,6181.12744140625,6189.236328125,-8.10931396484375, 15 | 14,7600.5380859375,7607.11669921875,-6.578573703765869, 16 | 15,8960.830078125,8967.54296875,-6.716297626495361, 17 | 16,2145.1650390625,2151.6181640625,-6.452633857727051, 18 | 17,8813.0146484375,8819.5380859375,-6.523341178894043, 19 | 18,11834.060546875,11840.7431640625,-6.687376499176025, 20 | 19,13627.7314453125,13634.45703125,-6.725391387939453, 21 | 20,14783.0087890625,14789.8486328125,-6.841118812561035, 22 | 21,1898.525390625,1904.828857421875,-6.30275821685791, 23 | 22,11544.9501953125,11551.5458984375,-6.600620746612549, 24 | 23,15527.9921875,15534.80078125,-6.80873966217041, 25 | 24,16560.462890625,16567.28125,-6.819511890411377, 26 | 25,17203.35546875,17210.19921875,-6.8430399894714355, 27 | 26,1735.3951416015625,1741.93408203125,-6.538581371307373, 28 | 27,12874.603515625,12881.345703125,-6.740387439727783, 29 | 28,17444.771484375,17451.55859375,-6.787528991699219, 30 | 29,18286.798828125,18293.630859375,-6.836643695831299, 31 | 30,18755.193359375,18762.025390625,-6.8322858810424805, 32 | 31,1524.648193359375,1530.775146484375,-6.127169132232666, 33 | 32,13828.6474609375,13835.2998046875,-6.657357692718506, 34 | 33,18687.52734375,18694.34765625,-6.815176963806152, 35 | 34,19470.57421875,19477.4140625,-6.834837436676025, 36 | 35,19829.240234375,19836.0703125,-6.821222305297852, 37 | 36,20029.00390625,20035.8515625,-6.854947090148926, 38 | 37,20144.501953125,20151.353515625,-6.8507843017578125, 39 | 38,20281.89453125,20288.7421875,-6.846615314483643, 40 | 39,20423.75390625,20430.650390625,-6.889188766479492, 41 | 40,20549.513671875,20556.359375,-6.846726894378662, 42 | 41,20599.734375,20606.5390625,-6.811519622802734, 43 | 42,20661.884765625,20668.673828125,-6.78491735458374, 44 | 43,20720.818359375,20727.60546875,-6.796228885650635, 45 | 44,20834.353515625,20841.125,-6.7719502449035645, 46 | 45,21023.98046875,21030.740234375,-6.755321025848389, 47 | 46,21188.884765625,21195.630859375,-6.747964382171631, 48 | 47,21331.1875,21337.9453125,-6.752517223358154, 49 | 48,21441.875,21448.59765625,-6.723874568939209, 50 | 49,21550.8828125,21557.591796875,-6.701201438903809, 51 | 50,21661.359375,21668.078125,-6.711892127990723, 52 | 51,1671.222900390625,1677.219482421875,-5.996818542480469, 53 | 52,15475.322265625,15482.001953125,-6.6762518882751465, 54 | 53,21405.7578125,21412.39453125,-6.643373012542725, 55 | 54,22032.5078125,22039.169921875,-6.658727169036865, 56 | 55,22250.134765625,22256.82421875,-6.67793607711792, 57 | 56,22288.830078125,22295.5078125,-6.668408393859863, 58 | 57,22251.123046875,22257.779296875,-6.663388252258301, 59 | 58,22057.28125,22063.96484375,-6.68434476852417, 60 | 59,21953.802734375,21960.521484375,-6.714709758758545, 61 | 60,21948.69140625,21955.34765625,-6.657466411590576, 62 | 61,22087.5859375,22094.21484375,-6.6216278076171875, 63 | 62,22197.283203125,22203.900390625,-6.621801853179932, 64 | 63,22335.5078125,22342.15234375,-6.633203029632568, 65 | 64,22459.697265625,22466.2578125,-6.561906814575195, 66 | 65,22615.13671875,22621.75,-6.612194538116455, 67 | 66,22713.95703125,22720.54296875,-6.600396633148193, 68 | 67,22762.287109375,22768.853515625,-6.568721771240234, 69 | 68,22798.765625,22805.3203125,-6.5493927001953125, 70 | 69,22802.30078125,22808.86328125,-6.551514625549316, 71 | 70,22808.6953125,22815.251953125,-6.559934616088867, 72 | 71,22825.27734375,22831.79296875,-6.5181660652160645, 73 | 72,22861.0859375,22867.59765625,-6.511868953704834, 74 | 73,22883.060546875,22889.5703125,-6.515264511108398, 75 | 74,22925.267578125,22931.802734375,-6.536333084106445, 76 | 75,22987.36328125,22993.859375,-6.495779991149902, 77 | 76,23042.642578125,23049.154296875,-6.507381916046143, 78 | 77,23107.31640625,23113.81640625,-6.505985260009766, 79 | 78,23140.298828125,23146.80078125,-6.503728866577148, 80 | 79,23121.71875,23128.20703125,-6.494779109954834, 81 | 80,23123.7890625,23130.2578125,-6.468773365020752, 82 | 81,23139.71875,23146.1875,-6.466261863708496, 83 | 82,23049.453125,23055.888671875,-6.43304443359375, 84 | 83,23050.81640625,23057.23828125,-6.4180521965026855, 85 | 84,23053.802734375,23060.19140625,-6.395423412322998, 86 | 85,23107.8046875,23114.1796875,-6.376999855041504, 87 | 86,23246.9375,23253.306640625,-6.374966144561768, 88 | 87,23333.662109375,23340.0390625,-6.375049114227295, 89 | 88,23443.091796875,23449.46875,-6.3719353675842285, 90 | 89,23514.2890625,23520.662109375,-6.377126693725586, 91 | 90,23564.984375,23571.369140625,-6.3810038566589355, 92 | 91,23559.18359375,23565.556640625,-6.364619731903076, 93 | 92,23552.5390625,23558.919921875,-6.364156246185303, 94 | 93,23519.060546875,23525.451171875,-6.391055583953857, 95 | 94,23547.64453125,23554.001953125,-6.366201400756836, 96 | 95,23535.58203125,23541.923828125,-6.339937210083008, 97 | 96,23537.31640625,23543.677734375,-6.369485855102539, 98 | 97,23578.1484375,23584.5078125,-6.358438014984131, 99 | 98,23575.158203125,23581.48828125,-6.326940059661865, 100 | 99,23597.40234375,23603.708984375,-6.303036212921143, 101 | 100,23547.091796875,23553.40234375,-6.311293125152588, 102 | -------------------------------------------------------------------------------- /torch_logs/test_vit_vae_mvtec_tile_loss_values.txt: -------------------------------------------------------------------------------- 1 | epoch,loss,rec_term,-beta*kld, 2 | 1,1272.4169921875,1392.7215576171875,-120.30499267578125, 3 | 2,1688.3067626953125,1732.8446044921875,-44.538185119628906, 4 | 3,1714.131591796875,1737.7825927734375,-23.651161193847656, 5 | 4,1728.46826171875,1742.9937744140625,-14.526482582092285, 6 | 5,1744.2891845703125,1754.369140625,-10.080055236816406, 7 | 6,1741.93017578125,1749.89697265625,-7.966800212860107, 8 | 7,1780.3360595703125,1787.7186279296875,-7.382688522338867, 9 | 8,1872.087158203125,1880.5927734375,-8.506059646606445, 10 | 9,2034.3828125,2043.746826171875,-9.363910675048828, 11 | 10,2215.71337890625,2224.73095703125,-9.017610549926758, 12 | 11,1718.876708984375,1726.63037109375,-7.753629207611084, 13 | 12,2273.729736328125,2280.121337890625,-6.39206600189209, 14 | 13,2474.5068359375,2481.8583984375,-7.351779460906982, 15 | 14,2639.449462890625,2646.6728515625,-7.2227702140808105, 16 | 15,2792.8369140625,2799.99169921875,-7.153892993927002, 17 | 16,1703.340576171875,1709.72900390625,-6.388490676879883, 18 | 17,2684.7890625,2690.676513671875,-5.887038707733154, 19 | 18,3096.43896484375,3102.686767578125,-6.247800827026367, 20 | 19,3385.486328125,3391.784423828125,-6.298841953277588, 21 | 20,3658.06201171875,3664.53955078125,-6.476823329925537, 22 | 21,1651.7772216796875,1657.9871826171875,-6.210011959075928, 23 | 22,3292.31640625,3298.0625,-5.746702671051025, 24 | 23,3947.225341796875,3953.551513671875,-6.324798107147217, 25 | 24,4304.396484375,4310.8701171875,-6.474338054656982, 26 | 25,4574.08154296875,4580.72216796875,-6.639566898345947, 27 | 26,1581.8782958984375,1588.287109375,-6.408655643463135, 28 | 27,3905.422607421875,3911.30029296875,-5.877096176147461, 29 | 28,4883.61181640625,4889.9619140625,-6.3506269454956055, 30 | 29,5283.703125,5290.189453125,-6.4850687980651855, 31 | 30,5540.7314453125,5547.39501953125,-6.664477825164795, 32 | 31,1544.0087890625,1549.983154296875,-5.973762035369873, 33 | 32,4599.46923828125,4605.60546875,-6.136614799499512, 34 | 33,5793.03173828125,5799.65673828125,-6.625733375549316, 35 | 34,6175.2177734375,6181.9169921875,-6.69929313659668, 36 | 35,6354.50537109375,6361.35302734375,-6.846960544586182, 37 | 36,6449.453125,6456.4384765625,-6.98342752456665, 38 | 37,6592.55078125,6599.6787109375,-7.126505374908447, 39 | 38,6793.509765625,6800.7421875,-7.23406457901001, 40 | 39,6993.095703125,7000.37158203125,-7.277365684509277, 41 | 40,7169.9716796875,7177.24169921875,-7.267228126525879, 42 | 41,7317.169921875,7324.505859375,-7.3364057540893555, 43 | 42,7439.7666015625,7447.18115234375,-7.415561199188232, 44 | 43,7540.9091796875,7548.41650390625,-7.50981330871582, 45 | 44,7622.09423828125,7629.716796875,-7.621334075927734, 46 | 45,7690.50390625,7698.21923828125,-7.715265274047852, 47 | 46,7801.92822265625,7809.6826171875,-7.756030082702637, 48 | 47,7939.01513671875,7946.7890625,-7.773240566253662, 49 | 48,8060.34326171875,8068.14404296875,-7.80089807510376, 50 | 49,8154.341796875,8162.1767578125,-7.834503650665283, 51 | 50,8241.40625,8249.2783203125,-7.871524333953857, 52 | 51,1338.25634765625,1344.404052734375,-6.147641181945801, 53 | 52,6099.26318359375,6106.6201171875,-7.355661392211914, 54 | 53,8242.689453125,8250.505859375,-7.815952301025391, 55 | 54,8612.138671875,8619.984375,-7.846371650695801, 56 | 55,8801.666015625,8809.5390625,-7.872689247131348, 57 | 56,8910.7470703125,8918.6533203125,-7.906557083129883, 58 | 57,8963.6923828125,8971.6337890625,-7.940813064575195, 59 | 58,8935.8515625,8943.826171875,-7.976507186889648, 60 | 59,8834.3232421875,8842.3349609375,-8.011957168579102, 61 | 60,8767.7880859375,8775.8359375,-8.050956726074219, 62 | 61,8833.40625,8841.4755859375,-8.066851615905762, 63 | 62,8939.2021484375,8947.2939453125,-8.096416473388672, 64 | 63,9036.5517578125,9044.6884765625,-8.136581420898438, 65 | 64,9116.4921875,9124.658203125,-8.165067672729492, 66 | 65,9184.4072265625,9192.599609375,-8.194713592529297, 67 | 66,9239.701171875,9247.9111328125,-8.21180248260498, 68 | 67,9284.1396484375,9292.3798828125,-8.237006187438965, 69 | 68,9308.185546875,9316.4306640625,-8.246399879455566, 70 | 69,9325.7275390625,9334.0009765625,-8.270928382873535, 71 | 70,9363.7373046875,9371.994140625,-8.25700569152832, 72 | 71,9441.4853515625,9449.7529296875,-8.269933700561523, 73 | 72,9535.1533203125,9543.439453125,-8.285029411315918, 74 | 73,9626.279296875,9634.5732421875,-8.292728424072266, 75 | 74,9701.1337890625,9709.4296875,-8.301007270812988, 76 | 75,9762.64453125,9770.9423828125,-8.297630310058594, 77 | 76,9820.8837890625,9829.1748046875,-8.290060043334961, 78 | 77,9867.171875,9875.4599609375,-8.284287452697754, 79 | 78,9905.0224609375,9913.3232421875,-8.305174827575684, 80 | 79,9945.0185546875,9953.3212890625,-8.304866790771484, 81 | 80,9985.4228515625,9993.7255859375,-8.303897857666016, 82 | 81,10028.4169921875,10036.71484375,-8.299776077270508, 83 | 82,10075.66796875,10083.9814453125,-8.313194274902344, 84 | 83,10116.263671875,10124.5751953125,-8.314276695251465, 85 | 84,10155.31640625,10163.6376953125,-8.322975158691406, 86 | 85,10187.025390625,10195.357421875,-8.327457427978516, 87 | 86,10216.40234375,10224.736328125,-8.335440635681152, 88 | 87,10239.896484375,10248.2451171875,-8.345924377441406, 89 | 88,10252.6044921875,10260.9580078125,-8.356954574584961, 90 | 89,10256.1435546875,10264.5009765625,-8.359593391418457, 91 | 90,10256.1103515625,10264.484375,-8.375662803649902, 92 | 91,10255.7666015625,10264.1591796875,-8.392746925354004, 93 | 92,10264.5751953125,10272.98828125,-8.416266441345215, 94 | 93,10294.46484375,10302.9033203125,-8.43930721282959, 95 | 94,10370.9951171875,10379.4462890625,-8.454097747802734, 96 | 95,10452.3251953125,10460.775390625,-8.449383735656738, 97 | 96,10517.353515625,10525.8037109375,-8.448058128356934, 98 | 97,10558.0732421875,10566.5283203125,-8.457788467407227, 99 | 98,10583.1845703125,10591.642578125,-8.461698532104492, 100 | 99,10596.0810546875,10604.546875,-8.467789649963379, 101 | 100,10604.7021484375,10613.173828125,-8.475193977355957, 102 | -------------------------------------------------------------------------------- /vae.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | from torch import nn 4 | from torchvision.models.resnet import resnet18 5 | 6 | class VAE(nn.Module): 7 | 8 | def __init__(self, img_size, nb_channels, latent_img_size, z_dim, rec_loss="xent", beta=1, delta=1): 9 | ''' 10 | ''' 11 | super(VAE, self).__init__() 12 | 13 | self.img_size = img_size 14 | self.nb_channels = nb_channels 15 | self.latent_img_size = latent_img_size 16 | self.z_dim = z_dim 17 | self.beta = beta 18 | self.rec_loss = rec_loss 19 | self.delta = delta 20 | self.nb_conv = int(np.log2(img_size // latent_img_size)) 21 | # the depth we will have at the end of the encoder given that a 22 | # convolution incease depth by 2 starting at 32 after the first 23 | self.max_depth_conv = 2 ** (4 + self.nb_conv) 24 | 25 | self.resnet = resnet18(pretrained=False) 26 | self.dino = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14') 27 | self.dino_entry = nn.Sequential( 28 | nn.Conv2d(self.nb_channels, 384, kernel_size=7, 29 | stride=2, padding=3, bias=False) 30 | ) 31 | self.resnet_entry = nn.Sequential( 32 | nn.Conv2d(self.nb_channels, 64, kernel_size=7, 33 | stride=2, padding=3, bias=False), 34 | self.resnet.bn1, 35 | self.resnet.relu, 36 | self.resnet.maxpool 37 | ) 38 | self.resnet18_layer_list = [ 39 | self.resnet.layer1, 40 | self.resnet.layer2, 41 | self.resnet.layer3, 42 | self.resnet.layer4 43 | ] 44 | self.encoder_layers = [self.resnet_entry] 45 | for i in range(1, self.nb_conv): 46 | try: 47 | self.encoder_layers.append(self.resnet18_layer_list[i - 1]) 48 | except IndexError: 49 | depth_in = 2 ** (4 + i) 50 | depth_out = 2 ** (4 + i + 1) 51 | self.encoder_layers.append(nn.Sequential( 52 | nn.Conv2d(depth_in, depth_out, 4, 2, 1), 53 | nn.BatchNorm2d(depth_out), 54 | nn.ReLU() 55 | )) 56 | self.conv_encoder = nn.Sequential( 57 | *self.encoder_layers, 58 | ) 59 | self.final_encoder = nn.Sequential( 60 | nn.Conv2d(self.max_depth_conv, self.z_dim * 2, kernel_size=1, 61 | stride=1, padding=0) 62 | ) 63 | 64 | self.initial_decoder = nn.Sequential( 65 | nn.ConvTranspose2d(self.z_dim, self.max_depth_conv, 66 | kernel_size=1, stride=1, padding=0), 67 | nn.BatchNorm2d(self.max_depth_conv), 68 | nn.ReLU() 69 | ) 70 | 71 | nb_conv_dec = self.nb_conv 72 | 73 | self.decoder_layers = [] 74 | for i in reversed(range(nb_conv_dec)): 75 | depth_in = 2 ** (4 + i + 1) 76 | depth_out = 2 ** (4 + i) 77 | if i == 0: 78 | depth_out = self.nb_channels 79 | self.decoder_layers.append(nn.Sequential( 80 | nn.ConvTranspose2d(depth_in, depth_out, 4, 2, 1), 81 | )) 82 | else: 83 | self.decoder_layers.append(nn.Sequential( 84 | nn.ConvTranspose2d(depth_in, depth_out, 4, 2, 1), 85 | nn.BatchNorm2d(depth_out), 86 | nn.ReLU() 87 | )) 88 | self.conv_decoder = nn.Sequential( 89 | *self.decoder_layers 90 | ) 91 | 92 | 93 | def encoder(self, x): 94 | x = self.conv_encoder(x) 95 | x = self.final_encoder(x) 96 | return x[:, :self.z_dim], x[:, self.z_dim:] 97 | 98 | def reparameterize(self, mu, logvar): 99 | if self.training: 100 | std = torch.exp(torch.mul(logvar, 0.5)) 101 | eps = torch.randn_like(std) 102 | return eps * std + mu 103 | else: 104 | return mu 105 | 106 | def decoder(self, z): 107 | z = self.initial_decoder(z) 108 | x = self.conv_decoder(z) 109 | x = nn.Sigmoid()(x) 110 | return x 111 | 112 | def forward(self, x): 113 | mu, logvar = self.encoder(x) 114 | z = self.reparameterize(mu, logvar) 115 | self.mu = mu 116 | print(mu.shape) 117 | self.logvar = logvar 118 | return self.decoder(z), (mu, logvar) 119 | 120 | def xent_continuous_ber(self, recon_x, x, pixelwise=False): 121 | ''' p(x_i|z_i) a continuous bernoulli ''' 122 | eps = 1e-6 123 | def log_norm_const(x): 124 | # numerically stable computation 125 | x = torch.clamp(x, eps, 1 - eps) 126 | x = torch.where((x < 0.49) | (x > 0.51), x, 0.49 * 127 | torch.ones_like(x)) 128 | return torch.log((2 * self.tarctanh(1 - 2 * x)) / 129 | (1 - 2 * x) + eps) 130 | if pixelwise: 131 | return (x * torch.log(recon_x + eps) + 132 | (1 - x) * torch.log(1 - recon_x + eps) + 133 | log_norm_const(recon_x)) 134 | else: 135 | return torch.sum(x * torch.log(recon_x + eps) + 136 | (1 - x) * torch.log(1 - recon_x + eps) + 137 | log_norm_const(recon_x), dim=(1, 2, 3)) 138 | 139 | def mean_from_lambda(self, l): 140 | ''' because the mean of a continuous bernoulli is not its lambda ''' 141 | l = torch.clamp(l, 10e-6, 1 - 10e-6) 142 | l = torch.where((l < 0.49) | (l > 0.51), l, 0.49 * 143 | torch.ones_like(l)) 144 | return l / (2 * l - 1) + 1 / (2 * self.tarctanh(1 - 2 * l)) 145 | 146 | def kld(self): 147 | # NOTE -kld actually 148 | return 0.5 * torch.sum( 149 | 1 + self.logvar - self.mu.pow(2) - self.logvar.exp(), 150 | dim=(1) 151 | ) 152 | 153 | def loss_function(self, recon_x, x): 154 | rec_term = self.xent_continuous_ber(recon_x, x) 155 | rec_term = torch.mean(rec_term) 156 | 157 | kld = torch.mean(self.kld()) 158 | 159 | L = (rec_term + self.beta * kld) 160 | 161 | loss = L 162 | 163 | loss_dict = { 164 | 'loss': loss, 165 | 'rec_term': rec_term, 166 | '-beta*kld': self.beta * kld 167 | } 168 | 169 | return loss, loss_dict 170 | 171 | def step(self, input_mb): 172 | recon_mb, _ = self.forward(input_mb) 173 | 174 | loss, loss_dict = self.loss_function(recon_mb, input_mb) 175 | 176 | recon_mb = self.mean_from_lambda(recon_mb) 177 | 178 | return loss, recon_mb, loss_dict 179 | 180 | def tarctanh(self, x): 181 | return 0.5 * torch.log((1+x)/(1-x)) 182 | 183 | 184 | -------------------------------------------------------------------------------- /vae_train.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import numpy as np 4 | import time 5 | 6 | 7 | from torchvision import transforms, utils 8 | import torch 9 | from torch import nn 10 | 11 | from utils import (get_train_dataloader, 12 | get_test_dataloader, 13 | load_model_parameters, 14 | load_vqvae, 15 | update_loss_dict, 16 | print_loss_logs, 17 | print_AUCROC_logs, 18 | parse_args 19 | ) 20 | 21 | import sys 22 | from vae_test import test_on_train 23 | 24 | def train(model, train_loader, device, optimizer, epoch): 25 | 26 | model.train() 27 | train_loss = 0 28 | loss_dict = {} 29 | 30 | 31 | for batch_idx, (input_mb, lbl) in enumerate(train_loader): 32 | print(batch_idx + 1, end=", ", flush=True) 33 | input_mb = input_mb.to(device) 34 | lbl = lbl.to(device) 35 | optimizer.zero_grad() # otherwise grads accumulate in backward 36 | 37 | loss, recon_mb, loss_dict_new = model.step( 38 | input_mb 39 | ) 40 | 41 | (-loss).backward() #calculate the gradient => đạo hàm 42 | train_loss += loss.item() 43 | loss_dict = update_loss_dict(loss_dict, loss_dict_new) 44 | optimizer.step() #chạy adam với loss đã được tính 45 | 46 | nb_mb_it = (len(train_loader.dataset) // input_mb.shape[0]) 47 | train_loss /= nb_mb_it 48 | loss_dict = {k:v / nb_mb_it for k, v in loss_dict.items()} 49 | print(loss_dict) 50 | return train_loss, input_mb, recon_mb, loss_dict, lbl 51 | 52 | 53 | def eval(model, test_loader, device): 54 | model.eval() 55 | input_mb, gt_mb = next(iter(test_loader)) 56 | gt_mb = gt_mb.to(device) 57 | input_mb = input_mb.to(device) 58 | recon_mb, opt_out = model(input_mb) 59 | recon_mb = model.mean_from_lambda(recon_mb) 60 | return input_mb, recon_mb, gt_mb, opt_out 61 | 62 | 63 | 64 | def main(args): 65 | test_aucroc_dict = {} 66 | device = torch.device( 67 | "cuda" if torch.cuda.is_available() and not args.force_cpu else "cpu" 68 | ) 69 | print("Cuda available ?", torch.cuda.is_available()) 70 | print("Pytorch device:", device) 71 | seed = 11 72 | torch.manual_seed(seed) 73 | if torch.cuda.is_available(): 74 | torch.cuda.manual_seed(seed) 75 | np.random.seed(seed) 76 | 77 | model = load_vqvae(args) 78 | model.to(device) 79 | 80 | train_dataloader, train_dataset = get_train_dataloader(args) 81 | test_dataloader, test_dataset = get_test_dataloader(args) 82 | 83 | nb_channels = args.nb_channels 84 | 85 | img_size = args.img_size 86 | batch_size = args.batch_size 87 | batch_size_test = args.batch_size_test 88 | 89 | print("Nb channels", nb_channels, "img_size", img_size, 90 | "mini batch size", batch_size) 91 | 92 | 93 | out_dir = './torch_logs' 94 | if not os.path.isdir(out_dir): 95 | os.mkdir(out_dir) 96 | checkpoints_dir ="./torch_checkpoints" 97 | if not os.path.isdir(checkpoints_dir): 98 | os.mkdir(checkpoints_dir) 99 | checkpoints_saved_dir ="./torch_checkpoints_saved" 100 | res_dir = './torch_results' 101 | if not os.path.isdir(res_dir): 102 | os.mkdir(res_dir) 103 | data_dir = './torch_datasets' 104 | if not os.path.isdir(data_dir): 105 | os.mkdir(data_dir) 106 | 107 | 108 | try: 109 | if args.force_train: 110 | raise FileNotFoundError 111 | file_name = f"{args.exp}_{args.params_id}.pth" 112 | model = load_model_parameters(model, file_name, checkpoints_dir, 113 | checkpoints_saved_dir, device) 114 | except FileNotFoundError: 115 | print("Starting training") 116 | #print([p for p in model.parameters()]) 117 | if args.model == "vae_grf": 118 | parameter_names = ['logsigma_prior', 'logrange_prior', 'mu_prior'] 119 | base_params = [p[1] for p in filter( 120 | lambda p: ((p[0] not in parameter_names) and 121 | (p[1].requires_grad)), 122 | model.named_parameters() 123 | )] 124 | vae_params = [p[1] for p in filter( 125 | lambda p: ((p[0] in parameter_names) and 126 | (p[1].requires_grad)), 127 | model.named_parameters() 128 | )] 129 | optimizer = torch.optim.Adam( 130 | [{'params':base_params}, 131 | {'params':vae_params, 132 | 'lr':100*args.lr 133 | } 134 | ], 135 | lr=args.lr 136 | ) 137 | else: 138 | optimizer = torch.optim.Adam( 139 | model.parameters(), 140 | lr=args.lr 141 | ) 142 | for epoch in range(args.num_epochs): 143 | print("Epoch", epoch + 1) 144 | loss, input_mb, recon_mb, loss_dict, lbl = train( 145 | model=model, 146 | train_loader=train_dataloader, 147 | device=device, 148 | optimizer=optimizer, 149 | epoch=epoch) 150 | 151 | if(args.intest): 152 | m_auc = test_on_train(args, model) 153 | test_aucroc_dict['aucroc']=m_auc 154 | print('epoch [{}/{}], train loss: {:.4f}'.format( 155 | epoch + 1, args.num_epochs, loss)) 156 | 157 | # print loss logs 158 | f_name = os.path.join(out_dir, f"{args.exp}_loss_values.txt") 159 | print_loss_logs(f_name, out_dir, loss_dict, epoch, args.exp) 160 | if(args.intest): 161 | if not os.path.isdir(os.path.join(out_dir,args.exp)): 162 | os.mkdir(os.path.join(out_dir,args.exp)) 163 | tf_name = os.path.join(out_dir, args.exp, f"{args.exp}_test_values.txt") 164 | print_AUCROC_logs(tf_name, out_dir, test_aucroc_dict, epoch, args.exp) 165 | # save model parameters 166 | if (epoch + 1) % 50 == 0 or epoch in [0, 4, 9, 24]: 167 | # to resume a training optimizer state dict and epoch 168 | # should also be saved 169 | torch.save(model.state_dict(), os.path.join( 170 | checkpoints_dir, f"{args.exp}_{epoch + 1}.pth" 171 | ) 172 | ) 173 | 174 | # print some reconstrutions 175 | if (epoch + 1) % 50 == 0 or epoch in [0, 4, 9, 14, 19, 24, 29, 49]: 176 | img_train = utils.make_grid( 177 | torch.cat(( 178 | input_mb, 179 | recon_mb, 180 | ), dim=0), nrow=batch_size 181 | ) 182 | utils.save_image( 183 | img_train, 184 | f"torch_results/{args.exp}_img_train_{epoch + 1}.png" 185 | ) 186 | model.eval() 187 | input_test_mb, recon_test_mb, _, opt_out = eval(model=model, 188 | test_loader=test_dataloader, 189 | device=device) 190 | 191 | model.train() 192 | img_test = utils.make_grid( 193 | torch.cat(( 194 | input_test_mb, 195 | recon_test_mb), 196 | dim=0), 197 | nrow=batch_size_test 198 | ) 199 | utils.save_image( 200 | img_test, 201 | f"torch_results/{args.exp}_img_test_{epoch + 1}.png" 202 | ) 203 | 204 | if __name__ == "__main__": 205 | args = parse_args() 206 | main(args) 207 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import matplotlib.pyplot as plt 4 | import torchvision 5 | import torch 6 | from datasets import * 7 | from vae import VAE 8 | from vae_grf import VAE_GRF 9 | # from vqvae import VQVAE 10 | from vit_vae import ViTVAE 11 | import time 12 | import argparse 13 | 14 | def parse_args(): 15 | parser = argparse.ArgumentParser() 16 | parser.add_argument("--params_id", default=100) 17 | parser.add_argument("--img_size", default=512, type=int) 18 | parser.add_argument("--batch_size", default=16, type=int) 19 | parser.add_argument("--batch_size_test", default=8, type=int) 20 | parser.add_argument("--num_epochs", default=2000, type=int) 21 | parser.add_argument("--latent_img_size", default=32, type=int) 22 | parser.add_argument("--z_dim", default=32, type=int) 23 | parser.add_argument("--lr", default=1e-4, type=float) 24 | parser.add_argument("--beta", default=1.0, type=float) 25 | parser.add_argument("--gamma", default=1, type=float) 26 | parser.add_argument("--delta", default=1, type=float) 27 | parser.add_argument("--vqvae_dist", default='mse') 28 | parser.add_argument("--num_embed", default=128, type=int) 29 | parser.add_argument("--exp", default=time.strftime("%Y%m%d-%H%M%S")) 30 | parser.add_argument("--dataset", default="livestock") 31 | parser.add_argument("--category", default=None) 32 | parser.add_argument("--fake_data_size", default=None) 33 | parser.add_argument("--defect", default=None) 34 | parser.add_argument( 35 | "--defect_list", 36 | type=lambda s: [item for item in s.split(',')] 37 | ) 38 | parser.add_argument("--rec_loss", default="xent") 39 | parser.add_argument("--nb_channels", default=3, type=int) 40 | parser.add_argument("--model", default="vae_grf") 41 | parser.add_argument("--corr_type", default="corr_exp") 42 | parser.add_argument("--force_train", dest='force_train', action='store_true') 43 | parser.add_argument("--intest", dest='intest', action='store_true') 44 | # parser.add_argument("--all_in", dest='all_in', action='store_true') 45 | parser.set_defaults(force_train=False) 46 | parser.add_argument("--force_cpu", dest='force_cpu', action='store_true') 47 | parser.set_defaults(force_train=False) 48 | 49 | return parser.parse_args() 50 | 51 | def load_vqvae(args): 52 | if args.model == "vae": 53 | print(args.nb_channels) 54 | model = VAE(latent_img_size=args.latent_img_size, 55 | z_dim=args.z_dim, 56 | img_size=args.img_size, 57 | nb_channels=args.nb_channels, 58 | beta=args.beta, 59 | ) 60 | elif args.model == "vae_grf": 61 | model = VAE_GRF(latent_img_size=args.latent_img_size, 62 | z_dim=args.z_dim, 63 | batch_size=args.batch_size, 64 | corr_type=args.corr_type, 65 | img_size=args.img_size, 66 | nb_channels=args.nb_channels, 67 | beta=args.beta, 68 | ) 69 | 70 | elif args.model == "vitvae": 71 | print(args.nb_channels) 72 | model = ViTVAE( 73 | # batch_size=args.batch_size, 74 | latent_img_size=args.latent_img_size, 75 | z_dim=args.z_dim, 76 | img_size=args.img_size, 77 | nb_channels=args.nb_channels, 78 | beta=args.beta, 79 | ) 80 | 81 | return model 82 | 83 | def load_model_parameters(model, file_name, dir1, dir2, device): 84 | print(f"Trying to load: {file_name}") 85 | try: 86 | state_dict = torch.load( 87 | os.path.join(dir1, file_name), 88 | map_location=device 89 | ) 90 | except FileNotFoundError: 91 | state_dict = torch.load( 92 | os.path.join(dir2, file_name), 93 | map_location=device 94 | ) 95 | model.load_state_dict(state_dict, strict=False) 96 | print(f"{file_name} loaded !") 97 | 98 | return model 99 | 100 | def get_train_dataloader(args, fake_dataset_size=None): 101 | if args.dataset == "livestock": 102 | train_dataset = LivestockTrainDataset( 103 | args.img_size, 104 | fake_dataset_size=1024 if fake_dataset_size is None else 105 | fake_dataset_size, 106 | ) 107 | elif args.dataset == "mvtec": 108 | train_dataset = MVTecTrainDataset( 109 | args.img_size, 110 | fake_dataset_size=1024 if fake_dataset_size is None else 111 | fake_dataset_size, 112 | ) 113 | elif args.dataset == "miad": 114 | 115 | train_dataset = MIADTrainDataset( 116 | args.img_size, 117 | fake_dataset_size=1024 if fake_dataset_size is None else 118 | fake_dataset_size 119 | ) 120 | else: 121 | raise RuntimeError("No / Wrong dataset provided") 122 | 123 | train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, 124 | shuffle=False if args.dataset == "ssl_vqvae" else True) 125 | 126 | return train_dataloader, train_dataset 127 | 128 | def get_test_dataloader(args, fake_dataset_size=30): 129 | if args.dataset == "livestock": 130 | test_dataset = LivestockTestDataset( 131 | args.img_size, 132 | fake_dataset_size=512 if fake_dataset_size is None else 133 | fake_dataset_size, 134 | ) 135 | elif args.dataset == "mvtec": 136 | test_dataset = MVTecTestDataset( 137 | args.img_size, 138 | fake_dataset_size=4 if fake_dataset_size is None else 139 | fake_dataset_size, 140 | ) 141 | elif args.dataset == "miad": 142 | test_dataset = MIADTestDataset( 143 | args.img_size, 144 | fake_dataset_size=128 if fake_dataset_size is None else 145 | fake_dataset_size, 146 | ) 147 | else: 148 | raise RuntimeError("No / Wrong dataset provided") 149 | 150 | test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size_test, 151 | ) 152 | 153 | return test_dataloader, test_dataset 154 | 155 | def tensor_img_to_01(t, share_B=False): 156 | ''' t is a BxCxHxW tensor, put its values in [0, 1] for each batch element 157 | if share_B is False otherwise normalization include all batch elements 158 | ''' 159 | t = torch.nan_to_num(t) 160 | if share_B: 161 | t = ((t - torch.amin(t, dim=(0, 1, 2, 3), keepdim=True)) / 162 | (torch.amax(t, dim=(0, 1, 2, 3), keepdim=True) - torch.amin(t, 163 | dim=(0, 1, 2,3), 164 | keepdim=True))) 165 | if not share_B: 166 | t = ((t - torch.amin(t, dim=(1, 2, 3), keepdim=True)) / 167 | (torch.amax(t, dim=(1, 2, 3), keepdim=True) - torch.amin(t, dim=(1, 2,3), 168 | keepdim=True))) 169 | return t 170 | 171 | def update_loss_dict(ld_old, ld_new): 172 | for k, v in ld_new.items(): 173 | if k in ld_old: 174 | ld_old[k] += v 175 | else: 176 | ld_old[k] = v 177 | return ld_old 178 | 179 | 180 | 181 | def print_loss_logs(f_name, out_dir, loss_dict, epoch, exp_name): 182 | if epoch == 0: 183 | with open(f_name, "w") as f: 184 | print("epoch,", end="", file=f) 185 | for k, v in loss_dict.items(): 186 | print(f"{k},", end="", file=f) 187 | print("\n", end="", file=f) 188 | # then, at every epoch 189 | with open(f_name, "a") as f: 190 | print(f"{epoch + 1},", end="", file=f) 191 | for k, v in loss_dict.items(): 192 | print(f"{v},", end="", file=f) 193 | print("\n", end="", file=f) 194 | if (epoch + 1) % 50 == 0 or epoch in [4, 9, 24]: 195 | # with this delimiter one spare column will be detected 196 | arr = np.genfromtxt(f_name, names=True, delimiter=",") 197 | fig, axis = plt.subplots(1) 198 | for i, col in enumerate(arr.dtype.names[1:-1]): 199 | axis.plot(arr[arr.dtype.names[0]], arr[col], label=col) 200 | axis.legend() 201 | 202 | fig.savefig(os.path.join(out_dir,exp_name, 203 | f"{exp_name}_loss_{epoch + 1}.png")) 204 | plt.close(fig) 205 | 206 | 207 | def print_AUCROC_logs(f_name, out_dir, loss_dict, epoch, exp_name): 208 | if epoch == 0: 209 | with open(f_name, "w") as f: 210 | print("epoch,", end="", file=f) 211 | for k, v in loss_dict.items(): 212 | print(f"{k},", end="", file=f) 213 | print("\n", end="", file=f) 214 | # then, at every epoch 215 | with open(f_name, "a") as f: 216 | print(f"{epoch + 1},", end="", file=f) 217 | for k, v in loss_dict.items(): 218 | print(f"{v},", end="", file=f) 219 | print("\n", end="", file=f) 220 | if (epoch + 1) % 50 == 0 or epoch in [4, 9, 24]: 221 | # with this delimiter one spare column will be detected 222 | arr = np.genfromtxt(f_name, names=True, delimiter=",") 223 | fig, axis = plt.subplots(1) 224 | for i, col in enumerate(arr.dtype.names[1:-1]): 225 | axis.plot(arr[arr.dtype.names[0]], arr[col], label=col) 226 | axis.legend() 227 | fig.savefig(os.path.join(out_dir,exp_name, 228 | f"{exp_name}_test_{epoch + 1}.png")) 229 | plt.close(fig) -------------------------------------------------------------------------------- /datasets.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import random 3 | import numpy as np 4 | import os 5 | from PIL import Image 6 | from torch.utils.data import Dataset, DataLoader 7 | from torchvision import transforms 8 | import torch 9 | import cv2 10 | import itertools 11 | 12 | # Định sẵn đường đẫn đến các dataset 13 | DEFAULT_LIVESTOCK_DIR = "./data/livestock/part_III_cropped" 14 | DEFAULT_MVTEC_DIR = "E:/UnitWTF/lab ai/mvtec_anomaly_detection/wood" 15 | DEFAULT_MIAD_DIR = "E:/UnitWTF/dataset/photovoltaic_module" 16 | 17 | # Traning Dataset for livestock 18 | class LivestockTrainDataset(Dataset): 19 | def __init__(self, img_size, fake_dataset_size): 20 | 21 | if os.path.isdir(DEFAULT_LIVESTOCK_DIR): 22 | self.img_dir = os.path.join(DEFAULT_LIVESTOCK_DIR, "Train") # set image dir 23 | else: 24 | self.img_dir = UNDEFINE # set undefine if not found 25 | 26 | #get a list of image path 27 | self.img_files = list( 28 | np.random.choice( 29 | [os.path.join(self.img_dir, img) 30 | for img in os.listdir(self.img_dir) 31 | if (os.path.isfile(os.path.join(self.img_dir, 32 | img)) and img.endswith('jpg'))], 33 | size=fake_dataset_size) 34 | ) 35 | 36 | # Tuỳ chỉnh độ dài data 37 | self.fake_dataset_size = fake_dataset_size # needed otherwise there are 38 | # 125000 images, and this is too much 39 | 40 | #Augmentation setting 41 | self.transform = transforms.Compose([ 42 | transforms.Resize(size=(img_size, img_size)), 43 | transforms.PILToTensor(), 44 | transforms.Lambda(lambda img: img.float()), 45 | transforms.Lambda(lambda img: img / 255.) 46 | ]) 47 | #number of image 48 | self.nb_img = len(self.img_files) 49 | #number of (color) channel 50 | self.nb_channels = 3 51 | 52 | #get length of data 53 | def __len__(self): 54 | return max(self.nb_img, self.fake_dataset_size) 55 | 56 | #get specific item in the dataset via index 57 | def __getitem__(self, index): 58 | index = index % self.nb_img 59 | img = Image.open(self.img_files[index]) 60 | 61 | return self.transform(img), 1 # one if the ground truth if there is one 62 | 63 | class LivestockTestDataset(Dataset): 64 | def __init__(self, img_size, fake_dataset_size): 65 | if os.path.isdir(DEFAULT_LIVESTOCK_DIR): 66 | self.img_dir = os.path.join(DEFAULT_LIVESTOCK_DIR, "Test") 67 | else: 68 | self.img_dir = UNDEFINE 69 | self.img_files = list( 70 | np.random.choice( 71 | [os.path.join(self.img_dir, img) 72 | for img in os.listdir(self.img_dir) 73 | if (os.path.isfile(os.path.join(self.img_dir, img)) 74 | and img.endswith('.jpg'))], 75 | size=fake_dataset_size) 76 | ) 77 | self.fake_dataset_size = fake_dataset_size # needed otherwise there are 78 | self.gt_files = [s.replace(".jpg", "_gt.png") for s in self.img_files] 79 | self.transform = transforms.Compose([ 80 | transforms.Resize(size=(img_size, img_size)), 81 | transforms.PILToTensor(), 82 | transforms.Lambda(lambda img: img.float()), 83 | transforms.Lambda(lambda img: img / 255.) 84 | ]) 85 | self.nb_img = len(self.img_files) # recompute the size, 86 | # fake_dataset_size may have changed it 87 | self.nb_channels = 3 88 | 89 | def __len__(self): 90 | return self.fake_dataset_size 91 | 92 | def __getitem__(self, index): 93 | img = Image.open(self.img_files[index]) 94 | gt = Image.open(self.gt_files[index]) 95 | 96 | return self.transform(img), self.transform(gt) 97 | 98 | 99 | class MVTecTrainDataset(Dataset): 100 | def __init__(self, img_size, fake_dataset_size): 101 | if os.path.isdir(DEFAULT_MVTEC_DIR): 102 | self.img_dir = os.path.join(DEFAULT_MVTEC_DIR, "train", "good") 103 | else: 104 | self.img_dir = UNDEFINE 105 | self.img_files = list( 106 | [os.path.join(self.img_dir, img) 107 | for img in os.listdir(self.img_dir) 108 | if (os.path.isfile(os.path.join(self.img_dir, 109 | img)) and img.endswith('png'))] 110 | ) 111 | self.fake_dataset_size = fake_dataset_size # needed otherwise there are 112 | # 125000 images, and this is too much 113 | self.transform = transforms.Compose([ 114 | transforms.Resize(size=(img_size, img_size)), 115 | transforms.PILToTensor(), 116 | transforms.Lambda(lambda img: img.float()), 117 | transforms.Lambda(lambda img: img / 255.) 118 | ]) 119 | self.nb_img = len(self.img_files) 120 | self.nb_channels = 3 121 | 122 | def __len__(self): 123 | return self.nb_img 124 | 125 | def __getitem__(self, index): 126 | index = index % self.nb_img 127 | img = Image.open(self.img_files[index]).convert("RGB") 128 | # img = Image.open(self.img_files[index]) 129 | return self.transform(img), 1 # one if the ground truth if there is one 130 | 131 | class MVTecTestDataset(Dataset): 132 | def __init__(self, img_size, fake_dataset_size): 133 | if os.path.isdir(DEFAULT_MVTEC_DIR): 134 | self.default_dir = os.path.join(DEFAULT_MVTEC_DIR, "test") 135 | self.img_dir = list(os.path.join(self.default_dir, img_fol) for img_fol in os.listdir(self.default_dir) if not img_fol.endswith("good")) 136 | # self.img_dir = os.path.join(DEFAULT_MVTEC_DIR, "test", "hole") 137 | 138 | else: 139 | self.img_dir = UNDEFINE 140 | 141 | self.img_files = list( 142 | list(itertools.chain.from_iterable([[os.path.join(img_fol, img) 143 | for img in os.listdir(img_fol) 144 | if (os.path.isfile(os.path.join(img_fol, img)) 145 | and img.endswith('.png'))] for img_fol in self.img_dir])) 146 | ) 147 | # self.img_files = list( 148 | # [os.path.join(self.img_dir, img) 149 | # for img in os.listdir(self.img_dir) 150 | # if (os.path.isfile(os.path.join(self.img_dir, img)) 151 | # and img.endswith('.png'))] 152 | # ) 153 | self.fake_dataset_size = fake_dataset_size # needed otherwise there are 154 | self.gt_files = [s.replace(".png", "_mask.png").replace("test","ground_truth") for s in self.img_files] 155 | self.transform = transforms.Compose([ 156 | transforms.Resize(size=(img_size, img_size)), 157 | transforms.PILToTensor(), 158 | transforms.Lambda(lambda img: img.float()), 159 | transforms.Lambda(lambda img: img / 255.) 160 | ]) 161 | self.nb_img = len(self.img_files) # recompute the size, 162 | # fake_dataset_size may have changed it 163 | self.nb_channels = 3 164 | 165 | def __len__(self): 166 | return self.nb_img 167 | 168 | def __getitem__(self, index): 169 | img = Image.open(self.img_files[index]).convert("RGB") #to turn binary image to RGB 170 | # img = Image.open(self.img_files[index]) 171 | gt = Image.open(self.gt_files[index]) 172 | 173 | return self.transform(img), self.transform(gt) 174 | 175 | 176 | class MIADTestDataset(Dataset): 177 | def __init__(self, img_size, fake_dataset_size): 178 | if os.path.isdir(DEFAULT_MIAD_DIR): 179 | self.img_dir = os.path.join(DEFAULT_MIAD_DIR, "test", "broken") 180 | else: 181 | self.img_dir = UNDEFINE 182 | self.img_files = list( 183 | np.random.choice( 184 | [os.path.join(self.img_dir, img) 185 | for img in os.listdir(self.img_dir) 186 | if (os.path.isfile(os.path.join(self.img_dir, img)) 187 | and img.endswith('.png'))], 188 | size=fake_dataset_size) 189 | ) 190 | self.fake_dataset_size = fake_dataset_size # needed otherwise there are 191 | self.gt_files = [s.replace(".png", "_mask.png").replace("test","ground_truth") for s in self.img_files] 192 | self.transform = transforms.Compose([ 193 | transforms.Resize(size=(img_size, img_size)), 194 | transforms.PILToTensor(), 195 | transforms.Lambda(lambda img: img.float()), 196 | transforms.Lambda(lambda img: img / 255.) 197 | ]) 198 | self.nb_img = len(self.img_files) # recompute the size, 199 | # fake_dataset_size may have changed it 200 | self.nb_channels = 3 201 | 202 | def __len__(self): 203 | return self.fake_dataset_size 204 | 205 | def __getitem__(self, index): 206 | img = Image.open(self.img_files[index]) 207 | gt = Image.open(self.gt_files[index]) 208 | 209 | return self.transform(img), self.transform(gt) 210 | 211 | class MIADTrainDataset(Dataset): 212 | def __init__(self, img_size, fake_dataset_size, all_in = False): 213 | if os.path.isdir(DEFAULT_MIAD_DIR): 214 | self.img_dir = os.path.join(DEFAULT_MIAD_DIR, "train", "good") 215 | else: 216 | self.img_dir = UNDEFINE 217 | print("all_in") 218 | # if not all_in: 219 | 220 | self.img_files = list( 221 | np.random.choice( 222 | [os.path.join(self.img_dir, img) 223 | for img in os.listdir(self.img_dir) 224 | if (os.path.isfile(os.path.join(self.img_dir, 225 | img)) and img.endswith('png'))], 226 | size=fake_dataset_size)# needed otherwise there are 227 | # 125000 images, and this is too much 228 | ) 229 | 230 | #UNCOMMENT TO RUN FULL DATASET 231 | 232 | # else: 233 | # self.img_files = list( 234 | 235 | # [os.path.join(self.img_dir, img) 236 | # for img in os.listdir(self.img_dir) 237 | # if (os.path.isfile(os.path.join(self.img_dir, 238 | # img)) and img.endswith('png'))] 239 | # ) 240 | # self.fake_dataset_size = fake_dataset_size 241 | self.transform = transforms.Compose([ 242 | transforms.Resize(size=(img_size, img_size)), 243 | transforms.PILToTensor(), 244 | transforms.Lambda(lambda img: img.float()), 245 | transforms.Lambda(lambda img: img / 255.) 246 | ]) 247 | self.nb_img = len(self.img_files) 248 | print("the number of image is:", self.nb_img) 249 | self.nb_channels = 3 250 | 251 | def __len__(self): 252 | return self.nb_img 253 | def __getitem__(self, index): 254 | index = index % self.nb_img 255 | 256 | img = Image.open(self.img_files[index]) 257 | 258 | return self.transform(img), 1 # one if the ground truth if there is one 259 | 260 | --------------------------------------------------------------------------------