Script started on Wed 24 May 2023 12:54:30 AM EDT
khippo@compute-2-11:~/rblur-code-package\(base) [hippo@compute-2-11 rblur-code-package]$ conda deactivate && conda activate rblur7
khippo@compute-2-11:~/rblur-code-package\(rblur7) [hippo@compute-2-11 rblur-code-package]$ conda deactivate && conda activate rblur7screen -r[Kdr n-r[3Pcd ../onda install -c fastchan fastai anacondapython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precisionhippohippohippohippo
[C[C[C[C[C[C[C[C[C[Chippo[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[92Pcd adversarialML/biologically_inspired_models/src/ln -s /home/mshah1/rblur-code-package/adversarialML -T /home/mshah1/anaconda3/envs/rblur5/lib/python3.9/site-packages/adversarialML
[hippoC[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Chippo[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cpwd[Kln -s /home/mshah1/rblur-code-package/adversarialML -T /home/mshah1/anaconda3/envs/rblur5/lib/python3.9/site-packages/adversarialML
[C[Chippo[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[81Pcd adversarialML/biologically_inspired_models/src/python main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
pyhippocan't open file '/home/mshah1/rblur-code-packahippon.py': [Errno 2] No such file or directoryhippo
khippo@compute-2-11:~/rblur-code-package\(rblur7) [hippo@compute-2-11 rblur-code-package]$ cd adversarialML/[Khippon -s [K[Kpwd
/hhippohah1/rblur-code-packagehippohippohippohippohippo
khippo@compute-2-11:~/rblur-code-package\(rblur7) [mshah1@compute-2-11 rblur-code-package]$ ln -s hippow)d)[C/adversarialML -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialMLhippohippo
kmshah1@compute-2-11:~/rblur-code-package\(rblur7) [mshah1@compute-2-11 rblur-code-package]$ ln -s $(pwd)/adversarialML -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML[Kmllib[C[C[C[C[C[C[C[C[C[C[C[C[C[13P[1@m[1@l[1@l[1@i[1@b
kmshah1@compuhippo1:~/rblur-code-package\(rblur7) [mshah1@compute-2-11 rblur-code-package]$ ln -s $(pwd)/mllib -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[CadversarialML -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML
[C[C[C[C[hippoC[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cpwd[Kython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cwd[Kln -s $(pwd)/adversarialML -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML
[C[C[C[C[hippoC[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[16Pmllib -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Kcd b[KadversarialML/biologically_inspired_models/src/
kmshah1@compuhippo1:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ cd adversarialML/biologically_inspired_models/src/
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cln -s $(pwd)/mllib -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[CadversarialML -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML
[hippoC[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[hippoCpwd[Kython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cconda deactivate && conda activate rblur7[K
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cpython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
Tracebhippoost recent call last):hippo
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 5, in <module>
    from mllib.tasks.base_tasks import AbstractTask
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/tasks/base_tasks.py", line 6, in <module>
    from mllib.datasets.dataset_factory import ImageDatasetFactory, SupportedDatasets
  hippo/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/datasets/dataset_factoryhippoline 17, in <module>
    from mllib.datasets.torchaudio_datasets import SpeechCommandDatasetWrapper, LibrispeechFilelistDataset
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/datasets/torchaudio_datasets.py", line 3, in <module>
    import sentencepiece as spm
ModuleNotFoundError: No module named 'sentencepiece'
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ pip install sentencepie[K[Keice
[31mERROR: Could not find a version that satisfies the requirement sentencepeice (from versions: none)[0m[31m
[0m[31mERROR: No matching distribution found for sentencepeice[0m[31m
[0mkmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ pip install sentencepeice[Ksentencepiece
Collecting sentencepiece
  Using cached sentencepiece-0.1.99-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)
Installing collected packages: sentencepiece
Successfully installed sentencepiece-0.1.99
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ vi env[K[K[K../../env[K[K[K../environment.yml 
[?1049h[?1h=[1;51r[34l[34h[?25h[23m[24m[0m[H[J[?25l[51;1H"../../../environment.yml" 403L, 11619C[1;1Hname: rblur7
channels:
  - huggingface
  - pytorch
  - anaconda
  - conda-forge
  - fastchan
  - oxfordcontrol
  - defaults
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=2_gnu
  - absl-py=1.3.0=pyhd8ed1ab_0
  - argon2-cffi=21.3.0=pyhd8ed1ab_0
  - argon2-cffi-bindings=21.2.0=py39hb9d737c_3
  - atk-1.0=2.36.0=h3371d22_4
  - attrs=21.4.0=pyhd8ed1ab_0
  - backcall=0.2.0=pyh9f0ad1d_0
  - backports=1.0=py_2
  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
  - beautifulsoup4=4.11.1=pyha770c72_0
  - blas=1.0=mkl
  - bleach=5.0.1=pyhd8ed1ab_0
  - brotli-bin=1.0.9=h166bdaf_8
  - brotlipy=0.7.0=py39hb9d737c_1004
  - bzip2=1.0.8=h7f98852_4
  - ca-certificates=2022.9.24=ha878542_0
  - cairo=1.16.0=h6cf1ce9_1008
  - cffi=1.15.0=py39h4bc2ebd_0
  - charset-normalizer=2.0.12=pyhd8ed1ab_0
  - click=8.1.3=py39hf3d152e_1
  - colorama=0.4.6=pyhd8ed1ab_0
  - cryptography=37.0.1=py39h9ce1e76_0
  - cudatoolkit=10.2.89=h713d32c_10
  - cycler=0.11.0=pyhd8ed1ab_0
  - cython=0.29.32=py39h6a678d5_0
  - dataclasses=0.8=pyhc8e2a94_3
  - debugpy=1.6.3=py39h5a03fae_1
  - decorator=5.1.1=pyhd8ed1ab_0
  - defusedxml=0.7.1=pyhd8ed1ab_0
  - dill=0.3.5.1=pyhd8ed1ab_0
  - docker-pycreds=0.4.0=py_0
  - einops=0.4.1=pyhd8ed1ab_0
  - entrypoints=0.4=pyhd8ed1ab_0
  - expat=2.5.0=h27087fc_0
  - ffmpeg=4.3=hf484d3e_0
  - fftw=3.3.8=nompi_hfc0cae8_1114
  - filelock=3.8.0=pyhd8ed1ab_0
  - flit-core=3.8.0=pyhd8ed1ab_0
  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0[1;1H[34h[?25h[?25l[23m[24m[0m[H[J[1;5H- s3transfer==0.6.0
    - safetensors==0.3.1
    - schema==0.7.5
    - scikit-image==0.19.3
    - segment-anything==1.0
    - semantic-version==2.10.0
    - simplejson==3.17.6
    - smart-open==5.2.1
    - smmap==5.0.0
    - soundfile==0.12.1
    - spacy==3.4.1
    - spacy-legacy==3.0.10
    - spacy-loggers==1.0.3
    - srsly==2.4.4
    - stack-data==0.5.0
    - strict-rfc3339==0.7
    - swagger-spec-validator==2.7.5
    - tensorboard
    - tensorboard-data-server==0.6.1
    - tensorboardx==2.5.1
    - tensorflow==2.11.0
    - tensorflow-addons==0.19.0
    - tensorflow-estimator==2.11.0
    - tensorflow-io-gcs-filesystem==0.29.0
    - termcolor==2.2.0
    - thinc==8.1.0
    - tifffile==2022.8.12
    - timm==0.8.21.dev0
    - tinycss2==1.1.1
    - torchattacks==3.2.6
    - torchmetrics==0.9.3
    - torchsummary==1.5.1
    - traitlets==5.3.0
    - typeguard==3.0.0b2
    - typer==0.4.2
    - urllib3=hippo12
    - validators==0.20.0
    - vit-keras==0.1.0
    - wasabi==0.10.1
    - webcolors==1.12
    - webdataset==0.2.20
    - webencodings==0.5.1
    - websocket-client==1.3.3
    - wheel==0.38.4
    - widgetsnbextension==4.hippohippohippohippohippohippo
    - wrapt==1.14.1
    - wurlitzer==3.0.2
    - xplique==0.4.3
prefix: /home/mshah1/anaconda3/envs/adversarialML-pt1.11
[34h[?25h[49;1H[48;1H[47;1H[46;1H[45;1H[44;1H[43;1H[42;1H[41;1H[40;1H[39;1H[38;1H[37;1H[36;1H[35;1H[34;1H[33;1H[32;1H[31;1H[30;1H[29;1H[28;1H[27;1H[26;1H[25;1H[24;1H[23;1H[22;1H[21;1H[20;1H[19;1H[18;1H[17;1H[16;1H[15;1H[14;1H[13;1H[12;1H[11;1H[10;1H[9;1H[8;1H[7;1H[6;1H[5;1H[4;1H[3;1H[2;1H[1;1H[?25l[1;50r[1;1H[L[1;51r[1;5H- ruamel-yaml-clib==0.2.7
[34h[?25h[?25l[1;50r[1;1H[L[1;51r[1;5H- ruamel-yaml==0.17.21
[34h[?25h[?25l[1;50r[1;1H[L[1;51r[1;5H- rich==12.5.1
[34h[?25h




hippohippo

[26C[9;30H[?25l[51;1H[1m-- INhippo-[9;31H[34h[?25h[?25l[10;50r[0m[10;1H[L[1;51r[10;1H[34h[?25h[?25l [34h[?25h[?25l [34h[?25h[?25l [34h[?25h[?25l [34h[?25h[?25l-[34h[?25h[?25l [34h[?25h[?25lsentencepiece-0.1.99[34h[?25h[?25l=0.1.99[34h[?25h[51;1H[K[10;21H[?25l[34h[?25h[?25l[51;1H:[34h[?25hx
[?25l"../../../environment.yml" 404L, 11647C written


[?1l>[34h[?25h[?1049lkmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ vi ../../../environment.yml [3Ppip install sentencepieceeiceython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[92Pcd adversarialML/biologically_inspired_models/src/
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cln -s $(pwd)/mllib -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[CadversarialML -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cpwd[Kython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomSchippoclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cconda deactivate && conda activate rblur7[K
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cscreen -r[Kdrconda deactivate && conda activate rblur7
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cpython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cwd[Kln -s $(pwd)/adversarialML -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[16Pmllib -T /home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[40Pcd adversarialML/biologically_inspired_models/src/
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cpython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cip install sentencepeice[Kiecevi ../../../environment.yml [Kpip install git+https://github.com/fra31/auto-attack
Collecting git+https://github.com/fra31/auto-attack
  Cloning https://github.com/fra31/auto-attack to /tmp/pip-req-build-h6uv9ufo
  Running command git clone --quiet https://github.com/fra31/auto-attack /tmp/pip-req-build-h6uv9ufo
  Resolved https://github.com/fra31/auto-attack to commit a39220048b3c9f2cca9a4d3a54604793c68eca7e
  Preparing metadata (setup.py) ... [?25l- \ | / done
[?25hBuilding wheels for collected packages: autoattack
  Building wheel for autoattack (setup.py) ... [?25l- \ | / - \ done
[?25h  Created wheel for autoattack: filename=autoattack-0.1-py3-none-any.whl size=36229 sha256=716bbd7408f928a08ae2db0283f00a39d379917c901b16b25ac781b911456e6b
  Stored in directory: /tmp/pip-ephem-wheel-cache-rykj_894/wheels/e5/00/6a/fb12d1eaa81d79f8c0585bdddc361ca48c9633e9549db68aef
Successfully built autoattack
Installing collected packages: autoattack
Successfully installed autoattack-0.1
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ pip install git+https://github.com/fra31/auto-attack
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[24Pvi ../../../environment.yml [3Ppip install sentencepieceeiceython main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
Namespace(task='ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18', ckp=None, num_test=2000, batch_size=256, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=False, attacks=['APGD'], eps_list=[0.0], run_randomized_smoothing_eval=False, rs_start_batch_idx=0, rs_end_batch_idx=None, center_fixation=False, five_fixations=False, bb_fixations=False, fixate_on_max_loc=False, view_scale=None, hscan_fixations=False, add_fixation_predictor=False, fixation_prediction_model='deepgazeII', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=False, use_clickme_data=False, num_fixations=1, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=False, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=True, debug=False, seed=45551323)
writing logs to /share/workhorse3/mshah1/biologically_inspired_models/logs/ecoset10_folder-0.0/Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'> torch.Size([1, 3, 224, 224])
[224 123  87  64  48  31  15] [10.802135213846604, 10.354928543635195, 9.923270285095539, 9.501554674323094, 8.531356795395943, 6.278959396936146, 2.4558932727738276]
[224 113  81  49  28  14] [9.886995580927815, 9.930499205002702, 9.988194256843602, 10.089318915518763, 10.351948368750191, 10.849174298342337]hippo
<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'> torch.Size([1, 3, 224, 224])
total parameters=44.622704M
trainable parameters=44.622698M
ImageDatasetFactory.ImageDatasetParams(cls=<class 'mllib.datasets.dataset_factory.ImageDatasetFactory'>, dataset=<SupportedDatasets.ECOSET10_FOLDER: 'ECOSET10_FOLDER'>, datafolder='/share/workhorse3/mshah1/ecoset-10', class_idxs=None, custom_transforms=(Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
), Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)), max_num_train=48000, max_num_test=1000, kwargs={}) 4800 859 100
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
)
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)

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1000 0
train_dataset_len: 48000, val_dataset_len: 859, test_dataset_len: 1000
AdversarialTrainer.TrainerParams(cls=<class 'adversarialML.biologically_inspired_models.src.trainers.MixedPrecisionAdversarialTrainer'>, training_params=TrainingParams(logdir='/share/workhorse3/mshah1/biologically_inspired_models/logs/ecoset10_folder-0.0/Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0', nepochs=60, early_stop_patience=50, tracked_metric='val_accuracy', tracking_mode='max', scheduler_step_after_epoch=False, debug=False), adversarial_params=AdversarialParams(training_attack_params=None, testing_attack_params=[None]))
GeneralClassifier(
  (feature_model): SequentialLayers(
    (layers): ModuleList(
      (0): GaussianNoiseLayer(std=0.25, neuronal=False)
      (1): RetinaBlurFilter(loc_mode=random_uniform, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, kernel_size=45, view_scale=random_uniform, beta=0.05)
    )
  )
  (classifier): XResNet18(
    (normalization_layer): NormalizationLayer(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    (preprocessing_layer): Identity()
    (resnet): XResNet(
      (0): ConvLayer(
        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (1): ConvLayer(
        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): ConvLayer(
        (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (4): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (5): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (6): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (7): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)hippohippohippohippo
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippohippohippohippo
          )hippohippohippohippo
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (conhippo: Sequential(
            (0): ConvLayer(
              hippoonv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              hippoeLU()
            )
            (1hippovLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )hippo
          (idpath): Sequential()
          (acthippoU(inplace=True)
        )
      )hippo
      (8): AdaptiveAvgPool2d(output_size=1)
      (9): fashippoyers.Flatten(full=False)
      (10): Dropout(p=0.0, inplace=False)
      (11): Idhippo()
    )
    (classifiehipponear(in_features=1024, out_features=10, bias=True)
    (logit_ensembler): Identity()
    (feature_ehippoer): Identity()
  )
  (logit_ensemhippo Identity()
  (loss_fn): CrossEntropyLoss()
)hippo
2023-05-24 01:00:22.390476: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-05-24 01:00:22.594532: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/mshah1/.local/cuda/10.2/lib64:/home/mshah1/.local/cuda/10.2/lib64:/opt/openmpi/lib:/home/mshah1/.local/cuda/8.0//lib64:/home/mshah1/.local/cuda/8.0//extras/CUPTI/lib64
20hippo24 01:00:22.594617: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore hippocudart dlerror if you do not have a GPU set up on your machine.hippohippo
2023-05-24 01:00:24.432143: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/mshah1/.local/cuda/10.2/lib64:/home/mshah1/.local/cuda/10.2/lib64:/opt/openmpi/lib:/home/mshah1/.local/cuda/8.0//lib64:/home/mshah1/.local/cuda/8.0//extras/CUPTI/lib64
20hippo24 01:00:24.433047: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:hippould not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/mshah1/.local/cuda/10.hippo4:/home/mshah1/.local/cuda/10.2/lib64:/opt/openmpi/lib:/home/mshah1/.local/cuda/8.0//lib64:/home/mshah1/.local/cuda/8.0//extras/CUPTI/lib64
2023-05-24 01:00:24.433142: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If hippould like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

0it [00:00, ?ihippo
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 0it [00:00, ?it/s]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 0it [00:21, ?it/s, best_metric=-inf, train_accuracy=0.133, train_loss=2.31]
EchippoNoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 1it [00:21, hippo/it, best_metric=-inf, train_accuracy=0.133, train_loss=2.31]hippo
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 1it [00:22, 21.94s/it, best_metric=-inf, train_accuracy=0.109, train_loss=2.32]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 2it [00:22,  9.19s/it, best_metric=-inf, train_accuracy=0.109, train_loss=2.32]
EchippoNoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 2it [00:22, hippo/it, best_metric=-inf, train_accuracy=0.0885, train_loss=2.31]hippo
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 3it [00:22,  5.12s/it, best_metric=-inf, train_accuracy=0.0885, train_loss=2.31]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 3it [00:22,  5.12s/it, best_metric=-inf, train_accuracy=0.102, train_loss=2.29] 
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 4it [00:22,  3.21s/it, best_metric=-inf, train_accuracy=0.102, train_loss=2.29]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 4it [00:23,  3.21s/it, best_metric=-inf, train_accuracy=0.105, train_loss=2.29]
Ecoset10NoisyRhippolurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 5it [00:23,  2.15s/it, best_metric=-inf, train_accuracy=0.105, train_loss=2.29]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 5it [00:23,  2.15s/it, best_metric=-inf, train_accuracy=0.109, train_loss=2.29]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 6it [00:23,  1.51s/it, best_metric=-inf, train_accuracy=0.109, train_loss=2.29]
EchippoNoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 6it [00:23, hippo/it, best_metric=-inf, train_accuracy=0.119, train_loss=2.29]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 7it [00:23,  1.11s/it, best_metric=-inf, train_accuracy=0.119, train_loss=2.29]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 7it [00:23,  1.11s/it, best_metric=-inf, train_accuracy=0.132, train_loss=2.28]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 8it [00:23,  1.18it/s, best_metric=-inf, trahippouracy=0.132, train_loss=2.28]
Ecoset10NoisyRetinhippo2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 8it [00:24,  1.18it/s, best_metric=-inf, train_accuracy=0.141, train_loss=2.27]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 9it [00:24,  1.50it/s, best_metric=-inf, train_accuracy=0.141, train_loss=2.27]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 9it [00:24,  1.50it/s, best_metric=-inf, train_accuracy=0.145, train_loss=2.26]
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 10it [00:24,  1.84it/s, best_metric=-inf, train_accuracy=0.145, train_loss=2.26]^C
Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18/0 epoch 0: : 10it [00:28,  2.86s/it, best_metric=-inf, train_accuracy=0.145, train_loss=2.26]
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 232, in <module>
    runner.run()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 213, in run
    self.train()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 194, in trainhippo
    self.trainer.train()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/trainers.py", line 178, in train
    metrics = super().train()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 270, in train
    train_output, train_metrics = self.train_loop(i, post_loop_fn=self.train_epoch_end)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 190, in train_loop
    outputs, metrics = self._batch_loop(self._optimization_wrapper(self.train_step),
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 173, in _batch_loop
    for i, batch in t:
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/tqdm/std.py", line 1195, in __iter__
    for obj in iterable:
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 530, in __next__
    data = self._next_data()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1207, in _next_data
    idx, data = self._get_data()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1163, in _get_data
    success, data = self._try_get_data()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1011, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/queue.py", line 180, in get
    self.not_empty.wait(remaining)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/threading.py", line 316, in wait
    gotit = waiter.acquire(True, timeout)
KeyboardInterrupt
^C^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.noisy_retina_blur.Ecoset10NoisyRetinaBlurS2500WRandomScalesCyclicLR1e_1RandAugmentXResNet2x18 --use_f16_precision
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[KCUDA_VISIBLE_DEVICES='1' python main.py --task ICLR22.baseline.Cifar10NoisyRetinaBlurCyclicLRAutoAugmentWideResNet4x22 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/cifar10-0.0/Cifar10CyclicLRA 
utoAugment --run_adv_attack_battery --attacks APGDL2_25 --eps_list .125 .25 .5 1. --batch_size 25 --multi_randaugment --num_test 10000Mhippo
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cpython main.py --task ICLR22.baseline.Cifar10NoisyRetinaBlurCyclicLRAutoAugmentWideResNet4x22 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/cifar10-0.0/Cifar10CyclicLRAutoAugment --run_adv_attac[25PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C^C


kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ CUDA_VISIBLE_DEVICES='3' python main.py --task ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18 --ckp /ocean/projects/cis220031p/mshah1/biologically_inspired_models/iclr22_logs 
/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch\=23-step\=117984.pt --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations 
 --num_test 1000
Namespace(task='ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18', ckp='/ocean/projects/cis220031p/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch=23-step=117984.pt', num_test=1000, batch_size=256, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=False, attacks=['APGD'], eps_list=[0.25], run_randomized_smoothing_eval=True, rs_start_batch_idx=150, rs_end_batch_idx=200, center_fixation=False, five_fixations=True, bb_fixations=False, fixate_on_max_loc=False, view_scale=3, hscan_fixations=False, add_fixation_predictor=False, fixation_prediction_model='deepgazeII', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=False, use_clickme_data=False, num_fixations=1, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=False, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 218, in <module>
    for ckp_pth in ckp_pths:
NameError: name 'ckp_pths' is not defined
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ CUDA_VISIBLE_DEVICES='3' python main.py --task ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18 --ckp /ocean/projects/cis220031p/mshah1/biologically_inspired_models/iclr22_logs/
/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch\=23-step\=117984.pt --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations 
 --num_test 1000
M[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations -[C[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --n[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --nu[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_t[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_te[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_tes[1PM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test[1P 1000M[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test [1P1000M[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 1[1P000M[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 10[C[C[KM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 100[C[KM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 1000[KM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 1000 
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kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ CUDA_VISIBLE_DEVICES='3' python main.py --task ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18 --ckp /ocean/projects/cis220031p/mshah1/biologically_inspired_models/iclr22_logs/
/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch\=23-step\=117984.pt --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations 
 --num_test 1000M[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C
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Namespace(task='ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18', ckp='/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch=23-step=117984.pt', num_test=1000, batch_size=256, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=False, attacks=['APGD'], eps_list=[0.25], run_randomized_smoothing_eval=True, rs_start_batch_idx=150, rs_end_batch_idx=200, center_fixation=False, five_fixations=True, bb_fixations=False, fixate_on_max_loc=False, view_scale=3, hscan_fixations=False, add_fixation_predictor=False, fixation_prediction_model='deepgazeII', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=False, use_clickme_data=False, num_fixations=1, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=False, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 218, in <module>
    for ckp_pth in ckp_pths:
NameError: name 'ckp_pths' is not defined
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ CUDA_VISIBLE_DEVICES='3' python main.py --task ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18 --ckp /imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmen
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1000MM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C_/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch\=23-step\=117984.pt --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 10[C0MM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cl/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch\=23-step\=117984.pt --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 1[C[C0MM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Co/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch\=23-step\=117984.pt --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 1000MM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cg/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch\=23-step\=117984.pt --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 1000MM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cs/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch\=23-step\=117984.pt --view_scale 3 --eps_list 0.25 --run_randomized_smoothing_eval --rs_start_batch_idx 150 --rs_end_batch_idx 200 --five_fixations --num_test 1000MM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C



Namespace(task='ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18', ckp='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/epoch=23-step=117984.pt', num_test=1000, batch_size=256, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=False, attacks=['APGD'], eps_list=[0.25], run_randomized_smoothing_eval=True, rs_start_batch_idx=150, rs_end_batch_idx=200, center_fixation=False, five_fixations=True, bb_fixations=False, fixate_on_max_loc=False, view_scale=3, hscan_fixations=False, add_fixation_predictor=False, fixation_prediction_model='deepgazeII', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=False, use_clickme_data=False, num_fixations=1, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=False, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1
None
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'> torch.Size([1, 3, 224, 224])
[224 123  87  64  48  31  15] [10.802135213846604, 10.354928543635195, 9.923270285095539, 9.501554674323094, 8.531356795395943, 6.278959396936146, 2.4558932727738276]
[224 113  81  49  28  14] [9.886995580927815, 9.930499205002702, 9.988194256843602, 10.089318915518763, 10.351948368750191, 10.849174298342337]
<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'> torch.Size([5, 3, 224, 224])
got unexpected keys: ['feature_model.layers.1.clr_kernels.0', 'feature_model.layers.1.clr_kernels.1', 'feature_model.layers.1.clr_kernels.2', 'feature_model.layers.1.clr_kernels.3', 'feature_model.layers.1.clr_kernels.4', 'feature_model.layers.1.clr_kernels.5', 'feature_model.layers.1.clr_kernels.6', 'feature_model.layers.1.gry_kernels.0', 'feature_model.layers.1.gry_kernels.1', 'feature_model.layers.1.gry_kernels.2', 'feature_model.layers.1.gry_kernels.3', 'feature_model.layers.1.gry_kernels.4', 'feature_model.layers.1.gry_kernels.5']
total parameters=45.637454M
trainable parameters=45.637448M
ImageDatasetFactory.ImageDatasetParams(cls=<class 'mllib.datasets.dataset_factory.ImageDatasetFactory'>, dataset=<SupportedDatasets.IMAGENET_FOLDER: 'IMAGENET_FOLDER'>, datafolder='/share/workhorse3/mshah1/imagenet/eval_dataset_dir', class_idxs=None, custom_transforms=(Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
), Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)), max_num_train=1275000, max_num_test=1000, kwargs={}) 1275 5000 1
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
)
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)

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1000 49000
train_dataset_len: 12750, val_dataset_len: 250, test_dataset_len: 1000
RandomizedSmoothingEvaluationTrainer.TrainerParams(cls=<class 'trainers.RandomizedSmoothingEvaluationTrainer'>, training_params=TrainingParams(logdir='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1', nepochs=25, early_stop_patience=50, tracked_metric='val_accuracy', tracking_mode='max', scheduler_step_after_epoch=False, debug=False), randomized_smoothing_params=RandomizedSmoothingParams(num_classes=1000, sigmas=[0.25], batch=256, N0=100, N=100000, alpha=0.001, mode='certify', start_idx=150, end_idx=200), exp_name='5Fixation-')
GeneralClassifier(
  (feature_model): SequentialLayers(
    (layers): ModuleList(
      (0): GaussianNoiseLayer(std=0.125, neuronal=False)
      (1): RetinaBlurFilter(loc_mode=five_fixations, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, kernel_size=45, view_scale=None, beta=0.05)
    )
  )
  (classifier): XResNet18(
    (normalization_layer): NormalizationLayer(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    (preprocessing_layer): Identity()
    (resnet): XResNet(
      (0): ConvLayer(
        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (1): ConvLayer(
        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): ConvLayer(
        (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (4): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (5): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (6): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)hippohippohippohippo
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()hippohippohippohippo
            )hippohippohippohippo
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (7): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (8): AdaptiveAvgPool2d(output_size=1)
      (9): fastai.layers.Flatten(full=False)
      (10): Dropout(p=0.0, inplace=False)
      (11): Identity()
    )
    (classifier): Linear(in_features=1024, out_features=1000, bias=True)
    (logit_ensembler): Identity()
    (feature_ensembler): Identity()
  )
  (logit_ensembler): LogitAverageEnsembler(n=5, act=Identity())
  (loss_fn): CrossEntropyLoss()
)
2023-05-24 02:31:33.887492: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-05-24 02:31:34.101651: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/mshah1/.local/cuda/10.2/lib64:/home/mshah1/.local/cuda/10.2/lib64:/opt/openmpi/lib:/home/mshah1/.local/cuda/8.0//lib64:/home/mshah1/.local/cuda/8.0//extras/CUPTI/lib64
2023-05-24 02:31:34.101695: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2023-05-24 02:31:35.690583: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/mshah1/.local/cuda/10.2/lib64:/home/mshah1/.local/cuda/10.2/lib64:/opt/openmpi/lib:/home/mshah1/.local/cuda/8.0//lib64:/home/mshah1/.local/cuda/8.0//extras/CUPTI/lib64
2023-05-24 02:31:35.690739: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/mshah1/.local/cuda/10.2/lib64:/home/mshah1/.local/cuda/10.2/lib64:/opt/openmpi/lib:/home/mshah1/.local/cuda/8.0//lib64:/home/mshah1/.local/cuda/8.0//extras/CUPTI/lib64
2023-05-24 02:31:35.690805: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

0it [00:00, ?it/s]
ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1 epoch 0: : 0it [00:00, ?it/s]start_idx:150	 end_idx:200


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ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1 epoch 0: : 0it [01:09, ?it/s]
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 226, in <module>
    runner.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 206, in test
    self.trainer.test()
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 592, in test
    test_outputs, test_metrics = self.test_loop(post_loop_fn=self.test_epoch_end)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 206, in test_loop
    outputs, metrics = self._batch_loop(self.test_step, self.test_loader, 0)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 175, in _batch_loop
    outputs, logs = func(batch, i)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 536, in test_step
    p,r = self._single_sample_step(smoothed_model, x_)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 510, in _single_sample_step
    return smoothed_model.certify(x, self.params.randomized_smoothing_params.N0,
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/adversarial/randomized_smoothing/core.py", line 43, in certify
    counts_estimation = self._sample_noise(x, n, batch_size)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/adversarial/randomized_smoothing/core.py", line 92, in _sample_noise
    predictions = self.base_classifier(batch + noise).argmax(1)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 959, in forward
    out = self.classifier(r, *fwd_args, **fwd_kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 1298, in forward
    x = self._get_feats(x)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 1288, in _get_feats
    feat = self.resnet(x)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/container.py", line 141, in forward
    input = module(input)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/container.py", line 141, in forward
    input = module(input)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/batchnorm.py", line 168, in forward
    return F.batch_norm(
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/functional.py", line 2421, in batch_norm
    return torch.batch_norm(
RuntimeError: CUDA out of memory. Tried to allocate 1.91 GiB (GPU 0; 10.75 GiB total capacity; 4.51 GiB already allocated; 1.73 GiB free; 8.12 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.vonenets.EcosetVOneBlockCyclicLRXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0/checkpoints/epoch\=24-step 
\=140800.pt --run_adv_attack_battery --batch_size 125 --num_test 500_000 --use_common_corruption_testset --add_fixed_noise_patch[31P --add_fixed_noise_patch[1P--add_fixed_noise_patch[1P --add_fixed_noise_patch[1P --add_fixed_noise_patch[1P --add_fixed_noise_patch[1P --add_fixed_noise_patch[1P --add_fixed_noise_patch[1P --add_fixed_noise_patch[1P --add_fixed_noise_patch2 --add_fixed_noise_patch5 --add_fixed_noise_patch0 --add_fixed_noise_patch[C --add_fixed_noise_patch- --add_fixed_noise_patch- --add_fixed_noise_patche --add_fixed_noise_patchp --add_fixed_noise_patchs --add_fixed_noise_patch_ --add_fixed_noise_patchl --add_fixed_noise_patchi --add_fixed_noise_patchs --add_fixed_noise_patcht --add_fixed_noise_patch[C --add_fixed_noise_patch0 --add_fixed_noise_patch. --add_fixed_noise_patch0 --add_fixed_noise_patch0 --add_fixed_noise_patch4 --add_fixed_noise_patch
Namespace(task='ICLR22.vonenets.EcosetVOneBlockCyclicLRXResNet2x18', ckp='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0/checkpoints/epoch=24-step=140800.pt', num_test=250, batch_size=125, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=True, attacks=['APGD'], eps_list=[0.004], run_randomized_smoothing_eval=False, rs_start_batch_idx=0, rs_end_batch_idx=None, center_fixation=False, five_fixations=False, bb_fixations=False, fixate_on_max_loc=False, view_scale=None, hscan_fixations=False, add_fixation_predictor=False, fixation_prediction_model='deepgazeII', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=False, use_clickme_data=False, num_fixations=1, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=True, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
None
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), VOneBlock.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'>, sf_corr=0.75, sf_max=9, sf_min=0, rand_param=False, gabor_seed=0, simple_channels=256, complex_channels=256, noise_mode='neuronal', noise_scale=0.35, noise_level=0.07, k_exc=25, image_size=224, visual_degrees=8, ksize=25, stride=4, model_arch=None, dropout_p=0.0, add_noise_during_inference=False, add_deterministic_noise_during_inference=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[64, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=None, preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(64, 64, 64), drop_layers=[0, 3]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0
None
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), VOneBlock.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'>, sf_corr=0.75, sf_max=9, sf_min=0, rand_param=False, gabor_seed=0, simple_channels=256, complex_channels=256, noise_mode='neuronal', noise_scale=0.35, noise_level=0.07, k_exc=25, image_size=224, visual_degrees=8, ksize=25, stride=4, model_arch=None, dropout_p=0.0, add_noise_during_inference=False, add_deterministic_noise_during_inference=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[64, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=None, preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(64, 64, 64), drop_layers=[0, 3]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'> torch.Size([1, 3, 224, 224])
{'sf_corr': 0.75, 'sf_max': 9, 'sf_min': 0, 'rand_param': False, 'gabor_seed': 0, 'simple_channels': 256, 'complex_channels': 256, 'noise_mode': 'neuronal', 'noise_scale': 0.35, 'noise_level': 0.07, 'k_exc': 25, 'image_size': 224, 'visual_degrees': 8, 'ksize': 25, 'stride': 4, 'model_arch': None}
Neuronal distributions gabor parameters
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/vonenet/vonenet/params.py:59: RuntimeWarning: invalid value encountered in true_divide
  ny_dist_marg = n_joint_dist / n_joint_dist.sum(axis=1, keepdims=True)
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /opt/conda/conda-bld/pytorch_1646755849709/work/aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Model:  VOneNet
<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'> torch.Size([1, 64, 56, 56])
total parameters=47.635438M
trainable parameters=45.715432M
ImageDatasetFactory.ImageDatasetParams(cls=<class 'mllib.datasets.dataset_factory.ImageDatasetFactory'>, dataset=<SupportedDatasets.ECOSET_FOLDER: 'ECOSET_FOLDER'>, datafolder='/share/workhorse3/mshah1/ecoset/eval_dataset_dir', class_idxs=None, custom_transforms=(ToTensor(), Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)), max_num_train=inf, max_num_test=250, kwargs={}) 5000 0 0
ToTensor()
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)

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0 28250
train_dataset_len: 48859, val_dataset_len: 1000, test_dataset_len: 0
LightningAdversarialTrainer.TrainerParams(cls=<class 'trainers.MultiAttackEvaluationTrainer'>, training_params=TrainingParams(logdir='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0', nepochs=25, early_stop_patience=50, tracked_metric='val_accuracy', tracking_mode='max', scheduler_step_after_epoch=False, debug=False), adversarial_params=AdversarialParams(training_attack_params=None, testing_attack_params=[('DetNoiseAPGD', TorchAttackAPGDInfParams(norm='Linf', eps=0.004, nsteps=100, n_restarts=1, seed=1684910078, loss='ce', eot_iter=1, rho=0.75, verbose=False))]))
GeneralClassifier(
  (feature_model): SequentialLayers(
    (layers): ModuleList(
      (0): NormalizationLayer(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
      (1): VOneBlock(
        (voneblock): Sequential(
          (0): VOneBlock(
            (simple_conv_q0): GFB()
            (simple_conv_q1): GFB()
            (simple): ReLU()
            (complex): Identity()
            (gabors): Identity()
            (noise): ReLU()
            (output): Identity()
          )
          (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
  )
  (classifier): XResNet18(
    (normalization_layer): Identity()
    (preprocessing_layer): Identity()
    (resnet): XResNet(
      (0): Identity()
      (1): ConvLayer(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): ConvLayer(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): Identity()
      (4): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (5): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (idphippoSequential()
          (act): ReLU(inplace=True)
        )hippo
      )
      (6): Seqhippol(
        (0): ResBlock(
          (conhippo: Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
  hippo      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)hippohippo
              (2): ReLU()hippo
            )
            (1): ConvLayer(
              (0):hippod(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
      hippo  (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      hippo)
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(hippo
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (7): Sequential(
        (0): ResBlock(
          (convpath): Sequential(hippo
            (0): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (8): AdaptiveAvgPool2d(output_size=1)
      (9): fastai.layers.Flatten(full=False)
      (10): Dropout(p=0.0, inplace=False)
      (11): Identity()
    )
    (classifier): Linear(in_features=1024, out_features=1000, bias=True)
    (logit_ensembler): Identity()
    (feature_ensembler): Identity()
  )
  (logit_ensembler): Identity()
  (loss_fn): CrossEntropyLoss()
)

0it [00:00, ?it/s]
EcosetVOneBlockCyclicLRXResNet2x18/0 epoch 0: : 0it [00:00, ?it/s]
EcosetVOneBlockCyclicLRXResNet2x18/0 epoch 0: : 0it [00:00, ?it/s]
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 226, in <module>
    runner.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 206, in test
    self.trainer.test()
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 215, in test
    test_outputs, test_metrics = self.test_loop(post_loop_fn=self.test_epoch_end)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 208, in test_loop
    outputs, metrics = post_loop_fn(outputs, metrics)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 366, in test_epoch_end
    labels = np.array(new_outputs['labels'])
KeyError: 'labels'
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.vonenets.EcosetVOneBlockCyclicLRXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0/checkpoints/epoch\=24-step\
\=140800.pt --run_adv_attack_battery --batch_size 125 --num_test 250 --eps_list 0.004 --add_fixed_noise_patch[C[C[C[C[C[C[C[C[C[C[C[C[1P[1P[1@0
Namespace(task='ICLR22.vonenets.EcosetVOneBlockCyclicLRXResNet2x18', ckp='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0/checkpoints/epoch=24-step=140800.pt', num_test=250, batch_size=10, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=True, attacks=['APGD'], eps_list=[0.004], run_randomized_smoothing_eval=False, rs_start_batch_idx=0, rs_end_batch_idx=None, center_fixation=False, five_fixations=False, bb_fixations=False, fixate_on_max_loc=False, view_scale=None, hscan_fixations=False, add_fixation_predictor=False, fixation_prediction_model='deepgazeII', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=False, use_clickme_data=False, num_fixations=1, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=True, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
None
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), VOneBlock.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'>, sf_corr=0.75, sf_max=9, sf_min=0, rand_param=False, gabor_seed=0, simple_channels=256, complex_channels=256, noise_mode='neuronal', noise_scale=0.35, noise_level=0.07, k_exc=25, image_size=224, visual_degrees=8, ksize=25, stride=4, model_arch=None, dropout_p=0.0, add_noise_during_inference=False, add_deterministic_noise_during_inference=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[64, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=None, preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(64, 64, 64), drop_layers=[0, 3]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0
None
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), VOneBlock.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'>, sf_corr=0.75, sf_max=9, sf_min=0, rand_param=False, gabor_seed=0, simple_channels=256, complex_channels=256, noise_mode='neuronal', noise_scale=0.35, noise_level=0.07, k_exc=25, image_size=224, visual_degrees=8, ksize=25, stride=4, model_arch=None, dropout_p=0.0, add_noise_during_inference=False, add_deterministic_noise_during_inference=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[64, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=None, preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(64, 64, 64), drop_layers=[0, 3]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'> torch.Size([1, 3, 224, 224])
{'sf_corr': 0.75, 'sf_max': 9, 'sf_min': 0, 'rand_param': False, 'gabor_seed': 0, 'simple_channels': 256, 'complex_channels': 256, 'noise_mode': 'neuronal', 'noise_scale': 0.35, 'noise_level': 0.07, 'k_exc': 25, 'image_size': 224, 'visual_degrees': 8, 'ksize': 25, 'stride': 4, 'model_arch': None}
Neuronal distributions gabor parameters
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/vonenet/vonenet/params.py:59: RuntimeWarning: invalid value encountered in true_divide
  ny_dist_marg = n_joint_dist / n_joint_dist.sum(axis=1, keepdims=True)
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /opt/conda/conda-bld/pytorch_1646755849709/work/aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Model:  VOneNet
<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'> torch.Size([1, 64, 56, 56])
total parameters=47.635438M
trainable parameters=45.715432M
ImageDatasetFactory.ImageDatasetParams(cls=<class 'mllib.datasets.dataset_factory.ImageDatasetFactory'>, dataset=<SupportedDatasets.ECOSET_FOLDER: 'ECOSET_FOLDER'>, datafolder='/share/workhorse3/mshah1/ecoset/eval_dataset_dir', class_idxs=None, custom_transforms=(ToTensor(), Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)), max_num_train=inf, max_num_test=250, kwargs={}) 5000 0 0
ToTensor()
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)

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0 28250
train_dataset_len: 48859, val_dataset_len: 1000, test_dataset_len: 0
LightningAdversarialTrainer.TrainerParams(cls=<class 'trainers.MultiAttackEvaluationTrainer'>, training_params=TrainingParams(logdir='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0', nepochs=25, early_stop_patience=50, tracked_metric='val_accuracy', tracking_mode='max', scheduler_step_after_epoch=False, debug=False), adversarial_params=AdversarialParams(training_attack_params=None, testing_attack_params=[('DetNoiseAPGD', TorchAttackAPGDInfParams(norm='Linf', eps=0.004, nsteps=100, n_restarts=1, seed=1684910345, loss='ce', eot_iter=1, rho=0.75, verbose=False))]))
GeneralClassifier(
  (feature_model): SequentialLayers(
    (layers): ModuleList(
      (0): NormalizationLayer(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
      (1): VOneBlock(
        (voneblock): Sequential(
          (0): VOneBlock(
            (simple_conv_q0): GFB()
            (simple_conv_q1): GFB()
            (simple): ReLU()
            (complex): Identity()
            (gabors): Identity()
            (noise): ReLU()
            (output): Identity()
          )
          (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
  )
  (classifier): XResNet18(
    (normalization_layer): Identity()
    (preprocessing_layer): Identity()
    (resnet): XResNet(
      (0): Identity()
      (1): ConvLayer(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): ConvLayer(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): Identity()
      (4): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (5): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0hippovLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )hippo
            (1): ConvLayer(
              hippoonv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (idpath): Sequential()
  hippo  (act): ReLU(inplace=True)hippohippo
        )hippo
      )
      (6): Sequential(
        (0): ResBlhippo
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
      hippo(1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      hippo  (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)hippo
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (7): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (8): AdaptiveAvgPool2d(output_size=1)
      (9): fastai.layers.Flatten(full=False)
      (10): Dropout(p=0.0, inplace=False)
      (11): Identity()
    )
    (classifier): Linear(in_features=1024, out_features=1000, bias=True)
    (logit_ensembler): Identity()
    (feature_ensembler): Identity()
  )
  (logit_ensembler): Identity()
  (loss_fn): CrossEntropyLoss()
)

0it [00:00, ?it/s]
EcosetVOneBlockCyclicLRXResNet2x18/0 epoch 0: : 0it [00:00, ?it/s]
EcosetVOneBlockCyclicLRXResNet2x18/0 epoch 0: : 0it [00:00, ?it/s]
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 226, in <module>
    runner.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 206, in test
    self.trainer.test()
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 215, in test
    test_outputs, test_metrics = self.test_loop(post_loop_fn=self.test_epoch_end)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 208, in test_loop
    outputs, metrics = post_loop_fn(outputs, metrics)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 366, in test_epoch_end
    labels = np.array(new_outputs['labels'])
KeyError: 'labels'
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.vonenets.EcosetVOneBlockCyclicLRXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0/checkpoints/epoch\=24-step\
\=140800.pt --run_adv_attack_battery --batch_size 10 --num_test 250 --eps_list 0.004 --add_fixed_noise_patch[C[C[C[C[C[C[C[C[C[3P[1@1[1@1[1@3[1@0
Namespace(task='ICLR22.vonenets.EcosetVOneBlockCyclicLRXResNet2x18', ckp='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0/checkpoints/epoch=24-step=140800.pt', num_test=1130, batch_size=10, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=True, attacks=['APGD'], eps_list=[0.004], run_randomized_smoothing_eval=False, rs_start_batch_idx=0, rs_end_batch_idx=None, center_fixation=False, five_fixations=False, bb_fixations=False, fixate_on_max_loc=False, view_scale=None, hscan_fixations=False, add_fixation_predictor=False, fixation_prediction_model='deepgazeII', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=False, use_clickme_data=False, num_fixations=1, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=True, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
None
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), VOneBlock.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'>, sf_corr=0.75, sf_max=9, sf_min=0, rand_param=False, gabor_seed=0, simple_channels=256, complex_channels=256, noise_mode='neuronal', noise_scale=0.35, noise_level=0.07, k_exc=25, image_size=224, visual_degrees=8, ksize=25, stride=4, model_arch=None, dropout_p=0.0, add_noise_during_inference=False, add_deterministic_noise_during_inference=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[64, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=None, preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(64, 64, 64), drop_layers=[0, 3]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0
None
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), VOneBlock.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'>, sf_corr=0.75, sf_max=9, sf_min=0, rand_param=False, gabor_seed=0, simple_channels=256, complex_channels=256, noise_mode='neuronal', noise_scale=0.35, noise_level=0.07, k_exc=25, image_size=224, visual_degrees=8, ksize=25, stride=4, model_arch=None, dropout_p=0.0, add_noise_during_inference=False, add_deterministic_noise_during_inference=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[64, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=None, preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(64, 64, 64), drop_layers=[0, 3]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'> torch.Size([1, 3, 224, 224])
{'sf_corr': 0.75, 'sf_max': 9, 'sf_min': 0, 'rand_param': False, 'gabor_seed': 0, 'simple_channels': 256, 'complex_channels': 256, 'noise_mode': 'neuronal', 'noise_scale': 0.35, 'noise_level': 0.07, 'k_exc': 25, 'image_size': 224, 'visual_degrees': 8, 'ksize': 25, 'stride': 4, 'model_arch': None}
Neuronal distributions gabor parameters
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/vonenet/vonenet/params.py:59: RuntimeWarning: invalid value encountered in true_divide
  ny_dist_marg = n_joint_dist / n_joint_dist.sum(axis=1, keepdims=True)
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /opt/conda/conda-bld/pytorch_1646755849709/work/aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Model:  VOneNet
<class 'adversarialML.biologically_inspired_models.src.retina_preproc.VOneBlock'> torch.Size([1, 64, 56, 56])
total parameters=47.635438M
trainable parameters=45.715432M
ImageDatasetFactory.ImageDatasetParams(cls=<class 'mllib.datasets.dataset_factory.ImageDatasetFactory'>, dataset=<SupportedDatasets.ECOSET_FOLDER: 'ECOSET_FOLDER'>, datafolder='/share/workhorse3/mshah1/ecoset/eval_dataset_dir', class_idxs=None, custom_transforms=(ToTensor(), Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)), max_num_train=inf, max_num_test=1130, kwargs={}) 5000 0 2
ToTensor()
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)

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1130 27120
train_dataset_len: 48859, val_dataset_len: 1000, test_dataset_len: 1130
LightningAdversarialTrainer.TrainerParams(cls=<class 'trainers.MultiAttackEvaluationTrainer'>, training_params=TrainingParams(logdir='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0', nepochs=25, early_stop_patience=50, tracked_metric='val_accuracy', tracking_mode='max', scheduler_step_after_epoch=False, debug=False), adversarial_params=AdversarialParams(training_attack_params=None, testing_attack_params=[('DetNoiseAPGD', TorchAttackAPGDInfParams(norm='Linf', eps=0.004, nsteps=100, n_restarts=1, seed=1684910414, loss='ce', eot_iter=1, rho=0.75, verbose=False))]))
GeneralClassifier(
  (feature_model): SequentialLayers(
    (layers): ModuleList(
      (0): NormalizationLayer(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
      (1): VOneBlock(
        (voneblock): Sequential(
          (0): VOneBlock(
            (simple_conv_q0): GFB()
            (simple_conv_q1): GFB()
            (simple): ReLU()
            (complex): Identity()
            (gabors): Identity()
            (noise): ReLU()
            (output): Identity()
          )
          (1): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
  )
  (classifier): XResNet18(
    (normalization_layer): Identity()
    (preprocessing_layer): Identity()
    (resnet): XResNet(
      (0): Identity()
      (1): ConvLayer(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): ConvLayer(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): Identity()
      (4): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (5): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              hippoonv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (acthippoU(inplace=True)
        )
        (1): Rhippok(
          (convpath): Sequential(
            (0hippovLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )hippo
            (1): ConvLayer(
              hippoonv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (idphippoSequential()
          (act): ReLU(inplace=True)
        )hippo
      )
      (6): Seqhippol(
        (0): ResBlock(
          (conhippo: Sequential(
            (0): ConvLayer(
              hippoonv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              hippoeLU()
            )
            (1hippovLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
  hippo  )hippo
  hippo  (idpath): Sequential(hippohippo
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)hippo
            (1): ConvLayer(hippo
              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)hippo
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)hippo
            )hippo
          )hippo
          (act): ReLU(inplace=True)hippo
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)hippo
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
      hippo(1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      hippo  (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (7): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (8): AdaptiveAvgPool2d(output_size=1)
      (9): fastai.layers.Flatten(full=False)
      (10): Dropout(p=0.0, inplace=False)
      (11): Identity()
    )
    (classifier): Linear(in_features=1024, out_features=1000, bias=True)
    (logit_ensembler): Identity()
    (feature_ensembler): Identity()
  )
  (logit_ensembler): Identity()
  (loss_fn): CrossEntropyLoss()
)

0it [00:00, ?it/s]
EcosetVOneBlockCyclicLRXResNet2x18/0 epoch 0: : 0it [00:00, ?it/s]
EcosetVOneBlockCyclicLRXResNet2x18/0 epoch 0: : 0it [00:10, ?it/s, best_metric=-inf, test_acc_DetNoiseAPGD-0.004=0.3]
EcosetVOneBlockCyclicLRXResNet2x18/0 epoch 0: : 1it [00:10, 10.42s/it, best_metric=-inf, test_acc_DetNoiseAPGD-0.004=0.3]^C
EcosetVOneBlockCyclicLRXResNet2x18/0 epoch 0: : 1it [00:13, 13.00s/it, best_metric=-inf, test_acc_DetNoiseAPGD-0.004=0.3]
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 226, in <module>
    runner.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 206, in test
    self.trainer.test()
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 215, in test
    test_outputs, test_metrics = self.test_loop(post_loop_fn=self.test_epoch_end)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 206, in test_loop
    outputs, metrics = self._batch_loop(self.test_step, self.test_loader, 0)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 175, in _batch_loop
    outputs, logs = func(batch, i)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 413, in test_step
    adv_batch = self._maybe_attack_batch(batch, atk if eps > 0 else None)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 106, in _maybe_attack_batch
    x = adv_attack(x, y)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attack.py", line 323, in __call__
    images = self.forward(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 61, in forward
    _, adv_images = self.perturb(images, labels, cheap=True)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 242, in perturb
    best_curr, acc_curr, loss_curr, adv_curr = self.attack_single_run(x_to_fool, y_to_fool)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 166, in attack_single_run
    logits = self.model(x_adv) # 1 forward pass (eot_iter = 1)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 947, in forward
    y = self.feature_model.forward(x, *fwd_args, **fwd_kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 809, in forward
    out = l(out, *fwd_args, **fwd_kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 804, in forward
    noise = self.detnoise.repeat_interleave(x.shape[0], dim=0).to(x.device)
KeyboardInterrupt
^C^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ ^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.vonenets.EcosetVOneBlockCyclicLRXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetVOneBlockCyclicLRXResNet2x18/0/checkpoints/epoch\=24-step\
\=140800.pt --run_adv_attack_battery --batch_size 10 --num_test 1130 --eps_list 0.004 --add_fixed_noise_patchM
[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[K

[KM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cpython main.py --task ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetNoisy 
RetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/ --run_adv_attack_battery --attacks PcFmap-APGDL2_25 --eps_list 0.5 1.5 2. 2.5 --batch_size 10 --num_test 2000 --add_fixation_predictor --add_fixed_noise_patch --view_scale 3 --num_fixati 
ons 5 --fixation_prediction_model deepgazeIII:rblur-6.1-7.0-7.1-in1k --precompute_fixation_map
Namespace(task='ICLR22.noisy_retina_blur.ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18', ckp='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1/checkpoints/', num_test=2000, batch_size=10, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=True, attacks=['PcFmap-APGDL2_25'], eps_list=[0.5, 1.5, 2.0, 2.5], run_randomized_smoothing_eval=False, rs_start_batch_idx=0, rs_end_batch_idx=None, center_fixation=False, five_fixations=False, bb_fixations=False, fixate_on_max_loc=False, view_scale=3, hscan_fixations=False, add_fixation_predictor=True, fixation_prediction_model='deepgazeIII:rblur-6.1-7.0-7.1-in1k', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=True, use_clickme_data=False, num_fixations=5, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=True, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=GaussianNoiseLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'>, std=0.125, add_noise_during_inference=False, add_deterministic_noise_during_inference=True, max_input_size=[3, 224, 224], neuronal_noise=False), retina_params=RetinaBlurFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, max_kernel_size=inf, view_scale=3, only_color=False, no_blur=False, scale=0.05, use_1d_gkernels=True, min_bincount=14, set_min_bin_to_1=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=GaussianNoiseLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'>, std=0.125, add_noise_during_inference=False, add_deterministic_noise_during_inference=True, max_input_size=[3, 224, 224], neuronal_noise=False), retina_params=RetinaBlurFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, max_kernel_size=inf, view_scale=3, only_color=False, no_blur=False, scale=0.05, use_1d_gkernels=True, min_bincount=14, set_min_bin_to_1=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=LogitAverageEnsembler.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.LogitAverageEnsembler'>, n=5, activation=<class 'torch.nn.modules.linear.Identity'>, reduction='mean'), loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1
RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=GaussianNoiseLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'>, std=0.125, add_noise_during_inference=False, add_deterministic_noise_during_inference=True, max_input_size=[3, 224, 224], neuronal_noise=False), retina_params=RetinaBlurFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, max_kernel_size=inf, view_scale=3, only_color=False, no_blur=False, scale=0.05, use_1d_gkernels=True, min_bincount=14, set_min_bin_to_1=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=GaussianNoiseLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'>, std=0.125, add_noise_during_inference=False, add_deterministic_noise_during_inference=True, max_input_size=[3, 224, 224], neuronal_noise=False), retina_params=RetinaBlurFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, max_kernel_size=inf, view_scale=3, only_color=False, no_blur=False, scale=0.05, use_1d_gkernels=True, min_bincount=14, set_min_bin_to_1=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=LogitAverageEnsembler.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.LogitAverageEnsembler'>, n=5, activation=<class 'torch.nn.modules.linear.Identity'>, reduction='mean'), loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'> torch.Size([1, 3, 224, 224])
[224 123  87  64  48  31  15] [10.802135213846604, 10.354928543635195, 9.923270285095539, 9.501554674323094, 8.531356795395943, 6.278959396936146, 2.4558932727738276]
[224 113  81  49  28  14] [9.886995580927815, 9.930499205002702, 9.988194256843602, 10.089318915518763, 10.351948368750191, 10.849174298342337]
CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.5714285714285714, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth')
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'> torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'> torch.Size([1, 3, 224, 224])
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py:557: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  y_hist = self.hist // w
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py:1274: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  frow = (fidx_ds // fmap_ds.shape[3]) * downsample_factor + downsample_factor//2
<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'> torch.Size([1, 3, 224, 224])
keeping feature_model.layers.1.preprocessor.noise_patch from target model
keeping feature_model.layers.1.fixation_model.centerbias_template from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.normalization_layer.mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.normalization_layer.std from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.2.0.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.2.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.weight from target modelhippo
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.0.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.running_var from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.0.1.running_var from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.running_var from target modelhippo
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.0.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.1.running_mean from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.0.0.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.classifier.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.classifier.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm0.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.conv0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.bias0.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.conv1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.bias1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm2.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm2.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.conv2.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.bias2.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.layernorm0.layernorm_part0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.layernorm0.layernorm_part0.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.conv0.conv_part0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.bias0.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.layernorm1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.layernorm1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.conv1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.bias1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.conv2.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.finalizer.center_bias_weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.finalizer.gauss.sigma from target model
got unexpected keys: ['feature_model.layers.1.clr_kernels.0', 'feature_model.layers.1.clr_kernels.1', 'feature_model.layers.1.clr_kernels.2', 'feature_model.layers.1.clr_kernels.3', 'feature_model.layers.1.clr_kernels.4', 'feature_model.layers.1.clr_kernels.5', 'feature_model.layers.1.clr_kernels.6', 'feature_model.layers.1.gry_kernels.0', 'feature_model.layers.1.gry_kernels.1', 'feature_model.layers.1.gry_kernels.2', 'feature_model.layers.1.gry_kernels.3', 'feature_model.layers.1.gry_kernels.4', 'feature_model.layers.1.gry_kernels.5']
total parameters=92.351897M
trainable parameters=45.637448M
ImageDatasetFactory.ImageDatasetParams(cls=<class 'mllib.datasets.dataset_factory.ImageDatasetFactory'>, dataset=<SupportedDatasets.IMAGENET_FOLDER: 'IMAGENET_FOLDER'>, datafolder='/share/workhorse3/mshah1/imagenet/eval_dataset_dir', class_idxs=None, custom_transforms=(Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
), Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)), max_num_train=1275000, max_num_test=2000, kwargs={}) 1275 5000 2
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
)
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)

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12750 250

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100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50000/50000 [00:00<00:00, 1909870.13it/s]
2000 48000
train_dataset_len: 12750, val_dataset_len: 250, test_dataset_len: 2000
LightningAdversarialTrainer.TrainerParams(cls=<class 'fixation_prediction.trainers.RetinaFilterWithFixationPredictionMultiAttackEvaluationTrainer'>, training_params=TrainingParams(logdir='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1', nepochs=25, early_stop_patience=50, tracked_metric='val_accuracy', tracking_mode='max', scheduler_step_after_epoch=False, debug=False), adversarial_params=AdversarialParams(training_attack_params=None, testing_attack_params=[('Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25', TorchAttackAPGDL2Params(norm='L2', eps=0.5, nsteps=25, n_restarts=1, seed=1684910522, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25', TorchAttackAPGDL2Params(norm='L2', eps=1.5, nsteps=25, n_restarts=1, seed=1684910522, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25', TorchAttackAPGDL2Params(norm='L2', eps=2.0, nsteps=25, n_restarts=1, seed=1684910522, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25', TorchAttackAPGDL2Params(norm='L2', eps=2.5, nsteps=25, n_restarts=1, seed=1684910522, loss='ce', eot_iter=1, rho=0.75, verbose=False))]))
GeneralClassifier(
  (feature_model): SequentialLayers(
    (layers): ModuleList(
      (0): IdentityLayer()
      (1): RetinaFilterWithFixationPrediction(
        (preprocessor): GaussianNoiseLayer(std=0.125, neuronal=False)
        (retina): RetinaBlurFilter(loc_mode=const, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, kernel_size=45, view_scale=3, beta=0.05)
        (fixation_model): CustomBackboneDeepGazeIII(
          (fixation_predictor): DeepGazeIIIModule(
            (features): mFeatureExtractor(
              (features): Sequential(
                (0): ToFloatImage()
                (1): GeneralClassifier(
                  (feature_model): SequentialLayers(
                    (layers): ModuleList(
                      (0): IdentityLayer()
                      (1): IdentityLayer()
                    )
                  )
                  (classifier): XResNet18(
                    (normalization_layer): NormalizationLayer(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
                    (preprocessing_layer): Identity()
                    (resnet): XResNet(
                      (0): ConvLayer(
                        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        (2): ReLU()
                      )
                      (1): ConvLayer(
                        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        (2): ReLU()
                      )
                      (2): ConvLayer(
                        (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        (2): ReLU()
                      )
                      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
                      (4): Sequential(
                        (0): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (act): ReLU(inplace=True)
                        )
                        (1): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential()
                          (act): ReLU(inplace=True)
                        )
                      )
                      (5): Sequential(
                        (0): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential(
                            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                            (1): ConvLayer(
                              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (act): ReLU(inplace=True)
                        )
                        (1): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential()
                          (act): ReLU(inplace=True)
                        )
                      )
                      (6): Sequential(
                        (0): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential(
                            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                            (1): ConvLayer(
                              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (act): ReLU(inplace=True)
                        )
                        (1): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential()
                          (act): ReLU(inplace=True)
                        )
                      )
                      (7): Sequential(
                        (0): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential(
                            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                            (1): ConvLayer(
                              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (act): ReLU(inplace=True)
                        )
                        (1): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential()
                          (act): ReLU(inplace=True)
                        )
                      )
                      (8): AdaptiveAvgPool2d(output_size=1)
                      (9): fastai.layers.Flatten(full=False)
                      (10): Dropout(p=0.0, inplace=False)
                      (11): Identity()
                    )
                    (classifier): Linear(in_features=1024, out_features=1000, bias=True)
                    (logit_ensembler): Identity()
                    (feature_ensembler): Identity()
                  )
                  (logit_ensembler): Identity()
                  (loss_fn): CrossEntropyLoss()
                )
              )
            )
            (saliency_network): Sequential(
              (layernorm0): LayerNorm(2560, eps=1e-12, center=True, scale=True)
              (conv0): Conv2d(2560, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bias0): Bias(channels=8)
              (softplus0): Softplus(beta=1, threshold=20)
              (layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
              (conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bias1): Bias(channels=16)
              (softplus1): Softplus(beta=1, threshold=20)
              (layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
              (conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bias2): Bias(channels=1)
              (softplus2): Softplus(beta=1, threshold=20)
            )
            (fixation_selection_network): Sequential(
              (layernorm0): LayerNormMultiInput(
                (layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
              )
              (conv0): Conv2dMultiInput(
                (conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              )
              (bias0): Bias(channels=128)
              (softplus0): Softplus(beta=1, threshold=20)
              (layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
              (conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bias1): Bias(channels=16)
              (softplus1): Softplus(beta=1, threshold=20)
              (conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
            )
            (finalizer): Finalizer(
              (gauss): GaussianFilterNd()
            )
          )
        )
      )
    )
  )
  (classifier): XResNet18(
    (normalization_layer): NormalizationLayer(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    (preprocessing_layer): Identity()
    (resnet): XResNet(
      (0): ConvLayer(
        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (1): ConvLayer(
        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): ConvLayer(
        (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (4): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (5): Sequential(hippo
        (0): ResBlock(
      hippoonvpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              hippoeLU()
            )
            (1hippovLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )hippo
          (idpath): Sequential(
            (0hippoPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              hippoonv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (acthippoU(inplace=True)
        )
        (1): Rhippok(
          (convpath): Sequential(
            (0hippovLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )hippo
            (1): ConvLayer(
              hippoonv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (idphippoSequential()
          (act): ReLU(inplace=True)
        )hippo
      )
      (6): Seqhippol(
        (0): ResBlock(
          (conhippo: Sequential(
            (0): ConvLayer(
              hippoonv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              hippoeLU()
            )
            (1hippovLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )hippo
          (idpath): Sequential(
            (0hippoPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  hippo    )hippo
  hippo  )hippohippo
          (act): ReLU(inplace=True)hippo
        )hippo
        (1): ResBlock(hippo
          (convpath): Sequential(hippo
            (0): Chippoer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)hippo
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)hippo
              (2): ReLU()hippo
            )hippo
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (7): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (8): AdaptiveAvgPool2d(output_size=1)
      (9): fastai.layers.Flatten(full=False)
      (10): Dropout(p=0.0, inplace=False)
      (11): Identity()
    )
    (classifier): Linear(in_features=1024, out_features=1000, bias=True)
    (logit_ensembler): Identity()
    (feature_ensembler): Identity()
  )
  (logit_ensembler): LogitAverageEnsembler(n=5, act=Identity())
  (loss_fn): CrossEntropyLoss()
)

0it [00:00, ?it/s]
ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1 epoch 0: : 0it [00:00, ?it/s]/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py:1184: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  rows = loc_idxs // w
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/_tensor.py:1142: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  ret = func(*args, **kwargs)

ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1 epoch 0: : 0it [00:53, ?it/s, best_metric=-inf, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-0.5=0.6, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-1.5=0.1, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.0=0.1, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.5=0.1]
ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1 epoch 0: : 1it [00:53, 53.57s/it, best_metric=-inf, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-0.5=0.6, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-1.5=0.1, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.0=0.1, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.5=0.1]
ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1 epoch 0: : 1it [01:46, 53.57s/it, best_metric=-inf, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-0.5=0.6, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-1.5=0.05, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.0=0.05, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.5=0.05]
ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1 epoch 0: : 2it [01:46, 53.37s/it, best_metric=-inf, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-0.5=0.6, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-1.5=0.05, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.0=0.05, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.5=0.05]^C
ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/1 epoch 0: : 2it [02:03, 61.98s/it, best_metric=-inf, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-0.5=0.6, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-1.5=0.05, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.0=0.05, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGDL2_25-2.5=0.05]
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 226, in <module>
    runner.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 206, in test
    self.trainer.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/trainers.py", line 215, in test
    test_outputs, test_metrics = self.test_loop(post_loop_fn=self.test_epoch_end)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 206, in test_loop
    outputs, metrics = self._batch_loop(self.test_step, self.test_loader, 0)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 175, in _batch_loop
    outputs, logs = func(batch, i)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/fixation_prediction/trainers.py", line 158, in test_step
    output = super().test_step((x,y), batch_idx)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/trainers.py", line 413, in test_step
    adv_batch = self._maybe_attack_batch(batch, atk if eps > 0 else None)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/trainers.py", line 106, in _maybe_attack_batch
    x = adv_attack(x, y)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attack.py", line 323, in __call__
    images = self.forward(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 61, in forward
    _, adv_images = self.perturb(images, labels, cheap=True)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 242, in perturb
    best_curr, acc_curr, loss_curr, adv_curr = self.attack_single_run(x_to_fool, y_to_fool)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/precomputed_fixation_attacks.py", line 96, in attack_single_run
    logits = self.model(torch.cat([x_adv, fmaps], 1)) # 1 forward pass (eot_iter = 1)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 947, in forward
    y = self.feature_model.forward(x, *fwd_args, **fwd_kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 809, in forward
    out = l(out, *fwd_args, **fwd_kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py", line 1337, in forward
    x_ = self.retina(x_)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 297, in forward
    filtered.append(self._forward_batch(b, _loc_idx))
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 436, in _forward_batch
    clr_filtered_img, cone_density_mat = self.apply_kernel(img, clr_isobox_w, clr_avg_bins, loc_idx, clr_kernels)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 386, in apply_kernel
    return _get_gaussian_filtered_image_and_density_mat_pytorch(img, isobox_w, avg_bins, loc_idx, 
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 104, in _get_gaussian_filtered_image_and_density_mat_pytorch
    filtered_crop = gblur_fn(crop, kern)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 71, in seperable_gaussian_blur_pytorch
    blurred_img = nn.functional.conv1d(blurred_img, W, groups=3)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/fastai/torch_core.py", line 374, in __torch_function__
    @classmethod
KeyboardInterrupt
^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ ^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.retina_warp.ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetRetinaWarpCyclicLRRandAugment 
XResNet2x18/0/checkpoints/epoch\=24-step\=122900.pt --run_adv_attack_battery --attacks APGD_25 --eps_list 0. .002 .004 .008 --batch_size 10 --num_test 2000 --add_fixation_predictor --num_fixations 5 --fixation_prediction_model deepgazeIII:rwarp-6.1-7.0-7. 
1-in1k
Namespace(task='ICLR22.retina_warp.ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18', ckp='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0/checkpoints/epoch=24-step=122900.pt', num_test=2000, batch_size=10, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=True, attacks=['APGD_25'], eps_list=[0.0, 0.002, 0.004, 0.008], run_randomized_smoothing_eval=False, rs_start_batch_idx=0, rs_end_batch_idx=None, center_fixation=False, five_fixations=False, bb_fixations=False, fixate_on_max_loc=False, view_scale=None, hscan_fixations=False, add_fixation_predictor=True, fixation_prediction_model='deepgazeIII:rwarp-6.1-7.0-7.1-in1k', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=False, use_clickme_data=False, num_fixations=5, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=False, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=None, retina_params=AbstractRetinaFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaWarp'>, input_shape=[3, 224, 224], loc_mode='random_uniform', loc=None, batch_size=32, straight_through=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/pretraining/step-0051.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=1, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=None, retina_params=AbstractRetinaFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaWarp'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/pretraining/step-0051.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=None, retina_params=AbstractRetinaFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaWarp'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/pretraining/step-0051.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=LogitAverageEnsembler.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.LogitAverageEnsembler'>, n=5, activation=<class 'torch.nn.modules.linear.Identity'>, reduction='mean'), loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=None, retina_params=AbstractRetinaFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaWarp'>, input_shape=[3, 224, 224], loc_mode='random_uniform', loc=None, batch_size=32, straight_through=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/pretraining/step-0051.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=1, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=None, retina_params=AbstractRetinaFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaWarp'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/pretraining/step-0051.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=None, retina_params=AbstractRetinaFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaWarp'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/pretraining/step-0051.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=LogitAverageEnsembler.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.LogitAverageEnsembler'>, n=5, activation=<class 'torch.nn.modules.linear.Identity'>, reduction='mean'), loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.5, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/pretraining/step-0051.pth')
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'> torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'> torch.Size([1, 3, 224, 224])
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keeping saliency_network.bias0.bias from target model
keeping saliency_network.layernorm1.weight from target model
keeping saliency_network.layernorm1.bias from target model
keeping saliency_network.conv1.weight from target model
keeping saliency_network.bias1.bias from target model
keeping saliency_network.layernorm2.weight from target model
keeping saliency_network.layernorm2.bias from target model
keeping saliency_network.conv2.weight from target model
keeping saliency_network.bias2.bias from target model
keeping fixation_selection_network.layernorm0.layernorm_part0.weight from target model
keeping fixation_selection_network.layernorm0.layernorm_part0.bias from target model
keeping fixation_selection_network.conv0.conv_part0.weight from target model
keeping fixation_selection_network.bias0.bias from target model
keeping fixation_selection_network.layernorm1.weight from target model
keeping fixation_selection_network.layernorm1.bias from target model
keeping fixation_selection_network.conv1.weight from target model
keeping fixation_selection_network.bias1.bias from target model
keeping fixation_selection_network.conv2.weight from target model
keeping finalizer.center_bias_weight from target model
keeping finalizer.gauss.sigma from target model
got unexpected keys: ['model', 'optimizer', 'scheduler', 'rng_state', 'step', 'loss']
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/retinawarp/retina/common.py:18: RuntimeWarning: overflow encountered in exp
  d.append(1. / np.sqrt(np.pi * rf) * np.exp(a * r[-1] / 2.))
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py:557: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  y_hist = self.hist // w
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py:1274: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  frow = (fidx_ds // fmap_ds.shape[3]) * downsample_factor + downsample_factor//2
keeping feature_model.fixation_model.centerbias_template from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.normalization_layer.mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.normalization_layer.std from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.bias from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.bias from target modelhippo
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.running_mean from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.2.0.weight from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.2.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.2.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.weight from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.bias from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.bias from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.0.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.0.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.0.1.bias from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.0.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.0.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.1.0.weight from target modelhippo
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.1.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.1.1.bias from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.1.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.1.1.running_var from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.bias from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.weight from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.weight from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.1.bias from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.0.0.weight from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.0.1.running_mean from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.1.1.running_mean from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.0.weight from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.running_var from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.weight from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.0.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.weight from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.bias from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.running_mean from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.running_var from target model
keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.0.weight from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.running_mean from target model
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keeping feature_model.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.running_mean from target model
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total parameters=92.351897M
trainable parameters=45.637448M
ImageDatasetFactory.ImageDatasetParams(cls=<class 'mllib.datasets.dataset_factory.ImageDatasetFactory'>, dataset=<SupportedDatasets.IMAGENET_FOLDER: 'IMAGENET_FOLDER'>, datafolder='/share/workhorse3/mshah1/imagenet/eval_dataset_dir', class_idxs=None, custom_transforms=(Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
), Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)), max_num_train=1275000, max_num_test=2000, kwargs={}) 1275 5000 2
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
)
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)

  0%|                                                                                                                                                                                                                                | 0/13000 [00:00<?, ?it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13000/13000 [00:00<00:00, 977255.17it/s]
12750 250

  0%|                                                                                                                                                                                                                                | 0/50000 [00:00<?, ?it/s]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50000/50000 [00:00<00:00, 2123182.21it/s]
2000 48000
train_dataset_len: 12750, val_dataset_len: 250, test_dataset_len: 2000
LightningAdversarialTrainer.TrainerParams(cls=<class 'trainers.MultiAttackEvaluationTrainer'>, training_params=TrainingParams(logdir='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0', nepochs=25, early_stop_patience=50, tracked_metric='val_accuracy', tracking_mode='max', scheduler_step_after_epoch=False, debug=False), adversarial_params=AdversarialParams(training_attack_params=None, testing_attack_params=[('Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25', TorchAttackAPGDInfParams(norm='Linf', eps=0.0, nsteps=25, n_restarts=1, seed=1684910775, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25', TorchAttackAPGDInfParams(norm='Linf', eps=0.002, nsteps=25, n_restarts=1, seed=1684910775, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25', TorchAttackAPGDInfParams(norm='Linf', eps=0.004, nsteps=25, n_restarts=1, seed=1684910775, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25', TorchAttackAPGDInfParams(norm='Linf', eps=0.008, nsteps=25, n_restarts=1, seed=1684910775, loss='ce', eot_iter=1, rho=0.75, verbose=False))]))
GeneralClassifier(
  (feature_model): RetinaFilterWithFixationPrediction(
    (preprocessor): Identity()
    (retina): RetinaWarp(loc_mode=const)
    (fixation_model): CustomBackboneDeepGazeIII(
      (fixation_predictor): DeepGazeIIIModule(
        (features): mFeatureExtractor(
          (features): Sequential(
            (0): ToFloatImage()
            (1): GeneralClassifier(
              (feature_model): SequentialLayers(
                (layers): ModuleList(
                  (0): IdentityLayer()
                  (1): IdentityLayer()
                )
              )
              (classifier): XResNet18(
                (normalization_layer): NormalizationLayer(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
                (preprocessing_layer): Identity()
                (resnet): XResNet(
                  (0): ConvLayer(
                    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                    (2): ReLU()
                  )
                  (1): ConvLayer(
                    (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                    (2): ReLU()
                  )
                  (2): ConvLayer(
                    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                    (2): ReLU()
                  )
                  (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
                  (4): Sequential(
                    (0): ResBlock(
                      (convpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (2): ReLU()
                        )
                        (1): ConvLayer(
                          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (idpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
                          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (act): ReLU(inplace=True)
                    )
                    (1): ResBlock(
                      (convpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (2): ReLU()
                        )
                        (1): ConvLayer(
                          (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (idpath): Sequential()
                      (act): ReLU(inplace=True)
                    )
                  )
                  (5): Sequential(
                    (0): ResBlock(
                      (convpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (2): ReLU()
                        )
                        (1): ConvLayer(
                          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (idpath): Sequential(
                        (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                        (1): ConvLayer(
                          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (act): ReLU(inplace=True)
                    )
                    (1): ResBlock(
                      (convpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (2): ReLU()
                        )
                        (1): ConvLayer(
                          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (idpath): Sequential()
                      (act): ReLU(inplace=True)
                    )
                  )
                  (6): Sequential(
                    (0): ResBlock(
                      (convpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (2): ReLU()
                        )
                        (1): ConvLayer(
                          (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (idpath): Sequential(
                        (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                        (1): ConvLayer(
                          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
                          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (act): ReLU(inplace=True)
                    )
                    (1): ResBlock(
                      (convpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (2): ReLU()
                        )
                        (1): ConvLayer(
                          (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (idpath): Sequential()
                      (act): ReLU(inplace=True)
                    )
                  )
                  (7): Sequential(
                    (0): ResBlock(
                      (convpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (2): ReLU()
                        )
                        (1): ConvLayer(
                          (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (idpath): Sequential(
                        (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                        (1): ConvLayer(
                          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
                          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (act): ReLU(inplace=True)
                    )
                    (1): ResBlock(
                      (convpath): Sequential(
                        (0): ConvLayer(
                          (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                          (2): ReLU()
                        )
                        (1): ConvLayer(
                          (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        )
                      )
                      (idpath): Sequential()
                      (act): ReLU(inplace=True)
                    )
                  )
                  (8): AdaptiveAvgPool2d(output_size=1)
                  (9): fastai.layers.Flatten(full=False)
                  (10): Dropout(p=0.0, inplace=False)
                  (11): Identity()
                )
                (classifier): Linear(in_features=1024, out_features=1000, bias=True)
                (logit_ensembler): Identity()
                (feature_ensembler): Identity()
              )
              (logit_ensembler): Identity()
              (loss_fn): CrossEntropyLoss()
            )
          )
        )
        (saliency_network): Sequential(
          (layernorm0): LayerNorm(2560, eps=1e-12, center=True, scale=True)
          (conv0): Conv2d(2560, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bias0): Bias(channels=8)
          (softplus0): Softplus(beta=1, threshold=20)
          (layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
          (conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bias1): Bias(channels=16)
          (softplus1): Softplus(beta=1, threshold=20)
          (layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
          (conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bias2): Bias(channels=1)
          (softplus2): Softplus(beta=1, threshold=20)
        )
        (fixation_selection_network): Sequential(
          (layernorm0): LayerNormMultiInput(
            (layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
          )
          (conv0): Conv2dMultiInput(
            (conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          )
          (bias0): Bias(channels=128)
          (softplus0): Softplus(beta=1, threshold=20)
          (layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
          (conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bias1): Bias(channels=16)
          (softplus1): Softplus(beta=1, threshold=20)
          (conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (finalizer): Finalizer(
          (gauss): GaussianFilterNd()
        )
      )
    )
  )
  (classifier): XResNet18(
    (normalization_layer): NormalizationLayer(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    (preprocessing_layer): Identity()
    (resnet): XResNet(
      (0): ConvLayer(
        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (1): ConvLayer(
        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): ConvLayer(
        (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (4): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)hippo
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1hippovLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )hippo
          (idpath): Sequential()
          (acthippoU(inplace=True)
        )
      )hippo
      (5): Sequential(
        (0): Rhippok(
          (convpath): Sequential(
            (0hippovLayer(
              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )hippo
            (1): ConvLayer(
              hippoonv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (idphippoSequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1hippovLayer(
              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )hippo
          (act): ReLU(inplace=True)
        )hippo
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
  hippo      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)hippohippo
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              hippoeLU()
            )
            (1hippovLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )hippo
          (idpath): Sequential()
          (acthippoU(inplace=True)
        )
      )hippo
      (6): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
  hippo      (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)hippo
  hippo      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)hippohippo
            )hippo
          )hippo
          (idpath): Sequential(hippo
            (0): Ahippo2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(hippo
              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)hippo
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )hippo
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
      hippo  (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      hippo  (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (7): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (8): AdaptiveAvgPool2d(output_size=1)
      (9): fastai.layers.Flatten(full=False)
      (10): Dropout(p=0.0, inplace=False)
      (11): Identity()
    )
    (classifier): Linear(in_features=1024, out_features=1000, bias=True)
    (logit_ensembler): Identity()
    (feature_ensembler): Identity()
  )
  (logit_ensembler): LogitAverageEnsembler(n=5, act=Identity())
  (loss_fn): CrossEntropyLoss()
)

0it [00:00, ?it/s]
ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 0it [00:00, ?it/s]/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/_tensor.py:1142: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  ret = func(*args, **kwargs)

ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 0it [01:01, ?it/s, best_metric=-inf, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.0=0.8, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.002=0.1, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.004=0, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.008=0]
ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 1it [01:01, 61.08s/it, best_metric=-inf, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.0=0.8, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.002=0.1, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.004=0, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.008=0]
ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 1it [02:02, 61.08s/it, best_metric=-inf, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.0=0.85, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.002=0.05, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.004=0, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.008=0]
ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 2it [02:02, 61.48s/it, best_metric=-inf, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.0=0.85, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.002=0.05, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.004=0, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.008=0]^C
ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 2it [02:36, 78.12s/it, best_metric=-inf, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.0=0.85, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.002=0.05, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.004=0, test_acc_Top5FixationsdeepgazeIII:rwarp-6.1-7.0-7.1-in1kFixationsAPGD_25-0.008=0]
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 226, in <module>
    runner.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 206, in test
    self.trainer.test()
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 215, in test
    test_outputs, test_metrics = self.test_loop(post_loop_fn=self.test_epoch_end)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 206, in test_loop
    outputs, metrics = self._batch_loop(self.test_step, self.test_loader, 0)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 175, in _batch_loop
    outputs, logs = func(batch, i)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 413, in test_step
    adv_batch = self._maybe_attack_batch(batch, atk if eps > 0 else None)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/trainers.py", line 106, in _maybe_attack_batch
    x = adv_attack(x, y)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attack.py", line 323, in __call__
    images = self.forward(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 61, in forward
    _, adv_images = self.perturb(images, labels, cheap=True)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 242, in perturb
    best_curr, acc_curr, loss_curr, adv_curr = self.attack_single_run(x_to_fool, y_to_fool)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 170, in attack_single_run
    grad += torch.autograd.grad(loss, [x_adv])[0].detach() # 1 backward pass (eot_iter = 1)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/autograd/__init__.py", line 242, in grad
    return handle_torch_function(
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/overrides.py", line 1394, in handle_torch_function
    result = torch_func_method(public_api, types, args, kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/fastai/torch_core.py", line 378, in __torch_function__
    res = super().__torch_function__(func, types, args, ifnone(kwargs, {}))
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/_tensor.py", line 1142, in __torch_function__
    ret = func(*args, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/autograd/__init__.py", line 275, in grad
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
KeyboardInterrupt
^C^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.retina_warp.ImagenetRetinaWarpCyclicLRRandAugmentXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/imagenet_folder-0.0/ImagenetRetinaWarpCyclicLRRandAugmentX
XResNet2x18/0/checkpoints/epoch\=24-step\=122900.pt --run_adv_attack_battery --attacks APGD_25 --eps_list 0. .002 .004 .008 --batch_size 10 --num_test 2000 --add_fixation_predictor --num_fixations 5 --fixation_prediction_model deepgazeIII:rwarp-6.1-7.0-7.1
1-in1k
^CTraceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 5, in <module>
    from mllib.tasks.base_tasks import AbstractTask
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/tasks/base_tasks.py", line 6, in <module>
    from mllib.datasets.dataset_factory import ImageDatasetFactory, SupportedDatasets
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/datasets/dataset_factory.py", line 13, in <module>
    from mllib.datasets.fixation_point_dataset import FixationPointDataset
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/datasets/fixation_point_dataset.py", line 5, in <module>
    from mllib.datasets.imagenet_filelist_dataset import ImagenetFileListDataset
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/datasets/imagenet_filelist_dataset.py", line 6, in <module>
    import webdataset as wds
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/webdataset/__init__.py", line 11, in <module>
    from .autodecode import (
  File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
  File "<frozen importlib._bootstrap>", line 982, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 925, in _find_spec
  File "<frozen importlib._bootstrap_external>", line 1423, in find_spec
  File "<frozen importlib._bootstrap_external>", line 1392, in _get_spec
  File "<frozen importlib._bootstrap_external>", line 1362, in _path_importer_cache
KeyboardInterrupt

kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ ^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.noisy_retina_blur.EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetNoisyRetinaBlurWRa 
ndomScalesCyclicLRRandAugmentXResNet2x18/0/checkpoints/epoch\=23-step\=135168.pt --run_adv_attack_battery --attacks PcFmap-APGD_25 --eps_list .002 .004 .008 .016 --batch_size 10 --num_test 1200 --add_fixation_predictor --add_fixed_noise_patch --view_scale 
 3 --num_fixations 5 --fixation_prediction_model deepgazeIII:rblur-6.1-7.0-7.1-in1k --precompute_fixation_map
Namespace(task='ICLR22.noisy_retina_blur.EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18', ckp='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/0/checkpoints/epoch=23-step=135168.pt', num_test=1200, batch_size=10, output_to_task_logdir=False, num_trainings=1, eval_only=False, prune_and_test=False, run_adv_attack_battery=True, attacks=['PcFmap-APGD_25'], eps_list=[0.002, 0.004, 0.008, 0.016], run_randomized_smoothing_eval=False, rs_start_batch_idx=0, rs_end_batch_idx=None, center_fixation=False, five_fixations=False, bb_fixations=False, fixate_on_max_loc=False, view_scale=3, hscan_fixations=False, add_fixation_predictor=True, fixation_prediction_model='deepgazeIII:rblur-6.1-7.0-7.1-in1k', retina_after_fixation=False, use_precomputed_fixations=False, precompute_fixation_map=True, use_clickme_data=False, num_fixations=5, many_fixations=False, disable_retina=False, straight_through_retina=False, disable_reconstruction=False, use_residual_img=False, use_common_corruption_testset=False, add_fixed_noise_patch=True, add_random_noise=False, multi_randaugment=False, use_lightning_lite=False, use_bf16_precision=False, use_f16_precision=False, debug=False, seed=45551323)
RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=GaussianNoiseLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'>, std=0.125, add_noise_during_inference=False, add_deterministic_noise_during_inference=True, max_input_size=[3, 224, 224], neuronal_noise=False), retina_params=RetinaBlurFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, max_kernel_size=inf, view_scale=3, only_color=False, no_blur=False, scale=0.05, use_1d_gkernels=True, min_bincount=14, set_min_bin_to_1=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=GaussianNoiseLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'>, std=0.125, add_noise_during_inference=False, add_deterministic_noise_during_inference=True, max_input_size=[3, 224, 224], neuronal_noise=False), retina_params=RetinaBlurFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, max_kernel_size=inf, view_scale=3, only_color=False, no_blur=False, scale=0.05, use_1d_gkernels=True, min_bincount=14, set_min_bin_to_1=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=565, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=565, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=LogitAverageEnsembler.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.LogitAverageEnsembler'>, n=5, activation=<class 'torch.nn.modules.linear.Identity'>, reduction='mean'), loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/0
RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=GaussianNoiseLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'>, std=0.125, add_noise_during_inference=False, add_deterministic_noise_during_inference=True, max_input_size=[3, 224, 224], neuronal_noise=False), retina_params=RetinaBlurFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, max_kernel_size=inf, view_scale=3, only_color=False, no_blur=False, scale=0.05, use_1d_gkernels=True, min_bincount=14, set_min_bin_to_1=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)
GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), RetinaFilterWithFixationPrediction.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'>, common_params=CommonModelParams(input_size=[4, 224, 224], num_units=None, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), preprocessing_params=GaussianNoiseLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.GaussianNoiseLayer'>, std=0.125, add_noise_during_inference=False, add_deterministic_noise_during_inference=True, max_input_size=[3, 224, 224], neuronal_noise=False), retina_params=RetinaBlurFilter.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.retina_preproc.RetinaBlurFilter'>, input_shape=[3, 224, 224], loc_mode='const', loc=None, batch_size=32, straight_through=False, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, max_kernel_size=inf, view_scale=3, only_color=False, no_blur=False, scale=0.05, use_1d_gkernels=True, min_bincount=14, set_min_bin_to_1=False), fixation_params=CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.1, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth'), fixation_model_ckp=None, freeze_fixation_model=True, target_downsample_factor=1, loc_sampling_temp=1.0, num_train_fixation_points=1, num_eval_fixation_points=5, apply_retina_before_fixation=True, salience_map_provided_as_input_channel=False, random_fixation_prob=0.0, disable=False, return_fixation_maps=False, return_fixated_images=True)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=565, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=565, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=LogitAverageEnsembler.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.LogitAverageEnsembler'>, n=5, activation=<class 'torch.nn.modules.linear.Identity'>, reduction='mean'), loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>)
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'> torch.Size([1, 3, 224, 224])
[224 123  87  64  48  31  15] [10.802135213846604, 10.354928543635195, 9.923270285095539, 9.501554674323094, 8.531356795395943, 6.278959396936146, 2.4558932727738276]
[224 113  81  49  28  14] [9.886995580927815, 9.930499205002702, 9.988194256843602, 10.089318915518763, 10.351948368750191, 10.849174298342337]
CustomBackboneDeepGazeIII.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.CustomBackboneDeepGazeIII'>, backbone_params=GeneralClassifier.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.GeneralClassifier'>, input_size=[3, 224, 224], feature_model_params=SequentialLayers.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.SequentialLayers'>, layer_params=[AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>), AbstractModel.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'>)], common_params=CommonModelParams(input_size=[3, 224, 224], num_units=None, activation=<class 'torch.nn.modules.linear.Identity'>, bias=True, dropout_p=0.0)), classifier_params=XResNet34.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.models.XResNet18'>, common_params=CommonModelParams(input_size=[3, 224, 224], num_units=1000, activation=<class 'torch.nn.modules.activation.ReLU'>, bias=True, dropout_p=0.0), normalization_layer_params=NormalizationLayer.ModelParams(cls=<class 'adversarialML.biologically_inspired_models.src.mlp_mixer_models.NormalizationLayer'>, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), preprocessing_layer_params=None, logit_ensembler_params=None, feature_ensembler_params=None, setup_feature_extraction=False, setup_classification=True, num_classes=1000, kernel_size=3, widen_factor=2, widen_stem=False, stem_sizes=(32, 32, 64), drop_layers=[]), logit_ensembler_params=None, loss_fn=<class 'torch.nn.modules.loss.CrossEntropyLoss'>), backbone_config={'feature_layers': ['1.classifier.resnet.6.1', '1.classifier.resnet.7.0', '1.classifier.resnet.7.1']}, random_fixation_prob=0.0, loc_sampling_temperature=1.0, mask_past_fixations=True, always_recompute_fmap=False, pretrained=True, min_image_dim=224, fixation_width_frac=0.5714285714285714, ckp_path='/home/mshah1/workhorse3/train_deepgaze3/ImagenetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/pretraining/final.pth')
torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'> torch.Size([1, 3, 224, 224])
<class 'adversarialML.biologically_inspired_models.src.models.IdentityLayer'> torch.Size([1, 3, 224, 224])
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py:557: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  y_hist = self.hist // w
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py:1274: UserWarning: __floordiv__ is deprecated, and its hippoor will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  frow = (fidx_ds // fmap_ds.shape[3]) * downsample_factor + downsample_factor//2
<class 'adversarialML.biologically_inspired_models.src.fixation_prediction.models.RetinaFilterWithFixationPrediction'> torch.Size([1, 3, 224, 224])
keeping feature_model.layers.1.preprocessor.noise_patch from target model
keeping feature_model.layers.1.fixation_model.centerbias_template from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.normalization_layer.mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.normalization_layer.std from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.2.0.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.2.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.2.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.weight from target modelhippo
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.0.idpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.0.0.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.1.0.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.4.1.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.0.idpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.5.1.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.0.0.weight from target model
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keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.0.idpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.6.1.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.0.idpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.0.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.1.running_mean from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.resnet.7.1.convpath.1.1.running_var from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.classifier.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.features.features.1.classifier.classifier.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm0.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.conv0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.bias0.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.conv1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.bias1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm2.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.layernorm2.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.conv2.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.saliency_network.bias2.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.layernorm0.layernorm_part0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.layernorm0.layernorm_part0.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.conv0.conv_part0.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.bias0.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.layernorm1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.layernorm1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.conv1.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.bias1.bias from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.fixation_selection_network.conv2.weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.finalizer.center_bias_weight from target model
keeping feature_model.layers.1.fixation_model.fixation_predictor.finalizer.gauss.sigma from target model
got unexpected keys: ['feature_model.layers.1.clr_kernels.0', 'feature_model.layers.1.clr_kernels.1', 'feature_model.layers.1.clr_kernels.2', 'feature_model.layers.1.clr_kernels.3', 'feature_model.layers.1.clr_kernels.4', 'feature_model.layers.1.clr_kernels.5', 'feature_model.layers.1.clr_kernels.6', 'feature_model.layers.1.gry_kernels.0', 'feature_model.layers.1.gry_kernels.1', 'feature_model.layers.1.gry_kernels.2', 'feature_model.layers.1.gry_kernels.3', 'feature_model.layers.1.gry_kernels.4', 'feature_model.layers.1.gry_kernels.5']
total parameters=91.906022M
trainable parameters=45.191573M
ImageDatasetFactory.ImageDatasetParams(cls=<class 'mllib.datasets.dataset_factory.ImageDatasetFactory'>, dataset=<SupportedDatasets.ECOSET_FOLDER: 'ECOSET_FOLDER'>, datafolder='/share/workhorse3/mshah1/ecoset/eval_dataset_dir', class_idxs=None, custom_transforms=(Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
), Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)), max_num_train=inf, max_num_test=1200, kwargs={}) 5000 0 2
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    RandomCrop(size=(224, 224), padding=None)
    RandomHorizontalFlip(p=0.5)
    RandAugment(num_ops=2, magnitude=15, num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)
    ToTensor()
)
Compose(
    Resize(size=224, interpolation=bilinear, max_size=None, antialias=None)
    CenterCrop(size=(224, 224))
    ToTensor()
)

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48859 0

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1130 27120
train_dataset_len: 48859, val_dataset_len: 1000, test_dataset_len: 1130
LightningAdversarialTrainer.TrainerParams(cls=<class 'fixation_prediction.trainers.RetinaFilterWithFixationPredictionMultiAttackEvaluationTrainer'>, training_params=TrainingParams(logdir='/share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/0', nepochs=25, early_stop_patience=50, tracked_metric='val_accuracy', tracking_mode='max', scheduler_step_after_epoch=False, debug=False), adversarial_params=AdversarialParams(training_attack_params=None, testing_attack_params=[('Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25', TorchAttackAPGDInfParams(norm='Linf', eps=0.002, nsteps=25, n_restarts=1, seed=1684911081, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25', TorchAttackAPGDInfParams(norm='Linf', eps=0.004, nsteps=25, n_restarts=1, seed=1684911081, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25', TorchAttackAPGDInfParams(norm='Linf', eps=0.008, nsteps=25, n_restarts=1, seed=1684911081, loss='ce', eot_iter=1, rho=0.75, verbose=False)), ('Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25', TorchAttackAPGDInfParams(norm='Linf', eps=0.016, nsteps=25, n_restarts=1, seed=1684911081, loss='ce', eot_iter=1, rho=0.75, verbose=False))]))
GeneralClassifier(
  (feature_model): SequentialLayers(
    (layers): ModuleList(
      (0): IdentityLayer()
      (1): RetinaFilterWithFixationPrediction(
        (preprocessor): GaussianNoiseLayer(std=0.125, neuronal=False)
        (retina): RetinaBlurFilter(loc_mode=const, cone_std=0.12, rod_std=0.09, max_rod_density=0.12, kernel_size=45, view_scale=3, beta=0.05)
        (fixation_model): CustomBackboneDeepGazeIII(
          (fixation_predictor): DeepGazeIIIModule(
            (features): mFeatureExtractor(
              (features): Sequential(
                (0): ToFloatImage()
                (1): GeneralClassifier(
                  (feature_model): SequentialLayers(
                    (layers): ModuleList(
                      (0): IdentityLayer()
                      (1): IdentityLayer()
                    )
                  )
                  (classifier): XResNet18(
                    (normalization_layer): NormalizationLayer(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
                    (preprocessing_layer): Identity()
                    (resnet): XResNet(
                      (0): ConvLayer(
                        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        (2): ReLU()
                      )
                      (1): ConvLayer(
                        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        (2): ReLU()
                      )
                      (2): ConvLayer(
                        (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                        (2): ReLU()
                      )
                      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
                      (4): Sequential(
                        (0): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (act): ReLU(inplace=True)
                        )
                        (1): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential()
                          (act): ReLU(inplace=True)
                        )
                      )
                      (5): Sequential(
                        (0): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential(
                            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                            (1): ConvLayer(
                              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (act): ReLU(inplace=True)
                        )
                        (1): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential()
                          (act): ReLU(inplace=True)
                        )
                      )
                      (6): Sequential(
                        (0): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential(
                            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                            (1): ConvLayer(
                              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (act): ReLU(inplace=True)
                        )
                        (1): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential()
                          (act): ReLU(inplace=True)
                        )
                      )
                      (7): Sequential(
                        (0): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential(
                            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
                            (1): ConvLayer(
                              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (act): ReLU(inplace=True)
                        )
                        (1): ResBlock(
                          (convpath): Sequential(
                            (0): ConvLayer(
                              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                              (2): ReLU()
                            )
                            (1): ConvLayer(
                              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
                              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                            )
                          )
                          (idpath): Sequential()
                          (act): ReLU(inplace=True)
                        )
                      )
                      (8): AdaptiveAvgPool2d(output_size=1)
                      (9): fastai.layers.Flatten(full=False)
                      (10): Dropout(p=0.0, inplace=False)
                      (11): Identity()
                    )
                    (classifier): Linear(in_features=1024, out_features=1000, bias=True)
                    (logit_ensembler): Identity()
                    (feature_ensembler): Identity()
                  )
                  (logit_ensembler): Identity()
                  (loss_fn): CrossEntropyLoss()
                )
              )
            )
            (saliency_network): Sequential(
              (layernorm0): LayerNorm(2560, eps=1e-12, center=True, scale=True)
              (conv0): Conv2d(2560, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bias0): Bias(channels=8)
              (softplus0): Softplus(beta=1, threshold=20)
              (layernorm1): LayerNorm(8, eps=1e-12, center=True, scale=True)
              (conv1): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bias1): Bias(channels=16)
              (softplus1): Softplus(beta=1, threshold=20)
              (layernorm2): LayerNorm(16, eps=1e-12, center=True, scale=True)
              (conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bias2): Bias(channels=1)
              (softplus2): Softplus(beta=1, threshold=20)
            )
            (fixation_selection_network): Sequential(
              (layernorm0): LayerNormMultiInput(
                (layernorm_part0): LayerNorm(1, eps=1e-12, center=True, scale=True)
              )
              (conv0): Conv2dMultiInput(
                (conv_part0): Conv2d(1, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              )
              (bias0): Bias(channels=128)
              (softplus0): Softplus(beta=1, threshold=20)
              (layernorm1): LayerNorm(128, eps=1e-12, center=True, scale=True)
              (conv1): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bias1): Bias(channels=16)
              (softplus1): Softplus(beta=1, threshold=20)
              (conv2): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
            )
            (finalizer): Finalizer(
              (gauss): GaussianFilterNd()
            )
          )
        )
      )
    )
  )
  (classifier): XResNet18(
    (normalization_layer): NormalizationLayer(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    (preprocessing_layer): Identity()
    (resnet): XResNet(
      (0): ConvLayer(
        (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (1): ConvLayer(
        (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (2): ConvLayer(
        (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (4): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)hippo
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      hippo)
          )
          (idpath): Sequential(
            (0): ConvLayer(
              hippoonv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (acthippoU(inplace=True)
        )
        (1): Rhippok(
          (convpath): Sequential(
            (0hippovLayer(
              (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )hippo
            (1): ConvLayer(
              hippoonv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (idphippoSequential()
          (act): ReLU(inplace=True)
        )hippo
      )
      (5): Seqhippol(
        (0): ResBlock(
          (conhippo: Sequential(
            (0): ConvLayer(
              hippoonv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              hippoeLU()
            )
            (1hippovLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )hippo
          (idpath): Sequential(
            (0hippoPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              hippoonv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )hippo
          )
          (acthippoU(inplace=True)
        )
        (1): Rhippok(
          (convpath): Sequential(
            (0hippovLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              hippoatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )hippo
            (1): ConvLayer(
              (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  hippo    )hippo
  hippo  )hippohippo
          (idpath): Sequential()
          (act): ReLU(inplace=True)hippo
        )
      )hippo
  hippo): Sequential(hippo
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (7): Sequential(
        (0): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential(
            (0): AvgPool2d(kernel_size=2, stride=2, padding=0)
            (1): ConvLayer(
              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (act): ReLU(inplace=True)
        )
        (1): ResBlock(
          (convpath): Sequential(
            (0): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              (2): ReLU()
            )
            (1): ConvLayer(
              (0): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
          )
          (idpath): Sequential()
          (act): ReLU(inplace=True)
        )
      )
      (8): AdaptiveAvgPool2d(output_size=1)
      (9): fastai.layers.Flatten(full=False)
      (10): Dropout(p=0.0, inplace=False)
      (11): Identity()
    )
    (classifier): Linear(in_features=1024, out_features=565, bias=True)
    (logit_ensembler): Identity()
    (feature_ensembler): Identity()
  )
  (logit_ensembler): LogitAverageEnsembler(n=5, act=Identity())
  (loss_fn): CrossEntropyLoss()
)

0it [00:00, ?it/s]
EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 0it [00:00, ?it/s]/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py:1184: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  rows = loc_idxs // w
/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/_tensor.py:1142: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  ret = func(*args, **kwargs)

EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 0it [00:53, ?it/s, best_metric=-inf, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.002=0.6, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.004=0.5, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.008=0.1, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.016=0]
EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 1it [00:53, 53.77s/it, best_metric=-inf, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.002=0.6, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.004=0.5, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.008=0.1, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.016=0]^C
EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/0 epoch 0: : 1it [01:03, 63.01s/it, best_metric=-inf, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.002=0.6, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.004=0.5, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.008=0.1, test_acc_Top5FixationsScale=3DetNoisedeepgazeIII:rblur-6.1-7.0-7.1-in1kFixationsPrecomputedFmapPcFmap-APGD_25-0.016=0]
Traceback (most recent call last):
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/main.py", line 226, in <module>
    runner.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/runners/base_runners.py", line 206, in test
    self.trainer.test()
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/trainers.py", line 215, in test
    test_outputs, test_metrics = self.test_loop(post_loop_fn=self.test_epoch_end)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 206, in test_loop
    outputs, metrics = self._batch_loop(self.test_step, self.test_loader, 0)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/mllib/trainers/base_trainers.py", line 175, in _batch_loop
    outputs, logs = func(batch, i)
  File "/home/mshah1/rblur-code-package/adversarialML/biologically_inspired_models/src/fixation_prediction/trainers.py", line 158, in test_step
    output = super().test_step((x,y), batch_idx)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/trainers.py", line 413, in test_step
    adv_batch = self._maybe_attack_batch(batch, atk if eps > 0 else None)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/trainers.py", line 106, in _maybe_attack_batch
    x = adv_attack(x, y)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attack.py", line 323, in __call__
    images = self.forward(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 61, in forward
    _, adv_images = self.perturb(images, labels, cheap=True)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torchattacks/attacks/apgd.py", line 242, in perturb
    best_curr, acc_curr, loss_curr, adv_curr = self.attack_single_run(x_to_fool, y_to_fool)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/precomputed_fixation_attacks.py", line 96, in attack_single_run
    logits = self.model(torch.cat([x_adv, fmaps], 1)) # 1 forward pass (eot_iter = 1)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 947, in forward
    y = self.feature_model.forward(x, *fwd_args, **fwd_kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/models.py", line 809, in forward
    out = l(out, *fwd_args, **fwd_kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/fixation_prediction/models.py", line 1337, in forward
    x_ = self.retina(x_)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 297, in forward
    filtered.append(self._forward_batch(b, _loc_idx))
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 436, in _forward_batch
    clr_filtered_img, cone_density_mat = self.apply_kernel(img, clr_isobox_w, clr_avg_bins, loc_idx, clr_kernels)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 386, in apply_kernel
    return _get_gaussian_filtered_image_and_density_mat_pytorch(img, isobox_w, avg_bins, loc_idx, 
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 104, in _get_gaussian_filtered_image_and_density_mat_pytorch
    filtered_crop = gblur_fn(crop, kern)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/adversarialML/biologically_inspired_models/src/retina_preproc.py", line 68, in seperable_gaussian_blur_pytorch
    img = rearrange(img, 'b c h w -> (b h) c w')
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/einops/einops.py", line 487, in rearrange
    return reduce(tensor, pattern, reduction='rearrange', **axes_lengths)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/einops/einops.py", line 410, in reduce
    return _apply_recipe(recipe, tensor, reduction_type=reduction)
  File "/home/mshah1/anaconda3/envs/rblur7/lib/python3.9/site-packages/einops/einops.py", line 233, in _apply_recipe
    _reconstruct_from_shape(recipe, backend.shape(tensor))
KeyboardInterrupt
^C^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ ^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ python main.py --task ICLR22.noisy_retina_blur.EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18 --ckp /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetNoisyRetinaBlurWRan
ndomScalesCyclicLRRandAugmentXResNet2x18/0/checkpoints/epoch\=23-step\=135168.pt --run_adv_attack_battery --attacks PcFmap-APGD_25 --eps_list .002 .004 .008 .016 --batch_size 10 --num_test 1200 --add_fixation_predictor --add_fixed_noise_patch --view_scale 
 3 --num_fixations 5 --fixation_prediction_model deepgazeIII:rblur-6.1-7.0-7.1-in1k --precompute_fixation_mapMM
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ndomScalesCyclicLRRandAugmentXResNet2x18/0/checkpoints/epoch\=23-step\=135168.pt --run_adv_attack_battery --attacks PcFmap-APGD_25 --eps_list .002 .004 .008 .016 --batch_size 10 --num_test 1200 --add_fixation_predictor --add_fixed_noise_patch --view_scale 
 3 --num_fixations 5 --fixation_prediction_model deepgazeIII:rblur-6.1-7.0-7.1-in1k --precompute_fixation_mapMM
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[KMM[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[C[Cless /share/workhorse3/mshah1/biologically_inspired_models/iclr22_logs/ecoset-0.0/EcosetNoisyRetinaBlurWRandomScalesCyclicLRRandAugmentXResNet2x18/0/checkpoints/epoch\=23-step\=135168.pt[K[K[K[K[K[K[K[K[K[K[K[K^C
kmshah1@compute-2-11:~/rblur-code-package/adversarialML/biologically_inspired_models/src\(rblur7) [mshah1@compute-2-11 src]$ exit

Script done on Wed 24 May 2023 09:14:18 AM EDT
