SYMBOL INDEX (410 symbols across 44 files)

FILE: examples/evaluate.py
  function run_evaluation (line 9) | def run_evaluation():
  function run_plotting (line 16) | def run_plotting():

FILE: examples/plotting_definition.py
  function plotting_definition_template (line 12) | def plotting_definition_template(df):
  function get_comparison_decision_makers (line 45) | def get_comparison_decision_makers(df, include_humans=True,

FILE: modelvshuman/cli.py
  function main (line 43) | def main(models, datasets, *args, **kwargs):

FILE: modelvshuman/datasets/base.py
  class Dataset (line 7) | class Dataset(object):
    method __init__ (line 19) | def __init__(self,
    method loader (line 60) | def loader(self):
    method loader (line 66) | def loader(self, new_loader):

FILE: modelvshuman/datasets/create_dataset.py
  function resize_crop_image (line 24) | def resize_crop_image(input_file,
  function create_dataset (line 37) | def create_dataset(original_dataset_path,
  function create_experiment (line 124) | def create_experiment(expt_name,
  function get_leading_zeros (line 191) | def get_leading_zeros(num, length=4):

FILE: modelvshuman/datasets/dataloaders.py
  class ImageFolderWithPaths (line 7) | class ImageFolderWithPaths(datasets.ImageFolder):
    method __init__ (line 15) | def __init__(self, *args, **kwargs):
    method __getitem__ (line 26) | def __getitem__(self, index):
  class PytorchLoader (line 41) | class PytorchLoader(object):
    method __call__ (line 44) | def __call__(self, path, resize, batch_size, num_workers,

FILE: modelvshuman/datasets/dataset_converters.py
  class ToTensorflow (line 6) | class ToTensorflow(object):
    method __init__ (line 9) | def __init__(self, pytorch_loader):
    method convert (line 14) | def convert(self, x):
    method __iter__ (line 19) | def __iter__(self):

FILE: modelvshuman/datasets/decision_mappings.py
  class DecisionMapping (line 10) | class DecisionMapping(ABC):
    method check_input (line 11) | def check_input(self, probabilities):
    method __call__ (line 16) | def __call__(self, probabilities):
  class ImageNetProbabilitiesTo1000ClassesMapping (line 20) | class ImageNetProbabilitiesTo1000ClassesMapping(DecisionMapping):
    method __init__ (line 22) | def __init__(self):
    method __call__ (line 25) | def __call__(self, probabilities):
  class ImageNetProbabilitiesTo16ClassesMapping (line 30) | class ImageNetProbabilitiesTo16ClassesMapping(DecisionMapping):
    method __init__ (line 33) | def __init__(self, aggregation_function=None):
    method __call__ (line 39) | def __call__(self, probabilities):

FILE: modelvshuman/datasets/experiments.py
  class Experiment (line 6) | class Experiment:
    method __post_init__ (line 14) | def __post_init__(self):
  class DatasetExperiments (line 76) | class DatasetExperiments:
  function get_experiments (line 81) | def get_experiments(dataset_names):

FILE: modelvshuman/datasets/imagenet.py
  class ImageNetParams (line 14) | class ImageNetParams:
  function imagenet_validation (line 25) | def imagenet_validation(*args, **kwargs):

FILE: modelvshuman/datasets/info_mappings.py
  class ImagePathToInformationMapping (line 4) | class ImagePathToInformationMapping(ABC):
    method __init__ (line 5) | def __init__(self):
    method __call__ (line 8) | def __call__(self, full_path):
  class ImageNetInfoMapping (line 12) | class ImageNetInfoMapping(ImagePathToInformationMapping):
    method __call__ (line 18) | def __call__(self, full_path):
  class ImageNetCInfoMapping (line 27) | class ImageNetCInfoMapping(ImagePathToInformationMapping):
    method __call__ (line 33) | def __call__(self, full_path):
  class InfoMappingWithSessions (line 44) | class InfoMappingWithSessions(ImagePathToInformationMapping):
    method __call__ (line 50) | def __call__(self, full_path):

FILE: modelvshuman/datasets/noise_generalisation.py
  class NoiseGeneralisationParams (line 21) | class NoiseGeneralisationParams:
  function _get_dataset (line 31) | def _get_dataset(name, params, *args, **kwargs):
  function colour (line 42) | def colour(*args, **kwargs):
  function contrast (line 49) | def contrast(*args, **kwargs):
  function high_pass (line 56) | def high_pass(*args, **kwargs):
  function low_pass (line 63) | def low_pass(*args, **kwargs):
  function phase_scrambling (line 70) | def phase_scrambling(*args, **kwargs):
  function power_equalisation (line 77) | def power_equalisation(*args, **kwargs):
  function false_colour (line 84) | def false_colour(*args, **kwargs):
  function rotation (line 91) | def rotation(*args, **kwargs):
  function eidolonI (line 98) | def eidolonI(*args, **kwargs):
  function eidolonII (line 105) | def eidolonII(*args, **kwargs):
  function eidolonIII (line 112) | def eidolonIII(*args, **kwargs):
  function uniform_noise (line 119) | def uniform_noise(*args, **kwargs):

FILE: modelvshuman/datasets/registry.py
  function register_dataset (line 6) | def register_dataset(name):
  function list_datasets (line 15) | def list_datasets():

FILE: modelvshuman/datasets/sketch.py
  function sketch (line 12) | def sketch(*args, **kwargs):

FILE: modelvshuman/datasets/stylized.py
  function stylized (line 12) | def stylized(*args, **kwargs):

FILE: modelvshuman/datasets/texture_shape.py
  class TextureShapeParams (line 17) | class TextureShapeParams:
  function _get_dataset (line 27) | def _get_dataset(name, *args, **kwargs):
  function original (line 37) | def original(*args, **kwargs):
  function greyscale (line 42) | def greyscale(*args, **kwargs):
  function texture (line 47) | def texture(*args, **kwargs):
  function edge (line 52) | def edge(*args, **kwargs):
  function silhouette (line 57) | def silhouette(*args, **kwargs):
  function cue_conflict (line 62) | def cue_conflict(*args, **kwargs):

FILE: modelvshuman/evaluation/evaluate.py
  function print_performance_to_csv (line 16) | def print_performance_to_csv(model_name, dataset_name,
  function print_predictions_to_console (line 55) | def print_predictions_to_console(softmax_output, top_n=5,
  class ResultPrinter (line 96) | class ResultPrinter():
    method __init__ (line 98) | def __init__(self, model_name, dataset,
    method create_session_csv (line 108) | def create_session_csv(self, session):
    method print_batch_to_csv (line 133) | def print_batch_to_csv(self, object_response,

FILE: modelvshuman/evaluation/metrics.py
  class Metric (line 15) | class Metric(ABC):
    method __init__ (line 16) | def __init__(self, name):
    method check_input (line 20) | def check_input(self, output, target, assert_ndarray=True):
    method update (line 29) | def update(self, predictions, targets, paths):
    method reset (line 33) | def reset(self):
    method value (line 38) | def value(self):
    method __str__ (line 41) | def __str__(self):
  class Accuracy (line 45) | class Accuracy(Metric):
    method __init__ (line 46) | def __init__(self, name=None, topk=1):
    method reset (line 52) | def reset(self):
    method update (line 56) | def update(self, predictions, targets, paths):
    method value (line 62) | def value(self):
    method __str__ (line 67) | def __str__(self):

FILE: modelvshuman/helper/human_categories.py
  function compute_imagenet_indices_for_category (line 16) | def compute_imagenet_indices_for_category(category):
  function get_human_object_recognition_categories (line 33) | def get_human_object_recognition_categories():
  function get_num_human_categories (line 45) | def get_num_human_categories():
  class HumanCategories (line 51) | class HumanCategories(object):
    method get_human_category_from_WNID (line 180) | def get_human_category_from_WNID(self, wnid):
    method get_imagenet_indices_for_category (line 198) | def get_imagenet_indices_for_category(self, category):

FILE: modelvshuman/helper/plotting_helper.py
  function get_short_imagename (line 7) | def get_short_imagename(imagename):
  function read_data (line 27) | def read_data(path):
  function read_all_csv_files_from_directory (line 35) | def read_all_csv_files_from_directory(dir_path):
  function get_experimental_data (line 49) | def get_experimental_data(dataset, print_name=False):
  function crop_pdfs_in_directory (line 72) | def crop_pdfs_in_directory(dir_path, suppress_output=True):

FILE: modelvshuman/helper/wordnet_functions.py
  function get_filenames_of_category (line 13) | def get_filenames_of_category(category, image_labels_path, categories):
  function hypernyms_in_ilsvrc2012_categories (line 44) | def hypernyms_in_ilsvrc2012_categories(entity):
  function get_hypernyms (line 57) | def get_hypernyms(categories_file, entity):
  function get_ilsvrc2012_training_WNID (line 78) | def get_ilsvrc2012_training_WNID(entity):
  function num_hypernyms_in_ilsvrc2012 (line 106) | def num_hypernyms_in_ilsvrc2012(entity):
  function get_ilsvrc2012_categories (line 112) | def get_ilsvrc2012_categories():
  function get_ilsvrc2012_WNIDs (line 128) | def get_ilsvrc2012_WNIDs():
  function get_category_from_line (line 144) | def get_category_from_line(line):
  function get_WNID_from_line (line 153) | def get_WNID_from_line(line):
  function get_WNID_from_index (line 160) | def get_WNID_from_index(index):

FILE: modelvshuman/model_evaluator.py
  function device (line 17) | def device():
  class ModelEvaluator (line 21) | class ModelEvaluator:
    method _pytorch_evaluator (line 23) | def _pytorch_evaluator(self, model_name, model, dataset, *args, **kwar...
    method _tensorflow_evaluator (line 61) | def _tensorflow_evaluator(self, model_name, model, dataset, *args, **k...
    method _get_datasets (line 95) | def _get_datasets(self, dataset_names, *args, **kwargs):
    method _to_tensorflow (line 102) | def _to_tensorflow(self, datasets):
    method _get_evaluator (line 110) | def _get_evaluator(self, framework):
    method _remove_model_from_cache (line 118) | def _remove_model_from_cache(self, framework, model_name):
    method __call__ (line 133) | def __call__(self, models, dataset_names, *args, **kwargs):

FILE: modelvshuman/models/pytorch/adversarially_robust/robust_models.py
  function _model (line 29) | def _model(arch, model_fn, pretrained, progress, use_data_parallel, **kw...
  function resnet50_l2_eps0 (line 44) | def resnet50_l2_eps0(pretrained=True, progress=True, use_data_parallel=F...
  function resnet50_l2_eps0_01 (line 51) | def resnet50_l2_eps0_01(pretrained=True, progress=True, use_data_paralle...
  function resnet50_l2_eps0_03 (line 58) | def resnet50_l2_eps0_03(pretrained=True, progress=True, use_data_paralle...
  function resnet50_l2_eps0_05 (line 65) | def resnet50_l2_eps0_05(pretrained=True, progress=True, use_data_paralle...
  function resnet50_l2_eps0_1 (line 72) | def resnet50_l2_eps0_1(pretrained=True, progress=True, use_data_parallel...
  function resnet50_l2_eps0_25 (line 79) | def resnet50_l2_eps0_25(pretrained=True, progress=True, use_data_paralle...
  function resnet50_l2_eps0_5 (line 86) | def resnet50_l2_eps0_5(pretrained=True, progress=True, use_data_parallel...
  function resnet50_l2_eps1 (line 93) | def resnet50_l2_eps1(pretrained=True, progress=True, use_data_parallel=F...
  function resnet50_l2_eps3 (line 100) | def resnet50_l2_eps3(pretrained=True, progress=True, use_data_parallel=F...
  function resnet50_l2_eps5 (line 107) | def resnet50_l2_eps5(pretrained=True, progress=True, use_data_parallel=F...

FILE: modelvshuman/models/pytorch/bagnets/kerasnet.py
  function bagnet9 (line 13) | def bagnet9():
  function bagnet17 (line 23) | def bagnet17():
  function bagnet33 (line 33) | def bagnet33():

FILE: modelvshuman/models/pytorch/bagnets/pytorchnet.py
  class Bottleneck (line 23) | class Bottleneck(nn.Module):
    method __init__ (line 26) | def __init__(self, inplanes, planes, stride=1, downsample=None, kernel...
    method forward (line 40) | def forward(self, x, **kwargs):
  class BagNet (line 67) | class BagNet(nn.Module):
    method __init__ (line 69) | def __init__(self, block, layers, strides=[1, 2, 2, 2], kernel3=[0, 0,...
    method _make_layer (line 95) | def _make_layer(self, block, planes, blocks, stride=1, kernel3=0, pref...
    method forward (line 114) | def forward(self, x):
  function bagnet33 (line 136) | def bagnet33(pretrained=False, strides=[2, 2, 2, 1], **kwargs):
  function bagnet17 (line 148) | def bagnet17(pretrained=False, strides=[2, 2, 2, 1], **kwargs):
  function bagnet9 (line 160) | def bagnet9(pretrained=False, strides=[2, 2, 2, 1], **kwargs):

FILE: modelvshuman/models/pytorch/model_zoo.py
  function model_pytorch (line 13) | def model_pytorch(model_name, *args):
  function resnet50_trained_on_SIN (line 21) | def resnet50_trained_on_SIN(model_name, *args):
  function resnet50_trained_on_SIN_and_IN (line 28) | def resnet50_trained_on_SIN_and_IN(model_name, *args):
  function resnet50_trained_on_SIN_and_IN_then_finetuned_on_IN (line 35) | def resnet50_trained_on_SIN_and_IN_then_finetuned_on_IN(model_name, *args):
  function bagnet9 (line 42) | def bagnet9(model_name, *args):
  function bagnet17 (line 50) | def bagnet17(model_name, *args):
  function bagnet33 (line 58) | def bagnet33(model_name, *args):
  function simclr_resnet50x1_supervised_baseline (line 66) | def simclr_resnet50x1_supervised_baseline(model_name, *args):
  function simclr_resnet50x4_supervised_baseline (line 73) | def simclr_resnet50x4_supervised_baseline(model_name, *args):
  function simclr_resnet50x1 (line 80) | def simclr_resnet50x1(model_name, *args):
  function simclr_resnet50x2 (line 87) | def simclr_resnet50x2(model_name, *args):
  function simclr_resnet50x4 (line 94) | def simclr_resnet50x4(model_name, *args):
  function InsDis (line 102) | def InsDis(model_name, *args):
  function MoCo (line 109) | def MoCo(model_name, *args):
  function MoCoV2 (line 116) | def MoCoV2(model_name, *args):
  function PIRL (line 123) | def PIRL(model_name, *args):
  function InfoMin (line 130) | def InfoMin(model_name, *args):
  function resnet50_l2_eps0 (line 137) | def resnet50_l2_eps0(model_name, *args):
  function resnet50_l2_eps0_01 (line 144) | def resnet50_l2_eps0_01(model_name, *args):
  function resnet50_l2_eps0_03 (line 151) | def resnet50_l2_eps0_03(model_name, *args):
  function resnet50_l2_eps0_05 (line 158) | def resnet50_l2_eps0_05(model_name, *args):
  function resnet50_l2_eps0_1 (line 165) | def resnet50_l2_eps0_1(model_name, *args):
  function resnet50_l2_eps0_25 (line 172) | def resnet50_l2_eps0_25(model_name, *args):
  function resnet50_l2_eps0_5 (line 179) | def resnet50_l2_eps0_5(model_name, *args):
  function resnet50_l2_eps1 (line 186) | def resnet50_l2_eps1(model_name, *args):
  function resnet50_l2_eps3 (line 193) | def resnet50_l2_eps3(model_name, *args):
  function resnet50_l2_eps5 (line 200) | def resnet50_l2_eps5(model_name, *args):
  function efficientnet_b0 (line 207) | def efficientnet_b0(model_name, *args):
  function efficientnet_es (line 215) | def efficientnet_es(model_name, *args):
  function efficientnet_b0_noisy_student (line 223) | def efficientnet_b0_noisy_student(model_name, *args):
  function efficientnet_l2_noisy_student_475 (line 231) | def efficientnet_l2_noisy_student_475(model_name, *args):
  function transformer_B16_IN21K (line 239) | def transformer_B16_IN21K(model_name, *args):
  function transformer_B32_IN21K (line 246) | def transformer_B32_IN21K(model_name, *args):
  function transformer_L16_IN21K (line 253) | def transformer_L16_IN21K(model_name, *args):
  function transformer_L32_IN21K (line 260) | def transformer_L32_IN21K(model_name, *args):
  function vit_small_patch16_224 (line 267) | def vit_small_patch16_224(model_name, *args):
  function vit_base_patch16_224 (line 276) | def vit_base_patch16_224(model_name, *args):
  function vit_large_patch16_224 (line 285) | def vit_large_patch16_224(model_name, *args):
  function cspresnet50 (line 294) | def cspresnet50(model_name, *args):
  function cspresnext50 (line 302) | def cspresnext50(model_name, *args):
  function cspdarknet53 (line 310) | def cspdarknet53(model_name, *args):
  function darknet53 (line 318) | def darknet53(model_name, *args):
  function dpn68 (line 326) | def dpn68(model_name, *args):
  function dpn68b (line 334) | def dpn68b(model_name, *args):
  function dpn92 (line 342) | def dpn92(model_name, *args):
  function dpn98 (line 350) | def dpn98(model_name, *args):
  function dpn131 (line 358) | def dpn131(model_name, *args):
  function dpn107 (line 366) | def dpn107(model_name, *args):
  function hrnet_w18_small (line 374) | def hrnet_w18_small(model_name, *args):
  function hrnet_w18_small (line 382) | def hrnet_w18_small(model_name, *args):
  function hrnet_w18_small_v2 (line 390) | def hrnet_w18_small_v2(model_name, *args):
  function hrnet_w18 (line 398) | def hrnet_w18(model_name, *args):
  function hrnet_w30 (line 406) | def hrnet_w30(model_name, *args):
  function hrnet_w40 (line 414) | def hrnet_w40(model_name, *args):
  function hrnet_w44 (line 422) | def hrnet_w44(model_name, *args):
  function hrnet_w48 (line 430) | def hrnet_w48(model_name, *args):
  function hrnet_w64 (line 438) | def hrnet_w64(model_name, *args):
  function selecsls42 (line 446) | def selecsls42(model_name, *args):
  function selecsls84 (line 454) | def selecsls84(model_name, *args):
  function selecsls42b (line 462) | def selecsls42b(model_name, *args):
  function selecsls60 (line 470) | def selecsls60(model_name, *args):
  function selecsls60b (line 478) | def selecsls60b(model_name, *args):
  function clip (line 486) | def clip(model_name, *args):
  function clipRN50 (line 493) | def clipRN50(model_name, *args):
  function resnet50_swsl (line 500) | def resnet50_swsl(model_name, *args):
  function ResNeXt101_32x16d_swsl (line 507) | def ResNeXt101_32x16d_swsl(model_name, *args):
  function BiTM_resnetv2_50x1 (line 514) | def BiTM_resnetv2_50x1(model_name, *args):
  function BiTM_resnetv2_50x3 (line 522) | def BiTM_resnetv2_50x3(model_name, *args):
  function BiTM_resnetv2_101x1 (line 530) | def BiTM_resnetv2_101x1(model_name, *args):
  function BiTM_resnetv2_101x3 (line 538) | def BiTM_resnetv2_101x3(model_name, *args):
  function BiTM_resnetv2_152x2 (line 546) | def BiTM_resnetv2_152x2(model_name, *args):
  function BiTM_resnetv2_152x4 (line 554) | def BiTM_resnetv2_152x4(model_name, *args):
  function resnet50_clip_hard_labels (line 562) | def resnet50_clip_hard_labels(model_name, *args):
  function resnet50_clip_soft_labels (line 573) | def resnet50_clip_soft_labels(model_name, *args):
  function swag_regnety_16gf_in1k (line 584) | def swag_regnety_16gf_in1k(model_name, *args):
  function swag_regnety_32gf_in1k (line 590) | def swag_regnety_32gf_in1k(model_name, *args):
  function swag_regnety_128gf_in1k (line 596) | def swag_regnety_128gf_in1k(model_name, *args):
  function swag_vit_b16_in1k (line 602) | def swag_vit_b16_in1k(model_name, *args):
  function swag_vit_l16_in1k (line 608) | def swag_vit_l16_in1k(model_name, *args):
  function swag_vit_h14_in1k (line 614) | def swag_vit_h14_in1k(model_name, *args):

FILE: modelvshuman/models/pytorch/pycontrast/pycontrast_resnet50.py
  function build_classifier (line 19) | def build_classifier(model_name, classes=1000):
  function InsDis (line 38) | def InsDis(pretrained=False, **kwargs):
  function CMC (line 54) | def CMC(pretrained=False, **kwargs):
  function MoCo (line 69) | def MoCo(pretrained=False, **kwargs):
  function MoCoV2 (line 84) | def MoCoV2(pretrained=False, **kwargs):
  function PIRL (line 99) | def PIRL(pretrained=False, **kwargs):
  function InfoMin (line 114) | def InfoMin(pretrained=False, **kwargs):

FILE: modelvshuman/models/pytorch/shapenet/texture_shape_models.py
  function load_model (line 24) | def load_model(model_name):

FILE: modelvshuman/models/pytorch/simclr/cores/cores.py
  class Core (line 11) | class Core:
    method initialize (line 12) | def initialize(self):
    method __repr__ (line 15) | def __repr__(self):
  class Core2d (line 24) | class Core2d(Core):
    method initialize (line 25) | def initialize(self, cuda=False):
    method put_to_cuda (line 29) | def put_to_cuda(self, cuda):
    method init_conv (line 34) | def init_conv(m):
  class TaskDrivenCore (line 41) | class TaskDrivenCore(Core2d, nn.Module):
    method __init__ (line 42) | def __init__(
    method forward (line 106) | def forward(self, input_):
    method regularizer (line 113) | def regularizer(self):
    method outchannels (line 118) | def outchannels(self):
    method initialize (line 128) | def initialize(self, cuda=False):
  class TaskDrivenCore2 (line 134) | class TaskDrivenCore2(Core2d, nn.Module):
    method __init__ (line 135) | def __init__(
    method forward (line 224) | def forward(self, input_):
    method regularizer (line 235) | def regularizer(self):
    method probe_model (line 239) | def probe_model(self):
    method outchannels (line 254) | def outchannels(self):
    method initialize (line 268) | def initialize(self, cuda=False):

FILE: modelvshuman/models/pytorch/simclr/utils/gdrive.py
  function _get_name (line 15) | def _get_name(id):
  function load_state_dict_from_google_drive (line 53) | def load_state_dict_from_google_drive(id, model_dir=None, map_location=N...

FILE: modelvshuman/models/pytorch/simclr/utils/mlayer.py
  function clip_model (line 7) | def clip_model(model, layer_name):
  function probe_model (line 48) | def probe_model(model, layer_name):
  class ModuleHook (line 64) | class ModuleHook():
    method __init__ (line 65) | def __init__(self, module):
    method hook_fn (line 70) | def hook_fn(self, module, input, output):
    method close (line 74) | def close(self):
  function hook_model (line 79) | def hook_model(model):
  function hook_model_module (line 100) | def hook_model_module(model, module):

FILE: modelvshuman/models/pytorch/simclr/utils/modules.py
  class Unnormalize (line 5) | class Unnormalize(nn.Module):
    method __init__ (line 9) | def __init__(self, mean=[0], std=[1], inplace=False):
    method forward (line 15) | def forward(self, x):
  function unnormalize (line 21) | def unnormalize(tensor, mean=[0], std=[1], inplace=False):

FILE: modelvshuman/models/pytorch/simclr/zoo/simclr.py
  function _model (line 45) | def _model(arch, pretrained, block, layers, width_mult, normalized_input...
  function simclr_resnet50x1_supervised_baseline (line 68) | def simclr_resnet50x1_supervised_baseline(pretrained=False, normalized_i...
  function simclr_resnet50x4_supervised_baseline (line 88) | def simclr_resnet50x4_supervised_baseline(pretrained=False, normalized_i...
  function simclr_resnet50x1 (line 108) | def simclr_resnet50x1(pretrained=False, normalized_inputs=True, use_data...
  function simclr_resnet50x2 (line 128) | def simclr_resnet50x2(pretrained=False, normalized_inputs=True, use_data...
  function simclr_resnet50x4 (line 148) | def simclr_resnet50x4(pretrained=False, normalized_inputs=True, use_data...
  function conv3x3 (line 169) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  function conv1x1 (line 175) | def conv1x1(in_planes, out_planes, stride=1):
  class BasicBlock (line 180) | class BasicBlock(nn.Module):
    method __init__ (line 184) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 202) | def forward(self, x):
  class Bottleneck (line 221) | class Bottleneck(nn.Module):
    method __init__ (line 225) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
    method forward (line 242) | def forward(self, x):
  class ResNet (line 265) | class ResNet(nn.Module):
    method __init__ (line 267) | def __init__(self, block, layers, num_classes=1000, zero_init_residual...
    method _make_layer (line 323) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    method _forward_impl (line 347) | def _forward_impl(self, x):
    method forward (line 369) | def forward(self, x):

FILE: modelvshuman/models/registry.py
  function register_model (line 8) | def register_model(framework):
  function list_models (line 17) | def list_models(framework):

FILE: modelvshuman/models/tensorflow/build_model.py
  function build_model_from_hub (line 6) | def build_model_from_hub(model_name):

FILE: modelvshuman/models/tensorflow/model_zoo.py
  function efficientnet_b0 (line 8) | def efficientnet_b0(model_name, *args):
  function resnet50 (line 14) | def resnet50(model_name, *args):
  function mobilenet_v1 (line 20) | def mobilenet_v1(model_name, *args):
  function inception_v1 (line 26) | def inception_v1(model_name, *args):

FILE: modelvshuman/models/wrappers/base.py
  class AbstractModel (line 3) | class AbstractModel(ABC):
    method softmax (line 6) | def softmax(self, logits):
    method forward_batch (line 10) | def forward_batch(self, images):

FILE: modelvshuman/models/wrappers/pytorch.py
  function device (line 15) | def device():
  function undo_default_preprocessing (line 19) | def undo_default_preprocessing(images):
  class PytorchModel (line 32) | class PytorchModel(AbstractModel):
    method __init__ (line 34) | def __init__(self, model, model_name, *args):
    method to_numpy (line 40) | def to_numpy(self, x):
    method softmax (line 46) | def softmax(self, logits):
    method forward_batch (line 53) | def forward_batch(self, images):
  class PyContrastPytorchModel (line 61) | class PyContrastPytorchModel(PytorchModel):
    method __init__ (line 67) | def __init__(self, model, classifier, model_name, *args):
    method forward_batch (line 72) | def forward_batch(self, images):
  class ViTPytorchModel (line 81) | class ViTPytorchModel(PytorchModel):
    method __init__ (line 83) | def __init__(self, model, model_name, img_size=(384, 384), *args):
    method forward_batch (line 87) | def forward_batch(self, images):
    method preprocess (line 98) | def preprocess(self):
  class ClipPytorchModel (line 109) | class ClipPytorchModel(PytorchModel):
    method __init__ (line 111) | def __init__(self, model, model_name, *args):
    method _get_zeroshot_weights (line 115) | def _get_zeroshot_weights(self, class_names, templates):
    method preprocess (line 130) | def preprocess(self):
    method forward_batch (line 140) | def forward_batch(self, images):
  class EfficientNetPytorchModel (line 155) | class EfficientNetPytorchModel(PytorchModel):
    method __init__ (line 157) | def __init__(self, model, model_name, *args):
    method preprocess (line 160) | def preprocess(self):
    method forward_batch (line 173) | def forward_batch(self, images):
  class SwagPytorchModel (line 185) | class SwagPytorchModel(PytorchModel):
    method __init__ (line 187) | def __init__(self, model, model_name, input_size, *args):
    method preprocess (line 191) | def preprocess(self):
    method forward_batch (line 202) | def forward_batch(self, images):

FILE: modelvshuman/models/wrappers/tensorflow.py
  function get_device (line 10) | def get_device(device=None):
  class TensorflowModel (line 20) | class TensorflowModel(AbstractModel):
    method __init__ (line 22) | def __init__(self, model, model_name, *args):
    method softmax (line 27) | def softmax(self, logits):
    method forward_batch (line 31) | def forward_batch(self, images):

FILE: modelvshuman/plotting/analyses.py
  class Analysis (line 16) | class Analysis(ABC):
    method __init__ (line 19) | def __init__(self, *args, **kwargs):
    method _check_dataframe (line 23) | def _check_dataframe(df):
    method analysis (line 27) | def analysis(self, *args, **kwargs):
    method get_result_df (line 31) | def get_result_df(self, *args, **kwars):
    method num_input_models (line 36) | def num_input_models(self):
  class ConfusionAnalysis (line 45) | class ConfusionAnalysis(Analysis):
    method __init__ (line 49) | def __init__(self):
    method analysis (line 54) | def analysis(self, df,
    method get_result_df (line 73) | def get_result_df():
  class ShapeBias (line 78) | class ShapeBias(Analysis):
    method __init__ (line 84) | def __init__(self):
    method analysis (line 89) | def analysis(self, df):
    method get_result_df (line 109) | def get_result_df(self):
    method get_texture_category (line 113) | def get_texture_category(self, imagename):
  class ErrorConsistency (line 131) | class ErrorConsistency(Analysis):
    method __init__ (line 138) | def __init__(self):
    method error_consistency (line 147) | def error_consistency(self, expected_consistency, observed_consistency):
    method analysis (line 161) | def analysis(self, df1, df2):
    method get_result_df (line 194) | def get_result_df(self, df, decision_makers, experiment, column="error...
  class XYAnalysis (line 242) | class XYAnalysis(Analysis):
    method get_result_df (line 244) | def get_result_df(self, df, decision_makers,
  class SixteenClassAccuracy (line 265) | class SixteenClassAccuracy(XYAnalysis):
    method __init__ (line 273) | def __init__(self):
    method analysis (line 281) | def analysis(self, df):
  class SixteenClassAccuracyDifference (line 290) | class SixteenClassAccuracyDifference(XYAnalysis):
    method __init__ (line 296) | def __init__(self):
    method analysis (line 303) | def analysis(self, df1, df2, norm=np.square):
  class Entropy (line 315) | class Entropy(XYAnalysis):
    method __init__ (line 322) | def __init__(self, num_categories=16):
    method analysis (line 330) | def analysis(self, df):
  function get_analysis_list (line 351) | def get_analysis_list(df, conditions, analysis):
  function get_percent_answers_per_category (line 365) | def get_percent_answers_per_category(df):

FILE: modelvshuman/plotting/colors.py
  function rgb (line 6) | def rgb(r, g, b, divide_by=255.0):

FILE: modelvshuman/plotting/decision_makers.py
  class DecisionMaker (line 16) | class DecisionMaker:
    method __post_init__ (line 24) | def __post_init__(self):
    method _get_ID (line 61) | def _get_ID(self):
    method _convert_file_name (line 67) | def _convert_file_name(self, plotting_name):
  function get_individual_decision_makers (line 75) | def get_individual_decision_makers(decision_maker_list):
  function get_human_and_model_decision_makers (line 85) | def get_human_and_model_decision_makers(decision_maker_list):
  function decision_maker_to_attributes (line 98) | def decision_maker_to_attributes(decision_maker_name, decision_maker_list):

FILE: modelvshuman/plotting/plot.py
  function plot (line 76) | def plot(plot_types,
  function plot_nonparallel (line 100) | def plot_nonparallel(plot_types,
  function get_datasets (line 192) | def get_datasets(dataset_names, *args, **kwargs):
  function get_dataset_names (line 200) | def get_dataset_names(plot_type):
  function get_permutations (line 221) | def get_permutations(elements):
  function exclude_conditions (line 236) | def exclude_conditions(dataset):
  function log (line 250) | def log(plot_type, dataset_name):
  function get_human_and_CNN_subjects (line 258) | def get_human_and_CNN_subjects(subjects):
  function get_raw_matrix (line 272) | def get_raw_matrix(dataset,
  function plotting_names_to_data_subjects (line 316) | def plotting_names_to_data_subjects(plotting_names,
  function get_mean_over_datasets (line 332) | def get_mean_over_datasets(colname,
  function x_y_plot (line 376) | def x_y_plot(figure_path,
  function confusion_matrix_helper (line 418) | def confusion_matrix_helper(data, output_filename,
  function plot_shape_bias_matrixplot (line 461) | def plot_shape_bias_matrixplot(datasets,
  function plot_shape_bias_boxplot (line 575) | def plot_shape_bias_boxplot(datasets,
  function plot_error_consistency (line 637) | def plot_error_consistency(datasets, decision_maker_fun, result_dir,
  function plot_matrix (line 644) | def plot_matrix(datasets, analysis,
  function sort_matrix_by_models_mean (line 695) | def sort_matrix_by_models_mean(result_dict):
  function sort_matrix_by_subjects_mean (line 724) | def sort_matrix_by_subjects_mean(result_dict):
  function plot_confusion_matrix (line 752) | def plot_confusion_matrix(datasets,
  function plot_accuracy (line 777) | def plot_accuracy(datasets, decision_maker_fun, result_dir):
  function plot_entropy (line 783) | def plot_entropy(datasets, decision_maker_fun, result_dir):
  function plot_error_consistency_lineplot (line 789) | def plot_error_consistency_lineplot(datasets, decision_maker_fun, result...
  function plot_general_analyses (line 795) | def plot_general_analyses(datasets, analysis, decision_maker_fun,
  function get_raw_benchmark_df (line 817) | def get_raw_benchmark_df(datasets, metric_names, decision_maker_fun,
  function print_benchmark_table_accuracy_to_latex (line 909) | def print_benchmark_table_accuracy_to_latex(df):
  function print_benchmark_table_humanlike_to_latex (line 938) | def print_benchmark_table_humanlike_to_latex(df):
  function plot_benchmark_barplot (line 976) | def plot_benchmark_barplot(datasets, decision_maker_fun, result_dir,
  function format_benchmark_df (line 1036) | def format_benchmark_df(df, decision_makers, metric_names,
  function barplot (line 1078) | def barplot(path, names, values, colors, ylabel=None,
  function plot_scatterplot (line 1114) | def plot_scatterplot(datasets,
  function scatter_plot_helper (line 1171) | def scatter_plot_helper(df, metric_x, metric_y, result_dir, dataset_name):

FILE: modelvshuman/utils.py
  function try_download_dataset_from_github (line 19) | def try_download_dataset_from_github(dataset_name):
  function load_dataset (line 41) | def load_dataset(name, *args, **kwargs):
  function no_op (line 59) | def no_op():
  function load_model (line 64) | def load_model(model_name, *args):
  class AverageMeter (line 79) | class AverageMeter(object):
    method __init__ (line 82) | def __init__(self, name, fmt=':f'):
    method reset (line 87) | def reset(self):
    method update (line 93) | def update(self, val, n=1):
    method __str__ (line 99) | def __str__(self):