SYMBOL INDEX (140 symbols across 11 files) FILE: augment.py class GaussianBlur (line 24) | class GaussianBlur(object): method __init__ (line 28) | def __init__(self, p=0.1, radius_min=0.1, radius_max=2.): method __call__ (line 33) | def __call__(self, img): class Solarization (line 45) | class Solarization(object): method __init__ (line 49) | def __init__(self, p=0.2): method __call__ (line 52) | def __call__(self, img): class gray_scale (line 58) | class gray_scale(object): method __init__ (line 62) | def __init__(self, p=0.2): method __call__ (line 66) | def __call__(self, img): class horizontal_flip (line 74) | class horizontal_flip(object): method __init__ (line 78) | def __init__(self, p=0.2,activate_pred=False): method __call__ (line 82) | def __call__(self, img): function new_data_aug_generator (line 90) | def new_data_aug_generator(args = None): FILE: datasets.py class INatDataset (line 13) | class INatDataset(ImageFolder): method __init__ (line 14) | def __init__(self, root, train=True, year=2018, transform=None, target... function build_dataset (line 56) | def build_dataset(is_train, args): function build_transform (line 82) | def build_transform(is_train, args): FILE: ekan.py class KANLinear (line 6) | class KANLinear(torch.nn.Module): method __init__ (line 7) | def __init__( method reset_parameters (line 56) | def reset_parameters(self): method b_splines (line 78) | def b_splines(self, x: torch.Tensor): method curve2coeff (line 113) | def curve2coeff(self, x: torch.Tensor, y: torch.Tensor): method scaled_spline_weight (line 146) | def scaled_spline_weight(self): method forward (line 153) | def forward(self, x: torch.Tensor): method update_grid (line 164) | def update_grid(self, x: torch.Tensor, margin=0.01): method regularization_loss (line 212) | def regularization_loss(self, regularize_activation=1.0, regularize_en... class KAN (line 235) | class KAN(torch.nn.Module): method __init__ (line 236) | def __init__( method forward (line 269) | def forward(self, x: torch.Tensor, update_grid=False): method regularization_loss (line 276) | def regularization_loss(self, regularize_activation=1.0, regularize_en... FILE: engine.py function train_one_epoch (line 19) | def train_one_epoch(model: torch.nn.Module, criterion: DistillationLoss, function evaluate (line 83) | def evaluate(data_loader, model, device): FILE: fasterkan.py class SplineLinear (line 8) | class SplineLinear(nn.Linear): method __init__ (line 9) | def __init__(self, in_features: int, out_features: int, init_scale: fl... method reset_parameters (line 13) | def reset_parameters(self) -> None: class ReflectionalSwitchFunction (line 17) | class ReflectionalSwitchFunction(nn.Module): method __init__ (line 18) | def __init__( method forward (line 33) | def forward(self, x): class FasterKANLayer (line 43) | class FasterKANLayer(nn.Module): method __init__ (line 44) | def __init__( method forward (line 66) | def forward(self, x, time_benchmark=False): class FasterKAN (line 103) | class FasterKAN(nn.Module): method __init__ (line 104) | def __init__( method forward (line 131) | def forward(self, x): FILE: kit.py class Attention (line 17) | class Attention(nn.Module): method __init__ (line 19) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at... method forward (line 30) | def forward(self, x): class Block (line 47) | class Block(nn.Module): method __init__ (line 49) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 63) | def forward(self, x): class Layer_scale_init_Block (line 70) | class Layer_scale_init_Block(nn.Module): method __init__ (line 73) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 89) | def forward(self, x): class Layer_scale_init_Block_paralx2 (line 97) | class Layer_scale_init_Block_paralx2(nn.Module): method __init__ (line 100) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 122) | def forward(self, x): class Block_paralx2 (line 130) | class Block_paralx2(nn.Module): method __init__ (line 133) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc... method forward (line 151) | def forward(self, x): class hMLP_stem (line 157) | class hMLP_stem(nn.Module): method __init__ (line 163) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=... method forward (line 181) | def forward(self, x): class vit_models (line 187) | class vit_models(nn.Module): method __init__ (line 193) | def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classe... method _init_weights (line 233) | def _init_weights(self, m): method no_weight_decay (line 243) | def no_weight_decay(self): method get_classifier (line 246) | def get_classifier(self): method get_num_layers (line 249) | def get_num_layers(self): method reset_classifier (line 252) | def reset_classifier(self, num_classes, global_pool=''): method forward_features (line 256) | def forward_features(self, x): method forward (line 272) | def forward(self, x): function deit_tiny_patch16_LS (line 286) | def deit_tiny_patch16_LS(pretrained=False, img_size=224, pretrained_21k=... function deit_small_patch16_LS (line 295) | def deit_small_patch16_LS(pretrained=False, img_size=224, pretrained_21k... function deit_medium_patch16_LS (line 317) | def deit_medium_patch16_LS(pretrained=False, img_size=224, pretrained_21... function deit_base_patch16_LS (line 338) | def deit_base_patch16_LS(pretrained=False, img_size=224, pretrained_21k=... function deit_large_patch16_LS (line 358) | def deit_large_patch16_LS(pretrained=False, img_size=224, pretrained_21k... function deit_huge_patch14_LS (line 378) | def deit_huge_patch14_LS(pretrained=False, img_size=224, pretrained_21k=... function deit_huge_patch14_52_LS (line 398) | def deit_huge_patch14_52_LS(pretrained=False, img_size=224, pretrained_2... function deit_huge_patch14_26x2_LS (line 407) | def deit_huge_patch14_26x2_LS(pretrained=False, img_size=224, pretrained... function deit_Giant_48x2_patch14_LS (line 416) | def deit_Giant_48x2_patch14_LS(pretrained=False, img_size=224, pretraine... function deit_giant_40x2_patch14_LS (line 425) | def deit_giant_40x2_patch14_LS(pretrained=False, img_size=224, pretraine... function deit_Giant_48_patch14_LS (line 433) | def deit_Giant_48_patch14_LS(pretrained=False, img_size=224, pretrained_... function deit_giant_40_patch14_LS (line 441) | def deit_giant_40_patch14_LS(pretrained=False, img_size=224, pretrained_... function deit_small_patch16_36_LS (line 453) | def deit_small_patch16_36_LS(pretrained=False, img_size=224, pretrained_... function deit_small_patch16_36 (line 462) | def deit_small_patch16_36(pretrained=False, img_size=224, pretrained_21k... function deit_small_patch16_18x2_LS (line 471) | def deit_small_patch16_18x2_LS(pretrained=False, img_size=224, pretraine... function deit_small_patch16_18x2 (line 480) | def deit_small_patch16_18x2(pretrained=False, img_size=224, pretrained_2... function deit_base_patch16_18x2_LS (line 489) | def deit_base_patch16_18x2_LS(pretrained=False, img_size=224, pretrained... function deit_base_patch16_18x2 (line 498) | def deit_base_patch16_18x2(pretrained=False, img_size=224, pretrained_21... function deit_base_patch16_36x1_LS (line 507) | def deit_base_patch16_36x1_LS(pretrained=False, img_size=224, pretrained... function deit_base_patch16_36x1 (line 516) | def deit_base_patch16_36x1(pretrained=False, img_size=224, pretrained_21... function create_model (line 522) | def create_model(model_name,**kwargs): FILE: losses.py class DistillationLoss (line 10) | class DistillationLoss(torch.nn.Module): method __init__ (line 15) | def __init__(self, base_criterion: torch.nn.Module, teacher_model: tor... method forward (line 25) | def forward(self, inputs, outputs, labels): FILE: main.py function get_args_parser (line 34) | def get_args_parser(): function main (line 195) | def main(args): FILE: models_kan.py class kanBlock (line 26) | class kanBlock(Block): method __init__ (line 28) | def __init__(self, dim, num_heads=8, hdim_kan=192, mlp_ratio=4., qkv_b... method forward (line 41) | def forward(self, x): class VisionKAN (line 48) | class VisionKAN(VisionTransformer): method __init__ (line 49) | def __init__(self, *args, num_heads=8, batch_size=16, **kwargs): class DistilledVisionTransformer (line 80) | class DistilledVisionTransformer(VisionTransformer): method __init__ (line 81) | def __init__(self, *args, **kwargs): method forward_features (line 92) | def forward_features(self, x): method forward (line 111) | def forward(self, x): function create_kan (line 121) | def create_kan(model_name, pretrained, **kwargs): function create_ViT (line 184) | def create_ViT(model_name, pretrained, **kwargs): function create_model (line 292) | def create_model(model_name,**kwargs): FILE: samplers.py class RASampler (line 8) | class RASampler(torch.utils.data.Sampler): method __init__ (line 16) | def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True... method __iter__ (line 38) | def __iter__(self): method __len__ (line 60) | def __len__(self): method set_epoch (line 63) | def set_epoch(self, epoch): FILE: utils.py class SmoothedValue (line 18) | class SmoothedValue(object): method __init__ (line 23) | def __init__(self, window_size=20, fmt=None): method update (line 31) | def update(self, value, n=1): method synchronize_between_processes (line 36) | def synchronize_between_processes(self): method median (line 50) | def median(self): method avg (line 55) | def avg(self): method global_avg (line 60) | def global_avg(self): method max (line 64) | def max(self): method value (line 68) | def value(self): method __str__ (line 71) | def __str__(self): class MetricLogger (line 80) | class MetricLogger(object): method __init__ (line 81) | def __init__(self, delimiter="\t"): method update (line 85) | def update(self, **kwargs): method __getattr__ (line 92) | def __getattr__(self, attr): method __str__ (line 100) | def __str__(self): method synchronize_between_processes (line 108) | def synchronize_between_processes(self): method add_meter (line 112) | def add_meter(self, name, meter): method log_every (line 115) | def log_every(self, iterable, print_freq, header=None): function _load_checkpoint_for_ema (line 162) | def _load_checkpoint_for_ema(model_ema, checkpoint): function setup_for_distributed (line 172) | def setup_for_distributed(is_master): function is_dist_avail_and_initialized (line 187) | def is_dist_avail_and_initialized(): function get_world_size (line 195) | def get_world_size(): function get_rank (line 201) | def get_rank(): function is_main_process (line 207) | def is_main_process(): function save_on_master (line 211) | def save_on_master(*args, **kwargs): function init_distributed_mode (line 216) | def init_distributed_mode(args):