SYMBOL INDEX (405 symbols across 23 files)

FILE: Diffusion_UKAN/Diffusion/Diffusion.py
  function extract (line 9) | def extract(v, t, x_shape):
  class GaussianDiffusionTrainer (line 19) | class GaussianDiffusionTrainer(nn.Module):
    method __init__ (line 20) | def __init__(self, model, beta_1, beta_T, T):
    method forward (line 37) | def forward(self, x_0):
  class GaussianDiffusionSampler (line 50) | class GaussianDiffusionSampler(nn.Module):
    method __init__ (line 51) | def __init__(self, model, beta_1, beta_T, T):
    method predict_xt_prev_mean_from_eps (line 67) | def predict_xt_prev_mean_from_eps(self, x_t, t, eps):
    method p_mean_variance (line 74) | def p_mean_variance(self, x_t, t):
    method forward (line 84) | def forward(self, x_T):

FILE: Diffusion_UKAN/Diffusion/Model.py
  class Swish (line 10) | class Swish(nn.Module):
    method forward (line 11) | def forward(self, x):
  class TimeEmbedding (line 14) | class TimeEmbedding(nn.Module):
    method __init__ (line 15) | def __init__(self, T, d_model, dim):
    method initialize (line 35) | def initialize(self):
    method forward (line 41) | def forward(self, t):
  class DownSample (line 46) | class DownSample(nn.Module):
    method __init__ (line 47) | def __init__(self, in_ch):
    method initialize (line 52) | def initialize(self):
    method forward (line 56) | def forward(self, x, temb):
  class UpSample (line 61) | class UpSample(nn.Module):
    method __init__ (line 62) | def __init__(self, in_ch):
    method initialize (line 67) | def initialize(self):
    method forward (line 71) | def forward(self, x, temb):
  class AttnBlock (line 79) | class AttnBlock(nn.Module):
    method __init__ (line 80) | def __init__(self, in_ch):
    method initialize (line 89) | def initialize(self):
    method forward (line 95) | def forward(self, x):
  class ResBlock (line 117) | class ResBlock(nn.Module):
    method __init__ (line 118) | def __init__(self, in_ch, out_ch, tdim, dropout, attn=False):
    method initialize (line 145) | def initialize(self):
    method forward (line 152) | def forward(self, x, temb):
  class KANLinear (line 162) | class KANLinear(torch.nn.Module):
    method __init__ (line 163) | def __init__(
    method reset_parameters (line 212) | def reset_parameters(self):
    method b_splines (line 234) | def b_splines(self, x: torch.Tensor):
    method curve2coeff (line 269) | def curve2coeff(self, x: torch.Tensor, y: torch.Tensor):
    method scaled_spline_weight (line 302) | def scaled_spline_weight(self):
    method forward (line 309) | def forward(self, x: torch.Tensor):
    method update_grid (line 322) | def update_grid(self, x: torch.Tensor, margin=0.01):
    method regularization_loss (line 370) | def regularization_loss(self, regularize_activation=1.0, regularize_en...
  class Ukan (line 392) | class Ukan(nn.Module):
    method __init__ (line 393) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 486) | def initialize(self):
    method forward (line 492) | def forward(self, x, t):
  class DW_bn_relu (line 524) | class DW_bn_relu(nn.Module):
    method __init__ (line 525) | def __init__(self, dim=768):
    method forward (line 531) | def forward(self, x, H, W):
  class kan (line 541) | class kan(nn.Module):
    method __init__ (line 542) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method _init_weights (line 674) | def _init_weights(self, m):
    method forward (line 690) | def forward(self, x, H, W):
  class Ukan_v3 (line 831) | class Ukan_v3(nn.Module):
    method __init__ (line 832) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 900) | def initialize(self):
    method forward (line 906) | def forward(self, x, t):
  class Ukan_v2 (line 944) | class Ukan_v2(nn.Module):
    method __init__ (line 945) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout,versi...
    method initialize (line 1012) | def initialize(self):
    method forward (line 1018) | def forward(self, x, t):
  class UNet (line 1055) | class UNet(nn.Module):
    method __init__ (line 1056) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 1103) | def initialize(self):
    method forward (line 1109) | def forward(self, x, t):
  class UNet_MLP (line 1137) | class UNet_MLP(nn.Module):
    method __init__ (line 1138) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 1203) | def initialize(self):
    method forward (line 1209) | def forward(self, x, t):

FILE: Diffusion_UKAN/Diffusion/Model_ConvKan.py
  class Swish (line 10) | class Swish(nn.Module):
    method forward (line 11) | def forward(self, x):
  class TimeEmbedding (line 14) | class TimeEmbedding(nn.Module):
    method __init__ (line 15) | def __init__(self, T, d_model, dim):
    method initialize (line 35) | def initialize(self):
    method forward (line 41) | def forward(self, t):
  class DownSample (line 46) | class DownSample(nn.Module):
    method __init__ (line 47) | def __init__(self, in_ch):
    method initialize (line 53) | def initialize(self):
    method forward (line 57) | def forward(self, x, temb):
  class UpSample (line 62) | class UpSample(nn.Module):
    method __init__ (line 63) | def __init__(self, in_ch):
    method initialize (line 69) | def initialize(self):
    method forward (line 73) | def forward(self, x, temb):
  class AttnBlock (line 81) | class AttnBlock(nn.Module):
    method __init__ (line 82) | def __init__(self, in_ch):
    method initialize (line 91) | def initialize(self):
    method forward (line 97) | def forward(self, x):
  class ResBlock (line 119) | class ResBlock(nn.Module):
    method __init__ (line 120) | def __init__(self, in_ch, out_ch, tdim, dropout, attn=False):
    method initialize (line 150) | def initialize(self):
    method forward (line 157) | def forward(self, x, temb):
  class UNet_ConvKan (line 166) | class UNet_ConvKan(nn.Module):
    method __init__ (line 167) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 215) | def initialize(self):
    method forward (line 221) | def forward(self, x, t):

FILE: Diffusion_UKAN/Diffusion/Model_UKAN_Hybrid.py
  class KANLinear (line 10) | class KANLinear(torch.nn.Module):
    method __init__ (line 11) | def __init__(
    method reset_parameters (line 60) | def reset_parameters(self):
    method b_splines (line 82) | def b_splines(self, x: torch.Tensor):
    method curve2coeff (line 117) | def curve2coeff(self, x: torch.Tensor, y: torch.Tensor):
    method scaled_spline_weight (line 150) | def scaled_spline_weight(self):
    method forward (line 157) | def forward(self, x: torch.Tensor):
    method update_grid (line 168) | def update_grid(self, x: torch.Tensor, margin=0.01):
    method regularization_loss (line 216) | def regularization_loss(self, regularize_activation=1.0, regularize_en...
  class KAN (line 239) | class KAN(torch.nn.Module):
    method __init__ (line 240) | def __init__(
    method forward (line 273) | def forward(self, x: torch.Tensor, update_grid=False):
    method regularization_loss (line 280) | def regularization_loss(self, regularize_activation=1.0, regularize_en...
  function conv1x1 (line 287) | def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
  function shift (line 292) | def shift(dim):
  class OverlapPatchEmbed (line 300) | class OverlapPatchEmbed(nn.Module):
    method __init__ (line 304) | def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, e...
    method _init_weights (line 319) | def _init_weights(self, m):
    method forward (line 334) | def forward(self, x):
  class Swish (line 343) | class Swish(nn.Module):
    method forward (line 344) | def forward(self, x):
  function swish (line 346) | def swish(x):
  class TimeEmbedding (line 351) | class TimeEmbedding(nn.Module):
    method __init__ (line 352) | def __init__(self, T, d_model, dim):
    method initialize (line 372) | def initialize(self):
    method forward (line 378) | def forward(self, t):
  class DownSample (line 383) | class DownSample(nn.Module):
    method __init__ (line 384) | def __init__(self, in_ch):
    method initialize (line 389) | def initialize(self):
    method forward (line 393) | def forward(self, x, temb):
  class UpSample (line 398) | class UpSample(nn.Module):
    method __init__ (line 399) | def __init__(self, in_ch):
    method initialize (line 404) | def initialize(self):
    method forward (line 408) | def forward(self, x, temb):
  class kan (line 415) | class kan(nn.Module):
    method __init__ (line 416) | def __init__(self, in_features, hidden_features=None, out_features=None):
    method _init_weights (line 446) | def _init_weights(self, m):
    method forward (line 462) | def forward(self, x, H, W):
  class shiftedBlock (line 469) | class shiftedBlock(nn.Module):
    method __init__ (line 470) | def __init__(self, dim,  mlp_ratio=4.,drop_path=0.,norm_layer=nn.Layer...
    method _init_weights (line 486) | def _init_weights(self, m):
    method forward (line 501) | def forward(self, x, H, W, temb):
  class DWConv (line 509) | class DWConv(nn.Module):
    method __init__ (line 510) | def __init__(self, dim=768):
    method forward (line 514) | def forward(self, x, H, W):
  class DW_bn_relu (line 522) | class DW_bn_relu(nn.Module):
    method __init__ (line 523) | def __init__(self, dim=768):
    method forward (line 529) | def forward(self, x, H, W):
  class SingleConv (line 539) | class SingleConv(nn.Module):
    method __init__ (line 540) | def __init__(self, in_ch, h_ch):
    method forward (line 552) | def forward(self, input, temb):
  class DoubleConv (line 556) | class DoubleConv(nn.Module):
    method __init__ (line 557) | def __init__(self, in_ch, h_ch):
    method forward (line 571) | def forward(self, input, temb):
  class D_SingleConv (line 575) | class D_SingleConv(nn.Module):
    method __init__ (line 576) | def __init__(self, in_ch, h_ch):
    method forward (line 587) | def forward(self, input, temb):
  class D_DoubleConv (line 591) | class D_DoubleConv(nn.Module):
    method __init__ (line 592) | def __init__(self, in_ch, h_ch):
    method forward (line 606) | def forward(self, input,temb):
  class AttnBlock (line 609) | class AttnBlock(nn.Module):
    method __init__ (line 610) | def __init__(self, in_ch):
    method initialize (line 619) | def initialize(self):
    method forward (line 625) | def forward(self, x):
  class ResBlock (line 647) | class ResBlock(nn.Module):
    method __init__ (line 648) | def __init__(self, in_ch, h_ch, tdim, dropout, attn=False):
    method initialize (line 675) | def initialize(self):
    method forward (line 682) | def forward(self, x, temb):
  class UKan_Hybrid (line 692) | class UKan_Hybrid(nn.Module):
    method __init__ (line 693) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 758) | def initialize(self):
    method forward (line 764) | def forward(self, x, t):

FILE: Diffusion_UKAN/Diffusion/Model_UMLP.py
  class KANLinear (line 10) | class KANLinear(torch.nn.Module):
    method __init__ (line 11) | def __init__(
    method reset_parameters (line 60) | def reset_parameters(self):
    method b_splines (line 82) | def b_splines(self, x: torch.Tensor):
    method curve2coeff (line 117) | def curve2coeff(self, x: torch.Tensor, y: torch.Tensor):
    method scaled_spline_weight (line 150) | def scaled_spline_weight(self):
    method forward (line 157) | def forward(self, x: torch.Tensor):
    method update_grid (line 168) | def update_grid(self, x: torch.Tensor, margin=0.01):
    method regularization_loss (line 216) | def regularization_loss(self, regularize_activation=1.0, regularize_en...
  class KAN (line 239) | class KAN(torch.nn.Module):
    method __init__ (line 240) | def __init__(
    method forward (line 273) | def forward(self, x: torch.Tensor, update_grid=False):
    method regularization_loss (line 280) | def regularization_loss(self, regularize_activation=1.0, regularize_en...
  function conv1x1 (line 287) | def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
  function shift (line 292) | def shift(dim):
  class OverlapPatchEmbed (line 300) | class OverlapPatchEmbed(nn.Module):
    method __init__ (line 304) | def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, e...
    method _init_weights (line 319) | def _init_weights(self, m):
    method forward (line 334) | def forward(self, x):
  class Swish (line 343) | class Swish(nn.Module):
    method forward (line 344) | def forward(self, x):
  function swish (line 346) | def swish(x):
  class TimeEmbedding (line 351) | class TimeEmbedding(nn.Module):
    method __init__ (line 352) | def __init__(self, T, d_model, dim):
    method initialize (line 372) | def initialize(self):
    method forward (line 378) | def forward(self, t):
  class DownSample (line 383) | class DownSample(nn.Module):
    method __init__ (line 384) | def __init__(self, in_ch):
    method initialize (line 389) | def initialize(self):
    method forward (line 393) | def forward(self, x, temb):
  class UpSample (line 398) | class UpSample(nn.Module):
    method __init__ (line 399) | def __init__(self, in_ch):
    method initialize (line 404) | def initialize(self):
    method forward (line 408) | def forward(self, x, temb):
  class kan (line 415) | class kan(nn.Module):
    method __init__ (line 416) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method _init_weights (line 450) | def _init_weights(self, m):
    method forward (line 466) | def forward(self, x, H, W):
  class shiftedBlock (line 475) | class shiftedBlock(nn.Module):
    method __init__ (line 476) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method _init_weights (line 492) | def _init_weights(self, m):
    method forward (line 507) | def forward(self, x, H, W, temb):
  class DWConv (line 516) | class DWConv(nn.Module):
    method __init__ (line 517) | def __init__(self, dim=768):
    method forward (line 521) | def forward(self, x, H, W):
  class DW_bn_relu (line 529) | class DW_bn_relu(nn.Module):
    method __init__ (line 530) | def __init__(self, dim=768):
    method forward (line 536) | def forward(self, x, H, W):
  class SingleConv (line 546) | class SingleConv(nn.Module):
    method __init__ (line 547) | def __init__(self, in_ch, h_ch):
    method forward (line 559) | def forward(self, input, temb):
  class DoubleConv (line 563) | class DoubleConv(nn.Module):
    method __init__ (line 564) | def __init__(self, in_ch, h_ch):
    method forward (line 578) | def forward(self, input, temb):
  class D_SingleConv (line 582) | class D_SingleConv(nn.Module):
    method __init__ (line 583) | def __init__(self, in_ch, h_ch):
    method forward (line 594) | def forward(self, input, temb):
  class D_DoubleConv (line 598) | class D_DoubleConv(nn.Module):
    method __init__ (line 599) | def __init__(self, in_ch, h_ch):
    method forward (line 613) | def forward(self, input,temb):
  class AttnBlock (line 616) | class AttnBlock(nn.Module):
    method __init__ (line 617) | def __init__(self, in_ch):
    method initialize (line 626) | def initialize(self):
    method forward (line 632) | def forward(self, x):
  class ResBlock (line 654) | class ResBlock(nn.Module):
    method __init__ (line 655) | def __init__(self, in_ch, h_ch, tdim, dropout, attn=False):
    method initialize (line 682) | def initialize(self):
    method forward (line 689) | def forward(self, x, temb):
  class UMLP (line 699) | class UMLP(nn.Module):
    method __init__ (line 700) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 788) | def initialize(self):
    method forward (line 794) | def forward(self, x, t):

FILE: Diffusion_UKAN/Diffusion/Train.py
  class UnlabeledDataset (line 32) | class UnlabeledDataset(Dataset):
    method __init__ (line 33) | def __init__(self, folder, transform=None, repeat_n=1):
    method __len__ (line 39) | def __len__(self):
    method __getitem__ (line 42) | def __getitem__(self, idx):
  function train (line 51) | def train(modelConfig: Dict):
  function eval_tmp (line 136) | def eval_tmp(modelConfig: Dict, nme: int):
  function eval (line 168) | def eval(modelConfig: Dict):

FILE: Diffusion_UKAN/Diffusion/UNet.py
  class Swish (line 10) | class Swish(nn.Module):
    method forward (line 11) | def forward(self, x):
  class TimeEmbedding (line 15) | class TimeEmbedding(nn.Module):
    method __init__ (line 16) | def __init__(self, T, d_model, dim):
    method initialize (line 36) | def initialize(self):
    method forward (line 42) | def forward(self, t):
  class DownSample (line 47) | class DownSample(nn.Module):
    method __init__ (line 48) | def __init__(self, in_ch):
    method initialize (line 53) | def initialize(self):
    method forward (line 57) | def forward(self, x, temb):
  class UpSample (line 62) | class UpSample(nn.Module):
    method __init__ (line 63) | def __init__(self, in_ch):
    method initialize (line 68) | def initialize(self):
    method forward (line 72) | def forward(self, x, temb):
  class AttnBlock (line 80) | class AttnBlock(nn.Module):
    method __init__ (line 81) | def __init__(self, in_ch):
    method initialize (line 90) | def initialize(self):
    method forward (line 96) | def forward(self, x):
  class ResBlock (line 118) | class ResBlock(nn.Module):
    method __init__ (line 119) | def __init__(self, in_ch, out_ch, tdim, dropout, attn=False):
    method initialize (line 146) | def initialize(self):
    method forward (line 153) | def forward(self, x, temb):
  class UNet (line 163) | class UNet(nn.Module):
    method __init__ (line 164) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 210) | def initialize(self):
    method forward (line 216) | def forward(self, x, t):
  class UNet_Baseline (line 240) | class UNet_Baseline(nn.Module):
    method __init__ (line 242) | def __init__(self, T, ch, ch_mult, attn, num_res_blocks, dropout):
    method initialize (line 284) | def initialize(self):
    method forward (line 290) | def forward(self, x, t):

FILE: Diffusion_UKAN/Diffusion/kan_utils/fastkanconv.py
  class PolynomialFunction (line 7) | class PolynomialFunction(nn.Module):
    method __init__ (line 8) | def __init__(self,
    method forward (line 13) | def forward(self, x):
  class BSplineFunction (line 16) | class BSplineFunction(nn.Module):
    method __init__ (line 17) | def __init__(self, grid_min: float = -2.,
    method basis_function (line 24) | def basis_function(self, i, k, t):
    method forward (line 37) | def forward(self, x):
  class ChebyshevFunction (line 42) | class ChebyshevFunction(nn.Module):
    method __init__ (line 43) | def __init__(self, degree: int = 4):
    method forward (line 47) | def forward(self, x):
  class FourierBasisFunction (line 53) | class FourierBasisFunction(nn.Module):
    method __init__ (line 54) | def __init__(self,
    method forward (line 62) | def forward(self, x):
  class RadialBasisFunction (line 69) | class RadialBasisFunction(nn.Module):
    method __init__ (line 70) | def __init__(
    method forward (line 82) | def forward(self, x):
  class SplineConv2D (line 88) | class SplineConv2D(nn.Conv2d):
    method __init__ (line 89) | def __init__(self,
    method reset_parameters (line 115) | def reset_parameters(self) -> None:
  class FastKANConvLayer (line 121) | class FastKANConvLayer(nn.Module):
    method __init__ (line 122) | def __init__(self,
    method forward (line 178) | def forward(self, x):

FILE: Diffusion_UKAN/Diffusion/kan_utils/kan.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: Diffusion_UKAN/Diffusion/utils.py
  class qkv_transform (line 4) | class qkv_transform(nn.Conv1d):
  function str2bool (line 7) | def str2bool(v):
  function count_params (line 16) | def count_params(model):
  class AverageMeter (line 20) | class AverageMeter(object):
    method __init__ (line 23) | def __init__(self):
    method reset (line 26) | def reset(self):
    method update (line 32) | def update(self, val, n=1):

FILE: Diffusion_UKAN/Main.py
  function main (line 7) | def main(model_config = None):
  function seed_all (line 23) | def seed_all(args):

FILE: Diffusion_UKAN/Main_Test.py
  function main (line 5) | def main(model_config = None):
  function seed_all (line 22) | def seed_all(args):

FILE: Diffusion_UKAN/Scheduler.py
  class GradualWarmupScheduler (line 3) | class GradualWarmupScheduler(_LRScheduler):
    method __init__ (line 4) | def __init__(self, optimizer, multiplier, warm_epoch, after_scheduler=...
    method get_lr (line 13) | def get_lr(self):
    method step (line 24) | def step(self, epoch=None, metrics=None):

FILE: Diffusion_UKAN/inception-score-pytorch/inception_score.py
  function inception_score (line 17) | def inception_score(imgs, cuda=True, batch_size=32, resize=False, splits...
  class UnlabeledDataset (line 75) | class UnlabeledDataset(torch.utils.data.Dataset):
    method __init__ (line 76) | def __init__(self, folder, transform=None):
    method __len__ (line 81) | def __len__(self):
    method __getitem__ (line 84) | def __getitem__(self, idx):
  class IgnoreLabelDataset (line 93) | class IgnoreLabelDataset(torch.utils.data.Dataset):
    method __init__ (line 94) | def __init__(self, orig):
    method __getitem__ (line 97) | def __getitem__(self, index):
    method __len__ (line 100) | def __len__(self):

FILE: Seg_UKAN/archs.py
  class KANLayer (line 27) | class KANLayer(nn.Module):
    method __init__ (line 28) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method _init_weights (line 114) | def _init_weights(self, m):
    method forward (line 130) | def forward(self, x, H, W):
  class KANBlock (line 150) | class KANBlock(nn.Module):
    method __init__ (line 151) | def __init__(self, dim, drop=0., drop_path=0., act_layer=nn.GELU, norm...
    method _init_weights (line 162) | def _init_weights(self, m):
    method forward (line 177) | def forward(self, x, H, W):
  class DWConv (line 183) | class DWConv(nn.Module):
    method __init__ (line 184) | def __init__(self, dim=768):
    method forward (line 188) | def forward(self, x, H, W):
  class DW_bn_relu (line 196) | class DW_bn_relu(nn.Module):
    method __init__ (line 197) | def __init__(self, dim=768):
    method forward (line 203) | def forward(self, x, H, W):
  class PatchEmbed (line 213) | class PatchEmbed(nn.Module):
    method __init__ (line 217) | def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, e...
    method _init_weights (line 232) | def _init_weights(self, m):
    method forward (line 247) | def forward(self, x):
  class ConvLayer (line 256) | class ConvLayer(nn.Module):
    method __init__ (line 257) | def __init__(self, in_ch, out_ch):
    method forward (line 268) | def forward(self, input):
  class D_ConvLayer (line 271) | class D_ConvLayer(nn.Module):
    method __init__ (line 272) | def __init__(self, in_ch, out_ch):
    method forward (line 283) | def forward(self, input):
  class UKAN (line 288) | class UKAN(nn.Module):
    method __init__ (line 289) | def __init__(self, num_classes, input_channels=3, deep_supervision=Fal...
    method forward (line 339) | def forward(self, x):

FILE: Seg_UKAN/config.py
  function _update_config_from_file (line 175) | def _update_config_from_file(config, cfg_file):
  function update_config (line 190) | def update_config(config, args):
  function get_config (line 222) | def get_config(args):

FILE: Seg_UKAN/dataset.py
  class Dataset (line 9) | class Dataset(torch.utils.data.Dataset):
    method __init__ (line 10) | def __init__(self, img_ids, img_dir, mask_dir, img_ext, mask_ext, num_...
    method __len__ (line 52) | def __len__(self):
    method __getitem__ (line 55) | def __getitem__(self, idx):

FILE: Seg_UKAN/kan.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: Seg_UKAN/losses.py
  class BCEDiceLoss (line 13) | class BCEDiceLoss(nn.Module):
    method __init__ (line 14) | def __init__(self):
    method forward (line 17) | def forward(self, input, target):
  class LovaszHingeLoss (line 30) | class LovaszHingeLoss(nn.Module):
    method __init__ (line 31) | def __init__(self):
    method forward (line 34) | def forward(self, input, target):

FILE: Seg_UKAN/metrics.py
  function iou_score (line 9) | def iou_score(output, target):
  function dice_coef (line 31) | def dice_coef(output, target):
  function indicators (line 41) | def indicators(output, target):

FILE: Seg_UKAN/train.py
  function list_type (line 47) | def list_type(s):
  function parse_args (line 53) | def parse_args():
  function train (line 138) | def train(config, train_loader, model, criterion, optimizer):
  function validate (line 186) | def validate(config, val_loader, model, criterion):
  function seed_torch (line 231) | def seed_torch(seed=1029):
  function main (line 242) | def main():

FILE: Seg_UKAN/utils.py
  class qkv_transform (line 4) | class qkv_transform(nn.Conv1d):
  function str2bool (line 7) | def str2bool(v):
  function count_params (line 16) | def count_params(model):
  class AverageMeter (line 20) | class AverageMeter(object):
    method __init__ (line 23) | def __init__(self):
    method reset (line 26) | def reset(self):
    method update (line 32) | def update(self, val, n=1):

FILE: Seg_UKAN/val.py
  function parse_args (line 28) | def parse_args():
  function seed_torch (line 38) | def seed_torch(seed=1029):
  function main (line 49) | def main():