SYMBOL INDEX (494 symbols across 85 files)

FILE: configs/default_celeba_configs.py
  function get_default_configs (line 5) | def get_default_configs():

FILE: configs/default_cifar10_configs.py
  function get_default_configs (line 5) | def get_default_configs():

FILE: configs/default_complex_configs.py
  function get_default_configs (line 5) | def get_default_configs():

FILE: configs/default_lsun_configs.py
  function get_default_configs (line 5) | def get_default_configs():

FILE: configs/subvp/cifar10_ddpm_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/subvp/cifar10_ddpmpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/subvp/cifar10_ddpmpp_deep_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/subvp/cifar10_ncsnpp_continuous.py
  function get_config (line 21) | def get_config():

FILE: configs/subvp/cifar10_ncsnpp_deep_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/AAPM_128_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/AAPM_256_ncsnpp_continuous.py
  function get_config (line 4) | def get_config():

FILE: configs/ve/Object5_fast.py
  function get_config (line 4) | def get_config():

FILE: configs/ve/Object5_ncsnpp_continuous.py
  function get_config (line 4) | def get_config():

FILE: configs/ve/bedroom_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/celeba_ncsnpp.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/celebahq_256_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/celebahq_ncsnpp_continuous.py
  function get_config (line 23) | def get_config():

FILE: configs/ve/church_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/cifar10_ddpm.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/cifar10_ncsnpp.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/cifar10_ncsnpp_continuous.py
  function get_config (line 21) | def get_config():

FILE: configs/ve/cifar10_ncsnpp_deep_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/fastmri_knee_128_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/fastmri_knee_256_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/fastmri_knee_320_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/fastmri_knee_320_ncsnpp_continuous_complex.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/fastmri_knee_320_ncsnpp_continuous_complex_magpha.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/fastmri_knee_320_ncsnpp_continuous_multi.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ffhq_256_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ffhq_ncsnpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsn/celeba.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsn/celeba_124.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsn/celeba_1245.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsn/celeba_5.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsn/cifar10.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsn/cifar10_124.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsn/cifar10_1245.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsn/cifar10_5.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsnv2/bedroom.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsnv2/celeba.py
  function get_config (line 22) | def get_config():

FILE: configs/ve/ncsnv2/cifar10.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/cifar10_ddpmpp.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/cifar10_ddpmpp_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/cifar10_ddpmpp_deep_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/cifar10_ncsnpp.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/cifar10_ncsnpp_continuous.py
  function get_config (line 21) | def get_config():

FILE: configs/vp/cifar10_ncsnpp_deep_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/ddpm/bedroom.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/ddpm/celebahq.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/ddpm/church.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/ddpm/cifar10.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/ddpm/cifar10_continuous.py
  function get_config (line 22) | def get_config():

FILE: configs/vp/ddpm/cifar10_unconditional.py
  function get_config (line 22) | def get_config():

FILE: controllable_generation_TV.py
  class lambda_schedule (line 18) | class lambda_schedule:
    method __init__ (line 19) | def __init__(self, total=2000):
    method get_current_lambda (line 22) | def get_current_lambda(self, i):
  class lambda_schedule_linear (line 24) | class lambda_schedule_linear(lambda_schedule):
    method __init__ (line 25) | def __init__(self, start_lamb=1.0, end_lamb=0.0):
    method get_current_lambda (line 30) | def get_current_lambda(self, i):
  class lambda_schedule_const (line 34) | class lambda_schedule_const(lambda_schedule):
    method __init__ (line 35) | def __init__(self, lamb=1.0):
    method get_current_lambda (line 39) | def get_current_lambda(self, i):
  function _Dz (line 43) | def _Dz(x): # Batch direction
  function _DzT (line 50) | def _DzT(x): # Batch direction
  function _Dx (line 62) | def _Dx(x):  # Batch direction
  function _DxT (line 69) | def _DxT(x):  # Batch direction
  function _Dy (line 80) | def _Dy(x):  # Batch direction
  function _DyT (line 87) | def _DyT(x):  # Batch direction
  function get_pc_radon_ADMM_TV (line 98) | def get_pc_radon_ADMM_TV(sde, predictor, corrector, inverse_scaler, snr,
  function get_pc_radon_ADMM_TV_vol (line 226) | def get_pc_radon_ADMM_TV_vol(sde, predictor, corrector, inverse_scaler, ...
  function get_pc_radon_ADMM_TV_all_vol (line 353) | def get_pc_radon_ADMM_TV_all_vol(sde, predictor, corrector, inverse_scal...
  function get_ADMM_TV (line 496) | def get_ADMM_TV(eps=1e-5, radon=None, save_progress=False, save_root=None,
  function get_ADMM_TV_isotropic (line 579) | def get_ADMM_TV_isotropic(eps=1e-5, radon=None, save_progress=False, sav...
  function prox_l21 (line 680) | def prox_l21(src, lamb, dim):
  function shrink (line 691) | def shrink(weight_src, lamb):
  function get_pc_radon_ADMM_TV_mri (line 695) | def get_pc_radon_ADMM_TV_mri(sde, predictor, corrector, inverse_scaler, ...

FILE: datasets.py
  function get_data_scaler (line 22) | def get_data_scaler(config):
  function get_data_inverse_scaler (line 31) | def get_data_inverse_scaler(config):
  function crop_resize (line 40) | def crop_resize(image, resolution):
  function resize_small (line 54) | def resize_small(image, resolution):
  function central_crop (line 63) | def central_crop(image, size):
  function get_dataset (line 70) | def get_dataset(config, uniform_dequantization=False, evaluation=False):
  class fastmri_knee (line 203) | class fastmri_knee(Dataset):
    method __init__ (line 205) | def __init__(self, root, is_complex=False):
    method __len__ (line 210) | def __len__(self):
    method __getitem__ (line 213) | def __getitem__(self, idx):
  class AAPM (line 223) | class AAPM(Dataset):
    method __init__ (line 224) | def __init__(self, root, sort):
    method __len__ (line 231) | def __len__(self):
    method __getitem__ (line 234) | def __getitem__(self, idx):
  class Object5 (line 241) | class Object5(Dataset):
    method __init__ (line 242) | def __init__(self, root, slice, fast=False):
    method __len__ (line 263) | def __len__(self):
    method __getitem__ (line 266) | def __getitem__(self, idx):
  class fastmri_knee_infer (line 274) | class fastmri_knee_infer(Dataset):
    method __init__ (line 276) | def __init__(self, root, sort=True, is_complex=False):
    method __len__ (line 283) | def __len__(self):
    method __getitem__ (line 286) | def __getitem__(self, idx):
  class fastmri_knee_magpha (line 296) | class fastmri_knee_magpha(Dataset):
    method __init__ (line 298) | def __init__(self, root):
    method __len__ (line 302) | def __len__(self):
    method __getitem__ (line 305) | def __getitem__(self, idx):
  class fastmri_knee_magpha_infer (line 311) | class fastmri_knee_magpha_infer(Dataset):
    method __init__ (line 313) | def __init__(self, root, sort=True):
    method __len__ (line 319) | def __len__(self):
    method __getitem__ (line 322) | def __getitem__(self, idx):
  function create_dataloader (line 328) | def create_dataloader(configs, evaluation=False, sort=True):
  function create_dataloader_regression (line 372) | def create_dataloader_regression(configs, evaluation=False):

FILE: evaluation.py
  function get_inception_model (line 31) | def get_inception_model(inceptionv3=False):
  function load_dataset_stats (line 39) | def load_dataset_stats(config):
  function classifier_fn_from_tfhub (line 55) | def classifier_fn_from_tfhub(output_fields, inception_model,
  function run_inception_jit (line 86) | def run_inception_jit(inputs,
  function run_inception_distributed (line 104) | def run_inception_distributed(input_tensor,

FILE: fastmri_utils.py
  function fft2c_old (line 16) | def fft2c_old(data: torch.Tensor, norm: str = "ortho") -> torch.Tensor:
  function ifft2c_old (line 41) | def ifft2c_old(data: torch.Tensor, norm: str = "ortho") -> torch.Tensor:
  function fft2c_new (line 67) | def fft2c_new(data: torch.Tensor, norm: str = "ortho") -> torch.Tensor:
  function ifft2c_new (line 92) | def ifft2c_new(data: torch.Tensor, norm: str = "ortho") -> torch.Tensor:
  function roll_one_dim (line 120) | def roll_one_dim(x: torch.Tensor, shift: int, dim: int) -> torch.Tensor:
  function roll (line 140) | def roll(
  function fftshift (line 163) | def fftshift(x: torch.Tensor, dim: Optional[List[int]] = None) -> torch....
  function ifftshift (line 186) | def ifftshift(x: torch.Tensor, dim: Optional[List[int]] = None) -> torch...

FILE: likelihood.py
  function get_div_fn (line 26) | def get_div_fn(fn):
  function get_likelihood_fn (line 40) | def get_likelihood_fn(sde, inverse_scaler, hutchinson_type='Rademacher',

FILE: losses.py
  function get_optimizer (line 28) | def get_optimizer(config, params):
  function optimization_manager (line 40) | def optimization_manager(config):
  function get_sde_loss_fn (line 57) | def get_sde_loss_fn(sde, train, reduce_mean=True, continuous=True, likel...
  function get_smld_loss_fn (line 105) | def get_smld_loss_fn(vesde, train, reduce_mean=False):
  function get_ddpm_loss_fn (line 129) | def get_ddpm_loss_fn(vpsde, train, reduce_mean=True):
  function get_step_fn (line 152) | def get_step_fn(sde, train, optimize_fn=None, reduce_mean=False, continu...
  function get_step_fn_regression (line 215) | def get_step_fn_regression(train, config, mask=None, loss_fn=None, optim...

FILE: main.py
  function main (line 38) | def main(argv):

FILE: models/ddpm.py
  class DDPM (line 40) | class DDPM(nn.Module):
    method __init__ (line 41) | def __init__(self, config):
    method forward (line 110) | def forward(self, x, labels):

FILE: models/ema.py
  class ExponentialMovingAverage (line 10) | class ExponentialMovingAverage:
    method __init__ (line 15) | def __init__(self, parameters, decay, use_num_updates=True):
    method update (line 32) | def update(self, parameters):
    method copy_to (line 53) | def copy_to(self, parameters):
    method store (line 66) | def store(self, parameters):
    method restore (line 76) | def restore(self, parameters):
    method state_dict (line 91) | def state_dict(self):
    method load_state_dict (line 95) | def load_state_dict(self, state_dict):

FILE: models/layers.py
  class SiLU (line 29) | class SiLU(nn.Module):
    method forward (line 30) | def forward(self, x):
  function get_act (line 33) | def get_act(config):
  function ncsn_conv1x1 (line 48) | def ncsn_conv1x1(in_planes, out_planes, stride=1, bias=True, dilation=1,...
  function variance_scaling (line 58) | def variance_scaling(scale, mode, distribution,
  function default_init (line 92) | def default_init(scale=1.):
  class Dense (line 98) | class Dense(nn.Module):
    method __init__ (line 100) | def __init__(self):
  function ddpm_conv1x1 (line 104) | def ddpm_conv1x1(in_planes, out_planes, stride=1, bias=True, init_scale=...
  function ncsn_conv3x3 (line 112) | def ncsn_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1,...
  function ddpm_conv3x3 (line 122) | def ddpm_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1,...
  class CRPBlock (line 137) | class CRPBlock(nn.Module):
    method __init__ (line 138) | def __init__(self, features, n_stages, act=nn.ReLU(), maxpool=True):
    method forward (line 151) | def forward(self, x):
  class CondCRPBlock (line 161) | class CondCRPBlock(nn.Module):
    method __init__ (line 162) | def __init__(self, features, n_stages, num_classes, normalizer, act=nn...
    method forward (line 175) | def forward(self, x, y):
  class RCUBlock (line 187) | class RCUBlock(nn.Module):
    method __init__ (line 188) | def __init__(self, features, n_blocks, n_stages, act=nn.ReLU()):
    method forward (line 200) | def forward(self, x):
  class CondRCUBlock (line 211) | class CondRCUBlock(nn.Module):
    method __init__ (line 212) | def __init__(self, features, n_blocks, n_stages, num_classes, normaliz...
    method forward (line 226) | def forward(self, x, y):
  class MSFBlock (line 238) | class MSFBlock(nn.Module):
    method __init__ (line 239) | def __init__(self, in_planes, features):
    method forward (line 248) | def forward(self, xs, shape):
  class CondMSFBlock (line 257) | class CondMSFBlock(nn.Module):
    method __init__ (line 258) | def __init__(self, in_planes, features, num_classes, normalizer):
    method forward (line 271) | def forward(self, xs, y, shape):
  class RefineBlock (line 281) | class RefineBlock(nn.Module):
    method __init__ (line 282) | def __init__(self, in_planes, features, act=nn.ReLU(), start=False, en...
    method forward (line 299) | def forward(self, xs, output_shape):
  class CondRefineBlock (line 317) | class CondRefineBlock(nn.Module):
    method __init__ (line 318) | def __init__(self, in_planes, features, num_classes, normalizer, act=n...
    method forward (line 337) | def forward(self, xs, y, output_shape):
  class ConvMeanPool (line 355) | class ConvMeanPool(nn.Module):
    method __init__ (line 356) | def __init__(self, input_dim, output_dim, kernel_size=3, biases=True, ...
    method forward (line 369) | def forward(self, inputs):
  class MeanPoolConv (line 376) | class MeanPoolConv(nn.Module):
    method __init__ (line 377) | def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
    method forward (line 381) | def forward(self, inputs):
  class UpsampleConv (line 388) | class UpsampleConv(nn.Module):
    method __init__ (line 389) | def __init__(self, input_dim, output_dim, kernel_size=3, biases=True):
    method forward (line 394) | def forward(self, inputs):
  class ConditionalResidualBlock (line 401) | class ConditionalResidualBlock(nn.Module):
    method __init__ (line 402) | def __init__(self, input_dim, output_dim, num_classes, resample=1, act...
    method forward (line 441) | def forward(self, x, y):
  class ResidualBlock (line 457) | class ResidualBlock(nn.Module):
    method __init__ (line 458) | def __init__(self, input_dim, output_dim, resample=None, act=nn.ELU(),
    method forward (line 498) | def forward(self, x):
  function get_timestep_embedding (line 519) | def get_timestep_embedding(timesteps, embedding_dim, max_positions=10000):
  function _einsum (line 533) | def _einsum(a, b, c, x, y):
  function contract_inner (line 538) | def contract_inner(x, y):
  class NIN (line 547) | class NIN(nn.Module):
    method __init__ (line 548) | def __init__(self, in_dim, num_units, init_scale=0.1):
    method forward (line 553) | def forward(self, x):
  class AttnBlock (line 559) | class AttnBlock(nn.Module):
    method __init__ (line 561) | def __init__(self, channels):
    method forward (line 569) | def forward(self, x):
  class Upsample (line 585) | class Upsample(nn.Module):
    method __init__ (line 586) | def __init__(self, channels, with_conv=False):
    method forward (line 592) | def forward(self, x):
  class Downsample (line 600) | class Downsample(nn.Module):
    method __init__ (line 601) | def __init__(self, channels, with_conv=False):
    method forward (line 607) | def forward(self, x):
  class ResnetBlockDDPM (line 620) | class ResnetBlockDDPM(nn.Module):
    method __init__ (line 622) | def __init__(self, act, in_ch, out_ch=None, temb_dim=None, conv_shortc...
    method forward (line 646) | def forward(self, x, temb=None):

FILE: models/layerspp.py
  class GaussianFourierProjection (line 32) | class GaussianFourierProjection(nn.Module):
    method __init__ (line 35) | def __init__(self, embedding_size=256, scale=1.0):
    method forward (line 39) | def forward(self, x):
  class Combine (line 44) | class Combine(nn.Module):
    method __init__ (line 47) | def __init__(self, dim1, dim2, method='cat'):
    method forward (line 52) | def forward(self, x, y):
  class AttnBlockpp (line 62) | class AttnBlockpp(nn.Module):
    method __init__ (line 65) | def __init__(self, channels, skip_rescale=False, init_scale=0.):
    method forward (line 75) | def forward(self, x):
  class Upsample (line 94) | class Upsample(nn.Module):
    method __init__ (line 95) | def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False,
    method forward (line 114) | def forward(self, x):
  class Downsample (line 129) | class Downsample(nn.Module):
    method __init__ (line 130) | def __init__(self, in_ch=None, out_ch=None, with_conv=False, fir=False,
    method forward (line 149) | def forward(self, x):
  class ResnetBlockDDPMpp (line 166) | class ResnetBlockDDPMpp(nn.Module):
    method __init__ (line 169) | def __init__(self, act, in_ch, out_ch=None, temb_dim=None, conv_shortc...
    method forward (line 193) | def forward(self, x, temb=None):
  class ResnetBlockBigGANpp (line 212) | class ResnetBlockBigGANpp(nn.Module):
    method __init__ (line 213) | def __init__(self, act, in_ch, out_ch=None, temb_dim=None, up=False, d...
    method forward (line 242) | def forward(self, x, temb=None):

FILE: models/ncsnpp.py
  class NCSNpp (line 35) | class NCSNpp(nn.Module):
    method __init__ (line 38) | def __init__(self, config):
    method forward (line 232) | def forward(self, x, time_cond):

FILE: models/ncsnv2.py
  function get_network (line 31) | def get_network(config):
  class NCSNv2 (line 44) | class NCSNv2(nn.Module):
    method __init__ (line 45) | def __init__(self, config):
    method _compute_cond_module (line 101) | def _compute_cond_module(self, module, x):
    method forward (line 106) | def forward(self, x, y):
  class NCSN (line 136) | class NCSN(nn.Module):
    method __init__ (line 137) | def __init__(self, config):
    method _compute_cond_module (line 191) | def _compute_cond_module(self, module, x, y):
    method forward (line 196) | def forward(self, x, y):
  class NCSNv2_128 (line 222) | class NCSNv2_128(nn.Module):
    method __init__ (line 224) | def __init__(self, config):
    method _compute_cond_module (line 279) | def _compute_cond_module(self, module, x):
    method forward (line 284) | def forward(self, x, y):
  class NCSNv2_256 (line 316) | class NCSNv2_256(nn.Module):
    method __init__ (line 318) | def __init__(self, config):
    method _compute_cond_module (line 381) | def _compute_cond_module(self, module, x):
    method forward (line 386) | def forward(self, x, y):

FILE: models/normalization.py
  function get_normalization (line 22) | def get_normalization(config, conditional=False):
  class ConditionalBatchNorm2d (line 43) | class ConditionalBatchNorm2d(nn.Module):
    method __init__ (line 44) | def __init__(self, num_features, num_classes, bias=True):
    method forward (line 57) | def forward(self, x, y):
  class ConditionalInstanceNorm2d (line 68) | class ConditionalInstanceNorm2d(nn.Module):
    method __init__ (line 69) | def __init__(self, num_features, num_classes, bias=True):
    method forward (line 82) | def forward(self, x, y):
  class ConditionalVarianceNorm2d (line 93) | class ConditionalVarianceNorm2d(nn.Module):
    method __init__ (line 94) | def __init__(self, num_features, num_classes, bias=False):
    method forward (line 101) | def forward(self, x, y):
  class VarianceNorm2d (line 110) | class VarianceNorm2d(nn.Module):
    method __init__ (line 111) | def __init__(self, num_features, bias=False):
    method forward (line 118) | def forward(self, x):
  class ConditionalNoneNorm2d (line 126) | class ConditionalNoneNorm2d(nn.Module):
    method __init__ (line 127) | def __init__(self, num_features, num_classes, bias=True):
    method forward (line 139) | def forward(self, x, y):
  class NoneNorm2d (line 149) | class NoneNorm2d(nn.Module):
    method __init__ (line 150) | def __init__(self, num_features, bias=True):
    method forward (line 153) | def forward(self, x):
  class InstanceNorm2dPlus (line 157) | class InstanceNorm2dPlus(nn.Module):
    method __init__ (line 158) | def __init__(self, num_features, bias=True):
    method forward (line 170) | def forward(self, x):
  class ConditionalInstanceNorm2dPlus (line 186) | class ConditionalInstanceNorm2dPlus(nn.Module):
    method __init__ (line 187) | def __init__(self, num_features, num_classes, bias=True):
    method forward (line 200) | def forward(self, x, y):

FILE: models/unet.py
  class ConvBlock (line 7) | class ConvBlock(nn.Module):
    method __init__ (line 13) | def __init__(self, in_chans, out_chans, stride=2):
    method forward (line 35) | def forward(self, tensor):
    method __repr__ (line 44) | def __repr__(self):
  class Unet (line 49) | class Unet(nn.Module):
    method __init__ (line 50) | def __init__(self, in_chans=1, out_chans=1, chans=64, num_pool_layers=...
    method forward (line 81) | def forward(self, tensor):

FILE: models/up_or_down_sampling.py
  function get_weight (line 14) | def get_weight(module,
  class Conv2d (line 23) | class Conv2d(nn.Module):
    method __init__ (line 26) | def __init__(self, in_ch, out_ch, kernel, up=False, down=False,
    method forward (line 45) | def forward(self, x):
  function naive_upsample_2d (line 59) | def naive_upsample_2d(x, factor=2):
  function naive_downsample_2d (line 66) | def naive_downsample_2d(x, factor=2):
  function upsample_conv_2d (line 72) | def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
  function conv_downsample_2d (line 144) | def conv_downsample_2d(x, w, k=None, factor=2, gain=1):
  function _setup_kernel (line 181) | def _setup_kernel(k):
  function _shape (line 191) | def _shape(x, dim):
  function upsample_2d (line 195) | def upsample_2d(x, k=None, factor=2, gain=1):
  function downsample_2d (line 227) | def downsample_2d(x, k=None, factor=2, gain=1):

FILE: models/utils.py
  function register_model (line 27) | def register_model(cls=None, *, name=None):
  function get_model (line 46) | def get_model(name):
  function get_sigmas (line 50) | def get_sigmas(config):
  function get_ddpm_params (line 63) | def get_ddpm_params(config):
  function create_model (line 88) | def create_model(config):
  function get_model_fn (line 97) | def get_model_fn(model, train=False):
  function get_score_fn (line 129) | def get_score_fn(sde, model, train=False, continuous=False):
  function to_flattened_numpy (line 181) | def to_flattened_numpy(x):
  function from_flattened_numpy (line 186) | def from_flattened_numpy(x, shape):

FILE: op/fused_act.py
  class FusedLeakyReLUFunctionBackward (line 20) | class FusedLeakyReLUFunctionBackward(Function):
    method forward (line 22) | def forward(ctx, grad_output, out, negative_slope, scale):
    method backward (line 43) | def backward(ctx, gradgrad_input, gradgrad_bias):
  class FusedLeakyReLUFunction (line 52) | class FusedLeakyReLUFunction(Function):
    method forward (line 54) | def forward(ctx, input, bias, negative_slope, scale):
    method backward (line 64) | def backward(ctx, grad_output):
  class FusedLeakyReLU (line 74) | class FusedLeakyReLU(nn.Module):
    method __init__ (line 75) | def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
    method forward (line 82) | def forward(self, input):
  function fused_leaky_relu (line 86) | def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):

FILE: op/fused_bias_act.cpp
  function fused_bias_act (line 11) | torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Te...
  function PYBIND11_MODULE (line 19) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: op/upfirdn2d.cpp
  function upfirdn2d (line 12) | torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor&...
  function PYBIND11_MODULE (line 21) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: op/upfirdn2d.py
  class UpFirDn2dBackward (line 19) | class UpFirDn2dBackward(Function):
    method forward (line 21) | def forward(
    method backward (line 63) | def backward(ctx, gradgrad_input):
  class UpFirDn2d (line 88) | class UpFirDn2d(Function):
    method forward (line 90) | def forward(ctx, input, kernel, up, down, pad):
    method backward (line 127) | def backward(ctx, grad_output):
  function upfirdn2d (line 145) | def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
  function upfirdn2d_native (line 159) | def upfirdn2d_native(

FILE: physics/ct.py
  class CT (line 5) | class CT():
    method __init__ (line 6) | def __init__(self, img_width, radon_view, uniform=True, circle=False, ...
    method A (line 20) | def A(self, x):
    method A_all (line 23) | def A_all(self, x):
    method A_all_dagger (line 26) | def A_all_dagger(self, x):
    method A_dagger (line 29) | def A_dagger(self, y):
    method AT (line 32) | def AT(self, y):
  class CT_LA (line 36) | class CT_LA():
    method __init__ (line 40) | def __init__(self, img_width, radon_view, uniform=True, circle=False, ...
    method A (line 49) | def A(self, x):
    method A_dagger (line 52) | def A_dagger(self, y):
    method AT (line 55) | def AT(self, y):

FILE: physics/inpainting.py
  class Inpainting (line 4) | class Inpainting():
    method __init__ (line 5) | def __init__(self, img_heigth=512, img_width=512, mode='random', mask_...
    method A (line 14) | def A(self, x):
    method A_dagger (line 17) | def A_dagger(self, x):

FILE: physics/radon/filters.py
  class AbstractFilter (line 9) | class AbstractFilter(nn.Module):
    method __init__ (line 10) | def __init__(self):
    method forward (line 13) | def forward(self, x):
    method _get_fourier_filter (line 24) | def _get_fourier_filter(self, size):
    method create_filter (line 38) | def create_filter(self, f):
  class RampFilter (line 41) | class RampFilter(AbstractFilter):
    method __init__ (line 42) | def __init__(self):
    method create_filter (line 45) | def create_filter(self, f):
  class HannFilter (line 48) | class HannFilter(AbstractFilter):
    method __init__ (line 49) | def __init__(self):
    method create_filter (line 52) | def create_filter(self, f):
  class LearnableFilter (line 57) | class LearnableFilter(AbstractFilter):
    method __init__ (line 58) | def __init__(self, filter_size):
    method forward (line 62) | def forward(self, x):

FILE: physics/radon/radon.py
  class Radon (line 11) | class Radon(nn.Module):
    method __init__ (line 12) | def __init__(self, in_size=None, theta=None, circle=True, dtype=torch....
    method forward (line 23) | def forward(self, x):
    method _create_grids (line 48) | def _create_grids(self, angles, grid_size, circle):
  class IRadon (line 62) | class IRadon(nn.Module):
    method __init__ (line 63) | def __init__(self, in_size=None, theta=None, circle=True,
    method forward (line 77) | def forward(self, x):
    method _create_yxgrid (line 118) | def _create_yxgrid(self, in_size, circle):
    method _XYtoT (line 124) | def _XYtoT(self, theta):
    method _create_grids (line 128) | def _create_grids(self, angles, grid_size, circle):

FILE: physics/radon/stackgram.py
  class Stackgram (line 9) | class Stackgram(nn.Module):
    method __init__ (line 10) | def __init__(self, out_size, theta=None, circle=True, mode='nearest', ...
    method forward (line 22) | def forward(self, x):
    method _create_grids (line 33) | def _create_grids(self, angles, grid_size):
  class IStackgram (line 41) | class IStackgram(nn.Module):
    method __init__ (line 42) | def __init__(self, out_size, theta=None, circle=True, mode='bilinear',...
    method forward (line 54) | def forward(self, x):
    method _create_grids (line 65) | def _create_grids(self, angles, grid_size):

FILE: physics/radon/utils.py
  function fftfreq (line 17) | def fftfreq(n):
  function deg2rad (line 27) | def deg2rad(x):

FILE: run_lib.py
  function train (line 47) | def train(config, workdir):
  function evaluate (line 168) | def evaluate(config,

FILE: sampling.py
  function register_predictor (line 35) | def register_predictor(cls=None, *, name=None):
  function register_corrector (line 54) | def register_corrector(cls=None, *, name=None):
  function get_predictor (line 73) | def get_predictor(name):
  function get_corrector (line 77) | def get_corrector(name):
  function get_sampling_fn (line 81) | def get_sampling_fn(config, sde, shape, inverse_scaler, eps):
  class Predictor (line 127) | class Predictor(abc.ABC):
    method __init__ (line 130) | def __init__(self, sde, score_fn, probability_flow=False):
    method update_fn (line 138) | def update_fn(self, x, t):
  class Corrector (line 152) | class Corrector(abc.ABC):
    method __init__ (line 155) | def __init__(self, sde, score_fn, snr, n_steps):
    method update_fn (line 163) | def update_fn(self, x, t):
  class EulerMaruyamaPredictor (line 178) | class EulerMaruyamaPredictor(Predictor):
    method __init__ (line 179) | def __init__(self, sde, score_fn, probability_flow=False):
    method update_fn (line 182) | def update_fn(self, x, t):
  class ReverseDiffusionPredictor (line 192) | class ReverseDiffusionPredictor(Predictor):
    method __init__ (line 193) | def __init__(self, sde, score_fn, probability_flow=False):
    method update_fn (line 196) | def update_fn(self, x, t):
  class AncestralSamplingPredictor (line 205) | class AncestralSamplingPredictor(Predictor):
    method __init__ (line 208) | def __init__(self, sde, score_fn, probability_flow=False):
    method vesde_update_fn (line 214) | def vesde_update_fn(self, x, t):
    method vpsde_update_fn (line 226) | def vpsde_update_fn(self, x, t):
    method update_fn (line 236) | def update_fn(self, x, t):
  class NonePredictor (line 244) | class NonePredictor(Predictor):
    method __init__ (line 247) | def __init__(self, sde, score_fn, probability_flow=False):
    method update_fn (line 250) | def update_fn(self, x, t):
  class LangevinCorrector (line 255) | class LangevinCorrector(Corrector):
    method __init__ (line 256) | def __init__(self, sde, score_fn, snr, n_steps):
    method update_fn (line 263) | def update_fn(self, x, t):
  class LangevinCorrectorCS (line 286) | class LangevinCorrectorCS(Corrector):
    method __init__ (line 288) | def __init__(self, sde, score_fn, snr, n_steps, sigma_min, sigma_max, N):
    method update_fn (line 295) | def update_fn(self, x, t, y, discrete_sigmas):
  class AnnealedLangevinDynamics (line 329) | class AnnealedLangevinDynamics(Corrector):
    method __init__ (line 335) | def __init__(self, sde, score_fn, snr, n_steps):
    method update_fn (line 342) | def update_fn(self, x, t):
  class NoneCorrector (line 366) | class NoneCorrector(Corrector):
    method __init__ (line 369) | def __init__(self, sde, score_fn, snr, n_steps):
    method update_fn (line 372) | def update_fn(self, x, t):
  function shared_predictor_update_fn (line 376) | def shared_predictor_update_fn(x, t, sde, model, predictor, probability_...
  function shared_corrector_update_fn (line 387) | def shared_corrector_update_fn(x, t, sde, model, corrector, continuous, ...
  function get_pc_sampler (line 406) | def get_pc_sampler(sde, shape, predictor, corrector, inverse_scaler, snr,
  function get_ode_sampler (line 473) | def get_ode_sampler(sde, shape, inverse_scaler,

FILE: sde_lib.py
  class SDE (line 7) | class SDE(abc.ABC):
    method __init__ (line 10) | def __init__(self, N):
    method T (line 21) | def T(self):
    method sde (line 26) | def sde(self, x, t):
    method marginal_prob (line 30) | def marginal_prob(self, x, t):
    method prior_sampling (line 35) | def prior_sampling(self, shape):
    method prior_logp (line 40) | def prior_logp(self, z):
    method discretize (line 52) | def discretize(self, x, t):
    method reverse (line 71) | def reverse(self, score_fn, probability_flow=False):
  class VPSDE (line 112) | class VPSDE(SDE):
    method __init__ (line 113) | def __init__(self, beta_min=0.1, beta_max=20, N=1000):
    method T (line 132) | def T(self):
    method sde (line 135) | def sde(self, x, t):
    method marginal_prob (line 141) | def marginal_prob(self, x, t):
    method prior_sampling (line 147) | def prior_sampling(self, shape):
    method prior_logp (line 150) | def prior_logp(self, z):
    method discretize (line 156) | def discretize(self, x, t):
  class subVPSDE (line 167) | class subVPSDE(SDE):
    method __init__ (line 168) | def __init__(self, beta_min=0.1, beta_max=20, N=1000):
    method T (line 182) | def T(self):
    method sde (line 185) | def sde(self, x, t):
    method marginal_prob (line 192) | def marginal_prob(self, x, t):
    method prior_sampling (line 198) | def prior_sampling(self, shape):
    method prior_logp (line 201) | def prior_logp(self, z):
  class VESDE (line 207) | class VESDE(SDE):
    method __init__ (line 208) | def __init__(self, sigma_min=0.01, sigma_max=50, N=1000):
    method T (line 223) | def T(self):
    method sde (line 226) | def sde(self, x, t):
    method marginal_prob (line 233) | def marginal_prob(self, x, t):
    method prior_sampling (line 238) | def prior_sampling(self, shape):
    method prior_logp (line 241) | def prior_logp(self, z):
    method discretize (line 246) | def discretize(self, x, t):

FILE: test/test_TV.py
  function test_adjoint (line 22) | def test_adjoint(A, AT):
  function test_prox_l21 (line 31) | def test_prox_l21():
  class Identity (line 54) | class Identity:
    method A (line 56) | def A(x):
    method AT (line 60) | def AT(y):
  function test_ADMM_TV_isotropic (line 63) | def test_ADMM_TV_isotropic():

FILE: utils.py
  function clear_color (line 16) | def clear_color(x):
  function clear (line 20) | def clear(x, normalize=True):
  function restore_checkpoint (line 27) | def restore_checkpoint(ckpt_dir, state, device, skip_sigma=False, skip_o...
  function save_checkpoint (line 50) | def save_checkpoint(ckpt_dir, state, name="checkpoint.pth"):
  function fft2 (line 65) | def fft2(x):
  function ifft2 (line 70) | def ifft2(x):
  function fft2_m (line 75) | def fft2_m(x):
  function ifft2_m (line 80) | def ifft2_m(x):
  function crop_center (line 85) | def crop_center(img, cropx, cropy):
  function normalize (line 92) | def normalize(img):
  function normalize_np (line 98) | def normalize_np(img):
  function normalize_np_kwarg (line 105) | def normalize_np_kwarg(img, maxv=1.0, minv=0.0):
  function normalize_complex (line 112) | def normalize_complex(img):
  function batchfy (line 120) | def batchfy(tensor, batch_size):
  function img_wise_min_max (line 126) | def img_wise_min_max(img):
  function patient_wise_min_max (line 134) | def patient_wise_min_max(img):
  function create_sphere (line 153) | def create_sphere(cx, cy, cz, r, resolution=256):
  class lambda_schedule (line 170) | class lambda_schedule:
    method __init__ (line 171) | def __init__(self, total=2000):
    method get_current_lambda (line 174) | def get_current_lambda(self, i):
  class lambda_schedule_linear (line 178) | class lambda_schedule_linear(lambda_schedule):
    method __init__ (line 179) | def __init__(self, start_lamb=1.0, end_lamb=0.0):
    method get_current_lambda (line 184) | def get_current_lambda(self, i):
  class lambda_schedule_const (line 188) | class lambda_schedule_const(lambda_schedule):
    method __init__ (line 189) | def __init__(self, lamb=1.0):
    method get_current_lambda (line 193) | def get_current_lambda(self, i):
  function image_grid (line 199) | def image_grid(x, sz=32):
  function show_samples (line 208) | def show_samples(x, sz=32):
  function image_grid_gray (line 217) | def image_grid_gray(x, size=32):
  function show_samples_gray (line 224) | def show_samples_gray(x, size=32, save=False, save_fname=None):
  function get_mask (line 235) | def get_mask(img, size, batch_size, type='gaussian2d', acc_factor=8, cen...
  function kspace_to_nchw (line 317) | def kspace_to_nchw(tensor):
  function nchw_to_kspace (line 342) | def nchw_to_kspace(tensor):
  function root_sum_of_squares (line 361) | def root_sum_of_squares(data, dim=0):
  function save_data (line 373) | def save_data(fname, arr):
  function mean_std (line 378) | def mean_std(vals: list):
  function cal_metric (line 381) | def cal_metric(comp, label):