SYMBOL INDEX (313 symbols across 36 files)

FILE: calc_metrics.py
  function subprocess_fn (line 28) | def subprocess_fn(rank, args, temp_dir):
  class CommaSeparatedList (line 76) | class CommaSeparatedList(click.ParamType):
    method convert (line 79) | def convert(self, value, param, ctx):
  function calc_metrics (line 96) | def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose):

FILE: dataset_resize.py
  function is_image_file (line 14) | def is_image_file(filename):
  function make_dataset (line 18) | def make_dataset(dir):
  function resize_dataset (line 35) | def resize_dataset(ctx, source, dest, width, height, crop):

FILE: dnnlib/util.py
  class EasyDict (line 40) | class EasyDict(dict):
    method __getattr__ (line 43) | def __getattr__(self, name: str) -> Any:
    method __setattr__ (line 49) | def __setattr__(self, name: str, value: Any) -> None:
    method __delattr__ (line 52) | def __delattr__(self, name: str) -> None:
  class Logger (line 56) | class Logger(object):
    method __init__ (line 59) | def __init__(self, file_name: str = None, file_mode: str = "w", should...
    method __enter__ (line 72) | def __enter__(self) -> "Logger":
    method __exit__ (line 75) | def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> N...
    method write (line 78) | def write(self, text: Union[str, bytes]) -> None:
    method flush (line 93) | def flush(self) -> None:
    method close (line 100) | def close(self) -> None:
  function set_cache_dir (line 120) | def set_cache_dir(path: str) -> None:
  function make_cache_dir_path (line 124) | def make_cache_dir_path(*paths: str) -> str:
  function format_time (line 139) | def format_time(seconds: Union[int, float]) -> str:
  function ask_yes_no (line 153) | def ask_yes_no(question: str) -> bool:
  function tuple_product (line 163) | def tuple_product(t: Tuple) -> Any:
  function get_dtype_and_ctype (line 187) | def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
  function is_pickleable (line 210) | def is_pickleable(obj: Any) -> bool:
  function get_module_from_obj_name (line 222) | def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, s...
  function get_obj_from_module (line 263) | def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
  function get_obj_by_name (line 273) | def get_obj_by_name(name: str) -> Any:
  function call_func_by_name (line 279) | def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
  function construct_class_by_name (line 287) | def construct_class_by_name(*args, class_name: str = None, **kwargs) -> ...
  function get_module_dir_by_obj_name (line 292) | def get_module_dir_by_obj_name(obj_name: str) -> str:
  function is_top_level_function (line 298) | def is_top_level_function(obj: Any) -> bool:
  function get_top_level_function_name (line 303) | def get_top_level_function_name(obj: Any) -> str:
  function list_dir_recursively_with_ignore (line 315) | def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] =...
  function copy_files_and_create_dirs (line 348) | def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
  function is_url (line 364) | def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
  function open_url (line 382) | def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, ve...

FILE: generate.py
  function make_dataset (line 25) | def make_dataset(dir, extension='npz'):
  function num_range (line 37) | def num_range(s: str) -> List[int]:
  function generate_blended_img (line 49) | def generate_blended_img(G_t, z=None, blend_weights=[0,0.25,0.5,0.75,1],...
  function generate_images (line 81) | def generate_images(

FILE: legacy.py
  function load_network (line 20) | def load_network(cfg, network_pkl, img_resolution, img_channels, c_dim):
  function load_network_pkl (line 53) | def load_network_pkl(f, force_fp16=False):
  class _TFNetworkStub (line 97) | class _TFNetworkStub(dnnlib.EasyDict):
  class _LegacyUnpickler (line 100) | class _LegacyUnpickler(pickle.Unpickler):
    method find_class (line 101) | def find_class(self, module, name):
  function _collect_tf_params (line 108) | def _collect_tf_params(tf_net):
  function _populate_module_params (line 121) | def _populate_module_params(module, *patterns):
  function convert_tf_generator (line 142) | def convert_tf_generator(tf_G):
  function convert_tf_discriminator (line 241) | def convert_tf_discriminator(tf_D):
  function convert_network_pickle (line 328) | def convert_network_pickle(source, dest, force_fp16):

FILE: metrics/frechet_inception_distance.py
  function compute_fid (line 20) | def compute_fid(opts, max_real, num_gen):

FILE: metrics/inception_score.py
  function compute_is (line 18) | def compute_is(opts, num_gen, num_splits):

FILE: metrics/kernel_inception_distance.py
  function compute_kid (line 18) | def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size):

FILE: metrics/lpips.py
  class LPIPSSampler (line 20) | class LPIPSSampler(torch.nn.Module):
    method __init__ (line 21) | def __init__(self, G, G_orig, G_kwargs, epsilon, crop):
    method forward (line 30) | def forward(self, c):
  function compute_lpips (line 59) | def compute_lpips(opts, num_samples, epsilon, crop, batch_size, resume='...

FILE: metrics/metric_main.py
  function register_metric (line 27) | def register_metric(fn):
  function is_valid_metric (line 32) | def is_valid_metric(metric):
  function list_valid_metrics (line 35) | def list_valid_metrics():
  function calc_metric (line 40) | def calc_metric(metric, **kwargs): # See metric_utils.MetricOptions for ...
  function report_metric (line 68) | def report_metric(result_dict, run_dir=None, snapshot_pkl=None):
  function fid50k_full (line 84) | def fid50k_full(opts):
  function kid50k_full (line 90) | def kid50k_full(opts):
  function pr50k3_full (line 96) | def pr50k3_full(opts):
  function ppl2_wend (line 102) | def ppl2_wend(opts):
  function is50k (line 107) | def is50k(opts):
  function fid50k (line 116) | def fid50k(opts):
  function kid50k (line 122) | def kid50k(opts):
  function pr50k3 (line 128) | def pr50k3(opts):
  function ppl_zfull (line 134) | def ppl_zfull(opts):
  function ppl_wfull (line 139) | def ppl_wfull(opts):
  function ppl_zend (line 144) | def ppl_zend(opts):
  function ppl_wend (line 149) | def ppl_wend(opts):
  function lpips2k (line 157) | def lpips2k(opts):
  function lpips_church2k (line 162) | def lpips_church2k(opts):

FILE: metrics/metric_utils.py
  class MetricOptions (line 21) | class MetricOptions:
    method __init__ (line 22) | def __init__(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1,...
  function get_feature_detector_name (line 37) | def get_feature_detector_name(url):
  function get_feature_detector (line 40) | def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, ra...
  class FeatureStats (line 55) | class FeatureStats:
    method __init__ (line 56) | def __init__(self, capture_all=False, capture_mean_cov=False, max_item...
    method set_num_features (line 66) | def set_num_features(self, num_features):
    method is_full (line 75) | def is_full(self):
    method append (line 78) | def append(self, x):
    method append_torch (line 95) | def append_torch(self, x, num_gpus=1, rank=0):
    method get_all (line 107) | def get_all(self):
    method get_all_torch (line 111) | def get_all_torch(self):
    method get_mean_cov (line 114) | def get_mean_cov(self):
    method save (line 121) | def save(self, pkl_file):
    method load (line 126) | def load(pkl_file):
  class ProgressMonitor (line 135) | class ProgressMonitor:
    method __init__ (line 136) | def __init__(self, tag=None, num_items=None, flush_interval=1000, verb...
    method update (line 151) | def update(self, cur_items):
    method sub (line 166) | def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0,...
  function compute_feature_stats_for_dataset (line 180) | def compute_feature_stats_for_dataset(opts, detector_url, detector_kwarg...
  function compute_feature_stats_for_generator (line 232) | def compute_feature_stats_for_generator(opts, detector_url, detector_kwa...

FILE: metrics/perceptual_path_length.py
  function slerp (line 23) | def slerp(a, b, t):
  class PPLSampler (line 36) | class PPLSampler(torch.nn.Module):
    method __init__ (line 37) | def __init__(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16):
    method forward (line 49) | def forward(self, c):
  function compute_ppl (line 95) | def compute_ppl(opts, num_samples, epsilon, space, sampling, crop, batch...

FILE: metrics/precision_recall.py
  function compute_distances (line 19) | def compute_distances(row_features, col_features, num_gpus, rank, col_ba...
  function compute_pr (line 36) | def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_...

FILE: projector_z.py
  function is_image_file (line 34) | def is_image_file(filename):
  function make_dataset (line 38) | def make_dataset(dir):
  function project (line 49) | def project(
  function project_loop (line 159) | def project_loop(
  function subprocess_fn (line 218) | def subprocess_fn(rank, args, temp_dir):
  function main (line 243) | def main(

FILE: torch_utils/custom_ops.py
  function _find_compiler_bindir (line 28) | def _find_compiler_bindir():
  function get_plugin (line 46) | def get_plugin(module_name, sources, **build_kwargs):

FILE: torch_utils/misc.py
  function constant (line 22) | def constant(value, shape=None, dtype=None, device=None, memory_format=N...
  function nan_to_num (line 49) | def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): #...
  class suppress_tracer_warnings (line 69) | class suppress_tracer_warnings(warnings.catch_warnings):
    method __enter__ (line 70) | def __enter__(self):
  function assert_shape (line 80) | def assert_shape(tensor, ref_shape):
  function profiled_function (line 98) | def profiled_function(fn):
  class InfiniteSampler (line 109) | class InfiniteSampler(torch.utils.data.Sampler):
    method __init__ (line 110) | def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed...
    method __iter__ (line 123) | def __iter__(self):
  function params_and_buffers (line 145) | def params_and_buffers(module):
  function named_params_and_buffers (line 149) | def named_params_and_buffers(module):
  function copy_params_and_buffers (line 153) | def copy_params_and_buffers(src_module, dst_module, require_all=False):
  function ddp_sync (line 167) | def ddp_sync(module, sync):
  function check_ddp_consistency (line 178) | def check_ddp_consistency(module, ignore_regex=None):
  function print_module_summary (line 192) | def print_module_summary(module, inputs, max_nesting=3, skip_redundant=T...

FILE: torch_utils/ops/bias_act.cpp
  function has_same_layout (line 16) | static bool has_same_layout(torch::Tensor x, torch::Tensor y)
  function bias_act (line 32) | static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::T...
  function PYBIND11_MODULE (line 94) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)

FILE: torch_utils/ops/bias_act.h
  type bias_act_kernel_params (line 12) | struct bias_act_kernel_params

FILE: torch_utils/ops/bias_act.py
  function _init (line 41) | def _init():
  function bias_act (line 55) | def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clam...
  function _bias_act_ref (line 94) | def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None,...
  function _bias_act_cuda (line 129) | def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):

FILE: torch_utils/ops/conv2d_gradfix.py
  function no_weight_gradients (line 26) | def no_weight_gradients():
  function conv2d (line 35) | def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, gr...
  function conv_transpose2d (line 40) | def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, outp...
  function _should_use_custom_op (line 47) | def _should_use_custom_op(input):
  function _tuple_of_ints (line 58) | def _tuple_of_ints(xs, ndim):
  function _conv2d_gradfix (line 68) | def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_pad...

FILE: torch_utils/ops/conv2d_resample.py
  function _get_weight_shape (line 21) | def _get_weight_shape(w):
  function _conv2d_wrapper (line 29) | def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False...
  function conv2d_resample (line 59) | def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, fli...

FILE: torch_utils/ops/fma.py
  function fma (line 15) | def fma(a, b, c): # => a * b + c
  class _FusedMultiplyAdd (line 20) | class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
    method forward (line 22) | def forward(ctx, a, b, c): # pylint: disable=arguments-differ
    method backward (line 29) | def backward(ctx, dout): # pylint: disable=arguments-differ
  function _unbroadcast (line 49) | def _unbroadcast(x, shape):

FILE: torch_utils/ops/grid_sample_gradfix.py
  function grid_sample (line 27) | def grid_sample(input, grid):
  function _should_use_custom_op (line 34) | def _should_use_custom_op():
  class _GridSample2dForward (line 44) | class _GridSample2dForward(torch.autograd.Function):
    method forward (line 46) | def forward(ctx, input, grid):
    method backward (line 54) | def backward(ctx, grad_output):
  class _GridSample2dBackward (line 61) | class _GridSample2dBackward(torch.autograd.Function):
    method forward (line 63) | def forward(ctx, grad_output, input, grid):
    method backward (line 70) | def backward(ctx, grad2_grad_input, grad2_grad_grid):

FILE: torch_utils/ops/upfirdn2d.cpp
  function upfirdn2d (line 16) | static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx...
  function PYBIND11_MODULE (line 98) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)

FILE: torch_utils/ops/upfirdn2d.h
  type upfirdn2d_kernel_params (line 14) | struct upfirdn2d_kernel_params
  type upfirdn2d_kernel_spec (line 45) | struct upfirdn2d_kernel_spec

FILE: torch_utils/ops/upfirdn2d.py
  function _init (line 26) | def _init():
  function _parse_scaling (line 37) | def _parse_scaling(scaling):
  function _parse_padding (line 46) | def _parse_padding(padding):
  function _get_filter_size (line 57) | def _get_filter_size(f):
  function setup_filter (line 72) | def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_fil...
  function upfirdn2d (line 120) | def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, ...
  function _upfirdn2d_ref (line 169) | def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gai...
  function _upfirdn2d_cuda (line 214) | def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
  function filter2d (line 272) | def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
  function upsample2d (line 308) | def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='c...
  function downsample2d (line 347) | def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, imp...

FILE: torch_utils/persistence.py
  function persistent_class (line 35) | def persistent_class(orig_class):
  function is_persistent (line 134) | def is_persistent(obj):
  function import_hook (line 147) | def import_hook(hook):
  function _reconstruct_persistent_obj (line 179) | def _reconstruct_persistent_obj(meta):
  function _module_to_src (line 206) | def _module_to_src(module):
  function _src_to_module (line 216) | def _src_to_module(src):
  function _check_pickleable (line 231) | def _check_pickleable(obj):

FILE: torch_utils/training_stats.py
  function init_multiprocessing (line 34) | def init_multiprocessing(rank, sync_device):
  function report (line 56) | def report(name, value):
  function report0 (line 103) | def report0(name, value):
  class Collector (line 113) | class Collector:
    method __init__ (line 133) | def __init__(self, regex='.*', keep_previous=True):
    method names (line 141) | def names(self):
    method update (line 147) | def update(self):
    method _get_delta (line 170) | def _get_delta(self, name):
    method num (line 180) | def num(self, name):
    method mean (line 188) | def mean(self, name):
    method std (line 198) | def std(self, name):
    method as_dict (line 212) | def as_dict(self):
    method __getitem__ (line 226) | def __getitem__(self, name):
  function _sync (line 234) | def _sync(names):

FILE: train.py
  class UserError (line 27) | class UserError(Exception):
  function setup_training_loop_kwargs (line 32) | def setup_training_loop_kwargs(
  function subprocess_fn (line 420) | def subprocess_fn(rank, args, temp_dir):
  class CommaSeparatedList (line 444) | class CommaSeparatedList(click.ParamType):
    method convert (line 447) | def convert(self, value, param, ctx):
  function main (line 505) | def main(ctx, outdir, dry_run, **config_kwargs):

FILE: training/augment.py
  function matrix (line 43) | def matrix(*rows, device=None):
  function translate2d (line 53) | def translate2d(tx, ty, **kwargs):
  function translate3d (line 60) | def translate3d(tx, ty, tz, **kwargs):
  function scale2d (line 68) | def scale2d(sx, sy, **kwargs):
  function scale3d (line 75) | def scale3d(sx, sy, sz, **kwargs):
  function rotate2d (line 83) | def rotate2d(theta, **kwargs):
  function rotate3d (line 90) | def rotate3d(v, theta, **kwargs):
  function translate2d_inv (line 100) | def translate2d_inv(tx, ty, **kwargs):
  function scale2d_inv (line 103) | def scale2d_inv(sx, sy, **kwargs):
  function rotate2d_inv (line 106) | def rotate2d_inv(theta, **kwargs):
  class AugmentPipe (line 117) | class AugmentPipe(torch.nn.Module):
    method __init__ (line 118) | def __init__(self,
    method forward (line 181) | def forward(self, images, debug_percentile=None):

FILE: training/dataset.py
  class Dataset (line 24) | class Dataset(torch.utils.data.Dataset):
    method __init__ (line 25) | def __init__(self,
    method _get_raw_labels (line 51) | def _get_raw_labels(self):
    method close (line 64) | def close(self): # to be overridden by subclass
    method _load_raw_image (line 67) | def _load_raw_image(self, raw_idx): # to be overridden by subclass
    method _load_raw_labels (line 70) | def _load_raw_labels(self): # to be overridden by subclass
    method __getstate__ (line 73) | def __getstate__(self):
    method __del__ (line 76) | def __del__(self):
    method __len__ (line 82) | def __len__(self):
    method __getitem__ (line 85) | def __getitem__(self, idx):
    method get_label (line 95) | def get_label(self, idx):
    method get_details (line 103) | def get_details(self, idx):
    method name (line 111) | def name(self):
    method image_shape (line 115) | def image_shape(self):
    method num_channels (line 119) | def num_channels(self):
    method resolution (line 124) | def resolution(self):
    method label_shape (line 130) | def label_shape(self):
    method label_dim (line 140) | def label_dim(self):
    method has_labels (line 145) | def has_labels(self):
    method has_onehot_labels (line 149) | def has_onehot_labels(self):
  class ImageFolderDataset (line 154) | class ImageFolderDataset(Dataset):
    method __init__ (line 155) | def __init__(self,
    method _file_ext (line 184) | def _file_ext(fname):
    method _get_zipfile (line 187) | def _get_zipfile(self):
    method _open_file (line 193) | def _open_file(self, fname):
    method close (line 200) | def close(self):
    method __getstate__ (line 207) | def __getstate__(self):
    method _load_raw_image (line 210) | def _load_raw_image(self, raw_idx):
    method _load_raw_labels (line 222) | def _load_raw_labels(self):

FILE: training/loss.py
  class FMLoss (line 27) | class FMLoss(torch.nn.Module):
    method __init__ (line 31) | def __init__(self,
    method _select_features (line 55) | def _select_features(self, img, block_features, block_rgbs):
    method forward (line 68) | def forward(self, z, c, sync):
  class Loss (line 104) | class Loss:
    method accumulate_gradients (line 105) | def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, ...
  class StyleGAN2Loss (line 110) | class StyleGAN2Loss(Loss):
    method __init__ (line 111) | def __init__(self, device, G_mapping, G_synthesis, D, augment_pipe=Non...
    method run_G (line 142) | def run_G(self, z, c, sync):
    method run_D (line 154) | def run_D(self, img, c, sync):
    method accumulate_gradients (line 161) | def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, ...

FILE: training/networks.py
  function normalize_2nd_moment (line 21) | def normalize_2nd_moment(x, dim=1, eps=1e-8):
  function modulated_conv2d (line 27) | def modulated_conv2d(
  class FullyConnectedLayer (line 89) | class FullyConnectedLayer(torch.nn.Module):
    method __init__ (line 90) | def __init__(self,
    method forward (line 116) | def forward(self, x):
  class Conv2dLayer (line 134) | class Conv2dLayer(torch.nn.Module):
    method __init__ (line 135) | def __init__(self,
    method forward (line 171) | def forward(self, x, gain=1):
  class MappingNetwork (line 185) | class MappingNetwork(torch.nn.Module):
    method __init__ (line 186) | def __init__(self,
    method forward (line 226) | def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, skip...
  class SynthesisLayer (line 266) | class SynthesisLayer(torch.nn.Module):
    method __init__ (line 267) | def __init__(self,
    method forward (line 312) | def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=...
    method sample_noise (line 335) | def sample_noise(self):
  class ToRGBLayer (line 341) | class ToRGBLayer(torch.nn.Module):
    method __init__ (line 342) | def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, co...
    method forward (line 359) | def forward(self, x, w, fused_modconv=True):
  class SynthesisBlock (line 368) | class SynthesisBlock(torch.nn.Module):
    method __init__ (line 369) | def __init__(self,
    method forward (line 435) | def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, **...
    method sample_noise (line 477) | def sample_noise(self):
  class SynthesisNetwork (line 487) | class SynthesisNetwork(torch.nn.Module):
    method __init__ (line 488) | def __init__(self,
    method forward (line 522) | def forward(self, ws, return_features=False, **block_kwargs):
    method sample_noise (line 548) | def sample_noise(self):
  class Generator (line 556) | class Generator(torch.nn.Module):
    method __init__ (line 557) | def __init__(self,
    method forward (line 576) | def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, retu...
    method sample_noise (line 586) | def sample_noise(self):
  class DiscriminatorBlock (line 592) | class DiscriminatorBlock(torch.nn.Module):
    method __init__ (line 593) | def __init__(self,
    method forward (line 643) | def forward(self, x, img, force_fp32=False):
  class MinibatchStdLayer (line 676) | class MinibatchStdLayer(torch.nn.Module):
    method __init__ (line 677) | def __init__(self, group_size, num_channels=1):
    method forward (line 682) | def forward(self, x):
  class DiscriminatorEpilogue (line 702) | class DiscriminatorEpilogue(torch.nn.Module):
    method __init__ (line 703) | def __init__(self,
    method forward (line 729) | def forward(self, x, img, cmap, force_fp32=False):
  class Discriminator (line 760) | class Discriminator(torch.nn.Module):
    method __init__ (line 761) | def __init__(self,
    method forward (line 804) | def forward(self, img, c, **block_kwargs):

FILE: training/training_loop.py
  function setup_snapshot_image_grid (line 29) | def setup_snapshot_image_grid(training_set, random_seed=0):
  function save_image_grid (line 70) | def save_image_grid(img, fname, drange, grid_size):
  function training_loop (line 90) | def training_loop(

FILE: utils/dataset_tool.py
  function error (line 28) | def error(msg):
  function maybe_min (line 34) | def maybe_min(a: int, b: Optional[int]) -> int:
  function file_ext (line 41) | def file_ext(name: Union[str, Path]) -> str:
  function is_image_ext (line 46) | def is_image_ext(fname: Union[str, Path]) -> bool:
  function open_image_folder (line 52) | def open_image_folder(source_dir, *, max_images: Optional[int]):
  function open_image_zip (line 80) | def open_image_zip(source, *, max_images: Optional[int]):
  function open_lmdb (line 109) | def open_lmdb(lmdb_dir: str, *, max_images: Optional[int]):
  function open_cifar10 (line 137) | def open_cifar10(tarball: str, *, max_images: Optional[int]):
  function open_mnist (line 169) | def open_mnist(images_gz: str, *, max_images: Optional[int]):
  function make_transform (line 199) | def make_transform(
  function open_dataset (line 252) | def open_dataset(source, *, max_images: Optional[int]):
  function open_dest (line 272) | def open_dest(dest: str) -> Tuple[str, Callable[[str, Union[bytes, str]]...
  function convert_dataset (line 313) | def convert_dataset(

FILE: utils/style_mixing.py
  function num_range (line 25) | def num_range(s: str) -> List[int]:
  function generate_style_mix (line 45) | def generate_style_mix(