SYMBOL INDEX (279 symbols across 23 files)

FILE: dataset_tool.py
  function error (line 27) | def error(msg):
  class TFRecordExporter (line 33) | class TFRecordExporter:
    method __init__ (line 34) | def __init__(self, tfrecord_dir, expected_images, print_progress=True,...
    method close (line 51) | def close(self):
    method choose_shuffled_order (line 61) | def choose_shuffled_order(self): # Note: Images and labels must be add...
    method add_image (line 66) | def add_image(self, img):
    method add_labels (line 91) | def add_labels(self, labels):
    method __enter__ (line 98) | def __enter__(self):
    method __exit__ (line 101) | def __exit__(self, *args):
  class ExceptionInfo (line 106) | class ExceptionInfo(object):
    method __init__ (line 107) | def __init__(self):
  class WorkerThread (line 113) | class WorkerThread(threading.Thread):
    method __init__ (line 114) | def __init__(self, task_queue):
    method run (line 118) | def run(self):
  class ThreadPool (line 131) | class ThreadPool(object):
    method __init__ (line 132) | def __init__(self, num_threads):
    method add_task (line 142) | def add_task(self, func, args=()):
    method get_result (line 148) | def get_result(self, func): # returns (result, args)
    method finish (line 155) | def finish(self):
    method __enter__ (line 159) | def __enter__(self): # for 'with' statement
    method __exit__ (line 162) | def __exit__(self, *excinfo):
    method process_items_concurrently (line 165) | def process_items_concurrently(self, item_iterator, process_func=lambd...
  function display (line 193) | def display(tfrecord_dir):
  function extract (line 219) | def extract(tfrecord_dir, output_dir):
  function compare (line 246) | def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
  function create_mnist (line 289) | def create_mnist(tfrecord_dir, mnist_dir):
  function create_mnistrgb (line 313) | def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_...
  function create_cifar10 (line 330) | def create_cifar10(tfrecord_dir, cifar10_dir):
  function create_cifar100 (line 357) | def create_cifar100(tfrecord_dir, cifar100_dir):
  function create_svhn (line 379) | def create_svhn(tfrecord_dir, svhn_dir):
  function create_lsun (line 406) | def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
  function create_lsun_wide (line 439) | def create_lsun_wide(tfrecord_dir, lmdb_dir, width=512, height=384, max_...
  function create_celeba (line 484) | def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
  function create_from_images (line 503) | def create_from_images(tfrecord_dir, image_dir, shuffle):
  function create_from_hdf5 (line 531) | def create_from_hdf5(tfrecord_dir, hdf5_filename, shuffle):
  function execute_cmdline (line 546) | def execute_cmdline(argv):

FILE: dnnlib/submission/_internal/run.py
  function main (line 22) | def main():

FILE: dnnlib/submission/run_context.py
  class RunContext (line 22) | class RunContext(object):
    method __init__ (line 35) | def __init__(self, submit_config: submit.SubmitConfig, config_module: ...
    method __enter__ (line 55) | def __enter__(self) -> "RunContext":
    method __exit__ (line 58) | def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> N...
    method update (line 61) | def update(self, loss: Any = 0, cur_epoch: Any = 0, max_epoch: Any = N...
    method should_stop (line 74) | def should_stop(self) -> bool:
    method get_time_since_start (line 78) | def get_time_since_start(self) -> float:
    method get_time_since_last_update (line 82) | def get_time_since_last_update(self) -> float:
    method get_last_update_interval (line 86) | def get_last_update_interval(self) -> float:
    method close (line 90) | def close(self) -> None:

FILE: dnnlib/submission/submit.py
  class SubmitTarget (line 30) | class SubmitTarget(Enum):
  class PathType (line 38) | class PathType(Enum):
  class SubmitConfig (line 53) | class SubmitConfig(util.EasyDict):
    method __init__ (line 75) | def __init__(self):
  function get_path_from_template (line 101) | def get_path_from_template(path_template: str, path_type: PathType = Pat...
  function get_template_from_path (line 123) | def get_template_from_path(path: str) -> str:
  function convert_path (line 130) | def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:
  function set_user_name_override (line 137) | def set_user_name_override(name: str) -> None:
  function get_user_name (line 143) | def get_user_name():
  function _create_run_dir_local (line 159) | def _create_run_dir_local(submit_config: SubmitConfig) -> str:
  function _get_next_run_id_local (line 180) | def _get_next_run_id_local(run_dir_root: str) -> int:
  function _populate_run_dir (line 196) | def _populate_run_dir(run_dir: str, submit_config: SubmitConfig) -> None:
  function run_wrapper (line 224) | def run_wrapper(submit_config: SubmitConfig) -> None:
  function submit_run (line 263) | def submit_run(submit_config: SubmitConfig, run_func_name: str, **run_fu...

FILE: dnnlib/tflib/autosummary.py
  function _create_var (line 42) | def _create_var(name: str, value_expr: TfExpression) -> TfExpression:
  function autosummary (line 74) | def autosummary(name: str, value: TfExpressionEx, passthru: TfExpression...
  function finalize_autosummaries (line 112) | def finalize_autosummaries() -> None:
  function save_summaries (line 170) | def save_summaries(file_writer, global_step=None):

FILE: dnnlib/tflib/network.py
  function import_handler (line 30) | def import_handler(handler_func):
  class Network (line 36) | class Network:
    method __init__ (line 74) | def __init__(self, name: str = None, func_name: Any = None, **static_k...
    method _init_fields (line 101) | def _init_fields(self) -> None:
    method _init_graph (line 126) | def _init_graph(self) -> None:
    method reset_own_vars (line 188) | def reset_own_vars(self) -> None:
    method reset_vars (line 192) | def reset_vars(self) -> None:
    method reset_trainables (line 196) | def reset_trainables(self) -> None:
    method get_output_for (line 200) | def get_output_for(self, *in_expr: TfExpression, return_as_list: bool ...
    method get_var_local_name (line 235) | def get_var_local_name(self, var_or_global_name: Union[TfExpression, s...
    method find_var (line 241) | def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfE...
    method get_var (line 246) | def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.n...
    method set_var (line 251) | def set_var(self, var_or_local_name: Union[TfExpression, str], new_val...
    method __getstate__ (line 256) | def __getstate__(self) -> dict:
    method __setstate__ (line 268) | def __setstate__(self, state: dict) -> None:
    method clone (line 302) | def clone(self, name: str = None, **new_static_kwargs) -> "Network":
    method copy_own_vars_from (line 317) | def copy_own_vars_from(self, src_net: "Network") -> None:
    method copy_vars_from (line 322) | def copy_vars_from(self, src_net: "Network") -> None:
    method copy_trainables_from (line 327) | def copy_trainables_from(self, src_net: "Network") -> None:
    method convert (line 332) | def convert(self, new_func_name: str, new_name: str = None, **new_stat...
    method setup_as_moving_average_of (line 342) | def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpre...
    method run (line 354) | def run(self,
    method list_ops (line 456) | def list_ops(self) -> List[TfExpression]:
    method list_layers (line 464) | def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpressi...
    method print_layers (line 507) | def print_layers(self, title: str = None, hide_layers_with_no_params: ...
    method setup_weight_histograms (line 536) | def setup_weight_histograms(self, title: str = None) -> None:
  function _handle_legacy_output_transforms (line 556) | def _handle_legacy_output_transforms(output_transform, dynamic_kwargs):
  function _legacy_output_transform_func (line 576) | def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_s...

FILE: dnnlib/tflib/optimizer.py
  class Optimizer (line 29) | class Optimizer:
    method __init__ (line 40) | def __init__(self,
    method register_gradients (line 67) | def register_gradients(self, loss: TfExpression, trainable_vars: Union...
    method apply_updates (line 102) | def apply_updates(self) -> tf.Operation:
    method reset_optimizer_state (line 182) | def reset_optimizer_state(self) -> None:
    method get_loss_scaling_var (line 187) | def get_loss_scaling_var(self, device: str) -> Union[tf.Variable, None]:
    method apply_loss_scaling (line 198) | def apply_loss_scaling(self, value: TfExpression) -> TfExpression:
    method undo_loss_scaling (line 207) | def undo_loss_scaling(self, value: TfExpression) -> TfExpression:

FILE: dnnlib/tflib/tfutil.py
  function run (line 23) | def run(*args, **kwargs) -> Any:
  function is_tf_expression (line 29) | def is_tf_expression(x: Any) -> bool:
  function shape_to_list (line 34) | def shape_to_list(shape: Iterable[tf.Dimension]) -> List[Union[int, None]]:
  function flatten (line 39) | def flatten(x: TfExpressionEx) -> TfExpression:
  function log2 (line 45) | def log2(x: TfExpressionEx) -> TfExpression:
  function exp2 (line 51) | def exp2(x: TfExpressionEx) -> TfExpression:
  function lerp (line 57) | def lerp(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfE...
  function lerp_clip (line 63) | def lerp_clip(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -...
  function absolute_name_scope (line 69) | def absolute_name_scope(scope: str) -> tf.name_scope:
  function absolute_variable_scope (line 74) | def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope:
  function _sanitize_tf_config (line 79) | def _sanitize_tf_config(config_dict: dict = None) -> dict:
  function init_tf (line 94) | def init_tf(config_dict: dict = None) -> None:
  function assert_tf_initialized (line 122) | def assert_tf_initialized():
  function create_session (line 128) | def create_session(config_dict: dict = None, force_as_default: bool = Fa...
  function init_uninitialized_vars (line 152) | def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None:
  function set_vars (line 182) | def set_vars(var_to_value_dict: dict) -> None:
  function create_var_with_large_initial_value (line 208) | def create_var_with_large_initial_value(initial_value: np.ndarray, *args...
  function convert_images_from_uint8 (line 218) | def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
  function convert_images_to_uint8 (line 228) | def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, s...

FILE: dnnlib/util.py
  class EasyDict (line 36) | class EasyDict(dict):
    method __getattr__ (line 39) | def __getattr__(self, name: str) -> Any:
    method __setattr__ (line 45) | def __setattr__(self, name: str, value: Any) -> None:
    method __delattr__ (line 48) | def __delattr__(self, name: str) -> None:
  class Logger (line 52) | class Logger(object):
    method __init__ (line 55) | def __init__(self, file_name: str = None, file_mode: str = "w", should...
    method __enter__ (line 68) | def __enter__(self) -> "Logger":
    method __exit__ (line 71) | def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> N...
    method write (line 74) | def write(self, text: str) -> None:
    method flush (line 87) | def flush(self) -> None:
    method close (line 94) | def close(self) -> None:
  function format_time (line 112) | def format_time(seconds: Union[int, float]) -> str:
  function ask_yes_no (line 126) | def ask_yes_no(question: str) -> bool:
  function tuple_product (line 136) | def tuple_product(t: Tuple) -> Any:
  function get_dtype_and_ctype (line 160) | def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
  function is_pickleable (line 183) | def is_pickleable(obj: Any) -> bool:
  function get_module_from_obj_name (line 195) | def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, s...
  function get_obj_from_module (line 236) | def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
  function get_obj_by_name (line 246) | def get_obj_by_name(name: str) -> Any:
  function call_func_by_name (line 252) | def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
  function get_module_dir_by_obj_name (line 260) | def get_module_dir_by_obj_name(obj_name: str) -> str:
  function is_top_level_function (line 266) | def is_top_level_function(obj: Any) -> bool:
  function get_top_level_function_name (line 271) | def get_top_level_function_name(obj: Any) -> str:
  function list_dir_recursively_with_ignore (line 280) | def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] =...
  function copy_files_and_create_dirs (line 313) | def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
  function is_url (line 329) | def is_url(obj: Any) -> bool:
  function open_url (line 345) | def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, ve...

FILE: generate_figures.py
  function load_Gs (line 31) | def load_Gs(url):
  function draw_uncurated_result_figure (line 41) | def draw_uncurated_result_figure(png, Gs, cx, cy, cw, ch, rows, lods, se...
  function draw_style_mixing_figure (line 59) | def draw_style_mixing_figure(png, Gs, w, h, src_seeds, dst_seeds, style_...
  function draw_noise_detail_figure (line 83) | def draw_noise_detail_figure(png, Gs, w, h, num_samples, seeds):
  function draw_noise_components_figure (line 103) | def draw_noise_components_figure(png, Gs, w, h, seeds, noise_ranges, fli...
  function draw_truncation_trick_figure (line 127) | def draw_truncation_trick_figure(png, Gs, w, h, seeds, psis):
  function main (line 144) | def main():

FILE: metrics/frechet_inception_distance.py
  class FID (line 21) | class FID(metric_base.MetricBase):
    method __init__ (line 22) | def __init__(self, num_images, minibatch_per_gpu, **kwargs):
    method _evaluate (line 27) | def _evaluate(self, Gs, num_gpus):

FILE: metrics/linear_separability.py
  function prob_normalize (line 66) | def prob_normalize(p):
  function mutual_information (line 71) | def mutual_information(p):
  function entropy (line 85) | def entropy(p):
  function conditional_entropy (line 95) | def conditional_entropy(p):
  class LS (line 104) | class LS(metric_base.MetricBase):
    method __init__ (line 105) | def __init__(self, num_samples, num_keep, attrib_indices, minibatch_pe...
    method _evaluate (line 113) | def _evaluate(self, Gs, num_gpus):

FILE: metrics/metric_base.py
  class MetricBase (line 36) | class MetricBase:
    method __init__ (line 37) | def __init__(self, name):
    method run (line 45) | def run(self, network_pkl, run_dir=None, dataset_args=None, mirror_aug...
    method get_result_str (line 72) | def get_result_str(self):
    method update_autosummaries (line 83) | def update_autosummaries(self):
    method _evaluate (line 87) | def _evaluate(self, Gs, num_gpus):
    method _report_result (line 90) | def _report_result(self, value, suffix='', fmt='%-10.4f'):
    method _get_cache_file_for_reals (line 93) | def _get_cache_file_for_reals(self, extension='pkl', **kwargs):
    method _iterate_reals (line 101) | def _iterate_reals(self, minibatch_size):
    method _iterate_fakes (line 109) | def _iterate_fakes(self, Gs, minibatch_size, num_gpus):
  class MetricGroup (line 119) | class MetricGroup:
    method __init__ (line 120) | def __init__(self, metric_kwarg_list):
    method run (line 123) | def run(self, *args, **kwargs):
    method get_result_str (line 127) | def get_result_str(self):
    method update_autosummaries (line 130) | def update_autosummaries(self):
  class DummyMetric (line 137) | class DummyMetric(MetricBase):
    method _evaluate (line 138) | def _evaluate(self, Gs, num_gpus):

FILE: metrics/perceptual_path_length.py
  function normalize (line 20) | def normalize(v):
  function slerp (line 24) | def slerp(a, b, t):
  class PPL (line 35) | class PPL(metric_base.MetricBase):
    method __init__ (line 36) | def __init__(self, num_samples, epsilon, space, sampling, minibatch_pe...
    method _evaluate (line 46) | def _evaluate(self, Gs, num_gpus):

FILE: pretrained_example.py
  function main (line 18) | def main():

FILE: run_metrics.py
  function run_pickle (line 20) | def run_pickle(submit_config, metric_args, network_pkl, dataset_args, mi...
  function run_snapshot (line 32) | def run_snapshot(submit_config, metric_args, run_id, snapshot):
  function run_all_snapshots (line 46) | def run_all_snapshots(submit_config, metric_args, run_id):
  function main (line 62) | def main():

FILE: train.py
  function main (line 177) | def main():

FILE: training/dataset.py
  function parse_tfrecord_tf (line 20) | def parse_tfrecord_tf(record):
  function parse_tfrecord_np (line 27) | def parse_tfrecord_np(record):
  class TFRecordDataset (line 37) | class TFRecordDataset:
    method __init__ (line 38) | def __init__(self,
    method configure (line 136) | def configure(self, minibatch_size, lod=0):
    method get_minibatch_tf (line 145) | def get_minibatch_tf(self): # => images, labels
    method get_minibatch_np (line 149) | def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
    method get_random_labels_tf (line 156) | def get_random_labels_tf(self, minibatch_size): # => labels
    method get_random_labels_np (line 163) | def get_random_labels_np(self, minibatch_size): # => labels
  class SyntheticDataset (line 171) | class SyntheticDataset:
    method __init__ (line 172) | def __init__(self, resolution=1024, num_channels=3, dtype='uint8', dyn...
    method configure (line 190) | def configure(self, minibatch_size, lod=0):
    method get_minibatch_tf (line 195) | def get_minibatch_tf(self): # => images, labels
    method get_minibatch_np (line 203) | def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
    method get_random_labels_tf (line 209) | def get_random_labels_tf(self, minibatch_size): # => labels
    method get_random_labels_np (line 213) | def get_random_labels_np(self, minibatch_size): # => labels
    method _generate_images (line 219) | def _generate_images(self, minibatch, lod, shape): # to be overridden ...
    method _generate_labels (line 222) | def _generate_labels(self, minibatch): # to be overridden by subclasses
  function load_dataset (line 228) | def load_dataset(class_name='training.dataset.TFRecordDataset', data_dir...

FILE: training/loss.py
  function fp32 (line 17) | def fp32(*values):
  function G_wgan (line 26) | def G_wgan(G, D, opt, training_set, minibatch_size): # pylint: disable=u...
  function D_wgan (line 34) | def D_wgan(G, D, opt, training_set, minibatch_size, reals, labels, # pyl...
  function D_wgan_gp (line 50) | def D_wgan_gp(G, D, opt, training_set, minibatch_size, reals, labels, # ...
  function D_hinge (line 83) | def D_hinge(G, D, opt, training_set, minibatch_size, reals, labels): # p...
  function D_hinge_gp (line 93) | def D_hinge_gp(G, D, opt, training_set, minibatch_size, reals, labels, #...
  function G_logistic_saturating (line 123) | def G_logistic_saturating(G, D, opt, training_set, minibatch_size): # py...
  function G_logistic_nonsaturating (line 131) | def G_logistic_nonsaturating(G, D, opt, training_set, minibatch_size): #...
  function D_logistic (line 139) | def D_logistic(G, D, opt, training_set, minibatch_size, reals, labels): ...
  function D_logistic_simplegp (line 150) | def D_logistic_simplegp(G, D, opt, training_set, minibatch_size, reals, ...

FILE: training/misc.py
  function open_file_or_url (line 26) | def open_file_or_url(file_or_url):
  function load_pkl (line 31) | def load_pkl(file_or_url):
  function save_pkl (line 35) | def save_pkl(obj, filename):
  function adjust_dynamic_range (line 42) | def adjust_dynamic_range(data, drange_in, drange_out):
  function create_image_grid (line 49) | def create_image_grid(images, grid_size=None):
  function convert_to_pil_image (line 66) | def convert_to_pil_image(image, drange=[0,1]):
  function save_image (line 79) | def save_image(image, filename, drange=[0,1], quality=95):
  function save_image_grid (line 86) | def save_image_grid(images, filename, drange=[0,1], grid_size=None):
  function locate_run_dir (line 92) | def locate_run_dir(run_id_or_run_dir):
  function list_network_pkls (line 113) | def list_network_pkls(run_id_or_run_dir, include_final=True):
  function locate_network_pkl (line 122) | def locate_network_pkl(run_id_or_run_dir_or_network_pkl, snapshot_or_net...
  function get_id_string_for_network_pkl (line 145) | def get_id_string_for_network_pkl(network_pkl):
  function load_network_pkl (line 152) | def load_network_pkl(run_id_or_run_dir_or_network_pkl, snapshot_or_netwo...
  function parse_config_for_previous_run (line 155) | def parse_config_for_previous_run(run_id):
  function load_dataset_for_previous_run (line 180) | def load_dataset_for_previous_run(run_id, **kwargs): # => dataset_obj, m...
  function apply_mirror_augment (line 187) | def apply_mirror_augment(minibatch):
  function setup_snapshot_image_grid (line 197) | def setup_snapshot_image_grid(G, training_set,

FILE: training/networks_progan.py
  function lerp (line 18) | def lerp(a, b, t): return a + (b - a) * t
  function lerp_clip (line 19) | def lerp_clip(a, b, t): return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
  function cset (line 20) | def cset(cur_lambda, new_cond, new_lambda): return lambda: tf.cond(new_c...
  function get_weight (line 25) | def get_weight(shape, gain=np.sqrt(2), use_wscale=False):
  function dense (line 38) | def dense(x, fmaps, gain=np.sqrt(2), use_wscale=False):
  function conv2d (line 48) | def conv2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
  function apply_bias (line 57) | def apply_bias(x):
  function leaky_relu (line 67) | def leaky_relu(x, alpha=0.2):
  function upscale2d (line 75) | def upscale2d(x, factor=2):
  function upscale2d_conv2d (line 89) | def upscale2d_conv2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
  function downscale2d (line 102) | def downscale2d(x, factor=2):
  function conv2d_downscale2d (line 113) | def conv2d_downscale2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=Fal...
  function pixel_norm (line 124) | def pixel_norm(x, epsilon=1e-8):
  function minibatch_stddev_layer (line 131) | def minibatch_stddev_layer(x, group_size=4, num_new_features=1):
  function G_paper (line 149) | def G_paper(
  function D_paper (line 238) | def D_paper(

FILE: training/networks_stylegan.py
  function _blur2d (line 22) | def _blur2d(x, f=[1,2,1], normalize=True, flip=False, stride=1):
  function _upscale2d (line 51) | def _upscale2d(x, factor=2, gain=1):
  function _downscale2d (line 70) | def _downscale2d(x, factor=2, gain=1):
  function blur2d (line 96) | def blur2d(x, f=[1,2,1], normalize=True):
  function upscale2d (line 108) | def upscale2d(x, factor=2):
  function downscale2d (line 120) | def downscale2d(x, factor=2):
  function get_weight (line 135) | def get_weight(shape, gain=np.sqrt(2), use_wscale=False, lrmul=1):
  function dense (line 154) | def dense(x, fmaps, **kwargs):
  function conv2d (line 164) | def conv2d(x, fmaps, kernel, **kwargs):
  function upscale2d_conv2d (line 174) | def upscale2d_conv2d(x, fmaps, kernel, fused_scale='auto', **kwargs):
  function conv2d_downscale2d (line 193) | def conv2d_downscale2d(x, fmaps, kernel, fused_scale='auto', **kwargs):
  function apply_bias (line 213) | def apply_bias(x, lrmul=1):
  function leaky_relu (line 223) | def leaky_relu(x, alpha=0.2):
  function pixel_norm (line 239) | def pixel_norm(x, epsilon=1e-8):
  function instance_norm (line 247) | def instance_norm(x, epsilon=1e-8):
  function style_mod (line 261) | def style_mod(x, dlatent, **kwargs):
  function apply_noise (line 270) | def apply_noise(x, noise_var=None, randomize_noise=True):
  function minibatch_stddev_layer (line 283) | def minibatch_stddev_layer(x, group_size=4, num_new_features=1):
  function G_style (line 302) | def G_style(
  function G_mapping (line 384) | def G_mapping(
  function G_synthesis (line 440) | def G_synthesis(
  function D_basic (line 564) | def D_basic(

FILE: training/training_loop.py
  function process_reals (line 26) | def process_reals(x, lod, mirror_augment, drange_data, drange_net):
  function training_schedule (line 55) | def training_schedule(
  function training_loop (line 112) | def training_loop(