SYMBOL INDEX (681 symbols across 58 files)

FILE: FQ-BigGAN/BigGAN.py
  function G_arch (line 19) | def G_arch(ch=64, attention='64', ksize='333333', dilation='111111'):
  class Generator (line 54) | class Generator(nn.Module):
    method __init__ (line 55) | def __init__(self, G_ch=64, dim_z=128, bottom_width=4, resolution=128,
    method init_weights (line 214) | def init_weights(self):
    method forward (line 235) | def forward(self, z, y):
  function D_arch (line 260) | def D_arch(ch=64, attention='64',ksize='333333', dilation='111111'):
  class Discriminator (line 288) | class Discriminator(nn.Module):
    method __init__ (line 289) | def __init__(self, D_ch=64, D_wide=True, resolution=128,
    method init_weights (line 385) | def init_weights(self):
    method forward (line 402) | def forward(self, x, y=None):
  class G_D (line 430) | class G_D(nn.Module):
    method __init__ (line 431) | def __init__(self, G, D):
    method forward (line 436) | def forward(self, z, gy, x=None, dy=None, train_G=False, return_G_z=Fa...

FILE: FQ-BigGAN/BigGANdeep.py
  class GBlock (line 23) | class GBlock(nn.Module):
    method __init__ (line 24) | def __init__(self, in_channels, out_channels,
    method forward (line 48) | def forward(self, x, y):
  function G_arch (line 67) | def G_arch(ch=64, attention='64', ksize='333333', dilation='111111'):
  class Generator (line 96) | class Generator(nn.Module):
    method __init__ (line 97) | def __init__(self, G_ch=64, G_depth=2, dim_z=128, bottom_width=4, reso...
    method init_weights (line 243) | def init_weights(self):
    method forward (line 265) | def forward(self, z, y):
  class DBlock (line 283) | class DBlock(nn.Module):
    method __init__ (line 284) | def __init__(self, in_channels, out_channels, which_conv=layers.SNConv...
    method shortcut (line 308) | def shortcut(self, x):
    method forward (line 315) | def forward(self, x):
  function D_arch (line 331) | def D_arch(ch=64, attention='64',ksize='333333', dilation='111111'):
  class Discriminator (line 359) | class Discriminator(nn.Module):
    method __init__ (line 361) | def __init__(self, D_ch=64, D_wide=True, D_depth=2, resolution=128,
    method init_weights (line 458) | def init_weights(self):
    method forward (line 475) | def forward(self, x, y=None):
  class G_D (line 492) | class G_D(nn.Module):
    method __init__ (line 493) | def __init__(self, G, D):
    method forward (line 498) | def forward(self, z, gy, x=None, dy=None, train_G=False, return_G_z=Fa...

FILE: FQ-BigGAN/TFHub/biggan_v1.py
  function l2normalize (line 12) | def l2normalize(v, eps=1e-4):
  function truncated_z_sample (line 16) | def truncated_z_sample(batch_size, z_dim, truncation=0.5, seed=None):
  function denorm (line 22) | def denorm(x):
  class SpectralNorm (line 27) | class SpectralNorm(nn.Module):
    method __init__ (line 28) | def __init__(self, module, name='weight', power_iterations=1):
    method _update_u_v (line 36) | def _update_u_v(self):
    method _made_params (line 50) | def _made_params(self):
    method _make_params (line 59) | def _make_params(self):
    method forward (line 76) | def forward(self, *args):
  class SelfAttention (line 81) | class SelfAttention(nn.Module):
    method __init__ (line 84) | def __init__(self, in_dim, activation=F.relu):
    method forward (line 98) | def forward(self, x):
  class ConditionalBatchNorm2d (line 115) | class ConditionalBatchNorm2d(nn.Module):
    method __init__ (line 116) | def __init__(self, num_features, num_classes, eps=1e-4, momentum=0.1):
    method forward (line 123) | def forward(self, x, y):
  class GBlock (line 131) | class GBlock(nn.Module):
    method __init__ (line 132) | def __init__(
    method forward (line 168) | def forward(self, input, condition=None):
  class Generator128 (line 197) | class Generator128(nn.Module):
    method __init__ (line 198) | def __init__(self, code_dim=120, n_class=1000, chn=96, debug=False):
    method forward (line 226) | def forward(self, input, class_id):
  class Generator256 (line 244) | class Generator256(nn.Module):
    method __init__ (line 245) | def __init__(self, code_dim=140, n_class=1000, chn=96, debug=False):
    method forward (line 272) | def forward(self, input, class_id):
  class Generator512 (line 290) | class Generator512(nn.Module):
    method __init__ (line 291) | def __init__(self, code_dim=128, n_class=1000, chn=96, debug=False):
    method forward (line 321) | def forward(self, input, class_id):
  class Discriminator (line 339) | class Discriminator(nn.Module):
    method __init__ (line 340) | def __init__(self, n_class=1000, chn=96, debug=False):
    method forward (line 376) | def forward(self, input, class_id):

FILE: FQ-BigGAN/TFHub/converter.py
  function dump_tfhub_to_hdf5 (line 47) | def dump_tfhub_to_hdf5(module_path, hdf5_path, redownload=False):
  class TFHub2Pytorch (line 80) | class TFHub2Pytorch(object):
    method __init__ (line 97) | def __init__(self, state_dict, tf_weights, resolution=256, load_ema=Tr...
    method load (line 106) | def load(self):
    method load_generator (line 110) | def load_generator(self):
    method load_linear (line 125) | def load_linear(self, name_pth, name_tf, bias=True):
    method load_snlinear (line 130) | def load_snlinear(self, name_pth, name_tf, bias=True):
    method load_colorize (line 137) | def load_colorize(self, name_pth, name_tf):
    method load_GBlock (line 140) | def load_GBlock(self, name_pth, name_tf):
    method load_convs (line 144) | def load_convs(self, name_pth, name_tf):
    method load_snconv (line 149) | def load_snconv(self, name_pth, name_tf, bias=True):
    method load_conv (line 158) | def load_conv(self, name_pth, name_tf, bias=True):
    method load_HyperBNs (line 166) | def load_HyperBNs(self, name_pth, name_tf):
    method load_ScaledCrossReplicaBNs (line 170) | def load_ScaledCrossReplicaBNs(self, name_pth, name_tf):
    method load_HyperBN (line 178) | def load_HyperBN(self, name_pth, name_tf):
    method load_attention (line 197) | def load_attention(self, name_pth, name_tf):
    method load_tf_tensor (line 205) | def load_tf_tensor(self, prefix, var, device='0'):
  function convert_from_v1 (line 210) | def convert_from_v1(hub_dict, resolution=128):
  function get_config (line 289) | def get_config(resolution):
  function convert_biggan (line 310) | def convert_biggan(resolution, weight_dir, redownload=False, no_ema=Fals...
  function generate_sample (line 334) | def generate_sample(G, z_dim, batch_size, filename, parallel=False):
  function parse_args (line 348) | def parse_args():

FILE: FQ-BigGAN/calculate_inception_moments.py
  function prepare_parser (line 19) | def prepare_parser():
  function run (line 49) | def run(config):
  function main (line 82) | def main():

FILE: FQ-BigGAN/datasets.py
  function is_image_file (line 20) | def is_image_file(filename):
  function find_classes (line 33) | def find_classes(dir):
  function make_dataset (line 40) | def make_dataset(dir, class_to_idx):
  function pil_loader (line 58) | def pil_loader(path):
  function accimage_loader (line 65) | def accimage_loader(path):
  function default_loader (line 74) | def default_loader(path):
  class ImageFolder (line 82) | class ImageFolder(data.Dataset):
    method __init__ (line 107) | def __init__(self, root, transform=None, target_transform=None,
    method __getitem__ (line 143) | def __getitem__(self, index):
    method __len__ (line 166) | def __len__(self):
    method __repr__ (line 169) | def __repr__(self):
  class ILSVRC_HDF5 (line 184) | class ILSVRC_HDF5(data.Dataset):
    method __init__ (line 185) | def __init__(self, root, transform=None, target_transform=None,
    method __getitem__ (line 208) | def __getitem__(self, index):
    method __len__ (line 238) | def __len__(self):
  class CIFAR10 (line 243) | class CIFAR10(dset.CIFAR10):
    method __init__ (line 245) | def __init__(self, root, train=True,
    method __getitem__ (line 326) | def __getitem__(self, index):
    method __len__ (line 347) | def __len__(self):
  class CIFAR100 (line 351) | class CIFAR100(CIFAR10):

FILE: FQ-BigGAN/inception_tf13.py
  function prepare_parser (line 29) | def prepare_parser():
  function run (line 44) | def run(config):
  function main (line 130) | def main():

FILE: FQ-BigGAN/inception_utils.py
  class WrapInception (line 27) | class WrapInception(nn.Module):
    method __init__ (line 28) | def __init__(self, net):
    method forward (line 35) | def forward(self, x):
  function torch_cov (line 89) | def torch_cov(m, rowvar=False):
  function sqrt_newton_schulz (line 124) | def sqrt_newton_schulz(A, numIters, dtype=None):
  function numpy_calculate_frechet_distance (line 144) | def numpy_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
  function torch_calculate_frechet_distance (line 200) | def torch_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
  function calculate_inception_score (line 235) | def calculate_inception_score(pred, num_splits=10):
  function accumulate_inception_activations (line 248) | def accumulate_inception_activations(sample, net, num_inception_images=5...
  function load_inception_net (line 261) | def load_inception_net(parallel=False):
  function prepare_inception_metrics (line 274) | def prepare_inception_metrics(dataset, parallel, no_fid=False):

FILE: FQ-BigGAN/layers.py
  function proj (line 16) | def proj(x, y):
  function gram_schmidt (line 21) | def gram_schmidt(x, ys):
  function power_iteration (line 28) | def power_iteration(W, u_, update=True, eps=1e-12):
  class identity (line 54) | class identity(nn.Module):
    method forward (line 55) | def forward(self, input):
  class SN (line 60) | class SN(object):
    method __init__ (line 61) | def __init__(self, num_svs, num_itrs, num_outputs, transpose=False, ep...
    method u (line 77) | def u(self):
    method sv (line 83) | def sv(self):
    method W_ (line 87) | def W_(self):
  class SNConv2d (line 103) | class SNConv2d(nn.Conv2d, SN):
    method __init__ (line 104) | def __init__(self, in_channels, out_channels, kernel_size, stride=1,
    method forward (line 110) | def forward(self, x):
  class SNLinear (line 116) | class SNLinear(nn.Linear, SN):
    method __init__ (line 117) | def __init__(self, in_features, out_features, bias=True,
    method forward (line 121) | def forward(self, x):
  class SNEmbedding (line 128) | class SNEmbedding(nn.Embedding, SN):
    method __init__ (line 129) | def __init__(self, num_embeddings, embedding_dim, padding_idx=None,
    method forward (line 137) | def forward(self, x):
  class Attention (line 144) | class Attention(nn.Module):
    method __init__ (line 145) | def __init__(self, ch, which_conv=SNConv2d, name='attention'):
    method forward (line 156) | def forward(self, x, y=None):
  function fused_bn (line 173) | def fused_bn(x, mean, var, gain=None, bias=None, eps=1e-5):
  function manual_bn (line 191) | def manual_bn(x, gain=None, bias=None, return_mean_var=False, eps=1e-5):
  class myBN (line 212) | class myBN(nn.Module):
    method __init__ (line 213) | def __init__(self, num_channels, eps=1e-5, momentum=0.1):
    method reset_stats (line 229) | def reset_stats(self):
    method forward (line 234) | def forward(self, x, gain, bias):
  function groupnorm (line 259) | def groupnorm(x, norm_style):
  class ccbn (line 278) | class ccbn(nn.Module):
    method __init__ (line 279) | def __init__(self, output_size, input_size, which_linear, eps=1e-5, mo...
    method forward (line 306) | def forward(self, x, y):
    method extra_repr (line 327) | def extra_repr(self):
  class bn (line 334) | class bn(nn.Module):
    method __init__ (line 335) | def __init__(self, output_size, eps=1e-5, momentum=0.1,
    method forward (line 360) | def forward(self, x, y=None):
  class GBlock (line 376) | class GBlock(nn.Module):
    method __init__ (line 377) | def __init__(self, in_channels, out_channels,
    method forward (line 399) | def forward(self, x, y):
  class DBlock (line 413) | class DBlock(nn.Module):
    method __init__ (line 414) | def __init__(self, in_channels, out_channels, which_conv=SNConv2d, wid...
    method shortcut (line 432) | def shortcut(self, x):
    method forward (line 445) | def forward(self, x):

FILE: FQ-BigGAN/losses.py
  function loss_dcgan_dis (line 5) | def loss_dcgan_dis(dis_fake, dis_real):
  function loss_dcgan_gen (line 11) | def loss_dcgan_gen(dis_fake):
  function loss_hinge_dis (line 17) | def loss_hinge_dis(dis_fake, dis_real):
  function loss_hinge_gen (line 27) | def loss_hinge_gen(dis_fake):

FILE: FQ-BigGAN/make_hdf5.py
  function prepare_parser (line 20) | def prepare_parser():
  function run (line 45) | def run(config):
  function main (line 102) | def main():

FILE: FQ-BigGAN/sample.py
  function run (line 24) | def run(config):
  function main (line 174) | def main():

FILE: FQ-BigGAN/sync_batchnorm/batchnorm.py
  function _sum_ft (line 24) | def _sum_ft(tensor):
  function _unsqueeze_ft (line 29) | def _unsqueeze_ft(tensor):
  class _SynchronizedBatchNorm (line 38) | class _SynchronizedBatchNorm(_BatchNorm):
    method __init__ (line 39) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
    method forward (line 48) | def forward(self, input, gain=None, bias=None):
    method __data_parallel_replicate__ (line 110) | def __data_parallel_replicate__(self, ctx, copy_id):
    method _data_parallel_master (line 120) | def _data_parallel_master(self, intermediates):
    method _compute_mean_std (line 147) | def _compute_mean_std(self, sum_, ssum, size):
  class SynchronizedBatchNorm1d (line 162) | class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
    method _check_input_dim (line 218) | def _check_input_dim(self, input):
  class SynchronizedBatchNorm2d (line 225) | class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
    method _check_input_dim (line 281) | def _check_input_dim(self, input):
  class SynchronizedBatchNorm3d (line 288) | class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
    method _check_input_dim (line 345) | def _check_input_dim(self, input):

FILE: FQ-BigGAN/sync_batchnorm/batchnorm_reimpl.py
  class BatchNorm2dReimpl (line 18) | class BatchNorm2dReimpl(nn.Module):
    method __init__ (line 27) | def __init__(self, num_features, eps=1e-5, momentum=0.1):
    method reset_running_stats (line 39) | def reset_running_stats(self):
    method reset_parameters (line 43) | def reset_parameters(self):
    method forward (line 48) | def forward(self, input_):

FILE: FQ-BigGAN/sync_batchnorm/comm.py
  class FutureResult (line 18) | class FutureResult(object):
    method __init__ (line 21) | def __init__(self):
    method put (line 26) | def put(self, result):
    method get (line 32) | def get(self):
  class SlavePipe (line 46) | class SlavePipe(_SlavePipeBase):
    method run_slave (line 49) | def run_slave(self, msg):
  class SyncMaster (line 56) | class SyncMaster(object):
    method __init__ (line 67) | def __init__(self, master_callback):
    method __getstate__ (line 78) | def __getstate__(self):
    method __setstate__ (line 81) | def __setstate__(self, state):
    method register_slave (line 84) | def register_slave(self, identifier):
    method run_master (line 102) | def run_master(self, master_msg):
    method nr_slaves (line 136) | def nr_slaves(self):

FILE: FQ-BigGAN/sync_batchnorm/replicate.py
  class CallbackContext (line 23) | class CallbackContext(object):
  function execute_replication_callbacks (line 27) | def execute_replication_callbacks(modules):
  class DataParallelWithCallback (line 50) | class DataParallelWithCallback(DataParallel):
    method replicate (line 64) | def replicate(self, module, device_ids):
  function patch_replication_callback (line 70) | def patch_replication_callback(data_parallel):

FILE: FQ-BigGAN/sync_batchnorm/unittest.py
  class TorchTestCase (line 15) | class TorchTestCase(unittest.TestCase):
    method assertTensorClose (line 16) | def assertTensorClose(self, x, y):

FILE: FQ-BigGAN/train.py
  function run (line 34) | def run(config):
  function main (line 219) | def main():

FILE: FQ-BigGAN/train_fns.py
  function dummy_training_function (line 14) | def dummy_training_function():
  function GAN_training_function (line 19) | def GAN_training_function(G, D, GD, z_, y_, ema, state_dict, config):
  function save_and_sample (line 106) | def save_and_sample(G, D, G_ema, z_, y_, fixed_z, fixed_y,
  function test (line 169) | def test(G, D, G_ema, z_, y_, state_dict, config, sample, get_inception_...

FILE: FQ-BigGAN/utility/extract_imagenet.py
  function create_imagenet_ext (line 5) | def create_imagenet_ext(src_path, tgt_path, num_class=20):

FILE: FQ-BigGAN/utils.py
  function prepare_parser (line 30) | def prepare_parser():
  function add_sample_parser (line 379) | def add_sample_parser(parser):
  class CenterCropLongEdge (line 457) | class CenterCropLongEdge(object):
    method __call__ (line 464) | def __call__(self, img):
    method __repr__ (line 473) | def __repr__(self):
  class RandomCropLongEdge (line 476) | class RandomCropLongEdge(object):
    method __call__ (line 483) | def __call__(self, img):
    method __repr__ (line 498) | def __repr__(self):
  class MultiEpochSampler (line 504) | class MultiEpochSampler(torch.utils.data.Sampler):
    method __init__ (line 513) | def __init__(self, data_source, num_epochs, start_itr=0, batch_size=128):
    method __iter__ (line 524) | def __iter__(self):
    method __len__ (line 542) | def __len__(self):
  function get_data_loaders (line 547) | def get_data_loaders(dataset, data_root=None, augment=False, batch_size=64,
  function seed_rng (line 610) | def seed_rng(seed):
  function update_config_roots (line 618) | def update_config_roots(config):
  function prepare_root (line 627) | def prepare_root(config):
  class ema (line 637) | class ema(object):
    method __init__ (line 638) | def __init__(self, source, target, decay=0.9999, start_itr=0):
    method update (line 653) | def update(self, itr=None):
  function ortho (line 669) | def ortho(model, strength=1e-4, blacklist=[]):
  function default_ortho (line 684) | def default_ortho(model, strength=1e-4, blacklist=[]):
  function toggle_grad (line 697) | def toggle_grad(model, on_or_off):
  function join_strings (line 705) | def join_strings(base_string, strings):
  function save_weights (line 710) | def save_weights(G, D, state_dict, weights_root, experiment_name,
  function load_weights (line 735) | def load_weights(G, D, state_dict, weights_root, experiment_name,
  class MetricsLogger (line 767) | class MetricsLogger(object):
    method __init__ (line 768) | def __init__(self, fname, reinitialize=False):
    method log (line 776) | def log(self, record=None, **kwargs):
  class MyLogger (line 794) | class MyLogger(object):
    method __init__ (line 795) | def __init__(self, fname, reinitialize=False, logstyle='%3.3f'):
    method reinit (line 804) | def reinit(self, item):
    method log (line 816) | def log(self, itr, **kwargs):
  function write_metadata (line 834) | def write_metadata(logs_root, experiment_name, config, state_dict):
  function progress (line 849) | def progress(items, desc='', total=None, min_delay=0.1, displaytype='s1k'):
  function sample (line 888) | def sample(G, z_, y_, config):
  function sample_sheet (line 900) | def sample_sheet(G, classes_per_sheet, num_classes, samples_per_class, p...
  function interp (line 934) | def interp(x0, x1, num_midpoints):
  function interp_sheet (line 941) | def interp_sheet(G, num_per_sheet, num_midpoints, num_classes, parallel,
  function print_grad_norms (line 978) | def print_grad_norms(net):
  function get_SVs (line 991) | def get_SVs(net, prefix):
  function name_from_config (line 999) | def name_from_config(config):
  function hashname (line 1052) | def hashname(name):
  function query_gpu (line 1063) | def query_gpu(indices):
  function count_parameters (line 1068) | def count_parameters(module):
  function sample_1hot (line 1074) | def sample_1hot(batch_size, num_classes, device='cuda'):
  class Distribution (line 1086) | class Distribution(torch.Tensor):
    method init_distribution (line 1088) | def init_distribution(self, dist_type, **kwargs):
    method sample_ (line 1096) | def sample_(self):
    method to (line 1105) | def to(self, *args, **kwargs):
  function prepare_z_y (line 1113) | def prepare_z_y(G_batch_size, dim_z, nclasses, device='cuda',
  function initiate_standing_stats (line 1128) | def initiate_standing_stats(net):
  function accumulate_standing_stats (line 1135) | def accumulate_standing_stats(net, z, y, nclasses, num_accumulations=16):
  class Adam16 (line 1155) | class Adam16(Optimizer):
    method __init__ (line 1156) | def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,weigh...
    method load_state_dict (line 1163) | def load_state_dict(self, state_dict):
    method step (line 1171) | def step(self, closure=None):

FILE: FQ-BigGAN/vq_layer.py
  class Quantize (line 6) | class Quantize(nn.Module):
    method __init__ (line 7) | def __init__(self, dim, n_embed, commitment=1.0, decay=0.8, eps=1e-5):
    method forward (line 21) | def forward(self, x, y=None):
    method embed_code (line 56) | def embed_code(self, embed_id):

FILE: FQ-StyleGAN/dataset_tool.py
  function error (line 26) | def error(msg):
  class TFRecordExporter (line 32) | class TFRecordExporter:
    method __init__ (line 33) | def __init__(self, tfrecord_dir, expected_images, print_progress=True,...
    method close (line 50) | def close(self):
    method choose_shuffled_order (line 60) | def choose_shuffled_order(self): # Note: Images and labels must be add...
    method add_image (line 65) | def add_image(self, img):
    method add_labels (line 90) | def add_labels(self, labels):
    method __enter__ (line 97) | def __enter__(self):
    method __exit__ (line 100) | def __exit__(self, *args):
  class ExceptionInfo (line 105) | class ExceptionInfo(object):
    method __init__ (line 106) | def __init__(self):
  class WorkerThread (line 112) | class WorkerThread(threading.Thread):
    method __init__ (line 113) | def __init__(self, task_queue):
    method run (line 117) | def run(self):
  class ThreadPool (line 130) | class ThreadPool(object):
    method __init__ (line 131) | def __init__(self, num_threads):
    method add_task (line 141) | def add_task(self, func, args=()):
    method get_result (line 147) | def get_result(self, func): # returns (result, args)
    method finish (line 154) | def finish(self):
    method __enter__ (line 158) | def __enter__(self): # for 'with' statement
    method __exit__ (line 161) | def __exit__(self, *excinfo):
    method process_items_concurrently (line 164) | def process_items_concurrently(self, item_iterator, process_func=lambd...
  function display (line 192) | def display(tfrecord_dir):
  function extract (line 218) | def extract(tfrecord_dir, output_dir):
  function compare (line 245) | def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
  function create_mnist (line 288) | def create_mnist(tfrecord_dir, mnist_dir):
  function create_mnistrgb (line 312) | def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_...
  function create_cifar10 (line 329) | def create_cifar10(tfrecord_dir, cifar10_dir):
  function create_cifar100 (line 356) | def create_cifar100(tfrecord_dir, cifar100_dir):
  function create_svhn (line 378) | def create_svhn(tfrecord_dir, svhn_dir):
  function create_lsun (line 405) | def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
  function create_lsun_wide (line 438) | def create_lsun_wide(tfrecord_dir, lmdb_dir, width=512, height=384, max_...
  function create_celeba (line 483) | def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
  function create_from_images (line 502) | def create_from_images(tfrecord_dir, image_dir, shuffle):
  function create_from_hdf5 (line 530) | def create_from_hdf5(tfrecord_dir, hdf5_filename, shuffle):
  function execute_cmdline (line 545) | def execute_cmdline(argv):

FILE: FQ-StyleGAN/dnnlib/submission/internal/local.py
  class TargetOptions (line 7) | class TargetOptions():
    method __init__ (line 8) | def __init__(self):
  class Target (line 11) | class Target():
    method __init__ (line 12) | def __init__(self):
    method finalize_submit_config (line 15) | def finalize_submit_config(self, submit_config, host_run_dir):
    method submit (line 19) | def submit(self, submit_config, host_run_dir):

FILE: FQ-StyleGAN/dnnlib/submission/run_context.py
  class RunContext (line 23) | class RunContext(object):
    method __init__ (line 35) | def __init__(self, submit_config: submit.SubmitConfig, config_module: ...
    method __enter__ (line 57) | def __enter__(self) -> "RunContext":
    method __exit__ (line 60) | def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> N...
    method update (line 63) | 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:
    method get (line 106) | def get():

FILE: FQ-StyleGAN/dnnlib/submission/submit.py
  class SubmitTarget (line 29) | class SubmitTarget(Enum):
  class PathType (line 37) | class PathType(Enum):
  class PlatformExtras (line 49) | class PlatformExtras:
    method __init__ (line 57) | def __init__(self):
  class SubmitConfig (line 64) | class SubmitConfig(util.EasyDict):
    method __init__ (line 87) | def __init__(self):
  function get_path_from_template (line 116) | def get_path_from_template(path_template: str, path_type: PathType = Pat...
  function get_template_from_path (line 138) | def get_template_from_path(path: str) -> str:
  function convert_path (line 144) | def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:
  function set_user_name_override (line 151) | def set_user_name_override(name: str) -> None:
  function get_user_name (line 157) | def get_user_name():
  function make_run_dir_path (line 173) | def make_run_dir_path(*paths):
  function _create_run_dir_local (line 192) | def _create_run_dir_local(submit_config: SubmitConfig) -> str:
  function _get_next_run_id_local (line 211) | def _get_next_run_id_local(run_dir_root: str) -> int:
  function _populate_run_dir (line 227) | def _populate_run_dir(submit_config: SubmitConfig, run_dir: str) -> None:
  function run_wrapper (line 256) | def run_wrapper(submit_config: SubmitConfig) -> None:
  function submit_run (line 310) | def submit_run(submit_config: SubmitConfig, run_func_name: str, **run_fu...

FILE: FQ-StyleGAN/dnnlib/tflib/autosummary.py
  function _create_var (line 45) | def _create_var(name: str, value_expr: TfExpression) -> TfExpression:
  function autosummary (line 77) | def autosummary(name: str, value: TfExpressionEx, passthru: TfExpression...
  function finalize_autosummaries (line 118) | def finalize_autosummaries() -> None:
  function save_summaries (line 177) | def save_summaries(file_writer, global_step=None):

FILE: FQ-StyleGAN/dnnlib/tflib/custom_ops.py
  function _find_compiler_bindir (line 36) | def _find_compiler_bindir():
  function _get_compute_cap (line 42) | def _get_compute_cap(device):
  function _get_cuda_gpu_arch_string (line 49) | def _get_cuda_gpu_arch_string():
  function _run_cmd (line 56) | def _run_cmd(cmd):
  function _prepare_nvcc_cli (line 63) | def _prepare_nvcc_cli(opts):
  function get_plugin (line 87) | def get_plugin(cuda_file):

FILE: FQ-StyleGAN/dnnlib/tflib/network.py
  function import_handler (line 29) | def import_handler(handler_func):
  class Network (line 35) | class Network:
    method __init__ (line 73) | def __init__(self, name: str = None, func_name: Any = None, **static_k...
    method _init_fields (line 100) | def _init_fields(self) -> None:
    method _init_graph (line 125) | def _init_graph(self) -> None:
    method reset_own_vars (line 187) | def reset_own_vars(self) -> None:
    method reset_vars (line 191) | def reset_vars(self) -> None:
    method reset_trainables (line 195) | def reset_trainables(self) -> None:
    method get_output_for (line 199) | def get_output_for(self, *in_expr: TfExpression, return_as_list: bool ...
    method get_var_local_name (line 234) | def get_var_local_name(self, var_or_global_name: Union[TfExpression, s...
    method find_var (line 240) | def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfE...
    method get_var (line 245) | def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.n...
    method set_var (line 250) | def set_var(self, var_or_local_name: Union[TfExpression, str], new_val...
    method __getstate__ (line 255) | def __getstate__(self) -> dict:
    method __setstate__ (line 267) | def __setstate__(self, state: dict) -> None:
    method clone (line 301) | def clone(self, name: str = None, **new_static_kwargs) -> "Network":
    method copy_own_vars_from (line 316) | def copy_own_vars_from(self, src_net: "Network") -> None:
    method copy_vars_from (line 321) | def copy_vars_from(self, src_net: "Network") -> None:
    method copy_trainables_from (line 326) | def copy_trainables_from(self, src_net: "Network") -> None:
    method convert (line 331) | def convert(self, new_func_name: str, new_name: str = None, **new_stat...
    method setup_as_moving_average_of (line 341) | def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpre...
    method run (line 353) | def run(self,
    method list_ops (line 455) | def list_ops(self) -> List[TfExpression]:
    method list_layers (line 463) | def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpressi...
    method print_layers (line 506) | def print_layers(self, title: str = None, hide_layers_with_no_params: ...
    method setup_weight_histograms (line 535) | def setup_weight_histograms(self, title: str = None) -> None:
  function _handle_legacy_output_transforms (line 555) | def _handle_legacy_output_transforms(output_transform, dynamic_kwargs):
  function _legacy_output_transform_func (line 575) | def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_s...

FILE: FQ-StyleGAN/dnnlib/tflib/ops/fused_bias_act.py
  function _get_plugin (line 15) | def _get_plugin():
  function fused_bias_act (line 34) | def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=Non...
  function _fused_bias_act_ref (line 72) | def _fused_bias_act_ref(x, b, axis, act, alpha, gain):
  function _fused_bias_act_cuda (line 100) | def _fused_bias_act_cuda(x, b, axis, act, alpha, gain):

FILE: FQ-StyleGAN/dnnlib/tflib/ops/upfirdn_2d.py
  function _get_plugin (line 14) | def _get_plugin():
  function upfirdn_2d (line 19) | def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, p...
  function _upfirdn_2d_ref (line 66) | def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, p...
  function _upfirdn_2d_cuda (line 105) | def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, ...
  function filter_2d (line 144) | def filter_2d(x, k, gain=1, data_format='NCHW', impl='cuda'):
  function upsample_2d (line 169) | def upsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='c...
  function downsample_2d (line 202) | def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl=...
  function upsample_conv_2d (line 234) | def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW',...
  function conv_downsample_2d (line 296) | def conv_downsample_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW...
  function _shape (line 337) | def _shape(tf_expr, dim_idx):
  function _setup_kernel (line 344) | def _setup_kernel(k):
  function _simple_upfirdn_2d (line 353) | def _simple_upfirdn_2d(x, k, up=1, down=1, pad0=0, pad1=0, data_format='...

FILE: FQ-StyleGAN/dnnlib/tflib/optimizer.py
  class Optimizer (line 28) | class Optimizer:
    method __init__ (line 40) | def __init__(self,
    method _get_device (line 84) | def _get_device(self, device_name: str):
    method register_gradients (line 114) | def register_gradients(self, loss: TfExpression, trainable_vars: Union...
    method apply_updates (line 156) | def apply_updates(self, allow_no_op: bool = False) -> tf.Operation:
    method reset_optimizer_state (line 266) | def reset_optimizer_state(self) -> None:
    method get_loss_scaling_var (line 271) | def get_loss_scaling_var(self, device: str) -> Union[tf.Variable, None]:
    method apply_loss_scaling (line 275) | def apply_loss_scaling(self, value: TfExpression) -> TfExpression:
    method undo_loss_scaling (line 282) | def undo_loss_scaling(self, value: TfExpression) -> TfExpression:
  class SimpleAdam (line 290) | class SimpleAdam:
    method __init__ (line 293) | def __init__(self, name="Adam", learning_rate=0.001, beta1=0.9, beta2=...
    method variables (line 301) | def variables(self):
    method compute_gradients (line 304) | def compute_gradients(self, loss, var_list, gate_gradients=tf.train.Op...
    method apply_gradients (line 308) | def apply_gradients(self, grads_and_vars):

FILE: FQ-StyleGAN/dnnlib/tflib/tfutil.py
  function run (line 28) | def run(*args, **kwargs) -> Any:
  function is_tf_expression (line 34) | def is_tf_expression(x: Any) -> bool:
  function shape_to_list (line 39) | def shape_to_list(shape: Iterable[tf.Dimension]) -> List[Union[int, None]]:
  function flatten (line 44) | def flatten(x: TfExpressionEx) -> TfExpression:
  function log2 (line 50) | def log2(x: TfExpressionEx) -> TfExpression:
  function exp2 (line 56) | def exp2(x: TfExpressionEx) -> TfExpression:
  function lerp (line 62) | def lerp(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfE...
  function lerp_clip (line 68) | def lerp_clip(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -...
  function absolute_name_scope (line 74) | def absolute_name_scope(scope: str) -> tf.name_scope:
  function absolute_variable_scope (line 79) | def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope:
  function _sanitize_tf_config (line 84) | def _sanitize_tf_config(config_dict: dict = None) -> dict:
  function init_tf (line 107) | def init_tf(config_dict: dict = None) -> None:
  function assert_tf_initialized (line 135) | def assert_tf_initialized():
  function create_session (line 141) | def create_session(config_dict: dict = None, force_as_default: bool = Fa...
  function init_uninitialized_vars (line 164) | def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None:
  function set_vars (line 194) | def set_vars(var_to_value_dict: dict) -> None:
  function create_var_with_large_initial_value (line 220) | def create_var_with_large_initial_value(initial_value: np.ndarray, *args...
  function convert_images_from_uint8 (line 230) | def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
  function convert_images_to_uint8 (line 240) | def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, s...

FILE: FQ-StyleGAN/dnnlib/util.py
  class EasyDict (line 35) | class EasyDict(dict):
    method __getattr__ (line 38) | def __getattr__(self, name: str) -> Any:
    method __setattr__ (line 44) | def __setattr__(self, name: str, value: Any) -> None:
    method __delattr__ (line 47) | def __delattr__(self, name: str) -> None:
  class Logger (line 51) | class Logger(object):
    method __init__ (line 54) | def __init__(self, file_name: str = None, file_mode: str = "w", should...
    method __enter__ (line 67) | def __enter__(self) -> "Logger":
    method __exit__ (line 70) | def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> N...
    method write (line 73) | def write(self, text: str) -> None:
    method flush (line 86) | def flush(self) -> None:
    method close (line 93) | def close(self) -> None:
  function format_time (line 111) | def format_time(seconds: Union[int, float]) -> str:
  function ask_yes_no (line 125) | def ask_yes_no(question: str) -> bool:
  function tuple_product (line 135) | def tuple_product(t: Tuple) -> Any:
  function get_dtype_and_ctype (line 159) | def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
  function is_pickleable (line 182) | def is_pickleable(obj: Any) -> bool:
  function get_module_from_obj_name (line 194) | def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, s...
  function get_obj_from_module (line 235) | def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
  function get_obj_by_name (line 245) | def get_obj_by_name(name: str) -> Any:
  function call_func_by_name (line 251) | def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
  function get_module_dir_by_obj_name (line 259) | def get_module_dir_by_obj_name(obj_name: str) -> str:
  function is_top_level_function (line 265) | def is_top_level_function(obj: Any) -> bool:
  function get_top_level_function_name (line 270) | def get_top_level_function_name(obj: Any) -> str:
  function list_dir_recursively_with_ignore (line 279) | def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] =...
  function copy_files_and_create_dirs (line 312) | def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
  function is_url (line 328) | def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
  function open_url (line 346) | def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, ve...

FILE: FQ-StyleGAN/metrics/frechet_inception_distance.py
  class FID (line 20) | class FID(metric_base.MetricBase):
    method __init__ (line 21) | def __init__(self, num_images, minibatch_per_gpu, **kwargs):
    method _evaluate (line 26) | def _evaluate(self, Gs, Gs_kwargs, num_gpus):

FILE: FQ-StyleGAN/metrics/inception_score.py
  class IS (line 18) | class IS(metric_base.MetricBase):
    method __init__ (line 19) | def __init__(self, num_images, num_splits, minibatch_per_gpu, **kwargs):
    method _evaluate (line 25) | def _evaluate(self, Gs, Gs_kwargs, num_gpus):

FILE: FQ-StyleGAN/metrics/linear_separability.py
  function prob_normalize (line 65) | def prob_normalize(p):
  function mutual_information (line 70) | def mutual_information(p):
  function entropy (line 84) | def entropy(p):
  function conditional_entropy (line 94) | def conditional_entropy(p):
  class LS (line 103) | class LS(metric_base.MetricBase):
    method __init__ (line 104) | def __init__(self, num_samples, num_keep, attrib_indices, minibatch_pe...
    method _evaluate (line 112) | def _evaluate(self, Gs, Gs_kwargs, num_gpus):

FILE: FQ-StyleGAN/metrics/metric_base.py
  class MetricBase (line 23) | class MetricBase:
    method __init__ (line 24) | def __init__(self, name):
    method close (line 34) | def close(self):
    method _reset (line 37) | def _reset(self, network_pkl=None, run_dir=None, data_dir=None, datase...
    method configure_progress_reports (line 55) | def configure_progress_reports(self, plo, phi, pmax, psec=15):
    method run (line 61) | def run(self, network_pkl, run_dir=None, data_dir=None, dataset_args=N...
    method get_result_str (line 79) | def get_result_str(self):
    method update_autosummaries (line 90) | def update_autosummaries(self):
    method _evaluate (line 94) | def _evaluate(self, Gs, Gs_kwargs, num_gpus):
    method _report_result (line 97) | def _report_result(self, value, suffix='', fmt='%-10.4f'):
    method _report_progress (line 100) | def _report_progress(self, pcur, pmax, status_str=''):
    method _get_cache_file_for_reals (line 110) | def _get_cache_file_for_reals(self, extension='pkl', **kwargs):
    method _get_dataset_obj (line 119) | def _get_dataset_obj(self):
    method _iterate_reals (line 124) | def _iterate_reals(self, minibatch_size):
    method _iterate_fakes (line 132) | def _iterate_fakes(self, Gs, minibatch_size, num_gpus):
    method _get_random_labels_tf (line 139) | def _get_random_labels_tf(self, minibatch_size):
  class MetricGroup (line 145) | class MetricGroup:
    method __init__ (line 146) | def __init__(self, metric_kwarg_list):
    method run (line 149) | def run(self, *args, **kwargs):
    method get_result_str (line 153) | def get_result_str(self):
    method update_autosummaries (line 156) | def update_autosummaries(self):
  class DummyMetric (line 163) | class DummyMetric(MetricBase):
    method _evaluate (line 164) | def _evaluate(self, Gs, Gs_kwargs, num_gpus):

FILE: FQ-StyleGAN/metrics/perceptual_path_length.py
  function normalize (line 19) | def normalize(v):
  function slerp (line 23) | def slerp(a, b, t):
  class PPL (line 34) | class PPL(metric_base.MetricBase):
    method __init__ (line 35) | def __init__(self, num_samples, epsilon, space, sampling, crop, miniba...
    method _evaluate (line 47) | def _evaluate(self, Gs, Gs_kwargs, num_gpus):

FILE: FQ-StyleGAN/metrics/precision_recall.py
  function batch_pairwise_distances (line 20) | def batch_pairwise_distances(U, V):
  class DistanceBlock (line 38) | class DistanceBlock():
    method __init__ (line 40) | def __init__(self, num_features, num_gpus):
    method pairwise_distances (line 55) | def pairwise_distances(self, U, V):
  class ManifoldEstimator (line 61) | class ManifoldEstimator():
    method __init__ (line 63) | def __init__(self, distance_block, features, row_batch_size, col_batch...
    method evaluate (line 96) | def evaluate(self, eval_features, return_realism=False, return_neighbo...
  function knn_precision_recall_features (line 138) | def knn_precision_recall_features(ref_features, eval_features, feature_n...
  class PR (line 171) | class PR(metric_base.MetricBase):
    method __init__ (line 172) | def __init__(self, num_images, nhood_size, minibatch_per_gpu, row_batc...
    method _evaluate (line 180) | def _evaluate(self, Gs, Gs_kwargs, num_gpus):

FILE: FQ-StyleGAN/pretrained_networks.py
  function get_path_or_url (line 57) | def get_path_or_url(path_or_gdrive_path):
  function load_networks (line 64) | def load_networks(path_or_gdrive_path):

FILE: FQ-StyleGAN/projector.py
  class Projector (line 16) | class Projector:
    method __init__ (line 17) | def __init__(self):
    method _info (line 50) | def _info(self, *args):
    method set_network (line 54) | def set_network(self, Gs, minibatch_size=1):
    method run (line 134) | def run(self, target_images):
    method start (line 147) | def start(self, target_images):
    method step (line 167) | def step(self):
    method get_cur_step (line 194) | def get_cur_step(self):
    method get_dlatents (line 197) | def get_dlatents(self):
    method get_noises (line 200) | def get_noises(self):
    method get_images (line 203) | def get_images(self):

FILE: FQ-StyleGAN/run_generator.py
  function generate_images (line 19) | def generate_images(network_pkl, seeds, truncation_psi):
  function style_mixing_example (line 40) | def style_mixing_example(network_pkl, row_seeds, col_seeds, truncation_p...
  function _parse_num_range (line 90) | def _parse_num_range(s):
  function main (line 119) | def main():

FILE: FQ-StyleGAN/run_metrics.py
  function run (line 20) | def run(network_pkl, metrics, dataset, data_dir, mirror_augment):
  function _str_to_bool (line 31) | def _str_to_bool(v):
  function main (line 52) | def main():

FILE: FQ-StyleGAN/run_projector.py
  function project_image (line 21) | def project_image(proj, targets, png_prefix, num_snapshots):
  function project_generated_images (line 34) | def project_generated_images(network_pkl, seeds, num_snapshots, truncati...
  function project_real_images (line 55) | def project_real_images(network_pkl, dataset_name, data_dir, num_images,...
  function _parse_num_range (line 73) | def _parse_num_range(s):
  function main (line 97) | def main():

FILE: FQ-StyleGAN/run_training.py
  function run (line 36) | def run(dataset, data_dir, result_dir, config_id, num_gpus, total_kimg, ...
  function _str_to_bool (line 134) | def _str_to_bool(v):
  function _parse_comma_sep (line 144) | def _parse_comma_sep(s):
  function main (line 166) | def main():

FILE: FQ-StyleGAN/training/dataset.py
  class TFRecordDataset (line 19) | class TFRecordDataset:
    method __init__ (line 20) | def __init__(self,
    method close (line 122) | def close(self):
    method configure (line 126) | def configure(self, minibatch_size, lod=0):
    method get_minibatch_tf (line 135) | def get_minibatch_tf(self): # => images, labels
    method get_minibatch_np (line 139) | def get_minibatch_np(self, minibatch_size, lod=0): # => images, labels
    method get_random_labels_tf (line 147) | def get_random_labels_tf(self, minibatch_size): # => labels
    method get_random_labels_np (line 155) | def get_random_labels_np(self, minibatch_size): # => labels
    method parse_tfrecord_tf (line 162) | def parse_tfrecord_tf(record):
    method parse_tfrecord_np (line 171) | def parse_tfrecord_np(record):
  function load_dataset (line 181) | def load_dataset(class_name=None, data_dir=None, verbose=False, **kwargs):

FILE: FQ-StyleGAN/training/loss.py
  function G_logistic (line 18) | def G_logistic(G, D, opt, training_set, minibatch_size):
  function G_logistic_ns (line 27) | def G_logistic_ns(G, D, opt, training_set, minibatch_size):
  function D_logistic (line 36) | def D_logistic(G, D, opt, training_set, minibatch_size, reals, labels):
  function D_logistic_r1 (line 52) | def D_logistic_r1(G, D, opt, training_set, minibatch_size, reals, labels...
  function D_logistic_r2 (line 85) | def D_logistic_r2(G, D, opt, training_set, minibatch_size, reals, labels...
  function G_wgan (line 107) | def G_wgan(G, D, opt, training_set, minibatch_size):
  function D_wgan (line 116) | def D_wgan(G, D, opt, training_set, minibatch_size, reals, labels, wgan_...
  function D_wgan_gp (line 134) | def D_wgan_gp(G, D, opt, training_set, minibatch_size, reals, labels, wg...
  function G_logistic_ns_pathreg (line 163) | def G_logistic_ns_pathreg(G, D, opt, training_set, minibatch_size, pl_mi...

FILE: FQ-StyleGAN/training/misc.py
  function open_file_or_url (line 20) | def open_file_or_url(file_or_url):
  function load_pkl (line 25) | def load_pkl(file_or_url):
  function save_pkl (line 29) | def save_pkl(obj, filename):
  function adjust_dynamic_range (line 36) | def adjust_dynamic_range(data, drange_in, drange_out):
  function create_image_grid (line 43) | def create_image_grid(images, grid_size=None):
  function convert_to_pil_image (line 60) | def convert_to_pil_image(image, drange=[0,1]):
  function save_image_grid (line 73) | def save_image_grid(images, filename, drange=[0,1], grid_size=None):
  function apply_mirror_augment (line 76) | def apply_mirror_augment(minibatch):
  function parse_config_for_previous_run (line 85) | def parse_config_for_previous_run(run_dir):
  function setup_snapshot_image_grid (line 95) | def setup_snapshot_image_grid(training_set,

FILE: FQ-StyleGAN/training/networks_stylegan.py
  function _blur2d (line 21) | def _blur2d(x, f=[1,2,1], normalize=True, flip=False, stride=1):
  function _upscale2d (line 50) | def _upscale2d(x, factor=2, gain=1):
  function _downscale2d (line 69) | def _downscale2d(x, factor=2, gain=1):
  function blur2d (line 95) | def blur2d(x, f=[1,2,1], normalize=True):
  function upscale2d (line 107) | def upscale2d(x, factor=2):
  function downscale2d (line 119) | def downscale2d(x, factor=2):
  function get_weight (line 134) | def get_weight(shape, gain=np.sqrt(2), use_wscale=False, lrmul=1):
  function dense (line 153) | def dense(x, fmaps, **kwargs):
  function conv2d (line 163) | def conv2d(x, fmaps, kernel, **kwargs):
  function upscale2d_conv2d (line 173) | def upscale2d_conv2d(x, fmaps, kernel, fused_scale='auto', **kwargs):
  function conv2d_downscale2d (line 192) | def conv2d_downscale2d(x, fmaps, kernel, fused_scale='auto', **kwargs):
  function apply_bias (line 212) | def apply_bias(x, lrmul=1):
  function leaky_relu (line 222) | def leaky_relu(x, alpha=0.2):
  function pixel_norm (line 238) | def pixel_norm(x, epsilon=1e-8):
  function instance_norm (line 246) | def instance_norm(x, epsilon=1e-8):
  function style_mod (line 260) | def style_mod(x, dlatent, **kwargs):
  function apply_noise (line 269) | def apply_noise(x, noise_var=None, randomize_noise=True):
  function minibatch_stddev_layer (line 282) | def minibatch_stddev_layer(x, group_size=4, num_new_features=1):
  function G_style (line 301) | def G_style(
  function G_mapping (line 383) | def G_mapping(
  function G_synthesis (line 439) | def G_synthesis(
  function D_basic (line 563) | def D_basic(

FILE: FQ-StyleGAN/training/networks_stylegan2.py
  function get_weight (line 22) | def get_weight(shape, gain=1, use_wscale=True, lrmul=1, weight_var='weig...
  function dense_layer (line 41) | def dense_layer(x, fmaps, gain=1, use_wscale=True, lrmul=1, weight_var='...
  function conv2d_layer (line 51) | def conv2d_layer(x, fmaps, kernel, up=False, down=False, resample_kernel...
  function apply_bias_act (line 66) | def apply_bias_act(x, act='linear', alpha=None, gain=None, lrmul=1, bias...
  function naive_upsample_2d (line 73) | def naive_upsample_2d(x, factor=2):
  function naive_downsample_2d (line 80) | def naive_downsample_2d(x, factor=2):
  function modulated_conv2d_layer (line 89) | def modulated_conv2d_layer(x, y, fmaps, kernel, up=False, down=False, de...
  function minibatch_stddev_layer (line 132) | def minibatch_stddev_layer(x, group_size=4, num_new_features=1):
  function G_main (line 151) | def G_main(
  function G_mapping (line 251) | def G_mapping(
  function G_synthesis_stylegan_revised (line 307) | def G_synthesis_stylegan_revised(
  function G_synthesis_stylegan2 (line 417) | def G_synthesis_stylegan2(
  function VectorQuantizerEMA (line 512) | def VectorQuantizerEMA(inputs, is_training=True, embedding_dim=512,
  function D_stylegan (line 593) | def D_stylegan(
  function D_stylegan2 (line 694) | def D_stylegan2(
  function D_stylegan2_quant (line 782) | def D_stylegan2_quant(

FILE: FQ-StyleGAN/training/training_loop.py
  function process_reals (line 22) | def process_reals(x, labels, lod, mirror_augment, drange_data, drange_net):
  function training_schedule (line 47) | def training_schedule(
  function training_loop (line 105) | def training_loop(

FILE: FQ-U-GAT-IT/UGATIT.py
  class UGATIT (line 12) | class UGATIT(object) :
    method __init__ (line 13) | def __init__(self, sess, args):
    method model_dir (line 127) | def model_dir(self):
    method generator (line 162) | def generator(self, x_init, reuse=False, scope="generator"):
    method MLP (line 220) | def MLP(self, x, use_bias=True, reuse=False, scope='MLP'):
    method discriminator (line 243) | def discriminator(self, x_init, reuse=False, scope="discriminator"):
    method discriminator_global (line 256) | def discriminator_global(self, x_init, reuse=False, scope='discriminat...
    method discriminator_local (line 297) | def discriminator_local(self, x_init, reuse=False, scope='discriminato...
    method generate_a2b (line 338) | def generate_a2b(self, x_A, reuse=False):
    method generate_b2a (line 343) | def generate_b2a(self, x_B, reuse=False):
    method discriminate_real (line 348) | def discriminate_real(self, x_A, x_B):
    method discriminate_fake (line 357) | def discriminate_fake(self, x_ba, x_ab):
    method gradient_panalty (line 367) | def gradient_panalty(self, real, fake, scope="discriminator_A"):
    method build_model (line 410) | def build_model(self):
    method train (line 564) | def train(self):
    method save (line 653) | def save(self, checkpoint_dir, step):
    method load (line 667) | def load(self, checkpoint_dir):
    method test (line 682) | def test(self, epoch):

FILE: FQ-U-GAT-IT/logger.py
  class Logger (line 3) | class Logger(object):
    method __init__ (line 4) | def __init__(self, output_file):
    method write (line 8) | def write(self, message):
    method flush (line 12) | def flush(self):

FILE: FQ-U-GAT-IT/main.py
  function parse_args (line 8) | def parse_args():
  function check_args (line 66) | def check_args(args):
  function main (line 99) | def main():

FILE: FQ-U-GAT-IT/ops.py
  function conv (line 16) | def conv(x, channels, kernel=4, stride=2, pad=0, pad_type='zero', use_bi...
  function fully_connected_with_w (line 54) | def fully_connected_with_w(x, use_bias=True, sn=False, reuse=False, scop...
  function fully_connected (line 82) | def fully_connected(x, units, use_bias=True, sn=False, scope='linear'):
  function flatten (line 104) | def flatten(x) :
  function resblock (line 111) | def resblock(x_init, channels, use_bias=True, scope='resblock_0'):
  function adaptive_ins_layer_resblock (line 124) | def adaptive_ins_layer_resblock(x_init, channels, gamma, beta, use_bias=...
  function up_sample (line 142) | def up_sample(x, scale_factor=2):
  function global_avg_pooling (line 148) | def global_avg_pooling(x):
  function global_max_pooling (line 152) | def global_max_pooling(x):
  function lrelu (line 160) | def lrelu(x, alpha=0.01):
  function relu (line 165) | def relu(x):
  function tanh (line 169) | def tanh(x):
  function sigmoid (line 172) | def sigmoid(x) :
  function adaptive_instance_layer_norm (line 179) | def adaptive_instance_layer_norm(x, gamma, beta, smoothing=True, scope='...
  function instance_norm (line 202) | def instance_norm(x, scope='instance_norm'):
  function layer_norm (line 208) | def layer_norm(x, scope='layer_norm') :
  function layer_instance_norm (line 213) | def layer_instance_norm(x, scope='layer_instance_norm') :
  function spectral_norm (line 235) | def spectral_norm(w, iteration=1):
  function L1_loss (line 270) | def L1_loss(x, y):
  function cam_loss (line 275) | def cam_loss(source, non_source) :
  function regularization_loss (line 284) | def regularization_loss(scope_name) :
  function discriminator_loss (line 300) | def discriminator_loss(loss_func, real, fake):
  function generator_loss (line 326) | def generator_loss(loss_func, fake):

FILE: FQ-U-GAT-IT/utils.py
  class ImageData (line 7) | class ImageData:
    method __init__ (line 9) | def __init__(self, load_size, channels, augment_flag):
    method image_processing (line 14) | def image_processing(self, filename):
  function load_test_data (line 28) | def load_test_data(image_path, size=256):
  function augmentation (line 39) | def augmentation(image, augment_size):
  function save_images (line 47) | def save_images(images, size, image_path):
  function inverse_transform (line 50) | def inverse_transform(images):
  function imsave (line 54) | def imsave(images, size, path):
  function merge (line 60) | def merge(images, size):
  function show_all_variables (line 70) | def show_all_variables():
  function check_folder (line 74) | def check_folder(log_dir):
  function str2bool (line 79) | def str2bool(x):

FILE: FQ-U-GAT-IT/vq_layer.py
  class VectorQuantizerEMA (line 4) | class VectorQuantizerEMA:
    method __init__ (line 17) | def __init__(self, embedding_dim, num_embeddings, commitment_cost, decay,
    method __call__ (line 27) | def __call__(self, inputs, reuse=False, layer=None, is_training=True):
    method embeddings (line 111) | def embeddings(self):
    method quantize (line 114) | def quantize(self, encoding_indices):