SYMBOL INDEX (200 symbols across 21 files)

FILE: data/__init__.py
  function find_dataset_using_name (line 18) | def find_dataset_using_name(dataset_name):
  function get_option_setter (line 41) | def get_option_setter(dataset_name):
  function create_dataset (line 47) | def create_dataset(opt):
  class CustomDatasetDataLoader (line 62) | class CustomDatasetDataLoader():
    method __init__ (line 65) | def __init__(self, opt):
    method set_epoch (line 83) | def set_epoch(self, epoch):
    method load_data (line 86) | def load_data(self):
    method __len__ (line 89) | def __len__(self):
    method __iter__ (line 93) | def __iter__(self):

FILE: data/base_dataset.py
  class BaseDataset (line 13) | class BaseDataset(data.Dataset, ABC):
    method __init__ (line 23) | def __init__(self, opt):
    method modify_commandline_options (line 34) | def modify_commandline_options(parser, is_train):
    method __len__ (line 47) | def __len__(self):
    method __getitem__ (line 52) | def __getitem__(self, index):
  function get_params (line 64) | def get_params(opt, size):
  function get_transform (line 82) | def get_transform(opt, params=None, grayscale=False, method=Image.BICUBI...
  function __make_power_2 (line 134) | def __make_power_2(img, base, method=Image.BICUBIC):
  function __random_zoom (line 144) | def __random_zoom(img, target_width, crop_width, method=Image.BICUBIC, f...
  function __scale_shortside (line 156) | def __scale_shortside(img, target_width, crop_width, method=Image.BICUBIC):
  function __trim (line 166) | def __trim(img, trim_width):
  function __scale_width (line 183) | def __scale_width(img, target_width, crop_width, method=Image.BICUBIC):
  function __crop (line 192) | def __crop(img, pos, size):
  function __patch (line 201) | def __patch(img, index, size):
  function __flip (line 217) | def __flip(img, flip):
  function __print_size_warning (line 223) | def __print_size_warning(ow, oh, w, h):

FILE: data/image_folder.py
  function is_image_file (line 20) | def is_image_file(filename):
  function make_dataset (line 24) | def make_dataset(dir, max_dataset_size=float("inf")):
  function default_loader (line 36) | def default_loader(path):
  class ImageFolder (line 40) | class ImageFolder(data.Dataset):
    method __init__ (line 42) | def __init__(self, root, transform=None, return_paths=False,
    method __getitem__ (line 55) | def __getitem__(self, index):
    method __len__ (line 65) | def __len__(self):

FILE: data/unaligned_dataset.py
  class UnalignedDataset (line 9) | class UnalignedDataset(BaseDataset):
    method __init__ (line 20) | def __init__(self, opt):
    method __getitem__ (line 39) | def __getitem__(self, index):
    method __len__ (line 72) | def __len__(self):

FILE: experiments/__init__.py
  function find_launcher_using_name (line 5) | def find_launcher_using_name(launcher_name):

FILE: experiments/__main__.py
  function find_launcher_using_name (line 5) | def find_launcher_using_name(launcher_name):

FILE: models/MSP.py
  class StyleExtractor (line 9) | class StyleExtractor(nn.Module):
    method __init__ (line 12) | def __init__(self, encoder, gpu_ids = []):
    method encode_with_intermediate (line 57) | def encode_with_intermediate(self, input):
    method forward (line 64) | def forward(self, input, index):
  class Projector (line 80) | class Projector(nn.Module):
    method __init__ (line 81) | def __init__(self, projector, gpu_ids = []):
    method forward (line 137) | def forward(self, input, index):
  function make_layers (line 151) | def make_layers(cfg, batch_norm=True):
  class InfoNCELoss (line 169) | class InfoNCELoss(nn.Module):
    method __init__ (line 171) | def __init__(self, temperature, feature_dim, queue_size):
    method forward (line 219) | def forward(self, query, key, style = 'real'):
    method dequeue_and_enqueue (line 265) | def dequeue_and_enqueue(self, keys, style = 'real'):

FILE: models/__init__.py
  function find_model_using_name (line 25) | def find_model_using_name(model_name):
  function get_option_setter (line 48) | def get_option_setter(model_name):
  function create_model (line 54) | def create_model(opt):

FILE: models/base_model.py
  class BaseModel (line 8) | class BaseModel(ABC):
    method __init__ (line 18) | def __init__(self, opt):
    method dict_grad_hook_factory (line 47) | def dict_grad_hook_factory(add_func=lambda x: x):
    method modify_commandline_options (line 58) | def modify_commandline_options(parser, is_train):
    method set_input (line 71) | def set_input(self, input):
    method forward (line 80) | def forward(self):
    method optimize_parameters (line 85) | def optimize_parameters(self):
    method setup (line 89) | def setup(self, opt):
    method parallelize (line 103) | def parallelize(self):
    method data_dependent_initialize (line 109) | def data_dependent_initialize(self, data):
    method eval (line 112) | def eval(self):
    method test (line 119) | def test(self):
    method compute_visuals (line 129) | def compute_visuals(self):
    method get_image_paths (line 133) | def get_image_paths(self):
    method update_learning_rate (line 137) | def update_learning_rate(self):
    method get_current_visuals (line 148) | def get_current_visuals(self):
    method get_current_losses (line 156) | def get_current_losses(self):
    method save_networks (line 164) | def save_networks(self, epoch):
    method __patch_instance_norm_state_dict (line 182) | def __patch_instance_norm_state_dict(self, state_dict, module, keys, i...
    method load_networks (line 196) | def load_networks(self, epoch):
    method print_networks (line 226) | def print_networks(self, verbose):
    method set_requires_grad (line 244) | def set_requires_grad(self, nets, requires_grad=False):
    method generate_visuals_for_evaluation (line 257) | def generate_visuals_for_evaluation(self, data, mode):

FILE: models/cast_model.py
  class CASTModel (line 13) | class CASTModel(BaseModel):
    method modify_commandline_options (line 19) | def modify_commandline_options(parser, is_train=True):
    method __init__ (line 47) | def __init__(self, opt):
    method optimize_parameters (line 133) | def optimize_parameters(self):
    method set_input (line 164) | def set_input(self, input):
    method forward (line 175) | def forward(self):
    method backward_D_basic (line 189) | def backward_D_basic(self, netD, content,style, fake):
    method backward_D_NCEloss (line 212) | def backward_D_NCEloss(self):
    method backward_D (line 240) | def backward_D(self):
    method compute_G_loss (line 258) | def compute_G_loss(self):
  function init_weights (line 311) | def init_weights(net, init_type='normal', init_gain=0.02):
  function init_net (line 345) | def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):

FILE: models/net.py
  class ADAIN_Encoder (line 61) | class ADAIN_Encoder(nn.Module):
    method __init__ (line 62) | def __init__(self, encoder, gpu_ids=[]):
    method encode_with_intermediate (line 78) | def encode_with_intermediate(self, input):
    method calc_mean_std (line 85) | def calc_mean_std(self, feat, eps=1e-5):
    method adain (line 95) | def adain(self, content_feat, style_feat):
    method forward (line 105) | def forward(self, content, style, encoded_only = False):
  class Decoder (line 114) | class Decoder(nn.Module):
    method __init__ (line 115) | def __init__(self, gpu_ids=[]):
    method forward (line 150) | def forward(self, adain_feat):

FILE: models/networks.py
  function get_filter (line 14) | def get_filter(filt_size=3):
  class Downsample (line 36) | class Downsample(nn.Module):
    method __init__ (line 37) | def __init__(self, channels, pad_type='reflect', filt_size=3, stride=2...
    method forward (line 52) | def forward(self, inp):
  class Upsample2 (line 62) | class Upsample2(nn.Module):
    method __init__ (line 63) | def __init__(self, scale_factor, mode='nearest'):
    method forward (line 68) | def forward(self, x):
  class Upsample (line 72) | class Upsample(nn.Module):
    method __init__ (line 73) | def __init__(self, channels, pad_type='repl', filt_size=4, stride=2):
    method forward (line 87) | def forward(self, inp):
  function get_pad_layer (line 95) | def get_pad_layer(pad_type):
  class Identity (line 107) | class Identity(nn.Module):
    method forward (line 108) | def forward(self, x):
  function get_norm_layer (line 112) | def get_norm_layer(norm_type='instance'):
  function get_scheduler (line 133) | def get_scheduler(optimizer, opt):
  function init_weights (line 162) | def init_weights(net, init_type='kaiming', init_gain=0.02, debug=False):
  function init_net (line 197) | def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[], debug=...
  function define_D (line 217) | def define_D(input_nc, ndf, netD, n_layers_D=3, image_size = 256, featur...
  class GANLoss (line 271) | class GANLoss(nn.Module):
    method __init__ (line 278) | def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=...
    method get_target_tensor (line 304) | def get_target_tensor(self, prediction, target_is_real):
    method __call__ (line 321) | def __call__(self, prediction, target_is_real):
  function cal_gradient_penalty (line 355) | def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed...
  class Normalize (line 392) | class Normalize(nn.Module):
    method __init__ (line 394) | def __init__(self, power=2):
    method forward (line 398) | def forward(self, x):
  class ResBlocks (line 408) | class ResBlocks(nn.Module):
    method __init__ (line 409) | def __init__(self, num_blocks, dim, norm='inst', activation='relu', pa...
    method forward (line 416) | def forward(self, x):
  function cat_feature (line 423) | def cat_feature(x, y):
  class Conv2dBlock (line 429) | class Conv2dBlock(nn.Module):
    method __init__ (line 430) | def __init__(self, input_dim, output_dim, kernel_size, stride,
    method forward (line 474) | def forward(self, x):
  class LinearBlock (line 483) | class LinearBlock(nn.Module):
    method __init__ (line 484) | def __init__(self, input_dim, output_dim, norm='none', activation='rel...
    method forward (line 519) | def forward(self, x):
  class LayerNorm (line 532) | class LayerNorm(nn.Module):
    method __init__ (line 533) | def __init__(self, num_features, eps=1e-5, affine=True):
    method forward (line 543) | def forward(self, x):
  class NLayerDiscriminator (line 554) | class NLayerDiscriminator(nn.Module):
    method __init__ (line 557) | def __init__(self, input_nc, ndf=64, n_layers=3, image_size = 256,norm...
    method forward (line 608) | def forward(self, input):
  class PixelDiscriminator (line 613) | class PixelDiscriminator(nn.Module):
    method __init__ (line 616) | def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
    method forward (line 640) | def forward(self, input):
  class PatchDiscriminator (line 644) | class PatchDiscriminator(NLayerDiscriminator):
    method __init__ (line 647) | def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNo...
    method forward (line 650) | def forward(self, input):
  class GroupedChannelNorm (line 659) | class GroupedChannelNorm(nn.Module):
    method __init__ (line 660) | def __init__(self, num_groups):
    method forward (line 664) | def forward(self, x):

FILE: models/torch_utils.py
  function concat_all_gather (line 10) | def concat_all_gather(tensor, world_size):
  function get_rank (line 19) | def get_rank(group=None):
  function get_world_size (line 26) | def get_world_size(group=None):
  function kaiming_init (line 33) | def kaiming_init(mod):
  function set_seed (line 41) | def set_seed(seed):
  function update_average (line 52) | def update_average(net, net_ema, m=0.999):
  function warmup_learning_rate (line 58) | def warmup_learning_rate(optimizer, lr, train_step, warmup_step):

FILE: options/base_options.py
  class BaseOptions (line 9) | class BaseOptions():
    method __init__ (line 16) | def __init__(self, cmd_line=None):
    method initialize (line 23) | def initialize(self, parser):
    method gather_options (line 77) | def gather_options(self):
    method print_options (line 114) | def print_options(self, opt):
    method parse (line 143) | def parse(self):

FILE: options/test_options.py
  class TestOptions (line 4) | class TestOptions(BaseOptions):
    method initialize (line 10) | def initialize(self, parser):

FILE: options/train_options.py
  class TrainOptions (line 4) | class TrainOptions(BaseOptions):
    method initialize (line 10) | def initialize(self, parser):

FILE: util/get_data.py
  class GetData (line 11) | class GetData(object):
    method __init__ (line 27) | def __init__(self, technique='cyclegan', verbose=True):
    method _print (line 35) | def _print(self, text):
    method _get_options (line 40) | def _get_options(r):
    method _present_options (line 46) | def _present_options(self):
    method _download_data (line 56) | def _download_data(self, dataset_url, save_path):
    method get (line 79) | def get(self, save_path, dataset=None):

FILE: util/html.py
  class HTML (line 6) | class HTML:
    method __init__ (line 14) | def __init__(self, web_dir, title, refresh=0):
    method get_image_dir (line 35) | def get_image_dir(self):
    method add_header (line 39) | def add_header(self, text):
    method add_images (line 48) | def add_images(self, ims, txts, links, width=400):
    method save (line 68) | def save(self):

FILE: util/image_pool.py
  class ImagePool (line 5) | class ImagePool():
    method __init__ (line 12) | def __init__(self, pool_size):
    method query (line 23) | def query(self, images):

FILE: util/util.py
  function str2bool (line 13) | def str2bool(v):
  function copyconf (line 24) | def copyconf(default_opt, **kwargs):
  function find_class_in_module (line 31) | def find_class_in_module(target_cls_name, module):
  function tensor2im (line 44) | def tensor2im(input_image, imtype=np.uint8):
  function diagnose_network (line 65) | def diagnose_network(net, name='network'):
  function save_image (line 84) | def save_image(image_numpy, image_path, aspect_ratio=1.0):
  function print_numpy (line 104) | def print_numpy(x, val=True, shp=False):
  function mkdirs (line 120) | def mkdirs(paths):
  function mkdir (line 133) | def mkdir(path):
  function correct_resize_label (line 143) | def correct_resize_label(t, size):
  function correct_resize (line 157) | def correct_resize(t, size, mode=Image.BICUBIC):

FILE: util/visualizer.py
  function save_images (line 15) | def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
  class Visualizer (line 46) | class Visualizer():
    method __init__ (line 52) | def __init__(self, opt):
    method reset (line 95) | def reset(self):
    method create_visdom_connections (line 99) | def create_visdom_connections(self):
    method display_current_results (line 106) | def display_current_results(self, visuals, epoch, save_result):
    method plot_current_losses (line 191) | def plot_current_losses(self, epoch, counter_ratio, losses):
    method print_current_losses (line 226) | def print_current_losses(self, epoch, iters, losses, t_comp, t_data):