SYMBOL INDEX (529 symbols across 44 files)

FILE: GFPGAN.py
  function tensor2img (line 13) | def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
  class GFPGANer (line 58) | class GFPGANer():
    method __init__ (line 74) | def __init__(self, device,model_path, upscale=2, arch='clean', channel...
    method enhance_allimg (line 114) | def enhance_allimg(self, img, has_aligned=False, only_center_face=Fals...
    method enhance (line 174) | def enhance(self, img, has_aligned=False, only_center_face=False, past...
  function GFPGANInit (line 225) | def GFPGANInit(device,face_enhancement_path):
  function GFPGANInfer (line 247) | def GFPGANInfer(img, restorer, aligned):

FILE: Gen_hyperlipsbase_videos.py
  function inference_list (line 33) | def inference_list():

FILE: HYPERLIPS.py
  function get_smoothened_boxes (line 11) | def get_smoothened_boxes(boxes, T):
  function face_detect (line 20) | def face_detect(images, detector,pad):
  function datagen (line 59) | def datagen(mels, detector,frames,img_size,hyper_batch_size,pads):
  function load_HyperLips (line 101) | def load_HyperLips(window,rescaling,path,path_hr,device):
  function main (line 107) | def main():
  class Hyperlips (line 112) | class Hyperlips():
    method __init__ (line 113) | def __init__(self,checkpoint_path_BASE=None,
    method _HyperlipsLoadModels (line 145) | def _HyperlipsLoadModels(self):
    method _HyperlipsInference (line 158) | def _HyperlipsInference(self,face_path,audio_path,outfile_path):

FILE: Inference_hyperlips.py
  function get_smoothened_mels (line 45) | def get_smoothened_mels(mel_chunks, T):
  function face_detect (line 55) | def face_detect(images, detector,pad):
  function datagen (line 95) | def datagen(mels, detector,face_path, resize_factor):
  function _load (line 138) | def _load(checkpoint_path, device):
  function load_HyperLipsHR (line 143) | def load_HyperLipsHR(path,path_hr,device):
  function load_HyperLipsBase (line 149) | def load_HyperLipsBase(path, device):
  function read_frames (line 158) | def read_frames(face_path, resize_factor):
  function main (line 172) | def main():
  class Hyperlips (line 177) | class Hyperlips():
    method __init__ (line 178) | def __init__(self):
    method _HyperlipsLoadModels (line 185) | def _HyperlipsLoadModels(self):
    method _HyperlipsInference (line 200) | def _HyperlipsInference(self):

FILE: Train_hyperlipsBase.py
  class Dataset (line 36) | class Dataset(object):
    method __init__ (line 37) | def __init__(self, split):
    method get_frame_id (line 40) | def get_frame_id(self, frame):
    method get_window (line 43) | def get_window(self, start_frame):
    method read_window (line 55) | def read_window(self, window_fnames):
    method crop_audio_window (line 71) | def crop_audio_window(self, spec, start_frame):
    method get_segmented_mels (line 82) | def get_segmented_mels(self, spec, start_frame):
    method prepare_window (line 97) | def prepare_window(self, window):
    method __len__ (line 104) | def __len__(self):
    method __getitem__ (line 107) | def __getitem__(self, idx):
  function save_sample_images (line 162) | def save_sample_images(x, g, gt, global_step, checkpoint_dir):
  function cosine_loss (line 176) | def cosine_loss(a, v, y):
  function get_sync_loss (line 188) | def get_sync_loss(mel, g):
  function train (line 199) | def train(device, model, disc, train_data_loader, test_data_loader, opti...
  function eval_model (line 297) | def eval_model(test_data_loader, global_step, device, model, disc):
  function save_checkpoint (line 352) | def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch, prefi...
  function _load (line 364) | def _load(checkpoint_path):
  function load_checkpoint (line 373) | def load_checkpoint(path, model, optimizer, reset_optimizer=False, overw...

FILE: Train_hyperlipsHR.py
  class Dataset (line 45) | class Dataset(object):
    method __init__ (line 46) | def __init__(self, split):
    method get_frame_id (line 53) | def get_frame_id(self, frame):
    method get_window (line 56) | def get_window(self, start_frame):
    method read_window (line 68) | def read_window(self, window_fnames):
    method read_window_base (line 85) | def read_window_base(self, window_fnames):
    method read_window_sketch (line 102) | def read_window_sketch(self, window_fnames):
    method read_window_sketch_base (line 129) | def read_window_sketch_base(self, window_fnames):
    method read_coord (line 155) | def read_coord(self,window_fnames):
    method prepare_window (line 174) | def prepare_window(self, window):
    method __len__ (line 181) | def __len__(self):
    method __getitem__ (line 184) | def __getitem__(self, idx):
  function save_sample_images (line 247) | def save_sample_images(x, g, gt,m, global_step, checkpoint_dir):
  class PerceptualLoss (line 259) | class PerceptualLoss(nn.Module):
    method __init__ (line 260) | def __init__(self):
    method forward (line 270) | def forward(self, high_resolution, fake_high_resolution):
  function cosine_loss (line 275) | def cosine_loss(a, v, y):
  function train (line 287) | def train(device, model, disc,train_data_loader, test_data_loader, optim...
  function eval_model (line 429) | def eval_model(test_data_loader, global_step, device, model):
  function save_checkpoint (line 470) | def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch, prefi...
  function _load (line 482) | def _load(checkpoint_path):
  function load_checkpoint (line 491) | def load_checkpoint(path, model, reset_optimizer=False, overwrite_global...

FILE: audio.py
  function load_wav (line 9) | def load_wav(path, sr):
  function save_wav (line 12) | def save_wav(wav, path, sr):
  function save_wavenet_wav (line 17) | def save_wavenet_wav(wav, path, sr):
  function preemphasis (line 20) | def preemphasis(wav, k, preemphasize=True):
  function inv_preemphasis (line 25) | def inv_preemphasis(wav, k, inv_preemphasize=True):
  function get_hop_size (line 30) | def get_hop_size():
  function linearspectrogram (line 37) | def linearspectrogram(wav):
  function melspectrogram (line 45) | def melspectrogram(wav):
  function _lws_processor (line 53) | def _lws_processor():
  function _stft (line 57) | def _stft(y):
  function num_frames (line 66) | def num_frames(length, fsize, fshift):
  function pad_lr (line 77) | def pad_lr(x, fsize, fshift):
  function librosa_pad_lr (line 87) | def librosa_pad_lr(x, fsize, fshift):
  function _linear_to_mel (line 93) | def _linear_to_mel(spectogram):
  function _build_mel_basis (line 99) | def _build_mel_basis():
  function _amp_to_db (line 105) | def _amp_to_db(x):
  function _db_to_amp (line 109) | def _db_to_amp(x):
  function _normalize (line 112) | def _normalize(S):
  function _denormalize (line 126) | def _denormalize(D):

FILE: color_syncnet_trainv3.py
  class Dataset (line 36) | class Dataset(object):
    method __init__ (line 37) | def __init__(self, split):
    method get_frame_id (line 40) | def get_frame_id(self, frame):
    method get_window (line 43) | def get_window(self, start_frame):
    method crop_audio_window (line 57) | def crop_audio_window(self, spec, start_frame):
    method read_window (line 71) | def read_window(self, window_fnames, flip_flag=False):
    method __len__ (line 90) | def __len__(self):
    method __getitem__ (line 93) | def __getitem__(self, idx):
  function cosine_loss (line 143) | def cosine_loss(a, v, y):
  function train (line 149) | def train(device, model, train_data_loader, test_data_loader, optimizer,
  function eval_model (line 202) | def eval_model(test_data_loader, global_step, device, model, checkpoint_...
  function save_checkpoint (line 233) | def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch):
  function _load (line 246) | def _load(checkpoint_path):
  function load_checkpoint (line 254) | def load_checkpoint(path, model, optimizer, reset_optimizer=False):

FILE: conv.py
  class Conv2d (line 5) | class Conv2d(nn.Module):
    method __init__ (line 6) | def __init__(self, cin, cout, kernel_size, stride, padding, residual=F...
    method forward (line 15) | def forward(self, x):
  class nonorm_Conv2d (line 21) | class nonorm_Conv2d(nn.Module):
    method __init__ (line 22) | def __init__(self, cin, cout, kernel_size, stride, padding, residual=F...
    method forward (line 29) | def forward(self, x):
  class Conv2dTranspose (line 33) | class Conv2dTranspose(nn.Module):
    method __init__ (line 34) | def __init__(self, cin, cout, kernel_size, stride, padding, output_pad...
    method forward (line 42) | def forward(self, x):

FILE: face_detection/api.py
  class LandmarksType (line 17) | class LandmarksType(Enum):
  class NetworkSize (line 30) | class NetworkSize(Enum):
    method __new__ (line 36) | def __new__(cls, value):
    method __int__ (line 41) | def __int__(self):
  class FaceAlignment (line 46) | class FaceAlignment:
    method __init__ (line 47) | def __init__(self, landmarks_type, network_size=NetworkSize.LARGE,
    method get_detections_for_batch (line 64) | def get_detections_for_batch(self, images):

FILE: face_detection/detection/core.py
  class FaceDetector (line 9) | class FaceDetector(object):
    method __init__ (line 18) | def __init__(self, device, verbose):
    method detect_from_image (line 32) | def detect_from_image(self, tensor_or_path):
    method detect_from_directory (line 54) | def detect_from_directory(self, path, extensions=['.jpg', '.png'], rec...
    method reference_scale (line 104) | def reference_scale(self):
    method reference_x_shift (line 108) | def reference_x_shift(self):
    method reference_y_shift (line 112) | def reference_y_shift(self):
    method tensor_or_path_to_ndarray (line 116) | def tensor_or_path_to_ndarray(tensor_or_path, rgb=True):

FILE: face_detection/detection/sfd/bbox.py
  function IOU (line 17) | def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2):
  function bboxlog (line 30) | def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
  function bboxloginv (line 37) | def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
  function nms (line 44) | def nms(dets, thresh):
  function encode (line 67) | def encode(matched, priors, variances):
  function decode (line 91) | def decode(loc, priors, variances):
  function batch_decode (line 111) | def batch_decode(loc, priors, variances):

FILE: face_detection/detection/sfd/detect.py
  function detect (line 19) | def detect(net, img, device):
  function batch_detect (line 58) | def batch_detect(net, imgs, device):
  function flip_detect (line 96) | def flip_detect(net, img, device):
  function pts_to_bb (line 109) | def pts_to_bb(pts):

FILE: face_detection/detection/sfd/net_s3fd.py
  class L2Norm (line 6) | class L2Norm(nn.Module):
    method __init__ (line 7) | def __init__(self, n_channels, scale=1.0):
    method forward (line 16) | def forward(self, x):
  class s3fd (line 22) | class s3fd(nn.Module):
    method __init__ (line 23) | def __init__(self):
    method forward (line 70) | def forward(self, x):

FILE: face_detection/detection/sfd/sfd_detector.py
  class SFDDetector (line 16) | class SFDDetector(FaceDetector):
    method __init__ (line 17) | def __init__(self, device, path_to_detector=os.path.join(os.path.dirna...
    method detect_from_image (line 31) | def detect_from_image(self, tensor_or_path):
    method detect_from_batch (line 41) | def detect_from_batch(self, images):
    method reference_scale (line 50) | def reference_scale(self):
    method reference_x_shift (line 54) | def reference_x_shift(self):
    method reference_y_shift (line 58) | def reference_y_shift(self):

FILE: face_detection/models.py
  function conv3x3 (line 7) | def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False):
  class ConvBlock (line 13) | class ConvBlock(nn.Module):
    method __init__ (line 14) | def __init__(self, in_planes, out_planes):
    method forward (line 33) | def forward(self, x):
  class Bottleneck (line 58) | class Bottleneck(nn.Module):
    method __init__ (line 62) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 75) | def forward(self, x):
  class HourGlass (line 98) | class HourGlass(nn.Module):
    method __init__ (line 99) | def __init__(self, num_modules, depth, num_features):
    method _generate_network (line 107) | def _generate_network(self, level):
    method _forward (line 119) | def _forward(self, level, inp):
    method forward (line 141) | def forward(self, x):
  class FAN (line 145) | class FAN(nn.Module):
    method __init__ (line 147) | def __init__(self, num_modules=1):
    method forward (line 174) | def forward(self, x):
  class ResNetDepth (line 204) | class ResNetDepth(nn.Module):
    method __init__ (line 206) | def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes...
    method _make_layer (line 229) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 246) | def forward(self, x):

FILE: face_detection/utils.py
  function _gaussian (line 11) | def _gaussian(
  function draw_gaussian (line 37) | def draw_gaussian(image, point, sigma):
  function transform (line 56) | def transform(point, center, scale, resolution, invert=False):
  function crop (line 92) | def crop(image, center, scale, resolution=256.0):
  function get_preds_fromhm (line 132) | def get_preds_fromhm(hm, center=None, scale=None):
  function get_preds_fromhm_batch (line 172) | def get_preds_fromhm_batch(hm, centers=None, scales=None):
  function shuffle_lr (line 212) | def shuffle_lr(parts, pairs=None):
  function flip (line 237) | def flip(tensor, is_label=False):
  function appdata_dir (line 259) | def appdata_dir(appname=None, roaming=False):

FILE: face_parsing/model.py
  class ConvBNReLU (line 14) | class ConvBNReLU(nn.Module):
    method __init__ (line 15) | def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args...
    method forward (line 26) | def forward(self, x):
    method init_weight (line 31) | def init_weight(self):
  class BiSeNetOutput (line 37) | class BiSeNetOutput(nn.Module):
    method __init__ (line 38) | def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
    method forward (line 44) | def forward(self, x):
    method init_weight (line 49) | def init_weight(self):
    method get_params (line 55) | def get_params(self):
  class AttentionRefinementModule (line 67) | class AttentionRefinementModule(nn.Module):
    method __init__ (line 68) | def __init__(self, in_chan, out_chan, *args, **kwargs):
    method forward (line 76) | def forward(self, x):
    method init_weight (line 85) | def init_weight(self):
  class ContextPath (line 92) | class ContextPath(nn.Module):
    method __init__ (line 93) | def __init__(self, device,*args, **kwargs):
    method forward (line 104) | def forward(self, x):
    method init_weight (line 127) | def init_weight(self):
    method get_params (line 133) | def get_params(self):
  class SpatialPath (line 146) | class SpatialPath(nn.Module):
    method __init__ (line 147) | def __init__(self, *args, **kwargs):
    method forward (line 155) | def forward(self, x):
    method init_weight (line 162) | def init_weight(self):
    method get_params (line 168) | def get_params(self):
  class FeatureFusionModule (line 180) | class FeatureFusionModule(nn.Module):
    method __init__ (line 181) | def __init__(self, in_chan, out_chan, *args, **kwargs):
    method forward (line 200) | def forward(self, fsp, fcp):
    method init_weight (line 212) | def init_weight(self):
    method get_params (line 218) | def get_params(self):
  class BiSeNet (line 230) | class BiSeNet(nn.Module):
    method __init__ (line 231) | def __init__(self,device, n_classes, *args, **kwargs):
    method forward (line 241) | def forward(self, x):
    method init_weight (line 256) | def init_weight(self):
    method get_params (line 262) | def get_params(self):

FILE: face_parsing/resnet.py
  function conv3x3 (line 14) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 20) | class BasicBlock(nn.Module):
    method __init__ (line 21) | def __init__(self, in_chan, out_chan, stride=1):
    method forward (line 36) | def forward(self, x):
  function create_layer_basic (line 51) | def create_layer_basic(in_chan, out_chan, bnum, stride=1):
  class Resnet18 (line 58) | class Resnet18(nn.Module):
    method __init__ (line 59) | def __init__(self,device):
    method forward (line 71) | def forward(self, x):
    method init_weight (line 82) | def init_weight(self,device):
    method get_params (line 93) | def get_params(self):

FILE: face_parsing/swap.py
  function init_parser (line 9) | def init_parser(pth_path, device):
  function image_to_parsing_img (line 20) | def image_to_parsing_img(img, net):
  function image_to_parsing (line 46) | def image_to_parsing(img, net):
  function image_to_parsing2 (line 66) | def image_to_parsing2(img, net):
  function get_mask (line 77) | def get_mask(parsing, classes):
  function swap_regions_img (line 83) | def swap_regions_img(source, target, net):
  function swap_regions (line 97) | def swap_regions(source, target, net):

FILE: gfpgan/gfpganv1_clean_arch.py
  class StyleGAN2GeneratorCSFT (line 12) | class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
    method __init__ (line 26) | def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_mu...
    method forward (line 35) | def forward(self,
  class ResBlock (line 121) | class ResBlock(nn.Module):
    method __init__ (line 130) | def __init__(self, in_channels, out_channels, mode='down'):
    method forward (line 141) | def forward(self, x):
  class GFPGANv1Clean (line 154) | class GFPGANv1Clean(nn.Module):
    method __init__ (line 175) | def __init__(
    method forward (line 278) | def forward(self, x, return_latents=False, return_rgb=True, randomize_...

FILE: gfpgan/stylegan2_clean_arch.py
  class NormStyleCode (line 10) | class NormStyleCode(nn.Module):
    method forward (line 12) | def forward(self, x):
  class ModulatedConv2d (line 24) | class ModulatedConv2d(nn.Module):
    method __init__ (line 39) | def __init__(self,
    method forward (line 65) | def forward(self, x, style):
    method __repr__ (line 101) | def __repr__(self):
  class StyleConv (line 106) | class StyleConv(nn.Module):
    method __init__ (line 118) | def __init__(self, in_channels, out_channels, kernel_size, num_style_f...
    method forward (line 126) | def forward(self, x, style, noise=None):
  class ToRGB (line 141) | class ToRGB(nn.Module):
    method __init__ (line 150) | def __init__(self, in_channels, num_style_feat, upsample=True):
    method forward (line 157) | def forward(self, x, style, skip=None):
  class ConstantInput (line 177) | class ConstantInput(nn.Module):
    method __init__ (line 185) | def __init__(self, num_channel, size):
    method forward (line 189) | def forward(self, batch):
  class StyleGAN2GeneratorClean (line 195) | class StyleGAN2GeneratorClean(nn.Module):
    method __init__ (line 206) | def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_mu...
    method make_noise (line 279) | def make_noise(self):
    method get_latent (line 290) | def get_latent(self, x):
    method mean_latent (line 293) | def mean_latent(self, num_latent):
    method forward (line 298) | def forward(self,

FILE: hparams.py
  function get_image_list (line 3) | def get_image_list(data_root, split):
  class HParams (line 15) | class HParams:
    method __init__ (line 16) | def __init__(self, **kwargs):
    method __getattr__ (line 22) | def __getattr__(self, key):
    method set_hparam (line 27) | def set_hparam(self, key, value):
  function hparams_debug_string (line 98) | def hparams_debug_string():

FILE: hparams_Base.py
  function get_image_list (line 3) | def get_image_list(data_root, split):
  class HParams (line 15) | class HParams:
    method __init__ (line 16) | def __init__(self, **kwargs):
    method __getattr__ (line 22) | def __getattr__(self, key):
    method set_hparam (line 27) | def set_hparam(self, key, value):
  function hparams_debug_string (line 99) | def hparams_debug_string():

FILE: hparams_HR.py
  function get_image_list (line 3) | def get_image_list(data_root, split):
  class HParams (line 15) | class HParams:
    method __init__ (line 16) | def __init__(self, **kwargs):
    method __getattr__ (line 22) | def __getattr__(self, key):
    method set_hparam (line 27) | def set_hparam(self, key, value):
  function hparams_debug_string (line 100) | def hparams_debug_string():

FILE: inference.py
  function inference_single (line 35) | def inference_single():

FILE: models/audio_v.py
  function load_wav (line 8) | def load_wav(path, sr):
  function save_wav (line 12) | def save_wav(wav, path, sr):
  function save_wavenet_wav (line 19) | def save_wavenet_wav(wav, path, sr):
  function preemphasis (line 23) | def preemphasis(wav, k, preemphasize=True):
  function inv_preemphasis (line 29) | def inv_preemphasis(wav, k, inv_preemphasize=True):
  function start_and_end_indices (line 36) | def start_and_end_indices(quantized, silence_threshold=2):
  function get_hop_size (line 50) | def get_hop_size(hparams):
  function linearspectrogram (line 58) | def linearspectrogram(wav, hparams):
  function melspectrogram (line 67) | def melspectrogram(wav, hparams):
  function inv_linear_spectrogram (line 76) | def inv_linear_spectrogram(linear_spectrogram, hparams):
  function inv_mel_spectrogram (line 94) | def inv_mel_spectrogram(mel_spectrogram, hparams):
  function _lws_processor (line 112) | def _lws_processor(hparams):
  function _griffin_lim (line 117) | def _griffin_lim(S, hparams):
  function _stft (line 130) | def _stft(y, hparams):
  function _istft (line 137) | def _istft(y, hparams):
  function num_frames (line 144) | def num_frames(length, fsize, fshift):
  function pad_lr (line 155) | def pad_lr(x, fsize, fshift):
  function librosa_pad_lr (line 167) | def librosa_pad_lr(x, fsize, fshift):
  function _linear_to_mel (line 176) | def _linear_to_mel(spectogram, hparams):
  function _mel_to_linear (line 183) | def _mel_to_linear(mel_spectrogram, hparams):
  function _build_mel_basis (line 190) | def _build_mel_basis(hparams):
  function _amp_to_db (line 196) | def _amp_to_db(x, hparams):
  function _db_to_amp (line 201) | def _db_to_amp(x):
  function _normalize (line 205) | def _normalize(S, hparams):
  function _denormalize (line 223) | def _denormalize(D, hparams):

FILE: models/conv.py
  class Conv2d (line 5) | class Conv2d(nn.Module):
    method __init__ (line 6) | def __init__(self, cin, cout, kernel_size, stride, padding, residual=F...
    method forward (line 15) | def forward(self, x):
  class nonorm_Conv2d (line 21) | class nonorm_Conv2d(nn.Module):
    method __init__ (line 22) | def __init__(self, cin, cout, kernel_size, stride, padding, residual=F...
    method forward (line 29) | def forward(self, x):
  class Conv2dTranspose (line 33) | class Conv2dTranspose(nn.Module):
    method __init__ (line 34) | def __init__(self, cin, cout, kernel_size, stride, padding, output_pad...
    method forward (line 42) | def forward(self, x):

FILE: models/decoder.py
  class RecurrentDecoder (line 7) | class RecurrentDecoder(nn.Module):
    method __init__ (line 8) | def __init__(self, feature_channels, decoder_channels):
    method forward (line 17) | def forward(self,
  class AvgPool (line 30) | class AvgPool(nn.Module):
    method __init__ (line 31) | def __init__(self):
    method forward_single_frame (line 35) | def forward_single_frame(self, s0):
    method forward_time_series (line 41) | def forward_time_series(self, s0):
    method forward (line 50) | def forward(self, s0):
  class BottleneckBlock (line 57) | class BottleneckBlock(nn.Module):
    method __init__ (line 58) | def __init__(self, channels):
    method forward (line 63) | def forward(self, x, r: Optional[Tensor]):
  class UpsamplingBlock (line 70) | class UpsamplingBlock(nn.Module):
    method __init__ (line 71) | def __init__(self, in_channels, skip_channels, src_channels, out_chann...
    method forward_single_frame (line 82) | def forward_single_frame(self, x, f, s, r: Optional[Tensor]):
    method forward_time_series (line 92) | def forward_time_series(self, x, f, s, r: Optional[Tensor]):
    method forward (line 107) | def forward(self, x, f, s, r: Optional[Tensor]):
  class OutputBlock (line 114) | class OutputBlock(nn.Module):
    method __init__ (line 115) | def __init__(self, in_channels, src_channels, out_channels):
    method forward_single_frame (line 127) | def forward_single_frame(self, x, s):
    method forward_time_series (line 134) | def forward_time_series(self, x, s):
    method forward (line 145) | def forward(self, x, s):
  class ConvGRU (line 152) | class ConvGRU(nn.Module):
    method __init__ (line 153) | def __init__(self,
    method forward_single_frame (line 168) | def forward_single_frame(self, x, h):
    method forward_time_series (line 174) | def forward_time_series(self, x, h):
    method forward (line 182) | def forward(self, x, h: Optional[Tensor]):
  class Projection (line 193) | class Projection(nn.Module):
    method __init__ (line 194) | def __init__(self, in_channels, out_channels):
    method forward_single_frame (line 198) | def forward_single_frame(self, x):
    method forward_time_series (line 201) | def forward_time_series(self, x):
    method forward (line 205) | def forward(self, x):

FILE: models/deep_guided_filter.py
  class DeepGuidedFilterRefiner (line 9) | class DeepGuidedFilterRefiner(nn.Module):
    method __init__ (line 10) | def __init__(self, in_channels=4,hid_channels=16):
    method forward_single_frame (line 24) | def forward_single_frame(self, fine_src, base_src, base_fgr, base_pha,...
    method forward_time_series (line 45) | def forward_time_series(self, fine_src, base_src, base_fgr, base_pha, ...
    method forward (line 57) | def forward(self, fine_src, base_src, base_fgr, base_pha, base_hid):

FILE: models/gfpganv1_clean_arch.py
  class StyleGAN2GeneratorCSFT (line 12) | class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
    method __init__ (line 26) | def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_mu...
    method forward (line 35) | def forward(self,
  class ResBlock (line 121) | class ResBlock(nn.Module):
    method __init__ (line 130) | def __init__(self, in_channels, out_channels, mode='down'):
    method forward (line 141) | def forward(self, x):
  class GFPGANv1Clean (line 154) | class GFPGANv1Clean(nn.Module):
    method __init__ (line 175) | def __init__(
    method forward (line 261) | def forward(self, x, return_latents=False, return_rgb=True, randomize_...

FILE: models/guided_filter_pytorch/box_filter.py
  function diff_x (line 4) | def diff_x(input, r):
  function diff_y (line 15) | def diff_y(input, r):
  class BoxFilter (line 26) | class BoxFilter(nn.Module):
    method __init__ (line 27) | def __init__(self, r):
    method forward (line 32) | def forward(self, x):

FILE: models/guided_filter_pytorch/guided_filter.py
  class FastGuidedFilter (line 8) | class FastGuidedFilter(nn.Module):
    method __init__ (line 9) | def __init__(self, r, eps=1e-8):
    method forward (line 17) | def forward(self, lr_x, lr_y, hr_x):
  class GuidedFilter (line 51) | class GuidedFilter(nn.Module):
    method __init__ (line 52) | def __init__(self, r, eps=1e-8):
    method forward (line 60) | def forward(self, x, y):
  class ConvGuidedFilter (line 92) | class ConvGuidedFilter(nn.Module):
    method __init__ (line 93) | def __init__(self, radius=1, norm=nn.BatchNorm2d):
    method forward (line 106) | def forward(self, x_lr, y_lr, x_hr):

FILE: models/hyperlayers.py
  class FCLayer (line 8) | class FCLayer(nn.Module):
    method __init__ (line 9) | def __init__(self, in_features, out_features):
    method forward (line 16) | def forward(self, input):
  class FCBlock (line 19) | class FCBlock(nn.Module):
    method __init__ (line 20) | def __init__(self,
    method __getitem__ (line 42) | def __getitem__(self,item):
    method init_weights (line 45) | def init_weights(self, m):
    method forward (line 49) | def forward(self, input):
  function partialclass (line 53) | def partialclass(cls, *args, **kwds):
  class HyperLayer (line 60) | class HyperLayer(nn.Module):
    method __init__ (line 62) | def __init__(self,
    method forward (line 80) | def forward(self, hyper_input):
  class HyperFC (line 88) | class HyperFC(nn.Module):
    method __init__ (line 91) | def __init__(self,
    method forward (line 120) | def forward(self, hyper_input):
  class BatchLinear (line 132) | class BatchLinear(nn.Module):
    method __init__ (line 133) | def __init__(self,
    method __repr__ (line 146) | def __repr__(self):
    method forward (line 149) | def forward(self, input):
  function last_hyper_layer_init (line 155) | def last_hyper_layer_init(m):
  class HyperLinear (line 161) | class HyperLinear(nn.Module):
    method __init__ (line 163) | def __init__(self,
    method forward (line 181) | def forward(self, hyper_input):#([1, 131072])
  class HyperConv (line 193) | class HyperConv(nn.Module):
    method __init__ (line 200) | def __init__(self,
    method forward (line 233) | def forward(self, x):

FILE: models/hypernetwork.py
  class HyperNetwork (line 3) | class HyperNetwork(nn.Module):
    method __init__ (line 5) | def __init__(self, in_dim=1, h_dim=32):
    method forward (line 21) | def forward(self, x):

FILE: models/layers.py
  class Upsample (line 14) | class Upsample(nn.Module):
    method __init__ (line 16) | def __init__(self, scale_factor, mode, align_corners):
    method forward (line 21) | def forward(self, x):
  class MultiSequential (line 24) | class MultiSequential(nn.Sequential):
    method forward (line 25) | def forward(self, *inputs):
  class Conv2d (line 35) | class Conv2d(nn.Module):
    method __init__ (line 36) | def __init__(self, in_channels, out_channels, kernel_size=3, padding=0):
    method forward (line 39) | def forward(self, x, hyp_out=None):
  class BatchConv2d (line 42) | class BatchConv2d(nn.Module):
    method __init__ (line 50) | def __init__(self, in_channels, out_channels, hyp_out_units, stride=1,
    method forward (line 66) | def forward(self, x, hyp_out, include_bias=True):
    method get_kernel (line 95) | def get_kernel(self):
    method get_bias (line 97) | def get_bias(self):
    method get_kernel_shape (line 99) | def get_kernel_shape(self):
    method get_bias_shape (line 101) | def get_bias_shape(self):
  class ClipByPercentile (line 104) | class ClipByPercentile(object):
    method __init__ (line 106) | def __init__(self, perc=99):
    method __call__ (line 109) | def __call__(self, img):
  class ZeroPad (line 117) | class ZeroPad(object):
    method __init__ (line 118) | def __init__(self, final_size):
    method __call__ (line 121) | def __call__(self, img):

FILE: models/lraspp.py
  class LRASPP (line 3) | class LRASPP(nn.Module):
    method __init__ (line 4) | def __init__(self, in_channels, out_channels):
    method forward_single_frame (line 17) | def forward_single_frame(self, x):
    method forward_time_series (line 20) | def forward_time_series(self, x):
    method forward (line 25) | def forward(self, x):

FILE: models/memory.py
  class Memory (line 7) | class Memory(nn.Module):
    method __init__ (line 8) | def __init__(self, radius=16.0, n_slot=96):
    method forward (line 21) | def forward(self, query, value=None, inference=False):

FILE: models/mobilenetv3.py
  function _make_divisible (line 17) | def _make_divisible(v: float, divisor: int, min_value: Optional[int] = N...
  function _log_api_usage_once (line 33) | def _log_api_usage_once(obj: str) -> None:  # type: ignore
  class Conv2d (line 47) | class Conv2d(torch.nn.Conv2d):
    method __init__ (line 48) | def __init__(self, *args, **kwargs):
  class ConvTranspose2d (line 56) | class ConvTranspose2d(torch.nn.ConvTranspose2d):
    method __init__ (line 57) | def __init__(self, *args, **kwargs):
  class BatchNorm2d (line 65) | class BatchNorm2d(torch.nn.BatchNorm2d):
    method __init__ (line 66) | def __init__(self, *args, **kwargs):
  class FrozenBatchNorm2d (line 78) | class FrozenBatchNorm2d(torch.nn.Module):
    method __init__ (line 85) | def __init__(
    method _load_from_state_dict (line 103) | def _load_from_state_dict(
    method forward (line 124) | def forward(self, x: Tensor) -> Tensor:
    method __repr__ (line 136) | def __repr__(self) -> str:
  class ConvNormActivation (line 142) | class ConvNormActivation(torch.nn.Sequential):
    method __init__ (line 157) | def __init__(
  class SElayer (line 197) | class SElayer(torch.nn.Module):
    method __init__ (line 207) | def __init__(
    method _scale (line 223) | def _scale(self, input: Tensor) -> Tensor:
    method forward (line 231) | def forward(self, input: Tensor) -> Tensor:
  class InvertedResidualConfig (line 235) | class InvertedResidualConfig:
    method __init__ (line 237) | def __init__(
    method adjust_channels (line 259) | def adjust_channels(channels: int, width_mult: float):
  class InvertedResidual (line 262) | class InvertedResidual(nn.Module):
    method __init__ (line 264) | def __init__(
    method forward (line 320) | def forward(self, input: Tensor) -> Tensor:
  class MobileNetV3 (line 328) | class MobileNetV3(nn.Module):
    method __init__ (line 329) | def __init__(
    method _forward_impl (line 419) | def _forward_impl(self, x: Tensor) -> Tensor:
    method forward (line 429) | def forward(self, x: Tensor) -> Tensor:
  class MobileNetV3LargeEncoder (line 435) | class MobileNetV3LargeEncoder(MobileNetV3):
    method __init__ (line 436) | def __init__(self, pretrained: bool = False,in_ch: int = 3):
    method forward_single_frame (line 474) | def forward_single_frame(self, x):
    method forward_time_series (line 501) | def forward_time_series(self, x):
    method forward (line 507) | def forward(self, x):

FILE: models/model.py
  class MattingNetwork (line 26) | class MattingNetwork(nn.Module):
    method __init__ (line 27) | def __init__(self,
    method forward (line 52) | def forward(self,
    method _interpolate (line 83) | def _interpolate(self, x: Tensor, scale_factor: float):

FILE: models/model_hyperlips.py
  function preprocess_sketch (line 66) | def preprocess_sketch(skecth,hr_size):
  function get_smoothened_landmarks (line 81) | def get_smoothened_landmarks(all_landmarks,windows_T,hr_size,base_size,):
  class FastGuidedFilterRefiner (line 155) | class FastGuidedFilterRefiner(nn.Module):
    method __init__ (line 156) | def __init__(self, *args, **kwargs):
    method forward_single_frame (line 160) | def forward_single_frame(self, fine_src, base_src, base_fgr, base_pha):
    method forward_time_series (line 171) | def forward_time_series(self, fine_src, base_src, base_fgr, base_pha):
    method forward (line 182) | def forward(self, fine_src, base_src, base_fgr, base_pha, base_hid):
  class FastGuidedFilter (line 189) | class FastGuidedFilter(nn.Module):
    method __init__ (line 190) | def __init__(self, r: int, eps: float = 1e-5):
    method forward (line 196) | def forward(self, lr_x, lr_y, hr_x):
  class BoxFilter (line 208) | class BoxFilter(nn.Module):
    method __init__ (line 209) | def __init__(self, r):
    method forward (line 213) | def forward(self, x):
  class Conv2d (line 226) | class Conv2d(nn.Module):
    method __init__ (line 227) | def __init__(self, cin, cout, kernel_size, stride, padding, residual=F...
    method forward (line 236) | def forward(self, x):
  class nonorm_Conv2d (line 242) | class nonorm_Conv2d(nn.Module):
    method __init__ (line 243) | def __init__(self, cin, cout, kernel_size, stride, padding, residual=F...
    method forward (line 250) | def forward(self, x):
  class Conv2dTranspose (line 254) | class Conv2dTranspose(nn.Module):
    method __init__ (line 255) | def __init__(self, cin, cout, kernel_size, stride, padding, output_pad...
    method forward (line 263) | def forward(self, x):
  class HyperFCNet (line 268) | class HyperFCNet(nn.Module):
    method __init__ (line 271) | def __init__(self,
    method forward (line 295) | def forward(self, x,f1, f2, f3, f4):#([1, 512])
    method double_conv (line 324) | def double_conv(self, in_channels, out_channels,hnet_hdim):
  class HyperLipsBase (line 369) | class HyperLipsBase(nn.Module):
    method __init__ (line 370) | def __init__(self):
    method forward (line 388) | def forward(self,audio_sequences: Tensor,face_sequences: Tensor):
    method _interpolate (line 415) | def _interpolate(self, x: Tensor, scale_factor: float):
  class HRDecoder (line 426) | class HRDecoder(nn.Module):
    method __init__ (line 427) | def __init__(self,rescaling=1):
    method forward (line 458) | def forward(self,x):
  class HRDecoder_disc_qual (line 466) | class HRDecoder_disc_qual(nn.Module):
    method __init__ (line 467) | def __init__(self):
    method forward (line 490) | def forward(self, face_sequences):
  class HyperLips_inference (line 501) | class HyperLips_inference(nn.Module):
    method __init__ (line 502) | def __init__(self,window_T,rescaling=1,base_model_checkpoint="",HRDeco...
    method forward (line 537) | def forward(self,
  class HyperCtrolDiscriminator (line 581) | class HyperCtrolDiscriminator(nn.Module):
    method __init__ (line 582) | def __init__(self):
    method get_lower_half (line 611) | def get_lower_half(self, face_sequences):
    method to_2d (line 614) | def to_2d(self, face_sequences):
    method perceptual_forward (line 619) | def perceptual_forward(self, false_face_sequences):
    method forward (line 632) | def forward(self, face_sequences):

FILE: models/resnet.py
  class ResNet50Encoder (line 5) | class ResNet50Encoder(ResNet):
    method __init__ (line 6) | def __init__(self, pretrained: bool = False,in_ch: int = 3):
    method forward_single_frame (line 22) | def forward_single_frame(self, x):
    method forward_time_series (line 37) | def forward_time_series(self, x):
    method forward (line 43) | def forward(self, x):

FILE: models/syncnet.py
  class SyncNet_color2 (line 7) | class SyncNet_color2(nn.Module):
    method __init__ (line 8) | def __init__(self):
    method forward (line 55) | def forward(self, audio_sequences, face_sequences): # audio_sequences ...
  class SyncNet_color (line 71) | class SyncNet_color(nn.Module):
    method __init__ (line 72) | def __init__(self):
    method forward (line 122) | def forward(self, audio_sequences, face_sequences):

FILE: preprocess.py
  function split_video_5s (line 94) | def split_video_5s(args):
  function get_sketch (line 153) | def get_sketch(hight,width,image,savepath):
  function get_landmarks (line 175) | def get_landmarks(image, face_mesh,hight,width):
  function get_mask (line 191) | def get_mask(hight,width,image,savepath):
  function data_process_hyper_base (line 214) | def data_process_hyper_base(args):
  function split_train_test_text (line 257) | def split_train_test_text(args):
  function data_process_hyper_hq_module (line 292) | def data_process_hyper_hq_module(args):