SYMBOL INDEX (195 symbols across 20 files)

FILE: clip/clip.py
  function _download (line 39) | def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
  function _transform (line 71) | def _transform(n_px):
  function available_models (line 81) | def available_models() -> List[str]:
  function load (line 86) | def load(name: str, device: Union[str, torch.device] = "cuda" if torch.c...
  function tokenize (line 185) | def tokenize(texts: Union[str, List[str]], context_length: int = 77, tru...

FILE: clip/model.py
  class Bottleneck (line 10) | class Bottleneck(nn.Module):
    method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1):
    method forward (line 40) | def forward(self, x: torch.Tensor):
  class AttentionPool2d (line 56) | class AttentionPool2d(nn.Module):
    method __init__ (line 57) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 66) | def forward(self, x):
  class ModifiedResNet (line 93) | class ModifiedResNet(nn.Module):
    method __init__ (line 101) | def __init__(self, layers, output_dim, heads, input_resolution=224, wi...
    method _make_layer (line 126) | def _make_layer(self, planes, blocks, stride=1):
    method forward (line 135) | def forward(self, x):
  class LayerNorm (line 153) | class LayerNorm(nn.LayerNorm):
    method forward (line 156) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 162) | class QuickGELU(nn.Module):
    method forward (line 163) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 167) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 168) | def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor ...
    method attention (line 181) | def attention(self, x: torch.Tensor):
    method forward (line 185) | def forward(self, x: torch.Tensor):
  class Transformer (line 191) | class Transformer(nn.Module):
    method __init__ (line 192) | def __init__(self, width: int, layers: int, heads: int, attn_mask: tor...
    method forward (line 198) | def forward(self, x: torch.Tensor):
  class VisionTransformer (line 202) | class VisionTransformer(nn.Module):
    method __init__ (line 203) | def __init__(self, input_resolution: int, patch_size: int, width: int,...
    method forward (line 219) | def forward(self, x: torch.Tensor):
  class CLIP (line 239) | class CLIP(nn.Module):
    method __init__ (line 240) | def __init__(self,
    method initialize_parameters (line 295) | def initialize_parameters(self):
    method build_attention_mask (line 324) | def build_attention_mask(self):
    method dtype (line 333) | def dtype(self):
    method encode_image (line 336) | def encode_image(self, image):
    method encode_text (line 339) | def encode_text(self, text):
    method forward (line 354) | def forward(self, image, text):
  function convert_weights (line 371) | def convert_weights(model: nn.Module):
  function build_model (line 395) | def build_model(state_dict: dict):

FILE: clip/simple_tokenizer.py
  function default_bpe (line 11) | def default_bpe():
  function bytes_to_unicode (line 16) | def bytes_to_unicode():
  function get_pairs (line 38) | def get_pairs(word):
  function basic_clean (line 50) | def basic_clean(text):
  function whitespace_clean (line 56) | def whitespace_clean(text):
  class SimpleTokenizer (line 62) | class SimpleTokenizer(object):
    method __init__ (line 63) | def __init__(self, bpe_path: str = default_bpe()):
    method bpe (line 80) | def bpe(self, token):
    method encode (line 121) | def encode(self, text):
    method decode (line 129) | def decode(self, tokens):

FILE: datasets/test_dataset.py
  class TestLatentsDataset (line 6) | class TestLatentsDataset(Dataset):
    method __init__ (line 7) | def __init__(self):
    method __len__ (line 22) | def __len__(self):
    method __getitem__ (line 26) | def __getitem__(self, index):

FILE: datasets/train_dataset.py
  class TrainLatentsDataset (line 8) | class TrainLatentsDataset(Dataset):
    method __init__ (line 9) | def __init__(self, opts, cycle=True):
    method __len__ (line 35) | def __len__(self):
    method __getitem__ (line 41) | def __getitem__(self, index):

FILE: delta_mapper.py
  class Mapper (line 10) | class Mapper(Module):
    method __init__ (line 12) | def __init__(self, in_channel=512, out_channel=512, norm=True, num_lay...
    method forward (line 22) | def forward(self, x):
  class DeltaMapper (line 26) | class DeltaMapper(Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 46) | def forward(self, sspace_feat, clip_feat):

FILE: generate_codes.py
  function save_image_pytorch (line 12) | def save_image_pytorch(img, name):
  function generate (line 24) | def generate(args, netG, device, mean_latent):

FILE: models/encoders/helpers.py
  class Flatten (line 11) | class Flatten(Module):
    method forward (line 12) | def forward(self, input):
  function l2_norm (line 16) | def l2_norm(input, axis=1):
  class Bottleneck (line 22) | class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
  function get_block (line 26) | def get_block(in_channel, depth, num_units, stride=2):
  function get_blocks (line 30) | def get_blocks(num_layers):
  class SEModule (line 57) | class SEModule(Module):
    method __init__ (line 58) | def __init__(self, channels, reduction):
    method forward (line 66) | def forward(self, x):
  class bottleneck_IR (line 76) | class bottleneck_IR(Module):
    method __init__ (line 77) | def __init__(self, in_channel, depth, stride):
    method forward (line 92) | def forward(self, x):
  class bottleneck_IR_SE (line 98) | class bottleneck_IR_SE(Module):
    method __init__ (line 99) | def __init__(self, in_channel, depth, stride):
    method forward (line 117) | def forward(self, x):
  function _upsample_add (line 123) | def _upsample_add(x, y):

FILE: models/encoders/model_irse.py
  class Backbone (line 9) | class Backbone(Module):
    method __init__ (line 10) | def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, ...
    method forward (line 44) | def forward(self, x):
  function IR_50 (line 51) | def IR_50(input_size):
  function IR_101 (line 57) | def IR_101(input_size):
  function IR_152 (line 63) | def IR_152(input_size):
  function IR_SE_50 (line 69) | def IR_SE_50(input_size):
  function IR_SE_101 (line 75) | def IR_SE_101(input_size):
  function IR_SE_152 (line 81) | def IR_SE_152(input_size):

FILE: models/encoders/psp_encoders.py
  class ProgressiveStage (line 12) | class ProgressiveStage(Enum):
  class GradualStyleBlock (line 34) | class GradualStyleBlock(Module):
    method __init__ (line 35) | def __init__(self, in_c, out_c, spatial):
    method forward (line 51) | def forward(self, x):
  class GradualStyleEncoder (line 58) | class GradualStyleEncoder(Module):
    method __init__ (line 59) | def __init__(self, num_layers, mode='ir', opts=None):
    method forward (line 95) | def forward(self, x):
  class Encoder4Editing (line 124) | class Encoder4Editing(Module):
    method __init__ (line 125) | def __init__(self, num_layers, stylegan_size, mode='ir'):
    method get_deltas_starting_dimensions (line 165) | def get_deltas_starting_dimensions(self):
    method set_progressive_stage (line 169) | def set_progressive_stage(self, new_stage: ProgressiveStage):
    method forward (line 173) | def forward(self, x):
  class BackboneEncoderUsingLastLayerIntoW (line 203) | class BackboneEncoderUsingLastLayerIntoW(Module):
    method __init__ (line 204) | def __init__(self, num_layers, mode='ir', opts=None):
    method forward (line 229) | def forward(self, x):

FILE: models/stylegan2/model.py
  class PixelNorm (line 10) | class PixelNorm(nn.Module):
    method __init__ (line 11) | def __init__(self):
    method forward (line 14) | def forward(self, input):
  function make_kernel (line 18) | def make_kernel(k):
  class Upsample (line 29) | class Upsample(nn.Module):
    method __init__ (line 30) | def __init__(self, kernel, factor=2):
    method forward (line 44) | def forward(self, input):
  class Downsample (line 50) | class Downsample(nn.Module):
    method __init__ (line 51) | def __init__(self, kernel, factor=2):
    method forward (line 65) | def forward(self, input):
  class Blur (line 71) | class Blur(nn.Module):
    method __init__ (line 72) | def __init__(self, kernel, pad, upsample_factor=1):
    method forward (line 84) | def forward(self, input):
  class EqualConv2d (line 90) | class EqualConv2d(nn.Module):
    method __init__ (line 91) | def __init__(
    method forward (line 110) | def forward(self, input):
    method __repr__ (line 121) | def __repr__(self):
  class EqualLinear (line 128) | class EqualLinear(nn.Module):
    method __init__ (line 129) | def __init__(
    method forward (line 147) | def forward(self, input):
    method __repr__ (line 159) | def __repr__(self):
  class ScaledLeakyReLU (line 165) | class ScaledLeakyReLU(nn.Module):
    method __init__ (line 166) | def __init__(self, negative_slope=0.2):
    method forward (line 171) | def forward(self, input):
  class ModulatedConv2d (line 177) | class ModulatedConv2d(nn.Module):
    method __init__ (line 178) | def __init__(
    method __repr__ (line 226) | def __repr__(self):
    method forward (line 232) | def forward(self, input, style):
  class NoiseInjection (line 276) | class NoiseInjection(nn.Module):
    method __init__ (line 277) | def __init__(self):
    method forward (line 282) | def forward(self, image, noise=None):
  class ConstantInput (line 290) | class ConstantInput(nn.Module):
    method __init__ (line 291) | def __init__(self, channel, size=4):
    method forward (line 296) | def forward(self, input):
  class StyledConv (line 303) | class StyledConv(nn.Module):
    method __init__ (line 304) | def __init__(
    method forward (line 331) | def forward(self, input, style, noise=None):
  class ToRGB (line 340) | class ToRGB(nn.Module):
    method __init__ (line 341) | def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[...
    method forward (line 350) | def forward(self, input, style, skip=None):
  class Generator (line 362) | class Generator(nn.Module):
    method __init__ (line 363) | def __init__(
    method make_noise (line 448) | def make_noise(self):
    method mean_latent (line 459) | def mean_latent(self, n_latent):
    method get_latent (line 467) | def get_latent(self, input):
    method forward (line 470) | def forward(
  class ConvLayer (line 544) | class ConvLayer(nn.Sequential):
    method __init__ (line 545) | def __init__(
  class ResBlock (line 593) | class ResBlock(nn.Module):
    method __init__ (line 594) | def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
    method forward (line 604) | def forward(self, input):
  class Discriminator (line 614) | class Discriminator(nn.Module):
    method __init__ (line 615) | def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
    method forward (line 654) | def forward(self, input):

FILE: models/stylegan2/op/fused_act.py
  class FusedLeakyReLU (line 9) | class FusedLeakyReLU(nn.Module):
    method __init__ (line 10) | def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
    method forward (line 17) | def forward(self, input):
  function fused_leaky_relu (line 21) | def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):

FILE: models/stylegan2/op/upfirdn2d.py
  function upfirdn2d (line 7) | def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
  function upfirdn2d_native (line 12) | def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, ...

FILE: options/test_options.py
  class TestOptions (line 3) | class TestOptions:
    method __init__ (line 5) | def __init__(self):
    method initialize (line 9) | def initialize(self):
    method parse (line 25) | def parse(self):

FILE: options/train_options.py
  class TrainOptions (line 3) | class TrainOptions:
    method __init__ (line 5) | def __init__(self):
    method initialize (line 9) | def initialize(self):
    method parse (line 25) | def parse(self):

FILE: scripts/inference.py
  function GetBoundary (line 26) | def GetBoundary(fs3,dt,threshold):
  function improved_ds (line 32) | def improved_ds(ds, select):
  function main (line 38) | def main(opts):

FILE: scripts/inference_real.py
  function get_keys (line 33) | def get_keys(d, name):
  class Imagedataset (line 39) | class Imagedataset(Dataset):
    method __init__ (line 40) | def __init__(self,
    method __len__ (line 60) | def __len__(self):
    method __getitem__ (line 63) | def __getitem__(self, index):
  function encoder_latent (line 73) | def encoder_latent(G, latent):
  function GetBoundary (line 94) | def GetBoundary(fs3,dt,threshold):
  function improved_ds (line 100) | def improved_ds(ds, select):
  function main (line 106) | def main(opts):

FILE: scripts/train.py
  function main (line 14) | def main(opts):

FILE: utils/map_tool.py
  function zeroshot_classifier (line 89) | def zeroshot_classifier(classnames, templates,model):
  function GetDt (line 103) | def GetDt(classnames,model):

FILE: utils/stylespace_util.py
  function conv_warper (line 10) | def conv_warper(layer, input, style, noise):
  function decoder (line 58) | def decoder(G, style_space, latent, noise):
  function decoder_validate (line 78) | def decoder_validate(G, style_space, latent):
  function encoder_noise (line 101) | def encoder_noise(G, noise):
  function encoder_latent (line 122) | def encoder_latent(G, latent):
  function split_stylespace (line 141) | def split_stylespace(style):
  function fuse_stylespace (line 157) | def fuse_stylespace(style):