SYMBOL INDEX (227 symbols across 23 files)

FILE: data/mvtec.py
  class MVTecSolver (line 5) | class MVTecSolver(object):
    method __init__ (line 12) | def __init__(self, root='data/mvtec'):
    method run (line 16) | def run(self):

FILE: data/visa.py
  class VisASolver (line 6) | class VisASolver(object):
    method __init__ (line 13) | def __init__(self, root='data/visa'):
    method run (line 19) | def run(self):

FILE: dataset.py
  class VisaDataset (line 10) | class VisaDataset(data.Dataset):
    method __init__ (line 11) | def __init__(self, root, transform, target_transform, mode='test', k_s...
    method __len__ (line 38) | def __len__(self):
    method get_cls_names (line 41) | def get_cls_names(self):
    method __getitem__ (line 44) | def __getitem__(self, index):
  class MVTecDataset (line 63) | class MVTecDataset(data.Dataset):
    method __init__ (line 64) | def __init__(self, root, transform, target_transform, aug_rate, mode='...
    method __len__ (line 92) | def __len__(self):
    method get_cls_names (line 95) | def get_cls_names(self):
    method combine_img (line 98) | def combine_img(self, cls_name):
    method __getitem__ (line 138) | def __getitem__(self, index):

FILE: few_shot.py
  function memory (line 4) | def memory(model_name, model, obj_list, dataset_dir, save_path, preproce...

FILE: loss.py
  class FocalLoss (line 7) | class FocalLoss(nn.Module):
    method __init__ (line 21) | def __init__(self, apply_nonlin=None, alpha=None, gamma=2, balance_ind...
    method forward (line 34) | def forward(self, logit, target):
  class BinaryDiceLoss (line 89) | class BinaryDiceLoss(nn.Module):
    method __init__ (line 90) | def __init__(self):
    method forward (line 93) | def forward(self, input, targets):

FILE: model.py
  class LinearLayer (line 5) | class LinearLayer(nn.Module):
    method __init__ (line 6) | def __init__(self, dim_in, dim_out, k, model):
    method forward (line 13) | def forward(self, tokens):

FILE: open_clip/coca_model.py
  class MultimodalCfg (line 45) | class MultimodalCfg(CLIPTextCfg):
  function _build_text_decoder_tower (line 53) | def _build_text_decoder_tower(
  class CoCa (line 79) | class CoCa(nn.Module):
    method __init__ (line 80) | def __init__(
    method set_grad_checkpointing (line 126) | def set_grad_checkpointing(self, enable=True):
    method _encode_image (line 135) | def _encode_image(self, images, out_layers, normalize=True):
    method _encode_text (line 139) | def _encode_text(self, text, normalize=True, embed_cls=True):
    method encode_image (line 148) | def encode_image(self, images, out_layers, normalize=True):
    method encode_text (line 152) | def encode_text(self, text, normalize=True, embed_cls=True):
    method forward (line 156) | def forward(self, image, text, embed_cls=True, image_latent=None, imag...
    method generate (line 173) | def generate(
    method _generate_beamsearch (line 296) | def _generate_beamsearch(
  function prepare_inputs_for_generation (line 445) | def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **...

FILE: open_clip/factory.py
  function _natural_key (line 29) | def _natural_key(string_):
  function _rescan_model_configs (line 33) | def _rescan_model_configs():
  function list_models (line 57) | def list_models():
  function add_model_config (line 62) | def add_model_config(path):
  function get_model_config (line 70) | def get_model_config(model_name):
  function get_tokenizer (line 77) | def get_tokenizer(model_name):
  function load_state_dict (line 87) | def load_state_dict(checkpoint_path: str, map_location='cpu'):
  function load_checkpoint (line 98) | def load_checkpoint(model, checkpoint_path, strict=True):
  function create_model (line 108) | def create_model(
  function create_loss (line 286) | def create_loss(args):
  function create_model_and_transforms (line 317) | def create_model_and_transforms(
  function create_model_from_pretrained (line 372) | def create_model_from_pretrained(

FILE: open_clip/hf_model.py
  class BaseModelOutput (line 21) | class BaseModelOutput:
  class PretrainedConfig (line 25) | class PretrainedConfig:
  function _camel2snake (line 32) | def _camel2snake(s):
  function register_pooler (line 40) | def register_pooler(cls):
  class MeanPooler (line 47) | class MeanPooler(nn.Module):
    method forward (line 50) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class MaxPooler (line 56) | class MaxPooler(nn.Module):
    method forward (line 59) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class ClsPooler (line 65) | class ClsPooler(nn.Module):
    method __init__ (line 68) | def __init__(self, use_pooler_output=True):
    method forward (line 73) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class HFTextEncoder (line 83) | class HFTextEncoder(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 137) | def forward(self, x: TensorType):
    method lock (line 154) | def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
    method set_grad_checkpointing (line 172) | def set_grad_checkpointing(self, enable=True):
    method init_parameters (line 175) | def init_parameters(self):

FILE: open_clip/loss.py
  function gather_features (line 19) | def gather_features(
  class ClipLoss (line 66) | class ClipLoss(nn.Module):
    method __init__ (line 68) | def __init__(
    method get_ground_truth (line 89) | def get_ground_truth(self, device, num_logits) -> torch.Tensor:
    method get_logits (line 102) | def get_logits(self, image_features, text_features, logit_scale):
    method forward (line 120) | def forward(self, image_features, text_features, logit_scale, output_d...
  class CoCaLoss (line 134) | class CoCaLoss(ClipLoss):
    method __init__ (line 135) | def __init__(
    method forward (line 160) | def forward(self, image_features, text_features, logits, labels, logit...
  class DistillClipLoss (line 176) | class DistillClipLoss(ClipLoss):
    method dist_loss (line 178) | def dist_loss(self, teacher_logits, student_logits):
    method forward (line 181) | def forward(

FILE: open_clip/model.py
  class CLIPVisionCfg (line 24) | class CLIPVisionCfg:
  class CLIPTextCfg (line 49) | class CLIPTextCfg:
  function get_cast_dtype (line 66) | def get_cast_dtype(precision: str):
  function _build_vision_tower (line 75) | def _build_vision_tower(
  function _build_text_tower (line 137) | def _build_text_tower(
  class CLIP (line 176) | class CLIP(nn.Module):
    method __init__ (line 179) | def __init__(
    method lock_image_tower (line 203) | def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 208) | def set_grad_checkpointing(self, enable=True):
    method encode_image (line 212) | def encode_image(self, image, out_layers, normalize: bool = False):
    method encode_text (line 216) | def encode_text(self, text, normalize: bool = False):
    method forward (line 230) | def forward(self, image, text):
  class CustomTextCLIP (line 242) | class CustomTextCLIP(nn.Module):
    method __init__ (line 245) | def __init__(
    method lock_image_tower (line 260) | def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
    method lock_text_tower (line 264) | def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm:...
    method set_grad_checkpointing (line 268) | def set_grad_checkpointing(self, enable=True):
    method encode_image (line 272) | def encode_image(self, image, normalize: bool = False):
    method encode_text (line 276) | def encode_text(self, text, normalize: bool = False):
    method forward (line 280) | def forward(self, image, text):
  function convert_weights_to_lp (line 292) | def convert_weights_to_lp(model: nn.Module, dtype=torch.float16):
  function convert_to_custom_text_state_dict (line 320) | def convert_to_custom_text_state_dict(state_dict: dict):
  function build_model_from_openai_state_dict (line 338) | def build_model_from_openai_state_dict(
  function trace_model (line 398) | def trace_model(model, batch_size=256, device=torch.device('cpu')):
  function resize_pos_embed (line 414) | def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', ...

FILE: open_clip/modified_resnet.py
  class Bottleneck (line 10) | class Bottleneck(nn.Module):
    method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1):
    method forward (line 42) | def forward(self, x: torch.Tensor):
  class AttentionPool2d (line 58) | class AttentionPool2d(nn.Module):
    method __init__ (line 59) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 68) | def forward(self, x):
  class ModifiedResNet (line 95) | class ModifiedResNet(nn.Module):
    method __init__ (line 103) | def __init__(self, layers, output_dim, heads, image_size=224, width=64):
    method _make_layer (line 132) | def _make_layer(self, planes, blocks, stride=1):
    method init_parameters (line 141) | def init_parameters(self):
    method lock (line 154) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 162) | def set_grad_checkpointing(self, enable=True):
    method stem (line 166) | def stem(self, x):
    method forward (line 173) | def forward(self, x, out_blocks):

FILE: open_clip/openai.py
  function list_openai_models (line 18) | def list_openai_models() -> List[str]:
  function load_openai_model (line 23) | def load_openai_model(

FILE: open_clip/pretrained.py
  function _pcfg (line 21) | def _pcfg(url='', hf_hub='', mean=None, std=None):
  function _clean_tag (line 235) | def _clean_tag(tag: str):
  function list_pretrained (line 240) | def list_pretrained(as_str: bool = False):
  function list_pretrained_models_by_tag (line 247) | def list_pretrained_models_by_tag(tag: str):
  function list_pretrained_tags_by_model (line 257) | def list_pretrained_tags_by_model(model: str):
  function is_pretrained_cfg (line 265) | def is_pretrained_cfg(model: str, tag: str):
  function get_pretrained_cfg (line 271) | def get_pretrained_cfg(model: str, tag: str):
  function get_pretrained_url (line 278) | def get_pretrained_url(model: str, tag: str):
  function download_pretrained_from_url (line 283) | def download_pretrained_from_url(
  function has_hf_hub (line 329) | def has_hf_hub(necessary=False):
  function download_pretrained_from_hf (line 337) | def download_pretrained_from_hf(
  function download_pretrained (line 348) | def download_pretrained(

FILE: open_clip/push_to_hf_hub.py
  function save_config_for_hf (line 27) | def save_config_for_hf(
  function save_for_hf (line 45) | def save_for_hf(
  function push_to_hf_hub (line 65) | def push_to_hf_hub(
  function push_pretrained_to_hf_hub (line 124) | def push_pretrained_to_hf_hub(
  function generate_readme (line 163) | def generate_readme(model_card: dict, model_name: str):

FILE: open_clip/timm_model.py
  class TimmModel (line 28) | class TimmModel(nn.Module):
    method __init__ (line 33) | def __init__(
    method lock (line 85) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 118) | def set_grad_checkpointing(self, enable=True):
    method forward (line 124) | def forward(self, x):

FILE: open_clip/tokenizer.py
  function default_bpe (line 21) | def default_bpe():
  function bytes_to_unicode (line 26) | def bytes_to_unicode():
  function get_pairs (line 48) | def get_pairs(word):
  function basic_clean (line 60) | def basic_clean(text):
  function whitespace_clean (line 66) | def whitespace_clean(text):
  class SimpleTokenizer (line 72) | class SimpleTokenizer(object):
    method __init__ (line 73) | def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
    method bpe (line 98) | def bpe(self, token):
    method encode (line 139) | def encode(self, text):
    method decode (line 147) | def decode(self, tokens):
  function decode (line 155) | def decode(output_ids: torch.Tensor):
  function tokenize (line 159) | def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> ...
  class HFTokenizer (line 191) | class HFTokenizer:
    method __init__ (line 194) | def __init__(self, tokenizer_name: str):
    method save_pretrained (line 198) | def save_pretrained(self, dest):
    method __call__ (line 201) | def __call__(self, texts: Union[str, List[str]], context_length: int =...

FILE: open_clip/transform.py
  class AugmentationCfg (line 16) | class AugmentationCfg:
  class ResizeMaxSize (line 26) | class ResizeMaxSize(nn.Module):
    method __init__ (line 28) | def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, ...
    method forward (line 37) | def forward(self, img):
  function _convert_to_rgb (line 52) | def _convert_to_rgb(image):
  function image_transform (line 56) | def image_transform(

FILE: open_clip/transformer.py
  class LayerNormFp32 (line 14) | class LayerNormFp32(nn.LayerNorm):
    method forward (line 17) | def forward(self, x: torch.Tensor):
  class LayerNorm (line 23) | class LayerNorm(nn.LayerNorm):
    method forward (line 26) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 32) | class QuickGELU(nn.Module):
    method forward (line 34) | def forward(self, x: torch.Tensor):
  class LayerScale (line 38) | class LayerScale(nn.Module):
    method __init__ (line 39) | def __init__(self, dim, init_values=1e-5, inplace=False):
    method forward (line 44) | def forward(self, x):
  class PatchDropout (line 48) | class PatchDropout(nn.Module):
    method __init__ (line 53) | def __init__(self, prob, exclude_first_token=True):
    method forward (line 59) | def forward(self, x):
  class Attention (line 88) | class Attention(nn.Module):
    method __init__ (line 89) | def __init__(
    method forward (line 128) | def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
  class AttentionalPooler (line 164) | class AttentionalPooler(nn.Module):
    method __init__ (line 165) | def __init__(
    method forward (line 179) | def forward(self, x: torch.Tensor):
    method _repeat (line 186) | def _repeat(self, query, N: int):
  class ResidualAttentionBlock (line 190) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 191) | def __init__(
    method attention (line 221) | def attention(
    method forward (line 236) | def forward(
  class CustomResidualAttentionBlock (line 252) | class CustomResidualAttentionBlock(nn.Module):
    method __init__ (line 253) | def __init__(
    method forward (line 287) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
  class Transformer (line 293) | class Transformer(nn.Module):
    method __init__ (line 294) | def __init__(
    method get_cast_dtype (line 316) | def get_cast_dtype(self) -> torch.dtype:
    method forward (line 319) | def forward(self, x: torch.Tensor, out_layers: list = [3, 6, 9],
  class VisionTransformer (line 342) | class VisionTransformer(nn.Module):
    method __init__ (line 345) | def __init__(
    method lock (line 415) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method init_parameters (line 448) | def init_parameters(self):
    method set_grad_checkpointing (line 469) | def set_grad_checkpointing(self, enable=True):
    method _global_pool (line 472) | def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.T...
    method forward (line 478) | def forward(self, x: torch.Tensor, out_layers: list):
  class TextTransformer (line 537) | class TextTransformer(nn.Module):
    method __init__ (line 540) | def __init__(
    method init_parameters (line 588) | def init_parameters(self):
    method set_grad_checkpointing (line 607) | def set_grad_checkpointing(self, enable=True):
    method build_attention_mask (line 610) | def build_attention_mask(self):
    method build_cls_mask (line 618) | def build_cls_mask(self, text, cast_dtype: torch.dtype):
    method _repeat (line 627) | def _repeat(self, t, N: int):
    method forward (line 630) | def forward(self, text):
  class MultimodalTransformer (line 665) | class MultimodalTransformer(Transformer):
    method __init__ (line 666) | def __init__(
    method init_parameters (line 707) | def init_parameters(self):
    method build_attention_mask (line 725) | def build_attention_mask(self):
    method forward (line 733) | def forward(self, image_embs, text_embs):
    method set_grad_checkpointing (line 756) | def set_grad_checkpointing(self, enable=True):

FILE: open_clip/utils.py
  function freeze_batch_norm_2d (line 8) | def freeze_batch_norm_2d(module, module_match={}, name=''):
  function _ntuple (line 48) | def _ntuple(n):

FILE: prompt_ensemble.py
  function encode_text_with_prompt_ensemble (line 8) | def encode_text_with_prompt_ensemble(model, objs, tokenizer, device):

FILE: test.py
  function setup_seed (line 23) | def setup_seed(seed):
  function normalize (line 32) | def normalize(pred, max_value=None, min_value=None):
  function apply_ad_scoremap (line 39) | def apply_ad_scoremap(image, scoremap, alpha=0.5):
  function cal_pro_score (line 47) | def cal_pro_score(masks, amaps, max_step=200, expect_fpr=0.3):
  function test (line 74) | def test(args):

FILE: train.py
  function setup_seed (line 22) | def setup_seed(seed):
  function train (line 31) | def train(args):