SYMBOL INDEX (199 symbols across 7 files)

FILE: CLUB_modules/mi_estimators.py
  class CLUBVec2Seq (line 13) | class CLUBVec2Seq(nn.Module):
    method __init__ (line 16) | def __init__(
    method temporal_avg_pool (line 47) | def temporal_avg_pool(self, x, mask=None):
    method get_mu_logvar (line 62) | def get_mu_logvar(self, seq, mask):
    method loglikeli (line 71) | def loglikeli(self, seq, vec, mask=None):
    method forward (line 82) | def forward(self, seq, vec, mask=None):
    method learning_loss (line 118) | def learning_loss(self, seq, vec, mask=None):
  class CLUBForCategorical (line 122) | class CLUBForCategorical(nn.Module): # Update 04/27/2022
    method __init__ (line 128) | def __init__(self, input_dim, label_num, hidden_size=None):
    method forward (line 144) | def forward(self, inputs, labels):
    method loglikeli (line 172) | def loglikeli(self, inputs, labels):
    method learning_loss (line 176) | def learning_loss(self, inputs, labels):
  class CLUB (line 180) | class CLUB(nn.Module):  # CLUB: Mutual Information Contrastive Learning ...
    method __init__ (line 191) | def __init__(self, x_dim, y_dim, hidden_size, is_sampled_version=False):
    method get_mu_logvar (line 205) | def get_mu_logvar(self, x_samples):
    method forward (line 210) | def forward(self, x_samples, y_samples):
    method loglikeli (line 237) | def loglikeli(self, x_samples, y_samples): # unnormalized loglikelihood
    method learning_loss (line 241) | def learning_loss(self, x_samples, y_samples):
  class MINE (line 245) | class MINE(nn.Module):
    method __init__ (line 246) | def __init__(self, x_dim, y_dim, hidden_size):
    method forward (line 252) | def forward(self, x_samples, y_samples):  # samples have shape [sample...
    method learning_loss (line 267) | def learning_loss(self, x_samples, y_samples):
  class NWJ (line 271) | class NWJ(nn.Module):
    method __init__ (line 272) | def __init__(self, x_dim, y_dim, hidden_size):
    method forward (line 278) | def forward(self, x_samples, y_samples):
    method learning_loss (line 291) | def learning_loss(self, x_samples, y_samples):
  class InfoNCE (line 295) | class InfoNCE(nn.Module):
    method __init__ (line 296) | def __init__(self, x_dim, y_dim, hidden_size):
    method forward (line 303) | def forward(self, x_samples, y_samples):  # samples have shape [sample...
    method learning_loss (line 316) | def learning_loss(self, x_samples, y_samples):
  function log_sum_exp (line 320) | def log_sum_exp(value, dim=None, keepdim=False):
  class L1OutUB (line 341) | class L1OutUB(nn.Module):  # naive upper bound
    method __init__ (line 342) | def __init__(self, x_dim, y_dim, hidden_size):
    method get_mu_logvar (line 353) | def get_mu_logvar(self, x_samples):
    method forward (line 358) | def forward(self, x_samples, y_samples):
    method loglikeli (line 374) | def loglikeli(self, x_samples, y_samples):
    method learning_loss (line 378) | def learning_loss(self, x_samples, y_samples):
  class VarUB (line 382) | class VarUB(nn.Module):  #    variational upper bound
    method __init__ (line 383) | def __init__(self, x_dim, y_dim, hidden_size):
    method get_mu_logvar (line 394) | def get_mu_logvar(self, x_samples):
    method forward (line 399) | def forward(self, x_samples, y_samples): #[nsample, 1]
    method loglikeli (line 403) | def loglikeli(self, x_samples, y_samples):
    method learning_loss (line 407) | def learning_loss(self, x_samples, y_samples):

FILE: CLUB_modules/mi_estimators_dist.py
  class CLUBVec2Seq (line 14) | class CLUBVec2Seq(nn.Module):
    method __init__ (line 17) | def __init__(
    method temporal_avg_pool (line 48) | def temporal_avg_pool(self, x, mask=None):
    method get_mu_logvar (line 63) | def get_mu_logvar(self, seq, mask):
    method loglikeli (line 72) | def loglikeli(self, seq, vec, mask=None):
    method forward (line 83) | def forward(self, seq, vec, mask=None):
    method learning_loss (line 126) | def learning_loss(self, seq, vec, mask=None):
  class CLUBForCategorical (line 130) | class CLUBForCategorical(nn.Module):
    method __init__ (line 138) | def __init__(self, input_dim, label_num, hidden_size=None):
    method forward (line 154) | def forward(self, inputs, labels):
    method loglikeli (line 201) | def loglikeli(self, inputs, labels):
    method learning_loss (line 205) | def learning_loss(self, inputs, labels):
  class CLUB (line 209) | class CLUB(nn.Module):  # CLUB: Mutual Information Contrastive Learning ...
    method __init__ (line 220) | def __init__(self, x_dim, y_dim, hidden_size, is_sampled_version=False):
    method get_mu_logvar (line 234) | def get_mu_logvar(self, x_samples):
    method forward (line 239) | def forward(self, x_samples, y_samples):
    method loglikeli (line 275) | def loglikeli(self, x_samples, y_samples): # unnormalized loglikelihood
    method learning_loss (line 279) | def learning_loss(self, x_samples, y_samples):

FILE: dataloader/dataloader.py
  class AudioMotionDataset (line 7) | class AudioMotionDataset(Dataset):
    method __init__ (line 8) | def __init__(self, text_file, wav_scp_file,description_file):
    method __getitem__ (line 30) | def __getitem__(self, index):
    method __len__ (line 37) | def __len__(self):
  function collate_fn (line 42) | def collate_fn(batch):

FILE: model2.py
  class KeywordsStoppingCriteria (line 20) | class KeywordsStoppingCriteria(StoppingCriteria):
    method __init__ (line 21) | def __init__(self, keywords_ids:list):
    method __call__ (line 24) | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTen...
  class MotionAudio (line 29) | class MotionAudio(pl.LightningModule):
    method __init__ (line 30) | def __init__(
    method init_Qformer (line 76) | def init_Qformer(self,num_query_token, vision_width, cross_attention_f...
    method mean_pooling (line 94) | def mean_pooling(self,model_output, attention_mask):
    method forward (line 102) | def forward(self, audio, describtion):
    method training_step (line 170) | def training_step(self, batch, batch_idx):
    method validation_step (line 175) | def validation_step(self, batch, batch_idx):
    method configure_optimizers (line 180) | def configure_optimizers(self):
    method inference (line 183) | def inference(self, audio):
    method post_processing (line 256) | def post_processing(self, sentences,device):
    method test_step (line 268) | def test_step(self, batch, batch_idx):
  function count_parameters (line 286) | def count_parameters(model):

FILE: module/Qformer.py
  class BertEmbeddings (line 41) | class BertEmbeddings(nn.Module):
    method __init__ (line 44) | def __init__(self, config):
    method forward (line 68) | def forward(
  class BertSelfAttention (line 101) | class BertSelfAttention(nn.Module):
    method __init__ (line 102) | def __init__(self, config, is_cross_attention):
    method save_attn_gradients (line 139) | def save_attn_gradients(self, attn_gradients):
    method get_attn_gradients (line 142) | def get_attn_gradients(self):
    method save_attention_map (line 145) | def save_attention_map(self, attention_map):
    method get_attention_map (line 148) | def get_attention_map(self):
    method transpose_for_scores (line 151) | def transpose_for_scores(self, x):
    method forward (line 159) | def forward(
  class BertSelfOutput (line 268) | class BertSelfOutput(nn.Module):
    method __init__ (line 269) | def __init__(self, config):
    method forward (line 275) | def forward(self, hidden_states, input_tensor):
  class BertAttention (line 282) | class BertAttention(nn.Module):
    method __init__ (line 283) | def __init__(self, config, is_cross_attention=False):
    method prune_heads (line 289) | def prune_heads(self, heads):
    method forward (line 312) | def forward(
  class BertIntermediate (line 339) | class BertIntermediate(nn.Module):
    method __init__ (line 340) | def __init__(self, config):
    method forward (line 348) | def forward(self, hidden_states):
  class BertOutput (line 354) | class BertOutput(nn.Module):
    method __init__ (line 355) | def __init__(self, config):
    method forward (line 361) | def forward(self, hidden_states, input_tensor):
  class BertLayer (line 368) | class BertLayer(nn.Module):
    method __init__ (line 369) | def __init__(self, config, layer_num):
    method forward (line 392) | def forward(
    method feed_forward_chunk (line 466) | def feed_forward_chunk(self, attention_output):
    method feed_forward_chunk_query (line 471) | def feed_forward_chunk_query(self, attention_output):
  class BertEncoder (line 477) | class BertEncoder(nn.Module):
    method __init__ (line 478) | def __init__(self, config):
    method forward (line 485) | def forward(
  class BertPooler (line 582) | class BertPooler(nn.Module):
    method __init__ (line 583) | def __init__(self, config):
    method forward (line 588) | def forward(self, hidden_states):
  class BertPredictionHeadTransform (line 597) | class BertPredictionHeadTransform(nn.Module):
    method __init__ (line 598) | def __init__(self, config):
    method forward (line 607) | def forward(self, hidden_states):
  class BertLMPredictionHead (line 614) | class BertLMPredictionHead(nn.Module):
    method __init__ (line 615) | def __init__(self, config):
    method forward (line 628) | def forward(self, hidden_states):
  class BertOnlyMLMHead (line 634) | class BertOnlyMLMHead(nn.Module):
    method __init__ (line 635) | def __init__(self, config):
    method forward (line 639) | def forward(self, sequence_output):
  class BertPreTrainedModel (line 644) | class BertPreTrainedModel(PreTrainedModel):
    method _init_weights (line 654) | def _init_weights(self, module):
  class BertModel (line 667) | class BertModel(BertPreTrainedModel):
    method __init__ (line 677) | def __init__(self, config, add_pooling_layer=False):
    method get_input_embeddings (line 689) | def get_input_embeddings(self):
    method set_input_embeddings (line 692) | def set_input_embeddings(self, value):
    method _prune_heads (line 695) | def _prune_heads(self, heads_to_prune):
    method get_extended_attention_mask (line 703) | def get_extended_attention_mask(
    method forward (line 794) | def forward(
  class BertLMHeadModel (line 958) | class BertLMHeadModel(BertPreTrainedModel):
    method __init__ (line 963) | def __init__(self, config):
    method get_output_embeddings (line 971) | def get_output_embeddings(self):
    method set_output_embeddings (line 974) | def set_output_embeddings(self, new_embeddings):
    method forward (line 977) | def forward(
    method prepare_inputs_for_generation (line 1087) | def prepare_inputs_for_generation(
    method _reorder_cache (line 1110) | def _reorder_cache(self, past, beam_idx):
  class BertForMaskedLM (line 1121) | class BertForMaskedLM(BertPreTrainedModel):
    method __init__ (line 1126) | def __init__(self, config):
    method get_output_embeddings (line 1134) | def get_output_embeddings(self):
    method set_output_embeddings (line 1137) | def set_output_embeddings(self, new_embeddings):
    method forward (line 1140) | def forward(

FILE: module/modeling_llama.py
  function _make_causal_mask (line 42) | def _make_causal_mask(
  function _expand_mask (line 60) | def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Option...
  class LlamaRMSNorm (line 74) | class LlamaRMSNorm(nn.Module):
    method __init__ (line 75) | def __init__(self, hidden_size, eps=1e-6):
    method forward (line 83) | def forward(self, hidden_states):
  class LlamaRotaryEmbedding (line 91) | class LlamaRotaryEmbedding(torch.nn.Module):
    method __init__ (line 92) | def __init__(self, dim, max_position_embeddings=2048, base=10000, devi...
    method forward (line 107) | def forward(self, x, seq_len=None):
  function rotate_half (line 124) | def rotate_half(x):
  function apply_rotary_pos_emb (line 131) | def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
  class LlamaMLP (line 142) | class LlamaMLP(nn.Module):
    method __init__ (line 143) | def __init__(
    method forward (line 155) | def forward(self, x):
  class LlamaAttention (line 159) | class LlamaAttention(nn.Module):
    method __init__ (line 162) | def __init__(self, config: LlamaConfig):
    method _shape (line 181) | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
    method forward (line 184) | def forward(
  class LlamaDecoderLayer (line 253) | class LlamaDecoderLayer(nn.Module):
    method __init__ (line 254) | def __init__(self, config: LlamaConfig):
    method forward (line 266) | def forward(
  class LlamaPreTrainedModel (line 342) | class LlamaPreTrainedModel(PreTrainedModel):
    method _init_weights (line 349) | def _init_weights(self, module):
    method _set_gradient_checkpointing (line 360) | def _set_gradient_checkpointing(self, module, value=False):
  class LlamaModel (line 433) | class LlamaModel(LlamaPreTrainedModel):
    method __init__ (line 441) | def __init__(self, config: LlamaConfig):
    method get_input_embeddings (line 454) | def get_input_embeddings(self):
    method set_input_embeddings (line 457) | def set_input_embeddings(self, value):
    method _prepare_decoder_attention_mask (line 461) | def _prepare_decoder_attention_mask(self, attention_mask, input_shape,...
    method forward (line 485) | def forward(
  class LlamaForCausalLM (line 613) | class LlamaForCausalLM(LlamaPreTrainedModel):
    method __init__ (line 616) | def __init__(self, config):
    method get_input_embeddings (line 625) | def get_input_embeddings(self):
    method set_input_embeddings (line 628) | def set_input_embeddings(self, value):
    method get_output_embeddings (line 631) | def get_output_embeddings(self):
    method set_output_embeddings (line 634) | def set_output_embeddings(self, new_embeddings):
    method set_decoder (line 637) | def set_decoder(self, decoder):
    method get_decoder (line 640) | def get_decoder(self):
    method forward (line 645) | def forward(
    method prepare_inputs_for_generation (line 732) | def prepare_inputs_for_generation(
    method _reorder_cache (line 763) | def _reorder_cache(past_key_values, beam_idx):
  class LlamaForSequenceClassification (line 787) | class LlamaForSequenceClassification(LlamaPreTrainedModel):
    method __init__ (line 788) | def __init__(self, config):
    method get_input_embeddings (line 797) | def get_input_embeddings(self):
    method set_input_embeddings (line 800) | def set_input_embeddings(self, value):
    method forward (line 804) | def forward(

FILE: tool/get_sentence_simi.py
  class SimiCal (line 13) | class SimiCal():
    method __init__ (line 14) | def __init__(self, device=torch.device('cuda')):
    method mean_pooling (line 25) | def mean_pooling(self, model_output, attention_mask):
    method cos_sim (line 30) | def cos_sim(self, a: Union[torch.Tensor, np.ndarray], b: Union[torch.T...
    method __call__ (line 51) | def __call__(self,inp1,inp2):
  function test_SimiCal (line 61) | def test_SimiCal():
  function calculate_mean_variance (line 66) | def calculate_mean_variance(lst):
  function predictSimiWrapper (line 75) | def predictSimiWrapper(fpath):