SYMBOL INDEX (172 symbols across 11 files)

FILE: pec_baseline/models/base_model.py
  function is_torch_available (line 32) | def is_torch_available():

FILE: pec_baseline/models/gpt/modeling_openai.py
  function load_tf_weights_in_openai_gpt (line 67) | def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folde...
  class Attention (line 151) | class Attention(nn.Module):
    method __init__ (line 152) | def __init__(self, nx, n_ctx, config, scale=False):
    method prune_heads (line 168) | def prune_heads(self, heads):
    method _attn (line 183) | def _attn(self, q, k, v, attention_mask=None, head_mask=None, output_a...
    method merge_heads (line 208) | def merge_heads(self, x):
    method split_heads (line 213) | def split_heads(self, x, k=False):
    method forward (line 221) | def forward(self, x, attention_mask=None, head_mask=None, output_atten...
  class MLP (line 239) | class MLP(nn.Module):
    method __init__ (line 240) | def __init__(self, n_state, config):  # in MLP: n_state=3072 (4 * n_embd)
    method forward (line 248) | def forward(self, x):
  class Block (line 254) | class Block(nn.Module):
    method __init__ (line 255) | def __init__(self, n_ctx, config, scale=False):
    method forward (line 263) | def forward(self, x, attention_mask=None, head_mask=None, output_atten...
  class OpenAIGPTPreTrainedModel (line 280) | class OpenAIGPTPreTrainedModel(PreTrainedModel):
    method _init_weights (line 291) | def _init_weights(self, module):
  class OpenAIGPTDoubleHeadsModelOutput (line 309) | class OpenAIGPTDoubleHeadsModelOutput(ModelOutput):
  class OpenAIGPTModel (line 415) | class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
    method __init__ (line 416) | def __init__(self, config):
    method get_input_embeddings (line 427) | def get_input_embeddings(self):
    method set_input_embeddings (line 430) | def set_input_embeddings(self, new_embeddings):
    method _prune_heads (line 433) | def _prune_heads(self, heads_to_prune):
    method forward (line 447) | def forward(
  class OpenAIGPTLMHeadModel (line 545) | class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
    method __init__ (line 546) | def __init__(self, config):
    method get_output_embeddings (line 553) | def get_output_embeddings(self):
    method set_output_embeddings (line 556) | def set_output_embeddings(self, new_embeddings):
    method forward (line 566) | def forward(
  class OpenAIGPTDoubleHeadsModel (line 633) | class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
    method __init__ (line 634) | def __init__(self, config):
    method get_output_embeddings (line 644) | def get_output_embeddings(self):
    method set_output_embeddings (line 647) | def set_output_embeddings(self, new_embeddings):
    method forward (line 653) | def forward(
  class OpenAIGPTForSequenceClassification (line 757) | class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
    method __init__ (line 758) | def __init__(self, config):
    method forward (line 773) | def forward(

FILE: pec_baseline/models/gpt/tokenization_openai.py
  function get_pairs (line 49) | def get_pairs(word):
  function text_standardize (line 62) | def text_standardize(text):
  class OpenAIGPTTokenizer (line 77) | class OpenAIGPTTokenizer(PreTrainedTokenizer):
    method __init__ (line 103) | def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
    method do_lower_case (line 128) | def do_lower_case(self):
    method vocab_size (line 132) | def vocab_size(self):
    method get_vocab (line 135) | def get_vocab(self):
    method bpe (line 138) | def bpe(self, token):
    method _tokenize (line 182) | def _tokenize(self, text):
    method _convert_token_to_id (line 197) | def _convert_token_to_id(self, token):
    method _convert_id_to_token (line 201) | def _convert_id_to_token(self, index):
    method convert_tokens_to_string (line 205) | def convert_tokens_to_string(self, tokens):
    method save_vocabulary (line 210) | def save_vocabulary(self, save_directory: str, filename_prefix: Option...

FILE: pec_baseline/models/gpt2/modeling_gpt2.py
  function load_tf_weights_in_gpt2 (line 76) | def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
  class GPT2Attention (line 133) | class GPT2Attention(nn.Module):
    method __init__ (line 134) | def __init__(self, config, is_cross_attention=False):
    method prune_heads (line 170) | def prune_heads(self, heads):
    method _attn (line 185) | def _attn(self, query, key, value, attention_mask=None, head_mask=None):
    method _split_heads (line 212) | def _split_heads(self, tensor, num_heads, attn_head_size):
    method _merge_heads (line 220) | def _merge_heads(self, tensor, num_heads, attn_head_size):
    method forward (line 228) | def forward(
  class GPT2MLP (line 279) | class GPT2MLP(nn.Module):
    method __init__ (line 280) | def __init__(self, intermediate_size, config):
    method forward (line 288) | def forward(self, hidden_states):
  class GPT2Block (line 296) | class GPT2Block(nn.Module):
    method __init__ (line 297) | def __init__(self, config):
    method forward (line 312) | def forward(
  class GPT2PreTrainedModel (line 374) | class GPT2PreTrainedModel(PreTrainedModel):
    method __init__ (line 386) | def __init__(self, *inputs, **kwargs):
    method _init_weights (line 389) | def _init_weights(self, module):
    method _set_gradient_checkpointing (line 405) | def _set_gradient_checkpointing(self, module, value=False):
  class GPT2DoubleHeadsModelOutput (line 411) | class GPT2DoubleHeadsModelOutput(ModelOutput):
  class GPT2Model (line 584) | class GPT2Model(GPT2PreTrainedModel):
    method __init__ (line 587) | def __init__(self, config):
    method parallelize (line 607) | def parallelize(self, device_map=None):
    method deparallelize (line 627) | def deparallelize(self):
    method get_input_embeddings (line 639) | def get_input_embeddings(self):
    method set_input_embeddings (line 642) | def set_input_embeddings(self, new_embeddings):
    method _prune_heads (line 645) | def _prune_heads(self, heads_to_prune):
    method forward (line 659) | def forward(
  class GPT2LMHeadModel (line 861) | class GPT2LMHeadModel(GPT2PreTrainedModel):
    method __init__ (line 864) | def __init__(self, config):
    method parallelize (line 876) | def parallelize(self, device_map=None):
    method deparallelize (line 888) | def deparallelize(self):
    method get_output_embeddings (line 895) | def get_output_embeddings(self):
    method set_output_embeddings (line 898) | def set_output_embeddings(self, new_embeddings):
    method prepare_inputs_for_generation (line 901) | def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
    method forward (line 936) | def forward(
    method _reorder_cache (line 1008) | def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.T...
  class GPT2DoubleHeadsModel (line 1029) | class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
    method __init__ (line 1032) | def __init__(self, config):
    method parallelize (line 1046) | def parallelize(self, device_map=None):
    method deparallelize (line 1059) | def deparallelize(self):
    method get_output_embeddings (line 1067) | def get_output_embeddings(self):
    method set_output_embeddings (line 1070) | def set_output_embeddings(self, new_embeddings):
    method prepare_inputs_for_generation (line 1073) | def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
    method forward (line 1105) | def forward(
    method _reorder_cache (line 1217) | def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.T...
  class GPT2ForSequenceClassification (line 1244) | class GPT2ForSequenceClassification(GPT2PreTrainedModel):
    method __init__ (line 1247) | def __init__(self, config):
    method forward (line 1266) | def forward(
  class GPT2ForTokenClassification (line 1357) | class GPT2ForTokenClassification(GPT2PreTrainedModel):
    method __init__ (line 1358) | def __init__(self, config):
    method forward (line 1385) | def forward(

FILE: pec_baseline/models/gpt2/tokenization_gpt2.py
  function bytes_to_unicode (line 71) | def bytes_to_unicode():
  function get_pairs (line 95) | def get_pairs(word):
  class GPT2Tokenizer (line 109) | class GPT2Tokenizer(PreTrainedTokenizer):
    method __init__ (line 161) | def __init__(
    method vocab_size (line 201) | def vocab_size(self):
    method get_vocab (line 204) | def get_vocab(self):
    method bpe (line 207) | def bpe(self, token):
    method _tokenize (line 249) | def _tokenize(self, text):
    method _convert_token_to_id (line 259) | def _convert_token_to_id(self, token):
    method _convert_id_to_token (line 263) | def _convert_id_to_token(self, index):
    method convert_tokens_to_string (line 267) | def convert_tokens_to_string(self, tokens):
    method save_vocabulary (line 273) | def save_vocabulary(self, save_directory: str, filename_prefix: Option...
    method prepare_for_tokenization (line 302) | def prepare_for_tokenization(self, text, is_split_into_words=False, **...
    method _build_conversation_input_ids (line 308) | def _build_conversation_input_ids(self, conversation: "Conversation") ...

FILE: pec_baseline/models/model_parameters.py
  function count_trainable_parameters (line 23) | def count_trainable_parameters(model):
  function count_total_parameters (line 29) | def count_total_parameters(model):
  function show_trainable_parameters (line 35) | def show_trainable_parameters(model):
  function set_freeze_by_names (line 42) | def set_freeze_by_names(model, layer_names, freeze=True):
  function freeze_by_names (line 51) | def freeze_by_names(model, layer_names):
  function unfreeze_by_names (line 54) | def unfreeze_by_names(model, layer_names):
  function set_freeze_by_idxs (line 57) | def set_freeze_by_idxs(model, idxs, freeze=True):
  function freeze_by_idxs (line 68) | def freeze_by_idxs(model, idxs):
  function unfreeze_by_idxs (line 71) | def unfreeze_by_idxs(model, idxs):
  function freeze_by_model_name (line 74) | def freeze_by_model_name(model, model_name):
  function unfreeze_by_model_name (line 79) | def unfreeze_by_model_name(model, model_name):

FILE: pec_baseline/train_model.py
  function setup_seed (line 63) | def setup_seed(seed):
  function average_distributed_scalar (line 73) | def average_distributed_scalar(scalar, args):
  function score_function (line 81) | def score_function(engine):
  function train (line 89) | def train():

FILE: pec_baseline/utils/base_util.py
  function is_torch_available (line 44) | def is_torch_available():
  function load_csv_data_from_dir (line 47) | def load_csv_data_from_dir(data_dir="../data/CPED",
  function shuffle_total_data (line 63) | def shuffle_total_data(data_path,
  function combine_csv_files (line 117) | def combine_csv_files(data_path="./MELD/",
  function save_speaker (line 159) | def save_speaker(data_path, save_path, row_name="Speaker", regen=False):
  function load_speaker (line 183) | def load_speaker(speakers_file): #speakers.txt
  function convert_speaker_to_id (line 201) | def convert_speaker_to_id(speakers_to_ids, speaker, unk_token="其他"):
  function convert_id_to_speaker (line 205) | def convert_id_to_speaker(ids_to_speakers, index, unk_token="其他"):
  function convert_cache_to_csv (line 209) | def convert_cache_to_csv(dataset_cache,output_dir):

FILE: pec_baseline/utils/cped_dataset.py
  function find_split_id_of_response (line 60) | def find_split_id_of_response(speaker_list,responder):
  function create_speaker_type (line 83) | def create_speaker_type(speaker_list,responder=None):
  function convert_emotion_to_tokens (line 104) | def convert_emotion_to_tokens(emotion_list,
  function convert_da_to_tokens (line 126) | def convert_da_to_tokens(da_list,
  function set_da_in_speaker (line 142) | def set_da_in_speaker(da_ids,input_ids,bos, eos, speaker1, speaker2, pad):
  function set_emotion_in_speaker (line 155) | def set_emotion_in_speaker(emotion_ids,input_ids,bos, eos, speaker1, spe...
  class CpedDataset (line 169) | class CpedDataset(Dataset):
    method __init__ (line 219) | def __init__(self,
    method __len__ (line 261) | def __len__(self):
    method __getitem__ (line 264) | def __getitem__(self, index):
    method process (line 350) | def process(self,
    method collate (line 410) | def collate(self, batch):
  function build_cped_dataloaders (line 472) | def build_cped_dataloaders(args,

FILE: pec_baseline/utils/cped_util.py
  function tokenize (line 102) | def tokenize(utterance, tokenizer):
  function cped_get_single_file (line 139) | def cped_get_single_file(file_path,
  function cped_get_single_cache_file (line 177) | def cped_get_single_cache_file(file_path,
  function cped_get_data_from_dir (line 226) | def cped_get_data_from_dir(dir_path,
  function cped_get_single_file_for_bert_gpt (line 279) | def cped_get_single_file_for_bert_gpt(file_path,
  function cped_get_data_from_dir_for_bert_gpt (line 322) | def cped_get_data_from_dir_for_bert_gpt(dir_path,

FILE: pec_baseline/utils/dataset_statistics.py
  function get_data_for_analysis (line 30) | def get_data_for_analysis(data_dir="../data/CPED",
  function get_totaldata_for_analysis (line 42) | def get_totaldata_for_analysis(data_path="/home/MMMTD/data/processed_cle...
  function get_row_statistics (line 51) | def get_row_statistics(data,row_name):
  function cout_dialogue_words (line 69) | def cout_dialogue_words(data,dialogue_id):
  function remove_element (line 78) | def remove_element(utt_list,word = " "):
  function statistics_utterance (line 85) | def statistics_utterance(data,row_name="Utterance"):
  function statistics_emotda (line 98) | def statistics_emotda(data,row_name="Emotion",dialogue_id="Dialogue_ID"):
  function statistics_avg_duration (line 111) | def statistics_avg_duration(all_data,data,dialogue_id="Dialogue_ID",Star...
  function print_speaker (line 128) | def print_speaker(data_path):
  function print_speaker_from_dir (line 136) | def print_speaker_from_dir(input_dir="/home/MMMTD/data/processed_cleaned...
  function statistic_speaker (line 148) | def statistic_speaker(data_path = "/home/MMMTD/data/MMMTD_cleaned_speake...
  function print_sentiment (line 159) | def print_sentiment(data_path, sentiment='中性情绪'):
  function print_sentiment_from_dir (line 167) | def print_sentiment_from_dir(input_dir="/home/MMMTD/data/processed_clean...
  function count_eda_array_from_data (line 180) | def count_eda_array_from_data(data,da_name = "DA", emotion_name = "Emoti...