SYMBOL INDEX (151 symbols across 17 files)

FILE: src/batcher/base.py
  function _pad_seq_right_to_n (line 14) | def _pad_seq_right_to_n(
  class EEGDataset (line 34) | class EEGDataset(Dataset):
    method __init__ (line 35) | def __init__(self, filenames, sample_keys, chunk_len=500, num_chunks=1...
    method __len__ (line 54) | def __len__(self):
    method __getitem__ (line 57) | def __getitem__(self, idx):
    method _pad_seq_right_to_n (line 64) | def _pad_seq_right_to_n(
    method load_single_file (line 75) | def load_single_file(self, filename):
    method load_tensor (line 87) | def load_tensor(self, filename):
    method reorder_channels (line 92) | def reorder_channels(self, data):
    method split_chunks (line 103) | def split_chunks(self, data, length=500, ovlp=50, num_chunks=10, start...
    method normalize (line 122) | def normalize(self, data):
    method preprocess_sample (line 131) | def preprocess_sample(

FILE: src/batcher/downstream_dataset.py
  class MotorImageryDataset (line 8) | class MotorImageryDataset(EEGDataset):
    method __init__ (line 9) | def __init__(self, filenames, sample_keys, chunk_len=500, num_chunks=1...
    method __len__ (line 27) | def __len__(self):
    method __getitem__ (line 30) | def __getitem__(self, idx):
    method map2pret (line 33) | def map2pret(self, data):
    method get_trials_from_single_subj (line 36) | def get_trials_from_single_subj(self, sub_id):
    method get_labels (line 72) | def get_labels(self, sub_id):
    method get_trials_all (line 79) | def get_trials_all(self):
    method bandpass_filter (line 98) | def bandpass_filter(self, data, lowcut, highcut, fs, order=5):

FILE: src/batcher/make.py
  function make_batcher (line 6) | def make_batcher(

FILE: src/decoder/gpt.py
  class GPTModel (line 9) | class GPTModel(torch.nn.Module):
    method __init__ (line 10) | def __init__(
    method switch_decoding_mode (line 53) | def switch_decoding_mode(
    method add_pooler_layer (line 66) | def add_pooler_layer(self):
    method add_decoding_head (line 80) | def add_decoding_head(
    method decode (line 113) | def decode(
    method forward (line 131) | def forward(
  class PretrainedGPT2 (line 155) | class PretrainedGPT2(GPTModel):
    method __init__ (line 157) | def __init__(

FILE: src/decoder/make_decoder.py
  function make_decoder (line 4) | def make_decoder(

FILE: src/decoder/unembedder.py
  class DeconvNet (line 8) | class DeconvNet(nn.Module):
    method __init__ (line 9) | def __init__(self, n_filters_time=40, n_channels=22, filter_time_lengt...
    method forward (line 18) | def forward(self, x):
  class UnEmbedder (line 26) | class UnEmbedder(torch.nn.Module):
    method __init__ (line 50) | def __init__(
    method stack_inputs (line 85) | def stack_inputs(
    method unstack_inputs (line 95) | def unstack_inputs(
    method forward (line 107) | def forward(
  function make_unembedder (line 122) | def make_unembedder(

FILE: src/embedder/base.py
  class EmbeddingModel (line 8) | class EmbeddingModel(torch.nn.Module):
    method __init__ (line 10) | def __init__(
    method _stack_inputs (line 47) | def _stack_inputs(
    method _unstack_inputs (line 57) | def _unstack_inputs(
    method forward (line 69) | def forward(
  class BaseEmbedder (line 82) | class BaseEmbedder(torch.nn.Module):
    method __init__ (line 83) | def __init__(self,
    method switch_decoding_mode (line 112) | def switch_decoding_mode(self, is_decoding_mode: bool=False) -> None:
    method _pad_tensor_left_by_n (line 121) | def _pad_tensor_left_by_n(
    method _round_to_precision (line 144) | def _round_to_precision(
    method embed_inputs (line 151) | def embed_inputs(
    method forward (line 157) | def forward(
    method decoding_loss (line 170) | def decoding_loss(
    method reconstruction_loss (line 184) | def reconstruction_loss(
    method prep_batch (line 198) | def prep_batch(
    method _root_loss (line 223) | def _root_loss(
    method loss (line 237) | def loss(

FILE: src/embedder/csm.py
  class CSMEmbedder (line 10) | class CSMEmbedder(BaseEmbedder):
    method __init__ (line 12) | def __init__(
    method _init_embeds (line 39) | def _init_embeds(self):
    method prep_batch (line 48) | def prep_batch(
    method mask_inputs (line 65) | def mask_inputs(
    method add_cls_embed (line 147) | def add_cls_embed(
    method masking_loss (line 217) | def masking_loss(
    method _root_loss (line 231) | def _root_loss(

FILE: src/embedder/csm_causal.py
  class CSMEmbedder (line 10) | class CSMEmbedder(BaseEmbedder):
    method __init__ (line 12) | def __init__(
    method _init_embeds (line 39) | def _init_embeds(self):
    method duplicate_batch (line 48) | def duplicate_batch(self, batch: Dict[str, torch.Tensor]) -> Dict[str,...
    method prep_batch (line 64) | def prep_batch(
    method mask_inputs (line 84) | def mask_inputs(
    method add_cls_embed (line 172) | def add_cls_embed(
    method masking_loss (line 242) | def masking_loss(
    method _root_loss (line 256) | def _root_loss(

FILE: src/embedder/make.py
  function make_embedder (line 6) | def make_embedder(

FILE: src/encoder/base.py
  function deprecated_args (line 15) | def deprecated_args(obj, *old_new_args):
  class EEGModuleMixin (line 32) | class EEGModuleMixin():
    method __init__ (line 73) | def __init__(
    method n_outputs (line 109) | def n_outputs(self):
    method n_chans (line 115) | def n_chans(self):
    method chs_info (line 125) | def chs_info(self):
    method n_times (line 131) | def n_times(self):
    method input_window_seconds (line 146) | def input_window_seconds(self):
    method sfreq (line 161) | def sfreq(self):
    method add_log_softmax (line 176) | def add_log_softmax(self):
    method input_shape (line 183) | def input_shape(self) -> Tuple[int]:
    method get_output_shape (line 187) | def get_output_shape(self) -> Tuple[int]:
    method load_state_dict (line 220) | def load_state_dict(self, state_dict, *args, **kwargs):
    method to_dense_prediction_model (line 232) | def to_dense_prediction_model(self, axis: Tuple[int] = (2, 3)) -> None:
    method get_torchinfo_statistics (line 275) | def get_torchinfo_statistics(
    method __str__ (line 309) | def __str__(self) -> str:

FILE: src/encoder/conformer_braindecode.py
  class EEGConformer (line 13) | class EEGConformer(EEGModuleMixin, nn.Module):
    method __init__ (line 77) | def __init__(
    method forward (line 158) | def forward(self, x: Tensor) -> Tensor:
    method get_fc_size (line 173) | def get_fc_size(self):
  class _PatchEmbedding (line 184) | class _PatchEmbedding(nn.Module):
    method __init__ (line 211) | def __init__(
    method forward (line 245) | def forward(self, x: Tensor) -> Tensor:
  class _MultiHeadAttention (line 252) | class _MultiHeadAttention(nn.Module):
    method __init__ (line 253) | def __init__(self, emb_size, num_heads, dropout):
    method forward (line 263) | def forward(self, x: Tensor, mask: Tensor = None) -> Tensor:
  class _ResidualAdd (line 287) | class _ResidualAdd(nn.Module):
    method __init__ (line 288) | def __init__(self, fn):
    method forward (line 292) | def forward(self, x, **kwargs):
  class _FeedForwardBlock (line 299) | class _FeedForwardBlock(nn.Sequential):
    method __init__ (line 300) | def __init__(self, emb_size, expansion, drop_p):
  class _TransformerEncoderBlock (line 309) | class _TransformerEncoderBlock(nn.Sequential):
    method __init__ (line 310) | def __init__(self, emb_size, att_heads, att_drop, forward_expansion=4):
  class _TransformerEncoder (line 332) | class _TransformerEncoder(nn.Sequential):
    method __init__ (line 350) | def __init__(self, att_depth, emb_size, att_heads, att_drop):
  class _FullyConnected (line 359) | class _FullyConnected(nn.Module):
    method __init__ (line 360) | def __init__(self, final_fc_length,
    method forward (line 395) | def forward(self, x):
  class _FinalLayer (line 401) | class _FinalLayer(nn.Module):
    method __init__ (line 402) | def __init__(self, n_classes, hidden_channels=32, return_features=Fals...
    method forward (line 429) | def forward(self, x):

FILE: src/model.py
  class Model (line 7) | class Model(torch.nn.Module):
    method __init__ (line 34) | def __init__(
    method from_pretrained (line 51) | def from_pretrained(
    method switch_ft_mode (line 86) | def switch_ft_mode(self, ft_encoder_only=False):
    method switch_decoding_mode (line 89) | def switch_decoding_mode(
    method compute_loss (line 113) | def compute_loss(
    method prep_batch (line 148) | def prep_batch(
    method forward (line 162) | def forward(

FILE: src/train_gpt.py
  function train (line 59) | def train(config: Dict=None) -> Trainer:
  function make_model (line 255) | def make_model(model_config: Dict=None):
  function get_config (line 356) | def get_config(args: argparse.Namespace=None) -> Dict:
  function get_args (line 428) | def get_args() -> argparse.ArgumentParser:

FILE: src/trainer/base.py
  class Trainer (line 52) | class Trainer(Trainer):
    method __init__ (line 53) | def __init__(
    method get_train_dataloader (line 62) | def get_train_dataloader(self) -> DataLoader:
    method get_eval_dataloader (line 113) | def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) ...
    method get_test_dataloader (line 163) | def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
    method prediction_step (line 622) | def prediction_step(
    method compute_loss (line 644) | def compute_loss(
    method _move_batch_to_device (line 675) | def _move_batch_to_device(

FILE: src/trainer/make.py
  class CSVLogCallback (line 12) | class CSVLogCallback(TrainerCallback):
    method __init__ (line 14) | def __init__(self):
    method on_log (line 19) | def on_log(
  function _cat_data_collator (line 71) | def _cat_data_collator(features: List) -> Dict[str, torch.tensor]:
  function decoding_accuracy_metrics (line 88) | def decoding_accuracy_metrics(eval_preds):
  function make_trainer (line 97) | def make_trainer(

FILE: src/utils.py
  function load_tuh_all (line 12) | def load_tuh_all(path):
  function load_pickle (line 39) | def load_pickle(filename):
  function read_threshold_sub (line 50) | def read_threshold_sub(csv_file, lower_bound=2599, upper_bound=1000000):
  function get_epi_files (line 61) | def get_epi_files(path, epi_csv, nonepi_csv, lower_bound=2599, upper_bou...
  function read_sub_list (line 73) | def read_sub_list(epi_list):
  function exclude_epi_subs (line 80) | def exclude_epi_subs(csv_file, epi_list, lower_bound=2599, upper_bound=1...
  function exclude_sz_subs (line 91) | def exclude_sz_subs(csv_file, lower_bound=2599, upper_bound=1000000, fil...
  function cv_split_bci (line 102) | def cv_split_bci(filenames):