SYMBOL INDEX (381 symbols across 35 files)

FILE: Quick_demo/Model/RadFM/blocks.py
  class PMC_CLIP_cfg (line 9) | class PMC_CLIP_cfg:
  class Bottleneck (line 25) | class Bottleneck(nn.Module):
    method __init__ (line 28) | def __init__(self, inplanes, planes, stride=1):
    method forward (line 57) | def forward(self, x: torch.Tensor):
  class AttentionPool2d (line 73) | class AttentionPool2d(nn.Module):
    method __init__ (line 74) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 83) | def forward(self, x):
  class ResNet (line 110) | class ResNet(nn.Module):
    method __init__ (line 115) | def __init__(
    method _make_layer (line 144) | def _make_layer(
    method init_parameters (line 157) | def init_parameters(self):
    method lock (line 163) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 171) | def set_grad_checkpointing(self, enable=True):
    method stem (line 175) | def stem(self, x):
    method forward (line 180) | def forward(self, x):
  class ModifiedResNet (line 200) | class ModifiedResNet(nn.Module):
    method __init__ (line 208) | def __init__(self, layers, output_dim, heads, image_size=224, width=64):
    method _make_layer (line 237) | def _make_layer(self, planes, blocks, stride=1):
    method init_parameters (line 246) | def init_parameters(self):
    method lock (line 259) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 267) | def set_grad_checkpointing(self, enable=True):
    method stem (line 271) | def stem(self, x):
    method forward (line 278) | def forward(self, x):
  class LayerNorm (line 294) | class LayerNorm(nn.LayerNorm):
    method forward (line 297) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 303) | class QuickGELU(nn.Module):
    method forward (line 305) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 309) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 310) | def __init__(
    method attention (line 330) | def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor]...
    method forward (line 333) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
  class PatchDropout (line 339) | class PatchDropout(nn.Module):
    method __init__ (line 344) | def __init__(self, prob, exclude_first_token=True):
    method forward (line 350) | def forward(self, x):
  class Transformer (line 379) | class Transformer(nn.Module):
    method __init__ (line 380) | def __init__(
    method forward (line 394) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...

FILE: Quick_demo/Model/RadFM/helpers.py
  function exists (line 11) | def exists(val):
  function FeedForward (line 15) | def FeedForward(dim, mult=4):
  class PerceiverAttention (line 25) | class PerceiverAttention(nn.Module):
    method __init__ (line 26) | def __init__(self, *, dim, dim_head=64, heads=8):
    method forward (line 39) | def forward(self, x, latents):
  class PerceiverResampler (line 68) | class PerceiverResampler(nn.Module):
    method __init__ (line 69) | def __init__(
    method forward (line 107) | def forward(self, x):
  class MaskedCrossAttention (line 138) | class MaskedCrossAttention(nn.Module):
    method __init__ (line 139) | def __init__(
    method forward (line 162) | def forward(self, x, media, media_locations=None, attend_previous=True):
  class GatedCrossAttentionBlock (line 232) | class GatedCrossAttentionBlock(nn.Module):
    method __init__ (line 233) | def __init__(
    method forward (line 256) | def forward(

FILE: Quick_demo/Model/RadFM/multimodality_model.py
  class MultiLLaMAForCausalLM (line 13) | class MultiLLaMAForCausalLM(nn.Module):
    method __init__ (line 18) | def __init__(self, lang_model_path):
    method forward (line 44) | def forward(self, lang_x, vision_x, attention_mask, labels, loss_rewei...
    method generate (line 119) | def generate(self, lang_x, vision_x):

FILE: Quick_demo/Model/RadFM/my_embedding_layer.py
  class MyEmbedding (line 18) | class MyEmbedding(nn.Module):
    method __init__ (line 22) | def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver...
    method forward (line 100) | def forward(self, text_input, vision_x, key_words_query=None):

FILE: Quick_demo/Model/RadFM/position_encoding.py
  class PositionEmbeddingSine (line 11) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 16) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
    method forward (line 27) | def forward(self, tensor_list):
  class PositionEmbeddingLearned (line 50) | class PositionEmbeddingLearned(nn.Module):
    method __init__ (line 54) | def __init__(self, num_pos_feats=256):
    method reset_parameters (line 60) | def reset_parameters(self):
    method forward (line 64) | def forward(self, tensor_list):
  class PositionEmbeddingLearned3d (line 77) | class PositionEmbeddingLearned3d(nn.Module):
    method __init__ (line 81) | def __init__(self, num_pos_feats=256,h_patch_num = 16, w_patch_num = 1...
    method reset_parameters (line 91) | def reset_parameters(self):
    method forward (line 96) | def forward(self, B, h, w, d,x):
  function build_position_encoding (line 107) | def build_position_encoding(args):

FILE: Quick_demo/Model/RadFM/transformer_decoder.py
  class TransformerDecoder (line 16) | class TransformerDecoder(nn.Module):
    method __init__ (line 18) | def __init__(self, decoder_layer, num_layers, norm=None, return_interm...
    method forward (line 25) | def forward(self, tgt, memory,
  class TransformerDecoderLayer (line 59) | class TransformerDecoderLayer(nn.Module):
    method __init__ (line 61) | def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
    method with_pos_embed (line 80) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 83) | def forward_post(self, tgt, memory,
    method forward_pre (line 109) | def forward_pre(self, tgt, memory,
    method forward (line 132) | def forward(self, tgt, memory,
  function _get_clones (line 147) | def _get_clones(module, N):
  function _get_activation_fn (line 152) | def _get_activation_fn(activation):

FILE: Quick_demo/Model/RadFM/utils.py
  function extend_instance (line 6) | def extend_instance(obj, mixin):
  function getattr_recursive (line 15) | def getattr_recursive(obj, att):
  function setattr_recursive (line 29) | def setattr_recursive(obj, att, val):
  function get_visual_encoder (line 40) | def get_visual_encoder(model_str):
  function vision_load_pretrain (line 69) | def vision_load_pretrain(resnet,model_path):

FILE: Quick_demo/Model/RadFM/vit_3d.py
  function pair (line 10) | def pair(t):
  class PreNorm (line 15) | class PreNorm(nn.Module):
    method __init__ (line 16) | def __init__(self, dim, fn):
    method forward (line 20) | def forward(self, x, **kwargs):
  class FeedForward (line 23) | class FeedForward(nn.Module):
    method __init__ (line 24) | def __init__(self, dim, hidden_dim, dropout = 0.):
    method forward (line 33) | def forward(self, x):
  class Attention (line 36) | class Attention(nn.Module):
    method __init__ (line 37) | def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
    method forward (line 55) | def forward(self, x):
  class Transformer (line 68) | class Transformer(nn.Module):
    method __init__ (line 69) | def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
    method forward (line 77) | def forward(self, x):
  class ViT (line 83) | class ViT(nn.Module):
    method __init__ (line 84) | def __init__(self, *, image_size, image_patch_size, frames, frame_patc...
    method forward (line 113) | def forward(self, video):

FILE: Quick_demo/test.py
  function get_tokenizer (line 14) | def get_tokenizer(tokenizer_path, max_img_size=100, image_num=32):
  function combine_and_preprocess (line 59) | def combine_and_preprocess(question, image_list, image_padding_tokens):
  function main (line 110) | def main():

FILE: src/Dataset/dataset/MedPix_dataset.py
  class MedPix_Single_Dataset (line 21) | class MedPix_Single_Dataset(Dataset):
    method __init__ (line 28) | def __init__(self, csv_path, img_root="/gpfs/home/cs/leijiayu/data/Med...
    method __len__ (line 146) | def __len__(self):
    method get_image (line 150) | def get_image(self, img_path):
    method __getitem__ (line 166) | def __getitem__(self, idx):
  class MedPix_Multi_Dataset (line 246) | class MedPix_Multi_Dataset(Dataset):
    method __init__ (line 253) | def __init__(self, csv_path, img_root="/gpfs/home/cs/leijiayu/data/Med...
    method __len__ (line 376) | def __len__(self):
    method get_image (line 380) | def get_image(self, img_path):
    method __getitem__ (line 396) | def __getitem__(self, idx):
  class MedPix_QA_Dataset (line 475) | class MedPix_QA_Dataset(Dataset):
    method __init__ (line 481) | def __init__(self, csv_path, img_root="/gpfs/home/cs/leijiayu/data/Med...
    method __len__ (line 498) | def __len__(self):
    method get_image (line 502) | def get_image(self, img_path):
    method __getitem__ (line 518) | def __getitem__(self, idx):

FILE: src/Dataset/dataset/binary.py
  class Binary_Dataset (line 24) | class Binary_Dataset(Dataset):
    method __init__ (line 37) | def __init__(self,csv_path,prompt_json_file):
    method __len__ (line 50) | def __len__(self):
    method __getitem__ (line 53) | def __getitem__(self, index):

FILE: src/Dataset/dataset/case_report.py
  class CaseReport_dataset (line 19) | class CaseReport_dataset(Dataset):
    method __init__ (line 26) | def __init__(self, csv_path, img_path):
    method __len__ (line 48) | def __len__(self):
    method __getitem__ (line 52) | def __getitem__(self, idx):

FILE: src/Dataset/dataset/chestxray.py
  class ChestXray_Dataset (line 24) | class ChestXray_Dataset(Dataset):
    method __init__ (line 38) | def __init__(self,csv_path,prompt_json_file):
    method __len__ (line 49) | def __len__(self):
    method __getitem__ (line 52) | def __getitem__(self, index):

FILE: src/Dataset/dataset/dicom_to_png_for_VinDR_sampled_using_mammo.py
  function dcm_to_png (line 14) | def dcm_to_png(dcm_path,save_png_path):
  function preprocess_csv (line 23) | def preprocess_csv(csv_path,data_dir,save_data_dir):

FILE: src/Dataset/dataset/jpg2nii_data_convert.py
  function get_image (line 13) | def get_image(single_image_dir,single_image_filenames):
  function convert_case (line 36) | def convert_case(case_id,image_root_dir,json_root_dir,save_case_dict,sav...

FILE: src/Dataset/dataset/nii2npy_for_radiopaedio.py
  function resize_array (line 13) | def resize_array(array_list, shape_list):
  function process_image_list (line 37) | def process_image_list(image_path_list):
  function process_json_file (line 76) | def process_json_file(json_file,save_json_file,save_root_dir):

FILE: src/Dataset/dataset/paper_inline.py
  class Paper_Inline_dataset (line 18) | class Paper_Inline_dataset(Dataset):
    method __init__ (line 25) | def __init__(self, csv_path, img_path, sample_sentence_length=50, max_...
    method __len__ (line 52) | def __len__(self):
    method __getitem__ (line 56) | def __getitem__(self, idx):
    method random_sample_sentence (line 83) | def random_sample_sentence(self, sentences_list, PMC_name):

FILE: src/Dataset/dataset/pmcoa.py
  class PMCOA_Dataset (line 25) | class PMCOA_Dataset(Dataset):
    method __init__ (line 47) | def __init__(self, csv_path, img_root_dir, prompt_json_file):
    method __len__ (line 78) | def __len__(self):
    method __getitem__ (line 82) | def __getitem__(self, index):

FILE: src/Dataset/dataset/radiopaedia.py
  class Radio_Modality_Dataset (line 28) | class Radio_Modality_Dataset(Dataset):
    method __init__ (line 41) | def __init__(self,csv_path,prompt_json_file,modality_json_file,down_sa...
    method resize_image (line 53) | def resize_image(self, image):
    method __len__ (line 69) | def __len__(self):
    method __getitem__ (line 72) | def __getitem__(self, index):
  class RadioVQA_Dataset (line 125) | class RadioVQA_Dataset(Dataset):
    method __init__ (line 139) | def __init__(self,csv_path):
    method __len__ (line 146) | def __len__(self):
    method __getitem__ (line 149) | def __getitem__(self, index):
  class RadioCaption_Dataset (line 186) | class RadioCaption_Dataset(Dataset):
    method __init__ (line 187) | def __init__(self,json_path,prompt_json_file):
    method __len__ (line 193) | def __len__(self):
    method __getitem__ (line 196) | def __getitem__(self, index):
  class Radiofeatures_Dataset (line 237) | class Radiofeatures_Dataset(Dataset):
    method __init__ (line 238) | def __init__(self,json_path,prompt_json_file,disease_prompt_json_file,...
    method __len__ (line 248) | def __len__(self):
    method __getitem__ (line 251) | def __getitem__(self, index):

FILE: src/Dataset/dataset/vqa.py
  class VQA_Dataset (line 24) | class VQA_Dataset(Dataset):
    method __init__ (line 36) | def __init__(self,csv_path):
    method __len__ (line 49) | def __len__(self):
    method __getitem__ (line 52) | def __getitem__(self, index):

FILE: src/Dataset/multi_dataset.py
  class umls_extractor (line 23) | class umls_extractor:
    method __init__ (line 27) | def __init__(self):
    method extract (line 34) | def extract(self, text):
  function find_position (line 48) | def find_position(label, key_embeddings):
  function stack_images (line 69) | def stack_images(images):
  class multi_dataset (line 115) | class multi_dataset(Dataset):
    method __init__ (line 120) | def __init__(self, text_tokenizer, max_seq=2048, max_img_size=100, ima...
    method __len__ (line 338) | def __len__(self):
    method __getitem__ (line 342) | def __getitem__(self, idx):
    method text_add_image (line 434) | def text_add_image(self, images, question, answer):

FILE: src/Dataset/multi_dataset_test.py
  function stack_images (line 20) | def stack_images(images):
  class multi_dataset (line 71) | class multi_dataset(Dataset):
    method __init__ (line 75) | def __init__(self, text_tokenizer, test_split='close', max_seq=2048, m...
    method __len__ (line 277) | def __len__(self):
    method __getitem__ (line 281) | def __getitem__(self, idx):
    method text_add_image (line 333) | def text_add_image(self, images, question, answer):

FILE: src/Dataset/multi_dataset_test_for_close.py
  function find_position (line 19) | def find_position(label, key_embeddings):
  function stack_images (line 30) | def stack_images(images):
  class multi_dataset_close (line 61) | class multi_dataset_close(Dataset):
    method __init__ (line 62) | def __init__(self, text_tokenizer, test_split = 'close', max_seq = 204...
    method __len__ (line 176) | def __len__(self):
    method __getitem__ (line 179) | def __getitem__(self, idx):
    method text_add_image (line 247) | def text_add_image(self,images,question,answer):

FILE: src/Model/RadFM/blocks.py
  class PMC_CLIP_cfg (line 9) | class PMC_CLIP_cfg:
  class Bottleneck (line 25) | class Bottleneck(nn.Module):
    method __init__ (line 28) | def __init__(self, inplanes, planes, stride=1):
    method forward (line 57) | def forward(self, x: torch.Tensor):
  class AttentionPool2d (line 73) | class AttentionPool2d(nn.Module):
    method __init__ (line 74) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 83) | def forward(self, x):
  class ResNet (line 110) | class ResNet(nn.Module):
    method __init__ (line 115) | def __init__(
    method _make_layer (line 144) | def _make_layer(
    method init_parameters (line 157) | def init_parameters(self):
    method lock (line 163) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 171) | def set_grad_checkpointing(self, enable=True):
    method stem (line 175) | def stem(self, x):
    method forward (line 180) | def forward(self, x):
  class ModifiedResNet (line 200) | class ModifiedResNet(nn.Module):
    method __init__ (line 208) | def __init__(self, layers, output_dim, heads, image_size=224, width=64):
    method _make_layer (line 237) | def _make_layer(self, planes, blocks, stride=1):
    method init_parameters (line 246) | def init_parameters(self):
    method lock (line 259) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 267) | def set_grad_checkpointing(self, enable=True):
    method stem (line 271) | def stem(self, x):
    method forward (line 278) | def forward(self, x):
  class LayerNorm (line 294) | class LayerNorm(nn.LayerNorm):
    method forward (line 297) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 303) | class QuickGELU(nn.Module):
    method forward (line 305) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 309) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 310) | def __init__(
    method attention (line 330) | def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor]...
    method forward (line 333) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
  class PatchDropout (line 339) | class PatchDropout(nn.Module):
    method __init__ (line 344) | def __init__(self, prob, exclude_first_token=True):
    method forward (line 350) | def forward(self, x):
  class Transformer (line 379) | class Transformer(nn.Module):
    method __init__ (line 380) | def __init__(
    method forward (line 394) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...

FILE: src/Model/RadFM/helpers.py
  function exists (line 11) | def exists(val):
  function FeedForward (line 15) | def FeedForward(dim, mult=4):
  class PerceiverAttention (line 25) | class PerceiverAttention(nn.Module):
    method __init__ (line 26) | def __init__(self, *, dim, dim_head=64, heads=8):
    method forward (line 39) | def forward(self, x, latents):
  class PerceiverResampler (line 68) | class PerceiverResampler(nn.Module):
    method __init__ (line 69) | def __init__(
    method forward (line 107) | def forward(self, x):
  class MaskedCrossAttention (line 138) | class MaskedCrossAttention(nn.Module):
    method __init__ (line 139) | def __init__(
    method forward (line 162) | def forward(self, x, media, media_locations=None, attend_previous=True):
  class GatedCrossAttentionBlock (line 232) | class GatedCrossAttentionBlock(nn.Module):
    method __init__ (line 233) | def __init__(
    method forward (line 256) | def forward(

FILE: src/Model/RadFM/multimodality_model.py
  class MultiLLaMAForCausalLM (line 13) | class MultiLLaMAForCausalLM(nn.Module):
    method __init__ (line 18) | def __init__(self, lang_model_path):
    method forward (line 44) | def forward(self, lang_x, vision_x, attention_mask, labels, loss_rewei...
    method generate (line 119) | def generate(self, lang_x, vision_x):

FILE: src/Model/RadFM/my_embedding_layer.py
  class MyEmbedding (line 18) | class MyEmbedding(nn.Module):
    method __init__ (line 22) | def __init__(self, num_embeddings=32000, embedding_dim=5120, perceiver...
    method forward (line 100) | def forward(self, text_input, vision_x, key_words_query=None):

FILE: src/Model/RadFM/position_encoding.py
  class PositionEmbeddingSine (line 11) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 16) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
    method forward (line 27) | def forward(self, tensor_list):
  class PositionEmbeddingLearned (line 50) | class PositionEmbeddingLearned(nn.Module):
    method __init__ (line 54) | def __init__(self, num_pos_feats=256):
    method reset_parameters (line 60) | def reset_parameters(self):
    method forward (line 64) | def forward(self, tensor_list):
  class PositionEmbeddingLearned3d (line 77) | class PositionEmbeddingLearned3d(nn.Module):
    method __init__ (line 81) | def __init__(self, num_pos_feats=256,h_patch_num = 16, w_patch_num = 1...
    method reset_parameters (line 91) | def reset_parameters(self):
    method forward (line 96) | def forward(self, B, h, w, d,x):
  function build_position_encoding (line 107) | def build_position_encoding(args):

FILE: src/Model/RadFM/transformer_decoder.py
  class TransformerDecoder (line 16) | class TransformerDecoder(nn.Module):
    method __init__ (line 18) | def __init__(self, decoder_layer, num_layers, norm=None, return_interm...
    method forward (line 25) | def forward(self, tgt, memory,
  class TransformerDecoderLayer (line 59) | class TransformerDecoderLayer(nn.Module):
    method __init__ (line 61) | def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
    method with_pos_embed (line 80) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 83) | def forward_post(self, tgt, memory,
    method forward_pre (line 109) | def forward_pre(self, tgt, memory,
    method forward (line 132) | def forward(self, tgt, memory,
  function _get_clones (line 147) | def _get_clones(module, N):
  function _get_activation_fn (line 152) | def _get_activation_fn(activation):

FILE: src/Model/RadFM/utils.py
  function extend_instance (line 6) | def extend_instance(obj, mixin):
  function getattr_recursive (line 15) | def getattr_recursive(obj, att):
  function setattr_recursive (line 29) | def setattr_recursive(obj, att, val):
  function get_visual_encoder (line 40) | def get_visual_encoder(model_str):
  function vision_load_pretrain (line 69) | def vision_load_pretrain(resnet,model_path):

FILE: src/Model/RadFM/vit_3d.py
  function pair (line 10) | def pair(t):
  class PreNorm (line 15) | class PreNorm(nn.Module):
    method __init__ (line 16) | def __init__(self, dim, fn):
    method forward (line 20) | def forward(self, x, **kwargs):
  class FeedForward (line 23) | class FeedForward(nn.Module):
    method __init__ (line 24) | def __init__(self, dim, hidden_dim, dropout = 0.):
    method forward (line 33) | def forward(self, x):
  class Attention (line 36) | class Attention(nn.Module):
    method __init__ (line 37) | def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
    method forward (line 55) | def forward(self, x):
  class Transformer (line 68) | class Transformer(nn.Module):
    method __init__ (line 69) | def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
    method forward (line 77) | def forward(self, x):
  class ViT (line 83) | class ViT(nn.Module):
    method __init__ (line 84) | def __init__(self, *, image_size, image_patch_size, frames, frame_patc...
    method forward (line 113) | def forward(self, video):

FILE: src/My_Trainer/trainer.py
  class Trainer (line 230) | class Trainer:
    method __init__ (line 315) | def __init__(
    method add_callback (line 701) | def add_callback(self, callback):
    method pop_callback (line 712) | def pop_callback(self, callback):
    method remove_callback (line 728) | def remove_callback(self, callback):
    method _move_model_to_device (line 739) | def _move_model_to_device(self, model, device):
    method _set_signature_columns_if_needed (line 745) | def _set_signature_columns_if_needed(self):
    method _remove_unused_columns (line 753) | def _remove_unused_columns(self, dataset: "datasets.Dataset", descript...
    method _get_collator_with_removed_columns (line 779) | def _get_collator_with_removed_columns(
    method _get_train_sampler (line 797) | def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
    method get_train_dataloader (line 868) | def get_train_dataloader(self) -> DataLoader:
    method _get_eval_sampler (line 936) | def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.u...
    method get_eval_dataloader (line 965) | def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) ...
    method get_test_dataloader (line 1015) | def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
    method create_optimizer_and_scheduler (line 1063) | def create_optimizer_and_scheduler(self, num_training_steps: int):
    method create_optimizer (line 1079) | def create_optimizer(self):
    method get_optimizer_cls_and_kwargs (line 1136) | def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any...
    method create_scheduler (line 1226) | def create_scheduler(self, num_training_steps: int, optimizer: torch.o...
    method num_examples (line 1243) | def num_examples(self, dataloader: DataLoader) -> int:
    method _hp_search_setup (line 1257) | def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
    method _report_to_hp_search (line 1298) | def _report_to_hp_search(self, trial: Union["optuna.Trial", Dict[str, ...
    method _tune_save_checkpoint (line 1316) | def _tune_save_checkpoint(self):
    method call_model_init (line 1329) | def call_model_init(self, trial=None):
    method torch_jit_model_eval (line 1343) | def torch_jit_model_eval(self, model, dataloader, training=False):
    method ipex_optimize_model (line 1381) | def ipex_optimize_model(self, model, training=False, dtype=torch.float...
    method _wrap_model (line 1404) | def _wrap_model(self, model, training=True, dataloader=None):
    method train (line 1601) | def train(
    method _inner_training_loop (line 1687) | def _inner_training_loop(
    method _get_output_dir (line 2099) | def _get_output_dir(self, trial):
    method _load_from_checkpoint (line 2119) | def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
    method _load_best_model (line 2190) | def _load_best_model(self):
    method _issue_warnings_after_load (line 2259) | def _issue_warnings_after_load(self, load_result):
    method _maybe_log_save_evaluate (line 2272) | def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignor...
    method _load_rng_state (line 2313) | def _load_rng_state(self, checkpoint):
    method _save_checkpoint (line 2354) | def _save_checkpoint(self, model, trial, metrics=None):
    method _load_optimizer_and_scheduler (line 2460) | def _load_optimizer_and_scheduler(self, checkpoint):
    method hyperparameter_search (line 2520) | def hyperparameter_search(
    method log (line 2616) | def log(self, logs: Dict[str, float]) -> None:
    method _prepare_input (line 2633) | def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torc...
    method _prepare_inputs (line 2651) | def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]])...
    method compute_loss_context_manager (line 2667) | def compute_loss_context_manager(self):
    method autocast_smart_context_manager (line 2673) | def autocast_smart_context_manager(self, cache_enabled: Optional[bool]...
    method training_step (line 2692) | def training_step(self, model: nn.Module, inputs: Dict[str, Union[torc...
    method compute_loss (line 2740) | def compute_loss(self, model, inputs, return_outputs=False):
    method is_local_process_zero (line 2772) | def is_local_process_zero(self) -> bool:
    method is_world_process_zero (line 2779) | def is_world_process_zero(self) -> bool:
    method save_model (line 2791) | def save_model(self, output_dir: Optional[str] = None, _internal_call:...
    method _save_tpu (line 2855) | def _save_tpu(self, output_dir: Optional[str] = None):
    method _save (line 2883) | def _save(self, output_dir: Optional[str] = None, state_dict=None):
    method store_flos (line 2915) | def store_flos(self):
    method _sorted_checkpoints (line 2926) | def _sorted_checkpoints(
    method _rotate_checkpoints (line 2950) | def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:
    method evaluate (line 2975) | def evaluate(
    method predict (line 3046) | def predict(
    method evaluation_loop (line 3108) | def evaluation_loop(
    method _nested_gather (line 3319) | def _nested_gather(self, tensors, name=None):
    method _pad_across_processes (line 3337) | def _pad_across_processes(self, tensor, pad_index=-100):
    method prediction_step (line 3371) | def prediction_step(
    method floating_point_ops (line 3476) | def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any...
    method init_git_repo (line 3494) | def init_git_repo(self, at_init: bool = False):
    method create_model_card (line 3541) | def create_model_card(
    method _push_from_checkpoint (line 3597) | def _push_from_checkpoint(self, checkpoint_folder):
    method push_to_hub (line 3639) | def push_to_hub(self, commit_message: Optional[str] = "End of training...
    method prediction_loop (line 3699) | def prediction_loop(
    method _gather_and_numpify (line 3845) | def _gather_and_numpify(self, tensors, name):
    method _add_sm_patterns_to_gitignore (line 3861) | def _add_sm_patterns_to_gitignore(self) -> None:

FILE: src/datasampler.py
  function make_batch (line 10) | def make_batch(index_list, batch_size, drop_last):
  function batch_generation (line 23) | def batch_generation(dataset,batch_size_2D, batch_size_3D,drop_last=Fals...
  class My_DistributedBatchSampler (line 44) | class My_DistributedBatchSampler(Sampler):
    method __init__ (line 59) | def __init__(self, dataset, num_replicas=None, rank=None, batch_size_2...
    method __iter__ (line 93) | def __iter__(self):
    method __len__ (line 120) | def __len__(self):
    method set_epoch (line 123) | def set_epoch(self, epoch: int) -> None:

FILE: src/test.py
  function setup_seed (line 18) | def setup_seed(seed):
  class ModelArguments (line 36) | class ModelArguments:
  class DataArguments (line 46) | class DataArguments:
  class TrainingArguments (line 54) | class TrainingArguments(transformers.TrainingArguments):
  class DataCollator (line 68) | class DataCollator(object):
    method __call__ (line 73) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function main (line 134) | def main():

FILE: src/train.py
  function compute_metrics (line 18) | def compute_metrics(eval_preds):
  class ModelArguments (line 35) | class ModelArguments:
  class DataArguments (line 46) | class DataArguments:
  class TrainingArguments (line 53) | class TrainingArguments(transformers.TrainingArguments):
  class DataCollator (line 67) | class DataCollator(object):
    method __call__ (line 73) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function main (line 137) | def main():