SYMBOL INDEX (1862 symbols across 193 files)

FILE: datasets_os/build.py
  class JointLoader (line 40) | class JointLoader(torchdata.IterableDataset):
    method __init__ (line 41) | def __init__(self, loaders, key_dataset):
    method __iter__ (line 51) | def __iter__(self):
    method __len__ (line 55) | def __len__(self):
  function filter_images_with_only_crowd_annotations (line 58) | def filter_images_with_only_crowd_annotations(dataset_dicts, dataset_nam...
  function get_detection_dataset_dicts (line 94) | def get_detection_dataset_dicts(
  function _test_loader_from_config (line 135) | def _test_loader_from_config(cfg, dataset_name, mapper=None):
  function build_detection_test_loader (line 167) | def build_detection_test_loader(
  function _train_loader_from_config (line 232) | def _train_loader_from_config(cfg, dataset_name, mapper, *, dataset=None...
  function build_detection_train_loader (line 263) | def build_detection_train_loader(
  function get_config_from_name (line 310) | def get_config_from_name(cfg, dataset_name):
  function build_train_dataloader (line 382) | def build_train_dataloader(cfg,tokenizer=None,data_args=None,preprocess=...

FILE: datasets_os/custom_dataset_dataloader.py
  function _custom_test_loader_from_config (line 35) | def _custom_test_loader_from_config(cfg, dataset_name, mapper=None):
  function build_custom_test_loader (line 61) | def build_custom_test_loader(
  function trivial_batch_collator (line 89) | def trivial_batch_collator(batch):
  function _custom_train_loader_from_config (line 93) | def _custom_train_loader_from_config(cfg, mapper=None, *, dataset=None, ...
  function build_custom_train_loader (line 150) | def build_custom_train_loader(
  function build_multi_dataset_batch_data_loader (line 191) | def build_multi_dataset_batch_data_loader(
  function get_detection_dataset_dicts_with_source (line 221) | def get_detection_dataset_dicts_with_source(
  class MultiDatasetSampler (line 256) | class MultiDatasetSampler(Sampler):
    method __init__ (line 257) | def __init__(
    method __iter__ (line 313) | def __iter__(self):
    method _infinite_indices (line 319) | def _infinite_indices(self):
  class MDAspectRatioGroupedDataset (line 331) | class MDAspectRatioGroupedDataset(torch.utils.data.IterableDataset):
    method __init__ (line 332) | def __init__(self, dataset, batch_size, num_datasets):
    method __iter__ (line 339) | def __iter__(self):
  class DIFFMDAspectRatioGroupedDataset (line 351) | class DIFFMDAspectRatioGroupedDataset(torch.utils.data.IterableDataset):
    method __init__ (line 352) | def __init__(self, dataset, batch_sizes, num_datasets):
    method __iter__ (line 359) | def __iter__(self):
  function repeat_factors_from_tag_frequency (line 371) | def repeat_factors_from_tag_frequency(dataset_dicts, repeat_thresh):

FILE: datasets_os/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py
  function convert_coco_poly_to_mask (line 21) | def convert_coco_poly_to_mask(segmentations, height, width):
  function build_transform_gen (line 38) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 72) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 88) | def __init__(
    method from_config (line 112) | def from_config(cls, cfg, is_train=True):
    method __call__ (line 123) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/coco_instruct_grounding_dataset_interactive_mapper.py
  function convert_coco_poly_to_mask (line 26) | def convert_coco_poly_to_mask(segmentations, height, width):
  function preprocess_multimodal (line 42) | def preprocess_multimodal(
  function build_transform_gen (line 65) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 121) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 137) | def __init__(
    method from_config (line 172) | def from_config(cls, cfg, is_train=True,tokenizer=None,data_args=None,...
    method __call__ (line 187) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/coco_instruct_grounding_dataset_mapper.py
  function convert_coco_poly_to_mask (line 37) | def convert_coco_poly_to_mask(segmentations, height, width):
  function preprocess_multimodal (line 53) | def preprocess_multimodal(
  function build_transform_gen (line 76) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 132) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 148) | def __init__(
    method from_config (line 181) | def from_config(cls, cfg, is_train=True,tokenizer=None,data_args=None,...
    method __call__ (line 196) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/coco_interactive_panoptic_new_baseline_dataset_mapper.py
  function filter_empty_instances_by_box (line 21) | def filter_empty_instances_by_box(
  function build_transform_gen (line 42) | def build_transform_gen(cfg, is_train):
  class COCOInteractivePanopticNewBaselineDatasetMapper (line 75) | class COCOInteractivePanopticNewBaselineDatasetMapper:
    method __init__ (line 91) | def __init__(
    method from_config (line 118) | def from_config(cls, cfg, is_train=True):
    method __call__ (line 129) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/coco_panoptic_interactive_dataset_mapper.py
  function filter_empty_instances_by_box (line 24) | def filter_empty_instances_by_box(
  function build_transform_gen (line 45) | def build_transform_gen(cfg, is_train):
  function convert_coco_poly_to_mask (line 78) | def convert_coco_poly_to_mask(segmentations, height, width):
  class COCOPanopticInteractiveDatasetMapper (line 96) | class COCOPanopticInteractiveDatasetMapper:
    method __init__ (line 112) | def __init__(
    method from_config (line 159) | def from_config(cls, cfg, is_train=True,tokenizer=None,data_args=None,...
    method __call__ (line 177) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py
  function build_transform_gen (line 21) | def build_transform_gen(cfg, is_train):
  class COCOPanopticNewBaselineDatasetMapper (line 54) | class COCOPanopticNewBaselineDatasetMapper:
    method __init__ (line 70) | def __init__(
    method from_config (line 97) | def from_config(cls, cfg, is_train=True):
    method __call__ (line 108) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/flickr_instance_new_baseline_dataset_mapper.py
  function convert_coco_poly_to_mask (line 22) | def convert_coco_poly_to_mask(segmentations, height, width):
  function build_transform_gen (line 39) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 95) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 111) | def __init__(
    method from_config (line 144) | def from_config(cls, cfg, is_train=True,tokenizer=None,data_args=None,...
    method __call__ (line 159) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/flickr_instance_new_baseline_dataset_mapper_.py
  function convert_coco_poly_to_mask (line 25) | def convert_coco_poly_to_mask(segmentations, height, width):
  function preprocess_multimodal (line 41) | def preprocess_multimodal(
  function build_transform_gen (line 64) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 120) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 136) | def __init__(
    method from_config (line 167) | def from_config(cls, cfg, is_train=True,tokenizer=None,data_args=None,...
    method __call__ (line 181) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/flickr_instance_new_baseline_dataset_mapper_end.py
  function convert_coco_poly_to_mask (line 22) | def convert_coco_poly_to_mask(segmentations, height, width):
  function build_transform_gen (line 39) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 95) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 111) | def __init__(
    method from_config (line 142) | def from_config(cls, cfg, is_train=True,tokenizer=None,data_args=None,...
    method __call__ (line 156) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/flickr_new_baseline_dataset_mapper.py
  function filter_empty_instances_by_box (line 21) | def filter_empty_instances_by_box(
  function build_transform_gen (line 42) | def build_transform_gen(cfg, is_train):
  class COCOInteractivePanopticNewBaselineDatasetMapper (line 75) | class COCOInteractivePanopticNewBaselineDatasetMapper:
    method __init__ (line 91) | def __init__(
    method from_config (line 118) | def from_config(cls, cfg, is_train=True):
    method __call__ (line 129) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/inference_mapper_with_gt.py
  class CoCoInferenceDatasetMapper (line 21) | class CoCoInferenceDatasetMapper:
    method __init__ (line 39) | def __init__(
    method from_config (line 87) | def from_config(cls, cfg, is_train: bool = True):
    method _transform_annotations (line 116) | def _transform_annotations(self, dataset_dict, transforms, image_shape):
    method __call__ (line 145) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/llava_dataset_mapper.py
  function convert_coco_poly_to_mask (line 22) | def convert_coco_poly_to_mask(segmentations, height, width):
  function build_transform_gen (line 39) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 95) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 111) | def __init__(
    method from_config (line 135) | def from_config(cls, cfg, is_train=True):
    method __call__ (line 146) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/refcoco_dataset_mapper.py
  function build_transform_gen (line 26) | def build_transform_gen(cfg, is_train):
  class RefCOCODatasetMapper (line 61) | class RefCOCODatasetMapper:
    method __init__ (line 77) | def __init__(
    method from_config (line 108) | def from_config(cls, cfg, is_train=True):
    method __call__ (line 126) | def __call__(self, dataset_dict):

FILE: datasets_os/dataset_mappers/vg_instance_new_baseline_dataset_mapper.py
  function convert_coco_poly_to_mask (line 22) | def convert_coco_poly_to_mask(segmentations, height, width):
  function build_transform_gen (line 39) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 95) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 111) | def __init__(
    method from_config (line 144) | def from_config(cls, cfg, is_train=True,tokenizer=None,data_args=None,...
    method __call__ (line 159) | def __call__(self, dataset_dict):

FILE: datasets_os/refer.py
  class REFER (line 45) | class REFER:
    method __init__ (line 46) | def __init__(self, data_root, dataset='refcoco', splitBy='unc'):
    method createIndex (line 79) | def createIndex(self):
    method getRefIds (line 143) | def getRefIds(self, image_ids=[], cat_ids=[], ref_ids=[], split=''):
    method getAnnIds (line 176) | def getAnnIds(self, image_ids=[], cat_ids=[], ref_ids=[]):
    method getImgIds (line 198) | def getImgIds(self, ref_ids=[]):
    method getCatIds (line 208) | def getCatIds(self):
    method loadRefs (line 211) | def loadRefs(self, ref_ids=[]):
    method loadAnns (line 217) | def loadAnns(self, ann_ids=[]):
    method loadImgs (line 223) | def loadImgs(self, image_ids=[]):
    method loadCats (line 229) | def loadCats(self, cat_ids=[]):
    method getRefBox (line 235) | def getRefBox(self, ref_id):
    method showRef (line 240) | def showRef(self, ref, seg_box='seg'):
    method getMask (line 286) | def getMask(self, ref):
    method showMask (line 338) | def showMask(self, ref):

FILE: datasets_os/registration/register_coco_instruct_grounding_dataset.py
  function get_metadata (line 41) | def get_metadata():
  function load_coco_json (line 46) | def load_coco_json(image_root, annot_json,conversation, metadata):
  function register_coco (line 112) | def register_coco(
  function register_all_coco (line 128) | def register_all_coco(root):

FILE: datasets_os/registration/register_coco_panoptic_annos_grounding_interactive.py
  function get_metadata (line 27) | def get_metadata():
  function load_coco_panoptic_json (line 71) | def load_coco_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, gr...
  function register_coco_panoptic_annos_caption_grounding_sem_seg (line 137) | def register_coco_panoptic_annos_caption_grounding_sem_seg(
  function register_all_coco_panoptic_annos_caption_grounding_sem_seg (line 169) | def register_all_coco_panoptic_annos_caption_grounding_sem_seg(root):

FILE: datasets_os/registration/register_flickr_dataset.py
  function get_metadata (line 29) | def get_metadata():
  function load_flickr_json (line 34) | def load_flickr_json(image_root, annot_json, metadata):
  function register_flickr (line 68) | def register_flickr(
  function register_all_flickr (line 84) | def register_all_flickr(root,anno_root):

FILE: datasets_os/registration/register_vg_dataset.py
  function get_metadata (line 25) | def get_metadata():
  function load_vg_json (line 30) | def load_vg_json(image_root, annot_json, metadata):
  function register_vg (line 59) | def register_vg(
  function register_all_vg (line 75) | def register_all_vg(root,anno_root):

FILE: datasets_os/semseg_loader.py
  function load_semseg (line 5) | def load_semseg(filename, loader_type):

FILE: gradio_demo/LLaVA_G_Demo.py
  function get_image_name (line 13) | def get_image_name(dir_save="./gradio_demo/tmp_files", prefix="click_img...
  function preprocess_multi_conv (line 21) | def preprocess_multi_conv(
  function filter_empty_box_mask (line 65) | def filter_empty_box_mask(text, boxes_image, masks_image):
  class InferenceDemo (line 101) | class InferenceDemo(object):
    method __init__ (line 102) | def __init__(self,
    method hitory2datadict (line 114) | def hitory2datadict(self, history, text):
    method inference (line 161) | def inference(self, data_dict):
  function generate_distinct_colors (line 176) | def generate_distinct_colors(count):
  function add_text (line 192) | def add_text(history, text, image, threshold_slider, temporature_slider,...
  function add_image (line 371) | def add_image(history, image):
  function add_interaction_click (line 383) | def add_interaction_click(history, image, interaction_selector):
  function bot (line 397) | def bot(history):
  function clear_history (line 400) | def clear_history(history, txt, img):
  function clear_response (line 402) | def clear_response(history):
  function upvote_one (line 412) | def upvote_one(history):
  function downvote_one (line 415) | def downvote_one(history):
  function flag_one (line 418) | def flag_one(history):

FILE: llava/conversation.py
  class SeparatorStyle (line 6) | class SeparatorStyle(Enum):
  class Conversation (line 16) | class Conversation:
    method get_prompt (line 29) | def get_prompt(self):
    method append_message (line 106) | def append_message(self, role, message):
    method get_images (line 109) | def get_images(self, return_pil=False):
    method to_gradio_chatbot (line 158) | def to_gradio_chatbot(self):
    method copy (line 191) | def copy(self):
    method dict (line 202) | def dict(self):

FILE: llava/eval/LLaVA_G_Eval.py
  function load_jsonl_file (line 22) | def load_jsonl_file(path_jsonl):
  function save_jsonl_file (line 29) | def save_jsonl_file(data, path_save):
  function load_benchmark (line 34) | def load_benchmark(image_root, path_benchmark):
  function preprocess_v1 (line 66) | def preprocess_v1(
  class Evaluator_MM (line 150) | class Evaluator_MM:
    method __init__ (line 151) | def __init__(self,
    method construct_model (line 176) | def construct_model(self, model_path, model_base=None, model_name=None...
    method construct_vision_model (line 302) | def construct_vision_model(self, path_vision_model_cfg):
    method load_parameters (line 384) | def load_parameters(self, path_model):
    method evaluate_sample (line 392) | def evaluate_sample(self, input_data, get_box=True, get_mask=False):
  class Evaluator_MM_Inter (line 402) | class Evaluator_MM_Inter(Evaluator_MM):
    method __init__ (line 403) | def __init__(self, model_path, path_vision_model_cfg=None, path_inter_...
    method construct_model (line 406) | def construct_model(self, model_path, model_base=None, model_name=None...
    method construct_vision_model (line 532) | def construct_vision_model(self, path_vision_model_cfg):
    method evaluate_sample (line 618) | def evaluate_sample(self, input_data):
  function formatting (line 623) | def formatting(text, boxes, question_id):
  function evaluate_ (line 695) | def evaluate_(path_benchmarks, dir_image, evaluator, matching_threshold):
  function evaluate (line 792) | def evaluate(args=None):

FILE: llava/eval/eval_gpt_review.py
  function get_eval (line 13) | def get_eval(content: str, max_tokens: int):
  function parse_score (line 39) | def parse_score(review):

FILE: llava/eval/eval_gpt_review_bench.py
  function get_eval (line 16) | def get_eval(content: str, max_tokens: int):
  function parse_score (line 41) | def parse_score(review):

FILE: llava/eval/eval_gpt_review_visual.py
  function get_eval (line 15) | def get_eval(content: str, max_tokens: int):
  function parse_score (line 40) | def parse_score(review):

FILE: llava/eval/eval_gpt_review_visual2.py
  function get_eval (line 15) | def get_eval(content: str, max_tokens: int):
  function parse_score (line 40) | def parse_score(review):

FILE: llava/eval/eval_science_qa.py
  function get_args (line 8) | def get_args():
  function convert_caps (line 19) | def convert_caps(results):
  function get_pred_idx (line 28) | def get_pred_idx(prediction, choices, options):

FILE: llava/eval/eval_science_qa_gpt4.py
  function get_args (line 9) | def get_args():
  function convert_caps (line 19) | def convert_caps(results):
  function get_pred_idx (line 28) | def get_pred_idx(prediction, choices, options):

FILE: llava/eval/eval_science_qa_gpt4_requery.py
  function get_args (line 9) | def get_args():
  function convert_caps (line 21) | def convert_caps(results):
  function get_pred_idx (line 30) | def get_pred_idx(prediction, choices, options):

FILE: llava/eval/generate_webpage_data_from_table.py
  function read_jsonl (line 10) | def read_jsonl(path: str, key: str=None):
  function trim_hanging_lines (line 23) | def trim_hanging_lines(s: str, n: int) -> str:

FILE: llava/eval/llava_mapper.py
  function convert_coco_poly_to_mask (line 25) | def convert_coco_poly_to_mask(segmentations, height, width):
  function preprocess_multimodal (line 41) | def preprocess_multimodal(
  function build_transform_gen (line 63) | def build_transform_gen(cfg, is_train):
  class COCOInstanceNewBaselineDatasetMapper (line 119) | class COCOInstanceNewBaselineDatasetMapper:
    method __init__ (line 135) | def __init__(
    method from_config (line 165) | def from_config(cls, cfg, is_train=True,tokenizer=None,image_processor...
    method __call__ (line 179) | def __call__(self, dataset_dict):

FILE: llava/eval/model_qa.py
  class KeywordsStoppingCriteria (line 14) | class KeywordsStoppingCriteria(StoppingCriteria):
    method __init__ (line 15) | def __init__(self, keywords, tokenizer, input_ids):
    method __call__ (line 21) | def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTe...
  function eval_model (line 33) | def eval_model(model_name, questions_file, answers_file):

FILE: llava/eval/model_vqa.py
  function split_list (line 18) | def split_list(lst, n):
  function get_chunk (line 24) | def get_chunk(lst, n, k):
  function eval_model (line 29) | def eval_model(args):

FILE: llava/eval/model_vqa_science.py
  function split_list (line 18) | def split_list(lst, n):
  function get_chunk (line 24) | def get_chunk(lst, n, k):
  function eval_model (line 29) | def eval_model(args):

FILE: llava/eval/qa_baseline_gpt35.py
  function get_answer (line 16) | def get_answer(question_id: int, question: str, max_tokens: int):

FILE: llava/eval/run_llava.py
  function load_image (line 17) | def load_image(image_file):
  function eval_model (line 26) | def eval_model(args):

FILE: llava/eval/summarize_gpt_review.py
  function parse_args (line 9) | def parse_args():

FILE: llava/eval/webpage/script.js
  function text2Markdown (line 35) | function text2Markdown(text) {
  function capitalizeFirstChar (line 41) | function capitalizeFirstChar(str) {
  function updateQuestionSelect (line 48) | function updateQuestionSelect(question_id) {
  function updateModelSelect (line 64) | function updateModelSelect() {
  function populateModels (line 70) | function populateModels(models) {
  function populateQuestions (line 81) | function populateQuestions(questions) {
  function displayQuestion (line 110) | function displayQuestion(index) {
  function displayAnswers (line 116) | function displayAnswers(index) {
  function switchQuestionAndCategory (line 203) | function switchQuestionAndCategory() {
  function updateExpandButtonVisibility (line 226) | function updateExpandButtonVisibility(card) {

FILE: llava/mm_utils.py
  function load_image_from_base64 (line 10) | def load_image_from_base64(image):
  function process_images (line 14) | def process_images(images, image_processor, model_cfg):
  function tokenizer_image_token (line 18) | def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOK...
  function tokenizer_image_token_inter (line 39) | def tokenizer_image_token_inter(prompt, tokenizer, image_token_index=IMA...
  function get_model_name_from_path (line 60) | def get_model_name_from_path(model_path):
  class KeywordsStoppingCriteria (line 71) | class KeywordsStoppingCriteria(StoppingCriteria):
    method __init__ (line 72) | def __init__(self, keywords, tokenizer, input_ids):
    method __call__ (line 83) | def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTe...

FILE: llava/model/apply_delta.py
  function apply_delta (line 13) | def apply_delta(base_model_path, target_model_path, delta_path):

FILE: llava/model/builder.py
  function load_pretrained_model (line 25) | def load_pretrained_model(model_path, model_base, model_name, load_8bit=...

FILE: llava/model/consolidate.py
  function consolidate_ckpt (line 13) | def consolidate_ckpt(src_path, dst_path):

FILE: llava/model/language_model/llava_llama.py
  class LlavaConfig (line 30) | class LlavaConfig(LlamaConfig):
  class LlavaLlamaModel (line 34) | class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
    method __init__ (line 37) | def __init__(self, config: LlamaConfig):
  class LlavaLlamaForCausalLM (line 41) | class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
    method __init__ (line 44) | def __init__(self, config):
    method get_model (line 53) | def get_model(self):
    method forward (line 56) | def forward(
    method prepare_inputs_for_generation (line 122) | def prepare_inputs_for_generation(

FILE: llava/model/language_model/llava_llama_gd.py
  class DataCollatorForSupervisedDataset (line 31) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 36) | def __call__(self, instances,tokenizer):
  class LlavaConfig (line 63) | class LlavaConfig(LlamaConfig):
  class LlavaLlamaModel (line 67) | class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
    method __init__ (line 70) | def __init__(self, config: LlamaConfig):
  class LlavaLlamaForCausalLM (line 74) | class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
    method __init__ (line 77) | def __init__(self, config):
    method get_model (line 86) | def get_model(self):
    method forward (line 89) | def forward(
    method prepare_inputs_for_generation (line 150) | def prepare_inputs_for_generation(
  class LlavaLlamaForCausalLM_gd (line 172) | class LlavaLlamaForCausalLM_gd(LlamaForCausalLM, LlavaMetaForCausalLM_gd):
    method __init__ (line 175) | def __init__(self, config):
    method get_model (line 184) | def get_model(self):
    method forward (line 187) | def forward(self,**batched_inputs):
    method forward_inner (line 206) | def forward_inner(
    method prepare_inputs_for_generation (line 348) | def prepare_inputs_for_generation(
    method forward_eval (line 370) | def forward_eval(self, inputs):
    method forward_inner_eval (line 376) | def forward_inner_eval(
    method auto_regressive_generate (line 407) | def auto_regressive_generate(self,
  class LlavaLlamaForCausalLM_joint (line 470) | class LlavaLlamaForCausalLM_joint(LlavaLlamaForCausalLM_gd):
    method forward (line 471) | def forward(self,**batched_inputs):
    method forward_inner (line 491) | def forward_inner(
  class LlavaLlamaForCausalLM_joint_2st (line 599) | class LlavaLlamaForCausalLM_joint_2st(LlavaLlamaForCausalLM_gd):
    method forward (line 600) | def forward(self,**batched_inputs):
    method forward_inner (line 620) | def forward_inner(
  class LlavaLlamaForCausalLM_joint_2st_it_only_ref_instr (line 737) | class LlavaLlamaForCausalLM_joint_2st_it_only_ref_instr(LlamaForCausalLM...
    method __init__ (line 740) | def __init__(self, config):
    method get_model (line 749) | def get_model(self):
    method forward (line 752) | def forward(self,**batched_inputs):
    method forward_inner (line 790) | def forward_inner(
    method forward_eval (line 870) | def forward_eval(self, batched_inputs):
    method forward_inner_eval (line 891) | def forward_inner_eval(
    method forward_inner_eval_interactive (line 922) | def forward_inner_eval_interactive(
    method auto_regressive_generate (line 967) | def auto_regressive_generate(self,

FILE: llava/model/language_model/llava_mpt.py
  class LlavaMPTConfig (line 30) | class LlavaMPTConfig(MPTConfig):
  class LlavaMPTModel (line 34) | class LlavaMPTModel(LlavaMetaModel, MPTModel):
    method __init__ (line 37) | def __init__(self, config: MPTConfig):
    method embed_tokens (line 41) | def embed_tokens(self, x):
  class LlavaMPTForCausalLM (line 45) | class LlavaMPTForCausalLM(MPTForCausalLM, LlavaMetaForCausalLM):
    method __init__ (line 49) | def __init__(self, config):
    method get_model (line 65) | def get_model(self):
    method _set_gradient_checkpointing (line 68) | def _set_gradient_checkpointing(self, module, value=False):
    method forward (line 72) | def forward(self, input_ids: torch.LongTensor, past_key_values: Option...
    method prepare_inputs_for_generation (line 91) | def prepare_inputs_for_generation(self, input_ids, past_key_values=Non...

FILE: llava/model/language_model/mpt/adapt_tokenizer.py
  function adapt_tokenizer_for_denoising (line 6) | def adapt_tokenizer_for_denoising(tokenizer: Tokenizer):
  class AutoTokenizerForMOD (line 25) | class AutoTokenizerForMOD(AutoTokenizer):
    method from_pretrained (line 37) | def from_pretrained(cls, *args, **kwargs):

FILE: llava/model/language_model/mpt/attention.py
  function _reset_is_causal (line 12) | def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, origina...
  function scaled_multihead_dot_product_attention (line 20) | def scaled_multihead_dot_product_attention(query, key, value, n_heads, p...
  function check_valid_inputs (line 64) | def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bflo...
  function flash_attn_fn (line 71) | def flash_attn_fn(query, key, value, n_heads, past_key_value=None, softm...
  function triton_flash_attn_fn (line 107) | def triton_flash_attn_fn(query, key, value, n_heads, past_key_value=None...
  class MultiheadAttention (line 151) | class MultiheadAttention(nn.Module):
    method __init__ (line 158) | def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton'...
    method forward (line 191) | def forward(self, x, past_key_value=None, attn_bias=None, attention_ma...
  class MultiQueryAttention (line 204) | class MultiQueryAttention(nn.Module):
    method __init__ (line 211) | def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton'...
    method forward (line 245) | def forward(self, x, past_key_value=None, attn_bias=None, attention_ma...
  function attn_bias_shape (line 258) | def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causa...
  function build_attn_bias (line 272) | def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False...
  function gen_slopes (line 283) | def gen_slopes(n_heads, alibi_bias_max=8, device=None):
  function build_alibi_bias (line 292) | def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, dev...

FILE: llava/model/language_model/mpt/blocks.py
  class MPTMLP (line 8) | class MPTMLP(nn.Module):
    method __init__ (line 10) | def __init__(self, d_model: int, expansion_ratio: int, device: Optiona...
    method forward (line 17) | def forward(self, x):
  class MPTBlock (line 20) | class MPTBlock(nn.Module):
    method __init__ (line 22) | def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, a...
    method forward (line 34) | def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torc...

FILE: llava/model/language_model/mpt/configuration_mpt.py
  class MPTConfig (line 7) | class MPTConfig(PretrainedConfig):
    method __init__ (line 10) | def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=2...
    method _set_config_defaults (line 90) | def _set_config_defaults(self, config, config_defaults):
    method _validate_config (line 96) | def _validate_config(self):

FILE: llava/model/language_model/mpt/custom_embedding.py
  class SharedEmbedding (line 6) | class SharedEmbedding(nn.Embedding):
    method forward (line 8) | def forward(self, input: Tensor, unembed: bool=False) -> Tensor:

FILE: llava/model/language_model/mpt/flash_attn_triton.py
  function _fwd_kernel (line 51) | def _fwd_kernel(Q, K, V, Bias, Out, Lse, TMP, softmax_scale, stride_qb, ...
  function _bwd_preprocess_do_o_dot (line 155) | def _bwd_preprocess_do_o_dot(Out, DO, Delta, stride_ob, stride_oh, strid...
  function _bwd_store_dk_dv (line 168) | def _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k,...
  function _bwd_kernel_one_col_block (line 184) | def _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LS...
  function init_to_zero (line 300) | def init_to_zero(name):
  function _bwd_kernel (line 306) | def _bwd_kernel(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, st...
  function _flash_attn_forward (line 329) | def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=...
  function _flash_attn_backward (line 366) | def _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, cau...
  class FlashAttnQKVPackedFunc (line 401) | class FlashAttnQKVPackedFunc(torch.autograd.Function):
    method forward (line 404) | def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None):
    method backward (line 419) | def backward(ctx, do):
  class FlashAttnKVPackedFunc (line 428) | class FlashAttnKVPackedFunc(torch.autograd.Function):
    method forward (line 431) | def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None):
    method backward (line 446) | def backward(ctx, do):
  class FlashAttnFunc (line 457) | class FlashAttnFunc(torch.autograd.Function):
    method forward (line 460) | def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None):
    method backward (line 475) | def backward(ctx, do):

FILE: llava/model/language_model/mpt/hf_prefixlm_converter.py
  function _convert_gpt_causal_lm_to_prefix_lm (line 29) | def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUS...
  function _convert_bloom_causal_lm_to_prefix_lm (line 113) | def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> Bl...
  function _convert_opt_causal_lm_to_prefix_lm (line 269) | def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTFor...
  function convert_hf_causal_lm_to_prefix_lm (line 335) | def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_...
  function add_bidirectional_mask_if_missing (line 401) | def add_bidirectional_mask_if_missing(batch: Dict[str, Any]):

FILE: llava/model/language_model/mpt/meta_init_context.py
  function init_empty_weights (line 6) | def init_empty_weights(include_buffers: bool=False):
  function init_on_device (line 37) | def init_on_device(device: torch.device, include_buffers: bool=False):

FILE: llava/model/language_model/mpt/modeling_mpt.py
  class MPTPreTrainedModel (line 28) | class MPTPreTrainedModel(PreTrainedModel):
  class MPTModel (line 33) | class MPTModel(MPTPreTrainedModel):
    method __init__ (line 35) | def __init__(self, config: MPTConfig):
    method get_input_embeddings (line 81) | def get_input_embeddings(self):
    method set_input_embeddings (line 84) | def set_input_embeddings(self, value):
    method _attn_bias (line 88) | def _attn_bias(self, device, dtype, attention_mask: Optional[torch.Byt...
    method _apply_prefix_mask (line 119) | def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: tor...
    method _apply_sequence_id (line 134) | def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: tor...
    method forward (line 144) | def forward(self, input_ids: torch.LongTensor, past_key_values: Option...
    method param_init_fn (line 222) | def param_init_fn(self, module):
    method fsdp_wrap_fn (line 226) | def fsdp_wrap_fn(self, module):
    method activation_checkpointing_fn (line 229) | def activation_checkpointing_fn(self, module):
  class MPTForCausalLM (line 232) | class MPTForCausalLM(MPTPreTrainedModel):
    method __init__ (line 234) | def __init__(self, config: MPTConfig):
    method get_input_embeddings (line 255) | def get_input_embeddings(self):
    method set_input_embeddings (line 258) | def set_input_embeddings(self, value):
    method get_output_embeddings (line 261) | def get_output_embeddings(self):
    method set_output_embeddings (line 264) | def set_output_embeddings(self, new_embeddings):
    method set_decoder (line 267) | def set_decoder(self, decoder):
    method get_decoder (line 270) | def get_decoder(self):
    method forward (line 273) | def forward(self, input_ids: torch.LongTensor, past_key_values: Option...
    method param_init_fn (line 291) | def param_init_fn(self, module):
    method fsdp_wrap_fn (line 295) | def fsdp_wrap_fn(self, module):
    method activation_checkpointing_fn (line 298) | def activation_checkpointing_fn(self, module):
    method prepare_inputs_for_generation (line 301) | def prepare_inputs_for_generation(self, input_ids, past_key_values=Non...
    method _reorder_cache (line 322) | def _reorder_cache(past_key_values, beam_idx):

FILE: llava/model/language_model/mpt/norm.py
  function _cast_if_autocast_enabled (line 3) | def _cast_if_autocast_enabled(tensor):
  class LPLayerNorm (line 14) | class LPLayerNorm(torch.nn.LayerNorm):
    method __init__ (line 16) | def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=Tru...
    method forward (line 19) | def forward(self, x):
  function rms_norm (line 27) | def rms_norm(x, weight=None, eps=1e-05):
  class RMSNorm (line 33) | class RMSNorm(torch.nn.Module):
    method __init__ (line 35) | def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=Non...
    method forward (line 43) | def forward(self, x):
  class LPRMSNorm (line 46) | class LPRMSNorm(RMSNorm):
    method __init__ (line 48) | def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=Non...
    method forward (line 51) | def forward(self, x):

FILE: llava/model/language_model/mpt/param_init_fns.py
  function torch_default_param_init_fn_ (line 10) | def torch_default_param_init_fn_(module: nn.Module, verbose: int=0, **kw...
  function fused_init_helper_ (line 17) | def fused_init_helper_(module: nn.Module, init_fn_):
  function generic_param_init_fn_ (line 28) | def generic_param_init_fn_(module: nn.Module, init_fn_, n_layers: int, d...
  function _normal_init_ (line 121) | def _normal_init_(std, mean=0.0):
  function _normal_param_init_fn_ (line 124) | def _normal_param_init_fn_(module: nn.Module, std: float, n_layers: int,...
  function baseline_param_init_fn_ (line 131) | def baseline_param_init_fn_(module: nn.Module, init_std: float, n_layers...
  function small_param_init_fn_ (line 137) | def small_param_init_fn_(module: nn.Module, n_layers: int, d_model: int,...
  function neox_param_init_fn_ (line 142) | def neox_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, ...
  function kaiming_uniform_param_init_fn_ (line 155) | def kaiming_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_m...
  function kaiming_normal_param_init_fn_ (line 162) | def kaiming_normal_param_init_fn_(module: nn.Module, n_layers: int, d_mo...
  function xavier_uniform_param_init_fn_ (line 169) | def xavier_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_mo...
  function xavier_normal_param_init_fn_ (line 176) | def xavier_normal_param_init_fn_(module: nn.Module, n_layers: int, d_mod...

FILE: llava/model/llava_arch.py
  class LlavaMetaModel (line 31) | class LlavaMetaModel:
    method __init__ (line 33) | def __init__(self, config):
    method get_vision_tower (line 40) | def get_vision_tower(self):
    method initialize_vision_modules (line 46) | def initialize_vision_modules(self, model_args, fsdp=None):
  class LlavaMetaForCausalLM (line 78) | class LlavaMetaForCausalLM(ABC):
    method get_model (line 81) | def get_model(self):
    method get_vision_tower (line 84) | def get_vision_tower(self):
    method encode_images (line 87) | def encode_images(self, images):
    method prepare_inputs_labels_for_multimodal (line 92) | def prepare_inputs_labels_for_multimodal(
    method initialize_vision_tokenizer (line 217) | def initialize_vision_tokenizer(self, model_args, tokenizer):
  class LlavaMetaForCausalLM_gd (line 298) | class LlavaMetaForCausalLM_gd(ABC):
    method get_model (line 301) | def get_model(self):
    method get_vision_tower (line 304) | def get_vision_tower(self):
    method encode_images (line 307) | def encode_images(self, images):
    method prepare_inputs_labels_for_multimodal (line 312) | def prepare_inputs_labels_for_multimodal(
    method initialize_vision_tokenizer (line 438) | def initialize_vision_tokenizer(self, model_args, tokenizer):
    method initialize_seg_modules (line 513) | def initialize_seg_modules(self, cfg):
    method freeze_seg_modules (line 518) | def freeze_seg_modules(self):
  class LlavaMetaForCausalLM_gd_interactive (line 523) | class LlavaMetaForCausalLM_gd_interactive(ABC):
    method get_model (line 526) | def get_model(self):
    method get_vision_tower (line 529) | def get_vision_tower(self):
    method encode_images (line 532) | def encode_images(self, images):
    method prepare_inputs_labels_for_multimodal (line 537) | def prepare_inputs_labels_for_multimodal(
    method prepare_inputs_labels_for_multimodal_NoInter (line 667) | def prepare_inputs_labels_for_multimodal_NoInter(
    method initialize_vision_tokenizer (line 793) | def initialize_vision_tokenizer(self, model_args, tokenizer):
    method initialize_seg_modules (line 869) | def initialize_seg_modules(self, cfg):
    method initialize_interactive_modules (line 874) | def initialize_interactive_modules(self, cfg):
    method freeze_seg_modules (line 882) | def freeze_seg_modules(self):

FILE: llava/model/make_delta.py
  function make_delta (line 13) | def make_delta(base_model_path, target_model_path, delta_path, hub_repo_...

FILE: llava/model/multimodal_encoder/builder.py
  function build_vision_tower (line 4) | def build_vision_tower(vision_tower_cfg, **kwargs):

FILE: llava/model/multimodal_encoder/clip_encoder.py
  class CLIPVisionTower (line 7) | class CLIPVisionTower(nn.Module):
    method __init__ (line 8) | def __init__(self, vision_tower, args, delay_load=False):
    method load_model (line 22) | def load_model(self):
    method feature_select (line 29) | def feature_select(self, image_forward_outs):
    method forward (line 40) | def forward(self, images):
    method dummy_feature (line 54) | def dummy_feature(self):
    method dtype (line 58) | def dtype(self):
    method device (line 62) | def device(self):
    method config (line 66) | def config(self):
    method hidden_size (line 73) | def hidden_size(self):
    method num_patches (line 77) | def num_patches(self):

FILE: llava/model/openseed/BaseModel.py
  function align_and_update_state_dicts (line 12) | def align_and_update_state_dicts(model_state_dict, ckpt_state_dict):
  class BaseModel (line 46) | class BaseModel(nn.Module):
    method __init__ (line 47) | def __init__(self, opt, module: nn.Module):
    method forward (line 52) | def forward(self, *inputs, **kwargs):
    method save_pretrained (line 56) | def save_pretrained(self, save_dir):
    method from_pretrained (line 59) | def from_pretrained(self, load_dir):

FILE: llava/model/openseed/architectures/build.py
  function build_model (line 4) | def build_model(config, **kwargs):

FILE: llava/model/openseed/architectures/openseed_model.py
  class OpenSeeD (line 25) | class OpenSeeD(nn.Module):
    method __init__ (line 31) | def __init__(
    method from_config (line 141) | def from_config(cls, cfg):
    method device (line 271) | def device(self):
    method forward (line 274) | def forward(self, batched_inputs, inference_task='seg'):
    method forward_seg (line 348) | def forward_seg(self, batched_inputs, task='seg',default_text_embeddin...
    method prepare_targets (line 541) | def prepare_targets(self, targets, images, task='seg'):
    method semantic_inference (line 564) | def semantic_inference(self, mask_cls, mask_pred):
    method panoptic_inference (line 583) | def panoptic_inference(self, mask_cls, mask_pred):
    method instance_inference (line 645) | def instance_inference(self, mask_cls, mask_pred, mask_box_result):
    method box_postprocess (line 686) | def box_postprocess(self, out_bbox, img_h, img_w):
    method forward_eval (line 694) | def forward_eval(self, batched_inputs, text_embeddings):
    method forward_inner_eval (line 708) | def forward_inner_eval(self, batched_inputs, task='seg',default_text_e...
  function get_segmentation_model (line 759) | def get_segmentation_model(cfg, **kwargs):

FILE: llava/model/openseed/architectures/openseed_model_decouple_train.py
  class OpenSeeD (line 26) | class OpenSeeD(nn.Module):
    method __init__ (line 32) | def __init__(
    method from_config (line 152) | def from_config(cls, cfg):
    method device (line 298) | def device(self):
    method forward (line 301) | def forward(self, batched_inputs, inference_task='seg'):
    method forward_seg (line 329) | def forward_seg(self, batched_inputs, task='seg'):
    method prepare_targets (line 478) | def prepare_targets(self, targets, images, task='seg'):
    method semantic_inference (line 501) | def semantic_inference(self, mask_cls, mask_pred):
    method panoptic_inference (line 520) | def panoptic_inference(self, mask_cls, mask_pred):
    method instance_inference (line 582) | def instance_inference(self, mask_cls, mask_pred, mask_box_result,spli...
    method box_postprocess (line 627) | def box_postprocess(self, out_bbox, img_h, img_w):
  function get_segmentation_model (line 636) | def get_segmentation_model(cfg, **kwargs):

FILE: llava/model/openseed/architectures/registry.py
  function register_model (line 3) | def register_model(fn):
  function model_entrypoints (line 9) | def model_entrypoints(model_name):
  function is_model (line 12) | def is_model(model_name):

FILE: llava/model/openseed/backbone/backbone.py
  class Backbone (line 11) | class Backbone(nn.Module):
    method __init__ (line 16) | def __init__(self):
    method forward (line 22) | def forward(self):
    method size_divisibility (line 32) | def size_divisibility(self) -> int:
    method output_shape (line 42) | def output_shape(self):

FILE: llava/model/openseed/backbone/build.py
  function build_backbone (line 6) | def build_backbone(config, **kwargs):

FILE: llava/model/openseed/backbone/focal.py
  class Mlp (line 24) | class Mlp(nn.Module):
    method __init__ (line 27) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 36) | def forward(self, x):
  class FocalModulation (line 44) | class FocalModulation(nn.Module):
    method __init__ (line 56) | def __init__(self, dim, proj_drop=0., focal_level=2, focal_window=7, f...
    method forward (line 89) | def forward(self, x):
  class FocalModulationBlock (line 118) | class FocalModulationBlock(nn.Module):
    method __init__ (line 132) | def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0.,
    method forward (line 166) | def forward(self, x):
  class BasicLayer (line 197) | class BasicLayer(nn.Module):
    method __init__ (line 214) | def __init__(self,
    method forward (line 264) | def forward(self, x, H, W):
  class PatchEmbed (line 287) | class PatchEmbed(nn.Module):
    method __init__ (line 299) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 322) | def forward(self, x):
  class FocalNet (line 340) | class FocalNet(nn.Module):
    method __init__ (line 364) | def __init__(self,
    method _freeze_stages (line 438) | def _freeze_stages(self):
    method init_weights (line 452) | def init_weights(self, pretrained=None):
    method load_weights (line 478) | def load_weights(self, pretrained_dict=None, pretrained_layers=[], ver...
    method forward (line 566) | def forward(self, x):
    method train (line 592) | def train(self, mode=True):
  class D2FocalNet (line 598) | class D2FocalNet(FocalNet, Backbone):
    method __init__ (line 599) | def __init__(self, cfg, input_shape):
    method forward (line 652) | def forward(self, x):
    method output_shape (line 669) | def output_shape(self):
    method size_divisibility (line 678) | def size_divisibility(self):
  function get_focal_backbone (line 682) | def get_focal_backbone(cfg):

FILE: llava/model/openseed/backbone/focal_dw.py
  class Mlp (line 24) | class Mlp(nn.Module):
    method __init__ (line 27) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 36) | def forward(self, x):
  class FocalModulation (line 44) | class FocalModulation(nn.Module):
    method __init__ (line 56) | def __init__(self, dim, proj_drop=0., focal_level=2, focal_window=7, f...
    method forward (line 89) | def forward(self, x):
  class FocalModulationBlock (line 118) | class FocalModulationBlock(nn.Module):
    method __init__ (line 132) | def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0.,
    method forward (line 168) | def forward(self, x):
  class BasicLayer (line 206) | class BasicLayer(nn.Module):
    method __init__ (line 223) | def __init__(self,
    method forward (line 275) | def forward(self, x, H, W):
  class PatchEmbed (line 368) | class PatchEmbed(nn.Module):
    method __init__ (line 380) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 410) | def forward(self, x):
  class FocalNet (line 434) | class FocalNet(nn.Module):
    method __init__ (line 458) | def __init__(self,
    method _freeze_stages (line 535) | def _freeze_stages(self):
    method init_weights (line 549) | def init_weights(self, pretrained=None):
    method load_weights (line 575) | def load_weights(self, pretrained_dict=None, pretrained_layers=[], ver...
    method forward (line 663) | def forward(self, x):
    method train (line 689) | def train(self, mode=True):
  class D2FocalNet (line 695) | class D2FocalNet(FocalNet, Backbone):
    method __init__ (line 696) | def __init__(self, cfg, input_shape):
    method forward (line 749) | def forward(self, x):
    method output_shape (line 766) | def output_shape(self):
    method size_divisibility (line 775) | def size_divisibility(self):
  function get_focal_backbone (line 779) | def get_focal_backbone(cfg):

FILE: llava/model/openseed/backbone/registry.py
  function register_backbone (line 4) | def register_backbone(fn):
  function model_entrypoints (line 10) | def model_entrypoints(model_name):
  function is_model (line 13) | def is_model(model_name):

FILE: llava/model/openseed/backbone/swin.py
  class Mlp (line 26) | class Mlp(nn.Module):
    method __init__ (line 29) | def __init__(
    method forward (line 40) | def forward(self, x):
  function window_partition (line 49) | def window_partition(x, window_size):
  function window_reverse (line 63) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 79) | class WindowAttention(nn.Module):
    method __init__ (line 92) | def __init__(
    method forward (line 136) | def forward(self, x, mask=None):
  class SwinTransformerBlock (line 180) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 197) | def __init__(
    method forward (line 241) | def forward(self, x, mask_matrix):
  class PatchMerging (line 309) | class PatchMerging(nn.Module):
    method __init__ (line 316) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 322) | def forward(self, x, H, W):
  class BasicLayer (line 351) | class BasicLayer(nn.Module):
    method __init__ (line 369) | def __init__(
    method forward (line 417) | def forward(self, x, H, W):
  class PatchEmbed (line 467) | class PatchEmbed(nn.Module):
    method __init__ (line 476) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 490) | def forward(self, x):
  class SwinTransformer (line 509) | class SwinTransformer(nn.Module):
    method __init__ (line 537) | def __init__(
    method _freeze_stages (line 629) | def _freeze_stages(self):
    method init_weights (line 646) | def init_weights(self, pretrained=None):
    method load_weights (line 663) | def load_weights(self, pretrained_dict=None, pretrained_layers=[], ver...
    method forward (line 730) | def forward(self, x):
    method train (line 763) | def train(self, mode=True):
  class D2SwinTransformer (line 769) | class D2SwinTransformer(SwinTransformer, Backbone):
    method __init__ (line 770) | def __init__(self, cfg, pretrain_img_size, patch_size, in_chans, embed...
    method forward (line 810) | def forward(self, x):
    method output_shape (line 827) | def output_shape(self):
    method size_divisibility (line 837) | def size_divisibility(self):
  function get_swin_backbone (line 842) | def get_swin_backbone(cfg):

FILE: llava/model/openseed/body/build.py
  function build_openseed_head (line 6) | def build_openseed_head(config, *args, **kwargs):

FILE: llava/model/openseed/body/decoder/build.py
  function build_decoder (line 5) | def build_decoder(config, *args, **kwargs):

FILE: llava/model/openseed/body/decoder/modules.py
  class SelfAttentionLayer (line 12) | class SelfAttentionLayer(nn.Module):
    method __init__ (line 14) | def __init__(self, d_model, nhead, dropout=0.0,
    method _reset_parameters (line 27) | def _reset_parameters(self):
    method with_pos_embed (line 32) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 35) | def forward_post(self, tgt,
    method forward_pre (line 47) | def forward_pre(self, tgt,
    method forward (line 59) | def forward(self, tgt,
  class CrossAttentionLayer (line 70) | class CrossAttentionLayer(nn.Module):
    method __init__ (line 72) | def __init__(self, d_model, nhead, dropout=0.0,
    method _reset_parameters (line 85) | def _reset_parameters(self):
    method with_pos_embed (line 90) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 93) | def forward_post(self, tgt, memory,
    method forward_pre (line 106) | def forward_pre(self, tgt, memory,
    method forward (line 120) | def forward(self, tgt, memory,
  class FFNLayer (line 132) | class FFNLayer(nn.Module):
    method __init__ (line 134) | def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
    method _reset_parameters (line 149) | def _reset_parameters(self):
    method with_pos_embed (line 154) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 157) | def forward_post(self, tgt):
    method forward_pre (line 163) | def forward_pre(self, tgt):
    method forward (line 169) | def forward(self, tgt):
  function _get_activation_fn (line 175) | def _get_activation_fn(activation):
  class MLP (line 186) | class MLP(nn.Module):
    method __init__ (line 189) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
    method forward (line 195) | def forward(self, x):

FILE: llava/model/openseed/body/decoder/openseed_decoder.py
  class OpenSeeDDecoder (line 26) | class OpenSeeDDecoder(nn.Module):
    method __init__ (line 28) | def __init__(
    method from_config (line 159) | def from_config(cls, cfg, in_channels, mask_classification, extra):
    method prepare_for_dn (line 191) | def prepare_for_dn(self, targets, tgt, refpoint_emb, batch_size):
    method dn_post_process (line 316) | def dn_post_process(self,outputs_class,outputs_coord,mask_dict,outputs...
    method get_valid_ratio (line 336) | def get_valid_ratio(self, mask):
    method pred_box (line 345) | def pred_box(self, reference, hs, ref0=None):
    method compute_similarity (line 363) | def compute_similarity(self, v_emb,name='default'):
    method forward (line 371) | def forward(self, x, mask_features, masks, targets=None, target_querie...
    method forward_prediction_heads (line 531) | def forward_prediction_heads(self, output, mask_features, pred_mask=Tr...
    method _set_aux_loss (line 546) | def _set_aux_loss(self, outputs_class, outputs_seg_masks, out_boxes=No...
  function get_maskdino_transformer_decoder (line 568) | def get_maskdino_transformer_decoder(cfg, in_channels, mask_classificati...

FILE: llava/model/openseed/body/decoder/openseed_decoder_decouple.py
  class MaskDINODecoder (line 25) | class MaskDINODecoder(nn.Module):
    method __init__ (line 27) | def __init__(
    method from_config (line 163) | def from_config(cls, cfg, in_channels, lang_encoder, mask_classificati...
    method prepare_for_dn (line 197) | def prepare_for_dn(self, targets, tgt, refpoint_emb, batch_size,task="...
    method dn_post_process (line 328) | def dn_post_process(self,outputs_class,outputs_coord,mask_dict,outputs...
    method get_valid_ratio (line 348) | def get_valid_ratio(self, mask):
    method pred_box (line 357) | def pred_box(self, reference, hs, ref0=None):
    method forward_cls (line 375) | def forward_cls(self, x, mask_features, masks, targets=None, target_qu...
    method forward (line 537) | def forward(self, x, mask_features, masks, targets=None, target_querie...
    method forward_prediction_heads (line 701) | def forward_prediction_heads(self, output, mask_features, pred_mask=Tr...
    method _set_aux_loss (line 716) | def _set_aux_loss(self, outputs_class, outputs_seg_masks, out_boxes=No...
  function get_maskdino_transformer_decoder (line 744) | def get_maskdino_transformer_decoder(cfg, in_channels, lang_encoder, mas...

FILE: llava/model/openseed/body/decoder/registry.py
  function register_decoder (line 3) | def register_decoder(fn):
  function model_entrypoints (line 9) | def model_entrypoints(model_name):
  function is_model (line 12) | def is_model(model_name):

FILE: llava/model/openseed/body/decoder/utils/dino_decoder.py
  class TransformerDecoder (line 18) | class TransformerDecoder(nn.Module):
    method __init__ (line 20) | def __init__(self, decoder_layer, num_layers, norm=None,
    method _reset_parameters (line 88) | def _reset_parameters(self):
    method forward (line 96) | def forward(self, tgt, memory,
  class DeformableTransformerDecoderLayer (line 196) | class DeformableTransformerDecoderLayer(nn.Module):
    method __init__ (line 198) | def __init__(self, d_model=256, d_ffn=1024,
    method rm_self_attn_modules (line 230) | def rm_self_attn_modules(self):
    method with_pos_embed (line 236) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 239) | def forward_ffn(self, tgt):
    method forward (line 246) | def forward(self,

FILE: llava/model/openseed/body/decoder/utils/utils.py
  class MLP (line 11) | class MLP(nn.Module):
    method __init__ (line 14) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
    method forward (line 20) | def forward(self, x):
  function inverse_sigmoid (line 26) | def inverse_sigmoid(x, eps=1e-5):
  function gen_encoder_output_proposals (line 33) | def gen_encoder_output_proposals(memory:Tensor, memory_padding_mask:Tens...
  function gen_sineembed_for_position (line 74) | def gen_sineembed_for_position(pos_tensor, dim=128):
  function _get_activation_fn (line 103) | def _get_activation_fn(activation):
  function _get_clones (line 118) | def _get_clones(module, N, layer_share=False):

FILE: llava/model/openseed/body/encoder/build.py
  function build_encoder (line 7) | def build_encoder(config, *args, **kwargs):

FILE: llava/model/openseed/body/encoder/encoder_deform.py
  class MSDeformAttnTransformerEncoderOnly (line 30) | class MSDeformAttnTransformerEncoderOnly(nn.Module):
    method __init__ (line 31) | def __init__(self, d_model=256, nhead=8,
    method _reset_parameters (line 49) | def _reset_parameters(self):
    method get_valid_ratio (line 58) | def get_valid_ratio(self, mask):
    method forward (line 67) | def forward(self, srcs, masks, pos_embeds, use_ckpt=False):
  class MSDeformAttnTransformerEncoderLayer (line 104) | class MSDeformAttnTransformerEncoderLayer(nn.Module):
    method __init__ (line 105) | def __init__(self,
    method with_pos_embed (line 125) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 128) | def forward_ffn(self, src):
    method forward (line 134) | def forward(self, src, pos, reference_points, spatial_shapes, level_st...
  class MSDeformAttnTransformerEncoder (line 146) | class MSDeformAttnTransformerEncoder(nn.Module):
    method __init__ (line 147) | def __init__(self, encoder_layer, num_layers):
    method get_reference_points (line 153) | def get_reference_points(spatial_shapes, valid_ratios, device):
    method forward (line 167) | def forward(self, src, spatial_shapes, level_start_index, valid_ratios...
  class OpenSeeDEncoder (line 179) | class OpenSeeDEncoder(nn.Module):
    method __init__ (line 184) | def __init__(
    method from_config (line 331) | def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec], *args, **...
    method forward_features (line 357) | def forward_features(self, features, masks):
  function get_maskdino_encoder_deform (line 436) | def get_maskdino_encoder_deform(cfg, input_shape):

FILE: llava/model/openseed/body/encoder/ops/functions/ms_deform_attn_func.py
  class MSDeformAttnFunction (line 32) | class MSDeformAttnFunction(Function):
    method forward (line 34) | def forward(ctx, value, value_spatial_shapes, value_level_start_index,...
    method backward (line 43) | def backward(ctx, grad_output):
  function ms_deform_attn_core_pytorch (line 52) | def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_lo...

FILE: llava/model/openseed/body/encoder/ops/modules/ms_deform_attn.py
  function _is_power_of_2 (line 28) | def _is_power_of_2(n):
  class MSDeformAttn (line 34) | class MSDeformAttn(nn.Module):
    method __init__ (line 35) | def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
    method _reset_parameters (line 66) | def _reset_parameters(self):
    method forward (line 82) | def forward(self, query, reference_points, input_flatten, input_spatia...

FILE: llava/model/openseed/body/encoder/ops/setup.py
  function get_extensions (line 26) | def get_extensions():

FILE: llava/model/openseed/body/encoder/ops/src/cpu/ms_deform_attn_cpu.cpp
  function ms_deform_attn_cpu_forward (line 22) | at::Tensor
  function ms_deform_attn_cpu_backward (line 34) | std::vector<at::Tensor>

FILE: llava/model/openseed/body/encoder/ops/src/ms_deform_attn.h
  function im2col_step (line 32) | int im2col_step)

FILE: llava/model/openseed/body/encoder/ops/src/vision.cpp
  function PYBIND11_MODULE (line 18) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: llava/model/openseed/body/encoder/ops/test.py
  function check_forward_equal_with_pytorch_double (line 35) | def check_forward_equal_with_pytorch_double():
  function check_forward_equal_with_pytorch_float (line 51) | def check_forward_equal_with_pytorch_float():
  function check_gradient_numerical (line 66) | def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_...

FILE: llava/model/openseed/body/encoder/registry.py
  function register_encoder (line 3) | def register_encoder(fn):
  function model_entrypoints (line 9) | def model_entrypoints(model_name):
  function is_model (line 12) | def is_model(model_name):

FILE: llava/model/openseed/body/encoder/transformer_encoder_fpn.py
  class BasePixelDecoder (line 22) | class BasePixelDecoder(nn.Module):
    method __init__ (line 23) | def __init__(
    method from_config (line 112) | def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
    method forward_features (line 123) | def forward_features(self, features):
    method forward (line 145) | def forward(self, features, targets=None):
  class TransformerEncoderOnly (line 151) | class TransformerEncoderOnly(nn.Module):
    method __init__ (line 152) | def __init__(
    method _reset_parameters (line 175) | def _reset_parameters(self):
    method forward (line 180) | def forward(self, src, mask, pos_embed):
  class TransformerEncoderPixelDecoder (line 193) | class TransformerEncoderPixelDecoder(BasePixelDecoder):
    method __init__ (line 195) | def __init__(
    method from_config (line 262) | def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
    method forward_features (line 276) | def forward_features(self, features):
    method forward (line 304) | def forward(self, features, targets=None):
  function get_transformer_encoder_fpn (line 312) | def get_transformer_encoder_fpn(cfg, input_shape):

FILE: llava/model/openseed/body/openseed_head.py
  class OpenSeeDHead (line 21) | class OpenSeeDHead(nn.Module):
    method __init__ (line 23) | def __init__(
    method from_config (line 56) | def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec], lang_enco...
    method forward (line 77) | def forward(self, features, mask=None,targets=None, target_queries=Non...
  function get_maskdino_head (line 86) | def get_maskdino_head(cfg, input_shape, lang_encoder, extra):

FILE: llava/model/openseed/body/registry.py
  function register_body (line 4) | def register_body(fn):
  function model_entrypoints (line 10) | def model_entrypoints(model_name):
  function is_model (line 13) | def is_model(model_name):

FILE: llava/model/openseed/body/transformer_blocks.py
  class Transformer (line 19) | class Transformer(nn.Module):
    method __init__ (line 20) | def __init__(
    method _reset_parameters (line 56) | def _reset_parameters(self):
    method forward (line 61) | def forward(self, src, mask, query_embed, pos_embed):
  class TransformerEncoder (line 78) | class TransformerEncoder(nn.Module):
    method __init__ (line 79) | def __init__(self, encoder_layer, num_layers, norm=None):
    method forward (line 85) | def forward(
  class TransformerDecoder (line 105) | class TransformerDecoder(nn.Module):
    method __init__ (line 106) | def __init__(self, decoder_layer, num_layers, norm=None, return_interm...
    method forward (line 113) | def forward(
  class TransformerEncoderLayer (line 154) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 155) | def __init__(
    method with_pos_embed (line 179) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 182) | def forward_post(
    method forward_pre (line 201) | def forward_pre(
    method forward (line 219) | def forward(
  class TransformerDecoderLayer (line 231) | class TransformerDecoderLayer(nn.Module):
    method __init__ (line 232) | def __init__(
    method with_pos_embed (line 259) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 262) | def forward_post(
    method forward_pre (line 293) | def forward_pre(
    method forward (line 324) | def forward(
  function _get_clones (line 358) | def _get_clones(module, N):
  function _get_activation_fn (line 362) | def _get_activation_fn(activation):

FILE: llava/model/openseed/language/LangEncoder/build.py
  function build_lang_encoder (line 10) | def build_lang_encoder(config_encoder, tokenizer, verbose, **kwargs):
  function build_tokenizer (line 19) | def build_tokenizer(config_encoder):

FILE: llava/model/openseed/language/LangEncoder/registry.py
  function register_lang_encoder (line 4) | def register_lang_encoder(fn):
  function lang_encoders (line 13) | def lang_encoders(model_name):
  function is_lang_encoder (line 17) | def is_lang_encoder(model_name):

FILE: llava/model/openseed/language/LangEncoder/transformer.py
  class LayerNorm (line 21) | class LayerNorm(nn.Module):
    method __init__ (line 22) | def __init__(self, hidden_size, eps=1e-12):
    method forward (line 30) | def forward(self, x):
  class QuickGELU (line 39) | class QuickGELU(nn.Module):
    method forward (line 40) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 44) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 45) | def __init__(self,
    method attention (line 63) | def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = ...
    method forward (line 75) | def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = No...
  class Transformer (line 81) | class Transformer(nn.Module):
    method __init__ (line 82) | def __init__(self,
    method dim_out (line 119) | def dim_out(self):
    method build_attention_mask (line 122) | def build_attention_mask(self):
    method _init_weights (line 130) | def _init_weights(self, m):
    method load_pretrained (line 142) | def load_pretrained(self, pretrained='', pretrained_layers=[], verbose...
    method no_weight_decay (line 188) | def no_weight_decay(self):
    method forward (line 194) | def forward(self, input_ids, attention_mask=None):
  function lang_encoder (line 210) | def lang_encoder(config_encoder, tokenizer, verbose, **kwargs):

FILE: llava/model/openseed/language/build.py
  function build_language_encoder (line 5) | def build_language_encoder(config, **kwargs):

FILE: llava/model/openseed/language/encoder.py
  class LanguageEncoder (line 13) | class LanguageEncoder(nn.Module):
    method __init__ (line 16) | def __init__(
    method from_config (line 33) | def from_config(cls, cfg):
    method get_text_embeddings (line 54) | def get_text_embeddings(self, class_names, name='default', is_eval=Fal...
    method forward_language (line 109) | def forward_language(self, texts, norm=True):
    method compute_similarity (line 123) | def compute_similarity(self, v_emb, name='default'):
  function get_language_model (line 131) | def get_language_model(cfg, **kwargs):

FILE: llava/model/openseed/language/registry.py
  function register_model (line 3) | def register_model(fn):
  function model_entrypoints (line 9) | def model_entrypoints(model_name):
  function is_model (line 12) | def is_model(model_name):

FILE: llava/model/openseed/language/vlpencoder.py
  class LanguageEncoder (line 19) | class LanguageEncoder(nn.Module):
    method __init__ (line 22) | def __init__(
    method from_config (line 46) | def from_config(cls, cfg):
    method get_text_embeddings (line 70) | def get_text_embeddings(self, class_names, name='default', is_eval=Fal...
    method get_text_token_embeddings (line 127) | def get_text_token_embeddings(self, txts, name='default', token=False,...
    method forward_language (line 142) | def forward_language(self, texts, norm=True):
    method forward_language_token (line 156) | def forward_language_token(self, texts, norm=False):
    method compute_similarity (line 174) | def compute_similarity(self, v_emb, name='default', fake=False):
  function get_language_model (line 184) | def get_language_model(cfg, **kwargs):

FILE: llava/model/openseed/modules/attention.py
  function multi_head_attention_forward (line 13) | def multi_head_attention_forward(
  class _LinearWithBias (line 324) | class _LinearWithBias(nn.Linear):
    method __init__ (line 327) | def __init__(self, in_features: int, out_features: int) -> None:
  class MultiheadAttention (line 331) | class MultiheadAttention(nn.Module):
    method __init__ (line 364) | def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bi...
    method _reset_parameters (line 403) | def _reset_parameters(self):
    method __setstate__ (line 419) | def __setstate__(self, state):
    method forward (line 426) | def forward(self, query: Tensor, key: Tensor, value: Tensor, key_paddi...

FILE: llava/model/openseed/modules/criterion.py
  function sigmoid_focal_loss (line 29) | def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, ...
  function dice_loss (line 58) | def dice_loss(
  function sigmoid_ce_loss (line 85) | def sigmoid_ce_loss(
  function calculate_uncertainty (line 110) | def calculate_uncertainty(logits):
  class SetCriterion (line 127) | class SetCriterion(nn.Module):
    method __init__ (line 134) | def __init__(self, num_classes, matcher, weight_dict, eos_coef, top_x_...
    method loss_labels_ce (line 168) | def loss_labels_ce(self, outputs, targets, indices, num_masks, layer_i...
    method loss_labels_masked (line 193) | def loss_labels_masked(self, outputs, targets, indices, num_boxes, log...
    method loss_labels (line 223) | def loss_labels(self, outputs, targets, indices, num_boxes, log=True, ...
    method loss_boxes (line 257) | def loss_boxes(self, outputs, targets, indices, num_boxes, layer_id=No...
    method loss_boxes_panoptic (line 285) | def loss_boxes_panoptic(self, outputs, targets, indices, num_boxes, la...
    method loss_masks (line 316) | def loss_masks(self, outputs, targets, indices, num_masks, layer_id=No...
    method prep_for_dn (line 374) | def prep_for_dn(self,mask_dict):
    method _get_src_permutation_idx (line 385) | def _get_src_permutation_idx(self, indices):
    method _get_tgt_permutation_idx (line 391) | def _get_tgt_permutation_idx(self, indices):
    method get_loss (line 397) | def get_loss(self, loss, outputs, targets, indices, num_masks=None, la...
    method forward (line 408) | def forward(self, outputs, targets, mask_dict=None, extra=None, task='...
    method __repr__ (line 529) | def __repr__(self):

FILE: llava/model/openseed/modules/matcher.py
  function batch_dice_loss (line 22) | def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):
  function batch_sigmoid_ce_loss (line 45) | def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):
  class HungarianMatcher (line 77) | class HungarianMatcher(nn.Module):
    method __init__ (line 85) | def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_d...
    method memory_efficient_forward (line 108) | def memory_efficient_forward(self, outputs, targets, cost=["cls", "box...
    method forward (line 206) | def forward(self, outputs, targets, cost=["cls", "box", "mask"], mode=...
    method __repr__ (line 235) | def __repr__(self, _repr_indent=4):

FILE: llava/model/openseed/modules/point_features.py
  function point_sample (line 21) | def point_sample(input, point_coords, **kwargs):
  function generate_regular_grid_point_coords (line 47) | def generate_regular_grid_point_coords(R, side_size, device):
  function get_uncertain_point_coords_with_randomness (line 65) | def get_uncertain_point_coords_with_randomness(
  function get_uncertain_point_coords_on_grid (line 121) | def get_uncertain_point_coords_on_grid(uncertainty_map, num_points):
  function point_sample_fine_grained_features (line 148) | def point_sample_fine_grained_features(features_list, feature_scales, bo...
  function get_point_coords_wrt_image (line 194) | def get_point_coords_wrt_image(boxes_coords, point_coords):
  function sample_point_labels (line 221) | def sample_point_labels(instances, point_coords):

FILE: llava/model/openseed/modules/position_encoding.py
  class PositionEmbeddingSine (line 12) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 18) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
    method forward (line 29) | def forward(self, x, mask=None):
    method __repr__ (line 54) | def __repr__(self, _repr_indent=4):

FILE: llava/model/openseed/modules/postprocessing.py
  function detector_postprocess (line 9) | def detector_postprocess(
  function bbox_postprocess (line 77) | def bbox_postprocess(result, input_size, img_size, output_height, output...
  function sem_seg_postprocess (line 99) | def sem_seg_postprocess(result, img_size, output_height, output_width):

FILE: llava/model/openseed/utils/box_ops.py
  function box_cxcywh_to_xyxy (line 9) | def box_cxcywh_to_xyxy(x):
  function box_xyxy_to_cxcywh (line 16) | def box_xyxy_to_cxcywh(x):
  function box_xywh_to_xyxy (line 22) | def box_xywh_to_xyxy(x):
  function box_iou (line 29) | def box_iou(boxes1, boxes2):
  function generalized_box_iou (line 45) | def generalized_box_iou(boxes1, boxes2):
  function masks_to_boxes (line 69) | def masks_to_boxes(masks):

FILE: llava/model/openseed/utils/config.py
  function configurable (line 7) | def configurable(init_func=None, *, from_config=None):
  function _called_with_cfg (line 94) | def _called_with_cfg(*args, **kwargs):
  function _get_args_from_config (line 111) | def _get_args_from_config(from_config_func, *args, **kwargs):

FILE: llava/model/openseed/utils/misc.py
  function _max_by_axis (line 26) | def _max_by_axis(the_list):
  class NestedTensor (line 34) | class NestedTensor(object):
    method __init__ (line 35) | def __init__(self, tensors, mask: Optional[Tensor]):
    method to (line 39) | def to(self, device):
    method decompose (line 50) | def decompose(self):
    method __repr__ (line 53) | def __repr__(self):
  function nested_tensor_from_tensor_list (line 56) | def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
  function _collate_and_pad_divisibility (line 98) | def _collate_and_pad_divisibility(tensor_list: list, div=32):
  function _onnx_nested_tensor_from_tensor_list (line 132) | def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> N...
  function is_dist_avail_and_initialized (line 161) | def is_dist_avail_and_initialized():

FILE: llava/model/semsam/BaseModel.py
  class BaseModel (line 12) | class BaseModel(nn.Module):
    method __init__ (line 13) | def __init__(self, opt, module: nn.Module):
    method forward (line 18) | def forward(self, *inputs, **kwargs):
    method save_pretrained (line 22) | def save_pretrained(self, save_dir):
    method from_pretrained (line 25) | def from_pretrained(self, load_dir):

FILE: llava/model/semsam/architectures/build.py
  function build_model (line 4) | def build_model(config, **kwargs):

FILE: llava/model/semsam/architectures/idino_model_partwhole_all_llm_ref_feats_all_det_pretrainv1.py
  function dice_loss (line 23) | def dice_loss(
  function iou_score_loss (line 49) | def iou_score_loss(inputs, targets):
  function sigmoid_ce_loss (line 59) | def sigmoid_ce_loss(
  function calculate_uncertainty (line 87) | def calculate_uncertainty(logits):
  function sigmoid_focal_loss (line 104) | def sigmoid_focal_loss(inputs, targets, alpha: float = 0.25, gamma: floa...
  class SemanticSAM (line 132) | class SemanticSAM(nn.Module):
    method __init__ (line 138) | def __init__(
    method from_config (line 264) | def from_config(cls, cfg):
    method device (line 418) | def device(self):
    method evaluate_demo (line 421) | def evaluate_demo(self, batched_inputs, all_whole=None, all_parts=None...
    method forward (line 478) | def forward(self, batched_inputs, inference_task='seg',detach=False):
    method forward_det_pretrain (line 499) | def forward_det_pretrain(self, batched_inputs, task='seg',
    method prepare_targets_sam (line 568) | def prepare_targets_sam(self, targets, images, prediction_switch, task...
  function get_segmentation_model (line 657) | def get_segmentation_model(cfg, **kwargs):

FILE: llava/model/semsam/architectures/registry.py
  function register_model (line 3) | def register_model(fn):
  function model_entrypoints (line 9) | def model_entrypoints(model_name):
  function is_model (line 12) | def is_model(model_name):

FILE: llava/model/semsam/backbone/backbone.py
  class Backbone (line 11) | class Backbone(nn.Module):
    method __init__ (line 16) | def __init__(self):
    method forward (line 22) | def forward(self):
    method size_divisibility (line 32) | def size_divisibility(self) -> int:
    method output_shape (line 42) | def output_shape(self):

FILE: llava/model/semsam/backbone/build.py
  function build_backbone (line 6) | def build_backbone(config, **kwargs):

FILE: llava/model/semsam/backbone/focal.py
  class Mlp (line 24) | class Mlp(nn.Module):
    method __init__ (line 27) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 36) | def forward(self, x):
  class FocalModulation (line 44) | class FocalModulation(nn.Module):
    method __init__ (line 56) | def __init__(self, dim, proj_drop=0., focal_level=2, focal_window=7, f...
    method forward (line 89) | def forward(self, x):
  class FocalModulationBlock (line 118) | class FocalModulationBlock(nn.Module):
    method __init__ (line 132) | def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0.,
    method forward (line 166) | def forward(self, x):
  class BasicLayer (line 197) | class BasicLayer(nn.Module):
    method __init__ (line 214) | def __init__(self,
    method forward (line 264) | def forward(self, x, H, W):
  class PatchEmbed (line 287) | class PatchEmbed(nn.Module):
    method __init__ (line 299) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 322) | def forward(self, x):
  class FocalNet (line 340) | class FocalNet(nn.Module):
    method __init__ (line 364) | def __init__(self,
    method _freeze_stages (line 438) | def _freeze_stages(self):
    method init_weights (line 452) | def init_weights(self, pretrained=None):
    method load_weights (line 478) | def load_weights(self, pretrained_dict=None, pretrained_layers=[], ver...
    method forward (line 566) | def forward(self, x):
    method train (line 592) | def train(self, mode=True):
  class D2FocalNet (line 598) | class D2FocalNet(FocalNet, Backbone):
    method __init__ (line 599) | def __init__(self, cfg, input_shape):
    method forward (line 652) | def forward(self, x):
    method output_shape (line 669) | def output_shape(self):
    method size_divisibility (line 678) | def size_divisibility(self):
  function get_focal_backbone (line 682) | def get_focal_backbone(cfg):

FILE: llava/model/semsam/backbone/focal_dw.py
  class Mlp (line 24) | class Mlp(nn.Module):
    method __init__ (line 27) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 36) | def forward(self, x):
  class FocalModulation (line 44) | class FocalModulation(nn.Module):
    method __init__ (line 56) | def __init__(self, dim, proj_drop=0., focal_level=2, focal_window=7, f...
    method forward (line 89) | def forward(self, x):
  class FocalModulationBlock (line 118) | class FocalModulationBlock(nn.Module):
    method __init__ (line 132) | def __init__(self, dim, mlp_ratio=4., drop=0., drop_path=0.,
    method forward (line 168) | def forward(self, x):
  class BasicLayer (line 206) | class BasicLayer(nn.Module):
    method __init__ (line 223) | def __init__(self,
    method forward (line 275) | def forward(self, x, H, W):
  class PatchEmbed (line 368) | class PatchEmbed(nn.Module):
    method __init__ (line 380) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 410) | def forward(self, x):
  class FocalNet (line 434) | class FocalNet(nn.Module):
    method __init__ (line 458) | def __init__(self,
    method _freeze_stages (line 535) | def _freeze_stages(self):
    method init_weights (line 549) | def init_weights(self, pretrained=None):
    method load_weights (line 575) | def load_weights(self, pretrained_dict=None, pretrained_layers=[], ver...
    method forward (line 663) | def forward(self, x):
    method train (line 689) | def train(self, mode=True):
  class D2FocalNet (line 695) | class D2FocalNet(FocalNet, Backbone):
    method __init__ (line 696) | def __init__(self, cfg, input_shape):
    method forward (line 749) | def forward(self, x):
    method output_shape (line 766) | def output_shape(self):
    method size_divisibility (line 775) | def size_divisibility(self):
  function get_focal_backbone (line 779) | def get_focal_backbone(cfg):

FILE: llava/model/semsam/backbone/registry.py
  function register_backbone (line 4) | def register_backbone(fn):
  function model_entrypoints (line 10) | def model_entrypoints(model_name):
  function is_model (line 13) | def is_model(model_name):

FILE: llava/model/semsam/backbone/swin.py
  class Mlp (line 26) | class Mlp(nn.Module):
    method __init__ (line 29) | def __init__(
    method forward (line 40) | def forward(self, x):
  function window_partition (line 49) | def window_partition(x, window_size):
  function window_reverse (line 63) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 79) | class WindowAttention(nn.Module):
    method __init__ (line 92) | def __init__(
    method forward (line 136) | def forward(self, x, mask=None):
  class SwinTransformerBlock (line 180) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 197) | def __init__(
    method forward (line 241) | def forward(self, x, mask_matrix):
  class PatchMerging (line 309) | class PatchMerging(nn.Module):
    method __init__ (line 316) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 322) | def forward(self, x, H, W):
  class BasicLayer (line 351) | class BasicLayer(nn.Module):
    method __init__ (line 369) | def __init__(
    method forward (line 417) | def forward(self, x, H, W):
  class PatchEmbed (line 467) | class PatchEmbed(nn.Module):
    method __init__ (line 476) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 490) | def forward(self, x):
  class SwinTransformer (line 509) | class SwinTransformer(nn.Module):
    method __init__ (line 537) | def __init__(
    method _freeze_stages (line 629) | def _freeze_stages(self):
    method init_weights (line 646) | def init_weights(self, pretrained=None):
    method load_weights (line 663) | def load_weights(self, pretrained_dict=None, pretrained_layers=[], ver...
    method forward (line 730) | def forward(self, x):
    method train (line 763) | def train(self, mode=True):
  class D2SwinTransformer (line 769) | class D2SwinTransformer(SwinTransformer, Backbone):
    method __init__ (line 770) | def __init__(self, cfg, pretrain_img_size, patch_size, in_chans, embed...
    method forward (line 810) | def forward(self, x):
    method output_shape (line 827) | def output_shape(self):
    method size_divisibility (line 837) | def size_divisibility(self):
  function get_swin_backbone (line 842) | def get_swin_backbone(cfg):

FILE: llava/model/semsam/backbone/swin_new.py
  class Mlp (line 21) | class Mlp(nn.Module):
    method __init__ (line 24) | def __init__(
    method forward (line 35) | def forward(self, x):
  function window_partition (line 44) | def window_partition(x, window_size):
  function window_reverse (line 58) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 74) | class WindowAttention(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 131) | def forward(self, x, mask=None):
  class SwinTransformerBlock (line 174) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 191) | def __init__(
    method forward (line 235) | def forward(self, x, mask_matrix):
  class PatchMerging (line 298) | class PatchMerging(nn.Module):
    method __init__ (line 305) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 311) | def forward(self, x, H, W):
  class BasicLayer (line 340) | class BasicLayer(nn.Module):
    method __init__ (line 358) | def __init__(
    method forward (line 406) | def forward(self, x, H, W):
  class PatchEmbed (line 456) | class PatchEmbed(nn.Module):
    method __init__ (line 465) | def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=...
    method forward (line 479) | def forward(self, x):
  class SwinTransformer (line 498) | class SwinTransformer(nn.Module):
    method __init__ (line 526) | def __init__(
    method _freeze_stages (line 618) | def _freeze_stages(self):
    method init_weights (line 635) | def init_weights(self, pretrained=None):
    method forward (line 651) | def forward(self, x):
    method train (line 680) | def train(self, mode=True):
  class D2SwinTransformer (line 687) | class D2SwinTransformer(SwinTransformer, Backbone):
    method __init__ (line 688) | def __init__(self, cfg, input_shape):
    method forward (line 743) | def forward(self, x):
    method output_shape (line 760) | def output_shape(self):
    method size_divisibility (line 769) | def size_divisibility(self):

FILE: llava/model/semsam/body/build.py
  function build_openseed_head (line 6) | def build_openseed_head(config, *args, **kwargs):

FILE: llava/model/semsam/body/decoder/build.py
  function build_decoder (line 5) | def build_decoder(config, *args, **kwargs):

FILE: llava/model/semsam/body/decoder/idino_decoder_no_iou_token_partwhole_all_llm.py
  class MaskDINODecoder (line 24) | class MaskDINODecoder(nn.Module):
    method __init__ (line 26) | def __init__(
    method from_config (line 178) | def from_config(cls, cfg, in_channels, lang_encoder, mask_classificati...
    method prepare_for_dn (line 213) | def prepare_for_dn(self, targets, tgt, refpoint_emb, batch_size):
    method prepare_for_dn_o3 (line 338) | def prepare_for_dn_o3(self, targets, tgt, refpoint_emb, batch_size):
    method prepare_for_dn_mo (line 463) | def prepare_for_dn_mo(self, targets, tgt, refpoint_emb, batch_size):
    method prepare_for_dn_mo_infer (line 563) | def prepare_for_dn_mo_infer(self, targets, tgt, refpoint_emb, batch_si...
    method dn_post_process (line 612) | def dn_post_process(self,outputs_class,outputs_coord,mask_dict,outputs...
    method get_valid_ratio (line 632) | def get_valid_ratio(self, mask):
    method pred_box (line 641) | def pred_box(self, reference, hs, ref0=None):
    method pred_box_old (line 662) | def pred_box_old(self, reference, hs, ref0=None):
    method forward (line 680) | def forward(self, x, mask_features, masks, targets=None, target_querie...
    method forward_o365 (line 797) | def forward_o365(self, x, mask_features, masks, targets=None, target_q...
    method forward_prediction_heads (line 906) | def forward_prediction_heads(self, output, mask_features, pred_mask=Tr...
    method idno_forward_prediction_heads (line 920) | def idno_forward_prediction_heads(self, output, mask_features, pred_ma...
    method _set_aux_loss (line 953) | def _set_aux_loss(self, outputs_class=None, outputs_seg_masks=None, ou...
  function get_maskdino_transformer_decoder (line 980) | def get_maskdino_transformer_decoder(cfg, in_channels, lang_encoder, mas...

FILE: llava/model/semsam/body/decoder/modules.py
  class SelfAttentionLayer (line 12) | class SelfAttentionLayer(nn.Module):
    method __init__ (line 14) | def __init__(self, d_model, nhead, dropout=0.0,
    method _reset_parameters (line 27) | def _reset_parameters(self):
    method with_pos_embed (line 32) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 35) | def forward_post(self, tgt,
    method forward_pre (line 47) | def forward_pre(self, tgt,
    method forward (line 59) | def forward(self, tgt,
  class CrossAttentionLayer (line 70) | class CrossAttentionLayer(nn.Module):
    method __init__ (line 72) | def __init__(self, d_model, nhead, dropout=0.0,
    method _reset_parameters (line 85) | def _reset_parameters(self):
    method with_pos_embed (line 90) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 93) | def forward_post(self, tgt, memory,
    method forward_pre (line 106) | def forward_pre(self, tgt, memory,
    method forward (line 120) | def forward(self, tgt, memory,
  class FFNLayer (line 132) | class FFNLayer(nn.Module):
    method __init__ (line 134) | def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
    method _reset_parameters (line 149) | def _reset_parameters(self):
    method with_pos_embed (line 154) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 157) | def forward_post(self, tgt):
    method forward_pre (line 163) | def forward_pre(self, tgt):
    method forward (line 169) | def forward(self, tgt):
  function _get_activation_fn (line 175) | def _get_activation_fn(activation):
  class MLP (line 186) | class MLP(nn.Module):
    method __init__ (line 189) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
    method forward (line 195) | def forward(self, x):

FILE: llava/model/semsam/body/decoder/registry.py
  function register_decoder (line 3) | def register_decoder(fn):
  function model_entrypoints (line 9) | def model_entrypoints(model_name):
  function is_model (line 12) | def is_model(model_name):

FILE: llava/model/semsam/body/decoder/utils/dino_decoder.py
  class TransformerDecoder (line 19) | class TransformerDecoder(nn.Module):
    method __init__ (line 21) | def __init__(self, decoder_layer, num_layers, norm=None,
    method _reset_parameters (line 89) | def _reset_parameters(self):
    method forward (line 97) | def forward(self, tgt, memory,
  class DeformableTransformerDecoderLayer (line 195) | class DeformableTransformerDecoderLayer(nn.Module):
    method __init__ (line 197) | def __init__(self, d_model=256, d_ffn=1024,
    method rm_self_attn_modules (line 229) | def rm_self_attn_modules(self):
    method with_pos_embed (line 235) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 238) | def forward_ffn(self, tgt):
    method forward (line 245) | def forward(self,

FILE: llava/model/semsam/body/decoder/utils/utils.py
  class MLP (line 11) | class MLP(nn.Module):
    method __init__ (line 14) | def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
    method forward (line 20) | def forward(self, x):
  function inverse_sigmoid (line 26) | def inverse_sigmoid(x, eps=1e-5):
  function gen_encoder_output_proposals (line 33) | def gen_encoder_output_proposals(memory:Tensor, memory_padding_mask:Tens...
  function gen_sineembed_for_position (line 74) | def gen_sineembed_for_position(pos_tensor, dim=128):
  function _get_activation_fn (line 103) | def _get_activation_fn(activation):
  function _get_clones (line 118) | def _get_clones(module, N, layer_share=False):

FILE: llava/model/semsam/body/encoder/build.py
  function build_encoder (line 7) | def build_encoder(config, *args, **kwargs):

FILE: llava/model/semsam/body/encoder/encoder_deform.py
  class MSDeformAttnTransformerEncoderOnly (line 29) | class MSDeformAttnTransformerEncoderOnly(nn.Module):
    method __init__ (line 30) | def __init__(self, d_model=256, nhead=8,
    method _reset_parameters (line 48) | def _reset_parameters(self):
    method get_valid_ratio (line 57) | def get_valid_ratio(self, mask):
    method forward (line 66) | def forward(self, srcs, masks, pos_embeds, use_ckpt=False):
  class MSDeformAttnTransformerEncoderLayer (line 103) | class MSDeformAttnTransformerEncoderLayer(nn.Module):
    method __init__ (line 104) | def __init__(self,
    method with_pos_embed (line 124) | def with_pos_embed(tensor, pos):
    method forward_ffn (line 127) | def forward_ffn(self, src):
    method forward (line 133) | def forward(self, src, pos, reference_points, spatial_shapes, level_st...
  class MSDeformAttnTransformerEncoder (line 145) | class MSDeformAttnTransformerEncoder(nn.Module):
    method __init__ (line 146) | def __init__(self, encoder_layer, num_layers):
    method get_reference_points (line 152) | def get_reference_points(spatial_shapes, valid_ratios, device):
    method forward (line 166) | def forward(self, src, spatial_shapes, level_start_index, valid_ratios...
  class MaskDINOEncoder (line 179) | class MaskDINOEncoder(nn.Module):
    method __init__ (line 184) | def __init__(
    method from_config (line 331) | def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec], *args, **...
    method forward_features (line 357) | def forward_features(self, features, masks):
  function get_maskdino_encoder_deform (line 428) | def get_maskdino_encoder_deform(cfg, input_shape):

FILE: llava/model/semsam/body/encoder/ops/functions/ms_deform_attn_func.py
  class MSDeformAttnFunction (line 32) | class MSDeformAttnFunction(Function):
    method forward (line 34) | def forward(ctx, value, value_spatial_shapes, value_level_start_index,...
    method backward (line 43) | def backward(ctx, grad_output):
  function ms_deform_attn_core_pytorch (line 52) | def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_lo...

FILE: llava/model/semsam/body/encoder/ops/modules/ms_deform_attn.py
  function _is_power_of_2 (line 28) | def _is_power_of_2(n):
  class MSDeformAttn (line 34) | class MSDeformAttn(nn.Module):
    method __init__ (line 35) | def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
    method _reset_parameters (line 66) | def _reset_parameters(self):
    method forward (line 82) | def forward(self, query, reference_points, input_flatten, input_spatia...

FILE: llava/model/semsam/body/encoder/ops/setup.py
  function get_extensions (line 26) | def get_extensions():

FILE: llava/model/semsam/body/encoder/ops/src/cpu/ms_deform_attn_cpu.cpp
  function ms_deform_attn_cpu_forward (line 22) | at::Tensor
  function ms_deform_attn_cpu_backward (line 34) | std::vector<at::Tensor>

FILE: llava/model/semsam/body/encoder/ops/src/ms_deform_attn.h
  function im2col_step (line 32) | int im2col_step)

FILE: llava/model/semsam/body/encoder/ops/src/vision.cpp
  function PYBIND11_MODULE (line 18) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: llava/model/semsam/body/encoder/ops/test.py
  function check_forward_equal_with_pytorch_double (line 35) | def check_forward_equal_with_pytorch_double():
  function check_forward_equal_with_pytorch_float (line 51) | def check_forward_equal_with_pytorch_float():
  function check_gradient_numerical (line 66) | def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_...

FILE: llava/model/semsam/body/encoder/registry.py
  function register_encoder (line 3) | def register_encoder(fn):
  function model_entrypoints (line 9) | def model_entrypoints(model_name):
  function is_model (line 12) | def is_model(model_name):

FILE: llava/model/semsam/body/encoder/transformer_encoder_fpn.py
  class BasePixelDecoder (line 22) | class BasePixelDecoder(nn.Module):
    method __init__ (line 23) | def __init__(
    method from_config (line 112) | def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
    method forward_features (line 123) | def forward_features(self, features):
    method forward (line 145) | def forward(self, features, targets=None):
  class TransformerEncoderOnly (line 151) | class TransformerEncoderOnly(nn.Module):
    method __init__ (line 152) | def __init__(
    method _reset_parameters (line 175) | def _reset_parameters(self):
    method forward (line 180) | def forward(self, src, mask, pos_embed):
  class TransformerEncoderPixelDecoder (line 193) | class TransformerEncoderPixelDecoder(BasePixelDecoder):
    method __init__ (line 195) | def __init__(
    method from_config (line 262) | def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
    method forward_features (line 276) | def forward_features(self, features):
    method forward (line 304) | def forward(self, features, targets=None):
  function get_transformer_encoder_fpn (line 312) | def get_transformer_encoder_fpn(cfg, input_shape):

FILE: llava/model/semsam/body/openseed_head.py
  class MaskDINOHead (line 21) | class MaskDINOHead(nn.Module):
    method __init__ (line 23) | def __init__(
    method from_config (line 56) | def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec], lang_enco...
    method forward (line 78) | def forward(self, features, mask=None, targets=None, target_queries=No...
    method layers (line 81) | def layers(self, features, mask=None,targets=None, target_queries=None...
  function get_maskdino_head (line 94) | def get_maskdino_head(cfg, input_shape, lang_encoder, extra):

FILE: llava/model/semsam/body/registry.py
  function register_body (line 4) | def register_body(fn):
  function model_entrypoints (line 10) | def model_entrypoints(model_name):
  function is_model (line 13) | def is_model(model_name):

FILE: llava/model/semsam/body/transformer_blocks.py
  class Transformer (line 19) | class Transformer(nn.Module):
    method __init__ (line 20) | def __init__(
    method _reset_parameters (line 56) | def _reset_parameters(self):
    method forward (line 61) | def forward(self, src, mask, query_embed, pos_embed):
  class TransformerEncoder (line 78) | class TransformerEncoder(nn.Module):
    method __init__ (line 79) | def __init__(self, encoder_layer, num_layers, norm=None):
    method forward (line 85) | def forward(
  class TransformerDecoder (line 105) | class TransformerDecoder(nn.Module):
    method __init__ (line 106) | def __init__(self, decoder_layer, num_layers, norm=None, return_interm...
    method forward (line 113) | def forward(
  class TransformerEncoderLayer (line 154) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 155) | def __init__(
    method with_pos_embed (line 179) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 182) | def forward_post(
    method forward_pre (line 201) | def forward_pre(
    method forward (line 219) | def forward(
  class TransformerDecoderLayer (line 231) | class TransformerDecoderLayer(nn.Module):
    method __init__ (line 232) | def __init__(
    method with_pos_embed (line 259) | def with_pos_embed(self, tensor, pos: Optional[Tensor]):
    method forward_post (line 262) | def forward_post(
    method forward_pre (line 293) | def forward_pre(
    method forward (line 324) | def forward(
  function _get_clones (line 358) | def _get_clones(module, N):
  function _get_activation_fn (line 362) | def _get_activation_fn(activation):

FILE: llava/model/semsam/language/LangEncoder/build.py
  function build_lang_encoder (line 10) | def build_lang_encoder(config_encoder, tokenizer, verbose, **kwargs):
  function build_tokenizer (line 19) | def build_tokenizer(config_encoder):

FILE: llava/model/semsam/language/LangEncoder/registry.py
  function register_lang_encoder (line 4) | def register_lang_encoder(fn):
  function lang_encoders (line 13) | def lang_encoders(model_name):
  function is_lang_encoder (line 17) | def is_lang_encoder(model_name):

FILE: llava/model/semsam/language/LangEncoder/transformer.py
  class LayerNorm (line 21) | class LayerNorm(nn.Module):
    method __init__ (line 22) | def __init__(self, hidden_size, eps=1e-12):
    method forward (line 30) | def forward(self, x):
  class QuickGELU (line 39) | class QuickGELU(nn.Module):
    method forward (line 40) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 44) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 45) | def __init__(self,
    method attention (line 63) | def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = ...
    method forward (line 75) | def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = No...
  class Transformer (line 81) | class Transformer(nn.Module):
    method __init__ (line 82) | def __init__(self,
    method dim_out (line 119) | def dim_out(self):
    method build_attention_mask (line 122) | def build_attention_mask(self):
    method _init_weights (line 130) | def _init_weights(self, m):
    method load_pretrained (line 142) | def load_pretrained(self, pretrained='', pretrained_layers=[], verbose...
    method no_weight_decay (line 188) | def no_weight_decay(self):
    method forward (line 194) | def forward(self, input_ids, attention_mask=None):
  function lang_encoder (line 210) | def lang_encoder(config_encoder, tokenizer, verbose, **kwargs):

FILE: llava/model/semsam/language/build.py
  function build_language_encoder (line 5) | def build_language_encoder(config, **kwargs):

FILE: llava/model/semsam/language/encoder.py
  class LanguageEncoder (line 13) | class LanguageEncoder(nn.Module):
    method __init__ (line 16) | def __init__(
    method from_config (line 33) | def from_config(cls, cfg):
    method get_text_embeddings (line 54) | def get_text_embeddings(self, class_names, name='default', is_eval=Fal...
    method forward_language (line 109) | def forward_language(self, texts, norm=True):
    method compute_similarity (line 123) | def compute_similarity(self, v_emb, name='default'):
  function get_language_model (line 131) | def get_language_model(cfg, **kwargs):

FILE: llava/model/semsam/language/fixencoder.py
  class LanguageEncoder (line 13) | class LanguageEncoder(nn.Module):
    method __init__ (line 16) | def __init__(
    method from_config (line 33) | def from_config(cls, cfg):
    method get_text_embeddings (line 54) | def get_text_embeddings(self, class_names, name='default', is_eval=Fal...
    method forward_language (line 109) | def forward_language(self, texts, norm=True):
    method compute_similarity (line 124) | def compute_similarity(self, v_emb, name='default'):
  function get_language_model (line 132) | def get_language_model(cfg, **kwargs):

FILE: llava/model/semsam/language/llama_encoder.py
  class ModelArguments (line 65) | class ModelArguments:
  class DataArguments (line 80) | class DataArguments:
  class TrainingArguments (line 91) | class TrainingArguments(transformers.TrainingArguments):
  function safe_save_model_for_hf_trainer (line 105) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
  function smart_tokenizer_and_embedding_resize (line 118) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 143) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 170) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 181) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 202) | def preprocess_multimodal(
  function preprocess (line 223) | def preprocess(
  class SupervisedDataset (line 253) | class SupervisedDataset(Dataset):
    method __init__ (line 256) | def __init__(self, data_path: str,
    method __len__ (line 269) | def __len__(self):
    method __getitem__ (line 272) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class LazySupervisedDataset (line 276) | class LazySupervisedDataset(Dataset):
    method __init__ (line 279) | def __init__(self, data_path: str,
    method __len__ (line 291) | def __len__(self):
    method __getitem__ (line 294) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 348) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 353) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 379) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function setup (line 402) | def setup(config_file):
  function get_language_model (line 415) | def get_language_model(cfg, **kwargs):
  function train (line 430) | def train():

FILE: llava/model/semsam/language/loss.py
  function is_dist_initialized (line 13) | def is_dist_initialized():
  function get_world_size (line 16) | def get_world_size():
  function get_rank (line 21) | def get_rank():
  function all_gather_grad (line 26) | def all_gather_grad(x):
  function vl_multilabel_contrastive_loss (line 34) | def vl_multilabel_contrastive_loss(image_feat, text_feat, temperature=1):
  function vl_contrastive_loss (line 93) | def vl_contrastive_loss(image_feat, text_feat, temperature=1):
  function all_gather_pickle (line 112) | def all_gather_pickle(data, device):
  function all_gather_arbitary_tensor (line 154) | def all_gather_arbitary_tensor(tensor):
  function ql_contrastive_loss (line 165) | def ql_contrastive_loss(image_feat, text_feat, temperature=1):
  function vl_similarity (line 178) | def vl_similarity(image_feat, text_feat, temperature=1):
  function ql_multi_contrastive_loss (line 184) | def ql_multi_contrastive_loss(image_feat, text_feat, text_hash, temperat...
  function image_text_contrastive_loss_queue (line 209) | def image_text_contrastive_loss_queue(image_feat_inp, text_feat_inp, lan...

FILE: llava/model/semsam/language/misc.py
  function vl_similarity (line 11) | def vl_similarity(image_feat, text_feat, temperature=1):
  function get_tag (line 17) | def get_tag(tokenized, tags):
  function get_noun_phrase (line 27) | def get_noun_phrase(tokenized):
  function text_noun_with_prompt_all (line 56) | def text_noun_with_prompt_all(text, phrase_prob=0.0, append_text=True):

FILE: llava/model/semsam/language/modeling_llama_os.py
  function _make_causal_mask (line 41) | def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, p...
  function _expand_mask (line 56) | def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Option...
  class LlamaRMSNorm (line 70) | class LlamaRMSNorm(nn.Module):
    method __init__ (line 71) | def __init__(self, hidden_size, eps=1e-6):
    method forward (line 79) | def forward(self, hidden_states):
  class LlamaRotaryEmbedding (line 90) | class LlamaRotaryEmbedding(torch.nn.Module):
    method __init__ (line 91) | def __init__(self, dim, max_position_embeddings=2048, base=10000, devi...
    method forward (line 105) | def forward(self, x, seq_len=None):
  function rotate_half (line 122) | def rotate_half(x):
  function apply_rotary_pos_emb (line 129) | def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0):
  class LlamaMLP (line 137) | class LlamaMLP(nn.Module):
    method __init__ (line 138) | def __init__(
    method forward (line 150) | def forward(self, x):
  class LlamaAttention (line 154) | class LlamaAttention(nn.Module):
    method __init__ (line 157) | def __init__(
    method _shape (line 194) | def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
    method forward (line 197) | def forward(
  class LlamaDecoderLayer (line 266) | class LlamaDecoderLayer(nn.Module):
    method __init__ (line 267) | def __init__(self, config: LlamaConfig):
    method forward (line 282) | def forward(
  class LlamaPreTrainedModel (line 356) | class LlamaPreTrainedModel(PreTrainedModel):
    method _init_weights (line 363) | def _init_weights(self, module):
    method _set_gradient_checkpointing (line 374) | def _set_gradient_checkpointing(self, module, value=False):
  class LlamaModel (line 443) | class LlamaModel(LlamaPreTrainedModel):
    method __init__ (line 451) | def __init__(self, config: LlamaConfig):
    method get_input_embeddings (line 472) | def get_input_embeddings(self):
    method get_output_embeddings (line 475) | def get_output_embeddings(self):
    method set_input_embeddings (line 478) | def set_input_embeddings(self, value):
    method _prepare_decoder_attention_mask (line 482) | def _prepare_decoder_attention_mask(self, attention_mask, input_shape,...
    method find_pattern_list (line 503) | def find_pattern_list(self, pattern, src):
    method forward (line 518) | def forward(
  class LlamaForCausalLM (line 783) | class LlamaForCausalLM(LlamaPreTrainedModel):
    method __init__ (line 786) | def __init__(self, config):
    method get_input_embeddings (line 795) | def get_input_embeddings(self):
    method set_input_embeddings (line 798) | def set_input_embeddings(self, value):
    method get_output_embeddings (line 801) | def get_output_embeddings(self):
    method set_output_embeddings (line 804) | def set_output_embeddings(self, new_embeddings):
    method set_decoder (line 807) | def set_decoder(self, decoder):
    method get_decoder (line 810) | def get_decoder(self):
    method forward (line 814) | def forward(
    method prepare_inputs_for_generation (line 958) | def prepare_inputs_for_generation(
    method _reorder_cache (line 981) | def _reorder_cache(past_key_values, beam_idx):
  class LlamaForSequenceClassification (line 1003) | class LlamaForSequenceClassification(LlamaPreTrainedModel):
    method __init__ (line 1006) | def __init__(self, config):
    method get_input_embeddings (line 1015) | def get_input_embeddings(self):
    method set_input_embeddings (line 1018) | def set_input_embeddings(self, value):
    method forward (line 1022) | def forward(

FILE: llava/model/semsam/language/registry.py
  function register_model (line 3) | def register_model(fn):
  function model_entrypoints (line 9) | def model_entrypoints(model_name):
  function is_model (line 12) | def is_model(model_name):

FILE: llava/model/semsam/language/vlpencoder.py
  class LanguageEncoder (line 19) | class LanguageEncoder(nn.Module):
    method __init__ (line 22) | def __init__(
    method from_config (line 46) | def from_config(cls, cfg):
    method get_text_embeddings (line 70) | def get_text_embeddings(self, class_names, name='default', is_eval=Fal...
    method get_text_token_embeddings (line 127) | def get_text_token_embeddings(self, txts, name='default', token=False,...
    method forward_language (line 142) | def forward_language(self, texts, norm=True):
    method forward_language_token (line 156) | def forward_language_token(self, texts, norm=False):
    method compute_similarity (line 174) | def compute_similarity(self, v_emb, name='default', fake=False):
  function get_language_model (line 184) | def get_language_model(cfg, **kwargs):

FILE: llava/model/semsam/modules/attention.py
  function multi_head_attention_forward (line 13) | def multi_head_attention_forward(
  class _LinearWithBias (line 324) | class _LinearWithBias(nn.Linear):
    method __init__ (line 327) | def __init__(self, in_features: int, out_features: int) -> None:
  class MultiheadAttention (line 331) | class MultiheadAttention(nn.Module):
    method __init__ (line 364) | def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bi...
    method _reset_parameters (line 403) | def _reset_parameters(self):
    method __setstate__ (line 419) | def __setstate__(self, state):
    method forward (line 426) | def forward(self, query: Tensor, key: Tensor, value: Tensor, key_paddi...

FILE: llava/model/semsam/modules/criterion_id_llm.py
  function sigmoid_focal_loss (line 24) | def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, ...
  function dice_loss (line 52) | def dice_loss(
  function iou_score_loss (line 78) | def iou_score_loss(inputs, targets):
  function sigmoid_ce_loss (line 88) | def sigmoid_ce_loss(
  function calculate_uncertainty (line 116) | def calculate_uncertainty(logits):
  class SetCriterionLLM (line 133) | class SetCriterionLLM(nn.Module):
    method __init__ (line 140) | def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,
    method loss_labels_ce (line 184) | def loss_labels_ce(self, outputs, targets, indices, num_masks):
    method loss_labels (line 202) | def loss_labels(self, outputs, targets, indices, num_boxes, log=True, ...
    method loss_labels_part (line 239) | def loss_labels_part(self, outputs, targets, indices, num_boxes, log=T...
    method loss_boxes_o365 (line 273) | def loss_boxes_o365(self, outputs, targets, indices, num_boxes, layer_...
    method loss_boxes (line 299) | def loss_boxes(self, outputs, targets, indices, num_boxes):
    method loss_boxes_panoptic (line 337) | def loss_boxes_panoptic(self, outputs, targets, indices, num_boxes):
    method loss_masks (line 362) | def loss_masks(self, outputs, targets, indices, num_masks):
    method loss_labels_o365 (line 461) | def loss_labels_o365(self, outputs, targets, indices, num_boxes, log=T...
    method prep_for_dn (line 489) | def prep_for_dn(self, mask_dict):
    method _get_src_permutation_idx (line 500) | def _get_src_permutation_idx(self, indices):
    method _get_tgt_permutation_idx (line 506) | def _get_tgt_permutation_idx(self, indices):
    method get_loss (line 512) | def get_loss(self, loss, outputs, targets, indices, num_masks):
    method forward (line 524) | def forward(self, outputs, targets, mask_dict=None, task='sam', extra=...
    method __repr__ (line 630) | def __repr__(self):

FILE: llava/model/semsam/modules/hooks.py
  class HookBase (line 13) | class HookBase:
    method before_train (line 50) | def before_train(self):
    method after_train (line 56) | def after_train(self):
    method before_step (line 62) | def before_step(self):
    method after_step (line 68) | def after_step(self):
    method state_dict (line 74) | def state_dict(self):
  class CallbackHook (line 129) | class CallbackHook(HookBase):
    method __init__ (line 134) | def __init__(self, *, before_train=None, after_train=None, before_step...
    method before_train (line 143) | def before_train(self):
    method after_train (line 147) | def after_train(self):
    method before_step (line 155) | def before_step(self):
    method after_step (line 159) | def after_step(self):
  class IterationTimer (line 164) | class IterationTimer(HookBase):
    method __init__ (line 176) | def __init__(self, warmup_iter=3):
    method before_train (line 187) | def before_train(self):
    method after_train (line 192) | def after_train(self):
    method before_step (line 218) | def before_step(self):
    method after_step (line 222) | def after_step(self):
  class PeriodicWriter (line 236) | class PeriodicWriter(HookBase):
    method __init__ (line 244) | def __init__(self, writers, period=20):
    method after_step (line 255) | def after_step(self):
    method after_train (line 262) | def after_train(self):
  class PeriodicCheckpointer (line 270) | class PeriodicCheckpointer(_PeriodicCheckpointer, HookBase):
    method before_train (line 281) | def before_train(self):
    method after_step (line 284) | def after_step(self):
  class BestCheckpointer (line 289) | class BestCheckpointer(HookBase):
    method __init__ (line 297) | def __init__(
    method _update_best (line 330) | def _update_best(self, val, iteration):
    method _best_checking (line 337) | def _best_checking(self):
    method after_step (line 370) | def after_step(self):
    method after_train (line 380) | def after_train(self):
  class LRScheduler (line 386) | class LRScheduler(HookBase):
    method __init__ (line 392) | def __init__(self, optimizer=None, scheduler=None):
    method before_train (line 405) | def before_train(self):
    method get_best_param_group_id (line 417) | def get_best_param_group_id(optimizer):
    method after_step (line 435) | def after_step(self):
    method scheduler (line 441) | def scheduler(self):
    method state_dict (line 444) | def state_dict(self):
    method load_state_dict (line 449) | def load_state_dict(self, state_dict):
  class TorchProfiler (line 456) | class TorchProfiler(HookBase):
    method __init__ (line 474) | def __init__(self, enable_predicate, output_dir, *, activities=None, s...
    method before_step (line 489) | def before_step(self):
    method after_step (line 514) | def after_step(self):
  class AutogradProfiler (line 536) | class AutogradProfiler(TorchProfiler):
    method __init__ (line 559) | def __init__(self, enable_predicate, output_dir, *, use_cuda=True):
    method before_step (line 573) | def before_step(self):
  class EvalHook (line 581) | class EvalHook(HookBase):
    method __init__ (line 588) | def __init__(self, eval_period, eval_function, eval_after_train=True):
    method _do_eval (line 607) | def _do_eval(self):
    method after_step (line 630) | def after_step(self):
    method after_train (line 637) | def after_train(self):
  class PreciseBN (line 646) | class PreciseBN(HookBase):
    method __init__ (line 656) | def __init__(self, period, model, data_loader, num_iter):
    method after_step (line 685) | def after_step(self):
    method update_stats (line 691) | def update_stats(self):
  class TorchMemoryStats (line 718) | class TorchMemoryStats(HookBase):
    method __init__ (line 723) | def __init__(self, period=20, max_runs=10):
    method after_step (line 735) | def after_step(self):

FILE: llava/model/semsam/modules/matcher.py
  function batch_dice_loss (line 22) | def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):
  function batch_sigmoid_ce_loss (line 45) | def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):
  class HungarianMatcher (line 77) | class HungarianMatcher(nn.Module):
    method __init__ (line 85) | def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_d...
    method memory_efficient_forward (line 108) | def memory_efficient_forward(self, outputs, targets, cost=["cls", "box...
    method grounding_forward (line 196) | def grounding_forward(self, outputs, targets, extra):
    method caption_forward_womask (line 259) | def caption_forward_womask(self, outputs, targets, extra):
    method caption_forward_wmask (line 295) | def caption_forward_wmask(self, outputs, targets, extra):
    method forward (line 365) | def forward(self, outputs, targets, cost=["cls", "box", "mask"], mode=...
    method __repr__ (line 396) | def __repr__(self, _repr_indent=4):

FILE: llava/model/semsam/modules/point_features.py
  function point_sample (line 21) | def point_sample(input, point_coords, **kwargs):
  function generate_regular_grid_point_coords (line 47) | def generate_regular_grid_point_coords(R, side_size, device):
  function get_uncertain_point_coords_with_randomness (line 65) | def get_uncertain_point_coords_with_randomness(
  function get_uncertain_point_coords_on_grid (line 121) | def get_uncertain_point_coords_on_grid(uncertainty_map, num_points):
  function point_sample_fine_grained_features (line 148) | def point_sample_fine_grained_features(features_list, feature_scales, bo...
  function get_point_coords_wrt_image (line 194) | def get_point_coords_wrt_image(boxes_coords, point_coords):
  function sample_point_labels (line 221) | def sample_point_labels(instances, point_coords):

FILE: llava/model/semsam/modules/position_encoding.py
  class PositionEmbeddingSine (line 12) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 18) | def __init__(self, num_pos_feats=64, temperature=10000, normalize=Fals...
    method forward (line 29) | def forward(self, x, mask=None):
    method __repr__ (line 54) | def __repr__(self, _repr_indent=4):

FILE: llava/model/semsam/modules/postprocessing.py
  function detector_postprocess (line 9) | def detector_postprocess(
  function bbox_postprocess (line 77) | def bbox_postprocess(result, input_size, img_size, output_height, output...
  function sem_seg_postprocess (line 99) | def sem_seg_postprocess(result, img_size, output_height, output_width):

FILE: llava/model/semsam/utils/box_ops.py
  function box_cxcywh_to_xyxy (line 9) | def box_cxcywh_to_xyxy(x):
  function box_xyxy_to_cxcywh (line 16) | def box_xyxy_to_cxcywh(x):
  function box_xywh_to_xyxy (line 22) | def box_xywh_to_xyxy(x):
  function box_iou (line 29) | def box_iou(boxes1, boxes2):
  function generalized_box_iou (line 45) | def generalized_box_iou(boxes1, boxes2):
  function masks_to_boxes (line 69) | def masks_to_boxes(masks):

FILE: llava/model/semsam/utils/config.py
  function configurable (line 7) | def configurable(init_func=None, *, from_config=None):
  function _called_with_cfg (line 95) | def _called_with_cfg(*args, **kwargs):
  function _get_args_from_config (line 112) | def _get_args_from_config(from_config_func, *args, **kwargs):

FILE: llava/model/semsam/utils/misc.py
  function get_iou (line 25) | def get_iou(gt_masks, pred_masks, ignore_label=-1):
  function _max_by_axis (line 34) | def _max_by_axis(the_list):
  class NestedTensor (line 42) | class NestedTensor(object):
    method __init__ (line 43) | def __init__(self, tensors, mask: Optional[Tensor]):
    method to (line 47) | def to(self, device):
    method decompose (line 58) | def decompose(self):
    method __repr__ (line 61) | def __repr__(self):
  function nested_tensor_from_tensor_list (line 64) | def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
  function _collate_and_pad_divisibility (line 106) | def _collate_and_pad_divisibility(tensor_list: list, div=32):
  function _onnx_nested_tensor_from_tensor_list (line 140) | def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> N...
  function is_dist_avail_and_initialized (line 169) | def is_dist_avail_and_initialized():
  function get_class_names (line 177) | def get_class_names(name, background=True):

FILE: llava/model/utils.py
  function auto_upgrade (line 4) | def auto_upgrade(config):

FILE: llava/serve/cli.py
  function load_image (line 18) | def load_image(image_file):
  function main (line 27) | def main(args):

FILE: llava/serve/controller.py
  class DispatchMethod (line 28) | class DispatchMethod(Enum):
    method from_str (line 33) | def from_str(cls, name):
  class WorkerInfo (line 43) | class WorkerInfo:
  function heart_beat_controller (line 51) | def heart_beat_controller(controller):
  class Controller (line 57) | class Controller:
    method __init__ (line 58) | def __init__(self, dispatch_method: str):
    method register_worker (line 69) | def register_worker(self, worker_name: str, check_heart_beat: bool,
    method get_worker_status (line 88) | def get_worker_status(self, worker_name: str):
    method remove_worker (line 101) | def remove_worker(self, worker_name: str):
    method refresh_all_workers (line 104) | def refresh_all_workers(self):
    method list_models (line 112) | def list_models(self):
    method get_worker_address (line 120) | def get_worker_address(self, model_name: str):
    method receive_heart_beat (line 173) | def receive_heart_beat(self, worker_name: str, queue_length: int):
    method remove_stable_workers_by_expiration (line 183) | def remove_stable_workers_by_expiration(self):
    method worker_api_generate_stream (line 193) | def worker_api_generate_stream(self, params):
    method worker_api_get_status (line 220) | def worker_api_get_status(self):
  function register_worker (line 243) | async def register_worker(request: Request):
  function refresh_all_workers (line 251) | async def refresh_all_workers():
  function list_models (line 256) | async def list_models():
  function get_worker_address (line 262) | async def get_worker_address(request: Request):
  function receive_heart_beat (line 269) | async def receive_heart_beat(request: Request):
  function worker_api_generate_stream (line 277) | async def worker_api_generate_stream(request: Request):
  function worker_api_get_status (line 284) | async def worker_api_get_status(request: Request):

FILE: llava/serve/gradio_web_server.py
  function get_conv_log_filename (line 32) | def get_conv_log_filename():
  function get_model_list (line 38) | def get_model_list():
  function load_demo (line 58) | def load_demo(url_params, request: gr.Request):
  function load_demo_refresh_model_list (line 78) | def load_demo_refresh_model_list(request: gr.Request):
  function vote_last_response (line 92) | def vote_last_response(state, vote_type, model_selector, request: gr.Req...
  function upvote_last_response (line 104) | def upvote_last_response(state, model_selector, request: gr.Request):
  function downvote_last_response (line 110) | def downvote_last_response(state, model_selector, request: gr.Request):
  function flag_last_response (line 116) | def flag_last_response(state, model_selector, request: gr.Request):
  function regenerate (line 122) | def regenerate(state, image_process_mode, request: gr.Request):
  function clear_history (line 132) | def clear_history(request: gr.Request):
  function add_text (line 138) | def add_text(state, text, image, image_process_mode, request: gr.Request):
  function http_bot (line 165) | def http_bot(state, model_selector, temperature, top_p, max_new_tokens, ...
  function build_demo (line 310) | def build_demo(embed_mode):

FILE: llava/serve/test_message.py
  function main (line 9) | def main():

FILE: llava/train/llama_flash_attn_monkey_patch.py
  function forward (line 19) | def forward(
  function _prepare_decoder_attention_mask (line 107) | def _prepare_decoder_attention_mask(
  function replace_llama_attn_with_flash_attn (line 114) | def replace_llama_attn_with_flash_attn():

FILE: llava/train/llava_trainer.py
  function maybe_zero_3 (line 8) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_mm_adapter_state_maybe_zero_3 (line 22) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  class LLaVATrainer (line 28) | class LLaVATrainer(Trainer):
    method _save_checkpoint (line 30) | def _save_checkpoint(self, model, trial, metrics=None):
    method _save (line 51) | def _save(self, output_dir: Optional[str] = None, state_dict=None):

FILE: llava/train/llava_trainer_gd.py
  function maybe_zero_3 (line 9) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_mm_adapter_state_maybe_zero_3 (line 23) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  class TrainerLLavaGD (line 29) | class TrainerLLavaGD(Trainer):
    method __init__ (line 114) | def __init__(
    method add_callback (line 508) | def add_callback(self, callback):
    method pop_callback (line 519) | def pop_callback(self, callback):
    method remove_callback (line 535) | def remove_callback(self, callback):
    method _move_model_to_device (line 546) | def _move_model_to_device(self, model, device):
    method _set_signature_columns_if_needed (line 552) | def _set_signature_columns_if_needed(self):
    method _remove_unused_columns (line 560) | def _remove_unused_columns(self, dataset: "datasets.Dataset", descript...
    method _get_collator_with_removed_columns (line 586) | def _get_collator_with_removed_columns(
    method _get_train_sampler (line 604) | def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
    method get_train_dataloader (line 629) | def get_train_dataloader(self) -> DataLoader:
    method get_train_dataloaderd2 (line 663) | def get_train_dataloaderd2(self) -> DataLoader:
    method _get_eval_sampler (line 666) | def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.u...
    method get_eval_dataloader (line 688) | def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) ...
    method get_test_dataloader (line 722) | def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
    method create_optimizer_and_scheduler (line 754) | def create_optimizer_and_scheduler(self, num_training_steps: int):
    method create_optimizer (line 770) | def create_optimizer(self):
    method get_optimizer_cls_and_kwargs (line 876) | def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any...
    method create_scheduler (line 998) | def create_scheduler(self, num_training_steps: int, optimizer: torch.o...
    method num_examples (line 1016) | def num_examples(self, dataloader: DataLoader) -> int:
    method _hp_search_setup (line 1030) | def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
    method _report_to_hp_search (line 1077) | def _report_to_hp_search(self, trial: Union["optuna.Trial", Dict[str, ...
    method _tune_save_checkpoint (line 1095) | def _tune_save_checkpoint(self):
    method call_model_init (line 1108) | def call_model_init(self, trial=None):
    method torch_jit_model_eval (line 1122) | def torch_jit_model_eval(self, model, dataloader, training=False):
    method ipex_optimize_model (line 1165) | def ipex_optimize_model(self, model, training=False, dtype=torch.float...
    method _wrap_model (line 1188) | def _wrap_model(self, model, training=True, dataloader=None):
    method train (line 1319) | def train(
    method _inner_training_loop (line 1403) | def _inner_training_loop(
    method _get_output_dir (line 1842) | def _get_output_dir(self, trial):
    method _load_from_checkpoint (line 1862) | def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
    method _load_best_model (line 1957) | def _load_best_model(self):
    method _issue_warnings_after_load (line 2044) | def _issue_warnings_after_load(self, load_result):
    method _maybe_log_save_evaluate (line 2057) | def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignor...
    method _load_rng_state (line 2110) | def _load_rng_state(self, checkpoint):
    method _save_checkpoint (line 2151) | def _save_checkpoint(self, model, trial, metrics=None):
    method _load_optimizer_and_scheduler (line 2272) | def _load_optimizer_and_scheduler(self, checkpoint):
    method hyperparameter_search (line 2350) | def hyperparameter_search(
    method log (line 2425) | def log(self, logs: Dict[str, float]) -> None:
    method _prepare_input (line 2442) | def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torc...
    method _prepare_inputs (line 2460) | def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]])...
    method compute_loss_context_manager (line 2476) | def compute_loss_context_manager(self):
    method autocast_smart_context_manager (line 2482) | def autocast_smart_context_manager(self, cache_enabled: Optional[bool]...
    method training_step (line 2498) | def training_step(self, model: nn.Module, inputs: Dict[str, Union[torc...
    method compute_loss (line 2542) | def compute_loss(self, model, inputs, return_outputs=False):
    method is_local_process_zero (line 2574) | def is_local_process_zero(self) -> bool:
    method is_world_process_zero (line 2581) | def is_world_process_zero(self) -> bool:
    method save_model (line 2593) | def save_model(self, output_dir: Optional[str] = None, _internal_call:...
    method _save_tpu (line 2648) | def _save_tpu(self, output_dir: Optional[str] = None):
    method _save (line 2676) | def _save(self, output_dir: Optional[str] = None, state_dict=None):
    method store_flos (line 2710) | def store_flos(self):
    method _sorted_checkpoints (line 2721) | def _sorted_checkpoints(
    method _rotate_checkpoints (line 2745) | def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:
    method evaluate (line 2770) | def evaluate(
    method predict (line 2841) | def predict(
    method evaluation_loop (line 2903) | def evaluation_loop(
    method _nested_gather (line 3114) | def _nested_gather(self, tensors, name=None):
    method prediction_step (line 3133) | def prediction_step(
    method floating_point_ops (line 3238) | def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any...
    method init_git_repo (line 3256) | def init_git_repo(self, at_init: bool = False):
    method create_model_card (line 3303) | def create_model_card(
    method _push_from_checkpoint (line 3359) | def _push_from_checkpoint(self, checkpoint_folder):
    method push_to_hub (line 3406) | def push_to_hub(self, commit_message: Optional[str] = "End of training...
    method prediction_loop (line 3466) | def prediction_loop(
    method _gather_and_numpify (line 3617) | def _gather_and_numpify(self, tensors, name):
    method _add_sm_patterns_to_gitignore (line 3633) | def _add_sm_patterns_to_gitignore(self) -> None:
    method create_accelerator_and_postprocess (line 3672) | def create_accelerator_and_postprocess(self):
  class LLaVATrainer (line 3707) | class LLaVATrainer(TrainerLLavaGD):
    method _save_checkpoint (line 3709) | def _save_checkpoint(self, model, trial, metrics=None):
    method _save (line 3730) | def _save(self, output_dir: Optional[str] = None, state_dict=None):

FILE: llava/train/llava_trainer_joint_train.py
  function maybe_zero_3 (line 13) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_mm_adapter_state_maybe_zero_3 (line 27) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  class DataCollatorForSupervisedDatasetEmpty (line 33) | class DataCollatorForSupervisedDatasetEmpty(object):
    method __call__ (line 38) | def __call__(self, instances: Sequence[Dict]):
  class TrainerLLavaGD (line 66) | class TrainerLLavaGD(Trainer):
    method __init__ (line 151) | def __init__(
    method add_callback (line 545) | def add_callback(self, callback):
    method pop_callback (line 556) | def pop_callback(self, callback):
    method remove_callback (line 572) | def remove_callback(self, callback):
    method _move_model_to_device (line 583) | def _move_model_to_device(self, model, device):
    method _set_signature_columns_if_needed (line 589) | def _set_signature_columns_if_needed(self):
    method _remove_unused_columns (line 597) | def _remove_unused_columns(self, dataset: "datasets.Dataset", descript...
    method _get_collator_with_removed_columns (line 623) | def _get_collator_with_removed_columns(
    method _get_train_sampler (line 641) | def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
    method get_train_dataloader (line 666) | def get_train_dataloader(self) -> DataLoader:
    method get_train_dataloaderd2 (line 701) | def get_train_dataloaderd2(self) -> DataLoader:
    method _get_eval_sampler (line 705) | def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.u...
    method get_eval_dataloader (line 727) | def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) ...
    method get_test_dataloader (line 761) | def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
    method create_optimizer_and_scheduler (line 793) | def create_optimizer_and_scheduler(self, num_training_steps: int):
    method create_optimizer (line 809) | def create_optimizer(self):
    method get_optimizer_cls_and_kwargs (line 944) | def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any...
    method create_scheduler (line 1066) | def create_scheduler(self, num_training_steps: int, optimizer: torch.o...
    method num_examples (line 1084) | def num_examples(self, dataloader: DataLoader) -> int:
    method _hp_search_setup (line 1098) | def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
    method _report_to_hp_search (line 1145) | def _report_to_hp_search(self, trial: Union["optuna.Trial", Dict[str, ...
    method _tune_save_checkpoint (line 1163) | def _tune_save_checkpoint(self):
    method call_model_init (line 1176) | def call_model_init(self, trial=None):
    method torch_jit_model_eval (line 1190) | def torch_jit_model_eval(self, model, dataloader, training=False):
    method ipex_optimize_model (line 1233) | def ipex_optimize_model(self, model, training=False, dtype=torch.float...
    method _wrap_model (line 1256) | def _wrap_model(self, model, training=True, dataloader=None):
    method train (line 1387) | def train(
    method _inner_training_loop (line 1471) | def _inner_training_loop(
    method _get_output_dir (line 1910) | def _get_output_dir(self, trial):
    method _load_from_checkpoint (line 1930) | def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
    method _load_best_model (line 2025) | def _load_best_model(self):
    method _issue_warnings_after_load (line 2112) | def _issue_warnings_after_load(self, load_result):
    method _maybe_log_save_evaluate (line 2125) | def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignor...
    method _load_rng_state (line 2178) | def _load_rng_state(self, checkpoint):
    method _save_checkpoint (line 2219) | def _save_checkpoint(self, model, trial, metrics=None):
    method _load_optimizer_and_scheduler (line 2340) | def _load_optimizer_and_scheduler(self, checkpoint):
    method hyperparameter_search (line 2418) | def hyperparameter_search(
    method log (line 2493) | def log(self, logs: Dict[str, float]) -> None:
    method _prepare_input (line 2510) | def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torc...
    method _prepare_inputs (line 2528) | def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]])...
    method compute_loss_context_manager (line 2544) | def compute_loss_context_manager(self):
    method autocast_smart_context_manager (line 2550) | def autocast_smart_context_manager(self, cache_enabled: Optional[bool]...
    method training_step (line 2566) | def training_step(self, model: nn.Module, inputs: Dict[str, Union[torc...
    method compute_loss (line 2610) | def compute_loss(self, model, inputs, return_outputs=False):
    method is_local_process_zero (line 2642) | def is_local_process_zero(self) -> bool:
    method is_world_process_zero (line 2649) | def is_world_process_zero(self) -> bool:
    method save_model (line 2661) | def save_model(self, output_dir: Optional[str] = None, _internal_call:...
    method _save_tpu (line 2716) | def _save_tpu(self, output_dir: Optional[str] = None):
    method _save (line 2744) | def _save(self, output_dir: Optional[str] = None, state_dict=None):
    method store_flos (line 2778) | def store_flos(self):
    method _sorted_checkpoints (line 2789) | def _sorted_checkpoints(
    method _rotate_checkpoints (line 2813) | def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:
    method evaluate (line 2838) | def evaluate(
    method predict (line 2909) | def predict(
    method evaluation_loop (line 2971) | def evaluation_loop(
    method _nested_gather (line 3182) | def _nested_gather(self, tensors, name=None):
    method prediction_step (line 3201) | def prediction_step(
    method floating_point_ops (line 3306) | def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any...
    method init_git_repo (line 3324) | def init_git_repo(self, at_init: bool = False):
    method create_model_card (line 3371) | def create_model_card(
    method _push_from_checkpoint (line 3427) | def _push_from_checkpoint(self, checkpoint_folder):
    method push_to_hub (line 3474) | def push_to_hub(self, commit_message: Optional[str] = "End of training...
    method prediction_loop (line 3534) | def prediction_loop(
    method _gather_and_numpify (line 3685) | def _gather_and_numpify(self, tensors, name):
    method _add_sm_patterns_to_gitignore (line 3701) | def _add_sm_patterns_to_gitignore(self) -> None:
    method create_accelerator_and_postprocess (line 3740) | def create_accelerator_and_postprocess(self):
  class LLaVATrainer (line 3775) | class LLaVATrainer(TrainerLLavaGD):
    method _save_checkpoint (line 3777) | def _save_checkpoint(self, model, trial, metrics=None):
    method _save (line 3798) | def _save(self, output_dir: Optional[str] = None, state_dict=None):

FILE: llava/train/train.py
  function rank0_print (line 43) | def rank0_print(*args):
  class ModelArguments (line 49) | class ModelArguments:
  class DataArguments (line 63) | class DataArguments:
  class TrainingArguments (line 74) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 108) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 123) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 148) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 156) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 162) | def find_all_linear_names(model):
  function safe_save_model_for_hf_trainer (line 176) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
  function smart_tokenizer_and_embedding_resize (line 195) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 220) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 247) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 258) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 279) | def preprocess_multimodal(
  function preprocess_llama_2 (line 303) | def preprocess_llama_2(
  function preprocess_v1 (line 385) | def preprocess_v1(
  function preprocess_mpt (line 467) | def preprocess_mpt(
  function preprocess_plain (line 533) | def preprocess_plain(
  function preprocess (line 555) | def preprocess(
  class LazySupervisedDataset (line 603) | class LazySupervisedDataset(Dataset):
    method __init__ (line 606) | def __init__(self, data_path: str,
    method __len__ (line 617) | def __len__(self):
    method __getitem__ (line 620) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 686) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 691) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 719) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function train (line 731) | def train():

FILE: llava/train/train_grounding_1st.py
  function rank0_print (line 44) | def rank0_print(*args):
  class ModelArguments (line 50) | class ModelArguments:
  class DataArguments (line 68) | class DataArguments:
  class TrainingArguments (line 79) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 117) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 132) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 157) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 165) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 171) | def find_all_linear_names(model):
  function safe_save_model_for_hf_trainer (line 185) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
  function smart_tokenizer_and_embedding_resize (line 204) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 229) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 256) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 267) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 288) | def preprocess_multimodal(
  function preprocess_llama_2 (line 312) | def preprocess_llama_2(
  function preprocess_v1 (line 394) | def preprocess_v1(
  function preprocess_mpt (line 476) | def preprocess_mpt(
  function preprocess_plain (line 542) | def preprocess_plain(
  function preprocess (line 564) | def preprocess(
  class LazySupervisedDataset (line 612) | class LazySupervisedDataset(Dataset):
    method __init__ (line 615) | def __init__(self, data_path: str,
    method __len__ (line 626) | def __len__(self):
    method __getitem__ (line 629) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 684) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 689) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 717) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function setup (line 730) | def setup(args):
  function train (line 743) | def train():

FILE: llava/train/train_joint_1st.py
  function rank0_print (line 44) | def rank0_print(*args):
  class ModelArguments (line 50) | class ModelArguments:
  class DataArguments (line 68) | class DataArguments:
  class TrainingArguments (line 79) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 116) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 131) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 156) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 164) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 170) | def find_all_linear_names(model):
  function safe_save_model_for_hf_trainer (line 184) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
  function smart_tokenizer_and_embedding_resize (line 203) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 228) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 255) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 266) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 287) | def preprocess_multimodal(
  function preprocess_llama_2 (line 311) | def preprocess_llama_2(
  function preprocess_v1 (line 393) | def preprocess_v1(
  function preprocess_mpt (line 475) | def preprocess_mpt(
  function preprocess_plain (line 541) | def preprocess_plain(
  function preprocess (line 563) | def preprocess(
  class LazySupervisedDataset (line 611) | class LazySupervisedDataset(Dataset):
    method __init__ (line 614) | def __init__(self, data_path: str,
    method __len__ (line 625) | def __len__(self):
    method __getitem__ (line 628) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 683) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 688) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDatasetEmpty (line 716) | class DataCollatorForSupervisedDatasetEmpty(object):
    method __call__ (line 721) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 749) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function setup (line 763) | def setup(args):
  function train (line 776) | def train():

FILE: llava/train/train_joint_2st.py
  function rank0_print (line 44) | def rank0_print(*args):
  class ModelArguments (line 50) | class ModelArguments:
  class DataArguments (line 68) | class DataArguments:
  class TrainingArguments (line 79) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 116) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 131) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 156) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 164) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 170) | def find_all_linear_names(model):
  function safe_save_model_for_hf_trainer (line 184) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
  function smart_tokenizer_and_embedding_resize (line 203) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 228) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 255) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 266) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 287) | def preprocess_multimodal(
  function preprocess_llama_2 (line 311) | def preprocess_llama_2(
  function preprocess_v1 (line 393) | def preprocess_v1(
  function preprocess_mpt (line 475) | def preprocess_mpt(
  function preprocess_plain (line 541) | def preprocess_plain(
  function preprocess (line 563) | def preprocess(
  class LazySupervisedDataset (line 611) | class LazySupervisedDataset(Dataset):
    method __init__ (line 614) | def __init__(self, data_path: str,
    method __len__ (line 625) | def __len__(self):
    method __getitem__ (line 628) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 683) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 688) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDatasetEmpty (line 716) | class DataCollatorForSupervisedDatasetEmpty(object):
    method __call__ (line 721) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 749) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function setup (line 763) | def setup(args):
  function train (line 776) | def train():

FILE: llava/train/train_joint_2st_interactive_refcoco_coco_instruction.py
  function rank0_print (line 44) | def rank0_print(*args):
  class ModelArguments (line 50) | class ModelArguments:
  class DataArguments (line 70) | class DataArguments:
  class TrainingArguments (line 81) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 118) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 133) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 158) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 166) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 172) | def find_all_linear_names(model):
  function safe_save_model_for_hf_trainer (line 186) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
  function smart_tokenizer_and_embedding_resize (line 205) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 230) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 257) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 268) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 289) | def preprocess_multimodal(
  function preprocess_llama_2 (line 313) | def preprocess_llama_2(
  function preprocess_v1 (line 395) | def preprocess_v1(
  function preprocess_mpt (line 477) | def preprocess_mpt(
  function preprocess_plain (line 543) | def preprocess_plain(
  function preprocess (line 565) | def preprocess(
  class LazySupervisedDataset (line 613) | class LazySupervisedDataset(Dataset):
    method __init__ (line 616) | def __init__(self, data_path: str,
    method __len__ (line 627) | def __len__(self):
    method __getitem__ (line 630) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 685) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 690) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDatasetEmpty (line 718) | class DataCollatorForSupervisedDatasetEmpty(object):
    method __call__ (line 723) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 751) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function setup (line 765) | def setup(args):
  function train (line 782) | def train():

FILE: llava/utils.py
  function build_logger (line 17) | def build_logger(logger_name, logger_filename):
  class StreamToLogger (line 60) | class StreamToLogger(object):
    method __init__ (line 64) | def __init__(self, logger, log_level=logging.INFO):
    method __getattr__ (line 70) | def __getattr__(self, attr):
    method write (line 73) | def write(self, buf):
    method flush (line 87) | def flush(self):
  function disable_torch_init (line 93) | def disable_torch_init():
  function violates_moderation (line 102) | def violates_moderation(text):
  function pretty_print_semaphore (line 123) | def pretty_print_semaphore(semaphore):

FILE: scripts/convert_sqa_to_llava.py
  function convert_to_llava (line 8) | def convert_to_llava(base_dir, split, prompt_format="QCM-LEPA"):
  function convert_to_jsonl (line 49) | def convert_to_jsonl(base_dir, split, prompt_format="QCM-LEPA"):
  function main (line 83) | def main(task, **kwargs):

FILE: scripts/convert_sqa_to_llava_base_prompt.py
  function get_question_text (line 1) | def get_question_text(problem):
  function get_context_text (line 6) | def get_context_text(problem, use_caption):
  function get_choice_text (line 15) | def get_choice_text(probelm, options):
  function get_answer (line 25) | def get_answer(problem, options):
  function get_lecture_text (line 29) | def get_lecture_text(problem):
  function get_solution_text (line 35) | def get_solution_text(problem):
  function create_one_example_chatbot (line 41) | def create_one_example_chatbot(format, question, context, choice, answer...
  function create_one_example (line 106) | def create_one_example(format, question, context, choice, answer, lectur...
  function create_one_example_gpt4 (line 162) | def create_one_example_gpt4(format, question, context, choice, answer, l...
  function build_prompt_chatbot (line 221) | def build_prompt_chatbot(problems, shot_qids, prompt_format, use_caption...
  function build_prompt (line 244) | def build_prompt(problems, shot_qids, test_qid, args):
  function build_prompt_gpt4 (line 291) | def build_prompt_gpt4(problems, shot_qids, test_qid, args):

FILE: scripts/merge_lora_weights.py
  function merge_lora (line 6) | def merge_lora(args):

FILE: utils/Config.py
  class CfgNode (line 3) | class CfgNode(_CfgNode):
    method merge_from_dict (line 23) | def merge_from_dict(self, dict):

FILE: utils/arguments.py
  function load_config_dict_to_opt (line 9) | def load_config_dict_to_opt(opt, config_dict):
  function load_opt_from_config_files (line 30) | def load_opt_from_config_files(conf_files):
  function load_opt_command (line 50) | def load_opt_command(args):
  function save_opt_to_json (line 93) | def save_opt_to_json(opt, conf_file):
  function save_opt_to_yaml (line 98) | def save_opt_to_yaml(opt, conf_file):

FILE: utils/dist.py
  function init_distributed_mode (line 19) | def init_distributed_mode(args):

FILE: utils/misc.py
  function hook_metadata (line 11) | def hook_metadata(metadata, name):
  function hook_opt (line 16) | def hook_opt(model, name):
  function hook_switcher (line 23) | def hook_switcher(model, name):
  class AverageMeter (line 44) | class AverageMeter(object):
    method __init__ (line 46) | def __init__(self):
    method reset (line 49) | def reset(self):
    method update (line 55) | def update(self, val, n=1, decay=0):

FILE: utils/model.py
  function register_norm_module (line 25) | def register_norm_module(cls):
  function align_and_update_state_dicts (line 29) | def align_and_update_state_dicts(model_state_dict, ckpt_state_dict):

FILE: utils/nms.py
  function matrix_nms (line 4) | def matrix_nms(seg_masks, cate_labels, cate_scores, kernel='gaussian', s...
  function matrix_nms_merge (line 26) | def matrix_nms_merge(seg_masks, cate_labels, cate_scores, kernel='gaussi...
  function multiclass_nms (line 61) | def multiclass_nms(multi_bboxes,

FILE: utils/prompt_engineering.py
  function get_prompt_templates (line 4) | def get_prompt_templates():
  function prompt_engineering (line 90) | def prompt_engineering(classnames, topk=1, suffix='.'):

FILE: utils/utils.py
  function slprint (line 4) | def slprint(x, name='x'):

FILE: utils/visualizer.py
  class ColorMode (line 37) | class ColorMode(Enum):
  class GenericMask (line 59) | class GenericMask:
    method __init__ (line 67) | def __init__(self, mask_or_polygons, height, width):
    method mask (line 99) | def mask(self):
    method polygons (line 105) | def polygons(self):
    method has_holes (line 111) | def has_holes(self):
    method mask_to_polygons (line 119) | def mask_to_polygons(self, mask):
    method polygons_to_mask (line 138) | def polygons_to_mask(self, polygons):
    method area (line 143) | def area(self):
    method bbox (line 146) | def bbox(self):
  class _PanopticPrediction (line 155) | class _PanopticPrediction:
    method __init__ (line 160) | def __init__(self, panoptic_seg, segments_info, metadata=None):
    method non_empty_mask (line 196) | def non_empty_mask(self):
    method semantic_masks (line 212) | def semantic_masks(self):
    method instance_masks (line 220) | def instance_masks(self):
  function _create_text_labels (line 230) | def _create_text_labels(classes, scores, class_names, is_crowd=None):
  class VisImage (line 257) | class VisImage:
    method __init__ (line 258) | def __init__(self, img, scale=1.0):
    method _setup_figure (line 269) | def _setup_figure(self, img):
    method reset_image (line 294) | def reset_image(self, img):
    method save (line 302) | def save(self, filepath):
    method get_image (line 310) | def get_image(self):
  class Visualizer (line 331) | class Visualizer:
    method __init__ (line 357) | def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=Co...
    method draw_instance_predictions (line 384) | def draw_instance_predictions(self, predictions):
    method draw_sem_seg (line 447) | def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.7):
    method draw_panoptic_seg (line 483) | def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshol...
    method draw_dataset_dict (line 549) | def draw_dataset_dict(self, dic):
    method overlay_instances (line 618) | def overlay_instances(
    method overlay_rotated_instances (line 760) | def overlay_rotated_instances(self, boxes=None, labels=None, assigned_...
    method draw_and_connect_keypoints (line 798) | def draw_and_connect_keypoints(self, keypoints):
    method draw_text (line 861) | def draw_text(
    method draw_box (line 908) | def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
    method draw_rotated_box_with_label (line 942) | def draw_rotated_box_with_label(
    method draw_circle (line 997) | def draw_circle(self, circle_coord, color, radius=3):
    method draw_line (line 1015) | def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=No...
    method draw_binary_mask (line 1046) | def draw_binary_mask(
    method draw_soft_mask (line 1097) | def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):
    method draw_polygon (line 1125) | def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
    method _jitter (line 1161) | def _jitter(self, color):
    method _create_grayscale_image (line 1181) | def _create_grayscale_image(self, mask=None):
    method _change_color_brightness (line 1192) | def _change_color_brightness(self, color, brightness_factor):
    method _convert_boxes (line 1217) | def _convert_boxes(self, boxes):
    method _convert_masks (line 1226) | def _convert_masks(self, masks_or_polygons):
    method _draw_text_in_mask (line 1249) | def _draw_text_in_mask(self, binary_mask, text, color):
    method _convert_keypoints (line 1267) | def _convert_keypoints(self, keypoints):
    method get_output (line 1273) | def get_output(self):