SYMBOL INDEX (925 symbols across 95 files)

FILE: train/dpo/dpo_trainer_2stages.py
  class DPOTrainer (line 60) | class DPOTrainer(Trainer):
    method __init__ (line 123) | def __init__(
    method _prepare_deepspeed (line 354) | def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
    method concatenated_inputs (line 385) | def concatenated_inputs(self, batch: Dict[str, Union[List, torch.LongT...
    method dpo_loss (line 424) | def dpo_loss(
    method _get_batch_logps (line 467) | def _get_batch_logps(
    method _get_noisy_batch_logps (line 501) | def _get_noisy_batch_logps(
    method concatenated_forward (line 550) | def concatenated_forward(
    method get_batch_metrics (line 629) | def get_batch_metrics(
    method compute_loss (line 681) | def compute_loss(
    method get_batch_samples (line 702) | def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor])...
    method prediction_step (line 739) | def prediction_step(
    method store_metrics (line 778) | def store_metrics(self, metrics: Dict[str, float], train_eval: Literal...
    method evaluation_loop (line 782) | def evaluation_loop(
    method log (line 832) | def log(self, logs: Dict[str, float]) -> None:

FILE: train/dpo/llava/conversation.py
  class SeparatorStyle (line 9) | class SeparatorStyle(Enum):
  class Conversation (line 19) | class Conversation:
    method get_prompt (line 32) | def get_prompt(self):
    method append_message (line 109) | def append_message(self, role, message):
    method process_image (line 112) | def process_image(self, image, image_process_mode, return_pil=False, i...
    method get_images (line 152) | def get_images(self, return_pil=False):
    method to_gradio_chatbot (line 162) | def to_gradio_chatbot(self):
    method copy (line 180) | def copy(self):
    method dict (line 191) | def dict(self):

FILE: train/dpo/llava/conversation_new.py
  class SeparatorStyle (line 6) | class SeparatorStyle(Enum):
  class Conversation (line 13) | class Conversation:
    method get_prompt (line 26) | def get_prompt(self):
    method append_message (line 51) | def append_message(self, role, message):
    method get_images (line 54) | def get_images(self, return_pil=False):
    method to_gradio_chatbot (line 103) | def to_gradio_chatbot(self):
    method copy (line 133) | def copy(self):
    method dict (line 143) | def dict(self):

FILE: train/dpo/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: train/dpo/llava/eval/eval_gpt_review_bench.py
  function get_eval (line 11) | def get_eval(content: str, max_tokens: int):
  function parse_score (line 36) | def parse_score(review):

FILE: train/dpo/llava/eval/eval_gpt_review_visual.py
  function get_eval (line 11) | def get_eval(content: str, max_tokens: int):
  function parse_score (line 36) | def parse_score(review):

FILE: train/dpo/llava/eval/eval_pope.py
  function eval_pope (line 5) | def eval_pope(answers, label_file):

FILE: train/dpo/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: train/dpo/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: train/dpo/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: train/dpo/llava/eval/eval_textvqa.py
  function get_args (line 9) | def get_args():
  function prompt_processor (line 17) | def prompt_processor(prompt):
  function eval_single (line 35) | def eval_single(annotation_file, result_file):

FILE: train/dpo/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: train/dpo/llava/eval/m4c_evaluator.py
  class EvalAIAnswerProcessor (line 7) | class EvalAIAnswerProcessor:
    method __init__ (line 178) | def __init__(self, *args, **kwargs):
    method word_tokenize (line 181) | def word_tokenize(self, word):
    method process_punctuation (line 186) | def process_punctuation(self, in_text):
    method process_digit_article (line 198) | def process_digit_article(self, in_text):
    method __call__ (line 213) | def __call__(self, item):
  class TextVQAAccuracyEvaluator (line 221) | class TextVQAAccuracyEvaluator:
    method __init__ (line 222) | def __init__(self):
    method _compute_answer_scores (line 225) | def _compute_answer_scores(self, raw_answers):
    method eval_pred_list (line 248) | def eval_pred_list(self, pred_list):
  class STVQAAccuracyEvaluator (line 260) | class STVQAAccuracyEvaluator:
    method __init__ (line 261) | def __init__(self):
    method eval_pred_list (line 264) | def eval_pred_list(self, pred_list):
  class STVQAANLSEvaluator (line 276) | class STVQAANLSEvaluator:
    method __init__ (line 277) | def __init__(self):
    method get_anls (line 282) | def get_anls(self, s1, s2):
    method eval_pred_list (line 289) | def eval_pred_list(self, pred_list):
  class TextCapsBleu4Evaluator (line 301) | class TextCapsBleu4Evaluator:
    method __init__ (line 302) | def __init__(self):
    method eval_pred_list (line 321) | def eval_pred_list(self, pred_list):

FILE: train/dpo/llava/eval/model_qa.py
  function eval_model (line 14) | def eval_model(model_name, questions_file, answers_file):

FILE: train/dpo/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: train/dpo/llava/eval/model_vqa_loader.py
  function split_list (line 19) | def split_list(lst, n):
  function get_chunk (line 25) | def get_chunk(lst, n, k):
  class CustomDataset (line 31) | class CustomDataset(Dataset):
    method __init__ (line 32) | def __init__(self, questions, image_folder, tokenizer, image_processor...
    method __getitem__ (line 39) | def __getitem__(self, index):
    method __len__ (line 60) | def __len__(self):
  function collate_fn (line 64) | def collate_fn(batch):
  function create_data_loader (line 72) | def create_data_loader(questions, image_folder, tokenizer, image_process...
  function eval_model (line 79) | def eval_model(args):

FILE: train/dpo/llava/eval/model_vqa_mmbench.py
  function split_list (line 22) | def split_list(lst, n):
  function get_chunk (line 28) | def get_chunk(lst, n, k):
  function is_none (line 33) | def is_none(value):
  function get_options (line 44) | def get_options(row, options):
  function eval_model (line 54) | def eval_model(args):

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

FILE: train/dpo/llava/eval/run_llava.py
  function image_parser (line 28) | def image_parser(args):
  function load_image (line 33) | def load_image(image_file):
  function load_images (line 42) | def load_images(image_files):
  function eval_model (line 50) | def eval_model(args):

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

FILE: train/dpo/llava/mm_utils.py
  function select_best_resolution (line 12) | def select_best_resolution(original_size, possible_resolutions):
  function resize_and_pad_image (line 42) | def resize_and_pad_image(image, target_resolution):
  function divide_to_patches (line 77) | def divide_to_patches(image, patch_size):
  function get_anyres_image_grid_shape (line 99) | def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
  function process_anyres_image (line 119) | def process_anyres_image(image, processor, grid_pinpoints):
  function load_image_from_base64 (line 148) | def load_image_from_base64(image):
  function expand2square (line 152) | def expand2square(pil_img, background_color):
  function process_images (line 166) | def process_images(images, image_processor, model_cfg):
  function tokenizer_image_token (line 185) | def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOK...
  function get_model_name_from_path (line 207) | def get_model_name_from_path(model_path):
  class KeywordsStoppingCriteria (line 215) | class KeywordsStoppingCriteria(StoppingCriteria):
    method __init__ (line 216) | def __init__(self, keywords, tokenizer, input_ids):
    method call_for_batch (line 230) | def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.F...
    method __call__ (line 243) | def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTe...

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

FILE: train/dpo/llava/model/builder.py
  function load_pretrained_model (line 26) | def load_pretrained_model(model_path, model_base, model_name, load_8bit=...

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

FILE: train/dpo/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 54) | def get_model(self):
    method forward (line 57) | def forward(
    method generate (line 105) | def generate(
    method prepare_inputs_for_generation (line 144) | def prepare_inputs_for_generation(self, input_ids, past_key_values=None,

FILE: train/dpo/llava/model/language_model/llava_mistral.py
  class LlavaMistralConfig (line 31) | class LlavaMistralConfig(MistralConfig):
  class LlavaMistralModel (line 35) | class LlavaMistralModel(LlavaMetaModel, MistralModel):
    method __init__ (line 38) | def __init__(self, config: MistralConfig):
  class LlavaMistralForCausalLM (line 42) | class LlavaMistralForCausalLM(MistralForCausalLM, LlavaMetaForCausalLM):
    method __init__ (line 45) | def __init__(self, config):
    method get_model (line 54) | def get_model(self):
    method forward (line 57) | def forward(
    method generate (line 105) | def generate(
    method prepare_inputs_for_generation (line 144) | def prepare_inputs_for_generation(self, input_ids, past_key_values=None,

FILE: train/dpo/llava/model/language_model/llava_mpt.py
  class LlavaMptConfig (line 25) | class LlavaMptConfig(MptConfig):
  class LlavaMptModel (line 29) | class LlavaMptModel(LlavaMetaModel, MptModel):
    method __init__ (line 32) | def __init__(self, config: MptConfig):
    method embed_tokens (line 36) | def embed_tokens(self, x):
  class LlavaMptForCausalLM (line 40) | class LlavaMptForCausalLM(MptForCausalLM, LlavaMetaForCausalLM):
    method __init__ (line 44) | def __init__(self, config):
    method get_model (line 53) | def get_model(self):
    method _set_gradient_checkpointing (line 56) | def _set_gradient_checkpointing(self, module, value=False):
    method forward (line 60) | def forward(
    method prepare_inputs_for_generation (line 87) | def prepare_inputs_for_generation(self, input_ids, past_key_values=Non...

FILE: train/dpo/llava/model/llava_arch.py
  class LlavaMetaModel (line 29) | class LlavaMetaModel:
    method __init__ (line 31) | def __init__(self, config):
    method get_vision_tower (line 43) | def get_vision_tower(self):
    method initialize_vision_modules (line 49) | def initialize_vision_modules(self, model_args, fsdp=None):
  function unpad_image (line 100) | def unpad_image(tensor, original_size):
  class LlavaMetaForCausalLM (line 131) | class LlavaMetaForCausalLM(ABC):
    method get_model (line 134) | def get_model(self):
    method get_vision_tower (line 137) | def get_vision_tower(self):
    method encode_images (line 140) | def encode_images(self, images):
    method prepare_inputs_labels_for_multimodal (line 145) | def prepare_inputs_labels_for_multimodal(
    method initialize_vision_tokenizer (line 326) | def initialize_vision_tokenizer(self, model_args, tokenizer):

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

FILE: train/dpo/llava/model/multimodal_encoder/builder.py
  function build_vision_tower (line 5) | def build_vision_tower(vision_tower_cfg, **kwargs):

FILE: train/dpo/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 24) | def load_model(self, device_map=None):
    method feature_select (line 35) | def feature_select(self, image_forward_outs):
    method forward (line 46) | def forward(self, images):
    method dummy_feature (line 60) | def dummy_feature(self):
    method dtype (line 64) | def dtype(self):
    method device (line 68) | def device(self):
    method config (line 72) | def config(self):
    method hidden_size (line 79) | def hidden_size(self):
    method num_patches_per_side (line 83) | def num_patches_per_side(self):
    method num_patches (line 87) | def num_patches(self):

FILE: train/dpo/llava/model/multimodal_projector/builder.py
  class IdentityMap (line 6) | class IdentityMap(nn.Module):
    method __init__ (line 7) | def __init__(self):
    method forward (line 10) | def forward(self, x, *args, **kwargs):
    method config (line 14) | def config(self):
  class SimpleResBlock (line 18) | class SimpleResBlock(nn.Module):
    method __init__ (line 19) | def __init__(self, channels):
    method forward (line 28) | def forward(self, x):
  function build_vision_projector (line 33) | def build_vision_projector(config, delay_load=False, **kwargs):

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

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

FILE: train/dpo/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: train/dpo/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 71) | def load_demo_refresh_model_list(request: gr.Request):
  function vote_last_response (line 82) | def vote_last_response(state, vote_type, model_selector, request: gr.Req...
  function upvote_last_response (line 94) | def upvote_last_response(state, model_selector, request: gr.Request):
  function downvote_last_response (line 100) | def downvote_last_response(state, model_selector, request: gr.Request):
  function flag_last_response (line 106) | def flag_last_response(state, model_selector, request: gr.Request):
  function regenerate (line 112) | def regenerate(state, image_process_mode, request: gr.Request):
  function clear_history (line 122) | def clear_history(request: gr.Request):
  function add_text (line 128) | def add_text(state, text, image, image_process_mode, request: gr.Request):
  function http_bot (line 154) | def http_bot(state, model_selector, temperature, top_p, max_new_tokens, ...
  function build_demo (line 315) | def build_demo(embed_mode, cur_dir=None, concurrency_count=10):

FILE: train/dpo/llava/serve/model_worker.py
  function heart_beat_worker (line 37) | def heart_beat_worker(controller):
  class ModelWorker (line 44) | class ModelWorker:
    method __init__ (line 45) | def __init__(self, controller_addr, worker_addr,
    method register_to_controller (line 75) | def register_to_controller(self):
    method send_heart_beat (line 87) | def send_heart_beat(self):
    method get_queue_length (line 108) | def get_queue_length(self):
    method get_status (line 115) | def get_status(self):
    method generate_stream (line 123) | def generate_stream(self, params):
    method generate_stream_gate (line 195) | def generate_stream_gate(self, params):
  function release_model_semaphore (line 225) | def release_model_semaphore(fn=None):
  function generate_stream (line 232) | async def generate_stream(request: Request):
  function get_status (line 248) | async def get_status(request: Request):

FILE: train/dpo/llava/serve/sglang_worker.py
  function heart_beat_worker (line 38) | def heart_beat_worker(controller):
  function pipeline (line 45) | def pipeline(s, prompt, max_tokens):
  class ModelWorker (line 54) | class ModelWorker:
    method __init__ (line 55) | def __init__(self, controller_addr, worker_addr, sgl_endpoint,
    method register_to_controller (line 85) | def register_to_controller(self):
    method send_heart_beat (line 97) | def send_heart_beat(self):
    method get_queue_length (line 118) | def get_queue_length(self):
    method get_status (line 125) | def get_status(self):
    method generate_stream (line 132) | async def generate_stream(self, params):
    method generate_stream_gate (line 172) | async def generate_stream_gate(self, params):
  function release_model_semaphore (line 195) | def release_model_semaphore(fn=None):
  function generate_stream (line 202) | async def generate_stream(request: Request):
  function get_status (line 218) | async def get_status(request: Request):

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

FILE: train/dpo/llava/train/llama_flash_attn_monkey_patch.py
  function forward (line 16) | def forward(
  function _prepare_decoder_attention_mask (line 98) | def _prepare_decoder_attention_mask(
  function replace_llama_attn_with_flash_attn (line 105) | def replace_llama_attn_with_flash_attn():

FILE: train/dpo/llava/train/llama_xformers_attn_monkey_patch.py
  function replace_llama_attn_with_xformers_attn (line 19) | def replace_llama_attn_with_xformers_attn():
  function xformers_forward (line 23) | def xformers_forward(

FILE: train/dpo/llava/train/llava_trainer.py
  function maybe_zero_3 (line 18) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_mm_adapter_state_maybe_zero_3 (line 32) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function split_to_even_chunks (line 38) | def split_to_even_chunks(indices, lengths, num_chunks):
  function get_modality_length_grouped_indices (line 60) | def get_modality_length_grouped_indices(lengths, batch_size, world_size,...
  function get_length_grouped_indices (line 88) | def get_length_grouped_indices(lengths, batch_size, world_size, generato...
  class LengthGroupedSampler (line 99) | class LengthGroupedSampler(Sampler):
    method __init__ (line 105) | def __init__(
    method __len__ (line 122) | def __len__(self):
    method __iter__ (line 125) | def __iter__(self):
  class LLaVATrainer (line 133) | class LLaVATrainer(DPOTrainer):
    method _get_train_sampler (line 135) | def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
    method create_optimizer (line 150) | def create_optimizer(self):
    method _save_checkpoint (line 239) | def _save_checkpoint(self, model, trial, metrics=None):
    method _save (line 260) | def _save(self, output_dir: Optional[str] = None, state_dict=None):

FILE: train/dpo/llava/train/train.py
  function rank0_print (line 44) | def rank0_print(*args):
  class ModelArguments (line 54) | class ModelArguments:
  class DataArguments (line 70) | class DataArguments:
  class TrainingArguments (line 80) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 115) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 130) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 155) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 163) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 169) | 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 224) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 249) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 276) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 287) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 308) | def preprocess_multimodal(
  function preprocess_llama_2 (line 332) | def preprocess_llama_2(
  function preprocess_v1 (line 414) | def preprocess_v1(
  function preprocess_mpt (line 500) | def preprocess_mpt(
  function preprocess_plain (line 588) | def preprocess_plain(
  function preprocess (line 610) | def preprocess(
  class LazySupervisedDataset (line 658) | class LazySupervisedDataset(Dataset):
    method __init__ (line 661) | def __init__(self, data_path: str,
    method __len__ (line 672) | def __len__(self):
    method lengths (line 676) | def lengths(self):
    method modality_lengths (line 684) | def modality_lengths(self):
    method __getitem__ (line 692) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 743) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 748) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 776) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function train (line 788) | def train(attn_implementation=None):

FILE: train/dpo/llava/train/train_dpo.py
  function rank0_print (line 46) | def rank0_print(*args):
  function pil_to_tensor (line 50) | def pil_to_tensor(image):
  function tensor_to_pil (line 54) | def tensor_to_pil(tensor):
  function add_diffusion_noise (line 58) | def add_diffusion_noise(image_tensor, noise_step):
  class ModelArguments (line 86) | class ModelArguments:
  class DataArguments (line 102) | class DataArguments:
  class TrainingArguments (line 112) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 147) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 162) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 187) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 195) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 201) | def find_all_linear_names(model):
  function safe_save_model_for_hf_trainer (line 217) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
  function smart_tokenizer_and_embedding_resize (line 256) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 281) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 308) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 319) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 340) | def preprocess_multimodal(
  function preprocess_llama_2 (line 364) | def preprocess_llama_2(
  function preprocess_v1 (line 446) | def preprocess_v1(
  function preprocess_plain (line 544) | def preprocess_plain(
  function preprocess (line 566) | def preprocess(
  class LazySupervisedDataset (line 620) | class LazySupervisedDataset(Dataset):
    method __init__ (line 623) | def __init__(self, data_path: str,
    method __len__ (line 634) | def __len__(self):
    method lengths (line 638) | def lengths(self):
    method modality_lengths (line 646) | def modality_lengths(self):
    method __getitem__ (line 654) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 732) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 737) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 794) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function train (line 806) | def train():

FILE: train/dpo/llava/train/train_dpo_inherent.py
  function rank0_print (line 45) | def rank0_print(*args):
  function pil_to_tensor (line 49) | def pil_to_tensor(image):
  function tensor_to_pil (line 53) | def tensor_to_pil(tensor):
  function add_diffusion_noise (line 57) | def add_diffusion_noise(image_tensor, noise_step):
  class ModelArguments (line 85) | class ModelArguments:
  class DataArguments (line 100) | class DataArguments:
  class TrainingArguments (line 110) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 145) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 160) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 185) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 193) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 199) | def find_all_linear_names(model):
  function safe_save_model_for_hf_trainer (line 215) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
  function smart_tokenizer_and_embedding_resize (line 254) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 279) | def _tokenize_fn(strings: Sequence[str],
  function _mask_targets (line 306) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 317) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 338) | def preprocess_multimodal(
  function preprocess_llama_2 (line 362) | def preprocess_llama_2(
  function preprocess_v1 (line 444) | def preprocess_v1(
  function preprocess_plain (line 542) | def preprocess_plain(
  function preprocess (line 564) | def preprocess(
  class LazySupervisedDataset (line 618) | class LazySupervisedDataset(Dataset):
    method __init__ (line 621) | def __init__(self, data_path: str,
    method __len__ (line 632) | def __len__(self):
    method lengths (line 636) | def lengths(self):
    method modality_lengths (line 644) | def modality_lengths(self):
    method __getitem__ (line 652) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 736) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 741) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 804) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function train (line 816) | def train():

FILE: train/dpo/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: train/dpo/llava_trainer_2stages.py
  function maybe_zero_3 (line 18) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_mm_adapter_state_maybe_zero_3 (line 32) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function split_to_even_chunks (line 38) | def split_to_even_chunks(indices, lengths, num_chunks):
  function get_modality_length_grouped_indices (line 60) | def get_modality_length_grouped_indices(lengths, batch_size, world_size,...
  function get_length_grouped_indices (line 88) | def get_length_grouped_indices(lengths, batch_size, world_size, generato...
  class LengthGroupedSampler (line 99) | class LengthGroupedSampler(Sampler):
    method __init__ (line 105) | def __init__(
    method __len__ (line 122) | def __len__(self):
    method __iter__ (line 125) | def __iter__(self):
  class LLaVATrainer (line 133) | class LLaVATrainer(DPOTrainer):
    method _get_train_sampler (line 135) | def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
    method create_optimizer (line 150) | def create_optimizer(self):
    method _save_checkpoint (line 239) | def _save_checkpoint(self, model, trial, metrics=None):
    method _save (line 260) | def _save(self, output_dir: Optional[str] = None, state_dict=None):

FILE: train/dpo/povid_infer.py
  function split_list (line 23) | def split_list(lst, n):
  function get_chunk (line 29) | def get_chunk(lst, n, k):
  function eval_model (line 34) | def eval_model(args):

FILE: train/dpo/predict.py
  function download_json (line 54) | def download_json(url: str, dest: Path):
  function download_weights (line 62) | def download_weights(baseurl: str, basedest: str, files: list[str]):
  class Predictor (line 78) | class Predictor(BasePredictor):
    method setup (line 79) | def setup(self) -> None:
    method predict (line 87) | def predict(
  function load_image (line 148) | def load_image(image_file):

FILE: train/dpo/scripts/convert_mmbench_for_submission.py
  function get_args (line 6) | def get_args():

FILE: train/dpo/scripts/convert_seed_for_submission.py
  function get_args (line 6) | def get_args():
  function eval_single (line 14) | def eval_single(result_file, eval_only_type=None):

FILE: train/dpo/scripts/convert_sqa_to_llava.py
  function convert_to_llava (line 8) | def convert_to_llava(base_dir, split, prompt_format="QCM-LEA"):
  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: train/dpo/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: train/dpo/scripts/convert_vizwiz_for_submission.py
  function parse_args (line 8) | def parse_args():

FILE: train/dpo/scripts/convert_vqav2_for_submission.py
  function parse_args (line 8) | def parse_args():

FILE: train/dpo/scripts/extract_mm_projector.py
  function parse_args (line 15) | def parse_args():

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

FILE: train/dpo/tool/dpo_trainer.py
  class DPOTrainer (line 60) | class DPOTrainer(Trainer):
    method __init__ (line 123) | def __init__(
    method _prepare_deepspeed (line 354) | def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
    method concatenated_inputs (line 385) | def concatenated_inputs(self, batch: Dict[str, Union[List, torch.LongT...
    method dpo_loss (line 424) | def dpo_loss(
    method _get_batch_logps (line 467) | def _get_batch_logps(
    method _get_noisy_batch_logps (line 501) | def _get_noisy_batch_logps(
    method concatenated_forward (line 550) | def concatenated_forward(
    method get_batch_metrics (line 629) | def get_batch_metrics(
    method compute_loss (line 681) | def compute_loss(
    method get_batch_samples (line 702) | def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor])...
    method prediction_step (line 739) | def prediction_step(
    method store_metrics (line 778) | def store_metrics(self, metrics: Dict[str, float], train_eval: Literal...
    method evaluation_loop (line 782) | def evaluation_loop(
    method log (line 832) | def log(self, logs: Dict[str, float]) -> None:

FILE: train/dpo/tool/dpo_trainer_inherent.py
  class DPOTrainer (line 56) | class DPOTrainer(Trainer):
    method __init__ (line 119) | def __init__(
    method _prepare_deepspeed (line 350) | def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
    method concatenated_inputs (line 381) | def concatenated_inputs(self, batch: Dict[str, Union[List, torch.LongT...
    method dpo_loss (line 420) | def dpo_loss(
    method _get_batch_logps (line 463) | def _get_batch_logps(
    method _get_noisy_batch_logps (line 497) | def _get_noisy_batch_logps(
    method concatenated_multi_forward (line 546) | def concatenated_multi_forward(
    method get_batch_metrics (line 641) | def get_batch_metrics(
    method compute_loss (line 704) | def compute_loss(
    method get_batch_samples (line 725) | def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor])...
    method prediction_step (line 762) | def prediction_step(
    method store_metrics (line 801) | def store_metrics(self, metrics: Dict[str, float], train_eval: Literal...
    method evaluation_loop (line 805) | def evaluation_loop(
    method log (line 855) | def log(self, logs: Dict[str, float]) -> None:

FILE: train/dpo/train_dpo_2stages.py
  function rank0_print (line 72) | def rank0_print(*args):
  function pil_to_tensor (line 77) | def pil_to_tensor(image):
  function tensor_to_pil (line 82) | def tensor_to_pil(tensor):
  function add_gaussian_noise (line 86) | def add_gaussian_noise(image, mean=0.0, stddev=0.1):
  function generate_gaussian_noise (line 93) | def generate_gaussian_noise(image, mean=0.0, stddev=0.1):
  function add_diffusion_noise (line 99) | def add_diffusion_noise(image_tensor, noise_step):
  class ModelArguments (line 130) | class ModelArguments:
  class DataArguments (line 149) | class DataArguments:
  class TrainingArguments (line 160) | class TrainingArguments(transformers.TrainingArguments):
  function maybe_zero_3 (line 195) | def maybe_zero_3(param, ignore_status=False, name=None):
  function get_peft_state_maybe_zero_3 (line 213) | def get_peft_state_maybe_zero_3(named_params, bias):
  function get_peft_state_non_lora_maybe_zero_3 (line 238) | def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only...
  function get_mm_adapter_state_maybe_zero_3 (line 248) | def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
  function find_all_linear_names (line 260) | def find_all_linear_names(model):
  function safe_save_model_for_hf_trainer (line 276) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output...
  function smart_tokenizer_and_embedding_resize (line 318) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 345) | def _tokenize_fn(
  function _mask_targets (line 372) | def _mask_targets(target, tokenized_lens, speakers):
  function _add_speaker_and_signal (line 383) | def _add_speaker_and_signal(header, source, get_conversation=True):
  function preprocess_multimodal (line 405) | def preprocess_multimodal(sources: Sequence[str], data_args: DataArgumen...
  function preprocess_llama_2 (line 435) | def preprocess_llama_2(
  function preprocess_v1 (line 521) | def preprocess_v1(
  function preprocess_plain (line 634) | def preprocess_plain(
  function preprocess (line 663) | def preprocess(
  class LazySupervisedDataset (line 730) | class LazySupervisedDataset(Dataset):
    method __init__ (line 733) | def __init__(
    method __len__ (line 747) | def __len__(self):
    method lengths (line 751) | def lengths(self):
    method modality_lengths (line 762) | def modality_lengths(self):
    method __getitem__ (line 772) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 895) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 900) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 967) | def make_supervised_data_module(
  function train (line 980) | def train():

FILE: train/open_clip/setup.py
  function _read_reqs (line 13) | def _read_reqs(relpath):

FILE: train/open_clip/src/open_clip/big_vision.py
  function load_big_vision_weights (line 9) | def load_big_vision_weights(model: CustomTextCLIP, checkpoint_path: str):

FILE: train/open_clip/src/open_clip/coca_model.py
  class MultimodalCfg (line 45) | class MultimodalCfg(CLIPTextCfg):
  function _build_text_decoder_tower (line 53) | def _build_text_decoder_tower(
  class CoCa (line 79) | class CoCa(nn.Module):
    method __init__ (line 80) | def __init__(
    method set_grad_checkpointing (line 134) | def set_grad_checkpointing(self, enable: bool = True):
    method _encode_image (line 139) | def _encode_image(self, images, normalize: bool = True):
    method _encode_text (line 144) | def _encode_text(self, text, normalize: bool = True):
    method encode_image (line 149) | def encode_image(self, images, normalize: bool = True):
    method encode_text (line 153) | def encode_text(self, text, normalize: bool = True):
    method forward (line 157) | def forward(
    method generate (line 187) | def generate(
    method _generate_beamsearch (line 310) | def _generate_beamsearch(
  function prepare_inputs_for_generation (line 459) | def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **...

FILE: train/open_clip/src/open_clip/factory.py
  function _natural_key (line 28) | def _natural_key(string_):
  function _rescan_model_configs (line 32) | def _rescan_model_configs():
  function list_models (line 56) | def list_models():
  function add_model_config (line 61) | def add_model_config(path):
  function get_model_config (line 69) | def get_model_config(model_name):
  function _get_hf_config (line 76) | def _get_hf_config(model_id, cache_dir=None):
  function get_tokenizer (line 83) | def get_tokenizer(
  function load_state_dict (line 127) | def load_state_dict(checkpoint_path: str, map_location='cpu'):
  function load_checkpoint (line 142) | def load_checkpoint(model, checkpoint_path, strict=True):
  function create_model (line 165) | def create_model(
  function create_loss (line 324) | def create_loss(args):
  function create_model_and_transforms (line 361) | def create_model_and_transforms(
  function create_model_from_pretrained (line 418) | def create_model_from_pretrained(

FILE: train/open_clip/src/open_clip/hf_model.py
  class BaseModelOutput (line 20) | class BaseModelOutput:
  class PretrainedConfig (line 24) | class PretrainedConfig:
  function _camel2snake (line 31) | def _camel2snake(s):
  function register_pooler (line 39) | def register_pooler(cls):
  class MeanPooler (line 46) | class MeanPooler(nn.Module):
    method forward (line 49) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class MaxPooler (line 55) | class MaxPooler(nn.Module):
    method forward (line 58) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class ClsPooler (line 64) | class ClsPooler(nn.Module):
    method __init__ (line 67) | def __init__(self, use_pooler_output=True):
    method forward (line 72) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class ClsLastHiddenStatePooler (line 83) | class ClsLastHiddenStatePooler(nn.Module):
    method __init__ (line 88) | def __init__(self):
    method forward (line 92) | def forward(self, x: BaseModelOutput, attention_mask: TensorType):
  class HFTextEncoder (line 96) | class HFTextEncoder(nn.Module):
    method __init__ (line 100) | def __init__(
    method forward (line 154) | def forward(self, x: TensorType):
    method lock (line 171) | def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
    method set_grad_checkpointing (line 189) | def set_grad_checkpointing(self, enable=True):
    method init_parameters (line 192) | def init_parameters(self):

FILE: train/open_clip/src/open_clip/loss.py
  function gather_features (line 19) | def gather_features(
  class ClipLoss (line 66) | class ClipLoss(nn.Module):
    method __init__ (line 68) | def __init__(
    method get_ground_truth (line 89) | def get_ground_truth(self, device, num_logits) -> torch.Tensor:
    method get_logits (line 102) | def get_logits(self, image_features, text_features, logit_scale):
    method forward (line 120) | def forward(self, image_features, text_features, logit_scale, output_d...
  class CoCaLoss (line 134) | class CoCaLoss(ClipLoss):
    method __init__ (line 135) | def __init__(
    method forward (line 160) | def forward(self, image_features, text_features, logits, labels, logit...
  class DistillClipLoss (line 180) | class DistillClipLoss(ClipLoss):
    method dist_loss (line 182) | def dist_loss(self, teacher_logits, student_logits):
    method forward (line 185) | def forward(
  function neighbour_exchange (line 219) | def neighbour_exchange(from_rank, to_rank, tensor, group=None):
  function neighbour_exchange_bidir (line 239) | def neighbour_exchange_bidir(left_rank, right_rank, tensor_to_left, tens...
  class NeighbourExchange (line 272) | class NeighbourExchange(torch.autograd.Function):
    method forward (line 274) | def forward(ctx, from_rank, to_rank, group, tensor):
    method backward (line 281) | def backward(ctx, grad_output):
  function neighbour_exchange_with_grad (line 285) | def neighbour_exchange_with_grad(from_rank, to_rank, tensor, group=None):
  class NeighbourExchangeBidir (line 289) | class NeighbourExchangeBidir(torch.autograd.Function):
    method forward (line 291) | def forward(ctx, left_rank, right_rank, group, tensor_to_left, tensor_...
    method backward (line 298) | def backward(ctx, *grad_outputs):
  function neighbour_exchange_bidir_with_grad (line 303) | def neighbour_exchange_bidir_with_grad(left_rank, right_rank, tensor_to_...
  class SigLipLoss (line 307) | class SigLipLoss(nn.Module):
    method __init__ (line 317) | def __init__(
    method get_ground_truth (line 337) | def get_ground_truth(self, device, dtype, num_logits, negative_only=Fa...
    method get_logits (line 343) | def get_logits(self, image_features, text_features, logit_scale, logit...
    method _loss (line 349) | def _loss(self, image_features, text_features, logit_scale, logit_bias...
    method forward (line 360) | def forward(self, image_features, text_features, logit_scale, logit_bi...

FILE: train/open_clip/src/open_clip/model.py
  class CLIPVisionCfg (line 27) | class CLIPVisionCfg:
  class CLIPTextCfg (line 58) | class CLIPTextCfg:
  function get_cast_dtype (line 86) | def get_cast_dtype(precision: str):
  function get_input_dtype (line 95) | def get_input_dtype(precision: str):
  function _build_vision_tower (line 104) | def _build_vision_tower(
  function _build_text_tower (line 173) | def _build_text_tower(
  class CLIP (line 220) | class CLIP(nn.Module):
    method __init__ (line 223) | def __init__(
    method lock_image_tower (line 256) | def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 261) | def set_grad_checkpointing(self, enable=True):
    method encode_image (line 265) | def encode_image(self, image, normalize: bool = False):
    method encode_text (line 269) | def encode_text(self, text, normalize: bool = False):
    method get_logits (line 288) | def get_logits(self, image, text):
    method forward (line 297) | def forward(
  class CustomTextCLIP (line 320) | class CustomTextCLIP(nn.Module):
    method __init__ (line 323) | def __init__(
    method lock_image_tower (line 346) | def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
    method lock_text_tower (line 350) | def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm:...
    method set_grad_checkpointing (line 354) | def set_grad_checkpointing(self, enable=True):
    method encode_image (line 358) | def encode_image(self, image, normalize: bool = False):
    method encode_text (line 362) | def encode_text(self, text, normalize: bool = False):
    method get_logits (line 366) | def get_logits(self, image, text):
    method forward (line 375) | def forward(
  function convert_weights_to_lp (line 398) | def convert_weights_to_lp(model: nn.Module, dtype=torch.float16):
  function convert_to_custom_text_state_dict (line 432) | def convert_to_custom_text_state_dict(state_dict: dict):
  function build_model_from_openai_state_dict (line 450) | def build_model_from_openai_state_dict(
  function trace_model (line 509) | def trace_model(model, batch_size=256, device=torch.device('cpu')):
  function resize_pos_embed (line 525) | def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', ...
  function resize_text_pos_embed (line 559) | def resize_text_pos_embed(state_dict, model, interpolation: str = 'linea...
  function get_model_preprocess_cfg (line 591) | def get_model_preprocess_cfg(model):
  function set_model_preprocess_cfg (line 608) | def set_model_preprocess_cfg(model, preprocess_cfg: Dict[str, Any]):
  function get_model_tokenize_cfg (line 615) | def get_model_tokenize_cfg(model):

FILE: train/open_clip/src/open_clip/modified_resnet.py
  class Bottleneck (line 10) | class Bottleneck(nn.Module):
    method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1):
    method forward (line 42) | def forward(self, x: torch.Tensor):
  class AttentionPool2d (line 58) | class AttentionPool2d(nn.Module):
    method __init__ (line 59) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 68) | def forward(self, x):
  class ModifiedResNet (line 95) | class ModifiedResNet(nn.Module):
    method __init__ (line 103) | def __init__(self, layers, output_dim, heads, image_size=224, width=64):
    method _make_layer (line 132) | def _make_layer(self, planes, blocks, stride=1):
    method init_parameters (line 141) | def init_parameters(self):
    method lock (line 154) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 162) | def set_grad_checkpointing(self, enable=True):
    method stem (line 166) | def stem(self, x):
    method forward (line 173) | def forward(self, x):

FILE: train/open_clip/src/open_clip/openai.py
  function list_openai_models (line 19) | def list_openai_models() -> List[str]:
  function load_openai_model (line 24) | def load_openai_model(

FILE: train/open_clip/src/open_clip/pos_embed.py
  function get_2d_sincos_pos_embed (line 20) | def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
  function get_2d_sincos_pos_embed_from_grid (line 38) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
  function get_1d_sincos_pos_embed_from_grid (line 49) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  function interpolate_pos_embed (line 75) | def interpolate_pos_embed(model, checkpoint_model):

FILE: train/open_clip/src/open_clip/pretrained.py
  function _pcfg (line 29) | def _pcfg(url='', hf_hub='', **kwargs):
  function _slpcfg (line 42) | def _slpcfg(url='', hf_hub='', **kwargs):
  function _apcfg (line 55) | def _apcfg(url='', hf_hub='', **kwargs):
  function _clean_tag (line 445) | def _clean_tag(tag: str):
  function list_pretrained (line 450) | def list_pretrained(as_str: bool = False):
  function list_pretrained_models_by_tag (line 457) | def list_pretrained_models_by_tag(tag: str):
  function list_pretrained_tags_by_model (line 467) | def list_pretrained_tags_by_model(model: str):
  function is_pretrained_cfg (line 475) | def is_pretrained_cfg(model: str, tag: str):
  function get_pretrained_cfg (line 481) | def get_pretrained_cfg(model: str, tag: str):
  function get_pretrained_url (line 488) | def get_pretrained_url(model: str, tag: str):
  function download_pretrained_from_url (line 493) | def download_pretrained_from_url(
  function has_hf_hub (line 539) | def has_hf_hub(necessary=False):
  function download_pretrained_from_hf (line 547) | def download_pretrained_from_hf(
  function download_pretrained (line 558) | def download_pretrained(

FILE: train/open_clip/src/open_clip/push_to_hf_hub.py
  function save_config_for_hf (line 40) | def save_config_for_hf(
  function save_for_hf (line 63) | def save_for_hf(
  function push_to_hf_hub (line 90) | def push_to_hf_hub(
  function push_pretrained_to_hf_hub (line 160) | def push_pretrained_to_hf_hub(
  function generate_readme (line 209) | def generate_readme(model_card: dict, model_name: str):

FILE: train/open_clip/src/open_clip/timm_model.py
  class TimmModel (line 28) | class TimmModel(nn.Module):
    method __init__ (line 32) | def __init__(
    method lock (line 110) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method set_grad_checkpointing (line 143) | def set_grad_checkpointing(self, enable=True):
    method forward (line 149) | def forward(self, x):

FILE: train/open_clip/src/open_clip/tokenizer.py
  function default_bpe (line 27) | def default_bpe():
  function bytes_to_unicode (line 32) | def bytes_to_unicode():
  function get_pairs (line 54) | def get_pairs(word):
  function basic_clean (line 66) | def basic_clean(text):
  function whitespace_clean (line 72) | def whitespace_clean(text):
  function _clean_canonicalize (line 78) | def _clean_canonicalize(x):
  function _clean_lower (line 83) | def _clean_lower(x):
  function _clean_whitespace (line 88) | def _clean_whitespace(x):
  function get_clean_fn (line 93) | def get_clean_fn(type: str):
  function canonicalize_text (line 104) | def canonicalize_text(text, *, keep_punctuation_exact_string=None):
  class SimpleTokenizer (line 127) | class SimpleTokenizer(object):
    method __init__ (line 128) | def __init__(
    method bpe (line 166) | def bpe(self, token):
    method encode (line 207) | def encode(self, text):
    method decode (line 215) | def decode(self, tokens):
    method __call__ (line 220) | def __call__(self, texts: Union[str, List[str]], context_length: Optio...
  function decode (line 265) | def decode(output_ids: torch.Tensor):
  function tokenize (line 270) | def tokenize(texts: Union[str, List[str]], context_length: int = DEFAULT...
  function random_mask_tokenize (line 274) | def random_mask_tokenize(
  function simple_mask_tokenize (line 303) | def simple_mask_tokenize(
  function syntax_mask_tokenize (line 325) | def syntax_mask_tokenize(
  function get_reduction_mask_fn (line 384) | def get_reduction_mask_fn(type: str):
  class HFTokenizer (line 397) | class HFTokenizer:
    method __init__ (line 400) | def __init__(
    method save_pretrained (line 419) | def save_pretrained(self, dest):
    method __call__ (line 422) | def __call__(self, texts: Union[str, List[str]], context_length: Optio...
    method set_language (line 449) | def set_language(self, src_lang):
  class SigLipTokenizer (line 456) | class SigLipTokenizer:
    method __init__ (line 466) | def __init__(
    method save_pretrained (line 490) | def save_pretrained(self, dest):
    method __call__ (line 493) | def __call__(self, texts: Union[str, List[str]], context_length: Optio...

FILE: train/open_clip/src/open_clip/transform.py
  class PreprocessCfg (line 17) | class PreprocessCfg:
    method __post_init__ (line 26) | def __post_init__(self):
    method num_channels (line 30) | def num_channels(self):
    method input_size (line 34) | def input_size(self):
  function merge_preprocess_dict (line 40) | def merge_preprocess_dict(
  function merge_preprocess_kwargs (line 57) | def merge_preprocess_kwargs(base: PreprocessCfg, **kwargs):
  class AugmentationCfg (line 62) | class AugmentationCfg:
  function _setup_size (line 75) | def _setup_size(size, error_msg):
  class ResizeKeepRatio (line 88) | class ResizeKeepRatio:
    method __init__ (line 94) | def __init__(
    method get_params (line 116) | def get_params(
    method __call__ (line 144) | def __call__(self, img):
    method __repr__ (line 160) | def __repr__(self):
  function center_crop_or_pad (line 167) | def center_crop_or_pad(img: torch.Tensor, output_size: List[int], fill=0...
  class CenterCropOrPad (line 207) | class CenterCropOrPad(torch.nn.Module):
    method __init__ (line 219) | def __init__(self, size, fill=0):
    method forward (line 224) | def forward(self, img):
    method __repr__ (line 234) | def __repr__(self) -> str:
  function _convert_to_rgb (line 238) | def _convert_to_rgb(image):
  class color_jitter (line 242) | class color_jitter(object):
    method __init__ (line 246) | def __init__(self, brightness=0., contrast=0., saturation=0., hue=0., ...
    method __call__ (line 251) | def __call__(self, img):
  class gray_scale (line 258) | class gray_scale(object):
    method __init__ (line 262) | def __init__(self, p=0.2):
    method __call__ (line 267) | def __call__(self, img):
  function image_transform (line 274) | def image_transform(
  function image_transform_v2 (line 393) | def image_transform_v2(

FILE: train/open_clip/src/open_clip/transformer.py
  class LayerNormFp32 (line 15) | class LayerNormFp32(nn.LayerNorm):
    method forward (line 18) | def forward(self, x: torch.Tensor):
  class LayerNorm (line 24) | class LayerNorm(nn.LayerNorm):
    method forward (line 27) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 33) | class QuickGELU(nn.Module):
    method forward (line 35) | def forward(self, x: torch.Tensor):
  class LayerScale (line 39) | class LayerScale(nn.Module):
    method __init__ (line 40) | def __init__(self, dim, init_values=1e-5, inplace=False):
    method forward (line 45) | def forward(self, x):
  class PatchDropout (line 49) | class PatchDropout(nn.Module):
    method __init__ (line 54) | def __init__(self, prob, exclude_first_token=True):
    method forward (line 60) | def forward(self, x):
  class Attention (line 89) | class Attention(nn.Module):
    method __init__ (line 90) | def __init__(
    method forward (line 129) | def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
  class AttentionalPooler (line 165) | class AttentionalPooler(nn.Module):
    method __init__ (line 166) | def __init__(
    method forward (line 180) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 188) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 189) | def __init__(
    method attention (line 216) | def attention(
    method forward (line 231) | def forward(
  class CustomResidualAttentionBlock (line 246) | class CustomResidualAttentionBlock(nn.Module):
    method __init__ (line 247) | def __init__(
    method forward (line 281) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
  function _expand_token (line 287) | def _expand_token(token, batch_size: int):
  class Transformer (line 291) | class Transformer(nn.Module):
    method __init__ (line 292) | def __init__(
    method get_cast_dtype (line 313) | def get_cast_dtype(self) -> torch.dtype:
    method forward (line 318) | def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] =...
  class VisionTransformer (line 328) | class VisionTransformer(nn.Module):
    method __init__ (line 331) | def __init__(
    method lock (line 435) | def lock(self, unlocked_groups=0, freeze_bn_stats=False):
    method init_parameters (line 468) | def init_parameters(self):
    method set_grad_checkpointing (line 489) | def set_grad_checkpointing(self, enable=True):
    method _global_pool (line 492) | def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.T...
    method forward (line 502) | def forward(self, x: torch.Tensor):
  function text_global_pool (line 550) | def text_global_pool(x, text: Optional[torch.Tensor] = None, pool_type: ...
  class TextTransformer (line 565) | class TextTransformer(nn.Module):
    method __init__ (line 568) | def __init__(
    method init_parameters (line 628) | def init_parameters(self):
    method set_grad_checkpointing (line 652) | def set_grad_checkpointing(self, enable=True):
    method build_causal_mask (line 655) | def build_causal_mask(self):
    method build_cls_mask (line 663) | def build_cls_mask(self, text, cast_dtype: torch.dtype):
    method forward (line 672) | def forward(self, text):
  class MultimodalTransformer (line 711) | class MultimodalTransformer(Transformer):
    method __init__ (line 712) | def __init__(
    method init_parameters (line 753) | def init_parameters(self):
    method build_attention_mask (line 771) | def build_attention_mask(self):
    method forward (line 779) | def forward(self, image_embs, text_embs):
    method set_grad_checkpointing (line 802) | def set_grad_checkpointing(self, enable=True):

FILE: train/open_clip/src/open_clip/utils.py
  function freeze_batch_norm_2d (line 9) | def freeze_batch_norm_2d(module, module_match={}, name=''):
  function _ntuple (line 49) | def _ntuple(n):
  function replace_linear (line 65) | def replace_linear(model, linear_replacement, include_modules=['c_fc', '...
  function convert_int8_model_to_inference_mode (line 84) | def convert_int8_model_to_inference_mode(model):

FILE: train/open_clip/src/open_clip/zero_shot_classifier.py
  function batched (line 9) | def batched(iterable, n):
  function build_zero_shot_classifier (line 21) | def build_zero_shot_classifier(
  function build_zero_shot_classifier_legacy (line 71) | def build_zero_shot_classifier_legacy(

FILE: train/open_clip/src/retrieve_clip_VQA.py
  function retrieve_topk_per_image (line 15) | def retrieve_topk_per_image(logits, val_k_list,retrieve_threshold=''):
  function get_logits (line 55) | def get_logits(image_features, text_features, logit_scale):
  function clean_data_info (line 62) | def clean_data_info(data_info):
  function split_and_clean_data_infos (line 75) | def split_and_clean_data_infos(batch_data_infos):
  function main (line 96) | def main(args):

FILE: train/open_clip/src/retrieve_clip_report.py
  function retrieve_topk_per_image (line 16) | def retrieve_topk_per_image(logits, val_k_list,retrieve_threshold=''):
  function get_logits (line 56) | def get_logits(image_features, text_features, logit_scale):
  function clean_data_info (line 63) | def clean_data_info(data_info):
  function split_and_clean_data_infos (line 76) | def split_and_clean_data_infos(batch_data_infos):
  function main (line 97) | def main(args):

FILE: train/open_clip/src/training/data.py
  class CsvDataset (line 41) | class CsvDataset(Dataset):
    method __init__ (line 42) | def __init__(
    method __len__ (line 55) | def __len__(self):
    method __getitem__ (line 58) | def __getitem__(self, idx):
  class IUXrayDataset (line 64) | class IUXrayDataset(Dataset):  # TODO
    method __init__ (line 65) | def __init__(
    method __len__ (line 116) | def __len__(self):
    method __getitem__ (line 122) | def __getitem__(self, idx):
  class PubMedVisionDataset (line 138) | class PubMedVisionDataset(Dataset):#TODO
    method __init__ (line 139) | def __init__(self, img_root,json_file, transforms, tokenizer=None,load...
    method __len__ (line 177) | def __len__(self):
    method __getitem__ (line 183) | def __getitem__(self, idx):
  class RadiologyDataset (line 200) | class RadiologyDataset(Dataset):  # TODO
    method __init__ (line 201) | def __init__(
    method __len__ (line 241) | def __len__(self):
    method __getitem__ (line 246) | def __getitem__(self, idx):
  class PathologyDataset (line 258) | class PathologyDataset(Dataset):  # TODO
    method __init__ (line 259) | def __init__(
    method __len__ (line 298) | def __len__(self):
    method __getitem__ (line 303) | def __getitem__(self, idx):
  class MimicVQADataset (line 316) | class MimicVQADataset(Dataset):  #
    method __init__ (line 317) | def __init__(
    method __len__ (line 357) | def __len__(self):
    method __getitem__ (line 363) | def __getitem__(self, idx):
  class IUXrayVQADataset (line 375) | class IUXrayVQADataset(Dataset):  # TODO
    method __init__ (line 376) | def __init__(
    method __len__ (line 416) | def __len__(self):
    method __getitem__ (line 422) | def __getitem__(self, idx):
  class pmc_oa_VQADataset (line 435) | class pmc_oa_VQADataset(Dataset):  # TODO
    method __init__ (line 436) | def __init__(
    method __len__ (line 478) | def __len__(self):
    method __getitem__ (line 484) | def __getitem__(self, idx):
  class HarvardVQADataset (line 496) | class HarvardVQADataset(Dataset):  # TODO
    method __init__ (line 497) | def __init__(
    method __len__ (line 537) | def __len__(self):
    method __getitem__ (line 543) | def __getitem__(self, idx):
  class quilt_1m_VQADataset (line 555) | class quilt_1m_VQADataset(Dataset):  # TODO
    method __init__ (line 556) | def __init__(
    method __len__ (line 596) | def __len__(self):
    method __getitem__ (line 602) | def __getitem__(self, idx):
  class quilt_1m_Dataset (line 615) | class quilt_1m_Dataset(Dataset):  # TODO
    method __init__ (line 616) | def __init__(
    method __len__ (line 657) | def __len__(self):
    method __getitem__ (line 663) | def __getitem__(self, idx):
  class pmc_oa_Dataset (line 683) | class pmc_oa_Dataset(Dataset):  # TODO
    method __init__ (line 684) | def __init__(
    method __len__ (line 729) | def __len__(self):
    method __getitem__ (line 735) | def __getitem__(self, idx):
  class IUXrayVQADataset_with_conf (line 756) | class IUXrayVQADataset_with_conf(Dataset):  # TODO
    method __init__ (line 757) | def __init__(
    method __len__ (line 828) | def __len__(self):
    method __getitem__ (line 834) | def __getitem__(self, idx):
    method map_confidence_to_k (line 847) | def map_confidence_to_k(self, confidence):
  class MimicDataset (line 874) | class MimicDataset(Dataset):  # TODO
    method __init__ (line 875) | def __init__(
    method __len__ (line 922) | def __len__(self):
    method __getitem__ (line 928) | def __getitem__(self, idx):
  class HarvardDataset (line 945) | class HarvardDataset(Dataset):  # TODO
    method __init__ (line 946) | def __init__(
    method __len__ (line 997) | def __len__(self):
    method __getitem__ (line 1003) | def __getitem__(self, idx):
  class SharedEpoch (line 1021) | class SharedEpoch:
    method __init__ (line 1022) | def __init__(self, epoch: int = 0):
    method set_value (line 1025) | def set_value(self, epoch):
    method get_value (line 1028) | def get_value(self):
  class DataInfo (line 1033) | class DataInfo:
    method set_epoch (line 1038) | def set_epoch(self, epoch):
  function expand_urls (line 1045) | def expand_urls(urls, weights=None):
  function get_dataset_size (line 1068) | def get_dataset_size(shards):
  function get_imagenet (line 1090) | def get_imagenet(args, preprocess_fns, split):
  function count_samples (line 1137) | def count_samples(dataloader):
  function filter_no_caption_or_no_image (line 1147) | def filter_no_caption_or_no_image(sample):
  function log_and_continue (line 1155) | def log_and_continue(exn):
  function group_by_keys_nothrow (line 1161) | def group_by_keys_nothrow(
  function tarfile_to_samples_nothrow (line 1195) | def tarfile_to_samples_nothrow(src, handler=log_and_continue):
  function pytorch_worker_seed (line 1203) | def pytorch_worker_seed(increment=0):
  class detshuffle2 (line 1223) | class detshuffle2(wds.PipelineStage):
    method __init__ (line 1224) | def __init__(
    method run (line 1236) | def run(self, src):
  class ResampledShards2 (line 1255) | class ResampledShards2(IterableDataset):
    method __init__ (line 1258) | def __init__(
    method __iter__ (line 1286) | def __iter__(self):
  function get_wds_dataset (line 1312) | def get_wds_dataset(
  function get_csv_dataset (line 1451) | def get_csv_dataset(args, preprocess_fn, is_train, epoch=0, tokenizer=No...
  function get_pmc_oa_Dataset (line 1480) | def get_pmc_oa_Dataset(
  function get_quilt_1m_Dataset (line 1520) | def get_quilt_1m_Dataset(
  function get_HarvardDataset (line 1560) | def get_HarvardDataset(
  function get_IUXrayDataset (line 1601) | def get_IUXrayDataset(
  function get_MimicDataset (line 1642) | def get_MimicDataset(
  class SyntheticDataset (line 1683) | class SyntheticDataset(Dataset):
    method __init__ (line 1684) | def __init__(
    method __len__ (line 1700) | def __len__(self):
    method __getitem__ (line 1703) | def __getitem__(self, idx):
  function get_synthetic_dataset (line 1709) | def get_synthetic_dataset(args, preprocess_fn, is_train, epoch=0, tokeni...
  function get_radiology_Dataset (line 1735) | def get_radiology_Dataset(
  function get_pathology_Dataset (line 1775) | def get_pathology_Dataset(
  function get_dataset_fn (line 1816) | def get_dataset_fn(data_path, dataset_type):
  function get_data (line 1851) | def get_data(

FILE: train/open_clip/src/training/distributed.py
  function is_global_master (line 12) | def is_global_master(args):
  function is_local_master (line 16) | def is_local_master(args):
  function is_master (line 20) | def is_master(args, local=False):
  function is_using_horovod (line 24) | def is_using_horovod():
  function is_using_distributed (line 35) | def is_using_distributed():
  function world_info_from_env (line 43) | def world_info_from_env():
  function init_distributed_device (line 63) | def init_distributed_device(args):
  function broadcast_object (line 117) | def broadcast_object(args, obj, src=0):
  function all_gather_object (line 130) | def all_gather_object(args, obj, dst=0):

FILE: train/open_clip/src/training/file_utils.py
  function remote_sync_s3 (line 10) | def remote_sync_s3(local_dir, remote_dir):
  function remote_sync_fsspec (line 20) | def remote_sync_fsspec(local_dir, remote_dir):
  function remote_sync (line 44) | def remote_sync(local_dir, remote_dir, protocol):
  function keep_running_remote_sync (line 54) | def keep_running_remote_sync(sync_every, local_dir, remote_dir, protocol):
  function start_sync_process (line 59) | def start_sync_process(sync_every, local_dir, remote_dir, protocol):
  function pt_save (line 64) | def pt_save(pt_obj, file_path):
  function pt_load (line 69) | def pt_load(file_path, map_location=None):
  function check_exists (line 77) | def check_exists(file_path):

FILE: train/open_clip/src/training/logger.py
  function setup_logging (line 4) | def setup_logging(log_file, level, include_host=False):

FILE: train/open_clip/src/training/main.py
  function random_seed (line 52) | def random_seed(seed=42, rank=0):
  function natural_key (line 58) | def natural_key(string_):
  function get_latest_checkpoint (line 63) | def get_latest_checkpoint(path: str, remote : bool):
  function main (line 79) | def main(args):
  function copy_codebase (line 519) | def copy_codebase(args):

FILE: train/open_clip/src/training/main_feature-work.py
  function random_seed (line 54) | def random_seed(seed=42, rank=0):
  function natural_key (line 60) | def natural_key(string_):
  function get_latest_checkpoint (line 65) | def get_latest_checkpoint(path: str, remote : bool):
  function get_checkpoint_from_id (line 80) | def get_checkpoint_from_id(path: str, remote : bool,checkpoint_id:int):
  function main (line 101) | def main(args):

FILE: train/open_clip/src/training/main_retrieve_report.py
  function random_seed (line 54) | def random_seed(seed=42, rank=0):
  function natural_key (line 60) | def natural_key(string_):
  function get_latest_checkpoint (line 65) | def get_latest_checkpoint(path: str, remote : bool):
  function get_checkpoint_from_id (line 80) | def get_checkpoint_from_id(path: str, remote : bool,checkpoint_id:int):
  function main (line 101) | def main(args):

FILE: train/open_clip/src/training/main_retrieve_report_harvard.py
  function random_seed (line 54) | def random_seed(seed=42, rank=0):
  function natural_key (line 60) | def natural_key(string_):
  function get_latest_checkpoint (line 65) | def get_latest_checkpoint(path: str, remote : bool):
  function get_checkpoint_from_id (line 80) | def get_checkpoint_from_id(path: str, remote : bool,checkpoint_id:int):
  function main (line 101) | def main(args):

FILE: train/open_clip/src/training/params.py
  function get_default_params (line 5) | def get_default_params(model_name):
  class ParseKwargs (line 14) | class ParseKwargs(argparse.Action):
    method __call__ (line 15) | def __call__(self, parser, namespace, values, option_string=None):
  function parse_args (line 26) | def parse_args(args):

FILE: train/open_clip/src/training/precision.py
  function get_autocast (line 5) | def get_autocast(precision):

FILE: train/open_clip/src/training/profiler.py
  function profile_fvcore (line 23) | def profile_fvcore(
  function profile_fvcore_text (line 44) | def profile_fvcore_text(
  function profile_fvcore_image (line 63) | def profile_fvcore_image(
  function profile_torch_image (line 82) | def profile_torch_image(model, image_input_size, batch_size=1, force_cpu...
  function profile_torch_text (line 96) | def profile_torch_text(model, text_input_size, batch_size=1, force_cpu=F...
  function profile_torch (line 110) | def profile_torch(model, text_input_size, image_input_size, batch_size=1...
  function count_params (line 125) | def count_params(model):
  function profile_model (line 128) | def profile_model(model_name, batch_size=1, profiler='torch'):
  function main (line 205) | def main():

FILE: train/open_clip/src/training/scheduler.py
  function assign_learning_rate (line 4) | def assign_learning_rate(optimizer, new_lr):
  function _warmup_lr (line 9) | def _warmup_lr(base_lr, warmup_length, step):
  function const_lr (line 13) | def const_lr(optimizer, base_lr, warmup_length, steps):
  function const_lr_cooldown (line 24) | def const_lr_cooldown(optimizer, base_lr, warmup_length, steps, cooldown...
  function cosine_lr (line 43) | def cosine_lr(optimizer, base_lr, warmup_length, steps):

FILE: train/open_clip/src/training/train.py
  class AverageMeter (line 23) | class AverageMeter(object):
    method __init__ (line 26) | def __init__(self):
    method reset (line 29) | def reset(self):
    method update (line 35) | def update(self, val, n=1):
  function postprocess_clip_output (line 42) | def postprocess_clip_output(model_out):
  function unwrap_model (line 50) | def unwrap_model(model):
  function backward (line 57) | def backward(total_loss, scaler):
  function train_one_epoch (line 64) | def train_one_epoch(model, data, loss, epoch, optimizer, scaler, schedul...
  function evaluate (line 252) | def evaluate(model, data, epoch, args, tb_writer=None, tokenizer=None):
  function retrieve_report (line 362) | def retrieve_report(model, data, epoch, args, tb_writer=None, tokenizer=...
  function get_clip_metrics (line 579) | def get_clip_metrics(image_features, text_features, logit_scale):
  function get_logits (line 598) | def get_logits(image_features, text_features, logit_scale):
  function maybe_compute_generative_loss (line 611) | def maybe_compute_generative_loss(model_out):

FILE: train/open_clip/src/training/zero_shot.py
  function accuracy (line 11) | def accuracy(output, target, topk=(1,)):
  function run (line 17) | def run(model, classifier, dataloader, args):
  function run_retrieve (line 43) | def run_retrieve(model, classifier, dataloader, args):
  function zero_shot_retrieve (line 69) | def zero_shot_retrieve(model, data, epoch, args, tokenizer=None):
  function zero_shot_eval (line 113) | def zero_shot_eval(model, data, epoch, args, tokenizer=None):