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Functions4,510 in github.com/Feng-Hong/WINO-DLLM

⨍ Functions 4,510◇ Types & classes 452↳ Endpoints 18
↓ 6 callersFunctionparse_score
(review)
MMaDA/lmms_eval/lmms_eval/tasks/videochatgpt/utils.py:428
↓ 6 callersMethodprocess_images
(self, images, image_processor, model_cfg)
MMaDA/lmms_eval/lmms_eval/models/auroracap.py:218
↓ 5 callersMethod_name_is_registered
(self, name)
MMaDA/lmms_eval/lmms_eval/tasks/__init__.py:162
↓ 5 callersMethodcardinal2chntext
(self)
MMaDA/lmms_eval/lmms_eval/tasks/librispeech/cn_tn.py:742
↓ 5 callersFunctioncompute_accuracy
Compute the accuracy of two bounding boxes based on a specified threshold. Parameters: - box1 (list of float): Bounding box [x_min, y_mi
MMaDA/lmms_eval/lmms_eval/tasks/refcoco+/utils_rec.py:128
↓ 5 callersFunctioncompute_accuracy
Compute the accuracy of two bounding boxes based on a specified threshold. Parameters: - box1 (list of float): Bounding box [x_min, y_mi
MMaDA/lmms_eval/lmms_eval/tasks/refcoco/utils_rec.py:128
↓ 5 callersFunctioncompute_accuracy
Compute the accuracy of two bounding boxes based on a specified threshold. Parameters: - box1 (list of float): Bounding box [x_min, y_mi
MMaDA/lmms_eval/lmms_eval/tasks/screenspot/utils_rec.py:94
↓ 5 callersFunctioncompute_accuracy
Compute the accuracy of two bounding boxes based on a specified threshold. Parameters: - box1 (list of float): Bounding box [x_min, y_mi
MMaDA/lmms_eval/lmms_eval/tasks/refcocog/utils_rec.py:128
↓ 5 callersFunctionconvert_str_to_dict
Parses the 'predict' string and returns a dictionary. Missing or unparseable content is handled gracefully. Parameters: - predict_st
MMaDA/lmms_eval/lmms_eval/tasks/ocrbench_v2/TEDS_metric.py:214
↓ 5 callersMethodcreate_options_prompt
(self, row_data, option_candidate)
MMaDA/lmms_eval/lmms_eval/tasks/mmbench/mmbench_evals.py:22
↓ 5 callersMethoddevice
(self)
MMaDA/lmms_eval/lmms_eval/models/ola.py:256
↓ 5 callersMethoddoc_to_text
(self, doc)
MMaDA/lmms_eval/lmms_eval/api/task.py:374
↓ 5 callersMethoddownload
Downloads and returns the task dataset. Override this method to download the dataset from a custom API. :param data_dir: str
MMaDA/lmms_eval/lmms_eval/api/task.py:239
↓ 5 callersMethodeval_result
(self, results, eval_method)
MMaDA/lmms_eval/lmms_eval/tasks/mmbench/mmbench_evals.py:239
↓ 5 callersMethodevaluate
The main entry point to evaluate all tasks in self.data based on the HF dataset’s metric info.
MMaDA/lmms_eval/lmms_eval/tasks/megabench/evaluator.py:92
↓ 5 callersFunctionget_eval_generic
(question, answer, pred, task, max_tokens: int, retries: int = 5)
MMaDA/lmms_eval/lmms_eval/tasks/videochatgpt/utils.py:218
↓ 5 callersMethodget_input_embeddings
(self)
MMaDA/lmms_eval/lmms_eval/models/model_mmada/modeling_llada.py:1520
↓ 5 callersMethodget_prompt
(self)
MMaDA/lmms_eval/lmms_eval/models/video_chatgpt/video_conversation.py:31
↓ 5 callersMethodhas_test_docs
Whether the task has a test set
MMaDA/lmms_eval/lmms_eval/api/task.py:306
↓ 5 callersFunctionload_defualt_config
()
MMaDA/lmms_eval/lmms_eval/tasks/plm_videobench/eval_utils.py:293
↓ 5 callersFunctionnonlinearity
(x)
MMaDA/lmms_eval/lmms_eval/models/model_mmada/common_modules.py:16
↓ 5 callersFunctionnonlinearity
(x)
MMaDA/models/common_modules.py:16
↓ 5 callersMethodnormalize_dict
Sort by value, while iterate over element if data is list
MMaDA/lmms_eval/lmms_eval/tasks/synthdog/donut_evaluator.py:95
↓ 5 callersMethodpost_init
(self, results: Dict[str, Any])
MMaDA/lmms_eval/lmms_eval/loggers/wandb_logger.py:54
↓ 5 callersFunctionpreprocess
(text)
MMaDA/lmms_eval/lmms_eval/tasks/gpqa/cot_zeroshot/utils.py:7
↓ 5 callersFunctionpreprocess
(text)
MMaDA/lmms_eval/lmms_eval/tasks/gpqa/n_shot/utils.py:7
↓ 5 callersFunctionpreprocess
(text)
MMaDA/lmms_eval/lmms_eval/tasks/gpqa/zeroshot/utils.py:7
↓ 5 callersFunctionpreprocess
(text)
MMaDA/lmms_eval/lmms_eval/tasks/gpqa/generative/utils.py:7
↓ 5 callersFunctionpreprocess
(text)
MMaDA/lmms_eval/lmms_eval/tasks/gpqa/cot_n_shot/utils.py:7
↓ 5 callersFunctionstr_to_bboxes
(bbox_list)
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/scoring/common/conversions.py:96
↓ 5 callersMethodto_dict
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[
MMaDA/lmms_eval/lmms_eval/models/mplug_owl_video/configuration_mplug_owl.py:274
↓ 4 callersMethod_get_config
(self, name)
MMaDA/lmms_eval/lmms_eval/tasks/__init__.py:207
↓ 4 callersFunction_non_meta_init_device
(config: ModelConfig)
LLaDA/modeling_llada.py:193
↓ 4 callersFunction_non_meta_init_device
(config: ModelConfig)
MMaDA/lmms_eval/lmms_eval/models/model_mmada/modeling_llada.py:193
↓ 4 callersFunction_non_meta_init_device
(config: ModelConfig)
MMaDA/models/modeling_llada.py:193
↓ 4 callersFunction_tokenize_str
(role, content)
MMaDA/lmms_eval/lmms_eval/models/cambrian.py:79
↓ 4 callersFunction_tokenize_str
(role, content)
MMaDA/lmms_eval/lmms_eval/models/model_utils/qwen/qwen_generate_utils.py:135
↓ 4 callersMethodbuild_description
(self)
MMaDA/lmms_eval/lmms_eval/tasks/ifeval/instructions.py:846
↓ 4 callersFunctioncalculate_iou
(box1, box2)
MMaDA/lmms_eval/lmms_eval/tasks/ocrbench_v2/IoUscore_metric.py:10
↓ 4 callersFunctioncalculate_iou
Calculate the IoU between predicted and target bounding boxes.
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/scoring/common/metrics.py:6
↓ 4 callersFunctionchn2num
(chinese_string, numbering_type=NUMBERING_TYPES[1])
MMaDA/lmms_eval/lmms_eval/tasks/librispeech/cn_tn.py:559
↓ 4 callersFunctioncompute_f1_score
Compute the F1-score for KIE task between predicted and ground truth dictionaries. Args: preds (dict): The predicted key-value pairs.
MMaDA/lmms_eval/lmms_eval/tasks/ocrbench_v2/TEDS_metric.py:386
↓ 4 callersFunctionconstruct_prompt
(doc, mc_prompt="", open_ended_prompt="", prompt_type="reasoning")
MMaDA/lmms_eval/lmms_eval/tasks/mmmu/utils.py:121
↓ 4 callersFunctionconvert_time_to_frame
(time_in_seconds, fps)
MMaDA/lmms_eval/lmms_eval/tasks/egoplan/utils.py:63
↓ 4 callersFunctiondict_to_html
(data)
MMaDA/lmms_eval/lmms_eval/tasks/ocrbench_v2/TEDS_metric.py:202
↓ 4 callersMethoddoc_to_target
(self, doc: dict)
MMaDA/lmms_eval/lmms_eval/api/task.py:1337
↓ 4 callersMethoddoc_to_text
(self, doc)
MMaDA/lmms_eval/lmms_eval/api/task.py:1300
↓ 4 callersFunctiondownsample_audio
(audio_array: np.ndarray, original_sr: int, target_sr: int)
MMaDA/lmms_eval/lmms_eval/models/model_utils/audio_processing.py:7
↓ 4 callersFunctionferret_aggregation
(results, category)
MMaDA/lmms_eval/lmms_eval/tasks/ferret/utils.py:187
↓ 4 callersFunctionfind_word_position
(string, word)
MMaDA/lmms_eval/lmms_eval/tasks/naturalbench/utils.py:129
↓ 4 callersMethodflatten
Convert Dictionary into Non-nested Dictionary Example: input(dict) { "menu": [
MMaDA/lmms_eval/lmms_eval/tasks/synthdog/donut_evaluator.py:27
↓ 4 callersFunctionfloatify
(num: str)
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/scoring/general_numerical_match.py:138
↓ 4 callersMethodfrom_string
(cls, s)
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/metric_type.py:188
↓ 4 callersFunctionget_file_datetime
Given the results and sample results filenames, extracts and returns the datetime.
MMaDA/lmms_eval/lmms_eval/utils.py:235
↓ 4 callersFunctionget_metric_aggregation
(name)
MMaDA/lmms_eval/lmms_eval/api/registry.py:151
↓ 4 callersFunctionget_rerank_incontext_example
(rerank_num)
MMaDA/lmms_eval/lmms_eval/tasks/mmsearch/utils/prompt_utils.py:25
↓ 4 callersFunctionget_scores
Calculate various scores based on the given results. Args: scores (dict or list): A dictionary or list containing results where each
MMaDA/lmms_eval/lmms_eval/tasks/naturalbench/utils.py:16
↓ 4 callersMethodhas_test_docs
(self)
MMaDA/lmms_eval/lmms_eval/api/task.py:1093
↓ 4 callersMethodhas_validation_docs
Whether the task has a validation set
MMaDA/lmms_eval/lmms_eval/api/task.py:301
↓ 4 callersMethodhas_validation_docs
(self)
MMaDA/lmms_eval/lmms_eval/api/task.py:1087
↓ 4 callersFunctioninput_ids_to_masked_buckets
(input_ids, mask_id, total_buckets=10)
MMaDA/lmms_eval/lmms_eval/models/model_mmada/training_utils.py:418
↓ 4 callersFunctioninput_ids_to_masked_buckets
(input_ids, mask_id, total_buckets=10)
MMaDA/models/training_utils.py:418
↓ 4 callersFunctionis_higher_better
(metric_name)
MMaDA/lmms_eval/lmms_eval/api/registry.py:160
↓ 4 callersFunctionis_non_str_iterable
(obj)
MMaDA/lmms_eval/lmms_eval/api/metrics.py:455
↓ 4 callersMethodlist_all_tasks
(self, list_groups=True, list_tags=True, list_subtasks=True)
MMaDA/lmms_eval/lmms_eval/tasks/__init__.py:97
↓ 4 callersFunctionllava_aggregation
(results, category)
MMaDA/lmms_eval/lmms_eval/tasks/llava-in-the-wild/utils.py:181
↓ 4 callersFunctionllava_aggregation
(results, category)
MMaDA/lmms_eval/lmms_eval/tasks/llava-in-the-wild/utils_ko.py:181
↓ 4 callersFunctionllava_aggregation
(results, category)
MMaDA/lmms_eval/lmms_eval/tasks/multilingual-llava-bench-in-the-wild/utils.py:178
↓ 4 callersFunctionllava_aggregation
(results, category)
MMaDA/lmms_eval/lmms_eval/tasks/llava-bench-coco/utils.py:181
↓ 4 callersFunctionparse_choice_img
(choice: str, img_token: str)
MMaDA/lmms_eval/lmms_eval/tasks/seedbench_2_plus/utils.py:8
↓ 4 callersFunctionparse_choice_img
(choice: str, img_token: str)
MMaDA/lmms_eval/lmms_eval/tasks/seedbench_2/utils.py:8
↓ 4 callersFunctionparse_pddl_attr_from_string
( s, attr_starter="(:", attr_ender=")", inner_starter="(", inner_ender=")", overlap=Fa
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/scoring/symbolic_planning.py:43
↓ 4 callersFunctionparse_subtitle_time
(time_str)
MMaDA/lmms_eval/lmms_eval/tasks/egoplan/utils.py:41
↓ 4 callersFunctionprocess_option_for_question
(sent)
MMaDA/lmms_eval/lmms_eval/tasks/vitatecs/utils.py:79
↓ 4 callersFunctionremove_sp
(text)
MMaDA/lmms_eval/lmms_eval/tasks/covost2/utils.py:77
↓ 4 callersFunctionremove_sp
(text, language)
MMaDA/lmms_eval/lmms_eval/tasks/open_asr/utils.py:52
↓ 4 callersMethodsample
Draw the first `n` samples in order from the specified split. Used for tasks with "canonical" ordered fewshot examples, such as MMLU
MMaDA/lmms_eval/lmms_eval/api/samplers.py:61
↓ 4 callersMethodsave_pretrained
(self, path)
MMaDA/lmms_eval/lmms_eval/models/model_mmada/training_utils.py:126
↓ 4 callersFunctionset_verbosity
Set the verbosity level for the 🤗 muse' root logger. Args: verbosity (`int`): Logging level, e.g., one of:
MMaDA/lmms_eval/lmms_eval/models/model_mmada/logging.py:144
↓ 4 callersFunctionset_verbosity
Set the verbosity level for the 🤗 muse' root logger. Args: verbosity (`int`): Logging level, e.g., one of:
MMaDA/models/logging.py:144
↓ 4 callersMethodstate_dict
r""" Returns the state of the ExponentialMovingAverage as a dict. This method is used by accelerate during checkpointing to save the
MMaDA/models/training_utils.py:204
↓ 4 callersFunctionstr_to_iterable
Converts a string representation of an iterable to an iterable.
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/scoring/common/conversions.py:45
↓ 4 callersFunctionstr_to_set
Converts a string representation of an iterable to a set.
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/scoring/common/conversions.py:86
↓ 4 callersMethodtokenize
(self, sent: str)
MMaDA/lmms_eval/lmms_eval/tasks/tedlium/utils.py:117
↓ 4 callersFunctionvqa_evaluation
(predict, answers)
MMaDA/lmms_eval/lmms_eval/tasks/ocrbench_v2/vqa_metric.py:26
↓ 4 callersFunctionwrap_html_table
The TEDS computation from PubTabNet code requires that the input html table should have <html>, <body>, and <table> tags. Add them if they ar
MMaDA/lmms_eval/lmms_eval/tasks/ocrbench_v2/TEDS_metric.py:501
↓ 3 callersMethod__init__
pass a string `regex` to run `re.compile(r"regex")` on. `fallback` defines the output returned if no matches for the regex are locate
MMaDA/lmms_eval/lmms_eval/filters/extraction.py:36
↓ 3 callersMethod__init__
(self, date=None, chntext=None)
MMaDA/lmms_eval/lmms_eval/tasks/librispeech/cn_tn.py:814
↓ 3 callersMethod__init__
( self, )
MMaDA/lmms_eval/lmms_eval/models/model_mmada/modeling_magvitv2.py:404
↓ 3 callersMethod__init__
( self, )
MMaDA/models/modeling_magvitv2.py:404
↓ 3 callersMethod_cast_if_autocast_enabled
(self, tensor: torch.Tensor, dtype: Optional[torch.dtype] = None)
LLaDA/modeling_llada.py:251
↓ 3 callersMethod_cast_if_autocast_enabled
(self, tensor: torch.Tensor, dtype: Optional[torch.dtype] = None)
MMaDA/lmms_eval/lmms_eval/models/model_mmada/modeling_llada.py:251
↓ 3 callersMethod_cast_if_autocast_enabled
(self, tensor: torch.Tensor, dtype: Optional[torch.dtype] = None)
MMaDA/models/modeling_llada.py:251
↓ 3 callersMethod_encode_image
(self, image, image_format)
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/scoring/vlm_as_judge.py:81
↓ 3 callersMethod_get_tasklist
(self, name)
MMaDA/lmms_eval/lmms_eval/tasks/__init__.py:216
↓ 3 callersFunction_get_video_file
(prefix: str, video_name: str, suffix: str)
MMaDA/lmms_eval/lmms_eval/tasks/_task_utils/video_loader.py:12
↓ 3 callersMethod_name_is_tag
(self, name)
MMaDA/lmms_eval/lmms_eval/tasks/__init__.py:172
↓ 3 callersMethod_parse
Try to parse a single answer.
MMaDA/lmms_eval/lmms_eval/tasks/megabench/metrics/parsing/answer_str_parse.py:18
↓ 3 callersFunction_process_text_and_mixed_media
Process the text prompt and the input medias when the media files contain both image and video. In this case, sample frames from the video an
MMaDA/lmms_eval/lmms_eval/tasks/megabench/image_video_utils.py:101
↓ 3 callersFunction_sacreformat
Format refs and preds for sacrebleu corpus calculation. It is very particular
MMaDA/lmms_eval/lmms_eval/api/metrics.py:459
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