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Functions2,270 in github.com/allenai/molmo2

⨍ Functions 2,270◇ Types & classes 480

↓ 1 callersMethodvideo_to_patches_and_tokens

( self, frames, frame_prefixes: List[List[int]], is_training=False, rn

olmo/preprocessing/video_preprocessor.py:117

↓ 1 callersFunctionvqa_score

Evaluation with VQA 2 style preprocessing

olmo/eval/vqa.py:100

↓ 1 callersFunctionvsi_bench_na_score

( pred: str, target: str, start: float = 0.5, end: float = 0.95, interval: float = 0.05, )

olmo/eval/vsi_bench_utils.py:38

↓ 1 callersMethodwrap

(cls, scheduler: Scheduler, warmup_start: int, warmup_end: int)

olmo/train/optim.py:403

↓ 1 callersMethodwrite_file

(self, dir: PathOrStr, fname: str, contents: Union[str, bytes, Callable])

olmo/train/checkpointer.py:201

↓ 1 callersFunctionwrite_json

(path: PathOrStr, data: Any, **kwargs)

(self, example_id, example)

tests/data/test_data_iterator.py:164

olmo/preprocessing/point_formatter.py:18

( self, messages, is_training=False, rng=None, image=None, vid

olmo/preprocessing/multimodal_preprocessor.py:50

(self, example, rng=np.random)

olmo/preprocessing/multimodal_preprocessor.py:207

Returns a formatted example and example metadata

olmo/preprocessing/data_formatter.py:1891

( self, video_frames: VideoFrames, is_training=False, rng=None, metada

olmo/preprocessing/video_preprocessor.py:181

(self, batch: List[Dict[str, Any]])

olmo/preprocessing/multimodal_collator.py:91

( self, image: ImageInput, is_training=False, rng=np.random, )

olmo/preprocessing/multicrop_preprocessor.py:114

( self, images, is_training=False, rng=None, )

olmo/preprocessing/multicrop_preprocessor.py:275

(self, example_id: int, example: Dict)

olmo/data/dynamic_packer.py:427

Decide what time stamps to sample

olmo/data/video_loader.py:134

Decide what frame indices to sample

olmo/data/video_loader.py:195

Load frames from a video

olmo/data/video_loader.py:304

(self, input_ids, scores)

olmo/hf_model/modeling_molmo_point.py:569

to_pool: [n_to_pool, pooling_dim, vit_dim] to_pool_mask: [n_to_pool, pooling_dim] returns: pooled_features: [n_to_po

olmo/hf_model/modeling_molmo_point.py:914

(self, hidden_states, slice_indices=None)

olmo/hf_model/modeling_molmo_point.py:1614

Args: text (`str`, `list[str]`, `list[list[str]]`): The sequence or batch of sequences to be encoded. Each seque

olmo/hf_model/processing_molmo2.py:250

(self, message, **kwargs)

olmo/eval/open_ended_qa_eval.py:36

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/open_ended_qa_eval.py:183

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:270

(self, metadatas, predictions, tokenizer, step=None, scores=None)

olmo/eval/evaluators.py:294

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:496

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:557

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:666

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:697

(self, metadata, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:858

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:934

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1171

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1262

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1310

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1354

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1383

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1545

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1689

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1780

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1827

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1927

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:1976

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2012

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2183

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2225

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2317

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2380

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2447

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2535

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2608

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2663

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2828

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2924

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:2953

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:3017

(self, metadatas, predictions, tokenizer, step=None)

olmo/eval/evaluators.py:3043

(self, predictions, example_metadata, tokenizer, device, step=None, **kwargs)

olmo/eval/inf_evaluator.py:49

(self, example, rng=np.random)

olmo/models/molmo_point/molmo_point_example_preprocessor.py:413

Returns a formatted example and example metadata

olmo/models/molmo_point/molmo_point_data_formatter.py:1110

to_pool: [n_to_pool, pooling_dim, vit_dim] to_pool_mask: [n_to_pool, pooling_dim] returns: pooled_features: [n_to_po

olmo/models/molmo_point/molmo_point_connector.py:177

Method__enter__

olmo/train/trainer.py:1449

(self, exc_type, exc_val, exc_tb)

olmo/train/trainer.py:1452

Method__getitem__

olmo/data/dataset.py:34

Method__getitem__

olmo/data/dataset.py:61

Method__getitem__

olmo/data/dataset.py:101

(self, buffer_size, verbose=False)

tests/data/test_data_iterator.py:156

(self, resource: str)

(self, tokenizer, bos_token_id=None, adds_space=False)

olmo/tokenizer.py:49

(self, fn, *args, **kwargs)

olmo/util.py:394

olmo/registry.py:22

:param max_text_len: truncate examples longer than this length :param include_metadata: whether to include the metadata in the out ba

olmo/preprocessing/multimodal_collator.py:68

(self, split, sample=None, counting=False, keep_in_memory=False)

olmo/data/pixmo_datasets.py:89

(self, split, kind="both", counting=False, keep_in_memory=False, max_points=None, max_total_p

olmo/data/pixmo_datasets.py:145

(self, split, split_groups=True, keep_in_memory=False)

olmo/data/pixmo_datasets.py:253

(self, split, prefix_how_many=True, keep_in_memory=False, style="synthetic_qa")

olmo/data/pixmo_datasets.py:308

(self, split, mode, prefix_how_many=True, keep_in_memory=False, flatten=False)

olmo/data/pixmo_datasets.py:381

(self, split, prefix_how_many=True, keep_in_memory=False, flat=False, skip_counting=False, sa

olmo/data/pixmo_datasets.py:431

(self, keep_in_memory=False)

olmo/data/pixmo_datasets.py:508

(self, split, keep_in_memory=False, styles=("multi_image_pointing", "multi_image_point_then_c

olmo/data/pixmo_datasets.py:568

(self, split, multi_image_only=False, max_images=None, prefix_how_many=True)

olmo/data/pixmo_datasets.py:611

(self, max_buffer_size: int, constraints: List[Constraint], verbosity=0, cp_world_size: int =

olmo/data/dynamic_packer.py:408

olmo/data/molmo2_datasets.py:263

olmo/data/molmo2_datasets.py:692

(self, split: str, mode: str = "point_count", point_sort_by: str = "xy", max_s

olmo/data/molmo2_datasets.py:781

olmo/data/molmo2_datasets.py:1050

olmo/data/molmo2_datasets.py:1141

olmo/data/molmo2_datasets.py:1254

(self, split, keep_in_memory=False, p_intent=0.8, use_name_as_label=False)

olmo/data/molmo2_datasets.py:1309

( self, split, include_video_caption=False, include_merged_caption=False,

olmo/data/molmo2_datasets.py:1390

(self, split, reasoning_type="ALL", demonstration_type="ALL")

olmo/data/academic_video_datasets.py:131

(self, split, format="original")

olmo/data/academic_video_datasets.py:195

olmo/data/academic_video_datasets.py:275

(self, split, flat: bool = False)

olmo/data/academic_video_datasets.py:336

(self, split, minimum=0.0)

olmo/data/academic_video_datasets.py:385

(self, split, answer_type="multi_choice", flat=False, max_per_video=None)

olmo/data/academic_video_datasets.py:572

(self, split, sample=None)

olmo/data/academic_video_datasets.py:771

olmo/data/academic_video_datasets.py:850

(self, split, allow_subtitle=True, difficulty="all", duration_group="all", with_subtitle=False)

olmo/data/academic_video_datasets.py:1062

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