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Functions1,238 in github.com/boheumd/MA-LMM

⨍ Functions 1,238◇ Types & classes 310↳ Endpoints 9

lavis/models/blip_models/nlvr_encoder.py:383

lavis/models/blip_models/nlvr_encoder.py:398

lavis/models/blip_models/nlvr_encoder.py:490

lavis/models/blip_models/nlvr_encoder.py:594

lavis/models/blip_models/nlvr_encoder.py:609

lavis/models/blip_models/nlvr_encoder.py:626

lavis/models/blip_models/nlvr_encoder.py:646

(self, config, add_pooling_layer=True)

lavis/models/blip_models/nlvr_encoder.py:688

(self, image_encoder, text_encoder, embed_dim, max_txt_len=40)

lavis/models/blip_models/blip_feature_extractor.py:38

lavis/models/blip_models/blip_retrieval.py:51

(self, image_encoder, text_encoder, embed_dim=256, max_txt_len=35)

lavis/models/blip_models/blip_image_text_matching.py:38

( self, image_encoder, text_encoder, text_decoder, queue_size,

lavis/models/blip_models/blip_pretrain.py:40

(self, image_encoder, text_decoder, prompt=None, max_txt_len=40)

lavis/models/blip_models/blip_caption.py:40

lavis/models/blip_models/blip.py:21

( self, image_encoder, text_encoder, text_decoder, use_distill=True,

lavis/models/albef_models/albef_vqa.py:42

( self, image_encoder, text_encoder, num_classes, momentum=0.995,

lavis/models/albef_models/albef_classification.py:31

( self, image_encoder, text_encoder, num_classes, momentum=0.995,

lavis/models/albef_models/albef_nlvr.py:29

( self, image_encoder, text_encoder, queue_size, embed_dim=256,

lavis/models/albef_models/albef_pretrain.py:41

( self, image_encoder, text_encoder, queue_size, embed_dim=256,

lavis/models/albef_models/albef_retrieval.py:45

(self, image_encoder, text_encoder, embed_dim=256, max_txt_len=30)

lavis/models/albef_models/albef_feature_extractor.py:28

( self, local_loss=False, gather_with_grad=False, cache_labels=False,

lavis/models/clip_models/loss.py:79

(self, bpe_path: str = default_bpe(), special_tokens=None)

lavis/models/clip_models/tokenizer.py:83

( self, max_size, interpolation=InterpolationMode.BICUBIC, fn="max", fill=0 )

lavis/models/clip_models/transform.py:29

( self, in_features: int, out_features: int = None, embed_dim: int = None,

lavis/models/clip_models/timm_model.py:139

( self, in_features: int, feat_size: Union[int, Tuple[int, int]], out_features

lavis/models/clip_models/timm_model.py:200

( self, max_res: int = 224, num_bands: int = 64, concat_grid=True, kee

lavis/models/clip_models/timm_model.py:387

(self, dim, max_res=224, linear_bands: bool = False)

lavis/models/clip_models/timm_model.py:488

(self, inplanes, planes, stride=1)

lavis/models/clip_models/model.py:53

( self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None )

lavis/models/clip_models/model.py:110

(self, layers, output_dim, heads, image_size=224, width=64)

lavis/models/clip_models/model.py:164

(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU)

lavis/models/clip_models/model.py:263

( self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU )

lavis/models/clip_models/model.py:289

( self, image_size: int, patch_size: int, width: int, layers: int,

lavis/models/clip_models/model.py:309

apply_lemmatizer: when set to True, postprocess predict_answers() result with lemmas.

lavis/models/blip2_models/blip2_t5_instruct.py:40

( self, vit_model="eva_clip_g", img_size=224, drop_path_rate=0, use_gr

lavis/models/blip2_models/blip2_qformer.py:45

apply_lemmatizer: when set to True, postprocess predict_answers() result with lemmas.

lavis/models/blip2_models/blip2_t5.py:40

lavis/models/blip2_models/Qformer.py:54

(self, config, is_cross_attention)

lavis/models/blip2_models/Qformer.py:112

lavis/models/blip2_models/Qformer.py:279

(self, config, is_cross_attention=False)

lavis/models/blip2_models/Qformer.py:293

lavis/models/blip2_models/Qformer.py:350

lavis/models/blip2_models/Qformer.py:365

lavis/models/blip2_models/Qformer.py:488

lavis/models/blip2_models/Qformer.py:593

lavis/models/blip2_models/Qformer.py:608

lavis/models/blip2_models/Qformer.py:625

lavis/models/blip2_models/Qformer.py:645

(self, config, add_pooling_layer=False)

lavis/models/blip2_models/Qformer.py:687

lavis/models/blip2_models/Qformer.py:973

lavis/models/blip2_models/Qformer.py:1136

LlamaRMSNorm is equivalent to T5LayerNorm

lavis/models/blip2_models/modeling_llama.py:76

(self, dim, max_position_embeddings=2048, base=10000, device=None)

lavis/models/blip2_models/modeling_llama.py:96

(self, config: LlamaConfig)

lavis/models/blip2_models/modeling_llama.py:164

(self, config: LlamaConfig)

lavis/models/blip2_models/modeling_llama.py:253

(self, config: LlamaConfig)

lavis/models/blip2_models/modeling_llama.py:440

lavis/models/blip2_models/modeling_llama.py:614

lavis/models/blip2_models/modeling_llama.py:789

Construct a layernorm module in the T5 style. No bias and no subtraction of mean.

lavis/models/blip2_models/modeling_t5.py:255

(self, config: T5Config)

lavis/models/blip2_models/modeling_t5.py:299

(self, config: T5Config)

lavis/models/blip2_models/modeling_t5.py:315

(self, config: T5Config)

lavis/models/blip2_models/modeling_t5.py:333

(self, config: T5Config, has_relative_attention_bias=False)

lavis/models/blip2_models/modeling_t5.py:351

(self, config, has_relative_attention_bias=False)

lavis/models/blip2_models/modeling_t5.py:624

lavis/models/blip2_models/modeling_t5.py:660

(self, config, embed_tokens=None)

lavis/models/blip2_models/modeling_t5.py:952

(self, config: T5Config)

lavis/models/blip2_models/modeling_t5.py:1458

(self, config: T5Config)

lavis/models/blip2_models/modeling_t5.py:1659

(self, config: T5Config)

lavis/models/blip2_models/modeling_t5.py:1966

( self, vit_model="eva_clip_g", img_size=224, drop_path_rate=0, use_gr

lavis/models/blip2_models/blip2_vicuna_instruct.py:25

( self, vit_model="eva_clip_g", img_size=224, drop_path_rate=0, use_gr

lavis/models/blip2_models/blip2_image_text_matching.py:27

(self, mean=None, std=None, n_frms=MAX_INT)

lavis/processors/alpro_processors.py:22

lavis/processors/alpro_processors.py:34

(self, target_size, interpolation_mode="bilinear")

lavis/processors/alpro_processors.py:63

( self, image_size=384, mean=None, std=None, min_scale=0.5, ma

lavis/processors/alpro_processors.py:83

(self, image_size=256, mean=None, std=None, n_frms=MAX_INT)

lavis/processors/alpro_processors.py:172

(self, visual_ft=["i3d_rgb"], audio_ft=["vggish"])

lavis/processors/gpt_processors.py:40

(self, visual_ft, audio_ft)

lavis/processors/gpt_processors.py:123

lavis/processors/base_processor.py:12

( self, image_size=224, mean=None, std=None, min_scale=0.9, max_scale=1.0 )

lavis/processors/clip_processors.py:21

(self, N=2, M=10, isPIL=False, augs=[])

lavis/processors/randaugment.py:327

(self, N=2, M=10, p=0.0, tensor_in_tensor_out=True, augs=[])

lavis/processors/randaugment.py:353

lavis/processors/transforms_video.py:35

( self, size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpo

lavis/processors/transforms_video.py:57

(self, crop_size)

lavis/processors/transforms_video.py:93

(self, resize_size, interpolation_mode)

lavis/processors/transforms_video.py:113

(self, resize_size)

lavis/processors/transforms_video.py:134

(self, mean, std, inplace=False)

lavis/processors/transforms_video.py:161

lavis/processors/transforms_video.py:183

lavis/processors/transforms_video.py:206

(self, mean=None, std=None)

lavis/processors/blip_processors.py:21

(self, prompt="", max_words=50)

lavis/processors/blip_processors.py:32

(self, max_words=50)

lavis/processors/blip_processors.py:75

( self, image_size=384, mean=None, std=None, min_scale=0.5, max_scale=1.0 )

lavis/processors/blip_processors.py:108

(self, image_size=384, mean=None, std=None)

lavis/processors/blip_processors.py:170

( self, image_size=364, mean=None, std=None, min_scale=0.5, max_scale=1.0 )

lavis/processors/blip_processors.py:201

( self, image_size=364, mean=None, std=None, min_scale=0.5, max_scale=1.0 )

lavis/processors/blip_processors.py:246

( self, image_size=224, mean=None, std=None, min_scale=0.5, max_scale=1.0 )

lavis/processors/blip_processors.py:298

lavis/processors/blip_processors.py:347

lavis/datasets/datasets/dataloader_utils.py:58

lavis/datasets/datasets/dataloader_utils.py:158

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