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Functions5,024 in github.com/OpenBMB/VisRAG

↓ 4 callersFunctioninterp_mode_to_str
(mode)
timm_modified/timm/data/transforms.py:94
↓ 4 callersFunctionis_model
Check if a model name exists
timm_modified/timm/models/_registry.py:276
↓ 4 callersFunctionis_no_jit
()
timm_modified/timm/layers/config.py:37
↓ 4 callersMethodload_pretrained
(self, checkpoint_path, prefix='resnet/')
timm_modified/timm/models/resnetv2.py:436
↓ 4 callersFunctionload_state_dict
( checkpoint_path: str, use_ema: bool = True, device: Union[str, torch.device] = 'cpu'
timm_modified/timm/models/_helpers.py:31
↓ 4 callersMethodlog
(self, log_dict)
src/openmatch/trainer/dense_trainer.py:178
↓ 4 callersMethodmerge
(self, other)
src/rsgrpo/verl/utils/seqlen_balancing.py:36
↓ 4 callersFunctionnum_groups
(group_size, channels)
timm_modified/timm/models/fastvit.py:23
↓ 4 callersFunctionnum_groups
(group_size, channels)
timm_modified/timm/models/_efficientnet_blocks.py:16
↓ 4 callersFunctionoffload_fsdp_optimizer
(optimizer: Optimizer, empty_cache: bool = True)
src/rsgrpo/verl/utils/fsdp_utils.py:112
↓ 4 callersFunctionpixel_freq_bands
( num_bands: int, max_freq: float = 224., linear_bands: bool = True, device: O
timm_modified/timm/layers/pos_embed_sincos.py:15
↓ 4 callersFunctionprocess_image
( image: Union[Dict[str, Any], ImageObject, str], min_pixels: Optional[int], max_pixels: Optional[int] )
src/rsgrpo/verl/utils/dataset.py:54
↓ 4 callersFunctionprocess_video
( video: str, min_pixels: Optional[int], max_pixels: Optional[int], video_fps: float, return_fps: bool = F
src/rsgrpo/verl/utils/dataset.py:81
↓ 4 callersFunctionrepeat_kv
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, he
src/openmatch/modeling/weighted_selection/MiniCPMV20/modeling_minicpm.py:338
↓ 4 callersFunctionset_layer
(model, layer, val)
timm_modified/timm/models/_prune.py:30
↓ 4 callersFunctionunfreeze
Unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place. Args: root_modul
timm_modified/timm/utils/model.py:209
↓ 4 callersMethodupsample
Feature map up-sampling.
timm_modified/timm/models/coat.py:288
↓ 3 callersMethod__init__
(self, dtype=torch.float32)
timm_modified/timm/data/transforms.py:36
↓ 3 callersMethod__init__
( self, in_channel, out_channels, kernel_size, stride=1, padding=None, dilation=1, gro
timm_modified/timm/layers/std_conv.py:32
↓ 3 callersMethod__init__
Args: cfg (dict): Model architecture configuration in_chans (int): Number of input channels (default: 3)
timm_modified/timm/models/vovnet.py:155
↓ 3 callersMethod__init__
( self, cfgs, num_classes=1000, width=1.0, in_chan
timm_modified/timm/models/ghostnet.py:187
↓ 3 callersMethod__init__
(self, *args)
timm_modified/timm/models/hrnet.py:491
↓ 3 callersMethod__init__
( self, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128), k_r=96
timm_modified/timm/models/dpn.py:151
↓ 3 callersMethod__init__
( self, dim, num_heads, mlp_ratio=4., qkv_bias=Tru
timm_modified/timm/models/vision_transformer_sam.py:200
↓ 3 callersMethod__init__
( self, growth_rate=32, block_config=(6, 12, 24, 16), num_clas
timm_modified/timm/models/densenet.py:163
↓ 3 callersMethod__init__
( self, dim, num_heads, head_dim_ratio=1., mlp_rat
timm_modified/timm/models/visformer.py:113
↓ 3 callersMethod__init__
( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int
timm_modified/timm/models/beit.py:265
↓ 3 callersMethod__init__
(self, feature_info: List[Dict], out_indices: Tuple[int])
timm_modified/timm/models/_features.py:28
↓ 3 callersMethod__init__
Args: in_chans: Number of input image channels. num_classes: Number of classes for classification head. g
timm_modified/timm/models/convnext.py:242
↓ 3 callersMethod__init__
(self, cfg, num_classes=1000, in_chans=3, drop_rate=0.0, global_pool='avg')
timm_modified/timm/models/selecsls.py:126
↓ 3 callersMethod__init__
(self, act_type='relu', gamma: float = 1.0, inplace=False)
timm_modified/timm/models/nfnet.py:64
↓ 3 callersMethod__init__
( self, img_size=224, patch_size=16, in_chans=3, n
timm_modified/timm/models/tnt.py:192
↓ 3 callersMethod__init__
( self, dim, num_heads=8, qkv_bias=False, attn_dro
timm_modified/timm/models/convit.py:43
↓ 3 callersMethod__init__
( self, in_chs: int, out_chs: Optional[int] = None, kernel_siz
timm_modified/timm/models/mobilevit.py:169
↓ 3 callersMethod__init__
Args: img_size: patch_size: in_chans: num_classes: global_pool: embe
timm_modified/timm/models/eva.py:342
↓ 3 callersMethod__init__
Args: cfg (RegNetCfg): Model architecture configuration in_chans (int): Number of input channels (default: 3)
timm_modified/timm/models/regnet.py:378
↓ 3 callersMethod__init__
(self)
src/rsgrpo/verl/single_controller/ray/base.py:475
↓ 3 callersMethod_build_model_optimizer
( self, model_config: ModelConfig, fsdp_config: FSDPConfig, optim_config: Opti
src/rsgrpo/verl/workers/fsdp_workers.py:149
↓ 3 callersFunction_cfg
(url='', **kwargs)
timm_modified/timm/models/efficientformer.py:512
↓ 3 callersFunction_cfg
(url='', **kwargs)
timm_modified/timm/models/convmixer.py:110
↓ 3 callersFunction_cfg
(url='', **kwargs)
timm_modified/timm/models/convit.py:391
↓ 3 callersFunction_cfg
(url='', **kwargs)
timm_modified/timm/models/sequencer.py:474
↓ 3 callersFunction_create_convit
(variant, pretrained=False, **kwargs)
timm_modified/timm/models/convit.py:384
↓ 3 callersFunction_create_convmixer
(variant, pretrained=False, **kwargs)
timm_modified/timm/models/convmixer.py:103
↓ 3 callersFunction_create_efficientformer
(variant, pretrained=False, **kwargs)
timm_modified/timm/models/efficientformer.py:536
↓ 3 callersFunction_create_efficientvit_large
(variant, pretrained=False, **kwargs)
timm_modified/timm/models/efficientvit_mit.py:1015
↓ 3 callersFunction_create_fx_model
(model, train=False)
timm_modified/tests/test_models.py:383
↓ 3 callersFunction_create_inception_next
(variant, pretrained=False, **kwargs)
timm_modified/timm/models/inception_next.py:408
↓ 3 callersFunction_create_nextvit
(variant, pretrained=False, **kwargs)
timm_modified/timm/models/nextvit.py:591
↓ 3 callersFunction_create_sequencer2d
(variant, pretrained=False, **kwargs)
timm_modified/timm/models/sequencer.py:459
↓ 3 callersMethod_fuse_bn_tensor
Method to fuse batchnorm layer with preceeding conv layer. Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95 Arg
timm_modified/timm/models/fastvit.py:229
↓ 3 callersMethod_fuse_bn_tensor
Method to fuse batchnorm layer with preceeding conv layer. Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
timm_modified/timm/models/byobnet.py:602
↓ 3 callersMethod_fuse_bn_tensor
Method to fuse batchnorm layer with preceeding conv layer. Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
timm_modified/timm/models/byobnet.py:791
↓ 3 callersFunction_gen_fbnetv3
FBNetV3 Paper: `FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining` - https://arxiv.org/abs/2006.02049 FIXME untes
timm_modified/timm/models/mobilenetv3.py:447
↓ 3 callersFunction_get_feature_info
(net, out_indices)
timm_modified/timm/models/_features.py:149
↓ 3 callersMethod_make_stage
(self, layer_config, num_in_chs, multi_scale_output=True)
timm_modified/timm/models/hrnet.py:693
↓ 3 callersMethod_make_transition_layer
(self, num_channels_pre_layer, num_channels_cur_layer)
timm_modified/timm/models/hrnet.py:651
↓ 3 callersFunction_mobilevitv2_block
(d, c, s, transformer_depth, patch_size=2, br=2.0, transformer_br=0.5)
timm_modified/timm/models/mobilevit.py:54
↓ 3 callersMethod_prepare_data
(self, data_args, shuffle_seed, cache_dir)
src/openmatch/dataset/train_dataset.py:47
↓ 3 callersFunction_run
(x, act_layer='')
timm_modified/tests/test_layers.py:32
↓ 3 callersFunction_set_extensions
(extensions)
timm_modified/timm/data/readers/img_extensions.py:10
↓ 3 callersFunction_setup_size
(size, error_msg="Please provide only two dimensions (h, w) for size.")
timm_modified/timm/data/transforms.py:104
↓ 3 callersFunction_test_basic_cases_template
(weight, bias, input, constructor, scheduler_constructors)
timm_modified/tests/test_optim.py:30
↓ 3 callersMethod_validate
(self)
src/rsgrpo/verl/trainer/ray_trainer.py:393
↓ 3 callersFunctionadaptive_avgmax_pool2d
(x, output_size: _int_tuple_2_t = 1)
timm_modified/timm/layers/adaptive_avgmax_pool.py:30
↓ 3 callersFunctionapply_rot_embed
(x: torch.Tensor, sin_emb, cos_emb)
timm_modified/timm/layers/pos_embed_sincos.py:198
↓ 3 callersFunctionapply_rotary_pos_emb
Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): Th
src/openmatch/modeling/weighted_selection/MiniCPMV20/modeling_minicpm.py:259
↓ 3 callersFunctionapply_rotary_pos_emb
Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): Th
src/openmatch/modeling/modeling_minicpmv/modeling_minicpm.py:259
↓ 3 callersFunctionconvert_dict_to_str
(data: Dict[str, Any])
src/rsgrpo/verl/utils/py_functional.py:118
↓ 3 callersFunctioncreate_aa
(aa_layer: Type[nn.Module], channels: int, stride: int = 2, enable: bool = True)
timm_modified/timm/models/resnet.py:33
↓ 3 callersFunctioncreate_feature_extractor
(model: nn.Module, return_nodes: Union[Dict[str, str], List[str]])
timm_modified/timm/models/_features_fx.py:95
↓ 3 callersMethodencode_query
(self, qry, **kwargs)
src/openmatch/modeling/dense_retrieval_model.py:230
↓ 3 callersMethodfilenames
(self, basename=False, absolute=False)
timm_modified/timm/data/dataset.py:80
↓ 3 callersFunctionfind_best_resize
(original_size, scale_resolution, patch_size, allow_upscale=False)
src/openmatch/modeling/weighted_selection/MiniCPMV20/modeling_minicpmv.py:579
↓ 3 callersFunctionfind_best_resize
(original_size, scale_resolution, patch_size, allow_upscale=False)
src/openmatch/modeling/modeling_minicpmv/modeling_minicpmv.py:544
↓ 3 callersFunctionfind_latest_ckpt
Find the latest checkpoint in the save path.
src/rsgrpo/verl/utils/checkpoint/checkpoint_manager.py:118
↓ 3 callersMethodfinish
(self)
src/rsgrpo/verl/utils/logger/logger.py:51
↓ 3 callersMethodfrom_single_dict
( cls, data: Dict[str, Union[torch.Tensor, NDArray]], meta_info: Optional[Dict[str, An
src/rsgrpo/verl/protocol.py:267
↓ 3 callersFunctiongather_seq_scatter_heads
A func to sync embedding input with alltoall in sequence parallel gather sequence dimension and scatter head dim: e.g. seq_dim: 1, head_d
src/rsgrpo/verl/utils/ulysses.py:63
↓ 3 callersMethodgenerate
( self, data_list=None, img_list=None, tokenizer=None, max_inp_length:
src/openmatch/modeling/weighted_selection/MiniCPMV20/modeling_minicpmv.py:274
↓ 3 callersMethodgenerate_sequences
(self, prompts: DataProto)
src/rsgrpo/verl/workers/fsdp_workers.py:550
↓ 3 callersFunctionget_arch_name
(model_name: str)
timm_modified/timm/models/_registry.py:39
↓ 3 callersFunctionget_channel_dim
(fmt: FormatT)
timm_modified/timm/layers/format.py:30
↓ 3 callersFunctionget_condconv_initializer
(initializer, num_experts, expert_shape)
timm_modified/timm/layers/cond_conv2d.py:21
↓ 3 callersFunctionget_init_weights_vit
(mode: str = 'jax', head_bias: float = 0.0)
timm_modified/timm/models/vision_transformer.py:754
↓ 3 callersMethodget_output
(self, device)
timm_modified/timm/models/_features.py:129
↓ 3 callersMethodget_params
Gets all parameters in all param_groups with gradients
timm_modified/timm/optim/adahessian.py:58
↓ 3 callersFunctionget_pretrained_cfg_value
Get a specific model default_cfg value by key. None if key doesn't exist.
timm_modified/timm/models/_registry.py:330
↓ 3 callersFunctionget_processor
Create a huggingface pretrained processor.
src/rsgrpo/verl/utils/tokenizer.py:40
↓ 3 callersMethodget_resource_pool
Get the resource pool of the worker.
src/rsgrpo/verl/trainer/ray_trainer.py:94
↓ 3 callersFunctionget_state_dict
(model, unwrap_fn=unwrap_model)
timm_modified/timm/utils/model.py:23
↓ 3 callersFunctionget_tokenizer
Create a huggingface pretrained tokenizer.
src/rsgrpo/verl/utils/tokenizer.py:21
↓ 3 callersFunctionget_ulysses_sequence_parallel_world_size
Get ulysses sequence parallel world size.
src/rsgrpo/verl/utils/ulysses.py:47
↓ 3 callersMethodindex_select
Select a subset of the DataProto via index. Args: index (list, ndarray, torch.Tensor): a list of indices to select. Retu
src/rsgrpo/verl/protocol.py:385
↓ 3 callersMethodinit_model
(self)
src/rsgrpo/verl/workers/fsdp_workers.py:353
↓ 3 callersFunctionis_numeric_data
(text)
src/openmatch/generation_utils.py:106
↓ 3 callersFunctionis_scriptable
()
timm_modified/timm/layers/config.py:75
↓ 3 callersFunctionis_transformers_version_greater_than
(content: str)
src/rsgrpo/verl/utils/py_functional.py:66
↓ 3 callersMethodlabel_name_to_description
(self, label: str, detailed: bool = False)
timm_modified/timm/data/imagenet_info.py:90
↓ 3 callersFunctionload_checkpoint
( model: torch.nn.Module, checkpoint_path: str, use_ema: bool = True, device:
timm_modified/timm/models/_helpers.py:62
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