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Functions260 in github.com/Curt-Park/yolo-world-with-efficientvit-sam

⨍ Functions 260◇ Types & classes 51
↓ 32 callersFunctionbuild_kwargs_from_config
(config: dict, target_func: callable)
efficientvit/models/utils/network.py:61
↓ 17 callersFunctionval2tuple
(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1)
efficientvit/models/utils/list.py:43
↓ 13 callersMethodload_state_dict
(self, state_dict: dict[float, dict[str, torch.Tensor]])
efficientvit/apps/utils/ema.py:47
↓ 11 callersMethod__init__
( self, in_channels: int, out_channels: int, kernel_size=3, stride=1,
efficientvit/models/nn/ops.py:148
↓ 8 callersMethodstate_dict
(self)
efficientvit/apps/utils/ema.py:44
↓ 5 callersFunctionbuild_efficientvit_sam
( image_encoder: EfficientViTSamImageEncoder, image_size: int )
efficientvit/models/efficientvit/sam.py:520
↓ 5 callersMethodis_alive
(self)
efficientvit/apps/data_provider/random_resolution/_data_worker.py:53
↓ 5 callersFunctionis_master
()
efficientvit/apps/utils/dist.py:45
↓ 5 callersMethodupdate
(self, val: torch.Tensor or int or float, delta_n=1)
efficientvit/apps/utils/metric.py:23
↓ 4 callersFunctionbuild_act
(name: str, **kwargs)
efficientvit/models/nn/act.py:24
↓ 4 callersMethodbuild_dataloader
( self, dataset: any or None, batch_size: int, n_worker: int, drop_las
efficientvit/apps/data_provider/base.py:126
↓ 4 callersFunctionget_device
(model: nn.Module)
efficientvit/models/utils/network.py:28
↓ 4 callersFunctionis_parallel
(model: nn.Module)
efficientvit/models/utils/network.py:22
↓ 4 callersFunctionload_state_dict_from_file
( file: str, only_state_dict=True )
efficientvit/models/utils/network.py:70
↓ 4 callersMethodstep
(self)
efficientvit/apps/trainer/run_config.py:110
↓ 3 callersMethod__init__
( self, dataset: Dataset[T_co], batch_size: Optional[int] = 1, shuffle: Option
efficientvit/apps/data_provider/random_resolution/_data_loader.py:221
↓ 3 callersMethod__init__
(self, size: int, fill: float = 0, pad_mode="corner")
efficientvit/models/efficientvit/sam.py:46
↓ 3 callersMethod_mark_worker_as_unavailable
(self, worker_id, shutdown=False)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:1493
↓ 3 callersMethod_reset
(self, loader, first_iter=False)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:681
↓ 3 callersMethod_try_get_data
(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:1230
↓ 3 callersMethod_try_put_index
(self)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:1466
↓ 3 callersMethodbuild_local_block
( in_channels: int, out_channels: int, stride: int, expand_ratio: float,
efficientvit/models/efficientvit/backbone.py:114
↓ 3 callersMethodbuild_local_block
( block: str, in_channels: int, out_channels: int, stride: int, expand
efficientvit/models/efficientvit/backbone.py:288
↓ 3 callersFunctionbuild_norm
(name="bn2d", num_features=None, **kwargs)
efficientvit/models/nn/norm.py:31
↓ 3 callersMethodcreate_fetcher
(kind, dataset, auto_collation, collate_fn, drop_last)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:64
↓ 3 callersFunctiondist_barrier
()
efficientvit/apps/utils/dist.py:49
↓ 3 callersFunctionefficientvit_backbone_l1
(**kwargs)
efficientvit/models/efficientvit/backbone.py:348
↓ 3 callersFunctionefficientvit_backbone_l2
(**kwargs)
efficientvit/models/efficientvit/backbone.py:357
↓ 3 callersMethodforward_main
(self, x: torch.Tensor)
efficientvit/models/nn/ops.py:516
↓ 3 callersFunctionget_dist_rank
()
efficientvit/apps/utils/dist.py:37
↓ 3 callersFunctionget_dist_size
()
efficientvit/apps/utils/dist.py:41
↓ 3 callersFunctionlist_sum
(x: list)
efficientvit/models/utils/list.py:16
↓ 3 callersFunctionload_config
Load a yaml file.
efficientvit/apps/utils/misc.py:102
↓ 3 callersFunctionparse_image_size
(size: int or str)
efficientvit/apps/data_provider/base.py:17
↓ 3 callersFunctionrandom_drop_data
(dataset, drop_size: int, seed: int, keys=("samples",))
efficientvit/apps/data_provider/base.py:25
↓ 3 callersFunctionsync_tensor
( tensor: torch.Tensor or float, reduce="mean" )
efficientvit/apps/utils/dist.py:57
↓ 3 callersFunctionval2list
(x: list or tuple or any, repeat_time=1)
efficientvit/models/utils/list.py:37
↓ 2 callersMethod_get_data
(self)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:1380
↓ 2 callersMethod_get_iterator
(self)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:403
↓ 2 callersMethod_next_index
(self)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:693
↓ 2 callersMethod_process_data
(self, data)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:1486
↓ 2 callersFunction_share_dist_seed
(generator, pg)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:117
↓ 2 callersMethod_shutdown_workers
(self)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:1521
↓ 2 callersMethod_sync
(self, val: torch.Tensor or int or float)
efficientvit/apps/utils/metric.py:20
↓ 2 callersMethodapply_coords
(self, coords: np.ndarray, im_size=None)
efficientvit/models/efficientvit/sam.py:263
↓ 2 callersMethodcheck_worker_number_rationality
(self)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:534
↓ 2 callersFunctionefficientvit_backbone_b0
(**kwargs)
efficientvit/models/efficientvit/backbone.py:153
↓ 2 callersFunctionefficientvit_backbone_b1
(**kwargs)
efficientvit/models/efficientvit/backbone.py:163
↓ 2 callersFunctionefficientvit_backbone_b2
(**kwargs)
efficientvit/models/efficientvit/backbone.py:173
↓ 2 callersFunctionefficientvit_backbone_b3
(**kwargs)
efficientvit/models/efficientvit/backbone.py:183
↓ 2 callersMethodget_candidates
()
efficientvit/apps/data_provider/random_resolution/controller.py:27
↓ 2 callersFunctionget_dist_local_rank
()
efficientvit/apps/utils/dist.py:53
↓ 2 callersMethodget_preprocess_shape
Compute the output size given input size and target long side length.
efficientvit/models/efficientvit/sam.py:87
↓ 2 callersFunctionget_same_padding
(kernel_size: int or tuple[int, ...])
efficientvit/models/utils/network.py:32
↓ 2 callersFunctionhash
(value)
efficientvit/apps/data_provider/random_resolution/_data_worker.py:194
↓ 2 callersFunctionparse_with_yaml
(config_str: str)
efficientvit/apps/utils/misc.py:19
↓ 2 callersFunctionpartial_update_config
(config: dict, partial_config: dict)
efficientvit/apps/utils/misc.py:63
↓ 2 callersMethodreset_image
(self)
efficientvit/models/efficientvit/sam.py:257
↓ 2 callersFunctionresize
( x: torch.Tensor, size: any or None = None, scale_factor: list[float] or None = None, mode: s
efficientvit/models/utils/network.py:40
↓ 2 callersFunctiontorch_random_choices
( src_list: list[any], generator: torch.Generator or None = None, k=1, weight_list: list[float
efficientvit/models/utils/random.py:48
↓ 2 callersMethodwrite_log
(self, log_str, prefix="valid", print_log=True, mode="a")
efficientvit/apps/trainer/base.py:49
↓ 1 callersMethod__call__
( self, feed_dict: dict or np.ndarray or Image.Image )
efficientvit/apps/data_provider/augment/color_aug.py:17
↓ 1 callersMethod__init__
( self, config: dict[str, any], mean: tuple[float, float, float], key="data" )
efficientvit/apps/data_provider/augment/color_aug.py:62
↓ 1 callersMethod__init__
( self, in_channels: int, width_list: list[int], n_classes=1000, dropo
efficientvit/models/efficientvit/cls.py:28
↓ 1 callersMethod__init__
( self, width_list: list[int], depth_list: list[int], in_channels=3, d
efficientvit/models/efficientvit/backbone.py:28
↓ 1 callersMethod__init__
( self, fid_list: list[str], in_channel_list: list[int], stride_list: list[int
efficientvit/models/efficientvit/seg.py:27
↓ 1 callersMethod__next__
(self)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:699
↓ 1 callersFunction_generate_state
(base_seed, worker_id)
efficientvit/apps/data_provider/random_resolution/_data_worker.py:179
↓ 1 callersFunction_get_distributed_settings
()
efficientvit/apps/data_provider/random_resolution/_data_loader.py:91
↓ 1 callersMethod_next_data
(self)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:696
↓ 1 callersMethod_reset
(self, loader, first_iter=False)
efficientvit/apps/data_provider/random_resolution/_data_loader.py:1193
↓ 1 callersMethod_train_one_epoch
(self, epoch: int)
efficientvit/apps/trainer/base.py:284
↓ 1 callersMethod_try_squeeze
(self, x: torch.Tensor)
efficientvit/models/nn/ops.py:120
↓ 1 callersMethod_validate
(self, model, data_loader, epoch)
efficientvit/apps/trainer/base.py:165
↓ 1 callersMethodapply_boxes
(self, boxes: np.ndarray, im_size=None)
efficientvit/models/efficientvit/sam.py:271
↓ 1 callersMethodapply_image
Expects a numpy array with shape HxWxC in uint8 format.
efficientvit/models/efficientvit/sam.py:77
↓ 1 callersMethodassign_active_image_size
(self, new_size: int or tuple[int, int])
efficientvit/apps/data_provider/base.py:168
↓ 1 callersMethodaug_image
(self, image: Image.Image)
efficientvit/apps/data_provider/augment/color_aug.py:14
↓ 1 callersMethodbuild_datasets
(self)
efficientvit/apps/data_provider/base.py:123
↓ 1 callersFunctionbuild_optimizer
( net_params, optimizer_name: str, optimizer_params: dict or None, init_lr: float )
efficientvit/apps/utils/opt.py:21
↓ 1 callersMethodbuild_optimizer
r"""require setting 'batch_per_epoch' before building optimizer & lr_scheduler
efficientvit/apps/trainer/run_config.py:63
↓ 1 callersMethodbuild_sub_train_loader
(self, n_samples: int, batch_size: int)
efficientvit/apps/data_provider/base.py:192
↓ 1 callersMethodbuild_train_transform
(self, image_size: tuple[int, int] or None = None)
efficientvit/apps/data_provider/base.py:120
↓ 1 callersMethodbuild_valid_transform
(self, image_size: tuple[int, int] or None = None)
efficientvit/apps/data_provider/base.py:117
↓ 1 callersFunctioncreate_sam_model
( name: str, pretrained=True, weight_url: str or None = None, **kwargs )
efficientvit/sam_model_zoo.py:26
↓ 1 callersFunctiondist_init
()
efficientvit/apps/utils/dist.py:23
↓ 1 callersFunctiondump_config
Dump a config file
efficientvit/apps/utils/misc.py:108
↓ 1 callersFunctionefficientvit_backbone_l0
(**kwargs)
efficientvit/models/efficientvit/backbone.py:339
↓ 1 callersFunctionefficientvit_backbone_l3
(**kwargs)
efficientvit/models/efficientvit/backbone.py:366
↓ 1 callersMethodforward
( self, feed_dict: dict or np.ndarray or Image.Image )
efficientvit/apps/data_provider/augment/color_aug.py:55
↓ 1 callersFunctionget_interpolate
(name: str)
efficientvit/apps/data_provider/random_resolution/controller.py:45
↓ 1 callersFunctioninit_modules
(model: nn.Module or list[nn.Module], init_type="trunc_normal")
efficientvit/apps/utils/init.py:12
↓ 1 callersFunctionlist_join
(x: list, sep="\t", format_str="%s")
efficientvit/models/utils/list.py:33
↓ 1 callersFunctionlist_mean
(x: list)
efficientvit/models/utils/list.py:20
↓ 1 callersMethodload_model
(self, model_fname=None)
efficientvit/apps/trainer/base.py:88
↓ 1 callersFunctionmix
(x, y)
efficientvit/apps/data_provider/random_resolution/_data_worker.py:202
↓ 1 callersFunctionnew_forward
(bn, mean_est, var_est)
efficientvit/models/nn/norm.py:67
↓ 1 callersMethodpostprocess_masks
( self, masks: torch.Tensor, input_size: tuple[int, ...], original_size: tuple
efficientvit/models/efficientvit/sam.py:225
↓ 1 callersMethodpredict
Predict masks for the given input prompts, using the currently set image. Arguments: point_coords (np.ndarray or None): A
efficientvit/models/efficientvit/sam.py:297
↓ 1 callersMethodpredict_torch
Predict masks for the given input prompts, using the currently set image. Input prompts are batched torch tensors and are expected to
efficientvit/models/efficientvit/sam.py:381
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