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Functions283 in github.com/AILab-CVC/YOLO-World

⨍ Functions 283◇ Types & classes 69
↓ 1 callersFunctionparse_args
()
deploy/easydeploy/tools/export_onnx.py:26
↓ 1 callersFunctionparse_args
()
deploy/easydeploy/tools/image-demo.py:20
↓ 1 callersFunctionparse_args
()
deploy/easydeploy/tools/build_engine.py:99
↓ 1 callersFunctionparse_args
()
deploy/easydeploy/examples/main_onnxruntime.py:36
↓ 1 callersFunctionparse_args
()
demo/image_demo.py:39
↓ 1 callersFunctionparse_args
()
demo/image_prompt_demo.py:50
↓ 1 callersFunctionparse_args
()
demo/gradio_demo.py:50
↓ 1 callersFunctionparse_args
()
demo/video_demo.py:15
↓ 1 callersMethodparse_data_info
Parse raw annotation to target format. Args: raw_data_info (dict): Raw data information load from ``ann_file`` Returns:
yolo_world/datasets/yolov5_cc3m_grounding.py:57
↓ 1 callersFunctionpath_to_list
(path: str)
deploy/easydeploy/examples/main_onnxruntime.py:22
↓ 1 callersMethodpred_by_feat
(self, cls_scores: List[Tensor], bbox_preds: List[Tensor],
deploy/easydeploy/model/model.py:88
↓ 1 callersFunctionpreprocess
(image, size=(640, 640))
deploy/tflite_demo.py:52
↓ 1 callersFunctionpreprocess
(config)
deploy/easydeploy/tools/image-demo.py:36
↓ 1 callersFunctionreparameterize_head
(state_dict, embeds)
tools/reparameterize_yoloworld.py:31
↓ 1 callersFunctionreparameterize_neck
(state_dict, embeds, type='conv')
tools/reparameterize_yoloworld.py:81
↓ 1 callersFunctionsample_random_class_bboxes
(bbox_labels, batch_size, min_area=64 * 64)
yolo_world/models/detectors/yolo_world_image.py:116
↓ 1 callersMethodselect_nms
(self)
deploy/easydeploy/model/model.py:161
↓ 1 callersFunctionselect_nms_index
( scores: Tensor, boxes: Tensor, nms_index: Tensor, batch_size: int, keep_top_k: int = -1,
deploy/easydeploy/nms/ort_nms.py:45
↓ 1 callersFunctionsimple_bbox_decode
(points, pred_bboxes, stride)
deploy/tflite_demo.py:87
↓ 1 callersFunctionsoftmax
(x: ndarray, axis: int = -1)
deploy/easydeploy/examples/numpy_coder.py:8
↓ 1 callersFunctionsort_nms_index
first sort the nms_index by batch, and then sort by score in every image result, final apply keep_top_k strategy. In the process, we can also get
deploy/easydeploy/nms/ort_nms.py:11
↓ 1 callersMethodtransform
(self, image, bboxes_per_image)
yolo_world/models/detectors/yolo_world_image.py:52
↓ 1 callersFunctionvisualize
(image, bboxes, labels, scores, texts)
deploy/tflite_demo.py:99
FunctionHSigmoid__forward
(self, x: torch.Tensor)
deploy/easydeploy/model/backend.py:22
Method__call__
(self, image: ndarray, new_size: Union[List[int], Tuple[int]] = (640, 640),
deploy/easydeploy/examples/preprocess.py:41
Method__call__
(self, feats: Union[List, Tuple], conf_thres: float, num_la
deploy/easydeploy/examples/numpy_coder.py:28
Method__call__
(self, model: nn.Module)
yolo_world/engine/optimizers/yolow_v5_optim_constructor.py:150
Method__call__
(self, results: dict)
yolo_world/datasets/transformers/mm_transforms.py:29
Method__call__
(self, results: dict)
yolo_world/datasets/transformers/mm_transforms.py:113
Method__getitem__
(self, idx)
yolo_world/datasets/mm_dataset.py:71
Method__init__
(self)
deploy/easydeploy/tools/image-demo.py:45
Method__init__
( self, checkpoint: Union[str, Path], opt_shape: Union[Tuple, List] = (1,
deploy/easydeploy/tools/build_engine.py:19
Method__init__
(self, orin_Focus: nn.Module)
deploy/easydeploy/backbone/focus.py:10
Method__init__
(self, orin_Focus: nn.Module)
deploy/easydeploy/backbone/focus.py:61
Method__init__
(self, *args, **kwargs)
deploy/easydeploy/backbone/common.py:8
Method__init__
(self, weight: Union[str, Path], device: Optional[torch.device])
deploy/easydeploy/model/backendwrapper.py:142
Method__init__
(self, baseModel: nn.Module, backend: MMYOLOBackend, postpr
deploy/easydeploy/model/model.py:27
Method__init__
(self, model_type: ModelType)
deploy/easydeploy/examples/preprocess.py:11
Method__init__
(self, model_type: ModelType, model_only: bool = False)
deploy/easydeploy/examples/numpy_coder.py:20
Method__init__
(self, optim_wrapper_cfg: dict, paramwise_cfg: Optional[dict] = None)
yolo_world/engine/optimizers/yolow_v5_optim_constructor.py:22
Method__init__
(self, dataset: Union[BaseDataset, dict], class_text_path: str = None,
yolo_world/datasets/mm_dataset.py:100
Method__init__
(self, pre_transform: Optional[Sequence[str]] = None, prob: float = 1.0,
yolo_world/datasets/transformers/mm_mix_img_transforms.py:44
Method__init__
(self, img_scale: Tuple[int, int] = (640, 640), bbox_clip_border: bool = Tru
yolo_world/datasets/transformers/mm_mix_img_transforms.py:592
Method__init__
(self, alpha: float = 32.0, beta: float = 32.0, pre_transfo
yolo_world/datasets/transformers/mm_mix_img_transforms.py:861
Method__init__
(self, img_scale: Tuple[int, int] = (640, 640), ratio_range: Tuple[float, fl
yolo_world/datasets/transformers/mm_mix_img_transforms.py:1014
Method__init__
(self, text_path: str = None, prompt_format: str = '{}', nu
yolo_world/datasets/transformers/mm_transforms.py:13
Method__init__
(self, text_path: str = None, prompt_format: str = '{}', mu
yolo_world/datasets/transformers/mm_transforms.py:103
Method__init__
(self, in_channels: List[int], out_channels: Union[List[int], int],
yolo_world/models/necks/yolo_world_pafpn.py:152
Method__init__
(self, model_name: str, frozen_modules: Sequence[str] = (),
yolo_world/models/backbones/mm_backbone.py:60
Method__init__
(self, text_embed_path: str = "", test_embed_path: str = None,
yolo_world/models/backbones/mm_backbone.py:146
Method__init__
(self, image_model: ConfigType, text_model: ConfigType, fro
yolo_world/models/backbones/mm_backbone.py:193
Method__init__
(self, *args, embed_dims: int, proto_channels: int,
yolo_world/models/dense_heads/yolo_world_seg_head.py:31
Method__init__
(self, embed_dims: int, init_cfg: OptConfigType = None, use
yolo_world/models/dense_heads/yolo_world_head.py:35
Method__init__
(self, embed_dims: int, norm_cfg: ConfigDict, init_cfg: Opt
yolo_world/models/dense_heads/yolo_world_head.py:75
Method__init__
(self, embed_dims: int, num_guide_embeds: int, norm_cfg: Co
yolo_world/models/dense_heads/yolo_world_head.py:117
Method__init__
(self, *args, embed_dims: int, use_bn_head: bool = False,
yolo_world/models/dense_heads/yolo_world_head.py:142
Method__init__
(self, *args, embed_dims: int, num_guide: int,
yolo_world/models/dense_heads/yolo_world_head.py:294
Method__init__
(self, *args, non_blocking: Optional[bool] = True, **kwargs)
yolo_world/models/data_preprocessors/data_preprocessor.py:21
Method__init__
(self, *args, mm_neck: bool = False, num_train_classes=80,
yolo_world/models/detectors/yolo_world.py:111
Method__init__
(self, *args, mm_neck: bool = False, num_train_classes=80,
yolo_world/models/detectors/yolo_world_image.py:167
Method__init__
(self, in_channels: int, out_channels: int, embed_channels:
yolo_world/models/layers/yolo_bricks.py:104
Method__init__
(self, in_channels: int, out_channels: int, embed_channels:
yolo_world/models/layers/yolo_bricks.py:181
Method__init__
( self, in_channels: int, out_channels: int, guide_channels: i
yolo_world/models/layers/yolo_bricks.py:260
Method__init__
( self, in_channels: int, out_channels: int, guide_channels: i
yolo_world/models/layers/yolo_bricks.py:316
Method__init__
( self, in_channels: int, out_channels: int, guide_channels: i
yolo_world/models/layers/yolo_bricks.py:373
Method__init__
(self, image_channels: List[int], text_channels: int, embed
yolo_world/models/layers/yolo_bricks.py:429
Method__init__
(self, in_channels: int, out_channels: int, guide_channels:
yolo_world/models/layers/yolo_bricks.py:509
Method__init__
( self, in_channels: int, out_channels: int, guide_channels: i
yolo_world/models/layers/yolo_bricks.py:554
Method__init__
(self, dim: int = 0, reduction: str = 'mean', loss_weight:
yolo_world/models/losses/dynamic_loss.py:14
Method__init__
(self, num_classes: int, topk: int = 13, alpha: float = 1,
yolo_world/models/assigner/task_aligned_assigner.py:11
Method__len__
(self)
yolo_world/datasets/mm_dataset.py:89
Method__repr__
(self)
yolo_world/datasets/transformers/mm_mix_img_transforms.py:504
Method__repr__
(self)
yolo_world/datasets/transformers/mm_mix_img_transforms.py:800
Method__repr__
(self)
yolo_world/datasets/transformers/mm_mix_img_transforms.py:1165
Method_forward
Network forward process. Usually includes backbone, neck and head forward without any post-processing.
yolo_world/models/detectors/yolo_world.py:63
Method_forward
Network forward process. Usually includes backbone, neck and head forward without any post-processing.
yolo_world/models/detectors/yolo_world.py:196
Method_forward
Network forward process. Usually includes backbone, neck and head forward without any post-processing.
yolo_world/models/detectors/yolo_world_image.py:217
Method_init_layers
initialize conv layers in YOLOv8 head.
yolo_world/models/dense_heads/yolo_world_seg_head.py:60
Method_init_layers
initialize conv layers in YOLOv8 head.
yolo_world/models/dense_heads/yolo_world_head.py:167
Method_join_prefix
Join ``self.data_root`` with ``self.data_prefix`` and ``self.ann_file``.
yolo_world/datasets/yolov5_cc3m_grounding.py:169
Method_join_prefix
Join ``self.data_root`` with ``self.data_prefix`` and ``self.ann_file``.
yolo_world/datasets/yolov5_mixed_grounding.py:173
Methodaug_test
Test function with test time augmentation.
yolo_world/models/dense_heads/yolo_world_seg_head.py:336
Methodaug_test
Test function with test time augmentation.
yolo_world/models/dense_heads/yolo_world_head.py:419
Methodbuild_bottom_up_layer
build bottom up layer. Args: idx (int): layer idx. Returns: nn.Module: The bottom up layer.
yolo_world/models/necks/yolo_world_pafpn.py:78
Methodbuild_top_down_layer
build top down layer. Args: idx (int): layer idx. Returns: nn.Module: The top down layer.
yolo_world/models/necks/yolo_world_pafpn.py:50
Functionefficient_nms
Wrapper function for `_efficient_nms`.
deploy/easydeploy/nms/trt_nms.py:219
Functionexport_model
(runner, text, max_num_boxes, score_thr, nms_thr)
demo/gradio_demo.py:125
Methodfilter_data
Filter annotations according to filter_cfg. Returns: List[dict]: Filtered results.
yolo_world/datasets/yolov5_cc3m_grounding.py:139
Methodfilter_data
Filter annotations according to filter_cfg. Returns: List[dict]: Filtered results.
yolo_world/datasets/yolov5_mixed_grounding.py:143
Methodforward
(self, x)
deploy/easydeploy/tools/image-demo.py:48
Methodforward
(self, x: Tensor)
deploy/easydeploy/backbone/focus.py:14
Methodforward
(self, x: Tensor)
deploy/easydeploy/backbone/focus.py:32
Methodforward
(self, x: Tensor)
deploy/easydeploy/backbone/focus.py:74
Methodforward
(self, x: Tensor)
deploy/easydeploy/backbone/common.py:11
Methodforward
(self, *inputs)
deploy/easydeploy/model/backendwrapper.py:103
Methodforward
(self, *inputs)
deploy/easydeploy/model/backendwrapper.py:184
Methodforward
(self, inputs: Tensor)
deploy/easydeploy/model/model.py:175
Methodforward
Non-Maximum Suppression (NMS) implementation. Args: boxes (Tensor): Bounding boxes of shape (batch_size, num_boxes, 4).
deploy/easydeploy/nms/ort_nms.py:103
Methodforward
( ctx, boxes: Tensor, scores: Tensor, background_class: int = -1, box_
deploy/easydeploy/nms/trt_nms.py:13
Methodforward
( ctx, boxes: Tensor, scores: Tensor, plugin_version: str = '1', share
deploy/easydeploy/nms/trt_nms.py:65
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