mmdet.apis
mmdet.datasets
datasets
api_wrappers
samplers
transforms
mmdet.engine
hooks
optimizers
runner
schedulers
mmdet.evaluation
functional
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mmdet.evaluation.functional.average_precision(recalls, precisions, mode=‘area’)[源代码]
Calculate average precision (for single or multiple scales).参数recalls (ndarray) – shape (num_scales, num_dets) or (num_dets, )precisions (ndarray) – shape (num_scales, num_dets) or (num_dets, )mode (str) – ‘area’ or ‘11points’, ‘area’ means calculating the area under precision-recall curve, ‘11points’ means calculating the average precision of recalls at [0, 0.1, …, 1]返回calculated average precision返回类型float or ndarray
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mmdet.evaluation.functional.bbox_overlaps(bboxes1, bboxes2, mode=‘iou’, eps=1e-06, use_legacy_coordinate=False)[源代码]
Calculate the ious between each bbox of bboxes1 and bboxes2.参数bboxes1 (ndarray) – Shape (n, 4)bboxes2 (ndarray) – Shape (k, 4)mode (str) – IOU (intersection over union) or IOF (intersection over foreground)use_legacy_coordinate (bool) – Whether to use coordinate system in mmdet v1.x. which means width, height should be calculated as ‘x2 - x1 + 1` and ‘y2 - y1 + 1’ respectively. Note when function is used in VOCDataset, it should be True to align with the official implementation http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar Default: False.返回Shape (n, k)返回类型ious (ndarray)
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mmdet.evaluation.functional.cityscapes_classes() → list[源代码]
Class names of Cityscapes.
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mmdet.evaluation.functional.coco_classes() → list[源代码]
Class names of COCO.
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mmdet.evaluation.functional.coco_panoptic_classes() → list[源代码]
Class names of COCO panoptic.
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mmdet.evaluation.functional.eval_map(det_results, annotations, scale_ranges=None, iou_thr=0.5, ioa_thr=None, dataset=None, logger=None, tpfp_fn=None, nproc=4, use_legacy_coordinate=False, use_group_of=False, eval_mode=‘area’)[源代码]
Evaluate mAP of a dataset.
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mmdet.evaluation.functional.eval_recalls(gts, proposals, proposal_nums=None, iou_thrs=0.5, logger=None, use_legacy_coordinate=False)[源代码]
Calculate recalls.
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mmdet.evaluation.functional.get_classes(dataset) → list[源代码]
Get class names of a dataset.
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mmdet.evaluation.functional.voc_classes() → list[源代码]
Class names of PASCAL VOC.
metrics
mmdet.models
backbones
data_preprocessors
dense_heads
detectors
layers
losses
necks
roi_heads
seg_heads
task_modules
test_time_augs
utils
mmdet.structures
structures
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classmmdet.structures.DetDataSample
A data structure interface of MMDetection. They are used as interfaces between different components.
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classmmdet.structures.ReIDDataSample
A data structure interface of ReID task.
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classmmdet.structures.TrackDataSample
A data structure interface of tracking task in MMDetection.
bbox
mask
mmdet.testing
mmdet.visualization
mmdet.utils
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classmmdet.utils.AvoidOOM(to_cpu=True, test=False)
Try to convert inputs to FP16 and CPU if got a PyTorch’s CUDA Out of Memory error.
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mmdet.utils.all_reduce_dict(py_dict, op=‘sum’, group=None, to_float=True)
Apply all reduce function for python dict object.
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mmdet.utils.allreduce_grads(params, coalesce=True, bucket_size_mb=- 1)
Allreduce gradients.
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mmdet.utils.collect_env()
Collect the information of the running environments.
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mmdet.utils.compat_cfg(cfg)
This function would modify some filed to keep the compatibility of config.
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mmdet.utils.find_latest_checkpoint(path, suffix=‘pth’)
Find the latest checkpoint from the working directory.
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mmdet.utils.register_all_modules(init_default_scope: bool = True) → None
Register all modules in mmdet into the registries.
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mmdet.utils.sync_random_seed(seed=None, device=‘cuda’)
Make sure different ranks share the same seed.
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