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Functions1,452 in github.com/MasterBin-IIAU/Unicorn

⨍ Functions 1,452◇ Types & classes 215↳ Endpoints 16
Method__init__
(self)
exps/default/unicorn_track_tiny_mot_only.py:11
Method__init__
(self, window_size=50)
unicorn/utils/metric.py:56
Method__init__
(self, window_size=20)
unicorn/utils/metric.py:101
Method__init__
Args: model (nn.Module): model to apply EMA. decay (float): ema decay reate. updates (int): counter of EM
unicorn/utils/ema.py:33
Method__init__
Args: level(str): log level string of loguru. Default value: "INFO". caller_names(tuple): caller names of redirected
unicorn/utils/logger.py:32
Method__init__
Supported lr schedulers: [cos, warmcos, multistep] Args: lr (float): learning rate. iters_per_peoch (int): n
unicorn/utils/lr_scheduler.py:10
Method__init__
(self, exp, args)
unicorn/core/trainer.py:51
Method__init__
(self, *args, mosaic=True, **kwargs)
unicorn/data/samplers.py:21
Method__init__
(self, loader)
unicorn/data/data_prefetcher.py:39
Method__init__
(self, loader)
unicorn/data/data_prefetcher.py:70
Method__init__
(self, loader)
unicorn/data/data_prefetcher.py:104
Method__init__
(self, loader)
unicorn/data/data_prefetcher.py:139
Method__init__
(self, max_labels=50, flip_prob=0.5, hsv_prob=1.0, legacy=False)
unicorn/data/data_augment.py:264
Method__init__
(self, max_labels=50, flip_prob=0.5, hsv_prob=1.0, legacy=False)
unicorn/data/data_augment.py:350
Method__init__
(self, swap=(2, 0, 1), legacy=False)
unicorn/data/data_augment.py:455
Method__init__
(self, max_labels=50, flip_prob=0.5, hsv_prob=1.0, legacy=False)
unicorn/data/data_augment.py:602
Method__init__
(self, max_labels=50, flip_prob=0.5, hsv_prob=1.0, legacy=False, d_rate=1/4)
unicorn/data/data_augment.py:692
Method__init__
(self, max_labels=50, flip_prob=0.5, hsv_prob=1.0, legacy=False, d_rate=1/4)
unicorn/data/data_augment.py:793
Method__init__
(self, *args, **kwargs)
unicorn/data/dataloading.py:48
Method__init__
Args: dataset(Dataset) : Pytorch dataset object. img_size (tuple): mosaic (bool): enable mosaic augmenta
unicorn/data/datasets/mosaicdetection_uni.py:139
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/bdd_omni.py:20
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/mot.py:16
Method__init__
args: root - The path to the TrackingNet folder, containing the training sets. image_loader (jpeg4py_loader) -
unicorn/data/datasets/tracking_net.py:41
Method__init__
(self, mask, class_id, track_id)
unicorn/data/datasets/mots_mot.py:21
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/mots_mot.py:74
Method__init__
args: root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k image_lo
unicorn/data/datasets/got10k.py:15
Method__init__
( self, img_size=(416, 416), preproc=None, min_sz=0, box_only=False
unicorn/data/datasets/youtube_vos.py:18
Method__init__
Args: dataset(Dataset) : Pytorch dataset object. img_size (tuple): mosaic (bool): enable mosaic augmenta
unicorn/data/datasets/mosaicdetection.py:251
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/coco_inst.py:21
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/coco_mots.py:23
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/bdd_omni_mots.py:20
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/coco_sot.py:21
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/saliency.py:16
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/mot_omni.py:18
Method__init__
(self, img_size, datasets, p_datasets, samples_per_epoch, mode="joint", fix=False, fix_id=None)
unicorn/data/datasets/omni_data.py:40
Method__init__
args: root - path to the lasot dataset. image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (ht
unicorn/data/datasets/lasot.py:15
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/davis.py:18
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/bdd.py:19
Method__init__
(self, class_to_ind=None, keep_difficult=True)
unicorn/data/datasets/voc.py:38
Method__init__
( self, data_dir, image_sets=[("2007", "trainval"), ("2012", "trainval")], img
unicorn/data/datasets/voc.py:102
Method__init__
COCO dataset initialization. Annotation data are read into memory by COCO API. Args: data_dir (str): dataset root directo
unicorn/data/datasets/coco.py:21
Method__init__
(self, datasets)
unicorn/data/datasets/datasets_wrapper.py:13
Method__init__
(self, datasets)
unicorn/data/datasets/datasets_wrapper.py:35
Method__init__
(self, tlwh, score)
unicorn/tracker/byte_tracker.py:15
Method__init__
(self, args, frame_rate=30)
unicorn/tracker/byte_tracker.py:148
Method__init__
(self, init_score_thr=0.8, obj_score_thr=0.5, match_score_t
unicorn/tracker/quasi_dense_embed_tracker.py:11
Method__init__
(self)
unicorn/tracker/kalman_filter.py:40
Method__init__
(self)
unicorn/exp/base_exp.py:20
Method__init__
(self)
unicorn/exp/unicorn_track_mask.py:32
Method__init__
(self)
unicorn/exp/unicorn_det_mask.py:23
Method__init__
(self)
unicorn/exp/unicorn_track.py:31
Method__init__
(self)
unicorn/exp/unicorn_det.py:22
Method__init__
Args: dataloader (Dataloader): evaluate dataloader. img_size (int): image size after preprocess. images are resized
unicorn/evaluators/bdd_evaluator.py:36
Method__init__
(self, data_root, seq_name, data_type)
unicorn/evaluators/evaluation.py:10
Method__init__
Args: dataloader (Dataloader): evaluate dataloader. img_size (int): image size after preprocess. images are resized
unicorn/evaluators/coco_inst_evaluator.py:44
Method__init__
Args: dataloader (Dataloader): evaluate dataloader. img_size (int): image size after preprocess. images are resized
unicorn/evaluators/voc_evaluator.py:24
Method__init__
Args: dataloader (Dataloader): evaluate dataloader. img_size (int): image size after preprocess. images are resized
unicorn/evaluators/mot_evaluator.py:81
Method__init__
Args: dataloader (Dataloader): evaluate dataloader. img_size (int): image size after preprocess. images are resized
unicorn/evaluators/coco_evaluator.py:33
Method__init__
Args: act (str): activation type of conv. Defalut value: "silu". depthwise (bool): whether apply depthwise conv in co
unicorn/models/yolo_head_det_mask.py:35
Method__init__
mhs_max_inst: max number of instances used in mhs
unicorn/models/unicorn.py:21
Method__init__
args: net - The network to train objective - The loss function
unicorn/models/unicorn.py:472
Method__init__
Args: act (str): activation type of conv. Defalut value: "silu". depthwise (bool): whether apply depthwise conv in co
unicorn/models/unicorn_head_mask.py:23
Method__init__
(self, d_model=256, nhead=8, num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,
unicorn/models/deformable_transformer.py:22
Method__init__
(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu",
unicorn/models/deformable_transformer.py:93
Method__init__
(self, encoder_layer, num_layers)
unicorn/models/deformable_transformer.py:135
Method__init__
Args: act (str): activation type of conv. Defalut value: "silu". depthwise (bool): whether apply depthwise conv in co
unicorn/models/yolo_head_det.py:31
Method__init__
(self, reduction="none", loss_type="iou")
unicorn/models/losses.py:10
Method__init__
(self, backbone=None, head=None)
unicorn/models/yolox.py:60
Method__init__
(self, encoder_layer, num_layers, norm=None)
unicorn/models/transformer_encoder.py:12
Method__init__
(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before
unicorn/models/transformer_encoder.py:51
Method__init__
Args: act (str): activation type of conv. Defalut value: "silu". depthwise (bool): whether apply depthwise conv in co
unicorn/models/unicorn_head.py:20
Method__init__
(self, num_pos_feats=256, sz=20)
unicorn/models/position_encoding.py:14
Method__init__
Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels
unicorn/models/ops/modules/ms_deform_attn.py:31
Method__init__
( self, dep_mul, wid_mul, out_features=("dark3", "dark4", "dark5"), de
unicorn/models/backbone/darknet.py:98
Method__init__
( self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu" )
unicorn/models/backbone/network_blocks.py:32
Method__init__
(self, in_channels, out_channels, ksize, stride=1, act="silu")
unicorn/models/backbone/network_blocks.py:60
Method__init__
( self, in_channels, out_channels, shortcut=True, expansion=0.5,
unicorn/models/backbone/network_blocks.py:81
Method__init__
(self, in_channels: int)
unicorn/models/backbone/network_blocks.py:107
Method__init__
( self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation="silu" )
unicorn/models/backbone/network_blocks.py:125
Method__init__
Args: in_channels (int): input channels. out_channels (int): output channels. n (int): number of Bottlene
unicorn/models/backbone/network_blocks.py:150
Method__init__
(self, in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0., layer_s
unicorn/models/backbone/convnext.py:71
Method__init__
(self, normalized_shape, eps=1e-6, data_format="channels_last")
unicorn/models/backbone/convnext.py:166
Method__init__
(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.)
unicorn/models/backbone/swin_transformer.py:85
Method__init__
(self, dim, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=True, qk_scale=Non
unicorn/models/backbone/swin_transformer.py:172
Method__init__
(self, dim, norm_layer=nn.LayerNorm)
unicorn/models/backbone/swin_transformer.py:264
Method__init__
(self, dim, depth, num_heads, window_size=
unicorn/models/backbone/swin_transformer.py:319
Method__init__
(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)
unicorn/models/backbone/swin_transformer.py:414
Method__init__
(self, pretrain_img_size=224, patch_size=4, in_chans=3,
unicorn/models/backbone/swin_transformer.py:476
Method__init__
( self, depth=1.0, width=1.0, in_features=("dark3", "dark4", "dark5"),
unicorn/models/backbone/yolo_pafpn_new.py:20
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
unicorn/models/backbone/resnet.py:41
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
unicorn/models/backbone/resnet.py:87
Method__init__
(self, cfg, in_channels=(192, 384, 768), sem_loss_on=True, use_raft=False, up_rate=8)
unicorn/models/condinst/mask_branch.py:18
Method__init__
Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`: Args: norm (nn.Module, optional): a normali
unicorn/models/condinst/conv_with_kaiming_uniform.py:58
Method__init__
(self, cfg, use_raft=False, up_rate=8)
unicorn/models/condinst/dynamic_mask_head.py:95
Method__isub__
(self, other)
external/lib/utils/tensor.py:87
Method__iter__
(self)
external/qdtrack/qdtrack/datasets/samplers/distributed_video_sampler.py:27
Method__iter__
(self)
unicorn/data/samplers.py:25
Method__itruediv__
(self, other)
external/lib/utils/tensor.py:125
Method__le__
(self, other)
external/lib/utils/tensor.py:169
Method__len__
(self)
external/lib/test/evaluation/uavdataset.py:44
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