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Functions274 in github.com/chaoyu1999/FPSAutomaticAiming

⨍ Functions 274◇ Types & classes 47
↓ 1 callersMethodend_epoch
(self, best_result=False)
utils/wandb_logging/wandb_utils.py:290
↓ 1 callersFunctionexif_size
(img)
utils/datasets.py:44
↓ 1 callersMethodfinish_run
(self)
utils/wandb_logging/wandb_utils.py:302
↓ 1 callersMethodforward
(self, x, augment=False, profile=False)
models/yolo.py:105
↓ 1 callersMethodfuse
(self)
models/yolo.py:167
↓ 1 callersFunctionfuse_conv_and_bn
(conv, bn)
utils/torch_utils.py:181
↓ 1 callersFunctionget_latest_run
(search_dir='.')
utils/general.py:46
↓ 1 callersFunctionget_token
(cookie="./cookie")
utils/google_utils.py:90
↓ 1 callersFunctiongit_describe
(path=Path(__file__).parent)
utils/torch_utils.py:54
↓ 1 callersFunctiongsutil_getsize
(url='')
utils/google_utils.py:13
↓ 1 callersFunctionhex2rgb
(h)
utils/plots.py:31
↓ 1 callersFunctionhist2d
(x, y, n=100)
utils/plots.py:37
↓ 1 callersFunctioninit_seeds
(seed=0)
utils/general.py:39
↓ 1 callersFunctioninit_torch_seeds
(seed=0)
utils/torch_utils.py:39
↓ 1 callersFunctioninitialize_weights
(model)
utils/torch_utils.py:144
↓ 1 callersFunctionintersect_dicts
(da, db, exclude=())
utils/torch_utils.py:139
↓ 1 callersFunctionkmean_anchors
Creates kmeans-evolved anchors from training dataset Arguments: path: path to dataset *.yaml, or a loaded dataset n:
utils/autoanchor.py:62
↓ 1 callersFunctionlabels_to_class_weights
(labels, nc=80)
utils/general.py:216
↓ 1 callersFunctionlabels_to_image_weights
(labels, nc=80, class_weights=np.ones(80))
utils/general.py:235
↓ 1 callersFunctionload_classifier
(name='resnet101', n=2)
utils/torch_utils.py:228
↓ 1 callersMethodlog_dataset_artifact
(self, data_file, single_cls, project, overwrite_config=False)
utils/wandb_logging/wandb_utils.py:193
↓ 1 callersMethodlog_model
(self, path, opt, epoch, fitness_score, best_model=False)
utils/wandb_logging/wandb_utils.py:179
↓ 1 callersMethodlog_training_progress
(self, predn, path, names)
utils/wandb_logging/wandb_utils.py:263
↓ 1 callersFunctionmodel_info
(model, verbose=False, img_size=640)
utils/torch_utils.py:204
↓ 1 callersMethodnms
(self, mode=True)
models/yolo.py:177
↓ 1 callersFunctionone_cycle
(y1=0.0, y2=1.0, steps=100)
utils/general.py:186
↓ 1 callersFunctionoutput_to_target
(output)
utils/plots.py:105
↓ 1 callersFunctionparse_model
(d, ch)
models/yolo.py:201
↓ 1 callersFunctionplot_evolution
(yaml_file='data/hyp.finetune.yaml')
utils/plots.py:321
↓ 1 callersFunctionplot_labels
(labels, names=(), save_dir=Path(''), loggers=None)
utils/plots.py:272
↓ 1 callersFunctionplot_pr_curve
(px, py, ap, save_dir='pr_curve.png', names=())
utils/metrics.py:186
↓ 1 callersFunctionplot_results
(start=0, stop=0, bucket='', id=(), labels=(), save_dir='')
utils/plots.py:400
↓ 1 callersFunctionplot_study_txt
(path='', x=None)
utils/plots.py:240
↓ 1 callersMethodprint
(self)
models/common.py:338
↓ 1 callersFunctionprint_mutation
(hyp, results, yaml_file='hyp_evolved.yaml', bucket='')
utils/general.py:528
↓ 1 callersMethodprocess_batch
Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Ar
utils/metrics.py:117
↓ 1 callersFunctionprocess_wandb_config_ddp_mode
(opt)
utils/wandb_logging/wandb_utils.py:56
↓ 1 callersFunctionprofile
(x, ops, n=100, device=None)
utils/torch_utils.py:96
↓ 1 callersFunctionresample_segments
(segments, n=1000)
utils/general.py:310
↓ 1 callersFunctionscale_img
(img, ratio=1.0, same_shape=False, gs=32)
utils/torch_utils.py:247
↓ 1 callersFunctionsegment2box
(segment, width=640, height=640)
utils/general.py:293
↓ 1 callersFunctionsegments2boxes
(segments)
utils/general.py:301
↓ 1 callersMethodsetup_training
(self, opt, data_dict)
utils/wandb_logging/wandb_utils.py:126
↓ 1 callersFunctionsmooth_BCE
(eps=0.1)
utils/loss.py:10
↓ 1 callersFunctionsparsity
(model)
utils/torch_utils.py:161
↓ 1 callersMethodupdate_attr
(self, model, include=(), exclude=('process_group', 'reducer'))
utils/torch_utils.py:301
Method__call__
(self, p, targets)
utils/loss.py:114
Method__getitem__
(self, index)
utils/datasets.py:518
Method__init__
(self, alpha=0.05)
utils/loss.py:17
Method__init__
(self, loss_fcn, gamma=1.5, alpha=0.25)
utils/loss.py:64
Method__init__
(self, model, autobalance=False)
utils/loss.py:90
Method__init__
(self, model, decay=0.9999, updates=0)
utils/torch_utils.py:279
Method__init__
(self, nc, conf=0.25, iou_thres=0.45)
utils/metrics.py:111
Method__init__
(self, *args, **kwargs)
utils/datasets.py:94
Method__init__
(self, sampler)
utils/datasets.py:114
Method__init__
(self, pipe='0', img_size=640, stride=32)
utils/datasets.py:203
Method__init__
(self, sources='streams.txt', img_size=640, stride=32)
utils/datasets.py:261
Method__init__
(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
utils/datasets.py:348
Method__init__
(self, c1, k=3)
utils/activations.py:66
Method__init__
(self, opt, name, run_id, data_dict, job_type='Training')
utils/wandb_logging/wandb_utils.py:81
Method__init__
(self)
PID/pid_demo.py:9
Method__init__
(self, exp_val, p, i, d)
PID/PID.py:5
Method__init__
(self, nc=80, anchors=(), ch=())
models/yolo.py:28
Method__init__
(self, c1, c2, k=1, s=1, p=None, g=1, act=True)
models/common.py:35
Method__init__
(self, c, num_heads)
models/common.py:50
Method__init__
(self, c1, c2, num_heads, num_layers)
models/common.py:67
Method__init__
(self, c1, c2, shortcut=True, g=1, e=0.5)
models/common.py:96
Method__init__
(self, c1, c2, n=1, shortcut=True, g=1, e=0.5)
models/common.py:109
Method__init__
(self, c1, c2, n=1, shortcut=True, g=1, e=0.5)
models/common.py:143
Method__init__
(self, c1, c2, k=(5, 9, 13))
models/common.py:151
Method__init__
(self, c1, c2, k=1, s=1, p=None, g=1, act=True)
models/common.py:165
Method__init__
(self, gain=2)
models/common.py:177
Method__init__
(self, gain=2)
models/common.py:191
Method__init__
(self, dimension=1)
models/common.py:205
Method__init__
(self)
models/common.py:219
Method__init__
(self, model)
models/common.py:232
Method__init__
(self, imgs, pred, files, times=None, names=None, shape=None)
models/common.py:298
Method__init__
(self, c1, c2, k=1, s=1, p=None, g=1)
models/common.py:378
Method__init__
(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False)
models/experimental.py:13
Method__init__
(self, c1, c2, k=1, s=1, g=1, act=True)
models/experimental.py:48
Method__init__
(self, c1, c2, k=3, s=1)
models/experimental.py:61
Method__init__
(self, c1, c2, k=(1, 3), s=1, equal_ch=True)
models/experimental.py:76
Method__init__
(self)
models/experimental.py:100
Method__iter__
(self)
utils/datasets.py:102
Method__iter__
(self)
utils/datasets.py:117
Method__iter__
(self)
utils/datasets.py:217
Method__iter__
(self)
utils/datasets.py:314
Method__len__
(self)
utils/datasets.py:99
Method__len__
(self)
utils/datasets.py:198
Method__len__
(self)
utils/datasets.py:256
Method__len__
(self)
utils/datasets.py:337
Method__len__
(self)
utils/datasets.py:509
Method__len__
(self)
models/common.py:372
Method__next__
(self)
utils/datasets.py:155
Method__next__
(self)
utils/datasets.py:221
Method__next__
(self)
utils/datasets.py:318
Method_print_biases
(self)
models/yolo.py:156
Functionabc
(x)
demo.py:4
Functionapply_classifier
(x, model, img, im0)
utils/general.py:559
Methodautoshape
(self)
models/common.py:236
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