Functions — github.com/WinstonJQ/YOLOv5-ROS

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Functions575 in github.com/WinstonJQ/YOLOv5-ROS

⨍ Functions 575◇ Types & classes 97 ↳ Endpoints 1

↓ 1 callersMethodforward

(self, x)

src/yolov5/models/common.py:159

↓ 1 callersMethodforward

(self, im, augment=False, visualize=False)

src/yolov5/models/common.py:475

↓ 1 callersFunctionfuse_conv_and_bn

(conv, bn)

src/yolov5/utils/torch_utils.py:247

↓ 1 callersFunctionget_args

(known=False)

src/yolov5/utils/loggers/comet/hpo.py:27

↓ 1 callersFunctionget_default_args

(func)

src/yolov5/utils/general.py:239

↓ 1 callersMethodget_depth

(self, u, v)

src/detect_explore.py:209

↓ 1 callersMethodget_depth

(self, u, v)

src/detect_explore_reid.py:270

↓ 1 callersFunctionget_latest_run

(search_dir='.')

src/yolov5/utils/general.py:245

↓ 1 callersFunctionget_token

(cookie="./cookie")

src/yolov5/utils/downloads.py:156

↓ 1 callersFunctiongit_describe

(path=ROOT)

src/yolov5/utils/general.py:290

↓ 1 callersFunctiongsutil_getsize

(url='')

src/yolov5/utils/downloads.py:30

↓ 1 callersMethodhex2rgb

(h)

src/yolov5/utils/plots.py:47

↓ 1 callersFunctionhist2d

(x, y, n=100)

src/yolov5/utils/plots.py:161

↓ 1 callersFunctioninitialize_weights

(model)

src/yolov5/utils/torch_utils.py:211

↓ 1 callersFunctionis_parallel

(model)

src/yolov5/utils/torch_utils.py:201

↓ 1 callersFunctionis_url

(url, check_online=True)

src/yolov5/utils/downloads.py:19

↓ 1 callersFunctionis_writeable

(dir, test=False)

src/yolov5/utils/general.py:92

↓ 1 callersFunctionkmean_anchors

Creates kmeans-evolved anchors from training dataset Arguments: dataset: path to data.yaml, or a loaded dataset n: n

src/yolov5/utils/autoanchor.py:67

↓ 1 callersFunctionlabels_to_class_weights

(labels, nc=80)

src/yolov5/utils/general.py:658

↓ 1 callersFunctionlabels_to_image_weights

(labels, nc=80, class_weights=np.ones(80))

src/yolov5/utils/general.py:677

↓ 1 callersMethodlog_dataset_artifact

Log the dataset as W&B artifact and return the new data file with W&B links arguments: data_file (str) -- the .yaml file wit

src/yolov5/utils/loggers/wandb/wandb_utils.py:330

↓ 1 callersMethodlog_graph

(self, model, imgsz=(640, 640))

src/yolov5/utils/loggers/__init__.py:368

↓ 1 callersMethodlog_image

(self, img, **kwargs)

src/yolov5/utils/loggers/comet/__init__.py:201

↓ 1 callersMethodlog_image_with_boxes

Draw the bounding boxes on a single image and report the result as a ClearML debug sample. arguments: image_path (PosixPath)

src/yolov5/utils/loggers/clearml/clearml_utils.py:125

↓ 1 callersMethodlog_metrics

(self, metrics, epoch)

src/yolov5/utils/loggers/__init__.py:340

↓ 1 callersMethodlog_predictions

(self, image, labelsn, path, shape, predn)

src/yolov5/utils/loggers/comet/__init__.py:240

↓ 1 callersMethodlog_training_progress

Build evaluation Table. Uses reference from validation dataset table. arguments: predn (list): list of predictions in the na

src/yolov5/utils/loggers/wandb/wandb_utils.py:444

↓ 1 callersFunctionmain

(opt)

src/yolov5/val.py:363

↓ 1 callersFunctionmain

(opt)

src/yolov5/export.py:605

↓ 1 callersFunctionmain

(opt)

src/yolov5/detect.py:248

↓ 1 callersFunctionmain

(opt)

src/yolov5/utils/benchmarks.py:155

↓ 1 callersFunctionmain

(opt)

src/yolov5/classify/predict.py:208

↓ 1 callersFunctionmain

(opt)

src/yolov5/classify/val.py:162

↓ 1 callersFunctionmain

(opt)

src/yolov5/models/tf.py:568

↓ 1 callersFunctionmethods

(instance)

src/yolov5/utils/general.py:199

↓ 1 callersFunctionmixup

(im, labels, im2, labels2)

src/yolov5/utils/augmentations.py:290

↓ 1 callersMethodon_params_update

(self, params: dict)

src/yolov5/utils/loggers/__init__.py:303

↓ 1 callersFunctionone_cycle

(y1=0.0, y2=1.0, steps=100)

src/yolov5/utils/general.py:627

↓ 1 callersFunctionoutput_to_target

(output)

src/yolov5/utils/plots.py:183

↓ 1 callersMethodpandas

(self)

src/yolov5/models/common.py:762

↓ 1 callersFunctionparse_model

(d, ch)

src/yolov5/models/yolo.py:273

↓ 1 callersFunctionparse_model

(d, ch, model, imgsz)

src/yolov5/models/tf.py:349

↓ 1 callersFunctionparse_opt

()

src/yolov5/val.py:332

↓ 1 callersFunctionparse_opt

()

src/yolov5/export.py:572

↓ 1 callersFunctionparse_opt

()

src/yolov5/detect.py:213

↓ 1 callersFunctionparse_opt

()

src/yolov5/classify/predict.py:183

↓ 1 callersFunctionparse_opt

()

src/yolov5/classify/val.py:143

↓ 1 callersFunctionparse_opt

()

src/yolov5/models/tf.py:556

↓ 1 callersFunctionplot_evolve

(evolve_csv='path/to/evolve.csv')

src/yolov5/utils/plots.py:420

↓ 1 callersFunctionplot_labels

(labels, names=(), save_dir=Path(''))

src/yolov5/utils/plots.py:346

↓ 1 callersFunctionplot_pr_curve

(px, py, ap, save_dir=Path('pr_curve.png'), names=())

src/yolov5/utils/metrics.py:325

↓ 1 callersFunctionplot_results

(file='path/to/results.csv', dir='')

src/yolov5/utils/plots.py:447

↓ 1 callersFunctionplot_val_study

(file='', dir='', x=None)

src/yolov5/utils/plots.py:300

↓ 1 callersMethodpreprocess

(self, img)

src/detect_tracker.py:176

↓ 1 callersMethodpreprocess

(self, img)

src/detect_explore.py:237

↓ 1 callersMethodpreprocess

Adapted from yolov5/utils/datasets.py LoadStreams class

src/detect.py:180

↓ 1 callersMethodpreprocess

Preprocess image for YOLO.

src/detect_new.py:122

↓ 1 callersMethodpreprocess

(self, img)

src/detect_explore_reid.py:277

↓ 1 callersMethodpreprocess_prediction

(self, image, labels, shape, pred)

src/yolov5/utils/loggers/comet/__init__.py:283

↓ 1 callersFunctionprint_mutation

(results, hyp, save_dir, bucket, prefix=colorstr('evolve: '))

src/yolov5/utils/general.py:922

↓ 1 callersFunctionprocess_batch

Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format. Arguments: detections (Array[N, 6]), x1, y1, x2

src/yolov5/val.py:72

↓ 1 callersMethodprocess_image

Process a single image and publish detection results.

src/detect_new.py:84

↓ 1 callersFunctionprocess_wandb_config_ddp_mode

(opt)

src/yolov5/utils/loggers/wandb/wandb_utils.py:85

↓ 1 callersMethodpublish_control_command

(self, target_x, target_y, target_z)

src/detect_explore.py:214

↓ 1 callersMethodregister_action

src/yolov5/utils/callbacks.py:38

↓ 1 callersFunctionrepresentative_dataset_gen

(dataset, ncalib=100)

src/yolov5/models/tf.py:520

↓ 1 callersFunctionresample_segments

(segments, n=1000)

src/yolov5/utils/general.py:763

↓ 1 callersFunctionreshape_classifier_output

(model, n=1000)

src/yolov5/utils/torch_utils.py:65

↓ 1 callersFunctionrun

( data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path' weights=ROOT / 'yolov5s.pt', # weig

src/yolov5/export.py:453

↓ 1 callersFunctionrun

( weights=ROOT / 'yolov5s.pt', # model.pt path(s) source=ROOT / 'data/images', # file/dir/UR

src/yolov5/detect.py:51

↓ 1 callersFunctionrun

( weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) b

src/yolov5/utils/benchmarks.py:49

↓ 1 callersFunctionrun

(parameters, opt)

src/yolov5/utils/loggers/comet/hpo.py:83

↓ 1 callersFunctionrun

( weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) source=ROOT / 'data/images', # file/di

src/yolov5/classify/predict.py:53

↓ 1 callersFunctionrun

( data=ROOT / '../datasets/mnist', # dataset dir weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)

src/yolov5/classify/val.py:44

↓ 1 callersFunctionrun

( weights=ROOT / 'yolov5s.pt', # weights path imgsz=(640, 640), # inference size h,w

src/yolov5/models/tf.py:531

↓ 1 callersMethodrun

(self)

src/replay_private.py:72

↓ 1 callersMethodrun

(self)

src/replay.py:88

↓ 1 callersFunctionsave_one_json

(predn, jdict, path, class_map)

src/yolov5/val.py:59

↓ 1 callersFunctionsave_one_txt

(predn, save_conf, shape, file)

src/yolov5/val.py:49

↓ 1 callersFunctionscale_img

(img, ratio=1.0, same_shape=False, gs=32)

src/yolov5/utils/torch_utils.py:296

↓ 1 callersFunctionsegment2box

(segment, width=640, height=640)

src/yolov5/utils/general.py:746

↓ 1 callersFunctionsegments2boxes

(segments)

src/yolov5/utils/general.py:754

↓ 1 callersFunctionset_logging

(name=None, verbose=VERBOSE)

src/yolov5/utils/general.py:106

↓ 1 callersFunctionset_opt_parameters

Update the opts Namespace with parameters from Comet's ExistingExperiment when resuming a run Args: opt (argparse.Namespace): Namespa

src/yolov5/utils/loggers/comet/comet_utils.py:66

↓ 1 callersMethodsetup_training

Setup the necessary processes for training YOLO models: - Attempt to download model checkpoint and dataset artifacts if opt.resume

src/yolov5/utils/loggers/wandb/wandb_utils.py:215

↓ 1 callersMethodshow

(self, labels=True)

src/yolov5/models/common.py:747

↓ 1 callersFunctionsmartCrossEntropyLoss

(label_smoothing=0.0)

src/yolov5/utils/torch_utils.py:45

↓ 1 callersFunctionsmart_resume

(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True)

src/yolov5/utils/torch_utils.py:359

↓ 1 callersFunctionsmooth_BCE

(eps=0.1)

src/yolov5/utils/loss.py:13

↓ 1 callersFunctionsparsity

(model)

src/yolov5/utils/torch_utils.py:228

↓ 1 callersFunctionsweep

()

src/yolov5/utils/loggers/wandb/sweep.py:17

↓ 1 callersFunctiontest

( weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) b

src/yolov5/utils/benchmarks.py:106

↓ 1 callersMethodupdate

(self, i, cap, stream)

src/yolov5/utils/dataloaders.py:333

↓ 1 callersMethodupdate_attr

(self, model, include=(), exclude=('process_group', 'reducer'))

src/yolov5/utils/torch_utils.py:428

↓ 1 callersMethodupdate_data_paths

(self, data_dict)

src/yolov5/utils/loggers/comet/__init__.py:356

↓ 1 callersMethodupload_dataset_artifact

(self)

src/yolov5/utils/loggers/comet/__init__.py:319

↓ 1 callersFunctionurl2file

(url)

src/yolov5/utils/general.py:560

↓ 1 callersFunctionuser_config_dir

(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR')

src/yolov5/utils/general.py:128

↓ 1 callersMethodval_one_image

Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel arguments:

src/yolov5/utils/loggers/wandb/wandb_utils.py:487

↓ 1 callersFunctionweb_project_name

(project)

src/yolov5/utils/loggers/__init__.py:399

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