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Functions1,443 in github.com/WangRongsheng/BestYOLO

↓ 1 callersMethodpreprocess_prediction
(self, image, labels, shape, pred)
utils/loggers/comet/__init__.py:288
↓ 1 callersMethodprint
(self)
deploy/gui/models/common.py:624
↓ 1 callersMethodprint
(self)
models/common.py:825
↓ 1 callersFunctionprocess_batch
Return correct prediction matrix Arguments: detections (array[N, 6]), x1, y1, x2, y2, conf, class labels (array[M, 5]), class
val.py:72
↓ 1 callersFunctionprocess_batch
(predictions, labels, iouv)
deploy/gui/val.py:54
↓ 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
deploy/gui/utils/metrics.py:125
↓ 1 callersFunctionprocess_mask
Crop before upsample. proto_out: [mask_dim, mask_h, mask_w] out_masks: [n, mask_dim], n is number of masks after nms bboxes: [n, 4],
utils/segment/general.py:43
↓ 1 callersFunctionprocess_wandb_config_ddp_mode
(opt)
deploy/gui/utils/wandb_logging/wandb_utils.py:60
↓ 1 callersFunctionprocess_wandb_config_ddp_mode
(opt)
deploy/gui/utils/loggers/wandb/wandb_utils.py:82
↓ 1 callersFunctionprocess_wandb_config_ddp_mode
(opt)
utils/loggers/wandb/wandb_utils.py:85
↓ 1 callersFunctionrandom_color
deploy/yolov5_TensorRT/include/common.h:115
↓ 1 callersMethodregister_action
Register a new action to a callback hook Args: hook The callback hook name to register the action to
deploy/gui/utils/callbacks.py:40
↓ 1 callersFunctionremove_dataset
()
deploy/yolov5_maketools/app.py:144
↓ 1 callersFunctionrenderPredictions
(res)
deploy/yolov5_tfjs_flask/static/js/yolov5_rt_tfjs.js:83
↓ 1 callersFunctionrepresentative_dataset_gen
(dataset, ncalib=100)
models/tf.py:554
↓ 1 callersFunctionresample_segments
(segments, n=1000)
deploy/gui/utils/general.py:639
↓ 1 callersMethodreset_parameters
(self)
models/common.py:1554
↓ 1 callersMethodreset_parameters
(self)
models/common.py:1619
↓ 1 callersFunctionreshape_classifier_output
(model, n=1000)
utils/torch_utils.py:66
↓ 1 callersMethodretranslateUi
(self, mainWindow)
deploy/gui/main_win/win.py:1054
↓ 1 callersMethodretranslateUi
(self, Form)
deploy/gui/dialog/rtsp_dialog.py:85
↓ 1 callersFunctionround_labels
(labels)
deploy/gui/utils/datasets.py:949
↓ 1 callersFunctionrun
( weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) b
benchmarks.py:52
↓ 1 callersFunctionrun
( data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path' weights=ROOT / 'yolov5s.pt', # weig
export.py:491
↓ 1 callersFunctionrun
( weights=ROOT / 'yolov5s.pt', # model path or triton URL source=ROOT / 'data/images', # fil
detect.py:52
↓ 1 callersFunctionrun
(weights='./yolov5s.pt', # weights path img_size=(640, 640), # image (height, width) batch_s
deploy/gui/export.py:27
↓ 1 callersFunctionrun
(weights='yolov5s.pt', # model.pt path(s) source='data/images', # file/dir/URL/glob, 0 for webcam
deploy/gui/detect.py:28
↓ 1 callersFunctionrun
(weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) batch_size
deploy/gui/utils/benchmarks.py:46
↓ 1 callersFunctionrun
(weights=ROOT / 'yolov5s.pt', # weights path imgsz=(640, 640), # inference size h,w batch_si
deploy/gui/models/tf.py:422
↓ 1 callersFunctionrun
( weights=ROOT / 'yolov5s.pt', # model path or triton URL source=ROOT / 'data/images', # fil
deploy/yolov5_multi-lable/ml_detect.py:52
↓ 1 callersFunctionrun
(parameters, opt)
utils/loggers/comet/hpo.py:83
↓ 1 callersFunctionrun
( weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) source=ROOT / 'data/images', # file/di
classify/predict.py:54
↓ 1 callersFunctionrun
( data=ROOT / '../datasets/mnist', # dataset dir weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s)
classify/val.py:45
↓ 1 callersFunctionrun
( weights=ROOT / 'yolov5s-seg.pt', # model.pt path(s) source=ROOT / 'data/images', # file/dir/URL/gl
segment/predict.py:54
↓ 1 callersFunctionrun
( weights=ROOT / 'yolov5s.pt', # weights path imgsz=(640, 640), # inference size h,w
models/tf.py:565
↓ 1 callersFunctionsave_one_json
(predn, jdict, path, class_map)
val.py:59
↓ 1 callersFunctionsave_one_json
(predn, jdict, path, class_map)
deploy/gui/val.py:42
↓ 1 callersFunctionsave_one_json
(predn, jdict, path, class_map, pred_masks)
segment/val.py:67
↓ 1 callersFunctionsave_one_txt
(predn, save_conf, shape, file)
val.py:49
↓ 1 callersFunctionsave_one_txt
(predn, save_conf, shape, file)
deploy/gui/val.py:32
↓ 1 callersFunctionsave_one_txt
(predn, save_conf, shape, file)
segment/val.py:57
↓ 1 callersFunctionscale_boxes
(img1_shape, boxes, img0_shape, ratio_pad=None)
utils/general-softnms.py:825
↓ 1 callersFunctionscale_img
(img, ratio=1.0, same_shape=False, gs=32)
deploy/gui/utils/torch_utils.py:254
↓ 1 callersFunctionscale_segments
(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False)
utils/general.py:843
↓ 1 callersMethodsearch_pt
(self)
deploy/gui/main.py:280
↓ 1 callersFunctionsegment2box
(segment, width=640, height=640)
deploy/gui/utils/general.py:622
↓ 1 callersFunctionsegments2boxes
(segments)
deploy/gui/utils/general.py:630
↓ 1 callersFunctionsegments2boxes
(segments)
utils/general.py:808
↓ 1 callersMethodsetShouldLog
deploy/yolov5_TensorRT/include/logging.h:102
↓ 1 callersFunctionset_logging
(name=LOGGING_NAME, verbose=True)
utils/general.py:118
↓ 1 callersFunctionset_logging
(name=LOGGING_NAME, verbose=True)
utils/general-softnms.py:119
↓ 1 callersFunctionset_opt_parameters
Update the opts Namespace with parameters from Comet's ExistingExperiment when resuming a run Args: opt (argparse.Namespace): Namespa
utils/loggers/comet/comet_utils.py:66
↓ 1 callersMethodsetupUi
(self, mainWindow)
deploy/gui/main_win/win.py:15
↓ 1 callersMethodsetupUi
(self, Form)
deploy/gui/dialog/rtsp_dialog.py:15
↓ 1 callersMethodsetup_training
(self, opt, data_dict)
deploy/gui/utils/wandb_logging/wandb_utils.py:155
↓ 1 callersMethodsetup_training
Setup the necessary processes for training YOLO models: - Attempt to download model checkpoint and dataset artifacts if opt.resume
deploy/gui/utils/loggers/wandb/wandb_utils.py:210
↓ 1 callersMethodsetup_training
Setup the necessary processes for training YOLO models: - Attempt to download model checkpoint and dataset artifacts if opt.resume
utils/loggers/wandb/wandb_utils.py:220
↓ 1 callersMethodshow
(self, labels=True)
models/common.py:791
↓ 1 callersMethodshow_msg
(self, msg)
deploy/gui/main.py:436
↓ 1 callersMethodsingle_mask_loss
(self, gt_mask, pred, proto, xyxy, area)
utils/segment/loss.py:112
↓ 1 callersFunctionsmartCrossEntropyLoss
(label_smoothing=0.0)
utils/torch_utils.py:46
↓ 1 callersFunctionsmooth_BCE
(eps=0.1)
deploy/gui/utils/loss.py:13
↓ 1 callersFunctionsmooth_BCE
(eps=0.1)
deploy/yolov5_multi-lable/ml_loss.py:17
↓ 1 callersFunctionsoft_nms
(bboxes, scores, iou_thresh=0.5,sigma=0.5,score_threshold=0.25)
utils/general-softnms.py:938
↓ 1 callersFunctionsparsity
(model)
deploy/gui/utils/torch_utils.py:187
↓ 1 callersFunctionsparsity
(model)
utils/torch_utils.py:229
↓ 1 callersFunctionsplit_dataset
()
deploy/yolov5_maketools/app.py:90
↓ 1 callersMethodsqueeze_targets
(self, targets)
deploy/yolov5_multi-lable/ml_loss.py:196
↓ 1 callersFunctionsweep
()
deploy/gui/utils/wandb_logging/sweep.py:14
↓ 1 callersFunctionsweep
()
deploy/gui/utils/loggers/wandb/sweep.py:17
↓ 1 callersFunctionsweep
()
utils/loggers/wandb/sweep.py:17
↓ 1 callersFunctiontest
( weights=ROOT / 'yolov5s.pt', # weights path imgsz=640, # inference size (pixels) b
benchmarks.py:114
↓ 1 callersFunctiontrain
(opt, device)
classify/train.py:56
↓ 1 callersFunctionunzip
(path)
deploy/gui/utils/datasets.py:953
↓ 1 callersMethodupdate
(self, i, cap, stream)
deploy/gui/utils/datasets.py:328
↓ 1 callersMethodupdate
(self, i, cap, stream)
utils/dataloaders.py:385
↓ 1 callersMethodupdate
Args: results: Dict{'boxes': Dict{}, 'masks': Dict{}}
utils/segment/metrics.py:157
↓ 1 callersMethodupdate_data_paths
(self, data_dict)
utils/loggers/comet/__init__.py:368
↓ 1 callersMethodupdate_params
(self, params)
utils/loggers/__init__.py:380
↓ 1 callersMethodupload_dataset_artifact
(self)
utils/loggers/comet/__init__.py:324
↓ 1 callersFunctionurl2file
(url)
utils/general.py:614
↓ 1 callersFunctionuser_config_dir
(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR')
deploy/gui/utils/general.py:88
↓ 1 callersFunctionuser_config_dir
(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR')
utils/general.py:147
↓ 1 callersFunctionuser_config_dir
(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR')
utils/general-softnms.py:148
↓ 1 callersMethodval_one_image
(self, pred, predn, path, names, im)
deploy/gui/utils/wandb_logging/wandb_utils.py:317
↓ 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:
deploy/gui/utils/loggers/wandb/wandb_utils.py:474
↓ 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:
utils/loggers/wandb/wandb_utils.py:492
↓ 1 callersFunctionvoc2yolo
(classes, rootpath, xmlname)
deploy/yolov5_maketools/app.py:63
↓ 1 callersMethodwarmup
(self, imgsz=(1, 3, 640, 640), half=False)
deploy/gui/models/common.py:458
↓ 1 callersFunctionweb_project_name
(project)
utils/loggers/__init__.py:399
↓ 1 callersFunctionx1y1x2y22xywh
(bbox)
wbf.py:16
↓ 1 callersFunctionxywh2x1y1x2y2
(bbox)
wbf.py:9
↓ 1 callersFunctionxyxy2xywhn
(x, w=640, h=640, clip=False, eps=0.0)
deploy/gui/utils/general.py:602
↓ 1 callersFunctionyaml_load
(file='data.yaml')
utils/general-softnms.py:593
FunctionLOG_ERROR
\brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR Example usage: LOG_ERROR(logger) << "hello world" <<
deploy/yolov5_TensorRT/include/logging.h:506
FunctionLOG_FATAL
\brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR ("fatal" severity) Example usage: LOG_FATAL(
deploy/yolov5_TensorRT/include/logging.h:519
FunctionLOG_INFO
\brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO Example usage: LOG_INFO(logger) << "hello world" << st
deploy/yolov5_TensorRT/include/logging.h:482
FunctionLOG_VERBOSE
\brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE Example usage: LOG_VERBOSE(logger) << "hello world"
deploy/yolov5_TensorRT/include/logging.h:470
FunctionLOG_WARN
\brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING Example usage: LOG_WARN(logger) << "hello world" <<
deploy/yolov5_TensorRT/include/logging.h:494
MethodLogStreamConsumer
\brief Creates a LogStreamConsumer which logs messages with level severity. Reportable severity determines if the messages are severe enough to be log
deploy/yolov5_TensorRT/include/logging.h:145
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