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Functions1,443 in github.com/WangRongsheng/BestYOLO

⨍ Functions 1,443◇ Types & classes 273↳ Endpoints 2
↓ 5 callersMethodshow
(self)
deploy/gui/models/common.py:629
↓ 5 callersFunctionsmart_DDP
(model)
utils/torch_utils.py:55
↓ 5 callersFunctionsmart_optimizer
(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5)
utils/torch_utils.py:318
↓ 5 callersMethodstop
(self)
deploy/gui/main.py:488
↓ 5 callersMethodtext
(self, xy, text, txt_color=(255, 255, 255))
deploy/gui/utils/plots.py:111
↓ 5 callersFunctiontorch_distributed_zero_first
Decorator to make all processes in distributed training wait for each local_master to do something.
deploy/gui/utils/torch_utils.py:34
↓ 5 callersMethodwarmup
(self, imgsz=(1, 3, 640, 640))
models/common.py:595
↓ 4 callersMethod__init__
(self, nc=80, anchors=(), ch=(), inplace=True)
models/yolo.py:44
↓ 4 callersMethod__init__
(self, nc=80, anchors=(), ch=(), inplace=True)
models/yolo_AuxOTA.py:44
↓ 4 callersFunctionautopad
(k, p=None, d=1)
models/common.py:38
↓ 4 callersMethodbackward
(ctx, grad_output)
models/ops_dcnv3/functions/dcnv3_func.py:51
↓ 4 callersMethodbox_label
(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255))
deploy/gui/utils/plots.py:82
↓ 4 callersFunctioncheck_amp
(model)
utils/general.py:564
↓ 4 callersFunctioncheck_git_status
(repo='ultralytics/yolov5', branch='master')
utils/general.py:320
↓ 4 callersFunctioncheck_version
(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False)
deploy/gui/utils/general.py:270
↓ 4 callersFunctioncheck_yaml
(file, suffix=('.yaml', '.yml'))
deploy/gui/utils/general.py:356
↓ 4 callersFunctiondcnv3_core_pytorch
( input, offset, mask, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_
models/ops_dcnv3/functions/dcnv3_func.py:148
↓ 4 callersFunctionfile_size
(path)
deploy/gui/utils/general.py:223
↓ 4 callersFunctioninit_seeds
(seed=0, deterministic=False)
utils/general.py:238
↓ 4 callersFunctionintersect_dicts
(da, db, exclude=())
utils/general.py:253
↓ 4 callersMethodlog_metrics
(self, metrics, epoch)
utils/loggers/__init__.py:340
↓ 4 callersMethodlog_model
(self, model_path, epoch=0, metadata={})
utils/loggers/__init__.py:373
↓ 4 callersFunctionmetric
(k)
deploy/gui/utils/autoanchor.py:35
↓ 4 callersFunctionmetric
(k)
utils/autoanchor.py:37
↓ 4 callersFunctionparse_opt
(known=False)
deploy/gui/train.py:480
↓ 4 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:134
↓ 4 callersMethodresult
(self)
utils/plots.py:179
↓ 4 callersFunctionsmart_resume
(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True)
utils/torch_utils.py:361
↓ 4 callersFunctionstrip_optimizer
(f='best.pt', s='')
deploy/gui/utils/general.py:770
↓ 4 callersMethodtext
(self, xy, text, txt_color=(255, 255, 255), anchor='top')
utils/plots.py:167
↓ 4 callersFunctiontrain
(hyp, opt, device, callbacks)
train.py:70
↓ 4 callersFunctiontrain
(hyp, # path/to/hyp.yaml or hyp dictionary opt, device, )
deploy/gui/train.py:56
↓ 4 callersFunctionxyn2xy
(x, w=640, h=640, padw=0, padh=0)
utils/general.py:792
↓ 4 callersFunctionyaml_load
(file='data.yaml')
utils/general.py:592
↓ 3 callersMethodLogger
deploy/yolov5_TensorRT/include/logging.h:223
↓ 3 callersMethod__init__
(self, n, weight=False)
deploy/gui/models/experimental.py:31
↓ 3 callersMethod__init__
(self, loss_fcn, gamma=1.5, alpha=0.25)
utils/loss_AuxOTA.py:39
↓ 3 callersMethod__init__
(self, loss_fcn, gamma=1.5, alpha=0.25)
utils/loss_OTA.py:34
↓ 3 callersMethod_forward_once
(self, x, profile=False, visualize=False)
models/yolo.py:114
↓ 3 callersMethod_forward_once
(self, x, profile=False, visualize=False)
models/yolo_AuxOTA.py:119
↓ 3 callersFunctionap_per_class
Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Ar
utils/metrics.py:31
↓ 3 callersFunctionattempt_download
(file, repo='ultralytics/yolov5')
deploy/gui/utils/downloads.py:43
↓ 3 callersFunctionautopad
(k, p=None)
deploy/gui/models/common.py:31
↓ 3 callersMethodbackward
(ctx, grad_output)
deploy/gui/utils/activations.py:40
↓ 3 callersFunctionbox_iou
Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments:
deploy/gui/utils/metrics.py:244
↓ 3 callersFunctioncheck_anchor_order
(m)
utils/autoanchor.py:19
↓ 3 callersFunctioncheck_anchors
(dataset, model, thr=4.0, imgsz=640)
utils/autoanchor.py:30
↓ 3 callersFunctioncheck_comet_resume
Restores run parameters to its original state based on the model checkpoint and logged Experiment parameters. Args: opt (argparse.Nam
utils/loggers/comet/comet_utils.py:124
↓ 3 callersFunctioncheck_font
(font=FONT, progress=False)
utils/general.py:488
↓ 3 callersFunctioncheck_imshow
()
deploy/gui/utils/general.py:330
↓ 3 callersFunctioncheck_imshow
(warn=False)
utils/general.py:428
↓ 3 callersFunctioncheck_suffix
(file='yolov5s.pt', suffix=('.pt',), msg='')
deploy/gui/utils/general.py:345
↓ 3 callersFunctioncheck_train_batch_size
(model, imgsz=640, amp=True)
utils/autobatch.py:15
↓ 3 callersFunctionclassify_transforms
(size=224)
utils/augmentations.py:347
↓ 3 callersFunctionclip_boxes
(boxes, shape)
utils/general.py:862
↓ 3 callersFunctionclip_coords
(boxes, shape)
deploy/gui/utils/general.py:664
↓ 3 callersFunctioncopy_paste
(im, labels, segments, p=0.5)
utils/augmentations.py:240
↓ 3 callersFunctioncreate_classification_dataloader
(path, imgsz=224, batch_size=16,
utils/dataloaders.py:1196
↓ 3 callersFunctioncreate_dataloader
(path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0,
deploy/gui/utils/datasets.py:94
↓ 3 callersFunctioncrop_mask
"Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong). Args: - masks sh
utils/segment/general.py:7
↓ 3 callersFunctionexport_formats
()
export.py:80
↓ 3 callersFunctionexport_onnx
(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:'))
export.py:133
↓ 3 callersFunctionfile_size
(path)
utils/general.py:282
↓ 3 callersMethodfogging_img
fogging single image :param img: src img :param brightness: brightness :param thickness: fog thickness, without fog w
deploy/fog_augment/synthetic_fog.py:95
↓ 3 callersFunctionget_latest_run
(search_dir='.')
utils/general.py:264
↓ 3 callersFunctionis_ascii
(s='')
utils/general.py:63
↓ 3 callersFunctionis_docker
()
deploy/gui/utils/general.py:188
↓ 3 callersFunctionlabels_to_class_weights
(labels, nc=80)
utils/general.py:712
↓ 3 callersFunctionlabels_to_image_weights
(labels, nc=80, class_weights=np.ones(80))
utils/general.py:731
↓ 3 callersMethodload_image
(self, i)
deploy/gui/utils/datasets.py:627
↓ 3 callersMethodlog_metrics
(self, log_dict, **kwargs)
utils/loggers/comet/__init__.py:193
↓ 3 callersFunctionmodel_info
(model, verbose=False, imgsz=640)
utils/torch_utils.py:272
↓ 3 callersFunctionone_cycle
(y1=0.0, y2=1.0, steps=100)
utils/general.py:681
↓ 3 callersFunctionparse_opt
(known=False)
train.py:440
↓ 3 callersFunctionparse_opt
()
benchmarks.py:146
↓ 3 callersFunctionplot_evolve
(evolve_csv='path/to/evolve.csv')
utils/plots.py:476
↓ 3 callersFunctionplot_images
(images, targets, paths=None, fname='images.jpg', names=None)
utils/plots.py:245
↓ 3 callersFunctionplot_images_and_masks
(images, targets, masks, paths=None, fname='images.jpg', names=None)
utils/segment/plots.py:17
↓ 3 callersFunctionplot_mc_curve
(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric')
deploy/gui/utils/metrics.py:324
↓ 3 callersFunctionplot_mc_curve
(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric')
utils/metrics.py:344
↓ 3 callersMethodpredict
(self, inputs, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
deploy/gui/models/tf.py:340
↓ 3 callersFunctionprint_mutation
(keys, results, hyp, save_dir, bucket, prefix=colorstr('evolve: '))
utils/general.py:1018
↓ 3 callersFunctionprint_results
(k, verbose=True)
deploy/gui/utils/autoanchor.py:97
↓ 3 callersFunctionprint_results
(k, verbose=True)
utils/autoanchor.py:99
↓ 3 callersFunctionprofile
YOLOv5 speed/memory/FLOPs profiler Usage: input = torch.randn(16, 3, 640, 640) m1 = lambda x: x * torch.sigmoid(x) m2 = n
utils/torch_utils.py:151
↓ 3 callersFunctionrandom_perspective
(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
deploy/gui/utils/augmentations.py:124
↓ 3 callersFunctionrandom_perspective
(im, targets=(), segments=(), degrees=10,
utils/augmentations.py:144
↓ 3 callersFunctionrun
( data, weights=None, # model.pt path(s) batch_size=32, # batch size imgsz=6
val.py:98
↓ 3 callersFunctionrun
(data, weights=None, # model.pt path(s) batch_size=32, # batch size imgsz=640, # in
deploy/gui/val.py:78
↓ 3 callersFunctionrun
( data, weights=None, # model.pt path(s) batch_size=32, # batch size imgsz=6
segment/val.py:129
↓ 3 callersFunctionsave_one_box
(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True)
deploy/gui/utils/plots.py:458
↓ 3 callersFunctionsave_one_box
(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True)
utils/plots.py:560
↓ 3 callersFunctionscale_image
img1_shape: model input shape, [h, w] img0_shape: origin pic shape, [h, w, 3] masks: [h, w, num]
utils/segment/general.py:70
↓ 3 callersFunctionselectColor
(index)
deploy/yolov5_tfjs_flask/static/js/yolov5_rt_tfjs.js:68
↓ 3 callersFunctionseverity_string
deploy/yolov5_TensorRT/include/common.h:32
↓ 3 callersFunctionunzip_file
(file, path=None, exclude=('.DS_Store', '__MACOSX'))
utils/general.py:604
↓ 3 callersMethodupdate_attr
(self, model, include=(), exclude=('process_group', 'reducer'))
utils/torch_utils.py:430
↓ 3 callersFunctionxywhn2xyxy
(x, w=640, h=640, padw=0, padh=0)
deploy/gui/utils/general.py:592
↓ 2 callersMethod__init__
(self, loss_fcn, gamma=1.5, alpha=0.25)
deploy/gui/utils/loss.py:37
↓ 2 callersMethod__init__
(self, c1, k=3)
deploy/gui/utils/activations.py:52
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