SYMBOL INDEX (683 symbols across 43 files)

FILE: benchmarks.py
  function run (line 52) | def run(
  function test (line 149) | def test(
  function parse_opt (line 222) | def parse_opt():
  function main (line 271) | def main(opt):

FILE: classify/predict.py
  function run (line 68) | def run(
  function parse_opt (line 207) | def parse_opt():
  function main (line 233) | def main(opt):

FILE: classify/train.py
  function train (line 78) | def train(opt, device):
  function parse_opt (line 315) | def parse_opt(known=False):
  function main (line 343) | def main(opt):
  function run (line 367) | def run(**kwargs):

FILE: classify/val.py
  function run (line 53) | def run(
  function parse_opt (line 150) | def parse_opt():
  function main (line 170) | def main(opt):

FILE: detect.py
  function run (line 69) | def run(
  function parse_opt (line 288) | def parse_opt():
  function main (line 365) | def main(opt):

FILE: export.py
  class iOSModel (line 93) | class iOSModel(torch.nn.Module):
    method __init__ (line 96) | def __init__(self, model, im):
    method forward (line 126) | def forward(self, x):
  function export_formats (line 164) | def export_formats():
  function try_export (line 201) | def try_export(inner_func):
  function export_torchscript (line 241) | def export_torchscript(model, im, file, optimize, prefix=colorstr("Torch...
  function export_onnx (line 291) | def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colors...
  function export_openvino (line 386) | def export_openvino(file, metadata, half, int8, data, prefix=colorstr("O...
  function export_paddle (line 475) | def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePadd...
  function export_coreml (line 524) | def export_coreml(model, im, file, int8, half, nms, prefix=colorstr("Cor...
  function export_engine (line 583) | def export_engine(model, im, file, half, dynamic, simplify, workspace=4,...
  function export_saved_model (line 680) | def export_saved_model(
  function export_pb (line 777) | def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")):
  function export_tflite (line 819) | def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, ...
  function export_edgetpu (line 888) | def export_edgetpu(file, prefix=colorstr("Edge TPU:")):
  function export_tfjs (line 957) | def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")):
  function add_tflite_metadata (line 1042) | def add_tflite_metadata(file, metadata, num_outputs):
  function pipeline_coreml (line 1131) | def pipeline_coreml(model, im, file, names, y, prefix=colorstr("CoreML P...
  function run (line 1312) | def run(
  function parse_opt (line 1493) | def parse_opt(known=False):
  function main (line 1547) | def main(opt):

FILE: hubconf.py
  function _create (line 16) | def _create(name, pretrained=True, channels=3, classes=80, autoshape=Tru...
  function custom (line 97) | def custom(path="path/to/model.pt", autoshape=True, _verbose=True, devic...
  function yolov5n (line 126) | def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _ve...
  function yolov5s (line 158) | def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _ve...
  function yolov5m (line 186) | def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _ve...
  function yolov5l (line 212) | def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _ve...
  function yolov5x (line 240) | def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _ve...
  function yolov5n6 (line 273) | def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _v...
  function yolov5s6 (line 300) | def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _v...
  function yolov5m6 (line 328) | def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _v...
  function yolov5l6 (line 361) | def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _v...
  function yolov5x6 (line 391) | def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _v...

FILE: models/common.py
  function autopad (line 48) | def autopad(k, p=None, d=1):  # kernel, padding, dilation
  class Conv (line 57) | class Conv(nn.Module):
    method __init__ (line 62) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
    method forward (line 71) | def forward(self, x):
    method forward_fuse (line 77) | def forward_fuse(self, x):
  class DWConv (line 84) | class DWConv(Conv):
    method __init__ (line 87) | def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out,...
  class DWConvTranspose2d (line 94) | class DWConvTranspose2d(nn.ConvTranspose2d):
    method __init__ (line 97) | def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, ke...
  class TransformerLayer (line 104) | class TransformerLayer(nn.Module):
    method __init__ (line 107) | def __init__(self, c, num_heads):
    method forward (line 119) | def forward(self, x):
  class TransformerBlock (line 128) | class TransformerBlock(nn.Module):
    method __init__ (line 131) | def __init__(self, c1, c2, num_heads, num_layers):
    method forward (line 141) | def forward(self, x):
  class Bottleneck (line 150) | class Bottleneck(nn.Module):
    method __init__ (line 153) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_ou...
    method forward (line 163) | def forward(self, x):
  class BottleneckCSP (line 169) | class BottleneckCSP(nn.Module):
    method __init__ (line 172) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ...
    method forward (line 186) | def forward(self, x):
  class CrossConv (line 194) | class CrossConv(nn.Module):
    method __init__ (line 197) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
    method forward (line 207) | def forward(self, x):
  class C3 (line 212) | class C3(nn.Module):
    method __init__ (line 215) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ...
    method forward (line 225) | def forward(self, x):
  class C3x (line 230) | class C3x(C3):
    method __init__ (line 233) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
  class C3TR (line 240) | class C3TR(C3):
    method __init__ (line 243) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
  class C3SPP (line 250) | class C3SPP(C3):
    method __init__ (line 253) | def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
  class C3Ghost (line 260) | class C3Ghost(C3):
    method __init__ (line 263) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
  class SPP (line 270) | class SPP(nn.Module):
    method __init__ (line 273) | def __init__(self, c1, c2, k=(5, 9, 13)):
    method forward (line 284) | def forward(self, x):
  class SPPF (line 296) | class SPPF(nn.Module):
    method __init__ (line 299) | def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
    method forward (line 307) | def forward(self, x):
  class Focus (line 319) | class Focus(nn.Module):
    method __init__ (line 322) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in,...
    method forward (line 330) | def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
  class GhostConv (line 336) | class GhostConv(nn.Module):
    method __init__ (line 339) | def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out,...
    method forward (line 348) | def forward(self, x):
  class GhostBottleneck (line 354) | class GhostBottleneck(nn.Module):
    method __init__ (line 357) | def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
    method forward (line 372) | def forward(self, x):
  class Contract (line 377) | class Contract(nn.Module):
    method __init__ (line 380) | def __init__(self, gain=2):
    method forward (line 387) | def forward(self, x):
  class Expand (line 398) | class Expand(nn.Module):
    method __init__ (line 401) | def __init__(self, gain=2):
    method forward (line 408) | def forward(self, x):
  class Concat (line 419) | class Concat(nn.Module):
    method __init__ (line 422) | def __init__(self, dimension=1):
    method forward (line 427) | def forward(self, x):
  class DetectMultiBackend (line 434) | class DetectMultiBackend(nn.Module):
    method __init__ (line 437) | def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), d...
    method forward (line 644) | def forward(self, im, augment=False, visualize=False):
    method from_numpy (line 726) | def from_numpy(self, x):
    method warmup (line 732) | def warmup(self, imgsz=(1, 3, 640, 640)):
    method _model_type (line 741) | def _model_type(p="path/to/model.pt"):
    method _load_metadata (line 760) | def _load_metadata(f=Path("path/to/meta.yaml")):
  class AutoShape (line 768) | class AutoShape(nn.Module):
    method __init__ (line 779) | def __init__(self, model, verbose=True):
    method _apply (line 795) | def _apply(self, fn):
    method forward (line 809) | def forward(self, ims, size=640, augment=False, profile=False):
  class Detections (line 877) | class Detections:
    method __init__ (line 880) | def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shap...
    method _run (line 900) | def _run(self, pprint=False, show=False, save=False, crop=False, rende...
    method show (line 955) | def show(self, labels=True):
    method save (line 962) | def save(self, labels=True, save_dir="runs/detect/exp", exist_ok=False):
    method crop (line 970) | def crop(self, save=True, save_dir="runs/detect/exp", exist_ok=False):
    method render (line 978) | def render(self, labels=True):
    method pandas (line 986) | def pandas(self):
    method tolist (line 996) | def tolist(self):
    method print (line 1011) | def print(self):
    method __len__ (line 1015) | def __len__(self):  # override len(results)
    method __str__ (line 1019) | def __str__(self):  # override print(results)
    method __repr__ (line 1023) | def __repr__(self):
  class Proto (line 1028) | class Proto(nn.Module):
    method __init__ (line 1031) | def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, num...
    method forward (line 1039) | def forward(self, x):
  class Classify (line 1044) | class Classify(nn.Module):
    method __init__ (line 1047) | def __init__(
    method forward (line 1060) | def forward(self, x):

FILE: models/experimental.py
  class Sum (line 14) | class Sum(nn.Module):
    method __init__ (line 17) | def __init__(self, n, weight=False):  # n: number of inputs
    method forward (line 28) | def forward(self, x):
  class MixConv2d (line 44) | class MixConv2d(nn.Module):
    method __init__ (line 47) | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):  # ch_in, ch...
    method forward (line 69) | def forward(self, x):
  class Ensemble (line 74) | class Ensemble(nn.ModuleList):
    method __init__ (line 77) | def __init__(self):
    method forward (line 81) | def forward(self, x, augment=False, profile=False, visualize=False):
  function attempt_load (line 92) | def attempt_load(weights, device=None, inplace=True, fuse=True):

FILE: models/tf.py
  class TFBN (line 51) | class TFBN(keras.layers.Layer):
    method __init__ (line 54) | def __init__(self, w=None):
    method call (line 65) | def call(self, inputs):
  class TFPad (line 70) | class TFPad(keras.layers.Layer):
    method __init__ (line 73) | def __init__(self, pad):
    method call (line 81) | def call(self, inputs):
  class TFConv (line 88) | class TFConv(keras.layers.Layer):
    method __init__ (line 91) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
    method call (line 112) | def call(self, inputs):
  class TFDWConv (line 117) | class TFDWConv(keras.layers.Layer):
    method __init__ (line 120) | def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
    method call (line 137) | def call(self, inputs):
  class TFDWConvTranspose2d (line 142) | class TFDWConvTranspose2d(keras.layers.Layer):
    method __init__ (line 145) | def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
    method call (line 166) | def call(self, inputs):
  class TFFocus (line 173) | class TFFocus(keras.layers.Layer):
    method __init__ (line 176) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
    method call (line 183) | def call(self, inputs):  # x(b,w,h,c) -> y(b,w/2,h/2,4c)
  class TFBottleneck (line 191) | class TFBottleneck(keras.layers.Layer):
    method __init__ (line 194) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):  # ch_i...
    method call (line 202) | def call(self, inputs):
  class TFCrossConv (line 209) | class TFCrossConv(keras.layers.Layer):
    method __init__ (line 212) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
    method call (line 222) | def call(self, inputs):
  class TFConv2d (line 227) | class TFConv2d(keras.layers.Layer):
    method __init__ (line 230) | def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
    method call (line 246) | def call(self, inputs):
  class TFBottleneckCSP (line 251) | class TFBottleneckCSP(keras.layers.Layer):
    method __init__ (line 254) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
    method call (line 268) | def call(self, inputs):
  class TFC3 (line 275) | class TFC3(keras.layers.Layer):
    method __init__ (line 278) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
    method call (line 287) | def call(self, inputs):
  class TFC3x (line 294) | class TFC3x(keras.layers.Layer):
    method __init__ (line 297) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
    method call (line 310) | def call(self, inputs):
  class TFSPP (line 315) | class TFSPP(keras.layers.Layer):
    method __init__ (line 318) | def __init__(self, c1, c2, k=(5, 9, 13), w=None):
    method call (line 328) | def call(self, inputs):
  class TFSPPF (line 334) | class TFSPPF(keras.layers.Layer):
    method __init__ (line 337) | def __init__(self, c1, c2, k=5, w=None):
    method call (line 346) | def call(self, inputs):
  class TFDetect (line 354) | class TFDetect(keras.layers.Layer):
    method __init__ (line 357) | def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None)...
    method call (line 375) | def call(self, inputs):
    method _make_grid (line 402) | def _make_grid(nx=20, ny=20):
  class TFSegment (line 409) | class TFSegment(TFDetect):
    method __init__ (line 412) | def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(64...
    method call (line 422) | def call(self, x):
  class TFProto (line 431) | class TFProto(keras.layers.Layer):
    method __init__ (line 434) | def __init__(self, c1, c_=256, c2=32, w=None):
    method call (line 442) | def call(self, inputs):
  class TFUpsample (line 447) | class TFUpsample(keras.layers.Layer):
    method __init__ (line 450) | def __init__(self, size, scale_factor, mode, w=None):  # warning: all ...
    method call (line 462) | def call(self, inputs):
  class TFConcat (line 467) | class TFConcat(keras.layers.Layer):
    method __init__ (line 470) | def __init__(self, dimension=1, w=None):
    method call (line 476) | def call(self, inputs):
  function parse_model (line 481) | def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
  class TFModel (line 554) | class TFModel:
    method __init__ (line 557) | def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgs...
    method predict (line 576) | def predict(
    method _xywh2xyxy (line 618) | def _xywh2xyxy(xywh):
  class AgnosticNMS (line 625) | class AgnosticNMS(keras.layers.Layer):
    method call (line 628) | def call(self, input, topk_all, iou_thres, conf_thres):
    method _nms (line 638) | def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25):  # agnosti...
  function activations (line 673) | def activations(act=nn.SiLU):
  function representative_dataset_gen (line 685) | def representative_dataset_gen(dataset, ncalib=100):
  function run (line 698) | def run(
  function parse_opt (line 724) | def parse_opt():
  function main (line 739) | def main(opt):

FILE: models/yolo.py
  class Detect (line 44) | class Detect(nn.Module):
    method __init__ (line 51) | def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detecti...
    method forward (line 64) | def forward(self, x):
    method _make_grid (line 93) | def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch...
  class Segment (line 106) | class Segment(Detect):
    method __init__ (line 109) | def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=T...
    method forward (line 121) | def forward(self, x):
  class BaseModel (line 130) | class BaseModel(nn.Module):
    method forward (line 133) | def forward(self, x, profile=False, visualize=False):
    method _forward_once (line 139) | def _forward_once(self, x, profile=False, visualize=False):
    method _profile_one_layer (line 153) | def _profile_one_layer(self, m, x, dt):
    method fuse (line 167) | def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
    method info (line 178) | def info(self, verbose=False, img_size=640):  # print model information
    method _apply (line 182) | def _apply(self, fn):
  class DetectionModel (line 194) | class DetectionModel(BaseModel):
    method __init__ (line 197) | def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):  ...
    method forward (line 242) | def forward(self, x, augment=False, profile=False, visualize=False):
    method _forward_augment (line 248) | def _forward_augment(self, x):
    method _descale_pred (line 263) | def _descale_pred(self, p, flips, scale, img_size):
    method _clip_augmented (line 280) | def _clip_augmented(self, y):
    method _initialize_biases (line 291) | def _initialize_biases(self, cf=None):  # initialize biases into Detec...
  class SegmentationModel (line 307) | class SegmentationModel(DetectionModel):
    method __init__ (line 310) | def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
  class ClassificationModel (line 315) | class ClassificationModel(BaseModel):
    method __init__ (line 318) | def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):  # yaml,...
    method _from_detection_model (line 323) | def _from_detection_model(self, model, nc=1000, cutoff=10):
    method _from_yaml (line 338) | def _from_yaml(self, cfg):
  function parse_model (line 343) | def parse_model(d, ch):  # model_dict, input_channels(3)

FILE: segment/predict.py
  function run (line 70) | def run(
  function parse_opt (line 260) | def parse_opt():
  function main (line 297) | def main(opt):

FILE: segment/train.py
  function train (line 100) | def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hy...
  function parse_opt (line 543) | def parse_opt(known=False):
  function main (line 588) | def main(opt, callbacks=Callbacks()):
  function run (line 747) | def run(**kwargs):

FILE: segment/val.py
  function save_one_txt (line 73) | def save_one_txt(predn, save_conf, shape, file):
  function save_one_json (line 83) | def save_one_json(predn, jdict, path, class_map, pred_masks):
  function process_batch (line 111) | def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=No...
  function run (line 149) | def run(
  function parse_opt (line 441) | def parse_opt():
  function main (line 474) | def main(opt):

FILE: train.py
  function train (line 105) | def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hy...
  function parse_opt (line 544) | def parse_opt(known=False):
  function main (line 613) | def main(opt, callbacks=Callbacks()):
  function run (line 810) | def run(**kwargs):

FILE: utils/__init__.py
  function emojis (line 9) | def emojis(str=""):
  class TryExcept (line 14) | class TryExcept(contextlib.ContextDecorator):
    method __init__ (line 17) | def __init__(self, msg=""):
    method __enter__ (line 23) | def __enter__(self):
    method __exit__ (line 29) | def __exit__(self, exc_type, value, traceback):
  function threaded (line 38) | def threaded(func):
  function join_threads (line 56) | def join_threads(verbose=False):
  function notebook_init (line 66) | def notebook_init(verbose=True):

FILE: utils/activations.py
  class SiLU (line 9) | class SiLU(nn.Module):
    method forward (line 13) | def forward(x):
  class Hardswish (line 20) | class Hardswish(nn.Module):
    method forward (line 24) | def forward(x):
  class Mish (line 31) | class Mish(nn.Module):
    method forward (line 35) | def forward(x):
  class MemoryEfficientMish (line 43) | class MemoryEfficientMish(nn.Module):
    class F (line 46) | class F(torch.autograd.Function):
      method forward (line 50) | def forward(ctx, x):
      method backward (line 58) | def backward(ctx, grad_output):
    method forward (line 67) | def forward(self, x):
  class FReLU (line 72) | class FReLU(nn.Module):
    method __init__ (line 75) | def __init__(self, c1, k=3):  # ch_in, kernel
    method forward (line 83) | def forward(self, x):
  class AconC (line 88) | class AconC(nn.Module):
    method __init__ (line 93) | def __init__(self, c1):
    method forward (line 102) | def forward(self, x):
  class MetaAconC (line 109) | class MetaAconC(nn.Module):
    method __init__ (line 115) | def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
    method forward (line 128) | def forward(self, x):

FILE: utils/augmentations.py
  class Albumentations (line 20) | class Albumentations:
    method __init__ (line 23) | def __init__(self, size=640):
    method __call__ (line 50) | def __call__(self, im, labels, p=1.0):
  function normalize (line 58) | def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
  function denormalize (line 63) | def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
  function augment_hsv (line 73) | def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
  function hist_equalize (line 92) | def hist_equalize(im, clahe=True, bgr=False):
  function replicate (line 103) | def replicate(im, labels):
  function letterbox (line 120) | def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True...
  function random_perspective (line 153) | def random_perspective(
  function copy_paste (line 235) | def copy_paste(im, labels, segments, p=0.5):
  function cutout (line 259) | def cutout(im, labels, p=0.5):
  function mixup (line 286) | def mixup(im, labels, im2, labels2):
  function box_candidates (line 295) | def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1...
  function classify_albumentations (line 303) | def classify_albumentations(
  function classify_transforms (line 348) | def classify_transforms(size=224):
  class LetterBox (line 355) | class LetterBox:
    method __init__ (line 358) | def __init__(self, size=(640, 640), auto=False, stride=32):
    method __call__ (line 367) | def __call__(self, im):  # im = np.array HWC
  class CenterCrop (line 381) | class CenterCrop:
    method __init__ (line 384) | def __init__(self, size=640):
    method __call__ (line 389) | def __call__(self, im):  # im = np.array HWC
  class ToTensor (line 397) | class ToTensor:
    method __init__ (line 400) | def __init__(self, half=False):
    method __call__ (line 407) | def __call__(self, im):  # im = np.array HWC in BGR order

FILE: utils/autoanchor.py
  function check_anchor_order (line 17) | def check_anchor_order(m):
  function check_anchors (line 28) | def check_anchors(dataset, model, thr=4.0, imgsz=640):
  function kmean_anchors (line 67) | def kmean_anchors(dataset="./data/coco128.yaml", n=9, img_size=640, thr=...

FILE: utils/autobatch.py
  function check_train_batch_size (line 13) | def check_train_batch_size(model, imgsz=640, amp=True):
  function autobatch (line 19) | def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):

FILE: utils/callbacks.py
  class Callbacks (line 7) | class Callbacks:
    method __init__ (line 10) | def __init__(self):
    method register_action (line 35) | def register_action(self, hook, name="", callback=None):
    method get_registered_actions (line 47) | def get_registered_actions(self, hook=None):
    method run (line 55) | def run(self, hook, *args, thread=False, **kwargs):

FILE: utils/dataloaders.py
  function get_hash (line 74) | def get_hash(paths):
  function exif_size (line 82) | def exif_size(img):
  function exif_transpose (line 92) | def exif_transpose(image):
  function seed_worker (line 119) | def seed_worker(worker_id):
  function create_dataloader (line 126) | def create_dataloader(
  class InfiniteDataLoader (line 185) | class InfiniteDataLoader(dataloader.DataLoader):
    method __init__ (line 191) | def __init__(self, *args, **kwargs):
    method __len__ (line 199) | def __len__(self):
    method __iter__ (line 203) | def __iter__(self):
  class _RepeatSampler (line 209) | class _RepeatSampler:
    method __init__ (line 216) | def __init__(self, sampler):
    method __iter__ (line 220) | def __iter__(self):
  class LoadScreenshots (line 226) | class LoadScreenshots:
    method __init__ (line 229) | def __init__(self, source, img_size=640, stride=32, auto=True, transfo...
    method __iter__ (line 260) | def __iter__(self):
    method __next__ (line 264) | def __next__(self):
  class LoadImages (line 279) | class LoadImages:
    method __init__ (line 282) | def __init__(self, path, img_size=640, stride=32, auto=True, transform...
    method __iter__ (line 321) | def __iter__(self):
    method __next__ (line 326) | def __next__(self):
    method _new_video (line 367) | def _new_video(self, path):
    method _cv2_rotate (line 375) | def _cv2_rotate(self, im):
    method __len__ (line 385) | def __len__(self):
  class LoadStreams (line 390) | class LoadStreams:
    method __init__ (line 393) | def __init__(self, sources="file.streams", img_size=640, stride=32, au...
    method update (line 441) | def update(self, i, cap, stream):
    method __iter__ (line 459) | def __iter__(self):
    method __next__ (line 464) | def __next__(self):
    method __len__ (line 481) | def __len__(self):
  function img2label_paths (line 486) | def img2label_paths(img_paths):
  class LoadImagesAndLabels (line 493) | class LoadImagesAndLabels(Dataset):
    method __init__ (line 499) | def __init__(
    method check_cache_ram (line 653) | def check_cache_ram(self, safety_margin=0.1, prefix=""):
    method cache_labels (line 672) | def cache_labels(self, path=Path("./labels.cache"), prefix=""):
    method __len__ (line 712) | def __len__(self):
    method __getitem__ (line 722) | def __getitem__(self, index):
    method load_image (line 800) | def load_image(self, i):
    method cache_images_to_disk (line 821) | def cache_images_to_disk(self, i):
    method load_mosaic (line 827) | def load_mosaic(self, index):
    method load_mosaic9 (line 888) | def load_mosaic9(self, index):
    method collate_fn (line 968) | def collate_fn(batch):
    method collate_fn4 (line 976) | def collate_fn4(batch):
  function flatten_recursive (line 1005) | def flatten_recursive(path=DATASETS_DIR / "coco128"):
  function extract_boxes (line 1015) | def extract_boxes(path=DATASETS_DIR / "coco128"):  # from utils.dataload...
  function autosplit (line 1051) | def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0...
  function verify_image_label (line 1078) | def verify_image_label(args):
  class HUBDatasetStats (line 1129) | class HUBDatasetStats:
    method __init__ (line 1144) | def __init__(self, path="coco128.yaml", autodownload=False):
    method _find_yaml (line 1163) | def _find_yaml(dir):
    method _unzip (line 1173) | def _unzip(self, path):
    method _hub_ops (line 1185) | def _hub_ops(self, f, max_dim=1920):
    method get_json (line 1205) | def get_json(self, save=False, verbose=False):
    method process_images (line 1245) | def process_images(self):
  class ClassificationDataset (line 1261) | class ClassificationDataset(torchvision.datasets.ImageFolder):
    method __init__ (line 1270) | def __init__(self, root, augment, imgsz, cache=False):
    method __getitem__ (line 1281) | def __getitem__(self, i):
  function create_classification_dataloader (line 1299) | def create_classification_dataloader(

FILE: utils/downloads.py
  function is_url (line 13) | def is_url(url, check=True):
  function gsutil_getsize (line 24) | def gsutil_getsize(url=""):
  function url_getsize (line 30) | def url_getsize(url="https://ultralytics.com/images/bus.jpg"):
  function curl_download (line 36) | def curl_download(url, filename, *, silent: bool = False) -> bool:
  function safe_download (line 56) | def safe_download(file, url, url2=None, min_bytes=1e0, error_msg=""):
  function attempt_download (line 80) | def attempt_download(file, repo="ultralytics/yolov5", release="v7.0"):

FILE: utils/flask_rest_api/restapi.py
  function predict (line 18) | def predict(model):

FILE: utils/general.py
  function is_ascii (line 65) | def is_ascii(s=""):
  function is_chinese (line 71) | def is_chinese(s="人工智能"):
  function is_colab (line 76) | def is_colab():
  function is_jupyter (line 81) | def is_jupyter():
  function is_kaggle (line 95) | def is_kaggle():
  function is_docker (line 100) | def is_docker() -> bool:
  function is_writeable (line 111) | def is_writeable(dir, test=False):
  function set_logging (line 128) | def set_logging(name=LOGGING_NAME, verbose=True):
  function user_config_dir (line 162) | def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"):
  class Profile (line 179) | class Profile(contextlib.ContextDecorator):
    method __init__ (line 182) | def __init__(self, t=0.0):
    method __enter__ (line 188) | def __enter__(self):
    method __exit__ (line 195) | def __exit__(self, type, value, traceback):
    method time (line 200) | def time(self):
  class Timeout (line 207) | class Timeout(contextlib.ContextDecorator):
    method __init__ (line 210) | def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors...
    method _timeout_handler (line 217) | def _timeout_handler(self, signum, frame):
    method __enter__ (line 221) | def __enter__(self):
    method __exit__ (line 227) | def __exit__(self, exc_type, exc_val, exc_tb):
  class WorkingDirectory (line 235) | class WorkingDirectory(contextlib.ContextDecorator):
    method __init__ (line 238) | def __init__(self, new_dir):
    method __enter__ (line 243) | def __enter__(self):
    method __exit__ (line 247) | def __exit__(self, exc_type, exc_val, exc_tb):
  function methods (line 252) | def methods(instance):
  function print_args (line 257) | def print_args(args: dict | None = None, show_file=True, show_func=False):
  function init_seeds (line 272) | def init_seeds(seed=0, deterministic=False):
  function intersect_dicts (line 289) | def intersect_dicts(da, db, exclude=()):
  function get_default_args (line 296) | def get_default_args(func):
  function get_latest_run (line 302) | def get_latest_run(search_dir="."):
  function file_age (line 309) | def file_age(path=__file__):
  function file_date (line 315) | def file_date(path=__file__):
  function file_size (line 321) | def file_size(path):
  function check_online (line 333) | def check_online():
  function git_describe (line 349) | def git_describe(path=ROOT):  # path must be a directory
  function check_git_status (line 360) | def check_git_status(repo="ultralytics/yolov5", branch="master"):
  function check_git_info (line 389) | def check_git_info(path="."):
  function check_python (line 410) | def check_python(minimum="3.7.0"):
  function check_version (line 415) | def check_version(current="0.0.0", minimum="0.0.0", name="version ", pin...
  function check_img_size (line 427) | def check_img_size(imgsz, s=32, floor=0):
  function check_imshow (line 441) | def check_imshow(warn=False):
  function check_suffix (line 457) | def check_suffix(file="yolov5s.pt", suffix=(".pt",), msg=""):
  function check_yaml (line 468) | def check_yaml(file, suffix=(".yaml", ".yml")):
  function check_file (line 473) | def check_file(file, suffix=""):
  function check_font (line 505) | def check_font(font=FONT, progress=False):
  function check_dataset (line 515) | def check_dataset(data, autodownload=True):
  function check_amp (line 580) | def check_amp(model):
  function yaml_load (line 608) | def yaml_load(file="data.yaml"):
  function yaml_save (line 614) | def yaml_save(file="data.yaml", data=None):
  function unzip_file (line 622) | def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX")):
  function url2file (line 634) | def url2file(url):
  function download (line 640) | def download(url, dir=".", unzip=True, delete=True, curl=False, threads=...
  function make_divisible (line 689) | def make_divisible(x, divisor):
  function clean_str (line 696) | def clean_str(s):
  function one_cycle (line 701) | def one_cycle(y1=0.0, y2=1.0, steps=100):
  function colorstr (line 708) | def colorstr(*input):
  function labels_to_class_weights (line 738) | def labels_to_class_weights(labels, nc=80):
  function labels_to_image_weights (line 759) | def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
  function coco80_to_coco91_class (line 766) | def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index...
  function xyxy2xywh (line 859) | def xyxy2xywh(x):
  function xywh2xyxy (line 869) | def xywh2xyxy(x):
  function xywhn2xyxy (line 879) | def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
  function xyxy2xywhn (line 889) | def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
  function xyn2xy (line 901) | def xyn2xy(x, w=640, h=640, padw=0, padh=0):
  function segment2box (line 909) | def segment2box(segment, width=640, height=640):
  function segments2boxes (line 922) | def segments2boxes(segments):
  function resample_segments (line 931) | def resample_segments(segments, n=1000):
  function scale_boxes (line 941) | def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
  function scale_segments (line 957) | def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, nor...
  function clip_boxes (line 978) | def clip_boxes(boxes, shape):
  function clip_segments (line 990) | def clip_segments(segments, shape):
  function non_max_suppression (line 1000) | def non_max_suppression(
  function strip_optimizer (line 1112) | def strip_optimizer(f="best.pt", s=""):  # from utils.general import *; ...
  function print_mutation (line 1128) | def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr...
  function apply_classifier (line 1183) | def apply_classifier(x, model, img, im0):
  function increment_path (line 1218) | def increment_path(path, exist_ok=False, sep="", mkdir=False):
  function imread (line 1252) | def imread(filename, flags=cv2.IMREAD_COLOR):
  function imwrite (line 1257) | def imwrite(filename, img):
  function imshow (line 1269) | def imshow(path, im):

FILE: utils/loggers/__init__.py
  function SummaryWriter (line 24) | def SummaryWriter(*args):
  class Loggers (line 65) | class Loggers:
    method __init__ (line 68) | def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, lo...
    method remote_dataset (line 145) | def remote_dataset(self):
    method on_train_start (line 157) | def on_train_start(self):
    method on_pretrain_routine_start (line 162) | def on_pretrain_routine_start(self):
    method on_pretrain_routine_end (line 167) | def on_pretrain_routine_end(self, labels, names):
    method on_train_batch_end (line 182) | def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
    method on_train_epoch_end (line 203) | def on_train_epoch_end(self, epoch):
    method on_val_start (line 211) | def on_val_start(self):
    method on_val_image_end (line 216) | def on_val_image_end(self, pred, predn, path, names, im):
    method on_val_batch_end (line 225) | def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):
    method on_val_end (line 233) | def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusi...
    method on_fit_epoch_end (line 245) | def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
    method on_model_save (line 278) | def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
    method on_train_end (line 292) | def on_train_end(self, last, best, epoch, results):
    method on_params_update (line 328) | def on_params_update(self, params: dict):
  class GenericLogger (line 336) | class GenericLogger:
    method __init__ (line 346) | def __init__(self, opt, console_logger, include=("tb", "wandb")):
    method log_metrics (line 366) | def log_metrics(self, metrics, epoch):
    method log_images (line 382) | def log_images(self, files, name="Images", epoch=0):
    method log_graph (line 394) | def log_graph(self, model, imgsz=(640, 640)):
    method log_model (line 399) | def log_model(self, model_path, epoch=0, metadata=None):
    method update_params (line 408) | def update_params(self, params):
  function log_tensorboard_graph (line 414) | def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
  function web_project_name (line 427) | def web_project_name(project):

FILE: utils/loggers/clearml/clearml_utils.py
  function construct_dataset (line 21) | def construct_dataset(clearml_info_string):
  class ClearmlLogger (line 63) | class ClearmlLogger:
    method __init__ (line 73) | def __init__(self, opt, hyp):
    method log_debug_samples (line 122) | def log_debug_samples(self, files, title="Debug Samples"):
    method log_image_with_boxes (line 137) | def log_image_with_boxes(self, image_path, boxes, class_names, image, ...

FILE: utils/loggers/comet/__init__.py
  class CometLogger (line 66) | class CometLogger:
    method __init__ (line 69) | def __init__(self, opt, hyp, run_id=None, job_type="Training", **exper...
    method _get_experiment (line 168) | def _get_experiment(self, mode, experiment_id=None):
    method log_metrics (line 203) | def log_metrics(self, log_dict, **kwargs):
    method log_parameters (line 207) | def log_parameters(self, log_dict, **kwargs):
    method log_asset (line 211) | def log_asset(self, asset_path, **kwargs):
    method log_asset_data (line 215) | def log_asset_data(self, asset, **kwargs):
    method log_image (line 219) | def log_image(self, img, **kwargs):
    method log_model (line 223) | def log_model(self, path, opt, epoch, fitness_score, best_model=False):
    method check_dataset (line 249) | def check_dataset(self, data_file):
    method log_predictions (line 262) | def log_predictions(self, image, labelsn, path, shape, predn):
    method preprocess_prediction (line 303) | def preprocess_prediction(self, image, labels, shape, pred):
    method add_assets_to_artifact (line 325) | def add_assets_to_artifact(self, artifact, path, asset_path, split):
    method upload_dataset_artifact (line 351) | def upload_dataset_artifact(self):
    method download_dataset_artifact (line 376) | def download_dataset_artifact(self, artifact_path):
    method update_data_paths (line 396) | def update_data_paths(self, data_dict):
    method on_pretrain_routine_end (line 409) | def on_pretrain_routine_end(self, paths):
    method on_train_start (line 422) | def on_train_start(self):
    method on_train_epoch_start (line 426) | def on_train_epoch_start(self):
    method on_train_epoch_end (line 430) | def on_train_epoch_end(self, epoch):
    method on_train_batch_start (line 436) | def on_train_batch_start(self):
    method on_train_batch_end (line 440) | def on_train_batch_end(self, log_dict, step):
    method on_train_end (line 448) | def on_train_end(self, files, save_dir, last, best, epoch, results):
    method on_val_start (line 476) | def on_val_start(self):
    method on_val_batch_start (line 480) | def on_val_batch_start(self):
    method on_val_batch_end (line 484) | def on_val_batch_end(self, batch_i, images, targets, paths, shapes, ou...
    method on_val_end (line 503) | def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusi...
    method on_fit_epoch_end (line 540) | def on_fit_epoch_end(self, result, epoch):
    method on_model_save (line 544) | def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
    method on_params_update (line 549) | def on_params_update(self, params):
    method finish_run (line 553) | def finish_run(self):

FILE: utils/loggers/comet/comet_utils.py
  function download_model_checkpoint (line 21) | def download_model_checkpoint(opt, experiment):
  function set_opt_parameters (line 69) | def set_opt_parameters(opt, experiment):
  function check_comet_weights (line 99) | def check_comet_weights(opt):
  function check_comet_resume (line 122) | def check_comet_resume(opt):

FILE: utils/loggers/comet/hpo.py
  function get_args (line 29) | def get_args(known=False):
  function run (line 88) | def run(parameters, opt):

FILE: utils/loggers/wandb/wandb_utils.py
  class WandbLogger (line 33) | class WandbLogger:
    method __init__ (line 45) | def __init__(self, opt, run_id=None, job_type="Training"):
    method setup_training (line 81) | def setup_training(self, opt):
    method log_model (line 112) | def log_model(self, path, opt, epoch, fitness_score, best_model=False):
    method val_one_image (line 144) | def val_one_image(self, pred, predn, path, names, im):
    method log (line 148) | def log(self, log_dict):
    method end_epoch (line 158) | def end_epoch(self):
    method finish_run (line 176) | def finish_run(self):
  function all_logging_disabled (line 187) | def all_logging_disabled(highest_level=logging.CRITICAL):

FILE: utils/loss.py
  function smooth_BCE (line 11) | def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues...
  class BCEBlurWithLogitsLoss (line 16) | class BCEBlurWithLogitsLoss(nn.Module):
    method __init__ (line 19) | def __init__(self, alpha=0.05):
    method forward (line 25) | def forward(self, pred, true):
  class FocalLoss (line 38) | class FocalLoss(nn.Module):
    method __init__ (line 41) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
    method forward (line 52) | def forward(self, pred, true):
  class QFocalLoss (line 75) | class QFocalLoss(nn.Module):
    method __init__ (line 78) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
    method forward (line 89) | def forward(self, pred, true):
  class ComputeLoss (line 107) | class ComputeLoss:
    method __init__ (line 113) | def __init__(self, model, autobalance=False):
    method __call__ (line 140) | def __call__(self, p, targets):  # predictions, targets
    method build_targets (line 192) | def build_targets(self, p, targets):

FILE: utils/metrics.py
  function fitness (line 15) | def fitness(x):
  function smooth (line 21) | def smooth(y, f=0.05):
  function ap_per_class (line 29) | def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir="....
  function compute_ap (line 95) | def compute_ap(recall, precision):
  class ConfusionMatrix (line 118) | class ConfusionMatrix:
    method __init__ (line 121) | def __init__(self, nc, conf=0.25, iou_thres=0.45):
    method process_batch (line 128) | def process_batch(self, detections, labels):
    method tp_fp (line 175) | def tp_fp(self):
    method plot (line 183) | def plot(self, normalize=True, save_dir="", names=()):
    method print (line 215) | def print(self):
  function bbox_iou (line 221) | def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, ...
  function box_iou (line 262) | def box_iou(box1, box2, eps=1e-7):
  function bbox_ioa (line 282) | def bbox_ioa(box1, box2, eps=1e-7):
  function wh_iou (line 303) | def wh_iou(wh1, wh2, eps=1e-7):
  function plot_pr_curve (line 315) | def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()):
  function plot_mc_curve (line 340) | def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabe...

FILE: utils/plots.py
  class Colors (line 31) | class Colors:
    method __init__ (line 34) | def __init__(self):
    method __call__ (line 61) | def __call__(self, i, bgr=False):
    method hex2rgb (line 67) | def hex2rgb(h):  # rgb order (PIL)
  function feature_visualization (line 75) | def feature_visualization(x, module_type, stage, n=32, save_dir=Path("ru...
  function hist2d (line 99) | def hist2d(x, y, n=100):
  function butter_lowpass_filtfilt (line 108) | def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
  function output_to_target (line 128) | def output_to_target(output, max_det=300):
  function plot_images (line 141) | def plot_images(images, targets, paths=None, fname="images.jpg", names=N...
  function plot_lr_scheduler (line 205) | def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""):
  function plot_val_txt (line 222) | def plot_val_txt():  # from utils.plots import *; plot_val()
  function plot_targets_txt (line 239) | def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
  function plot_val_study (line 252) | def plot_val_study(file="", dir="", x=None):  # from utils.plots import ...
  function plot_labels (line 303) | def plot_labels(labels, names=(), save_dir=Path("")):
  function imshow_cls (line 348) | def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=...
  function plot_evolve (line 378) | def plot_evolve(evolve_csv="path/to/evolve.csv"):  # from utils.plots im...
  function plot_results (line 405) | def plot_results(file="path/to/results.csv", dir=""):
  function profile_idetection (line 432) | def profile_idetection(start=0, stop=0, labels=(), save_dir=""):
  function save_one_box (line 465) | def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, squar...

FILE: utils/segment/augmentations.py
  function mixup (line 14) | def mixup(im, labels, segments, im2, labels2, segments2):
  function random_perspective (line 25) | def random_perspective(

FILE: utils/segment/dataloaders.py
  function create_dataloader (line 21) | def create_dataloader(
  class LoadImagesAndLabelsAndMasks (line 84) | class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/...
    method __init__ (line 87) | def __init__(
    method __getitem__ (line 124) | def __getitem__(self, index):
    method load_mosaic (line 231) | def load_mosaic(self, index):
    method collate_fn (line 294) | def collate_fn(batch):
  function polygon2mask (line 303) | def polygon2mask(img_size, polygons, color=1, downsample_ratio=1):
  function polygons2masks (line 322) | def polygons2masks(img_size, polygons, color, downsample_ratio=1):
  function polygons2masks_overlap (line 336) | def polygons2masks_overlap(img_size, segments, downsample_ratio=1):

FILE: utils/segment/general.py
  function crop_mask (line 9) | def crop_mask(masks, boxes):
  function process_mask_upsample (line 24) | def process_mask_upsample(protos, masks_in, bboxes, shape):
  function process_mask (line 37) | def process_mask(protos, masks_in, bboxes, shape, upsample=False):
  function process_mask_native (line 59) | def process_mask_native(protos, masks_in, bboxes, shape):
  function scale_image (line 78) | def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
  function mask_iou (line 102) | def mask_iou(mask1, mask2, eps=1e-7):
  function masks_iou (line 113) | def masks_iou(mask1, mask2, eps=1e-7):
  function masks2segments (line 124) | def masks2segments(masks, strategy="largest"):

FILE: utils/segment/loss.py
  class ComputeLoss (line 14) | class ComputeLoss:
    method __init__ (line 17) | def __init__(self, model, autobalance=False, overlap=False):
    method __call__ (line 47) | def __call__(self, preds, targets, masks):  # predictions, targets, model
    method single_mask_loss (line 117) | def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
    method build_targets (line 126) | def build_targets(self, p, targets):

FILE: utils/segment/metrics.py
  function fitness (line 9) | def fitness(x):
  function ap_per_class_box_and_mask (line 15) | def ap_per_class_box_and_mask(
  class Metric (line 56) | class Metric:
    method __init__ (line 59) | def __init__(self) -> None:
    method ap50 (line 68) | def ap50(self):
    method ap (line 77) | def ap(self):
    method mp (line 86) | def mp(self):
    method mr (line 95) | def mr(self):
    method map50 (line 104) | def map50(self):
    method map (line 113) | def map(self):
    method mean_results (line 121) | def mean_results(self):
    method class_result (line 125) | def class_result(self, i):
    method get_maps (line 129) | def get_maps(self, nc):
    method update (line 138) | def update(self, results):
  class Metrics (line 151) | class Metrics:
    method __init__ (line 154) | def __init__(self) -> None:
    method update (line 159) | def update(self, results):
    method mean_results (line 167) | def mean_results(self):
    method class_result (line 171) | def class_result(self, i):
    method get_maps (line 175) | def get_maps(self, nc):
    method ap_class_index (line 180) | def ap_class_index(self):

FILE: utils/segment/plots.py
  function plot_images_and_masks (line 19) | def plot_images_and_masks(images, targets, masks, paths=None, fname="ima...
  function plot_results_with_masks (line 115) | def plot_results_with_masks(file="path/to/results.csv", dir="", best=True):

FILE: utils/torch_utils.py
  function smart_inference_mode (line 36) | def smart_inference_mode(torch_1_9=check_version(torch.__version__, "1.9...
  function smartCrossEntropyLoss (line 46) | def smartCrossEntropyLoss(label_smoothing=0.0):
  function smart_DDP (line 57) | def smart_DDP(model):
  function reshape_classifier_output (line 72) | def reshape_classifier_output(model, n=1000):
  function torch_distributed_zero_first (line 98) | def torch_distributed_zero_first(local_rank: int):
  function device_count (line 107) | def device_count():
  function select_device (line 117) | def select_device(device="", batch_size=0, newline=True):
  function time_sync (line 154) | def time_sync():
  function profile (line 161) | def profile(input, ops, n=10, device=None):
  function is_parallel (line 215) | def is_parallel(model):
  function de_parallel (line 220) | def de_parallel(model):
  function initialize_weights (line 225) | def initialize_weights(model):
  function find_modules (line 240) | def find_modules(model, mclass=nn.Conv2d):
  function sparsity (line 245) | def sparsity(model):
  function prune (line 255) | def prune(model, amount=0.3):
  function fuse_conv_and_bn (line 266) | def fuse_conv_and_bn(conv, bn):
  function model_info (line 296) | def model_info(model, verbose=False, imgsz=640):
  function scale_img (line 326) | def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,...
  function copy_attr (line 340) | def copy_attr(a, b, include=(), exclude=()):
  function smart_optimizer (line 349) | def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e...
  function smart_hub_load (line 382) | def smart_hub_load(repo="ultralytics/yolov5", model="yolov5s", **kwargs):
  function smart_resume (line 397) | def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs...
  class EarlyStopping (line 421) | class EarlyStopping:
    method __init__ (line 424) | def __init__(self, patience=30):
    method __call__ (line 433) | def __call__(self, epoch, fitness):
  class ModelEMA (line 451) | class ModelEMA:
    method __init__ (line 457) | def __init__(self, model, decay=0.9999, tau=2000, updates=0):
    method update (line 467) | def update(self, model):
    method update_attr (line 479) | def update_attr(self, model, include=(), exclude=("process_group", "re...

FILE: utils/triton.py
  class TritonRemoteModel (line 11) | class TritonRemoteModel:
    method __init__ (line 18) | def __init__(self, url: str):
    method runtime (line 50) | def runtime(self):
    method __call__ (line 54) | def __call__(self, *args, **kwargs) -> torch.Tensor | tuple[torch.Tens...
    method _create_inputs (line 68) | def _create_inputs(self, *args, **kwargs):

FILE: val.py
  function save_one_txt (line 64) | def save_one_txt(predn, save_conf, shape, file):
  function save_one_json (line 106) | def save_one_json(predn, jdict, path, class_map):
  function process_batch (line 147) | def process_batch(detections, labels, iouv):
  function run (line 192) | def run(
  function parse_opt (line 489) | def parse_opt():
  function main (line 562) | def main(opt):