SYMBOL INDEX (325 symbols across 18 files)

FILE: face_detector.py
  class YoloDetector (line 22) | class YoloDetector:
    method __init__ (line 23) | def __init__(self, weights_name='yolov5n_state_dict.pt', config_name='...
    method init_detector (line 42) | def init_detector(self,weights_name,config_name):
    method _preprocess (line 58) | def _preprocess(self,imgs):
    method _postprocess (line 80) | def _postprocess(self, imgs, origimgs, pred, conf_thres, iou_thres):
    method get_frontal_predict (line 113) | def get_frontal_predict(self, box, points):
    method align (line 130) | def align(self, img, points):
    method predict (line 142) | def predict(self, imgs, conf_thres = 0.3, iou_thres = 0.5):
    method __call__ (line 187) | def __call__(self,*args):

FILE: models/common.py
  function autopad (line 15) | def autopad(k, p=None):  # kernel, padding
  function channel_shuffle (line 21) | def channel_shuffle(x, groups):
  function DWConv (line 33) | def DWConv(c1, c2, k=1, s=1, act=True):
  class Conv (line 37) | class Conv(nn.Module):
    method __init__ (line 39) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in,...
    method forward (line 46) | def forward(self, x):
    method fuseforward (line 49) | def fuseforward(self, x):
  class StemBlock (line 52) | class StemBlock(nn.Module):
    method __init__ (line 53) | def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
    method forward (line 61) | def forward(self, x):
  class Bottleneck (line 69) | class Bottleneck(nn.Module):
    method __init__ (line 71) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_ou...
    method forward (line 78) | def forward(self, x):
  class BottleneckCSP (line 81) | class BottleneckCSP(nn.Module):
    method __init__ (line 83) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ...
    method forward (line 94) | def forward(self, x):
  class C3 (line 100) | class C3(nn.Module):
    method __init__ (line 102) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ...
    method forward (line 110) | def forward(self, x):
  class ShuffleV2Block (line 113) | class ShuffleV2Block(nn.Module):
    method __init__ (line 114) | def __init__(self, inp, oup, stride):
    method depthwise_conv (line 147) | def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
    method forward (line 150) | def forward(self, x):
  class SPP (line 159) | class SPP(nn.Module):
    method __init__ (line 161) | def __init__(self, c1, c2, k=(5, 9, 13)):
    method forward (line 168) | def forward(self, x):
  class Focus (line 173) | class Focus(nn.Module):
    method __init__ (line 175) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in,...
    method forward (line 180) | def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
  class Contract (line 185) | class Contract(nn.Module):
    method __init__ (line 187) | def __init__(self, gain=2):
    method forward (line 191) | def forward(self, x):
  class Expand (line 199) | class Expand(nn.Module):
    method __init__ (line 201) | def __init__(self, gain=2):
    method forward (line 205) | def forward(self, x):
  class Concat (line 213) | class Concat(nn.Module):
    method __init__ (line 215) | def __init__(self, dimension=1):
    method forward (line 219) | def forward(self, x):
  class NMS (line 223) | class NMS(nn.Module):
    method __init__ (line 229) | def __init__(self):
    method forward (line 232) | def forward(self, x):
  class autoShape (line 235) | class autoShape(nn.Module):
    method __init__ (line 242) | def __init__(self, model):
    method autoshape (line 246) | def autoshape(self):
    method forward (line 250) | def forward(self, imgs, size=640, augment=False, profile=False):
  class Detections (line 297) | class Detections:
    method __init__ (line 299) | def __init__(self, imgs, pred, names=None):
    method display (line 312) | def display(self, pprint=False, show=False, save=False, render=False):
    method print (line 336) | def print(self):
    method show (line 339) | def show(self):
    method save (line 342) | def save(self):
    method render (line 345) | def render(self):
    method __len__ (line 349) | def __len__(self):
    method tolist (line 352) | def tolist(self):
  class Classify (line 361) | class Classify(nn.Module):
    method __init__ (line 363) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, k...
    method forward (line 369) | def forward(self, x):

FILE: models/experimental.py
  class CrossConv (line 11) | class CrossConv(nn.Module):
    method __init__ (line 13) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
    method forward (line 21) | def forward(self, x):
  class Sum (line 25) | class Sum(nn.Module):
    method __init__ (line 27) | def __init__(self, n, weight=False):  # n: number of inputs
    method forward (line 34) | def forward(self, x):
  class GhostConv (line 46) | class GhostConv(nn.Module):
    method __init__ (line 48) | def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out,...
    method forward (line 54) | def forward(self, x):
  class GhostBottleneck (line 59) | class GhostBottleneck(nn.Module):
    method __init__ (line 61) | def __init__(self, c1, c2, k, s):
    method forward (line 70) | def forward(self, x):
  class MixConv2d (line 74) | class MixConv2d(nn.Module):
    method __init__ (line 76) | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
    method forward (line 94) | def forward(self, x):
  class Ensemble (line 98) | class Ensemble(nn.ModuleList):
    method __init__ (line 100) | def __init__(self):
    method forward (line 103) | def forward(self, x, augment=False):
  function attempt_load (line 113) | def attempt_load(weights, map_location=None):

FILE: models/yolo.py
  class Detect (line 27) | class Detect(nn.Module):
    method __init__ (line 31) | def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
    method forward (line 45) | def forward(self, x):
    method _make_grid (line 88) | def _make_grid(nx=20, ny=20):
  class Model (line 93) | class Model(nn.Module):
    method __init__ (line 94) | def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None):  # model, input...
    method forward (line 129) | def forward(self, x, augment=False, profile=False):
    method forward_once (line 149) | def forward_once(self, x, profile=False):
    method _initialize_biases (line 170) | def _initialize_biases(self, cf=None):  # initialize biases into Detec...
    method _print_biases (line 180) | def _print_biases(self):
    method fuse (line 191) | def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
    method nms (line 201) | def nms(self, mode=True):  # add or remove NMS module
    method autoshape (line 215) | def autoshape(self):  # add autoShape module
    method info (line 221) | def info(self, verbose=False, img_size=640):  # print model information
  function parse_model (line 225) | def parse_model(d, ch):  # model_dict, input_channels(3)

FILE: utils/activations.py
  class SiLU (line 9) | class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
    method forward (line 11) | def forward(x):
  class Hardswish (line 15) | class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
    method forward (line 17) | def forward(x):
  class MemoryEfficientSwish (line 22) | class MemoryEfficientSwish(nn.Module):
    class F (line 23) | class F(torch.autograd.Function):
      method forward (line 25) | def forward(ctx, x):
      method backward (line 30) | def backward(ctx, grad_output):
    method forward (line 35) | def forward(self, x):
  class Mish (line 40) | class Mish(nn.Module):
    method forward (line 42) | def forward(x):
  class MemoryEfficientMish (line 46) | class MemoryEfficientMish(nn.Module):
    class F (line 47) | class F(torch.autograd.Function):
      method forward (line 49) | def forward(ctx, x):
      method backward (line 54) | def backward(ctx, grad_output):
    method forward (line 60) | def forward(self, x):
  class FReLU (line 65) | class FReLU(nn.Module):
    method __init__ (line 66) | def __init__(self, c1, k=3):  # ch_in, kernel
    method forward (line 71) | def forward(self, x):

FILE: utils/autoanchor.py
  function check_anchor_order (line 12) | def check_anchor_order(m):
  function check_anchors (line 23) | def check_anchors(dataset, model, thr=4.0, imgsz=640):
  function kmean_anchors (line 58) | def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0...

FILE: utils/datasets.py
  function get_hash (line 38) | def get_hash(files):
  function exif_size (line 43) | def exif_size(img):
  function create_dataloader (line 58) | def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, au...
  class InfiniteDataLoader (line 87) | class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
    method __init__ (line 93) | def __init__(self, *args, **kwargs):
    method __len__ (line 98) | def __len__(self):
    method __iter__ (line 101) | def __iter__(self):
  class _RepeatSampler (line 106) | class _RepeatSampler(object):
    method __init__ (line 113) | def __init__(self, sampler):
    method __iter__ (line 116) | def __iter__(self):
  class LoadImages (line 121) | class LoadImages:  # for inference
    method __init__ (line 122) | def __init__(self, path, img_size=640):
    method __iter__ (line 150) | def __iter__(self):
    method __next__ (line 154) | def __next__(self):
    method new_video (line 192) | def new_video(self, path):
    method __len__ (line 197) | def __len__(self):
  class LoadWebcam (line 201) | class LoadWebcam:  # for inference
    method __init__ (line 202) | def __init__(self, pipe='0', img_size=640):
    method __iter__ (line 215) | def __iter__(self):
    method __next__ (line 219) | def __next__(self):
    method __len__ (line 254) | def __len__(self):
  class LoadStreams (line 258) | class LoadStreams:  # multiple IP or RTSP cameras
    method __init__ (line 259) | def __init__(self, sources='streams.txt', img_size=640):
    method update (line 292) | def update(self, index, cap):
    method __iter__ (line 304) | def __iter__(self):
    method __next__ (line 308) | def __next__(self):
    method __len__ (line 327) | def __len__(self):
  function img2label_paths (line 331) | def img2label_paths(img_paths):
  class LoadImagesAndLabels (line 337) | class LoadImagesAndLabels(Dataset):  # for training/testing
    method __init__ (line 338) | def __init__(self, path, img_size=640, batch_size=16, augment=False, h...
    method cache_labels (line 437) | def cache_labels(self, path=Path('./labels.cache'), prefix=''):
    method __len__ (line 483) | def __len__(self):
    method __getitem__ (line 492) | def __getitem__(self, index):
    method collate_fn (line 569) | def collate_fn(batch):
    method collate_fn4 (line 576) | def collate_fn4(batch):
  function load_image (line 603) | def load_image(self, index):
  function augment_hsv (line 620) | def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
  function load_mosaic (line 639) | def load_mosaic(self, index):
  function load_mosaic9 (line 693) | def load_mosaic9(self, index):
  function replicate (line 763) | def replicate(img, labels):
  function letterbox (line 780) | def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=Tru...
  function random_perspective (line 813) | def random_perspective(img, targets=(), degrees=10, translate=.1, scale=...
  function box_candidates (line 900) | def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e...
  function cutout (line 908) | def cutout(image, labels):
  function create_folder (line 954) | def create_folder(path='./new'):
  function flatten_recursive (line 961) | def flatten_recursive(path='../coco128'):
  function extract_boxes (line 969) | def extract_boxes(path='../coco128/'):  # from utils.datasets import *; ...
  function autosplit (line 1004) | def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0)):  # from utils...

FILE: utils/face_datasets.py
  function get_hash (line 35) | def get_hash(files):
  function img2label_paths (line 39) | def img2label_paths(img_paths):
  function exif_size (line 44) | def exif_size(img):
  function create_dataloader (line 58) | def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, au...
  class InfiniteDataLoader (line 85) | class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
    method __init__ (line 91) | def __init__(self, *args, **kwargs):
    method __len__ (line 96) | def __len__(self):
    method __iter__ (line 99) | def __iter__(self):
  class _RepeatSampler (line 102) | class _RepeatSampler(object):
    method __init__ (line 109) | def __init__(self, sampler):
    method __iter__ (line 112) | def __iter__(self):
  class LoadFaceImagesAndLabels (line 116) | class LoadFaceImagesAndLabels(Dataset):  # for training/testing
    method __init__ (line 117) | def __init__(self, path, img_size=640, batch_size=16, augment=False, h...
    method cache_labels (line 216) | def cache_labels(self, path=Path('./labels.cache')):
    method __len__ (line 262) | def __len__(self):
    method __getitem__ (line 271) | def __getitem__(self, index):
    method collate_fn (line 405) | def collate_fn(batch):
  function showlabels (line 412) | def showlabels(img, boxs, landmarks):
  function load_mosaic_face (line 426) | def load_mosaic_face(self, index):
  function load_image (line 515) | def load_image(self, index):
  function augment_hsv (line 532) | def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
  function replicate (line 550) | def replicate(img, labels):
  function letterbox (line 567) | def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=Tru...
  function random_perspective (line 600) | def random_perspective(img, targets=(), degrees=10, translate=.1, scale=...
  function box_candidates (line 715) | def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1):  # bo...
  function cutout (line 723) | def cutout(image, labels):
  function create_folder (line 769) | def create_folder(path='./new'):
  function flatten_recursive (line 776) | def flatten_recursive(path='../coco128'):
  function extract_boxes (line 784) | def extract_boxes(path='../coco128/'):  # from utils.datasets import *; ...
  function autosplit (line 819) | def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0)):  # from utils...

FILE: utils/general.py
  function set_logging (line 30) | def set_logging(rank=-1):
  function init_seeds (line 36) | def init_seeds(seed=0):
  function get_latest_run (line 43) | def get_latest_run(search_dir='.'):
  function check_online (line 49) | def check_online():
  function check_git_status (line 59) | def check_git_status():
  function check_requirements (line 80) | def check_requirements(file='requirements.txt'):
  function check_img_size (line 88) | def check_img_size(img_size, s=32):
  function check_file (line 96) | def check_file(file):
  function check_dataset (line 107) | def check_dataset(dict):
  function make_divisible (line 127) | def make_divisible(x, divisor):
  function clean_str (line 132) | def clean_str(s):
  function one_cycle (line 137) | def one_cycle(y1=0.0, y2=1.0, steps=100):
  function colorstr (line 142) | def colorstr(*input):
  function labels_to_class_weights (line 167) | def labels_to_class_weights(labels, nc=80):
  function labels_to_image_weights (line 186) | def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
  function coco80_to_coco91_class (line 194) | def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index...
  function xyxy2xywh (line 206) | def xyxy2xywh(x):
  function xywh2xyxy (line 216) | def xywh2xyxy(x):
  function xywhn2xyxy (line 226) | def xywhn2xyxy(x, w=640, h=640, padw=32, padh=32):
  function scale_coords (line 236) | def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
  function clip_coords (line 252) | def clip_coords(boxes, img_shape):
  function bbox_iou (line 260) | def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=Fal...
  function box_iou (line 307) | def box_iou(box1, box2):
  function wh_iou (line 334) | def wh_iou(wh1, wh2):
  function jaccard_diou (line 342) | def jaccard_diou(box_a, box_b, iscrowd:bool=False):
  function non_max_suppression_face (line 379) | def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45...
  function non_max_suppression (line 459) | def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, cla...
  function strip_optimizer (line 552) | def strip_optimizer(f='weights/best.pt', s=''):  # from utils.general im...
  function print_mutation (line 566) | def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
  function apply_classifier (line 597) | def apply_classifier(x, model, img, im0):
  function increment_path (line 636) | def increment_path(path, exist_ok=True, sep=''):
  function filter_boxes (line 647) | def filter_boxes(boxes, min_size):
  function scale_coords_landmarks (line 654) | def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):

FILE: utils/google_utils.py
  function gsutil_getsize (line 13) | def gsutil_getsize(url=''):
  function attempt_download (line 19) | def attempt_download(file, repo='ultralytics/yolov5'):
  function gdrive_download (line 55) | def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zi...
  function get_token (line 90) | def get_token(cookie="./cookie"):

FILE: utils/infer_utils.py
  function decode_infer (line 5) | def decode_infer(output, stride):

FILE: utils/loss.py
  function smooth_BCE (line 10) | def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues...
  class BCEBlurWithLogitsLoss (line 15) | class BCEBlurWithLogitsLoss(nn.Module):
    method __init__ (line 17) | def __init__(self, alpha=0.05):
    method forward (line 22) | def forward(self, pred, true):
  class FocalLoss (line 32) | class FocalLoss(nn.Module):
    method __init__ (line 34) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
    method forward (line 42) | def forward(self, pred, true):
  class QFocalLoss (line 62) | class QFocalLoss(nn.Module):
    method __init__ (line 64) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
    method forward (line 72) | def forward(self, pred, true):
  class WingLoss (line 87) | class WingLoss(nn.Module):
    method __init__ (line 88) | def __init__(self, w=10, e=2):
    method forward (line 95) | def forward(self, x, t, sigma=1):
  class LandmarksLoss (line 104) | class LandmarksLoss(nn.Module):
    method __init__ (line 106) | def __init__(self, alpha=1.0):
    method forward (line 111) | def forward(self, pred, truel, mask):
  function compute_loss (line 116) | def compute_loss(p, targets, model):  # predictions, targets, model
  function build_targets (line 196) | def build_targets(p, targets, model):

FILE: utils/metrics.py
  function fitness (line 12) | def fitness(x):
  function ap_per_class (line 18) | def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='p...
  function compute_ap (line 79) | def compute_ap(recall, precision):
  class ConfusionMatrix (line 107) | class ConfusionMatrix:
    method __init__ (line 109) | def __init__(self, nc, conf=0.25, iou_thres=0.45):
    method process_batch (line 115) | def process_batch(self, detections, labels):
    method matrix (line 155) | def matrix(self):
    method plot (line 158) | def plot(self, save_dir='', names=()):
    method print (line 177) | def print(self):
  function plot_pr_curve (line 184) | def plot_pr_curve(px, py, ap, save_dir='.', names=()):

FILE: utils/plots.py
  function color_list (line 29) | def color_list():
  function hist2d (line 37) | def hist2d(x, y, n=100):
  function butter_lowpass_filtfilt (line 46) | def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
  function plot_one_box (line 57) | def plot_one_box(x, img, color=None, label=None, line_thickness=None):
  function plot_wh_methods (line 71) | def plot_wh_methods():  # from utils.plots import *; plot_wh_methods()
  function output_to_target (line 91) | def output_to_target(output):
  function plot_images (line 100) | def plot_images(images, targets, paths=None, fname='images.jpg', names=N...
  function plot_lr_scheduler (line 179) | def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
  function plot_test_txt (line 196) | def plot_test_txt():  # from utils.plots import *; plot_test()
  function plot_targets_txt (line 213) | def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
  function plot_study_txt (line 226) | def plot_study_txt(path='study/', x=None):  # from utils.plots import *;...
  function plot_labels (line 257) | def plot_labels(labels, save_dir=Path(''), loggers=None):
  function plot_evolution (line 301) | def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.pl...
  function profile_idetection (line 325) | def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
  function plot_results_overlay (line 357) | def plot_results_overlay(start=0, stop=0):  # from utils.plots import *;...
  function plot_results (line 380) | def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=...

FILE: utils/preprocess_utils.py
  function align_faces (line 3) | def align_faces(img, bbox=None, landmark=None, **kwargs):
  function face_distance (line 25) | def face_distance(vec1,vec2):

FILE: utils/torch_utils.py
  function torch_distributed_zero_first (line 26) | def torch_distributed_zero_first(local_rank: int):
  function init_torch_seeds (line 37) | def init_torch_seeds(seed=0):
  function git_describe (line 46) | def git_describe():
  function select_device (line 54) | def select_device(device='', batch_size=None):
  function time_synchronized (line 80) | def time_synchronized():
  function profile (line 87) | def profile(x, ops, n=100, device=None):
  function is_parallel (line 126) | def is_parallel(model):
  function intersect_dicts (line 130) | def intersect_dicts(da, db, exclude=()):
  function initialize_weights (line 135) | def initialize_weights(model):
  function find_modules (line 147) | def find_modules(model, mclass=nn.Conv2d):
  function sparsity (line 152) | def sparsity(model):
  function prune (line 161) | def prune(model, amount=0.3):
  function fuse_conv_and_bn (line 172) | def fuse_conv_and_bn(conv, bn):
  function model_info (line 195) | def model_info(model, verbose=False, img_size=640):
  function load_classifier (line 219) | def load_classifier(name='resnet101', n=2):
  function scale_img (line 238) | def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,...
  function copy_attr (line 251) | def copy_attr(a, b, include=(), exclude=()):
  class ModelEMA (line 260) | class ModelEMA:
    method __init__ (line 270) | def __init__(self, model, decay=0.9999, updates=0):
    method update (line 280) | def update(self, model):
    method update_attr (line 292) | def update_attr(self, model, include=(), exclude=('process_group', 're...

FILE: utils/wandb_logging/log_dataset.py
  function create_dataset_artifact (line 10) | def create_dataset_artifact(opt):

FILE: utils/wandb_logging/wandb_utils.py
  function remove_prefix (line 23) | def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
  function check_wandb_config_file (line 27) | def check_wandb_config_file(data_config_file):
  function get_run_info (line 34) | def get_run_info(run_path):
  function check_wandb_resume (line 42) | def check_wandb_resume(opt):
  function process_wandb_config_ddp_mode (line 56) | def process_wandb_config_ddp_mode(opt):
  class WandbLogger (line 80) | class WandbLogger():
    method __init__ (line 81) | def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
    method check_and_upload_dataset (line 115) | def check_and_upload_dataset(self, opt):
    method setup_training (line 126) | def setup_training(self, opt, data_dict):
    method download_dataset_artifact (line 159) | def download_dataset_artifact(self, path, alias):
    method download_model_artifact (line 167) | def download_model_artifact(self, opt):
    method log_model (line 179) | def log_model(self, path, opt, epoch, fitness_score, best_model=False):
    method log_dataset_artifact (line 193) | def log_dataset_artifact(self, data_file, single_cls, project, overwri...
    method map_val_table_path (line 222) | def map_val_table_path(self):
    method create_dataset_table (line 228) | def create_dataset_table(self, dataset, class_to_id, name='dataset'):
    method log_training_progress (line 263) | def log_training_progress(self, predn, path, names):
    method log (line 285) | def log(self, log_dict):
    method end_epoch (line 290) | def end_epoch(self, best_result=False):
    method finish_run (line 302) | def finish_run(self):