SYMBOL INDEX (609 symbols across 51 files)

FILE: config/__init__.py
  class ConfigDict (line 1) | class ConfigDict(dict):
  function from_dict (line 6) | def from_dict(obj) -> ConfigDict:

FILE: functions/div_loss.py
  function div_loss (line 7) | def div_loss(disc, real_x, fake_x, wp: int = 6, eps: float = 1e-6):

FILE: functions/get_param_groups.py
  function get_param_groups (line 6) | def get_param_groups(module) -> tuple[List, List, List]:

FILE: loader/m3fd.py
  class M3FD (line 20) | class M3FD(Dataset):
    method __init__ (line 28) | def __init__(self, root: str | Path, mode: Literal['train', 'val', 'pr...
    method __len__ (line 68) | def __len__(self) -> int:
    method __getitem__ (line 71) | def __getitem__(self, index: int) -> dict:
    method train_val_item (line 79) | def train_val_item(self, index: int) -> dict:
    method pred_item (line 137) | def pred_item(self, index: int) -> dict:
    method pred_save (line 158) | def pred_save(fus: Tensor, names: List[str | Path], shape: List[Size],...
    method pred_save_no_boxes (line 164) | def pred_save_no_boxes(fus: Tensor, names: List[str | Path], shape: Li...
    method pred_save_with_boxes (line 170) | def pred_save_with_boxes(fus: Tensor, names: List[str | Path], shape: ...
    method collate_fn (line 201) | def collate_fn(data: List[dict]) -> dict:

FILE: loader/roadscene.py
  class RoadScene (line 16) | class RoadScene(Dataset):
    method __init__ (line 20) | def __init__(self, root: str | Path, mode: Literal['train', 'val', 'pr...
    method __len__ (line 52) | def __len__(self) -> int:
    method __getitem__ (line 55) | def __getitem__(self, index: int) -> dict:
    method train_val_item (line 63) | def train_val_item(self, index: int) -> dict:
    method pred_item (line 89) | def pred_item(self, index: int) -> dict:
    method pred_save (line 110) | def pred_save(fus: Tensor, names: List[str | Path], shape: List[Size]):
    method collate_fn (line 116) | def collate_fn(data: List[dict]) -> dict:

FILE: loader/tno.py
  class TNO (line 16) | class TNO(Dataset):
    method __init__ (line 20) | def __init__(self, root: str | Path, mode: Literal['train', 'val', 'pr...
    method __len__ (line 52) | def __len__(self) -> int:
    method __getitem__ (line 55) | def __getitem__(self, index: int) -> dict:
    method train_val_item (line 63) | def train_val_item(self, index: int) -> dict:
    method pred_item (line 89) | def pred_item(self, index: int) -> dict:
    method pred_save (line 110) | def pred_save(fus: Tensor, names: List[str | Path], shape: List[Size]):
    method collate_fn (line 116) | def collate_fn(data: List[dict]) -> dict:

FILE: loader/utils/checker.py
  function check_image (line 15) | def check_image(root: Path, img_list: List[str]):
  function check_iqa (line 24) | def check_iqa(root: Path, img_list: List[str], config: ConfigDict):
  function check_labels (line 41) | def check_labels(root: Path, img_list: List[str]) -> List[Tensor]:
  function check_mask (line 54) | def check_mask(root: Path, img_list: List[str], config: ConfigDict):
  function get_max_size (line 71) | def get_max_size(root: Path, img_list: List[str]):

FILE: loader/utils/reader.py
  function gray_read (line 13) | def gray_read(img_path: str | Path) -> Tensor:
  function ycbcr_read (line 19) | def ycbcr_read(img_path: str | Path) -> Tuple[Tensor, Tensor]:
  function label_read (line 27) | def label_read(label_path: str | Path) -> Tensor:
  function img_write (line 34) | def img_write(img_t: Tensor, img_path: str | Path):
  function label_write (line 41) | def label_write(pred_i: Tensor, txt_path: str | Path):

FILE: module/detect/models/common.py
  function autopad (line 40) | def autopad(k, p=None):  # kernel, padding
  class Conv (line 47) | class Conv(nn.Module):
    method __init__ (line 49) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in,...
    method forward (line 55) | def forward(self, x):
    method forward_fuse (line 58) | def forward_fuse(self, x):
  class DWConv (line 62) | class DWConv(Conv):
    method __init__ (line 64) | def __init__(self, c1, c2, k=1, s=1, act=True):  # ch_in, ch_out, kern...
  class DWConvTranspose2d (line 68) | class DWConvTranspose2d(nn.ConvTranspose2d):
    method __init__ (line 70) | def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, ke...
  class TransformerLayer (line 74) | class TransformerLayer(nn.Module):
    method __init__ (line 76) | def __init__(self, c, num_heads):
    method forward (line 85) | def forward(self, x):
  class TransformerBlock (line 91) | class TransformerBlock(nn.Module):
    method __init__ (line 93) | def __init__(self, c1, c2, num_heads, num_layers):
    method forward (line 102) | def forward(self, x):
  class Bottleneck (line 110) | class Bottleneck(nn.Module):
    method __init__ (line 112) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_ou...
    method forward (line 119) | def forward(self, x):
  class BottleneckCSP (line 123) | class BottleneckCSP(nn.Module):
    method __init__ (line 125) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ...
    method forward (line 136) | def forward(self, x):
  class CrossConv (line 142) | class CrossConv(nn.Module):
    method __init__ (line 144) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
    method forward (line 152) | def forward(self, x):
  class C3 (line 156) | class C3(nn.Module):
    method __init__ (line 158) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ...
    method forward (line 166) | def forward(self, x):
  class C3x (line 170) | class C3x(C3):
    method __init__ (line 172) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
  class C3TR (line 178) | class C3TR(C3):
    method __init__ (line 180) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
  class C3SPP (line 186) | class C3SPP(C3):
    method __init__ (line 188) | def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
  class C3Ghost (line 194) | class C3Ghost(C3):
    method __init__ (line 196) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
  class SPP (line 202) | class SPP(nn.Module):
    method __init__ (line 204) | def __init__(self, c1, c2, k=(5, 9, 13)):
    method forward (line 211) | def forward(self, x):
  class SPPF (line 218) | class SPPF(nn.Module):
    method __init__ (line 220) | def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
    method forward (line 227) | def forward(self, x):
  class Focus (line 236) | class Focus(nn.Module):
    method __init__ (line 238) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in,...
    method forward (line 243) | def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
  class GhostConv (line 248) | class GhostConv(nn.Module):
    method __init__ (line 250) | def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out,...
    method forward (line 256) | def forward(self, x):
  class GhostBottleneck (line 261) | class GhostBottleneck(nn.Module):
    method __init__ (line 263) | def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
    method forward (line 278) | def forward(self, x):
  class Contract (line 282) | class Contract(nn.Module):
    method __init__ (line 284) | def __init__(self, gain=2):
    method forward (line 288) | def forward(self, x):
  class Expand (line 296) | class Expand(nn.Module):
    method __init__ (line 298) | def __init__(self, gain=2):
    method forward (line 302) | def forward(self, x):
  class Concat (line 310) | class Concat(nn.Module):
    method __init__ (line 312) | def __init__(self, dimension=1):
    method forward (line 316) | def forward(self, x):
  class DetectMultiBackend (line 320) | class DetectMultiBackend(nn.Module):
    method __init__ (line 322) | def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), d...
    method forward (line 462) | def forward(self, im, augment=False, visualize=False, val=False):
    method warmup (line 523) | def warmup(self, imgsz=(1, 3, 640, 640)):
    method model_type (line 532) | def model_type(p='path/to/model.pt'):
    method _load_metadata (line 544) | def _load_metadata(f='path/to/meta.yaml'):
  class AutoShape (line 551) | class AutoShape(nn.Module):
    method __init__ (line 561) | def __init__(self, model, verbose=True):
    method _apply (line 570) | def _apply(self, fn):
    method forward (line 582) | def forward(self, imgs, size=640, augment=False, profile=False):
  class Detections (line 646) | class Detections:
    method __init__ (line 648) | def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, ...
    method display (line 665) | def display(self, pprint=False, show=False, save=False, crop=False, re...
    method print (line 710) | def print(self):
    method show (line 714) | def show(self, labels=True):
    method save (line 717) | def save(self, labels=True, save_dir='runs/detect/exp'):
    method crop (line 721) | def crop(self, save=True, save_dir='runs/detect/exp'):
    method render (line 725) | def render(self, labels=True):
    method pandas (line 729) | def pandas(self):
    method tolist (line 739) | def tolist(self):
    method __len__ (line 748) | def __len__(self):
    method __str__ (line 751) | def __str__(self):
  class Classify (line 756) | class Classify(nn.Module):
    method __init__ (line 758) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, k...
    method forward (line 764) | def forward(self, x):

FILE: module/detect/models/experimental.py
  class Sum (line 15) | class Sum(nn.Module):
    method __init__ (line 17) | def __init__(self, n, weight=False):  # n: number of inputs
    method forward (line 24) | def forward(self, x):
  class MixConv2d (line 36) | class MixConv2d(nn.Module):
    method __init__ (line 38) | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):  # ch_in, ch...
    method forward (line 57) | def forward(self, x):
  class Ensemble (line 61) | class Ensemble(nn.ModuleList):
    method __init__ (line 63) | def __init__(self):
    method forward (line 66) | def forward(self, x, augment=False, profile=False, visualize=False):
  function attempt_load (line 74) | def attempt_load(weights, device=None, inplace=True, fuse=True):

FILE: module/detect/models/tf.py
  class TFBN (line 38) | class TFBN(keras.layers.Layer):
    method __init__ (line 40) | def __init__(self, w=None):
    method call (line 49) | def call(self, inputs):
  class TFPad (line 53) | class TFPad(keras.layers.Layer):
    method __init__ (line 55) | def __init__(self, pad):
    method call (line 62) | def call(self, inputs):
  class TFConv (line 66) | class TFConv(keras.layers.Layer):
    method __init__ (line 68) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
    method call (line 86) | def call(self, inputs):
  class TFDWConv (line 90) | class TFDWConv(keras.layers.Layer):
    method __init__ (line 92) | def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
    method call (line 108) | def call(self, inputs):
  class TFDWConvTranspose2d (line 112) | class TFDWConvTranspose2d(keras.layers.Layer):
    method __init__ (line 114) | def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
    method call (line 131) | def call(self, inputs):
  class TFFocus (line 135) | class TFFocus(keras.layers.Layer):
    method __init__ (line 137) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
    method call (line 142) | def call(self, inputs):  # x(b,w,h,c) -> y(b,w/2,h/2,4c)
  class TFBottleneck (line 148) | class TFBottleneck(keras.layers.Layer):
    method __init__ (line 150) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):  # ch_i...
    method call (line 157) | def call(self, inputs):
  class TFCrossConv (line 161) | class TFCrossConv(keras.layers.Layer):
    method __init__ (line 163) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
    method call (line 170) | def call(self, inputs):
  class TFConv2d (line 174) | class TFConv2d(keras.layers.Layer):
    method __init__ (line 176) | def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
    method call (line 188) | def call(self, inputs):
  class TFBottleneckCSP (line 192) | class TFBottleneckCSP(keras.layers.Layer):
    method __init__ (line 194) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
    method call (line 206) | def call(self, inputs):
  class TFC3 (line 212) | class TFC3(keras.layers.Layer):
    method __init__ (line 214) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
    method call (line 223) | def call(self, inputs):
  class TFC3x (line 227) | class TFC3x(keras.layers.Layer):
    method __init__ (line 229) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
    method call (line 239) | def call(self, inputs):
  class TFSPP (line 243) | class TFSPP(keras.layers.Layer):
    method __init__ (line 245) | def __init__(self, c1, c2, k=(5, 9, 13), w=None):
    method call (line 252) | def call(self, inputs):
  class TFSPPF (line 257) | class TFSPPF(keras.layers.Layer):
    method __init__ (line 259) | def __init__(self, c1, c2, k=5, w=None):
    method call (line 266) | def call(self, inputs):
  class TFDetect (line 273) | class TFDetect(keras.layers.Layer):
    method __init__ (line 275) | def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None)...
    method call (line 292) | def call(self, inputs):
    method _make_grid (line 316) | def _make_grid(nx=20, ny=20):
  class TFUpsample (line 323) | class TFUpsample(keras.layers.Layer):
    method __init__ (line 325) | def __init__(self, size, scale_factor, mode, w=None):  # warning: all ...
    method call (line 334) | def call(self, inputs):
  class TFConcat (line 338) | class TFConcat(keras.layers.Layer):
    method __init__ (line 340) | def __init__(self, dimension=1, w=None):
    method call (line 345) | def call(self, inputs):
  function parse_model (line 349) | def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
  class TFModel (line 403) | class TFModel:
    method __init__ (line 405) | def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgs...
    method predict (line 421) | def predict(self,
    method _xywh2xyxy (line 464) | def _xywh2xyxy(xywh):
  class AgnosticNMS (line 470) | class AgnosticNMS(keras.layers.Layer):
    method call (line 472) | def call(self, input, topk_all, iou_thres, conf_thres):
    method _nms (line 480) | def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25):  # agnosti...
  function activations (line 508) | def activations(act=nn.SiLU):
  function representative_dataset_gen (line 520) | def representative_dataset_gen(dataset, ncalib=100):
  function run (line 531) | def run(
  function parse_opt (line 556) | def parse_opt():
  function main (line 568) | def main(opt):

FILE: module/detect/models/yolo.py
  class Detect (line 38) | class Detect(nn.Module):
    method __init__ (line 43) | def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detecti...
    method forward (line 55) | def forward(self, x):
    method _make_grid (line 79) | def _make_grid(self, nx=20, ny=20, i=0):
  class Model (line 93) | class Model(nn.Module):
    method __init__ (line 95) | def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  ...
    method forward (line 133) | def forward(self, x, augment=False, profile=False, visualize=False):
    method _forward_augment (line 138) | def _forward_augment(self, x):
    method _forward_once (line 152) | def _forward_once(self, x, profile=False, visualize=False):
    method _descale_pred (line 165) | def _descale_pred(self, p, flips, scale, img_size):
    method _clip_augmented (line 182) | def _clip_augmented(self, y):
    method _profile_one_layer (line 193) | def _profile_one_layer(self, m, x, dt):
    method _initialize_biases (line 206) | def _initialize_biases(self, cf=None):  # initialize biases into Detec...
    method _print_biases (line 216) | def _print_biases(self):
    method fuse (line 229) | def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
    method info (line 239) | def info(self, verbose=False, img_size=640):  # print model information
    method _apply (line 242) | def _apply(self, fn):
  function parse_model (line 254) | def parse_model(d, ch):  # model_dict, input_channels(3)

FILE: module/detect/utils/__init__.py
  function notebook_init (line 7) | def notebook_init(verbose=True):

FILE: module/detect/utils/activations.py
  class SiLU (line 11) | class SiLU(nn.Module):
    method forward (line 14) | def forward(x):
  class Hardswish (line 18) | class Hardswish(nn.Module):
    method forward (line 21) | def forward(x):
  class Mish (line 26) | class Mish(nn.Module):
    method forward (line 29) | def forward(x):
  class MemoryEfficientMish (line 33) | class MemoryEfficientMish(nn.Module):
    class F (line 35) | class F(torch.autograd.Function):
      method forward (line 38) | def forward(ctx, x):
      method backward (line 43) | def backward(ctx, grad_output):
    method forward (line 49) | def forward(self, x):
  class FReLU (line 53) | class FReLU(nn.Module):
    method __init__ (line 55) | def __init__(self, c1, k=3):  # ch_in, kernel
    method forward (line 60) | def forward(self, x):
  class AconC (line 64) | class AconC(nn.Module):
    method __init__ (line 70) | def __init__(self, c1):
    method forward (line 76) | def forward(self, x):
  class MetaAconC (line 81) | class MetaAconC(nn.Module):
    method __init__ (line 87) | def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
    method forward (line 97) | def forward(self, x):

FILE: module/detect/utils/augmentations.py
  class Albumentations (line 15) | class Albumentations:
    method __init__ (line 17) | def __init__(self):
    method __call__ (line 39) | def __call__(self, im, labels, p=1.0):
  function augment_hsv (line 46) | def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
  function hist_equalize (line 62) | def hist_equalize(im, clahe=True, bgr=False):
  function replicate (line 73) | def replicate(im, labels):
  function letterbox (line 90) | def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True...
  function random_perspective (line 123) | def random_perspective(
  function copy_paste (line 221) | def copy_paste(im, labels, segments, p=0.5):
  function cutout (line 245) | def cutout(im, labels, p=0.5):
  function mixup (line 272) | def mixup(im, labels, im2, labels2):
  function box_candidates (line 280) | def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1...

FILE: module/detect/utils/autoanchor.py
  function check_anchor_order (line 18) | def check_anchor_order(m):
  function check_anchors (line 28) | def check_anchors(dataset, model, thr=4.0, imgsz=640):
  function kmean_anchors (line 68) | def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=...

FILE: module/detect/utils/autobatch.py
  function check_train_batch_size (line 15) | def check_train_batch_size(model, imgsz=640, amp=True):
  function autobatch (line 21) | def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):

FILE: module/detect/utils/benchmarks.py
  function run (line 49) | def run(
  function test (line 102) | def test(
  function parse_opt (line 134) | def parse_opt():
  function main (line 151) | def main(opt):

FILE: module/detect/utils/callbacks.py
  class Callbacks (line 7) | class Callbacks:
    method __init__ (line 12) | def __init__(self):
    method register_action (line 36) | def register_action(self, hook, name='', callback=None):
    method get_registered_actions (line 49) | def get_registered_actions(self, hook=None):
    method run (line 58) | def run(self, hook, *args, **kwargs):

FILE: module/detect/utils/dataloaders.py
  function get_hash (line 46) | def get_hash(paths):
  function exif_size (line 54) | def exif_size(img):
  function exif_transpose (line 67) | def exif_transpose(image):
  function seed_worker (line 93) | def seed_worker(worker_id):
  function create_dataloader (line 100) | def create_dataloader(
  class InfiniteDataLoader (line 157) | class InfiniteDataLoader(dataloader.DataLoader):
    method __init__ (line 163) | def __init__(self, *args, **kwargs):
    method __len__ (line 168) | def __len__(self):
    method __iter__ (line 171) | def __iter__(self):
  class _RepeatSampler (line 176) | class _RepeatSampler:
    method __init__ (line 183) | def __init__(self, sampler):
    method __iter__ (line 186) | def __iter__(self):
  class LoadImages (line 191) | class LoadImages:
    method __init__ (line 193) | def __init__(self, path, img_size=640, stride=32, auto=True):
    method __iter__ (line 224) | def __iter__(self):
    method __next__ (line 228) | def __next__(self):
    method new_video (line 265) | def new_video(self, path):
    method __len__ (line 270) | def __len__(self):
  class LoadWebcam (line 274) | class LoadWebcam:  # for inference
    method __init__ (line 276) | def __init__(self, pipe='0', img_size=640, stride=32):
    method __iter__ (line 283) | def __iter__(self):
    method __next__ (line 287) | def __next__(self):
    method __len__ (line 312) | def __len__(self):
  class LoadStreams (line 316) | class LoadStreams:
    method __init__ (line 318) | def __init__(self, sources='streams.txt', img_size=640, stride=32, aut...
    method update (line 364) | def update(self, i, cap, stream):
    method __iter__ (line 381) | def __iter__(self):
    method __next__ (line 385) | def __next__(self):
    method __len__ (line 404) | def __len__(self):
  function img2label_paths (line 408) | def img2label_paths(img_paths):
  class LoadImagesAndLabels (line 414) | class LoadImagesAndLabels(Dataset):
    method __init__ (line 419) | def __init__(
    method cache_labels (line 555) | def cache_labels(self, path=Path('./labels.cache'), prefix=''):
    method __len__ (line 595) | def __len__(self):
    method __getitem__ (line 604) | def __getitem__(self, index):
    method load_image (line 680) | def load_image(self, i):
    method cache_images_to_disk (line 698) | def cache_images_to_disk(self, i):
    method load_mosaic (line 704) | def load_mosaic(self, index):
    method load_mosaic9 (line 764) | def load_mosaic9(self, index):
    method collate_fn (line 843) | def collate_fn(batch):
    method collate_fn4 (line 850) | def collate_fn4(batch):
  function create_folder (line 879) | def create_folder(path='./new'):
  function flatten_recursive (line 886) | def flatten_recursive(path=DATASETS_DIR / 'coco128'):
  function extract_boxes (line 894) | def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataload...
  function autosplit (line 928) | def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0...
  function verify_image_label (line 952) | def verify_image_label(args):
  function dataset_stats (line 1004) | def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False...

FILE: module/detect/utils/downloads.py
  function is_url (line 19) | def is_url(url):
  function gsutil_getsize (line 28) | def gsutil_getsize(url=''):
  function safe_download (line 34) | def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
  function attempt_download (line 55) | def attempt_download(file, repo='ultralytics/yolov5', release='v6.1'):
  function gdrive_download (line 107) | def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zi...
  function get_token (line 142) | def get_token(cookie="./cookie"):

FILE: module/detect/utils/flask_rest_api/restapi.py
  function predict (line 19) | def predict():

FILE: module/detect/utils/general.py
  function is_kaggle (line 57) | def is_kaggle():
  function is_writeable (line 67) | def is_writeable(dir, test=False):
  function set_logging (line 81) | def set_logging(name=None, verbose=VERBOSE):
  function user_config_dir (line 100) | def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
  class Profile (line 116) | class Profile(contextlib.ContextDecorator):
    method __enter__ (line 118) | def __enter__(self):
    method __exit__ (line 121) | def __exit__(self, type, value, traceback):
  class Timeout (line 125) | class Timeout(contextlib.ContextDecorator):
    method __init__ (line 127) | def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors...
    method _timeout_handler (line 132) | def _timeout_handler(self, signum, frame):
    method __enter__ (line 135) | def __enter__(self):
    method __exit__ (line 140) | def __exit__(self, exc_type, exc_val, exc_tb):
  class WorkingDirectory (line 147) | class WorkingDirectory(contextlib.ContextDecorator):
    method __init__ (line 149) | def __init__(self, new_dir):
    method __enter__ (line 153) | def __enter__(self):
    method __exit__ (line 156) | def __exit__(self, exc_type, exc_val, exc_tb):
  function try_except (line 160) | def try_except(func):
  function threaded (line 171) | def threaded(func):
  function methods (line 181) | def methods(instance):
  function print_args (line 186) | def print_args(args: Optional[dict] = None, show_file=True, show_fcn=Fal...
  function init_seeds (line 197) | def init_seeds(seed=0, deterministic=False):
  function intersect_dicts (line 215) | def intersect_dicts(da, db, exclude=()):
  function get_latest_run (line 220) | def get_latest_run(search_dir='.'):
  function is_docker (line 226) | def is_docker():
  function is_colab (line 231) | def is_colab():
  function is_pip (line 240) | def is_pip():
  function is_ascii (line 245) | def is_ascii(s=''):
  function is_chinese (line 251) | def is_chinese(s='人工智能'):
  function emojis (line 256) | def emojis(str=''):
  function file_age (line 261) | def file_age(path=__file__):
  function file_date (line 267) | def file_date(path=__file__):
  function file_size (line 273) | def file_size(path):
  function check_online (line 285) | def check_online():
  function git_describe (line 295) | def git_describe(path=ROOT):  # path must be a directory
  function check_git_status (line 306) | def check_git_status():
  function check_python (line 325) | def check_python(minimum='3.7.0'):
  function check_version (line 330) | def check_version(current='0.0.0', minimum='0.0.0', name='version ', pin...
  function check_requirements (line 343) | def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(...
  function check_img_size (line 379) | def check_img_size(imgsz, s=32, floor=0):
  function check_imshow (line 391) | def check_imshow():
  function check_suffix (line 406) | def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
  function check_yaml (line 417) | def check_yaml(file, suffix=('.yaml', '.yml')):
  function check_file (line 422) | def check_file(file, suffix=''):
  function check_font (line 447) | def check_font(font=FONT, progress=False):
  function check_dataset (line 457) | def check_dataset(data, autodownload=True):
  function check_amp (line 517) | def check_amp(model):
  function url2file (line 545) | def url2file(url):
  function download (line 551) | def download(url, dir='.', unzip=True, delete=True, curl=False, threads=...
  function make_divisible (line 597) | def make_divisible(x, divisor):
  function clean_str (line 604) | def clean_str(s):
  function one_cycle (line 609) | def one_cycle(y1=0.0, y2=1.0, steps=100):
  function colorstr (line 614) | def colorstr(*input):
  function labels_to_class_weights (line 640) | def labels_to_class_weights(labels, nc=80):
  function labels_to_image_weights (line 659) | def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
  function coco80_to_coco91_class (line 666) | def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index...
  function xyxy2xywh (line 678) | def xyxy2xywh(x):
  function xywh2xyxy (line 688) | def xywh2xyxy(x):
  function xywhn2xyxy (line 698) | def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
  function xyxy2xywhn (line 708) | def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
  function xyn2xy (line 720) | def xyn2xy(x, w=640, h=640, padw=0, padh=0):
  function segment2box (line 728) | def segment2box(segment, width=640, height=640):
  function segments2boxes (line 736) | def segments2boxes(segments):
  function resample_segments (line 745) | def resample_segments(segments, n=1000):
  function scale_coords (line 755) | def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
  function clip_coords (line 771) | def clip_coords(boxes, shape):
  function non_max_suppression (line 783) | def non_max_suppression(
  function strip_optimizer (line 887) | def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; ...
  function print_mutation (line 903) | def print_mutation(results, hyp, save_dir, bucket, prefix=colorstr('evol...
  function apply_classifier (line 949) | def apply_classifier(x, model, img, im0):
  function increment_path (line 984) | def increment_path(path, exist_ok=False, sep='', mkdir=False):
  function imread (line 1014) | def imread(path, flags=cv2.IMREAD_COLOR):
  function imwrite (line 1018) | def imwrite(path, im):
  function imshow (line 1026) | def imshow(path, im):

FILE: module/detect/utils/loggers/__init__.py
  class Loggers (line 35) | class Loggers():
    method __init__ (line 37) | def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, lo...
    method on_train_start (line 90) | def on_train_start(self):
    method on_pretrain_routine_end (line 94) | def on_pretrain_routine_end(self):
    method on_train_batch_end (line 100) | def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
    method on_train_epoch_end (line 115) | def on_train_epoch_end(self, epoch):
    method on_val_image_end (line 120) | def on_val_image_end(self, pred, predn, path, names, im):
    method on_val_end (line 125) | def on_val_end(self):
    method on_fit_epoch_end (line 131) | def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
    method on_model_save (line 153) | def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
    method on_train_end (line 159) | def on_train_end(self, last, best, plots, epoch, results):
    method on_params_update (line 184) | def on_params_update(self, params):

FILE: module/detect/utils/loggers/wandb/log_dataset.py
  function create_dataset_artifact (line 10) | def create_dataset_artifact(opt):

FILE: module/detect/utils/loggers/wandb/sweep.py
  function sweep (line 17) | def sweep():

FILE: module/detect/utils/loggers/wandb/wandb_utils.py
  function remove_prefix (line 32) | def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
  function check_wandb_config_file (line 36) | def check_wandb_config_file(data_config_file):
  function check_wandb_dataset (line 43) | def check_wandb_dataset(data_file):
  function get_run_info (line 63) | def get_run_info(run_path):
  function check_wandb_resume (line 72) | def check_wandb_resume(opt):
  function process_wandb_config_ddp_mode (line 86) | def process_wandb_config_ddp_mode(opt):
  class WandbLogger (line 110) | class WandbLogger():
    method __init__ (line 124) | def __init__(self, opt, run_id=None, job_type='Training'):
    method check_and_upload_dataset (line 199) | def check_and_upload_dataset(self, opt):
    method setup_training (line 218) | def setup_training(self, opt):
    method download_dataset_artifact (line 273) | def download_dataset_artifact(self, path, alias):
    method download_model_artifact (line 293) | def download_model_artifact(self, opt):
    method log_model (line 311) | def log_model(self, path, opt, epoch, fitness_score, best_model=False):
    method log_dataset_artifact (line 340) | def log_dataset_artifact(self, data_file, single_cls, project, overwri...
    method map_val_table_path (line 402) | def map_val_table_path(self):
    method create_dataset_table (line 412) | def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_...
    method log_training_progress (line 464) | def log_training_progress(self, predn, path, names):
    method val_one_image (line 511) | def val_one_image(self, pred, predn, path, names, im):
    method log (line 539) | def log(self, log_dict):
    method end_epoch (line 550) | def end_epoch(self, best_result=False):
    method finish_run (line 587) | def finish_run(self):
  function all_logging_disabled (line 599) | def all_logging_disabled(highest_level=logging.CRITICAL):

FILE: module/detect/utils/loss.py
  function smooth_BCE (line 13) | def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues...
  class BCEBlurWithLogitsLoss (line 18) | class BCEBlurWithLogitsLoss(nn.Module):
    method __init__ (line 20) | def __init__(self, alpha=0.05):
    method forward (line 25) | def forward(self, pred, true):
  class FocalLoss (line 35) | class FocalLoss(nn.Module):
    method __init__ (line 37) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
    method forward (line 45) | def forward(self, pred, true):
  class QFocalLoss (line 65) | class QFocalLoss(nn.Module):
    method __init__ (line 67) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
    method forward (line 75) | def forward(self, pred, true):
  class ComputeLoss (line 91) | class ComputeLoss:
    method __init__ (line 95) | def __init__(self, model, autobalance=False):
    method __call__ (line 121) | def __call__(self, p, targets):  # predictions, targets
    method build_targets (line 177) | def build_targets(self, p, targets):

FILE: module/detect/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 96) | def compute_ap(recall, precision):
  class ConfusionMatrix (line 124) | class ConfusionMatrix:
    method __init__ (line 126) | def __init__(self, nc, conf=0.25, iou_thres=0.45):
    method process_batch (line 132) | def process_batch(self, detections, labels):
    method matrix (line 172) | def matrix(self):
    method tp_fp (line 175) | def tp_fp(self):
    method plot (line 181) | def plot(self, normalize=True, save_dir='', names=()):
    method print (line 211) | def print(self):
  function bbox_iou (line 216) | def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, ...
  function box_area (line 257) | def box_area(box):
  function box_iou (line 262) | def box_iou(box1, box2, eps=1e-7):
  function bbox_ioa (line 283) | def bbox_ioa(box1, box2, eps=1e-7):
  function wh_iou (line 305) | def wh_iou(wh1, wh2, eps=1e-7):
  function plot_pr_curve (line 316) | def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
  function plot_mc_curve (line 337) | def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabe...

FILE: module/detect/utils/plots.py
  class Colors (line 30) | class Colors:
    method __init__ (line 32) | def __init__(self):
    method __call__ (line 39) | def __call__(self, i, bgr=False):
    method hex2rgb (line 44) | def hex2rgb(h):  # rgb order (PIL)
  function check_pil_font (line 51) | def check_pil_font(font=FONT, size=10):
  class Annotator (line 67) | class Annotator:
    method __init__ (line 69) | def __init__(self, im, line_width=None, font_size=None, font='Arial.tt...
    method box_label (line 84) | def box_label(self, box, label='', color=(128, 128, 128), txt_color=(2...
    method rectangle (line 117) | def rectangle(self, xy, fill=None, outline=None, width=1):
    method text (line 121) | def text(self, xy, text, txt_color=(255, 255, 255)):
    method result (line 126) | def result(self):
  function feature_visualization (line 131) | def feature_visualization(x, module_type, stage, n=32, save_dir=Path('ru...
  function hist2d (line 159) | def hist2d(x, y, n=100):
  function butter_lowpass_filtfilt (line 168) | def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
  function output_to_target (line 181) | def output_to_target(output):
  function plot_images (line 191) | def plot_images(images, targets, paths=None, fname='images.jpg', names=N...
  function plot_lr_scheduler (line 252) | def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
  function plot_val_txt (line 269) | def plot_val_txt():  # from utils.plots import *; plot_val()
  function plot_targets_txt (line 286) | def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
  function plot_val_study (line 299) | def plot_val_study(file='', dir='', x=None):  # from utils.plots import ...
  function plot_labels (line 350) | def plot_labels(labels, names=(), save_dir=Path('')):
  function plot_evolve (line 397) | def plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots im...
  function plot_results (line 424) | def plot_results(file='path/to/results.csv', dir=''):
  function profile_idetection (line 450) | def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
  function save_one_box (line 481) | def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, squar...

FILE: module/detect/utils/torch_utils.py
  function smart_DDP (line 36) | def smart_DDP(model):
  function torch_distributed_zero_first (line 48) | def torch_distributed_zero_first(local_rank: int):
  function device_count (line 57) | def device_count():
  function select_device (line 67) | def select_device(device='', batch_size=0, newline=True):
  function time_sync (line 103) | def time_sync():
  function profile (line 110) | def profile(input, ops, n=10, device=None):
  function is_parallel (line 164) | def is_parallel(model):
  function de_parallel (line 169) | def de_parallel(model):
  function initialize_weights (line 174) | def initialize_weights(model):
  function find_modules (line 186) | def find_modules(model, mclass=nn.Conv2d):
  function sparsity (line 191) | def sparsity(model):
  function prune (line 200) | def prune(model, amount=0.3):
  function fuse_conv_and_bn (line 211) | def fuse_conv_and_bn(conv, bn):
  function model_info (line 236) | def model_info(model, verbose=False, img_size=640):
  function scale_img (line 263) | def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,...
  function copy_attr (line 275) | def copy_attr(a, b, include=(), exclude=()):
  function smart_optimizer (line 284) | def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, weight_d...
  class EarlyStopping (line 316) | class EarlyStopping:
    method __init__ (line 318) | def __init__(self, patience=30):
    method __call__ (line 324) | def __call__(self, epoch, fitness):
  class ModelEMA (line 341) | class ModelEMA:
    method __init__ (line 347) | def __init__(self, model, decay=0.9999, tau=2000, updates=0):
    method update (line 357) | def update(self, model):
    method update_attr (line 369) | def update_attr(self, model, include=(), exclude=('process_group', 're...

FILE: module/fuse/discriminator.py
  class Discriminator (line 4) | class Discriminator(nn.Module):
    method __init__ (line 9) | def __init__(self, dim: int = 32, size: tuple[int, int] = (224, 224)):
    method forward (line 30) | def forward(self, x: Tensor) -> Tensor:

FILE: module/fuse/generator.py
  class Generator (line 6) | class Generator(nn.Module):
    method __init__ (line 12) | def __init__(self, dim: int = 32, depth: int = 3):
    method forward (line 51) | def forward(self, ir: Tensor, vi: Tensor) -> Tensor:

FILE: module/saliency/u2net.py
  class REBNCONV (line 10) | class REBNCONV(nn.Module):
    method __init__ (line 11) | def __init__(self, in_ch=3, out_ch=3, dirate=1):
    method forward (line 18) | def forward(self, x):
  function _upsample_like (line 26) | def _upsample_like(src, tar):
  class RSU7 (line 32) | class RSU7(nn.Module):  # UNet07DRES(nn.Module):
    method __init__ (line 34) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 65) | def forward(self, x):
  class RSU6 (line 109) | class RSU6(nn.Module):  # UNet06DRES(nn.Module):
    method __init__ (line 111) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 138) | def forward(self, x):
  class RSU5 (line 177) | class RSU5(nn.Module):  # UNet05DRES(nn.Module):
    method __init__ (line 179) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 202) | def forward(self, x):
  class RSU4 (line 235) | class RSU4(nn.Module):  # UNet04DRES(nn.Module):
    method __init__ (line 237) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 256) | def forward(self, x):
  class RSU4F (line 283) | class RSU4F(nn.Module):  # UNet04FRES(nn.Module):
    method __init__ (line 285) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 300) | def forward(self, x):
  class U2NET (line 319) | class U2NET(nn.Module):
    method __init__ (line 321) | def __init__(self, in_ch=3, out_ch=1):
    method forward (line 357) | def forward(self, x):
  class U2NETP (line 423) | class U2NETP(nn.Module):
    method __init__ (line 425) | def __init__(self, in_ch=3, out_ch=1):
    method forward (line 461) | def forward(self, x):

FILE: pipeline/detect.py
  class Detect (line 22) | class Detect:
    method __init__ (line 27) | def __init__(self, config, mode: Literal['train', 'inference'], nc: in...
    method load_ckpt (line 79) | def load_ckpt(self, ckpt: dict):
    method load_ckpt_fuse (line 83) | def load_ckpt_fuse(self, ckpt: dict):
    method save_ckpt (line 102) | def save_ckpt(self) -> dict:
    method forward (line 106) | def forward(self, imgs: Tensor) -> Tensor:
    method eval (line 112) | def eval(self, imgs: Tensor, targets: Tensor, stats: List, preview: bo...
    method inference (line 165) | def inference(self, imgs: Tensor) -> Tensor:
    method criterion (line 175) | def criterion(self, imgs: Tensor, targets: Tensor) -> Tuple[Tensor, Li...
    method preview (line 192) | def preview(imgs: Tensor, preds: Tensor, conf_th: float = 0.6):
    method param_groups (line 226) | def param_groups(self) -> tuple[List, List, List]:
    method process_batch (line 234) | def process_batch(detections, labels, iou_v):

FILE: pipeline/fuse.py
  class Fuse (line 20) | class Fuse:
    method __init__ (line 25) | def __init__(self, config, mode: Literal['train', 'inference']):
    method load_ckpt (line 81) | def load_ckpt(self, ckpt: dict):
    method save_ckpt (line 101) | def save_ckpt(self) -> dict:
    method forward (line 107) | def forward(self, ir: Tensor, vi: Tensor) -> Tensor:
    method eval (line 113) | def eval(self, ir: Tensor, vi: Tensor) -> Tensor:
    method inference (line 119) | def inference(self, ir: Tensor, vi: Tensor) -> Tensor:
    method criterion_dis_t (line 125) | def criterion_dis_t(self, ir: Tensor, vi: Tensor, mk: Tensor) -> Tensor:
    method criterion_dis_d (line 151) | def criterion_dis_d(self, ir: Tensor, vi: Tensor, mk: Tensor) -> Tensor:
    method criterion_generator (line 179) | def criterion_generator(self, ir: Tensor, vi: Tensor, mk: Tensor, w1: ...
    method gradient (line 202) | def gradient(x: Tensor, eps: float = 1e-8) -> Tensor:
    method src_loss (line 208) | def src_loss(self, x: Tensor, y: Tensor) -> Tensor:
    method adv_loss (line 220) | def adv_loss(self, fus: Tensor, mk: Tensor) -> Tuple[Tensor, number, n...
    method param_groups (line 233) | def param_groups(self, key: Optional[Literal['g', 'd']] = None) -> tup...
    method g_params (line 247) | def g_params(self) -> tuple[List, List, List]:
    method d_params (line 250) | def d_params(self) -> tuple[List, List, List]:

FILE: pipeline/iqa.py
  class IQA (line 16) | class IQA:
    method __init__ (line 21) | def __init__(self, url: str):
    method inference (line 53) | def inference(self, src: str | Path, dst: str | Path):
    method modality_inference (line 58) | def modality_inference(self, src: str | Path, dst: str | Path, modalit...
    method extractor_inference (line 78) | def extractor_inference(self, x: Tensor) -> Tensor:
    method _imread (line 93) | def _imread(img_p: str | Path):

FILE: pipeline/saliency.py
  class Saliency (line 16) | class Saliency:
    method __init__ (line 21) | def __init__(self, url: str):
    method inference (line 52) | def inference(self, src: str | Path, dst: str | Path):
    method _imread (line 75) | def _imread(img_p: str | Path):

FILE: pipeline/train.py
  class Train (line 23) | class Train:
    method __init__ (line 28) | def __init__(self, environment_probe: EnvironmentProbe, config: dict):
    method train_dis_target (line 71) | def train_dis_target(self, ir: Tensor, vi: Tensor, mk: Tensor) -> Tensor:
    method train_dis_detail (line 101) | def train_dis_detail(self, ir: Tensor, vi: Tensor, mk: Tensor) -> Tensor:
    method gradient (line 131) | def gradient(self, x: Tensor, eps: float = 1e-6) -> Tensor:
    method train_generator (line 137) | def train_generator(self, ir: Tensor, vi: Tensor, mk: Tensor, s1: Tens...
    method run (line 182) | def run(self):
    method save (line 207) | def save(self, epoch: int):

FILE: scripts/infer_f.py
  class InferF (line 16) | class InferF:
    method __init__ (line 17) | def __init__(self, config: str | Path | ConfigDict, save_dir: str | Pa...
    method run (line 55) | def run(self):

FILE: scripts/infer_fd.py
  class InferFD (line 17) | class InferFD:
    method __init__ (line 18) | def __init__(self, config: str | Path | ConfigDict, save_dir: str | Pa...
    method run (line 64) | def run(self):

FILE: scripts/train_f.py
  class TrainF (line 22) | class TrainF:
    method __init__ (line 23) | def __init__(self, config: str | Path | ConfigDict, wandb_key: str):
    method run (line 101) | def run(self):
    method optim (line 169) | def optim(self, loss: Tensor):

FILE: scripts/train_fd.py
  class TrainFD (line 28) | class TrainFD:
    method __init__ (line 29) | def __init__(self, config: str | Path | ConfigDict, wandb_key: str):
    method run (line 109) | def run(self):

FILE: scripts/utils/smart_optimizer.py
  function smart_optimizer (line 8) | def smart_optimizer(config: ConfigDict, param_group: Tuple[List, List, L...

FILE: tools/choose_images.py
  function choose_images (line 9) | def choose_images(root: str | Path, mode: str = Literal['train', 'val', ...

FILE: tools/convert_to_png.py
  function convert_to_png (line 9) | def convert_to_png(src: str | Path, color: bool):

FILE: tools/data_preview.py
  function data_preview (line 16) | def data_preview(img_f: str | Path, lbl_f: str | Path, dst_f: str | Path...

FILE: tools/dict_to_device.py
  function dict_to_device (line 7) | def dict_to_device(d: Dict, device: Device) -> Dict | None:

FILE: tools/environment_probe.py
  class EnvironmentProbe (line 7) | class EnvironmentProbe:
    method __init__ (line 12) | def __init__(self):
    method memory_status (line 21) | def memory_status(self):

FILE: tools/generate_mask.py
  function generate_mask (line 7) | def generate_mask(url: str, src: str, dst: str):

FILE: tools/scenario_reader.py
  function scenario_counter (line 7) | def scenario_counter(src: str | Path):
  function generate_meta (line 35) | def generate_meta(root: str | Path):