SYMBOL INDEX (399 symbols across 59 files)

FILE: datasets.py
  class CustomDataset (line 20) | class CustomDataset(Dataset):
    method __init__ (line 21) | def __init__(
    method read_and_clean (line 60) | def read_and_clean(self):
    method resize (line 107) | def resize(self, im, square=False):
    method load_image_and_labels (line 117) | def load_image_and_labels(self, index):
    method load_pascal_voc (line 141) | def load_pascal_voc(self, image, image_name, image_resized):
    method load_yolo (line 217) | def load_yolo(self, image, image_name, image_resized):
    method check_image_and_annotation (line 292) | def check_image_and_annotation(
    method load_cutmix_image_and_boxes (line 341) | def load_cutmix_image_and_boxes(self, index, resize_factor=512):
    method __getitem__ (line 407) | def __getitem__(self, idx):
    method __len__ (line 460) | def __len__(self):
  function collate_fn (line 463) | def collate_fn(batch):
  function create_train_dataset (line 471) | def create_train_dataset(
  function create_valid_dataset (line 494) | def create_valid_dataset(
  function create_train_loader (line 514) | def create_train_loader(
  function create_valid_loader (line 527) | def create_valid_loader(

FILE: eval.py
  function evaluate (line 134) | def evaluate(

FILE: export.py
  function parse_opt (line 18) | def parse_opt():
  function main (line 56) | def main(args):

FILE: inference.py
  function collect_all_images (line 21) | def collect_all_images(dir_test):
  function parse_opt (line 39) | def parse_opt():
  function main (line 132) | def main(args):

FILE: inference_video.py
  function read_return_video_data (line 26) | def read_return_video_data(video_path):
  function parse_opt (line 34) | def parse_opt():
  function main (line 121) | def main(args):

FILE: models/create_fasterrcnn_model.py
  function return_fasterrcnn_resnet50_fpn (line 3) | def return_fasterrcnn_resnet50_fpn(
  function return_fasterrcnn_mobilenetv3_large_fpn (line 11) | def return_fasterrcnn_mobilenetv3_large_fpn(
  function return_fasterrcnn_mobilenetv3_large_320_fpn (line 19) | def return_fasterrcnn_mobilenetv3_large_320_fpn(
  function return_fasterrcnn_resnet18 (line 27) | def return_fasterrcnn_resnet18(
  function return_fasterrcnn_custom_resnet (line 35) | def return_fasterrcnn_custom_resnet(
  function return_fasterrcnn_darknet (line 43) | def return_fasterrcnn_darknet(
  function return_fasterrcnn_squeezenet1_0 (line 51) | def return_fasterrcnn_squeezenet1_0(
  function return_fasterrcnn_squeezenet1_1 (line 59) | def return_fasterrcnn_squeezenet1_1(
  function return_fasterrcnn_mini_darknet (line 67) | def return_fasterrcnn_mini_darknet(
  function return_fasterrcnn_squeezenet1_1_small_head (line 75) | def return_fasterrcnn_squeezenet1_1_small_head(
  function return_fasterrcnn_mini_squeezenet1_1_small_head (line 83) | def return_fasterrcnn_mini_squeezenet1_1_small_head(
  function return_fasterrcnn_mini_squeezenet1_1_tiny_head (line 91) | def return_fasterrcnn_mini_squeezenet1_1_tiny_head(
  function return_fasterrcnn_mbv3_small_nano_head (line 99) | def return_fasterrcnn_mbv3_small_nano_head(
  function return_fasterrcnn_mini_darknet_nano_head (line 107) | def return_fasterrcnn_mini_darknet_nano_head(
  function return_fasterrcnn_efficientnet_b0 (line 115) | def return_fasterrcnn_efficientnet_b0(
  function return_fasterrcnn_nano (line 123) | def return_fasterrcnn_nano(
  function return_fasterrcnn_resnet152 (line 131) | def return_fasterrcnn_resnet152(
  function return_fasterrcnn_resnet50_fpn_v2 (line 139) | def return_fasterrcnn_resnet50_fpn_v2(
  function return_fasterrcnn_convnext_small (line 147) | def return_fasterrcnn_convnext_small(
  function return_fasterrcnn_convnext_tiny (line 155) | def return_fasterrcnn_convnext_tiny(
  function return_fasterrcnn_resnet101 (line 163) | def return_fasterrcnn_resnet101(
  function return_fasterrcnn_vitdet (line 171) | def return_fasterrcnn_vitdet(
  function return_fasterrcnn_vitdet_tiny (line 179) | def return_fasterrcnn_vitdet_tiny(
  function return_fasterrcnn_mobilevit_xxs (line 187) | def return_fasterrcnn_mobilevit_xxs(
  function return_fasterrcnn_regnet_y_400mf (line 195) | def return_fasterrcnn_regnet_y_400mf(
  function return_fasterrcnn_vgg16 (line 203) | def return_fasterrcnn_vgg16(
  function return_fasterrcnn_dinov3_convnext_tiny (line 211) | def return_fasterrcnn_dinov3_convnext_tiny(
  function return_fasterrcnn_dinov3_vits16 (line 219) | def return_fasterrcnn_dinov3_vits16(
  function return_fasterrcnn_dinov3_convnext_tiny_multifeat (line 227) | def return_fasterrcnn_dinov3_convnext_tiny_multifeat(
  function return_fasterrcnn_dinov3_vits16plus (line 235) | def return_fasterrcnn_dinov3_vits16plus(
  function return_fasterrcnn_dinov3_vitb16 (line 243) | def return_fasterrcnn_dinov3_vitb16(
  function return_fasterrcnn_dinov3_vitl16 (line 251) | def return_fasterrcnn_dinov3_vitl16(
  function return_fasterrcnn_dinov3_vith16plus (line 259) | def return_fasterrcnn_dinov3_vith16plus(
  function return_fasterrcnn_dinov3_convnext_small (line 267) | def return_fasterrcnn_dinov3_convnext_small(
  function return_fasterrcnn_dinov3_convnext_base (line 275) | def return_fasterrcnn_dinov3_convnext_base(
  function return_fasterrcnn_dinov3_convnext_large (line 283) | def return_fasterrcnn_dinov3_convnext_large(

FILE: models/fasterrcnn_convnext_small.py
  function create_model (line 13) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_convnext_tiny.py
  function create_model (line 13) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_custom_resnet.py
  class ResidualBlock (line 8) | class ResidualBlock(nn.Module):
    method __init__ (line 12) | def __init__(
    method forward (line 28) | def forward(self, x):
  function create_resnet_block (line 37) | def create_resnet_block(
  class CustomResNet (line 51) | class CustomResNet(nn.Module):
    method __init__ (line 52) | def __init__(self, num_classes=10):
    method forward (line 64) | def forward(self, x):
  function create_model (line 75) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_darknet.py
  class DarkNet (line 9) | class DarkNet(nn.Module):
    method __init__ (line 10) | def __init__(self, initialize_weights=True, num_classes=1000):
    method _create_conv_layers (line 23) | def _create_conv_layers(self):
    method _pool (line 82) | def _pool(self):
    method _create_fc_layers (line 88) | def _create_fc_layers(self):
    method _initialize_weights (line 94) | def _initialize_weights(self):
    method forward (line 106) | def forward(self, x):
  function create_model (line 113) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_convnext_base.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 37) | def forward(self, x):
  function create_model (line 44) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_convnext_large.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 37) | def forward(self, x):
  function create_model (line 44) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_convnext_small.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 37) | def forward(self, x):
  function create_model (line 44) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_convnext_tiny.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 37) | def forward(self, x):
  function create_model (line 44) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_convnext_tiny_multifeat.py
  class Dinov3Backbone (line 28) | class Dinov3Backbone(nn.Module):
    method __init__ (line 29) | def __init__(self):
    method forward (line 48) | def forward(self, x):
  function create_model (line 62) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_vitb16.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 39) | def forward(self, x):
  function create_model (line 52) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_vith16plus.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 39) | def forward(self, x):
  function create_model (line 52) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_vitl16.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 39) | def forward(self, x):
  function create_model (line 52) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_vits16.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 39) | def forward(self, x):
  function create_model (line 52) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_dinov3_vits16plus.py
  class Dinov3Backbone (line 27) | class Dinov3Backbone(nn.Module):
    method __init__ (line 28) | def __init__(self):
    method forward (line 39) | def forward(self, x):
  function create_model (line 52) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_efficientnet_b0.py
  function create_model (line 12) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_efficientnet_b4.py
  function create_model (line 10) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_mbv3_large.py
  function create_model (line 13) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_mbv3_small_nano_head.py
  class TwoMLPHead (line 21) | class TwoMLPHead(nn.Module):
    method __init__ (line 30) | def __init__(self, in_channels, representation_size):
    method forward (line 36) | def forward(self, x):
  class FastRCNNPredictor (line 44) | class FastRCNNPredictor(nn.Module):
    method __init__ (line 54) | def __init__(self, in_channels, num_classes):
    method forward (line 59) | def forward(self, x):
  function create_model (line 71) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_mini_darknet.py
  class DarkNet (line 8) | class DarkNet(nn.Module):
    method __init__ (line 9) | def __init__(self, initialize_weights=True, num_classes=1000):
    method _create_conv_layers (line 22) | def _create_conv_layers(self):
    method _pool (line 74) | def _pool(self):
    method _create_fc_layers (line 80) | def _create_fc_layers(self):
    method _initialize_weights (line 86) | def _initialize_weights(self):
    method forward (line 98) | def forward(self, x):
  function create_model (line 105) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_mini_darknet_nano_head.py
  class TwoMLPHead (line 15) | class TwoMLPHead(nn.Module):
    method __init__ (line 24) | def __init__(self, in_channels, representation_size):
    method forward (line 30) | def forward(self, x):
  class FastRCNNPredictor (line 38) | class FastRCNNPredictor(nn.Module):
    method __init__ (line 48) | def __init__(self, in_channels, num_classes):
    method forward (line 53) | def forward(self, x):
  class DarkNet (line 66) | class DarkNet(nn.Module):
    method __init__ (line 67) | def __init__(self, initialize_weights=True, num_classes=1000):
    method _create_conv_layers (line 80) | def _create_conv_layers(self):
    method _pool (line 132) | def _pool(self):
    method _create_fc_layers (line 138) | def _create_fc_layers(self):
    method _initialize_weights (line 144) | def _initialize_weights(self):
    method forward (line 156) | def forward(self, x):
  function create_model (line 163) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_mini_squeezenet1_1_small_head.py
  class TwoMLPHead (line 19) | class TwoMLPHead(nn.Module):
    method __init__ (line 28) | def __init__(self, in_channels, representation_size):
    method forward (line 34) | def forward(self, x):
  class FastRCNNPredictor (line 42) | class FastRCNNPredictor(nn.Module):
    method __init__ (line 52) | def __init__(self, in_channels, num_classes):
    method forward (line 57) | def forward(self, x):
  function create_model (line 69) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_mini_squeezenet1_1_tiny_head.py
  class TwoMLPHead (line 19) | class TwoMLPHead(nn.Module):
    method __init__ (line 28) | def __init__(self, in_channels, representation_size):
    method forward (line 34) | def forward(self, x):
  class FastRCNNPredictor (line 42) | class FastRCNNPredictor(nn.Module):
    method __init__ (line 52) | def __init__(self, in_channels, num_classes):
    method forward (line 57) | def forward(self, x):
  function create_model (line 69) | def create_model(num_classes=81, pretrained=True, coco_model=True):

FILE: models/fasterrcnn_mobilenetv3_large_320_fpn.py
  function create_model (line 5) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_mobilenetv3_large_fpn.py
  function create_model (line 5) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_mobilevit_xxs.py
  function create_model (line 24) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_nano.py
  class TwoMLPHead (line 14) | class TwoMLPHead(nn.Module):
    method __init__ (line 23) | def __init__(self, in_channels, representation_size):
    method forward (line 29) | def forward(self, x):
  class FastRCNNPredictor (line 37) | class FastRCNNPredictor(nn.Module):
    method __init__ (line 47) | def __init__(self, in_channels, num_classes):
    method forward (line 52) | def forward(self, x):
  class NanoBackbone (line 65) | class NanoBackbone(nn.Module):
    method __init__ (line 66) | def __init__(self, initialize_weights=True, num_classes=1000):
    method _create_conv_layers (line 77) | def _create_conv_layers(self):
    method _initialize_weights (line 96) | def _initialize_weights(self):
  function create_model (line 108) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_regnet_y_400mf.py
  function create_model (line 15) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_resnet101.py
  function create_model (line 14) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_resnet152.py
  function create_model (line 14) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_resnet18.py
  function create_model (line 13) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_resnet50_fpn.py
  function create_model (line 5) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_resnet50_fpn_v2.py
  function create_model (line 5) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_squeezenet1_0.py
  function create_model (line 12) | def create_model(num_classes=81, pretrained=False, coco_model=False):

FILE: models/fasterrcnn_squeezenet1_1.py
  function create_model (line 12) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_squeezenet1_1_small_head.py
  class TwoMLPHead (line 17) | class TwoMLPHead(nn.Module):
    method __init__ (line 26) | def __init__(self, in_channels, representation_size):
    method forward (line 32) | def forward(self, x):
  class FastRCNNPredictor (line 40) | class FastRCNNPredictor(nn.Module):
    method __init__ (line 50) | def __init__(self, in_channels, num_classes):
    method forward (line 55) | def forward(self, x):
  function create_model (line 67) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_vgg16.py
  function create_model (line 13) | def create_model(num_classes, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_vitdet.py
  class ViT (line 25) | class ViT(Backbone):
    method __init__ (line 32) | def __init__(
    method _init_weights (line 133) | def _init_weights(self, m):
    method forward (line 142) | def forward(self, x):
  class SimpleFeaturePyramid (line 155) | class SimpleFeaturePyramid(Backbone):
    method __init__ (line 161) | def __init__(
    method padding_constraints (line 262) | def padding_constraints(self):
    method forward (line 268) | def forward(self, x):
  function create_model (line 294) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/fasterrcnn_vitdet_tiny.py
  class ViT (line 25) | class ViT(Backbone):
    method __init__ (line 32) | def __init__(
    method _init_weights (line 133) | def _init_weights(self, m):
    method forward (line 142) | def forward(self, x):
  class SimpleFeaturePyramid (line 155) | class SimpleFeaturePyramid(Backbone):
    method __init__ (line 161) | def __init__(
    method padding_constraints (line 262) | def padding_constraints(self):
    method forward (line 268) | def forward(self, x):
  function create_model (line 294) | def create_model(num_classes=81, pretrained=True, coco_model=False):

FILE: models/layers.py
  class Mlp (line 18) | class Mlp(nn.Module):
    method __init__ (line 19) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 28) | def forward(self, x):
  function drop_path (line 36) | def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by...
  class ShapeSpec (line 55) | class ShapeSpec:
  class DropPath (line 68) | class DropPath(nn.Module):
    method __init__ (line 71) | def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
    method forward (line 76) | def forward(self, x):
    method extra_repr (line 79) | def extra_repr(self):
  class Backbone (line 82) | class Backbone(nn.Module, metaclass=ABCMeta):
    method __init__ (line 86) | def __init__(self):
    method forward (line 93) | def forward(self):
    method size_divisibility (line 102) | def size_divisibility(self) -> int:
    method padding_constraints (line 113) | def padding_constraints(self) -> Dict[str, int]:
    method output_shape (line 131) | def output_shape(self):
  function get_rel_pos (line 144) | def get_rel_pos(q_size, k_size, rel_pos):
  function add_decomposed_rel_pos (line 175) | def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size):
  class Conv2d (line 205) | class Conv2d(torch.nn.Conv2d):
    method __init__ (line 210) | def __init__(self, *args, **kwargs):
    method forward (line 225) | def forward(self, x):
  class Attention (line 249) | class Attention(nn.Module):
    method __init__ (line 252) | def __init__(
    method forward (line 288) | def forward(self, x):
  class FrozenBatchNorm2d (line 306) | class FrozenBatchNorm2d(nn.Module):
    method __init__ (line 324) | def __init__(self, num_features, eps=1e-5):
    method forward (line 333) | def forward(self, x):
    method _load_from_state_dict (line 356) | def _load_from_state_dict(
    method __repr__ (line 373) | def __repr__(self):
    method convert_frozen_batchnorm (line 377) | def convert_frozen_batchnorm(cls, module):
  class CNNBlockBase (line 407) | class CNNBlockBase(nn.Module):
    method __init__ (line 419) | def __init__(self, in_channels, out_channels, stride):
    method freeze (line 432) | def freeze(self):
  class LayerNorm (line 445) | class LayerNorm(nn.Module):
    method __init__ (line 453) | def __init__(self, normalized_shape, eps=1e-6):
    method forward (line 460) | def forward(self, x):
  function get_norm (line 467) | def get_norm(norm, out_channels):
  class NaiveSyncBatchNorm (line 495) | class NaiveSyncBatchNorm(BatchNorm2d):
    method __init__ (line 518) | def __init__(self, *args, stats_mode="", **kwargs):
    method forward (line 523) | def forward(self, input):
  function c2_msra_fill (line 570) | def c2_msra_fill(module: nn.Module) -> None:
  class ResBottleneckBlock (line 584) | class ResBottleneckBlock(CNNBlockBase):
    method __init__ (line 590) | def __init__(
    method forward (line 635) | def forward(self, x):
  function window_partition (line 643) | def window_partition(x, window_size):
  function window_unpartition (line 665) | def window_unpartition(windows, window_size, pad_hw, hw):
  function get_abs_pos (line 686) | def get_abs_pos(abs_pos, has_cls_token, hw):
  class Block (line 716) | class Block(nn.Module):
    method __init__ (line 719) | def __init__(
    method forward (line 778) | def forward(self, x):
  class PatchEmbed (line 799) | class PatchEmbed(nn.Module):
    method __init__ (line 804) | def __init__(
    method forward (line 820) | def forward(self, x):
  class LastLevelMaxPool (line 826) | class LastLevelMaxPool(nn.Module):
    method __init__ (line 832) | def __init__(self):
    method forward (line 837) | def forward(self, x):

FILE: models/model_summary.py
  function summary (line 4) | def summary(model):

FILE: models/utils.py
  function get_world_size (line 9) | def get_world_size() -> int:
  function _assert_strides_are_log2_contiguous (line 16) | def _assert_strides_are_log2_contiguous(strides):
  function differentiable_all_reduce (line 25) | def differentiable_all_reduce(input: torch.Tensor) -> torch.Tensor:
  class _AllReduce (line 37) | class _AllReduce(Function):
    method forward (line 39) | def forward(ctx, input: torch.Tensor) -> torch.Tensor:
    method backward (line 47) | def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:

FILE: onnx_inference_image.py
  function collect_all_images (line 27) | def collect_all_images(dir_test):
  function to_numpy (line 45) | def to_numpy(tensor):
  function parse_opt (line 48) | def parse_opt():
  function main (line 114) | def main(args):

FILE: onnx_inference_video.py
  function read_return_video_data (line 31) | def read_return_video_data(video_path):
  function to_numpy (line 39) | def to_numpy(tensor):
  function parse_opt (line 42) | def parse_opt():
  function main (line 113) | def main(args):

FILE: sahi_inference.py
  function collect_all_images (line 32) | def collect_all_images(dir_test):
  function parse_opt (line 50) | def parse_opt():
  function main (line 177) | def main(args):

FILE: torch_utils/coco_eval.py
  class CocoEvaluator (line 13) | class CocoEvaluator:
    method __init__ (line 14) | def __init__(self, coco_gt, iou_types):
    method update (line 27) | def update(self, predictions):
    method synchronize_between_processes (line 43) | def synchronize_between_processes(self):
    method accumulate (line 48) | def accumulate(self):
    method summarize (line 52) | def summarize(self):
    method prepare (line 58) | def prepare(self, predictions, iou_type):
    method prepare_for_coco_detection (line 67) | def prepare_for_coco_detection(self, predictions):
    method prepare_for_coco_segmentation (line 91) | def prepare_for_coco_segmentation(self, predictions):
    method prepare_for_coco_keypoint (line 125) | def prepare_for_coco_keypoint(self, predictions):
  function convert_to_xywh (line 152) | def convert_to_xywh(boxes):
  function merge (line 157) | def merge(img_ids, eval_imgs):
  function create_common_coco_eval (line 179) | def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
  function evaluate (line 189) | def evaluate(imgs):

FILE: torch_utils/coco_utils.py
  class FilterAndRemapCocoCategories (line 12) | class FilterAndRemapCocoCategories:
    method __init__ (line 13) | def __init__(self, categories, remap=True):
    method __call__ (line 17) | def __call__(self, image, target):
  function convert_coco_poly_to_mask (line 30) | def convert_coco_poly_to_mask(segmentations, height, width):
  class ConvertCocoPolysToMask (line 47) | class ConvertCocoPolysToMask:
    method __call__ (line 48) | def __call__(self, image, target):
  function _coco_remove_images_without_annotations (line 103) | def _coco_remove_images_without_annotations(dataset, cat_list=None):
  function convert_to_coco_api (line 143) | def convert_to_coco_api(ds):
  function get_coco_api_from_dataset (line 196) | def get_coco_api_from_dataset(dataset):
  class CocoDetection (line 206) | class CocoDetection(torchvision.datasets.CocoDetection):
    method __init__ (line 207) | def __init__(self, img_folder, ann_file, transforms):
    method __getitem__ (line 211) | def __getitem__(self, idx):
  function get_coco (line 220) | def get_coco(root, image_set, transforms, mode="instances"):
  function get_coco_kp (line 248) | def get_coco_kp(root, image_set, transforms):

FILE: torch_utils/engine.py
  function train_one_epoch (line 12) | def train_one_epoch(
  function _get_iou_types (line 103) | def _get_iou_types(model):
  function evaluate (line 116) | def evaluate(

FILE: torch_utils/utils.py
  class SmoothedValue (line 13) | class SmoothedValue:
    method __init__ (line 18) | def __init__(self, window_size=20, fmt=None):
    method update (line 26) | def update(self, value, n=1):
    method synchronize_between_processes (line 31) | def synchronize_between_processes(self):
    method median (line 45) | def median(self):
    method avg (line 50) | def avg(self):
    method global_avg (line 55) | def global_avg(self):
    method max (line 59) | def max(self):
    method value (line 63) | def value(self):
    method __str__ (line 66) | def __str__(self):
  function all_gather (line 72) | def all_gather(data):
  function reduce_dict (line 88) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 115) | class MetricLogger:
    method __init__ (line 116) | def __init__(self, delimiter="\t"):
    method update (line 120) | def update(self, **kwargs):
    method __getattr__ (line 127) | def __getattr__(self, attr):
    method __str__ (line 134) | def __str__(self):
    method synchronize_between_processes (line 140) | def synchronize_between_processes(self):
    method add_meter (line 144) | def add_meter(self, name, meter):
    method log_every (line 147) | def log_every(self, iterable, print_freq, header=None):
  function collate_fn (line 205) | def collate_fn(batch):
  function mkdir (line 209) | def mkdir(path):
  function setup_for_distributed (line 217) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 233) | def is_dist_avail_and_initialized():
  function get_world_size (line 241) | def get_world_size():
  function get_rank (line 247) | def get_rank():
  function is_main_process (line 253) | def is_main_process():
  function save_on_master (line 257) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 262) | def init_distributed_mode(args):

FILE: train.py
  function parse_opt (line 60) | def parse_opt():
  function main (line 230) | def main(args):

FILE: utils/annotations.py
  function convert_detections (line 4) | def convert_detections(
  function convert_pre_track (line 36) | def convert_pre_track(
  function convert_post_track (line 51) | def convert_post_track(
  function inference_annotations (line 68) | def inference_annotations(
  function draw_text (line 130) | def draw_text(
  function annotate_fps (line 159) | def annotate_fps(orig_image, fps_text):

FILE: utils/general.py
  function init_seeds (line 13) | def init_seeds(seed=0, deterministic=False):
  class Averager (line 29) | class Averager:
    method __init__ (line 30) | def __init__(self):
    method send (line 34) | def send(self, value):
    method value (line 39) | def value(self):
    method reset (line 45) | def reset(self):
  class SaveBestModel (line 49) | class SaveBestModel:
    method __init__ (line 55) | def __init__(
    method __call__ (line 60) | def __call__(
  function show_tranformed_image (line 80) | def show_tranformed_image(train_loader, device, classes, colors):
  function save_loss_plot (line 135) | def save_loss_plot(
  function save_mAP (line 156) | def save_mAP(OUT_DIR, map_05, map):
  function visualize_mosaic_images (line 179) | def visualize_mosaic_images(boxes, labels, image_resized, classes):
  function save_model (line 197) | def save_model(
  function save_model_state (line 235) | def save_model_state(model, OUT_DIR, config, model_name):
  function denormalize (line 250) | def denormalize(x, mean=None, std=None):
  function save_validation_results (line 257) | def save_validation_results(images, detections, counter, out_dir, classe...
  function set_infer_dir (line 298) | def set_infer_dir():
  function set_training_dir (line 312) | def set_training_dir(dir_name=None, project_dir=None):
  function yaml_save (line 334) | def yaml_save(file_path=None, data={}):
  class EarlyStopping (line 342) | class EarlyStopping():
    method __init__ (line 347) | def __init__(self, patience=10, min_delta=0):
    method __call__ (line 360) | def __call__(self, map):

FILE: utils/logging.py
  function wandb_init (line 12) | def wandb_init(name):
  function set_log (line 18) | def set_log(log_dir):
  function log (line 31) | def log(content, *args):
  function coco_log (line 36) | def coco_log(log_dir, stats):
  function set_summary_writer (line 63) | def set_summary_writer(log_dir):
  function tensorboard_loss_log (line 67) | def tensorboard_loss_log(name, loss_np_arr, writer, epoch):
  function tensorboard_map_log (line 74) | def tensorboard_map_log(name, val_map_05, val_map, writer, epoch):
  function create_log_csv (line 84) | def create_log_csv(log_dir):
  function csv_log (line 98) | def csv_log(
  function overlay_on_canvas (line 130) | def overlay_on_canvas(bg, image):
  function wandb_log (line 139) | def wandb_log(
  function wandb_save_model (line 219) | def wandb_save_model(model_dir):
  class LogJSON (line 227) | class LogJSON():
    method __init__ (line 228) | def __init__(self, output_filename):
    method update (line 246) | def update(self, image, file_name, boxes, labels, classes):
    method save (line 290) | def save(self, output_filename):

FILE: utils/transforms.py
  function resize (line 8) | def resize(im, img_size=640, square=False):
  function get_train_aug (line 20) | def get_train_aug():
  function get_train_transform (line 37) | def get_train_transform():
  function transform_mosaic (line 45) | def transform_mosaic(mosaic, boxes, img_size=640):
  function get_valid_transform (line 77) | def get_valid_transform():
  function infer_transforms (line 85) | def infer_transforms(image):