SYMBOL INDEX (60 symbols across 13 files)

FILE: augmentations.py
  class DeviceAgnosticColorJitter (line 7) | class DeviceAgnosticColorJitter(T.ColorJitter):
    method __init__ (line 8) | def __init__(self, brightness: float = 0., contrast: float = 0., satur...
    method forward (line 12) | def forward(self, images: torch.Tensor) -> torch.Tensor:
  class DeviceAgnosticRandomResizedCrop (line 23) | class DeviceAgnosticRandomResizedCrop(T.RandomResizedCrop):
    method __init__ (line 24) | def __init__(self, size: Union[int, Tuple[int, int]], scale: float):
    method forward (line 28) | def forward(self, images: torch.Tensor) -> torch.Tensor:

FILE: commons.py
  class InfiniteDataLoader (line 11) | class InfiniteDataLoader(torch.utils.data.DataLoader):
    method __init__ (line 12) | def __init__(self, *args, **kwargs):
    method __iter__ (line 16) | def __iter__(self):
    method __next__ (line 19) | def __next__(self):
  function make_deterministic (line 28) | def make_deterministic(seed: int = 0):
  function setup_logging (line 44) | def setup_logging(output_folder: str, exist_ok: bool = False, console: s...

FILE: cosface_loss.py
  function cosine_sim (line 9) | def cosine_sim(x1: torch.Tensor, x2: torch.Tensor, dim: int = 1, eps: fl...
  class MarginCosineProduct (line 16) | class MarginCosineProduct(nn.Module):
    method __init__ (line 24) | def __init__(self, in_features: int, out_features: int, s: float = 30....
    method forward (line 33) | def forward(self, inputs: torch.Tensor, label: torch.Tensor) -> torch....
    method __repr__ (line 40) | def __repr__(self):

FILE: cosplace_model/cosplace_network.py
  class GeoLocalizationNet (line 28) | class GeoLocalizationNet(nn.Module):
    method __init__ (line 29) | def __init__(self, backbone : str, fc_output_dim : int, train_all_laye...
    method forward (line 48) | def forward(self, x):
  function get_pretrained_torchvision_model (line 54) | def get_pretrained_torchvision_model(backbone_name : str) -> torch.nn.Mo...
  function get_backbone (line 66) | def get_backbone(backbone_name : str, train_all_layers : bool) -> Tuple[...

FILE: cosplace_model/layers.py
  function gem (line 8) | def gem(x, p=torch.ones(1)*3, eps: float = 1e-6):
  class GeM (line 12) | class GeM(nn.Module):
    method __init__ (line 13) | def __init__(self, p=3, eps=1e-6):
    method forward (line 18) | def forward(self, x):
    method __repr__ (line 21) | def __repr__(self):
  class Flatten (line 25) | class Flatten(torch.nn.Module):
    method __init__ (line 26) | def __init__(self):
    method forward (line 29) | def forward(self, x):
  class L2Norm (line 34) | class L2Norm(nn.Module):
    method __init__ (line 35) | def __init__(self, dim=1):
    method forward (line 39) | def forward(self, x):

FILE: datasets/dataset_utils.py
  function read_images_paths (line 10) | def read_images_paths(dataset_folder, get_abs_path=False):

FILE: datasets/test_dataset.py
  class TestDataset (line 12) | class TestDataset(data.Dataset):
    method __init__ (line 13) | def __init__(self, dataset_folder, database_folder="database",
    method open_image (line 51) | def open_image(path):
    method __getitem__ (line 54) | def __getitem__(self, index):
    method __len__ (line 60) | def __len__(self):
    method __repr__ (line 63) | def __repr__(self):
    method get_positives (line 66) | def get_positives(self):

FILE: datasets/train_dataset.py
  class TrainDataset (line 18) | class TrainDataset(torch.utils.data.Dataset):
    method __init__ (line 19) | def __init__(self, args, dataset_folder, M=10, alpha=30, N=5, L=2,
    method open_image (line 71) | def open_image(path):
    method __getitem__ (line 74) | def __getitem__(self, class_num):
    method get_images_num (line 97) | def get_images_num(self):
    method __len__ (line 101) | def __len__(self):
    method initialize (line 106) | def initialize(dataset_folder, M, N, alpha, L, min_images_per_class, f...
    method get__class_id__group_id (line 147) | def get__class_id__group_id(utm_east, utm_north, heading, M, alpha, N,...

FILE: hubconf.py
  function get_trained_model (line 18) | def get_trained_model(backbone : str = "ResNet50", fc_output_dim : int =...

FILE: parser.py
  function parse_arguments (line 5) | def parse_arguments(is_training: bool = True):

FILE: test.py
  function test (line 19) | def test(args: Namespace, eval_ds: Dataset, model: torch.nn.Module,

FILE: util.py
  function move_to_device (line 10) | def move_to_device(optimizer: Type[torch.optim.Optimizer], device: str):
  function save_checkpoint (line 17) | def save_checkpoint(state: dict, is_best: bool, output_folder: str,
  function resume_train (line 26) | def resume_train(args: Namespace, output_folder: str, model: torch.nn.Mo...

FILE: visualizations.py
  function write_labels_to_image (line 18) | def write_labels_to_image(labels=["text1", "text2"]):
  function draw (line 29) | def draw(img, c=(0, 255, 0), thickness=20):
  function build_prediction_image (line 37) | def build_prediction_image(images_paths, preds_correct=None):
  function save_file_with_paths (line 66) | def save_file_with_paths(query_path, preds_paths, positives_paths, outpu...
  function save_preds (line 78) | def save_preds(predictions, eval_ds, output_folder, save_only_wrong_pred...