SYMBOL INDEX (169 symbols across 12 files)

FILE: eval_copy_detection.py
  class CopydaysDataset (line 33) | class CopydaysDataset():
    method __init__ (line 34) | def __init__(self, basedir):
    method get_block (line 50) | def get_block(self, i):
    method get_block_filenames (line 57) | def get_block_filenames(self, subdir_name):
    method eval_result (line 63) | def eval_result(self, ids, distances):
  function score_ap_from_ranks_1 (line 97) | def score_ap_from_ranks_1(ranks, nres):
  class ImgListDataset (line 128) | class ImgListDataset(torch.utils.data.Dataset):
    method __init__ (line 129) | def __init__(self, img_list, transform=None):
    method __getitem__ (line 133) | def __getitem__(self, i):
    method __len__ (line 141) | def __len__(self):
  function is_image_file (line 145) | def is_image_file(s):
  function extract_features (line 153) | def extract_features(image_list, model, args):

FILE: eval_image_retrieval.py
  class OxfordParisDataset (line 33) | class OxfordParisDataset(torch.utils.data.Dataset):
    method __init__ (line 34) | def __init__(self, dir_main, dataset, split, transform=None, imsize=No...
    method __len__ (line 58) | def __len__(self):
    method __getitem__ (line 61) | def __getitem__(self, index):
  function config_imname (line 74) | def config_imname(cfg, i):
  function config_qimname (line 78) | def config_qimname(cfg, i):

FILE: eval_knn.py
  function extract_feature_pipeline (line 30) | def extract_feature_pipeline(args):
  function extract_features (line 96) | def extract_features(model, data_loader, use_cuda=True, multiscale=False):
  function knn_classifier (line 143) | def knn_classifier(train_features, train_labels, test_features, test_lab...
  class ReturnIndexDataset (line 185) | class ReturnIndexDataset(datasets.ImageFolder):
    method __getitem__ (line 186) | def __getitem__(self, idx):

FILE: eval_linear.py
  function eval_linear (line 31) | def eval_linear(args):
  function train (line 153) | def train(model, linear_classifier, optimizer, loader, epoch, n, avgpool):
  function validate_network (line 196) | def validate_network(val_loader, model, linear_classifier, n, avgpool):
  class LinearClassifier (line 237) | class LinearClassifier(nn.Module):
    method __init__ (line 239) | def __init__(self, dim, num_labels=1000):
    method forward (line 246) | def forward(self, x):

FILE: eval_video_segmentation.py
  function eval_video_tracking_davis (line 38) | def eval_video_tracking_davis(args, model, frame_list, video_dir, first_...
  function restrict_neighborhood (line 85) | def restrict_neighborhood(h, w):
  function norm_mask (line 102) | def norm_mask(mask):
  function label_propagation (line 113) | def label_propagation(args, model, frame_tar, list_frame_feats, list_seg...
  function extract_feature (line 153) | def extract_feature(model, frame, return_h_w=False):
  function imwrite_indexed (line 166) | def imwrite_indexed(filename, array, color_palette):
  function to_one_hot (line 176) | def to_one_hot(y_tensor, n_dims=None):
  function read_frame_list (line 191) | def read_frame_list(video_dir):
  function read_frame (line 197) | def read_frame(frame_dir, scale_size=[480]):
  function read_seg (line 224) | def read_seg(seg_dir, factor, scale_size=[480]):
  function color_normalize (line 244) | def color_normalize(x, mean=[0.485, 0.456, 0.406], std=[0.228, 0.224, 0....

FILE: hubconf.py
  function dino_vits16 (line 22) | def dino_vits16(pretrained=True, **kwargs):
  function dino_vits8 (line 37) | def dino_vits8(pretrained=True, **kwargs):
  function dino_vitb16 (line 52) | def dino_vitb16(pretrained=True, **kwargs):
  function dino_vitb8 (line 67) | def dino_vitb8(pretrained=True, **kwargs):
  function dino_resnet50 (line 82) | def dino_resnet50(pretrained=True, **kwargs):
  function dino_xcit_small_12_p16 (line 98) | def dino_xcit_small_12_p16(pretrained=True, **kwargs):
  function dino_xcit_small_12_p8 (line 112) | def dino_xcit_small_12_p8(pretrained=True, **kwargs):
  function dino_xcit_medium_24_p16 (line 126) | def dino_xcit_medium_24_p16(pretrained=True, **kwargs):
  function dino_xcit_medium_24_p8 (line 140) | def dino_xcit_medium_24_p8(pretrained=True, **kwargs):

FILE: main_dino.py
  function get_args_parser (line 41) | def get_args_parser():
  function train_dino (line 132) | def train_dino(args):
  function train_one_epoch (line 301) | def train_one_epoch(student, teacher, teacher_without_ddp, dino_loss, da...
  class DINOLoss (line 363) | class DINOLoss(nn.Module):
    method __init__ (line 364) | def __init__(self, out_dim, ncrops, warmup_teacher_temp, teacher_temp,
    method forward (line 380) | def forward(self, student_output, teacher_output, epoch):
    method update_center (line 407) | def update_center(self, teacher_output):
  class DataAugmentationDINO (line 419) | class DataAugmentationDINO(object):
    method __init__ (line 420) | def __init__(self, global_crops_scale, local_crops_scale, local_crops_...
    method __call__ (line 458) | def __call__(self, image):

FILE: run_with_submitit.py
  function parse_args (line 27) | def parse_args():
  function get_shared_folder (line 40) | def get_shared_folder() -> Path:
  function get_init_file (line 49) | def get_init_file():
  class Trainer (line 58) | class Trainer(object):
    method __init__ (line 59) | def __init__(self, args):
    method __call__ (line 62) | def __call__(self):
    method checkpoint (line 68) | def checkpoint(self):
    method _setup_gpu_args (line 77) | def _setup_gpu_args(self):
  function main (line 89) | def main():

FILE: utils.py
  class GaussianBlur (line 36) | class GaussianBlur(object):
    method __init__ (line 40) | def __init__(self, p=0.5, radius_min=0.1, radius_max=2.):
    method __call__ (line 45) | def __call__(self, img):
  class Solarization (line 57) | class Solarization(object):
    method __init__ (line 61) | def __init__(self, p):
    method __call__ (line 64) | def __call__(self, img):
  function load_pretrained_weights (line 71) | def load_pretrained_weights(model, pretrained_weights, checkpoint_key, m...
  function load_pretrained_linear_weights (line 112) | def load_pretrained_linear_weights(linear_classifier, model_name, patch_...
  function clip_gradients (line 132) | def clip_gradients(model, clip):
  function cancel_gradients_last_layer (line 144) | def cancel_gradients_last_layer(epoch, model, freeze_last_layer):
  function restart_from_checkpoint (line 152) | def restart_from_checkpoint(ckp_path, run_variables=None, **kwargs):
  function cosine_scheduler (line 187) | def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warm...
  function bool_flag (line 201) | def bool_flag(s):
  function fix_random_seeds (line 215) | def fix_random_seeds(seed=31):
  class SmoothedValue (line 224) | class SmoothedValue(object):
    method __init__ (line 229) | def __init__(self, window_size=20, fmt=None):
    method update (line 237) | def update(self, value, n=1):
    method synchronize_between_processes (line 242) | def synchronize_between_processes(self):
    method median (line 256) | def median(self):
    method avg (line 261) | def avg(self):
    method global_avg (line 266) | def global_avg(self):
    method max (line 270) | def max(self):
    method value (line 274) | def value(self):
    method __str__ (line 277) | def __str__(self):
  function reduce_dict (line 286) | def reduce_dict(input_dict, average=True):
  class MetricLogger (line 313) | class MetricLogger(object):
    method __init__ (line 314) | def __init__(self, delimiter="\t"):
    method update (line 318) | def update(self, **kwargs):
    method __getattr__ (line 325) | def __getattr__(self, attr):
    method __str__ (line 333) | def __str__(self):
    method synchronize_between_processes (line 341) | def synchronize_between_processes(self):
    method add_meter (line 345) | def add_meter(self, name, meter):
    method log_every (line 348) | def log_every(self, iterable, print_freq, header=None):
  function get_sha (line 403) | def get_sha():
  function is_dist_avail_and_initialized (line 423) | def is_dist_avail_and_initialized():
  function get_world_size (line 431) | def get_world_size():
  function get_rank (line 437) | def get_rank():
  function is_main_process (line 443) | def is_main_process():
  function save_on_master (line 447) | def save_on_master(*args, **kwargs):
  function setup_for_distributed (line 452) | def setup_for_distributed(is_master):
  function init_distributed_mode (line 467) | def init_distributed_mode(args):
  function accuracy (line 502) | def accuracy(output, target, topk=(1,)):
  function _no_grad_trunc_normal_ (line 512) | def _no_grad_trunc_normal_(tensor, mean, std, a, b):
  function trunc_normal_ (line 548) | def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
  class LARS (line 553) | class LARS(torch.optim.Optimizer):
    method __init__ (line 557) | def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, eta=0.001,
    method step (line 565) | def step(self):
  class MultiCropWrapper (line 594) | class MultiCropWrapper(nn.Module):
    method __init__ (line 603) | def __init__(self, backbone, head):
    method forward (line 610) | def forward(self, x):
  function get_params_groups (line 632) | def get_params_groups(model):
  function has_batchnorms (line 646) | def has_batchnorms(model):
  class PCA (line 654) | class PCA():
    method __init__ (line 658) | def __init__(self, dim=256, whit=0.5):
    method train_pca (line 663) | def train_pca(self, cov):
    method apply (line 689) | def apply(self, x):
  function compute_ap (line 708) | def compute_ap(ranks, nres):
  function compute_map (line 743) | def compute_map(ranks, gnd, kappas=[]):
  function multi_scale (line 815) | def multi_scale(samples, model):

FILE: video_generation.py
  class VideoGenerator (line 40) | class VideoGenerator:
    method __init__ (line 41) | def __init__(self, args):
    method run (line 48) | def run(self):
    method _extract_frames_from_video (line 102) | def _extract_frames_from_video(self, inp: str, out: str):
    method _generate_video_from_images (line 119) | def _generate_video_from_images(self, inp: str, out: str):
    method _inference (line 150) | def _inference(self, inp: str, out: str):
    method __load_model (line 240) | def __load_model(self):
  function parse_args (line 297) | def parse_args():

FILE: vision_transformer.py
  function drop_path (line 27) | def drop_path(x, drop_prob: float = 0., training: bool = False):
  class DropPath (line 38) | class DropPath(nn.Module):
    method __init__ (line 41) | def __init__(self, drop_prob=None):
    method forward (line 45) | def forward(self, x):
  class Mlp (line 49) | class Mlp(nn.Module):
    method __init__ (line 50) | def __init__(self, in_features, hidden_features=None, out_features=Non...
    method forward (line 59) | def forward(self, x):
  class Attention (line 68) | class Attention(nn.Module):
    method __init__ (line 69) | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, at...
    method forward (line 80) | def forward(self, x):
  class Block (line 95) | class Block(nn.Module):
    method __init__ (line 96) | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_sc...
    method forward (line 107) | def forward(self, x, return_attention=False):
  class PatchEmbed (line 116) | class PatchEmbed(nn.Module):
    method __init__ (line 119) | def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=...
    method forward (line 128) | def forward(self, x):
  class VisionTransformer (line 134) | class VisionTransformer(nn.Module):
    method __init__ (line 136) | def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_clas...
    method _init_weights (line 165) | def _init_weights(self, m):
    method interpolate_pos_encoding (line 174) | def interpolate_pos_encoding(self, x, w, h):
    method prepare_tokens (line 196) | def prepare_tokens(self, x):
    method forward (line 209) | def forward(self, x):
    method get_last_selfattention (line 216) | def get_last_selfattention(self, x):
    method get_intermediate_layers (line 225) | def get_intermediate_layers(self, x, n=1):
  function vit_tiny (line 236) | def vit_tiny(patch_size=16, **kwargs):
  function vit_small (line 243) | def vit_small(patch_size=16, **kwargs):
  function vit_base (line 250) | def vit_base(patch_size=16, **kwargs):
  class DINOHead (line 257) | class DINOHead(nn.Module):
    method __init__ (line 258) | def __init__(self, in_dim, out_dim, use_bn=False, norm_last_layer=True...
    method _init_weights (line 281) | def _init_weights(self, m):
    method forward (line 287) | def forward(self, x):

FILE: visualize_attention.py
  function apply_mask (line 38) | def apply_mask(image, mask, color, alpha=0.5):
  function random_colors (line 44) | def random_colors(N, bright=True):
  function display_instances (line 55) | def display_instances(image, mask, fname="test", figsize=(5, 5), blur=Fa...