SYMBOL INDEX (80 symbols across 8 files)

FILE: data.py
  class NTUDataset (line 22) | class NTUDataset(Dataset):
    method __init__ (line 23) | def __init__(self, x, y):
    method __len__ (line 27) | def __len__(self):
    method __getitem__ (line 30) | def __getitem__(self, index):
  class NTUDataLoaders (line 33) | class NTUDataLoaders(object):
    method __init__ (line 34) | def __init__(self, dataset ='NTU', case = 0, aug = 1, seg = 30):
    method get_train_loader (line 44) | def get_train_loader(self, batch_size, num_workers):
    method get_val_loader (line 54) | def get_val_loader(self, batch_size, num_workers):
    method get_test_loader (line 65) | def get_test_loader(self, batch_size, num_workers):
    method get_train_size (line 70) | def get_train_size(self):
    method get_val_size (line 73) | def get_val_size(self):
    method get_test_size (line 76) | def get_test_size(self):
    method create_datasets (line 79) | def create_datasets(self):
    method collate_fn_fix_train (line 102) | def collate_fn_fix_train(self, batch):
    method collate_fn_fix_val (line 134) | def collate_fn_fix_val(self, batch):
    method collate_fn_fix_test (line 147) | def collate_fn_fix_test(self, batch):
    method Tolist_fix (line 161) | def Tolist_fix(self, joints, y, train = 1):
    method sub_seq (line 177) | def sub_seq(self, seqs, seq , train = 1):
  class AverageMeter (line 209) | class AverageMeter(object):
    method __init__ (line 211) | def __init__(self):
    method reset (line 214) | def reset(self):
    method update (line 220) | def update(self, val, n=1):
  function turn_two_to_one (line 227) | def turn_two_to_one(seq):
  function _rot (line 239) | def _rot(rot):
  function _transform (line 262) | def _transform(x, theta):

FILE: data/ntu/get_raw_denoised_data.py
  function denoising_by_length (line 71) | def denoising_by_length(ske_name, bodies_data):
  function get_valid_frames_by_spread (line 92) | def get_valid_frames_by_spread(points):
  function denoising_by_spread (line 108) | def denoising_by_spread(ske_name, bodies_data):
  function denoising_by_motion (line 149) | def denoising_by_motion(ske_name, bodies_data, bodies_motion):
  function denoising_bodies_data (line 174) | def denoising_bodies_data(bodies_data):
  function get_one_actor_points (line 223) | def get_one_actor_points(body_data, num_frames):
  function remove_missing_frames (line 238) | def remove_missing_frames(ske_name, joints, colors):
  function get_bodies_info (line 281) | def get_bodies_info(bodies_data):
  function get_two_actors_points (line 290) | def get_two_actors_points(bodies_data):
  function get_raw_denoised_data (line 367) | def get_raw_denoised_data():

FILE: data/ntu/get_raw_skes_data.py
  function get_raw_bodies_data (line 10) | def get_raw_bodies_data(skes_path, ske_name, frames_drop_skes, frames_dr...
  function get_raw_skes_data (line 94) | def get_raw_skes_data():

FILE: data/ntu/seq_transformation.py
  function remove_nan_frames (line 31) | def remove_nan_frames(ske_name, ske_joints, nan_logger):
  function seq_translation (line 44) | def seq_translation(skes_joints):
  function frame_translation (line 79) | def frame_translation(skes_joints, skes_name, frames_cnt):
  function align_frames (line 109) | def align_frames(skes_joints, frames_cnt):
  function one_hot_vector (line 130) | def one_hot_vector(labels):
  function split_train_val (line 139) | def split_train_val(train_indices, method='sklearn', ratio=0.05):
  function split_dataset (line 156) | def split_dataset(skes_joints, label, performer, camera, evaluation, sav...
  function get_indices (line 185) | def get_indices(performer, camera, evaluation='CS'):

FILE: fit.py
  function add_fit_args (line 3) | def add_fit_args(parser):

FILE: main.py
  function main (line 42) | def main():
  function train (line 153) | def train(train_loader, model, criterion, optimizer, epoch):
  function validate (line 183) | def validate(val_loader, model, criterion):
  function test (line 203) | def test(test_loader, model, checkpoint, lable_path, pred_path):
  function accuracy (line 235) | def accuracy(output, target):
  function save_checkpoint (line 244) | def save_checkpoint(state, filename='checkpoint.pth.tar', is_best=False):
  function get_n_params (line 249) | def get_n_params(model):
  class LabelSmoothingLoss (line 258) | class LabelSmoothingLoss(nn.Module):
    method __init__ (line 259) | def __init__(self, classes, smoothing=0.0, dim=-1):
    method forward (line 266) | def forward(self, pred, target):

FILE: model.py
  class SGN (line 7) | class SGN(nn.Module):
    method __init__ (line 8) | def __init__(self, num_classes, dataset, seg, args, bias = True):
    method forward (line 49) | def forward(self, input):
    method one_hot (line 79) | def one_hot(self, bs, spa, tem):
  class norm_data (line 92) | class norm_data(nn.Module):
    method __init__ (line 93) | def __init__(self, dim= 64):
    method forward (line 98) | def forward(self, x):
  class embed (line 105) | class embed(nn.Module):
    method __init__ (line 106) | def __init__(self, dim = 3, dim1 = 128, norm = True, bias = False):
    method forward (line 125) | def forward(self, x):
  class cnn1x1 (line 129) | class cnn1x1(nn.Module):
    method __init__ (line 130) | def __init__(self, dim1 = 3, dim2 =3, bias = True):
    method forward (line 134) | def forward(self, x):
  class local (line 138) | class local(nn.Module):
    method __init__ (line 139) | def __init__(self, dim1 = 3, dim2 = 3, bias = False):
    method forward (line 149) | def forward(self, x1):
  class gcn_spa (line 161) | class gcn_spa(nn.Module):
    method __init__ (line 162) | def __init__(self, in_feature, out_feature, bias = False):
    method forward (line 170) | def forward(self, x1, g):
  class compute_g_spa (line 178) | class compute_g_spa(nn.Module):
    method __init__ (line 179) | def __init__(self, dim1 = 64 *3, dim2 = 64*3, bias = False):
    method forward (line 187) | def forward(self, x1):

FILE: util.py
  function make_dir (line 11) | def make_dir(dataset):
  function get_num_classes (line 22) | def get_num_classes(dataset):