SYMBOL INDEX (58 symbols across 10 files)

FILE: src/accuracy.py
  function accuracy_check (line 10) | def accuracy_check(mask, prediction):
  function accuracy_check_for_batch (line 28) | def accuracy_check_for_batch(masks, predictions, batch_size):

FILE: src/advanced_model.py
  class Double_conv (line 10) | class Double_conv(nn.Module):
    method __init__ (line 14) | def __init__(self, in_ch, out_ch):
    method forward (line 28) | def forward(self, x):
  class Conv_down (line 33) | class Conv_down(nn.Module):
    method __init__ (line 37) | def __init__(self, in_ch, out_ch):
    method forward (line 47) | def forward(self, x):
  class Conv_up (line 53) | class Conv_up(nn.Module):
    method __init__ (line 57) | def __init__(self, in_ch, out_ch):
    method forward (line 67) | def forward(self, x1, x2):
  function extract_img (line 76) | def extract_img(size, in_tensor):
  class CleanU_Net (line 88) | class CleanU_Net(nn.Module):
    method __init__ (line 89) | def __init__(self, in_channels, out_channels):
    method forward (line 103) | def forward(self, x):

FILE: src/dataset.py
  class SEMDataTrain (line 16) | class SEMDataTrain(Dataset):
    method __init__ (line 18) | def __init__(self, image_path, mask_path, in_size=572, out_size=388):
    method __getitem__ (line 33) | def __getitem__(self, index):
    method __len__ (line 121) | def __len__(self):
  class SEMDataVal (line 129) | class SEMDataVal(Dataset):
    method __init__ (line 130) | def __init__(self, image_path, mask_path, in_size=572, out_size=388):
    method __getitem__ (line 143) | def __getitem__(self, index):
    method __len__ (line 201) | def __len__(self):
  class SEMDataTest (line 206) | class SEMDataTest(Dataset):
    method __init__ (line 208) | def __init__(self, image_path, in_size=572, out_size=388):
    method __getitem__ (line 221) | def __getitem__(self, index):
    method __len__ (line 260) | def __len__(self):

FILE: src/mean_std.py
  function normalize_image (line 6) | def normalize_image(image):
  function find_mean (line 22) | def find_mean(image_path):
  function find_stdev (line 44) | def find_stdev(image_path):

FILE: src/modules.py
  function train_model (line 13) | def train_model(model, data_train, criterion, optimizer):
  function get_loss_train (line 34) | def get_loss_train(model, data_train, criterion):
  function validate_model (line 54) | def validate_model(model, data_val, criterion, epoch, make_prediction=Tr...
  function test_model (line 82) | def test_model(model_path, data_test, epoch, save_folder_name='predictio...
  function save_prediction_image (line 104) | def save_prediction_image(stacked_img, im_name, epoch, save_folder_name=...
  function polarize (line 126) | def polarize(img):

FILE: src/post_processing.py
  function postprocess (line 5) | def postprocess(image_path):

FILE: src/pre_processing.py
  function add_elastic_transform (line 7) | def add_elastic_transform(image, alpha, sigma, pad_size=30, seed=None):
  function flip (line 35) | def flip(image, option_value):
  function add_gaussian_noise (line 59) | def add_gaussian_noise(image, mean=0, std=1):
  function add_uniform_noise (line 75) | def add_uniform_noise(image, low=-10, high=10):
  function change_brightness (line 91) | def change_brightness(image, value):
  function ceil_floor_image (line 105) | def ceil_floor_image(image):
  function approximate_image (line 118) | def approximate_image(image):
  function normalization1 (line 131) | def normalization1(image, mean, std):
  function normalization2 (line 146) | def normalization2(image, max, min):
  function stride_size (line 158) | def stride_size(image_len, crop_num, crop_size):
  function multi_cropping (line 170) | def multi_cropping(image, crop_size, crop_num1, crop_num2):
  function cropping (line 221) | def cropping(image, crop_size, dim1, dim2):
  function add_padding (line 235) | def add_padding(image, in_size, out_size, mode):
  function division_array (line 250) | def division_array(crop_size, crop_num1, crop_num2, dim1, dim2):
  function image_concatenate (line 273) | def image_concatenate(image, crop_num1, crop_num2, dim1, dim2):

FILE: src/result_visualization.py
  function plotloss (line 16) | def plotloss(csvfile):

FILE: src/save_history.py
  function export_history (line 6) | def export_history(header, value, folder, file_name):
  function save_models (line 35) | def save_models(model, path, epoch):

FILE: src/simple_model.py
  class CleanU_Net (line 9) | class CleanU_Net(nn.Module):
    method __init__ (line 11) | def __init__(self):
    method forward (line 125) | def forward(self, x):