SYMBOL INDEX (101 symbols across 7 files)

FILE: data.py
  class SamsungDataset (line 30) | class SamsungDataset(torch.utils.data.Dataset):
    method __init__ (line 32) | def __init__(self, data_dict, transform=None, normaliser=2 ** 8 - 1, i...
    method __len__ (line 44) | def __len__(self):
    method __getitem__ (line 51) | def __getitem__(self, idx):
  class Dataset (line 88) | class Dataset(torch.utils.data.Dataset):
    method __init__ (line 90) | def __init__(self, data_dict, transform=None, normaliser=2 ** 8 - 1, i...
    method __len__ (line 105) | def __len__(self):
    method __getitem__ (line 114) | def __getitem__(self, idx):
  class DataLoader (line 203) | class DataLoader():
    method __init__ (line 205) | def __init__(self, data_dirpath, img_ids_filepath):
    method load_data (line 218) | def load_data(self):
    method perform_inference (line 228) | def perform_inference(self, net, data_dirpath):
  class Adobe5kDataLoader (line 238) | class Adobe5kDataLoader(DataLoader):
    method __init__ (line 240) | def __init__(self, data_dirpath, img_ids_filepath):
    method load_data (line 252) | def load_data(self):
  class SamsungDataLoader (line 328) | class SamsungDataLoader(DataLoader):
    method __init__ (line 330) | def __init__(self, data_dirpath, img_ids_filepath):
    method load_data (line 342) | def load_data(self):

FILE: main.py
  function main (line 39) | def main():

FILE: metric.py
  class Evaluator (line 26) | class Evaluator():
    method __init__ (line 28) | def __init__(self, criterion, data_loader, split_name, log_dirpath):
    method evaluate (line 41) | def evaluate(self, net, epoch=0):

FILE: model.py
  class CURLLoss (line 30) | class CURLLoss(nn.Module):
    method __init__ (line 32) | def __init__(self, ssim_window_size=5, alpha=0.5):
    method create_window (line 45) | def create_window(self, window_size, num_channel):
    method gaussian (line 62) | def gaussian(self, window_size, sigma):
    method compute_ssim (line 75) | def compute_ssim(self, img1, img2):
    method compute_msssim (line 123) | def compute_msssim(self, img1, img2):
    method forward (line 173) | def forward(self, predicted_img_batch, target_img_batch, gradient_regu...
  class CURLLayer (line 262) | class CURLLayer(nn.Module):
    method __init__ (line 266) | def __init__(self, num_in_channels=64, num_out_channels=64):
    method make_init_network (line 281) | def make_init_network(self):
    method forward (line 325) | def forward(self, x):
  class Block (line 419) | class Block(nn.Module):
    method __init__ (line 421) | def __init__(self):
    method conv3x3 (line 430) | def conv3x3(self, in_channels, out_channels, stride=1):
  class ConvBlock (line 444) | class ConvBlock(Block, nn.Module):
    method __init__ (line 446) | def __init__(self, num_in_channels, num_out_channels, stride=1):
    method forward (line 461) | def forward(self, x):
  class MaxPoolBlock (line 473) | class MaxPoolBlock(Block, nn.Module):
    method __init__ (line 475) | def __init__(self):
    method forward (line 486) | def forward(self, x):
  class GlobalPoolingBlock (line 498) | class GlobalPoolingBlock(Block, nn.Module):
    method __init__ (line 500) | def __init__(self, receptive_field):
    method forward (line 510) | def forward(self, x):
  class CURLNet (line 522) | class CURLNet(nn.Module):
    method __init__ (line 524) | def __init__(self):
    method forward (line 535) | def forward(self, img):

FILE: raw_ted.py
  class Flatten (line 22) | class Flatten(nn.Module):
    method forward (line 24) | def forward(self, x):
  class TED (line 35) | class TED(nn.Module):
    method __init__ (line 37) | def __init__(self):
    method forward (line 97) | def forward(self, x):
  class LocalNet (line 188) | class LocalNet(nn.Module):
    method forward (line 190) | def forward(self, x_in):
    method __init__ (line 203) | def __init__(self, in_channels=16, out_channels=64):
  class MidNet2 (line 219) | class MidNet2(nn.Module):
    method forward (line 221) | def forward(self, x_in):
    method __init__ (line 236) | def __init__(self, in_channels=16):
  class MidNet4 (line 252) | class MidNet4(nn.Module):
    method forward (line 254) | def forward(self, x_in):
    method __init__ (line 269) | def __init__(self, in_channels=16):
  class SimpleUpsampler (line 285) | class SimpleUpsampler(nn.Sequential):
    method __init__ (line 287) | def __init__(self, scale):
  function DownSamplingShuffle (line 300) | def DownSamplingShuffle(x):
  class TEDModel (line 318) | class TEDModel(nn.Module):
    method __init__ (line 320) | def __init__(self):
    method forward (line 333) | def forward(self, image):

FILE: rgb_ted.py
  class Flatten (line 16) | class Flatten(nn.Module):
    method forward (line 18) | def forward(self, x):
  class TED (line 29) | class TED(nn.Module):
    method __init__ (line 31) | def __init__(self):
    method forward (line 89) | def forward(self, x):
  class LocalNet (line 179) | class LocalNet(nn.Module):
    method forward (line 181) | def forward(self, x_in):
    method __init__ (line 194) | def __init__(self, in_channels=16, out_channels=64):
  class MidNet2 (line 210) | class MidNet2(nn.Module):
    method forward (line 212) | def forward(self, x_in):
    method __init__ (line 227) | def __init__(self, in_channels=16):
  class MidNet4 (line 243) | class MidNet4(nn.Module):
    method forward (line 245) | def forward(self, x_in):
    method __init__ (line 260) | def __init__(self, in_channels=16):
  class TEDModel (line 276) | class TEDModel(nn.Module):
    method __init__ (line 278) | def __init__(self):
    method forward (line 291) | def forward(self, img):

FILE: util.py
  class ImageProcessing (line 26) | class ImageProcessing(object):
    method rgb_to_lab (line 29) | def rgb_to_lab(img, is_training=True):
    method lab_to_rgb (line 91) | def lab_to_rgb(img, is_training=True):
    method swapimdims_3HW_HW3 (line 149) | def swapimdims_3HW_HW3(img):
    method swapimdims_HW3_3HW (line 164) | def swapimdims_HW3_3HW(img):
    method load_image (line 179) | def load_image(img_filepath, normaliser):
    method normalise_image (line 191) | def normalise_image(img, normaliser):
    method compute_mse (line 203) | def compute_mse(original, result):
    method compute_psnr (line 215) | def compute_psnr(image_batchA, image_batchB, max_intensity):
    method compute_ssim (line 239) | def compute_ssim(image_batchA, image_batchB):
    method hsv_to_rgb (line 263) | def hsv_to_rgb(img):
    method rgb_to_hsv (line 313) | def rgb_to_hsv(img):
    method apply_curve (line 395) | def apply_curve(img, C, slope_sqr_diff, channel_in, channel_out,
    method adjust_hsv (line 440) | def adjust_hsv(img, S):
    method adjust_rgb (line 495) | def adjust_rgb(img, R):
    method adjust_lab (line 546) | def adjust_lab(img, L):