SYMBOL INDEX (214 symbols across 12 files)

FILE: net/model.py
  function to_3d (line 17) | def to_3d(x):
  function to_4d (line 20) | def to_4d(x,h,w):
  class BiasFree_LayerNorm (line 24) | class BiasFree_LayerNorm(nn.Module):
    method __init__ (line 25) | def __init__(self, normalized_shape):
    method forward (line 36) | def forward(self, x):
  class WithBias_LayerNorm (line 41) | class WithBias_LayerNorm(nn.Module):
    method __init__ (line 42) | def __init__(self, normalized_shape):
    method forward (line 54) | def forward(self, x):
  class LayerNorm (line 60) | class LayerNorm(nn.Module):
    method __init__ (line 61) | def __init__(self, dim, LayerNorm_type):
    method forward (line 68) | def forward(self, x):
  class FeedForward (line 75) | class FeedForward(nn.Module):
    method __init__ (line 76) | def __init__(self, dim, ffn_expansion_factor, bias):
    method forward (line 87) | def forward(self, x):
  class Attention (line 97) | class Attention(nn.Module):
    method __init__ (line 98) | def __init__(self, dim, num_heads, bias):
    method forward (line 107) | def forward(self, x):
  class Downsample (line 133) | class Downsample(nn.Module):
    method __init__ (line 134) | def __init__(self, n_feat):
    method forward (line 140) | def forward(self, x):
  class Upsample (line 143) | class Upsample(nn.Module):
    method __init__ (line 144) | def __init__(self, n_feat):
    method forward (line 150) | def forward(self, x):
  class TransformerBlock (line 156) | class TransformerBlock(nn.Module):
    method __init__ (line 157) | def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNo...
    method forward (line 165) | def forward(self, x):
  class Chanel_Cross_Attention (line 173) | class Chanel_Cross_Attention(nn.Module):
    method __init__ (line 174) | def __init__(self, dim, num_head, bias):
    method forward (line 188) | def forward(self, x, y):
  class OverlapPatchEmbed (line 218) | class OverlapPatchEmbed(nn.Module):
    method __init__ (line 219) | def __init__(self, in_c=3, embed_dim=48, bias=False):
    method forward (line 224) | def forward(self, x):
  class SpatialGate (line 231) | class SpatialGate(nn.Module):
    method __init__ (line 232) | def __init__(self):
    method forward (line 237) | def forward(self, x):
  class ChannelGate (line 247) | class ChannelGate(nn.Module):
    method __init__ (line 248) | def __init__(self, dim):
    method forward (line 259) | def forward(self, x):
  class FreRefine (line 269) | class FreRefine(nn.Module):
    method __init__ (line 270) | def __init__(self, dim):
    method forward (line 277) | def forward(self, low, high):
  class FreModule (line 289) | class FreModule(nn.Module):
    method __init__ (line 290) | def __init__(self, dim, num_heads, bias, in_dim=3):
    method forward (line 313) | def forward(self, x, y):
    method shift (line 327) | def shift(self, x):
    method unshift (line 332) | def unshift(self, x):
    method fft (line 337) | def fft(self, x, n=128):
  class AdaIR (line 372) | class AdaIR(nn.Module):
    method __init__ (line 373) | def __init__(self,
    method forward (line 426) | def forward(self, inp_img,noise_emb = None):

FILE: test.py
  class AdaIRModel (line 18) | class AdaIRModel(pl.LightningModule):
    method __init__ (line 19) | def __init__(self):
    method forward (line 24) | def forward(self,x):
    method training_step (line 27) | def training_step(self, batch, batch_idx):
    method lr_scheduler_step (line 38) | def lr_scheduler_step(self,scheduler,metric):
    method configure_optimizers (line 42) | def configure_optimizers(self):
  function test_Denoise (line 49) | def test_Denoise(net, dataset, sigma=15):
  function test_Derain_Dehaze (line 73) | def test_Derain_Dehaze(net, dataset, task="derain"):

FILE: train.py
  class AdaIRModel (line 17) | class AdaIRModel(pl.LightningModule):
    method __init__ (line 18) | def __init__(self):
    method forward (line 23) | def forward(self,x):
    method training_step (line 26) | def training_step(self, batch, batch_idx):
    method lr_scheduler_step (line 37) | def lr_scheduler_step(self,scheduler,metric):
    method configure_optimizers (line 41) | def configure_optimizers(self):
  function main (line 48) | def main():

FILE: utils/dataset_utils.py
  class AdaIRTrainDataset (line 15) | class AdaIRTrainDataset(Dataset):
    method __init__ (line 16) | def __init__(self, args):
    method _init_ids (line 38) | def _init_ids(self):
    method _init_clean_ids (line 52) | def _init_clean_ids(self):
    method _init_hazy_ids (line 79) | def _init_hazy_ids(self):
    method _init_deblur_ids (line 90) | def _init_deblur_ids(self):
    method _init_enhance_ids (line 101) | def _init_enhance_ids(self):
    method _init_rs_ids (line 110) | def _init_rs_ids(self):
    method _crop_patch (line 121) | def _crop_patch(self, img_1, img_2):
    method _get_gt_name (line 132) | def _get_gt_name(self, rainy_name):
    method _get_deblur_name (line 137) | def _get_deblur_name(self, deblur_name):
    method _get_enhance_name (line 142) | def _get_enhance_name(self, enhance_name):
    method _get_nonhazy_name (line 147) | def _get_nonhazy_name(self, hazy_name):
    method _merge_ids (line 154) | def _merge_ids(self):
    method __getitem__ (line 172) | def __getitem__(self, idx):
    method __len__ (line 222) | def __len__(self):
  class DenoiseTestDataset (line 226) | class DenoiseTestDataset(Dataset):
    method __init__ (line 227) | def __init__(self, args):
    method _init_clean_ids (line 237) | def _init_clean_ids(self):
    method _add_gaussian_noise (line 243) | def _add_gaussian_noise(self, clean_patch):
    method set_sigma (line 248) | def set_sigma(self, sigma):
    method __getitem__ (line 251) | def __getitem__(self, clean_id):
    method tile_degrad (line 259) | def tile_degrad(input_,tile=128,tile_overlap =0):
    method __len__ (line 282) | def __len__(self):
  class DerainDehazeDataset (line 286) | class DerainDehazeDataset(Dataset):
    method __init__ (line 287) | def __init__(self, args, task="derain",addnoise = False,sigma = None):
    method _add_gaussian_noise (line 299) | def _add_gaussian_noise(self, clean_patch):
    method _init_input_ids (line 304) | def _init_input_ids(self):
    method _get_gt_path (line 325) | def _get_gt_path(self, degraded_name):
    method set_dataset (line 340) | def set_dataset(self, task):
    method __getitem__ (line 344) | def __getitem__(self, idx):
    method __len__ (line 358) | def __len__(self):
  class TestSpecificDataset (line 362) | class TestSpecificDataset(Dataset):
    method __init__ (line 363) | def __init__(self, args):
    method _init_clean_ids (line 371) | def _init_clean_ids(self, root):
    method __getitem__ (line 391) | def __getitem__(self, idx):
    method __len__ (line 399) | def __len__(self):

FILE: utils/degradation_utils.py
  class Degradation (line 11) | class Degradation(object):
    method __init__ (line 12) | def __init__(self, args):
    method _add_gaussian_noise (line 21) | def _add_gaussian_noise(self, clean_patch, sigma):
    method _degrade_by_type (line 27) | def _degrade_by_type(self, clean_patch, degrade_type):
    method degrade (line 40) | def degrade(self, clean_patch_1, clean_patch_2, degrade_type=None):
    method single_degrade (line 50) | def single_degrade(self,clean_patch,degrade_type = None):

FILE: utils/image_io.py
  function prepare_hazy_image (line 15) | def prepare_hazy_image(file_name):
  function prepare_gt_img (line 20) | def prepare_gt_img(file_name, SOTS=True):
  function crop_a_image (line 29) | def crop_a_image(img, d=10):
  function crop_image (line 40) | def crop_image(img, d=32):
  function crop_np_image (line 63) | def crop_np_image(img_np, d=32):
  function crop_torch_image (line 67) | def crop_torch_image(img, d=32):
  function get_params (line 85) | def get_params(opt_over, net, net_input, downsampler=None):
  function get_image_grid (line 114) | def get_image_grid(images_np, nrow=8):
  function plot_image_grid (line 127) | def plot_image_grid(name, images_np, interpolation='lanczos', output_pat...
  function save_image_np (line 152) | def save_image_np(name, image_np, output_path="output/"):
  function save_image_tensor (line 157) | def save_image_tensor(image_tensor, output_path="output/"):
  function video_to_images (line 163) | def video_to_images(file_name, name):
  function images_to_video (line 169) | def images_to_video(images_dir, name, gray=True):
  function save_heatmap (line 182) | def save_heatmap(name, image_np):
  function save_graph (line 190) | def save_graph(name, graph_list, output_path="output/"):
  function create_augmentations (line 196) | def create_augmentations(np_image):
  function create_video_augmentations (line 210) | def create_video_augmentations(np_video):
  function save_graphs (line 224) | def save_graphs(name, graph_dict, output_path="output/"):
  function load (line 241) | def load(path):
  function get_image (line 247) | def get_image(path, imsize=-1):
  function prepare_gt (line 269) | def prepare_gt(file_name):
  function prepare_image (line 284) | def prepare_image(file_name):
  function prepare_gray_image (line 297) | def prepare_gray_image(file_name):
  function pil_to_np (line 302) | def pil_to_np(img_PIL, with_transpose=True):
  function median (line 321) | def median(img_np_list):
  function average (line 338) | def average(img_np_list):
  function np_to_pil (line 353) | def np_to_pil(img_np):
  function np_to_torch (line 372) | def np_to_torch(img_np):
  function torch_to_np (line 384) | def torch_to_np(img_var):

FILE: utils/image_utils.py
  class EdgeComputation (line 14) | class EdgeComputation(nn.Module):
    method __init__ (line 15) | def __init__(self, test=False):
    method forward (line 18) | def forward(self, x):
  function crop_patch (line 49) | def crop_patch(im, pch_size):
  function crop_img (line 59) | def crop_img(image, base=64):
  function slice_image2patches (line 68) | def slice_image2patches(image, patch_size=64, overlap=0):
  function splice_patches2image (line 83) | def splice_patches2image(patches, image_size, overlap=0):
  function data_augmentation (line 101) | def data_augmentation(image, mode):
  function random_augmentation (line 134) | def random_augmentation(*args):
  function weights_init_normal_ (line 142) | def weights_init_normal_(m):
  function weights_init_normal (line 153) | def weights_init_normal(m):
  function weights_init_xavier (line 164) | def weights_init_xavier(m):
  function weights_init_kaiming (line 175) | def weights_init_kaiming(m):
  function weights_init_orthogonal (line 186) | def weights_init_orthogonal(m):
  function init_weights (line 198) | def init_weights(net, init_type='normal'):
  function np_to_torch (line 212) | def np_to_torch(img_np):
  function torch_to_np (line 224) | def torch_to_np(img_var):
  function save_image (line 236) | def save_image(name, image_np, output_path="output/normal/"):
  function np_to_pil (line 244) | def np_to_pil(img_np):

FILE: utils/imresize.py
  function imresize (line 6) | def imresize(im, scale_factor=None, output_shape=None, kernel=None, anti...
  function fix_scale_and_size (line 48) | def fix_scale_and_size(input_shape, output_shape, scale_factor):
  function contributions (line 77) | def contributions(in_length, out_length, scale, kernel, kernel_width, an...
  function resize_along_dim (line 137) | def resize_along_dim(im, dim, weights, field_of_view):
  function numeric_kernel (line 157) | def numeric_kernel(im, kernel, scale_factor, output_shape, kernel_shift_...
  function kernel_shift (line 172) | def kernel_shift(kernel, sf):
  function cubic (line 202) | def cubic(x):
  function lanczos2 (line 210) | def lanczos2(x):
  function box (line 216) | def box(x):
  function lanczos3 (line 220) | def lanczos3(x):
  function linear (line 226) | def linear(x):
  function np_imresize (line 230) | def np_imresize(im, scale_factor=None, output_shape=None, kernel=None, a...

FILE: utils/loss_utils.py
  class GANLoss (line 6) | class GANLoss(nn.Module):
    method __init__ (line 7) | def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_...
    method get_target_tensor (line 20) | def get_target_tensor(self, input, target_is_real):
    method __call__ (line 36) | def __call__(self, input, target_is_real):

FILE: utils/pytorch_ssim/__init__.py
  function gaussian (line 8) | def gaussian(window_size, sigma):
  function create_window (line 13) | def create_window(window_size, channel):
  function _ssim (line 19) | def _ssim(img1, img2, window, window_size, channel, size_average = True):
  class SSIM (line 45) | class SSIM(torch.nn.Module):
    method __init__ (line 46) | def __init__(self, window_size = 11, size_average = True):
    method forward (line 53) | def forward(self, img1, img2):
  function ssim (line 70) | def ssim(img1, img2, window_size = 11, size_average = True):

FILE: utils/schedulers.py
  class MultiStepRestartLR (line 11) | class MultiStepRestartLR(_LRScheduler):
    method __init__ (line 24) | def __init__(self,
    method get_lr (line 39) | def get_lr(self):
  class LinearLR (line 53) | class LinearLR(_LRScheduler):
    method __init__ (line 63) | def __init__(self,
    method get_lr (line 70) | def get_lr(self):
  class VibrateLR (line 76) | class VibrateLR(_LRScheduler):
    method __init__ (line 86) | def __init__(self,
    method get_lr (line 93) | def get_lr(self):
  function get_position_from_periods (line 119) | def get_position_from_periods(iteration, cumulative_period):
  class CosineAnnealingRestartLR (line 140) | class CosineAnnealingRestartLR(_LRScheduler):
    method __init__ (line 160) | def __init__(self,
    method get_lr (line 176) | def get_lr(self):
  class CosineAnnealingRestartCyclicLR (line 190) | class CosineAnnealingRestartCyclicLR(_LRScheduler):
    method __init__ (line 207) | def __init__(self,
    method get_lr (line 223) | def get_lr(self):
  class LinearWarmupCosineAnnealingLR (line 239) | class LinearWarmupCosineAnnealingLR(_LRScheduler):
    method __init__ (line 270) | def __init__(
    method get_lr (line 295) | def get_lr(self) -> List[float]:
    method _get_closed_form_lr (line 332) | def _get_closed_form_lr(self) -> List[float]:
  function linear_warmup_decay (line 350) | def linear_warmup_decay(warmup_steps, total_steps, cosine=True, linear=F...

FILE: utils/val_utils.py
  class AverageMeter (line 8) | class AverageMeter():
    method __init__ (line 11) | def __init__(self):
    method reset (line 14) | def reset(self):
    method update (line 21) | def update(self, val, n=1):
  function accuracy (line 29) | def accuracy(output, target, topk=(1,)):
  function compute_psnr_ssim (line 50) | def compute_psnr_ssim(recoverd, clean):
  function compute_niqe (line 67) | def compute_niqe(image):
  class timer (line 74) | class timer():
    method __init__ (line 75) | def __init__(self):
    method tic (line 79) | def tic(self):
    method toc (line 82) | def toc(self):
    method hold (line 85) | def hold(self):
    method release (line 88) | def release(self):
    method reset (line 94) | def reset(self):