SYMBOL INDEX (176 symbols across 10 files)

FILE: dataset.py
  class FWIDataset (line 25) | class FWIDataset(Dataset):
    method __init__ (line 37) | def __init__(self, anno, preload=True, sample_ratio=1, file_size=500,
    method load_every (line 57) | def load_every(self, batch):
    method __getitem__ (line 71) | def __getitem__(self, idx):
    method __len__ (line 86) | def __len__(self):

FILE: gan_train.py
  function train_one_epoch (line 45) | def train_one_epoch(model, model_d, criterion_g, criterion_d, optimizer_...
  function evaluate (line 98) | def evaluate(model, criterion, dataloader, device, writer):
  function main (line 121) | def main(args):
  function parse_args (line 301) | def parse_args():

FILE: network.py
  function replace_legacy (line 28) | def replace_legacy(old_dict):
  class Conv2DwithBN (line 38) | class Conv2DwithBN(nn.Module):
    method __init__ (line 39) | def __init__(self, in_fea, out_fea,
    method forward (line 51) | def forward(self, x):
  class ResizeConv2DwithBN (line 54) | class ResizeConv2DwithBN(nn.Module):
    method __init__ (line 55) | def __init__(self, in_fea, out_fea, scale_factor=2, mode='nearest'):
    method forward (line 63) | def forward(self, x):
  class Conv2DwithBN_Tanh (line 66) | class Conv2DwithBN_Tanh(nn.Module):
    method __init__ (line 67) | def __init__(self, in_fea, out_fea, kernel_size=3, stride=1, padding=1):
    method forward (line 74) | def forward(self, x):
  class ConvBlock (line 77) | class ConvBlock(nn.Module):
    method __init__ (line 78) | def __init__(self, in_fea, out_fea, kernel_size=3, stride=1, padding=1...
    method forward (line 88) | def forward(self, x):
  class ConvBlock_Tanh (line 92) | class ConvBlock_Tanh(nn.Module):
    method __init__ (line 93) | def __init__(self, in_fea, out_fea, kernel_size=3, stride=1, padding=1...
    method forward (line 101) | def forward(self, x):
  class DeconvBlock (line 105) | class DeconvBlock(nn.Module):
    method __init__ (line 106) | def __init__(self, in_fea, out_fea, kernel_size=2, stride=2, padding=0...
    method forward (line 114) | def forward(self, x):
  class ResizeBlock (line 118) | class ResizeBlock(nn.Module):
    method __init__ (line 119) | def __init__(self, in_fea, out_fea, scale_factor=2, mode='nearest', no...
    method forward (line 128) | def forward(self, x):
  class InversionNet (line 135) | class InversionNet(nn.Module):
    method __init__ (line 136) | def __init__(self, dim1=32, dim2=64, dim3=128, dim4=256, dim5=512, sam...
    method forward (line 165) | def forward(self,x):
  class FCN4_Deep_Resize_2 (line 197) | class FCN4_Deep_Resize_2(nn.Module):
    method __init__ (line 198) | def __init__(self, dim1=32, dim2=64, dim3=128, dim4=256, dim5=512, rat...
    method forward (line 227) | def forward(self,x):
  class Discriminator (line 260) | class Discriminator(nn.Module):
    method __init__ (line 261) | def __init__(self, dim1=32, dim2=64, dim3=128, dim4=256, **kwargs):
    method forward (line 273) | def forward(self, x):
  class Conv_HPGNN (line 287) | class Conv_HPGNN(nn.Module):
    method __init__ (line 288) | def __init__(self, in_fea, out_fea, kernel_size=None, stride=None, pad...
    method forward (line 298) | def forward(self, x):
  class Deconv_HPGNN (line 302) | class Deconv_HPGNN(nn.Module):
    method __init__ (line 303) | def __init__(self, in_fea, out_fea, kernel_size, **kwargs):
    method forward (line 312) | def forward(self, x):

FILE: pytorch_ssim.py
  function gaussian (line 9) | 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 41) | class SSIM(torch.nn.Module):
    method __init__ (line 42) | def __init__(self, window_size = 11, size_average = True):
    method forward (line 49) | def forward(self, img1, img2):
  function ssim (line 67) | def ssim(img1, img2, window_size = 11, size_average = True):

FILE: scheduler.py
  class WarmupMultiStepLR (line 23) | class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
    method __init__ (line 24) | def __init__(
    method get_lr (line 52) | def get_lr(self):

FILE: test.py
  function evaluate (line 41) | def evaluate(model, criterions, dataloader, device, k, ctx,
  function main (line 107) | def main(args):
  function parse_args (line 193) | def parse_args():

FILE: train.py
  function train_one_epoch (line 43) | def train_one_epoch(model, criterion, optimizer, lr_scheduler,
  function evaluate (line 78) | def evaluate(model, criterion, dataloader, device, writer):
  function main (line 102) | def main(args):
  function parse_args (line 273) | def parse_args():

FILE: transforms.py
  function crop (line 24) | def crop(vid, i, j, h, w):
  function center_crop (line 28) | def center_crop(vid, output_size):
  function hflip (line 37) | def hflip(vid):
  function resize (line 44) | def resize(vid, size, interpolation='bilinear'):
  function random_resize (line 54) | def random_resize(vid, size, random_factor, interpolation='bilinear'):
  function pad (line 67) | def pad(vid, padding, fill=0, padding_mode="constant"):
  function to_normalized_float_tensor (line 73) | def to_normalized_float_tensor(vid):
  function normalize (line 77) | def normalize(vid, mean, std):
  function minmax_normalize (line 83) | def minmax_normalize(vid, vmin, vmax, scale=2):
  function minmax_denormalize (line 88) | def minmax_denormalize(vid, vmin, vmax, scale=2):
  function add_noise (line 93) | def add_noise(data, snr):
  function log_transform (line 102) | def log_transform(data, k=1, c=0):
  function log_transform_tensor (line 105) | def log_transform_tensor(data, k=1, c=0):
  function exp_transform (line 108) | def exp_transform(data, k=1, c=0):
  function tonumpy_denormalize (line 111) | def tonumpy_denormalize(vid, vmin, vmax, exp=True, k=1, c=0, scale=2):
  class RandomCrop (line 120) | class RandomCrop(object):
    method __init__ (line 121) | def __init__(self, size):
    method get_params (line 125) | def get_params(vid, output_size):
    method __call__ (line 136) | def __call__(self, vid):
  class CenterCrop (line 141) | class CenterCrop(object):
    method __init__ (line 142) | def __init__(self, size):
    method __call__ (line 145) | def __call__(self, vid):
  class Resize (line 149) | class Resize(object):
    method __init__ (line 150) | def __init__(self, size):
    method __call__ (line 153) | def __call__(self, vid):
  class RandomResize (line 156) | class RandomResize(object):
    method __init__ (line 157) | def __init__(self, size, random_factor=1.25):
    method __call__ (line 161) | def __call__(self, vid):
  class ToFloatTensorInZeroOne (line 164) | class ToFloatTensorInZeroOne(object):
    method __call__ (line 165) | def __call__(self, vid):
  class Normalize (line 169) | class Normalize(object):
    method __init__ (line 170) | def __init__(self, mean, std):
    method __call__ (line 174) | def __call__(self, vid):
  class MinMaxNormalize (line 177) | class MinMaxNormalize(object):
    method __init__ (line 178) | def __init__(self, datamin, datamax, scale=2):
    method __call__ (line 183) | def __call__(self, vid):
  class RandomHorizontalFlip (line 186) | class RandomHorizontalFlip(object):
    method __init__ (line 187) | def __init__(self, p=0.5):
    method __call__ (line 190) | def __call__(self, vid):
  class Pad (line 195) | class Pad(object):
    method __init__ (line 196) | def __init__(self, padding, fill=0):
    method __call__ (line 200) | def __call__(self, vid):
  class TemporalDownsample (line 203) | class TemporalDownsample(object):
    method __init__ (line 204) | def __init__(self, rate=1):
    method __call__ (line 207) | def __call__(self, vid):
  class AddNoise (line 210) | class AddNoise(object):
    method __init__ (line 211) | def __init__(self, snr=10):
    method __call__ (line 214) | def __call__(self, vid):
  class PCD (line 217) | class PCD(object):
    method __init__ (line 218) | def __init__(self, n_comp=8):
    method __call__ (line 221) | def __call__(self, data):
  class StackPCD (line 231) | class StackPCD(object):
    method __init__ (line 232) | def __init__(self, n_comp=(32, 8)):
    method __call__ (line 237) | def __call__(self, data):
  class LogTransform (line 257) | class LogTransform(object):
    method __init__ (line 258) | def __init__(self, k=1, c=0):
    method __call__ (line 262) | def __call__(self, data):
  class ToTensor (line 265) | class ToTensor(object):
    method __call__ (line 269) | def __call__(self, sample):

FILE: utils.py
  class SmoothedValue (line 33) | class SmoothedValue(object):
    method __init__ (line 38) | def __init__(self, window_size=20, fmt=None):
    method update (line 46) | def update(self, value, n=1):
    method synchronize_between_processes (line 51) | def synchronize_between_processes(self):
    method median (line 65) | def median(self):
    method avg (line 70) | def avg(self):
    method global_avg (line 75) | def global_avg(self):
    method max (line 79) | def max(self):
    method value (line 83) | def value(self):
    method __str__ (line 86) | def __str__(self):
  class MetricLogger (line 95) | class MetricLogger(object):
    method __init__ (line 96) | def __init__(self, delimiter="\t"):
    method update (line 100) | def update(self, **kwargs):
    method __getattr__ (line 107) | def __getattr__(self, attr):
    method __str__ (line 115) | def __str__(self):
    method synchronize_between_processes (line 123) | def synchronize_between_processes(self):
    method add_meter (line 127) | def add_meter(self, name, meter):
    method log_every (line 130) | def log_every(self, iterable, print_freq, header=None):
  class ContentLoss (line 191) | class ContentLoss(nn.Module):
    method __init__ (line 192) | def __init__(self, args):
    method forward (line 201) | def forward(self, model, input, target):
  class IdenticalLoss (line 211) | class IdenticalLoss(nn.Module):
    method __init__ (line 212) | def __init__(self, args):
    method forward (line 221) | def forward(self, model_s2v, model_v2s, input):
  class NMSELoss (line 231) | class NMSELoss(nn.Module):
    method __init__ (line 232) | def __init__(self):
    method forward (line 235) | def forward(self, pred, gt):
  class CycleLoss (line 239) | class CycleLoss(nn.Module):
    method __init__ (line 240) | def __init__(self, args):
    method forward (line 249) | def forward(self, data, label, pred_s=None, pred_v=None, recon_s=None,...
  class _CycleLoss (line 272) | class _CycleLoss(nn.Module):
    method __init__ (line 273) | def __init__(self, args):
    method forward (line 282) | def forward(self, data, label, pred_s=None, pred_v=None, recon_s=None,...
  function accuracy (line 303) | def accuracy(output, target, topk=(1,)):
  function mkdir (line 320) | def mkdir(path):
  function setup_for_distributed (line 328) | def setup_for_distributed(is_master):
  function is_dist_avail_and_initialized (line 343) | def is_dist_avail_and_initialized():
  function get_world_size (line 351) | def get_world_size():
  function get_rank (line 357) | def get_rank():
  function is_main_process (line 363) | def is_main_process():
  function save_on_master (line 367) | def save_on_master(*args, **kwargs):
  function init_distributed_mode (line 372) | def init_distributed_mode(args):
  class Wasserstein_GP (line 398) | class Wasserstein_GP(nn.Module):
    method __init__ (line 399) | def __init__(self, device, lambda_gp):
    method forward (line 404) | def forward(self, real, fake, model):
    method compute_gradient_penalty (line 411) | def compute_gradient_penalty(self, model, real_samples, fake_samples):
  class VGGPerceptualLoss (line 428) | class VGGPerceptualLoss(nn.Module):
    method __init__ (line 429) | def __init__(self, resize=True):
    method forward (line 447) | def forward(self, input, target, rescale=True, feature_layers=[1]):
  function cal_psnr (line 470) | def cal_psnr(gt, data, max_value):

FILE: vis.py
  function plot_velocity (line 12) | def plot_velocity(output, target, path, vmin=None, vmax=None):
  function plot_single_velocity (line 38) | def plot_single_velocity(label, path):
  function plot_seismic (line 71) | def plot_seismic(output, target, path, vmin=-1e-5, vmax=1e-5):
  function plot_single_seismic (line 94) | def plot_single_seismic(data, path):