SYMBOL INDEX (88 symbols across 14 files)

FILE: Backbone/CNN1D.py
  class CNN1D (line 3) | class CNN1D(nn.Module):
    method __init__ (line 4) | def __init__(self, num_out = 10):
    method forward (line 27) | def forward(self, x):

FILE: Backbone/MLPNet.py
  class MLPNet (line 3) | class MLPNet(nn.Module):
    method __init__ (line 4) | def __init__(self, num_in = 1024, num_out = 10):
    method forward (line 28) | def forward(self, x):

FILE: Backbone/ResNet1D.py
  function conv3x1 (line 14) | def conv3x1(in_planes, out_planes, stride=1):
  function conv1x1 (line 19) | def conv1x1(in_planes, out_planes, stride=1):
  class BasicBlock (line 24) | class BasicBlock(nn.Module):
    method __init__ (line 27) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 37) | def forward(self, x):
  class Bottleneck (line 56) | class Bottleneck(nn.Module):
    method __init__ (line 60) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 72) | def forward(self, x):
  class ResNet (line 95) | class ResNet(nn.Module):
    method __init__ (line 97) | def __init__(self, block, layers, in_channel=1, out_channel=10, zero_i...
    method _make_layer (line 129) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 145) | def forward(self, x):
  function resnet18 (line 162) | def resnet18(pretrained=False, **kwargs):
  class resnet18_features (line 175) | class resnet18_features(nn.Module):
    method __init__ (line 176) | def __init__(self, pretrained=False):
    method forward (line 181) | def forward(self, x):
    method output_num (line 185) | def output_num(self):

FILE: DANN.py
  function parse_args (line 22) | def parse_args():
  class FeatureNet (line 69) | class FeatureNet(nn.Module):
    method __init__ (line 70) | def __init__(self, args):
    method forward (line 88) | def forward(self, x):
  class Classifier (line 93) | class Classifier(nn.Module):
    method __init__ (line 94) | def __init__(self, args, num_out=10):
    method forward (line 101) | def forward(self, logits):
  function calc_coeff (line 107) | def calc_coeff(iter_num, high=1.0, low=0.0, alpha=10.0, max_iter=10000.0):
  function grl_hook (line 112) | def grl_hook(coeff):
  class Discriminator (line 117) | class Discriminator(nn.Module):
    method __init__ (line 118) | def __init__(self, args, num_out = 1, max_iter=10000.0, trade_off_adve...
    method forward (line 145) | def forward(self, x):
  function loaddata (line 162) | def loaddata(args):
  function tester (line 179) | def tester(featurenet, classifier, dataloader):
  function trainer (line 199) | def trainer(args):

FILE: DDC.py
  function parse_args (line 22) | def parse_args():
  class FeatureNet (line 70) | class FeatureNet(nn.Module):
    method __init__ (line 71) | def __init__(self, args):
    method forward (line 89) | def forward(self, x):
  class Classifier (line 94) | class Classifier(nn.Module):
    method __init__ (line 95) | def __init__(self, args, num_out=10):
    method forward (line 102) | def forward(self, logits):
  function loaddata (line 108) | def loaddata(args):
  function tester (line 129) | def tester(featurenet, classifier, dataloader):
  function trainer (line 150) | def trainer(args):

FILE: OSDABP.py
  function parse_args (line 21) | def parse_args():
  class GradReverse (line 68) | class GradReverse(Function):
    method forward (line 70) | def forward(ctx, x):
    method backward (line 74) | def backward(ctx, grad_output):
  function grad_reverse (line 78) | def grad_reverse(x):
  class FeatureNet (line 82) | class FeatureNet(nn.Module):
    method __init__ (line 83) | def __init__(self, args):
    method forward (line 98) | def forward(self, x):
  class Classifier (line 103) | class Classifier(nn.Module):
    method __init__ (line 104) | def __init__(self, args, num_out=10):
    method forward (line 111) | def forward(self, logits, reverse = False):
  function loaddata (line 119) | def loaddata(args):
  function bce_loss (line 136) | def bce_loss(output, target):
  function tester (line 144) | def tester(featurenet, classifier, dataloader):
  function trainer (line 165) | def trainer(args):

FILE: PreparData/CWRU.py
  function CWRU (line 20) | def CWRU(datadir, load, labels, window, normalization, backbone, fft):
  function CWRUloader (line 52) | def CWRUloader(args, load, label_set, number="all"):

FILE: PreparData/preprocess.py
  function transformation (line 3) | def transformation(sub_data, fft, normalization, backbone):

FILE: Utils/logger.py
  function setlogger (line 3) | def setlogger(path):

FILE: Utils/utils.py
  function accuracy (line 7) | def accuracy(outputs, labels):
  function save_log (line 18) | def save_log(obj, path):
  function read_pkl (line 22) | def read_pkl(path):
  function save_model (line 27) | def save_model(model, args):
  function DataSplite (line 33) | def DataSplite(args, data, label):
  function optimizer (line 68) | def optimizer(args, parameter_list):

FILE: classification.py
  function parse_args (line 21) | def parse_args():
  class FeatureNet (line 64) | class FeatureNet(nn.Module):
    method __init__ (line 65) | def __init__(self, args):
    method forward (line 83) | def forward(self, x):
  class Classifier (line 88) | class Classifier(nn.Module):
    method __init__ (line 89) | def __init__(self, args, num_out=10):
    method forward (line 96) | def forward(self, logits):
  function loaddata (line 102) | def loaddata(args):
  function tester (line 111) | def tester(featurenet, classifier, dataloader):
  function trainer (line 132) | def trainer(args):

FILE: loss/CORAL.py
  function CORAL_loss (line 12) | def CORAL_loss(source, target):
  function compute_covariance (line 36) | def compute_covariance(data):

FILE: loss/MKMMD.py
  function guassian_kernel (line 11) | def guassian_kernel(source, target, kernel_mul=2.0, kernel_num=5, fix_si...
  function MKMMD (line 27) | def MKMMD(source, target, kernel_mul=2.0, kernel_num=5, fix_sigma=None):

FILE: loss/MMDLinear.py
  function MMDLinear (line 12) | def MMDLinear(source_activation, target_activation):