SYMBOL INDEX (122 symbols across 11 files)

FILE: dataset/aug.py
  function load_transforms (line 9) | def load_transforms(name):
  class Solarization (line 77) | class Solarization:
    method __call__ (line 80) | def __call__(self, img: Image) -> Image:
  class GBlur (line 92) | class GBlur(object):
    method __init__ (line 93) | def __init__(self, p):
    method __call__ (line 96) | def __call__(self, img):
  class AddGaussianNoise (line 104) | class AddGaussianNoise(object):
    method __init__ (line 105) | def __init__(self, mean=0., std=1.):
    method __call__ (line 109) | def __call__(self, tensor):
    method __repr__ (line 112) | def __repr__(self):
  class ContrastiveLearningViewGenerator (line 116) | class ContrastiveLearningViewGenerator(object):
    method __init__ (line 117) | def __init__(self, num_patch = 4):
    method __call__ (line 121) | def __call__(self, x):

FILE: dataset/aug4img.py
  class Solarization (line 12) | class Solarization:
    method __call__ (line 15) | def __call__(self, img: Image) -> Image:
  class GBlur (line 27) | class GBlur(object):
    method __init__ (line 28) | def __init__(self, p):
    method __call__ (line 31) | def __call__(self, img):
  class AddGaussianNoise (line 39) | class AddGaussianNoise(object):
    method __init__ (line 40) | def __init__(self, mean=0., std=1.):
    method __call__ (line 44) | def __call__(self, tensor):
    method __repr__ (line 47) | def __repr__(self):
  class ContrastiveLearningViewGenerator (line 51) | class ContrastiveLearningViewGenerator(object):
    method __init__ (line 52) | def __init__(self, num_patch = 4):
    method __call__ (line 56) | def __call__(self, x):

FILE: dataset/datasets.py
  function load_dataset (line 5) | def load_dataset(data_name, train=True, num_patch = 4, path="./data/"):
  function sparse2coarse (line 48) | def sparse2coarse(targets):

FILE: evaluate.py
  function compute_accuracy (line 60) | def compute_accuracy(y_pred, y_true):
  function chunk_avg (line 68) | def chunk_avg(x,n_chunks=2,normalize=False):
  function test (line 77) | def test(net, train_loader, test_loader):
  function chunk_avg (line 136) | def chunk_avg(x,n_chunks=2,normalize=False):

FILE: func.py
  class WeightedKNNClassifier (line 24) | class WeightedKNNClassifier(Metric):
    method __init__ (line 25) | def __init__(
    method update (line 62) | def update(
    method set_tk (line 90) | def set_tk(self, T, k):
    method compute (line 95) | def compute(self) -> Tuple[float]:
  function linear (line 181) | def linear(train_features, train_labels, test_features, test_labels, lr=...
  function accuracy (line 240) | def accuracy(output, target, topk=(1,)):
  class tensor_dataset (line 256) | class tensor_dataset(data.Dataset):
    method __init__ (line 257) | def __init__(self,x,y):
    method __getitem__ (line 262) | def __getitem__(self,indx):
    method __len__ (line 265) | def __len__(self):
  function set_gamma (line 271) | def set_gamma(loss_fn,epoch,total_epoch=500,warmup_epoch=100,gamma_min=0...
  function warmup_lr (line 280) | def warmup_lr(optimizer,epoch,base_lr,warmup_epoch=10):
  function marginal_H (line 285) | def marginal_H(logits):
  function chunk_avg (line 292) | def chunk_avg(x,n_chunks=2,normalize=False):
  function cluster_match (line 300) | def cluster_match(cluster_mtx,label_mtx,n_classes=10,print_result=True):
  function cluster_merge_match (line 330) | def cluster_merge_match(cluster_mtx,label_mtx,print_result=True):
  function cluster_acc (line 344) | def cluster_acc(test_loader,net,device,print_result=False,save_name_img=...
  function save_cluster_imgs (line 378) | def save_cluster_imgs(cluster_mtx,x_mtx,save_name,npercluster=100):
  function save_latent_pca_figure (line 399) | def save_latent_pca_figure(z_mtx,cluster_mtx,save_name):
  function analyze_latent (line 418) | def analyze_latent(z_mtx,cluster_mtx):

FILE: lars.py
  class LARS (line 5) | class LARS(Optimizer):
    method __init__ (line 26) | def __init__(self, params, lr, len_reduced, momentum=0.9, use_nesterov...
    method step (line 48) | def step(self, epoch=None, closure=None):
  class LARSWrapper (line 146) | class LARSWrapper:
    method __init__ (line 147) | def __init__(
    method defaults (line 184) | def defaults(self):
    method defaults (line 188) | def defaults(self, defaults):
    method __class__ (line 192) | def __class__(self):
    method state (line 196) | def state(self):
    method state (line 200) | def state(self, state):
    method param_groups (line 204) | def param_groups(self):
    method param_groups (line 208) | def param_groups(self, value):
    method step (line 212) | def step(self, closure=None):
    method update_p (line 234) | def update_p(self, p, group, weight_decay):

FILE: loss.py
  class contrastive_loss (line 5) | class contrastive_loss(nn.Module):
    method __init__ (line 6) | def __init__(self):
    method forward (line 9) | def forward(self,x,labels):
  class SimCLR (line 15) | class SimCLR(nn.Module):
    method __init__ (line 16) | def __init__(self,temperature=0.5,n_views=2,contrastive=False):
    method info_nce_loss (line 26) | def info_nce_loss(self,X):
    method forward (line 60) | def forward(self,X):
  class Z_loss (line 65) | class Z_loss(nn.Module):
    method __init__ (line 66) | def __init__(self,):
    method forward (line 70) | def forward(self,z):
  class TotalCodingRate (line 76) | class TotalCodingRate(nn.Module):
    method __init__ (line 77) | def __init__(self, eps=0.01):
    method compute_discrimn_loss (line 81) | def compute_discrimn_loss(self, W):
    method forward (line 89) | def forward(self,X):
  class MaximalCodingRateReduction (line 92) | class MaximalCodingRateReduction(torch.nn.Module):
    method __init__ (line 93) | def __init__(self, eps=0.01, gamma=1):
    method compute_discrimn_loss (line 98) | def compute_discrimn_loss(self, W):
    method compute_compress_loss (line 106) | def compute_compress_loss(self, W, Pi):
    method forward (line 118) | def forward(self, X, Y, num_classes=None):

FILE: main.py
  function chunk_avg (line 67) | def chunk_avg(x,n_chunks=2,normalize=False):
  class Similarity_Loss (line 76) | class Similarity_Loss(nn.Module):
    method __init__ (line 77) | def __init__(self, ):
    method forward (line 81) | def forward(self, z_list, z_avg):
  function cal_TCR (line 96) | def cal_TCR(z, criterion, num_patches):
  function main (line 146) | def main():

FILE: mcr/loss.py
  class MCRGANloss (line 6) | class MCRGANloss(nn.Module):
    method __init__ (line 8) | def __init__(self, gam1=1., gam2=1., gam3=1., eps=0.5, numclasses=1000...
    method forward (line 18) | def forward(self, Z, Z_bar, real_label, ith_inner_loop, num_inner_loop):
    method old_version (line 25) | def old_version(self, Z, Z_bar, real_label, ith_inner_loop, num_inner_...
    method debug (line 63) | def debug(self, Z, Z_bar, real_label):
    method compute_discrimn_loss (line 67) | def compute_discrimn_loss(self, Z):
    method compute_compress_loss (line 75) | def compute_compress_loss(self, Z, Pi):
    method deltaR (line 90) | def deltaR(self, Z, Y, num_classes):
    method gumb_compress_loss (line 108) | def gumb_compress_loss(self, Z, P):
    method pseudo_label_loss (line 123) | def pseudo_label_loss(self, Z, logits, thres = 1.4):

FILE: model/model.py
  function getmodel (line 9) | def getmodel(arch):
  class encoder (line 32) | class encoder(nn.Module):
    method __init__ (line 33) | def __init__(self,z_dim=1024,hidden_dim=4096, norm_p=2, arch = "resnet...
    method forward (line 46) | def forward(self, x, is_test = False):

FILE: model/resnet.py
  class BasicBlock (line 7) | class BasicBlock(nn.Module):
    method __init__ (line 9) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 26) | def forward(self, x):
  class Bottleneck (line 33) | class Bottleneck(nn.Module):
    method __init__ (line 36) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 55) | def forward(self, x):
  class ResNet (line 64) | class ResNet(nn.Module):
    method __init__ (line 65) | def __init__(self, block, blocks_config, first_config, first_pool=False):
    method _make_layer (line 80) | def _make_layer(self, block, planes, num_blocks, stride):
    method forward (line 88) | def forward(self, x):
  function Resnet10MNIST (line 103) | def Resnet10MNIST():
  function Resnet10CIFAR (line 111) | def Resnet10CIFAR():
  function Resnet18imgs (line 119) | def Resnet18imgs():
  function Resnet18CIFAR (line 127) | def Resnet18CIFAR():
  function Resnet18STL10 (line 134) | def Resnet18STL10():
  function Resnet34CIFAR (line 142) | def Resnet34CIFAR():
  function Resnet34STL10 (line 149) | def Resnet34STL10():