SYMBOL INDEX (282 symbols across 27 files)

FILE: checkpoint/test_robust.py
  function accuracy (line 40) | def accuracy(output, target, topk=(1,)):

FILE: checkpoint/utils.py
  class AverageMeter (line 11) | class AverageMeter(object):
    method __init__ (line 13) | def __init__(self):
    method reset (line 16) | def reset(self):
    method update (line 22) | def update(self, val, n=1):
  class RecorderMeter (line 29) | class RecorderMeter(object):
    method __init__ (line 31) | def __init__(self, total_epoch):
    method reset (line 34) | def reset(self, total_epoch):
    method update (line 44) | def update(self, idx, train_loss, train_acc, val_loss, val_acc):
    method max_accuracy (line 53) | def max_accuracy(self, istrain):
    method plot_curve (line 58) | def plot_curve(self, save_path):
  function time_string (line 104) | def time_string():
  function convert_secs2time (line 109) | def convert_secs2time(epoch_time):
  function time_file_str (line 115) | def time_file_str():
  function to_one_hot (line 120) | def to_one_hot(inp,num_classes,device='cuda'):
  function unsqueeze3 (line 127) | def unsqueeze3(tensor):
  function cost_matrix (line 130) | def cost_matrix(width):
  function K_prox (line 146) | def K_prox(x, H, transpose=False):
  function K (line 154) | def K(x, xi1):
  function nan_recover (line 157) | def nan_recover(tensor, thres=1e100):
  function barycenter_conv2d (line 161) | def barycenter_conv2d(input1, input2, reg=2e-3, weights=None, numItermax...
  function mixup_process (line 318) | def mixup_process(out, target_reweighted, mixup_alpha=1.0, loss_batch=No...
  function mixup_data (line 387) | def mixup_data(x, y, alpha):
  function get_lambda (line 400) | def get_lambda(alpha=1.0, alpha2=None):
  class Cutout (line 412) | class Cutout(object):
    method __init__ (line 418) | def __init__(self, n_holes, length):
    method apply (line 422) | def apply(self, img):
  function get_images_edges_cvh (line 452) | def get_images_edges_cvh(channel, height, width):
  function cut_3d_graph (line 470) | def cut_3d_graph(unary_cost, pairwise_cost, cost_v, cost_h, cost_c, n_it...
  function graphcut_multi (line 509) | def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_label...
  function graphcut_multi_float (line 545) | def graphcut_multi_float(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n...
  function neigh_penalty (line 586) | def neigh_penalty(input1, input2, k, dim=2):
  function mixup_box (line 603) | def mixup_box(input1, input2, grad1, grad2, method='random', alpha=0.5):
  function mixup_graph (line 666) | def mixup_graph(input1, grad1, indices, block_num=2, method='random', al...
  function mask_transport (line 844) | def mask_transport(mask, grad_pool, eps=0.01, t_type='full'):
  function transport_image (line 877) | def transport_image(img, plan, batch_size, block_num, block_size):
  function create_val_folder (line 890) | def create_val_folder(data_set_path):
  function aug (line 919) | def aug(image, preprocess):

FILE: imagenet/mixup.py
  function cost_matrix (line 7) | def cost_matrix(width):
  function graphcut_multi (line 30) | def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_label...
  function neigh_penalty (line 68) | def neigh_penalty(input1, input2, k):
  function mixup_graph (line 82) | def mixup_graph(input1,
  function mask_transport (line 200) | def mask_transport(mask, grad_pool, eps=0.01):
  function transport_image (line 225) | def transport_image(img, plan, block_num, block_size):

FILE: imagenet/models/densenet.py
  class Bottleneck (line 5) | class Bottleneck(nn.Module):
    method __init__ (line 6) | def __init__(self, nChannels, growthRate):
    method forward (line 14) | def forward(self, x):
  class SingleLayer (line 20) | class SingleLayer(nn.Module):
    method __init__ (line 21) | def __init__(self, nChannels, growthRate):
    method forward (line 26) | def forward(self, x):
  class Transition (line 31) | class Transition(nn.Module):
    method __init__ (line 32) | def __init__(self, nChannels, nOutChannels):
    method forward (line 37) | def forward(self, x):
  class DenseNet (line 42) | class DenseNet(nn.Module):
    method __init__ (line 43) | def __init__(self, growthRate, depth, reduction, nClasses, bottleneck):
    method _make_dense (line 80) | def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck):
    method forward (line 90) | def forward(self, x):
  function densenet100_12 (line 99) | def densenet100_12(num_classes=10):
  function densenet100_24 (line 104) | def densenet100_24(num_classes=10):

FILE: imagenet/models/preresnet.py
  class PreActBlock (line 13) | class PreActBlock(nn.Module):
    method __init__ (line 17) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 29) | def forward(self, x):
  class PreActBottleneck (line 38) | class PreActBottleneck(nn.Module):
    method __init__ (line 42) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 56) | def forward(self, x):
  class PreActResNet (line 66) | class PreActResNet(nn.Module):
    method __init__ (line 67) | def __init__(self, block, num_blocks, initial_channels, num_classes, s...
    method _make_layer (line 79) | def _make_layer(self, block, planes, num_blocks, stride):
    method compute_h1 (line 87) | def compute_h1(self,x):
    method compute_h2 (line 93) | def compute_h2(self,x):
    method forward (line 100) | def forward(self, x, target= None, mixup=False, mixup_hidden=False, ar...
  function preactresnet18 (line 145) | def preactresnet18(num_classes=10, dropout = False, stride=1):
  function preactresnet34 (line 148) | def preactresnet34(num_classes=10, dropout = False, stride=1):
  function preactresnet50 (line 151) | def preactresnet50(num_classes=10, dropout = False, stride=1):
  function preactresnet101 (line 154) | def preactresnet101(num_classes=10, dropout = False, stride=1):
  function preactresnet152 (line 157) | def preactresnet152(num_classes=10, dropout = False, stride=1):
  function test (line 160) | def test():

FILE: imagenet/models/pyramidnet.py
  function conv3x3 (line 7) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 13) | class BasicBlock(nn.Module):
    method __init__ (line 16) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 27) | def forward(self, x):
  class Bottleneck (line 55) | class Bottleneck(nn.Module):
    method __init__ (line 58) | def __init__(self, inplanes, planes, stride=1, downsample=None, reduct...
    method forward (line 72) | def forward(self, x):
  class PyramidNet (line 106) | class PyramidNet(nn.Module):
    method __init__ (line 108) | def __init__(self, dataset, depth, alpha, num_classes, bottleneck=False):
    method pyramidal_make_layer (line 181) | def pyramidal_make_layer(self, block, block_depth, stride=1):
    method forward (line 197) | def forward(self, x):

FILE: imagenet/models/resnet.py
  function conv3x3 (line 6) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 12) | class BasicBlock(nn.Module):
    method __init__ (line 15) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 26) | def forward(self, x):
  class Bottleneck (line 45) | class Bottleneck(nn.Module):
    method __init__ (line 48) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 62) | def forward(self, x):
  class ResNet (line 83) | class ResNet(nn.Module):
    method __init__ (line 84) | def __init__(self, dataset, depth, num_classes, bottleneck=False):
    method _make_layer (line 131) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 148) | def forward(self, x):

FILE: imagenet/models/resnext.py
  class ResNeXtBottleneck (line 14) | class ResNeXtBottleneck(nn.Module):
    method __init__ (line 19) | def __init__(self, inplanes, planes, cardinality, base_width, stride=1...
    method forward (line 36) | def forward(self, x):
  class CifarResNeXt (line 54) | class CifarResNeXt(nn.Module):
    method __init__ (line 59) | def __init__(self, block, depth, cardinality, base_width, num_classes,...
    method _make_layer (line 92) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 109) | def forward(self, x, target= None, mixup=False, mixup_hidden=False, mi...
  function resnext29_16_64 (line 161) | def resnext29_16_64(num_classes=10,dropout=False, per_img_std = False):
  function resnext29_8_64 (line 170) | def resnext29_8_64(num_classes=10, dropout=False, per_img_std = False):

FILE: imagenet/models/wide_resnet.py
  function conv3x3 (line 18) | def conv3x3(in_planes, out_planes, stride=1):
  function conv_init (line 21) | def conv_init(m):
  class wide_basic (line 30) | class wide_basic(nn.Module):
    method __init__ (line 31) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 44) | def forward(self, x):
  class Wide_ResNet (line 51) | class Wide_ResNet(nn.Module):
    method __init__ (line 53) | def __init__(self, depth, widen_factor, num_classes, stride = 1):
    method _wide_layer (line 72) | def _wide_layer(self, block, planes, num_blocks, stride):
    method forward (line 83) | def forward(self, x, target= None, mixup=False, mixup_hidden=False, ar...
  function wrn28_10 (line 128) | def wrn28_10(num_classes=10, dropout = False, stride = 1):
  function wrn28_2 (line 133) | def wrn28_2(num_classes=10, dropout = False, stride = 1):

FILE: imagenet/test.py
  function main (line 80) | def main():
  function validate (line 168) | def validate(val_loader, model, criterion):
  class AverageMeter (line 211) | class AverageMeter(object):
    method __init__ (line 213) | def __init__(self):
    method reset (line 216) | def reset(self):
    method update (line 222) | def update(self, val, n=1):
  function accuracy (line 229) | def accuracy(output, target, topk=(1, )):

FILE: imagenet/train.py
  function str2bool (line 34) | def str2bool(v):
  function main (line 139) | def main():
  function train (line 337) | def train(train_loader, model, criterion, criterion_batch, optimizer, ep...
  function rand_bbox (line 462) | def rand_bbox(size, lam):
  function validate (line 481) | def validate(val_loader, model, criterion, epoch):
  function save_checkpoint (line 529) | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
  class AverageMeter (line 539) | class AverageMeter(object):
    method __init__ (line 541) | def __init__(self):
    method reset (line 544) | def reset(self):
    method update (line 550) | def update(self, val, n=1):
  function adjust_learning_rate (line 557) | def adjust_learning_rate(optimizer, epoch):
  function get_learning_rate (line 572) | def get_learning_rate(optimizer):
  function accuracy (line 579) | def accuracy(output, target, topk=(1, )):

FILE: imagenet/utils.py
  class Compose (line 9) | class Compose(object):
    method __init__ (line 21) | def __init__(self, transforms):
    method __call__ (line 24) | def __call__(self, img):
    method __repr__ (line 29) | def __repr__(self):
  class Lighting (line 38) | class Lighting(object):
    method __init__ (line 40) | def __init__(self, alphastd, eigval, eigvec):
    method __call__ (line 45) | def __call__(self, img):
  class Grayscale (line 58) | class Grayscale(object):
    method __call__ (line 59) | def __call__(self, img):
  class Saturation (line 67) | class Saturation(object):
    method __init__ (line 68) | def __init__(self, var):
    method __call__ (line 71) | def __call__(self, img):
  class Brightness (line 77) | class Brightness(object):
    method __init__ (line 78) | def __init__(self, var):
    method __call__ (line 81) | def __call__(self, img):
  class Contrast (line 87) | class Contrast(object):
    method __init__ (line 88) | def __init__(self, var):
    method __call__ (line 91) | def __call__(self, img):
  class ColorJitter (line 98) | class ColorJitter(object):
    method __init__ (line 99) | def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4):
    method __call__ (line 104) | def __call__(self, img):

FILE: imagenet_fast/lib/utils.py
  class AverageMeter (line 14) | class AverageMeter(object):
    method __init__ (line 16) | def __init__(self):
    method reset (line 19) | def reset(self):
    method update (line 25) | def update(self, val, n=1):
  function adjust_learning_rate (line 32) | def adjust_learning_rate(initial_lr, optimizer, epoch, n_repeats):
  function fgsm (line 39) | def fgsm(gradz, step_size):
  function accuracy (line 43) | def accuracy(output, target, topk=(1,)):
  function initiate_logger (line 60) | def initiate_logger(output_path, evaluate):
  function get_model_names (line 72) | def get_model_names():
  function pad_str (line 77) | def pad_str(msg, total_len=70):
  function parse_config_file (line 81) | def parse_config_file(args):
  function save_checkpoint (line 94) | def save_checkpoint(state, is_best, filepath, epoch):
  function cost_matrix (line 103) | def cost_matrix(width):
  function graphcut_multi (line 118) | def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_label...
  function graphcut_multi_float (line 154) | def graphcut_multi_float(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n...
  function neigh_penalty (line 188) | def neigh_penalty(input1, input2, k):
  function get_mask (line 201) | def get_mask(input1, grad1, block_num, indices, alpha=0.5, beta=1.2, gam...
  function transport (line 269) | def transport(input1, grad1, indices, block_num, mask, eps=0.8):
  function mask_transport (line 294) | def mask_transport(mask, grad_pool, eps=0.01):
  function transport_image (line 321) | def transport_image(img, plan, block_num, block_size):
  function create_val_folder (line 336) | def create_val_folder(data_set_path):

FILE: imagenet_fast/lib/validation.py
  function validate (line 8) | def validate(val_loader, model, criterion, configs, logger):

FILE: imagenet_fast/main_fast.py
  function parse_args (line 30) | def parse_args():
  function main (line 70) | def main():
  function train (line 203) | def train(train_loader, model, optimizer, epoch, lr_schedule, clean_lam=...

FILE: imagenet_fast/main_test.py
  function parse_args (line 29) | def parse_args():
  function compute_mce (line 81) | def compute_mce(corruption_accs):
  function main (line 91) | def main():
  function train (line 173) | def train(train_loader, model, optimizer, epoch, lr_schedule, half=False):
  function test (line 310) | def test(net, test_loader):
  function test_c (line 334) | def test_c(net, test_transform):

FILE: imagenet_fast/models/imagenet_resnet.py
  function conv3x3 (line 7) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 13) | class BasicBlock(nn.Module):
    method __init__ (line 16) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 26) | def forward(self, x):
  class Bottleneck (line 45) | class Bottleneck(nn.Module):
    method __init__ (line 48) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 61) | def forward(self, x):
  class ResNet (line 84) | class ResNet(nn.Module):
    method __init__ (line 86) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 110) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 127) | def forward(self, x, manifold=False, graphcut=False, lam=None, permute...
  function resnet18 (line 171) | def resnet18(num_classes=1000):
  function resnet34 (line 181) | def resnet34(num_classes=1000):
  function resnet50 (line 191) | def resnet50(num_classes=1000, dropout = False, per_img_std = False, str...
  function resnet101 (line 201) | def resnet101(num_classes=1000):
  function resnet152 (line 211) | def resnet152(num_classes=1000):

FILE: imagenet_fast/resize.py
  function resize_img (line 18) | def resize_img(p, im, fn, sz):
  function resizes (line 28) | def resizes(p, fn):
  function resize_imgs (line 34) | def resize_imgs(p):

FILE: load_data.py
  function load_data_subset (line 10) | def load_data_subset(batch_size,
  function create_val_folder (line 161) | def create_val_folder(data_set_path):

FILE: logger.py
  function copy_script_to_folder (line 14) | def copy_script_to_folder(caller_path, folder):
  function time_string (line 21) | def time_string():
  function convert_secs2time (line 28) | def convert_secs2time(epoch_time):
  class RecorderMeter (line 35) | class RecorderMeter(object):
    method __init__ (line 37) | def __init__(self, total_epoch):
    method reset (line 40) | def reset(self, total_epoch):
    method update (line 51) | def update(self, idx, train_loss, train_acc, val_loss, val_acc):
    method max_accuracy (line 61) | def max_accuracy(self, istrain):
    method plot_curve (line 66) | def plot_curve(self, save_path):
  class AverageMeter (line 111) | class AverageMeter(object):
    method __init__ (line 113) | def __init__(self):
    method reset (line 116) | def reset(self):
    method update (line 122) | def update(self, val, n=1):
  function plotting (line 129) | def plotting(exp_dir):

FILE: main.py
  function str2bool (line 28) | def str2bool(v):
  function experiment_name_non_mnist (line 181) | def experiment_name_non_mnist(dataset=args.dataset,
  function print_log (line 242) | def print_log(print_string, log, end='\n'):
  function save_checkpoint (line 253) | def save_checkpoint(state, is_best, save_path, filename):
  function adjust_learning_rate (line 262) | def adjust_learning_rate(optimizer, epoch, gammas, schedule):
  function accuracy (line 276) | def accuracy(output, target, topk=(1, )):
  function train (line 299) | def train(train_loader, model, optimizer, epoch, args, log, mp=None):
  function validate (line 424) | def validate(val_loader, model, log, fgsm=False, eps=4, rand_init=False,...
  function main (line 480) | def main():

FILE: mixup.py
  function to_one_hot (line 9) | def to_one_hot(inp, num_classes, device='cuda'):
  function cost_matrix (line 16) | def cost_matrix(width, device='cuda'):
  function mixup_process (line 44) | def mixup_process(out,
  function get_lambda (line 120) | def get_lambda(alpha=1.0, alpha2=None):
  function graphcut_multi (line 132) | def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_label...
  function neigh_penalty (line 170) | def neigh_penalty(input1, input2, k):
  function mixup_box (line 184) | def mixup_box(input1, input2, alpha=0.5, device='cuda'):
  function mixup_graph (line 207) | def mixup_graph(input1,
  function mask_transport (line 340) | def mask_transport(mask, grad_pool, eps=0.01):
  function transport_image (line 367) | def transport_image(img, plan, batch_size, block_num, block_size):

FILE: models/preresnet.py
  class PreActBlock (line 14) | class PreActBlock(nn.Module):
    method __init__ (line 18) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 38) | def forward(self, x):
  class PreActBottleneck (line 47) | class PreActBottleneck(nn.Module):
    method __init__ (line 51) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 68) | def forward(self, x):
  class PreActResNet (line 78) | class PreActResNet(nn.Module):
    method __init__ (line 79) | def __init__(self, block, num_blocks, initial_channels, num_classes, s...
    method _make_layer (line 95) | def _make_layer(self, block, planes, num_blocks, stride):
    method compute_h1 (line 103) | def compute_h1(self, x):
    method compute_h2 (line 109) | def compute_h2(self, x):
    method forward (line 116) | def forward(self,
  function preactresnet18 (line 174) | def preactresnet18(num_classes=10, dropout=False, stride=1):
  function preactresnet34 (line 178) | def preactresnet34(num_classes=10, dropout=False, stride=1):
  function preactresnet50 (line 182) | def preactresnet50(num_classes=10, dropout=False, stride=1):
  function preactresnet101 (line 186) | def preactresnet101(num_classes=10, dropout=False, stride=1):
  function preactresnet152 (line 190) | def preactresnet152(num_classes=10, dropout=False, stride=1):
  function test (line 194) | def test():

FILE: models/wide_resnet.py
  function conv3x3 (line 18) | def conv3x3(in_planes, out_planes, stride=1):
  function conv_init (line 22) | def conv_init(m):
  class wide_basic (line 32) | class wide_basic(nn.Module):
    method __init__ (line 33) | def __init__(self, in_planes, planes, stride=1):
    method forward (line 45) | def forward(self, x):
  class Wide_ResNet (line 53) | class Wide_ResNet(nn.Module):
    method __init__ (line 54) | def __init__(self, depth, widen_factor, num_classes, stride=1):
    method _wide_layer (line 73) | def _wide_layer(self, block, planes, num_blocks, stride):
    method forward (line 83) | def forward(self,
  function wrn28_10 (line 142) | def wrn28_10(num_classes=10, dropout=False, stride=1):
  function wrn28_2 (line 147) | def wrn28_2(num_classes=10, dropout=False, stride=1):

FILE: unit_test/test_exact.py
  function mask_transport (line 6) | def mask_transport(mask, grad_pool, eps=0.01, n_iter=5, t_type='full'):
  function cost_matrix (line 49) | def cost_matrix(width=16, dist=2):

FILE: unit_test/test_graphcut.py
  function test_graph (line 8) | def test_graph(unary1, unary2, pw_x, pw_y, beta, n_labels=2, verbose=Fal...
  function graphcut_multi (line 63) | def graphcut_multi(unary1, unary2, pw_x, pw_y, beta, n_labels=2):

FILE: unit_test/test_heuristic.py
  function cost_matrix (line 16) | def cost_matrix(width=16, dist=2, device='cuda'):
  function mask_transport (line 32) | def mask_transport(mask, grad_pool, eps=0.01, n_iter=None, t_type='full'...
  function transport_image (line 71) | def transport_image(img, plan, batch_size, block_num, block_size):
  function transport_image_loop (line 85) | def transport_image_loop(img, plan, batch_size, block_num, block_size):