SYMBOL INDEX (930 symbols across 86 files)

FILE: benchmarks/GSCNN-master/config.py
  function assert_and_infer_cfg (line 74) | def assert_and_infer_cfg(args, make_immutable=True):

FILE: benchmarks/GSCNN-master/datasets/__init__.py
  function setup_loaders (line 13) | def setup_loaders(args):

FILE: benchmarks/GSCNN-master/datasets/cityscapes.py
  function colorize_mask (line 36) | def colorize_mask(mask):
  function add_items (line 43) | def add_items(items, aug_items, cities, img_path, mask_path, mask_postfi...
  function make_cv_splits (line 53) | def make_cv_splits(img_dir_name):
  function make_split_coarse (line 88) | def make_split_coarse(img_path):
  function make_test_split (line 102) | def make_test_split(img_dir_name):
  function make_dataset (line 109) | def make_dataset(quality, mode, maxSkip=0, fine_coarse_mult=6, cv_split=0):
  class CityScapes (line 150) | class CityScapes(data.Dataset):
    method __init__ (line 152) | def __init__(self, quality, mode, maxSkip=0, joint_transform=None, sli...
    method _eval_get_item (line 183) | def _eval_get_item(self, img, mask, scales, flip_bool):
    method __getitem__ (line 201) | def __getitem__(self, index):
    method __len__ (line 245) | def __len__(self):
  function make_dataset_video (line 249) | def make_dataset_video():
  class CityScapesVideo (line 263) | class CityScapesVideo(data.Dataset):
    method __init__ (line 265) | def __init__(self, transform=None):
    method __getitem__ (line 271) | def __getitem__(self, index):
    method __len__ (line 280) | def __len__(self):

FILE: benchmarks/GSCNN-master/datasets/cityscapes_labels.py
  function assureSingleInstanceName (line 168) | def assureSingleInstanceName( name ):

FILE: benchmarks/GSCNN-master/datasets/edge_utils.py
  function mask_to_onehot (line 11) | def mask_to_onehot(mask, num_classes):
  function onehot_to_mask (line 20) | def onehot_to_mask(mask):
  function onehot_to_multiclass_edges (line 28) | def onehot_to_multiclass_edges(mask, radius, num_classes):
  function onehot_to_binary_edges (line 49) | def onehot_to_binary_edges(mask, radius, num_classes):

FILE: benchmarks/GSCNN-master/datasets/rellis.py
  function colorize_mask (line 38) | def colorize_mask(mask):
  class Rellis (line 45) | class Rellis(data.Dataset):
    method __init__ (line 47) | def __init__(self, mode, joint_transform=None, sliding_crop=None,
    method _eval_get_item (line 94) | def _eval_get_item(self, img, mask, scales, flip_bool):
    method read_files (line 110) | def read_files(self):
    method convert_label (line 132) | def convert_label(self, label, inverse=False):
    method __getitem__ (line 143) | def __getitem__(self, index):
    method __len__ (line 190) | def __len__(self):
  function make_dataset_video (line 194) | def make_dataset_video():
  class CityScapesVideo (line 208) | class CityScapesVideo(data.Dataset):
    method __init__ (line 210) | def __init__(self, transform=None):
    method __getitem__ (line 216) | def __getitem__(self, index):
    method __len__ (line 225) | def __len__(self):

FILE: benchmarks/GSCNN-master/loss.py
  function get_loss (line 14) | def get_loss(args):
  class JointEdgeSegLoss (line 41) | class JointEdgeSegLoss(nn.Module):
    method __init__ (line 42) | def __init__(self, classes, weight=None, reduction='mean', ignore_inde...
    method bce2d (line 62) | def bce2d(self, input, target):
    method edge_attention (line 95) | def edge_attention(self, input, target, edge):
    method forward (line 101) | def forward(self, inputs, targets):
  class ImageBasedCrossEntropyLoss2d (line 114) | class ImageBasedCrossEntropyLoss2d(nn.Module):
    method __init__ (line 116) | def __init__(self, classes, weight=None, size_average=True, ignore_ind...
    method calculateWeights (line 126) | def calculateWeights(self, target):
    method forward (line 135) | def forward(self, inputs, targets):
  class CrossEntropyLoss2d (line 153) | class CrossEntropyLoss2d(nn.Module):
    method __init__ (line 154) | def __init__(self, weight=None, size_average=True, ignore_index=255):
    method forward (line 159) | def forward(self, inputs, targets):

FILE: benchmarks/GSCNN-master/my_functionals/DualTaskLoss.py
  function perturbate_input_ (line 38) | def perturbate_input_(input, n_elements=200):
  function _sample_gumbel (line 50) | def _sample_gumbel(shape, eps=1e-10):
  function _gumbel_softmax_sample (line 62) | def _gumbel_softmax_sample(logits, tau=1, eps=1e-10):
  function _one_hot_embedding (line 76) | def _one_hot_embedding(labels, num_classes):
  class DualTaskLoss (line 90) | class DualTaskLoss(nn.Module):
    method __init__ (line 91) | def __init__(self, cuda=False):
    method forward (line 96) | def forward(self, input_logits, gts, ignore_pixel=255):

FILE: benchmarks/GSCNN-master/my_functionals/GatedSpatialConv.py
  class GatedSpatialConv2d (line 15) | class GatedSpatialConv2d(_ConvNd):
    method __init__ (line 16) | def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
    method forward (line 47) | def forward(self, input_features, gating_features):
    method reset_parameters (line 60) | def reset_parameters(self):
  class Conv2dPad (line 66) | class Conv2dPad(nn.Conv2d):
    method forward (line 67) | def forward(self, input):
  class HighFrequencyGatedSpatialConv2d (line 70) | class HighFrequencyGatedSpatialConv2d(_ConvNd):
    method __init__ (line 71) | def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
    method forward (line 137) | def forward(self, input_features, gating_features):
    method reset_parameters (line 156) | def reset_parameters(self):
  function t (line 161) | def t():

FILE: benchmarks/GSCNN-master/my_functionals/custom_functional.py
  function calc_pad_same (line 12) | def calc_pad_same(in_siz, out_siz, stride, ksize):
  function conv2d_same (line 22) | def conv2d_same(input, kernel, groups,bias=None,stride=1,padding=0,dilat...
  function gradient_central_diff (line 38) | def gradient_central_diff(input, cuda):
  function compute_single_sided_diferences (line 55) | def compute_single_sided_diferences(o_x, o_y, input):
  function numerical_gradients_2d (line 66) | def numerical_gradients_2d(input, cuda=False):
  function convTri (line 81) | def convTri(input, r, cuda=False):
  function compute_normal (line 125) | def compute_normal(E, cuda=False):
  function compute_normal_2 (line 147) | def compute_normal_2(E, cuda=False):
  function compute_grad_mag (line 169) | def compute_grad_mag(E, cuda=False):

FILE: benchmarks/GSCNN-master/network/Resnet.py
  function conv3x3 (line 57) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 63) | class BasicBlock(nn.Module):
    method __init__ (line 66) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 82) | def forward(self, x):
  class Bottleneck (line 101) | class Bottleneck(nn.Module):
    method __init__ (line 104) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 117) | def forward(self, x):
  class ResNet (line 140) | class ResNet(nn.Module):
    method __init__ (line 142) | def __init__(self, block, layers, num_classes=1000):
    method _make_layer (line 164) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 181) | def forward(self, x):
  function resnet18 (line 199) | def resnet18(pretrained=True, **kwargs):
  function resnet34 (line 211) | def resnet34(pretrained=True, **kwargs):
  function resnet50 (line 223) | def resnet50(pretrained=True, **kwargs):
  function resnet101 (line 235) | def resnet101(pretrained=True, **kwargs):
  function resnet152 (line 247) | def resnet152(pretrained=True, **kwargs):

FILE: benchmarks/GSCNN-master/network/SEresnext.py
  class SEModule (line 75) | class SEModule(nn.Module):
    method __init__ (line 77) | def __init__(self, channels, reduction):
    method forward (line 87) | def forward(self, x):
  class Bottleneck (line 97) | class Bottleneck(nn.Module):
    method forward (line 101) | def forward(self, x):
  class SEBottleneck (line 124) | class SEBottleneck(Bottleneck):
    method __init__ (line 130) | def __init__(self, inplanes, planes, groups, reduction, stride=1,
  class SEResNetBottleneck (line 148) | class SEResNetBottleneck(Bottleneck):
    method __init__ (line 156) | def __init__(self, inplanes, planes, groups, reduction, stride=1,
  class SEResNeXtBottleneck (line 173) | class SEResNeXtBottleneck(Bottleneck):
    method __init__ (line 179) | def __init__(self, inplanes, planes, groups, reduction, stride=1,
  class SENet (line 197) | class SENet(nn.Module):
    method __init__ (line 199) | def __init__(self, block, layers, groups, reduction, dropout_p=0.2,
    method _make_layer (line 318) | def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
    method features (line 338) | def features(self, x):
    method logits (line 346) | def logits(self, x):
    method forward (line 354) | def forward(self, x):
  function initialize_pretrained_model (line 360) | def initialize_pretrained_model(model, num_classes, settings):
  function se_resnext50_32x4d (line 374) | def se_resnext50_32x4d(num_classes=1000):
  function se_resnext101_32x4d (line 384) | def se_resnext101_32x4d(num_classes=1000):

FILE: benchmarks/GSCNN-master/network/__init__.py
  function get_net (line 10) | def get_net(args, criterion):
  function get_model (line 25) | def get_model(network, num_classes, criterion, trunk):

FILE: benchmarks/GSCNN-master/network/gscnn.py
  class Crop (line 45) | class Crop(nn.Module):
    method __init__ (line 46) | def __init__(self, axis, offset):
    method forward (line 51) | def forward(self, x, ref):
  class MyIdentity (line 66) | class MyIdentity(nn.Module):
    method __init__ (line 67) | def __init__(self, axis, offset):
    method forward (line 72) | def forward(self, x, ref):
  class SideOutputCrop (line 81) | class SideOutputCrop(nn.Module):
    method __init__ (line 86) | def __init__(self, num_output, kernel_sz=None, stride=None, upconv_pad...
    method forward (line 104) | def forward(self, res, reference=None):
  class _AtrousSpatialPyramidPoolingModule (line 113) | class _AtrousSpatialPyramidPoolingModule(nn.Module):
    method __init__ (line 125) | def __init__(self, in_dim, reduction_dim=256, output_stride=16, rates=...
    method forward (line 163) | def forward(self, x, edge):
  class GSCNN (line 182) | class GSCNN(nn.Module):
    method __init__ (line 196) | def __init__(self, num_classes, trunk=None, criterion=None):
    method forward (line 263) | def forward(self, inp, gts=None):

FILE: benchmarks/GSCNN-master/network/mynn.py
  function Norm2d (line 16) | def Norm2d(in_channels):
  function initialize_weights (line 25) | def initialize_weights(*models):

FILE: benchmarks/GSCNN-master/network/wider_resnet.py
  function bnrelu (line 46) | def bnrelu(channels):
  class GlobalAvgPool2d (line 50) | class GlobalAvgPool2d(nn.Module):
    method __init__ (line 52) | def __init__(self):
    method forward (line 56) | def forward(self, inputs):
  class IdentityResidualBlock (line 61) | class IdentityResidualBlock(nn.Module):
    method __init__ (line 63) | def __init__(self,
    method forward (line 164) | def forward(self, x):
  class WiderResNet (line 182) | class WiderResNet(nn.Module):
    method __init__ (line 184) | def __init__(self,
    method forward (line 244) | def forward(self, img):
  class WiderResNetA2 (line 260) | class WiderResNetA2(nn.Module):
    method __init__ (line 262) | def __init__(self,
    method forward (line 359) | def forward(self, img):

FILE: benchmarks/GSCNN-master/optimizer.py
  function get_optimizer (line 12) | def get_optimizer(args, net):
  function restore_snapshot (line 48) | def restore_snapshot(args, net, optimizer, snapshot):
  function forgiving_state_restore (line 64) | def forgiving_state_restore(net, loaded_dict):

FILE: benchmarks/GSCNN-master/train.py
  function convert_label (line 123) | def convert_label(label, inverse=False):
  function convert_color (line 156) | def convert_color(label, color_map):
  function main (line 173) | def main():
  function train (line 279) | def train(train_loader, net, criterion, optimizer, curr_epoch, writer):
  function validate (line 393) | def validate(val_loader, net, criterion, optimizer, curr_epoch, writer):
  function evaluate (line 459) | def evaluate(val_loader, net):

FILE: benchmarks/GSCNN-master/transforms/joint_transforms.py
  class Compose (line 40) | class Compose(object):
    method __init__ (line 41) | def __init__(self, transforms):
    method __call__ (line 44) | def __call__(self, img, mask):
  class RandomCrop (line 51) | class RandomCrop(object):
    method __init__ (line 65) | def __init__(self, size, ignore_index=0, nopad=True):
    method __call__ (line 74) | def __call__(self, img, mask, centroid=None):
  class ResizeHeight (line 125) | class ResizeHeight(object):
    method __init__ (line 126) | def __init__(self, size, interpolation=Image.BICUBIC):
    method __call__ (line 130) | def __call__(self, img, mask):
  class CenterCrop (line 137) | class CenterCrop(object):
    method __init__ (line 138) | def __init__(self, size):
    method __call__ (line 144) | def __call__(self, img, mask):
  class CenterCropPad (line 153) | class CenterCropPad(object):
    method __init__ (line 154) | def __init__(self, size, ignore_index=0):
    method __call__ (line 161) | def __call__(self, img, mask):
  class PadImage (line 192) | class PadImage(object):
    method __init__ (line 193) | def __init__(self, size, ignore_index):
    method __call__ (line 198) | def __call__(self, img, mask):
  class RandomHorizontallyFlip (line 217) | class RandomHorizontallyFlip(object):
    method __call__ (line 218) | def __call__(self, img, mask):
  class FreeScale (line 225) | class FreeScale(object):
    method __init__ (line 226) | def __init__(self, size):
    method __call__ (line 229) | def __call__(self, img, mask):
  class Scale (line 234) | class Scale(object):
    method __init__ (line 239) | def __init__(self, size):
    method __call__ (line 242) | def __call__(self, img, mask):
  class ScaleMin (line 259) | class ScaleMin(object):
    method __init__ (line 264) | def __init__(self, size):
    method __call__ (line 267) | def __call__(self, img, mask):
  class Resize (line 284) | class Resize(object):
    method __init__ (line 289) | def __init__(self, size):
    method __call__ (line 292) | def __call__(self, img, mask):
  class RandomSizedCrop (line 301) | class RandomSizedCrop(object):
    method __init__ (line 302) | def __init__(self, size):
    method __call__ (line 305) | def __call__(self, img, mask):
  class RandomRotate (line 335) | class RandomRotate(object):
    method __init__ (line 336) | def __init__(self, degree):
    method __call__ (line 339) | def __call__(self, img, mask):
  class RandomSizeAndCrop (line 345) | class RandomSizeAndCrop(object):
    method __init__ (line 346) | def __init__(self, size, crop_nopad,
    method __call__ (line 354) | def __call__(self, img, mask, centroid=None):
  class SlidingCropOld (line 375) | class SlidingCropOld(object):
    method __init__ (line 376) | def __init__(self, crop_size, stride_rate, ignore_label):
    method _pad (line 381) | def _pad(self, img, mask):
    method __call__ (line 390) | def __call__(self, img, mask):
  class SlidingCrop (line 427) | class SlidingCrop(object):
    method __init__ (line 428) | def __init__(self, crop_size, stride_rate, ignore_label):
    method _pad (line 433) | def _pad(self, img, mask):
    method __call__ (line 442) | def __call__(self, img, mask):
  class ClassUniform (line 480) | class ClassUniform(object):
    method __init__ (line 481) | def __init__(self, size, crop_nopad, scale_min=0.5, scale_max=2.0, ign...
    method detect_peaks (line 500) | def detect_peaks(self, image):
    method __call__ (line 536) | def __call__(self, img, mask):

FILE: benchmarks/GSCNN-master/transforms/transforms.py
  class RandomVerticalFlip (line 46) | class RandomVerticalFlip(object):
    method __call__ (line 47) | def __call__(self, img):
  class DeNormalize (line 53) | class DeNormalize(object):
    method __init__ (line 54) | def __init__(self, mean, std):
    method __call__ (line 58) | def __call__(self, tensor):
  class MaskToTensor (line 64) | class MaskToTensor(object):
    method __call__ (line 65) | def __call__(self, img):
  class RelaxedBoundaryLossToTensor (line 68) | class RelaxedBoundaryLossToTensor(object):
    method __init__ (line 69) | def __init__(self,ignore_id, num_classes):
    method new_one_hot_converter (line 74) | def new_one_hot_converter(self,a):
    method __call__ (line 81) | def __call__(self,img):
  class ResizeHeight (line 124) | class ResizeHeight(object):
    method __init__ (line 125) | def __init__(self, size, interpolation=Image.BILINEAR):
    method __call__ (line 129) | def __call__(self, img):
  class FreeScale (line 135) | class FreeScale(object):
    method __init__ (line 136) | def __init__(self, size, interpolation=Image.BILINEAR):
    method __call__ (line 140) | def __call__(self, img):
  class FlipChannels (line 144) | class FlipChannels(object):
    method __call__ (line 145) | def __call__(self, img):
  class RandomGaussianBlur (line 149) | class RandomGaussianBlur(object):
    method __call__ (line 150) | def __call__(self, img):
  class RandomBilateralBlur (line 157) | class RandomBilateralBlur(object):
    method __call__ (line 158) | def __call__(self, img):
  function _is_pil_image (line 170) | def _is_pil_image(img):
  function adjust_brightness (line 177) | def adjust_brightness(img, brightness_factor):
  function adjust_contrast (line 197) | def adjust_contrast(img, contrast_factor):
  function adjust_saturation (line 217) | def adjust_saturation(img, saturation_factor):
  function adjust_hue (line 237) | def adjust_hue(img, hue_factor):
  class ColorJitter (line 282) | class ColorJitter(object):
    method __init__ (line 295) | def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
    method get_params (line 302) | def get_params(brightness, contrast, saturation, hue):
    method __call__ (line 337) | def __call__(self, img):

FILE: benchmarks/GSCNN-master/utils/AttrDict.py
  class AttrDict (line 34) | class AttrDict(dict):
    method __init__ (line 38) | def __init__(self, *args, **kwargs):
    method __getattr__ (line 42) | def __getattr__(self, name):
    method __setattr__ (line 50) | def __setattr__(self, name, value):
    method immutable (line 62) | def immutable(self, is_immutable):
    method is_immutable (line 75) | def is_immutable(self):

FILE: benchmarks/GSCNN-master/utils/f_boundary.py
  function eval_mask_boundary (line 59) | def eval_mask_boundary(seg_mask,gt_mask,num_classes,num_proc=10,bound_th...
  function db_eval_boundary_wrapper (line 104) | def db_eval_boundary_wrapper(args):
  function db_eval_boundary (line 108) | def db_eval_boundary(foreground_mask,gt_mask, ignore_mask,bound_th=0.008):
  function seg2bmap (line 173) | def seg2bmap(seg,width=None,height=None):

FILE: benchmarks/GSCNN-master/utils/image_page.py
  class ImagePage (line 9) | class ImagePage(object):
    method __init__ (line 22) | def __init__(self, experiment_name, html_filename):
    method _print_header (line 28) | def _print_header(self):
    method _print_footer (line 37) | def _print_footer(self):
    method _print_table_header (line 41) | def _print_table_header(self, table_name):
    method _print_table_footer (line 47) | def _print_table_footer(self):
    method _print_table_guts (line 52) | def _print_table_guts(self, img_fn, descr):
    method add_table (line 63) | def add_table(self, img_label_pairs):
    method _write_table (line 66) | def _write_table(self, table):
    method write_page (line 73) | def write_page(self):
  function main (line 82) | def main():

FILE: benchmarks/GSCNN-master/utils/misc.py
  function make_exp_name (line 23) | def make_exp_name(args, parser):
  function save_log (line 60) | def save_log(prefix, output_dir, date_str):
  function save_code (line 74) | def save_code(exp_path, date_str):
  function prep_experiment (line 85) | def prep_experiment(args, parser):
  class AverageMeter (line 109) | class AverageMeter(object):
    method __init__ (line 111) | def __init__(self):
    method reset (line 114) | def reset(self):
    method update (line 120) | def update(self, val, n=1):
  function evaluate_eval (line 128) | def evaluate_eval(args, net, optimizer, val_loss, mf_score, hist, dump_i...
  function fast_hist (line 274) | def fast_hist(label_pred, label_true, num_classes):
  function print_evaluate_results (line 283) | def print_evaluate_results(hist, iu, writer=None, epoch=0, dataset=None):

FILE: benchmarks/HRNet-Semantic-Segmentation-HRNet-OCR/hubconf.py
  function hrnet_w48_cityscapes (line 16) | def hrnet_w48_cityscapes(pretrained=False, **kwargs):

FILE: benchmarks/HRNet-Semantic-Segmentation-HRNet-OCR/tools/_init_paths.py
  function add_path (line 15) | def add_path(path):

FILE: benchmarks/HRNet-Semantic-Segmentation-HRNet-OCR/tools/test.py
  function parse_args (line 34) | def parse_args():
  function main (line 56) | def main():

FILE: benchmarks/HRNet-Semantic-Segmentation-HRNet-OCR/tools/train.py
  function parse_args (line 36) | def parse_args():
  function get_sampler (line 55) | def get_sampler(dataset):
  function main (line 63) | def main():

FILE: benchmarks/KPConv-PyTorch-master/cpp_wrappers/cpp_neighbors/neighbors/neighbors.cpp
  function brute_neighbors (line 5) | void brute_neighbors(vector<PointXYZ>& queries, vector<PointXYZ>& suppor...
  function ordered_neighbors (line 58) | void ordered_neighbors(vector<PointXYZ>& queries,
  function batch_ordered_neighbors (line 125) | void batch_ordered_neighbors(vector<PointXYZ>& queries,
  function batch_nanoflann_neighbors (line 211) | void batch_nanoflann_neighbors(vector<PointXYZ>& queries,

FILE: benchmarks/KPConv-PyTorch-master/cpp_wrappers/cpp_neighbors/wrapper.cpp
  type PyModuleDef (line 35) | struct PyModuleDef
  function PyMODINIT_FUNC (line 48) | PyMODINIT_FUNC PyInit_radius_neighbors(void)
  function PyObject (line 58) | static PyObject* batch_neighbors(PyObject* self, PyObject* args, PyObjec...

FILE: benchmarks/KPConv-PyTorch-master/cpp_wrappers/cpp_subsampling/grid_subsampling/grid_subsampling.cpp
  function grid_subsampling (line 5) | void grid_subsampling(vector<PointXYZ>& original_points,
  function batch_grid_subsampling (line 109) | void batch_grid_subsampling(vector<PointXYZ>& original_points,

FILE: benchmarks/KPConv-PyTorch-master/cpp_wrappers/cpp_subsampling/grid_subsampling/grid_subsampling.h
  function class (line 10) | class SampledData
  function update_all (line 42) | void update_all(const PointXYZ p, vector<float>::iterator f_begin, vecto...
  function update_features (line 55) | void update_features(const PointXYZ p, vector<float>::iterator f_begin)
  function update_classes (line 62) | void update_classes(const PointXYZ p, vector<int>::iterator l_begin)
  function update_points (line 74) | void update_points(const PointXYZ p)

FILE: benchmarks/KPConv-PyTorch-master/cpp_wrappers/cpp_subsampling/wrapper.cpp
  type PyModuleDef (line 39) | struct PyModuleDef
  function PyMODINIT_FUNC (line 52) | PyMODINIT_FUNC PyInit_grid_subsampling(void)
  function PyObject (line 62) | static PyObject* batch_subsampling(PyObject* self, PyObject* args, PyObj...
  function PyObject (line 338) | static PyObject* cloud_subsampling(PyObject* self, PyObject* args, PyObj...

FILE: benchmarks/KPConv-PyTorch-master/cpp_wrappers/cpp_utils/cloud/cloud.cpp
  function PointXYZ (line 27) | PointXYZ max_point(std::vector<PointXYZ> points)
  function PointXYZ (line 48) | PointXYZ min_point(std::vector<PointXYZ> points)

FILE: benchmarks/KPConv-PyTorch-master/cpp_wrappers/cpp_utils/cloud/cloud.h
  function class (line 40) | class PointXYZ
  function const (line 58) | float operator [] (int i) const
  function dot (line 66) | float dot(const PointXYZ P) const
  function sq_norm (line 71) | float sq_norm()
  function PointXYZ (line 76) | PointXYZ cross(const PointXYZ P) const
  function PointXYZ (line 110) | inline PointXYZ operator + (const PointXYZ A, const PointXYZ B)
  function PointXYZ (line 115) | inline PointXYZ operator - (const PointXYZ A, const PointXYZ B)
  function PointXYZ (line 120) | inline PointXYZ operator * (const PointXYZ P, const float a)
  function PointXYZ (line 125) | inline PointXYZ operator * (const float a, const PointXYZ P)
  function operator (line 135) | inline bool operator == (const PointXYZ A, const PointXYZ B)
  function PointXYZ (line 140) | inline PointXYZ floor(const PointXYZ P)
  function kdtree_get_pt (line 150) | struct PointCloud

FILE: benchmarks/KPConv-PyTorch-master/cpp_wrappers/cpp_utils/nanoflann/nanoflann.hpp
  function T (line 79) | T pi_const() {
  type has_resize (line 87) | struct has_resize : std::false_type {}
  type has_resize<T, decltype((void)std::declval<T>().resize(1), 0)> (line 90) | struct has_resize<T, decltype((void)std::declval<T>().resize(1), 0)>
  type has_assign (line 93) | struct has_assign : std::false_type {}
  type has_assign<T, decltype((void)std::declval<T>().assign(1, 0), 0)> (line 96) | struct has_assign<T, decltype((void)std::declval<T>().assign(1, 0), 0)>
  function resize (line 103) | inline typename std::enable_if<has_resize<Container>::value, void>::type
  function resize (line 113) | inline typename std::enable_if<!has_resize<Container>::value, void>::type
  function assign (line 123) | inline typename std::enable_if<has_assign<Container>::value, void>::type
  function assign (line 132) | inline typename std::enable_if<!has_assign<Container>::value, void>::type
  class KNNResultSet (line 142) | class KNNResultSet {
    method KNNResultSet (line 155) | inline KNNResultSet(CountType capacity_)
    method init (line 158) | inline void init(IndexType *indices_, DistanceType *dists_) {
    method CountType (line 166) | inline CountType size() const { return count; }
    method full (line 168) | inline bool full() const { return count == capacity; }
    method addPoint (line 175) | inline bool addPoint(DistanceType dist, IndexType index) {
    type IndexDist_Sorter (line 208) | struct IndexDist_Sorter {
    class RadiusResultSet (line 220) | class RadiusResultSet {
      method RadiusResultSet (line 230) | inline RadiusResultSet(
      method init (line 237) | inline void init() { clear(); }
      method clear (line 238) | inline void clear() { m_indices_dists.clear(); }
      method size (line 240) | inline size_t size() const { return m_indices_dists.size(); }
      method full (line 242) | inline bool full() const { return true; }
      method addPoint (line 249) | inline bool addPoint(DistanceType dist, IndexType index) {
      method DistanceType (line 255) | inline DistanceType worstDist() const { return radius; }
      method worst_item (line 261) | std::pair<IndexType, DistanceType> worst_item() const {
    method save_value (line 279) | void save_value(FILE *stream, const T &value, size_t count = 1) {
    method save_value (line 284) | void save_value(FILE *stream, const std::vector<T> &value) {
    method load_value (line 291) | void load_value(FILE *stream, T &value, size_t count = 1) {
    method load_value (line 298) | void load_value(FILE *stream, std::vector<T> &value) {
    type Metric (line 315) | struct Metric {}
    type L1_Adaptor (line 324) | struct L1_Adaptor {
      method L1_Adaptor (line 330) | L1_Adaptor(const DataSource &_data_source) : data_source(_data_sourc...
      method DistanceType (line 332) | inline DistanceType evalMetric(const T *a, const size_t b_idx, size_...
      method DistanceType (line 363) | inline DistanceType accum_dist(const U a, const V b, const size_t) c...
    type L2_Adaptor (line 375) | struct L2_Adaptor {
      method L2_Adaptor (line 381) | L2_Adaptor(const DataSource &_data_source) : data_source(_data_sourc...
      method DistanceType (line 383) | inline DistanceType evalMetric(const T *a, const size_t b_idx, size_...
      method DistanceType (line 411) | inline DistanceType accum_dist(const U a, const V b, const size_t) c...
    type L2_Simple_Adaptor (line 423) | struct L2_Simple_Adaptor {
      method L2_Simple_Adaptor (line 429) | L2_Simple_Adaptor(const DataSource &_data_source)
      method DistanceType (line 432) | inline DistanceType evalMetric(const T *a, const size_t b_idx,
      method DistanceType (line 443) | inline DistanceType accum_dist(const U a, const V b, const size_t) c...
    type SO2_Adaptor (line 455) | struct SO2_Adaptor {
      method SO2_Adaptor (line 461) | SO2_Adaptor(const DataSource &_data_source) : data_source(_data_sour...
      method DistanceType (line 463) | inline DistanceType evalMetric(const T *a, const size_t b_idx,
      method DistanceType (line 471) | inline DistanceType accum_dist(const U a, const V b, const size_t) c...
    type SO3_Adaptor (line 489) | struct SO3_Adaptor {
      method SO3_Adaptor (line 495) | SO3_Adaptor(const DataSource &_data_source)
      method DistanceType (line 498) | inline DistanceType evalMetric(const T *a, const size_t b_idx,
      method DistanceType (line 504) | inline DistanceType accum_dist(const U a, const V b, const size_t id...
    type metric_L1 (line 510) | struct metric_L1 : public Metric {
      type traits (line 511) | struct traits {
    type metric_L2 (line 516) | struct metric_L2 : public Metric {
      type traits (line 517) | struct traits {
    type metric_L2_Simple (line 522) | struct metric_L2_Simple : public Metric {
      type traits (line 523) | struct traits {
    type metric_SO2 (line 528) | struct metric_SO2 : public Metric {
      type traits (line 529) | struct traits {
    type metric_SO3 (line 534) | struct metric_SO3 : public Metric {
      type traits (line 535) | struct traits {
    type KDTreeSingleIndexAdaptorParams (line 546) | struct KDTreeSingleIndexAdaptorParams {
      method KDTreeSingleIndexAdaptorParams (line 547) | KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10)
    type SearchParams (line 554) | struct SearchParams {
      method SearchParams (line 557) | SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ ...
    method T (line 578) | inline T *allocate(size_t count = 1) {
    class PooledAllocator (line 601) | class PooledAllocator {
      method internal_init (line 612) | void internal_init() {
      method PooledAllocator (line 626) | PooledAllocator() { internal_init(); }
      method free_all (line 634) | void free_all() {
      method T (line 702) | T *allocate(const size_t count = 1) {
    type array_or_vector_selector (line 715) | struct array_or_vector_selector {
    type array_or_vector_selector<-1, T> (line 719) | struct array_or_vector_selector<-1, T> {
    class KDTreeBaseClass (line 739) | class KDTreeBaseClass {
      method freeIndex (line 744) | void freeIndex(Derived &obj) {
      type Node (line 754) | struct Node {
        type leaf (line 758) | struct leaf {
        type nonleaf (line 761) | struct nonleaf {
      type Interval (line 771) | struct Interval {
      method size (line 813) | size_t size(const Derived &obj) const { return obj.m_size; }
      method veclen (line 816) | size_t veclen(const Derived &obj) {
      method ElementType (line 821) | inline ElementType dataset_get(const Derived &obj, size_t idx,
      method usedMemory (line 830) | size_t usedMemory(Derived &obj) {
      method computeMinMax (line 836) | void computeMinMax(const Derived &obj, IndexType *ind, IndexType count,
      method NodePtr (line 857) | NodePtr divideTree(Derived &obj, const IndexType left, const IndexTy...
      method middleSplit_ (line 909) | void middleSplit_(Derived &obj, IndexType *ind, IndexType count,
      method planeSplit (line 967) | void planeSplit(Derived &obj, IndexType *ind, const IndexType count,
      method DistanceType (line 1005) | DistanceType computeInitialDistances(const Derived &obj,
      method save_tree (line 1024) | void save_tree(Derived &obj, FILE *stream, NodePtr tree) {
      method load_tree (line 1034) | void load_tree(Derived &obj, FILE *stream, NodePtr &tree) {
      method saveIndex_ (line 1050) | void saveIndex_(Derived &obj, FILE *stream) {
      method loadIndex_ (line 1064) | void loadIndex_(Derived &obj, FILE *stream) {
    class KDTreeSingleIndexAdaptor (line 1116) | class KDTreeSingleIndexAdaptor
      method KDTreeSingleIndexAdaptor (line 1122) | KDTreeSingleIndexAdaptor(
      method KDTreeSingleIndexAdaptor (line 1170) | KDTreeSingleIndexAdaptor(const int dimensionality,
      method buildIndex (line 1190) | void buildIndex() {
      method findNeighbors (line 1221) | bool findNeighbors(RESULTSET &result, const ElementType *vec,
      method knnSearch (line 1254) | size_t knnSearch(const ElementType *query_point, const size_t num_cl...
      method radiusSearch (line 1279) | size_t
      method radiusSearchCustomCallback (line 1297) | size_t radiusSearchCustomCallback(
      method init_vind (line 1309) | void init_vind() {
      method computeBoundingBox (line 1318) | void computeBoundingBox(BoundingBox &bbox) {
      method searchLevel (line 1348) | bool searchLevel(RESULTSET &result_set, const ElementType *vec,
      method saveIndex (line 1419) | void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
      method loadIndex (line 1426) | void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
    class KDTreeSingleIndexDynamicAdaptor_ (line 1468) | class KDTreeSingleIndexDynamicAdaptor_
      method KDTreeSingleIndexDynamicAdaptor_ (line 1519) | KDTreeSingleIndexDynamicAdaptor_(
      method KDTreeSingleIndexDynamicAdaptor_ (line 1536) | KDTreeSingleIndexDynamicAdaptor_
      method buildIndex (line 1555) | void buildIndex() {
      method findNeighbors (line 1584) | bool findNeighbors(RESULTSET &result, const ElementType *vec,
      method knnSearch (line 1616) | size_t knnSearch(const ElementType *query_point, const size_t num_cl...
      method radiusSearch (line 1641) | size_t
      method radiusSearchCustomCallback (line 1659) | size_t radiusSearchCustomCallback(
      method computeBoundingBox (line 1669) | void computeBoundingBox(BoundingBox &bbox) {
      method searchLevel (line 1699) | void searchLevel(RESULTSET &result_set, const ElementType *vec,
      method saveIndex (line 1765) | void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
      method loadIndex (line 1772) | void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
    class KDTreeSingleIndexDynamicAdaptor (line 1791) | class KDTreeSingleIndexDynamicAdaptor {
      method First0Bit (line 1825) | int First0Bit(IndexType num) {
      method init (line 1835) | void init() {
      method KDTreeSingleIndexDynamicAdaptor (line 1860) | KDTreeSingleIndexDynamicAdaptor(const int dimensionality,
      method KDTreeSingleIndexDynamicAdaptor (line 1881) | KDTreeSingleIndexDynamicAdaptor(
      method addPoints (line 1886) | void addPoints(IndexType start, IndexType end) {
      method removePoint (line 1909) | void removePoint(size_t idx) {
      method findNeighbors (line 1929) | bool findNeighbors(RESULTSET &result, const ElementType *vec,
    type KDTreeEigenMatrixAdaptor (line 1957) | struct KDTreeEigenMatrixAdaptor {
      method KDTreeEigenMatrixAdaptor (line 1971) | KDTreeEigenMatrixAdaptor(const size_t dimensionality,
      method KDTreeEigenMatrixAdaptor (line 1990) | KDTreeEigenMatrixAdaptor(const self_t &) = delete;
      method query (line 2002) | inline void query(const num_t *query_point, const size_t num_closest,
      method self_t (line 2013) | const self_t &derived() const { return *this; }
      method self_t (line 2014) | self_t &derived() { return *this; }
      method kdtree_get_point_count (line 2017) | inline size_t kdtree_get_point_count() const {
      method num_t (line 2022) | inline num_t kdtree_get_pt(const IndexType idx, size_t dim) const {
      method kdtree_get_bbox (line 2031) | bool kdtree_get_bbox(BBOX & /*bb*/) const {

FILE: benchmarks/KPConv-PyTorch-master/datasets/ModelNet40.py
  class ModelNet40Dataset (line 51) | class ModelNet40Dataset(PointCloudDataset):
    method __init__ (line 54) | def __init__(self, config, train=True, orient_correction=True):
    method __len__ (line 150) | def __len__(self):
    method __getitem__ (line 156) | def __getitem__(self, idx_list):
    method load_subsampled_clouds (line 232) | def load_subsampled_clouds(self, orient_correction):
  class ModelNet40Sampler (line 322) | class ModelNet40Sampler(Sampler):
    method __init__ (line 325) | def __init__(self, dataset: ModelNet40Dataset, use_potential=True, bal...
    method __iter__ (line 348) | def __iter__(self):
    method __len__ (line 434) | def __len__(self):
    method calibration (line 440) | def calibration(self, dataloader, untouched_ratio=0.9, verbose=False):
  class ModelNet40CustomBatch (line 681) | class ModelNet40CustomBatch:
    method __init__ (line 684) | def __init__(self, input_list):
    method pin_memory (line 714) | def pin_memory(self):
    method to (line 731) | def to(self, device):
    method unstack_points (line 745) | def unstack_points(self, layer=None):
    method unstack_neighbors (line 749) | def unstack_neighbors(self, layer=None):
    method unstack_pools (line 753) | def unstack_pools(self, layer=None):
    method unstack_elements (line 757) | def unstack_elements(self, element_name, layer=None, to_numpy=True):
  function ModelNet40Collate (line 808) | def ModelNet40Collate(batch_data):
  function debug_sampling (line 818) | def debug_sampling(dataset, sampler, loader):
  function debug_timing (line 837) | def debug_timing(dataset, sampler, loader):
  function debug_show_clouds (line 879) | def debug_show_clouds(dataset, sampler, loader):
  function debug_batch_and_neighbors_calib (line 934) | def debug_batch_and_neighbors_calib(dataset, sampler, loader):
  class ModelNet40WorkerInitDebug (line 971) | class ModelNet40WorkerInitDebug:
    method __init__ (line 974) | def __init__(self, dataset):
    method __call__ (line 978) | def __call__(self, worker_id):

FILE: benchmarks/KPConv-PyTorch-master/datasets/Rellis.py
  class RellisDataset (line 54) | class RellisDataset(PointCloudDataset):
    method __init__ (line 57) | def __init__(self, config, set='training', balance_classes=True):
    method __len__ (line 210) | def __len__(self):
    method __getitem__ (line 216) | def __getitem__(self, batch_i):
    method load_calib_poses (line 560) | def load_calib_poses(self):
    method parse_calibration (line 746) | def parse_calibration(self, filename):
    method parse_poses (line 773) | def parse_poses(self, filename, calibration):
  class RellisSampler (line 808) | class RellisSampler(Sampler):
    method __init__ (line 811) | def __init__(self, dataset: RellisDataset):
    method __iter__ (line 825) | def __iter__(self):
    method __len__ (line 926) | def __len__(self):
    method calib_max_in (line 932) | def calib_max_in(self, config, dataloader, untouched_ratio=0.8, verbos...
    method calibration (line 1049) | def calibration(self, dataloader, untouched_ratio=0.9, verbose=False, ...
  class RellisCustomBatch (line 1324) | class RellisCustomBatch:
    method __init__ (line 1327) | def __init__(self, input_list):
    method pin_memory (line 1367) | def pin_memory(self):
    method to (line 1386) | def to(self, device):
    method unstack_points (line 1402) | def unstack_points(self, layer=None):
    method unstack_neighbors (line 1406) | def unstack_neighbors(self, layer=None):
    method unstack_pools (line 1410) | def unstack_pools(self, layer=None):
    method unstack_elements (line 1414) | def unstack_elements(self, element_name, layer=None, to_numpy=True):
  function RellisCollate (line 1465) | def RellisCollate(batch_data):
  function debug_timing (line 1475) | def debug_timing(dataset, loader):
  function debug_class_w (line 1520) | def debug_class_w(dataset, loader):

FILE: benchmarks/KPConv-PyTorch-master/datasets/S3DIS.py
  class S3DISDataset (line 53) | class S3DISDataset(PointCloudDataset):
    method __init__ (line 56) | def __init__(self, config, set='training', use_potentials=True, load_d...
    method __len__ (line 225) | def __len__(self):
    method __getitem__ (line 231) | def __getitem__(self, batch_i):
    method potential_item (line 242) | def potential_item(self, batch_i, debug_workers=False):
    method random_item (line 495) | def random_item(self, batch_i):
    method prepare_S3DIS_ply (line 620) | def prepare_S3DIS_ply(self):
    method load_subsampled_clouds (line 694) | def load_subsampled_clouds(self):
    method load_evaluation_points (line 869) | def load_evaluation_points(self, file_path):
  class S3DISSampler (line 885) | class S3DISSampler(Sampler):
    method __init__ (line 888) | def __init__(self, dataset: S3DISDataset):
    method __iter__ (line 902) | def __iter__(self):
    method __len__ (line 955) | def __len__(self):
    method fast_calib (line 961) | def fast_calib(self):
    method calibration (line 1038) | def calibration(self, dataloader, untouched_ratio=0.9, verbose=False, ...
  class S3DISCustomBatch (line 1291) | class S3DISCustomBatch:
    method __init__ (line 1294) | def __init__(self, input_list):
    method pin_memory (line 1330) | def pin_memory(self):
    method to (line 1350) | def to(self, device):
    method unstack_points (line 1367) | def unstack_points(self, layer=None):
    method unstack_neighbors (line 1371) | def unstack_neighbors(self, layer=None):
    method unstack_pools (line 1375) | def unstack_pools(self, layer=None):
    method unstack_elements (line 1379) | def unstack_elements(self, element_name, layer=None, to_numpy=True):
  function S3DISCollate (line 1430) | def S3DISCollate(batch_data):
  function debug_upsampling (line 1440) | def debug_upsampling(dataset, loader):
  function debug_timing (line 1474) | def debug_timing(dataset, loader):
  function debug_show_clouds (line 1519) | def debug_show_clouds(dataset, loader):
  function debug_batch_and_neighbors_calib (line 1574) | def debug_batch_and_neighbors_calib(dataset, loader):

FILE: benchmarks/KPConv-PyTorch-master/datasets/SemanticKitti.py
  class SemanticKittiDataset (line 54) | class SemanticKittiDataset(PointCloudDataset):
    method __init__ (line 57) | def __init__(self, config, set='training', balance_classes=True):
    method __len__ (line 194) | def __len__(self):
    method __getitem__ (line 200) | def __getitem__(self, batch_i):
    method load_calib_poses (line 542) | def load_calib_poses(self):
    method parse_calibration (line 661) | def parse_calibration(self, filename):
    method parse_poses (line 688) | def parse_poses(self, filename, calibration):
  class SemanticKittiSampler (line 723) | class SemanticKittiSampler(Sampler):
    method __init__ (line 726) | def __init__(self, dataset: SemanticKittiDataset):
    method __iter__ (line 740) | def __iter__(self):
    method __len__ (line 831) | def __len__(self):
    method calib_max_in (line 837) | def calib_max_in(self, config, dataloader, untouched_ratio=0.8, verbos...
    method calibration (line 954) | def calibration(self, dataloader, untouched_ratio=0.9, verbose=False, ...
  class SemanticKittiCustomBatch (line 1229) | class SemanticKittiCustomBatch:
    method __init__ (line 1232) | def __init__(self, input_list):
    method pin_memory (line 1272) | def pin_memory(self):
    method to (line 1291) | def to(self, device):
    method unstack_points (line 1307) | def unstack_points(self, layer=None):
    method unstack_neighbors (line 1311) | def unstack_neighbors(self, layer=None):
    method unstack_pools (line 1315) | def unstack_pools(self, layer=None):
    method unstack_elements (line 1319) | def unstack_elements(self, element_name, layer=None, to_numpy=True):
  function SemanticKittiCollate (line 1370) | def SemanticKittiCollate(batch_data):
  function debug_timing (line 1380) | def debug_timing(dataset, loader):
  function debug_class_w (line 1425) | def debug_class_w(dataset, loader):

FILE: benchmarks/KPConv-PyTorch-master/datasets/common.py
  function grid_subsampling (line 44) | def grid_subsampling(points, features=None, labels=None, sampleDl=0.1, v...
  function batch_grid_subsampling (line 77) | def batch_grid_subsampling(points, batches_len, features=None, labels=None,
  function batch_neighbors (line 185) | def batch_neighbors(queries, supports, q_batches, s_batches, radius):
  class PointCloudDataset (line 205) | class PointCloudDataset(Dataset):
    method __init__ (line 208) | def __init__(self, name):
    method __len__ (line 226) | def __len__(self):
    method __getitem__ (line 232) | def __getitem__(self, idx):
    method init_labels (line 239) | def init_labels(self):
    method augmentation_transform (line 248) | def augmentation_transform(self, points, normals=None, verbose=False):
    method big_neighborhood_filter (line 332) | def big_neighborhood_filter(self, neighbors, layer):
    method classification_inputs (line 344) | def classification_inputs(self,
    method segmentation_inputs (line 457) | def segmentation_inputs(self,

FILE: benchmarks/KPConv-PyTorch-master/kernels/kernel_points.py
  function create_3D_rotations (line 44) | def create_3D_rotations(axis, angle):
  function spherical_Lloyd (line 78) | def spherical_Lloyd(radius, num_cells, dimension=3, fixed='center', appr...
  function kernel_point_optimization_debug (line 258) | def kernel_point_optimization_debug(radius, num_points, num_kernels=1, d...
  function load_kernels (line 400) | def load_kernels(radius, num_kpoints, dimension, fixed, lloyd=False):

FILE: benchmarks/KPConv-PyTorch-master/models/architectures.py
  function p2p_fitting_regularizer (line 21) | def p2p_fitting_regularizer(net):
  class KPCNN (line 57) | class KPCNN(nn.Module):
    method __init__ (line 62) | def __init__(self, config):
    method forward (line 136) | def forward(self, batch, config):
    method loss (line 151) | def loss(self, outputs, labels):
    method accuracy (line 174) | def accuracy(outputs, labels):
  class KPFCNN (line 189) | class KPFCNN(nn.Module):
    method __init__ (line 194) | def __init__(self, config, lbl_values, ign_lbls):
    method forward (line 322) | def forward(self, batch, config):
    method loss (line 345) | def loss(self, outputs, labels):
    method accuracy (line 377) | def accuracy(self, outputs, labels):

FILE: benchmarks/KPConv-PyTorch-master/models/blocks.py
  function gather (line 35) | def gather(x, idx, method=2):
  function radius_gaussian (line 69) | def radius_gaussian(sq_r, sig, eps=1e-9):
  function closest_pool (line 79) | def closest_pool(x, inds):
  function max_pool (line 94) | def max_pool(x, inds):
  function global_average (line 113) | def global_average(x, batch_lengths):
  class KPConv (line 143) | class KPConv(nn.Module):
    method __init__ (line 145) | def __init__(self, kernel_size, p_dim, in_channels, out_channels, KP_e...
    method reset_parameters (line 216) | def reset_parameters(self):
    method init_KP (line 222) | def init_KP(self):
    method forward (line 237) | def forward(self, q_pts, s_pts, neighb_inds, x):
    method __repr__ (line 375) | def __repr__(self):
  function block_decider (line 386) | def block_decider(block_name,
  class BatchNormBlock (line 429) | class BatchNormBlock(nn.Module):
    method __init__ (line 431) | def __init__(self, in_dim, use_bn, bn_momentum):
    method reset_parameters (line 449) | def reset_parameters(self):
    method forward (line 452) | def forward(self, x):
    method __repr__ (line 463) | def __repr__(self):
  class UnaryBlock (line 469) | class UnaryBlock(nn.Module):
    method __init__ (line 471) | def __init__(self, in_dim, out_dim, use_bn, bn_momentum, no_relu=False):
    method forward (line 492) | def forward(self, x, batch=None):
    method __repr__ (line 499) | def __repr__(self):
  class SimpleBlock (line 506) | class SimpleBlock(nn.Module):
    method __init__ (line 508) | def __init__(self, block_name, in_dim, out_dim, radius, layer_ind, con...
    method forward (line 548) | def forward(self, x, batch):
  class ResnetBottleneckBlock (line 563) | class ResnetBottleneckBlock(nn.Module):
    method __init__ (line 565) | def __init__(self, block_name, in_dim, out_dim, radius, layer_ind, con...
    method forward (line 620) | def forward(self, features, batch):
  class GlobalAverageBlock (line 651) | class GlobalAverageBlock(nn.Module):
    method __init__ (line 653) | def __init__(self):
    method forward (line 660) | def forward(self, x, batch):
  class NearestUpsampleBlock (line 664) | class NearestUpsampleBlock(nn.Module):
    method __init__ (line 666) | def __init__(self, layer_ind):
    method forward (line 674) | def forward(self, x, batch):
    method __repr__ (line 677) | def __repr__(self):
  class MaxPoolBlock (line 682) | class MaxPoolBlock(nn.Module):
    method __init__ (line 684) | def __init__(self, layer_ind):
    method forward (line 692) | def forward(self, x, batch):

FILE: benchmarks/KPConv-PyTorch-master/plot_convergence.py
  function running_mean (line 51) | def running_mean(signal, n, axis=0, stride=1):
  function IoU_class_metrics (line 83) | def IoU_class_metrics(all_IoUs, smooth_n):
  function load_confusions (line 97) | def load_confusions(filename, n_class):
  function load_training_results (line 110) | def load_training_results(path):
  function load_single_IoU (line 137) | def load_single_IoU(filename, n_parts):
  function load_snap_clouds (line 149) | def load_snap_clouds(path, dataset, only_last=False):
  function compare_trainings (line 199) | def compare_trainings(list_of_paths, list_of_labels=None):
  function compare_convergences_segment (line 332) | def compare_convergences_segment(dataset, list_of_paths, list_of_names=N...
  function compare_convergences_classif (line 448) | def compare_convergences_classif(list_of_paths, list_of_labels=None):
  function compare_convergences_SLAM (line 563) | def compare_convergences_SLAM(dataset, list_of_paths, list_of_names=None):
  function experiment_name_1 (line 691) | def experiment_name_1():
  function experiment_name_2 (line 720) | def experiment_name_2():

FILE: benchmarks/KPConv-PyTorch-master/test_models.py
  function model_choice (line 49) | def model_choice(chosen_log):

FILE: benchmarks/KPConv-PyTorch-master/train_ModelNet40.py
  class Modelnet40Config (line 46) | class Modelnet40Config(Config):

FILE: benchmarks/KPConv-PyTorch-master/train_Rellis.py
  class RellisConfig (line 46) | class RellisConfig(Config):

FILE: benchmarks/KPConv-PyTorch-master/train_S3DIS.py
  class S3DISConfig (line 43) | class S3DISConfig(Config):

FILE: benchmarks/KPConv-PyTorch-master/train_SemanticKitti.py
  class SemanticKittiConfig (line 46) | class SemanticKittiConfig(Config):

FILE: benchmarks/KPConv-PyTorch-master/utils/config.py
  class bcolors (line 23) | class bcolors:
  class Config (line 34) | class Config:
    method __init__ (line 190) | def __init__(self):
    method load (line 234) | def load(self, path):
    method save (line 277) | def save(self):

FILE: benchmarks/KPConv-PyTorch-master/utils/mayavi_visu.py
  function show_ModelNet_models (line 42) | def show_ModelNet_models(all_points):
  function show_ModelNet_examples (line 106) | def show_ModelNet_examples(clouds, cloud_normals=None, cloud_labels=None):
  function show_neighbors (line 191) | def show_neighbors(query, supports, neighbors):
  function show_input_batch (line 271) | def show_input_batch(batch):

FILE: benchmarks/KPConv-PyTorch-master/utils/metrics.py
  function fast_confusion (line 35) | def fast_confusion(true, pred, label_values=None):
  function metrics (line 121) | def metrics(confusions, ignore_unclassified=False):
  function smooth_metrics (line 158) | def smooth_metrics(confusions, smooth_n=0, ignore_unclassified=False):
  function IoU_from_confusions (line 204) | def IoU_from_confusions(confusions):

FILE: benchmarks/KPConv-PyTorch-master/utils/ply.py
  function parse_header (line 62) | def parse_header(plyfile, ext):
  function parse_mesh_header (line 82) | def parse_mesh_header(plyfile, ext):
  function read_ply (line 116) | def read_ply(filename, triangular_mesh=False):
  function header_properties (line 199) | def header_properties(field_list, field_names):
  function write_ply (line 217) | def write_ply(filename, field_list, field_names, triangular_faces=None):
  function describe_element (line 331) | def describe_element(name, df):

FILE: benchmarks/KPConv-PyTorch-master/utils/tester.py
  class ModelTester (line 52) | class ModelTester:
    method __init__ (line 57) | def __init__(self, net, chkp_path=None, on_gpu=True):
    method classification_test (line 85) | def classification_test(self, net, test_loader, config, num_votes=100,...
    method cloud_segmentation_test (line 177) | def cloud_segmentation_test(self, net, test_loader, config, num_votes=...
    method slam_segmentation_test (line 467) | def slam_segmentation_test(self, net, test_loader, config, num_votes=1...
    method rellis_segmentation_test (line 776) | def rellis_segmentation_test(self, net, test_loader, config, num_votes...

FILE: benchmarks/KPConv-PyTorch-master/utils/trainer.py
  class ModelTrainer (line 54) | class ModelTrainer:
    method __init__ (line 59) | def __init__(self, net, config, chkp_path=None, finetune=False, on_gpu...
    method train (line 125) | def train(self, net, training_loader, val_loader, config):
    method validation (line 280) | def validation(self, net, val_loader, config: Config):
    method object_classification_validation (line 293) | def object_classification_validation(self, net, val_loader, config):
    method cloud_segmentation_validation (line 413) | def cloud_segmentation_validation(self, net, val_loader, config, debug...
    method slam_segmentation_validation (line 652) | def slam_segmentation_validation(self, net, val_loader, config, debug=...

FILE: benchmarks/KPConv-PyTorch-master/utils/visualizer.py
  class ModelVisualizer (line 51) | class ModelVisualizer:
    method __init__ (line 56) | def __init__(self, net, config, chkp_path, on_gpu=True):
    method show_deformable_kernels (line 99) | def show_deformable_kernels(self, net, loader, config, deform_idx=0):
  function show_ModelNet_models (line 447) | def show_ModelNet_models(all_points):

FILE: benchmarks/KPConv-PyTorch-master/visualize_deformations.py
  function model_choice (line 47) | def model_choice(chosen_log):

FILE: benchmarks/SalsaNext/train/common/avgmeter.py
  class AverageMeter (line 4) | class AverageMeter(object):
    method __init__ (line 7) | def __init__(self):
    method reset (line 10) | def reset(self):
    method update (line 16) | def update(self, val, n=1):

FILE: benchmarks/SalsaNext/train/common/laserscan.py
  class LaserScan (line 10) | class LaserScan:
    method __init__ (line 14) | def __init__(self, project=False, H=64, W=1024, fov_up=3.0, fov_down=-...
    method reset (line 27) | def reset(self):
    method size (line 60) | def size(self):
    method __len__ (line 64) | def __len__(self):
    method open_scan (line 67) | def open_scan(self, filename):
    method set_points (line 96) | def set_points(self, points, remissions=None):
    method do_range_projection (line 134) | def do_range_projection(self):
  class SemLaserScan (line 198) | class SemLaserScan(LaserScan):
    method __init__ (line 202) | def __init__(self, sem_color_dict=None, project=False, H=64, W=1024, f...
    method reset (line 233) | def reset(self):
    method open_label (line 257) | def open_label(self, filename):
    method set_label (line 278) | def set_label(self, label):
    method colorize (line 300) | def colorize(self):
    method do_label_projection (line 309) | def do_label_projection(self):

FILE: benchmarks/SalsaNext/train/common/laserscanvis.py
  class LaserScanVis (line 10) | class LaserScanVis:
    method __init__ (line 13) | def __init__(self, scan, scan_names, label_names, offset=0,
    method reset (line 29) | def reset(self):
    method get_mpl_colormap (line 115) | def get_mpl_colormap(self, cmap_name):
    method update_scan (line 126) | def update_scan(self):
    method key_press (line 193) | def key_press(self, event):
    method draw (line 205) | def draw(self, event):
    method destroy (line 211) | def destroy(self):
    method run (line 217) | def run(self):

FILE: benchmarks/SalsaNext/train/common/logger.py
  class Logger (line 14) | class Logger(object):
    method __init__ (line 16) | def __init__(self, log_dir):
    method scalar_summary (line 20) | def scalar_summary(self, tag, value, step):
    method image_summary (line 27) | def image_summary(self, tag, images, step):
    method histo_summary (line 52) | def histo_summary(self, tag, values, step, bins=1000):

FILE: benchmarks/SalsaNext/train/common/summary.py
  function summary (line 10) | def summary(model, input_size, batch_size=-1, device="cuda"):

FILE: benchmarks/SalsaNext/train/common/sync_batchnorm/batchnorm.py
  function _sum_ft (line 25) | def _sum_ft(tensor):
  function _unsqueeze_ft (line 30) | def _unsqueeze_ft(tensor):
  class _SynchronizedBatchNorm (line 40) | class _SynchronizedBatchNorm(_BatchNorm):
    method __init__ (line 41) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
    method forward (line 51) | def forward(self, input):
    method __data_parallel_replicate__ (line 86) | def __data_parallel_replicate__(self, ctx, copy_id):
    method _data_parallel_master (line 96) | def _data_parallel_master(self, intermediates):
    method _compute_mean_std (line 119) | def _compute_mean_std(self, sum_, ssum, size):
  class SynchronizedBatchNorm1d (line 136) | class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
    method _check_input_dim (line 192) | def _check_input_dim(self, input):
  class SynchronizedBatchNorm2d (line 199) | class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
    method _check_input_dim (line 255) | def _check_input_dim(self, input):
  class SynchronizedBatchNorm3d (line 262) | class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
    method _check_input_dim (line 319) | def _check_input_dim(self, input):
  function convert_model (line 326) | def convert_model(module):

FILE: benchmarks/SalsaNext/train/common/sync_batchnorm/comm.py
  class FutureResult (line 18) | class FutureResult(object):
    method __init__ (line 21) | def __init__(self):
    method put (line 26) | def put(self, result):
    method get (line 32) | def get(self):
  class SlavePipe (line 47) | class SlavePipe(_SlavePipeBase):
    method run_slave (line 50) | def run_slave(self, msg):
  class SyncMaster (line 57) | class SyncMaster(object):
    method __init__ (line 68) | def __init__(self, master_callback):
    method __getstate__ (line 79) | def __getstate__(self):
    method __setstate__ (line 82) | def __setstate__(self, state):
    method register_slave (line 85) | def register_slave(self, identifier):
    method run_master (line 103) | def run_master(self, master_msg):
    method nr_slaves (line 137) | def nr_slaves(self):

FILE: benchmarks/SalsaNext/train/common/sync_batchnorm/replicate.py
  class CallbackContext (line 23) | class CallbackContext(object):
  function execute_replication_callbacks (line 27) | def execute_replication_callbacks(modules):
  class DataParallelWithCallback (line 50) | class DataParallelWithCallback(DataParallel):
    method replicate (line 64) | def replicate(self, module, device_ids):
  function patch_replication_callback (line 71) | def patch_replication_callback(data_parallel):

FILE: benchmarks/SalsaNext/train/common/warmupLR.py
  class warmupLR (line 6) | class warmupLR(toptim._LRScheduler):
    method __init__ (line 13) | def __init__(self, optimizer, lr, warmup_steps, momentum, decay):
    method get_lr (line 40) | def get_lr(self):
    method step (line 43) | def step(self, epoch=None):

FILE: benchmarks/SalsaNext/train/tasks/semantic/dataset/kitti/parser.py
  function is_scan (line 27) | def is_scan(filename):
  function is_label (line 31) | def is_label(filename):
  function my_collate (line 35) | def my_collate(batch):
  class SemanticKitti (line 62) | class SemanticKitti(Dataset):
    method __init__ (line 64) | def __init__(self, root,    # directory where data is
    method __getitem__ (line 155) | def __getitem__(self, index):
    method __len__ (line 249) | def __len__(self):
    method map (line 253) | def map(label, mapdict):
  class Parser (line 279) | class Parser():
    method __init__ (line 281) | def __init__(self,
    method get_train_batch (line 374) | def get_train_batch(self):
    method get_train_set (line 378) | def get_train_set(self):
    method get_valid_batch (line 381) | def get_valid_batch(self):
    method get_valid_set (line 385) | def get_valid_set(self):
    method get_test_batch (line 388) | def get_test_batch(self):
    method get_test_set (line 392) | def get_test_set(self):
    method get_train_size (line 395) | def get_train_size(self):
    method get_valid_size (line 398) | def get_valid_size(self):
    method get_test_size (line 401) | def get_test_size(self):
    method get_n_classes (line 404) | def get_n_classes(self):
    method get_original_class_string (line 407) | def get_original_class_string(self, idx):
    method get_xentropy_class_string (line 410) | def get_xentropy_class_string(self, idx):
    method to_original (line 413) | def to_original(self, label):
    method to_xentropy (line 417) | def to_xentropy(self, label):
    method to_color (line 421) | def to_color(self, label):

FILE: benchmarks/SalsaNext/train/tasks/semantic/dataset/rellis/parser.py
  function is_scan (line 27) | def is_scan(filename):
  function is_label (line 31) | def is_label(filename):
  function my_collate (line 35) | def my_collate(batch):
  class Rellis (line 67) | class Rellis(Dataset):
    method __init__ (line 69) | def __init__(self, root,    # directory where data is
    method __getitem__ (line 145) | def __getitem__(self, index):
    method __len__ (line 242) | def __len__(self):
    method map (line 246) | def map(label, mapdict):
  class Parser (line 272) | class Parser():
    method __init__ (line 274) | def __init__(self,
    method get_train_batch (line 368) | def get_train_batch(self):
    method get_train_set (line 372) | def get_train_set(self):
    method get_valid_batch (line 375) | def get_valid_batch(self):
    method get_valid_set (line 379) | def get_valid_set(self):
    method get_test_batch (line 382) | def get_test_batch(self):
    method get_test_set (line 386) | def get_test_set(self):
    method get_train_size (line 389) | def get_train_size(self):
    method get_valid_size (line 392) | def get_valid_size(self):
    method get_test_size (line 395) | def get_test_size(self):
    method get_n_classes (line 398) | def get_n_classes(self):
    method get_original_class_string (line 401) | def get_original_class_string(self, idx):
    method get_xentropy_class_string (line 404) | def get_xentropy_class_string(self, idx):
    method to_original (line 407) | def to_original(self, label):
    method to_xentropy (line 411) | def to_xentropy(self, label):
    method to_color (line 415) | def to_color(self, label):

FILE: benchmarks/SalsaNext/train/tasks/semantic/evaluate_iou.py
  function save_to_log (line 17) | def save_to_log(logdir,logfile,message):
  function eval (line 23) | def eval(test_sequences,splits,pred):

FILE: benchmarks/SalsaNext/train/tasks/semantic/infer.py
  function str2bool (line 14) | def str2bool(v):

FILE: benchmarks/SalsaNext/train/tasks/semantic/infer2.py
  function str2bool (line 14) | def str2bool(v):

FILE: benchmarks/SalsaNext/train/tasks/semantic/modules/Lovasz_Softmax.py
  function isnan (line 31) | def isnan(x):
  function mean (line 35) | def mean(l, ignore_nan=False, empty=0):
  function lovasz_grad (line 56) | def lovasz_grad(gt_sorted):
  function lovasz_softmax (line 71) | def lovasz_softmax(probas, labels, classes='present', per_image=False, i...
  function lovasz_softmax_flat (line 89) | def lovasz_softmax_flat(probas, labels, classes='present'):
  function flatten_probas (line 120) | def flatten_probas(probas, labels, ignore=None):
  class Lovasz_softmax (line 139) | class Lovasz_softmax(nn.Module):
    method __init__ (line 140) | def __init__(self, classes='present', per_image=False, ignore=None):
    method forward (line 146) | def forward(self, probas, labels):

FILE: benchmarks/SalsaNext/train/tasks/semantic/modules/SalsaNext.py
  class ResContextBlock (line 10) | class ResContextBlock(nn.Module):
    method __init__ (line 11) | def __init__(self, in_filters, out_filters):
    method forward (line 25) | def forward(self, x):
  class ResBlock (line 42) | class ResBlock(nn.Module):
    method __init__ (line 43) | def __init__(self, in_filters, out_filters, dropout_rate, kernel_size=...
    method forward (line 73) | def forward(self, x):
  class UpBlock (line 112) | class UpBlock(nn.Module):
    method __init__ (line 113) | def __init__(self, in_filters, out_filters, dropout_rate, drop_out=True):
    method forward (line 142) | def forward(self, x, skip):
  class SalsaNext (line 173) | class SalsaNext(nn.Module):
    method __init__ (line 174) | def __init__(self, nclasses):
    method forward (line 195) | def forward(self, x):

FILE: benchmarks/SalsaNext/train/tasks/semantic/modules/SalsaNextAdf.py
  function keep_variance_fn (line 17) | def keep_variance_fn(x):
  class ResContextBlock (line 20) | class ResContextBlock(nn.Module):
    method __init__ (line 21) | def __init__(self, in_filters, out_filters):
    method forward (line 35) | def forward(self, x):
  class ResBlock (line 52) | class ResBlock(nn.Module):
    method __init__ (line 53) | def __init__(self, in_filters, out_filters, kernel_size=(3, 3), stride=1,
    method forward (line 85) | def forward(self, x):
  class UpBlock (line 126) | class UpBlock(nn.Module):
    method __init__ (line 127) | def __init__(self, in_filters, out_filters,drop_out=True, p=0.2):
    method forward (line 155) | def forward(self, x, skip):
  class SalsaNextUncertainty (line 198) | class SalsaNextUncertainty(nn.Module):
    method __init__ (line 199) | def __init__(self, nclasses,p=0.2):
    method forward (line 222) | def forward(self, x):

FILE: benchmarks/SalsaNext/train/tasks/semantic/modules/adf.py
  function resize2D (line 39) | def resize2D(inputs, size_targets, mode="bilinear"):
  function resize2D_as (line 53) | def resize2D_as(inputs, output_as, mode="bilinear"):
  function normcdf (line 58) | def normcdf(value, mu=0.0, stddev=1.0):
  function _normal_log_pdf (line 63) | def _normal_log_pdf(value, mu, stddev):
  function normpdf (line 69) | def normpdf(value, mu=0.0, stddev=1.0):
  class AvgPool2d (line 73) | class AvgPool2d(nn.Module):
    method __init__ (line 74) | def __init__(self, keep_variance_fn=None,kernel_size=2):
    method forward (line 79) | def forward(self, inputs_mean, inputs_variance):
  class Softmax (line 94) | class Softmax(nn.Module):
    method __init__ (line 95) | def __init__(self, dim=1, keep_variance_fn=None):
    method forward (line 100) | def forward(self, features_mean, features_variance, eps=1e-5):
  class ReLU (line 125) | class ReLU(nn.Module):
    method __init__ (line 126) | def __init__(self, keep_variance_fn=None):
    method forward (line 130) | def forward(self, features_mean, features_variance):
  class LeakyReLU (line 143) | class LeakyReLU(nn.Module):
    method __init__ (line 144) | def __init__(self, negative_slope=0.01, keep_variance_fn=None):
    method forward (line 149) | def forward(self, features_mean, features_variance):
  class Dropout (line 173) | class Dropout(nn.Module):
    method __init__ (line 176) | def __init__(self, p: float = 0.5, keep_variance_fn=None, inplace=False):
    method forward (line 184) | def forward(self, inputs_mean, inputs_variance):
  class MaxPool2d (line 202) | class MaxPool2d(nn.Module):
    method __init__ (line 203) | def __init__(self, keep_variance_fn=None):
    method _max_pool_internal (line 207) | def _max_pool_internal(self, mu_a, mu_b, var_a, var_b):
    method _max_pool_1x2 (line 221) | def _max_pool_1x2(self, inputs_mean, inputs_variance):
    method _max_pool_2x1 (line 230) | def _max_pool_2x1(self, inputs_mean, inputs_variance):
    method forward (line 239) | def forward(self, inputs_mean, inputs_variance):
  class Linear (line 245) | class Linear(nn.Module):
    method __init__ (line 246) | def __init__(self, in_features, out_features, bias=True, keep_variance...
    method forward (line 257) | def forward(self, inputs_mean, inputs_variance):
  class BatchNorm2d (line 265) | class BatchNorm2d(nn.Module):
    method __init__ (line 270) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
    method reset_running_stats (line 295) | def reset_running_stats(self):
    method reset_parameters (line 301) | def reset_parameters(self):
    method _check_input_dim (line 307) | def _check_input_dim(self, input):
    method forward (line 310) | def forward(self, inputs_mean, inputs_variance):
  class Conv2d (line 344) | class Conv2d(_ConvNd):
    method __init__ (line 345) | def __init__(self, in_channels, out_channels, kernel_size, stride=1,
    method forward (line 357) | def forward(self, inputs_mean, inputs_variance):
  class ConvTranspose2d (line 367) | class ConvTranspose2d(_ConvTransposeMixin, _ConvNd):
    method __init__ (line 368) | def __init__(self, in_channels, out_channels, kernel_size, stride=1,
    method forward (line 381) | def forward(self, inputs_mean, inputs_variance, output_size=None):
  function concatenate_as (line 394) | def concatenate_as(tensor_list, tensor_as, dim, mode="bilinear"):
  class Sequential (line 402) | class Sequential(nn.Module):
    method __init__ (line 403) | def __init__(self, *args):
    method _get_item_by_idx (line 412) | def _get_item_by_idx(self, iterator, idx):
    method __getitem__ (line 421) | def __getitem__(self, idx):
    method __setitem__ (line 427) | def __setitem__(self, idx, module):
    method __delitem__ (line 431) | def __delitem__(self, idx):
    method __len__ (line 439) | def __len__(self):
    method __dir__ (line 442) | def __dir__(self):
    method forward (line 447) | def forward(self, inputs, inputs_variance):

FILE: benchmarks/SalsaNext/train/tasks/semantic/modules/ioueval.py
  class iouEval (line 9) | class iouEval:
    method __init__ (line 10) | def __init__(self, n_classes, device, ignore=None):
    method num_classes (line 21) | def num_classes(self):
    method reset (line 24) | def reset(self):
    method addBatch (line 30) | def addBatch(self, x, y):  # x=preds, y=targets
    method getStats (line 58) | def getStats(self):
    method getIoU (line 70) | def getIoU(self):
    method getacc (line 78) | def getacc(self):

FILE: benchmarks/SalsaNext/train/tasks/semantic/modules/trainer.py
  function keep_variance_fn (line 25) | def keep_variance_fn(x):
  function one_hot_pred_from_label (line 28) | def one_hot_pred_from_label(y_pred, labels):
  class SoftmaxHeteroscedasticLoss (line 37) | class SoftmaxHeteroscedasticLoss(torch.nn.Module):
    method __init__ (line 38) | def __init__(self):
    method forward (line 42) | def forward(self, outputs, targets, eps=1e-5):
  function save_to_log (line 50) | def save_to_log(logdir, logfile, message):
  function save_checkpoint (line 57) | def save_checkpoint(to_save, logdir, suffix=""):
  class Trainer (line 63) | class Trainer():
    method __init__ (line 64) | def __init__(self, ARCH, DATA, datadir, logdir, path=None,uncertainty=...
    method calculate_estimate (line 190) | def calculate_estimate(self, epoch, iter):
    method get_mpl_colormap (line 199) | def get_mpl_colormap(cmap_name):
    method make_log_img (line 208) | def make_log_img(depth, mask, pred, gt, color_fn):
    method save_to_log (line 225) | def save_to_log(logdir, logger, info, epoch, w_summary=False, model=No...
    method train (line 247) | def train(self):
    method train_epoch (line 344) | def train_epoch(self, train_loader, model, criterion, optimizer, epoch...
    method validate (line 499) | def validate(self, val_loader, model, criterion, evaluator, class_func...

FILE: benchmarks/SalsaNext/train/tasks/semantic/modules/user.py
  class User (line 24) | class User():
    method __init__ (line 25) | def __init__(self, ARCH, DATA, datadir, logdir, modeldir,split,uncerta...
    method infer (line 88) | def infer(self):
    method infer_subset (line 120) | def infer_subset(self, loader, to_orig_fn,cnn,knn):

FILE: benchmarks/SalsaNext/train/tasks/semantic/modules/user2.py
  class User (line 24) | class User():
    method __init__ (line 25) | def __init__(self, ARCH, DATA, datadir, logdir, modeldir,split,uncerta...
    method infer (line 88) | def infer(self):
    method infer_subset (line 120) | def infer_subset(self, loader, to_orig_fn,cnn,knn):

FILE: benchmarks/SalsaNext/train/tasks/semantic/postproc/KNN.py
  function get_gaussian_kernel (line 11) | def get_gaussian_kernel(kernel_size=3, sigma=2, channels=1):
  class KNN (line 36) | class KNN(nn.Module):
    method __init__ (line 37) | def __init__(self, params, nclasses):
    method forward (line 54) | def forward(self, proj_range, unproj_range, proj_argmax, px, py):

FILE: benchmarks/SalsaNext/train/tasks/semantic/train.py
  function remove_exponent (line 20) | def remove_exponent(d):
  function millify (line 23) | def millify(n, precision=0, drop_nulls=True, prefixes=[]):
  function str2bool (line 37) | def str2bool(v):

FILE: utils/convert_ply2bin.py
  function load_from_bin (line 5) | def load_from_bin(bin_path):
  function convert_ply2bin (line 9) | def convert_ply2bin(ply_path,bin_path=None):

FILE: utils/label_convert.py
  function convert_label (line 11) | def convert_label(label, label_mapping, inverse=False):
  function convert_color (line 21) | def convert_color(label, color_map):
  function save_output (line 27) | def save_output(label_dir, output_dir, config_path):

FILE: utils/plyreader.py
  class PlyObject (line 4) | class PlyObject:
    method __init__ (line 5) | def __init__(self,header) -> None:
    method __str__ (line 9) | def __str__(self):
    method __getitem__ (line 12) | def __getitem__(self, key):
    method element_info (line 15) | def element_info(self,key):
  class PlyReader (line 18) | class PlyReader:
    method __init__ (line 29) | def __init__(self):
    method _headerparse (line 32) | def _headerparse(self,f):
    method open (line 69) | def open(self,filename):

FILE: utils/stereo_camerainfo_pub.py
  class NerianStereoInfoPub (line 6) | class NerianStereoInfoPub():
    method __init__ (line 7) | def __init__(self):
    method get_infos (line 22) | def get_infos(self, info):
    method cb_l (line 44) | def cb_l(self, m):
    method cb_r (line 52) | def cb_r(self, m):