SYMBOL INDEX (498 symbols across 38 files)

FILE: Datasets/cowmask.py
  function gaussian_kernels (line 34) | def gaussian_kernels(sigma, max_sigma):
  function cow_masks (line 49) | def cow_masks(mask_size, log_sigma_range, max_sigma, prop_range):

FILE: Datasets/flowlib.py
  function show_flow (line 30) | def show_flow(filename):
  function point_vec (line 42) | def point_vec(img,flow,skip=10):
  function visualize_flow (line 63) | def visualize_flow(flow, mode='Y'):
  function read_flow (line 101) | def read_flow(filename):
  function write_flo (line 122) | def write_flo(flow, filename):
  function write_flow (line 147) | def write_flow(flow, filename):
  function save_flow_image (line 166) | def save_flow_image(flow, image_file):
  function flowfile_to_imagefile (line 178) | def flowfile_to_imagefile(flow_file, image_file):
  function segment_flow (line 189) | def segment_flow(flow):
  function flow_error (line 226) | def flow_error(tu, tv, u, v):
  function flow_to_image (line 281) | def flow_to_image(flow):
  function evaluate_flow_file (line 319) | def evaluate_flow_file(gt_file, pred_file):
  function evaluate_flow (line 334) | def evaluate_flow(gt_flow, pred_flow):
  function read_disp_png (line 350) | def read_disp_png(file_name):
  function disp_to_flowfile (line 368) | def disp_to_flowfile(disp, filename):
  function read_image (line 396) | def read_image(filename):
  function warp_image (line 407) | def warp_image(im, flow):
  function pfm_to_flo (line 452) | def pfm_to_flo(pfm_file):
  function scale_image (line 459) | def scale_image(image, new_range):
  function compute_color (line 474) | def compute_color(u, v):
  function make_color_wheel (line 518) | def make_color_wheel():
  function read_flo_file (line 568) | def read_flo_file(filename):
  function read_png_file (line 593) | def read_png_file(flow_file):
  function read_pfm_file (line 618) | def read_pfm_file(flow_file):
  function resample (line 629) | def resample(img, sz):

FILE: Datasets/segmask_gt.py
  function dataloader (line 22) | def dataloader(filepath, fpass='frames_cleanpass', level=6):
  function default_loader (line 79) | def default_loader(path):
  function flow_loader (line 82) | def flow_loader(path):
  function load_exts (line 90) | def load_exts(cam_file):
  function disparity_loader (line 103) | def disparity_loader(path):
  function triangulation (line 112) | def triangulation(disp, xcoord, ycoord, bl=1, fl = 450, cx = 479.5, cy =...
  function exp_loader (line 121) | def exp_loader(index, iml0s, iml1s, flowl0s, disp0s=None, dispcs=None, c...
  function motionmask (line 214) | def motionmask(flowl0, depthflow, RT01):

FILE: Datasets/tartanTrajFlowDataset.py
  class TrajFolderDataset (line 16) | class TrajFolderDataset(Dataset):
    method __init__ (line 19) | def __init__(self, imgfolder, transform = None,
    method __len__ (line 38) | def __len__(self):
    method __getitem__ (line 41) | def __getitem__(self, idx):

FILE: Datasets/util_flow.py
  function readPFM (line 26) | def readPFM(file):
  function save_pfm (line 65) | def save_pfm(file, image, scale = 1):
  function ReadMiddleburyFloFile (line 92) | def ReadMiddleburyFloFile(path):
  function ReadKittiPngFile (line 122) | def ReadKittiPngFile(path):
  function WriteMiddleburyFloFile (line 159) | def WriteMiddleburyFloFile(path, width, height, u, v, mask=None):
  function write_flow (line 181) | def write_flow(path,flow):
  function WriteKittiPngFile (line 197) | def WriteKittiPngFile(path, width, height, u, v, mask=None):
  function ConvertMiddleburyFloToKittiPng (line 219) | def ConvertMiddleburyFloToKittiPng(src_path, dest_path):
  function ConvertKittiPngToMiddleburyFlo (line 223) | def ConvertKittiPngToMiddleburyFlo(src_path, dest_path):
  function ParseFilenameKitti (line 228) | def ParseFilenameKitti(filename):
  function read_calib_file (line 239) | def read_calib_file(filepath):
  function load_calib_cam_to_cam (line 255) | def load_calib_cam_to_cam(cam_to_cam_file):

FILE: Datasets/utils.py
  class Compose (line 29) | class Compose(object):
    method __init__ (line 42) | def __init__(self, transforms):
    method __call__ (line 45) | def __call__(self, img):
  class DownscaleFlow (line 51) | class DownscaleFlow(object):
    method __init__ (line 56) | def __init__(self, scale=4):
    method __call__ (line 62) | def __call__(self, sample):
  class CropCenter (line 76) | class CropCenter(object):
    method __init__ (line 81) | def __init__(self, size):
    method __call__ (line 87) | def __call__(self, sample):
  class ResizeData (line 119) | class ResizeData(object):
    method __init__ (line 123) | def __init__(self, size):
    method __call__ (line 129) | def __call__(self, sample):
  class ToTensor (line 149) | class ToTensor(object):
    method __call__ (line 150) | def __call__(self, sample):
  function tensor2img (line 168) | def tensor2img(tensImg,mean,std):
  function bilinear_interpolate (line 180) | def bilinear_interpolate(img, h, w):
  function calculate_angle_distance_from_du_dv (line 206) | def calculate_angle_distance_from_du_dv(du, dv, flagDegree=False):
  function visflow (line 220) | def visflow(flownp, maxF=500.0, n=8, mask=None, hueMax=179, angShift=0.0):
  function dataset_intrinsics (line 251) | def dataset_intrinsics(dataset='tartanair', is_15mm=False):
  function plot_traj (line 278) | def plot_traj(gtposes, estposes, vis=False, savefigname=None, title=''):
  function make_intrinsics_layer (line 295) | def make_intrinsics_layer(w, h, fx, fy, ox, oy):
  function load_kiiti_intrinsics (line 303) | def load_kiiti_intrinsics(filename):
  function load_sceneflow_extrinsics (line 326) | def load_sceneflow_extrinsics(filename):

FILE: DytanVO.py
  class DytanVO (line 48) | class DytanVO(object):
    method __init__ (line 49) | def __init__(self, vo_model_name, seg_model_name, image_height, image_...
    method load_vo_model (line 100) | def load_vo_model(self, model, modelname):
    method load_seg_model (line 120) | def load_seg_model(self, model, modelname):
    method test_batch (line 130) | def test_batch(self, sample, intrinsics, seg_thresh, iter_num):
    method initialize_segnet_input (line 229) | def initialize_segnet_input(self, imgL_o, intrinsics):
    method transform_segnet_input (line 255) | def transform_segnet_input(self, imgL_o, imgR_o):

FILE: Network/PWC/PWCNet.py
  function conv (line 16) | def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dila...
  function predict_flow (line 22) | def predict_flow(in_planes):
  function deconv (line 25) | def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
  class PWCDCNet (line 30) | class PWCDCNet(nn.Module):
    method __init__ (line 35) | def __init__(self, md=4, flow_norm=20.0):
    method warp (line 133) | def warp(self, x, flo):
    method multi_scale_conv (line 171) | def multi_scale_conv(self, conv0_func, conv1_func, conv2_func, conv3_f...
    method concate_two_layers (line 179) | def concate_two_layers(self, pred_func, decon_func, upfeat_func, feat_...
    method forward (line 191) | def forward(self,x):
  function pwc_dc_net (line 236) | def pwc_dc_net(path=None):

FILE: Network/PWC/correlation.py
  function cupy_kernel (line 235) | def cupy_kernel(strFunction, objVariables):
  function cupy_launch (line 274) | def cupy_launch(strFunction, strKernel):
  class _FunctionCorrelation (line 278) | class _FunctionCorrelation(torch.autograd.Function):
    method forward (line 280) | def forward(self, first, second):
    method backward (line 333) | def backward(self, gradOutput):
  function FunctionCorrelation (line 385) | def FunctionCorrelation(tenFirst, tenSecond):
  class ModuleCorrelation (line 389) | class ModuleCorrelation(torch.nn.Module):
    method __init__ (line 390) | def __init__(self):
    method forward (line 394) | def forward(self, tenFirst, tenSecond):

FILE: Network/VOFlowNet.py
  function conv (line 38) | def conv(in_planes, out_planes, kernel_size=3, stride=2, padding=1, dila...
  function linear (line 51) | def linear(in_planes, out_planes):
  class BasicBlock (line 57) | class BasicBlock(nn.Module):
    method __init__ (line 59) | def __init__(self, inplanes, planes, stride, downsample, pad, dilation):
    method forward (line 68) | def forward(self, x):
  class VOFlowRes (line 78) | class VOFlowRes(nn.Module):
    method __init__ (line 79) | def __init__(self):
    method _make_layer (line 111) | def _make_layer(self, block, planes, blocks, stride, pad, dilation):
    method forward (line 125) | def forward(self, x):

FILE: Network/VONet.py
  class VONet (line 39) | class VONet(nn.Module):
    method __init__ (line 40) | def __init__(self):
    method forward (line 46) | def forward(self, x, only_flow=False, only_pose=False):

FILE: Network/rigidmask/VCNplus.py
  class flow_reg (line 16) | class flow_reg(nn.Module):
    method __init__ (line 25) | def __init__(self, size, ent=False, maxdisp = int(4), fac=1):
    method forward (line 44) | def forward(self, x):
  class WarpModule (line 91) | class WarpModule(nn.Module):
    method __init__ (line 95) | def __init__(self, size):
    method forward (line 105) | def forward(self, x, flo):
  function get_grid (line 127) | def get_grid(B,H,W):
  class SegNet (line 135) | class SegNet(nn.Module):
    method __init__ (line 139) | def __init__(self, size, md=[4,4,4,4,4], fac=1., exp_unc=True):
    method corrf (line 365) | def corrf(self, refimg_fea, targetimg_fea,maxdisp, fac=1):
    method cost_matching (line 390) | def cost_matching(self,up_flow, c1, c2, flowh, enth, level):
    method affine (line 445) | def affine(self,pref,flow, pw=1):
    method forward_VCN (line 470) | def forward_VCN(self, im):
    method forward (line 528) | def forward(self,im,disc_aux=None,flowdc=None):

FILE: Network/rigidmask/conv4d.py
  function conv4d (line 13) | def conv4d(data,filters,bias=None,permute_filters=True,use_half=False):
  class Conv4d (line 59) | class Conv4d(_ConvNd):
    method __init__ (line 64) | def __init__(self, in_channels, out_channels, kernel_size, bias=True, ...
    method forward (line 90) | def forward(self, input):
  class fullConv4d (line 97) | class fullConv4d(torch.nn.Module):
    method __init__ (line 98) | def __init__(self, in_channels, out_channels, kernel_size, bias=True, ...
    method forward (line 105) | def forward(self, input):
  class butterfly4D (line 112) | class butterfly4D(torch.nn.Module):
    method __init__ (line 116) | def __init__(self, fdima, fdimb, withbn=True, full=True,groups=1):
    method forward (line 127) | def forward(self,x):
  class projfeat4d (line 153) | class projfeat4d(torch.nn.Module):
    method __init__ (line 157) | def __init__(self, in_planes, out_planes, stride, with_bn=True,groups=1):
    method forward (line 164) | def forward(self,x):
  class sepConv4d (line 173) | class sepConv4d(torch.nn.Module):
    method __init__ (line 177) | def __init__(self, in_planes, out_planes, stride=(1,1,1), with_bn=True...
    method forward (line 209) | def forward(self,x):
  class sepConv4dBlock (line 223) | class sepConv4dBlock(torch.nn.Module):
    method __init__ (line 228) | def __init__(self, in_planes, out_planes, stride=(1,1,1), with_bn=True...
    method forward (line 243) | def forward(self,x):

FILE: Network/rigidmask/det.py
  function create_model (line 24) | def create_model(arch, heads, head_conv,num_input):
  function load_model (line 31) | def load_model(model, model_path, optimizer=None, resume=False,
  function save_model (line 86) | def save_model(path, epoch, model, optimizer=None):

FILE: Network/rigidmask/det_losses.py
  function _slow_neg_loss (line 18) | def _slow_neg_loss(pred, gt):
  function _neg_loss (line 43) | def _neg_loss(pred, gt, heat_logits):
  function _not_faster_neg_loss (line 71) | def _not_faster_neg_loss(pred, gt):
  function _slow_reg_loss (line 88) | def _slow_reg_loss(regr, gt_regr, mask):
  function _reg_loss (line 99) | def _reg_loss(regr, gt_regr, mask):
  class FocalLoss (line 116) | class FocalLoss(nn.Module):
    method __init__ (line 118) | def __init__(self):
    method forward (line 122) | def forward(self, out, target, logits):
  class RegLoss (line 125) | class RegLoss(nn.Module):
    method __init__ (line 133) | def __init__(self):
    method forward (line 136) | def forward(self, output, mask, ind, target):
  class RegL1Loss (line 141) | class RegL1Loss(nn.Module):
    method __init__ (line 142) | def __init__(self):
    method forward (line 145) | def forward(self, output, mask, ind, target):
  class NormRegL1Loss (line 153) | class NormRegL1Loss(nn.Module):
    method __init__ (line 154) | def __init__(self):
    method forward (line 157) | def forward(self, output, mask, ind, target):
  class RegWeightedL1Loss (line 167) | class RegWeightedL1Loss(nn.Module):
    method __init__ (line 168) | def __init__(self):
    method forward (line 171) | def forward(self, output, mask, ind, target):
  class L1Loss (line 179) | class L1Loss(nn.Module):
    method __init__ (line 180) | def __init__(self):
    method forward (line 183) | def forward(self, output, mask, ind, target):
  class BinRotLoss (line 189) | class BinRotLoss(nn.Module):
    method __init__ (line 190) | def __init__(self):
    method forward (line 193) | def forward(self, output, mask, ind, rotbin, rotres):
  function compute_res_loss (line 198) | def compute_res_loss(output, target):
  function compute_bin_loss (line 202) | def compute_bin_loss(output, target, mask):
  function compute_rot_loss (line 207) | def compute_rot_loss(output, target_bin, target_res, mask):

FILE: Network/rigidmask/det_utils.py
  function _sigmoid (line 8) | def _sigmoid(x):
  function _gather_feat (line 12) | def _gather_feat(feat, ind, mask=None):
  function _transpose_and_gather_feat (line 22) | def _transpose_and_gather_feat(feat, ind):
  function flip_tensor (line 28) | def flip_tensor(x):
  function flip_lr (line 33) | def flip_lr(x, flip_idx):
  function flip_lr_off (line 41) | def flip_lr_off(x, flip_idx):

FILE: Network/rigidmask/networks/DCNv2/DCN/dcn_v2.py
  class _DCNv2 (line 16) | class _DCNv2(Function):
    method forward (line 18) | def forward(ctx, input, offset, mask, weight, bias,
    method backward (line 37) | def backward(ctx, grad_output):
  class DCNv2 (line 57) | class DCNv2(nn.Module):
    method __init__ (line 59) | def __init__(self, in_channels, out_channels,
    method reset_parameters (line 75) | def reset_parameters(self):
    method forward (line 83) | def forward(self, input, offset, mask):
  class DCN (line 97) | class DCN(DCNv2):
    method __init__ (line 99) | def __init__(self, in_channels, out_channels,
    method init_offset (line 114) | def init_offset(self):
    method forward (line 118) | def forward(self, input):
  class _DCNv2Pooling (line 132) | class _DCNv2Pooling(Function):
    method forward (line 134) | def forward(ctx, input, rois, offset,
    method backward (line 163) | def backward(ctx, grad_output):
  class DCNv2Pooling (line 187) | class DCNv2Pooling(nn.Module):
    method __init__ (line 189) | def __init__(self,
    method forward (line 208) | def forward(self, input, rois, offset):
  class DCNPooling (line 223) | class DCNPooling(DCNv2Pooling):
    method __init__ (line 225) | def __init__(self,
    method forward (line 259) | def forward(self, input, rois):

FILE: Network/rigidmask/networks/DCNv2/DCN/src/cpu/dcn_v2_cpu.cpp
  function dcn_v2_cpu_forward (line 22) | at::Tensor
  function dcn_v2_cpu_backward (line 113) | std::vector<at::Tensor> dcn_v2_cpu_backward(const at::Tensor &input,

FILE: Network/rigidmask/networks/DCNv2/DCN/src/cpu/dcn_v2_im2col_cpu.cpp
  function dmcn_im2col_bilinear_cpu (line 27) | float dmcn_im2col_bilinear_cpu(const float *bottom_data, const int data_...
  function dmcn_get_gradient_weight_cpu (line 58) | float dmcn_get_gradient_weight_cpu(float argmax_h, float argmax_w,
  function dmcn_get_coordinate_weight_cpu (line 84) | float dmcn_get_coordinate_weight_cpu(float argmax_h, float argmax_w,
  function modulated_deformable_im2col_cpu_kernel (line 127) | void modulated_deformable_im2col_cpu_kernel(const int n, const float *da...
  function modulated_deformable_col2im_cpu_kernel (line 198) | void modulated_deformable_col2im_cpu_kernel(const int n, const float *da...
  function modulated_deformable_col2im_coord_cpu_kernel (line 259) | void modulated_deformable_col2im_coord_cpu_kernel(const int n, const flo...
  function modulated_deformable_im2col_cpu (line 331) | void modulated_deformable_im2col_cpu(const float* data_im, const float* ...
  function modulated_deformable_col2im_cpu (line 353) | void modulated_deformable_col2im_cpu(const float* data_col, const float*...
  function modulated_deformable_col2im_coord_cpu (line 375) | void modulated_deformable_col2im_coord_cpu(const float* data_col, const ...

FILE: Network/rigidmask/networks/DCNv2/DCN/src/cpu/dcn_v2_psroi_pooling_cpu.cpp
  function T (line 34) | T bilinear_interp_cpu(
  function DeformablePSROIPoolForwardKernelCpu (line 59) | void DeformablePSROIPoolForwardKernelCpu(
  function DeformablePSROIPoolBackwardAccKernelCpu (line 149) | void DeformablePSROIPoolBackwardAccKernelCpu(
  function dcn_v2_psroi_pooling_cpu_forward (line 278) | std::tuple<at::Tensor, at::Tensor>
  function dcn_v2_psroi_pooling_cpu_backward (line 350) | std::tuple<at::Tensor, at::Tensor>

FILE: Network/rigidmask/networks/DCNv2/DCN/src/vision.cpp
  function PYBIND11_MODULE (line 4) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

FILE: Network/rigidmask/networks/DCNv2/DCN/testcpu.py
  function conv_identify (line 20) | def conv_identify(weight, bias):
  function check_zero_offset (line 32) | def check_zero_offset():
  function check_gradient_dconv (line 69) | def check_gradient_dconv():
  function check_pooling_zero_offset (line 100) | def check_pooling_zero_offset():
  function check_gradient_dpooling (line 134) | def check_gradient_dpooling():
  function example_dconv (line 169) | def example_dconv():
  function example_dpooling (line 183) | def example_dpooling():
  function example_mdpooling (line 226) | def example_mdpooling():

FILE: Network/rigidmask/networks/DCNv2/DCN/testcuda.py
  function conv_identify (line 20) | def conv_identify(weight, bias):
  function check_zero_offset (line 32) | def check_zero_offset():
  function check_gradient_dconv (line 69) | def check_gradient_dconv():
  function check_pooling_zero_offset (line 100) | def check_pooling_zero_offset():
  function check_gradient_dpooling (line 134) | def check_gradient_dpooling():
  function example_dconv (line 169) | def example_dconv():
  function example_dpooling (line 183) | def example_dpooling():
  function example_mdpooling (line 226) | def example_mdpooling():

FILE: Network/rigidmask/networks/DCNv2/setup.py
  function get_extensions (line 18) | def get_extensions():

FILE: Network/rigidmask/networks/dlav0.py
  function get_model_url (line 18) | def get_model_url(data='imagenet', name='dla34', hash='ba72cf86'):
  function conv3x3 (line 22) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 28) | class BasicBlock(nn.Module):
    method __init__ (line 29) | def __init__(self, inplanes, planes, stride=1, dilation=1):
    method forward (line 42) | def forward(self, x, residual=None):
  class Bottleneck (line 59) | class Bottleneck(nn.Module):
    method __init__ (line 62) | def __init__(self, inplanes, planes, stride=1, dilation=1):
    method forward (line 79) | def forward(self, x, residual=None):
  class BottleneckX (line 100) | class BottleneckX(nn.Module):
    method __init__ (line 104) | def __init__(self, inplanes, planes, stride=1, dilation=1):
    method forward (line 123) | def forward(self, x, residual=None):
  class Root (line 144) | class Root(nn.Module):
    method __init__ (line 145) | def __init__(self, in_channels, out_channels, kernel_size, residual):
    method forward (line 154) | def forward(self, *x):
  class Tree (line 165) | class Tree(nn.Module):
    method __init__ (line 166) | def __init__(self, levels, block, in_channels, out_channels, stride=1,
    method forward (line 205) | def forward(self, x, residual=None, children=None):
  class DLA (line 221) | class DLA(nn.Module):
    method __init__ (line 222) | def __init__(self, levels, channels, num_classes=1000,
    method _make_level (line 260) | def _make_level(self, block, inplanes, planes, blocks, stride=1):
    method _make_conv_level (line 277) | def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation...
    method forward (line 289) | def forward(self, x):
    method load_pretrained_model (line 304) | def load_pretrained_model(self,  data='imagenet', name='dla34', hash='...
  function dla34 (line 319) | def dla34(pretrained, **kwargs):  # DLA-34
  function dla46_c (line 328) | def dla46_c(pretrained=None, **kwargs):  # DLA-46-C
  function dla46x_c (line 338) | def dla46x_c(pretrained=None, **kwargs):  # DLA-X-46-C
  function dla60x_c (line 348) | def dla60x_c(pretrained, **kwargs):  # DLA-X-60-C
  function dla60 (line 358) | def dla60(pretrained=None, **kwargs):  # DLA-60
  function dla60x (line 368) | def dla60x(pretrained=None, **kwargs):  # DLA-X-60
  function dla102 (line 378) | def dla102(pretrained=None, **kwargs):  # DLA-102
  function dla102x (line 387) | def dla102x(pretrained=None, **kwargs):  # DLA-X-102
  function dla102x2 (line 396) | def dla102x2(pretrained=None, **kwargs):  # DLA-X-102 64
  function dla169 (line 405) | def dla169(pretrained=None, **kwargs):  # DLA-169
  function set_bn (line 414) | def set_bn(bn):
  class Identity (line 420) | class Identity(nn.Module):
    method __init__ (line 421) | def __init__(self):
    method forward (line 424) | def forward(self, x):
  function fill_up_weights (line 428) | def fill_up_weights(up):
  class IDAUp (line 440) | class IDAUp(nn.Module):
    method __init__ (line 441) | def __init__(self, node_kernel, out_dim, channels, up_factors):
    method forward (line 482) | def forward(self, layers):
  class DLAUp (line 499) | class DLAUp(nn.Module):
    method __init__ (line 500) | def __init__(self, channels, scales=(1, 2, 4, 8, 16), in_channels=None):
    method forward (line 515) | def forward(self, layers):
  function fill_fc_weights (line 524) | def fill_fc_weights(layers):
  class DLASeg (line 533) | class DLASeg(nn.Module):
    method __init__ (line 534) | def __init__(self, base_name, heads,
    method forward (line 600) | def forward(self, x):
  function get_pose_net (line 642) | def get_pose_net(num_layers, heads, head_conv=256, down_ratio=4):

FILE: Network/rigidmask/networks/large_hourglass.py
  class convolution (line 17) | class convolution(nn.Module):
    method __init__ (line 18) | def __init__(self, k, inp_dim, out_dim, stride=1, with_bn=True):
    method forward (line 26) | def forward(self, x):
  class fully_connected (line 32) | class fully_connected(nn.Module):
    method __init__ (line 33) | def __init__(self, inp_dim, out_dim, with_bn=True):
    method forward (line 42) | def forward(self, x):
  class residual (line 48) | class residual(nn.Module):
    method __init__ (line 49) | def __init__(self, k, inp_dim, out_dim, stride=1, with_bn=True):
    method forward (line 65) | def forward(self, x):
  function make_layer (line 76) | def make_layer(k, inp_dim, out_dim, modules, layer=convolution, **kwargs):
  function make_layer_revr (line 82) | def make_layer_revr(k, inp_dim, out_dim, modules, layer=convolution, **k...
  class MergeUp (line 89) | class MergeUp(nn.Module):
    method forward (line 90) | def forward(self, up1, up2):
  function make_merge_layer (line 93) | def make_merge_layer(dim):
  function make_pool_layer (line 99) | def make_pool_layer(dim):
  function make_unpool_layer (line 102) | def make_unpool_layer(dim):
  function make_kp_layer (line 105) | def make_kp_layer(cnv_dim, curr_dim, out_dim):
  function make_inter_layer (line 111) | def make_inter_layer(dim):
  function make_cnv_layer (line 114) | def make_cnv_layer(inp_dim, out_dim):
  class kp_module (line 117) | class kp_module(nn.Module):
    method __init__ (line 118) | def __init__(
    method forward (line 167) | def forward(self, x):
  class exkp (line 176) | class exkp(nn.Module):
    method __init__ (line 177) | def __init__(
    method forward (line 253) | def forward(self, image):
  function make_hg_layer (line 277) | def make_hg_layer(kernel, dim0, dim1, mod, layer=convolution, **kwargs):
  class HourglassNet (line 283) | class HourglassNet(exkp):
    method __init__ (line 284) | def __init__(self, heads, num_stacks=2):
  function get_large_hourglass_net (line 298) | def get_large_hourglass_net(num_layers, heads, head_conv):

FILE: Network/rigidmask/networks/msra_resnet.py
  function conv3x3 (line 28) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 34) | class BasicBlock(nn.Module):
    method __init__ (line 37) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 47) | def forward(self, x):
  class Bottleneck (line 66) | class Bottleneck(nn.Module):
    method __init__ (line 69) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 84) | def forward(self, x):
  class PoseResNet (line 107) | class PoseResNet(nn.Module):
    method __init__ (line 109) | def __init__(self, block, layers, heads, head_conv, **kwargs):
    method _make_layer (line 154) | def _make_layer(self, block, planes, blocks, stride=1):
    method _get_deconv_cfg (line 171) | def _get_deconv_cfg(self, deconv_kernel, index):
    method _make_deconv_layer (line 184) | def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
    method forward (line 211) | def forward(self, x):
    method init_weights (line 228) | def init_weights(self, num_layers, pretrained=True):
  function get_pose_net (line 275) | def get_pose_net(num_layers, heads, head_conv):

FILE: Network/rigidmask/networks/pose_dla_dcn.py
  function get_model_url (line 21) | def get_model_url(data='imagenet', name='dla34', hash='ba72cf86'):
  function conv3x3 (line 25) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 31) | class BasicBlock(nn.Module):
    method __init__ (line 32) | def __init__(self, inplanes, planes, stride=1, dilation=1):
    method forward (line 45) | def forward(self, x, residual=None):
  class Bottleneck (line 62) | class Bottleneck(nn.Module):
    method __init__ (line 65) | def __init__(self, inplanes, planes, stride=1, dilation=1):
    method forward (line 82) | def forward(self, x, residual=None):
  class BottleneckX (line 103) | class BottleneckX(nn.Module):
    method __init__ (line 107) | def __init__(self, inplanes, planes, stride=1, dilation=1):
    method forward (line 126) | def forward(self, x, residual=None):
  class Root (line 147) | class Root(nn.Module):
    method __init__ (line 148) | def __init__(self, in_channels, out_channels, kernel_size, residual):
    method forward (line 157) | def forward(self, *x):
  class Tree (line 168) | class Tree(nn.Module):
    method __init__ (line 169) | def __init__(self, levels, block, in_channels, out_channels, stride=1,
    method forward (line 208) | def forward(self, x, residual=None, children=None):
  class DLA (line 224) | class DLA(nn.Module):
    method __init__ (line 225) | def __init__(self, levels, channels, num_classes=1000,
    method _make_level (line 257) | def _make_level(self, block, inplanes, planes, blocks, stride=1):
    method _make_conv_level (line 274) | def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation...
    method forward (line 286) | def forward(self, x):
    method load_pretrained_model (line 294) | def load_pretrained_model(self, data='imagenet', name='dla34', hash='b...
  function dla34 (line 309) | def dla34(pretrained=True, **kwargs):  # DLA-34
  class Identity (line 317) | class Identity(nn.Module):
    method __init__ (line 319) | def __init__(self):
    method forward (line 322) | def forward(self, x):
  function fill_fc_weights (line 326) | def fill_fc_weights(layers):
  function fill_up_weights (line 333) | def fill_up_weights(up):
  class DeformConv (line 345) | class DeformConv(nn.Module):
    method __init__ (line 346) | def __init__(self, chi, cho):
    method forward (line 354) | def forward(self, x):
  class IDAUp (line 360) | class IDAUp(nn.Module):
    method __init__ (line 362) | def __init__(self, o, channels, up_f):
    method forward (line 380) | def forward(self, layers, startp, endp):
  class DLAUp (line 390) | class DLAUp(nn.Module):
    method __init__ (line 391) | def __init__(self, startp, channels, scales, in_channels=None):
    method forward (line 407) | def forward(self, layers):
  class Interpolate (line 416) | class Interpolate(nn.Module):
    method __init__ (line 417) | def __init__(self, scale, mode):
    method forward (line 422) | def forward(self, x):
  class DLASeg (line 427) | class DLASeg(nn.Module):
    method __init__ (line 428) | def __init__(self, base_name, heads, pretrained, down_ratio, final_ker...
    method forward (line 470) | def forward(self, x):
  function get_pose_net (line 485) | def get_pose_net(num_layers, heads, head_conv=256, down_ratio=4,num_inpu...

FILE: Network/rigidmask/networks/resnet_dcn.py
  function conv3x3 (line 32) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 38) | class BasicBlock(nn.Module):
    method __init__ (line 41) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 51) | def forward(self, x):
  class Bottleneck (line 70) | class Bottleneck(nn.Module):
    method __init__ (line 73) | def __init__(self, inplanes, planes, stride=1, downsample=None):
    method forward (line 88) | def forward(self, x):
  function fill_up_weights (line 110) | def fill_up_weights(up):
  function fill_fc_weights (line 121) | def fill_fc_weights(layers):
  class PoseResNet (line 130) | class PoseResNet(nn.Module):
    method __init__ (line 132) | def __init__(self, block, layers, heads, head_conv):
    method _make_layer (line 179) | def _make_layer(self, block, planes, blocks, stride=1):
    method _get_deconv_cfg (line 196) | def _get_deconv_cfg(self, deconv_kernel, index):
    method _make_deconv_layer (line 209) | def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
    method forward (line 248) | def forward(self, x):
    method init_weights (line 265) | def init_weights(self, num_layers):
  function get_pose_net (line 285) | def get_pose_net(num_layers, heads, head_conv=256):

FILE: Network/rigidmask/submodule.py
  class residualBlock (line 12) | class residualBlock(nn.Module):
    method __init__ (line 15) | def __init__(self, in_channels, n_filters, stride=1, downsample=None,d...
    method forward (line 31) | def forward(self, x):
  function conv (line 43) | def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dila...
  class conv2DBatchNorm (line 51) | class conv2DBatchNorm(nn.Module):
    method __init__ (line 52) | def __init__(self, in_channels, n_filters, k_size,  stride, padding, d...
    method forward (line 71) | def forward(self, inputs):
  class conv2DBatchNormRelu (line 75) | class conv2DBatchNormRelu(nn.Module):
    method __init__ (line 76) | def __init__(self, in_channels, n_filters, k_size,  stride, padding, d...
    method forward (line 95) | def forward(self, inputs):
  class pyramidPooling (line 99) | class pyramidPooling(nn.Module):
    method __init__ (line 101) | def __init__(self, in_channels, with_bn=True, levels=4):
    method forward (line 111) | def forward(self, x):
  class pspnet (line 133) | class pspnet(nn.Module):
    method __init__ (line 137) | def __init__(self, is_proj=True,groups=1):
    method _make_layer (line 193) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 206) | def forward(self, x):
  class pspnet_s (line 249) | class pspnet_s(nn.Module):
    method __init__ (line 253) | def __init__(self, is_proj=True,groups=1):
    method _make_layer (line 309) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 322) | def forward(self, x):
  class bfmodule (line 366) | class bfmodule(nn.Module):
    method __init__ (line 367) | def __init__(self, inplanes, outplanes):
    method _make_layer (line 409) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 422) | def forward(self, x):
  class bfmodule_feat (line 452) | class bfmodule_feat(nn.Module):
    method __init__ (line 453) | def __init__(self, inplanes, outplanes):
    method _make_layer (line 495) | def _make_layer(self, block, planes, blocks, stride=1):
    method forward (line 508) | def forward(self, x):
  function compute_geo_costs (line 539) | def compute_geo_costs(rot, trans, Ex, Kinv, hp0, hp1, tau, Kinv_n=None):
  function get_skew_mat (line 568) | def get_skew_mat(transx,rotx):
  function sampson_err (line 581) | def sampson_err(x1h, x2h, F):
  function get_intrinsics (line 589) | def get_intrinsics(intr, noise=False):
  function testEss (line 661) | def testEss(K0,K1,R,T,p1,p2):

FILE: evaluator/evaluate_ate_scale.py
  function align (line 49) | def align(model,data,calc_scale=False):
  function plot_traj (line 102) | def plot_traj(ax,stamps,traj,style,color,label):

FILE: evaluator/evaluate_kitti.py
  function trajectory_distances (line 9) | def trajectory_distances(poses):
  function last_frame_from_segment_length (line 19) | def last_frame_from_segment_length(dist,first_frame,length):
  function rotation_error (line 25) | def rotation_error(pose_error):
  function translation_error (line 33) | def translation_error(pose_error):
  function calculate_sequence_error (line 45) | def calculate_sequence_error(poses_gt,poses_result,lengths=[10,20,30,40,...
  function calculate_ave_errors (line 86) | def calculate_ave_errors(errors,lengths=[10,20,30,40,50,60,70,80]):
  function evaluate (line 105) | def evaluate(gt, data,kittitype=True):
  function main (line 114) | def  main():

FILE: evaluator/evaluate_rpe.py
  function ominus (line 44) | def ominus(a,b):
  function compute_distance (line 57) | def compute_distance(transform):
  function compute_angle (line 63) | def compute_angle(transform):
  function distances_along_trajectory (line 70) | def distances_along_trajectory(traj):
  function evaluate_trajectory (line 83) | def evaluate_trajectory(traj_gt, traj_est, param_max_pairs=10000, param_...

FILE: evaluator/evaluator_base.py
  function transform_trajs (line 13) | def transform_trajs(gt_traj, est_traj, cal_scale):
  function quats2SEs (line 22) | def quats2SEs(gt_traj, est_traj):
  function per_frame_scale_alignment (line 27) | def per_frame_scale_alignment(gt_motions, est_motions):
  class ATEEvaluator (line 37) | class ATEEvaluator(object):
    method __init__ (line 38) | def __init__(self):
    method evaluate (line 42) | def evaluate(self, gt_traj, est_traj, scale):
  class RPEEvaluator (line 69) | class RPEEvaluator(object):
    method __init__ (line 70) | def __init__(self):
    method evaluate (line 74) | def evaluate(self, gt_SEs, est_SEs):
  class KittiEvaluator (line 90) | class KittiEvaluator(object):
    method __init__ (line 91) | def __init__(self):
    method evaluate (line 95) | def evaluate(self, gt_SEs, est_SEs, kittitype):

FILE: evaluator/tartanair_evaluator.py
  class TartanAirEvaluator (line 10) | class TartanAirEvaluator:
    method __init__ (line 11) | def __init__(self, scale = False, round=1):
    method evaluate_one_trajectory (line 16) | def evaluate_one_trajectory(self, gt_traj, est_traj, scale=False, kitt...

FILE: evaluator/trajectory_transform.py
  function shift0 (line 7) | def shift0(traj):
  function ned2cam (line 21) | def ned2cam(traj):
  function cam2ned (line 39) | def cam2ned(traj):
  function trajectory_transform (line 58) | def trajectory_transform(gt_traj, est_traj):
  function rescale_bk (line 71) | def rescale_bk(poses_gt, poses):
  function pose2trans (line 88) | def pose2trans(pose_data):
  function rescale (line 98) | def rescale(poses_gt, poses):
  function trajectory_scale (line 118) | def trajectory_scale(traj, scale):
  function timestamp_associate (line 123) | def timestamp_associate(first_list, second_list, max_difference):

FILE: evaluator/transformation.py
  function line2mat (line 8) | def line2mat(line_data):
  function mat2line (line 16) | def mat2line(mat_data):
  function motion2pose (line 24) | def motion2pose(data):
  function pose2motion (line 41) | def pose2motion(data, skip=0):
  function SE2se (line 56) | def SE2se(SE_data):
  function SO2so (line 62) | def SO2so(SO_data):
  function so2SO (line 65) | def so2SO(so_data):
  function se2SE (line 68) | def se2SE(se_data):
  function se_mean (line 74) | def se_mean(se_datas):
  function ses_mean (line 84) | def ses_mean(se_datas):
  function ses2poses (line 93) | def ses2poses(data):
  function ses2poses_quat (line 106) | def ses2poses_quat(data):
  function SEs2ses (line 122) | def SEs2ses(motion_data):
  function so2quat (line 131) | def so2quat(so_data):
  function quat2so (line 140) | def quat2so(quat_data):
  function sos2quats (line 151) | def sos2quats(so_datas,mean_std=[[1],[1]]):
  function SO2quat (line 163) | def SO2quat(SO_data):
  function quat2SO (line 167) | def quat2SO(quat_data):
  function pos_quat2SE (line 171) | def pos_quat2SE(quat_data):
  function pos_quats2SEs (line 180) | def pos_quats2SEs(quat_datas):
  function pos_quats2SE_matrices (line 189) | def pos_quats2SE_matrices(quat_datas):
  function SE2pos_quat (line 200) | def SE2pos_quat(SE_data):
  function SEs2ses (line 206) | def SEs2ses(data):
  function ses2SEs (line 217) | def ses2SEs(data):
  function SE2quat (line 228) | def SE2quat(SE_data):
  function quat2SE (line 238) | def quat2SE(quat_data):
  function SEs2quats (line 249) | def SEs2quats(SEs_data):
  function quats2SEs (line 261) | def quats2SEs(quat_datas):
  function motion_ses2pose_quats (line 273) | def motion_ses2pose_quats(data):
  function pose_quats2motion_ses (line 283) | def pose_quats2motion_ses(data):
  function kitti2tartan (line 293) | def kitti2tartan(traj):
  function tartan2kitti (line 313) | def tartan2kitti(traj):

FILE: vo_trajectory_from_folder.py
  function get_args (line 16) | def get_args():