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Functions1,028 in github.com/RozDavid/UnScene3D

⨍ Functions 1,028◇ Types & classes 235

Function__len__

pseudo_masks/datasets/dataset.py:229

utils/utils.py:344

pseudo_masks/datasets/dataloader.py:39

pseudo_masks/datasets/dataloader.py:76

pseudo_masks/datasets/dataset.py:150

pseudo_masks/datasets/dataset.py:220

pseudo_masks/datasets/dataset.py:490

datasets/outdoor_semseg.py:81

datasets/freemask_semseg.py:177

datasets/semseg.py:325

(cls, value, name)

models/modules/common.py:47

pseudo_masks/datasets/dataloader.py:33

pseudo_masks/datasets/dataloader.py:66

models/misc.py:44

(self, _repr_indent=4)

models/matcher.py:193

models/criterion.py:278

pseudo_masks/datasets/evaluation/scannet_benchmark_utils/util_3d.py:128

benchmark/util_3d.py:122

Method_augment_coords_to_feats

(self, coords, feats, labels=None)

pseudo_masks/datasets/s3dis.py:35

Method_augment_locfeat

(self, pointcloud)

pseudo_masks/datasets/scannet.py:308

Method_camera2world_transform

(no_rgb_points_3d, R, T)

utils/pc_utils.py:269

Method_get_tgt_permutation_idx

(self, indices)

models/criterion.py:225

Method_make_gen

datasets/preprocessing/s3dis_preprocessing.py:73

Method_parse_scene_subscene

datasets/preprocessing/s3dis_preprocessing.py:219

Method_parse_scene_subscene

datasets/preprocessing/stpls3d_preprocessing.py:221

Method_read_json

datasets/preprocessing/base_preprocessing.py:158

Method_remap_model_output

datasets/freemask_semseg.py:436

Method_remap_model_output

datasets/semseg.py:605

Method_world2camera_transform

(no_rgb_points_3d, R, T)

utils/pc_utils.py:274

utils/cuda_utils/include/cutil_math.h:1290

Functionaggregation_backward_cuda

utils/pointops2/src/aggregation/aggregation_cuda.cpp:18

Functionaggregation_forward_cuda

utils/pointops2/src/aggregation/aggregation_cuda.cpp:8

MethodangleBetween

returns the angle between two vectors *in degrees

utils/cpp_utils/include/vec3.h:262

Functionassign_image_features_to_point_cloud

(batched_cloud, image_features, camera_poses, camera_intrinsics, depth_images, depth_treshold=2.5)

utils/pc_utils.py:444

Functionassign_image_features_to_point_cloud_full_scenes

(batched_cloud, image_features, camera_poses, camera_intrinsics, depth_images, depth_treshold=2.5, projected_p

utils/pc_utils.py:507

FunctionatomicMax

utils/cuda_utils/include/cudaUtil.h:119

Functionattention_step1_backward_cuda

utils/pointops2/src/attention/attention_cuda.cpp:18

Functionattention_step1_backward_cuda_v2

utils/pointops2/src/attention_v2/attention_cuda_v2.cpp:18

Functionattention_step1_forward_cuda

utils/pointops2/src/attention/attention_cuda.cpp:7

Functionattention_step1_forward_cuda_v2

utils/pointops2/src/attention_v2/attention_cuda_v2.cpp:7

Functionattention_step2_backward_cuda

utils/pointops2/src/attention/attention_cuda.cpp:44

Functionattention_step2_backward_cuda_v2

utils/pointops2/src/attention_v2/attention_cuda_v2.cpp:44

Functionattention_step2_forward_cuda

utils/pointops2/src/attention/attention_cuda.cpp:32

Functionattention_step2_forward_cuda_v2

utils/pointops2/src/attention_v2/attention_cuda_v2.cpp:32

Functionattention_step2_with_rel_pos_value_backward_cuda

utils/pointops2/src/rpe/relative_pos_encoding_cuda.cpp:45

Functionattention_step2_with_rel_pos_value_backward_cuda_v2

utils/pointops2/src/rpe_v2/relative_pos_encoding_cuda_v2.cpp:96

Functionattention_step2_with_rel_pos_value_forward_cuda

utils/pointops2/src/rpe/relative_pos_encoding_cuda.cpp:32

Functionattention_step2_with_rel_pos_value_forward_cuda_v2

utils/pointops2/src/rpe_v2/relative_pos_encoding_cuda_v2.cpp:83

Functionavg_pool

( kernel_size, stride=1, dilation=1, conv_type=ConvType.HYPERCUBE, in_coords_key=None,

models/modules/common.py:191

Functionavg_unpool

(kernel_size, stride=1, dilation=1, conv_type=ConvType.HYPERCUBE, D=-1)

models/modules/common.py:219

Methodbackproject

Backproject a depth map into 3D points (camera coordinate system). Attach color if RGB image is provided, otherwise use gray [128 128 128] col

utils/pc_utils.py:175

(ctx, grad_image, grad_indexes_image, grad_mapping_nums)

utils/cuda_utils/raycast_image.py:105

(ctx, grad_image, grad_indexes_image, grad_vox_dist_weights, grad_mapping_nums)

utils/cuda_utils/raycast_image.py:211

(ctx, grad_query_points, grad_query_inds, grad_query_ind_weights, grad_accum_voxel_weights)

utils/cuda_utils/cuda_utils.py:36

input: grad_out: (m, c, nsample) output: (n, c), None

utils/pointops2/functions/pointops_ablation.py:64

input: grad_out: (n, nsample, c) output: grad_input1: (n, c), grad_input2: (n, c)

utils/pointops2/functions/pointops_ablation.py:119

input: grad_out: (n, c) output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight : (n, nsample, c')

utils/pointops2/functions/pointops_ablation.py:149

input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b) output: (n, c)

utils/pointops2/functions/pointops_ablation.py:203

input: grad_out: (m, c, nsample) output: (n, c), None

utils/pointops2/functions/pointops2.py:64

input: grad_out: (n, nsample, c) output: grad_input1: (n, c), grad_input2: (n, c)

utils/pointops2/functions/pointops2.py:118

input: grad_out: (n, c) output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight : (n, nsample, c')

utils/pointops2/functions/pointops2.py:148

input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b) output: (n, c)

utils/pointops2/functions/pointops2.py:202

input: grad_output: (N, h, C//h) output: (M, h), (N, h, C//h), None, None

utils/pointops2/functions/pointops.py:101

input: grad_output: (N, h, C//h) output: (M, h), (N, h, C//h), None, None

utils/pointops2/functions/pointops.py:163

input: grad_output: (N, h, C//h) output: (M, h), (N, h, C//h), None, None

utils/pointops2/functions/pointops.py:227

input: grad_output: (L, h, C//h) output: (M, h), (N, h, C//h), None, None

utils/pointops2/functions/pointops.py:289

input: grad_output: [M, h] output: (N, h, hdim), None, (L, h, hdim, 3), None

utils/pointops2/functions/pointops.py:334

input: grad_output: [M, h] output: (N, h, hdim), None, (L, h, hdim, 3), None

utils/pointops2/functions/pointops.py:405

input: grad_output: [M, h] output: (N, h, hdim), None, (L, h, hdim, 3), None

utils/pointops2/functions/pointops.py:481

input: grad_output: (N, h, C//h) output: (M, h), (N, h, C//h), None, None, (L, h, hdim, 3), None

utils/pointops2/functions/pointops.py:539

input: grad_output: (N, h, C//h) output: (M, h), (N, h, C//h), None, None, (L, h, hdim, 3), None

utils/pointops2/functions/pointops.py:603

input: grad_out: (n, nsample, c) output: grad_input1: (n, c), grad_input2: (n, c)

utils/pointops2/functions/pointops.py:706

input: grad_out: (n, c) output: grad_input: (n, c), grad_position: (n, nsample, c), grad_weight : (n, nsample, c')

utils/pointops2/functions/pointops.py:736

input: xyz: (m, 3), new_xyz: (n, 3), input: (m, c), offset: (b), new_offset: (b) output: (n, c)

utils/pointops2/functions/pointops.py:817

(ctx, xy_pred_projections_grad, xz_pred_projections_grad, yz_pred_projections_grad, xy_target

models/noise_robust_loss.py:59

Methodbackward_debug

(occ3d, indexes_image, grad_image, d_voxels, mapping3dto2d_num)

utils/cuda_utils/raycast_image.py:122

Methodbackward_debug

(occ3d, indexes_image, vox_dist_weights, grad_image, d_voxels, mapping3dto2d_num)

utils/cuda_utils/raycast_image.py:225

Methodbackward_debug

(grad_query_points, d_voxels, interpolation_indices, interpolation_weights, accum_voxel_weights)

utils/cuda_utils/cuda_utils.py:47

Functionbatch_dice_loss

Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The pred

models/matcher.py:12

Functionbatch_sigmoid_ce_loss

Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with t

models/matcher.py:35

Functionbox2d_iou

Compute 2D bounding box IoU. Input: box1: tuple of (xmin,ymin,xmax,ymax) box2: tuple of (xmin,ymin,xmax,ymax) Output:

utils/votenet_utils/box_util.py:170

pseudo_masks/datasets/dataset.py:76

Functioncalculate_uncertainty

We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the foreground class in `classes`. Args:

models/criterion.py:76

Methodcamera_matrix

utils/pc_utils.py:317

utils/cpp_utils/include/vec3.h:382

utils/cuda_utils/include/cuda_SimpleMatrixUtil.h:1314

MethodcheckMINF

utils/cuda_utils/include/cuda_SimpleMatrixUtil.h:1299

MethodcheckQNAN

utils/cuda_utils/include/cuda_SimpleMatrixUtil.h:1329

Functioncheck_aspect

(crop_range, aspect_min)

datasets/random_cuboid.py:6

Functionclean_segments

(comps, min_vert_num=500)

utils/cpp_utils/test/test_segmentor.py:72

Functioncolorize_pointcloud

(xyz, label, num_labels=200, ignore_label=255)

utils/pc_utils.py:325

Methodcompute_color_mean_std

( self, train_database_path: str = "./data/processed/freemask/train_database.yaml" )

datasets/preprocessing/freemask_preprocessing.py:222

Methodcompute_color_mean_std

( self, train_database_path: str = "" )

datasets/preprocessing/s3dis_preprocessing.py:165

Methodcompute_color_mean_std

( self, train_database_path: str = "./data/processed/arkit/train_database.yaml" )

datasets/preprocessing/arkit_preprocessing.py:151

Methodcompute_color_mean_std

( self, train_database_path: str = "./data/processed/stpls3d/train_database.yaml" )

datasets/preprocessing/stpls3d_preprocessing.py:186

Methodcompute_color_mean_std

( self, train_database_path: str = "./data/processed/scannet/train_database.yaml" )

datasets/preprocessing/scannet_preprocessing.py:213

Methodconcat_label

(cls, target, xyz, label)

utils/pc_utils.py:438

Methodconfigure_optimizers

trainer/trainer.py:953

Methodconvert_mat2cfl

pseudo_masks/datasets/dataset.py:306

Functionconvert_region_type

Convert the integer region_type to the corresponding RegionType enum object.

models/modules/common.py:73

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