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hub / github.com/RozDavid/UnScene3D / types & classes

Types & classes235 in github.com/RozDavid/UnScene3D

↓ 4 callersClassNoGpu
datasets/utils.py:670
↓ 4 callersClassOrderedSet
utils/utils.py:340
↓ 3 callersClassNestedTensor
models/misc.py:25
↓ 3 callersClassProject2DFeaturesCUDA
utils/cuda_utils/raycast_image.py:18
↓ 2 callersClassGenericMLP
models/modules/helpers_3detr.py:45
↓ 2 callersClassPositionEmbeddingCoordsSine
models/position_embedding.py:43
↓ 2 callersClassSELayer
models/modules/senet_block.py:8
↓ 2 callersClassVoxelizer
pseudo_masks/datasets/voxelizer.py:13
↓ 1 callersClassCloudInstance
pseudo_masks/datasets/scannet_free.py:8
↓ 1 callersClassCrossAttentionLayer
models/mask3d.py:548
↓ 1 callersClassEvaluator
pseudo_masks/datasets/evaluation/evaluate_semantic_instance.py:46
↓ 1 callersClassEvaluator
pseudo_masks/datasets/evaluation/evaluate_semantic_label.py:32
↓ 1 callersClassFFNLayer
models/mask3d.py:611
↓ 1 callersClassInfSampler
Samples elements randomly, without replacement. Arguments: data_source (Dataset): dataset to sample from
pseudo_masks/datasets/dataloader.py:12
↓ 1 callersClassInstance
pseudo_masks/datasets/evaluation/scannet_benchmark_utils/util_3d.py:88
↓ 1 callersClassInstance
benchmark/util_3d.py:82
↓ 1 callersClassInstanceSegmentation
trainer/trainer.py:44
↓ 1 callersClassIoU
Computes the intersection over union (IoU) per class and corresponding mean (mIoU). Intersection over union (IoU) is a common evaluation metr
models/metrics/metrics.py:4
↓ 1 callersClassPositionalEncoding3D
models/mask3d.py:459
↓ 1 callersClassProjectionFunctionWrapper
models/noise_robust_loss.py:74
↓ 1 callersClassProjectionMaskLoss
models/noise_robust_loss.py:105
↓ 1 callersClassRandomCuboid
RandomCuboid augmentation from DepthContrast [https://arxiv.org/abs/2101.02691] We slightly modify this operation to account for object detec
datasets/random_cuboid.py:17
↓ 1 callersClassRegularCheckpointing
trainer/trainer.py:38
↓ 1 callersClassSelfAttentionLayer
models/mask3d.py:491
↓ 1 callersClassTimer
A simple timer.
utils/utils.py:369
↓ 1 callersClasscfl_collate_fn_factory
Generates collate function for coords, feats, labels. Args: limit_numpoints: If 0 or False, does not alter batch size. If positive inte
utils/transforms.py:274
ClassARKit_10cmDataset
pseudo_masks/datasets/arkit.py:176
ClassARKit_2cmDataset
pseudo_masks/datasets/arkit.py:172
ClassARKit_Dataset
pseudo_masks/datasets/arkit.py:3
ClassAggregation
utils/pointops2/functions/pointops_ablation.py:134
ClassAggregation
utils/pointops2/functions/pointops2.py:133
ClassAggregation
utils/pointops2/functions/pointops.py:721
ClassAttentionStep1
utils/pointops2/functions/pointops.py:78
ClassAttentionStep1_v2
utils/pointops2/functions/pointops.py:138
ClassAttentionStep2
utils/pointops2/functions/pointops.py:203
ClassAttentionStep2WithRelPosValue
utils/pointops2/functions/pointops.py:517
ClassAttentionStep2WithRelPosValue_v2
utils/pointops2/functions/pointops.py:580
ClassAttentionStep2_v2
utils/pointops2/functions/pointops.py:264
ClassAverageMeter
Computes and stores the average and current value
utils/utils.py:417
ClassBasePreprocessing
datasets/preprocessing/base_preprocessing.py:17
ClassBasicBlock
models/modules/resnet_block.py:67
ClassBasicBlockBase
models/modules/resnet_block.py:7
ClassBasicBlockIN
models/modules/resnet_block.py:71
ClassBasicBlockINBN
models/modules/resnet_block.py:75
ClassBatchNormDim1Swap
Used for nn.Transformer that uses a HW x N x C rep
models/modules/helpers_3detr.py:7
ClassBatchNormDim1Swap
Used for nn.Transformer that uses a HW x N x C rep
models/modules/3detr_helpers.py:7
ClassBottleneck
models/modules/resnet_block.py:140
ClassBottleneckBase
models/modules/resnet_block.py:79
ClassBottleneckIN
models/modules/resnet_block.py:144
ClassBottleneckINBN
models/modules/resnet_block.py:148
EnumCUTBoolean
/////////////////////////////////////////////////////////////////////// CUT bool type ////////////////////////////////////////////////////////////////
utils/cuda_utils/include/cutil_inline_runtime.h:35
ClassCamera
utils/pc_utils.py:126
ClassChromaticAutoContrast
utils/transforms.py:40
ClassChromaticJitter
utils/transforms.py:66
ClassChromaticScale
utils/transforms.py:80
ClassChromaticTranslation
Add random color to the image, input must be an array in [0,255] or a PIL image
utils/transforms.py:23
ClassCompose
Composes several transforms together.
utils/transforms.py:248
ClassConfusionMatrix
Constructs a confusion matrix for a multi-class classification problems. Does not support multi-label, multi-class problems. Keyword argumen
models/metrics/confusionmatrix.py:5
ClassConvType
Define the kernel region type
models/modules/common.py:34
ClassCustom30M
models/res16unet.py:376
ClassDatasetPhase
pseudo_masks/datasets/dataset.py:23
ClassDictDataset
pseudo_masks/datasets/dataset.py:91
ClassDinoNet
models/encoders_2d/dino.py:7
ClassDistributedInfSampler
pseudo_masks/datasets/dataloader.py:45
ClassDotProdWithIdx
utils/pointops2/functions/pointops.py:316
ClassDotProdWithIdx_v2
utils/pointops2/functions/pointops.py:368
ClassDotProdWithIdx_v3
utils/pointops2/functions/pointops.py:442
ClassElasticDistortion
utils/transforms.py:202
ClassExpTimer
Exponential Moving Average Timer
utils/utils.py:403
ClassFreeMaskPreprocessing
datasets/preprocessing/freemask_preprocessing.py:14
ClassFreeMaskPreprocessing
datasets/preprocessing/arkit_preprocessing.py:15
ClassFreeMaskVoxelizeCollate
datasets/utils.py:181
ClassFurthestSampling
utils/pointops2/functions/pointops_ablation.py:10
ClassFurthestSampling
utils/pointops2/functions/pointops2.py:10
ClassFurthestSampling
utils/pointops2/functions/pointops.py:10
ClassGenericMLP
models/modules/3detr_helpers.py:45
ClassGrouping
utils/pointops2/functions/pointops_ablation.py:48
ClassGrouping
utils/pointops2/functions/pointops2.py:48
ClassGrouping
utils/pointops2/functions/pointops.py:48
ClassHashTimeBatch
utils/utils.py:491
ClassHighDimensionalModel
Base network for all spatio (temporal) chromatic sparse convnet
models/model.py:20
ClassHueSaturationTranslation
utils/transforms.py:92
ClassHungarianMatcher
This class computes an assignment between the targets and the predictions of the network For efficiency reasons, the targets don't include the no
models/matcher.py:67
ClassInterpolation
utils/pointops2/functions/pointops_ablation.py:182
ClassInterpolation
utils/pointops2/functions/pointops2.py:181
ClassInterpolation
utils/pointops2/functions/pointops.py:796
ClassInterpolationFunction
utils/cuda_utils/cuda_utils.py:16
ClassKNNQuery
utils/pointops2/functions/pointops_ablation.py:30
ClassKNNQuery
utils/pointops2/functions/pointops2.py:30
ClassKNNQuery
utils/pointops2/functions/pointops.py:30
ClassLSegNet
models/encoders_2d/lseg.py:8
ClassLidarDataset
datasets/outdoor_semseg.py:14
ClassLogger
utils/votenet_utils/tf_logger.py:15
ClassMask3D
models/mask3d.py:16
ClassMatterportPreprocessing
datasets/preprocessing/matterport_preprocessing.py:21
ClassMinkUNetBase
models/resunet.py:11
ClassMinkUNetHyper
models/resunet.py:294
ClassMinkUNetHyper14INBN
models/resunet.py:539
ClassModel
Base network for all sparse convnet By default, all networks are segmentation networks.
models/model.py:4
ClassNoGpuMask
datasets/utils.py:689
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