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Types & classes48 in github.com/Abraham190137/TactileACT

⨍ Functions 267◇ Types & classes 48
↓ 6 callersClassConditionalResidualBlock1D
diffusion/network.py:82
↓ 4 callersClassClipProjectionHead
clip_pretraining.py:66
↓ 4 callersClassDiffusionEpisodicDataset
diffusion/dataset.py:49
↓ 3 callersClassClipDataset
A dataset for training the CLIP model. This dataset will return a set of images from a single episode, making sure they are at least min_dis
clip_pretraining.py:113
↓ 3 callersClassConv1dBlock
Conv1d --> GroupNorm --> Mish
diffusion/network.py:64
↓ 3 callersClassNestedTensor
detr/detr/util/misc.py:284
↓ 3 callersClassNormalizeDiffusionActionQpos
diffusion/dataset.py:6
↓ 3 callersClassPositionEmbeddingLearned
Absolute pos embedding, learned.
detr/detr/models/position_encoding.py:55
↓ 3 callersClassPositionEmbeddingSine
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized t
detr/detr/models/position_encoding.py:14
↓ 2 callersClassACTPolicy
policy.py:26
↓ 2 callersClassBackbone
ResNet backbone with frozen BatchNorm.
detr/detr/models/backbone.py:76
↓ 2 callersClassClipDataset
clip_pretraining_no_pos.py:101
↓ 2 callersClassClipDataset
failed_DDP.py:112
↓ 2 callersClassClipDataset
diffusion/clip_pretraining.py:109
↓ 2 callersClassClipProjectionHead
clip_pretraining_no_pos.py:60
↓ 2 callersClassClipProjectionHead
failed_DDP.py:71
↓ 2 callersClassEpisodicDatasetDelta
utils.py:180
↓ 2 callersClassJoiner
detr/detr/models/backbone.py:89
↓ 2 callersClassNormalizeDeltaActionQpos
utils.py:43
↓ 2 callersClassPreprocessData
Preprocesses the data for the ACT model. Behaves like the dataset class used in the training loop, but does not inherit from torch.utils.data.Dat
diffusion/robot_operation_old.py:35
↓ 2 callersClassSmoothedValue
Track a series of values and provide access to smoothed values over a window or the global series average.
detr/detr/util/misc.py:27
↓ 2 callersClassTransformer
detr/detr/models/transformer.py:21
↓ 2 callersClassTransformerEncoder
detr/detr/models/transformer.py:110
↓ 2 callersClassTransformerEncoderLayer
detr/detr/models/transformer.py:181
↓ 2 callersClassclip_projection_head
diffusion/clip_pretraining.py:74
↓ 1 callersClassAsyncInput
robot_operation.py:182
↓ 1 callersClassConditionalUnet1D
diffusion/network.py:132
↓ 1 callersClassDETRVAE
This is the DETR module that performs object detection
detrvae.py:28
↓ 1 callersClassDebugController
visualization_utils.py:6
↓ 1 callersClassDebugController
diffusion/visualization.py:5
↓ 1 callersClassDownsample1d
diffusion/network.py:47
↓ 1 callersClassMyJoiner
policy.py:15
↓ 1 callersClassNormalizeActionQpos
utils.py:14
↓ 1 callersClassNormalizeActionQpos
diffusion/utils.py:14
↓ 1 callersClassNormalizeDeltaActionQpos
diffusion/utils.py:31
↓ 1 callersClassPreprocessData
Preprocesses the data for the ACT model. Behaves like the dataset class used in the training loop, but does not inherit from torch.utils.data.Dat
robot_operation.py:76
↓ 1 callersClassPreprocessData
Preprocesses the data for the ACT model. Behaves like the dataset class used in the training loop, but does not inherit from torch.utils.data.Dat
diffusion/robot_operation.py:69
↓ 1 callersClassSinusoidalPosEmb
diffusion/network.py:32
↓ 1 callersClassTransformerDecoder
detr/detr/models/transformer.py:135
↓ 1 callersClassTransformerDecoderLayer
detr/detr/models/transformer.py:253
↓ 1 callersClassUpsample1d
diffusion/network.py:55
ClassBackboneBase
detr/detr/models/backbone.py:60
ClassEpisodicDataset
utils.py:75
ClassEpisodicDataset
diffusion/utils.py:54
ClassEpisodicDatasetDelta
diffusion/utils.py:159
ClassFrozenBatchNorm2d
BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt
detr/detr/models/backbone.py:21
ClassMetricLogger
detr/detr/util/misc.py:159
ClasssyncInput
robot_operation.py:211