open3d.ml.torch.models.PointTransformer#
- class open3d.ml.torch.models.PointTransformer(name='PointTransformer', blocks=[2, 2, 2, 2, 2], in_channels=6, num_classes=13, voxel_size=0.04, max_voxels=80000, batcher='ConcatBatcher', augment=None, **kwargs)#
Semantic Segmentation model. Based on PointTransformer architecture https://arxiv.org/pdf/2012.09164.pdf
Uses Encoder-Decoder architecture with Transformer layers.
- name#
Name of model. Default to “PointTransformer”.
- blocks#
Number of Bottleneck layers.
- in_channels#
Number of features(default 6).
- num_classes#
Number of classes.
- voxel_size#
Voxel length for subsampling.
- max_voxels#
Maximum number of voxels.
- batcher#
Batching method for dataloader.
- augment#
dictionary for augmentation.
- __init__(name='PointTransformer', blocks=[2, 2, 2, 2, 2], in_channels=6, num_classes=13, voxel_size=0.04, max_voxels=80000, batcher='ConcatBatcher', augment=None, **kwargs)#
Initialize.
- Parameters:
cfg (cfg object or str) – cfg object or path to cfg file
dataset_path (str) – path to the dataset
**kwargs (dict) – Dict of args
- forward(batch)#
Forward pass for the model.
- Parameters:
inputs – A dict object for inputs with following keys point (tf.float32): Input pointcloud (N,3) feat (tf.float32): Input features (N, 3) row_splits (tf.int64): row splits for batches (b+1,)
- Returns:
Returns the probability distribution.
- get_loss(sem_seg_loss, results, inputs, device)#
Calculate the loss on output of the model.
- Parameters:
sem_seg_loss – Object of type SemSegLoss.
results – Output of the model.
inputs – Input of the model.
device – device(cpu or cuda).
- Returns:
Returns loss, labels and scores.
- get_optimizer(cfg_pipeline)#
Returns an optimizer object for the model.
- Parameters:
cfg_pipeline – A Config object with the configuration of the pipeline.
- Returns:
Returns a new optimizer object.
- inference_begin()#
Function called right before running inference.
- Parameters:
data – A data from the dataset.
- inference_end(inputs, results)#
This function is called after the inference.
This function can be implemented to apply post-processing on the network outputs.
- Parameters:
results – The model outputs as returned by the call() function. Post-processing is applied on this object.
- Returns:
Returns True if the inference is complete and otherwise False. Returning False can be used to implement inference for large point clouds which require multiple passes.
- inference_preprocess()#
This function prepares the inputs for the model.
- Returns:
The inputs to be consumed by the call() function of the model.
- preprocess(data, attr)#
Data preprocessing function.
This function is called before training to preprocess the data from a dataset. It consists of subsampling pointcloud with voxelization.
- Parameters:
data – A sample from the dataset.
attr – The corresponding attributes.
- Returns:
Returns the preprocessed data
- transform(data, attr)#
Transform function for the point cloud and features.
This function is called after preprocess method. It consists of calling augmentation and normalizing the pointcloud.
- Parameters:
data – A sample from the dataset.
attr – The corresponding attributes.
- Returns:
Returns dictionary data with keys (point, feat, label).
- update_probs(inputs, results, test_probs)#