open3d.ml.torch.models.PointTransformer¶
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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.
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name¶ Name of model. Default to “PointTransformer”.
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blocks¶ Number of Bottleneck layers.
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in_channels¶ Number of features(default 6).
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num_classes¶ Number of classes.
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voxel_size¶ Voxel length for subsampling.
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max_voxels¶ Maximum number of voxels.
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batcher¶ Batching method for dataloader.
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augment¶ dictionary for augmentation.
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__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
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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.
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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.
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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.
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inference_begin()¶ Function called right before running inference.
- Parameters
data – A data from the dataset.
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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.
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inference_preprocess()¶ This function prepares the inputs for the model.
- Returns
The inputs to be consumed by the call() function of the model.
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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
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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).
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update_probs(inputs, results, test_probs, test_labels)¶
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training: bool¶
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