open3d.ml.tf.models.SparseConvUnet#
- class open3d.ml.tf.models.SparseConvUnet(*args, **kwargs)#
Semantic Segmentation model.
Uses UNet architecture replacing convolutions with Sparse Convolutions.
- name#
Name of model. Default to “SparseConvUnet”.
- device#
Which device to use (cpu or cuda).
- voxel_size#
Voxel length for subsampling.
- multiplier#
min length of feature length in each layer.
- conv_block_reps#
repetition of Unet Blocks.
- residual_blocks#
Whether to use Residual Blocks.
- in_channels#
Number of features(default 3 for color).
- num_classes#
Number of classes.
- __init__(name='SparseConvUnet', device='cuda', multiplier=16, voxel_size=0.05, conv_block_reps=1, residual_blocks=False, in_channels=3, num_classes=20, grid_size=4096, augment=None, **kwargs)#
- call(inputs, training=False)#
- get_batch_gen(dataset, steps_per_epoch=None, batch_size=1)#
- get_loss(Loss, results, inputs)#
Calculate the loss on output of the model.
- 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(data)#
Function called right before running inference.
- Parameters:
data – A data from the dataset.
- inference_end(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.
- Parameters:
data – A sample from the dataset.
attr – The corresponding attributes.
- Returns:
Returns the preprocessed data
- transform(point, feat, label, lengths)#
Transform function for the point cloud and features.
- Parameters:
args – A list of tf Tensors.