open3d.ml.tf.models.PointPillars¶
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class
open3d.ml.tf.models.
PointPillars
(*args, **kwargs)¶ Object detection model. Based on the PointPillars architecture https://github.com/nutonomy/second.pytorch.
- Parameters
name (string) – Name of model. Default to “PointPillars”.
voxel_size – voxel edge lengths with format [x, y, z].
point_cloud_range – The valid range of point coordinates as [x_min, y_min, z_min, x_max, y_max, z_max].
voxelize – Config of PointPillarsVoxelization module.
voxelize_encoder – Config of PillarFeatureNet module.
scatter – Config of PointPillarsScatter module.
backbone – Config of backbone module (SECOND).
neck – Config of neck module (SECONDFPN).
head – Config of anchor head module.
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__init__
(name='PointPillars', point_cloud_range=[0, - 40.0, - 3, 70.0, 40.0, 1], classes=['car'], voxelize={}, voxel_encoder={}, scatter={}, backbone={}, neck={}, head={}, loss={}, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
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augment_data
(data, attr)¶
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call
(inputs, training=True)¶ Forward pass.
- Parameters
inputs – tuple/list of inputs (points, bboxes, labels, calib)
training – toggle training run
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extract_feats
(points, training=False)¶ Extract features from points.
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get_batch_gen
(dataset, steps_per_epoch=None, batch_size=1)¶
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get_optimizer
(cfg)¶ 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_end
(results, inputs)¶ 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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load_gt_database
(pickle_path, min_points_dict, sample_dict)¶
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loss
(results, inputs, training=True)¶ Computes loss.
- Parameters
results – results of forward pass (scores, bboxes, dirs)
inputs – tuple/list of gt inputs (points, bboxes, labels, calib)
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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
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transform
(data, attr)¶ Transform function for the point cloud and features.
- Parameters
args – A list of tf Tensors.
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voxelize
(points)¶ Apply hard voxelization to points.