open3d.ml.torch.pipelines.ObjectDetection#
- class open3d.ml.torch.pipelines.ObjectDetection(model, dataset=None, name='ObjectDetection', main_log_dir='./logs/', device='cuda', split='train', **kwargs)#
Pipeline for object detection.
- __init__(model, dataset=None, name='ObjectDetection', main_log_dir='./logs/', device='cuda', split='train', **kwargs)#
Initialize.
- Parameters:
model – A network model.
dataset – A dataset, or None for inference model.
device – ‘cuda’ or ‘cpu’.
distributed – Whether to use multiple gpus.
kwargs –
- Returns:
The corresponding class.
- Return type:
class
- get_3d_summary(infer_bboxes_batch, inputs_batch, epoch, results=None, save_gt=True)#
Create visualization for input point cloud and network output bounding boxes.
- Parameters:
infer_bboxes_batch (Sequence[Sequence[BoundingBox3D]) – Batch of predicted bounding boxes from inference_end()
(Sequence[Sequence[bbox_objs (inputs_batch) – Object3D, point: array(N,3)]]): Batch of ground truth boxes and pointclouds.
epoch (int) – step
results (torch.FloatTensor) – Model output (only required for RPN stage of PointRCNN).
save_gt (bool) – Save ground truth (for ‘train’ or ‘valid’ stages).
- Returns:
- [Dict] visualizations of inputs and outputs suitable to save as an
Open3D for TensorBoard summary.
- load_ckpt(ckpt_path=None, is_resume=True)#
- run_inference(data)#
Run inference on given data.
- Parameters:
data – A raw data.
- Returns:
Returns the inference results.
- run_test()#
Run test with test data split, computes mean average precision of the prediction results.
- run_train()#
Run training with train data split.
- run_valid(epoch=0)#
Run validation with validation data split, computes mean average precision and the loss of the prediction results.
- Parameters:
epoch (int) – step for TensorBoard summary. Defaults to 0 if unspecified.
- save_ckpt(epoch)#
- save_config(writer)#
Save experiment configuration with tensorboard summary.
- save_logs(writer, epoch)#