open3d.ml.torch.pipelines.SemanticSegmentation

class open3d.ml.torch.pipelines.SemanticSegmentation(model, dataset=None, name='SemanticSegmentation', batch_size=4, val_batch_size=4, test_batch_size=3, max_epoch=100, learning_rate=0.01, lr_decays=0.95, save_ckpt_freq=20, adam_lr=0.01, scheduler_gamma=0.95, momentum=0.98, main_log_dir='./logs/', device='gpu', split='train', train_sum_dir='train_log', **kwargs)

Pipeline for semantic segmentation.

__init__(model, dataset=None, name='SemanticSegmentation', batch_size=4, val_batch_size=4, test_batch_size=3, max_epoch=100, learning_rate=0.01, lr_decays=0.95, save_ckpt_freq=20, adam_lr=0.01, scheduler_gamma=0.95, momentum=0.98, main_log_dir='./logs/', device='gpu', split='train', train_sum_dir='train_log', **kwargs)

Initialize.

Parameters

• model – A network model.

• dataset – A dataset, or None for inference model.

• devce – ‘gpu’ or ‘cpu’.

• kwargs –

Returns

The corresponding class.

Return type

class

get_batcher(device, split='training')

load_ckpt(ckpt_path=None, is_resume=True)

run_inference(data)

Run inference on a given data.

Parameters

data – A raw data.

Returns

Returns the inference results.

run_test()

Run testing on test sets.

run_train()

Run training on train sets

save_ckpt(epoch)

save_config(writer)

Save experiment configuration with tensorboard summary

save_logs(writer, epoch)