cloudViewer.ml.torch.pipelines.ObjectDetection

class cloudViewer.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.

• devce – ‘gpu’ or ‘cpu’.

• kwargs –

Returns:

The corresponding class.

Return type:

class

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()

Run validation with validation data split, computes mean average precision and the loss of the prediction results.

save_ckpt(epoch)

save_config(writer)

Save experiment configuration with tensorboard summary.

save_logs(writer, epoch)