Inference and Testing

This section describes how to use the trained models for testing using a test set or running inference for any given videos.

To test using our trained models, download the required checkpoint, config, and dataset metadata from the Models section.

Computing accuacy using test set

  • Add a key called pretrained to the first line of the config, pointing to the .ckpt path

  • Add a sub-section called test_pipeline to the data section of the config.

  • This section is of same format as valid_pipeline section.

  • Finally, run the following snippet to compute accuracy for the given pose test set:

import omegaconf
from openhands.apis.inference import InferenceModel

cfg = omegaconf.OmegaConf.load("path/to/config.yaml")
model = InferenceModel(cfg=cfg)
model.init_from_checkpoint_if_available()
if cfg.data.test_pipeline.dataset.inference_mode:
    model.test_inference()
else:
    model.compute_test_accuracy()

Predicting for any given videos

  • In the same config as described above, just change the root_dir to point to any desired folder containing videos (or pose files)

  • Set an additional variable called inference_mode as true, to indicate that there will be no ground truth.

  • You can now run the inference using the same snippet above.