def demo_how(params, globals, logger):
"""Demo where asmk is applied to the HOW descriptors from eccv'20 paper, replicating reported
results. Params is a dictionary with parameters for each step."""
demo_params = params.pop("demo_how")
asmk = initialize(params, demo_params, globals, logger)
asmk = train_codebook(asmk, demo_params, globals, logger)
# Create db and evaluate datasets
for dataset in demo_params['eval_datasets']:
desc = io_helpers.load_pickle(f"{globals['root_path']}/features/{dataset}_" \
f"{demo_params['eval_features']}.pkl")
logger.info(f"Loaded DB and query descriptors for {dataset}")
asmk_dataset = build_ivf(asmk, dataset, desc, globals, logger)
query_ivf(asmk_dataset, dataset, desc, globals, logger)
def train_codebook(asmk, demo_params, globals, logger):
"""The first step of asmk method - training the codebook"""
codebook_path = f"{globals['exp_path']}/codebook.pkl"
features_path = f"{globals['root_path']}/features/{demo_params['codebook_dataset']}_" \
f"{demo_params['codebook_features']}.pkl"
desc = io_helpers.load_pickle(features_path)
logger.info(f"Loaded descriptors for codebook")
asmk = asmk.train_codebook(desc['vecs'], cache_path=codebook_path)
metadata = asmk.metadata['train_codebook']
logger.debug(f"Using {metadata['index_class']} index")
if "load_time" in metadata:
logger.info("Loaded pre-trained codebook")
else:
logger.info(f"Codebook trained in {metadata['train_time']:.1f}s")
logger.debug(f"Vectors for codebook clustered in {metadata['cluster_time']:.1f}s " \
f"and indexed in {metadata['index_time']:.1f}s")
return asmk