def train_sample_places_low_shot(
low_shot_trainer: SVMLowShotTrainer,
k_values: List[int],
sample_inds: List[int],
sample_num: int,
output_dir: str,
layername: str,
cfg: AttrDict,
):
# setup the environment variables
set_env_vars(local_rank=0, node_id=0, cfg=cfg)
for low_shot_kvalue in k_values:
checkpoint_dir = f"{output_dir}/sample{sample_num}_k{low_shot_kvalue}"
train_data = merge_features(checkpoint_dir, "train", layername)
train_features = train_data["features"]
train_targets = train_data["targets"]
checkpoint_dir = f"{output_dir}/sample{sample_inds[0]}_k{k_values[0]}"
test_data = merge_features(checkpoint_dir, "test", layername)
test_features = test_data["features"]
test_targets = test_data["targets"]
low_shot_trainer.train(train_features, train_targets, sample_num,
low_shot_kvalue)
low_shot_trainer.test(test_features, test_targets, sample_num,
low_shot_kvalue)
def train_places_low_shot(
k_values: List[int],
sample_inds: List[int],
output_dir: str,
layername: str,
cfg: AttrDict,
):
low_shot_trainer = SVMLowShotTrainer(cfg["SVM"],
layer=layername,
output_dir=output_dir)
# we have extracted the features in the
running_tasks = [
mp.Process(
target=train_sample_places_low_shot,
args=(
low_shot_trainer,
k_values,
sample_inds,
sample_num,
output_dir,
layername,
cfg,
),
) for sample_num in sample_inds
]
for running_task in running_tasks:
running_task.start()
for running_task in running_tasks:
running_task.join()
results = low_shot_trainer.aggregate_stats(k_values, sample_inds)
logging.info(f"All Done for layer: {layername}")
return results
def train_voc07_low_shot(
k_values: List[int],
sample_inds: List[int],
output_dir: str,
layername: str,
cfg: AttrDict,
):
dataset_name = cfg["SVM"]["low_shot"]["dataset_name"]
low_shot_trainer = SVMLowShotTrainer(cfg["SVM"],
layer=layername,
output_dir=output_dir)
train_data = merge_features(output_dir, "train", layername)
train_features, train_targets = train_data["features"], train_data[
"targets"]
test_data = merge_features(output_dir, "test", layername)
test_features, test_targets = test_data["features"], test_data["targets"]
# now we want to create the low-shot samples based on the kind of dataset.
# We only create low-shot samples for training. We test on the full dataset.
generate_low_shot_samples(dataset_name, train_targets, k_values,
sample_inds, output_dir, layername)
# Now, we train and test the low-shot SVM for every sample and k-value.
for sample_num in sample_inds:
for low_shot_kvalue in k_values:
train_targets = load_file(
f"{output_dir}/{layername}_sample{sample_num}_k{low_shot_kvalue}.npy"
)
low_shot_trainer.train(train_features, train_targets, sample_num,
low_shot_kvalue)
low_shot_trainer.test(test_features, test_targets, sample_num,
low_shot_kvalue)
# now we aggregate the stats across all independent samples and for each
# k-value and report mean/min/max/std stats
results = low_shot_trainer.aggregate_stats(k_values, sample_inds)
logging.info("All Done!")
return results