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Added top k filtering for bayesian ssd 

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens 2019-08-29 14:02:57 +02:00
parent 599f7d8957
commit 9b3d9a550b
1 changed files with 29 additions and 1 deletions
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30
src/twomartens/masterthesis/ssd.py
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@ -214,6 +214,10 @@ def predict(generator: callable,
confidence_threshold=confidence_threshold,
),
apply_entropy_threshold_func=_apply_entropy_threshold,
apply_top_k_func=functools.partial(
_apply_top_k,
top_k=top_k
),
get_observations_func=_get_observations,
transform_func=functools.partial(
_transform_predictions,
@ -300,7 +304,7 @@ def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
dropout_step: callable, vanilla_step: callable,
save_images: callable, decode_func: callable,
decode_func_dropout: callable, get_observations_func: callable,
apply_entropy_threshold_func: callable,
apply_entropy_threshold_func: callable, apply_top_k_func: callable,
transform_func: callable, save_func: callable,
use_entropy_threshold: bool, entropy_threshold_min: float,
entropy_threshold_max: float) -> None:
@ -329,6 +333,7 @@ def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
for entropy_threshold in entropy_thresholds:
if use_dropout:
decoded_predictions = apply_entropy_threshold_func(observations, entropy_threshold=entropy_threshold)
decoded_predictions = apply_top_k_func(decoded_predictions)
else:
decoded_predictions = decode_func(predictions, entropy_threshold=entropy_threshold)
if not saved_images_decoding:
@ -421,6 +426,29 @@ def _apply_entropy_threshold(observations: Sequence[np.ndarray], entropy_thresho
return final_observations
def _apply_top_k(detections: Sequence[np.ndarray], top_k: float) -> List[np.ndarray]:
final_detections = []
batch_size = len(detections)
data_type = np.dtype([('image_id', np.int32),
('confidence', 'f4'),
('entropy', 'f4'),
('xmin', 'f4'),
('ymin', 'f4'),
('xmax', 'f4'),
('ymax', 'f4')])
for i in range(batch_size):
image_detections = detections[i]
image_detections_structured = np.array(image_detections, dtype=data_type)
descending_indices = np.argsort(-image_detections_structured['confidence'])
image_detections_sorted = np.asarray(image_detections_structured[descending_indices])
top_k_indices = np.argpartition(image_detections_sorted[:, 1],
kth=image_detections_sorted.shape[0] - top_k,
axis=0)[image_detections_sorted.shape[0] - top_k:]
final_detections.append(image_detections_sorted[top_k_indices])
return final_detections
def _transform_predictions(decoded_predictions: np.ndarray, inverse_transforms: Sequence[np.ndarray],
inverse_transform_func: callable) -> np.ndarray: