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Changed filtering to occur per-class 

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens 2019-09-08 21:47:00 +02:00
parent aaaea5f942
commit a41f607ca9
2 changed files with 29 additions and 9 deletions
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3
src/twomartens/masterthesis/cli.py
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@ -274,7 +274,8 @@ def _ssd_test(args: argparse.Namespace) -> None:
paths.output_path,
conf_obj.paths.coco,
use_dropout,
nr_digits)
nr_digits,
conf_obj.parameters.nr_classes)
def _ssd_evaluate(args: argparse.Namespace) -> None:

35
src/twomartens/masterthesis/ssd.py
View File

@ -159,7 +159,8 @@ def predict(generator: callable,
output_path: str,
coco_path: str,
use_dropout: bool,
nr_digits: int) -> None:
nr_digits: int,
nr_classes: int) -> None:
"""
Run trained SSD on the given data set.
@ -184,6 +185,7 @@ def predict(generator: callable,
coco_path: the path to the COCO data set
use_dropout: if True, multiple forward passes and observations will be used
nr_digits: number of digits needed to print largest batch number
nr_classes: number of classes
"""
output_file, label_output_file = _predict_prepare_paths(output_path, use_dropout)
@ -213,7 +215,11 @@ def predict(generator: callable,
image_size=image_size,
confidence_threshold=confidence_threshold,
),
apply_entropy_threshold_func=_apply_entropy_threshold,
apply_entropy_threshold_func=functools.partial(
_apply_entropy_filtering,
confidence_threshold=confidence_threshold,
nr_classes=nr_classes
),
apply_top_k_func=functools.partial(
_apply_top_k,
top_k=top_k
@ -332,7 +338,8 @@ def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
observations = get_observations_func(decoded_predictions)
for entropy_threshold in entropy_thresholds:
if use_dropout:
decoded_predictions = apply_entropy_threshold_func(observations, entropy_threshold=entropy_threshold)
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)
@ -408,7 +415,10 @@ def _decode_predictions_dropout(predictions: np.ndarray,
)
def _apply_entropy_threshold(observations: Sequence[np.ndarray], entropy_threshold: float) -> List[np.ndarray]:
def _apply_entropy_filtering(observations: Sequence[np.ndarray],
entropy_threshold: float,
confidence_threshold: float,
nr_classes: int) -> List[np.ndarray]:
final_observations = []
batch_size = len(observations)
for i in range(batch_size):
@ -417,10 +427,19 @@ def _apply_entropy_threshold(observations: Sequence[np.ndarray], entropy_thresho
continue
filtered_image_observations = observations[i][observations[i][:, -1] < entropy_threshold]
final_image_observations = np.copy(filtered_image_observations[:, -8:-1])
final_image_observations[:, 0] = np.argmax(filtered_image_observations[:, :-5], axis=-1)
final_image_observations[:, 1] = np.amax(filtered_image_observations[:, :-5], axis=-1)
final_image_observations[:, 2] = filtered_image_observations[:, -1]
final_image_observations = []
for class_id in range(1, nr_classes):
single_class = filtered_image_observations[:, [class_id, -1, -5, -4, -3, -2]]
threshold_met = single_class[single_class[:, 0] > confidence_threshold]
if threshold_met.shape[0] > 0:
output = np.zeros((single_class.shape[0], single_class.shape[1] + 1))
output[:, 0] = class_id
output[:, 1:] = single_class
final_image_observations.append(output)
if final_image_observations:
final_image_observations = np.concatenate(final_image_observations, axis=0)
else:
final_image_observations = np.array(final_image_observations)
final_observations.append(final_image_observations)
return final_observations