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Added use_nms config option

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-09-13 11:47:18 +02:00
parent 3d78309603
commit 90d64ae7c7
3 changed files with 24 additions and 18 deletions
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35
src/twomartens/masterthesis/cli.py
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@ -266,23 +266,24 @@ def _ssd_test(args: argparse.Namespace) -> None:
nr_digits = _get_nr_digits(generators.length, conf_obj.parameters.batch_size) nr_digits = _get_nr_digits(generators.length, conf_obj.parameters.batch_size)
steps_per_epoch = _get_nr_batches(generators.length, conf_obj.parameters.batch_size) steps_per_epoch = _get_nr_batches(generators.length, conf_obj.parameters.batch_size)
ssd.predict(generators.generator, ssd.predict(generator=generators.generator,
ssd_model, model=ssd_model,
steps_per_epoch, steps_per_epoch=steps_per_epoch,
conf_obj.parameters.ssd_image_size, image_size=conf_obj.parameters.ssd_image_size,
conf_obj.parameters.batch_size, batch_size=conf_obj.parameters.batch_size,
conf_obj.parameters.ssd_forward_passes_per_image, forward_passes_per_image=conf_obj.parameters.ssd_forward_passes_per_image,
conf_obj.parameters.ssd_use_entropy_threshold, use_nms=conf_obj.parameters.ssd_use_nms,
conf_obj.parameters.ssd_entropy_threshold_min, use_entropy_threshold=conf_obj.parameters.ssd_use_entropy_threshold,
conf_obj.parameters.ssd_entropy_threshold_max, entropy_threshold_min=conf_obj.parameters.ssd_entropy_threshold_min,
conf_obj.parameters.ssd_confidence_threshold, entropy_threshold_max=conf_obj.parameters.ssd_entropy_threshold_max,
conf_obj.parameters.ssd_iou_threshold, confidence_threshold=conf_obj.parameters.ssd_confidence_threshold,
conf_obj.parameters.ssd_top_k, iou_threshold=conf_obj.parameters.ssd_iou_threshold,
paths.output_path, top_k=conf_obj.parameters.ssd_top_k,
conf_obj.paths.coco, output_path=paths.output_path,
use_dropout, coco_path=conf_obj.paths.coco,
nr_digits, use_dropout=use_dropout,
conf_obj.parameters.nr_classes) nr_digits=nr_digits,
nr_classes=conf_obj.parameters.nr_classes)
def _ssd_evaluate(args: argparse.Namespace) -> None: def _ssd_evaluate(args: argparse.Namespace) -> None:

1
src/twomartens/masterthesis/config.py
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@ -62,6 +62,7 @@ _CONFIG_PROPS = {
"ssd_iou_threshold": (float, "0.45"), "ssd_iou_threshold": (float, "0.45"),
"ssd_top_k": (int, "200"), "ssd_top_k": (int, "200"),
"ssd_dropout_rate": (float, "0.5"), "ssd_dropout_rate": (float, "0.5"),
"ssd_use_nms": (bool, "True"),
"ssd_use_entropy_threshold": (bool, "False"), "ssd_use_entropy_threshold": (bool, "False"),
"ssd_entropy_threshold_min": (float, "0.1"), "ssd_entropy_threshold_min": (float, "0.1"),
"ssd_entropy_threshold_max": (float, "2.5"), "ssd_entropy_threshold_max": (float, "2.5"),

6
src/twomartens/masterthesis/ssd.py
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@ -150,6 +150,7 @@ def predict(generator: callable,
image_size: int, image_size: int,
batch_size: int, batch_size: int,
forward_passes_per_image: int, forward_passes_per_image: int,
use_nms: bool,
use_entropy_threshold: bool, use_entropy_threshold: bool,
entropy_threshold_min: float, entropy_threshold_min: float,
entropy_threshold_max: float, entropy_threshold_max: float,
@ -174,6 +175,7 @@ def predict(generator: callable,
batch_size: number of items in every batch batch_size: number of items in every batch
forward_passes_per_image: specifies number of forward passes per image forward_passes_per_image: specifies number of forward passes per image
used by DropoutSSD used by DropoutSSD
use_nms: if True non-maximum suppression will be used for Bayesian SSD
use_entropy_threshold: if True entropy thresholding is applied use_entropy_threshold: if True entropy thresholding is applied
entropy_threshold_min: specifies the minimum threshold for the entropy entropy_threshold_min: specifies the minimum threshold for the entropy
entropy_threshold_max: specifies the maximum threshold for the entropy entropy_threshold_max: specifies the maximum threshold for the entropy
@ -219,7 +221,8 @@ def predict(generator: callable,
_apply_entropy_filtering, _apply_entropy_filtering,
confidence_threshold=confidence_threshold, confidence_threshold=confidence_threshold,
nr_classes=nr_classes, nr_classes=nr_classes,
iou_threshold=iou_threshold iou_threshold=iou_threshold,
use_nms=use_nms
), ),
apply_top_k_func=functools.partial( apply_top_k_func=functools.partial(
_apply_top_k, _apply_top_k,
@ -322,6 +325,7 @@ def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
if use_entropy_threshold: if use_entropy_threshold:
nr_steps = math.floor((entropy_threshold_max - entropy_threshold_min) * 10) nr_steps = math.floor((entropy_threshold_max - entropy_threshold_min) * 10)
entropy_thresholds = [round(i / 10 + entropy_threshold_min, 1) for i in range(nr_steps)] entropy_thresholds = [round(i / 10 + entropy_threshold_min, 1) for i in range(nr_steps)]
# entropy_thresholds = [1.4]
else: else:
entropy_thresholds = [0] entropy_thresholds = [0]