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Changed mode argument to training argument to better reflect requirements

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-06-13 15:12:37 +02:00
parent c260b7d824
commit 49a320ca12
2 changed files with 5 additions and 5 deletions
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4
src/twomartens/masterthesis/cli.py
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@ -75,13 +75,13 @@ def _ssd_train(args: argparse.Namespace) -> None:
predictor_sizes=ssd_model.predictor_sizes,
batch_size=batch_size,
resized_shape=(image_size, image_size),
mode="training")
training=True)
val_generator, val_length = \
data.load_scenenet_data(file_names_val, instances_val, args.coco_path,
predictor_sizes=ssd_model.predictor_sizes,
batch_size=batch_size,
resized_shape=(image_size, image_size),
mode="validation")
training=False)
del file_names_train, instances_train, file_names_val, instances_val
nr_batches_train = int(math.ceil(train_length / float(batch_size)))

6
src/twomartens/masterthesis/data.py
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@ -237,7 +237,7 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
coco_path: str, predictor_sizes: np.ndarray,
batch_size: int,
resized_shape: Sequence[int],
mode: str) -> Tuple[callable, int]:
training: bool) -> Tuple[callable, int]:
"""
Loads the SceneNet RGB-D data and returns a data set.
@ -248,7 +248,7 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
predictor_sizes: sizes of the predictor layers
batch_size: size of every batch
resized_shape: shape of input images to SSD
mode: one of "validation" or "training"
training: True if training data is desired
Returns:
scenenet data set generator
@ -290,7 +290,7 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
labels=final_labels
)
if mode == "training":
if training:
shuffle = True
transformations = [data_augmentation_chain_original_ssd.SSDDataAugmentation(
img_width=resized_shape[0],