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Predictor sizes only required for training

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-07-09 12:20:37 +02:00
parent 4bdd069650
commit b123d676bc
1 changed files with 5 additions and 2 deletions
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7
src/twomartens/masterthesis/data.py
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@ -234,12 +234,13 @@ def _load_images_callback(resized_shape: Sequence[int]) -> Callable[
def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
instances: Sequence[Sequence[Sequence[dict]]],
coco_path: str, predictor_sizes: np.ndarray,
coco_path: str,
batch_size: int,
resized_shape: Sequence[int],
training: bool,
evaluation: bool,
augment: bool,
predictor_sizes: Optional[np.ndarray],
nr_trajectories: Optional[int] = None) -> Tuple[callable, int]:
"""
Loads the SceneNet RGB-D data and returns a data set.
@ -248,12 +249,12 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
photo_paths: contains a list of image paths per trajectory
instances: instance data per frame per trajectory
coco_path: path to the COCO data set
predictor_sizes: sizes of the predictor layers
batch_size: size of every batch
resized_shape: shape of input images to SSD
training: True if training data is desired
evaluation: True if evaluation-ready data is desired
augment: True if training data should be augmented
predictor_sizes: sizes of the predictor layers, can be None for evaluation
nr_trajectories: number of trajectories to consider
Returns:
@ -327,6 +328,8 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
'original_labels'}
label_encoder = None
else:
if predictor_sizes is None:
raise ValueError("predictor_sizes cannot be None for training/validation")
label_encoder = ssd_input_encoder.SSDInputEncoder(
img_height=resized_shape[0],
img_width=resized_shape[1],