Disable augmentation of input for now
Signed-off-by: Jim Martens <github@2martens.de>
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@ -75,13 +75,13 @@ def _ssd_train(args: argparse.Namespace) -> None:
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predictor_sizes=ssd_model.predictor_sizes,
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batch_size=batch_size,
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resized_shape=(image_size, image_size),
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training=True, evaluation=False)
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training=True, evaluation=False, augment=False)
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val_generator, val_length = \
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data.load_scenenet_data(file_names_val, instances_val, args.coco_path,
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predictor_sizes=ssd_model.predictor_sizes,
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batch_size=batch_size,
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resized_shape=(image_size, image_size),
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training=False, evaluation=False)
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training=False, evaluation=False, augment=False)
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del file_names_train, instances_train, file_names_val, instances_val
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if args.debug:
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@ -238,7 +238,8 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
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batch_size: int,
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resized_shape: Sequence[int],
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training: bool,
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evaluation: bool) -> Tuple[callable, int]:
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evaluation: bool,
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augment: bool) -> Tuple[callable, int]:
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"""
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Loads the SceneNet RGB-D data and returns a data set.
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@ -251,6 +252,7 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
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resized_shape: shape of input images to SSD
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training: True if training data is desired
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evaluation: True if evaluation-ready data is desired
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augment: True if training data should be augmented
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Returns:
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scenenet data set generator
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@ -296,14 +298,14 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
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labels=final_labels
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)
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if training:
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shuffle = True
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shuffle = True if training else False
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if training and augment:
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transformations = [data_augmentation_chain_original_ssd.SSDDataAugmentation(
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img_width=resized_shape[0],
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img_height=resized_shape[1]
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)]
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else:
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shuffle = False
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transformations = [
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object_detection_2d_photometric_ops.ConvertTo3Channels(),
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object_detection_2d_geometric_ops.Resize(height=resized_shape[0],
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