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Changed code to save debug images from processed but not encoded labels

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-07-10 15:38:06 +02:00
parent 21cef4a09e
commit 093cbe472e
3 changed files with 9 additions and 6 deletions
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5
src/twomartens/masterthesis/cli.py
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@ -345,7 +345,7 @@ def _ssd_train_get_generators(load_data: callable,
predictor_sizes=predictor_sizes, predictor_sizes=predictor_sizes,
batch_size=batch_size, batch_size=batch_size,
image_size=image_size, image_size=image_size,
training=True, evaluation=False, augment=False, training=True, evaluation=True, augment=False,
nr_trajectories=nr_trajectories) nr_trajectories=nr_trajectories)
val_generator, val_length = \ val_generator, val_length = \
@ -368,8 +368,9 @@ def _ssd_debug_save_images(args: argparse.Namespace, save_images_on_debug: bool,
train_length -= batch_size train_length -= batch_size
train_images = train_data[0] train_images = train_data[0]
train_labels = train_data[1] train_labels = train_data[1]
train_labels_not_encoded = train_data[2]
save_images(train_images, train_labels, summary_path) save_images(train_images, train_labels_not_encoded, summary_path)
return train_length return train_length

2
src/twomartens/masterthesis/data.py
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@ -320,6 +320,8 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
] ]
returns = {'processed_images', 'encoded_labels'} returns = {'processed_images', 'encoded_labels'}
if training and evaluation:
returns = {'processed_images', 'encoded_labels', 'processed_labels'}
if not training and evaluation: if not training and evaluation:
returns = { returns = {

8
src/twomartens/masterthesis/debug.py
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@ -70,10 +70,10 @@ def save_ssd_train_images(images: np.ndarray, labels: np.ndarray,
current_axis = pyplot.gca() current_axis = pyplot.gca()
for instance in instances: for instance in instances:
xmin = (instance[-12] + instance[-8]) * image_size xmin = (instance[-12]) * image_size
ymin = (instance[-11] + instance[-7]) * image_size ymin = (instance[-11]) * image_size
xmax = (instance[-10] + instance[-6]) * image_size xmax = (instance[-10]) * image_size
ymax = (instance[-9] + instance[-5]) * image_size ymax = (instance[-9]) * image_size
class_id = np.argmax(instance[:-12], axis=0) class_id = np.argmax(instance[:-12], axis=0)
if class_id == 0: if class_id == 0:
continue continue