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Added labels to data set

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Padded them to make the tensors homogeneous. The zero tensors can be
removed later when evaluating. They are part of data set primarily
to ensure mapping between prediction result and corresponding labels.

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-05-10 15:36:20 +02:00
parent 50fd116bc7
commit c77d7bc157
3 changed files with 23 additions and 6 deletions
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17
src/twomartens/masterthesis/data.py
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@ -255,6 +255,7 @@ def load_scenenet_val(photo_paths: Sequence[Sequence[str]],
annotation_file_train = f"{coco_path}/annotations/instances_train2014.json"
cats_to_classes, _, _, _ = coco_utils.get_coco_category_maps(annotation_file_train)
max_nr_labels = -1
for trajectory in trajectories:
traj_image_paths, traj_instances = trajectory
@ -270,15 +271,25 @@ def load_scenenet_val(photo_paths: Sequence[Sequence[str]],
bbox[3]
])
len_labels = len(labels)
if len_labels > max_nr_labels:
max_nr_labels = len_labels
final_image_paths.append(image_path)
final_labels.append(labels)
empty_label = [0, 0, 0, 0, 0]
for labels in final_labels:
len_labels = len(labels)
if len_labels < max_nr_labels:
labels += empty_label * (max_nr_labels - len_labels)
length_dataset = len(final_image_paths)
path_dataset = tf.data.Dataset.from_tensor_slices(final_image_paths)
# label_dataset = tf.data.Dataset.from_tensor_slices(final_labels)
# dataset = tf.data.Dataset.zip((path_dataset, label_dataset))
dataset = path_dataset.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=length_dataset, count=num_epochs))
label_dataset = tf.data.Dataset.from_tensor_slices(final_labels)
dataset = tf.data.Dataset.zip((path_dataset, label_dataset))
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size=batch_size)
dataset = dataset.map(_load_images_ssd_callback(resized_shape))
dataset = dataset.prefetch(1)

3
src/twomartens/masterthesis/main.py
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@ -196,7 +196,8 @@ def _ssd_val(args: argparse.Namespace) -> None:
instances = pickle.load(file)
scenenet_data, nr_digits = data.load_scenenet_val(file_names_photos, instances, args.coco_path,
batch_size=batch_size, resized_shape=(image_size, image_size))
batch_size=batch_size,
resized_shape=(image_size, image_size))
del file_names_photos, instances
use_summary_writer = summary_ops_v2.create_file_writer(

9
src/twomartens/masterthesis/ssd.py
View File

@ -174,9 +174,11 @@ def _predict_one_epoch(dataset: tf.data.Dataset,
# prepare filename
filename = 'ssd_predictions'
label_filename = 'ssd_labels'
if use_dropout:
filename = f"dropout-{filename}"
output_file = os.path.join(output_path, filename)
label_output_file = os.path.join(output_path, label_filename)
# go through the data set
counter = 0
@ -184,7 +186,7 @@ def _predict_one_epoch(dataset: tf.data.Dataset,
from tensorflow.python.eager import context
for inputs in dataset:
for inputs, labels in dataset:
decoded_predictions_batch = []
if use_dropout:
for _ in range(forward_passes_per_image):
@ -200,14 +202,17 @@ def _predict_one_epoch(dataset: tf.data.Dataset,
if nr_digits is not None:
counter_str = str(counter).zfill(nr_digits)
filename = f"{output_file}-{counter_str}.npy"
label_filename = f"{label_output_file}-{counter_str}.npy"
else:
filename = f"{output_file}-{counter:d}.npy"
label_filename = f"{label_output_file}-{counter:d}.npy"
with open(filename, 'wb') as file:
with open(filename, 'wb') as file, open(label_filename, 'wb') as label_file:
decoded_predictions_batch_np = np.array(decoded_predictions_batch)
del decoded_predictions_batch
np.save(file, decoded_predictions_batch_np, allow_pickle=False, fix_imports=False)
del decoded_predictions_batch_np
np.save(label_file, labels, allow_pickle=False, fix_imports=False)
counter += 1