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Added functionality to use SSD with COCO data set

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-07-23 11:21:06 +02:00
parent e20167182f
commit d6047665ef
3 changed files with 177 additions and 14 deletions
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36
src/twomartens/masterthesis/cli.py
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@ -227,6 +227,7 @@ def _ssd_test(args: argparse.Namespace) -> None:
batch_size, image_size, learning_rate, \
forward_passes_per_image, nr_classes, iou_threshold, dropout_rate, \
use_entropy_threshold, entropy_threshold_min, entropy_threshold_max, \
use_coco, \
top_k, nr_trajectories, test_pretrained, \
coco_path, output_path, weights_path, ground_truth_path = _ssd_test_get_config_values(args, conf.get_property)
@ -252,6 +253,8 @@ def _ssd_test(args: argparse.Namespace) -> None:
ssd.compile_model(ssd_model, learning_rate, loss_func)
test_generator, length_dataset, test_debug_generator = _ssd_test_get_generators(args,
use_coco,
data.load_coco_val_ssd,
data.load_scenenet_data,
file_names,
instances,
@ -531,6 +534,7 @@ def _ssd_test_get_config_values(args: argparse.Namespace,
config_get: Callable[[str], Union[str, float, int, bool]]
) -> Tuple[int, int, float, int, int, float, float,
bool, float, float,
bool,
int, int, bool,
str, str, str, str]:
@ -544,6 +548,7 @@ def _ssd_test_get_config_values(args: argparse.Namespace,
use_entropy_threshold = config_get("Parameters.ssd_use_entropy_threshold")
entropy_threshold_min = config_get("Parameters.ssd_entropy_threshold_min")
entropy_threshold_max = config_get("Parameters.ssd_entropy_threshold_max")
use_coco = config_get("Parameters.ssd_use_coco")
top_k = config_get("Parameters.ssd_top_k")
nr_trajectories = config_get("Parameters.nr_trajectories")
test_pretrained = config_get("Parameters.ssd_test_pretrained")
@ -569,6 +574,8 @@ def _ssd_test_get_config_values(args: argparse.Namespace,
entropy_threshold_min,
entropy_threshold_max,
#
use_coco,
#
top_k,
nr_trajectories,
test_pretrained,
@ -798,7 +805,9 @@ def _ssd_train_get_generators(args: argparse.Namespace,
def _ssd_test_get_generators(args: argparse.Namespace,
load_data: callable,
use_coco: bool,
load_data_coco: callable,
load_data_scenenet: callable,
file_names: Sequence[Sequence[str]],
instances: Sequence[Sequence[Sequence[dict]]],
coco_path: str,
@ -807,16 +816,27 @@ def _ssd_test_get_generators(args: argparse.Namespace,
nr_trajectories: int,
predictor_sizes: Sequence[Sequence[int]]) -> Tuple[Generator, int, Generator]:
from twomartens.masterthesis import data
if nr_trajectories == -1:
nr_trajectories = None
generator, length, debug_generator = load_data(file_names, instances, coco_path,
predictor_sizes=predictor_sizes,
batch_size=batch_size,
image_size=image_size,
training=False, evaluation=True, augment=False,
debug=args.debug,
nr_trajectories=nr_trajectories)
if use_coco:
generator, length, debug_generator = load_data_coco(data.clean_dataset,
data.group_bboxes_to_images,
coco_path,
batch_size,
image_size,
training=False, evaluation=True, augment=False,
debug=args.debug)
else:
generator, length, debug_generator = load_data_scenenet(file_names, instances, coco_path,
predictor_sizes=predictor_sizes,
batch_size=batch_size,
image_size=image_size,
training=False, evaluation=True, augment=False,
debug=args.debug,
nr_trajectories=nr_trajectories)
return generator, length, debug_generator

1
src/twomartens/masterthesis/config.py
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@ -63,6 +63,7 @@ _CONFIG_PROPS = {
"ssd_entropy_threshold_min": (float, "0.1"),
"ssd_entropy_threshold_max": (float, "2.5"),
"ssd_test_pretrained": (bool, "False"),
"ssd_use_coco": (bool, "False"),
"nr_trajectories": (int, "-1")
}
}

154
src/twomartens/masterthesis/data.py
View File

@ -78,7 +78,7 @@ def load_coco_train(data_path: str, category: int,
file_names.update(file_names_val)
ids_to_images = {image['id']: image for image in images}
checked_file_names, checked_bboxes = _clean_dataset(annotations, file_names, ids_to_images)
checked_file_names, checked_bboxes = clean_dataset(annotations, file_names, ids_to_images)
length_dataset = len(checked_file_names)
# build image data set
@ -119,7 +119,7 @@ def load_coco_val(data_path: str, category: int,
file_names = {image['id']: f"{data_path}/val2014/{image['file_name']}" for image in images}
ids_to_images = {image['id']: image for image in images}
checked_file_names, checked_bboxes = _clean_dataset(annotations, file_names, ids_to_images)
checked_file_names, checked_bboxes = clean_dataset(annotations, file_names, ids_to_images)
length_dataset = len(checked_file_names)
# build image data set
@ -133,8 +133,8 @@ def load_coco_val(data_path: str, category: int,
return dataset
def _clean_dataset(annotations: Sequence[dict], file_names: Mapping[str, str],
ids_to_images: Mapping[str, dict]) -> Tuple[List[str], List[List[float]]]:
def clean_dataset(annotations: Sequence[dict], file_names: Mapping[str, str],
ids_to_images: Mapping[str, dict]) -> Tuple[List[str], List[List[float]]]:
"""
Cleans a given data set from problematic cases and returns cleaned version.
@ -152,7 +152,7 @@ def _clean_dataset(annotations: Sequence[dict], file_names: Mapping[str, str],
img_id = annotation['image_id']
image = ids_to_images[img_id]
file_name = file_names[img_id]
bbox = annotation['bbox']
bbox = annotation['bbox'] # type: List[float]
target_height = round(bbox[3])
target_width = round(bbox[2])
image_width, image_height = image['width'], image['height']
@ -178,9 +178,16 @@ def _clean_dataset(annotations: Sequence[dict], file_names: Mapping[str, str],
continue
bbox[2] = target_width
bbox[3] = target_height
new_bbox = [
annotation['category_id'],
x1,
y1,
round(bbox[0] + bbox[2]),
round(bbox[1] + bbox[3])
]
checked_file_names.append(file_name)
checked_bboxes.append(bbox)
checked_bboxes.append(new_bbox)
return checked_file_names, checked_bboxes
@ -232,6 +239,141 @@ def _load_images_callback(resized_shape: Sequence[int]) -> Callable[
return _load_images
def group_bboxes_to_images(file_names: Sequence[str], bboxes: Sequence[Sequence[int]]) -> Tuple[List[str],
List[List[List[int]]]]:
return_labels = {}
for file_name, bbox in zip(file_names, bboxes):
if file_name not in return_labels:
return_labels[file_name] = []
return_labels[file_name].append(bbox)
return list(return_labels.keys()), list(return_labels.values())
def load_coco_val_ssd(clean_dataset: callable,
group_bboxes_to_images: callable,
coco_path: str,
batch_size: int,
image_size: int,
training: bool,
evaluation: bool,
augment: bool,
debug: bool,
predictor_sizes: Optional[np.ndarray]) -> Tuple[Generator, int, Optional[Generator]]:
"""
Loads the COCO minival2014/val2017 data and returns a data set.
Args:
clean_dataset: function that cleans the data set
group_bboxes_to_images: function that groups bounding boxes to corresponding file name
coco_path: path to the COCO data set
batch_size: batch size
image_size: size of images after resizing them
training: True if training data is desired
evaluation: True if evaluation-ready data is desired
augment: True if training data should be augmented
debug: True if a more extensive generator should be added to output
predictor_sizes: sizes of the predictor layers, can be None for evaluation
Returns:
coco data set generator
length of dataset
generator which offers processed_labels as well (only if debug is True)
"""
from pycocotools import coco
from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
annotation_file_minival = f"{coco_path}/annotations/instances_minival2014.json"
resized_shape = (image_size, image_size)
coco_val = coco.COCO(annotation_file_minival)
img_ids = coco_val.getImgIds() # return all image IDs belonging to given category
images = coco_val.loadImgs(img_ids) # load all images
annotation_ids = coco_val.getAnnIds(img_ids)
annotations = coco_val.loadAnns(annotation_ids) # load all image annotations
file_names = {image['id']: f"{coco_path}/val2014/{image['file_name']}" for image in images}
ids_to_images = {image['id']: image for image in images}
annotation_file_train = f"{coco_path}/annotations/instances_train2014.json"
cats_to_classes, _, _, _ = coco_utils.get_coco_category_maps(annotation_file_train)
checked_image_paths, checked_bboxes = clean_dataset(annotations, file_names, ids_to_images)
final_image_paths, final_labels = group_bboxes_to_images(checked_image_paths, checked_bboxes)
data_generator = object_detection_2d_data_generator.DataGenerator(
filenames=final_image_paths,
labels=final_labels
)
shuffle = True if training else False
if training and augment:
transformations = [data_augmentation_chain_original_ssd.SSDDataAugmentation(
img_width=resized_shape[0],
img_height=resized_shape[1]
)]
else:
transformations = [
object_detection_2d_photometric_ops.ConvertTo3Channels(),
object_detection_2d_geometric_ops.Resize(height=resized_shape[0],
width=resized_shape[1])
]
returns = {'processed_images', 'encoded_labels'}
returns_debug = {'processed_images', 'encoded_labels', 'processed_labels'}
if not training and evaluation:
returns = {
'processed_images',
'filenames',
'inverse_transform',
'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],
n_classes=len(cats_to_classes), # 80
predictor_sizes=predictor_sizes,
steps=[8, 16, 32, 64, 100, 300],
coords="corners",
aspect_ratios_per_layer=[[1.0, 2.0, 0.5],
[1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
[1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
[1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
[1.0, 2.0, 0.5],
[1.0, 2.0, 0.5]]
)
if debug:
debug_generator = data_generator.generate(
batch_size=batch_size,
shuffle=shuffle,
transformations=transformations,
label_encoder=label_encoder,
returns=returns_debug,
keep_images_without_gt=False
)
else:
debug_generator = None
length_dataset = data_generator.dataset_size
generator = data_generator.generate(
batch_size=batch_size,
shuffle=shuffle,
transformations=transformations,
label_encoder=label_encoder,
returns=returns,
keep_images_without_gt=False
)
return generator, length_dataset, debug_generator
def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
instances: Sequence[Sequence[Sequence[dict]]],
coco_path: str,