def create_generators(args, preprocess_image):
""" Create generators for training and validation.
Args
args : parseargs object containing configuration for generators.
preprocess_image : Function that preprocesses an image for the network.
"""
common_args = {
'batch_size' : args.batch_size,
'image_min_side' : args.image_min_side,
'image_max_side' : args.image_max_side,
'preprocess_image' : preprocess_image,
}
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_rotation=-0.05,
max_rotation=0.05,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
#min_shear=-0.1,
#max_shear=0.1,
min_scaling=(0.8, 0.8),
max_scaling=(1.2, 1.2),
flip_x_chance=0.5,
#flip_y_chance=0.5,
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from ..preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
'train2017',
transform_generator=transform_generator,
**common_args
)
validation_generator = CocoGenerator(
args.coco_path,
'val2017',
**common_args
)
elif args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(
args.pascal_path,
'trainval',
transform_generator=transform_generator,
**common_args
)
validation_generator = PascalVocGenerator(
args.pascal_path,
'test',
**common_args
)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(
args.annotations,
args.classes,
transform_generator=transform_generator,
**common_args
)
if args.val_annotations:
validation_generator = CSVGenerator(
args.val_annotations,
args.classes,
**common_args
)
else:
validation_generator = None
elif args.dataset_type == 'oid':
train_generator = OpenImagesGenerator(
args.main_dir,
subset='train',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
transform_generator=transform_generator,
**common_args
)
validation_generator = OpenImagesGenerator(
args.main_dir,
subset='validation',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
**common_args
)
elif args.dataset_type == 'kitti':
train_generator = KittiGenerator(
args.kitti_path,
subset='train',
transform_generator=transform_generator,
**common_args
)
validation_generator = KittiGenerator(
args.kitti_path,
subset='val',
**common_args
)
else:
raise ValueError('Invalid data type received: {}'.format(args.dataset_type))
return train_generator, validation_generator
def create_generators(args):
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_translation=(-0.3, -0.3),
max_translation=(0.3, 0.3),
min_scaling=(0.2, 0.2),
max_scaling=(2, 2),
flip_x_chance=0.5,
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from ..preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
'train2017',
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = CocoGenerator(args.coco_path,
'val2017',
batch_size=args.batch_size)
elif args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(
args.pascal_path,
'trainval',
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = PascalVocGenerator(args.pascal_path,
'test',
batch_size=args.batch_size)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(args.annotations,
args.classes,
transform_generator=transform_generator,
batch_size=args.batch_size)
if args.val_annotations:
validation_generator = CSVGenerator(args.val_annotations,
args.classes,
batch_size=args.batch_size)
else:
validation_generator = None
elif args.dataset_type == 'oid':
train_generator = OpenImagesGenerator(
args.main_dir,
subset='train',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
fixed_labels=args.fixed_labels,
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = OpenImagesGenerator(
args.main_dir,
subset='validation',
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
fixed_labels=args.fixed_labels,
batch_size=args.batch_size)
elif args.dataset_type == 'kitti':
train_generator = KittiGenerator(
args.kitti_path,
subset='train',
transform_generator=transform_generator,
batch_size=args.batch_size)
validation_generator = KittiGenerator(args.kitti_path,
subset='val',
batch_size=args.batch_size)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return train_generator, validation_generator
def create_generator(args, config):
""" Create the data generators.
Args:
args: parseargs arguments object.
"""
# create random transform generator for augmenting training data
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from ..preprocessing.coco import CocoGenerator
generator = CocoGenerator(args.coco_path,
args.coco_set,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'pascal':
generator = PascalVocGenerator(args.pascal_path,
args.pascal_set,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'csv':
generator = CSVGenerator(args.annotations,
args.classes,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'oid':
generator = OpenImagesGenerator(
args.main_dir,
subset=args.subset,
version=args.version,
labels_filter=args.labels_filter,
fixed_labels=args.fixed_labels,
annotation_cache_dir=args.annotation_cache_dir,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'kitti':
generator = KittiGenerator(args.kitti_path,
subset=args.subset,
transform_generator=transform_generator,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side)
elif args.dataset_type == 'onthefly':
validation_generator = onthefly.OnTheFlyGenerator(
args.annotations, batch_size=args.batch_size, config=config)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return generator
def create_generators(args, preprocess_image):
""" Create generators for training and validation.
Args
args : parseargs object containing configuration for generators.
preprocess_image : Function that preprocesses an image for the network.
"""
common_args = {
"batch_size": args.batch_size,
"config": args.config,
"image_min_side": args.image_min_side,
"image_max_side": args.image_max_side,
"preprocess_image": preprocess_image,
}
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
if args.dataset_type == "coco":
# import here to prevent unnecessary dependency on cocoapi
from keras_retinanet.preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
"train2017",
transform_generator=transform_generator,
**common_args)
validation_generator = CocoGenerator(args.coco_path,
"val2017",
shuffle_groups=False,
**common_args)
elif args.dataset_type == "pascal":
train_generator = PascalVocGenerator(
args.pascal_path,
"trainval",
transform_generator=transform_generator,
**common_args)
validation_generator = PascalVocGenerator(args.pascal_path,
"test",
shuffle_groups=False,
**common_args)
elif args.dataset_type == "csv":
train_generator = CSVGenerator(args.annotations,
args.classes,
transform_generator=transform_generator,
**common_args)
if args.val_annotations:
validation_generator = CSVGenerator(args.val_annotations,
args.classes,
shuffle_groups=False,
**common_args)
else:
validation_generator = None
elif args.dataset_type == "oid":
train_generator = OpenImagesGenerator(
args.main_dir,
subset="train",
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
transform_generator=transform_generator,
**common_args)
validation_generator = OpenImagesGenerator(
args.main_dir,
subset="validation",
version=args.version,
labels_filter=args.labels_filter,
annotation_cache_dir=args.annotation_cache_dir,
parent_label=args.parent_label,
shuffle_groups=False,
**common_args)
elif args.dataset_type == "kitti":
train_generator = KittiGenerator(
args.kitti_path,
subset="train",
transform_generator=transform_generator,
**common_args)
validation_generator = KittiGenerator(args.kitti_path,
subset="val",
shuffle_groups=False,
**common_args)
else:
raise ValueError("Invalid data type received: {}".format(
args.dataset_type))
return train_generator, validation_generator