def create_generator(args):
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from preprocessing.coco import CocoGenerator
validation_generator = CocoGenerator(args.coco_path, 'val2017')
elif args.dataset_type == 'pascal':
validation_generator = PascalVocGenerator(args.pascal_path,
'test',
image_min_side=800,
image_max_side=1430,
classes={
"hathead": 0,
"nohathead": 1
})
elif args.dataset_type == 'csv':
validation_generator = CSVGenerator(
args.annotations,
args.classes,
)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return validation_generator
def create_generator(args):
""" Create generators for evaluation.
"""
if args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
# from ..preprocessing.coco import CocoGenerator
from preprocessing.coco import CocoGenerator
validation_generator = CocoGenerator(
args.coco_path,
'val2017',
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config)
elif args.dataset_type == 'pascal':
validation_generator = PascalVocGenerator(
args.pascal_path,
'train',
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config)
elif args.dataset_type == 'csv':
validation_generator = CSVGenerator(args.annotations,
args.classes,
image_min_side=args.image_min_side,
image_max_side=args.image_max_side,
config=args.config)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return validation_generator
def create_generator(args):
# 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)
elif args.dataset_type == 'pascal':
generator = PascalVocGenerator(
args.pascal_path,
args.pascal_set,
# classes={"bluehat": 0, 'whitehat': 1, 'yellohat': 2, 'redhat': 3, 'blackhat': 4, 'no_hat': 5},
classes={
'hathead': 0,
'nohathead': 1
},
transform_generator=transform_generator,
image_min_side=1080,
image_max_side=1920,
anchor_ratios=[0.5, 1, 1.5],
anchor_scales=[0, 0.33333333, 0.66666667],
anchor_sizes=[16, 32, 64, 128, 256],
anchor_strides=[8, 16, 32, 64, 128])
elif args.dataset_type == 'csv':
generator = CSVGenerator(args.annotations,
args.classes,
transform_generator=transform_generator)
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)
elif args.dataset_type == 'kitti':
generator = KittiGenerator(args.kitti_path,
subset=args.subset,
transform_generator=transform_generator)
else:
raise ValueError('Invalid data type received: {}'.format(
args.dataset_type))
return generator
def create_generator(args):
# 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
)
elif args.dataset_type == 'pascal':
generator = PascalVocGenerator(
args.pascal_path,
args.pascal_set,
classes={"rebar": 0},
transform_generator=transform_generator
)
elif args.dataset_type == 'csv':
generator = CSVGenerator(
args.annotations,
args.classes,
transform_generator=transform_generator
)
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
)
elif args.dataset_type == 'kitti':
generator = KittiGenerator(
args.kitti_path,
subset=args.subset,
transform_generator=transform_generator
)
else:
raise ValueError('Invalid data type received: {}'.format(args.dataset_type))
return generator
def main(args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# make sure keras is the minimum required version
check_keras_version()
# optionally choose specific GPU
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
# create the model
print('Loading model, this may take a second...')
model = keras.models.load_model(args.model, custom_objects=custom_objects)
# create a generator for testing data
test_generator = CocoGenerator(args.coco_path, args.set)
evaluate_coco(test_generator, model, args.score_threshold)
def create_generators(configs):
"""create a input data generator"""
# create random transform generator for augmenting training data
if not configs["Data_Augmentation"]['only_x_flip']:
transform_generator = random_transform_generator(
min_rotation=configs['Data_Augmentation']['rotation'][0],
max_rotation=configs['Data_Augmentation']['rotation'][1],
min_translation=configs['Data_Augmentation']['min_translation'],
max_translation=configs['Data_Augmentation']['max_translation'],
min_shear=configs['Data_Augmentation']['shear'][0],
max_shear=configs['Data_Augmentation']['shear'][1],
min_scaling=configs['Data_Augmentation']['min_scaling'],
max_scaling=configs['Data_Augmentation']['max_scaling'],
flip_x_chance=0.5,
gray=configs['Data_Augmentation']['gray'],
inverse_color=configs['Data_Augmentation']['inverse_color'],
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
if configs['Dataset']['dataset_type'] == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from preprocessing.coco import CocoGenerator
train_generator = CocoGenerator(
configs['Dataset']['dataset_path'],
'train2017',
transform_generator=transform_generator,
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'])
validation_generator = CocoGenerator(
configs['Dataset']['dataset_path'],
'val2017',
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'])
elif configs['Dataset']['dataset_type'] == 'pascal':
train_generator = PascalVocGenerator(
configs['Dataset']['dataset_path'],
'trainval',
classes=configs['Dataset']['classes'],
transform_generator=transform_generator,
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'],
anchor_ratios=configs['Anchors']['ratios'],
anchor_scales=configs['Anchors']['scales'],
anchor_sizes=configs['Anchors']['sizes'],
anchor_strides=configs['Anchors']['strides'])
validation_generator = PascalVocGenerator(
configs['Dataset']['dataset_path'],
'test',
classes=configs['Dataset']['classes'],
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'],
anchor_ratios=configs['Anchors']['ratios'],
anchor_scales=configs['Anchors']['scales'],
anchor_sizes=configs['Anchors']['sizes'],
anchor_strides=configs['Anchors']['strides'])
elif configs['Dataset']['dataset_type'] == 'csv':
train_generator = CSVGenerator(
configs['Dataset']['csv_data_file'],
configs['Dataset']['csv_classes_file'],
transform_generator=transform_generator,
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'])
if configs['Dataset']['csv_val_annotations']:
validation_generator = CSVGenerator(
configs['Dataset']['csv_val_annotations'],
configs['Dataset']['csv_classes_file'],
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'])
else:
validation_generator = None
elif configs['Dataset']['dataset_type'] == 'oid':
train_generator = OpenImagesGenerator(
configs['Dataset']['dataset_path'],
subset='train',
version=configs['Dataset']['version'],
labels_filter=configs['Dataset']['oid_labels_filter'],
annotation_cache_dir=configs['Dataset']
['oid_annotation_cache_dir'],
fixed_labels=configs['Dataset']['fixed_labels'],
transform_generator=transform_generator,
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'])
validation_generator = OpenImagesGenerator(
configs['Dataset']['dataset_path'],
subset='validation',
version=configs['Dataset']['version'],
labels_filter=configs['Dataset']['oid_labels_filter'],
annotation_cache_dir=configs['Dataset']
['oid_annotation_cache_dir'],
fixed_labels=configs['Dataset']['fixed_labels'],
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'])
elif configs['Dataset']['dataset_type'] == 'kitti':
train_generator = KittiGenerator(
configs['Dataset']['dataset_path'],
subset='train',
transform_generator=transform_generator,
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'])
validation_generator = KittiGenerator(
configs['Dataset']['dataset_path'],
subset='val',
batch_size=configs['Train']['batch_size'],
image_min_side=configs['Train']['image_min_side'],
image_max_side=configs['Train']['image_max_side'])
else:
raise ValueError('Invalid data type received: {}'.format(
configs['Dataset']['dataset_type']))
return train_generator, validation_generator