def parse_args(args):
""" Parse the arguments.
"""
parser = argparse.ArgumentParser(description='Evaluation script for a RetinaNet network.')
subparsers = parser.add_subparsers(help='Arguments for specific dataset types.', dest='dataset_type')
subparsers.required = True
coco_parser = subparsers.add_parser('coco')
coco_parser.add_argument('coco_path', help='Path to dataset directory (ie. /tmp/COCO).')
pascal_parser = subparsers.add_parser('pascal')
pascal_parser.add_argument('pascal_path', help='Path to dataset directory (ie. /tmp/VOCdevkit).')
data_dir = annotation_path()
args_annotations = data_dir + '/annotations_test.csv'
csv_parser = subparsers.add_parser('csv')
csv_parser.add_argument('--annotations', help='Path to CSV file containing annotations for evaluation.',
default=args_annotations)
csv_parser.add_argument('--classes', help='Path to a CSV file containing class label mapping.',
default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'class_mappings.csv'))
parser.add_argument('--hard_score_rate', help='', default=1.)
parser.add_argument('model', help='Path to RetinaNet model.')
parser.add_argument('--base_dir', help='Path to base dir for CSV file.',
default=image_path())
parser.add_argument('--convert-model',
help='Convert the model to an inference model (ie. the input is a training model).', type=int,
default=1)
parser.add_argument('--backbone', help='The backbone of the model.', default='resnet50')
parser.add_argument('--gpu', help='Id of the GPU to use (as reported by nvidia-smi).')
parser.add_argument('--score-threshold', help='Threshold on score to filter detections with (defaults to 0.05).',
default=0.1, type=float)
parser.add_argument('--iou-threshold', help='IoU Threshold to count for a positive detection (defaults to 0.5).',
default=0.75, type=float)
parser.add_argument('--save-path', help='Path for saving images with detections (doesn\'t work for COCO).')
parser.add_argument('--image-min-side', help='Rescale the image so the smallest side is min_side.', type=int,
default=800)
parser.add_argument('--image-max-side', help='Rescale the image if the largest side is larger than max_side.',
type=int, default=1333)
return parser.parse_args(args)
def parse_args(args):
""" Parse the arguments.
"""
parser = argparse.ArgumentParser(
description='Simple training script for training a RetinaNet network.')
subparsers = parser.add_subparsers(
help='Arguments for specific dataset types.', dest='dataset_type')
subparsers.required = True
coco_parser = subparsers.add_parser('coco')
coco_parser.add_argument('coco_path',
help='Path to dataset directory (ie. /tmp/COCO).')
pascal_parser = subparsers.add_parser('pascal')
pascal_parser.add_argument(
'pascal_path', help='Path to dataset directory (ie. /tmp/VOCdevkit).')
kitti_parser = subparsers.add_parser('kitti')
kitti_parser.add_argument(
'kitti_path', help='Path to dataset directory (ie. /tmp/kitti).')
def csv_list(string):
return string.split(',')
oid_parser = subparsers.add_parser('oid')
oid_parser.add_argument('main_dir', help='Path to dataset directory.')
oid_parser.add_argument('--version',
help='The current dataset version is v4.',
default='v4')
oid_parser.add_argument('--labels-filter',
help='A list of labels to filter.',
type=csv_list,
default=None)
oid_parser.add_argument('--annotation-cache-dir',
help='Path to store annotation cache.',
default='.')
oid_parser.add_argument('--fixed-labels',
help='Use the exact specified labels.',
default=False)
data_dir = annotation_path()
args_annotations = data_dir + '/annotations_train.csv'
args_classes = data_dir + '/class_mappings_train.csv'
args_val_annotations = data_dir + '/annotations_val.csv'
args_snapshot_path = root_dir() + '/snapshot'
args_tensorboard_dir = root_dir() + '/logs'
csv_parser = subparsers.add_parser('csv')
csv_parser.add_argument(
'--annotations',
help='Path to CSV file containing annotations for training.',
default=args_annotations)
csv_parser.add_argument(
'--classes',
help='Path to a CSV file containing class label mapping.',
default=args_classes)
csv_parser.add_argument(
'--val-annotations',
help=
'Path to CSV file containing annotations for validation (optional).',
default=args_val_annotations)
csv_parser.add_argument('--base_dir',
help='Path to base dir for CSV file.',
default=image_path())
group = parser.add_mutually_exclusive_group()
group.add_argument('--snapshot', help='Resume training from a snapshot.')
group.add_argument(
'--imagenet-weights',
help=
'Initialize the model with pretrained imagenet weights. This is the default behaviour.',
action='store_const',
const=True,
default=False)
group.add_argument('--weights',
help='Initialize the model with weights from a file.')
group.add_argument('--no-weights',
help='Don\'t initialize the model with any weights.',
dest='imagenet_weights',
action='store_const',
const=False)
parser.add_argument('--backbone',
help='Backbone model used by retinanet.',
default='resnet50',
type=str)
parser.add_argument('--batch-size',
help='Size of the batches.',
default=1,
type=int)
parser.add_argument(
'--gpu', help='Id of the GPU to use (as reported by nvidia-smi).')
parser.add_argument('--multi-gpu',
help='Number of GPUs to use for parallel processing.',
type=int,
default=0)
parser.add_argument(
'--multi-gpu-force',
help='Extra flag needed to enable (experimental) multi-gpu support.',
action='store_true')
parser.add_argument('--epochs',
help='Number of epochs to train.',
type=int,
default=150)
parser.add_argument('--steps',
help='Number of steps per epoch.',
type=int,
default=10000)
parser.add_argument(
'--snapshot-path',
help=
'Path to store snapshots of models during training (defaults to \'./snapshots\')',
default=args_snapshot_path)
parser.add_argument('--tensorboard-dir',
help='Log directory for Tensorboard output',
default=args_tensorboard_dir)
parser.add_argument('--no-snapshots',
help='Disable saving snapshots.',
dest='snapshots',
action='store_false')
parser.add_argument('--no-evaluation',
help='Disable per epoch evaluation.',
dest='evaluation',
action='store_false')
parser.add_argument('--freeze-backbone',
help='Freeze training of backbone layers.',
action='store_true')
parser.add_argument('--random-transform',
help='Randomly transform image and annotations.',
action='store_true')
parser.add_argument(
'--image-min-side',
help='Rescale the image so the smallest side is min_side.',
type=int,
default=800)
parser.add_argument(
'--image-max-side',
help='Rescale the image if the largest side is larger than max_side.',
type=int,
default=1333)
return check_args(parser.parse_args(args))