tta_model = dict(
    type='DetTTAModel',
    tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))

img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
    dict(
        type='TestTimeAug',
        transforms=[
            [
                dict(type='Resize', scale=s, keep_ratio=True)
                for s in img_scales
            ],
            [
                # ``RandomFlip`` must be placed before ``Pad``, otherwise
                # bounding box coordinates after flipping cannot be
                # recovered correctly.
                dict(type='RandomFlip', prob=1.),
                dict(type='RandomFlip', prob=0.)
            ],
            [
                dict(
                    type='Pad',
                    size=(960, 960),
                    pad_val=dict(img=(114, 114, 114))),
            ],
            [
                dict(
                    type='PackDetInputs',
                    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                               'scale_factor', 'flip', 'flip_direction'))
            ]
        ])
]