Function _demo_mm_inputs

tests/test_models/test_forward/utils.py:47–125

Create a superset of inputs needed to run test or train batches. Args: input_shape (tuple): input batch dimensions num_items (None | List[int]): specifies the number of boxes in each batch item num_classes (int): number of different

(
        input_shape=(1, 3, 300, 300),
        num_items=None,
        num_classes=10,
        with_track=False)

def _demo_mm_inputs(
        input_shape=(1, 3, 300, 300),
        num_items=None,
        num_classes=10,
        with_track=False):
    """Create a superset of inputs needed to run test or train batches.

    Args:
        input_shape (tuple):
            input batch dimensions

        num_items (None | List[int]):
            specifies the number of boxes in each batch item

        num_classes (int):
            number of different labels a box might have
    """
    from mmdet.core import BitmapMasks

    (N, C, H, W) = input_shape

    rng = np.random.RandomState(0)

    imgs = rng.rand(*input_shape)

    img_metas = [{
        'img_shape': (H, W, C),
        'ori_shape': (H, W, C),
        'pad_shape': (H, W, C),
        'filename': '<demo>.png',
        'scale_factor': 1.0,
        'flip': False,
        'frame_id': 0,
        'img_norm_cfg': {
            'mean': (128.0, 128.0, 128.0),
            'std': (10.0, 10.0, 10.0)
        }
    } for i in range(N)]

    gt_bboxes = []
    gt_labels = []
    gt_masks = []
    gt_instance_ids = []

    for batch_idx in range(N):
        if num_items is None:
            num_boxes = rng.randint(1, 10)
        else:
            num_boxes = num_items[batch_idx]

        cx, cy, bw, bh = rng.rand(num_boxes, 4).T

        tl_x = ((cx * W) - (W * bw / 2)).clip(0, W)
        tl_y = ((cy * H) - (H * bh / 2)).clip(0, H)
        br_x = ((cx * W) + (W * bw / 2)).clip(0, W)
        br_y = ((cy * H) + (H * bh / 2)).clip(0, H)

        boxes = np.vstack([tl_x, tl_y, br_x, br_y]).T