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Refactor anchor generator and box coder  
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"""
CommandLine:
    pytest tests/test_anchor.py
    xdoctest tests/test_anchor.py zero

"""
import torch


def test_aligned_anchor_generator():
    from mmdet3d.core.anchor import build_anchor_generator
    if torch.cuda.is_available():
        device = 'cuda'
    else:
        device = 'cpu'

    anchor_generator_cfg = dict(
        type='AlignedAnchor3DRangeGenerator',
        ranges=[[-51.2, -51.2, -1.80, 51.2, 51.2, -1.80]],
        strides=[1, 2, 4],
        sizes=[
            [0.8660, 2.5981, 1.],  # 1.5/sqrt(3)
            [0.5774, 1.7321, 1.],  # 1/sqrt(3)
            [1., 1., 1.],
            [0.4, 0.4, 1],
        ],
        custom_values=[0, 0],
        rotations=[0, 1.57],
        size_per_range=False,
        reshape_out=True)

    featmap_sizes = [(256, 256), (128, 128), (64, 64)]
    anchor_generator = build_anchor_generator(anchor_generator_cfg)
    assert anchor_generator.num_base_anchors == 8

    # check base anchors
    expected_grid_anchors = [
        torch.tensor([[
            -51.0000, -51.0000, -1.8000, 0.8660, 2.5981, 1.0000, 0.0000,
            0.0000, 0.0000
        ],
                      [
                          -51.0000, -51.0000, -1.8000, 0.4000, 0.4000, 1.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -50.6000, -51.0000, -1.8000, 0.4000, 0.4000, 1.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -50.2000, -51.0000, -1.8000, 1.0000, 1.0000, 1.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -49.8000, -51.0000, -1.8000, 1.0000, 1.0000, 1.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -49.4000, -51.0000, -1.8000, 0.5774, 1.7321, 1.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -49.0000, -51.0000, -1.8000, 0.5774, 1.7321, 1.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -48.6000, -51.0000, -1.8000, 0.8660, 2.5981, 1.0000,
                          1.5700, 0.0000, 0.0000
                      ]],
                     device=device),
        torch.tensor([[
            -50.8000, -50.8000, -1.8000, 1.7320, 5.1962, 2.0000, 0.0000,
            0.0000, 0.0000
        ],
                      [
                          -50.8000, -50.8000, -1.8000, 0.8000, 0.8000, 2.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -50.0000, -50.8000, -1.8000, 0.8000, 0.8000, 2.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -49.2000, -50.8000, -1.8000, 2.0000, 2.0000, 2.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -48.4000, -50.8000, -1.8000, 2.0000, 2.0000, 2.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -47.6000, -50.8000, -1.8000, 1.1548, 3.4642, 2.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -46.8000, -50.8000, -1.8000, 1.1548, 3.4642, 2.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -46.0000, -50.8000, -1.8000, 1.7320, 5.1962, 2.0000,
                          1.5700, 0.0000, 0.0000
                      ]],
                     device=device),
        torch.tensor([[
            -50.4000, -50.4000, -1.8000, 3.4640, 10.3924, 4.0000, 0.0000,
            0.0000, 0.0000
        ],
                      [
                          -50.4000, -50.4000, -1.8000, 1.6000, 1.6000, 4.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -48.8000, -50.4000, -1.8000, 1.6000, 1.6000, 4.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -47.2000, -50.4000, -1.8000, 4.0000, 4.0000, 4.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -45.6000, -50.4000, -1.8000, 4.0000, 4.0000, 4.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -44.0000, -50.4000, -1.8000, 2.3096, 6.9284, 4.0000,
                          1.5700, 0.0000, 0.0000
                      ],
                      [
                          -42.4000, -50.4000, -1.8000, 2.3096, 6.9284, 4.0000,
                          0.0000, 0.0000, 0.0000
                      ],
                      [
                          -40.8000, -50.4000, -1.8000, 3.4640, 10.3924, 4.0000,
                          1.5700, 0.0000, 0.0000
                      ]],
                     device=device)
    ]
    multi_level_anchors = anchor_generator.grid_anchors(
        featmap_sizes, device=device)
    expected_multi_level_shapes = [
        torch.Size([524288, 9]),
        torch.Size([131072, 9]),
        torch.Size([32768, 9])
    ]
    for i, single_level_anchor in enumerate(multi_level_anchors):
        assert single_level_anchor.shape == expected_multi_level_shapes[i]
        # set [:56:7] thus it could cover 8 (len(size) * len(rotations))
        # anchors on 8 location
        assert single_level_anchor[:56:7].allclose(expected_grid_anchors[i])