コード例 #1
0
ファイル: prop_patches.py プロジェクト: xnyr/neural-imagen
def test_extract_patches_even(array, coords):
    pb = patches.PatchBuilder(patch_size=2)
    result = pb.extract(array)

    for y, x in coords:
        y = y % (array.shape[2] - 1)
        x = x % (array.shape[3] - 1)
        column = torch.cat(
            [
                array[:, :, y + 0, x + 0],
                array[:, :, y + 0, x + 1],
                array[:, :, y + 1, x + 0],
                array[:, :, y + 1, x + 1],
            ],
            dim=1,
        )
        assert (result[:, :, y, x] == column).all()

    column_br = torch.cat(
        [
            array[:, :, -1, -1],
            array[:, :, -1, -1],
            array[:, :, -1, -1],
            array[:, :, -1, -1],
        ],
        dim=1,
    )
    assert (result[:, :, -1, -1] == column_br).all()
コード例 #2
0
ファイル: prop_patches.py プロジェクト: xnyr/neural-imagen
def test_reconstruct_patches_even(array):
    pb = patches.PatchBuilder(patch_size=2, weights=(1.0, 0.0, 0.0, 0.0))
    intermed = pb.extract(array)
    repro = pb.reconstruct(intermed)

    assert array.shape == repro.shape
    assert (array == repro).all()
コード例 #3
0
ファイル: prop_patches.py プロジェクト: xnyr/neural-imagen
def test_reconstruct_patches_identity(array):
    pb = patches.PatchBuilder(patch_size=1)
    intermed = pb.extract(array)
    repro = pb.reconstruct(intermed)

    assert array.shape == repro.shape
    assert (array == repro).all()
コード例 #4
0
def test_extract_patches_odd(array, coords):
    pb = patches.PatchBuilder(patch_size=3)
    result = pb.extract(array)

    for y, x in coords:
        y = y % (array.shape[0] - 2)
        x = x % (array.shape[1] - 2)
        column = torch.cat([
            array[y + 0, x + 0], array[y + 0, x + 1], array[y + 0, x + 2],
            array[y + 1, x + 0], array[y + 1, x + 1], array[y + 1, x + 2],
            array[y + 2, x + 0], array[y + 2, x + 1], array[y + 2, x + 2]
        ],
                           dim=0)
        assert (result[y + 1, x + 1] == column).all()

    column_br = torch.cat([
        array[-2, -2], array[-2, -1], array[-2, -1], array[-1, -2],
        array[-1, -1], array[-1, -1], array[-1, -2], array[-1, -1], array[-1,
                                                                          -1]
    ],
                          dim=0)
    assert (result[-1, -1] == column_br).all()
コード例 #5
0
ファイル: prop_patches.py プロジェクト: xnyr/neural-imagen
def test_extract_size(array, patch_size):
    pb = patches.PatchBuilder(patch_size)
    result = pb.extract(array)
    assert result.shape[1] == array.shape[1] * (patch_size**2)
    assert result.shape[2:] == array.shape[2:]
コード例 #6
0
ファイル: prop_patches.py プロジェクト: xnyr/neural-imagen
def test_extract_min_max(patch_size):
    pb = patches.PatchBuilder(patch_size)
    assert abs(pb.min) <= pb.max
    assert (pb.max - pb.min) + 1 == patch_size
    assert len(pb.coords) == patch_size