'block': P.L2Loss(), 'desc_inputs': [Tensor(np.array([1, 2, 3, 4]), mstype.float16)], 'desc_bprop': []}), ('L2Loss_2', { 'block': P.L2Loss(), 'desc_inputs': [Tensor(np.array([[1, 1], [2, 2], [3, 3], [4, 4]]), mstype.float16)], 'desc_bprop': []}), ] test_case_array_ops = [ ('SpaceToDepth', { 'block': P.SpaceToDepth(2), 'desc_inputs': [[1, 3, 2, 2]], 'desc_bprop': [[1, 12, 1, 1]]}), ('DepthToSpace', { 'block': P.DepthToSpace(2), 'desc_inputs': [[1, 12, 1, 1]], 'desc_bprop': [[1, 3, 2, 2]]}), ('Split', { 'block': P.Split(1, 2), 'desc_inputs': [Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))], 'skip': ['backward']}), ('Argmax', { 'block': P.Argmax(), 'desc_inputs': [[128, 32, 32, 64]], 'desc_bprop': [0], 'skip': ['backward']}), ('Argmin', { 'block': P.Argmin(), 'desc_inputs': [[128, 32, 32, 64]], 'desc_bprop': [1],
def __init__(self, upscale): super(PixelShuffle, self).__init__() self.pixel_shuffle = P.DepthToSpace(upscale)
def __init__(self): super(DepthToSpaceNet, self).__init__() block_size = 2 self.depth_to_space = P.DepthToSpace(block_size)