'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)
