'block': P.TruncatedNormal(),
'desc_const': [(1, 2, 3)],
'desc_inputs': [],
'skip': ['backward'],
'add_fake_input': True}),
('Select', {
'block': P.Select(),
'desc_inputs': [Tensor(np.array([[True, False, False], [False, True, True]])),
[2, 3], [2, 3]],
'desc_bprop': [[2, 3]]}),
('Rank', {
'block': P.Rank(),
'desc_inputs': [[2, 3]],
'skip': ['backward']}),
('InvertPermutation', {
'block': P.InvertPermutation(),
'desc_const': [(0, 3, 1, 2)],
'desc_inputs': [],
'skip': ['backward']}),
('Square', {
'block': P.Square(),
'desc_inputs': [[4]],
'desc_bprop': [[4]]}),
('Rsqrt', {
'block': P.Rsqrt(),
'desc_inputs': [[4]],
'desc_bprop': [[4]]}),
('Sqrt', {
'block': P.Sqrt(),
'desc_inputs': [[4]],
'desc_bprop': [[4]]}),
def test_invert_permutation():
invert_permutation = P.InvertPermutation()
x = (3, 4, 0, 2, 1)
output = invert_permutation(x)
expect = (2, 4, 3, 0, 1)
assert np.all(output == expect)
from mindspore._checkparam import Rel
from mindspore.nn import Optimizer
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum
from mindspore.train import Model
from ....dataset_mock import MindData
context.set_context(mode=context.GRAPH_MODE, enable_sparse=True)
reduce_sum = P.ReduceSum()
unsorted_segment_sum = P.UnsortedSegmentSum()
transpose = P.Transpose()
shape_op = P.Shape()
reshape = P.Reshape()
size_op = P.Size()
invert_permutation = P.InvertPermutation()
logical_and = P.LogicalAnd()
def get_axis(x):
shape = shape_op(x)
length = F.tuple_len(shape)
perm = F.make_range(0, length)
return perm
class MSELoss(nn.Cell):
def __init__(self):
super(MSELoss, self).__init__()
self.reduce_sum = P.ReduceSum()
self.square = P.Square()
