'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()