forked from mindspore-Ecosystem/mindspore
35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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import mindspore.ops as ops
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from mindspore import Tensor
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from mindspore import context
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class Net(nn.Cell):
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def __init__(self, reduction='mean'):
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super(Net, self).__init__()
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self.reduction = reduction
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def construct(self, x, label):
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loss = ops.hinge_embedding_loss(x, label, reduction=self.reduction)
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return loss
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_hinge_embedding_loss_abnormal(mode):
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"""
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Feature: HingeEmbeddingLoss
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Description: Verify abnormal inputs of HingeEmbeddingLoss
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Expectation: raise ValueError
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"""
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context.set_context(mode=mode)
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net = Net(reduction='mean')
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arr1 = np.array([0.9, -1.2, 2, 0.8, 3.9, 2, 1, 0, -1]).reshape((3, 3))
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arr2 = np.array([1, 1, -1, 1]).reshape((2, 2))
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a = Tensor(arr1, mstype.float32)
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b = Tensor(arr2, mstype.float32)
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with pytest.raises(ValueError):
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net(a, b)
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