forked from mindspore-Ecosystem/mindspore
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
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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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, full=False, reduction='mean'):
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super(Net, self).__init__()
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self.loss = nn.GaussianNLLLoss(full=full, reduction=reduction)
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def construct(self, x, label, v):
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loss = self.loss(x, label, v)
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return loss
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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@pytest.mark.parametrize('full', [True, False])
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def test_gaussian_nll_loss_full(mode, full):
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"""
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Feature: GaussianNLLLoss with reduction='mean'
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Description: Verify the result of GaussianNLLLoss
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Expectation: success
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"""
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context.set_context(mode=mode)
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net = Net(full=full)
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arr1 = np.arange(8).reshape((4, 2))
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arr2 = np.array([2, 3, 1, 4, 6, 4, 4, 9]).reshape((4, 2))
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a = Tensor(arr1, mstype.float32)
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b = Tensor(arr2, mstype.float32)
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var = Tensor(np.ones((4, 1)), mstype.float32)
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output = net(a, b, var)
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if full:
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expected = np.array(2.35644, np.float32)
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else:
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expected = np.array(1.4375, np.float32)
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assert np.allclose(output.asnumpy(), expected)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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@pytest.mark.parametrize('reduction', ['mean', 'sum', 'none'])
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def test_gaussian_nll_loss_reduction(mode, reduction):
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"""
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Feature: GaussianNLLLoss with full=False
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Description: Verify the result of GaussianNLLLoss
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Expectation: success
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"""
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context.set_context(mode=mode)
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net = Net(reduction=reduction)
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arr1 = np.arange(8).reshape((4, 2))
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arr2 = np.array([2, 3, 1, 4, 6, 4, 4, 9]).reshape((4, 2))
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a = Tensor(arr1, mstype.float32)
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b = Tensor(arr2, mstype.float32)
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var = Tensor(np.ones((4, 1)), mstype.float32)
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output = net(a, b, var)
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if reduction == 'mean':
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expected = np.array(1.4375, np.float32)
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elif reduction == 'sum':
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expected = np.array(11.5, np.float32)
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else:
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expected = np.array([[2.0000, 2.0000], [0.5000, 0.5000],
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[2.0000, 0.5000], [2.0000, 2.0000]], np.float32)
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assert np.allclose(output.asnumpy(), expected)
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