forked from mindspore-Ecosystem/mindspore
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops.composite import GradOperation
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from mindspore.ops.operations import _inner_ops as inner
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, input_x, dout):
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return self.grad(self.network)(input_x, dout)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.HSigmoid = P.HSigmoid()
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def construct(self, x):
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return self.HSigmoid(x)
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class DynamicNet(nn.Cell):
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def __init__(self):
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super(DynamicNet, self).__init__()
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self.HSigmoid = P.HSigmoid()
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self.d = inner.GpuConvertToDynamicShape()
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def construct(self, x):
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x = self.d(x)
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return self.HSigmoid(x)
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def generate_testcases(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.array([-1, -2, 0, 4, 5]).astype(nptype)
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net = Net()
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output = net(Tensor(x))
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expect = np.array([0.33333334, 0.16666667, 0.5, 1, 1]).astype(nptype)
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np.testing.assert_almost_equal(output.asnumpy(), expect)
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sens = np.array([-1.45, 0.63, 0.34, 6.43, 34.6]).astype(nptype)
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backward_net = Grad(Net())
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output = backward_net(Tensor(x), Tensor(sens))
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expect = np.array([-0.2416667, 0.1049999, 5.66666685e-02, 0, 0]).astype(nptype)
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np.testing.assert_almost_equal(output[0].asnumpy(), expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x = np.array([-1, -2, 0, 4, 5]).astype(nptype)
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net = Net()
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output = net(Tensor(x))
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expect = np.array([0.33333334, 0.16666667, 0.5, 1, 1]).astype(nptype)
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np.testing.assert_almost_equal(output.asnumpy(), expect)
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sens = np.array([-1.45, 0.63, 0.34, 6.43, 34.6]).astype(nptype)
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backward_net = Grad(Net())
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output = backward_net(Tensor(x), Tensor(sens))
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expect = np.array([-0.2416667, 0.1049999, 5.66666685e-02, 0, 0]).astype(nptype)
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np.testing.assert_almost_equal(output[0].asnumpy(), expect)
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def generate_dynamic_testcase(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.array([-1, -2, 0, 2, 1]).astype(nptype)
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net = DynamicNet()
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output = net(Tensor(x))
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expect = np.array([0.33333334, 0.16666667, 0.5, 0.8333333, 0.6666667]).astype(nptype)
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np.testing.assert_almost_equal(output.asnumpy(), expect)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_hsigmoid_dynamic_float32():
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generate_dynamic_testcase(np.float32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_hsigmoid_float32():
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generate_testcases(np.float32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_hsigmoid_float16():
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generate_testcases(np.float16)
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