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61 lines
2.1 KiB
61 lines
2.1 KiB
# Copyright 2020 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, Parameter
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from mindspore.ops import operations as P
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import mindspore.common.dtype as mstype
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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var_np = np.random.rand(3, 3).astype(np.float32)
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accum_np = np.random.rand(3, 3).astype(np.float32)
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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.apply_adagrad = P.ApplyAdagrad()
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self.var = Parameter(Tensor(var_np), name="var")
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self.accum = Parameter(Tensor(accum_np), name="accum")
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def construct(self, lr, grad):
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return self.apply_adagrad(self.var, self.accum, lr, grad)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_apply_adagrad():
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# numpy op
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grident_np = np.random.rand(3, 3).astype(np.float32)
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expect_accum_np = accum_np + grident_np * grident_np
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expect_var_np = var_np - (0.001 * grident_np * (1 / np.sqrt(expect_accum_np + 1e-6)))
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net = Net()
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lr = Tensor(0.001, mstype.float32)
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grad = Tensor(grident_np)
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out = net(lr, grad)
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res_var_mindspore = out[0].asnumpy()
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res_accum_mindspore = out[1].asnumpy()
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eps = np.array([1e-6 for i in range(9)]).reshape(3, 3)
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assert np.all(expect_var_np - res_var_mindspore < eps)
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assert np.all(expect_accum_np - res_accum_mindspore < eps)
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