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91 lines
2.8 KiB
91 lines
2.8 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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""" test_parser_construct """
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import pytest
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import numpy as np
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from mindspore import context
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from mindspore.nn import Cell
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from mindspore.common.tensor 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.common.parameter import Parameter
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def setup_module():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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@pytest.mark.level0
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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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def test_parser_construct():
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class ParentNet(Cell):
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def __init__(self):
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super().__init__()
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self.relu = P.ReLU()
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def construct(self, x):
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return self.relu(x)
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class UncleNet(Cell):
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def __init__(self):
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super(UncleNet, self).__init__()
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self.sigmoid = P.Sigmoid()
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def construct(self, x):
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return self.sigmoid(x)
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class Net(UncleNet, ParentNet):
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def __init__(self):
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super().__init__()
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super(UncleNet, self).__init__()
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def construct(self, x):
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return super(UncleNet, self).construct(x)
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input_np_x = np.ones([2, 3, 4, 5]).astype(np.float32)
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out_np = np.ones([2, 3, 4, 5]).astype(np.float32)
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input_me = Tensor(input_np_x)
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output_grad_me = Tensor(out_np)
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net = Net()
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out_me = net(input_me)
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net1 = Net()
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grad = GradOperation(sens_param=True)
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grad_op = grad(net1)
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grad_me = grad_op(input_me, output_grad_me)
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assert np.allclose(input_np_x, out_me.asnumpy(), 0.001, 0.001)
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assert np.allclose(input_np_x, grad_me.asnumpy(), 0.001, 0.001)
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@pytest.mark.level0
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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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def test_sit_parser_input_parameter():
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def tensor_add(x, y):
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add = P.Add()
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z = add(x, y)
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return z
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x = Tensor(np.ones([2, 2]).astype(np.float32))
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x = Parameter(x, name="x")
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y = Tensor(np.ones([2, 2]).astype(np.float32))
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y = Parameter(y, name="y")
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grad = GradOperation(get_all=True, get_by_list=False, sens_param=False)
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with pytest.raises(TypeError):
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grad(tensor_add)(x, y)
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