forked from mindspore-Ecosystem/mindspore
87 lines
3.1 KiB
Python
87 lines
3.1 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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import mindspore.ops as P
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from mindspore.nn.optim import Momentum
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from mindspore.common import ParameterTuple
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class GradofParams(nn.Cell):
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def __init__(self, net, sens=False):
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super().__init__()
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self.grad = P.GradOperation(get_all=False, get_by_list=True, sens_param=sens)
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self.net = net
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self.params = ParameterTuple(self.net.trainable_params())
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def construct(self, *x):
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out = self.grad(self.net, self.params)(*x)
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return out
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@pytest.mark.level1
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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.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_pynative_temporary_cell_variables():
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context.set_context(mode=context.PYNATIVE_MODE)
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.add = P.Add()
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self.conv = nn.Conv2d(1, 1, 3, weight_init='ones', pad_mode='pad')
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self.relu = nn.ReLU()
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def construct(self, x):
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x = self.conv(x)
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x = self.relu(x)
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x = self.add(x, x)
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return x
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class TempCellNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.add = P.Add()
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self.conv = nn.Conv2d(1, 1, 3, weight_init='ones', pad_mode='pad')
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def construct(self, x):
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x = self.conv(x)
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x = nn.ReLU()(x)
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x = self.add(x, x)
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return x
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input_data = Tensor(np.random.randn(1, 1, 224, 224).astype(np.float32))
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# The first net run
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net = Net()
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backnet = GradofParams(net)
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
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grad_first = backnet(input_data)
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optimizer(grad_first)
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grad_second = backnet(input_data)
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# The second net run
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compare_net = TempCellNet()
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compare_backnet = GradofParams(compare_net)
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compare_optimizer = Momentum(filter(lambda x: x.requires_grad, compare_net.get_parameters()), 0.1, 0.9)
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compare_grad_first = compare_backnet(input_data)
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compare_optimizer(compare_grad_first)
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compare_grad_second = compare_backnet(input_data)
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# compare result
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assert np.allclose(grad_first[0].asnumpy(), compare_grad_first[0].asnumpy(), 0.01, 0.01)
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assert np.allclose(grad_second[0].asnumpy(), compare_grad_second[0].asnumpy(), 0.01, 0.01)
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