forked from huawei/mindspore2022
52 lines
1.9 KiB
Python
52 lines
1.9 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.nn import TrainOneStepCell, WithLossCell
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from tests.st.networks.models.lenet import LeNet
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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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_lenet():
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'''
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Feature: AdaFactor
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Description: Test AdaFactor
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Expectation: Run lenet success
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'''
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data = Tensor(np.ones([32, 3, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([32]).astype(np.int32))
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net = LeNet()
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net.batch_size = 32
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learning_rate = 0.01
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optimizer = nn.AdaFactor(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate,
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scale_parameter=False, relative_step=False, beta1=0)
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criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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net_with_criterion = WithLossCell(net, criterion)
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train_network = TrainOneStepCell(net_with_criterion, optimizer) # optimizer
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train_network.set_train()
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loss = []
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for _ in range(10):
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res = train_network(data, label)
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loss.append(res.asnumpy())
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assert np.all(loss[-1] < 0.1)
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