forked from mindspore-Ecosystem/mindspore
90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
# Copyright 2019 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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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 mindspore.nn.optim import Momentum
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class AlexNet(nn.Cell):
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def __init__(self, num_classes=10):
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super(AlexNet, self).__init__()
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self.batch_size = 32
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self.conv1 = nn.Conv2d(3, 96, 11, stride=4, pad_mode="valid")
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self.conv2 = nn.Conv2d(96, 256, 5, stride=1, pad_mode="same")
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self.conv3 = nn.Conv2d(256, 384, 3, stride=1, pad_mode="same")
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self.conv4 = nn.Conv2d(384, 384, 3, stride=1, pad_mode="same")
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self.conv5 = nn.Conv2d(384, 256, 3, stride=1, pad_mode="same")
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="valid")
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self.flatten = nn.Flatten()
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self.fc1 = nn.Dense(6 * 6 * 256, 4096)
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self.fc2 = nn.Dense(4096, 4096)
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self.fc3 = nn.Dense(4096, num_classes)
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def construct(self, x):
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x = self.conv1(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv2(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.conv3(x)
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x = self.relu(x)
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x = self.conv4(x)
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x = self.relu(x)
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x = self.conv5(x)
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x = self.relu(x)
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x = self.max_pool2d(x)
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.relu(x)
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x = self.fc3(x)
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return x
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_trainTensor(num_classes=10, epoch=15, batch_size=32):
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net = AlexNet(num_classes)
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lr = 0.1
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momentum = 0.9
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, momentum, weight_decay=0.0001)
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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)
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train_network.set_train()
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losses = []
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for i in range(0, epoch):
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data = Tensor(np.ones([batch_size, 3, 227, 227]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([batch_size]).astype(np.int32))
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loss = train_network(data, label).asnumpy()
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losses.append(loss)
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assert losses[-1] < 0.01
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