forked from mindspore-Ecosystem/mindspore
86 lines
2.8 KiB
Python
86 lines
2.8 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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"""
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Function:
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test network
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Usage:
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python test_network_main.py --net lenet --target Ascend
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"""
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import argparse
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import numpy as np
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from models.alexnet import AlexNet
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from models.lenet import LeNet
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from models.resnetv1_5 import resnet50
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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="Ascend")
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def train(net, data, label):
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learning_rate = 0.01
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momentum = 0.9
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optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
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criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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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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res = train_network(data, label)
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print(res)
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assert res
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def test_resnet50():
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data = Tensor(np.ones([32, 3, 224, 224]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([32]).astype(np.int32))
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net = resnet50(32, 10)
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train(net, data, label)
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def test_lenet():
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data = Tensor(np.ones([32, 1, 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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train(net, data, label)
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def test_alexnet():
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data = Tensor(np.ones([32, 3, 227, 227]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([32]).astype(np.int32))
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net = AlexNet()
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train(net, data, label)
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parser = argparse.ArgumentParser(description='MindSpore Testing Network')
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parser.add_argument('--net', default='resnet50', type=str, help='net name')
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parser.add_argument('--device', default='Ascend', type=str, help='device target')
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if __name__ == "__main__":
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args = parser.parse_args()
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context.set_context(device_target=args.device)
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if args.net == 'resnet50':
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test_resnet50()
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elif args.net == 'lenet':
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test_lenet()
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elif args.net == 'alexnet':
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test_alexnet()
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else:
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print("Please add net name like --net lenet")
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