mindspore2022/tests/st/networks/test_network_main.py

# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Function:
    test network
Usage:
    python test_network_main.py --net lenet --target Ascend
"""
import argparse

import numpy as np
from models.alexnet import AlexNet
from models.lenet import LeNet
from models.resnetv1_5 import resnet50

import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum

context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")


def train(net, data, label):
    learning_rate = 0.01
    momentum = 0.9

    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
    criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
    net_with_criterion = WithLossCell(net, criterion)
    train_network = TrainOneStepCell(net_with_criterion, optimizer)  # optimizer
    train_network.set_train()
    res = train_network(data, label)
    print(res)
    assert res


def test_resnet50():
    data = Tensor(np.ones([32, 3, 224, 224]).astype(np.float32) * 0.01)
    label = Tensor(np.ones([32]).astype(np.int32))
    net = resnet50(32, 10)
    train(net, data, label)


def test_lenet():
    data = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
    label = Tensor(np.ones([32]).astype(np.int32))
    net = LeNet()
    train(net, data, label)


def test_alexnet():
    data = Tensor(np.ones([32, 3, 227, 227]).astype(np.float32) * 0.01)
    label = Tensor(np.ones([32]).astype(np.int32))
    net = AlexNet()
    train(net, data, label)


parser = argparse.ArgumentParser(description='MindSpore Testing Network')
parser.add_argument('--net', default='resnet50', type=str, help='net name')
parser.add_argument('--device', default='Ascend', type=str, help='device target')
if __name__ == "__main__":
    args = parser.parse_args()
    context.set_context(device_target=args.device)
    if args.net == 'resnet50':
        test_resnet50()
    elif args.net == 'lenet':
        test_lenet()
    elif args.net == 'alexnet':
        test_alexnet()
    else:
        print("Please add net name like --net lenet")