forked from mindspore-Ecosystem/mindspore
53 lines
2.5 KiB
Python
53 lines
2.5 KiB
Python
# Copyright 2020 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.
|
|
# ============================================================================
|
|
"""
|
|
##############test vgg16 example on cifar10#################
|
|
python eval.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
|
|
"""
|
|
import argparse
|
|
import mindspore.nn as nn
|
|
from mindspore.nn.optim.momentum import Momentum
|
|
from mindspore.train.model import Model
|
|
from mindspore import context
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
from mindspore.model_zoo.vgg import vgg16
|
|
from config import cifar_cfg as cfg
|
|
import dataset
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Cifar10 classification')
|
|
parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
|
|
help='device where the code will be implemented. (Default: Ascend)')
|
|
parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
|
|
parser.add_argument('--checkpoint_path', type=str, default=None, help='checkpoint file path.')
|
|
parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
|
|
args_opt = parser.parse_args()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
|
|
context.set_context(device_id=args_opt.device_id)
|
|
|
|
net = vgg16(num_classes=cfg.num_classes)
|
|
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
|
|
weight_decay=cfg.weight_decay)
|
|
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
|
|
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
|
|
|
|
param_dict = load_checkpoint(args_opt.checkpoint_path)
|
|
load_param_into_net(net, param_dict)
|
|
net.set_train(False)
|
|
dataset = dataset.create_dataset(args_opt.data_path, 1, False)
|
|
res = model.eval(dataset)
|
|
print("result: ", res)
|