You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
80 lines
3.1 KiB
80 lines
3.1 KiB
# 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.
|
|
# ============================================================================
|
|
"""eval mobilenet_v1."""
|
|
import os
|
|
import argparse
|
|
from mindspore import context
|
|
from mindspore.common import set_seed
|
|
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
|
from mindspore.train.model import Model
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
from src.CrossEntropySmooth import CrossEntropySmooth
|
|
from src.mobilenet_v1 import mobilenet_v1 as mobilenet
|
|
|
|
parser = argparse.ArgumentParser(description='Image classification')
|
|
parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
|
|
|
|
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
|
|
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
|
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
|
|
args_opt = parser.parse_args()
|
|
|
|
set_seed(1)
|
|
|
|
if args_opt.dataset == 'cifar10':
|
|
from src.config import config1 as config
|
|
from src.dataset import create_dataset1 as create_dataset
|
|
else:
|
|
from src.config import config2 as config
|
|
from src.dataset import create_dataset2 as create_dataset
|
|
|
|
if __name__ == '__main__':
|
|
target = args_opt.device_target
|
|
|
|
# init context
|
|
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
|
|
if target != "GPU":
|
|
device_id = int(os.getenv('DEVICE_ID'))
|
|
context.set_context(device_id=device_id)
|
|
|
|
# create dataset
|
|
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
|
|
target=target)
|
|
step_size = dataset.get_dataset_size()
|
|
|
|
# define net
|
|
net = mobilenet(class_num=config.class_num)
|
|
|
|
# load checkpoint
|
|
param_dict = load_checkpoint(args_opt.checkpoint_path)
|
|
load_param_into_net(net, param_dict)
|
|
net.set_train(False)
|
|
|
|
# define loss, model
|
|
if args_opt.dataset == "imagenet2012":
|
|
if not config.use_label_smooth:
|
|
config.label_smooth_factor = 0.0
|
|
loss = CrossEntropySmooth(sparse=True, reduction='mean',
|
|
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
|
|
else:
|
|
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
|
|
|
# define model
|
|
model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
|
|
|
|
# eval model
|
|
res = model.eval(dataset)
|
|
print("result:", res, "ckpt=", args_opt.checkpoint_path)
|