forked from mindspore-Ecosystem/mindspore
57 lines
2.3 KiB
Python
57 lines
2.3 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.
|
|
# ============================================================================
|
|
"""
|
|
eval.
|
|
"""
|
|
import os
|
|
import argparse
|
|
from dataset import create_dataset
|
|
from config import config
|
|
from mindspore import context
|
|
from mindspore.model_zoo.mobilenet import mobilenet_v2
|
|
from mindspore.train.model import Model
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
|
from mindspore.common import dtype as mstype
|
|
|
|
parser = argparse.ArgumentParser(description='Image classification')
|
|
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')
|
|
args_opt = parser.parse_args()
|
|
|
|
device_id = int(os.getenv('DEVICE_ID'))
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False)
|
|
|
|
if __name__ == '__main__':
|
|
loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
|
|
net = mobilenet_v2(num_classes=config.num_classes)
|
|
net.to_float(mstype.float16)
|
|
for _, cell in net.cells_and_names():
|
|
if isinstance(cell, nn.Dense):
|
|
cell.add_flags_recursive(fp32=True)
|
|
|
|
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
|
|
step_size = dataset.get_dataset_size()
|
|
|
|
if args_opt.checkpoint_path:
|
|
param_dict = load_checkpoint(args_opt.checkpoint_path)
|
|
load_param_into_net(net, param_dict)
|
|
net.set_train(False)
|
|
|
|
model = Model(net, loss_fn=loss, metrics={'acc'})
|
|
res = model.eval(dataset)
|
|
print("result:", res, "ckpt=", args_opt.checkpoint_path)
|