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.
58 lines
2.1 KiB
58 lines
2.1 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.
|
|
"""
|
|
from mindspore import nn
|
|
from mindspore.train.model import Model
|
|
from mindspore.common import dtype as mstype
|
|
|
|
from src.dataset import create_dataset
|
|
from src.config import set_config
|
|
from src.args import eval_parse_args
|
|
from src.models import define_net, load_ckpt
|
|
from src.utils import switch_precision, set_context
|
|
|
|
if __name__ == '__main__':
|
|
args_opt = eval_parse_args()
|
|
config = set_config(args_opt)
|
|
backbone_net, head_net, net = define_net(config)
|
|
|
|
#load the trained checkpoint file to the net for evaluation
|
|
if args_opt.head_ckpt:
|
|
load_ckpt(backbone_net, args_opt.pretrain_ckpt)
|
|
load_ckpt(head_net, args_opt.head_ckpt)
|
|
else:
|
|
load_ckpt(net, args_opt.pretrain_ckpt)
|
|
|
|
set_context(config)
|
|
switch_precision(net, mstype.float16, config)
|
|
|
|
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, config=config)
|
|
step_size = dataset.get_dataset_size()
|
|
if step_size == 0:
|
|
raise ValueError("The step_size of dataset is zero. Check if the images count of train dataset is more \
|
|
than batch_size in config.py")
|
|
|
|
net.set_train(False)
|
|
|
|
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
|
model = Model(net, loss_fn=loss, metrics={'acc'})
|
|
|
|
res = model.eval(dataset)
|
|
print(f"result:{res}\npretrain_ckpt={args_opt.pretrain_ckpt}")
|
|
if args_opt.head_ckpt:
|
|
print(f"head_ckpt={args_opt.head_ckpt}")
|