forked from mindspore-Ecosystem/mindspore
60 lines
2.5 KiB
Python
60 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.
|
|
# ============================================================================
|
|
"""evaluate imagenet"""
|
|
import argparse
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import context
|
|
from mindspore.train.model import Model
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
|
|
from src.config import efficientnet_b0_config_gpu as cfg
|
|
from src.dataset import create_dataset_val
|
|
from src.efficientnet import efficientnet_b0
|
|
from src.loss import LabelSmoothingCrossEntropy
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='image classification evaluation')
|
|
parser.add_argument('--checkpoint', type=str, default='', help='checkpoint of efficientnet (Default: None)')
|
|
parser.add_argument('--data_path', type=str, default='', help='Dataset path')
|
|
parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
|
|
args_opt = parser.parse_args()
|
|
|
|
if args_opt.platform != 'GPU':
|
|
raise ValueError("Only supported GPU training.")
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform)
|
|
|
|
net = efficientnet_b0(num_classes=cfg.num_classes,
|
|
drop_rate=cfg.drop,
|
|
drop_connect_rate=cfg.drop_connect,
|
|
global_pool=cfg.gp,
|
|
bn_tf=cfg.bn_tf,
|
|
)
|
|
|
|
ckpt = load_checkpoint(args_opt.checkpoint)
|
|
load_param_into_net(net, ckpt)
|
|
net.set_train(False)
|
|
val_data_url = args_opt.data_path
|
|
dataset = create_dataset_val(cfg.batch_size, val_data_url, workers=cfg.workers, distributed=False)
|
|
loss = LabelSmoothingCrossEntropy(smooth_factor=cfg.smoothing)
|
|
eval_metrics = {'Loss': nn.Loss(),
|
|
'Top1-Acc': nn.Top1CategoricalAccuracy(),
|
|
'Top5-Acc': nn.Top5CategoricalAccuracy()}
|
|
model = Model(net, loss, optimizer=None, metrics=eval_metrics)
|
|
|
|
metrics = model.eval(dataset)
|
|
print("metric: ", metrics)
|