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sun_zhongqian authored 2021-12-16 17:02 . remove redundance code
# 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 lenet example ########################
eval lenet according to model file:
python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
"""
import os
from src.model_utils.config import config
from src.model_utils.moxing_adapter import moxing_wrapper
from src.dataset import create_dataset
from src.lenet import LeNet5
import mindspore.nn as nn
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
def modelarts_process():
config.ckpt_path = config.ckpt_file
@moxing_wrapper(pre_process=modelarts_process)
def eval_lenet():
print('eval with config: ', config)
context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
network = LeNet5(config.num_classes)
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
model = Model(network, net_loss, metrics={"Accuracy": Accuracy()})
print("============== Starting Testing ==============")
param_dict = load_checkpoint(config.ckpt_path)
load_param_into_net(network, param_dict)
ds_eval = create_dataset(os.path.join(config.data_path, "test"),
config.batch_size,)
if ds_eval.get_dataset_size() == 0:
raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
acc = model.eval(ds_eval)
print("============== {} ==============".format(acc))
if __name__ == "__main__":
eval_lenet()
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