53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
# Copyright 2021 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.
|
|
# ============================================================================
|
|
"""Evaluation process"""
|
|
|
|
import os
|
|
|
|
from mindspore import nn
|
|
from mindspore import context
|
|
from mindspore.train import Model
|
|
from mindspore.nn.metrics import Accuracy
|
|
from mindspore.train.serialization import load_checkpoint
|
|
|
|
from src.moxing_adapter import moxing_wrapper
|
|
from src.config import config
|
|
from src.dataset import create_lenet_dataset
|
|
from src.foo import LeNet5
|
|
|
|
|
|
@moxing_wrapper()
|
|
def eval_lenet5():
|
|
"""Evaluation of lenet5"""
|
|
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")
|
|
net_opt = nn.Momentum(network.trainable_params(), config.lr, config.momentum)
|
|
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
|
|
|
|
print("============== Starting Testing ==============")
|
|
load_checkpoint(config.ckpt_path, network)
|
|
ds_eval = create_lenet_dataset(os.path.join(config.data_path, "test"), config.batch_size, 1)
|
|
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_lenet5()
|