|
- # 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.
- # ============================================================================
- """
- ##############test hardnet example on imagenet#################
- python3 eval.py
- """
- import argparse
- import random
- import numpy as np
- from mindspore import context
- from mindspore import dataset
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.train import Model
-
- from src.dataset import create_dataset_ImageNet
- from src.HarDNet import HarDNet85
- from src.EntropyLoss import CrossEntropySmooth
- from src.config import config
-
- random.seed(1)
- np.random.seed(1)
- dataset.config.set_seed(1)
-
- parser = argparse.ArgumentParser(description='Image classification')
- parser.add_argument('--use_hardnet', type=bool, default=True, help='Enable HarnetUnit')
- parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
-
- parser.add_argument('--dataset_path', type=str, default='/data/imagenet_original/val/',
- help='Dataset path')
- parser.add_argument('--ckpt_path', type=str,
- default='/home/hardnet/result/HarDNet-150_625.ckpt',
- help='if mode is test, must provide path where the trained ckpt file')
- parser.add_argument('--label_smooth_factor', type=float, default=0.1, help='label_smooth_factor')
- parser.add_argument('--device_id', type=int, default=0, help='device_id')
- args = parser.parse_args()
-
- def test(ckpt_path):
- """run eval"""
- target = args.device_target
- # init context
- context.set_context(mode=context.GRAPH_MODE,
- device_target=target,
- save_graphs=False,
- device_id=args.device_id)
-
- # dataset
- predict_data = create_dataset_ImageNet(dataset_path=args.dataset_path,
- do_train=False,
- repeat_num=1,
- batch_size=config.batch_size,
- target=target)
- step_size = predict_data.get_dataset_size()
- if step_size == 0:
- raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
-
- # define net
- network = HarDNet85(num_classes=config.class_num)
-
- # load checkpoint
- param_dict = load_checkpoint(ckpt_path)
- load_param_into_net(network, param_dict)
-
- # define loss, model
- loss = CrossEntropySmooth(smooth_factor=args.label_smooth_factor,
- num_classes=config.class_num)
-
- model = Model(network, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
- print("Dataset path: {}".format(args.dataset_path))
- print("Ckpt path :{}".format(ckpt_path))
- print("Class num: {}".format(config.class_num))
- print("Backbone hardnet")
- print("============== Starting Testing ==============")
- acc = model.eval(predict_data)
- print("==============Acc: {} ==============".format(acc))
-
-
- if __name__ == '__main__':
- path = args.ckpt_path
- test(path)
|