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- # 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.
- # ============================================================================
- """
- python eval.py
- """
- import argparse
- import os
-
- 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.resnet_ibn import resnet50_ibn_a
- from src.loss import SoftmaxCrossEntropyExpand
- from src.dataset import create_dataset_ImageNet as create_dataset
- from src.lr_generator import lr_generator
- from src.config import cfg
-
- parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
-
- # Datasets
- parser.add_argument('--train_url', default='output path', type=str)
- parser.add_argument('--data_url', default='data path', type=str)
- parser.add_argument('--ckpt_url', default='checkpoint path', type=str)
- parser.add_argument('--eval_url', default='val data path', type=str)
- # Optimization options
- parser.add_argument('--epochs', default=100, type=int, metavar='N',
- help='number of total epochs to run')
- parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
- help='manual epoch number (useful on restarts)')
- parser.add_argument('--train_batch', default=256, type=int, metavar='N',
- help='train batch size (default: 256)')
- parser.add_argument('--test_batch', default=100, type=int, metavar='N',
- help='test batch size (default: 100)')
- # Checkpoints
- parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',
- help='path to save checkpoint (default: checkpoint)')
-
- # Device options
- parser.add_argument('--device_target', type=str, default='Ascend', choices=['GPU', 'Ascend', 'CPU'])
- parser.add_argument('--device_num', type=int, default=1)
- parser.add_argument('--device_id', type=int, default=0)
-
- args = parser.parse_args()
-
- if __name__ == "__main__":
- train_epoch = 1
- step = 60
- target = args.device_target
- context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
- context.set_context(device_id=args.device_id, enable_auto_mixed_precision=True)
-
- lr = lr_generator(cfg.lr, train_epoch, steps_per_epoch=step)
- net = resnet50_ibn_a(num_classes=cfg.class_num, pretrained=False)
- criterion = SoftmaxCrossEntropyExpand(sparse=True)
- optimizer = nn.SGD(params=net.trainable_params(), learning_rate=lr,
- momentum=cfg.momentum, weight_decay=cfg.weight_decay)
- model = Model(net, loss_fn=criterion, optimizer=optimizer, metrics={"top_1_accuracy", "top_5_accuracy"})
-
- print("============== Starting Testing ==============")
- # load the saved model for evaluation
- param_dict = load_checkpoint(args.checkpoint)
- # load parameter to the network
- load_param_into_net(net, param_dict)
- # load testing dataset
- ds_eval = create_dataset(os.path.join(args.eval_url), do_train=False, repeat_num=1,
- batch_size=cfg.test_batch, target=target)
- acc = model.eval(ds_eval, dataset_sink_mode=False)
- print("============== Accuracy:{} ==============".format(acc))
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