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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.
- # ============================================================================
- """evaluate_imagenet"""
- import argparse
- import os
- import ast
- 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 config_gpu, config_ascend, config_cpu
- from src.dataset import create_dataset_imagenet, create_dataset_cifar10
- from src.inception_v2 import inception_v2_base
- from src.loss import CrossEntropy_Val
-
- CFG_DICT = {
- "Ascend": config_ascend,
- "GPU": config_gpu,
- "CPU": config_cpu,
- }
-
- DS_DICT = {
- "imagenet": create_dataset_imagenet,
- "cifar10": create_dataset_cifar10,
- }
-
- if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='image classification evaluation')
- parser.add_argument("--data_url", type=str, help='data path for eval')
- parser.add_argument("--train_url", type=str, help='log')
- parser.add_argument("--run_online", type=ast.literal_eval)
- parser.add_argument('--ckpt_url', type=str, default='', help='checkpoint of inception-v2 (Default: None)')
- parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
- parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend'), help='run platform')
- parser.add_argument('--device_target', type=str, default='Ascend', choices=('Ascend'), help='run platform')
- parser.add_argument('--result_url', type=str, help='log')
- args_opt = parser.parse_args()
- cfg = CFG_DICT[args_opt.platform]
- cfg.work_nums = 1
- if args_opt.run_online:
- import moxing as mox
- cfg.ckpt_path = "/cache/checkpoint_inceptionv2/checkpoint.ckpt"
- Imagenet_root = "/cache/data_eval_url"
- mox.file.copy_parallel(args_opt.data_url, Imagenet_root)
- mox.file.copy_parallel(args_opt.ckpt_url, cfg.ckpt_path)
- else:
- cfg.ckpt_path = args_opt.ckpt_url
- Imagenet_root = args_opt.data_url
- if args_opt.platform == 'Ascend':
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(device_id=device_id)
- create_dataset = DS_DICT[cfg.ds_type]
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform)
- net = inception_v2_base(num_classes=cfg.num_classes)
- ckpt = load_checkpoint(cfg.ckpt_path)
- load_param_into_net(net, ckpt)
- net.set_train(False)
- cfg.rank = 0
- cfg.group_size = 1
- # eval-dataset
- root = os.path.join(Imagenet_root, 'val')
- dataset = create_dataset(root, cfg, False)
- loss = CrossEntropy_Val(smooth_factor=0.1, num_classes=cfg.num_classes)
- 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, dataset_sink_mode=cfg.ds_sink_mode)
- print("metric: ", metrics)
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