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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.
- # ============================================================================
- """eval resnet."""
- import os
- import ast
- import argparse
- import numpy as np
- from mindspore import context
- from mindspore.common import set_seed
- from mindspore.train.model import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from src.resnet import resnet50
- from src.dataset import create_dataset0 as create_dataset
- from src.utility import GetDatasetGenerator_eval, recall_topk_parallel
-
- parser = argparse.ArgumentParser(description='Image classification')
- # modelarts parameter
- parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
- parser.add_argument('--ckpt_url', type=str, default=None, help='ckpt path')
- parser.add_argument('--checkpoint_name', type=str, default='resnet-120_625.ckpt', help='Checkpoint file')
- # Ascend parameter
- parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
- parser.add_argument('--ckpt_path', type=str, default=None, help='Checkpoint file path')
- parser.add_argument('--device_id', type=int, default=0, help='Device id')
- parser.add_argument('--run_modelarts', type=ast.literal_eval, default=False, help='Run distribute')
- args_opt = parser.parse_args()
- set_seed(1)
-
- if __name__ == '__main__':
-
- if args_opt.run_modelarts:
- import moxing as mox
- device_id = int(os.getenv('DEVICE_ID'))
- device_num = int(os.getenv('RANK_SIZE'))
- context.set_context(device_id=device_id)
- local_data_url = '/cache/data/'
- local_ckpt_url = '/cache/ckpt/'
- mox.file.copy_parallel(args_opt.data_url, local_data_url)
- mox.file.copy_parallel(args_opt.ckpt_url, local_ckpt_url)
- DATA_DIR = local_data_url
- else:
- device_id = args_opt.device_id
- device_num = 1
- context.set_context(device_id=args_opt.device_id)
- DATA_DIR = args_opt.dataset_path
-
- context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', save_graphs=False)
-
- #dataset
- VAL_LIST = DATA_DIR + "/test_half.txt"
- dataset_generator_val = GetDatasetGenerator_eval(DATA_DIR, VAL_LIST)
-
- eval_dataset = create_dataset(dataset_generator_val, do_train=False, batch_size=30,
- device_num=device_num, rank_id=device_id)
- step_size = eval_dataset.get_dataset_size()
-
- # define net
- net = resnet50(class_num=5184)
-
- # load checkpoint
- if args_opt.run_modelarts:
- checkpoint_path = os.path.join(local_ckpt_url, args_opt.checkpoint_name)
- else:
- checkpoint_path = args_opt.ckpt_path
- param_dict = load_checkpoint(checkpoint_path)
- load_param_into_net(net.backbone, param_dict)
- net.set_train(False)
-
- # define model
- model_eval = Model(net.backbone)
- f, l = [], []
- for data in eval_dataset.create_dict_iterator():
- out = model_eval.predict(data['image'])
- f.append(out.asnumpy())
- l.append(data['label'].asnumpy())
- f = np.vstack(f)
- l = np.hstack(l)
- recall = recall_topk_parallel(f, l, k=1)
- print("eval_recall:", recall, "ckpt=", checkpoint_path)
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