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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.
- # ============================================================================
- """ntsnet eval."""
- import argparse
- import ast
- import os
- from mindspore import context, set_seed, Tensor, load_checkpoint, load_param_into_net, ops
- import mindspore.common.dtype as mstype
-
- from src.config import config
- from src.dataset import create_dataset_test
- from src.network import NTS_NET
-
- parser = argparse.ArgumentParser(description='ntsnet eval running')
- parser.add_argument("--run_modelart", type=ast.literal_eval, default=False, help="Run on modelArt, default is false.")
- parser.add_argument('--data_url', default=None, help='Directory contains CUB_200_2011 dataset.')
- parser.add_argument('--train_url', default=None, help='Directory contains checkpoint file and eval.log')
- parser.add_argument('--ckpt_filename', default=None, help='checkpoint file name')
- parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
- args = parser.parse_args()
- run_modelart = args.run_modelart
- if not run_modelart:
- device_id = args.device_id
- else:
- device_id = int(os.getenv('DEVICE_ID'))
-
- batch_size = config.batch_size
-
- resnet50Path = ""
-
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
- context.set_context(device_id=device_id)
-
- if run_modelart:
- import moxing as mox
-
- local_input_url = '/cache/data' + str(device_id)
- local_output_url = '/cache/ckpt' + str(device_id)
- mox.file.copy_parallel(src_url=args.data_url, dst_url=local_input_url)
- mox.file.copy_parallel(src_url=os.path.join(args.train_url, args.ckpt_filename),
- dst_url=os.path.join(local_output_url, args.ckpt_filename))
- mox.file.copy_parallel(src_url=os.path.join(args.train_url, "eval.log"),
- dst_url=os.path.join(local_output_url, "eval.log"))
- else:
- local_input_url = args.data_url
- local_output_url = args.train_url
-
-
- def print2file(obj1, obj2):
- with open(os.path.join(local_output_url, 'eval.log'), 'a') as f:
- f.write(str(obj1))
- f.write(' ')
- f.write(str(obj2))
- f.write(' \r\n')
-
-
- if __name__ == '__main__':
- set_seed(1)
- test_data_set = create_dataset_test(test_path=os.path.join(local_input_url, "CUB_200_2011/test"),
- batch_size=batch_size)
- test_data_loader = test_data_set.create_dict_iterator(output_numpy=True)
-
- ntsnet = NTS_NET(topK=6, resnet50Path=resnet50Path)
- param_dict = load_checkpoint(os.path.join(local_output_url, args.ckpt_filename))
- load_param_into_net(ntsnet, param_dict)
- ntsnet.set_train(False)
- success_num = 0.0
- total_num = 0.0
- for _, data in enumerate(test_data_loader):
- image_data = Tensor(data['image'], mstype.float32)
- label = Tensor(data["label"], mstype.int32)
- _, scrutinizer_out, _, _ = ntsnet(image_data)
- result_label, _ = ops.ArgMaxWithValue(1)(scrutinizer_out)
- success_num = success_num + sum((result_label == label).asnumpy())
- total_num = total_num + float(image_data.shape[0])
- print2file("ckpt file name: ", args.ckpt_filename)
- print2file("accuracy: ", round(success_num / total_num, 3))
- if run_modelart:
- mox.file.copy_parallel(src_url=os.path.join(local_output_url, "eval.log"),
- dst_url=os.path.join(args.train_url, "eval.log"))
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