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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 squeezenet.final"""
- import os
- import ast
- import argparse
- from mindspore import context
- from mindspore.common import set_seed
- from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
- from mindspore.train.model import Model
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from src.CrossEntropySmooth import CrossEntropySmooth
- from src.squeezenet import SqueezeNet as squeezenet
- from src.dataset import create_dataset_imagenet as create_dataset
- from src.config import config_imagenet as config
-
- local_data_url = '/cache/data'
- local_ckpt_url = '/cache/ckpt.ckpt'
-
- parser = argparse.ArgumentParser(description='Image classification')
- parser.add_argument('--dataset', type=str, default='imagenet', help='Dataset.')
- parser.add_argument('--net', type=str, default='squeezenet', help='Model.')
- parser.add_argument('--run_cloudbrain', type=ast.literal_eval, default=False,
- help='Whether it is running on CloudBrain platform.')
- parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
- parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
- parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
- parser.add_argument('--data_url', type=str, default="None", help='Datapath')
- parser.add_argument('--train_url', type=str, default="None", help='Train output path')
- args_opt = parser.parse_args()
-
- set_seed(1)
-
-
- if __name__ == '__main__':
-
- target = args_opt.device_target
- if args_opt.device_target != "Ascend":
- raise ValueError("Unsupported device target.")
-
- # init context
- device_id = os.getenv('DEVICE_ID')
- device_id = int(device_id) if device_id else 0
- context.set_context(mode=context.GRAPH_MODE,
- device_target=target,
- device_id=device_id)
-
- # create dataset
- if args_opt.run_cloudbrain:
- import moxing as mox
- mox.file.copy_parallel(args_opt.checkpoint_path, local_ckpt_url)
- mox.file.copy_parallel(args_opt.data_url, local_data_url)
- dataset = create_dataset(dataset_path=local_data_url,
- do_train=False,
- repeat_num=1,
- batch_size=config.batch_size,
- target=target,
- run_distribute=False)
- else:
- dataset = create_dataset(dataset_path=args_opt.dataset_path,
- do_train=False,
- repeat_num=1,
- batch_size=config.batch_size,
- target=target,
- run_distribute=False)
- step_size = dataset.get_dataset_size()
-
- # define net
- net = squeezenet(num_classes=config.class_num)
-
- # load checkpoint
- if args_opt.run_cloudbrain:
- param_dict = load_checkpoint(local_ckpt_url)
- else:
- param_dict = load_checkpoint(args_opt.checkpoint_path)
- load_param_into_net(net, param_dict)
- net.set_train(False)
-
- # define loss
- if args_opt.dataset == "imagenet":
- if not config.use_label_smooth:
- config.label_smooth_factor = 0.0
- loss = CrossEntropySmooth(sparse=True,
- reduction='mean',
- smooth_factor=config.label_smooth_factor,
- num_classes=config.class_num)
- else:
- loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-
- # define model
- model = Model(net,
- loss_fn=loss,
- metrics={'top_1_accuracy', 'top_5_accuracy'})
-
- # eval model
- res = model.eval(dataset)
- print("result:", res, "ckpt=", local_ckpt_url)
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