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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.
- # ============================================================================
- """train GENet."""
- import os
- import argparse
- 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.CrossEntropySmooth import CrossEntropySmooth
- from src.GENet import GE_resnet50 as Net
- from src.dataset import create_dataset
-
- parser = argparse.ArgumentParser(description='Image classification')
- parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
- parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
- parser.add_argument('--train_url', type=str, default=None, help='Dataset path')
- parser.add_argument('--device_target', type=str, default='Ascend', choices=("Ascend", "GPU", "CPU"),
- help="Device target, support Ascend, GPU and CPU.")
- parser.add_argument('--extra', type=str, default="False",
- help='whether to use Depth-wise conv to down sample')
- parser.add_argument('--mlp', type=str, default="True",
- help='bottleneck . whether to use 1*1 conv')
- parser.add_argument('--is_modelarts', type=str, default="False", help='is train on modelarts')
- args_opt = parser.parse_args()
-
- if args_opt.extra.lower() == "false":
- from src.config import config3 as config
- else:
- if args_opt.mlp.lower() == "false":
- from src.config import config2 as config
- else:
- from src.config import config1 as config
-
- if args_opt.is_modelarts == "True":
- import moxing as mox
-
- set_seed(1)
-
- def trans_char_to_bool(str_):
- """
- Args:
- str_: string
-
- Returns:
- bool
- """
- result = False
- if str_.lower() == "true":
- result = True
- return result
-
- if __name__ == '__main__':
- target = args_opt.device_target
- local_data_url = args_opt.data_url
- local_pretrained_url = args_opt.checkpoint_path
-
- if args_opt.is_modelarts == "True":
- local_data_url = "/cache/data"
- mox.file.copy_parallel(args_opt.data_url, local_data_url)
- local_pretrained_path = "/cache/pretrained"
- mox.file.make_dirs(local_pretrained_path)
- filename = "pretrained.ckpt"
- local_pretrained_url = os.path.join(local_pretrained_path, filename)
- mox.file.copy(args_opt.checkpoint_path, local_pretrained_url)
-
- # init context
- context.set_context(mode=context.GRAPH_MODE,
- device_target=target,
- save_graphs=False)
-
- if target == "Ascend":
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(device_id=device_id)
-
- # create dataset
- dataset = create_dataset(dataset_path=local_data_url,
- do_train=False,
- batch_size=config.batch_size,
- target=target)
- step_size = dataset.get_dataset_size()
-
- # define net
- mlp = trans_char_to_bool(args_opt.mlp)
- extra = trans_char_to_bool(args_opt.extra)
- # define net
- net = Net(class_num=config.class_num, extra=extra, mlp=mlp)
-
- # load checkpoint
- param_dict = load_checkpoint(local_pretrained_url)
- load_param_into_net(net, param_dict)
- net.set_train(False)
-
- # define loss, model
-
- 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)
-
- # 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=", args_opt.checkpoint_path)
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