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92 lines
3.5 KiB
92 lines
3.5 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""DPN model eval with MindSpore"""
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import os
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import argparse
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from mindspore import context
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.train.model import Model
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from mindspore.common import set_seed
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.dpn import dpns
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from src.config import config
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from src.imagenet_dataset import classification_dataset
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set_seed(1)
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# set context
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend", save_graphs=False, device_id=device_id)
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def parse_args():
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"""parameters"""
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parser = argparse.ArgumentParser('dpn evaluating')
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# dataset related
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parser.add_argument('--data_dir', type=str, default='', help='eval data dir')
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# network related
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parser.add_argument('--pretrained', type=str, default='', help='ckpt path to load')
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args, _ = parser.parse_known_args()
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args.image_size = config.image_size
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args.num_classes = config.num_classes
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args.batch_size = config.batch_size
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args.num_parallel_workers = config.num_parallel_workers
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args.backbone = config.backbone
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args.loss_scale_num = config.loss_scale_num
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args.rank = config.rank
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args.group_size = config.group_size
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args.dataset = config.dataset
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return args
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def dpn_evaluate(args):
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# create evaluate dataset
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eval_path = os.path.join(args.data_dir, 'val')
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eval_dataset = classification_dataset(eval_path,
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image_size=args.image_size,
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num_parallel_workers=args.num_parallel_workers,
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per_batch_size=args.batch_size,
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max_epoch=1,
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rank=args.rank,
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shuffle=False,
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group_size=args.group_size,
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mode='eval')
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# create network
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net = dpns[args.backbone](num_classes=args.num_classes)
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# load checkpoint
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load_param_into_net(net, load_checkpoint(args.pretrained))
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print("load checkpoint from [{}].".format(args.pretrained))
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# loss
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if args.dataset == "imagenet-1K":
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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else:
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if not args.label_smooth:
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args.label_smooth_factor = 0.0
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loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes)
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# create model
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model = Model(net, amp_level="O2", keep_batchnorm_fp32=False, loss_fn=loss,
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metrics={'top_1_accuracy', 'top_5_accuracy'})
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# evaluate
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output = model.eval(eval_dataset)
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print(f'Evaluation result: {output}.')
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if __name__ == '__main__':
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dpn_evaluate(parse_args())
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print('DPN evaluate success!')
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