forked from mindspore-Ecosystem/mindspore
66 lines
2.8 KiB
Python
66 lines
2.8 KiB
Python
# 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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"""
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######################## eval lenet example ########################
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eval lenet according to model file:
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python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
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"""
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import os
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import argparse
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train import Model
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from mindspore.nn.metrics import Accuracy
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from src.dataset import create_dataset
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from src.config import mnist_cfg as cfg
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from src.lenet_fusion import LeNet5 as LeNet5Fusion
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parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
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parser.add_argument('--device_target', type=str, default="Ascend",
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choices=['Ascend', 'GPU'],
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help='device where the code will be implemented (default: Ascend)')
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parser.add_argument('--data_path', type=str, default="./MNIST_Data",
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help='path where the dataset is saved')
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parser.add_argument('--ckpt_path', type=str, default="",
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help='if mode is test, must provide path where the trained ckpt file')
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parser.add_argument('--dataset_sink_mode', type=bool, default=True,
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help='dataset_sink_mode is False or True')
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args = parser.parse_args()
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if __name__ == "__main__":
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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ds_eval = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1)
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step_size = ds_eval.get_dataset_size()
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# define fusion network
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network = LeNet5Fusion(cfg.num_classes)
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# define loss
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net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
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# define network optimization
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net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
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# call back and monitor
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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# load check point into network
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param_dict = load_checkpoint(args.ckpt_path)
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load_param_into_net(network, param_dict)
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print("============== Starting Testing ==============")
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acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
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print("============== {} ==============".format(acc))
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