forked from mindspore-Ecosystem/mindspore
64 lines
2.5 KiB
Python
64 lines
2.5 KiB
Python
# Copyright 2021 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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"""Training process"""
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import os
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from mindspore import nn
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from mindspore import context
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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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.moxing_adapter import moxing_wrapper
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from src.config import config
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from src.dataset import create_lenet_dataset
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from src.foo import LeNet5
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@moxing_wrapper()
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def train_lenet5():
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"""
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Train lenet5
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"""
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config.ckpt_path = config.output_path
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context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
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ds_train = create_lenet_dataset(os.path.join(config.data_path, "train"), config.batch_size, num_parallel_workers=1)
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if ds_train.get_dataset_size() == 0:
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raise ValueError("Please check dataset size > 0 and batch_size <= dataset size")
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network = LeNet5(config.num_classes)
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net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
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net_opt = nn.Momentum(network.trainable_params(), config.lr, config.momentum)
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time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
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directory=None if config.ckpt_path == "" else config.ckpt_path,
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config=config_ck)
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if config.device_target != "Ascend":
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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else:
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}, amp_level="O2")
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print("============== Starting Training ==============")
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model.train(config.epoch_size, ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()])
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if __name__ == '__main__':
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train_lenet5()
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