forked from mindspore-Ecosystem/mindspore
79 lines
3.6 KiB
Python
79 lines
3.6 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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#################train vgg16 example on cifar10########################
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python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
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"""
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import argparse
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import random
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.train.model import Model
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from mindspore import context
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
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from mindspore.model_zoo.vgg import vgg16
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import dataset
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from config import cifar_cfg as cfg
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random.seed(1)
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np.random.seed(1)
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def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
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"""Set learning rate."""
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lr_each_step = []
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total_steps = steps_per_epoch * total_epochs
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decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
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for i in range(total_steps):
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if i < decay_epoch_index[0]:
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lr_each_step.append(lr_max)
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elif i < decay_epoch_index[1]:
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lr_each_step.append(lr_max * 0.1)
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elif i < decay_epoch_index[2]:
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lr_each_step.append(lr_max * 0.01)
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else:
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lr_each_step.append(lr_max * 0.001)
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current_step = global_step
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lr_each_step = np.array(lr_each_step).astype(np.float32)
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learning_rate = lr_each_step[current_step:]
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return learning_rate
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Cifar10 classification')
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parser.add_argument('--device_target', type=str, default='Ascend', 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='./cifar', help='path where the dataset is saved')
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parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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context.set_context(device_id=args_opt.device_id)
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context.set_context(enable_mem_reuse=True, enable_hccl=False)
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net = vgg16(batch_size=cfg.batch_size, num_classes=cfg.num_classes)
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lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=50000 // cfg.batch_size)
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum, weight_decay=cfg.weight_decay)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
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dataset = dataset.create_dataset(args_opt.data_path, cfg.epoch_size)
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batch_num = dataset.get_dataset_size()
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config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix="train_vgg_cifar10", directory="./", config=config_ck)
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loss_cb = LossMonitor()
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model.train(cfg.epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
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