main.py

# LeNet5 MNIST

import os
# os.environ['DEVICE_ID'] = '0'

import mindspore as ms
import mindspore.context as context
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as CV

from mindspore import nn
from mindspore.train import Model
from mindspore.train.callback import LossMonitor

context.set_context(mode=context.GRAPH_MODE, device_target='Ascend') # Ascend, CPU, GPU


def create_dataset(data_dir, training=True, batch_size=32, resize=(32, 32),
                   rescale=1/(255*0.3081), shift=-0.1307/0.3081, buffer_size=64):
    data_train = os.path.join(data_dir, 'train') # 训练集信息
    data_test = os.path.join(data_dir, 'test') # 测试集信息
    ds = ms.dataset.MnistDataset(data_train if training else data_test)

    ds = ds.map(input_columns=["image"], operations=[CV.Resize(resize), CV.Rescale(rescale, shift), CV.HWC2CHW()])
    ds = ds.map(input_columns=["label"], operations=C.TypeCast(ms.int32))
    # When `dataset_sink_mode=True` on Ascend, append `ds = ds.repeat(num_epochs) to the end
    ds = ds.shuffle(buffer_size=buffer_size).batch(batch_size, drop_remainder=True)

    return ds


class LeNet5(nn.Cell):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5, stride=1, pad_mode='valid')
        self.conv2 = nn.Conv2d(6, 16, 5, stride=1, pad_mode='valid')
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()
        self.fc1 = nn.Dense(400, 120)
        self.fc2 = nn.Dense(120, 84)
        self.fc3 = nn.Dense(84, 10)

    def construct(self, x):
        x = self.relu(self.conv1(x))
        x = self.pool(x)
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)

        return x


def train(data_dir, lr=0.01, momentum=0.9, num_epochs=3):
    ds_train = create_dataset(data_dir)
    ds_eval = create_dataset(data_dir, training=False)

    net = LeNet5()
    loss = nn.loss.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
    opt = nn.Momentum(net.trainable_params(), lr, momentum)
    loss_cb = LossMonitor(per_print_times=ds_train.get_dataset_size())

    model = Model(net, loss, opt, metrics={'acc', 'loss'})
    # dataset_sink_mode can be True when using Ascend
    model.train(num_epochs, ds_train, callbacks=[loss_cb], dataset_sink_mode=False)
    metrics = model.eval(ds_eval, dataset_sink_mode=False)
    print('Metrics:', metrics)


if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--data_url', required=False, default='MNIST', help='Location of data.')
    parser.add_argument('--train_url', required=False, default=None, help='Location of training outputs.')
    args, unknown = parser.parse_known_args()

    if args.data_url.startswith('s3'):
        import moxing
        moxing.file.copy_parallel(src_url=args.data_url, dst_url='MNIST')
        args.data_url = 'MNIST'

    train(args.data_url)