docker run -it mindspore/mindspore-cpu:0.3.0-alpha /bin/bash

conda create -n mindspore python=3.7.5 
conda activate mindspore

conda install numpy

pip install mindspore-0.3.0-cp37-cp37m-win_amd64.whl

def download_dataset():
    """Download the dataset from http://yann.lecun.com/exdb/mnist/."""
    print("******Downloading the MNIST dataset******")
    train_path = "./MNIST_Data/train/"
    test_path = "./MNIST_Data/test/"
    train_path_check = os.path.exists(train_path)
    test_path_check = os.path.exists(test_path)
    if train_path_check == False and test_path_check ==False:
        os.makedirs(train_path)
        os.makedirs(test_path)
    train_url = {"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz", "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"}
    test_url = {"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz", "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"}
    for url in train_url:
        url_parse = urlparse(url)
        # split the file name from url
        file_name = os.path.join(train_path,url_parse.path.split('/')[-1])
        if not os.path.exists(file_name.replace('.gz','')):
            file = urllib.request.urlretrieve(url, file_name)
            unzipfile(file_name)
            os.remove(file_name)
    for url in test_url:
        url_parse = urlparse(url)
        # split the file name from url
        file_name = os.path.join(test_path,url_parse.path.split('/')[-1])
        if not os.path.exists(file_name.replace('.gz','')):
            file = urllib.request.urlretrieve(url, file_name)
            unzipfile(file_name)
            os.remove(file_name)

def create_dataset(data_path, batch_size=32, repeat_size=1,
                   num_parallel_workers=1):
    """ create dataset for train or test
    Args:
        data_path: Data path
        batch_size: The number of data records in each group
        repeat_size: The number of replicated data records
        num_parallel_workers: The number of parallel workers
    """
    # define dataset
    mnist_ds = ds.MnistDataset(data_path)


    # define operation parameters
    resize_height, resize_width = 32, 32
    rescale = 1.0 / 255.0
    shift = 0.0
    rescale_nml = 1 / 0.3081
    shift_nml = -1 * 0.1307 / 0.3081


    # define map operations
    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Resize images to (32, 32)
    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) # normalize images
    rescale_op = CV.Rescale(rescale, shift) # rescale images
    hwc2chw_op = CV.HWC2CHW() # change shape from (height, width, channel) to (channel, height, width) to fit network.
    type_cast_op = C.TypeCast(mstype.int32) # change data type of label to int32 to fit network


    # apply map operations on images
    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)


    # apply DatasetOps
    buffer_size = 10000
    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
    mnist_ds = mnist_ds.repeat(repeat_size)


    return mnist_ds

import mindspore.nn as nnfrom mindspore.common.initializer import TruncatedNormal
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):    """Conv layer weight initial."""    weight = weight_variable()    return nn.Conv2d(in_channels, out_channels,                     kernel_size=kernel_size, stride=stride, padding=padding,                     weight_init=weight, has_bias=False, pad_mode="valid")
def fc_with_initialize(input_channels, out_channels):    """Fc layer weight initial."""    weight = weight_variable()    bias = weight_variable()    return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable():    """Weight initial."""    return TruncatedNormal(0.02)

class LeNet5(nn.Cell):    """Lenet network structure."""    # define the operator required    def __init__(self):        super(LeNet5, self).__init__()        self.conv1 = conv(1, 6, 5)        self.conv2 = conv(6, 16, 5)        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)        self.fc2 = fc_with_initialize(120, 84)        self.fc3 = fc_with_initialize(84, 10)        self.relu = nn.ReLU()        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)        self.flatten = nn.Flatten()
    # use the preceding operators to construct networks    def construct(self, x):        x = self.conv1(x)        x = self.relu(x)        x = self.max_pool2d(x)        x = self.conv2(x)        x = self.relu(x)        x = self.max_pool2d(x)        x = self.flatten(x)        x = self.fc1(x)        x = self.relu(x)        x = self.fc2(x)        x = self.relu(x)        x = self.fc3(x)        return x

from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
# define the loss functionnet_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')

# learning rate setting
lr = 0.01
momentum = 0.9
# define the optimizer
net_opt = nn.Momentum(network.trainable_params(), lr, momentum)

config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)
# save the network model and parameters for subsequence fine-tuning
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)

from mindspore.nn.metrics import Accuracyfrom mindspore.train.callback import LossMonitorfrom mindspore.train import Model
def train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb, sink_mode):    """define the training method"""    print("============== Starting Training ==============")    #load training dataset    ds_train = create_dataset(os.path.join(mnist_path, "train"), 32, repeat_size)    model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=sink_mode)
epoch_size = 1
mnist_path = "./MNIST_Data
# group layers into an object with training and evaluation features
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb)

parser = argparse.ArgumentParser(description='MindSpore LeNet Example')
parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
                        help='device where the code will be implemented (default: CPU)')
args = parser.parse_args(args=[])
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)

...
epoch: 1 step: 262, loss is 1.9212162
epoch: 1 step: 263, loss is 1.8498616
epoch: 1 step: 264, loss is 1.7990671
epoch: 1 step: 265, loss is 1.9492403
epoch: 1 step: 266, loss is 2.0305142
epoch: 1 step: 267, loss is 2.0657792
epoch: 1 step: 268, loss is 1.9582214
epoch: 1 step: 269, loss is 0.9459006
epoch: 1 step: 270, loss is 0.8167224
epoch: 1 step: 271, loss is 0.7432692
...

def test_net(args, network, model, mnist_path):
    """Define the evaluation method."""
    print("============== Starting Testing ==============")
    # load the saved model for evaluation
    param_dict = load_checkpoint("checkpoint_lenet-1_1875.ckpt")
    # load parameter to the network
    load_param_into_net(network, param_dict)
    # load testing dataset
    ds_eval = create_dataset(os.path.join(mnist_path, "test"))
    acc = model.eval(ds_eval, dataset_sink_mode=False)
    print("============== Accuracy:{} ==============".format(acc))

test_net(args, network, model, mnist_path)
============== Starting Testing ==============
============== Accuracy:{'Accuracy': 0.9663461538461539} ==============

└─对象存储/mine-ms-dataset/mine-cifar-10
    ├─train
    │      data_batch_1.bin
    │      data_batch_2.bin
    │      data_batch_3.bin
    │      data_batch_4.bin
    │      data_batch_5.bin
    │
    └─eval
        test_batch.bin

└─对象存储/mine-resnet50-train
    ├─mine-resnet50_cifar10_train
    │      dataset.py
    │      resnet50_train.py
    │
    ├─output
    └─log