MindSpore框架学习

MindSpore框架学习

研一时候的笔记

手写数字Mnist识别

把大象放进冰箱共需要3步：

第一步:打开冰箱门;

第二步:把大象装进去;

第三步:关好冰箱门。

将一个复杂的任务分解成一个个步骤，按照步骤一点点去实现就可以了。手写数字Mnist识别的任务也是一样，将该任务分解成以下一个个步骤：

1. 加载数据
2. 定义网络模型
3. 定义损失函数
4. 定义优化器
5. Train(训练)的过程：
   • 加载一批数据
   • 将数据传入模型，输出预测结果
   • 将预测结果与ground truth label 两者传入损失函数，计算一个损失值。
   • 求损失值对于网络中每个权重w_i 的一个梯度，即对w_i 求偏导的过程。
   • 利用优化器更新w_i 的值。
   • 重复以上过程，直到 Loss 收敛。

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Lenet Tutorial"""
import os
import mindspore.dataset as ds
import mindspore.nn as nn
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
import mindspore.ops.operations as P
from mindspore.common.initializer import TruncatedNormal
import mindspore.dataset.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
from mindspore.dataset.vision import Inter
from mindspore.nn.metrics import Accuracy
from mindspore.common import dtype as mstype
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits




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


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.batch_size = 32
        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.reshape = P.Reshape()

    # 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.reshape(x, (self.batch_size, -1))
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.fc3(x)
        return x


def train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb):
    """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=False)


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))


if __name__ == "__main__":

    #运行设备
    device_target = "CPU" # ['Ascend', 'GPU', 'CPU'] 
    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)

    # learning rate 学习率
    lr = 0.01
    epoch_size = 1
    mnist_path = "./MNIST_Data"

    # define the loss function 损失函数
    net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    repeat_size = epoch_size
    
    # create the network
    network = LeNet5()
    # define the optimizer
    net_opt = nn.Momentum(network.trainable_params(), lr, 0.9)
    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)
    # 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)
    test_net(args, network, model, mnist_path)