昇思MindSpore 25天学习打卡营|day10

作者：繁依Fanyi0 | 2024-07-18 08:09:16

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前段时间忙着小学期的实验还有答辩，今天刚刚忙完，就赶着来学习MindSpore，继续完成打卡。

ResNet50迁移学习

加载数据集

狼狗数据集提取自ImageNet分类数据集，使用mindspore.dataset.ImageFolderDataset接口来加载数据集，并进行相关图像增强操作。

首先执行过程定义一些输入：


batch_size = 18                             # 批量大小
image_size = 224                            # 训练图像空间大小
num_epochs = 5                             # 训练周期数
lr = 0.001                                  # 学习率
momentum = 0.9                              # 动量
workers = 4                                 # 并行线程个数

对数据集的加载并进行相关图像增强操作：


import mindspore as ms
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
 
# 数据集目录路径
data_path_train = "./datasets-Canidae/data/Canidae/train/"
data_path_val = "./datasets-Canidae/data/Canidae/val/"
 
# 创建训练数据集
 
def create_dataset_canidae(dataset_path, usage):
    """数据加载"""
    data_set = ds.ImageFolderDataset(dataset_path,
                                     num_parallel_workers=workers,
                                     shuffle=True,)
 
    # 数据增强操作
    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
    scale = 32
 
    if usage == "train":
        # Define map operations for training dataset
        trans = [
            vision.RandomCropDecodeResize(size=image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
            vision.RandomHorizontalFlip(prob=0.5),
            vision.Normalize(mean=mean, std=std),
            vision.HWC2CHW()
        ]
    else:
        # Define map operations for inference dataset
        trans = [
            vision.Decode(),
            vision.Resize(image_size + scale),
            vision.CenterCrop(image_size),
            vision.Normalize(mean=mean, std=std),
            vision.HWC2CHW()
        ]
 
 
    # 数据映射操作
    data_set = data_set.map(
        operations=trans,
        input_columns='image',
        num_parallel_workers=workers)
 
 
    # 批量操作
    data_set = data_set.batch(batch_size)
 
    return data_set
 
 
dataset_train = create_dataset_canidae(data_path_train, "train")
step_size_train = dataset_train.get_dataset_size()
 
dataset_val = create_dataset_canidae(data_path_val, "val")
step_size_val = dataset_val.get_dataset_size()

数据可视化

从mindspore.dataset.ImageFolderDataset接口加载的训练数据返回值为字典，用户可通过create_dict_iterator 接口创建数据迭代器，使用next迭代访问数据集。上文中我设置的batch_size是18，所以用next一次可获取18个图像及标签数据。


data = next(dataset_train.create_dict_iterator())
images = data["image"]
labels = data["label"]
 
print("Tensor of image", images.shape)
print("Labels:", labels)

Tensor of image (18, 3, 224, 224)
Labels: [0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0]

对获取到的图像及标签数据进行可视化，标题为图像对应的label名称


import matplotlib.pyplot as plt
import numpy as np
 
# class_name对应label，按文件夹字符串从小到大的顺序标记label
class_name = {0: "dogs", 1: "wolves"}
 
plt.figure(figsize=(5, 5))
for i in range(4):
    # 获取图像及其对应的label
    data_image = images[i].asnumpy()
    data_label = labels[i]
    # 处理图像供展示使用
    data_image = np.transpose(data_image, (1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    data_image = std * data_image + mean
    data_image = np.clip(data_image, 0, 1)
    # 显示图像
    plt.subplot(2, 2, i+1)
    plt.imshow(data_image)
    plt.title(class_name[int(labels[i].asnumpy())])
    plt.axis("off")
 
plt.show()

训练模型

使用ResNet50模型进行训练。搭建好模型框架后，通过将pretrained参数设置为True来下载ResNet50的预训练模型并将权重参数加载到网络中。

构建Resnet50网络


from typing import Type, Union, List, Optional
from mindspore import nn, train
from mindspore.common.initializer import Normal
 
 
weight_init = Normal(mean=0, sigma=0.02)
gamma_init = Normal(mean=1, sigma=0.02)


class ResidualBlockBase(nn.Cell):
    expansion: int = 1  # 最后一个卷积核数量与第一个卷积核数量相等
 
    def __init__(self, in_channel: int, out_channel: int,
                 stride: int = 1, norm: Optional[nn.Cell] = None,
                 down_sample: Optional[nn.Cell] = None) -> None:
        super(ResidualBlockBase, self).__init__()
        if not norm:
            self.norm = nn.BatchNorm2d(out_channel)
        else:
            self.norm = norm
 
        self.conv1 = nn.Conv2d(in_channel, out_channel,
                               kernel_size=3, stride=stride,
                               weight_init=weight_init)
        self.conv2 = nn.Conv2d(in_channel, out_channel,
                               kernel_size=3, weight_init=weight_init)
        self.relu = nn.ReLU()
        self.down_sample = down_sample
 
    def construct(self, x):
        """ResidualBlockBase construct."""
        identity = x  # shortcuts分支
 
        out = self.conv1(x)  # 主分支第一层：3*3卷积层
        out = self.norm(out)
        out = self.relu(out)
        out = self.conv2(out)  # 主分支第二层：3*3卷积层
        out = self.norm(out)
 
        if self.down_sample is not None:
            identity = self.down_sample(x)
        out += identity  # 输出为主分支与shortcuts之和
        out = self.relu(out)
 
        return out
 
class ResidualBlock(nn.Cell):
    expansion = 4  # 最后一个卷积核的数量是第一个卷积核数量的4倍
 
    def __init__(self, in_channel: int, out_channel: int,
                 stride: int = 1, down_sample: Optional[nn.Cell] = None) -> None:
        super(ResidualBlock, self).__init__()
 
        self.conv1 = nn.Conv2d(in_channel, out_channel,
                               kernel_size=1, weight_init=weight_init)
        self.norm1 = nn.BatchNorm2d(out_channel)
        self.conv2 = nn.Conv2d(out_channel, out_channel,
                               kernel_size=3, stride=stride,
                               weight_init=weight_init)
        self.norm2 = nn.BatchNorm2d(out_channel)
        self.conv3 = nn.Conv2d(out_channel, out_channel * self.expansion,
                               kernel_size=1, weight_init=weight_init)
        self.norm3 = nn.BatchNorm2d(out_channel * self.expansion)
 
        self.relu = nn.ReLU()
        self.down_sample = down_sample
 
    def construct(self, x):
 
        identity = x  # shortscuts分支
 
        out = self.conv1(x)  # 主分支第一层：1*1卷积层
        out = self.norm1(out)
        out = self.relu(out)
        out = self.conv2(out)  # 主分支第二层：3*3卷积层
        out = self.norm2(out)
        out = self.relu(out)
        out = self.conv3(out)  # 主分支第三层：1*1卷积层
        out = self.norm3(out)
 
        if self.down_sample is not None:
            identity = self.down_sample(x)
 
        out += identity  # 输出为主分支与shortcuts之和
        out = self.relu(out)
 
        return out
 
def make_layer(last_out_channel, block: Type[Union[ResidualBlockBase, ResidualBlock]],
               channel: int, block_nums: int, stride: int = 1):
    down_sample = None  # shortcuts分支
 
 
    if stride != 1 or last_out_channel != channel * block.expansion:
 
        down_sample = nn.SequentialCell([
            nn.Conv2d(last_out_channel, channel * block.expansion,
                      kernel_size=1, stride=stride, weight_init=weight_init),
            nn.BatchNorm2d(channel * block.expansion, gamma_init=gamma_init)
        ])
 
    layers = []
    layers.append(block(last_out_channel, channel, stride=stride, down_sample=down_sample))
 
    in_channel = channel * block.expansion
    # 堆叠残差网络
    for _ in range(1, block_nums):
 
        layers.append(block(in_channel, channel))
 
    return nn.SequentialCell(layers)
 
from mindspore import load_checkpoint, load_param_into_net
 
 
class ResNet(nn.Cell):
    def __init__(self, block: Type[Union[ResidualBlockBase, ResidualBlock]],
                 layer_nums: List[int], num_classes: int, input_channel: int) -> None:
        super(ResNet, self).__init__()
 
        self.relu = nn.ReLU()
        # 第一个卷积层，输入channel为3（彩色图像），输出channel为64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, weight_init=weight_init)
        self.norm = nn.BatchNorm2d(64)
        # 最大池化层，缩小图片的尺寸
        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
        # 各个残差网络结构块定义，
        self.layer1 = make_layer(64, block, 64, layer_nums[0])
        self.layer2 = make_layer(64 * block.expansion, block, 128, layer_nums[1], stride=2)
        self.layer3 = make_layer(128 * block.expansion, block, 256, layer_nums[2], stride=2)
        self.layer4 = make_layer(256 * block.expansion, block, 512, layer_nums[3], stride=2)
        # 平均池化层
        self.avg_pool = nn.AvgPool2d()
        # flattern层
        self.flatten = nn.Flatten()
        # 全连接层
        self.fc = nn.Dense(in_channels=input_channel, out_channels=num_classes)
 
    def construct(self, x):
 
        x = self.conv1(x)
        x = self.norm(x)
        x = self.relu(x)
        x = self.max_pool(x)
 
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
 
        x = self.avg_pool(x)
        x = self.flatten(x)
        x = self.fc(x)
 
        return x
 
 
def _resnet(model_url: str, block: Type[Union[ResidualBlockBase, ResidualBlock]],
            layers: List[int], num_classes: int, pretrained: bool, pretrianed_ckpt: str,
            input_channel: int):
    model = ResNet(block, layers, num_classes, input_channel)
 
    if pretrained:
        # 加载预训练模型
        download(url=model_url, path=pretrianed_ckpt, replace=True)
        param_dict = load_checkpoint(pretrianed_ckpt)
        load_param_into_net(model, param_dict)
 
    return model
 
 
def resnet50(num_classes: int = 1000, pretrained: bool = False):
    "ResNet50模型"
    resnet50_url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/models/application/resnet50_224_new.ckpt"
    resnet50_ckpt = "./LoadPretrainedModel/resnet50_224_new.ckpt"
    return _resnet(resnet50_url, ResidualBlock, [3, 4, 6, 3], num_classes,
                   pretrained, resnet50_ckpt, 2048)

固定特征进行训练

使用固定特征进行训练的时候，需要冻结除最后一层之外的所有网络层。通过设置 requires_grad 冻结参数，以便不在反向传播中计算梯度。


import mindspore as ms
import matplotlib.pyplot as plt
import os
import time
 
net_work = resnet50(pretrained=True)
 
# 全连接层输入层的大小
in_channels = net_work.fc.in_channels
# 输出通道数大小为狼狗分类数2
head = nn.Dense(in_channels, 2)
# 重置全连接层
net_work.fc = head
 
# 平均池化层kernel size为7
avg_pool = nn.AvgPool2d(kernel_size=7)
# 重置平均池化层
net_work.avg_pool = avg_pool
 
# 冻结除最后一层外的所有参数
for param in net_work.get_parameters():
    if param.name not in ["fc.weight", "fc.bias"]:
        param.requires_grad = False
 
# 定义优化器和损失函数
opt = nn.Momentum(params=net_work.trainable_params(), learning_rate=lr, momentum=0.5)
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
 
 
def forward_fn(inputs, targets):
    logits = net_work(inputs)
    loss = loss_fn(logits, targets)
 
    return loss
 
grad_fn = ms.value_and_grad(forward_fn, None, opt.parameters)
 
def train_step(inputs, targets):
    loss, grads = grad_fn(inputs, targets)
    opt(grads)
    return loss
 
# 实例化模型
model1 = train.Model(net_work, loss_fn, opt, metrics={"Accuracy": train.Accuracy()})

训练和评估

开始训练模型，与没有预训练模型相比，将节约一大半时间，因为此时可以不用计算部分梯度。保存评估精度最高的ckpt文件于当前路径的./BestCheckpoint/resnet50-best-freezing-param.ckpt。


import mindspore as ms
import matplotlib.pyplot as plt
import os
import time
dataset_train = create_dataset_canidae(data_path_train, "train")
step_size_train = dataset_train.get_dataset_size()
 
dataset_val = create_dataset_canidae(data_path_val, "val")
step_size_val = dataset_val.get_dataset_size()
 
num_epochs = 5
 
# 创建迭代器
data_loader_train = dataset_train.create_tuple_iterator(num_epochs=num_epochs)
data_loader_val = dataset_val.create_tuple_iterator(num_epochs=num_epochs)
best_ckpt_dir = "./BestCheckpoint"
best_ckpt_path = "./BestCheckpoint/resnet50-best-freezing-param.ckpt"


import mindspore as ms
import matplotlib.pyplot as plt
import os
import time
# 开始循环训练
print("Start Training Loop ...")
 
best_acc = 0
 
for epoch in range(num_epochs):
    losses = []
    net_work.set_train()
 
    epoch_start = time.time()
 
    # 为每轮训练读入数据
    for i, (images, labels) in enumerate(data_loader_train):
        labels = labels.astype(ms.int32)
        loss = train_step(images, labels)
        losses.append(loss)
 
    # 每个epoch结束后，验证准确率
 
    acc = model1.eval(dataset_val)['Accuracy']
 
    epoch_end = time.time()
    epoch_seconds = (epoch_end - epoch_start) * 1000
    step_seconds = epoch_seconds/step_size_train
 
    print("-" * 20)
    print("Epoch: [%3d/%3d], Average Train Loss: [%5.3f], Accuracy: [%5.3f]" % (
        epoch+1, num_epochs, sum(losses)/len(losses), acc
    ))
    print("epoch time: %5.3f ms, per step time: %5.3f ms" % (
        epoch_seconds, step_seconds
    ))
 
    if acc > best_acc:
        best_acc = acc
        if not os.path.exists(best_ckpt_dir):
            os.mkdir(best_ckpt_dir)
        ms.save_checkpoint(net_work, best_ckpt_path)
 
print("=" * 80)
print(f"End of validation the best Accuracy is: {best_acc: 5.3f}, "
      f"save the best ckpt file in {best_ckpt_path}", flush=True)

Start Training Loop ...
--------------------
Epoch: [  1/  5], Average Train Loss: [0.702], Accuracy: [0.717]
epoch time: 123599.202 ms, per step time: 8828.514 ms
--------------------
Epoch: [  2/  5], Average Train Loss: [0.585], Accuracy: [0.817]
epoch time: 738.841 ms, per step time: 52.774 ms
--------------------
Epoch: [  3/  5], Average Train Loss: [0.532], Accuracy: [0.933]
epoch time: 738.426 ms, per step time: 52.745 ms
--------------------
Epoch: [  4/  5], Average Train Loss: [0.454], Accuracy: [0.967]
epoch time: 709.648 ms, per step time: 50.689 ms
--------------------
Epoch: [  5/  5], Average Train Loss: [0.412], Accuracy: [0.933]
epoch time: 743.312 ms, per step time: 53.094 ms
================================================================================
End of validation the best Accuracy is:  0.967, save the best ckpt file in ./BestCheckpoint/resnet50-best-freezing-param.ckpt

可视化模型预测

使用固定特征得到的best.ckpt文件对验证集的狼和狗图像数据进行预测。若预测字体为蓝色即为预测正确，若预测文字为红色则预测错误。


import matplotlib.pyplot as plt
import mindspore as ms
 
def visualize_model(best_ckpt_path, val_ds):
    net = resnet50()
    # 全连接层输入层的大小
    in_channels = net.fc.in_channels
    # 输出通道数大小为狼狗分类数2
    head = nn.Dense(in_channels, 2)
    # 重置全连接层
    net.fc = head
    # 平均池化层kernel size为7
    avg_pool = nn.AvgPool2d(kernel_size=7)
    # 重置平均池化层
    net.avg_pool = avg_pool
    # 加载模型参数
    param_dict = ms.load_checkpoint(best_ckpt_path)
    ms.load_param_into_net(net, param_dict)
    model = train.Model(net)
    # 加载验证集的数据进行验证
    data = next(val_ds.create_dict_iterator())
    images = data["image"].asnumpy()
    labels = data["label"].asnumpy()
    class_name = {0: "dogs", 1: "wolves"}
    # 预测图像类别
    output = model.predict(ms.Tensor(data['image']))
    pred = np.argmax(output.asnumpy(), axis=1)
 
    # 显示图像及图像的预测值
    plt.figure(figsize=(5, 5))
    for i in range(4):
        plt.subplot(2, 2, i + 1)
        # 若预测正确，显示为蓝色；若预测错误，显示为红色
        color = 'blue' if pred[i] == labels[i] else 'red'
        plt.title('predict:{}'.format(class_name[pred[i]]), color=color)
        picture_show = np.transpose(images[i], (1, 2, 0))
        mean = np.array([0.485, 0.456, 0.406])
        std = np.array([0.229, 0.224, 0.225])
        picture_show = std * picture_show + mean
        picture_show = np.clip(picture_show, 0, 1)
        plt.imshow(picture_show)
        plt.axis('off')
 
    plt.show()

visualize_model(best_ckpt_path, dataset_val)

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