昇思25天学习打卡营第12天 | ResNet50图像分类

内容介绍：

ResNet50网络是2015年由微软实验室的何恺明提出，获得ILSVRC2015图像分类竞赛第一名。在ResNet网络提出之前，传统的卷积神经网络都是将一系列的卷积层和池化层堆叠得到的，但当网络堆叠到一定深度时，就会出现退化问题。

具体内容：

1. 导包

from download import download
import mindspore as ms
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms
from mindspore import dtype as mstype
import matplotlib.pyplot as plt
import numpy as np
from typing import Type, Union, List, Optional
import mindspore.nn as nn
from mindspore.common.initializer import Normal
from mindspore import load_checkpoint, load_param_into_net
import mindspore.ops as ops
import os

2. 下载数据

url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz"download(url, "./datasets-cifar10-bin", kind="tar.gz", replace=True)

3. 加载数据

data_dir = "./datasets-cifar10-bin/cifar-10-batches-bin"  # 数据集根目录
batch_size = 256  # 批量大小
image_size = 32  # 训练图像空间大小
workers = 4  # 并行线程个数
num_classes = 10  # 分类数量def create_dataset_cifar10(dataset_dir, usage, resize, batch_size, workers):data_set = ds.Cifar10Dataset(dataset_dir=dataset_dir,usage=usage,num_parallel_workers=workers,shuffle=True)trans = []if usage == "train":trans += [vision.RandomCrop((32, 32), (4, 4, 4, 4)),vision.RandomHorizontalFlip(prob=0.5)]trans += [vision.Resize(resize),vision.Rescale(1.0 / 255.0, 0.0),vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),vision.HWC2CHW()]target_trans = transforms.TypeCast(mstype.int32)# 数据映射操作data_set = data_set.map(operations=trans,input_columns='image',num_parallel_workers=workers)data_set = data_set.map(operations=target_trans,input_columns='label',num_parallel_workers=workers)# 批量操作data_set = data_set.batch(batch_size)return data_set# 获取处理后的训练与测试数据集dataset_train = create_dataset_cifar10(dataset_dir=data_dir,usage="train",resize=image_size,batch_size=batch_size,workers=workers)
step_size_train = dataset_train.get_dataset_size()dataset_val = create_dataset_cifar10(dataset_dir=data_dir,usage="test",resize=image_size,batch_size=batch_size,workers=workers)
step_size_val = dataset_val.get_dataset_size()

4. 可视化

data_iter = next(dataset_train.create_dict_iterator())images = data_iter["image"].asnumpy()
labels = data_iter["label"].asnumpy()
print(f"Image shape: {images.shape}, Label shape: {labels.shape}")# 训练数据集中，前六张图片所对应的标签
print(f"Labels: {labels[:6]}")classes = []with open(data_dir + "/batches.meta.txt", "r") as f:for line in f:line = line.rstrip()if line:classes.append(line)# 训练数据集的前六张图片
plt.figure()
for i in range(6):plt.subplot(2, 3, i + 1)image_trans = np.transpose(images[i], (1, 2, 0))mean = np.array([0.4914, 0.4822, 0.4465])std = np.array([0.2023, 0.1994, 0.2010])image_trans = std * image_trans + meanimage_trans = np.clip(image_trans, 0, 1)plt.title(f"{classes[labels[i]]}")plt.imshow(image_trans)plt.axis("off")
plt.show()

5. 构建网络

# 初始化卷积层与BatchNorm的参数
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 = normself.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_sampledef 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_sampledef 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)

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64self.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, pretrained_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=pretrained_ckpt, replace=True)param_dict = load_checkpoint(pretrained_ckpt)load_param_into_net(model, param_dict)return modeldef 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)

6. 模型训练

# 定义ResNet50网络
network = resnet50(pretrained=True)# 全连接层输入层的大小
in_channel = network.fc.in_channels
fc = nn.Dense(in_channels=in_channel, out_channels=10)
# 重置全连接层
network.fc = fc

# 设置学习率
num_epochs = 5
lr = nn.cosine_decay_lr(min_lr=0.00001, max_lr=0.001, total_step=step_size_train * num_epochs,step_per_epoch=step_size_train, decay_epoch=num_epochs)
# 定义优化器和损失函数
opt = nn.Momentum(params=network.trainable_params(), learning_rate=lr, momentum=0.9)
loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')def forward_fn(inputs, targets):logits = network(inputs)loss = loss_fn(logits, targets)return lossgrad_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

# 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)# 最佳模型存储路径
best_acc = 0
best_ckpt_dir = "./BestCheckpoint"
best_ckpt_path = "./BestCheckpoint/resnet50-best.ckpt"if not os.path.exists(best_ckpt_dir):os.mkdir(best_ckpt_dir)

def train(data_loader, epoch):"""模型训练"""losses = []network.set_train(True)for i, (images, labels) in enumerate(data_loader):loss = train_step(images, labels)if i % 100 == 0 or i == step_size_train - 1:print('Epoch: [%3d/%3d], Steps: [%3d/%3d], Train Loss: [%5.3f]' %(epoch + 1, num_epochs, i + 1, step_size_train, loss))losses.append(loss)return sum(losses) / len(losses)def evaluate(data_loader):"""模型验证"""network.set_train(False)correct_num = 0.0  # 预测正确个数total_num = 0.0  # 预测总数for images, labels in data_loader:logits = network(images)pred = logits.argmax(axis=1)  # 预测结果correct = ops.equal(pred, labels).reshape((-1, ))correct_num += correct.sum().asnumpy()total_num += correct.shape[0]acc = correct_num / total_num  # 准确率return acc

# 开始循环训练
print("Start Training Loop ...")for epoch in range(num_epochs):curr_loss = train(data_loader_train, epoch)curr_acc = evaluate(data_loader_val)print("-" * 50)print("Epoch: [%3d/%3d], Average Train Loss: [%5.3f], Accuracy: [%5.3f]" % (epoch+1, num_epochs, curr_loss, curr_acc))print("-" * 50)# 保存当前预测准确率最高的模型if curr_acc > best_acc:best_acc = curr_accms.save_checkpoint(network, 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)

7. 可视化

def visualize_model(best_ckpt_path, dataset_val):num_class = 10  # 对狼和狗图像进行二分类net = resnet50(num_class)# 加载模型参数param_dict = ms.load_checkpoint(best_ckpt_path)ms.load_param_into_net(net, param_dict)# 加载验证集的数据进行验证data = next(dataset_val.create_dict_iterator())images = data["image"]labels = data["label"]# 预测图像类别output = net(data['image'])pred = np.argmax(output.asnumpy(), axis=1)# 图像分类classes = []with open(data_dir + "/batches.meta.txt", "r") as f:for line in f:line = line.rstrip()if line:classes.append(line)# 显示图像及图像的预测值plt.figure()for i in range(6):plt.subplot(2, 3, 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.asnumpy()[i] else 'red'plt.title('predict:{}'.format(classes[pred[i]]), color=color)picture_show = np.transpose(images.asnumpy()[i], (1, 2, 0))mean = np.array([0.4914, 0.4822, 0.4465])std = np.array([0.2023, 0.1994, 0.2010])picture_show = std * picture_show + meanpicture_show = np.clip(picture_show, 0, 1)plt.imshow(picture_show)plt.axis('off')plt.show()# 使用测试数据集进行验证
visualize_model(best_ckpt_path=best_ckpt_path, dataset_val=dataset_val)

ResNet作为图像分类领域的经典模型，其深度残差学习的思想让我对神经网络有了更深的理解。通过引入跳跃连接（shortcut connections），ResNet解决了深度神经网络在训练过程中容易出现的梯度消失和表示瓶颈问题，使得网络能够更深、更宽，从而具有更强的特征表示能力。在构建基于ResNet的图像分类模型时，我深入了解了网络结构的设计原理和参数配置，这对我的深度学习能力有了显著的提升。