实现pytorch版的mobileNetV1

mobileNet具体细节，在前面已做了分析记录：轻量化网络-MobileNet系列-CSDN博客

这里是根据网络结构，搭建模型，用于图像分类任务。

1. 网络结构和基本组件

2. 搭建组件

（1）普通的卷积组件：CBL?= Conv2d + BN + ReLU6;

（2）深度可分离卷积：DwCBL? = Conv dw+ Conv dp；

Conv dw+ Conv dp = {Conv2d(3x3) + BN + ReLU6 }? + {Conv2d(1x1) + BN + ReLU6};

Conv dw是3x3的深度卷积，通过步长控制是否进行下采样；

Conv dp是1x1的逐点卷积，通过控制输出通道数，控制通道维度的变化；

# 普通卷积
class CBN(nn.Module):
    def __init__(self, in_c, out_c, stride=1):
        super(CBN, self).__init__()
        self.conv = nn.Conv2d(in_c, out_c, 3, stride, padding=1, bias=False)
        self.bn = nn.BatchNorm2d(out_c)
        self.relu = nn.ReLU6(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

# 深度可分离卷积: 深度卷积(3x3x1) + 逐点卷积（1x1xc卷积)
class DwCBN(nn.Module):
    def __init__(self, in_c, out_c, stride=1):
        super(DwCBN, self).__init__()
        # conv3x3x1, 深度卷积，通过步长，只控制是否缩小特征hw
        self.conv3x3 = nn.Conv2d(in_c, in_c, 3, stride, padding=1, groups=in_c, bias=False)
        self.bn1 = nn.BatchNorm2d(in_c)
        self.relu1 = nn.ReLU6(inplace=True)
        # conv1x1xc, 逐点卷积，通过控制输出通道数，控制通道维度的变化
        self.conv1x1 = nn.Conv2d(in_c, out_c, 1, stride=1, padding=0, bias=False)
        self.bn2 = nn.BatchNorm2d(out_c)
        self.relu2 = nn.ReLU6(inplace=True)

    def forward(self, x):
        x = self.conv3x3(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv1x1(x)
        x = self.bn2(x)
        x = self.relu2(x)
        return x

3. 搭建网络

class MobileNetV1(nn.Module):
    def __init__(self, class_num=1000):
        super(MobileNetV1, self).__init__()
        self.stage1 = torch.nn.Sequential(
            CBN(3, 32, 2),  # 下采样/2
            DwCBN(32, 64, 1)
        )
        self.stage2 = torch.nn.Sequential(
            DwCBN(64, 128, 2),  # 下采样/4
            DwCBN(128, 128, 1)
        )
        self.stage3 = torch.nn.Sequential(
            DwCBN(128, 256, 2),  # 下采样/8
            DwCBN(256, 256, 1)
        )
        self.stage4 = torch.nn.Sequential(
            DwCBN(256, 512, 2),  # 下采样/16
            DwCBN(512, 512, 1),  # 5个
            DwCBN(512, 512, 1),
            DwCBN(512, 512, 1),
            DwCBN(512, 512, 1),
            DwCBN(512, 512, 1),
        )
        self.stage5 = torch.nn.Sequential(
            DwCBN(512, 1024, 2),  # 下采样/32
            DwCBN(1024, 1024, 1)
        )

        # classifier
        self.avg_pooling = torch.nn.AdaptiveAvgPool2d((1, 1))
        self.fc = torch.nn.Linear(1024, class_num, bias=True)

        # self.classifier = torch.nn.Softmax()  # 原始的softmax值
        # torch.log_softmax 首先计算 softmax 然后再取对数，因此在数值上更加稳定。
        # 在分类网络在训练过程中，通常使用交叉熵损失函数（Cross-Entropy Loss）。
        # torch.nn.CrossEntropyLoss 会在内部进行 softmax 操作，因此在网络的最后一层不需要手动加上 softmax 操作。

    def forward(self, x):
        scale1 = self.stage1(x)  # /2
        scale2 = self.stage2(scale1)
        scale3 = self.stage3(scale2)
        scale4 = self.stage4(scale3)
        scale5 = self.stage5(scale4)  # /32. 7x7

        x = self.avg_pooling(scale5)  # (b,1024,7,7)->(b,1024,1,1)
        x = torch.flatten(x, 1)  # (b,1024,1,1)->(b,1024,)
        x = self.fc(x)  # (b,1024,)  -> (b,1000,)
        return x


if __name__ == '__main__':
    m1 = MobileNetV1(class_num=1000)
    input_data = torch.randn(64, 3, 224, 224)
    output = m1.forward(input_data)
    print(output.shape)

4. 训练验证

import torch
import torchvision
import torchvision.transforms as transforms
from torch import nn, optim

from mobilenetv1 import MobileNetV1


def validate(model, val_loader, criterion, device):
    model.eval()  # Set the model to evaluation mode
    total_correct = 0
    total_samples = 0

    with torch.no_grad():
        for val_inputs, val_labels in val_loader:
            val_inputs, val_labels = val_inputs.to(device), val_labels.to(device)

            val_outputs = model(val_inputs)
            _, predicted = torch.max(val_outputs, 1)
            total_samples += val_labels.size(0)
            total_correct += (predicted == val_labels).sum().item()

    accuracy = total_correct / total_samples
    model.train()  # Set the model back to training mode
    return accuracy


if __name__ == '__main__':
    # 下载并准备数据集
    # Define image transformations (adjust as needed)
    transform = transforms.Compose([
        transforms.Resize((224, 224)),  # Resize images to a consistent size
        transforms.ToTensor(),  # converts to PIL Image to a Pytorch Tensor and scales values to the range [0, 1]
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),  # Adjust normalization values. val = (val - mean) / std.
    ])

    # Create ImageFolder dataset
    data_folder = r"D:\zxq\data\car_or_dog"
    dataset = torchvision.datasets.ImageFolder(root=data_folder, transform=transform)

    # Optionally, split the dataset into training and validation sets
    # Adjust the `split_ratio` as needed
    split_ratio = 0.8
    train_size = int(split_ratio * len(dataset))
    val_size = len(dataset) - train_size

    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])

    # Create DataLoader for training and validation
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4)
    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=4, shuffle=False, num_workers=4)

    # 初始化模型、损失函数和优化器
    net = MobileNetV1(class_num=2)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

    # 训练模型
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print(device)
    net.to(device)
    for epoch in range(20):  # 例如，训练 20 个周期
        for i, data in enumerate(train_loader, 0):
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)  # 将数据移动到GPU

            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            if i % 100 == 0:
                print("epoch/step: {}/{}: loss: {}".format(epoch, i, loss.item()))

        # Validation after each epoch
        val_accuracy = validate(net, val_loader, criterion, device)
        print("Epoch {} - Validation Accuracy: {:.2%}".format(epoch, val_accuracy))

    print('Finished Training')

待续。。。