怎么用torchsummary查看deeplabv3_resnet50模型

您可以按照以下步骤使用torchsummary查看deeplabv3_resnet50模型:

1. 导入所需的库:

import torch
import torchsummary
from torchvision.models import deeplabv3_resnet50

2. 加载deeplabv3_resnet50模型:

model = deeplabv3_resnet50(pretrained=False, progress=True)

3. 使用torchsummary查看模型信息:

torchsummary.summary(model, (3, 224, 224))

其中，(3, 224, 224)是输入数据的维度。通过运行上述代码，您将看到deeplabv3_resnet50模型的详细信息，包括每个层的输出形状和总参数数量。

相关问题

deeplabv3_resnet50完整代码

### DeepLabV3_ResNet50 的完整代码实现

为了构建并训练一个基于 PyTorch 的 DeepLabV3 使用 ResNet50 作为骨干网络，完整的代码实现涉及多个方面，包括数据加载、模型定义、损失函数配置以及训练循环等。下面提供了一个简化版的实现示例。

#### 数据准备
首先需要准备好用于训练的数据集，并创建相应的 DataLoader 来批量读取图片及其标签：

from torchvision import datasets, transforms
import torch.utils.data as data

transform = transforms.Compose([
    transforms.Resize((512, 512)),
    transforms.ToTensor(),
])

dataset_train = datasets.Cityscapes(root='./data/cityscapes', split='train', mode='fine', target_type='semantic', transform=transform)
dataloader_train = data.DataLoader(dataset_train, batch_size=8, shuffle=True)

dataset_val = datasets.Cityscapes(root='./data/cityscapes', split='val', mode='fine', target_type='semantic', transform=transform)
dataloader_val = data.DataLoader(dataset_val, batch_size=8, shuffle=False)

#### 模型初始化
接着实例化带有 ResNet50 骨干网的 DeepLabV3 模型，并加载预训练权重来加速收敛过程[^1]：

import torchvision.models.segmentation as segmentation_models

model = segmentation_models.deeplabv3_resnet50(pretrained_backbone=True, num_classes=19) # Cityscapes has 19 classes
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device);

#### 训练设置
指定优化器和学习率调度策略，同时设定交叉熵损失函数处理多类别分割任务：

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
criterion = torch.nn.CrossEntropyLoss(ignore_index=255).to(device) # ignore index is used for padding or unknown class in cityscape dataset.

#### 开始训练
最后编写训练逻辑，在每个 epoch 中迭代整个训练集，并定期验证性能以调整超参数或保存最佳模型版本：

num_epochs = 20

for epoch in range(num_epochs):
    model.train()
    
    running_loss = []
    for images, targets in dataloader_train:
        optimizer.zero_grad()

        outputs = model(images.to(device))['out']
        
        loss = criterion(outputs, targets.squeeze(1).long().to(device))
        loss.backward()
        optimizer.step()

        running_loss.append(loss.item())
    
    scheduler.step()
    
    avg_loss = sum(running_loss)/len(running_loss)
    print(f"Epoch {epoch}, Loss: {avg_loss:.4f}")

上述代码片段展示了如何利用 PyTorch 和 torchvision 库快速搭建起一个简单的语义分割实验环境。当然实际应用时还需要考虑更多细节如评估指标计算、可视化预测结果等功能扩展。

pytorch代码 deeplabv2_resnet101

Sure! Here's an example of PyTorch code for training DeepLabV2 with ResNet101 as the backbone:

import torch
import torch.nn as nn
import torchvision.models as models

# Define DeepLabV2 model
class DeepLabV2(nn.Module):
    def __init__(self, num_classes):
        super(DeepLabV2, self).__init__()
        self.resnet = models.resnet101(pretrained=True)
        
        # Remove the fully connected layer and average pooling layer
        del self.resnet.fc
        del self.resnet.avgpool
        
        self.conv1 = nn.Conv2d(2048, 256, kernel_size=1)
        self.conv2 = nn.Conv2d(256, num_classes, kernel_size=1)
        
    def forward(self, x):
        x = self.resnet.conv1(x)
        x = self.resnet.bn1(x)
        x = self.resnet.relu(x)
        x = self.resnet.maxpool(x)
        
        x = self.resnet.layer1(x)
        x = self.resnet.layer2(x)
        x = self.resnet.layer3(x)
        x = self.resnet.layer4(x)
        
        x = self.conv1(x)
        x = self.conv2(x)
        
        return x

# Create an instance of the model
num_classes = 21  # Number of classes in the dataset
model = DeepLabV2(num_classes)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# Training loop
for epoch in range(num_epochs):
    for images, labels in dataloader:
        optimizer.zero_grad()
        
        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # Backward pass and optimization
        loss.backward()
        optimizer.step()

This code defines the DeepLabV2 model with ResNet101 as the backbone. It removes the fully connected layer and average pooling layer from the ResNet101 model and replaces them with convolutional layers. The model is trained using the CrossEntropyLoss and optimized using stochastic gradient descent (SGD).

Please note that this code is a simplified example and may need modification based on your specific dataset and requirements.