ModelScope可视化微调的代码主要包括以下几个部分：

1、导入所需库

2、加载预训练模型

3、准备数据集

4、定义损失函数和优化器

5、进行微调

6、评估模型性能

下面是详细的代码实现：

1. 导入所需库
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from modelscope import VisualizationModel
2. 加载预训练模型
model = VisualizationModel()
model.load_state_dict(torch.load('pretrained_model.pth'))
3. 准备数据集
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_dataset = datasets.ImageFolder(root='train_data', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
4. 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
5. 进行微调
num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch + 1}, Loss: {running_loss / (i + 1)}')
6. 评估模型性能
test_dataset = datasets.ImageFolder(root='test_data', transform=transform)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print(f'Accuracy: {100 * correct / total}%')

这段代码首先导入了所需的库，然后加载了预训练模型，接着，准备了训练和测试数据集，并定义了损失函数和优化器，在微调过程中，进行了多个epoch的训练，并在每个epoch后输出了当前的损失值，评估了模型在测试集上的性能。