ModelScope可视化微调的代码主要包括以下几个部分:
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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。
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