Help me write a code for training 8-class image recognition based on mixmatch using Pytorch.

Sure, I can help you with that! Here's some sample PyTorch code for training an 8-class image recognition model using mixmatch:

import torch
import torchvision
from torch.utils.data import DataLoader
from mixmatch import MixMatch  # assuming you already have a MixMatch implementation

# define your dataset and data loaders here
train_dataset = ...  # replace ... with your training dataset
val_dataset = ...  # replace ... with your validation dataset
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)

# define your image recognition model here
model = ...  # replace ... with your PyTorch model

# define your loss function and optimizer here
criterion = ...  # replace ... with your loss function
optimizer = ...  # replace ... with your optimizer

# train the model using mixmatch
mixmatch = MixMatch(model, criterion, optimizer, alpha=0.75, T=0.5, K=2)  # set hyperparameters here
for epoch in range(num_epochs):
    mixmatch.train(train_loader)
    accuracy = mixmatch.validate(val_loader)
    print(f"Epoch {epoch}: Validation accuracy = {accuracy}")

This is just a starting point, and you'll need to fill in the specifics of your model, dataset, loss function, and optimizer. But hopefully it gives you a good idea of how to use mixmatch in PyTorch for image recognition. Let me know if you have any other questions!