如何写一个可以识别图像数字的基于VIT大模型的简单代码？

直接上代码，源代码来自于kaggle上的大神，经过简单修改得到

# 模型# 数据# 训练fromtorch.utils.dataimportDataset,DataLoaderimportnumpyfromPILimportImageclassconvertDataset(Dataset):def__init__(self,data,transform)->None:super().__init__()self.data=data self.transform=transform self.labels=self.data.iloc[:,0].values self.images=self.data.iloc[:,1:].values.astype(numpy.uint8)def__len__(self):returnlen(self.data)def__getitem__(self,index):label=self.labels[index]image=self.images[index].reshape(28,28)image=Image.fromarray(image,"L")image=self.transform(image)returnimage,labeldefmain():# 模型importtorch.nnasnnimporttimm dict_path="D:\\kaggle\\digit-recognizer\\dict.pth"importosclassVIT(nn.Module):def__init__(self,*args,**kwargs)->None:super().__init__(*args,**kwargs)self.model=timm.create_model("vit_base_patch16_224",pretrained=True)self.model.head=nn.Linear(self.model.head.in_features,10)# type:ignoreifos.path.exists(dict_path):print("load dict")self.model.load_state_dict(torch.load(dict_path))defforward(self,x):returnself.model(x)importtorch device=torch.device("cuda"iftorch.cuda.is_availableelse"cpu")model=VIT().to(device)# 数据importpandas train_data=pandas.read_csv("input/train.csv")fromtorchvisionimporttransforms transform=transforms.Compose([transforms.Resize((224,224)),transforms.Grayscale(num_output_channels=3),transforms.ToTensor(),transforms.Normalize((0.5,),(0.5,))])train_dataset=convertDataset(train_data,transform)train_dataloader=DataLoader(train_dataset,batch_size=20,shuffle=True,num_workers=1,pin_memory=True)# 训练epoches=5optimizer=torch.optim.Adam(model.parameters(),lr=1e-4)criterion=nn.CrossEntropyLoss()importtimeimporttorch.autograd.profilerasprofilerforepochinrange(epoches):model.train()batch=0forimages,labelsintrain_dataloader:start_time=time.time()images,labels=images.to(device),labels.to(device)optimizer.zero_grad()output=model(images)loss=criterion(output,labels)loss.backward()optimizer.step()batch+=1total=len(labels)start_time1=time.time()_,predicted=output.max(1)correct=(predicted==labels).sum().item()print(f"batch:{batch}/{len(train_dataloader)}",f"epoch:{epoch}/{epoches}",f"loss:{loss.item():.4f}",f"accuracy:{correct/total:.4f}",f"spend:{time.time()-start_time:.2f}")ifbatch%1000==0:torch.save(model.model.state_dict(),dict_path)if__name__=="__main__":main()