1.导入相应的包 

!pip -q install vit_pytorch linformer

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from linformer import Linformer
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
from tqdm.notebook import tqdm
import torchvision

2.定义数据处理操作

因为CIFAR-10数据是32*32像素大小，我们需要将它转化为224*224像素大小。

transforms_train = transforms.Compose([
    transforms.Resize((256,256)),
    transforms.RandomResizedCrop(224,scale=(0.64,1.0),ratio=(1.0,1.0)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                        [0.229, 0.224, 0.225])
])
transforms_test = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                        [0.229, 0.224, 0.225])
])

3.导入数据

train_data = torchvision.datasets.CIFAR10(root="./cifar10", train=True, download=True, transform=transforms_train)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=128, shuffle=True, num_workers=2)

test_data = torchvision.datasets.CIFAR10(root='./cifar10', train=False,
                                           download=False, transform=transforms_test)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=128,
                                             shuffle=False, num_workers=2)
train_data_size = len(train_data)
test_data_size = len(test_data)

4.定义ViT函数

from einops import rearrange, repeat
from einops.layers.torch import Rearrange

# helpers

def pair(t):
    return t if isinstance(t, tuple) else (t, t)

# classes

class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout = 0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.LayerNorm(dim),
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        return self.net(x)

class Attention(nn.Module):
    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
        super().__init__()
        inner_dim = dim_head *  heads
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head ** -0.5

        self.norm = nn.LayerNorm(dim)

        self.attend = nn.Softmax(dim = -1)
        self.dropout = nn.Dropout(dropout)

        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()

    def forward(self, x):
        x = self.norm(x)

        qkv = self.to_qkv(x).chunk(3, dim = -1)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)

        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)
        attn = self.dropout(attn)

        out = torch.matmul(attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
                FeedForward(dim, mlp_dim, dropout = dropout)
            ]))

    def forward(self, x):
        for attn, ff in self.layers:
            x = attn(x) + x
            x = ff(x) + x

        return self.norm(x)

class ViT(nn.Module):
    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
        super().__init__()
        image_height, image_width = pair(image_size)
        patch_height, patch_width = pair(patch_size)

        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'

        num_patches = (image_height // patch_height) * (image_width // patch_width)
        patch_dim = channels * patch_height * patch_width
        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'

        self.to_patch_embedding = nn.Sequential(
            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
            nn.LayerNorm(patch_dim),
            nn.Linear(patch_dim, dim),
            nn.LayerNorm(dim),
        )

        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        self.dropout = nn.Dropout(emb_dropout)

        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)

        self.pool = pool
        self.to_latent = nn.Identity()

        self.mlp_head = nn.Linear(dim, num_classes)

    def forward(self, img):
        x = self.to_patch_embedding(img)
        b, n, _ = x.shape

        cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b)
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.pos_embedding[:, :(n + 1)]
        x = self.dropout(x)

        x = self.transformer(x)

        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]

        x = self.to_latent(x)
        return self.mlp_head(x)

5.实例化模型并且转移到GPU上

model = ViT(
image_size = 224,
patch_size = 16,
num_classes = 10,
dim = 1024,
depth = 6,
heads = 16,
mlp_dim = 2048,
dropout = 0.1,
emb_dropout = 0.1
    )
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)

6.选取损失函数和优化器

LR = 0.001
epoch = 100
loss_func = nn.CrossEntropyLoss()
loss_func = loss_func.to(device)
optimizer = torch.optim.SGD(model.parameters(),lr=LR,weight_decay=0.001,momentum=0.9)

7.训练模块

total_train_step = 0
for i in range(epoch):
    print(f'---------------第{i + 1}轮训练---------------')
    total_train_acc = 0
    total_train_loss = 0
    model.train()
    for j,data in enumerate(train_loader):
        inputs,labels = data
        inputs,labels = inputs.to(device),labels.to(device)
        outputs = model(inputs)
        loss = loss_func(outputs,labels)
            
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        acc = (outputs.argmax(1) == labels).sum()
        total_train_acc = total_train_acc + acc
        total_train_loss += loss
        total_train_step += 1

        if total_train_step % 100 == 0:
            print("训练次数：{}, Loss: {}".format(total_train_step, loss.item()))
                
    print("整体训练集集上的Loss: {}".format(total_train_loss))
    print("整体训练集上的正确率: {}".format(total_train_acc / train_data_size)) 
        
    model.eval()
    total_test_acc = 0
    total_test_loss = 0
    with torch.no_grad():
        for h,data in enumerate(test_loader):
            inputs_test, labels_test = data
            inputs_test, labels_test = inputs_test.to(device), labels_test.to(device)
            outputs = model(inputs_test)
            loss_test = loss_func(outputs, labels_test)
            total_test_loss += loss_test
            acc_test = (outputs.argmax(1) == labels_test).sum()
            total_test_acc += acc_test

    print("整体测试集上的Loss: {}".format(total_test_loss))
    print("整体测试集上的正确率: {}".format(total_test_acc / test_data_size))


torch.save(model,'./model')

初学，欢迎大家指出错误。