Transfer Learning

Pokemon Dataset

通过网络上收集宝可梦的图片，制作图像分类数据集。我收集了5种宝可梦，分别是皮卡丘，超梦，杰尼龟，小火龙，妙蛙种子

数据集链接： https://pan.baidu.com/s/1Kept7FF88lb8TqPZMD_Yxw 提取码：1sdd

一共有1168张宝可梦的图片，其中皮卡丘234张，超梦239张，杰尼龟223张，小火龙238张，妙蛙种子234张

每个目录由神奇宝贝名字命名，对应目录下是该神奇宝贝的图片，图片的格式有jpg,png,jpeg三种

数据集的划分如下（训练集60%，验证集20%，测试集20%）。这个比例不是针对每一类提取，而是针对总体的1168张

iuaUNnQ.png!web

Load Data

在PyTorch中定义数据集主要涉及到两个主要的类：Dataset和DataLoder

DataSet类

DataSet类是PyTorch中所有数据集加载类中都应该继承的父类，它的两个私有成员函数 __len__() 和 __getitem__() 必须被重载，否则将触发错误提示

其中 __len__ 应该返回数据集的样本数量，而 __getitem__() 实现通过索引返回样本数据的功能

首先看一个自定义Dataset的例子

class NumbersDataset(Dataset):
    def __init__(self, training=True):
        if training:
            self.samples = list(range(1, 1001))
        else:
            self.samples = list(range(1001, 1501))
            
    def __len__(self):
        return len(self.samples)
    
    def __getitem__(self, idx):
        return self.samples[idx]

然后需要对图片做Preprocessing

Image Resize：224*224 for ResNet18
Data Argumentation：Rotate & Crop
Normalize：Mean & std
ToTensor

首先我们在 __init__() 函数里将name->label，这里的name就是文件夹的名字，然后拆分数据集，按照6:2:2的比例

class Pokemon(Dataset):
    def __init__(self, root, resize, model):
        super(Pokemon, self).__init__()
        
        self.root = root
        self.resize = resize

        self.name2label = {} # 将文件夹的名字映射为label(数字)
        for name in sorted(os.listdir(os.path.join(root))):
            if not os.path.isdir(os.path.join(root, name)):
                continue
            self.name2label[name] = len(self.name2label.keys())

        # image, label
        self.images, self.labels = self.load_csv('images.csv')
        
        if model == 'train': # 60%
            self.images = self.images[:int(0.6*len(self.images))]
            self.labels = self.labels[:int(0.6*len(self.labels))]
        elif model == 'val': # 20%
            self.images = self.images[int(0.6*len(self.images)):int(0.8*len(self.images))]
            self.labels = self.labels[int(0.6*len(self.labels)):int(0.8*len(self.labels))]
        else: # 20%
            self.images = self.images[int(0.8*len(self.images)):]
            self.labels = self.labels[int(0.8*len(self.labels)):]

其中 load_csv() 函数的作用是将所有的图片名（名字里包含完整的路径）以及label都存到csv文件里，例如，有一个图片的路径是 pokemon\\bulbasaur\\00000000.png ，对应的label是0，那么csv就会写入一行 pokemon\\bulbasaur\\00000000.png, 0 ，总共写入了1167行（有一张图片既不是png，也不是jpg和jpeg，找不到，算了）。 load_csv() 函数具体如下所示

def load_csv(self, filename):
    if not os.path.exists(os.path.join(self.root, filename)):
        images = []
        for name in self.name2label.keys():
            images += glob.glob(os.path.join(self.root, name, '*.png'))
            images += glob.glob(os.path.join(self.root, name, '*.jpg'))
            images += glob.glob(os.path.join(self.root, name, '*.jpeg'))

        random.shuffle(images)
        with open(os.path.join(self.root, filename), mode='w', newline='') as f:
            writer = csv.writer(f)
            for img in images: # pokemon\\bulbasaur\\00000000.png
                name = img.split(os.sep)[-2] # bulbasaur
                label = self.name2label[name]
                # pokemon\\bulbasaur\\00000000.png 0
                writer.writerow([img, label])
            print('writen into csv file:', filename)

    # read csv file
    images, labels = [], []
    with open(os.path.join(self.root, filename)) as f:
        reader = csv.reader(f)
        for row in reader:
            image, label = row
            label = int(label)
            images.append(image)
            labels.append(label)
    assert len(images) == len(labels)
    return images, labels

然后是 __len__() 函数的代码

def __len__(self):
    return len(self.images)

最后是 __getitem__() 函数的代码，这个比较复杂，因为我们现在只有图片的string path(字符串形式的路径)，要先转成三通道的image data，这个利用PIL库中的 Image.open(path).convert('RGB') 函数可以完成。图片读取出来以后，要经过一系列的transforms，具体代码如下

def __getitem__(self, idx):
    # idx [0~len(images)]
    # self.images, self.labels
    # pokemon\\bulbasaur\\00000000.png    0
    img, label = self.images[idx], self.labels[idx]
    tf = transforms.Compose([
        lambda x:Image.open(x).convert('RGB'), # string path => image data
        transforms.Resize((int(self.resize*1.25), int(self.resize*1.25))),
        transforms.RandomRotation(15),
        transforms.CenterCrop(self.resize),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])
    img = tf(img)
    label = torch.tensor(label)

    return img, label

Normalize的参数是PyTorch推荐的，直接写上就可以了

JzAJjma.png!web

DataLoader类

Dataset类是读入数据集并对读入的数据进行了索引，但是光有这个功能是不够的，在实际加载数据集的过程中，我们的数据量往往都很大，因此还需要以下几个功能：

每次读入一些批次：batch_size
可以对数据进行随机读取，打乱数据的顺序(shuffling)
可以并行加载数据集（利用多核处理器加快载入数据的效率）

为此，就需要DataLoader类了，它里面常用的参数有：

batch_size：每个batch的大小
shuffle：是否进行shuffle操作
num_works：加载数据的时候使用几个进程

DataLoader这个类并不需要我们自己设计代码，只需要利用它读取我们设计好的Dataset的子类即可

db = Pokemon('pokemon', 224, 'train')
lodder = DataLoader(db, batch_size=32, shuffle=True, num_workers=4)

完整代码如下：

import torch
import os, glob
import random, csv
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image

class Pokemon(Dataset):
    def __init__(self, root, resize, model):
        super(Pokemon, self).__init__()
        
        self.root = root
        self.resize = resize

        self.name2label = {} # 将文件夹的名字映射为label(数字)
        for name in sorted(os.listdir(os.path.join(root))):
            if not os.path.isdir(os.path.join(root, name)):
                continue
            self.name2label[name] = len(self.name2label.keys())

        # image, label
        self.images, self.labels = self.load_csv('images.csv')
        
        if model == 'train': # 60%
            self.images = self.images[:int(0.6*len(self.images))]
            self.labels = self.labels[:int(0.6*len(self.labels))]
        elif model == 'val': # 20%
            self.images = self.images[int(0.6*len(self.images)):int(0.8*len(self.images))]
            self.labels = self.labels[int(0.6*len(self.labels)):int(0.8*len(self.labels))]
        else: # 20%
            self.images = self.images[int(0.8*len(self.images)):]
            self.labels = self.labels[int(0.8*len(self.labels)):]
    
    def load_csv(self, filename):
        if not os.path.exists(os.path.join(self.root, filename)):
            images = []
            for name in self.name2label.keys():
                images += glob.glob(os.path.join(self.root, name, '*.png'))
                images += glob.glob(os.path.join(self.root, name, '*.jpg'))
                images += glob.glob(os.path.join(self.root, name, '*.jpeg'))

            random.shuffle(images)
            with open(os.path.join(self.root, filename), mode='w', newline='') as f:
                writer = csv.writer(f)
                for img in images: # pokemon\\bulbasaur\\00000000.png
                    name = img.split(os.sep)[-2] # bulbasaur
                    label = self.name2label[name]
                    # pokemon\\bulbasaur\\00000000.png 0
                    writer.writerow([img, label])
                print('writen into csv file:', filename)

        # read csv file
        images, labels = [], []
        with open(os.path.join(self.root, filename)) as f:
            reader = csv.reader(f)
            for row in reader:
                image, label = row
                label = int(label)
                images.append(image)
                labels.append(label)
        assert len(images) == len(labels)
        return images, labels
        
    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        # idx [0~len(images)]
        # self.images, self.labels
        # pokemon\\bulbasaur\\00000000.png    0
        img, label = self.images[idx], self.labels[idx]
        tf = transforms.Compose([
            lambda x:Image.open(x).convert('RGB'), # string path => image data
            transforms.Resize((int(self.resize*1.25), int(self.resize*1.25))),
            transforms.RandomRotation(15),
            transforms.CenterCrop(self.resize),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ])
        img = tf(img)
        label = torch.tensor(label)
        
        return img, label

db = Pokemon('pokemon', 224, 'train')
lodder = DataLoader(db, batch_size=32, shuffle=True, num_workers=8)

Build Model

用PyTorch搭建ResNet其实在我之前的文章已经讲过了，这里直接拿来用，修改一下里面的参数就行了

import torch
import torch.nn as nn
import torch.nn.functional as F

class ResBlk(nn.Module):
    def __init__(self, ch_in, ch_out, stride=1):
        super(ResBlk, self).__init__()
        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(ch_out)
        
        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(ch_out)
        
        self.extra = nn.Sequential()
        if ch_out != ch_in:
            self.extra = nn.Sequential(
                nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),
                nn.BatchNorm2d(ch_out),
            )
        
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))

        # short cut
        out = self.extra(x) + out
        out = F.relu(out)
        
        return out
        
class ResNet18(nn.Module):
    def __init__(self, num_class):
        super(ResNet18, self).__init__()
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=3, padding=0),
            nn.BatchNorm2d(16),
        )
        # followed 4 blocks
        
        # [b, 16, h, w] => [b, 32, h, w]
        self.blk1 = ResBlk(16, 32, stride=3)
        # [b, 32, h, w] => [b, 64, h, w]
        self.blk2 = ResBlk(32, 64, stride=3)
        # [b, 64, h, w] => [b, 128, h, w]
        self.blk3 = ResBlk(64, 128, stride=2)
        # [b, 128, h, w] => [b, 256, h, w]
        self.blk4 = ResBlk(128, 256, stride=2)
        
        self.outlayer = nn.Linear(256*3*3, num_class)
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        
        x = self.blk1(x)
        x = self.blk2(x)
        x = self.blk3(x)
        x = self.blk4(x)
        
        x = x.view(x.size(0), -1)
        x = self.outlayer(x)
        
        return x

Train and Test

训练的时候，严格按照Training和Test的逻辑，就是在训练epoch的过程中，间断的做一次validation，然后看一下当前的validation accuracy是不是最高的，如果是最高的，就把当前的模型参数保存起来。training完以后，加载最好的模型，再做testing。这就是非常严格的训练逻辑。代码如下：

batchsz = 32
lr = 1e-3
epochs = 10
device = torch.device('cuda')
torch.manual_seed(1234)

train_db = Pokemon('pokemon', 224, model='train')
val_db = Pokemon('pokemon', 224, model='val')
test_db = Pokemon('pokemon', 224, model='test')
train_loader = DataLoader(train_db, batch_size=batchsz, shuffle=True, num_workers=2)
val_loader = DataLoader(val_db, batch_size=batchsz, num_workers=2)
test_loader = DataLoader(test_db, batch_size=batchsz, num_workers=2)


def evalute(model, loader):
    correct = 0
    total = len(loader.dataset)
    for x,y in loader:
        with torch.no_grad():
            logits = model(x)
            pred = logits.argmax(dim=1)
        correct += torch.eq(pred, y).sum().float().item()
    return correct / total

def main():
    model = ResNet18(5)
    optimizer = optim.Adam(model.parameters(), lr=lr)
    criteon = nn.CrossEntropyLoss()
    
    best_acc, best_epoch = 0, 0
    for epoch in range(epochs):
        for step, (x, y) in enumerate(train_loader):
            # x:[b, 3, 224, 224], y:[b]
            logits = model(x)
            loss = criteon(logits, y)
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
        if epoch % 2 == 0:
            val_acc = evalute(model, val_loader)
            if val_acc > best_acc:
                best_epoch = epoch
                best_acc = val_acc
                torch.save(model.state_dict(), 'best.mdl')
                
    print('best acc:', best_acc, 'best_epoch', best_epoch)
    
    model.load_state_dict(torch.load('best.mdl'))
    print('loaded from ckt!')
    
    test_acc = evalute(model, test_loader)
    print('test_acc:', test_acc)

截至到目前为止，能完整运行的代码如下：

import torch
import os, glob
import warnings
import random, csv
from PIL import Image
from torch import optim, nn
import torch.nn.functional as F
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
warnings.filterwarnings('ignore')


class Pokemon(Dataset):
    def __init__(self, root, resize, model):
        super(Pokemon, self).__init__()
        
        self.root = root
        self.resize = resize

        self.name2label = {} # 将文件夹的名字映射为label(数字)
        for name in sorted(os.listdir(os.path.join(root))):
            if not os.path.isdir(os.path.join(root, name)):
                continue
            self.name2label[name] = len(self.name2label.keys())

        # image, label
        self.images, self.labels = self.load_csv('images.csv')
        
        if model == 'train': # 60%
            self.images = self.images[:int(0.6*len(self.images))]
            self.labels = self.labels[:int(0.6*len(self.labels))]
        elif model == 'val': # 20%
            self.images = self.images[int(0.6*len(self.images)):int(0.8*len(self.images))]
            self.labels = self.labels[int(0.6*len(self.labels)):int(0.8*len(self.labels))]
        else: # 20%
            self.images = self.images[int(0.8*len(self.images)):]
            self.labels = self.labels[int(0.8*len(self.labels)):]
    
    def load_csv(self, filename):
        if not os.path.exists(os.path.join(self.root, filename)):
            images = []
            for name in self.name2label.keys():
                images += glob.glob(os.path.join(self.root, name, '*.png'))
                images += glob.glob(os.path.join(self.root, name, '*.jpg'))
                images += glob.glob(os.path.join(self.root, name, '*.jpeg'))

            random.shuffle(images)
            with open(os.path.join(self.root, filename), mode='w', newline='') as f:
                writer = csv.writer(f)
                for img in images: # pokemon\\bulbasaur\\00000000.png
                    name = img.split(os.sep)[-2] # bulbasaur
                    label = self.name2label[name]
                    # pokemon\\bulbasaur\\00000000.png 0
                    writer.writerow([img, label])
                print('writen into csv file:', filename)

        # read csv file
        images, labels = [], []
        with open(os.path.join(self.root, filename)) as f:
            reader = csv.reader(f)
            for row in reader:
                image, label = row
                label = int(label)
                images.append(image)
                labels.append(label)
        assert len(images) == len(labels)
        return images, labels
        
    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        # idx [0~len(images)]
        # self.images, self.labels
        # pokemon\\bulbasaur\\00000000.png    0
        img, label = self.images[idx], self.labels[idx]
        tf = transforms.Compose([
            lambda x:Image.open(x).convert('RGB'), # string path => image data
            transforms.Resize((int(self.resize*1.25), int(self.resize*1.25))),
            transforms.RandomRotation(15),
            transforms.CenterCrop(self.resize),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ])
        img = tf(img)
        label = torch.tensor(label)
        
        return img, label

class ResBlk(nn.Module):
    def __init__(self, ch_in, ch_out, stride=1):
        super(ResBlk, self).__init__()
        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(ch_out)
        
        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(ch_out)
        
        self.extra = nn.Sequential()
        if ch_out != ch_in:
            self.extra = nn.Sequential(
                nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),
                nn.BatchNorm2d(ch_out),
            )
        
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))

        # short cut
        out = self.extra(x) + out
        out = F.relu(out)
        
        return out
        
class ResNet18(nn.Module):
    def __init__(self, num_class):
        super(ResNet18, self).__init__()
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=3, padding=0),
            nn.BatchNorm2d(16),
        )
        # followed 4 blocks
        
        # [b, 16, h, w] => [b, 32, h, w]
        self.blk1 = ResBlk(16, 32, stride=3)
        # [b, 32, h, w] => [b, 64, h, w]
        self.blk2 = ResBlk(32, 64, stride=3)
        # [b, 64, h, w] => [b, 128, h, w]
        self.blk3 = ResBlk(64, 128, stride=2)
        # [b, 128, h, w] => [b, 256, h, w]
        self.blk4 = ResBlk(128, 256, stride=2)
        
        self.outlayer = nn.Linear(256*3*3, num_class)
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        
        x = self.blk1(x)
        x = self.blk2(x)
        x = self.blk3(x)
        x = self.blk4(x)
        
        x = x.view(x.size(0), -1)
        x = self.outlayer(x)
        
        return x
    
batchsz = 32
lr = 1e-3
epochs = 10
device = torch.device('cuda')
torch.manual_seed(1234)

train_db = Pokemon('pokemon', 224, model='train')
val_db = Pokemon('pokemon', 224, model='val')
test_db = Pokemon('pokemon', 224, model='test')
train_loader = DataLoader(train_db, batch_size=batchsz, shuffle=True, num_workers=2)
val_loader = DataLoader(val_db, batch_size=batchsz, num_workers=2)
test_loader = DataLoader(test_db, batch_size=batchsz, num_workers=2)


def evalute(model, loader):
    correct = 0
    total = len(loader.dataset)
    for x,y in loader:
        with torch.no_grad():
            logits = model(x)
            pred = logits.argmax(dim=1)
        correct += torch.eq(pred, y).sum().float().item()
    return correct / total

def main():
    model = ResNet18(5)
    optimizer = optim.Adam(model.parameters(), lr=lr)
    criteon = nn.CrossEntropyLoss()
    
    best_acc, best_epoch = 0, 0
    for epoch in range(epochs):
        for step, (x, y) in enumerate(train_loader):
            # x:[b, 3, 224, 224], y:[b]
            logits = model(x)
            loss = criteon(logits, y)
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
        if epoch % 2 == 0:
            val_acc = evalute(model, val_loader)
            if val_acc > best_acc:
                best_epoch = epoch
                best_acc = val_acc
                torch.save(model.state_dict(), 'best.mdl')
                
    print('best acc:', best_acc, 'best_epoch', best_epoch)
    
    model.load_state_dict(torch.load('best.mdl'))
    print('loaded from ckt!')
    
    test_acc = evalute(model, test_loader)
    print('test_acc:', test_acc)

if __name__ == '__main__':
    main()

运行上面的代码，基本上最终test accuracy可以达到0.88左右。如果想要提升的话，就需要使用更多工程上的tricks或者调参

当然还有一种方法，就是迁移学习，我们先看下面这张图，这张图展示的问题在于，当数据很少的情况下（第一张图），模型训练的结果可能会有很多情况（第二张图），当然最终输出就一个结果。然而这个结果可能test accuracy并不高。就比方说我们的pokemon图片，只有1000多张，算是一个比较少的数据集了，但是由于pokemon和ImageNet都是图片，它们可能存在某些共性。那我们能不能用ImageNet的一些train好的模型，拿来帮助我们解决一下特定的图片分类任务，这就是Transfer Learning，也就是在A任务上train好一个分类器，再transfer到B上去

vyUnQ3Q.png!web

我个人理解Transfer Learning的作用是这样的，我们都知道神经网络初始化参数非常重要，有时候初始化不好，可能就会导致最终效果非常差。现在我们用一个在A任务上已经训练好了的网络，相当于帮你做了一个很好的初始化，你在这个网络的基础上，去做B任务，如果这两个任务比较接近的话，夸张一点说，这个网络的训练可能就只需要微调一下，就能在B任务上显示出非常好的效果

下图展示的是一个真实的Transfer Learning的过程，左边是已经training好的网络，我们利用这个网络的公有部分，吸取它的common knowledge，然后把最后一层去掉，换成我们需要的

jqeYNzI.png!web

先上核心代码

import torch.nn as nn
from torchvision.models import resnet18

class Flatten(nn.Module):
    def __init__(self):
        super(Flatten, self).__init__()
    
    def forward(self, x):
        shape = torch.prod(torch.tensor(x.shape[1:])).item()
        return x.view(-1, shape) 

trained_model = resnet18(pretrained=True)
model = nn.Sequential(*list(trained_model.children())[:-1],# [b, 512, 1, 1]
                      Flatten(), # [b, 512, 1, 1] => [b, 512]
                      nn.Linear(512, 5) # [b, 512] => [b, 5]
                     )

PyTorch中有已经训练好的各种规格的resnet，第一次使用需要下载。我们不要resnet18的最后一层，所以要用 list(trained_model.children())[:-1] 把除了最后一层以外的所有层都取出来，保存在list中，然后用 * 将其list展开，之后接一个我们自定义的Flatten层，作用是将output打平，打平以后才能送到Linear层去

上面几行代码就实现了Transfer Learning，而且不需要我们自己实现resnet，完整代码如下

import torch
import os, glob
import warnings
import random, csv
from PIL import Image
from torch import optim, nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision.models import resnet18
from torch.utils.data import Dataset, DataLoader
warnings.filterwarnings('ignore')
from matplotlib import pyplot as plt


class Pokemon(Dataset):
    def __init__(self, root, resize, model):
        super(Pokemon, self).__init__()
        
        self.root = root
        self.resize = resize

        self.name2label = {} # 将文件夹的名字映射为label(数字)
        for name in sorted(os.listdir(os.path.join(root))):
            if not os.path.isdir(os.path.join(root, name)):
                continue
            self.name2label[name] = len(self.name2label.keys())

        # image, label
        self.images, self.labels = self.load_csv('images.csv')
        
        if model == 'train': # 60%
            self.images = self.images[:int(0.6*len(self.images))]
            self.labels = self.labels[:int(0.6*len(self.labels))]
        elif model == 'val': # 20%
            self.images = self.images[int(0.6*len(self.images)):int(0.8*len(self.images))]
            self.labels = self.labels[int(0.6*len(self.labels)):int(0.8*len(self.labels))]
        else: # 20%
            self.images = self.images[int(0.8*len(self.images)):]
            self.labels = self.labels[int(0.8*len(self.labels)):]
    
    def load_csv(self, filename):
        if not os.path.exists(os.path.join(self.root, filename)):
            images = []
            for name in self.name2label.keys():
                images += glob.glob(os.path.join(self.root, name, '*.png'))
                images += glob.glob(os.path.join(self.root, name, '*.jpg'))
                images += glob.glob(os.path.join(self.root, name, '*.jpeg'))

            random.shuffle(images)
            with open(os.path.join(self.root, filename), mode='w', newline='') as f:
                writer = csv.writer(f)
                for img in images: # pokemon\\bulbasaur\\00000000.png
                    name = img.split(os.sep)[-2] # bulbasaur
                    label = self.name2label[name]
                    # pokemon\\bulbasaur\\00000000.png 0
                    writer.writerow([img, label])
                print('writen into csv file:', filename)

        # read csv file
        images, labels = [], []
        with open(os.path.join(self.root, filename)) as f:
            reader = csv.reader(f)
            for row in reader:
                image, label = row
                label = int(label)
                images.append(image)
                labels.append(label)
        assert len(images) == len(labels)
        return images, labels
        
    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        # idx [0~len(images)]
        # self.images, self.labels
        # pokemon\\bulbasaur\\00000000.png    0
        img, label = self.images[idx], self.labels[idx]
        tf = transforms.Compose([
            lambda x:Image.open(x).convert('RGB'), # string path => image data
            transforms.Resize((int(self.resize*1.25), int(self.resize*1.25))),
            transforms.RandomRotation(15),
            transforms.CenterCrop(self.resize),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ])
        img = tf(img)
        label = torch.tensor(label)
        
        return img, label
    
class Flatten(nn.Module):
    def __init__(self):
        super(Flatten, self).__init__()
    
    def forward(self, x):
        shape = torch.prod(torch.tensor(x.shape[1:])).item()
        return x.view(-1, shape)    
    
batchsz = 32
lr = 1e-3
epochs = 10
device = torch.device('cuda')
torch.manual_seed(1234)

train_db = Pokemon('pokemon', 224, model='train')
val_db = Pokemon('pokemon', 224, model='val')
test_db = Pokemon('pokemon', 224, model='test')
train_loader = DataLoader(train_db, batch_size=batchsz, shuffle=True, num_workers=2)
val_loader = DataLoader(val_db, batch_size=batchsz, num_workers=2)
test_loader = DataLoader(test_db, batch_size=batchsz, num_workers=2)


def evalute(model, loader):
    correct = 0
    total = len(loader.dataset)
    for x,y in loader:
        with torch.no_grad():
            logits = model(x)
            pred = logits.argmax(dim=1)
        correct += torch.eq(pred, y).sum().float().item()
    return correct / total

def main():
    trained_model = resnet18(pretrained=True)
    model = nn.Sequential(*list(trained_model.children())[:-1],# [b, 512, 1, 1]
                          Flatten(), # [b, 512, 1, 1] => [b, 512]
                          nn.Linear(512, 5)
                         )
    optimizer = optim.Adam(model.parameters(), lr=lr)
    criteon = nn.CrossEntropyLoss()
    
    best_acc, best_epoch = 0, 0
    for epoch in range(epochs):
        for step, (x, y) in enumerate(train_loader):
            # x:[b, 3, 224, 224], y:[b]
            logits = model(x)
            loss = criteon(logits, y)
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
        if epoch % 2 == 0:
            val_acc = evalute(model, val_loader)
            if val_acc > best_acc:
                best_epoch = epoch
                best_acc = val_acc
                torch.save(model.state_dict(), 'best.mdl')
                
    print('best acc:', best_acc, 'best_epoch', best_epoch)
    
    model.load_state_dict(torch.load('best.mdl'))
    print('loaded from ckt!')
    
    test_acc = evalute(model, test_loader)
    print('test_acc:', test_acc)

if __name__ == '__main__':
    main()

最终test accuracy在0.94左右，比我们自己从0开始训练效果好了很多

Transfer Learning

Pokemon Dataset

Load Data

DataSet类

DataLoader类

Build Model

Train and Test

Transfer Learning

Recommend

微软提出Petridish，完美解决问题的神经网络？

ALBERT：用于语言表征自监督学习的轻量级 BERT

【信号与系统】05 - 滤波、采样和通信

Negative Feedback Is Positive

反向代理_系统设计笔记6

Making Sense of Asynchrony in Interactive Data Visualizations

跨域我知道，存储我知道，那跨域存储呢？

如何把握“黑天鹅”带来的机会？

iPhone 9 售价 399 美元起？

德国支持特斯拉设立超级工厂：可获政府补贴

About Joyk