pytorch 迁移学习猫狗大战 note

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我是靠谱客的博主落后抽屉，最近开发中收集的这篇文章主要介绍pytorch 迁移学习猫狗大战 note，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

import os
from pathlib import Path
from PIL import Image
from torch.utils.data import DataLoader,Dataset
import numpy as np
from torchvision import transforms, models
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import torch 
from torch import nn
import torch.optim as optim
import torch.nn.functional as F
import random
import time
%matplotlib inline


# 定义猫为0，狗为1
label_dict = {'cat':0
              ,'dog':1
             }

path = Path('D:图像数据集kaggle_cat_vs_dog')

os.listdir(path)    #train, test1两个文件夹， test1中存放测试集数据，本次实验暂时不用

# 得到所有文件名
all_file_name = os.listdir(path/'train')
# 随机打乱
random.shuffle(all_file_name)
# 得到打乱后的标签
all_labels = [label_dict[i.split('.')[0]] for i in all_file_name]
# 训练/验证集拆分
Xtrain, Xvalid, Ytrain, Yvalid = train_test_split(all_file_name
                                                ,all_labels
                                                ,test_size=0.3)

# 图像预处理
train_transforms = transforms.Compose([transforms.RandomRotation(30),
                                       transforms.RandomResizedCrop(224),
                                       transforms.RandomHorizontalFlip(),
                                       transforms.ToTensor(),
                                       transforms.Normalize([0.485, 0.456, 0.406],
                                                            [0.229, 0.224, 0.225])])
 
test_transforms = transforms.Compose([transforms.Resize(255),
                                      transforms.CenterCrop(224),
                                      transforms.ToTensor(),
                                      transforms.Normalize([0.485, 0.456, 0.406],
                                                           [0.229, 0.224, 0.225])])

# 建立dataset
class CatvsDogDataset(Dataset):
    def __init__(self, path, mode, file_names, labels):
        '''
        mode: 1.train(train and valid)  2.test1(test)
        '''
        self.data_list = [path/mode/file for file in file_names]
        self.labels = labels
        self.mode = mode
        
    def __getitem__(self, index):
        img = Image.open(self.data_list[index])
        label = self.labels[index]
        if self.mode == 'train':
            return train_transforms(img), torch.LongTensor([label])
        else:
            return test_transforms(img)
        
    def __len__(self):
        return len(self.labels)

# 看看长什么样
#img = Image.open(path/'train'/Xtrain[0])
#print(img.size)
#print(train_transforms(img).shape)
#print(train_transforms(img).numpy().shape)
#print(np.transpose(train_transforms(img).numpy(),[1,2,0]).shape)
#print((train_transforms(img).permute(1,2,0)).shape)

#img = np.array(img)
#plt.imshow(img)

# 建立dataloader
train_loader = DataLoader(CatvsDogDataset(path, 'train', Xtrain, Ytrain)
                              ,shuffle=True
                              ,batch_size=32
#                           ,pin_memory=True
#                           ,num_workers=2
                              )
valid_loader = DataLoader(CatvsDogDataset(path, 'train', Xvalid, Yvalid)
                               ,shuffle=True
                               ,batch_size=32
#                           ,pin_memory=True
#                           ,num_workers=2
                              )

# 测试一下dataloader能不能用
#tmpx,tmpy = next(iter(train_loader))
#print(tmpx.shape)
#print(tmpy.shape)

# 定义特征提取的预训练网络
class Feature_net(nn.Module):
    def __init__(self, model):
        super().__init__()
        
        if model == 'vgg':
            vgg = models.vgg19(pretrained=True   #用人家训练好的现成的网络参数
                              )
            # 获取模型的特征提取层(所有的卷积相关的层)
            self.feature = nn.Sequential(*list(vgg.children())[:-2])
#             self.feature.add_module(name='global average'
#                                     ,module=nn.AvgPool2d(9)
#                                    )
        elif model == 'inception_v3':
            inception = models.inception_v3(pretrained=True)
            self.feature = nn.Sequential(*list(inception.childrenldren())[:-1])
            self.feature._modules.pop('13')
            self.feature.add_module('global average', nn.AvgPool2d(35))
        elif model == 'resnet152':
            resnet = models.resnet152(pretrained=True)
            self.feature = nn.Sequential(*list(resnet.children())[:-1])
            
    def forward(self, x):
        x = self.feature(x)
        x = x.view(x.size(0), -1)
        return x

# # nn.AvgPool2d()
# # nn.AdaptiveAvgPool2d() #自适应平均池化层
# model = models.vgg16(pretrained = True)
# print(model)

# print(nn.Sequential(*list(model.children())[:-2]))

# model = models.inception_v3(pretrained=True)
# print(model)
# model = models.resnet152(pretrained=True)
# print(model)

# model.add_module()
# model._modules.pop('avgpool')
# model._modules

# 自己定义全连接层
class Classifier(nn.Module):
    def __init__(self, dim, n_class):
        super().__init__()
        self.fc = nn.Sequential(
                    nn.Linear(dim, 1000)
                    ,nn.ReLU(inplace=True)
                    ,nn.Dropout(0.5)
                    ,nn.Linear(1000, n_class)
                    )
    
    def forward(self, x):
        x = self.fc(x)
        return x

model_fe = Feature_net('vgg')
for parma in model_fe.parameters():
    parma.requires_grad = False     #固定卷积层的参数
    
model_clf = Classifier(25088, 2)
# if torch.cuda.is_available():
#     model_fe = model_fe.cuda()
#     model_clf = model_clf.cuda()
model_fe = model_fe.cuda()
model_clf = model_clf.cuda()

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model_clf.parameters())

now = lambda x:time.time()
begin_time = now()
###################
# 电脑配置烂+dataloader存在IO阻塞的问题，这里只训练一轮
model_clf.train()
for epoch in range(1):
    sum_loss = 0.0
    for i, data in enumerate(train_loader):
        imgs, labels = data
        imgs, labels = imgs.cuda(), labels.cuda()
        if torch.cuda.is_available():
            imgs, labels = imgs.cuda(), labels.cuda()
        x = model_fe(imgs) 
        x = model_clf(x)
        loss = criterion(x, labels.squeeze())  
        optimizer.zero_grad()  
        loss.backward()  
        optimizer.step()  
        # print(loss)
        sum_loss += loss.item()
        if i % 100 == 99:
            print('[%d,%d] loss:%.03f' %
                  (epoch + 1, i + 1, sum_loss / 100))
            sum_loss = 0.0
##################
print(f'耗时:{now()-begin_time}s')

#测一下
tmpx,tmpy = next(iter(valid_loader))
print(tmpx.shape)
with torch.no_grad():
    model_fe.eval()
    model_clf.eval()
    out = model_fe(tmpx.cuda())
    out = model_clf(out)
print(torch.max(out,1)[1])
print(tmpy.squeeze())
print(sum(torch.max(out,1)[1].cpu() == tmpy.squeeze()).numpy()/32)

# 测试一下模型在验证集上的表现
model_fe.eval()    
model_clf.eval()
with torch.no_grad():
    eval_acc = 0
    for data in valid_loader:
        img, label = data
        if torch.cuda.is_available():
            img = img.cuda()
            label = label.cuda()
        out = model_fe(img)
        out = model_clf(out)    
        _, pred = torch.max(out, 1)
        num_correct = (pred == label.squeeze()).sum()
        eval_acc += num_correct.item()
    print(f'Acc: {eval_acc/len(Yvalid)}')

#output acc 0.934