第五部分 猫狗大战（Colab）
5.1 基础知识储备
1.Pytorch：transforms的二十二个方法，推荐博客链接：
5.2 代码详解
1.导入相关库，检查是否在使用GPU设备（Colab）
import numpy as np
import matplotlib.pyplot as plt
import os
import torch
import torch.nn as nn
import torchvision
from torchvision import models,transforms,datasets
import time
import json
# 判断是否存在GPU设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('Using gpu: %s ' % torch.cuda.is_available())
2.下载数据集（缩小版数据集）
! wget http://fenggao-image.stor.sinaapp.com/dogscats.zip
! unzip dogscats.zip
3.数据处理
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format = transforms.Compose([
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
data_dir = './dogscats'
dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), vgg_format)
for x in ['train', 'valid']}
dset_sizes = {x: len(dsets[x]) for x in ['train', 'valid']}
dset_classes = dsets['train'].classes
代码详解：
transforms.Normalize()：对图片进行标准化处理，使用Imagenet的均值和标准差
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]为Imagenet预设模型transforms.Compose()：串联多个图片变换的操作
CenterCrop(224)：依据给定的size从中心裁剪
transforms.ToTensor()：将PIL Image（Python Image Library）或者ndarray（N维数组对象）转化为tensor（张量），并且归一化至[0-1]
datasets.ImageFolder(os.path.join(data_dir, x)：加载该路径的数据
下图为dsets的属性：
# 通过下面代码可以查看 dsets 的一些属性
print(dsets['train'].classes)
print(dsets['train'].class_to_idx)
print(dsets['train'].imgs[:5])
print('dset_sizes: ', dset_sizes)
loader_train = torch.utils.data.DataLoader(dsets['train'], batch_size=64, shuffle=True, num_workers=6)
loader_valid = torch.utils.data.DataLoader(dsets['valid'], batch_size=5, shuffle=False, num_workers=6)
'''
valid 数据一共有2000张图，每个batch是5张，因此，下面进行遍历一共会输出到 400
同时，把第一个 batch 保存到 inputs_try, labels_try，分别查看
'''
count = 1
for data in loader_valid:
print(count, end='\n')
if count == 1:
inputs_try,labels_try = data
count +=1
print(labels_try)
print(inputs_try.shape)
代码详解：
loader_train = torch.utils.data.DataLoader(dsets['train'], batch_size=64, shuffle=True, num_workers=6)：
输入的数据类型为dataset；每次输入数据的行数为batch_size；shuffle为true，即将输入数据的顺序打乱（数据为无序列特征的可以使用）；num_workers：使用6个子进程来导入数据。
显示图片
# 显示图片的小程序
def imshow(inp, title=None):
# Imshow for Tensor.
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = np.clip(std * inp + mean, 0,1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
# 显示 labels_try 的5张图片，即valid里第一个batch的5张图片
out = torchvision.utils.make_grid(inputs_try)
imshow(out, title=[dset_classes[x] for x in labels_try])
4.创建VGG Model
直接使用预训练好的VGG模型进行预测，使用softmax对结果进行预处理，展示识别结果。
softmax详解，
#下载ImgaeNet1000个类的JSON文件
!wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json
model_vgg = models.vgg16(pretrained=True)
with open('./imagenet_class_index.json') as f:
class_dict = json.load(f)
dic_imagenet = [class_dict[str(i)][1] for i in range(len(class_dict))]
inputs_try , labels_try = inputs_try.to(device), labels_try.to(device)
model_vgg = model_vgg.to(device)
outputs_try = model_vgg(inputs_try)
print(outputs_try)
print(outputs_try.shape)
'''
可以看到结果为5行，1000列的数据，每一列代表对每一种目标识别的结果。
但是我也可以观察到，结果非常奇葩，有负数，有正数，
为了将VGG网络输出的结果转化为对每一类的预测概率，我们把结果输入到 Softmax 函数
'''
m_softm = nn.Softmax(dim=1)
probs = m_softm(outputs_try)
vals_try,pred_try = torch.max(probs,dim=1)
print( 'prob sum: ', torch.sum(probs,1))
print( 'vals_try: ', vals_try)
print( 'pred_try: ', pred_try)
print([dic_imagenet[i] for i in pred_try.data])
imshow(torchvision.utils.make_grid(inputs_try.data.cpu()),
title=[dset_classes[x] for x in labels_try.data.cpu()])
代码详解：
VGG16详细过程，见博客https://blog.csdn.net/qq_43270687/article/details/93471659
inputs_try.to(device)：将所有最开始读取数据时的tensor变量copy一份到device所指定的GPU上去，之后的运算都在GPU上进行。
print(model_vgg)
model_vgg_new = model_vgg;
for param in model_vgg_new.parameters():
param.requires_grad = False
model_vgg_new.classifier._modules['6'] = nn.Linear(4096, 2)
model_vgg_new.classifier._modules['7'] = torch.nn.LogSoftmax(dim = 1)
model_vgg_new = model_vgg_new.to(device)
print(model_vgg_new.classifier)
代码详解
model_vgg_new.parameters()：返回一个迭代器，迭代器每次生成的是Tensor类型的数据。
param.requires_grad：所有的tensor都有.requires_grad属性，requires_grad=True（要求梯度）
kernel_size表示卷积核的大小为3X3的，stride表示步长，padding表示的是填充值。
5.修改最后一层，冻结前面层的参数
'''
第一步：创建损失函数和优化器
损失函数 NLLLoss() 的 输入 是一个对数概率向量和一个目标标签.
它不会为我们计算对数概率，适合最后一层是log_softmax()的网络.
'''
criterion = nn.NLLLoss()
# 学习率
lr = 0.001
# 随机梯度下降
optimizer_vgg = torch.optim.SGD(model_vgg_new.classifier[6].parameters(),lr = lr)
'''
第二步：训练模型
'''
def train_model(model,dataloader,size,epochs=1,optimizer=None):
model.train()
for epoch in range(epochs):
running_loss = 0.0
running_corrects = 0
count = 0
for inputs,classes in dataloader:
inputs = inputs.to(device)
classes = classes.to(device)
outputs = model(inputs)
loss = criterion(outputs,classes)
optimizer = optimizer
optimizer.zero_grad()
loss.backward()
optimizer.step()
_,preds = torch.max(outputs.data,1)
# statistics
running_loss += loss.data.item()
running_corrects += torch.sum(preds == classes.data)
count += len(inputs)
print('Training: No. ', count, ' process ... total: ', size)
epoch_loss = running_loss / size
epoch_acc = running_corrects.data.item() / size
print('Loss: {:.4f} Acc: {:.4f}'.format(
epoch_loss, epoch_acc))
# 模型训练
train_model(model_vgg_new,loader_train,size=dset_sizes['train'], epochs=1,
optimizer=optimizer_vgg)
上述代码采用的SGD优化器，且epoch为1，在接下来的代码中，修改为Adam优化器，epoch的次数选择为10。
def train_model(model,criterion,optimizer,num_epochs):
model.train()
print(0)
#保存验证集上准确率最高的模型
best_model = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
running_loss = 0.0
running_corrects = 0
for inputs,labels in loader_train:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
_,preds = torch.max(outputs,1)
loss = criterion(outputs,labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
running_corrects += (preds == labels).sum().item()
epoch_loss = running_loss / dset_sizes['train']
epoch_acc = running_corrects / dset_sizes['train']
print("Train Loss:{:.4f} Acc:{:.4f}".format(epoch_loss,epoch_acc))
with torch.set_grad_enabled(False):
model.eval()
running_loss = 0.0
running_corrects = 0
for inputs,labels in loader_valid:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_,preds = torch.max(outputs,1)
loss = criterion(outputs,labels)
running_loss += loss.item()
running_corrects += (preds == labels).sum().item()
epoch_loss = running_loss / dset_sizes['valid']
epoch_acc = running_corrects / dset_sizes['valid']
print("Valid Loss:{:.4f} Acc:{:.4f}".format(epoch_loss,epoch_acc))
if epoch_acc > best_acc:
best_model = copy.deepcopy(model.state_dict())
best_acc = epoch_acc
print("Best val Acc:{:.4f}".format(best_acc))
model.load_state_dict(best_model)
return model
def test_model(model):
pred = []
for inputs in test_loader:
inputs = inputs.to(device)
outputs = model(inputs)
_,preds = torch.max(outputs,1)
for i in preds:
pred.append(i.item())
return pred
model_vgg = models.vgg19(pretrained=True)
for param in model_vgg.features.parameters():
param.requires_grad = False
model_vgg.classifier._modules['0'] = nn.Linear(25088, 1024)
model_vgg.classifier._modules['3'] = nn.Linear(1024, 128)
model_vgg.classifier._modules['6'] = nn.Linear(128, 2)
model_vgg.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
#Adam是一种学习率自适应的算法
optimizer = optim.Adam(model_vgg.parameters(), lr=0.001)
model = train_model(model_vgg, criterion, optimizer,
num_epochs=10)
pre = test_model(model)
import csv
f = open('result.csv','w',encoding='utf-8',newline="")
csv_writer = csv.writer(f)
for i,pred in enumerate(pre):a
csv_writer.writerow([i,pred])
f.close()
总结
通过此次对进阶练习的学习，清楚的认识到了自己的不足，且收获很多，为我今后的学习指明了方向（特别感谢解志杰同学的帮助！！）。以上代码取材于老师给的材料，目前的实力还没能达到修改其他内容，仅仅对VGG模型和模型训练的过程有初步的了解，因此保留原代码添加注释，在今后学习的过程中再回顾。