【第4次作业】CNN实战

使用VGG模型进行猫狗大战

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

1、下载数据

! wget https://static.leiphone.com/cat_dog.rar

! unrar x cat_dog.rar

2、数据处理

datasets 是 torchvision 中的一个包，可以用做加载图像数据。它可以以多线程（multi-thread）的形式从硬盘中读取数据，使用 mini-batch 的形式，在网络训练中向 GPU 输送。在使用CNN处理图像时，需要进行预处理。图片将被整理成 224×224×3 的大小，同时还将进行归一化处理。

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,
            ])

#这里进行了修改，包括训练数据、验证数据、以及测试数据，分别在三个目录train/val/test
import shutil
data_dir = './cat_dog'
os.mkdir("./cat_dog/train/cat")
os.mkdir("./cat_dog/train/dog")
os.mkdir("./cat_dog/val/cat")
os.mkdir("./cat_dog/val/dog")
for i in range(10000):
  cat_name = './cat_dog/train/cat_'+str(i)+'.jpg';
  dog_name = './cat_dog/train/dog_'+str(i)+'.jpg';
  shutil.move(cat_name,"./cat_dog/train/cat")
  shutil.move(dog_name,"./cat_dog/train/dog")

for i in range(1000):
  cat_name = './cat_dog/val/cat_'+str(i)+'.jpg';
  dog_name = './cat_dog/val/dog_'+str(i)+'.jpg';
  shutil.move(cat_name,"./cat_dog/val/cat")
  shutil.move(dog_name,"./cat_dog/val/dog")
#读取测试问题的数据集

test_path = "./cat_dog/test/dogs_cats"
os.mkdir(test_path)
#移动到test_path
for i in range(2000):
  name = './cat_dog/test/'+str(i)+'.jpg'
  shutil.move(name,"./cat_dog/test/dogs_cats")

file_list=os.listdir("./cat_dog/test/dogs_cats")
#将图片名补全，防止读取顺序不对
for file in file_list:
  #填充0后名字总共10位，包括扩展名
  filename = file.zfill(10)
  new_name =''.join(filename)
  os.rename(test_path+'/'+file,test_path+'/'+new_name)
#将所有图片数据放到dsets内
dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), vgg_format)
         for x in ['train','val','test']}
dset_sizes = {x: len(dsets[x]) for x in ['train','val','test']}
dset_classes = dsets['train'].classes

loader_train = torch.utils.data.DataLoader(dsets['train'], batch_size=64, shuffle=True, num_workers=6)
loader_valid = torch.utils.data.DataLoader(dsets['val'], batch_size=5, shuffle=False, num_workers=6)
#加入测试集
loader_test = torch.utils.data.DataLoader(dsets['test'], 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_test:
    print(count, end=',')
    if count%50==0:
      print()
    if count == 1:
        inputs_try,labels_try = data
    count +=1

print(labels_try)
print(inputs_try.shape)

# 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])

3. 创建 VGG Model

!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()])

4. 修改最后一层，冻结前面层的参数

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)

5. 训练并测试全连接层

包括三个步骤：第1步，创建损失函数和优化器；第2步，训练模型；第3步，测试模型。

'''
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)

#验证模型正确率的代码
def test_model(model,dataloader,size):
    model.eval()
    predictions = np.zeros(size)
    all_classes = np.zeros(size)
    all_proba = np.zeros((size,2))
    i = 0
    running_loss = 0.0
    running_corrects = 0
    for inputs,classes in dataloader:
        inputs = inputs.to(device)
        classes = classes.to(device)
        outputs = model(inputs)
        loss = criterion(outputs,classes)
        _,preds = torch.max(outputs.data,1)
        # statistics
        running_loss += loss.data.item()
        running_corrects += torch.sum(preds == classes.data)
        predictions[i:i+len(classes)] = preds.to('cpu').numpy()
        all_classes[i:i+len(classes)] = classes.to('cpu').numpy()
        all_proba[i:i+len(classes),:] = outputs.data.to('cpu').numpy()
        i += len(classes)
        print('validing: No. ', i, ' 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))
    return predictions, all_proba, all_classes

#predictions, all_proba, all_classes = test_model(model_vgg_new,loader_valid,size=dset_sizes['val'])
#如果使用的是已有的模型，应该跑下面这行代码
predictions, all_proba, all_classes = test_model(model_new,loader_valid,size=dset_sizes['val'])

#这个是对测试集进行预测的代码
def result_model(model,dataloader,size):
    model.eval()
    predictions=np.zeros((size,2),dtype='int')
    i = 0
    for inputs,classes in dataloader:
        inputs = inputs.to(device)
        outputs = model(inputs)
        #_表示的就是具体的value，preds表示下标，1表示在行上操作取最大值，返回类别
        _,preds = torch.max(outputs.data,1)
        predictions[i:i+len(classes),1] = preds.to('cpu').numpy();
        predictions[i:i+len(classes),0] = np.linspace(i,i+len(classes)-1,len(classes))
        #可在过程中看到部分结果
        print(predictions[i:i+len(classes),:])
        i += len(classes)
        print('creating: No. ', i, ' process ... total: ', size)
    return predictions

result = result_model(model_vgg_new,loader_test,size=dset_sizes['test'])
#如果使用的是已有的模型，应该跑下面这行代码
result = result_model(model_new,loader_test,size=dset_sizes['test'])

#这里是生成结果的文件，上传到AI研习社可以看到正确率
np.savetxt("./cat_dog/result.csv",result,fmt="%d",delimiter=",")

6. 可视化模型预测结果（主观分析）

主观分析就是把预测的结果和相对应的测试图像输出出来看看，一般有四种方式：

随机查看一些预测正确的图片
随机查看一些预测错误的图片
预测正确，同时具有较大的probability的图片
预测错误，同时具有较大的probability的图片
最不确定的图片，比如说预测概率接近0.5的图片

# 单次可视化显示的图片个数
n_view = 8
correct = np.where(predictions==all_classes)[0]
from numpy.random import random, permutation
idx = permutation(correct)[:n_view]
print('random correct idx: ', idx)
loader_correct = torch.utils.data.DataLoader([dsets['valid'][x] for x in idx],
                  batch_size = n_view,shuffle=True)
for data in loader_correct:
    inputs_cor,labels_cor = data
# Make a grid from batch
out = torchvision.utils.make_grid(inputs_cor)
imshow(out, title=[l.item() for l in labels_cor])

print(all_classes)
# 类似的思路，可以显示错误分类的图片，这里不再重复代码