pytorch 咖啡豆识别

• 🍨 本文为🔗365天深度学习训练营** 中的学习记录博客**
• 🍦 参考文章地址： 365天深度学习训练营-第P6周：好莱坞明星识别
• 🍖 作者：K同学啊****

一、前期准备

1.设置GPU

import torch
from torch import nn
import torchvision
from torchvision import transforms,datasets,models
import matplotlib.pyplot as plt
import os,PIL,pathlib

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

device(type='cuda')

2.导入数据

data_dir = './49-data/'
data_dir = pathlib.Path(data_dir)

data_paths = list(data_dir.glob('*'))
classNames = [str(path).split('\\')[1] for path in data_paths]
classNames

['Dark', 'Green', 'Light', 'Medium']

train_transforms = transforms.Compose([
    transforms.Resize([224,224]),# resize输入图片
    transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换成tensor
    transforms.Normalize(
        mean = [0.485, 0.456, 0.406],
        std = [0.229,0.224,0.225]) # 从数据集中随机抽样计算得到
])

test_transforms = transforms.Compose([
    transforms.Resize([224,224]),# resize输入图片
    transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换成tensor
    transforms.Normalize(
        mean = [0.485, 0.456, 0.406],
        std = [0.229,0.224,0.225]) # 从数据集中随机抽样计算得到
])

total_data = datasets.ImageFolder(data_dir,transform=train_transforms)
total_data

Dataset ImageFolder
    Number of datapoints: 1200
    Root location: 49-data
    StandardTransform
Transform: Compose(
               Resize(size=[224, 224], interpolation=PIL.Image.BILINEAR)
               ToTensor()
               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
           )

total_data.class_to_idx

{'Dark': 0, 'Green': 1, 'Light': 2, 'Medium': 3}

3.数据集划分

train_size = int(0.8*len(total_data))
test_size = len(total_data) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(total_data,[train_size,test_size])
train_dataset,test_dataset

train_size,test_size

(960, 240)

batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset,
                                       batch_size=batch_size,
                                       shuffle=True,
                                       num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
                                       batch_size=batch_size,
                                       shuffle=True,
                                       num_workers=1)

imgs, labels = next(iter(train_dl))
imgs.shape

torch.Size([32, 3, 224, 224])

import numpy as np

 # 指定图片大小，图像大小为20宽、5高的绘图(单位为英寸inch)
plt.figure(figsize=(20, 5)) 
for i, imgs in enumerate(imgs[:20]):
    npimg = imgs.numpy().transpose((1,2,0))
    npimg = npimg * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))
    npimg = npimg.clip(0, 1)
    # 将整个figure分成2行10列，绘制第i+1个子图。
    plt.subplot(2, 10, i+1)
    plt.imshow(npimg)
    plt.axis('off')

for X,y in test_dl:
    print('Shape of X [N, C, H, W]:', X.shape)
    print('Shape of y:', y.shape)
    break

Shape of X [N, C, H, W]: torch.Size([32, 3, 224, 224])
Shape of y: torch.Size([32])

二、构建简单的CNN网络

1. 搭建模型

import torch.nn.functional as F

# class vgg16(nn.Module):
    
#     def __init__(self):
#         super(vgg16,self).__init__()
        
#         self.block1 = nn.Sequential(
#             nn.Conv2d(3,64,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.Conv2d(64,64,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
#         )
        
#         self.block2 = nn.Sequential(
#             nn.Conv2d(64,128,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.Conv2d(128,128,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
#         )
        
#         self.block3 = nn.Sequential(
#             nn.Conv2d(128,256,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.Conv2d(256,256,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.Conv2d(256,256,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
#         )
        
#         self.block4 = nn.Sequential(
#             nn.Conv2d(256,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
#         )
        
#         self.block5 = nn.Sequential(
#             nn.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.Conv2d(512,512,kernel_size=(3,3),stride=(1,1),padding=(1,1)),
#             nn.ReLU(),
#             nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))
#         )
    
#         self.classifier = nn.Sequential(
#             nn.Linear(in_features=512*7*7, out_features=4096),
#             nn.ReLU(),
#             nn.Linear(in_features=4096,out_features=4096),
#             nn.ReLU(),
#             nn.Linear(in_features=4096,out_features=4)
#         )
    
#         def forward(self,x):
            
#             x = self.block1(x)
#             x = self.block2(x)
#             x = self.block3(x)
#             x = self.block4(x)
#             x = self.block5(x)
#             x = torch.flatten(x, start_dim=1)
#             x = self.classifier(x)
            
#             return x

# model = vgg16().to(device)
# model 

from torchvision.models import vgg16
    
model = vgg16(pretrained = True).to(device)
for param in model.parameters(): # 只训练输出层
    param.requires_grad = False

model.classifier._modules['6'] = nn.Linear(4096,len(classNames))
model.to(device)
model

VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace=True)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace=True)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace=True)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace=True)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace=True)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace=True)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace=True)
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU(inplace=True)
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU(inplace=True)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace=True)
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace=True)
    (2): Dropout(p=0.5, inplace=False)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace=True)
    (5): Dropout(p=0.5, inplace=False)
    (6): Linear(in_features=4096, out_features=4, bias=True)
  )
)

2.查看模型详情

import torchsummary as summary
summary.summary(model,(3,224,224))

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 224, 224]           1,792
              ReLU-2         [-1, 64, 224, 224]               0
            Conv2d-3         [-1, 64, 224, 224]          36,928
              ReLU-4         [-1, 64, 224, 224]               0
         MaxPool2d-5         [-1, 64, 112, 112]               0
            Conv2d-6        [-1, 128, 112, 112]          73,856
              ReLU-7        [-1, 128, 112, 112]               0
            Conv2d-8        [-1, 128, 112, 112]         147,584
              ReLU-9        [-1, 128, 112, 112]               0
        MaxPool2d-10          [-1, 128, 56, 56]               0
           Conv2d-11          [-1, 256, 56, 56]         295,168
             ReLU-12          [-1, 256, 56, 56]               0
           Conv2d-13          [-1, 256, 56, 56]         590,080
             ReLU-14          [-1, 256, 56, 56]               0
           Conv2d-15          [-1, 256, 56, 56]         590,080
             ReLU-16          [-1, 256, 56, 56]               0
        MaxPool2d-17          [-1, 256, 28, 28]               0
           Conv2d-18          [-1, 512, 28, 28]       1,180,160
             ReLU-19          [-1, 512, 28, 28]               0
           Conv2d-20          [-1, 512, 28, 28]       2,359,808
             ReLU-21          [-1, 512, 28, 28]               0
           Conv2d-22          [-1, 512, 28, 28]       2,359,808
             ReLU-23          [-1, 512, 28, 28]               0
        MaxPool2d-24          [-1, 512, 14, 14]               0
           Conv2d-25          [-1, 512, 14, 14]       2,359,808
             ReLU-26          [-1, 512, 14, 14]               0
           Conv2d-27          [-1, 512, 14, 14]       2,359,808
             ReLU-28          [-1, 512, 14, 14]               0
           Conv2d-29          [-1, 512, 14, 14]       2,359,808
             ReLU-30          [-1, 512, 14, 14]               0
        MaxPool2d-31            [-1, 512, 7, 7]               0
AdaptiveAvgPool2d-32            [-1, 512, 7, 7]               0
           Linear-33                 [-1, 4096]     102,764,544
             ReLU-34                 [-1, 4096]               0
          Dropout-35                 [-1, 4096]               0
           Linear-36                 [-1, 4096]      16,781,312
             ReLU-37                 [-1, 4096]               0
          Dropout-38                 [-1, 4096]               0
           Linear-39                    [-1, 4]          16,388
================================================================
Total params: 134,276,932
Trainable params: 16,388
Non-trainable params: 134,260,544
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 218.77
Params size (MB): 512.23
Estimated Total Size (MB): 731.57
----------------------------------------------------------------

三、训练模型

# 设置优化器
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)#要训练什么参数/
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.92)#学习率每5个epoch衰减成原来的1/10
loss_fn = nn.CrossEntropyLoss()

1. 编写训练函数

# 训练循环
def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)  # 训练集的大小，一共900张图片
    num_batches = len(dataloader)   # 批次数目，29（900/32）

    train_loss, train_acc = 0, 0  # 初始化训练损失和正确率
    
    for X, y in dataloader:  # 获取图片及其标签
        X, y = X.to(device), y.to(device)
        
        # 计算预测误差
        pred = model(X)          # 网络输出
        loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失
        
        # 反向传播
        optimizer.zero_grad()  # grad属性归零
        loss.backward()        # 反向传播
        optimizer.step()       # 每一步自动更新
        
        # 记录acc与loss
        train_acc  += (pred.argmax(1) == y).type(torch.float).sum().item()
        train_loss += loss.item()
            
    train_acc  /= size
    train_loss /= num_batches

    return train_acc, train_loss

2.编写测试函数

def test (dataloader, model, loss_fn):
    size        = len(dataloader.dataset)  # 测试集的大小，一共10000张图片
    num_batches = len(dataloader)          # 批次数目，8（255/32=8，向上取整）
    test_loss, test_acc = 0, 0
    
    # 当不进行训练时，停止梯度更新，节省计算内存消耗
    with torch.no_grad():
        for imgs, target in dataloader:
            imgs, target = imgs.to(device), target.to(device)
            
            # 计算loss
            target_pred = model(imgs)
            loss        = loss_fn(target_pred, target)
            
            test_loss += loss.item()
            test_acc  += (target_pred.argmax(1) == target).type(torch.float).sum().item()

    test_acc  /= size
    test_loss /= num_batches

    return test_acc, test_loss

3、正式训练

epochs     = 20
train_loss = []
train_acc  = []
test_loss  = []
test_acc   = []
best_acc = 0

for epoch in range(epochs):
    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
    
    scheduler.step()#学习率衰减
    
    model.eval()
    epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
    
    # 保存最优模型
    if epoch_test_acc > best_acc:
        best_acc = epoch_train_acc
        state = {
            'state_dict': model.state_dict(),#字典里key就是各层的名字，值就是训练好的权重
            'best_acc': best_acc,
            'optimizer' : optimizer.state_dict(),
        }
        
    train_acc.append(epoch_train_acc)
    train_loss.append(epoch_train_loss)
    test_acc.append(epoch_test_acc)
    test_loss.append(epoch_test_loss)
    
    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')
    print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))
print('Done')
print('best_acc：',best_acc)

Epoch:18, Train_acc:93.5%, Train_loss:0.270, Test_acc:95.4%，Test_loss:0.223
Epoch:19, Train_acc:94.5%, Train_loss:0.241, Test_acc:95.8%，Test_loss:0.223
Epoch:20, Train_acc:94.4%, Train_loss:0.243, Test_acc:96.2%，Test_loss:0.207
Done
best_acc： 0.94375

四、结果可视化

1.Loss与Accuracy图

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息
plt.rcParams['font.sans-serif']    = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号
plt.rcParams['figure.dpi']         = 100        #分辨率

epochs_range = range(epochs)

plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)

plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

2.指定图片进行预测

from PIL import Image

classes = list(total_data.class_to_idx)

def predict_one_img(image_path,model,transform,classes):
    test_img = Image.open(image_path).convert('RGB')
    plt.imshow(test_img)
    test_img = transform(test_img)
    img = test_img.to(device).unsqueeze(0)
    model.eval()
    output = model(img)
    
    _,pred = torch.max(output,1)
    pred_class = classes[pred]
    print(f'预测结果是：{pred_class}')

predict_one_img('./49-data/Dark/dark (1).png', model, train_transforms, classNames)

预测结果是：Dark