Pytorch从零开始实战------彩色图像识别
本系列来源于365天深度学习训练营
原作者K同学
文章目录
环境准备
本文基于Jupyter notebook,使用Python3.8,Pytorch2.0.1+cu118,torchvision0.15.2,需读者自行配置好环境且有一些深度学习理论基础。
老规矩,还是导入常用包
python
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torchvision
import torch.nn.functional as F
import random
from time import time
import random
import numpy as np
import pandas as pd
import datetime
import gc
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True' # 用于避免jupyter环境突然关闭
torch.backends.cudnn.benchmark=True # 用于加速GPU运算的代码
创建设备对象
python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
设置随机数种子
python
torch.manual_seed(428)
torch.cuda.manual_seed(428)
torch.cuda.manual_seed_all(428)
random.seed(428)
np.random.seed(428)
数据集
本文使用CIFAR-10数据集,CIFAR-10数据集包含60000张 32x32的彩色图片,共分为10种类别,每种类别6000张。其中训练集包含50000张图片,测试机包含10000张图片。
我们使用torchvision.datasets下载数据集。
python
train_ds = torchvision.datasets.CIFAR10('data', train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_ds = torchvision.datasets.CIFAR10('data', train=True, transform=torchvision.transforms.ToTensor(), download=True)
随机展示五张图片,下面这个函数是从别人那里抄的,挺好用的。
python
def plotsample(data):
fig, axs = plt.subplots(1, 5, figsize=(10, 10)) #建立子图
for i in range(5):
num = random.randint(0, len(data) - 1) #首先选取随机数,随机选取五次
#抽取数据中对应的图像对象,make_grid函数可将任意格式的图像的通道数升为3,而不改变图像原始的数据
#而展示图像用的imshow函数最常见的输入格式也是3通道
npimg = torchvision.utils.make_grid(data[num][0]).numpy()
nplabel = data[num][1] #提取标签
#将图像由(3, weight, height)转化为(weight, height, 3),并放入imshow函数中读取
axs[i].imshow(np.transpose(npimg, (1, 2, 0)))
axs[i].set_title(nplabel) #给每个子图加上标签
axs[i].axis("off") #消除每个子图的坐标轴
plotsample(train_ds)
展示出现有点模糊
还是之前的套路,使用DataLoder将它按照batch_size批量划分,并将训练集顺序打乱。
python
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_ds, batch_size=batch_size, shuffle=True)
test_dl = torch.utils.data.DataLoader(test_ds, batch_size=batch_size, shuffle=True)
查看dataloader的一个批次
python
imgs, label = next(iter(train_dl))
imgs.shape # 3通道 32 * 32
模型选择
本文还是使用自定义简单的卷积神经网络,下面是两个函数原型,卷积函数默认步幅是1,padding是0,最大池化函数默认步长跟最大池化窗口大小一致。
torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)
torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)
最后输出的图像尺寸是2 * 2,128通道,将它们拉平,线性层需要填入512来接收。
python
num_classes = 10
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3)
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3)
self.pool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3)
self.pool3 = nn.MaxPool2d(kernel_size=2)
self.fc1 = nn.Linear(512, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self, x):
x = self.pool1(F.relu(self.conv1(x)))
x = self.pool2(F.relu(self.conv2(x)))
x = self.pool3(F.relu(self.conv3(x)))
x = torch.flatten(x, start_dim=1) # 从第一个维度开始拉平
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
使用summary查看模型,这次用的是torchsummary,展示的也不错,可以看见每层结构。
python
from torchsummary import summary
# 将模型转移到GPU中
model = Model().to(device)
summary(model, input_size=(3, 32, 32))
模型训练
定义损失函数、学习率、优化算法。
python
loss_fn = nn.CrossEntropyLoss()
learn_rate = 0.01
opt = torch.optim.SGD(model.parameters(), lr=learn_rate)
跟以前的文章一样,编写训练函数。
python
def train(dataloader, model, loss_fn, opt):
size = len(dataloader.dataset)
num_batches = len(dataloader)
train_acc, train_loss = 0, 0
for X, y in dataloader:
X, y = X.to(device), y.to(device)
pred = model(X)
loss = loss_fn(pred, y)
opt.zero_grad()
loss.backward()
opt.step()
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
测试函数。
python
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_acc, test_loss = 0, 0
with torch.no_grad():
for X, y in dataloader:
X, y = X.to(device), y.to(device)
pred = model(X)
loss = loss_fn(pred, y)
test_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
test_loss += loss.item()
test_acc /= size
test_loss /= num_batches
return test_acc, test_loss
开始训练,这次epochs设置为10轮。
python
import time
epochs = 10
train_loss = []
train_acc = []
test_loss = []
test_acc = []
T1 = time.time()
for epoch in range(epochs):
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
model.eval() # 确保模型不会进行训练操作
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
print("epoch:%d, train_acc:%.1f%%, train_loss:%.3f, test_acc:%.1f%%, test_loss:%.3f"
% (epoch + 1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss))
print("Done")
T2 = time.time()
print('程序运行时间:%s毫秒' % ((T2 - T1)*1000))
运行结果:在训练集上准确率达到59.7%,测试集达到59.9%,效果不是很好,说明还是欠拟合,需要未来改造模型或者增加训练轮数。
数据可视化
使用matplotlib进行训练数据、测试数据的可视化
python
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()