pytorch学习记录四【cifar示例完整代码 / 利用GPU进行训练 / 测试】
1.item：
import torch
a = torch.tensor(5)
print(a) # tensor(5)
print(a.item()) # 5
2.求准确率的小test
import torch
# 预测概率 此处有两个输入。每行代表一个输入图片的预测输出。例如第一行代表：为第0种种类的概率为0.1，为第1种种类的概率为0.2
outputs = torch.tensor([[0.1, 0.2],
[0.3, 0.4]])
# 预测结果
preds = outputs.argmax(1) # tensor([1, 1]) 参数1表示横向看。表示的意思是：每行的概率匹配。 比如这里代表两个图片都是第一种种类
# 真实结果
targets = torch.tensor([0, 1]) # 这是真实结果
# 准确个数
print((preds == targets).sum()) # 表示我们的预测是否准确。为false + true = 1
3.cifar示例完整代码
model.py
# 搭建神经网络
import torch
from torch import nn
class cifar_model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
if __name__ == '__main__':
# 可以利用这里进行测试
test_cifar = cifar_model()
input = torch.ones((64, 3, 32, 32))
output = test_cifar(input)
print(output.shape) # torch.Size([64, 10]) # 检查模型的正确性
训练train.py
import torch
import torchvision
from tensorboardX import SummaryWriter
from torch import nn
from torch.utils.data import DataLoader
from cifar_src.model import *
# 准备数据集
train_data = torchvision.datasets.CIFAR10("../cifar_data", train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10("../cifar_data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
# length 长度
train_data_size = len(train_data) # 50000
test_data_size = len(test_data) # 10000
print("训练数据集的长度为:{}".format(train_data_size))
print("测试数据集的长度为:{}".format(test_data_size))
# 利用Dataloader来加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型
cifarr = cifar_model()
# 损失函数
loss_fn = nn.CrossEntropyLoss()
# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(cifarr.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("../cifar_log")
for i in range(epoch):
print("————————第{}轮训练开始————————".format(i+1))
# 训练步骤开始
cifarr.train()
for data in train_dataloader:
imgs, targets = data
outputs = cifarr(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad() # 优化器梯度清零
loss.backward() # 反向传播，计算梯度
optimizer.step() # 根据梯度和优化器，更新权重
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
print("训练次数:{}, Loss:{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
# 验证步骤
cifarr.eval()
total_test_loss = 0
total_accuracy = 0 # 总体的准确率
with torch.no_grad(): # 强制之后的内容不进行计算图构建
for data in test_dataloader:
imgs, targets = data
outputs = cifarr(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
print("整体测试集上的loss:{}".format(total_test_loss))
print("整体测试集上的正确率:{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
total_test_step = total_test_step + 1
torch.save(cifarr, "cifar_{}.pth".format(i))
print("模型已保存")
writer.close()
4.利用GPU进行训练1：.cuda：
命令行nvidia-smi
利用GPU训练的方法：
对网络模型、数据（输入，标注）、损失函数调用.cuda
加入GPU的完整代码
import time
import torch
import torchvision
from tensorboardX import SummaryWriter
from torch import nn
from torch.utils.data import DataLoader
# 准备数据集
train_data = torchvision.datasets.CIFAR10("../cifar_data", train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10("../cifar_data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
# length 长度
train_data_size = len(train_data) # 50000
test_data_size = len(test_data) # 10000
print("训练数据集的长度为:{}".format(train_data_size))
print("测试数据集的长度为:{}".format(test_data_size))
# 利用Dataloader来加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型
class cifar_model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
cifarr = cifar_model()
if torch.cuda.is_available():
cifarr = cifarr.cuda()
# 损失函数
loss_fn = nn.CrossEntropyLoss()
if torch.cuda.is_available():
loss_fn = loss_fn.cuda()
# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(cifarr.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("../cifar_log")
start_time = time.time()
for i in range(epoch):
print("————————第{}轮训练开始————————".format(i+1))
# 训练步骤开始
cifarr.train()
for data in train_dataloader:
imgs, targets = data
if torch.cuda.is_available():
imgs = imgs.cuda()
targets = targets.cuda()
outputs = cifarr(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad() # 优化器梯度清零
loss.backward() # 反向传播，计算梯度
optimizer.step() # 根据梯度和优化器，更新权重
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
end_time = time.time()
print(end_time-start_time)
print("训练次数:{}, Loss:{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
# 验证步骤
cifarr.eval()
total_test_loss = 0
total_accuracy = 0 # 总体的准确率
with torch.no_grad(): # 强制之后的内容不进行计算图构建
for data in test_dataloader:
imgs, targets = data
if torch.cuda.is_available():
imgs = imgs.cuda()
targets = targets.cuda()
outputs = cifarr(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
print("整体测试集上的loss:{}".format(total_test_loss))
print("整体测试集上的正确率:{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
total_test_step = total_test_step + 1
torch.save(cifarr, "cifar_{}.pth".format(i))
print("模型已保存")
writer.close()
5.利用GPU进行训练2：.to(device)：
完整代码：
import time
import torch
import torchvision
from tensorboardX import SummaryWriter
from torch import nn
from torch.utils.data import DataLoader
# 定义训练的设备
# device = torch.device("cuda")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 准备数据集
train_data = torchvision.datasets.CIFAR10("../cifar_data", train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10("../cifar_data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
# length 长度
train_data_size = len(train_data) # 50000
test_data_size = len(test_data) # 10000
print("训练数据集的长度为:{}".format(train_data_size))
print("测试数据集的长度为:{}".format(test_data_size))
# 利用Dataloader来加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型
class cifar_model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
cifarr = cifar_model()
cifarr = cifarr.to(device)
# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)
# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(cifarr.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("../cifar_log")
start_time = time.time()
for i in range(epoch):
print("————————第{}轮训练开始————————".format(i+1))
# 训练步骤开始
cifarr.train()
for data in train_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = cifarr(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad() # 优化器梯度清零
loss.backward() # 反向传播，计算梯度
optimizer.step() # 根据梯度和优化器，更新权重
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
end_time = time.time()
print(end_time-start_time)
print("训练次数:{}, Loss:{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
# 验证步骤
cifarr.eval()
total_test_loss = 0
total_accuracy = 0 # 总体的准确率
with torch.no_grad(): # 强制之后的内容不进行计算图构建
for data in test_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = cifarr(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
print("整体测试集上的loss:{}".format(total_test_loss))
print("整体测试集上的正确率:{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
total_test_step = total_test_step + 1
torch.save(cifarr, "cifar_{}.pth".format(i))
print("模型已保存")
writer.close()
6.测试
注意，如果是用GPU训练的网络结构进行测试，那测试时输入的图片也要用cuda
否则会报这个错
正确完整代码：
test.py
from PIL import Image
import time
import torch
import torchvision
from tensorboardX import SummaryWriter
from torch import nn
from torch.utils.data import DataLoader
from torchvision import transforms
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
imgae_path = "../cifar_img/dog3.jpg"
image = Image.open(imgae_path)
image = image.convert('RGB')
transform = torchvision.transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor()])
image = transform(image)
print(image.shape) # 因为cifar网络要求输入32*32的图片
class cifar_model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
model = torch.load("cifar_9.pth")
print(model)
image = torch.reshape(image, (1, 3, 32, 32))
image = image.to(device)
model.eval()
with torch.no_grad():
output = model(image)
print(output)
print(output.argmax(1))
输出7
至于看7是什么，可以在训练数据的这里打断点
7.最终的完整代码
目录
model.py
# 搭建神经网络
import torch
from torch import nn
class cifar_model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
if __name__ == '__main__':
test_cifar = cifar_model()
input = torch.ones((64, 3, 32, 32))
output = test_cifar(input)
print(output.shape) # torch.Size([64, 10]) # 检查模型的正确性
train.py
import time
import torch
import torchvision
from tensorboardX import SummaryWriter
from torch import nn
from torch.utils.data import DataLoader
# 定义训练的设备
# device = torch.device("cuda")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 准备数据集
train_data = torchvision.datasets.CIFAR10("../cifar_data", train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10("../cifar_data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
# length 长度
train_data_size = len(train_data) # 50000
test_data_size = len(test_data) # 10000
print("训练数据集的长度为:{}".format(train_data_size))
print("测试数据集的长度为:{}".format(test_data_size))
# 利用Dataloader来加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型
class cifar_model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
cifarr = cifar_model()
cifarr = cifarr.to(device)
# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)
# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(cifarr.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("../cifar_log")
start_time = time.time()
for i in range(epoch):
print("————————第{}轮训练开始————————".format(i+1))
# 训练步骤开始
cifarr.train()
for data in train_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = cifarr(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad() # 优化器梯度清零
loss.backward() # 反向传播，计算梯度
optimizer.step() # 根据梯度和优化器，更新权重
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
end_time = time.time()
print(end_time-start_time)
print("训练次数:{}, Loss:{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
# 验证步骤
cifarr.eval()
total_test_loss = 0
total_accuracy = 0 # 总体的准确率
with torch.no_grad(): # 强制之后的内容不进行计算图构建
for data in test_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = cifarr(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
print("整体测试集上的loss:{}".format(total_test_loss))
print("整体测试集上的正确率:{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
total_test_step = total_test_step + 1
torch.save(cifarr, "cifar_{}.pth".format(i))
print("模型已保存")
writer.close()
test.py
from PIL import Image
import time
import torch
import torchvision
from tensorboardX import SummaryWriter
from torch import nn
from torch.utils.data import DataLoader
from torchvision import transforms
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
imgae_path = "../cifar_img/dog3.jpg"
image = Image.open(imgae_path)
image = image.convert('RGB')
transform = torchvision.transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor()])
image = transform(image)
print(image.shape) # 因为cifar网络要求输入32*32的图片
class cifar_model(nn.Module):
def __init__(self) -> None:
super().__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, padding=2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, padding=2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
model = torch.load("cifar_9.pth")
print(model)
image = torch.reshape(image, (1, 3, 32, 32))
image = image.to(device)
model.eval()
with torch.no_grad():
output = model(image)
print(output)
print(output.argmax(1))