20_完整模型验证套路
完整的模型验证套路
利用已经训练好的模型，然后给它提供输入
1. GPU训练30轮次
import torchvision
import torch
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import time
# 定义训练的设备
#device = torch.device("cpu")
device = torch.device("cuda") # 使用 GPU 方式一
#device = torch.device("cuda:0") # 使用 GPU 方式二
#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# from model import * 相当于把 model中的所有内容写到这里，这里直接把 model 写在这里
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3,32,5,1,2), # 输入通道3，输出通道32，卷积核尺寸5×5，步长1，填充2
nn.MaxPool2d(2),
nn.Conv2d(32,32,5,1,2),
nn.MaxPool2d(2),
nn.Conv2d(32,64,5,1,2),
nn.MaxPool2d(2),
nn.Flatten(), # 展平后变成 64*4*4 了
nn.Linear(64*4*4,64),
nn.Linear(64,10)
)
def forward(self, x):
x = self.model1(x)
return x
# 准备数据集
train_data = torchvision.datasets.CIFAR10("./dataset",train=True,transform=torchvision.transforms.ToTensor(),download=True)
test_data = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
# length 长度
train_data_size = len(train_data)
test_data_size = len(test_data)
# 如果train_data_size=10，则打印：训练数据集的长度为：10
print("训练数据集的长度：{}".format(train_data_size))
print("测试数据集的长度：{}".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
tudui = Tudui()
tudui = tudui.to(device) # 也可以不赋值，直接 tudui.to(device)
# 损失函数
loss_fn = nn.CrossEntropyLoss() # 交叉熵，fn 是 fuction 的缩写
loss_fn = loss_fn.to(device) # 也可以不赋值，直接loss_fn.to(device)
# 优化器
learning = 0.01 # 1e-2 就是 0.01 的意思
optimizer = torch.optim.SGD(tudui.parameters(),learning) # 随机梯度下降优化器
# 设置网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮次
epoch = 30
# 添加 tensorboard
writer = SummaryWriter("logs")
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
tudui.train() # 当网络中有dropout层、batchnorm层时，这些层能起作用
for data in train_dataloader:
imgs, targets = data
imgs = imgs.to(device) # 也可以不赋值，直接 imgs.to(device)
targets = targets.to(device) # 也可以不赋值，直接 targets.to(device)
outputs = tudui(imgs)
loss = loss_fn(outputs, targets) # 计算实际输出与目标输出的差距
# 优化器对模型调优
optimizer.zero_grad() # 梯度清零
loss.backward() # 反向传播，计算损失函数的梯度
optimizer.step() # 根据梯度，对网络的参数进行调优
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
end_time = time.time()
print(end_time - start_time) # 运行训练一百次后的时间间隔
print("训练次数：{}，Loss：{}".format(total_train_step,loss.item())) # 方式二：获得loss值
writer.add_scalar("train_loss",loss.item(),total_train_step)
# 测试步骤开始（每一轮训练后都查看在测试数据集上的loss情况）
tudui.eval() # 当网络中有dropout层、batchnorm层时，这些层不能起作用
total_test_loss = 0
total_accuracy = 0
with torch.no_grad(): # 没有梯度了
for data in test_dataloader: # 测试数据集提取数据
imgs, targets = data # 数据放到cuda上
imgs = imgs.to(device) # 也可以不赋值，直接 imgs.to(device)
targets = targets.to(device) # 也可以不赋值，直接 targets.to(device)
outputs = tudui(imgs)
loss = loss_fn(outputs, targets) # 仅data数据在网络模型上的损失
total_test_loss = total_test_loss + loss.item() # 所有loss
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(tudui, "./model/tudui_{}.pth".format(i)) # 保存每一轮训练后的结果
#torch.save(tudui.state_dict(),"tudui_{}.path".format(i)) # 保存方式二
print("模型已保存")
writer.close()
2. 验证狗是否识别
① 完整的模型验证(测试，demo)套路，利用已经训练好的模型，然后给它提供输入。
import torchvision
from PIL import Image
from torch import nn
import torch
image_path = "imgs/dog.png"
image = Image.open(image_path) # PIL类型的Image
image = image.convert("RGB") # 4通道的RGBA转为3通道的RGB图片，一般png图片还有透明度通道
print(image)
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),
torchvision.transforms.ToTensor()])
image = transform(image)
print(image.shape)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3,32,5,1,2),
nn.MaxPool2d(2),
nn.Conv2d(32,32,5,1,2),
nn.MaxPool2d(2),
nn.Conv2d(32,64,5,1,2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(64*4*4,64),
nn.Linear(64,10)
)
def forward(self, x):
x = self.model1(x)
return x
# GPU上训练的东西映射到CPU上
model = torch.load("model/tudui_29.pth",map_location=torch.device('cpu'))
print(model)
# 转为四维，符合网络输入需求
image = torch.reshape(image,(1,3,32,32))
#使用模型测试模式
model.eval()
# 不进行梯度计算，节约内存
with torch.no_grad():
output = model(image)
output = model(image)
print(output)
# 概率最大类别的输出
print(output.argmax(1))
3. 验证飞机是否识别
import torchvision
from PIL import Image
from torch import nn
import torch
image_path = "imgs/plane.png"
image = Image.open(image_path) # PIL类型的Image
image = image.convert("RGB") # 4通道的RGBA转为3通道的RGB图片
print(image)
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),
torchvision.transforms.ToTensor()])
image = transform(image)
print(image.shape)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = nn.Sequential(
nn.Conv2d(3,32,5,1,2),
nn.MaxPool2d(2),
nn.Conv2d(32,32,5,1,2),
nn.MaxPool2d(2),
nn.Conv2d(32,64,5,1,2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(64*4*4,64),
nn.Linear(64,10)
)
def forward(self, x):
x = self.model1(x)
return x
model = torch.load("model/tudui_29.pth",map_location=torch.device('cpu')) # GPU上训练的东西映射到CPU上
print(model)
image = torch.reshape(image,(1,3,32,32)) # 转为四维，符合网络输入需求
model.eval()
with torch.no_grad(): # 不进行梯度计算，减少内存计算
output = model(image)
output = model(image)
print(output)
print(output.argmax(1)) # 概率最大类别的输出
<PIL.Image.Image image mode=RGB size=245x181 at 0x1A236055908>
torch.Size([3, 32, 32])
Tudui(
(model1): Sequential(
(0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Flatten(start_dim=1, end_dim=-1)
(7): Linear(in_features=1024, out_features=64, bias=True)
(8): Linear(in_features=64, out_features=10, bias=True)
)
)
tensor([[ 3.9218, 1.2536, 1.1701, 0.1064, 2.2779, -4.4623, 0.0393, -4.2867,
2.3805, -2.1321]], grad_fn=<AddmmBackward0>)
tensor([0])
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