PyTorch代码笔记
跟着B站小土堆PyTorch教程写的代码笔记
完整代码:
链接:https://pan.baidu.com/s/1-ZePujIeVjeZeCdq7Xek0Q
提取码:4vse
1_tensorboard
Tensorboard是一个可视化工具,能够可视化神经网络内部的组织、结构、及其训练过程。
from torch.utils.tensorboard import SummaryWriter
from PIL import Image
import numpy as np
#tensorboard --logdir=logs/xxxlogs
writer = SummaryWriter("logs")
img_path = "dataset/antbee/train/ants/0013035.jpg"
img_PIL = Image.open(img_path)
img_array = np.array(img_PIL)
#img_PIL.show()
print(type(img_array))
print(img_array.shape)
writer.add_image("ant", img_array, 2, dataformats='HWC')
for i in range(100):
writer.add_scalar(tag="y=2x", scalar_value=2*i, global_step=i)
writer.flush()
writer.close()
2_transform
Transforms是pytorch的图像处理工具包,是torchvision模块下的一个一个类的集合,可以对图像或数据进行格式变换,裁剪,缩放,旋转等。
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
import cv2
img_path = "dataset/antbee/train/ants/0013035.jpg"
img = Image.open(img_path)
writer = SummaryWriter("logs")
tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)
#print(tensor_img)
writer.add_image("tensor_img", tensor_img)
writer.close()
#cv_img = cv2.imread(img_path)
#print(cv_img)
3_useful_transform
常用transform
ToTensor:把PIL.Image或ndarray从 (H x W x C)形状转换为 (C x H x W) 的tensor
Normalize:对图像进行标准化
Resize:调整PILImage对象的尺寸,注意不能是用io.imread或者cv2.imread读取的图片,这两种方法得到的是ndarray
Compose:串联多个图片变换的操作
RandomCrop:随机裁剪图片
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
img = Image.open("dataset/antbee/train/ants/0013035.jpg")
print(img)
writer = SummaryWriter("logs")
#ToTensor
tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)
writer.add_image("tensor_img", tensor_img)
#Normalize
print(tensor_img[0][0][0])
trans_norm = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
img_norm = trans_norm(tensor_img)
print(img_norm[0][0][0])
writer.add_image("Normalize", img_norm)
#Resize
print(img.size)
trans_resize = transforms.Resize((512, 512))
img_resize = trans_resize(img)
img_resize = trans_resize(tensor_img)
writer.add_image("Resize", img_resize, 0)
print(img_resize)
#Compose
trans_resize_2 = transforms.Resize(512)
trans_compose = transforms.Compose([trans_resize_2, tensor_trans])
img_resize_2 = trans_compose(img)
writer.add_image("Resize", img_resize_2, 1)
#RandomCrop
trans_random = transforms.RandomCrop((500, 600))
trans_compose_2 = transforms.Compose([trans_random, tensor_trans])
for i in range(20):
img_crop = trans_compose_2(img)
writer.add_image("RandomCrop", img_crop, i)
writer.close()
4_data
torchvision中的数据集使用
import torchvision
from torch.utils.tensorboard import SummaryWriter
dataset_transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor()
])
train_set = torchvision.datasets.CIFAR10(root="dataset", train=True, transform=dataset_transform, download=False)
test_set = torchvision.datasets.CIFAR10(root="dataset", train=False, transform=dataset_transform, download=False)
# print(train_set[0])
# print(train_set.classes)
#
# img, target = train_set[0]
# print(img)
# print(target)
# print(train_set.classes[target])
# img.show()
#print(train_set[0])
writer = SummaryWriter("logs/datalogs")
for i in range(10):
img, target = train_set[i]
writer.add_image("data", img, i)
writer.close()
5_dataloader
DataLoader是Pytorch中用来处理模型输入数据的一个工具类。
import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
train_data = torchvision.datasets.CIFAR10("dataset", train=True, transform=torchvision.transforms.ToTensor())
test_data = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor())
trian_loader = DataLoader(dataset=train_data, batch_size=64, shuffle=True, num_workers=0, drop_last=False)
test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True, num_workers=0, drop_last=False)
img, target = train_data[0]
print(img.shape)
print(target)
writer = SummaryWriter("logs/dataloadlogs")
for epoh in range(2):
step = 0
for data in test_loader:
imgs, targets = data
#print(imgs.shape)
#print(targets)
writer.add_images("epoh {}".format(epoh), imgs, step)
step = step+1
print(step)
writer.close()
6_module
神经网络的基本骨架——nn.Module的使用
import torch
import torch.nn as nn
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
output = input + 1
return output
model = Model()
x = torch.tensor(1.0)
output = model(x)
print(output)
7_conv
卷积理解
import torch
import torch.nn.functional as F
input = torch.tensor([[1, 2, 0, 3, 1],
[0, 1, 2, 3, 1],
[1, 2, 1, 0, 0],
[5, 2, 3, 1, 1],
[2, 1, 0, 1, 1]])
kernel = torch.tensor([[1, 2, 1],
[0, 1, 0],
[2, 1, 0]])
input = torch.reshape(input, (1, 1, 5, 5))
kernel = torch.reshape(kernel, (1, 1, 3, 3))
output = F.conv2d(input, kernel, stride=1)
print(output)
output2 = F.conv2d(input, kernel, stride=2)
print(output2)
output3 = F.conv2d(input, kernel, stride=1, padding=1)
print(output3)
8_conv2d
卷积层
import torchvision
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=64)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.conv1 = Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=0)
def forward(self, x):
x = self.conv1(x)
return x
tudui = Tudui()
print(tudui)
writer = SummaryWriter("logs/convlogs")
step = 0
for data in dataloader:
imgs, target = data
output = tudui(imgs)
#print(imgs.shape)
#print(output.shape)
writer.add_images("input", imgs, step)
output = torch.reshape(output, (-1, 3, 30, 30))
print(set, output.shape)
writer.add_images("output", output, step)
step = step + 1
9_maxpool
最大池化
import torch
from torch import nn
from torch.nn import MaxPool2d
import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=64)
# input = torch.tensor([[1, 2, 0, 3, 1],
# [0, 1, 2, 3, 1],
# [1, 2, 1, 0, 0],
# [5, 2, 3, 1, 1],
# [2, 1, 0, 1, 1]], dtype=torch.float32)
#
# input = torch.reshape(input, (-1, 1, 5, 5))
# print(input.shape)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=True)
def forward(self, input):
output = self.maxpool1(input)
return output
tudui = Tudui()
# output = tudui(input)
# print(output)
writer = SummaryWriter("logs/poollogs")
step = 0
for data in dataloader:
imgs, target = data
writer.add_images("input", imgs, step)
output = tudui(imgs)
writer.add_images("output", output, step)
step = step + 1
writer.close()
10_nolinear
非线性激活
import torch
from torch import nn
from torch.nn import ReLU, Sigmoid
import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10("data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=64)
#relu
# input = torch.tensor([[1, -0.5],
# [-1, 3]])
#
# output = torch.reshape(input, (-1, 1, 2, 2))
# print(output.shape)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.relu1 = ReLU()
self.sigmoid1 = Sigmoid()
def forward(self, input):
output = self.sigmoid1(input)
return output
tudui = Tudui()
# output = tudui(input)
# print(output)
writer = SummaryWriter("logs/sigmoidlogs")
step = 0
for data in dataloader:
imgs, target = data
writer.add_images("input", imgs, step)
output = tudui(imgs)
writer.add_images("output", output, step)
step = step + 1
writer.close()
11_linear
线性层
import torch
from torch import nn
from torch.nn import Linear
import torchvision
from torch.utils.data import DataLoader
dataset = torchvision.datasets.CIFAR10("data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=64, drop_last=True)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.linear1 = Linear(196608, 10)
def forward(self, input):
output = self.linear1(input)
return output
tudui = Tudui()
for data in dataloader:
imgs, target = data
print(imgs.shape)
#output = torch.reshape(imgs, (1, 1, 1, -1))
output = torch.flatten(imgs)
print(output.shape)
output = tudui(output)
print(output.shape)
12_sequential
搭建小实战和Sequential的使用
import torch
from torch import nn
from torch.utils.tensorboard import SummaryWriter
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 5, padding=2)
self.maxpool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(32, 32, 5, padding=2)
self.maxpool2 = nn.MaxPool2d(2)
self.conv3 = nn.Conv2d(32, 64, 5, padding=2)
self.maxpool3 = nn.MaxPool2d(2)
self.flatten = nn.Flatten()
self.linear1 = nn.Linear(1024, 64)
self.linear2 = nn.Linear(64, 10)
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.conv1(x)
# x = self.maxpool1(x)
# x = self.conv2(x)
# x = self.maxpool2(x)
# x = self.conv3(x)
# x = self.maxpool3(x)
# x = self.flatten(x)
# x = self.linear1(x)
# x = self.linear2(x)
x = self.model1(x)
return x
tudui = Tudui()
print(tudui)
input = torch.ones((64, 3, 32, 32))
output = tudui(input)
print(output.shape)
writer = SummaryWriter("logs/seqlogs")
writer.add_graph(tudui, input)
writer.close()
13_loss
损失函数理解
import torch
from torch import nn
outputs = torch.tensor([1, 2, 3], dtype=torch.float32)
targets = torch.tensor([1, 2, 5], dtype=torch.float32)
outputs = torch.reshape(outputs, (1, 1, 1, 3))
targets = torch.reshape(targets, (1, 1, 1, 3))
loss = nn.L1Loss()
result = loss(outputs, targets)
loss_mse = nn.MSELoss()
result_mse = loss_mse(outputs, targets)
print(result)
print(result_mse)
x = torch.tensor([0.1, 0.2, 0.3])
y = torch.tensor([1])
x = torch.reshape(x, (1, 3))
loss_cross = nn.CrossEntropyLoss()
result_cross = loss_cross(x, y)
print(result_cross)
14_lossnetwork
损失函数+神经网络
import torch
from torch import nn
import torchvision
from torch.utils.data import DataLoader
dataset = torchvision.datasets.CIFAR10("data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=1)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__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
loss = nn.CrossEntropyLoss()
tudui = Tudui()
for data in dataloader:
imgs, target = data
outputs = tudui(imgs)
result_loss = loss(outputs, target)
# print(outputs)
# print(target)
# print(result_loss)
result_loss.backward()
print("ok")
15_optimzer
优化器
import torch
from torch import nn
import torchvision
from torch.utils.data import DataLoader
dataset = torchvision.datasets.CIFAR10("data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
dataloader = DataLoader(dataset, batch_size=1)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__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
loss = nn.CrossEntropyLoss()
tudui = Tudui()
optim = torch.optim.SGD(tudui.parameters(), lr=0.01)
for epoh in range(20):
running_loss = 0.0
for data in dataloader:
imgs, targets = data
outputs = tudui(imgs)
result_loss = loss(outputs, targets)
optim.zero_grad()
result_loss.backward()
optim.step()
running_loss = running_loss + result_loss
print(running_loss)
16_model
现有网络模型的使用及修改
import torchvision
from torch import nn
vgg16_flase = torchvision.models.vgg16(progress=False)
vgg16_ture = torchvision.models.vgg16(progress=True)
print(vgg16_ture)
train_data = torchvision.datasets.CIFAR10('data', train=True, transform=torchvision.transforms.ToTensor(), download=True)
#添加
vgg16_ture.add_module('add_linear', nn.Linear(1000, 10))
print(vgg16_ture)
#修改
vgg16_flase.classifier[6] = nn.Linear(4096, 10)
print(vgg16_flase)
17_modelsave
模型保存
import torch
import torchvision
from torch import nn
vgg16 = torchvision.models.vgg16(progress=False)
#1、保存模型+参数
torch.save(vgg16, "model/vgg16_method1.pth")
#2、保存参数(字典形式)
torch.save(vgg16.state_dict(), "model/vgg16_method2.pth")
#陷阱
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = nn.Sequential(
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
tudui = Tudui()
torch.save(tudui, "model/tudui_method.pth")
18_modelload
模型加载
import torch
import torchvision
from torch import nn
#1、保存模型+参数
model = torch.load("model/vgg16_method1.pth")
#print(model)
#2、保存参数(字典形式)
vgg16 = torchvision.models.vgg16(progress=False)
vgg16.load_state_dict(torch.load("model/vgg16_method2.pth"))
#model = torch.load("model/vgg16_method2.pth")
#print(model)
#陷阱,需要有模型定义
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model1 = nn.Sequential(
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model1(x)
return x
model = torch.load("model/tudui_method.pth")
print(model)
完整项目—model.py
模型定义
import torch
from torch import nn
#搭建神经网络
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model = 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(1024, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
# if __name__ == '__main__':
# tudui = Tudui()
# input = torch.ones((64, 3, 32, 32))
# output = tudui(input)
# print(output.shape)
完整项目—train.py
CPU训练
import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model import *
#准备数据集
train_data = torchvision.datasets.CIFAR10('data', train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10('data', train=False, transform=torchvision.transforms.ToTensor(), download=True)
#数据集长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集长度:{}".format(train_data_size))
print("测试数据集长度:{}".format(test_data_size))
#加载数据集(Dataloader)
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
#创建网络模型
tudui = Tudui()
#损失函数
loss_fn = nn.CrossEntropyLoss()
#优化器
#learning_rate = 0.01
learning_rate = 1e-2
optimizer = torch.optim.SGD(tudui.parameters(), lr=learning_rate)
#参数
total_train_step = 0
total_test_step = 0
epoh = 10
#添加tensorbooard
writer = SummaryWriter("logs/trainlogs")
for i in range(epoh):
print("-----第{}轮训练-----".format(i+1))
#训练开始
tudui.train()#模型状态
for data in train_dataloader:
imgs, targets = data
outputs = tudui(imgs)
loss = loss_fn(outputs, targets)
#优化器调优
optimizer.zero_grad()
loss.backward()
optimizer.step()
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)
#测试
tudui.eval()#模型状态
total_test_loss = 0
total_accuracy = 0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
outputs = tudui(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("测试集loss:{}".format(total_test_loss))
print("测试集正确率: {}".format(total_accuracy / test_data_size))
total_test_step = total_test_step + 1
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy / test_data_size, total_test_step)
torch.save(tudui, "model/tudui_{}.pth".format(i))
writer.close()
完整项目—train_gpu1.py
GPU训练,方式1
import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model import *
#准备数据集
train_data = torchvision.datasets.CIFAR10('data', train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10('data', train=False, transform=torchvision.transforms.ToTensor(), download=True)
#数据集长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集长度:{}".format(train_data_size))
print("测试数据集长度:{}".format(test_data_size))
#加载数据集(Dataloader)
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
#创建网络模型
tudui = Tudui()
if torch.cuda.is_available():#1、网络模型
tudui = tudui.cuda()
#损失函数
loss_fn = nn.CrossEntropyLoss()
if torch.cuda.is_available():#2、损失函数
loss_fn = loss_fn.cuda()
#优化器
#learning_rate = 0.01
learning_rate = 1e-2
optimizer = torch.optim.SGD(tudui.parameters(), lr=learning_rate)
#参数
total_train_step = 0
total_test_step = 0
epoh = 10
#添加tensorbooard
writer = SummaryWriter("logs/trainlogs")
for i in range(epoh):
print("-----第{}轮训练-----".format(i+1))
#训练开始
tudui.train()#模型状态
for data in train_dataloader:
imgs, targets = data
if torch.cuda.is_available():#3、数据
imgs = imgs.cuda()
targets = targets.cuda()
outputs = tudui(imgs)
loss = loss_fn(outputs, targets)
#优化器调优
optimizer.zero_grad()
loss.backward()
optimizer.step()
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)
#测试
tudui.eval()#模型状态
total_test_loss = 0
total_accuracy = 0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
if torch.cuda.is_available():#3、数据
imgs = imgs.cuda()
targets = targets.cuda()
outputs = tudui(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("测试集loss:{}".format(total_test_loss))
print("测试集正确率: {}".format(total_accuracy / test_data_size))
total_test_step = total_test_step + 1
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy / test_data_size, total_test_step)
torch.save(tudui, "model/tudui_{}.pth".format(i))
writer.close()
完整项目—train_gpu2.py
GPU训练,方式2文章来源:https://www.toymoban.com/news/detail-724259.html
import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model import *
#定义训练的设备
#device = torch.device("cpu")
device = torch.device("cuda:0")
#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#准备数据集
train_data = torchvision.datasets.CIFAR10('data', train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10('data', train=False, transform=torchvision.transforms.ToTensor(), download=True)
#数据集长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集长度:{}".format(train_data_size))
print("测试数据集长度:{}".format(test_data_size))
#加载数据集(Dataloader)
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)
#损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)
#优化器
#learning_rate = 0.01
learning_rate = 1e-2
optimizer = torch.optim.SGD(tudui.parameters(), lr=learning_rate)
#参数
total_train_step = 0
total_test_step = 0
epoh = 10
#添加tensorbooard
writer = SummaryWriter("logs/trainlogs")
for i in range(epoh):
print("-----第{}轮训练-----".format(i+1))
#训练开始
tudui.train()#模型状态
for data in train_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = tudui(imgs)
loss = loss_fn(outputs, targets)
#优化器调优
optimizer.zero_grad()
loss.backward()
optimizer.step()
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)
#测试
tudui.eval()#模型状态
total_test_loss = 0
total_accuracy = 0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = tudui(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("测试集loss:{}".format(total_test_loss))
print("测试集正确率: {}".format(total_accuracy / test_data_size))
total_test_step = total_test_step + 1
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy / test_data_size, total_test_step)
torch.save(tudui, "model/tudui_{}.pth".format(i))
writer.close()
完整项目—test.py
测试部分文章来源地址https://www.toymoban.com/news/detail-724259.html
import torch
import torchvision
from PIL import Image
from model import *
img_path = "test_imgs/1.jpg"
image = Image.open(img_path)
print(image)
image = image.convert('RGB')
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),
torchvision.transforms.ToTensor()])
image = transform(image)
print(image.shape)
model = torch.load("model/tudui_9.pth", map_location=torch.device('cpu'))
print(model)
image = torch.reshape(image, (1, 3, 32, 32))
print(image.shape)
model.eval()
with torch.no_grad():
#image = image.cuda()
output = model(image)
print(output)
print(output.argmax(1))
# 'airplane'=0
# 'automobile'=1
# 'brid'=2
# 'cat'=3
# 'deer'=4
# 'dog'=5
# 'frog'=6
# 'horse'=7
# 'ship'=8
# 'truck'=9
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