.gitignore

@@ -1,164 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-CIFAR10
-model
-logs
-.idea
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/

QCQ_ResNet18.py

@@ -1,62 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-class ResBlock(nn.Module):
-    def __init__(self, ch_in, ch_out, stride=2):
-        super(ResBlock, self).__init__()
-        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)  # ! (h-3+2)/2 + 1 = h/2 图像尺寸减半
-        self.bn1 = nn.BatchNorm2d(ch_out)
-        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)  # ! h-3+2*1+1=h 图像尺寸没变化
-        self.bn2 = nn.BatchNorm2d(ch_out)
-
-        self.extra = nn.Sequential(
-            nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),  # ! 这句话是针对原图像尺寸写的，要进行element wise add
-            # ! 因此图像尺寸也必须减半，(h-1)/2+1=h/2 图像尺寸减半
-            nn.BatchNorm2d(ch_out)
-        )
-
-    def forward(self, x):
-        out = x
-        x = torch.relu(self.bn1(self.conv1(x)))
-        x = self.bn2(self.conv2(x))
-        # shortcut
-        # ! element wise add [b,ch_in,h,w] [b,ch_out,h,w] 必须当ch_in = ch_out时才能进行相加
-        out = x + self.extra(out)  # todo self.extra强制把输出通道变成一致
-        return out
-
-
-class ResNet18(nn.Module):
-    def __init__(self):
-        super(ResNet18, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),  # ! 图像尺寸不变
-            nn.BatchNorm2d(64)
-        )
-        # 4个ResBlock
-        #  [b,64,h,w] --> [b,128,h,w]
-        self.block1 = ResBlock(64, 128)
-        #  [b,128,h,w] --> [b,256,h,w]
-        self.block2 = ResBlock(128, 256)
-        #  [b,256,h,w] --> [b,512,h,w]
-        self.block3 = ResBlock(256, 512)
-        #  [b,512,h,w] --> [b,512,h,w]
-        self.block4 = ResBlock(512, 512)
-
-        self.outlayer = nn.Linear(512, 10)
-
-    def forward(self, x):
-        x = torch.relu(self.conv1(x))
-        # [b,64,h,w] --> [b,1024,h,w]
-        x = self.block1(x)
-        x = self.block2(x)
-        x = self.block3(x)
-        x = self.block4(x)
-        # print("after conv:",x.shape)
-        # [b,512,h,w] --> [b,512,1,1]
-        x = F.adaptive_avg_pool2d(x, [1, 1])
-        # flatten
-        x = x.view(x.shape[0], -1)
-        x = self.outlayer(x)
-        return x

QCQ_VGG16.py

@@ -1,44 +0,0 @@
-
-from torch import nn
-
-
-cfg = {'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']}
-class QCQ(nn.Module):
-
-    def __init__(self, vgg):
-        super(QCQ, self).__init__()
-        self.features = self._make_layers(vgg)
-        self.dense = nn.Sequential(
-            nn.Linear(512, 4096),
-            nn.ReLU(inplace=True),
-            nn.Dropout(0.4),
-            nn.Linear(4096, 4096),
-            nn.ReLU(inplace=True),
-            nn.Dropout(0.4),
-        )
-        self.classifier = nn.Linear(4096, 10)
-
-    def forward(self, x):
-        out = self.features(x)
-        out = out.view(out.size(0), -1)
-        out = self.dense(out)
-        out = self.classifier(out)
-        return out
-
-    def _make_layers(self, vgg):
-        layers = []
-        in_channels = 3
-        for x in vgg:
-            if x == 'M':
-                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
-            else:
-                layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
-                           nn.BatchNorm2d(x),
-                           nn.ReLU(inplace=True)]
-                in_channels = x
-
-        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
-        return nn.Sequential(*layers)
-
-
-

README.md

@@ -1,10 +1,2 @@
 # CIFAR10
-CIFAR10学习
 
-我的环境：  
-python      3.6.13  
-torch       1.10.2  
-cuda        11.3  
-torchvision 0.11.3  
-pillow      8.4.0  
-tensorboard 2.10.1  

demo.py

@@ -1,97 +0,0 @@
-import os
-
-import PIL
-import torchvision.transforms
-from PIL import Image
-
-
-from QCQ_VGG16 import *
-from QCQ_ResNet18 import *
-
-CIFAR10_class = ['airplane','automobile','brid','cat','deer','dog','frog','horse','ship','truck']
-vgg = [96, 96, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']
-
-def readImage(img_path='img/test.png'):
-    img = Image.open(img_path).convert('RGB')
-    transform= torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),torchvision.transforms.ToTensor()])
-    img = transform(img)
-    print(img.shape)
-    img = torch.reshape(img, (1, 3, 32, 32))
-    return img
-
-
-def DrawImageTxt(imageFile, targetImageFile, txtnum):
-    # 设置字体大小
-    font = PIL.ImageFont.truetype('img/abc.ttf', 50)
-    # 打开文件
-    im = Image.open(imageFile)
-    # 字体坐标
-    draw = PIL.ImageDraw.Draw(im)
-    draw.text((0, 0), txtnum, (255, 255, 0), font=font)
-    # 保存
-    im.save(targetImageFile)
-    # 关闭
-    im.close()
-
-
-if __name__=='__main__':
-    print("通过训练好的模型识别十种事物：飞机,汽车,鸟,猫,鹿,狗,青蛙,马,船,卡车")
-    device = 'cuda'
-    models=[]
-    i=1
-    for root, dirs, files in os.walk('model'):
-        for file in files:
-            if file.__contains__('.pth'):
-                file_path=root+'/'+file
-                models.append(file_path)
-                print(f'{i}.'+file)
-                i+=1
-    if models.__len__()==0:
-        print('model文件夹中没有pth模型文件')
-    else:
-        select = int(input('选择一个模型\n'))
-        model_path=models[select-1]
-        if model_path.__contains__('VGG16'):
-            qcq_test=QCQ(vgg)
-        elif model_path.__contains__('RestNet18'):
-            qcq_test=ResNet18()
-        else:
-            print('选择的模型名称中既不包含"VGG16"，也不包含"RestNet18"')
-
-        qcq_test.load_state_dict(torch.load(model_path, map_location=torch.device(device)))
-
-        imgs=[]
-        i=1
-        for root, dirs, files in os.walk('img'):
-            for file in files:
-                if file.__contains__('.png') or file.__contains__('.jpg'):
-                    if not file.__contains__('pre'):
-                        file_path = root + '/' + file
-                        imgs.append(file_path)
-                        print(f'{i}.' + file)
-                        i+=1
-        if imgs.__len__() == 0:
-            print('img文件夹中没有图片')
-        else:
-            select = int(input('选择一个测试图片,\n'))
-            image_path = imgs[select-1]
-            image_name,image_type = image_path.split('.')
-            targetImageFile=image_name+'_pre.png'
-            img=readImage(image_path)
-            qcq_test.eval()
-            with torch.no_grad():
-                output = qcq_test(img)
-                pre=output.argmax(1)
-                txtnum = CIFAR10_class[pre.item()]
-                DrawImageTxt(image_path, targetImageFile, txtnum)
-                print(output)
-                print(pre)
-                print(txtnum)
-            Image.open(targetImageFile).show()
-
-
-
-
-
-
-

train_CIFAR10_complete.py

@@ -1,124 +0,0 @@
-import os.path
-
-
-import torchvision.datasets
-from torch.utils.tensorboard import SummaryWriter
-
-from QCQ_VGG16 import *
-from QCQ_ResNet18 import *
-from torch.utils.data import DataLoader
-
-# 训练数据集
-train_data = torchvision.datasets.CIFAR10("CIFAR10/CIFAR10_train", train=True,
-                                          transform=torchvision.transforms.ToTensor(), download=True)
-# 测试数据集
-test_data = torchvision.datasets.CIFAR10("CIFAR10/CIFAR10_test", 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("训练数据集的长度为：{}".format(train_data_size))
-print("测试数据集的长度为：{}".format(test_data_size))
-
-# dataloader加载数据集
-train_dataloader = DataLoader(train_data, batch_size=64)
-test_dataloader = DataLoader(test_data, batch_size=64)
-
-# 定义运行设备
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# 创建神经网络模型
-vgg = [96, 96, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']
-
-select=input("选择模型:\n"
-             "1.VGG16\n"
-             "2.RestNet18\n")
-if select=='1':
-    qcq = QCQ(vgg)
-    model_name='VGG16'
-    learning_rate = 1e-2
-    optimizer = torch.optim.SGD(qcq.parameters(), lr=learning_rate,weight_decay=5e-3)
-    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.4, last_epoch=-1)
-    # 训练轮数
-    epoch = 10
-else:
-    qcq = ResNet18()
-    model_name = 'RestNet18'
-    # 优化器,学习率
-    learning_rate = 1e-3
-    optimizer = torch.optim.Adam(qcq.parameters(), lr=learning_rate)
-    # 训练轮数
-    epoch = 50
-qcq = qcq.to(device)
-
-# 创建损失函数
-loss_fun = nn.CrossEntropyLoss().to(device)
-loss_fun = loss_fun.to(device)
-
-
-
-# 设置训练网络的一些参数
-# 训练次数
-total_train_step = 0
-# 测试次数
-total_test_step = 0
-
-
-# 添加tensorboard
-if not os.path.exists('logs'):
-    os.mkdir('logs')
-writer = SummaryWriter('logs')
-
-for i in range(epoch):
-    print("--------第%5d   轮训练开始--------" % (i + 1))
-
-    # 训练步骤开始
-    qcq.train()
-    for data in train_dataloader:
-        imgs, targets = data
-
-        imgs = imgs.to(device)
-        targets = targets.to(device)
-
-        outputs = qcq(imgs)
-        loss = loss_fun(outputs, targets)
-        # 模型优化
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-        total_train_step += 1
-        if total_train_step % 100 == 0:
-            print('训练次数：%-8d,Loss：%f' % (total_train_step, loss.item()))
-            writer.add_scalar('train_loss', loss.item(), total_train_step)
-
-    # 测试步骤开始
-    qcq.eval()
-    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 = qcq(imgs)
-            loss = loss_fun(outputs, targets)
-            total_test_loss += loss.item()
-            accuracy = (outputs.argmax(1) == targets).sum()
-            total_accuracy += accuracy
-    print('整体测试集上test_loss：%f' % total_test_loss)
-    print('整体测试集上正确率：%f' % (total_accuracy / test_data_size))
-    writer.add_scalar('test_loss', total_test_loss, total_test_step)
-    writer.add_scalar('test_accuracy_rate', total_accuracy / test_data_size, total_test_step)
-    if select=='1': scheduler.step()
-    total_test_step += 1
-
-    # 模型每轮保存
-    if not os.path.exists('model'):
-        os.mkdir('model')
-    torch.save(qcq.state_dict(), 'model/'+model_name+'_%d_epoch.pth' % (i+1))
-    print("模型自动保存成功")
-
-writer.close()