├── .gitattributes ├── .gitignore ├── LICENSE ├── README.en.md ├── README.md ├── data ├── __init__.py ├── myModel_46.pth ├── myModel_import2.py └── net_graph.png ├── example_img ├── c52d245900b9bae9565b6cf4d4781130.jpeg ├── pic │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 3.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png └── pic2 │ ├── c1.png │ ├── c2.png │ ├── c3.png │ ├── c4.png │ ├── c5.png │ ├── c6.png │ ├── c7.png │ ├── c8.png │ ├── d1.png │ ├── d2.png │ ├── d3.png │ ├── d4.png │ ├── d5.png │ ├── d6.png │ ├── d7.png │ └── d8.png ├── modelDemo.py ├── process ├── OpenJupyterLab.bat ├── Train7gpu.ipynb ├── __init__.py ├── make_qrcode.py ├── myModelGraph.py └── openTensorboard.bat ├── readme_static └── readme_img │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── net.png │ ├── p1.png │ └── p2.png ├── requirements.txt ├── test.py └── 开源许可证中文翻译.txt /.gitattributes: -------------------------------------------------------------------------------- 1 | *.ipynb linguist-language=python 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .idea 2 | __pycache__ 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 bytesc 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.en.md: -------------------------------------------------------------------------------- 1 | # Image_Classify_WebGUI_CIFAR10 2 | 3 | ✨**Intelligent Image Classification Web Applcation based on Convolutional Neural Networks and the CIFAR10 Dataset** : Image classification visualization interface, image classification front-end web page, image classification Demo display-Pywebio. AI artificial intelligence image classification-Pytorch. CIFAR10 dataset, small model. 100% pure Python code, lightweight, easy to reproduce. 4 | 5 | [简体中文文档](./README.md) 6 | 7 | [Personal website: www.bytesc.top](http://www.bytesc.top) includes online project demonstrations. 8 | 9 | 🔔 If you have any project-related questions, feel free to raise an `issue` in this project, I will usually reply within 24 hours. 10 | 11 | ## Project Introduction 12 | * 1. Use pytorch to implement intelligent classification of CIFAR10 dataset images 13 | * 2. Use a small model, lightweight, with a 76% accuracy rate 14 | * 3. Use pywebio as the web visualization framework, no need for front-end language, written in pure python. Lightweight, easy to reproduce, easy to deploy 15 | 16 | Network structure used 17 | ![image](./readme_static/readme_img/net.png) 18 | 19 | ## Screenshot of the effect 20 | ![image](./readme_static/readme_img/1.png) 21 | ![image](./readme_static/readme_img/2.png) 22 | ![image](./readme_static/readme_img/3.png) 23 | 24 | ## How to use 25 | Python version 3.9 26 | 27 | First install dependencies 28 | > pip install -r requirement.txt 29 | 30 | modelDemo.py is the project entry point, run this file to start the server 31 | > python modelDemo.py 32 | 33 | Copy the link to the browser and open it 34 | ![image](./readme_static/readme_img/p1.png) 35 | Click "Demo" to enter the Web interface 36 | ![image](./readme_static/readme_img/p2.png) 37 | 38 | After that, you can also click "Upload File" and select an image file from the example_img folder to upload and test 39 | 40 | ## Project structure 41 | ``` 42 | └─Image_Classify_WebGUI_CIFAR10 43 | ├─data 44 | │ └─logs_import 45 | ├─example_img 46 | ├─process 47 | │ └─logs 48 | └─readme_static 49 | ``` 50 | * The data folder stores some static resources, including the trained model.pth 51 | * The process folder stores some process files, including the model training program, etc. 52 | * readme_static stores static resources used in the readme document 53 | * The example_img folder contains some images that can be used for testing 54 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Image_Classify_WebGUI_CIFAR10 2 | 3 | ✨ **基于卷积神经网络(CNN)和 CIFAR10 数据集的图像智能分类**：图像分类可视化界面，图像分类前端网页，图像分类Demo展示-Pywebio。AI人工智能图像分类-Pytorch。CIFAR10数据集，小模型。100%纯Python代码，轻量化，易复现 4 | 5 | [English Readme](./README.en.md)🚩 6 | 7 | [个人网站：www.bytesc.top](http://www.bytesc.top) 包含项目在线演示。 8 | 9 | 🔔 如有项目相关问题，欢迎在本项目提出`issue`，我一般会在 24 小时内回复。 10 | 11 | ## 项目简介 12 | * 1, 使用pytorch实现CIFAR10数据集图片的智能分类 13 | * 2, 使用小模型，轻量化，76%准确率 14 | * 3，使用pywebio作为web可视化框架，无需前端语言，使用纯python编写。轻量化，易复现，易部署 15 | 16 | 使用的网络结构 17 | ![image](./readme_static/readme_img/net.png) 18 | 19 | ## 效果截图 20 | ![image](./readme_static/readme_img/1.png) 21 | ![image](./readme_static/readme_img/2.png) 22 | ![image](./readme_static/readme_img/3.png) 23 | 24 | ## 如何使用 25 | python版本3.9 26 | 27 | 先安装依赖 28 | > pip install -r requirement.txt 29 | 30 | modelDemo.py是项目入口，运行此文件即可启动服务器 31 | > python modelDemo.py 32 | 33 | 复制链接到浏览器打开 34 | ![image](./readme_static/readme_img/p1.png) 35 | 点击”Demo“即可进入Web界面 36 | ![image](./readme_static/readme_img/p2.png) 37 | 38 | 之后，也可以点击“上传文件”，选择example_img文件夹内图片文件上传测试 39 | 40 | ## 项目结构 41 | ``` 42 | └─Image_Classify_WebGUI_CIFAR10 43 | ├─data 44 | │ └─logs_import 45 | ├─example_img 46 | ├─process 47 | │ └─logs 48 | └─readme_static 49 | ``` 50 | * data文件夹存放部分静态资源，包括训练好的模型.pth 51 | * process文件夹存放一些过程文件，包括模型的训练程序等 52 | * readme_static存放readme文档中用的静态资源 53 | * example_img文件夹内存放了一些图片，可用于测试 54 | 55 | # 开源许可证 56 | 57 | 此翻译版本仅供参考，以 LICENSE 文件中的英文版本为准 58 | 59 | MIT 开源许可证： 60 | 61 | 版权所有 (c) 2023 bytesc 62 | 63 | 特此授权，免费向任何获得本软件及相关文档文件（以下简称“软件”）副本的人提供使用、复制、修改、合并、出版、发行、再许可和/或销售软件的权利，但须遵守以下条件： 64 | 65 | 上述版权声明和本许可声明应包含在所有副本或实质性部分中。 66 | 67 | 本软件按“原样”提供，不作任何明示或暗示的保证，包括但不限于适销性、特定用途适用性和非侵权性。在任何情况下，作者或版权持有人均不对因使用本软件而产生的任何索赔、损害或其他责任负责，无论是在合同、侵权或其他方面。 68 | -------------------------------------------------------------------------------- /data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytesc/Image_Classify_WebGUI_CIFAR10/981131d8148eb1492cbcfe073fb7b030e276b105/data/__init__.py -------------------------------------------------------------------------------- /data/myModel_46.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytesc/Image_Classify_WebGUI_CIFAR10/981131d8148eb1492cbcfe073fb7b030e276b105/data/myModel_46.pth -------------------------------------------------------------------------------- /data/myModel_import2.py: -------------------------------------------------------------------------------- 1 | import torch.nn 2 | 3 | 4 | 5 | class CIFAR10Classify01(torch.nn.Module): 6 | def __init__(self): 7 | super(CIFAR10Classify01, self).__init__() 8 | self.model1 = torch.nn.Sequential( 9 | torch.nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2, ), 10 | torch.nn.MaxPool2d(kernel_size=2), 11 | torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2, ), 12 | torch.nn.MaxPool2d(kernel_size=2), 13 | torch.nn.BatchNorm2d(32), 14 | torch.nn.Sigmoid(), 15 | torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2, ), 16 | torch.nn.MaxPool2d(2), 17 | torch.nn.Flatten(), 18 | # 第一个linear的in_features可以由print(x.shape)得到 19 | torch.nn.Linear(in_features=1024, out_features=64), 20 | torch.nn.Linear(in_features=64, out_features=10) 21 | # 分类问题的最终out_features和类数相同 22 | ) 23 | 24 | def forward(self, x): 25 | x = self.model1(x) 26 | return x 27 | 28 | 29 | if __name__ == "__main__": 30 | import torch.utils.tensorboard 31 | model = CIFAR10Classify01() 32 | test_tensor = torch.ones(64, 3, 32, 32) 33 | # 用形状和实际数据集相同的tenser测试网络是否可用 34 | output = model(test_tensor) 35 | print(output.shape) 36 | # output.shape[0]==batch_size, output.shape[1]==分类数量 37 | 38 | writer = torch.utils.tensorboard.SummaryWriter("logs_import") 39 | writer.add_graph(model, test_tensor) 40 | writer.close() 41 | 42 | import os 43 | os.system("tensorboard --logdir=logs_import --port=998") 44 | -------------------------------------------------------------------------------- /data/net_graph.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytesc/Image_Classify_WebGUI_CIFAR10/981131d8148eb1492cbcfe073fb7b030e276b105/data/net_graph.png 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torchvision.transforms 3 | import torch.nn 4 | 5 | import pywebio 6 | from io import BytesIO 7 | 8 | from data.myModel_import2 import * 9 | 10 | 11 | @pywebio.config(title="卷积神经网络Demo", description="基于CIFAR10数据集的图像分类") 12 | def page1(): 13 | train_set_classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] 14 | graph_img = PIL.Image.open("./data/net_graph.png") 15 | 16 | def show_info(): 17 | pywebio.output.put_markdown("# 基于CIFAR10数据集的图像分类") 18 | pywebio.output.put_html("
") 19 | pywebio.output.put_table([ 20 | [pywebio.output.span('数据集', row=1), pywebio.output.span('支持类别', col=2)], 21 | ['CIFAR10', train_set_classes] 22 | ]) 23 | pywebio.output.put_html("
") 24 | 25 | show_net = [pywebio.output.put_text('net'), 26 | pywebio.output.put_image(graph_img)] 27 | 28 | def popup_window(title, content): 29 | pywebio.output.popup(title=title, content=content) 30 | 31 | show_info() 32 | pywebio.output.put_buttons(['查看网络结构'], [lambda: popup_window("网络结构", show_net)]) 33 | # pywebio.output.put_buttons(['点击查看网络结构'], [popup_window]) 34 | pywebio.input.actions("", [{'label': "上传图片", 'value': "", 'color': 'success', }]) 35 | inpic = pywebio.input.file_upload(label="上传图片 please upload a image") 36 | pywebio.output.popup("加载中", [ 37 | pywebio.output.put_loading(), 38 | ]) 39 | 40 | # img_path = "./pic2/102.png" 41 | img = PIL.Image.open(BytesIO(inpic['content'])) 42 | # pywebio.output.put_image(inpic['content']) 43 | img = img.convert("RGB") 44 | # print(img.size) 45 | transform01 = torchvision.transforms.Compose([ 46 | torchvision.transforms.Resize((32, 32)), 47 | torchvision.transforms.ToTensor() 48 | ]) 49 | img = transform01(img) 50 | img = torch.reshape(img, (1, 3, 32, 32)) 51 | # print(img.shape) 52 | 53 | model = torch.load("data/myModel_46.pth", map_location=torch.device('cpu')) 54 | # print(model) 55 | 56 | # model.eval() 57 | with torch.no_grad(): 58 | output = model(img) 59 | # print(output) 60 | # print(output.argmax(1)) 61 | # print(train_set.classes[output.argmax(1).item()]) 62 | # pywebio.output.put_text(train_set.classes[output.argmax(1).item()]) 63 | pywebio.output.popup(title='识别结果', 64 | content=[ 65 | pywebio.output.put_markdown("分类结果：\n # " + train_set_classes[output.argmax(1).item()]), 66 | pywebio.output.put_image(None if not inpic else inpic['content'])]) 67 | 68 | # img = torch.reshape(img, (3, 32, 32)) 69 | # transform2 = torchvision.transforms.Compose([ 70 | # torchvision.transforms.Resize((160, 160)), 71 | # torchvision.transforms.ToPILImage() 72 | # ]) 73 | # img = transform2(img) 74 | # 75 | # img_bytes = BytesIO() 76 | # img.save(img_bytes, format="JPEG") 77 | # img = img_bytes.getvalue() 78 | # pywebio.output.put_image(img, height="512", width="512") 79 | # pywebio.output.put_image(inpic['content'], height="512", width="512") 80 | # pywebio.output.put_image(inpic['content']) 81 | del model, inpic, img 82 | 83 | 84 | if __name__ == "__main__": 85 | # page1() 86 | pywebio.start_server( 87 | applications=[page1, ], 88 | debug=True, 89 | cdn=False, 90 | auto_open_webbrowser=False, 91 | remote_access=False, 92 | port=6007 93 | ) 94 | -------------------------------------------------------------------------------- /process/OpenJupyterLab.bat: -------------------------------------------------------------------------------- 1 | jupyter lab 2 | 3 | -------------------------------------------------------------------------------- /process/Train7gpu.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "cc1e2529-a2e4-4bf9-8c3b-b3a037e06fd1", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import torchvision\n", 11 | "import torch.nn\n", 12 | "import torch.utils.tensorboard\n", 13 | "\n", 14 | "import torchvision.transforms\n", 15 | "from torch.utils.data import DataLoader" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 2, 21 | "id": "efb32107-c090-43bf-9b5c-44ce2c5b4904", 22 | "metadata": {}, 23 | "outputs": [ 24 | { 25 | "name": "stdout", 26 | "output_type": "stream", 27 | "text": [ 28 | "Files already downloaded and verified\n", 29 | "Files already downloaded and verified\n" 30 | ] 31 | } 32 | ], 33 | "source": [ 34 | "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", 35 | "# 准备数据集\n", 36 | "train_data = torchvision.datasets.CIFAR10(\"../CIFAR10\", train=True,\n", 37 | " transform=torchvision.transforms.ToTensor(), download=True)\n", 38 | "# cifar10是默认piltenser，要转化为pytorchtensor\n", 39 | "\n", 40 | "test_data = torchvision.datasets.CIFAR10(\"../CIFAR10\", train=False, download=True,\n", 41 | " transform=torchvision.transforms.ToTensor())\n", 42 | "# print(len(train_data))\n", 43 | "# print(len(test_data))\n", 44 | "train_data_size = len(train_data)\n", 45 | "test_data_size = len(test_data)\n", 46 | "# 用dataloader加载数据集,加载为一组64个\n", 47 | "train_dataloader = DataLoader(dataset=train_data, batch_size=64)\n", 48 | "test_dataloader = DataLoader(dataset=test_data, batch_size=64)" 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": 3, 54 | "id": "67cca792-7dd3-4ead-b657-ca5336aa978b", 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "# 添加tensorboard可视化\n", 59 | "writer = torch.utils.tensorboard.SummaryWriter(\"./logs\")" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 4, 65 | "id": "eb1bb815-35c3-4d5e-8a3b-64e3726088bf", 66 | "metadata": {}, 67 | "outputs": [ 68 | { 69 | "data": { 70 | "text/plain": [ 71 | "CIFAR10Classify01(\n", 72 | " (model1): Sequential(\n", 73 | " (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", 74 | " (1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", 75 | " (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", 76 | " (3): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", 77 | " (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", 78 | " (5): Sigmoid()\n", 79 | " (6): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", 80 | " (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", 81 | " (8): Flatten(start_dim=1, end_dim=-1)\n", 82 | " (9): Linear(in_features=1024, out_features=64, bias=True)\n", 83 | " (10): Linear(in_features=64, out_features=10, bias=True)\n", 84 | " )\n", 85 | ")" 86 | ] 87 | }, 88 | "execution_count": 4, 89 | "metadata": {}, 90 | "output_type": "execute_result" 91 | } 92 | ], 93 | "source": [ 94 | "# 搭建神经网络\n", 95 | "class CIFAR10Classify01(torch.nn.Module):\n", 96 | " def __init__(self):\n", 97 | " super(CIFAR10Classify01, self).__init__()\n", 98 | " self.model1 = torch.nn.Sequential(\n", 99 | " torch.nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2, ),\n", 100 | " torch.nn.MaxPool2d(kernel_size=2),\n", 101 | " torch.nn.BatchNorm2d(32),\n", 102 | " torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2, ),\n", 103 | " torch.nn.MaxPool2d(kernel_size=2),\n", 104 | " torch.nn.Sigmoid(),\n", 105 | " torch.nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2, ),\n", 106 | " torch.nn.MaxPool2d(2),\n", 107 | " torch.nn.Flatten(),\n", 108 | " # 第一个linear的in_features可以由print(x.shape)得到\n", 109 | " torch.nn.Linear(in_features=1024, out_features=64),\n", 110 | " torch.nn.Linear(in_features=64, out_features=10)\n", 111 | " # 分类问题的最终out_features和类数相同\n", 112 | " )\n", 113 | "\n", 114 | " def forward(self, x):\n", 115 | " x = self.model1(x)\n", 116 | " return x\n", 117 | " \n", 118 | "myModel = CIFAR10Classify01()\n", 119 | "myModel.to(device)" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 5, 125 | "id": "d605e974-7266-4ee3-ab72-954314233065", 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [ 129 | "# 定义损失函数\n", 130 | "loss_fn = torch.nn.CrossEntropyLoss()\n", 131 | "loss_fn.to(device)\n", 132 | "\n", 133 | "# 定义优化器\n", 134 | "learning_rate = 1e-2\n", 135 | "optim = torch.optim.SGD(params=myModel.parameters(), lr=learning_rate)\n", 136 | "# SGD随机梯度下降" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 6, 142 | "id": "4f6defce-7903-4db5-94e0-90433d4917fd", 143 | "metadata": {}, 144 | "outputs": [ 145 | { 146 | "name": "stdout", 147 | "output_type": "stream", 148 | "text": [ 149 | "------0------\n" 150 | ] 151 | }, 152 | { 153 | "ename": "RuntimeError", 154 | "evalue": "running_mean should contain 3 elements not 32", 155 | "output_type": "error", 156 | "traceback": [ 157 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 158 | "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", 159 | "Input \u001b[1;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 10\u001b[0m imgs \u001b[38;5;241m=\u001b[39m imgs\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m 11\u001b[0m targets \u001b[38;5;241m=\u001b[39m targets\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m---> 12\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmyModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimgs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# 优化器梯度清零\u001b[39;00m\n\u001b[0;32m 14\u001b[0m optim\u001b[38;5;241m.\u001b[39mzero_grad()\n", 160 | "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1129\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", 161 | "Input \u001b[1;32mIn [4]\u001b[0m, in \u001b[0;36mCIFAR10Classify01.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m---> 22\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", 162 | "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1129\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", 163 | "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\container.py:139\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m 138\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n", 164 | "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1129\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", 165 | "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\batchnorm.py:168\u001b[0m, in \u001b[0;36m_BatchNorm.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 161\u001b[0m bn_training \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunning_mean \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunning_var \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 163\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 164\u001b[0m \u001b[38;5;124;03mBuffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be\u001b[39;00m\n\u001b[0;32m 165\u001b[0m \u001b[38;5;124;03mpassed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are\u001b[39;00m\n\u001b[0;32m 166\u001b[0m \u001b[38;5;124;03mused for normalization (i.e. in eval mode when buffers are not None).\u001b[39;00m\n\u001b[0;32m 167\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m--> 168\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 169\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 170\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# If buffers are not to be tracked, ensure that they won't be updated\u001b[39;49;00m\n\u001b[0;32m 171\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunning_mean\u001b[49m\n\u001b[0;32m 172\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrack_running_stats\u001b[49m\n\u001b[0;32m 173\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 174\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunning_var\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m 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179\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 180\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", 166 | "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\functional.py:2438\u001b[0m, in \u001b[0;36mbatch_norm\u001b[1;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[0;32m 2435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m training:\n\u001b[0;32m 2436\u001b[0m _verify_batch_size(\u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39msize())\n\u001b[1;32m-> 2438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2439\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrunning_mean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrunning_var\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmomentum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackends\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcudnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menabled\u001b[49m\n\u001b[0;32m 2440\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", 167 | "\u001b[1;31mRuntimeError\u001b[0m: running_mean should contain 3 elements not 32" 168 | ] 169 | } 170 | ], 171 | "source": [ 172 | "# 设置训练网络参数\n", 173 | "train_step = 0\n", 174 | "test_step = 0\n", 175 | "epoch = 100\n", 176 | "for i in range(epoch):\n", 177 | " train_loss = 0\n", 178 | " print(\"------{}------\".format(i))\n", 179 | " myModel.train()\n", 180 | " for imgs, targets in train_dataloader:\n", 181 | " imgs = imgs.to(device)\n", 182 | " targets = targets.to(device)\n", 183 | " output = myModel(imgs)\n", 184 | " # 优化器梯度清零\n", 185 | " optim.zero_grad()\n", 186 | " # 计算损失\n", 187 | " loss = loss_fn(output, targets)\n", 188 | " # 反向传播算梯度\n", 189 | " loss.backward()\n", 190 | " # 优化器优化模型\n", 191 | " optim.step()\n", 192 | " train_loss += loss.item()\n", 193 | " train_step += 1\n", 194 | " # print(loss)\n", 195 | " writer.add_scalar(\"trainloss\", train_loss/len(train_data), train_step)\n", 196 | " print(\"running_loss: \", train_loss/len(train_data))\n", 197 | "\n", 198 | " # 验证每轮学习效果\n", 199 | " test_loss = 0\n", 200 | " total_accuracy = 0\n", 201 | " with torch.no_grad():\n", 202 | " # 取消梯度，验证时不调优\n", 203 | " myModel.eval()\n", 204 | " for imgs, targets in test_dataloader:\n", 205 | " imgs = imgs.to(device)\n", 206 | " targets = targets.to(device)\n", 207 | " output = myModel(imgs)\n", 208 | " loss = loss_fn(output, targets)\n", 209 | " test_loss += loss.item()\n", 210 | " accuracy = (output.argmax(1) == targets).sum()\n", 211 | " total_accuracy += accuracy\n", 212 | " test_step += 1\n", 213 | " writer.add_scalar(\"testloss\", test_loss/len(test_data), test_step)\n", 214 | " writer.add_scalar(\"total_accuracy\", total_accuracy/len(test_data), test_step)\n", 215 | " print(\"test_loss: \", test_loss/len(test_data))\n", 216 | " print(\"total_accuracy: \", total_accuracy/len(test_data))\n", 217 | " torch.save(myModel, \"./myModel_{}.pth\".format(i))\n", 218 | "\n", 219 | "writer.close()" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": null, 225 | "id": "16cef185-cf09-4eab-b5a4-643ae14148fc", 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [ 229 | "%reload_ext tensorboard\n" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": null, 235 | "id": "d3280638-14cd-47f8-9717-c6eb86613ca5", 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "\n", 240 | "from tensorboard import notebook\n", 241 | "notebook.list()\n", 242 | "notebook.start(\"--logdir ./logs --port=709\")" 243 | ] 244 | } 245 | ], 246 | "metadata": { 247 | "kernelspec": { 248 | "display_name": "Python 3 (ipykernel)", 249 | "language": "python", 250 | "name": "python3" 251 | }, 252 | "language_info": { 253 | "codemirror_mode": { 254 | "name": "ipython", 255 | "version": 3 256 | }, 257 | "file_extension": ".py", 258 | "mimetype": "text/x-python", 259 | "name": "python", 260 | "nbconvert_exporter": "python", 261 | "pygments_lexer": "ipython3", 262 | "version": "3.10.6" 263 | } 264 | }, 265 | "nbformat": 4, 266 | "nbformat_minor": 5 267 | } 268 | -------------------------------------------------------------------------------- /process/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytesc/Image_Classify_WebGUI_CIFAR10/981131d8148eb1492cbcfe073fb7b030e276b105/process/__init__.py -------------------------------------------------------------------------------- /process/make_qrcode.py: -------------------------------------------------------------------------------- 1 | import qrcode 2 | 3 | ad = input() 4 | img = qrcode.make(ad) 5 | 6 | img.show() 7 | -------------------------------------------------------------------------------- /process/myModelGraph.py: -------------------------------------------------------------------------------- 1 | import torch.nn 2 | import torchvision.datasets 3 | import torchvision.transforms 4 | import torch.utils.tensorboard 5 | from torch.utils.data import DataLoader 6 | 7 | import sys 8 | sys.path.append('..') 9 | from data.myModel_import2 import * 10 | 11 | 12 | # 用ones测试模型是否可用 13 | model2 = CIFAR10Classify01() 14 | print(model2) 15 | iinput = torch.ones((64, 3, 32, 32)) 16 | output = model2(iinput) 17 | print(output.shape) 18 | 19 | 20 | writer = torch.utils.tensorboard.SummaryWriter("logs") 21 | writer.add_graph(model2, iinput) 22 | writer.close() 23 | 24 | -------------------------------------------------------------------------------- /process/openTensorboard.bat: -------------------------------------------------------------------------------- 1 | tensorboard --logdir=logs --port=998 -------------------------------------------------------------------------------- /readme_static/readme_img/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytesc/Image_Classify_WebGUI_CIFAR10/981131d8148eb1492cbcfe073fb7b030e276b105/readme_static/readme_img/1.png -------------------------------------------------------------------------------- /readme_static/readme_img/2.png: 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/readme_static/readme_img/p1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytesc/Image_Classify_WebGUI_CIFAR10/981131d8148eb1492cbcfe073fb7b030e276b105/readme_static/readme_img/p1.png -------------------------------------------------------------------------------- /readme_static/readme_img/p2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bytesc/Image_Classify_WebGUI_CIFAR10/981131d8148eb1492cbcfe073fb7b030e276b105/readme_static/readme_img/p2.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torch==2.0.0 2 | torchvision==0.15.1 3 | pywebio==1.7.1 4 | 5 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /开源许可证中文翻译.txt: -------------------------------------------------------------------------------- 1 | 此翻译版本仅供参考，以 LICENSE 文件中的英文版本为准 2 | 3 | MIT 开源许可证： 4 | 5 | 版权所有 (c) 2023 bytesc 6 | 7 | 特此授权，免费向任何获得本软件及相关文档文件（以下简称“软件”）副本的人提供使用、复制、修改、合并、出版、发行、再许可和/或销售软件的权利，但须遵守以下条件： 8 | 9 | 上述版权声明和本许可声明应包含在所有副本或实质性部分中。 10 | 11 | 本软件按“原样”提供，不作任何明示或暗示的保证，包括但不限于适销性、特定用途适用性和非侵权性。在任何情况下，作者或版权持有人均不对因使用本软件而产生的任何索赔、损害或其他责任负责，无论是在合同、侵权或其他方面。 12 | --------------------------------------------------------------------------------