# PaddleClas实现图像分类 练习 **Repository Path**: cun-jiang/PaddleClasPractice ## Basic Information - **Project Name**: PaddleClas实现图像分类 练习 - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-08-11 - **Last Updated**: 2022-02-17 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # [AI训练营]paddleclas实现图像分类练习 ## 项目背景 1.这是Ai训练营的baseline，经过这段时间的学习，感觉还是有许多地方不懂，于是跟着baseline做一遍 2.该项目完成食物5分类 3.基于baseline的代码，根据自己的理解做了一点注释 ```python # 先导入库 from sklearn.utils import shuffle import os import pandas as pd import numpy as np from PIL import Image import paddle import paddle.nn as nn import random ``` ```python # 忽略（垃圾）警告信息 # 在python中运行代码经常会遇到的情况是——代码可以正常运行但是会提示警告，有时特别讨厌。 # 那么如何来控制警告输出呢？其实很简单，python通过调用warnings模块中定义的warn()函数来发出警告。我们可以通过警告过滤器进行控制是否发出警告消息。 import warnings warnings.filterwarnings("ignore") ``` ### 解压数据集，查看数据的结构 ```python # 项目挂载的数据集先解压出来，待解压完毕，刷新后可发现左侧文件夹根目录出现五个zip #代码前面的！意思是在终端执行，直接把代码放到终端执行出来的效果也是一样的 !unzip -oq /home/aistudio/data/data103736/五种图像分类数据集.zip ``` 左侧可以看到如图所示五个zip ![](https://ai-studio-static-online.cdn.bcebos.com/f8bc5b21a0ba49b4b78b6e7b18ac0341dfb14cf545b14c83b1f597b6ee8109bb) ```python # 本项目以食物分类为例进行介绍，因为分类大多数情况下是不存在标签文件的，猫分类已经有了标签，省去了数据处理的操作 # (此处需要你根据自己的选择进行解压对应的文件) # 解压完毕左侧出现文件夹，即为需要分类的文件 #文件一般建议用英文，用中文有时会出问题 !unzip -oq /home/aistudio/食物5分类.zip ``` ```python # 查看结构，正为一个类别下有一系列对应的图片 !tree foods/ ``` **五类食物图片** 1. beef_carpaccio 2. baby_back_ribs 3. beef_tartare 4. apple_pie 5. baklava 具体结构如下： ``` foods/ ├── apple_pie │ ├── 1005649.jpg │ ├── 1011328.jpg │ ├── 101251.jpg ``` ### 拿到总的训练数据txt ```python import os # -*- coding: utf-8 -*- # 根据官方paddleclas的提示，我们需要把图像变为两个txt文件 # train_list.txt（训练集） # val_list.txt（验证集） # 先把路径搞定 比如：foods/beef_carpaccio/855780.jpg ,读取到并写入txt # 根据左侧生成的文件夹名字来写根目录 dirpath = "foods" # 先得到总的txt后续再进行划分，因为要划分出验证集，所以要先打乱，因为原本是有序的 def get_all_txt(): all_list = [] i = 0 # 标记总文件数量 j = 0 # 标记文件类别 for root,dirs,files in os.walk(dirpath): # 分别代表根目录、文件夹、文件 for file in files: i = i + 1 # 文件中每行格式： 图像相对路径 图像的label_id（数字类别）（注意：中间有空格）。 imgpath = os.path.join(root,file) all_list.append(imgpath+" "+str(j)+"\n") j = j + 1 allstr = ''.join(all_list) f = open('all_list.txt','w',encoding='utf-8') f.write(allstr) return all_list , i all_list,all_lenth = get_all_txt() print(all_lenth) ``` 5000 ### 数据打乱 ```python # 把数据打乱 all_list = shuffle(all_list) allstr = ''.join(all_list) f = open('all_list.txt','w',encoding='utf-8') f.write(allstr) print("打乱成功，并重新写入文本") ``` 打乱成功，并重新写入文本 ### 数据划分 ```python # 按照比例划分数据集 食品的数据有5000张图片，不算大数据，一般9:1即可 train_size = int(all_lenth * 0.9) train_list = all_list[:train_size] val_list = all_list[train_size:] print(len(train_list)) print(len(val_list)) ``` 4500 500 ```python # 运行cell，生成训练集txt train_txt = ''.join(train_list) f_train = open('train_list.txt','w',encoding='utf-8') f_train.write(train_txt) f_train.close() print("train_list.txt 生成成功！") # 运行cell，生成验证集txt val_txt = ''.join(val_list) f_val = open('val_list.txt','w',encoding='utf-8') f_val.write(val_txt) f_val.close() print("val_list.txt 生成成功！") ``` train_list.txt 生成成功！ val_list.txt 生成成功！ ### 补充 我第一次做的跟这个差不多，txt文件是用以下代码做的 ``` import random import os #生成train.txt和val.txt #train.txt和val.txt是对数据集的划分，看到下面的ratio=0.9，意为按9：1划分为训练集和验证集 random.seed(2020) xml_dir = '/home/aistudio/work/Annotations'#标签文件地址 #xml后缀文件是标签文件，打开Annotations文件夹，可以看见里面的文件是xml后缀的 img_dir = '/home/aistudio/work/images'#图像文件地址 #该文件地址是存放图片的，跟上面的文件是对应的 path_list = list() for img in os.listdir(img_dir): #os.listdir会返回指定路径下的文件 img_path = os.path.join(img_dir,img) #os.path.join是将两个或多个拼起来 xml_path = os.path.join(xml_dir,img.replace('jpg', 'xml')) path_list.append((img_path, xml_path))#将标签与图片对应 #可能还是有些同学不是很清楚是什么意思，举个例子可能好理解些 #已知img_dir='/home/aistudio/work/images'路径下都是图片 #假设这些图片是为1~20为名字的jpg #那么os.listdir(img_dir)返回的是1.jpg，2.jpg……20.jpg #for img in os.listdir(img_dir)就是上面的每一个jpg #img_path = os.path.join(img_dir,img)拼接出来的就是这样： #/home/aistudio/work/images/1.jpg #/home/aistudio/work/images/2.jpg #…… #xml_path 得到的就是/home/aistudio/work/images/2.xml #两者的区别只是将最后面的jpg换成xml #path_list就是把上面img_path和xml_path连起来 random.shuffle(path_list)#shuffle意为洗牌，意思是将数据集打乱 ratio = 0.9 train_f = open('/home/aistudio/work/train.txt','w') #生成训练文件 #open就是打开，后面的'w'是写入命令，即write val_f = open('/home/aistudio/work/val.txt' ,'w')#生成验证文件 for i ,content in enumerate(path_list): #enumerate(path_list)是一个枚举 #i,content分别是索引和值 #可以这样理解：path_list是一个C语言里的数组 #i为数组的序号，意思是第i个；content则为第i个的内容 img, xml = content text = img + ' ' + xml + '\n' if i < len(path_list) * ratio: train_f.write(text)#写入 else: val_f.write(text) train_f.close() val_f.close() #生成标签文档 label = ['smoke']#设置你想检测的类别 with open('/home/aistudio/work/label_list.txt', 'w') as f: for text in label: f.write(text+'\n') ``` ## 安装paddleclas 数据集核实完搞定成功的前提下，可以准备更改原文档的参数进行实现自己的图片分类了！ 这里采用paddleclas的2.2版本，好用！ ```python # 先把paddleclas安装上再说 # 安装paddleclas以及相关三方包(好像studio自带的已经够用了，无需安装了) !git clone https://gitee.com/paddlepaddle/PaddleClas.git -b release/2.2 # 我这里安装相关包时，花了30几分钟还有错误提示，不管他即可 #!pip install --upgrade -r PaddleClas/requirements.txt -i https://mirror.baidu.com/pypi/simple ``` Cloning into 'PaddleClas'... remote: Enumerating objects: 538, done. remote: Counting objects: 100% (538/538), done. remote: Compressing objects: 100% (323/323), done. remote: Total 15290 (delta 347), reused 349 (delta 210), pack-reused 14752 Receiving objects: 100% (15290/15290), 113.56 MiB | 13.49 MiB/s, done. Resolving deltas: 100% (10239/10239), done. Checking connectivity... done. ```python #因为后续paddleclas的命令需要在PaddleClas目录下，所以进入PaddleClas根目录，执行此命令 %cd PaddleClas !ls ``` /home/aistudio/PaddleClas dataset hubconf.py MANIFEST.in README_ch.md requirements.txt deploy __init__.py paddleclas.py README_en.md setup.py docs LICENSE ppcls README.md tools ```python # 将图片移动到paddleclas下面的数据集里面 # 至于为什么现在移动，也是我的一点小技巧，防止之前移动的话，生成的txt的路径是全路径，反而需要去掉路径的一部分 !mv ../foods/ dataset/ ``` ```python # 挪动文件到对应目录 !mv ../all_list.txt dataset/foods !mv ../train_list.txt dataset/foods !mv ../val_list.txt dataset/foods ``` ### 修改配置文件 #### 说明 主要是以下几点：分类数、图片总量、训练和验证的路径、图像尺寸、数据预处理、训练和预测的num_workers: 0 路径如下： >PaddleClas/ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml （主要的参数已经进行注释，一定要过一遍） ``` # global configs Global: checkpoints: null pretrained_model: null output_dir: ./output/ # 使用GPU训练 device: gpu # 每几个轮次保存一次 save_interval: 1 eval_during_train: True # 每几个轮次验证一次 eval_interval: 1 # 训练轮次 epochs: 20 print_batch_step: 1 use_visualdl: True #开启可视化（目前平台不可用） # used for static mode and model export # 图像大小 image_shape: [3, 224, 224] save_inference_dir: ./inference # training model under @to_static to_static: False # model architecture Arch: # 采用的网络 name: ResNet50 # 类别数 多了个0类 0-5 0无用 class_num: 6 # loss function config for traing/eval process Loss: Train: - CELoss: weight: 1.0 Eval: - CELoss: weight: 1.0 Optimizer: name: Momentum momentum: 0.9 lr: name: Piecewise learning_rate: 0.015 decay_epochs: [30, 60, 90] values: [0.1, 0.01, 0.001, 0.0001] regularizer: name: 'L2' coeff: 0.0005 # data loader for train and eval DataLoader: Train: dataset: name: ImageNetDataset # 根路径 image_root: ./dataset/ # 前面自己生产得到的训练集文本路径 cls_label_path: ./dataset/foods/train_list.txt # 数据预处理 transform_ops: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - RandFlipImage: flip_code: 1 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' sampler: name: DistributedBatchSampler batch_size: 128 drop_last: False shuffle: True loader: num_workers: 0 use_shared_memory: True Eval: dataset: name: ImageNetDataset # 根路径 image_root: ./dataset/ # 前面自己生产得到的验证集文本路径 cls_label_path: ./dataset/foods/val_list.txt # 数据预处理 transform_ops: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' sampler: name: DistributedBatchSampler batch_size: 128 drop_last: False shuffle: True loader: num_workers: 0 use_shared_memory: True Infer: infer_imgs: ./dataset/foods/beef_carpaccio/855780.jpg batch_size: 10 transforms: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' - ToCHWImage: PostProcess: name: Topk # 输出的可能性最高的前topk个 topk: 5 # 标签文件 需要自己新建文件 class_id_map_file: ./dataset/label_list.txt Metric: Train: - TopkAcc: topk: [1, 5] Eval: - TopkAcc: topk: [1, 5] ``` #### 标签文件 这个是在预测时生成对照的依据，在上个文件有提到这个 ``` # 标签文件 需要自己新建文件 class_id_map_file: dataset/label_list.txt ``` 按照对应的进行编写： ![](https://ai-studio-static-online.cdn.bcebos.com/0e40a0afaa824ba9b70778aa7931a3baf2a421bcb81b4b0f83632da4e4ddc0ef) 如食品分类(要对照之前的txt的类别确认无误) ``` 1 beef_carpaccio 2 baby_back_ribs 3 beef_tartare 4 apple_pie 5 baklava ``` ![](https://ai-studio-static-online.cdn.bcebos.com/6b3a4a244ed34517bcf4be5fcc629ab10d081eb0af7c4532a795583d19939a82) ![](https://ai-studio-static-online.cdn.bcebos.com/8ee69dbad8bf4e0f9c743645a4438cc035446d052d4b409a88c1cc25b58dcf83) ### 开始训练 ```python # 提示，运行过程中可能存在坏图的情况，但是不用担心，训练过程不受影响。 # 仅供参考，我只跑了五轮，准确率很低 !python3 tools/train.py \ -c ./ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml ``` [2021/08/10 22:11:17] root INFO: Already save model in ./output/ResNet50/latest ### 预测一张 ```python # 更换为你训练的网络，需要预测的文件，上面训练所得到的的最优模型文件 # 我这里是不严谨的，直接使用训练集的图片进行验证，大家可以去百度搜一些相关的图片传上来，进行预测 !python3 tools/infer.py \ -c ./ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml \ -o Infer.infer_imgs=dataset/foods/baby_back_ribs/319516.jpg \ -o Global.pretrained_model=output/ResNet50/best_model ``` /home/aistudio/PaddleClas/ppcls/arch/backbone/model_zoo/vision_transformer.py:15: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Callable [2021/08/10 22:12:14] root INFO: =========================================================== == PaddleClas is powered by PaddlePaddle ! == =========================================================== == == == For more info please go to the following website. == == == == https://github.com/PaddlePaddle/PaddleClas == =========================================================== [2021/08/10 22:12:14] root INFO: Arch : [2021/08/10 22:12:14] root INFO: class_num : 6 [2021/08/10 22:12:14] root INFO: name : ResNet50 [2021/08/10 22:12:14] root INFO: DataLoader : [2021/08/10 22:12:14] root INFO: Eval : [2021/08/10 22:12:14] root INFO: dataset : [2021/08/10 22:12:14] root INFO: cls_label_path : ./dataset/foods/val_list.txt [2021/08/10 22:12:14] root INFO: image_root : ./dataset/ [2021/08/10 22:12:14] root INFO: name : ImageNetDataset [2021/08/10 22:12:14] root INFO: transform_ops : [2021/08/10 22:12:14] root INFO: DecodeImage : [2021/08/10 22:12:14] root INFO: channel_first : False [2021/08/10 22:12:14] root INFO: to_rgb : True [2021/08/10 22:12:14] root INFO: ResizeImage : [2021/08/10 22:12:14] root INFO: resize_short : 256 [2021/08/10 22:12:14] root INFO: CropImage : [2021/08/10 22:12:14] root INFO: size : 224 [2021/08/10 22:12:14] root INFO: NormalizeImage : [2021/08/10 22:12:14] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/10 22:12:14] root INFO: order : [2021/08/10 22:12:14] root INFO: scale : 1.0/255.0 [2021/08/10 22:12:14] root INFO: std : [0.229, 0.224, 0.225] [2021/08/10 22:12:14] root INFO: loader : [2021/08/10 22:12:14] root INFO: num_workers : 0 [2021/08/10 22:12:14] root INFO: use_shared_memory : True [2021/08/10 22:12:14] root INFO: sampler : [2021/08/10 22:12:14] root INFO: batch_size : 128 [2021/08/10 22:12:14] root INFO: drop_last : False [2021/08/10 22:12:14] root INFO: name : DistributedBatchSampler [2021/08/10 22:12:14] root INFO: shuffle : True [2021/08/10 22:12:14] root INFO: Train : [2021/08/10 22:12:14] root INFO: dataset : [2021/08/10 22:12:14] root INFO: cls_label_path : ./dataset/foods/train_list.txt [2021/08/10 22:12:14] root INFO: image_root : ./dataset/ [2021/08/10 22:12:14] root INFO: name : ImageNetDataset [2021/08/10 22:12:14] root INFO: transform_ops : [2021/08/10 22:12:14] root INFO: DecodeImage : [2021/08/10 22:12:14] root INFO: channel_first : False [2021/08/10 22:12:14] root INFO: to_rgb : True [2021/08/10 22:12:14] root INFO: ResizeImage : [2021/08/10 22:12:14] root INFO: resize_short : 256 [2021/08/10 22:12:14] root INFO: CropImage : [2021/08/10 22:12:14] root INFO: size : 224 [2021/08/10 22:12:14] root INFO: RandFlipImage : [2021/08/10 22:12:14] root INFO: flip_code : 1 [2021/08/10 22:12:14] root INFO: NormalizeImage : [2021/08/10 22:12:14] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/10 22:12:14] root INFO: order : [2021/08/10 22:12:14] root INFO: scale : 1.0/255.0 [2021/08/10 22:12:14] root INFO: std : [0.229, 0.224, 0.225] [2021/08/10 22:12:14] root INFO: loader : [2021/08/10 22:12:14] root INFO: num_workers : 0 [2021/08/10 22:12:14] root INFO: use_shared_memory : True [2021/08/10 22:12:14] root INFO: sampler : [2021/08/10 22:12:14] root INFO: batch_size : 128 [2021/08/10 22:12:14] root INFO: drop_last : False [2021/08/10 22:12:14] root INFO: name : DistributedBatchSampler [2021/08/10 22:12:14] root INFO: shuffle : True [2021/08/10 22:12:14] root INFO: Global : [2021/08/10 22:12:14] root INFO: checkpoints : None [2021/08/10 22:12:14] root INFO: device : gpu [2021/08/10 22:12:14] root INFO: epochs : 20 [2021/08/10 22:12:14] root INFO: eval_during_train : True [2021/08/10 22:12:14] root INFO: eval_interval : 1 [2021/08/10 22:12:14] root INFO: image_shape : [3, 224, 224] [2021/08/10 22:12:14] root INFO: output_dir : ./output/ [2021/08/10 22:12:14] root INFO: pretrained_model : output/ResNet50/best_model [2021/08/10 22:12:14] root INFO: print_batch_step : 1 [2021/08/10 22:12:14] root INFO: save_inference_dir : ./inference [2021/08/10 22:12:14] root INFO: save_interval : 1 [2021/08/10 22:12:14] root INFO: to_static : False [2021/08/10 22:12:14] root INFO: use_visualdl : True [2021/08/10 22:12:14] root INFO: Infer : [2021/08/10 22:12:14] root INFO: PostProcess : [2021/08/10 22:12:14] root INFO: class_id_map_file : ./dataset/label_list.txt [2021/08/10 22:12:14] root INFO: name : Topk [2021/08/10 22:12:14] root INFO: topk : 5 [2021/08/10 22:12:14] root INFO: batch_size : 10 [2021/08/10 22:12:14] root INFO: infer_imgs : dataset/foods/baby_back_ribs/319516.jpg [2021/08/10 22:12:14] root INFO: transforms : [2021/08/10 22:12:14] root INFO: DecodeImage : [2021/08/10 22:12:14] root INFO: channel_first : False [2021/08/10 22:12:14] root INFO: to_rgb : True [2021/08/10 22:12:14] root INFO: ResizeImage : [2021/08/10 22:12:14] root INFO: resize_short : 256 [2021/08/10 22:12:14] root INFO: CropImage : [2021/08/10 22:12:14] root INFO: size : 224 [2021/08/10 22:12:14] root INFO: NormalizeImage : [2021/08/10 22:12:14] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/10 22:12:14] root INFO: order : [2021/08/10 22:12:14] root INFO: scale : 1.0/255.0 [2021/08/10 22:12:14] root INFO: std : [0.229, 0.224, 0.225] [2021/08/10 22:12:14] root INFO: ToCHWImage : None [2021/08/10 22:12:14] root INFO: Loss : [2021/08/10 22:12:14] root INFO: Eval : [2021/08/10 22:12:14] root INFO: CELoss : [2021/08/10 22:12:14] root INFO: weight : 1.0 [2021/08/10 22:12:14] root INFO: Train : [2021/08/10 22:12:14] root INFO: CELoss : [2021/08/10 22:12:14] root INFO: weight : 1.0 [2021/08/10 22:12:14] root INFO: Metric : [2021/08/10 22:12:14] root INFO: Eval : [2021/08/10 22:12:14] root INFO: TopkAcc : [2021/08/10 22:12:14] root INFO: topk : [1, 5] [2021/08/10 22:12:14] root INFO: Train : [2021/08/10 22:12:14] root INFO: TopkAcc : [2021/08/10 22:12:14] root INFO: topk : [1, 5] [2021/08/10 22:12:14] root INFO: Optimizer : [2021/08/10 22:12:14] root INFO: lr : [2021/08/10 22:12:14] root INFO: decay_epochs : [30, 60, 90] [2021/08/10 22:12:14] root INFO: learning_rate : 0.015 [2021/08/10 22:12:14] root INFO: name : Piecewise [2021/08/10 22:12:14] root INFO: values : [0.1, 0.01, 0.001, 0.0001] [2021/08/10 22:12:14] root INFO: momentum : 0.9 [2021/08/10 22:12:14] root INFO: name : Momentum [2021/08/10 22:12:14] root INFO: regularizer : [2021/08/10 22:12:14] root INFO: coeff : 0.0005 [2021/08/10 22:12:14] root INFO: name : L2 W0810 22:12:14.715823 1852 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1 W0810 22:12:14.721333 1852 device_context.cc:422] device: 0, cuDNN Version: 7.6. [2021/08/10 22:12:18] root INFO: train with paddle 2.1.0 and device CUDAPlace(0) /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations if data.dtype == np.object: [{'class_ids': [3, 5, 4, 2, 1], 'scores': [0.82606, 0.10882, 0.04856, 0.01256, 0.0036], 'file_name': 'dataset/foods/baby_back_ribs/319516.jpg', 'label_names': ['beef_tartare', 'baklava', 'apple_pie', 'baby_back_ribs', 'beef_carpaccio']}] ## 运行结果 ![结果](https://ai-studio-static-online.cdn.bcebos.com/59d12b8a48cb482f91c58d5a72782bcb3856944a06734728b236afe02410d151) ## 修改参数 修改一些参数，看看会不会有更好的效果 验证用的图片保持不变 ### 修改模型 先只修改模型看看 ```python !python3 tools/train.py \ -c ./ppcls/configs/quick_start/new_user/ModifyParameter.yaml ``` [2021/08/10 22:36:58] root INFO: Already save model in ./output/MobileNetV1/latest ```python !python3 tools/infer.py \ -c ./ppcls/configs/quick_start/new_user/ModifyParameter.yaml \ -o Infer.infer_imgs=dataset/foods/baby_back_ribs/319516.jpg \ -o Global.pretrained_model=output/MobileNetV1/best_model ``` /home/aistudio/PaddleClas/ppcls/arch/backbone/model_zoo/vision_transformer.py:15: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Callable [2021/08/10 22:40:03] root INFO: =========================================================== == PaddleClas is powered by PaddlePaddle ! == =========================================================== == == == For more info please go to the following website. == == == == https://github.com/PaddlePaddle/PaddleClas == =========================================================== [2021/08/10 22:40:03] root INFO: Arch : [2021/08/10 22:40:03] root INFO: class_num : 6 [2021/08/10 22:40:03] root INFO: name : MobileNetV1 [2021/08/10 22:40:03] root INFO: DataLoader : [2021/08/10 22:40:03] root INFO: Eval : [2021/08/10 22:40:03] root INFO: dataset : [2021/08/10 22:40:03] root INFO: cls_label_path : ./dataset/foods/val_list.txt [2021/08/10 22:40:03] root INFO: image_root : ./dataset/ [2021/08/10 22:40:03] root INFO: name : ImageNetDataset [2021/08/10 22:40:03] root INFO: transform_ops : [2021/08/10 22:40:03] root INFO: DecodeImage : [2021/08/10 22:40:03] root INFO: channel_first : False [2021/08/10 22:40:03] root INFO: to_rgb : True [2021/08/10 22:40:03] root INFO: ResizeImage : [2021/08/10 22:40:03] root INFO: resize_short : 256 [2021/08/10 22:40:03] root INFO: CropImage : [2021/08/10 22:40:03] root INFO: size : 224 [2021/08/10 22:40:03] root INFO: NormalizeImage : [2021/08/10 22:40:03] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/10 22:40:03] root INFO: order : [2021/08/10 22:40:03] root INFO: scale : 1.0/255.0 [2021/08/10 22:40:03] root INFO: std : [0.229, 0.224, 0.225] [2021/08/10 22:40:03] root INFO: loader : [2021/08/10 22:40:03] root INFO: num_workers : 0 [2021/08/10 22:40:03] root INFO: use_shared_memory : True [2021/08/10 22:40:03] root INFO: sampler : [2021/08/10 22:40:03] root INFO: batch_size : 128 [2021/08/10 22:40:03] root INFO: drop_last : False [2021/08/10 22:40:03] root INFO: name : DistributedBatchSampler [2021/08/10 22:40:03] root INFO: shuffle : True [2021/08/10 22:40:03] root INFO: Train : [2021/08/10 22:40:03] root INFO: dataset : [2021/08/10 22:40:03] root INFO: cls_label_path : ./dataset/foods/train_list.txt [2021/08/10 22:40:03] root INFO: image_root : ./dataset/ [2021/08/10 22:40:03] root INFO: name : ImageNetDataset [2021/08/10 22:40:03] root INFO: transform_ops : [2021/08/10 22:40:03] root INFO: DecodeImage : [2021/08/10 22:40:03] root INFO: channel_first : False [2021/08/10 22:40:03] root INFO: to_rgb : True [2021/08/10 22:40:03] root INFO: ResizeImage : [2021/08/10 22:40:03] root INFO: resize_short : 256 [2021/08/10 22:40:03] root INFO: CropImage : [2021/08/10 22:40:03] root INFO: size : 224 [2021/08/10 22:40:03] root INFO: RandFlipImage : [2021/08/10 22:40:03] root INFO: flip_code : 1 [2021/08/10 22:40:03] root INFO: NormalizeImage : [2021/08/10 22:40:03] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/10 22:40:03] root INFO: order : [2021/08/10 22:40:03] root INFO: scale : 1.0/255.0 [2021/08/10 22:40:03] root INFO: std : [0.229, 0.224, 0.225] [2021/08/10 22:40:03] root INFO: loader : [2021/08/10 22:40:03] root INFO: num_workers : 0 [2021/08/10 22:40:03] root INFO: use_shared_memory : True [2021/08/10 22:40:03] root INFO: sampler : [2021/08/10 22:40:03] root INFO: batch_size : 128 [2021/08/10 22:40:03] root INFO: drop_last : False [2021/08/10 22:40:03] root INFO: name : DistributedBatchSampler [2021/08/10 22:40:03] root INFO: shuffle : True [2021/08/10 22:40:03] root INFO: Global : [2021/08/10 22:40:03] root INFO: checkpoints : None [2021/08/10 22:40:03] root INFO: device : gpu [2021/08/10 22:40:03] root INFO: epochs : 20 [2021/08/10 22:40:03] root INFO: eval_during_train : True [2021/08/10 22:40:03] root INFO: eval_interval : 1 [2021/08/10 22:40:03] root INFO: image_shape : [3, 224, 224] [2021/08/10 22:40:03] root INFO: output_dir : ./output/ [2021/08/10 22:40:03] root INFO: pretrained_model : output/MobileNetV1/best_model [2021/08/10 22:40:03] root INFO: print_batch_step : 1 [2021/08/10 22:40:03] root INFO: save_inference_dir : ./inference [2021/08/10 22:40:03] root INFO: save_interval : 1 [2021/08/10 22:40:03] root INFO: to_static : False [2021/08/10 22:40:03] root INFO: use_visualdl : True [2021/08/10 22:40:03] root INFO: Infer : [2021/08/10 22:40:03] root INFO: PostProcess : [2021/08/10 22:40:03] root INFO: class_id_map_file : ./dataset/label_list.txt [2021/08/10 22:40:03] root INFO: name : Topk [2021/08/10 22:40:03] root INFO: topk : 5 [2021/08/10 22:40:03] root INFO: batch_size : 10 [2021/08/10 22:40:03] root INFO: infer_imgs : dataset/foods/baby_back_ribs/319516.jpg [2021/08/10 22:40:03] root INFO: transforms : [2021/08/10 22:40:03] root INFO: DecodeImage : [2021/08/10 22:40:03] root INFO: channel_first : False [2021/08/10 22:40:03] root INFO: to_rgb : True [2021/08/10 22:40:03] root INFO: ResizeImage : [2021/08/10 22:40:03] root INFO: resize_short : 256 [2021/08/10 22:40:03] root INFO: CropImage : [2021/08/10 22:40:03] root INFO: size : 224 [2021/08/10 22:40:03] root INFO: NormalizeImage : [2021/08/10 22:40:03] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/10 22:40:03] root INFO: order : [2021/08/10 22:40:03] root INFO: scale : 1.0/255.0 [2021/08/10 22:40:03] root INFO: std : [0.229, 0.224, 0.225] [2021/08/10 22:40:03] root INFO: ToCHWImage : None [2021/08/10 22:40:03] root INFO: Loss : [2021/08/10 22:40:03] root INFO: Eval : [2021/08/10 22:40:03] root INFO: CELoss : [2021/08/10 22:40:03] root INFO: weight : 1.0 [2021/08/10 22:40:03] root INFO: Train : [2021/08/10 22:40:03] root INFO: CELoss : [2021/08/10 22:40:03] root INFO: weight : 1.0 [2021/08/10 22:40:03] root INFO: Metric : [2021/08/10 22:40:03] root INFO: Eval : [2021/08/10 22:40:03] root INFO: TopkAcc : [2021/08/10 22:40:03] root INFO: topk : [1, 5] [2021/08/10 22:40:03] root INFO: Train : [2021/08/10 22:40:03] root INFO: TopkAcc : [2021/08/10 22:40:03] root INFO: topk : [1, 5] [2021/08/10 22:40:03] root INFO: Optimizer : [2021/08/10 22:40:03] root INFO: lr : [2021/08/10 22:40:03] root INFO: decay_epochs : [30, 60, 90] [2021/08/10 22:40:03] root INFO: learning_rate : 0.015 [2021/08/10 22:40:03] root INFO: name : Piecewise [2021/08/10 22:40:03] root INFO: values : [0.1, 0.01, 0.001, 0.0001] [2021/08/10 22:40:03] root INFO: momentum : 0.9 [2021/08/10 22:40:03] root INFO: name : Momentum [2021/08/10 22:40:03] root INFO: regularizer : [2021/08/10 22:40:03] root INFO: coeff : 0.0005 [2021/08/10 22:40:03] root INFO: name : L2 W0810 22:40:03.151469 3139 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1 W0810 22:40:03.156962 3139 device_context.cc:422] device: 0, cuDNN Version: 7.6. [2021/08/10 22:40:06] root INFO: train with paddle 2.1.0 and device CUDAPlace(0) /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations if data.dtype == np.object: [{'class_ids': [4, 2, 1, 3, 5], 'scores': [0.49584, 0.16217, 0.13516, 0.11661, 0.09011], 'file_name': 'dataset/foods/baby_back_ribs/319516.jpg', 'label_names': ['apple_pie', 'baby_back_ribs', 'beef_carpaccio', 'beef_tartare', 'baklava']}] ## 结果 感觉效果一般般 ![结果](https://ai-studio-static-online.cdn.bcebos.com/1b6a489aa53b49e0b157dbba44634ec04b11954d7e484e4a8f9ad74dda95a4a2) ### 修改更多参数 ModifyMoreParameters.yaml修改了模型、epoch等参数 ```python !python3 tools/train.py \ -c ./ppcls/configs/quick_start/new_user/ModifyMoreParameters.yaml ``` [2021/08/11 00:03:45] root INFO: Already save model in ./output/DarkNet53/latest ```python !python3 tools/infer.py \ -c ./ppcls/configs/quick_start/new_user/ModifyMoreParameters.yaml \ -o Infer.infer_imgs=dataset/foods/baby_back_ribs/319516.jpg \ -o Global.pretrained_model=output/DarkNet53/best_model ``` /home/aistudio/PaddleClas/ppcls/arch/backbone/model_zoo/vision_transformer.py:15: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Callable [2021/08/11 00:10:06] root INFO: =========================================================== == PaddleClas is powered by PaddlePaddle ! == =========================================================== == == == For more info please go to the following website. == == == == https://github.com/PaddlePaddle/PaddleClas == =========================================================== [2021/08/11 00:10:06] root INFO: Arch : [2021/08/11 00:10:06] root INFO: class_num : 6 [2021/08/11 00:10:06] root INFO: name : DarkNet53 [2021/08/11 00:10:06] root INFO: DataLoader : [2021/08/11 00:10:06] root INFO: Eval : [2021/08/11 00:10:06] root INFO: dataset : [2021/08/11 00:10:06] root INFO: cls_label_path : ./dataset/foods/val_list.txt [2021/08/11 00:10:06] root INFO: image_root : ./dataset/ [2021/08/11 00:10:06] root INFO: name : ImageNetDataset [2021/08/11 00:10:06] root INFO: transform_ops : [2021/08/11 00:10:06] root INFO: DecodeImage : [2021/08/11 00:10:06] root INFO: channel_first : False [2021/08/11 00:10:06] root INFO: to_rgb : True [2021/08/11 00:10:06] root INFO: ResizeImage : [2021/08/11 00:10:06] root INFO: resize_short : 256 [2021/08/11 00:10:06] root INFO: CropImage : [2021/08/11 00:10:06] root INFO: size : 224 [2021/08/11 00:10:06] root INFO: NormalizeImage : [2021/08/11 00:10:06] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/11 00:10:06] root INFO: order : [2021/08/11 00:10:06] root INFO: scale : 1.0/255.0 [2021/08/11 00:10:06] root INFO: std : [0.229, 0.224, 0.225] [2021/08/11 00:10:06] root INFO: loader : [2021/08/11 00:10:06] root INFO: num_workers : 0 [2021/08/11 00:10:06] root INFO: use_shared_memory : True [2021/08/11 00:10:06] root INFO: sampler : [2021/08/11 00:10:06] root INFO: batch_size : 128 [2021/08/11 00:10:06] root INFO: drop_last : False [2021/08/11 00:10:06] root INFO: name : DistributedBatchSampler [2021/08/11 00:10:06] root INFO: shuffle : True [2021/08/11 00:10:06] root INFO: Train : [2021/08/11 00:10:06] root INFO: dataset : [2021/08/11 00:10:06] root INFO: cls_label_path : ./dataset/foods/train_list.txt [2021/08/11 00:10:06] root INFO: image_root : ./dataset/ [2021/08/11 00:10:06] root INFO: name : ImageNetDataset [2021/08/11 00:10:06] root INFO: transform_ops : [2021/08/11 00:10:06] root INFO: DecodeImage : [2021/08/11 00:10:06] root INFO: channel_first : False [2021/08/11 00:10:06] root INFO: to_rgb : True [2021/08/11 00:10:06] root INFO: ResizeImage : [2021/08/11 00:10:06] root INFO: resize_short : 256 [2021/08/11 00:10:06] root INFO: CropImage : [2021/08/11 00:10:06] root INFO: size : 224 [2021/08/11 00:10:06] root INFO: RandFlipImage : [2021/08/11 00:10:06] root INFO: flip_code : 1 [2021/08/11 00:10:06] root INFO: NormalizeImage : [2021/08/11 00:10:06] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/11 00:10:06] root INFO: order : [2021/08/11 00:10:06] root INFO: scale : 1.0/255.0 [2021/08/11 00:10:06] root INFO: std : [0.229, 0.224, 0.225] [2021/08/11 00:10:06] root INFO: loader : [2021/08/11 00:10:06] root INFO: num_workers : 0 [2021/08/11 00:10:06] root INFO: use_shared_memory : True [2021/08/11 00:10:06] root INFO: sampler : [2021/08/11 00:10:06] root INFO: batch_size : 128 [2021/08/11 00:10:06] root INFO: drop_last : False [2021/08/11 00:10:06] root INFO: name : DistributedBatchSampler [2021/08/11 00:10:06] root INFO: shuffle : True [2021/08/11 00:10:06] root INFO: Global : [2021/08/11 00:10:06] root INFO: checkpoints : None [2021/08/11 00:10:06] root INFO: device : gpu [2021/08/11 00:10:06] root INFO: epochs : 100 [2021/08/11 00:10:06] root INFO: eval_during_train : True [2021/08/11 00:10:06] root INFO: eval_interval : 1 [2021/08/11 00:10:06] root INFO: image_shape : [3, 224, 224] [2021/08/11 00:10:06] root INFO: output_dir : ./output/ [2021/08/11 00:10:06] root INFO: pretrained_model : output/DarkNet53/best_model [2021/08/11 00:10:06] root INFO: print_batch_step : 1 [2021/08/11 00:10:06] root INFO: save_inference_dir : ./inference [2021/08/11 00:10:06] root INFO: save_interval : 1 [2021/08/11 00:10:06] root INFO: to_static : False [2021/08/11 00:10:06] root INFO: use_visualdl : True [2021/08/11 00:10:06] root INFO: Infer : [2021/08/11 00:10:06] root INFO: PostProcess : [2021/08/11 00:10:06] root INFO: class_id_map_file : ./dataset/label_list.txt [2021/08/11 00:10:06] root INFO: name : Topk [2021/08/11 00:10:06] root INFO: topk : 5 [2021/08/11 00:10:06] root INFO: batch_size : 10 [2021/08/11 00:10:06] root INFO: infer_imgs : dataset/foods/baby_back_ribs/319516.jpg [2021/08/11 00:10:06] root INFO: transforms : [2021/08/11 00:10:06] root INFO: DecodeImage : [2021/08/11 00:10:06] root INFO: channel_first : False [2021/08/11 00:10:06] root INFO: to_rgb : True [2021/08/11 00:10:06] root INFO: ResizeImage : [2021/08/11 00:10:06] root INFO: resize_short : 256 [2021/08/11 00:10:06] root INFO: CropImage : [2021/08/11 00:10:06] root INFO: size : 224 [2021/08/11 00:10:06] root INFO: NormalizeImage : [2021/08/11 00:10:06] root INFO: mean : [0.485, 0.456, 0.406] [2021/08/11 00:10:06] root INFO: order : [2021/08/11 00:10:06] root INFO: scale : 1.0/255.0 [2021/08/11 00:10:06] root INFO: std : [0.229, 0.224, 0.225] [2021/08/11 00:10:06] root INFO: ToCHWImage : None [2021/08/11 00:10:06] root INFO: Loss : [2021/08/11 00:10:06] root INFO: Eval : [2021/08/11 00:10:06] root INFO: CELoss : [2021/08/11 00:10:06] root INFO: weight : 1.0 [2021/08/11 00:10:06] root INFO: Train : [2021/08/11 00:10:06] root INFO: CELoss : [2021/08/11 00:10:06] root INFO: weight : 1.0 [2021/08/11 00:10:06] root INFO: Metric : [2021/08/11 00:10:06] root INFO: Eval : [2021/08/11 00:10:06] root INFO: TopkAcc : [2021/08/11 00:10:06] root INFO: topk : [1, 5] [2021/08/11 00:10:06] root INFO: Train : [2021/08/11 00:10:06] root INFO: TopkAcc : [2021/08/11 00:10:06] root INFO: topk : [1, 5] [2021/08/11 00:10:06] root INFO: Optimizer : [2021/08/11 00:10:06] root INFO: lr : [2021/08/11 00:10:06] root INFO: decay_epochs : [30, 60, 90] [2021/08/11 00:10:06] root INFO: learning_rate : 0.015 [2021/08/11 00:10:06] root INFO: name : Piecewise [2021/08/11 00:10:06] root INFO: values : [0.1, 0.01, 0.001, 0.0001] [2021/08/11 00:10:06] root INFO: momentum : 0.9 [2021/08/11 00:10:06] root INFO: name : Momentum [2021/08/11 00:10:06] root INFO: regularizer : [2021/08/11 00:10:06] root INFO: coeff : 0.0005 [2021/08/11 00:10:06] root INFO: name : L2 W0811 00:10:06.905934 7037 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1 W0811 00:10:06.911123 7037 device_context.cc:422] device: 0, cuDNN Version: 7.6. [2021/08/11 00:10:11] root INFO: train with paddle 2.1.0 and device CUDAPlace(0) /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations if data.dtype == np.object: [{'class_ids': [3, 5, 4, 2, 1], 'scores': [0.59343, 0.16844, 0.11845, 0.09278, 0.02689], 'file_name': 'dataset/foods/baby_back_ribs/319516.jpg', 'label_names': ['beef_tartare', 'baklava', 'apple_pie', 'baby_back_ribs', 'beef_carpaccio']}] ## 结果 ![](https://ai-studio-static-online.cdn.bcebos.com/44a7f12fdc05428093579f11d8e5fc0cfa503a70e96b43e18306b06240970cef) ## 最后 通过这次学习，基本了解了paddleclas的使用方法。 想部署到手机上试试