PaddleHub之《青春有你2》作业：五人识别

一、任务简介

图像分类是计算机视觉的重要领域，它的目标是将图像分类到预定义的标签。近期，许多研究者提出很多不同种类的神经网络，并且极大的提升了分类算法的性能。本文以自己创建的数据集：青春有你2中选手识别为例子，介绍如何使用PaddleHub进行图像分类任务。

#CPU环境启动请务必执行该指令 %set_env CPU_NUM=1 

env: CPU_NUM=1 

#安装paddlehub !pip install paddlehub==1.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple 

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting paddlehub==1.6.0 [?25l  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7f/9f/6617c2b8e9c5d847803ae89924b58bccd1b8fb2c98aa00e16531540591f2/paddlehub-1.6.0-py3-none-any.whl (206kB)      |████████████████████████████████| 215kB 10.0MB/s eta 0:00:01 [?25hRequirement already satisfied: protobuf>=3.6.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub==1.6.0) (3.10.0) Requirement already satisfied: pandas; python_version >= "3" in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub==1.6.0) (0.23.4) Requirement already satisfied: requests in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub==1.6.0) (2.22.0) Requirement already satisfied: sentencepiece in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub==1.6.0) (0.1.85) Requirement already satisfied: chardet==3.0.4 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from paddlehub==1.6.0) (3.0.4) Requirement already satisfied: gunicorn>=19.10.0; 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二、任务实践

Step1、基础工作

加载数据文件

导入python包

!unzip -o file.zip -d ./dataset/ 

unzip:  cannot find or open file.zip, file.zip.zip or file.zip.ZIP. 

!unzip ./data/train.zip -d ./dataset/train !ls 

Archive:  ./data/train.zip replace ./dataset/train/anqi/anqi0.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename:  

import paddlehub as hub 

Step2、加载预训练模型

接下来我们要在PaddleHub中选择合适的预训练模型来Finetune，由于是图像分类任务，因此我们使用经典的ResNet-50作为预训练模型。PaddleHub提供了丰富的图像分类预训练模型，包括了最新的神经网络架构搜索类的PNASNet，我们推荐您尝试不同的预训练模型来获得更好的性能。

import os  def generate_train_tlist(): # 待搜索的目录路径     result=[]     path = "dataset/train" # 待搜索的名称     stars = {'yushuxin': 0, 'xujiaqi': 1, 'zhaoxiaotang': 2, 'anqi': 3, r'wangchengxuan': 4} for root, dirs, files in os.walk(path): for f in files:             ff = os.path.join(root, f) # print(ff)             fff='t'+ff.strip('dataset/') # print(fff) # print(f)             name=f[:-5] # print(name) # print(' %s %d'% ( fff, stars[name]))             result.append('%s %d'% ( fff, stars[name])) return result train_list=generate_train_tlist() with open("./dataset/train_list.txt", "w") as f: for line in train_list: print(line)         f.writelines(line)         f.writelines("n") 

train/anqi/anqi4.jpg 3 train/anqi/anqi8.jpg 3 train/anqi/anqi7.jpg 3 train/anqi/anqi3.jpg 3 train/anqi/anqi0.jpg 3 train/anqi/anqi5.jpg 3 train/anqi/anqi2.jpg 3 train/anqi/anqi6.jpg 3 train/anqi/anqi9.jpg 3 train/anqi/anqi1.jpg 3 train/wangchengxuan/wangchengxuan5.jpg 4 train/wangchengxuan/wangchengxuan7.jpg 4 train/wangchengxuan/wangchengxuan4.jpg 4 train/wangchengxuan/wangchengxuan2.jpg 4 train/wangchengxuan/wangchengxuan9.jpg 4 train/wangchengxuan/wangchengxuan8.jpg 4 train/wangchengxuan/wangchengxuan3.jpg 4 train/wangchengxuan/wangchengxuan0.jpg 4 train/wangchengxuan/wangchengxuan6.jpg 4 train/wangchengxuan/wangchengxuan1.jpg 4 train/yushuxin/yushuxin0.jpg 0 train/yushuxin/yushuxin9.jpg 0 train/yushuxin/yushuxin5.jpg 0 train/yushuxin/yushuxin4.jpg 0 train/yushuxin/yushuxin3.jpg 0 train/yushuxin/yushuxin2.jpg 0 train/yushuxin/yushuxin7.jpg 0 train/yushuxin/yushuxin6.jpg 0 train/yushuxin/yushuxin8.jpg 0 train/yushuxin/yushuxin1.jpg 0 train/xujiaqi/xujiaqi4.jpg 1 train/xujiaqi/xujiaqi2.jpg 1 train/xujiaqi/xujiaqi6.jpg 1 train/xujiaqi/xujiaqi1.jpg 1 train/xujiaqi/xujiaqi9.jpg 1 train/xujiaqi/xujiaqi8.jpg 1 train/xujiaqi/xujiaqi5.jpg 1 train/xujiaqi/xujiaqi3.jpg 1 train/xujiaqi/xujiaqi0.jpg 1 train/xujiaqi/xujiaqi7.jpg 1 train/zhaoxiaotang/zhaoxiaotang7.jpg 2 train/zhaoxiaotang/zhaoxiaotang6.jpg 2 train/zhaoxiaotang/zhaoxiaotang1.jpg 2 train/zhaoxiaotang/zhaoxiaotang9.jpg 2 train/zhaoxiaotang/zhaoxiaotang2.jpg 2 train/zhaoxiaotang/zhaoxiaotang0.jpg 2 train/zhaoxiaotang/zhaoxiaotang4.jpg 2 train/zhaoxiaotang/zhaoxiaotang5.jpg 2 train/zhaoxiaotang/zhaoxiaotang8.jpg 2 train/zhaoxiaotang/zhaoxiaotang3.jpg 2 

!hub install ernie 

Module ernie already installed in /home/aistudio/.paddlehub/modules/ernie 

module = hub.Module(name="resnet_v2_50_imagenet") 

[2020-04-26 13:03:09,478] [    INFO] - Installing resnet_v2_50_imagenet module [2020-04-26 13:03:09,498] [    INFO] - Module resnet_v2_50_imagenet already installed in /home/aistudio/.paddlehub/modules/resnet_v2_50_imagenet 

Step3、数据准备

接着需要加载图片数据集。我们使用自定义的数据进行体验，请查看适配自定义数据

from paddlehub.dataset.base_cv_dataset import BaseCVDataset     class DemoDataset(BaseCVDataset): def __init__(self): # 数据集存放位置                self.dataset_dir = "dataset" super(DemoDataset, self).__init__(            base_path=self.dataset_dir,            train_list_file="train_list.txt", #    validate_list_file="validate_list.txt",            test_list_file="test_list.txt",            label_list_file="label_list.txt", ) dataset = DemoDataset() 

Step4、生成数据读取器

接着生成一个图像分类的reader，reader负责将dataset的数据进行预处理，接着以特定格式组织并输入给模型进行训练。

当我们生成一个图像分类的reader时，需要指定输入图片的大小

data_reader = hub.reader.ImageClassificationReader(     image_width=module.get_expected_image_width(),     image_height=module.get_expected_image_height(),     images_mean=module.get_pretrained_images_mean(),     images_std=module.get_pretrained_images_std(),     dataset=dataset) 

[2020-04-26 13:07:58,826] [    INFO] - Dataset label map = {'虞书欣': 0, '许佳琪': 1, '赵小棠': 2, '安崎': 3, '王承渲': 4} 

Step5、配置策略

在进行Finetune前，我们可以设置一些运行时的配置，例如如下代码中的配置，表示：

• use_cuda：设置为False表示使用CPU进行训练。如果您本机支持GPU，且安装的是GPU版本的PaddlePaddle，我们建议您将这个选项设置为True；

• epoch：迭代轮数；

• batch_size：每次训练的时候，给模型输入的每批数据大小为32，模型训练时能够并行处理批数据，因此batch_size越大，训练的效率越高，但是同时带来了内存的负荷，过大的batch_size可能导致内存不足而无法训练，因此选择一个合适的batch_size是很重要的一步；

• log_interval：每隔10 step打印一次训练日志；

• eval_interval：每隔50 step在验证集上进行一次性能评估；

• checkpoint_dir：将训练的参数和数据保存到cv_finetune_turtorial_demo目录中；

• strategy：使用DefaultFinetuneStrategy策略进行finetune；

更多运行配置，请查看RunConfig

同时PaddleHub提供了许多优化策略，如AdamWeightDecayStrategy、ULMFiTStrategy、DefaultFinetuneStrategy等，详细信息参见策略

config = hub.RunConfig(     use_cuda=True, #是否使用GPU训练，默认为False；     num_epoch=3, #Fine-tune的轮数；     checkpoint_dir="cv_finetune_turtorial_demo" ,#模型checkpoint保存路径, 若用户没有指定，程序会自动生成；     batch_size=3, #训练的批大小，如果使用GPU，请根据实际情况调整batch_size； # eval_interval=3,                           #模型评估的间隔，默认每100个step评估一次验证集；     log_interval=10,     strategy=hub.finetune.strategy.DefaultFinetuneStrategy()) #Fine-tune优化策略； 

[2020-04-26 13:06:01,681] [    INFO] - Checkpoint dir: cv_finetune_turtorial_demo 

Step6、组建Finetune Task

有了合适的预训练模型和准备要迁移的数据集后，我们开始组建一个Task。

由于该数据设置是一个二分类的任务，而我们下载的分类module是在ImageNet数据集上训练的千分类模型，所以我们需要对模型进行简单的微调，把模型改造为一个二分类模型：

1. 获取module的上下文环境，包括输入和输出的变量，以及Paddle Program；
2. 从输出变量中找到特征图提取层feature_map；
3. 在feature_map后面接入一个全连接层，生成Task；

input_dict, output_dict, program = module.context(trainable=True) img = input_dict["image"] feature_map = output_dict["feature_map"] feed_list = [img.name]  task = hub.ImageClassifierTask(     data_reader=data_reader,     feed_list=feed_list,     feature=feature_map,     num_classes=dataset.num_labels,     config=config) 

[2020-04-26 13:06:04,337] [    INFO] - 267 pretrained paramaters loaded by PaddleHub 

Step5、开始Finetune

我们选择finetune_and_eval接口来进行模型训练，这个接口在finetune的过程中，会周期性的进行模型效果的评估，以便我们了解整个训练过程的性能变化。

run_states = task.finetune_and_eval() 

[2020-04-26 13:06:10,841] [    INFO] - Strategy with slanted triangle learning rate, L2 regularization,  [2020-04-26 13:06:10,873] [    INFO] - Try loading checkpoint from cv_finetune_turtorial_demo/ckpt.meta [2020-04-26 13:06:10,874] [    INFO] - PaddleHub model checkpoint not found, start from scratch... [2020-04-26 13:06:10,909] [    INFO] - PaddleHub finetune start [2020-04-26 13:06:12,540] [   TRAIN] - step 10 / 50: loss=0.89901 acc=0.73333 [step/sec: 7.16] [2020-04-26 13:06:13,915] [   TRAIN] - step 20 / 50: loss=0.38457 acc=1.00000 [step/sec: 10.22] [2020-04-26 13:06:15,447] [   TRAIN] - step 30 / 50: loss=0.11394 acc=1.00000 [step/sec: 6.95] [2020-04-26 13:06:16,902] [   TRAIN] - step 40 / 50: loss=0.06314 acc=1.00000 [step/sec: 7.48] [2020-04-26 13:06:18,353] [   TRAIN] - step 50 / 50: loss=0.04763 acc=1.00000 [step/sec: 7.62] [2020-04-26 13:06:18,432] [    INFO] - Load the best model from cv_finetune_turtorial_demo/best_model [2020-04-26 13:06:18,433] [    INFO] - Evaluation on test dataset start share_vars_from is set, scope is ignored. [2020-04-26 13:06:19,083] [    EVAL] - [test dataset evaluation result] loss=0.00011 acc=1.00000 [step/sec: 19.32] [2020-04-26 13:06:19,085] [    INFO] - Saving model checkpoint to cv_finetune_turtorial_demo/step_51 [2020-04-26 13:06:20,090] [    INFO] - PaddleHub finetune finished. 

Step6、预测

当Finetune完成后，我们使用模型来进行预测，先通过以下命令来获取测试的图片

import numpy as np import matplotlib.pyplot as plt  import matplotlib.image as mpimg  with open("dataset/test_list.txt","r") as f:     filepath = f.readlines() # print(filepath)  data = [filepath[0].split(" ")[0],filepath[1].split(" ")[0],filepath[2].split(" ")[0],filepath[3].split(" ")[0],filepath[4].split(" ")[0]] print(data) label_map = dataset.label_dict() index = 0 run_states = task.predict(data=data) results = [run_state.run_results for run_state in run_states] print(results) print(50*'*') for batch_result in results: print(batch_result) print(50*'*')     batch_result = np.argmax(batch_result, axis=2)[0] print(batch_result) for result in batch_result:         index += 1         result = label_map[result] print("input %i is %s, and the predict result is %s" % (index, data[index - 1], result)) 

[2020-04-26 13:08:42,487] [    INFO] - PaddleHub predict start [2020-04-26 13:08:42,487] [    INFO] - Load the best model from cv_finetune_turtorial_demo/best_model   ['dataset/test/yushuxin.jpg', 'dataset/test/xujiaqi.jpg', 'dataset/test/zhaoxiaotang.jpg', 'dataset/test/anqi.jpg', 'dataset/test/wangchengxuan.jpg'] [[array([[9.99820173e-01, 7.76551133e-06, 3.23944623e-05, 1.07691012e-04,         3.19731771e-05],        [2.23369602e-06, 9.99973655e-01, 9.16190515e-07, 2.26883185e-05,         5.21339530e-07],        [2.94848701e-06, 1.81872983e-05, 9.99861002e-01, 8.90642877e-06,         1.08885535e-04]], dtype=float32)], [array([[2.0881200e-06, 4.9753922e-05, 5.9350541e-06, 9.9994111e-01,         1.0736142e-06],        [3.4572211e-05, 3.4797737e-05, 3.2656546e-05, 3.2816235e-05,         9.9986517e-01]], dtype=float32)]] ************************************************** [array([[9.99820173e-01, 7.76551133e-06, 3.23944623e-05, 1.07691012e-04,         3.19731771e-05],        [2.23369602e-06, 9.99973655e-01, 9.16190515e-07, 2.26883185e-05,         5.21339530e-07],        [2.94848701e-06, 1.81872983e-05, 9.99861002e-01, 8.90642877e-06,         1.08885535e-04]], dtype=float32)] ************************************************** [0 1 2] input 1 is dataset/test/yushuxin.jpg, and the predict result is 虞书欣 input 2 is dataset/test/xujiaqi.jpg, and the predict result is 许佳琪 input 3 is dataset/test/zhaoxiaotang.jpg, and the predict result is 赵小棠 [array([[2.0881200e-06, 4.9753922e-05, 5.9350541e-06, 9.9994111e-01,         1.0736142e-06],        [3.4572211e-05, 3.4797737e-05, 3.2656546e-05, 3.2816235e-05,         9.9986517e-01]], dtype=float32)] ************************************************** [3 4] input 4 is dataset/test/anqi.jpg, and the predict result is 安崎 input 5 is dataset/test/wangchengxuan.jpg, and the predict result is 王承渲   share_vars_from is set, scope is ignored. [2020-04-26 13:08:42,793] [    INFO] - PaddleHub predict finished.