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百度PY-Day4理论课课堂笔记
课件：点击下载课程大纲：深度学习难点PaddleHub是什么PaddleHub的效果PaddleHub的优势Pa...
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26
2020/04

百度PY-Day4理论课课堂笔记

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课件：点击下载

课程大纲：

深度学习难点

语义鸿沟




PaddleHub是什么

PaddleHub的效果

PaddleHub的优势


PaddleHub的理念

PaddleHub安装

PaddleHub体验

作业：

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
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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 dataset/file.zip -d ./dataset/
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。

module = hub.Module(name="resnet_v2_50_imagenet")
[2020-04-26 14:31:19,068] [    INFO] - Installing resnet_v2_50_imagenet module
[2020-04-26 14:31:19,086] [    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 14:31:19,424] [    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=False,                              #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=10,                           #模型评估的间隔，默认每100个step评估一次验证集；
    strategy=hub.finetune.strategy.DefaultFinetuneStrategy())  #Fine-tune优化策略；
[2020-04-26 14:41:58,841] [    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，把模型改造为一个五分类模型：

  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 14:42:05,578] [    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 14:52:14,227] [    INFO] - PaddleHub finetune start
[2020-04-26 14:52:27,151] [   TRAIN] - step 20 / 407: loss=1.40396 acc=0.50000 [step/sec: 0.78]
[2020-04-26 14:52:27,152] [    INFO] - Evaluation on dev dataset start
share_vars_from is set, scope is ignored.
[2020-04-26 14:52:27,995] [    EVAL] - [dev dataset evaluation result] loss=1.58546 acc=0.25000 [step/sec: 3.36]
[2020-04-26 14:52:27,996] [    EVAL] - best model saved to cv_finetune_turtorial_demo/best_model [best acc=0.25000]
[2020-04-26 14:52:41,352] [   TRAIN] - step 30 / 407: loss=1.09900 acc=0.50000 [step/sec: 0.80]
[2020-04-26 14:52:41,354] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:52:42,152] [    EVAL] - [dev dataset evaluation result] loss=1.49427 acc=0.25000 [step/sec: 3.58]
[2020-04-26 14:52:54,764] [   TRAIN] - step 40 / 407: loss=1.20739 acc=0.53333 [step/sec: 0.79]
[2020-04-26 14:52:54,765] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:52:55,571] [    EVAL] - [dev dataset evaluation result] loss=1.46154 acc=0.50000 [step/sec: 3.53]
[2020-04-26 14:52:55,572] [    EVAL] - best model saved to cv_finetune_turtorial_demo/best_model [best acc=0.50000]
[2020-04-26 14:53:09,099] [   TRAIN] - step 50 / 407: loss=1.17680 acc=0.46667 [step/sec: 0.79]
[2020-04-26 14:53:09,100] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:53:09,905] [    EVAL] - [dev dataset evaluation result] loss=1.60205 acc=0.25000 [step/sec: 3.55]
[2020-04-26 14:53:22,546] [   TRAIN] - step 60 / 407: loss=1.14852 acc=0.50000 [step/sec: 0.79]
[2020-04-26 14:53:22,547] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:53:23,339] [    EVAL] - [dev dataset evaluation result] loss=1.74368 acc=0.25000 [step/sec: 3.62]
[2020-04-26 14:53:36,011] [   TRAIN] - step 70 / 407: loss=1.02741 acc=0.53333 [step/sec: 0.79]
[2020-04-26 14:53:36,012] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:53:36,815] [    EVAL] - [dev dataset evaluation result] loss=1.87898 acc=0.25000 [step/sec: 3.56]
[2020-04-26 14:53:49,368] [   TRAIN] - step 80 / 407: loss=1.18922 acc=0.56667 [step/sec: 0.80]
[2020-04-26 14:53:49,369] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:53:50,182] [    EVAL] - [dev dataset evaluation result] loss=1.82476 acc=0.25000 [step/sec: 3.55]
[2020-04-26 14:54:02,730] [   TRAIN] - step 90 / 407: loss=1.47436 acc=0.40000 [step/sec: 0.80]
[2020-04-26 14:54:02,732] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:54:03,541] [    EVAL] - [dev dataset evaluation result] loss=1.71757 acc=0.25000 [step/sec: 3.52]
[2020-04-26 14:54:16,199] [   TRAIN] - step 100 / 407: loss=1.41214 acc=0.50000 [step/sec: 0.79]
[2020-04-26 14:54:16,200] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:54:17,012] [    EVAL] - [dev dataset evaluation result] loss=2.49438 acc=0.25000 [step/sec: 3.52]
[2020-04-26 14:54:29,679] [   TRAIN] - step 110 / 407: loss=1.09387 acc=0.50000 [step/sec: 0.79]
[2020-04-26 14:54:29,680] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:54:30,484] [    EVAL] - [dev dataset evaluation result] loss=2.29288 acc=0.50000 [step/sec: 3.56]
[2020-04-26 14:54:43,248] [   TRAIN] - step 120 / 407: loss=1.05538 acc=0.60000 [step/sec: 0.78]
[2020-04-26 14:54:43,249] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:54:44,050] [    EVAL] - [dev dataset evaluation result] loss=1.73628 acc=0.50000 [step/sec: 3.59]
[2020-04-26 14:54:56,782] [   TRAIN] - step 130 / 407: loss=1.27089 acc=0.46667 [step/sec: 0.79]
[2020-04-26 14:54:56,783] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:54:57,582] [    EVAL] - [dev dataset evaluation result] loss=1.30656 acc=0.66667 [step/sec: 3.58]
[2020-04-26 14:54:57,583] [    EVAL] - best model saved to cv_finetune_turtorial_demo/best_model [best acc=0.66667]
[2020-04-26 14:55:10,939] [   TRAIN] - step 140 / 407: loss=1.05220 acc=0.56667 [step/sec: 0.79]
[2020-04-26 14:55:10,941] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:55:11,742] [    EVAL] - [dev dataset evaluation result] loss=1.30367 acc=0.50000 [step/sec: 3.57]
[2020-04-26 14:55:24,392] [   TRAIN] - step 150 / 407: loss=0.92734 acc=0.41667 [step/sec: 0.76]
[2020-04-26 14:55:24,393] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:55:25,174] [    EVAL] - [dev dataset evaluation result] loss=1.86053 acc=0.50000 [step/sec: 3.70]
[2020-04-26 14:55:37,651] [   TRAIN] - step 160 / 407: loss=0.62062 acc=0.80000 [step/sec: 0.80]
[2020-04-26 14:55:37,652] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:55:38,433] [    EVAL] - [dev dataset evaluation result] loss=2.13082 acc=0.41667 [step/sec: 3.70]
[2020-04-26 14:55:50,865] [   TRAIN] - step 170 / 407: loss=0.87543 acc=0.63333 [step/sec: 0.80]
[2020-04-26 14:55:50,866] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:55:51,652] [    EVAL] - [dev dataset evaluation result] loss=0.89465 acc=0.66667 [step/sec: 3.68]
[2020-04-26 14:56:04,174] [   TRAIN] - step 180 / 407: loss=1.10530 acc=0.50000 [step/sec: 0.80]
[2020-04-26 14:56:04,176] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:56:04,970] [    EVAL] - [dev dataset evaluation result] loss=1.55989 acc=0.66667 [step/sec: 3.60]
[2020-04-26 14:56:17,498] [   TRAIN] - step 190 / 407: loss=0.80936 acc=0.66667 [step/sec: 0.80]
[2020-04-26 14:56:17,500] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:56:18,304] [    EVAL] - [dev dataset evaluation result] loss=2.20594 acc=0.41667 [step/sec: 3.54]
[2020-04-26 14:56:31,020] [   TRAIN] - step 200 / 407: loss=0.79162 acc=0.66667 [step/sec: 0.79]
[2020-04-26 14:56:31,021] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:56:31,812] [    EVAL] - [dev dataset evaluation result] loss=2.33450 acc=0.41667 [step/sec: 3.64]
[2020-04-26 14:56:44,558] [   TRAIN] - step 210 / 407: loss=1.08516 acc=0.50000 [step/sec: 0.78]
[2020-04-26 14:56:44,560] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:56:45,349] [    EVAL] - [dev dataset evaluation result] loss=0.92071 acc=0.66667 [step/sec: 3.66]
[2020-04-26 14:56:58,028] [   TRAIN] - step 220 / 407: loss=0.60862 acc=0.80000 [step/sec: 0.79]
[2020-04-26 14:56:58,029] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:56:58,818] [    EVAL] - [dev dataset evaluation result] loss=1.81622 acc=0.41667 [step/sec: 3.66]
[2020-04-26 14:57:11,493] [   TRAIN] - step 230 / 407: loss=0.71227 acc=0.76667 [step/sec: 0.79]
[2020-04-26 14:57:11,495] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:57:12,301] [    EVAL] - [dev dataset evaluation result] loss=2.00925 acc=0.25000 [step/sec: 3.54]
[2020-04-26 14:57:24,971] [   TRAIN] - step 240 / 407: loss=1.14968 acc=0.56667 [step/sec: 0.79]
[2020-04-26 14:57:24,972] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:57:25,772] [    EVAL] - [dev dataset evaluation result] loss=1.51558 acc=0.50000 [step/sec: 3.57]
[2020-04-26 14:57:38,404] [   TRAIN] - step 250 / 407: loss=1.03684 acc=0.56667 [step/sec: 0.79]
[2020-04-26 14:57:38,405] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:57:39,209] [    EVAL] - [dev dataset evaluation result] loss=1.59875 acc=0.25000 [step/sec: 3.55]
[2020-04-26 14:57:51,739] [   TRAIN] - step 260 / 407: loss=0.87253 acc=0.66667 [step/sec: 0.80]
[2020-04-26 14:57:51,740] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:57:52,531] [    EVAL] - [dev dataset evaluation result] loss=2.15564 acc=0.50000 [step/sec: 3.65]
[2020-04-26 14:58:05,287] [   TRAIN] - step 270 / 407: loss=0.74611 acc=0.76667 [step/sec: 0.78]
[2020-04-26 14:58:05,288] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:58:06,103] [    EVAL] - [dev dataset evaluation result] loss=1.80917 acc=0.66667 [step/sec: 3.49]
[2020-04-26 14:58:18,807] [   TRAIN] - step 280 / 407: loss=0.61917 acc=0.80000 [step/sec: 0.79]
[2020-04-26 14:58:18,808] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:58:45,892] [   TRAIN] - step 300 / 407: loss=0.54226 acc=0.80000 [step/sec: 0.78]
[2020-04-26 14:58:45,894] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:58:46,678] [    EVAL] - [dev dataset evaluation result] loss=0.99110 acc=0.58333 [step/sec: 3.67]
[2020-04-26 14:58:59,598] [   TRAIN] - step 310 / 407: loss=0.95645 acc=0.66667 [step/sec: 0.77]
[2020-04-26 14:58:59,599] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:59:00,394] [    EVAL] - [dev dataset evaluation result] loss=1.16195 acc=0.41667 [step/sec: 3.66]
[2020-04-26 14:59:13,256] [   TRAIN] - step 320 / 407: loss=0.66790 acc=0.73333 [step/sec: 0.78]
[2020-04-26 14:59:13,257] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:59:14,036] [    EVAL] - [dev dataset evaluation result] loss=1.87141 acc=0.25000 [step/sec: 3.72]
[2020-04-26 14:59:26,719] [   TRAIN] - step 330 / 407: loss=0.89824 acc=0.70000 [step/sec: 0.79]
[2020-04-26 14:59:26,720] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:59:27,508] [    EVAL] - [dev dataset evaluation result] loss=2.16238 acc=0.50000 [step/sec: 3.67]
[2020-04-26 14:59:53,776] [   TRAIN] - step 350 / 407: loss=0.66608 acc=0.80000 [step/sec: 0.79]
[2020-04-26 14:59:53,777] [    INFO] - Evaluation on dev dataset start
[2020-04-26 14:59:54,587] [    EVAL] - [dev dataset evaluation result] loss=2.02997 acc=0.25000 [step/sec: 3.51]
[2020-04-26 15:00:20,672] [   TRAIN] - step 370 / 407: loss=0.90537 acc=0.60000 [step/sec: 0.79]
[2020-04-26 15:00:20,674] [    INFO] - Evaluation on dev dataset start
[2020-04-26 15:00:21,479] [    EVAL] - [dev dataset evaluation result] loss=2.12513 acc=0.50000 [step/sec: 3.55]
[2020-04-26 15:00:47,782] [   TRAIN] - step 390 / 407: loss=0.72874 acc=0.76667 [step/sec: 0.78]
[2020-04-26 15:00:47,783] [    INFO] - Evaluation on dev dataset start
[2020-04-26 15:00:48,586] [    EVAL] - [dev dataset evaluation result] loss=2.07444 acc=0.50000 [step/sec: 3.57]
[2020-04-26 15:01:01,435] [   TRAIN] - step 400 / 407: loss=0.62824 acc=0.80000 [step/sec: 0.78]
[2020-04-26 15:01:01,437] [    INFO] - Evaluation on dev dataset start
[2020-04-26 15:01:02,242] [    EVAL] - [dev dataset evaluation result] loss=1.72133 acc=0.25000 [step/sec: 3.55]

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()

data = [filepath[0].split(" ")[0],filepath[1].split(" ")[0],filepath[2].split(" ")[0],filepath[3].split(" ")[0],filepath[4].split(" ")[0]]

label_map = dataset.label_dict()
index = 0
run_states = task.predict(data=data)
results = [run_state.run_results for run_state in run_states]

for batch_result in results:
    print(batch_result)
    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 15:02:07,700] [    INFO] - PaddleHub predict start
[2020-04-26 15:02:07,701] [    INFO] - The best model has been loaded
share_vars_from is set, scope is ignored.
[2020-04-26 15:02:08,568] [    INFO] - PaddleHub predict finished.
[array([[0.10850388, 0.03892484, 0.3766357 , 0.3425531 , 0.13338248],
       [0.22734572, 0.02854738, 0.04086622, 0.4217964 , 0.28144428],
       [0.05866686, 0.04220689, 0.6305476 , 0.13286346, 0.13571516]],
      dtype=float32)]
[2 3 2]
input 1 is dataset/test/yushuxin.jpg, and the predict result is 赵小棠
input 2 is dataset/test/xujiaqi.jpg, and the predict result is 安崎
input 3 is dataset/test/zhaoxiaotang.jpg, and the predict result is 赵小棠
[array([[0.08772717, 0.02577625, 0.05073174, 0.4185046 , 0.41726026],
       [0.00707476, 0.00106204, 0.0092694 , 0.16083124, 0.82176256]],
      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

说明：

由于前两个人的测试集纯手工下载且数量太少，预测结果出现了错误。后三人各使用爬虫爬取了上百张，预测结果正确，不过先就这样吧，熟悉一下平台的使用和人脸识别的基本方法。训练集图片爬取和整理方法见这里！

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最后修改：2020 年 04 月 26 日 03 : 45 PM
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