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
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Installing collected packages: paddlehub
Found existing installation: paddlehub 1.5.0
Uninstalling paddlehub-1.5.0:
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Successfully installed paddlehub-1.6.0
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
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train/yushuxin/yushuxin2.jpg 0
train/yushuxin/yushuxin7.jpg 0
train/yushuxin/yushuxin6.jpg 0
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train/yushuxin/yushuxin1.jpg 0
train/xujiaqi/xujiaqi4.jpg 1
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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
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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")
[32m[2020-04-26 13:03:09,478] [ INFO] - Installing resnet_v2_50_imagenet module[0m
[32m[2020-04-26 13:03:09,498] [ INFO] - Module resnet_v2_50_imagenet already installed in /home/aistudio/.paddlehub/modules/resnet_v2_50_imagenet[0m
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)
[32m[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}[0m
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；
[32m[2020-04-26 13:06:01,681] [ INFO] - Checkpoint dir: cv_finetune_turtorial_demo[0m
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：
- 获取module的上下文环境，包括输入和输出的变量，以及Paddle Program；
- 从输出变量中找到特征图提取层feature_map；
- 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)
[32m[2020-04-26 13:06:04,337] [ INFO] - 267 pretrained paramaters loaded by PaddleHub[0m
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()
[32m[2020-04-26 13:06:10,841] [ INFO] - Strategy with slanted triangle learning rate, L2 regularization, [0m
[32m[2020-04-26 13:06:10,873] [ INFO] - Try loading checkpoint from cv_finetune_turtorial_demo/ckpt.meta[0m
[32m[2020-04-26 13:06:10,874] [ INFO] - PaddleHub model checkpoint not found, start from scratch...[0m
[32m[2020-04-26 13:06:10,909] [ INFO] - PaddleHub finetune start[0m
[36m[2020-04-26 13:06:12,540] [ TRAIN] - step 10 / 50: loss=0.89901 acc=0.73333 [step/sec: 7.16][0m
[36m[2020-04-26 13:06:13,915] [ TRAIN] - step 20 / 50: loss=0.38457 acc=1.00000 [step/sec: 10.22][0m
[36m[2020-04-26 13:06:15,447] [ TRAIN] - step 30 / 50: loss=0.11394 acc=1.00000 [step/sec: 6.95][0m
[36m[2020-04-26 13:06:16,902] [ TRAIN] - step 40 / 50: loss=0.06314 acc=1.00000 [step/sec: 7.48][0m
[36m[2020-04-26 13:06:18,353] [ TRAIN] - step 50 / 50: loss=0.04763 acc=1.00000 [step/sec: 7.62][0m
[32m[2020-04-26 13:06:18,432] [ INFO] - Load the best model from cv_finetune_turtorial_demo/best_model[0m
[32m[2020-04-26 13:06:18,433] [ INFO] - Evaluation on test dataset start[0m
share_vars_from is set, scope is ignored.
[34m[2020-04-26 13:06:19,083] [ EVAL] - [test dataset evaluation result] loss=0.00011 acc=1.00000 [step/sec: 19.32][0m
[32m[2020-04-26 13:06:19,085] [ INFO] - Saving model checkpoint to cv_finetune_turtorial_demo/step_51[0m
[32m[2020-04-26 13:06:20,090] [ INFO] - PaddleHub finetune finished.[0m
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))
[32m[2020-04-26 13:08:42,487] [ INFO] - PaddleHub predict start[0m
[32m[2020-04-26 13:08:42,487] [ INFO] - Load the best model from cv_finetune_turtorial_demo/best_model[0m
['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.
[32m[2020-04-26 13:08:42,793] [ INFO] - PaddleHub predict finished.[0m