Введение в задачу распознавания эмоций на лице на основе PaddlePaddle

машинное обучение

1. Введение в задачу распознавания эмоций лица

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1. Предыстория соревнований

Выражение лица является важным способом распространения эмоциональной информации человека и координации межличностных отношений.Распознавание выражения лица относится к выбору состояний выражения лица из статичных фотографий или видеорядов для определения эмоциональных и психологических изменений персонажей. Люди привыкли усваивать невербальные сигналы от выражений лица в повседневной жизни, поэтому могут ли компьютеры выполнять аналогичные задачи? Ответ — да, но его нужно научить распознавать эмоции.

2. Конкурсные задания

Чтобы завершить распознавание определенных эмоций по фотографии лица, участникам необходимо построить задачу распознавания эмоций на основе данных обучающего набора и сделать прогнозы на изображениях тестового набора, чтобы идентифицировать семь эмоций лица.

3. Описание данных

Тестовые данные состоят из тренировочного набора и тестового набора, а набор данных тренировочного набора хранится в папках с разными эмоциями. в:

Учебный набор: изображения лиц мощностью 2,8 Вт;

Тестовый набор: изображения лиц 7K;

Для упрощения задачи изображение вопроса содержит только одно лицо, а размер всех изображений составляет 48*48 пикселей. Метки настроений, включенные в набор данных, включают следующие 7 категорий:

  • angry
  • disgusted
  • fearful
  • happy
  • neutral
  • sad
  • surprised

2. Обработка данных

1. Подготовка среды для гребли

Версии следующие:

  • (from versions: 0.1.0, 0.1.1, 0.1.2, 0.1.3, 0.1.4, 0.1.5, 0.1.6, 0.1.7, 0.1.8, 0.1.9, 0.2.0, 0.2.1, 0.3.0, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4, 1.0.5, 1.0.6, 1.0.7, 1.0.8, 1.1.0, 1.1.1, 1.1.5, 1.1.6, 1.2.0, 1.2.1, 1.2.2, 1.2.3, 1.2.4, 1.2.5, 1.2.6, 1.2.7, 1.2.8, 1.3.0, 1.3.1, 1.3.2, 1.3.3, 1.3.4, 1.3.5, 1.3.6, 1.3.7, 1.3.8, 1.3.9, 1.3.10, 1.3.11, 2.0.0rc0, 2.0.0rc3)
  • Здесь используйте 2.0.0rc3, эта версия немного отличается от предыдущей апи, вы должны быть внимательны, иначе будут необъяснимые ошибки!
! pip install paddlex==2.0.0rc3 -i https://mirror.baidu.com/pypi/simple

Looking in indexes: https://mirror.baidu.com/pypi/simple
Requirement already satisfied: paddlex==2.0.0rc3 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (2.0.0rc3)
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2. Распаковка данных

# %cd ~
# !unzip data/data100671/Datawhale_人脸情绪识别_数据集.zip -d data

# !unzip -oq /home/aistudio/data/Datawhale_人脸情绪识别_数据集/test.zip
# !unzip -oq /home/aistudio/data/Datawhale_人脸情绪识别_数据集/train.zip
# !cp data/Datawhale_人脸情绪识别_数据集/sample_submit.csv .

3. Баланс данных

Отвратительно всего 436, данные крайне неравномерны, а план сбалансирован

/home/aistudio/train/angry
3995
/home/aistudio/train/disgusted
436
/home/aistudio/train/fearful
4097
/home/aistudio/train/neutral
4965
/home/aistudio/train/sad
4830
/home/aistudio/train/surprised
3171

%cd ~/train/angry
!ls -lR | grep "^-"| wc -l
%cd ~/train/disgusted/
!ls -lR | grep "^-"| wc -l
%cd ~/train/fearful/
!ls -lR | grep "^-"| wc -l
%cd ~/train/neutral/
!ls -lR | grep "^-"| wc -l
%cd ~/train/sad/
!ls -lR | grep "^-"| wc -l
%cd ~/train/surprised/
!ls -lR | grep "^-"| wc -l

/home/aistudio/train/angry
4965
/home/aistudio/train/disgusted
4965
/home/aistudio/train/fearful
4965
/home/aistudio/train/neutral
4965
/home/aistudio/train/sad
4965
/home/aistudio/train/surprised
4965

# # 数据均衡到4965，仅执行一次
# import os
# import shutil

# def get_file_list(target_path):
#     img_list = os.listdir(target_path)
#     img_list=[os.path.join(target_path, item) for item in img_list]
#     return img_list


# def cpfile(file_list, max_num):
#     current_num=len(file_list)
#     while(current_num<max_num):
#         i=current_num%current_num
#         current_path= '/'.join(file_list[i].split('/')[:-1])
#         filename=file_list[i].split('/')[-1]
#         new_path = os.path.join(str(current_path), 'new_' + str(current_num) + filename)
#         shutil.copy(file_list[i], new_path)
#         current_num=current_num+1


# train_dir=os.listdir('/home/aistudio/train')
# train_dir.remove('.DS_Store')
# img_paths=[os.path.join('/home/aistudio/train', item) for item in train_dir]
# print(img_paths)
# for item in img_paths:
#     img_list=get_file_list(item)
#     cpfile(img_list, 4965)
# print("数据以均衡，各分类均为4965张！")

%cd ~/train/angry
!ls -lR | grep "^-"| wc -l
%cd ~/train/disgusted/
!ls -lR | grep "^-"| wc -l
%cd ~/train/fearful/
!ls -lR | grep "^-"| wc -l
%cd ~/train/neutral/
!ls -lR | grep "^-"| wc -l
%cd ~/train/sad/
!ls -lR | grep "^-"| wc -l
%cd ~/train/surprised/
!ls -lR | grep "^-"| wc -l

/home/aistudio/train/angry
4965
/home/aistudio/train/disgusted
4965
/home/aistudio/train/fearful
4965
/home/aistudio/train/neutral
4965
/home/aistudio/train/sad
4965
/home/aistudio/train/surprised
4965

3. Создайте список данных

Разделить на наборы данных train и eval

2021-07-18 21:53:18 [INFO]	Dataset split starts...
2021-07-18 21:53:18 [INFO]	Dataset split done.
2021-07-18 21:53:18 [INFO]	Train samples: 22968
2021-07-18 21:53:18 [INFO]	Eval samples: 5741
2021-07-18 21:53:18 [INFO]	Test samples: 0
2021-07-18 21:53:18 [INFO]	Split files saved in ./train

%cd ~/
!paddlex --split_dataset --format ImageNet --dataset_dir ./train --val_value 0.2

4. Определите преобразования

import matplotlib
matplotlib.use('Agg') 
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import paddlex as pdx

from paddlex import transforms as T
dir(T)

['ArrangeClassifier',
 'ArrangeDetector',
 'ArrangeSegmenter',
 'BatchRandomResize',
 'BatchRandomResizeByShort',
 'CenterCrop',
 'Compose',
 'Decode',
 'MixupImage',
 'Normalize',
 'Padding',
 'RandomBlur',
 'RandomCrop',
 'RandomDistort',
 'RandomExpand',
 'RandomHorizontalFlip',
 'RandomResize',
 'RandomResizeByShort',
 'RandomScaleAspect',
 'RandomVerticalFlip',
 'Resize',
 'ResizeByLong',
 'ResizeByShort',
 'T',
 '_BatchPadding',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'arrange_transforms',
 'batch_operators',
 'box_utils',
 'build_transforms',
 'functions',
 'operators']

from paddlex import transforms as T

train_transforms = T.Compose([T.RandomBlur(), 
                            T.RandomHorizontalFlip(), T.Normalize()])
# train_transforms = T.Compose([T.RandomBlur(), T.RandomDistort(brightness_range=0.9, brightness_prob=0.4, contrast_range=0.4, contrast_prob=0.5, saturation_range=0.4),
#                             T.RandomHorizontalFlip(), T.Normalize()])                            
eval_transforms = T.Compose([T.Normalize()])

4. Определите набор данных

train_dataset = pdx.datasets.ImageNet(
    data_dir='train',
    file_list='train/train_list.txt',
    label_list='train/labels.txt',
    transforms=train_transforms,
    shuffle=True)
    
eval_dataset = pdx.datasets.ImageNet(
    data_dir='train',
    file_list='train/val_list.txt',
    label_list='train/labels.txt',
    transforms=eval_transforms)

2021-07-19 21:37:07 [INFO]	Starting to read file list from dataset...
2021-07-19 21:37:08 [INFO]	29604 samples in file train/train_list.txt
2021-07-19 21:37:08 [INFO]	Starting to read file list from dataset...
2021-07-19 21:37:08 [INFO]	7401 samples in file train/val_list.txt

3. Выберите модель PaddleX и обучите ее

1. Выбор модели и обучение

num_classes = len(train_dataset.labels)
model = pdx.cls.ResNet101_vd_ssld(num_classes=num_classes)
model.train(num_epochs=10,
            train_dataset=train_dataset,
            train_batch_size=128,
            eval_dataset=eval_dataset,
            lr_decay_epochs=[4, 6, 8],
            save_interval_epochs=1,
            learning_rate=0.025,
            save_dir='output/ResNet101_vd_ssld',
            use_vdl=True)

2. Журнал тренировок

2021-07-18 22:01:55 [INFO]	[TRAIN] Epoch=10/10, Step=7/57, loss=0.612496, acc1=0.797500, acc5=1.000000, lr=0.000063, time_each_step=0.13s, eta=0:0:6
2021-07-18 22:01:56 [INFO]	[TRAIN] Epoch=10/10, Step=17/57, loss=0.707318, acc1=0.740000, acc5=0.997500, lr=0.000063, time_each_step=0.08s, eta=0:0:3
2021-07-18 22:01:57 [INFO]	[TRAIN] Epoch=10/10, Step=27/57, loss=0.733735, acc1=0.735000, acc5=0.997500, lr=0.000063, time_each_step=0.07s, eta=0:0:2
2021-07-18 22:01:57 [INFO]	[TRAIN] Epoch=10/10, Step=37/57, loss=0.822799, acc1=0.682500, acc5=0.992500, lr=0.000063, time_each_step=0.07s, eta=0:0:1
2021-07-18 22:01:58 [INFO]	[TRAIN] Epoch=10/10, Step=47/57, loss=0.722330, acc1=0.722500, acc5=0.997500, lr=0.000063, time_each_step=0.07s, eta=0:0:0
2021-07-18 22:01:59 [INFO]	[TRAIN] Epoch=10/10, Step=57/57, loss=0.781480, acc1=0.730000, acc5=0.985000, lr=0.000063, time_each_step=0.07s, eta=0:0:0
2021-07-18 22:01:59 [INFO]	[TRAIN] Epoch 10 finished, loss=0.72172904, acc1=0.74149126, acc5=0.99293864 .
2021-07-18 22:01:59 [INFO]	Start to evaluate(total_samples=5741, total_steps=15)...
2021-07-18 22:02:01 [INFO]	[EVAL] Finished, Epoch=10, acc1=0.554805, acc5=0.973047 .
2021-07-18 22:02:01 [INFO]	Current evaluated best model on eval_dataset is epoch_8, acc1=0.5554988384246826
2021-07-18 22:02:01 [INFO]	Model saved in output/mobilenetv3_large_ssld/epoch_10.

4. Прогноз

1. Создайте список прогнозов

import pandas as pd

test=pd.read_csv('sample_submit.csv')
test.head()
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
name label
0 00001.png sad
1 00002.png sad
2 00003.png sad
3 00004.png sad
4 00005.png sad 
test.info

2. Прогноз

import paddlex as pdx
import os
model = pdx.load_model('0.7377796769142151/')
labels=[]
for index, item in test.iterrows():  
    image_name = os.path.join('test', item['name'])
    label = model.predict(image_name)
    labels.append(label)
print("Predict Done:", len(labels))

2021-07-19 21:47:03 [INFO]	Model[ResNet101_vd_ssld] loaded.


/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:


Predict Done: 7178

3. Сохраните и отправьте

test['label']=labels

for i in range(len(labels)):
    test['label'][i]=test['label'][i][0]['category']

print(test['label'][0])

angry

# 不要index
test.to_csv('result.csv', index=False)

!zip result.zip result.csv

  adding: result.csv (deflated 82%)

4. Результаты

返回分数	0.65534	result.csv		livingbody	2021-07-19 01:52:09
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