PaddleX宝石图像分类

!pip install paddlex

!unzip -oq /home/aistudio/data/data146107/dataset.zip -d /home/aistudio/data
!mkdir data/dataset/train
import pandas as pd #引入pandas包
train_data_labels=pd.read_table('data/dataset/train.txt',sep='\t',header=None)
train_data_labels[1].value_counts()
import os
import shutil
for i in range(train_data_labels.shape[0]):
    src = 'data/dataset/images/'+ train_data_labels.iloc[i,0]
    dst = 'data/dataset/train/{}'.format(train_data_labels.iloc[i,1])
    if not os.path.exists(dst):
        os.makedirs(dst) 
    shutil.copy(src, dst)

!paddlex --split_dataset --format ImageNet --dataset_dir data/dataset/train --val_value 0.2 --test_value 0

# 导入Python库
import matplotlib
matplotlib.use('Agg') 
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import paddlex as pdx
from paddlex import transforms as T

# 设置数据增强的方式
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/apis/transforms/transforms.md
train_transforms = T.Compose([
    T.RandomCrop(crop_size=224), 
    T.RandomHorizontalFlip(), 
    T.RandomVerticalFlip(),
    T.RandomScaleAspect(min_scale=0.5, aspect_ratio=0.33),
    T.RandomDistort(),
    T.RandomBlur(),
    T.Normalize(),
])
eval_transforms = T.Compose([
    T.ResizeByShort(short_size=256), 
    T.CenterCrop(crop_size=224), 
    T.Normalize()
])

# 定义训练和验证所用的数据集
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/apis/datasets.md
train_dataset = pdx.datasets.ImageNet(
    data_dir='data/dataset/train',
    file_list='data/dataset/train/train_list.txt',
    label_list='data/dataset/train/labels.txt',
    transforms=train_transforms,
    shuffle=True)
eval_dataset = pdx.datasets.ImageNet(
    data_dir='data/dataset/train',
    file_list='data/dataset/train/val_list.txt',
    label_list='data/dataset/train/labels.txt',
    transforms=eval_transforms)

# 初始化模型，并进行训练
num_classes = len(train_dataset.labels)
model = pdx.cls.ResNet50_vd_ssld(num_classes=num_classes)

# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0.0/docs/apis/models/classification.md
# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/tree/release/2.0.0/docs/parameters.md
model.train(
    num_epochs=100,
    train_dataset=train_dataset,
    train_batch_size=128,
    eval_dataset=eval_dataset,
    lr_decay_epochs=[4, 6, 8],
    learning_rate=0.025,
    save_dir='output/resnet50',
    use_vdl=True)

# 将Test.txt加载成列表
import os
test_files = []
with open('data/dataset/test.txt', 'r') as file_to_read:
    for line in file_to_read:
        test_files.append(os.path.join('data/dataset/images/',line.strip()))

# 批量预测
import paddlex as pdx
# 模型载入（记得根据模型修改路径）
model = pdx.load_model('output/resnet50/best_model')
result_list = model.predict(test_files)

result_list

# 将预测结果写入result.txt
import pandas as pd
test_data = pd.read_table('data/dataset/test.txt',sep='\t',header=None)
with open('result.txt', mode='w') as file_out:
    for i in range(len(result_list)):
        file_out.write('%s\t%s\n'%(test_data.values.tolist()[i][0] , result_list[i][0].get('category')))