PaddleX图像分类
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!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')))