47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import os
|
|
import random
|
|
from torch.utils.data import DataLoader
|
|
import torchvision.transforms as transforms
|
|
import torchvision.datasets as datasets
|
|
import matplotlib.pyplot as plt
|
|
|
|
|
|
class HWDB(object):
|
|
def __init__(self,path, transform):
|
|
# 预处理过程
|
|
|
|
traindir = os.path.join(path, 'train')
|
|
testdir = os.path.join(path, 'test')
|
|
|
|
self.trainset = datasets.ImageFolder(traindir, transform)
|
|
self.testset = datasets.ImageFolder(testdir, transform)
|
|
self.train_size = len(self.trainset)
|
|
self.test_size = len(self.testset)
|
|
self.num_classes = len(self.trainset.classes)
|
|
self.class_to_idx = self.trainset.class_to_idx
|
|
|
|
def get_sample(self, index=0):
|
|
sample = self.trainset[index]
|
|
sample_img, sample_label = sample
|
|
return sample_img, sample_label
|
|
|
|
def get_loader(self, batch_size=100):
|
|
trainloader = DataLoader(self.trainset, batch_size=batch_size, shuffle=True)
|
|
testloader = DataLoader(self.testset, batch_size=batch_size, shuffle=True)
|
|
return trainloader, testloader
|
|
|
|
|
|
if __name__ == '__main__':
|
|
transform = transforms.Compose([
|
|
transforms.Resize((64, 64)),
|
|
transforms.ToTensor(),
|
|
])
|
|
dataset = HWDB(path=r'data', transform=transform)
|
|
print("训练集数量：", dataset.train_size)
|
|
print("测试集数量：", dataset.test_size)
|
|
print("类别数量：", dataset.num_classes)
|
|
index = random.randint(0, dataset.train_size)
|
|
img = dataset.get_sample(index)[0][0]
|
|
plt.imshow(img, cmap='gray')
|
|
plt.show()
|