51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
import os
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from torch.utils.data import DataLoader
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import torchvision.transforms as transforms
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import torchvision.datasets as datasets
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import matplotlib.pyplot as plt
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class HWDB(object):
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def __init__(self, transform, path='./data'):
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# 预处理过程
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traindir = os.path.join(path, 'train')
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testdir = os.path.join(path, 'test')
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self.trainset = datasets.ImageFolder(traindir, transform)
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self.testset = datasets.ImageFolder(testdir, transform)
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self.train_size = len(self.trainset)
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self.test_size = len(self.testset)
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def get_sample(self, index=0):
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sample = self.trainset[index]
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sample_img, sample_label = sample
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return sample_img, sample_label
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def get_loader(self, batch_size=100):
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trainloader = DataLoader(self.trainset, batch_size=batch_size, shuffle=True)
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testloader = DataLoader(self.testset, batch_size=batch_size, shuffle=True)
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return trainloader, testloader
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if __name__ == '__main__':
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transform = transforms.Compose([
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# transforms.Grayscale(),
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transforms.Resize((64, 64)),
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transforms.ToTensor(),
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])
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dataset = HWDB(transform=transform, path=r'data')
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print(dataset.train_size)
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print(dataset.test_size)
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for i in [1020, 120, 2000, 6000, 1000]:
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img, label = dataset.get_sample(i)
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img = img[0]
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print(label)
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plt.imshow(img, cmap='gray')
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plt.show()
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train_loader, test_loader = dataset.get_loader()
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# for (img, label) in train_loader:
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# print(img)
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# print(label)
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