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70 lines
2.2 KiB
70 lines
2.2 KiB
"""Dataset setting and data loader for MNIST_M."""
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import torch
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from torchvision import transforms
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import torch.utils.data as data
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from PIL import Image
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import os
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class GetLoader(data.Dataset):
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def __init__(self, data_root, data_list, transform=None):
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self.root = data_root
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self.transform = transform
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f = open(data_list, 'r')
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data_list = f.readlines()
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f.close()
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self.n_data = len(data_list)
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self.img_paths = []
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self.img_labels = []
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for data in data_list:
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self.img_paths.append(data[:-3])
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self.img_labels.append(data[-2])
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def __getitem__(self, item):
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img_paths, labels = self.img_paths[item], self.img_labels[item]
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imgs = Image.open(os.path.join(self.root, img_paths)).convert('RGB')
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if self.transform is not None:
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imgs = self.transform(imgs)
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labels = int(labels)
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return imgs, labels
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def __len__(self):
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return self.n_data
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def get_mnistm(dataset_root, batch_size, train):
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"""Get MNISTM datasets loader."""
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# image pre-processing
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pre_process = transforms.Compose([transforms.Resize(28),
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transforms.ToTensor(),
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transforms.Normalize(
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mean=(0.5, 0.5, 0.5),
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std=(0.5, 0.5, 0.5)
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)])
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# datasets and data_loader
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if train:
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train_list = os.path.join(dataset_root, 'mnist_m','mnist_m_train_labels.txt')
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mnistm_dataset = GetLoader(
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data_root=os.path.join(dataset_root, 'mnist_m', 'mnist_m_train'),
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data_list=train_list,
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transform=pre_process)
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else:
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train_list = os.path.join(dataset_root, 'mnist_m', 'mnist_m_test_labels.txt')
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mnistm_dataset = GetLoader(
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data_root=os.path.join(dataset_root, 'mnist_m', 'mnist_m_test'),
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data_list=train_list,
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transform=pre_process)
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mnistm_dataloader = torch.utils.data.DataLoader(
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dataset=mnistm_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=8)
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return mnistm_dataloader
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