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dataset.py
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dataset.py
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#!/usr/bin/env python3
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import transforms
from PIL import Image
class MyDataset(Dataset):
def __init__(self, image_paths, transform=None):
self.image_paths = image_paths
self.transform = transform
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
img_path = self.image_paths[idx]
img = Image.open(img_path).convert("RGB")
if self.transform is not None:
img = self.transform(img)
return img, img_path
class MyDataLoader(DataLoader):
def __init__(self, image_paths, batch_size, shuffle=False, num_workers=0, width=224, height=224):
self.image_paths = image_paths
self.batch_size = batch_size
self.shuffle = shuffle
self.num_workers = num_workers
self.transform = transforms.Compose([
transforms.Resize((width, height)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
self.dataset = MyDataset(self.image_paths, self.transform)
super().__init__(self.dataset, batch_size=self.batch_size, shuffle=self.shuffle, num_workers=self.num_workers)