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dataset.py
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dataset.py
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import os
import torch
import random
import numpy as np
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
# Note: You should write a DataLoader suitable for your own Dataset!!!
class SimpleDataset(Dataset):
def __init__(self, root, mode='train'):
self.root = root
self.mode = mode
self.image_dir = os.path.join(self.root, 'data')
folders = sorted(os.listdir(self.image_dir))
self.image_list = [os.path.join(self.image_dir, file) for file in folders]
def __len__(self):
return len(self.image_list)
def __getitem__(self, index):
image = self.image_list[index]
text = image.split('/')[-1]
prompt = text.replace('_', ' ')[:-4]
image = Image.open(image).convert('RGB')
# image = image.resize((512, 512))
image = image.resize((1024, 1024))
image = transforms.ToTensor()(image)
image = torch.from_numpy(np.ascontiguousarray(image)).float()
if self.mode == 'train':
p = random.uniform(0, 1)
if p < 0.1:
prompt = ''
# normalize
image = image * 2. - 1.
return {"image": image, "prompt": prompt}
if __name__ == '__main__':
train_dataset = SimpleDataset(root="./")
print(train_dataset.__len__())
train_data = DataLoader(train_dataset, batch_size=1, num_workers=1, shuffle=False)
# B C H W
for i, data in enumerate(train_data):
print(i)
print(data['image'].shape)
print(data['prompt'])