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ucf101_test.py
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ucf101_test.py
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import os
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import random
import glob
class UCF(Dataset):
def __init__(self, data_root , ext="png"):
super().__init__()
self.data_root = data_root
self.file_list = sorted(os.listdir(self.data_root))
self.transforms = transforms.Compose([
transforms.CenterCrop((224,224)),
transforms.ToTensor(),
])
def __getitem__(self, idx):
imgpath = os.path.join(self.data_root , self.file_list[idx])
imgpaths = [os.path.join(imgpath , "frame0.png") , os.path.join(imgpath , "frame1.png") ,os.path.join(imgpath , "frame2.png") ,os.path.join(imgpath , "frame3.png") ,os.path.join(imgpath , "framet.png")]
images = [Image.open(img) for img in imgpaths]
images = [self.transforms(img) for img in images]
return images[:-1] , [images[-1]]
def __len__(self):
return len(self.file_list)
def get_loader(data_root, batch_size, shuffle, num_workers, test_mode=True):
dataset = UCF(data_root)
return DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True)
if __name__ == "__main__":
dataset = UCF_triplet("./ucf_test/")
print(len(dataset))
dataloader = DataLoader(dataset , batch_size=1, shuffle=True, num_workers=0)