I am trying to read the images (CIFAR 10 = 50000 training sample) from the local machine and creating the dataset. The problem is that the kernel of Jupyter is killed abruptly. I tried to use the kernal of anaconda by running file_name.py in the terminal but I have the same issue. When I debugged the problem, the kernel was killed sometimes after loading 25000 images and sometimes after 45000 images. I would mention that I resized the images from 3232 to 224224 to fit the input of ResNet-18.
Do you think it is resources-based problem? Moreover, I did not use GPU while loading images to create a dataset because I will use GPU in the training step.
The kernel dies before it finishes this code (inside Dataset Class):
for sub_dir in os.listdir(self.dir_path): files_lst = os.listdir(os.path.join(self.dir_path,sub_dir)) for name in files_lst: directory = os.path.join(self.dir_path,sub_dir,name) image = Image.open(directory) image = transform(image) data_lst.append(image) target_lst.append(sub_dir) self.dataset = torch.stack(data_lst, 0) self.labels = torch.tensor(target_lst,dtype=torch.long)
The specifications of my laptop are: 11th Gen intel core vPro i9, RTX A4000 Laptop GPU, 32Gb RAM.
Thanks for any help.