# DataLoader issues in Google Colab: RuntimeError **URL:** \ **Category:** Uncategorized\ **Created:** [March 6, 2021, 10:58pm UTC](https://discuss.pytorch.org/t/dataloader-issues-in-google-colab-runtimeerror/113931 "2021-03-06T22:58:30Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![Bobby\_Joe\_Hill](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/bobby_joe_hill/32/35479_2.png) [@Bobby\_Joe\_Hill](https://discuss.pytorch.org/u/Bobby_Joe_Hill)\ **Post date:** [March 6, 2021, 10:58pm UTC](https://discuss.pytorch.org/t/dataloader-issues-in-google-colab-runtimeerror/113931/1 "2021-03-06T22:58:30Z")
Hi, I am having issues with using a custom dataset in **Google Colab**. What I am doing is I have images in Google Drive (NIFTI MRIs) which I am transforming to 4x240x240 sample and 240x240 label numpy arrays. Then I save them locally in Google Colab as .npy files. So, there are two folders with over 18000 files each. Then I use the following code for my **dataset** : ```auto class my_dataset(Dataset): def __init__ (self, img_path, seg_path, transform=None, target_transform=None): ... self.file_num = len(os.listdir(img_path)) def __getitem__ (self, idx): img = np.load(os.path.join(self.img_path, "sample"+str(idx+1)+".npy")) seg = np.load(os.path.join(self.seg_path, "segmentation"+str(idx+1)+".npy")) if self.transform: out_img = self.transform(img) if self.target_transform: try: out_seg = self.target_transform(seg) except TypeError: out_seg = self.target_transform(seg.astype("int64")) return out_img, out_seg def __len__ (self): return self.file_num ``` If I just create an instance of the dataset class and return one item ```auto data = BraTS2020_dataset("images", "segmentations", transform=ToTensor(), target_transform=ToTensor()) sample, label = data. __getitem__ (0) ``` it works perfectly fine but if I use the defaut **Dataloader** class ```auto trainloader = DataLoader(data, batch_size=4, shuffle=True, num_workers=2) example = next(iter(trainloader)) ``` it sometimes works but other times I get a **RuntimeError** in one worker process: ```auto RuntimeError: Caught RuntimeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch return self.collate_fn(data) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py", line 83, in default_collate return [default_collate(samples) for samples in transposed] File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py", line 83, in return [default_collate(samples) for samples in transposed] File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py", line 55, in default_collate return torch.stack(batch, 0, out=out) RuntimeError: result type Float can't be cast to the desired output type Short ``` I do not understand where the cast is done. Does this maybe have something to do with how Google Colab handles multiprocessing? ---
**Author:** ![Bobby\_Joe\_Hill](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/bobby_joe_hill/32/35479_2.png) [@Bobby\_Joe\_Hill](https://discuss.pytorch.org/u/Bobby_Joe_Hill)\ **Post date:** [March 12, 2021, 1:57pm UTC](https://discuss.pytorch.org/t/dataloader-issues-in-google-colab-runtimeerror/113931/2 "2021-03-12T13:57:41Z")
I think I found the problem: If I cast the numpy arrays img and seg as int64 directly there is no error occuring anymore. NIFTI images can potentially have float values. torch.stack could not cast the inputs to the right datatype, giving an error. With this code I can reproduce the error: ```auto from torch import Tensor, stack, short, float, ones import numpy as np a = ones([2,2],dtype=short) b = ones([2,2],dtype=float) storage = a.storage()._new_shared(8) out = a.new(storage) stack([b,a], out=out) ``` ```auto --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) in () 6 storage = a.storage()._new_shared(8) 7 out = a.new(storage) ----> 8 stack([b,a], out=out) RuntimeError: result type Float can't be cast to the desired output type Short ``` while the following code gets no error: ```auto from torch import Tensor, stack, short, float, ones import numpy as np a = ones([2,2],dtype=short) b = ones([2,2],dtype=float) stack([b,a]) ``` The problem is the inplace stacking and predefinition to save memory which collate of the dataloader does. So, in conclusion, if you are having similar issues check your datatypes.