Removing warnings in the EEG-GNN example. (#5925)

Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>

examples/pytorch/eeg-gcnn/main.py

@@ -7,12 +7,22 @@ import torch.nn as nn
 
 from dgl.dataloading import GraphDataLoader
 from EEGGraphDataset import EEGGraphDataset
-from joblib import load
+from joblib import dump, load
 from sklearn import preprocessing
 from sklearn.metrics import balanced_accuracy_score, roc_auc_score
 from sklearn.model_selection import train_test_split
 from torch.utils.data import WeightedRandomSampler
 
+
+def _load_memory_mapped_array(file_name):
+    # Due to a legacy problem related to memory alignment in joblib [1], the
+    # data provided in the example may not be byte-aligned. This can be risky
+    # when loading with mmap_mode. To fix the issue, load and re-dump the data.
+    # [1] https://joblib.readthedocs.io/en/latest/developing.html#release-1-2-0
+    dump(load(file_name), file_name)
+    return load(file_name, mmap_mode="r")
+
+
 if __name__ == "__main__":
     # argparse commandline args
     parser = argparse.ArgumentParser(
@@ -77,7 +87,7 @@ if __name__ == "__main__":
     print(f" Using device: {_DEVICE} {torch.cuda.get_device_name(_DEVICE)}")
 
     # load patient level indices
-    _DATASET_INDEX = pd.read_csv("master_metadata_index.csv")
+    _DATASET_INDEX = pd.read_csv("master_metadata_index.csv", low_memory=False)
     all_subjects = _DATASET_INDEX["patient_ID"].astype("str").unique()
     print(f"Subject list fetched! Total subjects are {len(all_subjects)}.")
 
@@ -89,10 +99,8 @@ if __name__ == "__main__":
     num_feats = args.num_feats
 
     # set up input and targets from files
-    memmap_x = f"psd_features_data_X"
-    memmap_y = f"labels_y"
-    x = load(memmap_x, mmap_mode="r")
-    y = load(memmap_y, mmap_mode="r")
+    x = _load_memory_mapped_array(f"psd_features_data_X")
+    y = _load_memory_mapped_array(f"labels_y")
 
     # normalize psd features data
     normd_x = []