add QM9_dataset_class.py

Graph/GraphMAE_MQ9/QM9_dataset_class.py

@@ -0,0 +1,51 @@
+import os
+
+from tqdm import tqdm
+import networkx as nx
+import torch
+from torch.utils.data import Dataset
+
+atom_number_index_dict = {
+    1: 0,  # H
+    6: 1,  # C
+    7: 2,  # N
+    8: 3,  # O
+    9: 4  # F
+}
+atom_index_number_dict = {v: k for k, v in atom_number_index_dict.items()}
+max_atom_number = max(atom_number_index_dict.keys())
+
+
+def atom_number2index(atom_number):
+    return atom_number_index_dict[atom_number]
+
+
+def atom_index2number(atom_index):
+    return atom_index_number_dict[atom_index]
+
+
+class PreprocessedQM9Dataset(Dataset):
+    def __init__(self, dataset):
+        self.dataset = dataset
+        self.processed_data = []
+        if dataset is not None:
+            self._preprocess()
+    def _preprocess(self):
+        i = 0
+        for g, label in tqdm(self.dataset):
+            g.ndata["Z_index"] = torch.tensor([atom_number2index(z.item()) for z in g.ndata["Z"]])
+            g.ndata["sample_idx"] = i
+            self.processed_data.append((g, label))
+
+    def __len__(self):
+        return len(self.processed_data)
+
+    def __getitem__(self, idx):
+        return self.processed_data[idx]
+
+    def save_dataset(self, save_dir):
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+        torch.save(self.processed_data, os.path.join(save_dir,"QM9_dataset_processed.pt"))
+    def load_dataset(self, dataset_path):
+        self.processed_data = torch.load(dataset_path)