Merge pull request #1 from mahsayazdani/gpu-specific

Merge gpu-specific to main

drug/datasets.py

 
 
 class DDInteractionDataset(Dataset):
-    def __init__(self, root = "\\drug/data/", transform=None, pre_transform=None, pre_filter=None):
+    def __init__(self, root = "\\drug/data/", transform=None, pre_transform=None, pre_filter=None, gpu_id=None):
+        self.gpu_id = gpu_id
         super(DDInteractionDataset, self).__init__(os.path.dirname(os.path.abspath(os.path.dirname( __file__ ))) + "/drug/data/", transform, pre_transform, pre_filter)
 
+
     @property
     def num_features(self):
         return self._num_features
 
         # ---------------------------------------------------------------
         data = Data(x = node_features, edge_index = edge_index)
+ 
+        if self.gpu_id is not None:
+            data = data.cuda(self.gpu_id)
 
         if self.pre_filter is not None and not self.pre_filter(data):
             pass
         data = torch.load(osp.join(self.processed_dir, 'ddi_graph_dataset.pt'))
         return data
 
-
-ddiDataset = DDInteractionDataset(root = "drug/data/")
-print(ddiDataset.get().edge_index.t())
+# run for checking
+# ddiDataset = DDInteractionDataset(root = "drug/data/")
+# print(ddiDataset.get().edge_index.t())
 # print(ddiDataset.get().x)
-print(ddiDataset.num_features)
+# print(ddiDataset.num_features)

drug/models.py

 
 # base from this notebook: https://colab.research.google.com/drive/1LJir3T6M6Omc2Vn2GV2cDW_GV2YfI53_?usp=sharing#scrollTo=jNsToorfSgS0
 class GCN(torch.nn.Module):
-    def __init__(self, num_features, hidden_channels, gpu_id=None): # num_features = dataset.num_features
+    def __init__(self, num_features, hidden_channels): # num_features = dataset.num_features
         super(GCN, self).__init__()
         torch.manual_seed(42)
 
         # Initialize the layers
         self.conv1 = GCNConv(num_features, hidden_channels)
         self.conv2 = GCNConv(hidden_channels, num_features)
-        self.gpu_id = gpu_id
 
     def forward(self, x, edge_index):
         # First Message Passing Layer (Transformation)
         x = x.to(torch.float32)
-        if self.gpu_id is not None:
-            x = x.cuda(self.gpu_id)
-            edge_index = edge_index.cuda(self.gpu_id)
         x = self.conv1(x, edge_index)
         x = x.relu()
         x = F.dropout(x, p=0.5, training=self.training)

predictor/cross_validation.py

         drug1_id = drug1_id.cuda(gpu_id)
         drug2_id = drug2_id.cuda(gpu_id)
         cell_feat = cell_feat.cuda(gpu_id)
-        pass
+        y_true = y_true.cuda(gpu_id)
+
     if train:
         y_pred = model(drug1_id, drug2_id, cell_feat)
     else:
                 logging.info("Inner loop completed. Mean valid loss: {:.4f}".format(inner_loss))
                 logging.info("-" * n_delimiter)
                 losses.append(inner_loss)
+
+        torch.cuda.memory_summary(device=None, abbreviated=False)
         gc.collect()
         torch.cuda.empty_cache()
         time.sleep(10)

predictor/model/models.py

 class Connector(nn.Module):
     def __init__(self, gpu_id=None):
         super(Connector, self).__init__()
-
-        self.ddiDataset = DDInteractionDataset()
-        self.gcn = GCN(self.ddiDataset.num_features, self.ddiDataset.num_features // 2, gpu_id)
+        self.ddiDataset = DDInteractionDataset(gpu_id = gpu_id)
+        self.gcn = GCN(self.ddiDataset.num_features, self.ddiDataset.num_features // 2)
         
         #Cell line features
         # np.load('cell_feat.npy')