--- a
+++ b/Test.py
@@ -0,0 +1,58 @@
+from torch_geometric import __version__ as pyg_version
+from torch import __version__ as torch_version
+from torch import device, as_tensor, max
+from torch.cuda import is_available, can_device_access_peer
+from Network import CHD_GNN
+from Utilities import CHD_Dataset
+from pandas import read_csv
+from Graph_Conversion import Convert_To_Image
+from matplotlib import pyplot as plt
+from torch.distributed import init_process_group
+from os import environ
+
+DIRECTORY = '/home/sojo/Documents/ImageCHD/ImageCHD_dataset/'
+
+print(pyg_version)
+print(torch_version)
+print(is_available())
+print(can_device_access_peer(device('cuda:1'), device('cuda:0')))
+
+init_process_group('nccl')
+local_rank = int(environ['LOCAL_RANK'])
+global_rank = int(environ['RANK'])
+batch_size = int(environ['WORLD_SIZE'])
+
+if global_rank == 0:
+    print('PyG version: ', pyg_version)
+    print('Torch version: ', torch_version)
+    print('GPU available: ', is_available())
+    print(batch_size)
+
+# gpu = device('cuda:0')
+# print(gpu)
+# gpu = device('cuda:1')
+# print(gpu)
+# testing = CHD_GNN().to(gpu)
+# metadata = read_csv(filepath_or_buffer = DIRECTORY + 'train_dataset_info.csv')
+# dataset = CHD_Dataset(metadata = metadata, directory = DIRECTORY)
+# sample = dataset.get(76)
+
+# print(sample.x.type())
+# print(sample.edge_index.type())
+# print(sample.y.type())
+
+# print(sample.x[0][0].type())
+# print(sample.edge_index[0][0].type())
+# print(sample.y[0][0].type())
+
+# out = testing(sample.x, sample.edge_index)
+# print(out.shape)
+# print(out.type())
+# _, label = max(out, dim = 1)
+# print(label)
+# print(label.shape)
+# print(label.type())
+
+# result = Convert_To_Image(label, sample.adj_count)
+# plt.imshow(result, cmap = 'gray')
+# plt.show()
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