test added

docs/source/index.rst

@@ -174,6 +174,6 @@ experiment with PyTorch.
     Graph Convolutional Network
     ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-    This example implements the `Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/pdf/1609.02907.pdf>` __ paper on the `CORA <https://en.wikipedia.org/wiki/MNIST_database>`__ database.
+    This example implements the `Semi-Supervised Classification with Graph Convolutional Networks <https://arxiv.org/pdf/1609.02907.pdf>`__ paper on the CORA database.
 
-    `GO TO EXAMPLE <https://github.com/pytorch/examples/tree/main/graph_conv_network>`__ :opticon:`link-external` 
+    `GO TO EXAMPLE <https://github.com/pytorch/examples/tree/main/gcn>`__ :opticon:`link-external` 

gcn/main.py

@@ -213,13 +213,29 @@ def test(model, criterion, input, target, mask):
                         help='dimension of the hidden representation (default: 16)')
     parser.add_argument('--val-every', type=int, default=20,
                         help='epochs to wait for print training and validation evaluation (default: 20)')
-    parser.add_argument('--include-bias', type=bool, default=False,
+    parser.add_argument('--include-bias', action='store_true', default=False,
                         help='use bias term in convolutions (default: False)')
-    parser.add_argument('--no-cuda', type=bool, default=False,
-                        help='disable CUDA training (default: False)')
+    parser.add_argument('--no-cuda', action='store_true', default=False,
+                        help='disables CUDA training')
+    parser.add_argument('--no-mps', action='store_true', default=False,
+                        help='disables macOS GPU training')
+    parser.add_argument('--dry-run', action='store_true', default=False,
+                        help='quickly check a single pass')
+    parser.add_argument('--seed', type=int, default=1, metavar='S',
+                        help='random seed (default: 1)')
     args = parser.parse_args()
 
-    device = 'cpu' if args.no_cuda else device
+    use_cuda = not args.no_cuda and torch.cuda.is_available()
+    use_mps = not args.no_mps and torch.backends.mps.is_available()
+
+    torch.manual_seed(args.seed)
+
+    if use_cuda:
+        device = torch.device('cuda')
+    elif use_mps:
+        device = torch.device('mps')
+    else:
+        device = torch.device('cpu')
     print(f'Using {device} device')
 
     cora_url = 'https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz'
@@ -239,6 +255,8 @@ def test(model, criterion, input, target, mask):
 
     for epoch in range(args.epochs):
         train_iter(epoch + 1, gcn, optimizer, criterion, (features, adj_mat), labels, idx_train, idx_val, args.val_every)
+        if args.dry_run:
+            break
 
     loss_test, acc_test = test(gcn, criterion, (features, adj_mat), labels, idx_test)
     print(f'Test set results: loss {loss_test:.4f} accuracy {acc_test:.4f}')

gcn/requirements.txt

@@ -1,3 +1,4 @@
-numpy
-requests
 torch
+torchvision
+requests
+numpy

run_python_examples.sh

@@ -172,6 +172,11 @@ function word_language_model() {
   python main.py --epochs 1 --dry-run $CUDA_FLAG --mps || error "word_language_model failed"
 }
 
+function gcn() {
+  start
+  python main.py --epochs 1 --dry-run || error "graph convolutional network failed"
+}
+
 function clean() {
   cd $BASE_DIR
   echo "running clean to remove cruft"
@@ -192,7 +197,8 @@ function clean() {
     super_resolution/model_epoch_1.pth \
     time_sequence_prediction/predict*.pdf \
     time_sequence_prediction/traindata.pt \
-    word_language_model/model.pt || error "couldn't clean up some files"
+    word_language_model/model.pt \
+    gcn/cora/ || error "couldn't clean up some files"
 
   git checkout fast_neural_style/images/output-images/amber-candy.jpg || error "couldn't clean up fast neural style image"
 }
@@ -217,6 +223,7 @@ function run_all() {
   vision_transformer
   word_language_model
   fx
+  gcn
 }
 
 # by default, run all examples