Add CI for Siamese Network Example

.github/workflows/unit-tests.yml

@@ -101,7 +101,7 @@ jobs:
 
       - name: Run Mypy
         # https://github.com/pytorch/ignite/pull/2780
-        # 
+        #
         if: ${{ matrix.os == 'ubuntu-latest' && matrix.pytorch-channel == 'pytorch-nightly'}}
         run: |
           bash ./tests/run_code_style.sh mypy
@@ -189,3 +189,8 @@ jobs:
         run: |
           # Super-Resolution
           python examples/super_resolution/main.py --upscale_factor 3 --crop_size 180 --batch_size 4 --test_batch_size 100 --n_epochs 1 --lr 0.001 --threads 2 --debug
+      - name: Run Siamese Network Example
+        if: ${{ matrix.os == 'ubuntu-latest' }}
+        run: |
+          # Siamese Network
+          python examples/siamese_network/siamese_network.py --batch-size 256 --test-batch-size 256 --epochs 4 --lr 0.95 --gamma 0.97 --num-workers 5

examples/siamese_network/README.md

@@ -1,10 +1,33 @@
-# Siamese Network example on MNIST dataset
+# Siamese Network example on CIFAR10 dataset
 
-This example is ported over from [pytorch/examples/siamese_network](https://github.com/pytorch/examples/tree/main/siamese_network)
+This example is inspired from [pytorch/examples/siamese_network](https://github.com/pytorch/examples/tree/main/siamese_network). It illustrates the implementation of Siamese Network for checking image similarity in CIFAR10 dataset.
 
-Usage:
+## Usage:
 
 ```
 pip install -r requirements.txt
-python siamese_network.py
+python siamese_network.py [-h] [--batch-size BATCHSIZE] [--test-batch-size TESTBATCHSIZE] [--epochs EPOCHS]
+                          [--lr LEARNINGRATE] [--gamma GAMMA] [--no-cuda][--no-mps] [--dry-run]
+                          [--seed SEED] [--log-interval LOGINTERVAL] [--save-model] [--num-workers NUMWORKERS]
+
+optional arguments:
+    -h, --help          shows usage and exits
+    --batch-size        sets training batch size
+    --test-batch-size   sets testing batch size
+    --epochs            sets number of training epochs
+    --lr                sets learning rate
+    --gamma             sets gamma parameter for LR Scheduler
+    --no-cuda           disables CUDA training
+    --no-mps            disables macOS GPU training
+    --dry-run           runs model over a single pass
+    --seed              sets random seed
+    --log-interval      sets number of epochs before logging results
+    --save-model        saves current model
+    --num-workers       sets number of processes generating parallel batches
+```
+
+## Example Usage:
+
+```
+python siamese_network.py --batch-size 64 --test-batch-size 256 --epochs 14 --lr 0.95 --gamma 0.97 --num-workers 5
 ```

examples/siamese_network/siamese_network.py

@@ -260,15 +260,15 @@ def main():
     # adds training defaults and support for terminal arguments
     parser = argparse.ArgumentParser(description="PyTorch Siamese network Example")
     parser.add_argument(
-        "--batch-size", type=int, default=256, metavar="N", help="input batch size for training (default: 64)"
+        "--batch-size", type=int, default=256, metavar="N", help="input batch size for training (default: 256)"
     )
     parser.add_argument(
-        "--test-batch-size", type=int, default=256, metavar="N", help="input batch size for testing (default: 1000)"
+        "--test-batch-size", type=int, default=256, metavar="N", help="input batch size for testing (default: 256)"
     )
-    parser.add_argument("--epochs", type=int, default=10, metavar="N", help="number of epochs to train (default: 14)")
+    parser.add_argument("--epochs", type=int, default=10, metavar="N", help="number of epochs to train (default: 10)")
     parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)")
     parser.add_argument(
-        "--gamma", type=float, default=0.95, metavar="M", help="Learning rate step gamma (default: 0.7)"
+        "--gamma", type=float, default=0.95, metavar="M", help="Learning rate step gamma (default: 0.95)"
     )
     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")
@@ -281,15 +281,23 @@ def main():
         metavar="N",
         help="how many batches to wait before logging training status",
     )
-    parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model")
-    parser.add_argument("--num-workers", default=4, help="number of processes generating parallel batches")
+    parser.add_argument("--save-model", action="store_true", default=False, help="saves model parameters")
+    parser.add_argument("--num-workers", type=int, default=4, help="number of parallel batches (default: 4)")
     args = parser.parse_args()
 
     # set manual seed
     manual_seed(args.seed)
 
     # set device
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    use_cuda = not args.no_cuda and torch.cuda.is_available()
+    use_mps = not args.no_mps and torch.backends.mps.is_available()
+
+    if use_cuda:
+        device = torch.device("cuda")
+    elif use_mps:
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
 
     # data loading
     train_dataset = MatcherDataset("../data", train=True, download=True)