Use torch.accelerator API in Siamese Network example (#1337)

Author: eromomon

siamese_network/README.md

@@ -1,7 +1,37 @@
 # Siamese Network Example
+Siamese network for image similarity estimation.
+The network is composed of two identical networks, one for each input.
+The output of each network is concatenated and passed to a linear layer.
+The output of the linear layer passed through a sigmoid function.
+[FaceNet](https://arxiv.org/pdf/1503.03832.pdf) is a variant of the Siamese network.
+This implementation varies from FaceNet as we use the `ResNet-18` model from
+[Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) as our feature extractor.
+In addition, we aren't using `TripletLoss` as the MNIST dataset is simple, so `BCELoss` can do the trick.
 
 ```bash
 pip install -r requirements.txt
 python main.py
 # CUDA_VISIBLE_DEVICES=2 python main.py # to specify GPU id to ex. 2
 ```
+Optionally, you can add the following arguments to customize your execution.
+
+```bash
+--batch-size input batch size for training (default: 64)
+--test-batch-size input batch size for testing (default: 1000)
+--epochs number of epochs to train (default: 14)
+--lr learning rate (default: 1.0)
+--gamma learning rate step gamma (default: 0.7)
+--accel use accelerator
+--dry-run quickly check a single pass
+--seed random seed (default: 1)
+--log-interval how many batches to wait before logging training status
+--save-model Saving the current Model
+```
+
+To execute in an GPU, add the --accel argument to the command. For example:
+
+```bash
+python main.py --accel
+```
+
+This command will execute the example on the detected GPU.

siamese_network/main.py

@@ -105,7 +105,7 @@ def group_examples(self):
  """
 
  # get the targets from MNIST dataset
- np_arr = np.array(self.dataset.targets.clone())
+ np_arr = np.array(self.dataset.targets.clone(), dtype=None, copy=None)
 
  # group examples based on class
  self.grouped_examples = {}
@@ -247,10 +247,8 @@ def main():
  help='learning rate (default: 1.0)')
  parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
  help='Learning rate step gamma (default: 0.7)')
- 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('--accel', action='store_true', 
+ help='use accelerator')
  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',
@@ -260,22 +258,25 @@ def main():
  parser.add_argument('--save-model', action='store_true', default=False,
  help='For Saving the current Model')
  args = parser.parse_args()
- 
- 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")
+ if args.accel and not torch.accelerator.is_available():
+ print("ERROR: accelerator is not available, try running on CPU")
+ sys.exit(1)
+ if not args.accel and torch.accelerator.is_available():
+ print("WARNING: accelerator is available, run with --accel to enable it")
+
+ if args.accel:
+ device = torch.accelerator.current_accelerator()
  else:
  device = torch.device("cpu")
+ 
+ print(f"Using device: {device}")
 
  train_kwargs = {'batch_size': args.batch_size}
  test_kwargs = {'batch_size': args.test_batch_size}
- if use_cuda:
+ if device=="cuda":
  cuda_kwargs = {'num_workers': 1,
  'pin_memory': True,
  'shuffle': True}

siamese_network/requirements.txt

@@ -1,2 +1,2 @@
 torch
-torchvision==0.20.0
+torchvision