Cosmetics

README.md

@@ -309,7 +309,7 @@ Few pointers to get you started:
 
 - [Quick Start Guide: Essentials of getting a project up and running](https://pytorch.org/ignite/quickstart.html)
 - [Concepts of the library: Engine, Events & Handlers, State, Metrics](https://pytorch.org/ignite/concepts.html)
-- Full-featured template examples for common tasks (coming soon ...)
+- Full-featured template examples (coming soon ...)
 
 
 <!-- ############################################################################################################### -->

examples/contrib/cifar10/main.py

@@ -196,11 +196,7 @@ def get_dataflow(config):
     )
 
     test_loader = idist.auto_dataloader(
-        test_dataset,
-        batch_size=2 * config["batch_size"],
-        num_workers=config["num_workers"],
-        shuffle=False,
-        pin_memory="cuda" in idist.device().type,
+        test_dataset, batch_size=2 * config["batch_size"], num_workers=config["num_workers"], shuffle=False,
     )
     return train_loader, test_loader
 

examples/contrib/cifar100_amp_benchmark/benchmark_nvidia_apex.py

@@ -40,7 +40,6 @@ def train_step(engine, batch):
         y_pred = model(x)
         loss = criterion(y_pred, y)
 
-        # Runs the forward pass with autocasting.
         with amp.scale_loss(loss, optimizer) as scaled_loss:
             scaled_loss.backward()