Satisfy cargo fmt

coreylowman · Feb 15, 2023 · 42dab6d · 42dab6d
1 parent 33a4329
commit 42dab6d
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Showing 11 changed files with 27 additions and 20 deletions.
diff --git a/examples/01-tensor.rs b/examples/01-tensor.rs
@@ -11,7 +11,6 @@ type Device = dfdx::tensor::Cpu;
 #[cfg(feature = "cuda")]
 type Device = dfdx::tensor::Cuda;
 
-
 fn main() {
     // a device is required to create & modify tensors.
     // we will use the Cpu device here for simplicity

diff --git a/examples/02-ops.rs b/examples/02-ops.rs
@@ -12,7 +12,6 @@ type Device = Cpu;
 #[cfg(feature = "cuda")]
 type Device = dfdx::tensor::Cuda;
 
-
 fn main() {
     let dev = Device::default();
 

diff --git a/examples/03-nn.rs b/examples/03-nn.rs
@@ -12,7 +12,6 @@ type Device = dfdx::tensor::Cpu;
 #[cfg(feature = "cuda")]
 type Device = dfdx::tensor::Cuda;
 
-
 fn main() {
     let dev = Device::default();
 

diff --git a/examples/05-optim.rs b/examples/05-optim.rs
@@ -15,7 +15,6 @@ type Device = dfdx::tensor::Cpu;
 #[cfg(feature = "cuda")]
 type Device = dfdx::tensor::Cuda;
 
-
 // first let's declare our neural network to optimze
 type Mlp = (
     (Linear<5, 32>, ReLU),

diff --git a/examples/07-custom-module.rs b/examples/07-custom-module.rs
@@ -3,8 +3,8 @@
 use dfdx::{
     gradients::Tape,
     nn::{
-        BuildModule,
-        Module, modules::{Linear, ReLU},
+        modules::{Linear, ReLU},
+        BuildModule, Module,
     },
     shapes::{Rank1, Rank2},
     tensor::{HasErr, SampleTensor, Tensor},
@@ -16,7 +16,6 @@ type Device = dfdx::tensor::Cpu;
 #[cfg(feature = "cuda")]
 type Device = dfdx::tensor::Cuda;
 
-
 /// Custom model struct
 /// This case is trivial and should be done with a tuple of linears and relus,
 /// but it demonstrates how to build models with custom behavior
@@ -28,7 +27,7 @@ struct Mlp<const IN: usize, const INNER: usize, const OUT: usize> {
 
 // BuildModule lets you randomize a model's parameters
 impl<const IN: usize, const INNER: usize, const OUT: usize> BuildModule<Device, f32>
-for Mlp<IN, INNER, OUT>
+    for Mlp<IN, INNER, OUT>
 {
     fn try_build(device: &Device) -> Result<Self, <Device as HasErr>::Err> {
         Ok(Self {
@@ -41,7 +40,7 @@ for Mlp<IN, INNER, OUT>
 
 // impl Module for single item
 impl<const IN: usize, const INNER: usize, const OUT: usize> Module<Tensor<Rank1<IN>, f32, Device>>
-for Mlp<IN, INNER, OUT>
+    for Mlp<IN, INNER, OUT>
 {
     type Output = Tensor<Rank1<OUT>, f32, Device>;
 
@@ -53,8 +52,13 @@ for Mlp<IN, INNER, OUT>
 }
 
 // impl Module for batch of items
-impl<const BATCH: usize, const IN: usize, const INNER: usize, const OUT: usize, T: Tape<Device>>
-Module<Tensor<Rank2<BATCH, IN>, f32, Device, T>> for Mlp<IN, INNER, OUT>
+impl<
+        const BATCH: usize,
+        const IN: usize,
+        const INNER: usize,
+        const OUT: usize,
+        T: Tape<Device>,
+    > Module<Tensor<Rank2<BATCH, IN>, f32, Device, T>> for Mlp<IN, INNER, OUT>
 {
     type Output = Tensor<Rank2<BATCH, OUT>, f32, Device, T>;
 

diff --git a/examples/08-tensor-broadcast-reduce.rs b/examples/08-tensor-broadcast-reduce.rs
@@ -13,7 +13,6 @@ type Device = dfdx::tensor::Cpu;
 #[cfg(feature = "cuda")]
 type Device = dfdx::tensor::Cuda;
 
-
 fn main() {
     let dev = Device::default();
     let a = dev.tensor([1.0f32, 2.0, 3.0]);

diff --git a/examples/09-tensor-permute.rs b/examples/09-tensor-permute.rs
@@ -12,7 +12,6 @@ type Device = dfdx::tensor::Cpu;
 #[cfg(feature = "cuda")]
 type Device = dfdx::tensor::Cuda;
 
-
 fn main() {
     let dev = Device::default();
 

diff --git a/examples/10-tensor-index.rs b/examples/10-tensor-index.rs
@@ -12,7 +12,6 @@ type Device = dfdx::tensor::Cpu;
 #[cfg(feature = "cuda")]
 type Device = dfdx::tensor::Cuda;
 
-
 fn main() {
     let dev = Device::default();
 

diff --git a/examples/nightly-resnet18.rs b/examples/nightly-resnet18.rs
@@ -51,7 +51,6 @@ fn main() {
     let dev = Device::default();
     let m = dev.build_module::<Resnet18<1000>, f32>();
 
-
     let x: Tensor<Rank3<3, 224, 224>, f32, _> = dev.sample_normal();
     let start = Instant::now();
     for _ in 0..PROBES {
@@ -64,7 +63,10 @@ fn main() {
     for _ in 0..PROBES {
         let _y = m.forward(x.clone());
     }
-    println!("Average batched (16) forward: {:?}", start.elapsed() / PROBES);
+    println!(
+        "Average batched (16) forward: {:?}",
+        start.elapsed() / PROBES
+    );
 }
 
 #[cfg(not(feature = "nightly"))]

diff --git a/examples/rl-dqn.rs b/examples/rl-dqn.rs
@@ -14,7 +14,6 @@ type Device = Cpu;
 #[cfg(feature = "cuda")]
 type Device = Cuda;
 
-
 const BATCH: usize = 64;
 const STATE: usize = 4;
 const ACTION: usize = 2;
@@ -79,6 +78,11 @@ fn main() {
         }
         target_q_net.clone_from(&q_net);
 
-        println!("Epoch {} in {:?}: q loss={:#.3}", epoch + 1, start.elapsed(), total_loss / 20.0);
+        println!(
+            "Epoch {} in {:?}: q loss={:#.3}",
+            epoch + 1,
+            start.elapsed(),
+            total_loss / 20.0
+        );
     }
 }
diff --git a/examples/rl-ppo.rs b/examples/rl-ppo.rs
@@ -13,7 +13,6 @@ type Device = Cpu;
 #[cfg(feature = "cuda")]
 type Device = Cuda;
 
-
 const BATCH: usize = 64;
 const STATE: usize = 4;
 const ACTION: usize = 2;
@@ -82,6 +81,11 @@ fn main() {
         }
         target_pi_net.clone_from(&pi_net);
 
-        println!("Epoch {} in {:?}: loss={:#.3}", epoch + 1, start.elapsed(), total_loss / 20.0);
+        println!(
+            "Epoch {} in {:?}: loss={:#.3}",
+            epoch + 1,
+            start.elapsed(),
+            total_loss / 20.0
+        );
     }
 }