Detach the tensors on batch-norm eval. (#1702)

* Detach the tensors on batch-norm eval.

* Fix pyo3 bindings.

* Black tweak.

* Formatting.

* Also update the pyo3-onnx formatting.

* Apply black.

2 files changed, 5 insertions, 5 deletions

diff --git a/candle-examples/examples/reinforcement-learning/ddpg.rs b/candle-examples/examples/reinforcement-learning/ddpg.rs
index 1ce4889e..5309eaf6 100644
--- a/candle-examples/examples/reinforcement-learning/ddpg.rs
+++ b/candle-examples/examples/reinforcement-learning/ddpg.rs
@@ -411,7 +411,7 @@ impl DDPG<'_> {
     pub fn actions(&mut self, state: &Tensor) -> Result<f32> {
         let actions = self
             .actor
-            .forward(&state.detach()?.unsqueeze(0)?)?
+            .forward(&state.detach().unsqueeze(0)?)?
             .squeeze(0)?;
         let actions = if self.train {
             (actions + self.ou_noise.sample()?)?
diff --git a/candle-examples/examples/reinforcement-learning/policy_gradient.rs b/candle-examples/examples/reinforcement-learning/policy_gradient.rs
index 044cbfcd..6c355fe6 100644
--- a/candle-examples/examples/reinforcement-learning/policy_gradient.rs
+++ b/candle-examples/examples/reinforcement-learning/policy_gradient.rs
@@ -74,7 +74,7 @@ pub fn run() -> Result<()> {
         loop {
             let action = {
                 let action_probs: Vec<f32> =
-                    softmax(&model.forward(&state.detach()?.unsqueeze(0)?)?, 1)?
+                    softmax(&model.forward(&state.detach().unsqueeze(0)?)?, 1)?
                         .squeeze(0)?
                         .to_vec1()?;
                 weighted_sample(action_probs, &mut rng)? as i64
@@ -109,7 +109,7 @@ pub fn run() -> Result<()> {
 
         let rewards = Tensor::from_vec(accumulate_rewards(&steps), batch_size, &Device::Cpu)?
             .to_dtype(DType::F32)?
-            .detach()?;
+            .detach();
 
         let actions_mask = {
             let actions: Vec<i64> = steps.iter().map(|s| s.action).collect();
@@ -126,12 +126,12 @@ pub fn run() -> Result<()> {
                         .unwrap()
                 })
                 .collect();
-            Tensor::stack(&actions_mask, 0)?.detach()?
+            Tensor::stack(&actions_mask, 0)?.detach()
         };
 
         let states = {
             let states: Vec<Tensor> = steps.into_iter().map(|s| s.state).collect();
-            Tensor::stack(&states, 0)?.detach()?
+            Tensor::stack(&states, 0)?.detach()
         };
 
         let log_probs = actions_mask