Merge pull request #366 from robertknight/slice-copy-inferred-type

Infer layout for `slice_copy` based on layout and range

rten-tensor/src/tensor.rs

@@ -1,4 +1,5 @@
 use std::borrow::Cow;
+use std::fmt::Debug;
 use std::mem::MaybeUninit;
 use std::ops::{Index, IndexMut, Range};
 
@@ -339,27 +340,42 @@ pub trait AsView: Layout {
     ///
     /// This is more expensive than [`slice`](AsView::slice) as it copies the
     /// data, but is more flexible as it supports ranges with negative steps.
-    fn slice_copy<R: Clone + IntoSliceItems>(&self, range: R) -> Tensor<Self::Elem>
+    #[allow(clippy::type_complexity)]
+    fn slice_copy<R: Clone + IntoSliceItems + IndexCount>(
+        &self,
+        range: R,
+    ) -> TensorBase<Vec<Self::Elem>, <Self::Layout as SliceWith<R, R::Count>>::Layout>
     where
         Self::Elem: Clone,
+        Self::Layout: SliceWith<
+            R,
+            R::Count,
+            Layout: for<'a> Layout<Index<'a>: TryFrom<&'a [usize], Error: Debug>>,
+        >,
     {
         self.slice_copy_in(GlobalAlloc::new(), range)
     }
 
     /// Variant of [`slice_copy`](AsView::slice_copy) which takes an allocator.
-    fn slice_copy_in<A: Alloc, R: Clone + IntoSliceItems>(
+    #[allow(clippy::type_complexity)]
+    fn slice_copy_in<A: Alloc, R: Clone + IntoSliceItems + IndexCount>(
         &self,
         pool: A,
         range: R,
-    ) -> Tensor<Self::Elem>
+    ) -> TensorBase<Vec<Self::Elem>, <Self::Layout as SliceWith<R, R::Count>>::Layout>
     where
         Self::Elem: Clone,
+        Self::Layout: SliceWith<
+            R,
+            R::Count,
+            Layout: for<'a> Layout<Index<'a>: TryFrom<&'a [usize], Error: Debug>>,
+        >,
     {
         // Fast path for slice ranges supported by `Tensor::slice`. This includes
         // all ranges except those with a negative step. This benefits from
         // optimizations that `Tensor::to_tensor` has for slices that are already
         // contiguous or have a small number of dims.
-        if let Ok(slice_view) = self.try_slice_dyn(range.clone()) {
+        if let Ok(slice_view) = self.try_slice_with(range.clone()) {
             return slice_view.to_tensor_in(pool);
         }
 
@@ -388,7 +404,9 @@ pub trait AsView: Layout {
             sliced_data.set_len(sliced_len);
         }
 
-        Tensor::from_data(&sliced_shape, sliced_data)
+        let sliced_shape = sliced_shape.as_slice().try_into().expect("slice failed");
+
+        TensorBase::from_data(sliced_shape, sliced_data)
     }
 
     /// Return a view of this tensor with all dimensions of size 1 removed.