Helper function to build 3d arrays.

src/device.rs

@@ -61,6 +61,25 @@ impl<S: crate::WithDType, const N: usize, const M: usize> NdArray for &[[S; N];
     }
 }
 
+impl<S: crate::WithDType, const N1: usize, const N2: usize, const N3: usize> NdArray
+    for &[[[S; N3]; N2]; N1]
+{
+    fn shape(&self) -> Result<Shape> {
+        Ok(Shape::from((N1, N2, N3)))
+    }
+
+    fn to_cpu_storage(&self) -> CpuStorage {
+        let mut vec = Vec::new();
+        vec.reserve(N1 * N2 * N3);
+        for i1 in 0..N1 {
+            for i2 in 0..N2 {
+                vec.extend(self[i1][i2])
+            }
+        }
+        S::to_cpu_storage_owned(vec)
+    }
+}
+
 impl Device {
     pub fn new_cuda(ordinal: usize) -> Result<Self> {
         Ok(Self::Cuda(crate::CudaDevice::new(ordinal)?))

tests/tensor_tests.rs

@@ -72,9 +72,8 @@ fn tensor_2d_transpose() -> Result<()> {
 
 #[test]
 fn softmax() -> Result<()> {
-    let data = &[3f32, 1., 4., 1., 5., 9., 2., 1., 7., 8., 2., 8.];
+    let data = &[[[3f32, 1., 4.], [1., 5., 9.]], [[2., 1., 7.], [8., 2., 8.]]];
     let tensor = Tensor::new(data, &Device::Cpu)?;
-    let tensor = tensor.reshape((2, 2, 3))?;
     let t0 = tensor.log()?.softmax(0)?;
     let t1 = tensor.log()?.softmax(1)?;
     let t2 = tensor.log()?.softmax(2)?;