Update ndarray to 0.16 and ndarray-rand to 0.15

Cargo.lock

@@ -3519,22 +3519,24 @@ checksum = "308d96db8debc727c3fd9744aac51751243420e46edf401010908da7f8d5e57c"
 
 [[package]]
 name = "ndarray"
-version = "0.15.6"
+version = "0.16.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "adb12d4e967ec485a5f71c6311fe28158e9d6f4bc4a447b474184d0f91a8fa32"
+checksum = "882ed72dce9365842bf196bdeedf5055305f11fc8c03dee7bb0194a6cad34841"
 dependencies = [
  "matrixmultiply",
  "num-complex",
  "num-integer",
  "num-traits",
+ "portable-atomic",
+ "portable-atomic-util",
  "rawpointer",
 ]
 
 [[package]]
 name = "ndarray-rand"
-version = "0.14.0"
+version = "0.15.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "65608f937acc725f5b164dcf40f4f0bc5d67dc268ab8a649d3002606718c4588"
+checksum = "f093b3db6fd194718dcdeea6bd8c829417deae904e3fcc7732dabcd4416d25d8"
 dependencies = [
  "ndarray",
  "rand",
@@ -4338,9 +4340,18 @@ checksum = "22686f4785f02a4fcc856d3b3bb19bf6c8160d103f7a99cc258bddd0251dc7f2"
 
 [[package]]
 name = "portable-atomic"
-version = "1.5.1"
+version = "1.7.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265"
+
+[[package]]
+name = "portable-atomic-util"
+version = "0.2.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3bccab0e7fd7cc19f820a1c8c91720af652d0c88dc9664dd72aef2614f04af3b"
+checksum = "fcdd8420072e66d54a407b3316991fe946ce3ab1083a7f575b2463866624704d"
+dependencies = [
+ "portable-atomic",
+]
 
 [[package]]
 name = "powerfmt"

Cargo.toml

@@ -198,8 +198,8 @@ mime_guess2 = "2.0" # infer MIME type by file extension, and map mime to file ex
 mint = "0.5.9"
 mp4 = "0.14.0"
 natord = "1.0.9"
-ndarray = "0.15"
-ndarray-rand = "0.14"
+ndarray = "0.16"
+ndarray-rand = "0.15"
 never = "0.1"
 nohash-hasher = "0.2"
 notify = "6.0"

crates/store/re_types/src/datatypes/tensor_data_ext.rs

@@ -8,9 +8,6 @@ use crate::archetypes::EncodedImage;
 
 use super::{TensorBuffer, TensorData, TensorDimension};
 
-// Much of the following duplicates code from: `crates/re_components/src/tensor.rs`, which
-// will eventually go away as the Tensor migration is completed.
-
 // ----------------------------------------------------------------------------
 
 impl TensorData {
@@ -169,7 +166,6 @@ macro_rules! tensor_from_ndarray {
             type Error = TensorCastError;
 
             fn try_from(value: ndarray::Array<$type, D>) -> Result<Self, Self::Error> {
-                let value = value.as_standard_layout();
                 let shape = value
                     .shape()
                     .iter()
@@ -178,13 +174,26 @@ macro_rules! tensor_from_ndarray {
                         name: None,
                     })
                     .collect();
-                value
-                    .is_standard_layout()
-                    .then(|| TensorData {
-                        shape,
-                        buffer: TensorBuffer::$variant(value.to_owned().into_raw_vec().into()),
-                    })
-                    .ok_or(TensorCastError::NotContiguousStdOrder)
+
+                let vec = if value.is_standard_layout() {
+                    let (mut vec, offset) = value.into_raw_vec_and_offset();
+                    // into_raw_vec_and_offset() guarantees that the logical element order (.iter()) matches the internal
+                    // storage order in the returned vector if the array is in standard layout.
+                    if let Some(offset) = offset {
+                        vec.drain(..offset);
+                        vec
+                    } else {
+                        debug_assert!(vec.is_empty());
+                        vec
+                    }
+                } else {
+                    value.into_iter().collect::<Vec<_>>()
+                };
+
+                Ok(Self {
+                    shape,
+                    buffer: TensorBuffer::$variant(vec.into()),
+                })
             }
         }
 
@@ -311,13 +320,18 @@ impl<D: ::ndarray::Dimension> TryFrom<::ndarray::Array<half::f16, D>> for Tensor
             })
             .collect();
         if value.is_standard_layout() {
+            let (vec, offset) = value.into_raw_vec_and_offset();
+            // into_raw_vec_and_offset() guarantees that the logical element order (.iter()) matches the internal
+            // storage order in the returned vector if the array is in standard layout.
+            let vec_slice = if let Some(offset) = offset {
+                &vec[offset..]
+            } else {
+                debug_assert!(vec.is_empty());
+                &vec
+            };
             Ok(Self {
                 shape,
-                buffer: TensorBuffer::F16(
-                    bytemuck::cast_slice(value.into_raw_vec().as_slice())
-                        .to_vec()
-                        .into(),
-                ),
+                buffer: TensorBuffer::F16(Vec::from(bytemuck::cast_slice(vec_slice)).into()),
             })
         } else {
             Ok(Self {

crates/store/re_types/tests/types/tensor.rs

@@ -106,7 +106,7 @@ fn convert_tensor_to_ndarray_f32() {
 }
 
 #[test]
-fn convert_ndarray_u8_to_tensor() {
+fn convert_ndarray_f64_to_tensor() {
     let n = ndarray::array![[1., 2., 3.], [4., 5., 6.]];
     let t = TensorData::try_from(n).unwrap();
 
@@ -125,6 +125,89 @@ fn convert_ndarray_slice_to_tensor() {
     assert_eq!(t.shape(), &[TensorDimension::unnamed(2)]);
 }
 
+#[test]
+fn convert_ndarray_to_tensor_both_layouts() {
+    #[rustfmt::skip]
+    let row_major_vec = vec![
+        1, 2, 3,
+        4, 5, 6,
+        7, 8, 9
+    ];
+    #[rustfmt::skip]
+    let col_major_vec = vec![
+        1, 4, 7,
+        2, 5, 8,
+        3, 6, 9
+    ];
+
+    let shape = ndarray::Ix2(3, 3);
+
+    let row_major = ndarray::Array::from_vec(row_major_vec)
+        .into_shape_with_order((shape, ndarray::Order::RowMajor))
+        .unwrap();
+
+    let col_major = ndarray::Array::from_vec(col_major_vec)
+        .into_shape_with_order((shape, ndarray::Order::ColumnMajor))
+        .unwrap();
+
+    // make sure that the offset is in fact negative, in case ndarray behavior changes
+    let rm = row_major.clone();
+    let cm = col_major.clone();
+    let (_, rm_offset) = rm.into_raw_vec_and_offset();
+    let (_, cm_offset) = cm.into_raw_vec_and_offset();
+    assert_eq!(rm_offset.unwrap(), 0);
+    assert_eq!(cm_offset.unwrap(), 0);
+
+    let tensor_row_major = TensorData::try_from(row_major).unwrap();
+    let tensor_col_major = TensorData::try_from(col_major).unwrap();
+
+    assert_eq!(tensor_row_major, tensor_col_major);
+}
+
+#[test]
+fn convert_ndarray_to_tensor_both_layouts_nonzero_offset() {
+    #[rustfmt::skip]
+    let row_major_vec = vec![
+        1, 2, 3,
+        4, 5, 6,
+        7, 8, 9
+    ];
+    #[rustfmt::skip]
+    let col_major_vec = vec![
+        1, 4, 7,
+        2, 5, 8,
+        3, 6, 9
+    ];
+
+    let shape = ndarray::Ix2(3, 3);
+
+    let row_major = ndarray::Array::from_vec(row_major_vec)
+        .into_shape_with_order((shape, ndarray::Order::RowMajor))
+        .unwrap();
+    let row_major_nonzero_offset = row_major.slice_move(ndarray::s![1.., ..]);
+
+    let col_major = ndarray::Array::from_vec(col_major_vec)
+        .into_shape_with_order((shape, ndarray::Order::ColumnMajor))
+        .unwrap();
+    let col_major_nonzero_offset = col_major.slice_move(ndarray::s![1.., ..]);
+
+    // make sure that the offset is in fact negative, in case ndarray behavior changes
+    let rmno = row_major_nonzero_offset.clone();
+    let cmno = col_major_nonzero_offset.clone();
+    let (_, rm_offset) = rmno.into_raw_vec_and_offset();
+    let (_, cm_offset) = cmno.into_raw_vec_and_offset();
+    assert!(rm_offset.unwrap() > 0);
+    assert!(cm_offset.unwrap() > 0);
+
+    let tensor_row_major_nonzero_offset = TensorData::try_from(row_major_nonzero_offset).unwrap();
+    let tensor_col_major_nonzero_offset = TensorData::try_from(col_major_nonzero_offset).unwrap();
+
+    assert_eq!(
+        tensor_row_major_nonzero_offset,
+        tensor_col_major_nonzero_offset
+    );
+}
+
 #[test]
 fn check_slices() {
     let t = TensorData::new(

crates/viewer/re_space_view_tensor/src/space_view_class.rs

@@ -424,7 +424,7 @@ pub fn selected_tensor_slice<'a, T: Copy>(
         // This is important for above width/height conversion to work since this assumes at least 2 dimensions.
         tensor
             .view()
-            .into_shape(ndarray::IxDyn(&[tensor.len(), 1]))
+            .into_shape_with_order(ndarray::IxDyn(&[tensor.len(), 1]))
             .unwrap()
     } else {
         tensor.view()

docs/snippets/all/archetypes/image_send_columns.rs

@@ -31,7 +31,7 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
 
     // Split up the image data into several components referencing the underlying data.
     let image_size_in_bytes = width * height * 3;
-    let blob = rerun::datatypes::Blob::from(images.into_raw_vec());
+    let blob = rerun::datatypes::Blob::from(images.into_raw_vec_and_offset().0);
     let image_column = times
         .iter()
         .map(|&t| {