Show tensors shaped [H, W, 1, 1] as images (and more!)

crates/re_data_ui/src/image.rs

@@ -540,17 +540,17 @@ fn tensor_pixel_value_ui(
         }
     });
 
-    let text = match tensor.num_dim() {
-        2 => tensor.get(&[y, x]).map(|v| format!("Val: {v}")),
-        3 => match tensor.shape()[2].size {
-            0 => Some("Cannot preview 0-size channel".to_owned()),
-            1 => tensor.get(&[y, x, 0]).map(|v| format!("Val: {v}")),
+    let text = if let Some([_, _, channel]) = tensor.image_height_width_channels() {
+        match channel {
+            1 => tensor
+                .get_with_image_coords(x, y, 0)
+                .map(|v| format!("Val: {v}")),
             3 => {
                 // TODO(jleibs): Track RGB ordering somehow -- don't just assume it
                 if let (Some(r), Some(g), Some(b)) = (
-                    tensor.get(&[y, x, 0]),
-                    tensor.get(&[y, x, 1]),
-                    tensor.get(&[y, x, 2]),
+                    tensor.get_with_image_coords(x, y, 0),
+                    tensor.get_with_image_coords(x, y, 1),
+                    tensor.get_with_image_coords(x, y, 2),
                 ) {
                     match (r, g, b) {
                         (TensorElement::U8(r), TensorElement::U8(g), TensorElement::U8(b)) => {
@@ -565,10 +565,10 @@ fn tensor_pixel_value_ui(
             4 => {
                 // TODO(jleibs): Track RGB ordering somehow -- don't just assume it
                 if let (Some(r), Some(g), Some(b), Some(a)) = (
-                    tensor.get(&[y, x, 0]),
-                    tensor.get(&[y, x, 1]),
-                    tensor.get(&[y, x, 2]),
-                    tensor.get(&[y, x, 3]),
+                    tensor.get_with_image_coords(x, y, 0),
+                    tensor.get_with_image_coords(x, y, 1),
+                    tensor.get_with_image_coords(x, y, 2),
+                    tensor.get_with_image_coords(x, y, 3),
                 ) {
                     match (r, g, b, a) {
                         (
@@ -585,9 +585,13 @@ fn tensor_pixel_value_ui(
                     None
                 }
             }
-            channels => Some(format!("Cannot preview {channels}-channel image")),
-        },
-        dims => Some(format!("Cannot preview {dims}-dimensional image")),
+            channel => Some(format!("Cannot preview {channel}-size channel image")),
+        }
+    } else {
+        Some(format!(
+            "Cannot preview tensors with a shape of {:?}",
+            tensor.shape()
+        ))
     };
 
     if let Some(text) = text {

crates/re_log_types/src/component_types/tensor.rs

@@ -399,50 +399,118 @@ impl Tensor {
         self.shape.as_slice()
     }
 
+    /// Returns the shape of the tensor with all trailing dimensions of size 1 ignored.
+    ///
+    /// If all dimension sizes are one, this returns only the first dimension.
+    #[inline]
+    pub fn shape_short(&self) -> &[TensorDimension] {
+        if self.shape.is_empty() {
+            &self.shape
+        } else {
+            self.shape
+                .iter()
+                .enumerate()
+                .rev()
+                .find(|(_, dim)| dim.size != 1)
+                .map_or(&self.shape[0..1], |(i, _)| &self.shape[..(i + 1)])
+        }
+    }
+
     #[inline]
     pub fn num_dim(&self) -> usize {
         self.shape.len()
     }
 
-    /// If this tensor is shaped as an image, return the height, width, and channels/depth of it.
+    /// If the tensor can be interpreted as an image, return the height, width, and channels/depth of it.
     pub fn image_height_width_channels(&self) -> Option<[u64; 3]> {
-        if self.shape.len() == 2 {
-            Some([self.shape[0].size, self.shape[1].size, 1])
-        } else if self.shape.len() == 3 {
-            let channels = self.shape[2].size;
-            // gray, rgb, rgba
-            if matches!(channels, 1 | 3 | 4) {
-                Some([self.shape[0].size, self.shape[1].size, channels])
-            } else {
-                None
+        let shape_short = self.shape_short();
+
+        match shape_short.len() {
+            1 => {
+                // Special case: Nx1(x1x1x...) tensors are treated as Nx1 grey images.
+                if self.shape.len() >= 2 {
+                    Some([shape_short[0].size, 1, 1])
+                } else {
+                    None
+                }
             }
-        } else {
-            None
+            2 => Some([shape_short[0].size, shape_short[1].size, 1]),
+            3 => {
+                let channels = shape_short[2].size;
+                if matches!(channels, 3 | 4) {
+                    // rgb, rgba
+                    Some([shape_short[0].size, shape_short[1].size, channels])
+                } else {
+                    None
+                }
+            }
+            _ => None,
         }
     }
 
+    /// Returns true if the tensor can be interpreted as an image.
     pub fn is_shaped_like_an_image(&self) -> bool {
-        self.num_dim() == 2
-            || self.num_dim() == 3 && {
-                matches!(
-                    self.shape.last().unwrap().size,
-                    // gray, rgb, rgba
-                    1 | 3 | 4
-                )
-            }
+        self.image_height_width_channels().is_some()
     }
 
+    /// Returns true if either all dimensions have size 1 or only a single dimension has a size larger than 1.
+    ///
+    /// Empty tensors return false.
     #[inline]
     pub fn is_vector(&self) -> bool {
-        let shape = &self.shape;
-        shape.len() == 1 || { shape.len() == 2 && (shape[0].size == 1 || shape[1].size == 1) }
+        if self.shape.is_empty() {
+            false
+        } else {
+            self.shape.iter().filter(|dim| dim.size > 1).count() <= 1
+        }
     }
 
     #[inline]
     pub fn meaning(&self) -> TensorDataMeaning {
         self.meaning
     }
 
+    /// Query with x, y, channel indices.
+    ///
+    /// Allows to query values for any image like tensor even if it has more or less dimensions than 3.
+    /// (useful for sampling e.g. `N x M x C x 1` tensor which is a valid image)
+    #[inline]
+    pub fn get_with_image_coords(&self, x: u64, y: u64, channel: u64) -> Option<TensorElement> {
+        match self.shape.len() {
+            1 => {
+                if y == 0 && channel == 0 {
+                    self.get(&[x])
+                } else {
+                    None
+                }
+            }
+            2 => {
+                if channel == 0 {
+                    self.get(&[y, x])
+                } else {
+                    None
+                }
+            }
+            3 => self.get(&[y, x, channel]),
+            4 => {
+                // Optimization for common case, next case handles this too.
+                if self.shape[3].size == 1 {
+                    self.get(&[y, x, channel, 0])
+                } else {
+                    None
+                }
+            }
+            dim => self.image_height_width_channels().and_then(|_| {
+                self.get(
+                    &[x, y, channel]
+                        .into_iter()
+                        .chain(std::iter::repeat(0).take(dim - 3))
+                        .collect::<Vec<u64>>(),
+                )
+            }),
+        }
+    }
+
     pub fn get(&self, index: &[u64]) -> Option<TensorElement> {
         let mut stride: usize = 1;
         let mut offset: usize = 0;
@@ -1080,3 +1148,119 @@ fn test_arrow() {
     let tensors_out: Vec<Tensor> = TryIntoCollection::try_into_collection(array).unwrap();
     assert_eq!(tensors_in, tensors_out);
 }
+
+#[test]
+fn test_tensor_shape_utilities() {
+    fn generate_tensor_from_shape(sizes: &[u64]) -> Tensor {
+        let shape = sizes
+            .iter()
+            .map(|&size| TensorDimension { size, name: None })
+            .collect();
+        let num_elements = sizes.iter().fold(0, |acc, &size| acc * size);
+        let data = (0..num_elements).map(|i| i as u32).collect::<Vec<_>>();
+
+        Tensor {
+            tensor_id: TensorId(std::default::Default::default()),
+            shape,
+            data: TensorData::U32(data.into()),
+            meaning: TensorDataMeaning::Unknown,
+            meter: None,
+        }
+    }
+
+    // Empty tensor.
+    {
+        let tensor = generate_tensor_from_shape(&[]);
+
+        assert_eq!(tensor.image_height_width_channels(), None);
+        assert_eq!(tensor.shape_short(), tensor.shape());
+        assert!(!tensor.is_vector());
+        assert!(!tensor.is_shaped_like_an_image());
+    }
+
+    // Single dimension tensors.
+    for shape in [vec![4], vec![1]] {
+        let tensor = generate_tensor_from_shape(&shape);
+
+        assert_eq!(tensor.image_height_width_channels(), None);
+        assert_eq!(tensor.shape_short(), &tensor.shape()[0..1]);
+        assert!(tensor.is_vector());
+        assert!(!tensor.is_shaped_like_an_image());
+    }
+
+    // Single element, but it might be interpreted as a 1x1 grey image!
+    for shape in [
+        vec![1, 1],
+        vec![1, 1, 1],
+        vec![1, 1, 1, 1],
+        vec![1, 1, 1, 1, 1],
+    ] {
+        let tensor = generate_tensor_from_shape(&shape);
+
+        assert_eq!(tensor.image_height_width_channels(), Some([1, 1, 1]));
+        assert_eq!(tensor.shape_short(), &tensor.shape()[0..1]);
+        assert!(tensor.is_vector());
+        assert!(tensor.is_shaped_like_an_image());
+    }
+    // Color/Grey 2x4 images
+    for shape in [
+        vec![4, 2],
+        vec![4, 2, 1],
+        vec![4, 2, 1, 1],
+        vec![4, 2, 3],
+        vec![4, 2, 3, 1, 1],
+        vec![4, 2, 4],
+        vec![4, 2, 4, 1, 1, 1, 1],
+    ] {
+        let tensor = generate_tensor_from_shape(&shape);
+        let channels = shape.get(2).cloned().unwrap_or(1);
+
+        assert_eq!(tensor.image_height_width_channels(), Some([4, 2, channels]));
+        assert_eq!(
+            tensor.shape_short(),
+            &tensor.shape()[0..(2 + (channels != 1) as usize)]
+        );
+        assert!(!tensor.is_vector());
+        assert!(tensor.is_shaped_like_an_image());
+    }
+
+    // Grey 1x4 images
+    for shape in [
+        vec![4, 1],
+        vec![4, 1, 1],
+        vec![4, 1, 1, 1],
+        vec![4, 1, 1, 1, 1],
+    ] {
+        let tensor = generate_tensor_from_shape(&shape);
+
+        assert_eq!(tensor.image_height_width_channels(), Some([4, 1, 1]));
+        assert_eq!(tensor.shape_short(), &tensor.shape()[0..1]);
+        assert!(tensor.is_vector());
+        assert!(tensor.is_shaped_like_an_image());
+    }
+
+    // Grey 4x1 images
+    for shape in [
+        vec![1, 4],
+        vec![1, 4, 1],
+        vec![1, 4, 1, 1],
+        vec![1, 4, 1, 1, 1],
+    ] {
+        let tensor = generate_tensor_from_shape(&shape);
+
+        assert_eq!(tensor.image_height_width_channels(), Some([1, 4, 1]));
+        assert_eq!(tensor.shape_short(), &tensor.shape()[0..2]);
+        assert!(tensor.is_vector());
+        assert!(tensor.is_shaped_like_an_image());
+    }
+
+    // Non images & non vectors without trailing dimensions
+    for shape in [vec![4, 2, 5], vec![1, 1, 1, 2, 4]] {
+        let tensor = generate_tensor_from_shape(&shape);
+
+        assert_eq!(tensor.image_height_width_channels(), None);
+        assert_eq!(tensor.shape_short(), tensor.shape());
+        assert!(!tensor.is_vector());
+        assert!(!tensor.is_shaped_like_an_image());
+    }
+}

crates/re_viewer/src/ui/space_view_heuristics.rs

@@ -188,9 +188,7 @@ fn default_created_space_views_from_candidates(
                     &[],
                 ) {
                     for tensor in entity_view.iter_primary_flattened() {
-                        if tensor.is_shaped_like_an_image() {
-                            debug_assert!(matches!(tensor.shape.len(), 2 | 3));
-
+                        if let Some([height, width, _]) = tensor.image_height_width_channels() {
                             if query_latest_single::<re_log_types::DrawOrder>(
                                 entity_db,
                                 entity_path,
@@ -205,9 +203,8 @@ fn default_created_space_views_from_candidates(
                                     .push(entity_path.clone());
                             } else {
                                 // Otherwise, distinguish buckets by image size.
-                                let dim = (tensor.shape[0].size, tensor.shape[1].size);
                                 images_by_bucket
-                                    .entry(ImageBucketing::BySize(dim))
+                                    .entry(ImageBucketing::BySize((height, width)))
                                     .or_default()
                                     .push(entity_path.clone());
                             }

crates/re_viewer_context/src/gpu_bridge/tensor_to_gpu.rs

@@ -465,27 +465,14 @@ fn pad_and_narrow_and_cast<T: Copy + Pod>(
 fn height_width_depth(tensor: &Tensor) -> anyhow::Result<[u32; 3]> {
     use anyhow::Context as _;
 
-    let shape = &tensor.shape();
-
-    anyhow::ensure!(
-        shape.len() == 2 || shape.len() == 3,
-        "Expected a 2D or 3D tensor, got {shape:?}",
-    );
+    let Some([height, width, channel]) = tensor.image_height_width_channels() else {
+        anyhow::bail!("Tensor is not an image");
+    };
 
     let [height, width] = [
-        u32::try_from(shape[0].size).context("tensor too large")?,
-        u32::try_from(shape[1].size).context("tensor too large")?,
+        u32::try_from(height).context("Image height is too large")?,
+        u32::try_from(width).context("Image width is too large")?,
     ];
-    let depth = if shape.len() == 2 { 1 } else { shape[2].size };
-
-    anyhow::ensure!(
-        depth == 1 || depth == 3 || depth == 4,
-        "Expected depth of 1,3,4 (gray, RGB, RGBA), found {depth:?}. Tensor shape: {shape:?}"
-    );
-    debug_assert!(
-        tensor.is_shaped_like_an_image(),
-        "We should make the same checks above, but with actual error messages"
-    );
 
-    Ok([height, width, depth as u32])
+    Ok([height, width, channel as u32])
 }

examples/rust/api_demo/src/main.rs

@@ -251,7 +251,7 @@ fn demo_rects(rec_stream: &RecordingStream) -> anyhow::Result<()> {
     use ndarray_rand::{rand_distr::Uniform, RandomExt as _};
 
     // Add an image
-    let img = Array::<u8, _>::from_elem((1024, 1024, 3).f(), 128);
+    let img = Array::<u8, _>::from_elem((1024, 1024, 3, 1).f(), 128);
     MsgSender::new("rects_demo/img")
         .with_timepoint(sim_time(1 as _))
         .with_component(&[Tensor::try_from(img.as_standard_layout().view())?])?