&[usize] -> TVec<usize> all the shapes

api/rs/src/lib.rs

@@ -364,7 +364,7 @@ impl ValueInterface for Value {
         let dt = to_internal_dt(dt);
         let len = shape.iter().product::<usize>() * dt.size_of();
         anyhow::ensure!(len == data.len());
-        let tensor = unsafe { Tensor::from_raw_dt(dt, shape, data)? };
+        let tensor = unsafe { Tensor::from_raw_dt(dt, shape.into(), data)? };
         Ok(Value(tensor.into_tvalue()))
     }
 

core/src/ops/array/broadcast.rs

@@ -24,7 +24,7 @@ impl EvalOp for MultiBroadcastTo {
         inputs: TVec<TValue>,
     ) -> TractResult<TVec<TValue>> {
         let shape = self.shape.eval_to_usize(&session.resolved_symbols)?;
-        Ok(tvec!(inputs[0].broadcast_to_shape(&shape)?.into_tvalue()))
+        Ok(tvec!(inputs[0].broadcast_to_shape(shape.into_owned())?.into_tvalue()))
     }
 }
 

core/src/ops/array/gather.rs

@@ -29,10 +29,10 @@ impl Gather {
     unsafe fn eval_t<T: Datum>(&self, data: TValue, indices: &TValue) -> TractResult<TValue> {
         let data_view = data.to_array_view_unchecked::<T>();
         let indices = indices.to_array_view::<i64>()?;
-        let output_shape = &*self.compute_output_shape(data.shape(), indices.shape())?;
+        let output_shape = self.compute_output_shape(data.shape(), indices.shape())?;
         let mut output = Tensor::uninitialized::<T>(output_shape)?;
         let mut output_view = output.to_array_view_mut::<T>()?;
-        for coords in tract_ndarray::indices(output_shape) {
+        for coords in tract_ndarray::indices(output_view.shape()) {
             let ocoords = coords.as_array_view();
             let ocoords = ocoords.as_slice().unwrap();
             let mut icoords: TVec<usize> = ocoords[0..self.axis].into();

core/src/ops/array/gather_nd.rs

@@ -91,7 +91,7 @@ impl EvalOp for GatherNd {
         let indices = indices.cast_to::<i32>()?;
         let indices = indices.to_array_view::<i32>()?;
         unsafe {
-            let mut output = Tensor::uninitialized_dt(data.datum_type(), &shape)?;
+            let mut output = Tensor::uninitialized_dt(data.datum_type(), shape)?;
             dispatch_datum_by_size!(Self::eval_t(data.datum_type())(
                 self,
                 &mut output,

core/src/ops/array/one_hot.rs

@@ -57,7 +57,7 @@ impl EvalOp for OneHot {
         let mut shape: TVec<usize> = input.shape().into();
         shape.insert(self.axis, self.dim);
         unsafe {
-            let mut output = self.off.broadcast_scalar_to_shape(&shape)?;
+            let mut output = self.off.broadcast_scalar_to_shape(shape)?;
             dispatch_datum_by_size!(Self::eval_t(self.off.datum_type())(
                 self,
                 &input,

core/src/ops/array/range.rs

@@ -37,7 +37,7 @@ impl Range {
         len: usize,
     ) -> TractResult<Tensor> {
         unsafe {
-            let mut result = Tensor::uninitialized::<T>(&[len])?;
+            let mut result = Tensor::uninitialized::<T>(tvec!(len))?;
             let mut v = start.to_scalar::<T>()?.clone();
             let step = step.to_scalar::<T>()?;
             for i in 0..len {

core/src/ops/array/reshape.rs

@@ -18,8 +18,6 @@ impl Op for FiniteReshape {
     op_as_typed_op!();
 }
 
-
-
 impl EvalOp for FiniteReshape {
     fn is_stateless(&self) -> bool {
         true
@@ -29,7 +27,7 @@ impl EvalOp for FiniteReshape {
         let input = args_1!(inputs);
         let mut tensor = input.into_tensor();
         unsafe {
-            tensor.set_shape_unchecked(&self.shape);
+            tensor.set_shape_unchecked(self.shape.clone());
         }
         Ok(tvec!(tensor.into_tvalue()))
     }

core/src/ops/array/slice.rs

@@ -86,7 +86,7 @@ fn eval_slice(input: &Tensor, axis: usize, start: usize, end: usize) -> TractRes
     unsafe {
         let mut shape: TVec<_> = input.shape().into();
         shape[axis] = end - start;
-        let mut tensor = Tensor::uninitialized_dt(input.datum_type(), &shape)?;
+        let mut tensor = Tensor::uninitialized_dt(input.datum_type(), shape)?;
         tensor.assign_slice_unchecked(.., input, start..end, axis);
         Ok(tvec!(tensor.into_tvalue()))
     }

core/src/ops/array/topk.rs

@@ -31,9 +31,9 @@ impl EvalOp for Topk {
         let k = k.cast_to_scalar::<i64>()? as usize;
         output_shape[self.axis] = k;
         let dt = input.datum_type();
-        let mut output_values = Tensor::zero_dt(dt, &output_shape)?;
-        let mut output_indices = Tensor::zero::<i64>(&output_shape)?;
-        let mut iterating_shape = output_shape.clone();
+        let mut output_values = Tensor::zero_dt(dt, output_shape.clone())?;
+        let mut output_indices = Tensor::zero::<i64>(output_shape.clone())?;
+        let mut iterating_shape = output_shape;
         iterating_shape[self.axis] = 1;
         let mut output_indices_view = output_indices.to_array_view_mut::<i64>()?;
         for coords in tract_ndarray::indices(&*iterating_shape) {

core/src/ops/binary.rs

@@ -101,7 +101,7 @@ pub trait BinMiniOp: fmt::Debug + dyn_clone::DynClone + Send + Sync + 'static +
                 self.eval_in_a(&mut a, &b)?;
                 Ok(a)
             } else {
-                let mut c = unsafe { Tensor::uninitialized_dt(c_dt, &c_shape)? };
+                let mut c = unsafe { Tensor::uninitialized_dt(c_dt, c_shape)? };
                 self.eval_out_of_place(&mut c, &a, &b)?;
                 Ok(c)
             }
@@ -584,7 +584,7 @@ macro_rules! bin_to_super_type {
                             let b = b.to_array_view::<u8>()?;
                             let c_shape = $crate::broadcast::multi_broadcast(&[a.shape(), b.shape()])
                                 .context("no broadcast solution")?;
-                            let mut c = Tensor::zero_dt(*c_dt, &c_shape)?;
+                            let mut c = Tensor::zero_dt(*c_dt, c_shape)?;
                             let view = c.to_array_view_mut::<u8>()?;
                             $crate::ndarray::Zip::from(view).and_broadcast(a).and_broadcast(b).for_each(|c, a, b| {
                                 *c = (scale_by($q_op_on_f32(
@@ -613,7 +613,7 @@ macro_rules! bin_to_super_type {
                             let b = b.cast_to_dt(accumulator_dt)?.into_owned();
                             let c_shape = $crate::broadcast::multi_broadcast(&[a.shape(), b.shape()])
                                 .context("no broadcast solution")?;
-                            let mut c = Tensor::zero_dt(accumulator_dt, &c_shape)?;
+                            let mut c = Tensor::zero_dt(accumulator_dt, c_shape)?;
                             match accumulator_dt {
                                 DatumType::F32 => {
                                     let view = c.to_array_view_mut::<f32>()?;

core/src/ops/cast.rs

@@ -32,7 +32,7 @@ impl EvalOp for Cast {
         if input.datum_type() == self.to {
             Ok(tvec!(input))
         } else if input.datum_type() == TDim::datum_type() {
-            let mut tmp = Tensor::zero_dt(i64::datum_type(), input.shape())?;
+            let mut tmp = Tensor::zero_dt(i64::datum_type(), input.shape().into())?;
             for (dim, i) in
                 tract_itertools::izip!(input.as_slice::<TDim>()?, tmp.as_slice_mut::<i64>()?)
             {

core/src/ops/change_axes.rs

@@ -360,7 +360,7 @@ impl AxisOp {
             Reshape(at, from, to) => {
                 let mut shape: TVec<usize> = tensor.shape().into();
                 self.change_shape_array(&mut shape, false)?;
-                if tensor.set_shape(&shape).is_ok() {
+                if tensor.set_shape(shape).is_ok() {
                     Ok(())
                 } else if broadcasting
                     && tensor.shape().iter().skip(*at).take(from.len()).all(|d| *d == 1)
@@ -1116,7 +1116,7 @@ mod proptests {
 
         fn input(&self) -> TractResult<Tensor> {
             unsafe {
-                let mut t = Tensor::uninitialized::<i64>(&self.input)?;
+                let mut t = Tensor::uninitialized::<i64>(self.input.clone())?;
                 for i in 0..t.len() {
                     t.as_slice_mut().unwrap()[i] = i as i64;
                 }

core/src/ops/cnn/conv/depth_wise.rs

@@ -99,7 +99,7 @@ macro_rules! impl_eval {
                 mul: impl Fn(T, T) -> T + Copy + 'static,
             ) -> TractResult<TVec<TValue>> {
                 let (img, kernel, bias) = args_3!(inputs);
-                let mut output = unsafe { Tensor::uninitialized::<T>(&dw.output_shape.shape)? };
+                let mut output = unsafe { Tensor::uninitialized::<T>(dw.output_shape.shape.clone())? };
                 let iptr = img.as_ptr::<T>()?;
                 let optr = output.as_ptr_mut::<T>()?;
                 let k_stride_i = kernel.strides()[1];

core/src/ops/cnn/conv/im2col.rs

@@ -146,7 +146,7 @@ impl EvalOp for Im2Col {
         unsafe {
             let mut input = inputs.remove(0).into_tensor();
             let pad_value: Option<&Tensor> = if inputs.len() > 0 { Some(&inputs[0]) } else { None };
-            let mut output = Tensor::uninitialized::<Opaque>(&geometry.packed_shape)?;
+            let mut output = Tensor::uninitialized::<Opaque>(geometry.packed_shape.clone())?;
             if !self.pool_spec.data_format.has_n() {
                 input.insert_axis(0)?;
             }
@@ -162,7 +162,7 @@ impl EvalOp for Im2Col {
                     for g in 0..self.group {
                         let mut data = Tensor::uninitialized_aligned_dt(
                             input.datum_type(),
-                            &[geometry.b_pack.len(geometry.k, geometry.n)],
+                            tvec![geometry.b_pack.len(geometry.k, geometry.n)],
                             geometry.b_pack.alignment(),
                         )?;
                         dispatch_copy_by_size!(Patcher::patch(input.datum_type())(
@@ -264,7 +264,7 @@ impl Patcher {
     ) -> TractResult<()> {
         unsafe {
             let pad_value = *pad_value.to_scalar_unchecked();
-            let mut mega_matrix = Tensor::uninitialized::<T>(&[geometry.k, geometry.n])?;
+            let mut mega_matrix = Tensor::uninitialized::<T>(tvec![geometry.k, geometry.n])?;
             let mut mega_matrix_view = mega_matrix.to_array_view_mut_unchecked::<T>();
             let ptr = input.as_ptr_unchecked::<T>();
             let ptr = ptr.add(geometry.input_shape_with_n.c_stride() * (g * geometry.ci_per_group));

core/src/ops/cnn/deconv/deconv_sum.rs

@@ -73,7 +73,7 @@ impl DeconvSum {
         let mut tensor = bias.into_tensor();
         let hw = *gemm.shape().last().unwrap();
         let n = *output_shape.n().unwrap_or(&1);
-        let n_o_hkwk_hw = gemm.into_tensor().into_shape(&[
+        let n_o_hkwk_hw = gemm.into_tensor().into_shape(tvec![
             n,
             *output_shape.c(),
             self.pool_spec.kernel_shape.iter().product(),

core/src/ops/cnn/patches.rs

@@ -813,7 +813,7 @@ pub mod test {
         fn reference_sumpool(&self) -> Tensor {
             let input_shape = self.input_shape();
             let output_shape = self.output_shape();
-            let mut output = Tensor::zero::<f32>(&output_shape.shape).unwrap();
+            let mut output = Tensor::zero::<f32>(output_shape.shape.clone()).unwrap();
             for geo_out in tract_ndarray::indices(output_shape.hw_dims()) {
                 for geo_ker in tract_ndarray::indices(&*self.patch.spec.kernel_shape) {
                     let geo_in: TVec<isize> = izip!(
@@ -845,7 +845,7 @@ pub mod test {
         fn check_visitor(&self) {
             let input_shape = self.input_shape();
             let output_shape = self.output_shape();
-            let mut output = Tensor::zero::<f32>(&output_shape.shape).unwrap();
+            let mut output = Tensor::zero::<f32>(output_shape.shape.clone()).unwrap();
             self.patch.visit_output(|visitor| {
                 for (_k, offset_in) in visitor.valid_offsets_ker_in() {
                     for c in 0..*output_shape.c() {
@@ -862,7 +862,7 @@ pub mod test {
         fn check_zone_visitor(&self) {
             let input_shape = self.input_shape();
             let output_shape = self.output_shape();
-            let mut output = Tensor::zero::<f32>(&output_shape.shape).unwrap();
+            let mut output = Tensor::zero::<f32>(output_shape.shape.clone()).unwrap();
             for zone in &self.patch.zones {
                 zone.visit_output(&self.patch, |visitor| {
                     for (_k, offset_in) in visitor.valid_offsets_ker_in() {
@@ -945,7 +945,7 @@ pub mod test {
     #[test]
     fn test_visitor_1() {
         let input_shape = NCHW.from_n_c_hw(1, 1, [2, 2]).unwrap();
-        let input = Tensor::zero::<f32>(&input_shape.shape).unwrap();
+        let input = Tensor::zero::<f32>(input_shape.shape.clone()).unwrap();
         let patch = PatchSpec::for_data_shape(input_shape.clone())
             .with_kernel_shape(tvec![2, 1])
             .with_padding(PaddingSpec::SameLower)

core/src/ops/cnn/sumpool.rs

@@ -107,8 +107,9 @@ impl EvalOp for LirSumPool {
         let input = args_1!(inputs);
         let geo = self.geometry.to_concrete(input.shape())?;
         let values = if input.datum_type().is_float() {
-            let mut values =
-                unsafe { Tensor::uninitialized_dt(input.datum_type(), &geo.output_shape.shape)? };
+            let mut values = unsafe {
+                Tensor::uninitialized_dt(input.datum_type(), geo.output_shape.shape.clone())?
+            };
             dispatch_floatlike!(Self::eval_t(input.datum_type())(
                 self,
                 &*input,
@@ -117,8 +118,9 @@ impl EvalOp for LirSumPool {
             ))?;
             values
         } else {
-            let mut values =
-                unsafe { Tensor::uninitialized_dt(DatumType::F32, &geo.output_shape.shape)? };
+            let mut values = unsafe {
+                Tensor::uninitialized_dt(DatumType::F32, geo.output_shape.shape.clone())?
+            };
             let input_f32 = input.cast_to_dt(DatumType::F32)?;
             self.eval_t::<f32>(input_f32.as_ref(), values.as_ptr_mut()?, geo.as_ref())?;
             values.cast_to_dt(input.datum_type())?.into_owned()

core/src/ops/downsample/mod.rs

@@ -59,7 +59,7 @@ impl EvalOp for Downsample {
             let t = if self.modulo > input.shape()[self.axis] {
                 let mut shape: TVec<usize> = input.shape().into();
                 shape[self.axis] = 0;
-                Tensor::uninitialized_dt(input.datum_type(), &shape)?
+                Tensor::uninitialized_dt(input.datum_type(), shape)?
             } else {
                 let slice = ndarray::Slice::new(self.modulo as isize, None, self.stride);
                 unsafe fn do_slice<T: Datum>(
@@ -86,7 +86,10 @@ impl EvalOp for Downsample {
 impl TypedOp for Downsample {
     fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
         ensure!(self.axis < inputs[0].rank());
-        ensure!(self.modulo == 0 || self.stride > 0, "non-zero modulo is only defined with forward strides");
+        ensure!(
+            self.modulo == 0 || self.stride > 0,
+            "non-zero modulo is only defined with forward strides"
+        );
         let mut downed = inputs[0].clone();
         let down_len = self.transform_dim(&downed.shape[self.axis]);
         downed.shape.set(self.axis, down_len);

core/src/ops/einsum/as_blas.rs

@@ -84,11 +84,11 @@ impl EvalOp for SGemm {
         let n = c_shape[rank - 1];
         let k = a.shape()[rank - 1];
         unsafe {
-            let mut c = Tensor::uninitialized::<f32>(&c_shape)?;
+            let mut c = Tensor::uninitialized::<f32>(c_shape)?;
             let c_ptr = c.as_ptr_mut::<f32>()?;
             let silent_a_axis = c.rank() - a.rank();
             let silent_b_axis = c.rank() - b.rank();
-            for prefix in ndarray::indices(&c_shape[0..rank - 2]) {
+            for prefix in ndarray::indices(&c.shape()[0..rank - 2]) {
                 let mut a_ptr = a_ptr;
                 let mut b_ptr = b_ptr;
                 let mut c_ptr = c_ptr;