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

Author: kali

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>()?;