refactor!: allow zero-copy from_array for array views with TensorRef

This has all sorts of fun breaking changes:
- `ort::inputs!` no longer yields an `ort::Result<...>` (thank God)
- `Tensor::from_array` now only accepts owned data.
- Introduce `TensorRef::from_array_view` and `TensorRefMut::from_array_view_mut`.
- `TryFrom<A>` is no longer implemented for `Tensor<T>` for any variants.

This opens the door to new optimizations on top of fixing a few unsoundness issues.

TODO: update docs

tests/mnist.rs

@@ -4,7 +4,8 @@ use image::{ImageBuffer, Luma, Pixel, imageops::FilterType};
 use ort::{
 	inputs,
 	session::{Session, builder::GraphOptimizationLevel},
-	tensor::ArrayExtensions
+	tensor::ArrayExtensions,
+	value::TensorRef
 };
 use test_log::test;
 
@@ -45,7 +46,7 @@ fn mnist_5() -> ort::Result<()> {
 	});
 
 	// Perform the inference
-	let outputs = session.run(inputs![array]?)?;
+	let outputs = session.run(inputs![TensorRef::from_array_view(&array)?])?;
 
 	let mut probabilities: Vec<(usize, f32)> = outputs[0]
 		.try_extract_tensor()?

tests/squeezenet.rs

@@ -10,7 +10,8 @@ use ndarray::s;
 use ort::{
 	Error, inputs,
 	session::{Session, builder::GraphOptimizationLevel},
-	tensor::ArrayExtensions
+	tensor::ArrayExtensions,
+	value::TensorRef
 };
 use test_log::test;
 
@@ -70,7 +71,7 @@ fn squeezenet_mushroom() -> ort::Result<()> {
 	}
 
 	// Perform the inference
-	let outputs = session.run(inputs![array]?)?;
+	let outputs = session.run(inputs![TensorRef::from_array_view(&array)?])?;
 
 	// Downloaded model does not have a softmax as final layer; call softmax on second axis
 	// and iterate on resulting probabilities, creating an index to later access labels.

tests/upsample.rs

@@ -4,7 +4,8 @@ use image::RgbImage;
 use ndarray::{Array, ArrayViewD, CowArray, Ix4};
 use ort::{
 	inputs,
-	session::{Session, builder::GraphOptimizationLevel}
+	session::{Session, builder::GraphOptimizationLevel},
+	value::TensorRef
 };
 use test_log::test;
 
@@ -69,7 +70,7 @@ fn upsample() -> ort::Result<()> {
 	let array = convert_image_to_cow_array(&image_buffer);
 
 	// Perform the inference
-	let outputs = session.run(inputs![&array]?)?;
+	let outputs = session.run(inputs![TensorRef::from_array_view(&array)?])?;
 
 	assert_eq!(outputs.len(), 1);
 	let output: ArrayViewD<f32> = outputs[0].try_extract_tensor()?;
@@ -106,7 +107,7 @@ fn upsample_with_ort_model() -> ort::Result<()> {
 	let array = convert_image_to_cow_array(&image_buffer);
 
 	// Perform the inference
-	let outputs = session.run(inputs![&array]?)?;
+	let outputs = session.run(inputs![TensorRef::from_array_view(&array)?])?;
 
 	assert_eq!(outputs.len(), 1);
 	let output: ArrayViewD<f32> = outputs[0].try_extract_tensor()?;

tests/vectorizer.rs

@@ -26,11 +26,7 @@ fn vectorizer() -> ort::Result<()> {
 
 	let array = ndarray::CowArray::from(ndarray::Array::from_shape_vec((1,), vec!["document".to_owned()]).unwrap());
 
-	// Just one input
-	let input_tensor_values = inputs![Tensor::from_string_array(&array)?]?;
-
-	// Perform the inference
-	let outputs = session.run(input_tensor_values)?;
+	let outputs = session.run(inputs![Tensor::from_string_array(&array)?])?;
 	assert_eq!(outputs[0].try_extract_tensor::<f32>()?, ArrayD::from_shape_vec(IxDyn(&[1, 9]), vec![0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).unwrap());
 
 	Ok(())