support running tests with --no-default-features

src/adapter.rs

@@ -165,13 +165,15 @@ mod tests {
 
 	#[test]
 	fn test_lora() -> crate::Result<()> {
-		let model = Session::builder()?.commit_from_file("tests/data/lora_model.onnx")?;
-		let lora = Adapter::from_file("tests/data/adapter.orl", None)?;
+		let model = std::fs::read("tests/data/lora_model.onnx").expect("");
+		let session = Session::builder()?.commit_from_memory(&model)?;
+		let lora = std::fs::read("tests/data/adapter.orl").expect("");
+		let lora = Adapter::from_memory(&lora, None)?;
 
 		let mut run_options = RunOptions::new()?;
 		run_options.add_adapter(&lora)?;
 
-		let output: Tensor<f32> = model
+		let output: Tensor<f32> = session
 			.run_with_options(crate::inputs![Tensor::<f32>::from_array(([4, 4], vec![1.0; 16]))?], &run_options)?
 			.remove("output")
 			.expect("")
@@ -187,7 +189,8 @@ mod tests {
 
 	#[test]
 	fn test_lora_from_memory() -> crate::Result<()> {
-		let model = Session::builder()?.commit_from_file("tests/data/lora_model.onnx")?;
+		let model = std::fs::read("tests/data/lora_model.onnx").expect("");
+		let session = Session::builder()?.commit_from_memory(&model)?;
 
 		let lora_bytes = std::fs::read("tests/data/adapter.orl").expect("");
 		let lora = Adapter::from_memory(&lora_bytes, None)?;
@@ -196,7 +199,7 @@ mod tests {
 		let mut run_options = RunOptions::new()?;
 		run_options.add_adapter(&lora)?;
 
-		let output: Tensor<f32> = model
+		let output: Tensor<f32> = session
 			.run_with_options(crate::inputs![Tensor::<f32>::from_array(([4, 4], vec![1.0; 16]))?], &run_options)?
 			.remove("output")
 			.expect("")

src/io_binding.rs

@@ -269,16 +269,19 @@ mod tests {
 	use core::cmp::Ordering;
 
 	use image::{ImageBuffer, Luma, Pixel};
+	#[cfg(feature = "ndarray")]
 	use ndarray::{Array2, Array4, Axis};
 
+	#[cfg(feature = "ndarray")]
+	use crate::tensor::ArrayExtensions;
 	use crate::{
 		Result,
 		memory::{AllocationDevice, AllocatorType, MemoryInfo, MemoryType},
 		session::Session,
-		tensor::ArrayExtensions,
 		value::{Tensor, TensorValueTypeMarker, Value}
 	};
 
+	#[cfg(feature = "ndarray")]
 	fn get_image() -> Array4<f32> {
 		let image_buffer: ImageBuffer<Luma<u8>, Vec<u8>> = image::open("tests/data/mnist_5.jpg").expect("failed to load image").to_luma8();
 		ndarray::Array::from_shape_fn((1, 1, 28, 28), |(_, c, j, i)| {
@@ -288,6 +291,7 @@ mod tests {
 		})
 	}
 
+	#[cfg(feature = "ndarray")]
 	fn extract_probabilities<T: TensorValueTypeMarker>(output: &Value<T>) -> Result<Vec<(usize, f32)>> {
 		let mut probabilities: Vec<(usize, f32)> = output
 			.try_extract_tensor()?
@@ -302,6 +306,7 @@ mod tests {
 
 	// not terribly useful since CI is CPU-only, but it at least ensures the API won't segfault or something silly
 	#[test]
+	#[cfg(all(feature = "ndarray", feature = "fetch-models"))]
 	fn test_mnist_input_bound() -> Result<()> {
 		let session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
 
@@ -319,6 +324,7 @@ mod tests {
 	}
 
 	#[test]
+	#[cfg(all(feature = "ndarray", feature = "fetch-models"))]
 	fn test_mnist_input_output_bound() -> Result<()> {
 		let session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
 
@@ -338,6 +344,7 @@ mod tests {
 	}
 
 	#[test]
+	#[cfg(all(feature = "ndarray", feature = "fetch-models"))]
 	fn test_send_iobinding() -> Result<()> {
 		let session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
 
@@ -362,6 +369,7 @@ mod tests {
 	}
 
 	#[test]
+	#[cfg(all(feature = "ndarray", feature = "fetch-models"))]
 	fn test_mnist_clear_bounds() -> Result<()> {
 		let session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
 

src/lib.rs

@@ -22,9 +22,6 @@ pub mod __private {
 	pub extern crate core;
 }
 
-#[cfg(all(test, not(feature = "fetch-models")))]
-compile_error!("`cargo test --features fetch-models`!!1!");
-
 pub mod adapter;
 pub mod environment;
 pub mod error;

src/operator/tests.rs

@@ -77,9 +77,10 @@ impl Operator for CustomOpTwo {
 
 #[test]
 fn test_custom_ops() -> crate::Result<()> {
+	let model = std::fs::read("tests/data/custom_op_test.onnx").expect("");
 	let session = Session::builder()?
 		.with_operators(OperatorDomain::new("test.customop")?.add(CustomOpOne)?.add(CustomOpTwo)?)?
-		.commit_from_file("tests/data/custom_op_test.onnx")?;
+		.commit_from_memory(&model)?;
 
 	let allocator = session.allocator();
 	let mut value1 = Tensor::<f32>::new(allocator, [3, 5])?;

src/session/input.rs

@@ -126,6 +126,7 @@ mod tests {
 	use crate::value::{DynTensor, Tensor};
 
 	#[test]
+	#[cfg(feature = "std")]
 	fn test_hashmap_static_keys() -> crate::Result<()> {
 		let v: Vec<f32> = vec![1., 2., 3., 4., 5.];
 		let shape = vec![v.len() as i64];
@@ -138,6 +139,7 @@ mod tests {
 	}
 
 	#[test]
+	#[cfg(feature = "std")]
 	fn test_hashmap_string_keys() -> crate::Result<()> {
 		let v: Vec<f32> = vec![1., 2., 3., 4., 5.];
 		let shape = vec![v.len() as i64];

src/value/impl_map.rs

@@ -284,8 +284,6 @@ impl<K: IntoTensorElementType + Debug + Clone + Hash + Eq + 'static, V: IntoTens
 }
 
 impl<K: IntoTensorElementType + Debug + Clone + Hash + Eq, V: PrimitiveTensorElementType + Debug + Clone> Value<MapValueType<K, V>> {
-	#[cfg(feature = "std")]
-	#[cfg_attr(docsrs, doc(cfg(feature = "std")))]
 	pub fn extract_raw_map(&self) -> Vec<(K, V)> {
 		self.try_extract_raw_map().expect("Failed to extract map")
 	}

src/value/impl_tensor/mod.rs

@@ -275,6 +275,7 @@ pub(crate) fn calculate_tensor_size(shape: &[i64]) -> usize {
 mod tests {
 	use alloc::sync::Arc;
 
+	#[cfg(feature = "ndarray")]
 	use ndarray::{ArcArray1, Array1, CowArray};
 
 	use super::Tensor;

src/value/mod.rs

@@ -491,7 +491,7 @@ mod tests {
 		let value = Sequence::new([Map::<String, f32>::new(map_contents)?])?;
 
 		for map in value.extract_sequence(&Allocator::default()) {
-			let map = map.extract_map();
+			let map = map.extract_raw_map().into_iter().collect::<std::collections::HashMap<_, _>>();
 			assert_eq!(map["meaning"], 42.0);
 			assert_eq!(map["pi"], core::f32::consts::PI);
 		}