chore(*): format code

examples/async-gpt2-api/examples/async-gpt2-api.rs

@@ -1,17 +1,17 @@
 use std::{path::Path, sync::Arc};
 
 use axum::{
+	Router,
 	extract::{FromRef, State},
 	response::{
-		sse::{Event, KeepAlive},
-		Sse
+		Sse,
+		sse::{Event, KeepAlive}
 	},
-	routing::post,
-	Router
+	routing::post
 };
 use futures::Stream;
-use ndarray::{array, concatenate, s, Array1, ArrayViewD, Axis};
-use ort::{inputs, CUDAExecutionProvider, GraphOptimizationLevel, Session};
+use ndarray::{Array1, ArrayViewD, Axis, array, concatenate, s};
+use ort::{CUDAExecutionProvider, GraphOptimizationLevel, Session, inputs};
 use rand::Rng;
 use tokenizers::Tokenizer;
 use tokio::net::TcpListener;

examples/gpt2/examples/gpt2-no-ndarray.rs

@@ -4,7 +4,7 @@ use std::{
 	sync::Arc
 };
 
-use ort::{inputs, CUDAExecutionProvider, GraphOptimizationLevel, Session};
+use ort::{CUDAExecutionProvider, GraphOptimizationLevel, Session, inputs};
 use rand::Rng;
 use tokenizers::Tokenizer;
 

examples/gpt2/examples/gpt2.rs

@@ -3,8 +3,8 @@ use std::{
 	path::Path
 };
 
-use ndarray::{array, concatenate, s, Array1, ArrayViewD, Axis};
-use ort::{inputs, CUDAExecutionProvider, GraphOptimizationLevel, Session};
+use ndarray::{Array1, ArrayViewD, Axis, array, concatenate, s};
+use ort::{CUDAExecutionProvider, GraphOptimizationLevel, Session, inputs};
 use rand::Rng;
 use tokenizers::Tokenizer;
 

examples/modnet/examples/modnet.rs

@@ -2,10 +2,10 @@
 
 use std::{ops::Mul, path::Path};
 
-use image::{imageops::FilterType, GenericImageView, ImageBuffer, Rgba};
+use image::{GenericImageView, ImageBuffer, Rgba, imageops::FilterType};
 use ndarray::Array;
-use ort::{inputs, CUDAExecutionProvider, Session};
-use show_image::{event, AsImageView, WindowOptions};
+use ort::{CUDAExecutionProvider, Session, inputs};
+use show_image::{AsImageView, WindowOptions, event};
 
 #[show_image::main]
 fn main() -> ort::Result<()> {
@@ -57,13 +57,10 @@ fn main() -> ort::Result<()> {
 	let window = show_image::context()
 		.run_function_wait(move |context| -> Result<_, String> {
 			let mut window = context
-				.create_window(
-					"ort + modnet",
-					WindowOptions {
-						size: Some([img_width, img_height]),
-						..WindowOptions::default()
-					}
-				)
+				.create_window("ort + modnet", WindowOptions {
+					size: Some([img_width, img_height]),
+					..WindowOptions::default()
+				})
 				.map_err(|e| e.to_string())?;
 			window.set_image("photo", &output.as_image_view().map_err(|e| e.to_string())?);
 			Ok(window.proxy())

examples/phi-3-vision/src/image_process.rs

@@ -9,7 +9,7 @@
 //! to be used with the Phi-3 vision model, adapting the original Python code to Rust.
 use anyhow::Result;
 use image::{DynamicImage, GenericImageView, ImageBuffer};
-use ndarray::{s, Array2, Array4, Array5, Axis};
+use ndarray::{Array2, Array4, Array5, Axis, s};
 
 /// see https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cpu/blob/main/cpu-int4-rtn-block-32-acc-level-4/processor_config.json
 /// NOTE: The default setting in processor_config.json is num_crops = 16,
@@ -24,7 +24,7 @@ pub struct Phi3VImageProcessor {
 	num_crops: usize,
 	image_mean: Vec<f32>,
 	image_std: Vec<f32>,
-	do_convert_rgb: bool,
+	do_convert_rgb: bool
 }
 
 impl Phi3VImageProcessor {
@@ -33,7 +33,7 @@ impl Phi3VImageProcessor {
 			num_crops: NUM_CROPS,
 			image_mean: OPENAI_CLIP_MEAN.to_vec(),
 			image_std: OPENAI_CLIP_STD.to_vec(),
-			do_convert_rgb: true,
+			do_convert_rgb: true
 		}
 	}
 
@@ -72,7 +72,7 @@ impl Phi3VImageProcessor {
 		Ok(BatchFeature {
 			pixel_values,
 			image_sizes,
-			num_img_tokens: vec![num_img_tokens as i64],
+			num_img_tokens: vec![num_img_tokens as i64]
 		})
 	}
 
@@ -99,11 +99,7 @@ impl Phi3VImageProcessor {
 		let resized = image.resize_exact(new_width, new_height, image::imageops::FilterType::Lanczos3);
 		let padded = self.padding_336(&resized);
 
-		if transposed {
-			padded.rotate90()
-		} else {
-			padded
-		}
+		if transposed { padded.rotate90() } else { padded }
 	}
 
 	fn padding_336(&self, image: &DynamicImage) -> DynamicImage {
@@ -188,5 +184,5 @@ impl Phi3VImageProcessor {
 pub struct BatchFeature {
 	pub pixel_values: Array5<f32>,
 	pub image_sizes: Array2<i64>,
-	pub num_img_tokens: Vec<i64>,
+	pub num_img_tokens: Vec<i64>
 }

examples/phi-3-vision/src/main.rs

@@ -1,10 +1,10 @@
 mod image_process;
+use std::{path::Path, time::Instant};
+
 use anyhow::Result;
 use image::DynamicImage;
-use ndarray::{s, Array, Array2, Array3, Array4, ArrayView, Ix3, Ix4};
+use ndarray::{Array, Array2, Array3, Array4, ArrayView, Ix3, Ix4, s};
 use ort::{Session, Tensor};
-use std::path::Path;
-use std::time::Instant;
 use tokenizers::Tokenizer;
 
 const VISION_MODEL_NAME: &'static str = "phi-3-v-128k-instruct-vision.onnx";
@@ -59,7 +59,7 @@ fn merge_text_and_image_embeddings(
 	inputs_embeds: &Array3<f32>,
 	attention_mask: &Array2<i64>,
 	visual_features: &Array3<f32>,
-	image_token_position: usize,
+	image_token_position: usize
 ) -> (Array3<f32>, Array2<i64>) {
 	let mut combined_embeds = Array3::zeros((1, inputs_embeds.shape()[1] + visual_features.shape()[1], inputs_embeds.shape()[2]));
 
@@ -96,7 +96,7 @@ fn merge_text_and_image_embeddings(
 fn format_chat_template(img: &Option<DynamicImage>, txt: &str) -> String {
 	match img {
 		Some(_) => format!("<s><|user|>\n<|image_1|>\n{txt}<|end|>\n<|assistant|>\n", txt = txt),
-		None => format!("<s><|user|>\n{txt}<|end|>\n<|assistant|>\n", txt = txt),
+		None => format!("<s><|user|>\n{txt}<|end|>\n<|assistant|>\n", txt = txt)
 	}
 }
 
@@ -106,7 +106,7 @@ pub async fn generate_text(
 	text_embedding_model: &Session,
 	generation_model: &Session,
 	image: &Option<DynamicImage>,
-	text: &str,
+	text: &str
 ) -> Result<()> {
 	let (mut inputs_embeds, mut attention_mask) = {
 		let visual_features = get_image_embedding(&vision_model, &image)?;
@@ -161,9 +161,11 @@ pub async fn generate_text(
 		//
 		// The current implementation uses a simple greedy decoding strategy:
 		// - We select the token with the highest probability (argmax) from the logits.
-		// - This approach always chooses the most likely next token, which can lead to deterministic and potentially repetitive outputs.
+		// - This approach always chooses the most likely next token, which can lead to deterministic and potentially repetitive
+		//   outputs.
 		//
-		// Note: More advanced sampling strategies (e.g., temperature scaling, top-k, top-p sampling) are not implemented in the current version.
+		// Note: More advanced sampling strategies (e.g., temperature scaling, top-k, top-p sampling) are not implemented in the
+		// current version.
 		//
 		// The selected token ID will be in the range [0, VOCAB_SIZE - 1].
 		let logits: ArrayView<f32, _> = model_outputs["logits"].try_extract_tensor::<f32>()?.into_dimensionality::<Ix3>()?;

examples/sentence-transformers/examples/semantic-similarity.rs

@@ -1,6 +1,6 @@
 use std::path::Path;
 
-use ndarray::{s, Array1, Array2, Axis, Ix2};
+use ndarray::{Array1, Array2, Axis, Ix2, s};
 use ort::{CUDAExecutionProvider, Error, GraphOptimizationLevel, Session};
 use tokenizers::Tokenizer;
 

examples/training/examples/train-clm-simple.rs

@@ -5,7 +5,7 @@ use std::{
 };
 
 use kdam::BarExt;
-use ndarray::{concatenate, s, Array1, Array2, ArrayViewD, Axis};
+use ndarray::{Array1, Array2, ArrayViewD, Axis, concatenate, s};
 use ort::{Allocator, CUDAExecutionProvider, CheckpointStrategy, Session, SessionBuilder, Trainer, TrainerCallbacks, TrainingArguments};
 use rand::RngCore;
 use tokenizers::Tokenizer;

examples/training/examples/train-clm.rs

@@ -5,7 +5,7 @@ use std::{
 };
 
 use kdam::BarExt;
-use ndarray::{concatenate, s, Array1, Array2, ArrayViewD, Axis};
+use ndarray::{Array1, Array2, ArrayViewD, Axis, concatenate, s};
 use ort::{Allocator, CUDAExecutionProvider, Checkpoint, Session, SessionBuilder, Trainer};
 use rand::RngCore;
 use tokenizers::Tokenizer;

examples/yolov8/examples/yolov8.rs

@@ -2,11 +2,11 @@
 
 use std::path::Path;
 
-use image::{imageops::FilterType, GenericImageView};
-use ndarray::{s, Array, Axis};
-use ort::{inputs, CUDAExecutionProvider, Session, SessionOutputs};
+use image::{GenericImageView, imageops::FilterType};
+use ndarray::{Array, Axis, s};
+use ort::{CUDAExecutionProvider, Session, SessionOutputs, inputs};
 use raqote::{DrawOptions, DrawTarget, LineJoin, PathBuilder, SolidSource, Source, StrokeStyle};
-use show_image::{event, AsImageView, WindowOptions};
+use show_image::{AsImageView, WindowOptions, event};
 
 #[derive(Debug, Clone, Copy)]
 struct BoundingBox {
@@ -137,13 +137,10 @@ fn main() -> ort::Result<()> {
 	let window = show_image::context()
 		.run_function_wait(move |context| -> Result<_, String> {
 			let mut window = context
-				.create_window(
-					"ort + YOLOv8",
-					WindowOptions {
-						size: Some([img_width, img_height]),
-						..WindowOptions::default()
-					}
-				)
+				.create_window("ort + YOLOv8", WindowOptions {
+					size: Some([img_width, img_height]),
+					..WindowOptions::default()
+				})
 				.map_err(|e| e.to_string())?;
 			window.set_image("baseball", &original_img.as_image_view().map_err(|e| e.to_string())?);
 			window.set_overlay("yolo", &overlay.as_image_view().map_err(|e| e.to_string())?, true);