Remove some extra whitelines. (#2513)

candle-examples/examples/mobileclip/main.rs

@@ -60,7 +60,6 @@ fn load_images<T: AsRef<std::path::Path>>(
     image_size: usize,
 ) -> anyhow::Result<Tensor> {
     let mut images = vec![];
-
     for path in paths {
         let tensor = candle_examples::imagenet::load_image_with_std_mean(
             path,
@@ -70,9 +69,7 @@ fn load_images<T: AsRef<std::path::Path>>(
         )?;
         images.push(tensor);
     }
-
     let images = Tensor::stack(&images, 0)?;
-
     Ok(images)
 }
 
@@ -80,24 +77,17 @@ pub fn main() -> anyhow::Result<()> {
     let args = Args::parse();
 
     let model_name = args.which.model_name();
-
     let api = hf_hub::api::sync::Api::new()?;
     let api = api.model(model_name);
-
     let model_file = if args.use_pth {
         api.get("open_clip_pytorch_model.bin")?
     } else {
         api.get("open_clip_model.safetensors")?
     };
-
     let tokenizer = api.get("tokenizer.json")?;
-
     let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
-
     let config = &args.which.config();
-
     let device = candle_examples::device(args.cpu)?;
-
     let vec_imgs = match args.images {
         Some(imgs) => imgs,
         None => vec![
@@ -105,9 +95,7 @@ pub fn main() -> anyhow::Result<()> {
             "candle-examples/examples/yolo-v8/assets/bike.jpg".to_string(),
         ],
     };
-
     let images = load_images(&vec_imgs, config.image_size)?.to_device(&device)?;
-
     let vb = if args.use_pth {
         VarBuilder::from_pth(&model_file, DType::F32, &device)?
     } else {
@@ -115,22 +103,15 @@ pub fn main() -> anyhow::Result<()> {
     };
 
     let model = mobileclip::MobileClipModel::new(vb, config)?;
-
     let (input_ids, vec_seq) = tokenize_sequences(args.sequences, &tokenizer, &device)?;
-
     let (_logits_per_text, logits_per_image) = model.forward(&images, &input_ids)?;
-
     let softmax_image = softmax(&logits_per_image, 1)?;
-
     let softmax_image_vec = softmax_image.flatten_all()?.to_vec1::<f32>()?;
-
     println!("softmax_image_vec: {:?}", softmax_image_vec);
-
     let probability_vec = softmax_image_vec
         .iter()
         .map(|v| v * 100.0)
         .collect::<Vec<f32>>();
-
     let probability_per_image = probability_vec.len() / vec_imgs.len();
 
     for (i, img) in vec_imgs.iter().enumerate() {
@@ -171,7 +152,6 @@ pub fn tokenize_sequences(
     };
 
     let mut tokens = vec![];
-
     for seq in vec_seq.clone() {
         let encoding = tokenizer.encode(seq, true).map_err(E::msg)?;
         tokens.push(encoding.get_ids().to_vec());
@@ -185,8 +165,6 @@ pub fn tokenize_sequences(
             token_vec.extend(vec![pad_id; len_diff]);
         }
     }
-
     let input_ids = Tensor::new(tokens, device)?;
-
     Ok((input_ids, vec_seq))
 }