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,
@@ -70,67 +69,49 @@ fn load_images<T: AsRef<std::path::Path>>(
  )?;
  images.push(tensor);
  }
-
  let images = Tensor::stack(&images, 0)?;
-
  Ok(images)
 }
 
 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![
  "candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg".to_string(),
  "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 {
  unsafe { VarBuilder::from_mmaped_safetensors(&[model_file.clone()], DType::F32, &device)? }
  };
 
  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))
 }

candle-transformers/src/models/fastvit.rs

@@ -495,7 +495,6 @@ fn fastvit_model(cfg: &Config, nclasses: Option<usize>, vb: VarBuilder) -> Resul
  .apply(&stage3)?
  .apply(&stage4)?
  .apply(&final_conv)?;
-
  match &cls {
  None => Ok(xs),
  Some(cls) => xs.mean(D::Minus2)?.mean(D::Minus1)?.apply(cls),

candle-transformers/src/models/mobileclip.rs

@@ -22,7 +22,6 @@ impl MobileClipConfig {
  pub fn s1() -> Self {
  let text_config = text_model::Config::vit_base_patch32();
  let vision_config = fastvit::Config::mci1();
-
  Self {
  text_config,
  vision_config,
@@ -32,7 +31,6 @@ impl MobileClipConfig {
  pub fn s2() -> Self {
  let text_config = text_model::Config::vit_base_patch32();
  let vision_config = fastvit::Config::mci2();
-
  Self {
  text_config,
  vision_config,
@@ -45,12 +43,10 @@ impl MobileClipModel {
  pub fn new(vs: VarBuilder, c: &MobileClipConfig) -> Result<Self> {
  let vision_model = fastvit::fastvit(&c.vision_config, 512, vs.pp("visual.trunk"))?;
  let text_model = text_model::OpenClipTextTransformer::new(vs.pp("text"), &c.text_config)?;
-
  let text_projection = vs.get(
  (c.text_config.embed_dim, c.text_config.projection_dim),
  "text.text_projection",
  )?;
-
  let logit_scale = vs.get(&[], "logit_scale")?;
  Ok(Self {
  text_model,