Add Mobilenet v4 (#2325)

* Support different resolutions in load_image()

* Added MobilenetV4 model.

* Add MobileNetv4 to README

README.md

@@ -236,7 +236,7 @@ If you have an addition to this list, please submit a pull request.
  - MetaVoice-1B, text-to-speech model.
  - Computer Vision Models.
  - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT,
- ConvNeXTv2, MobileOne, EfficientVit (MSRA).
+ ConvNeXTv2, MobileOne, EfficientVit (MSRA), MobileNetv4.
  - yolo-v3, yolo-v8.
  - Segment-Anything Model (SAM).
  - SegFormer.

candle-examples/examples/mobilenetv4/README.md

@@ -0,0 +1,18 @@
+# candle-mobilenetv4
+
+[MobileNetV4 - Universal Models for the Mobile Ecosystem](https://arxiv.org/abs/2404.10518)
+This candle implementation uses pre-trained MobileNetV4 models from timm for inference.
+The classification head has been trained on the ImageNet dataset and returns the probabilities for the top-5 classes.
+
+## Running an example
+
+```
+$ cargo run --example mobilenetv4 --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which medium
+loaded image Tensor[dims 3, 256, 256; f32]
+model built
+unicycle, monocycle : 20.18%
+mountain bike, all-terrain bike, off-roader: 19.77%
+bicycle-built-for-two, tandem bicycle, tandem: 15.91%
+crash helmet : 1.15%
+tricycle, trike, velocipede: 0.67%
+```

candle-examples/examples/mobilenetv4/main.rs

@@ -0,0 +1,106 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use clap::{Parser, ValueEnum};
+
+use candle::{DType, IndexOp, D};
+use candle_nn::{Module, VarBuilder};
+use candle_transformers::models::mobilenetv4;
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+ Small,
+ Medium,
+ Large,
+ HybridMedium,
+ HybridLarge,
+}
+
+impl Which {
+ fn model_filename(&self) -> String {
+ let name = match self {
+ Self::Small => "conv_small.e2400_r224",
+ Self::Medium => "conv_medium.e500_r256",
+ Self::HybridMedium => "hybrid_medium.ix_e550_r256",
+ Self::Large => "conv_large.e600_r384",
+ Self::HybridLarge => "hybrid_large.ix_e600_r384",
+ };
+ format!("timm/mobilenetv4_{}_in1k", name)
+ }
+
+ fn resolution(&self) -> u32 {
+ match self {
+ Self::Small => 224,
+ Self::Medium => 256,
+ Self::HybridMedium => 256,
+ Self::Large => 384,
+ Self::HybridLarge => 384,
+ }
+ }
+ fn config(&self) -> mobilenetv4::Config {
+ match self {
+ Self::Small => mobilenetv4::Config::small(),
+ Self::Medium => mobilenetv4::Config::medium(),
+ Self::HybridMedium => mobilenetv4::Config::hybrid_medium(),
+ Self::Large => mobilenetv4::Config::large(),
+ Self::HybridLarge => mobilenetv4::Config::hybrid_large(),
+ }
+ }
+}
+
+#[derive(Parser)]
+struct Args {
+ #[arg(long)]
+ model: Option<String>,
+
+ #[arg(long)]
+ image: String,
+
+ /// Run on CPU rather than on GPU.
+ #[arg(long)]
+ cpu: bool,
+
+ #[arg(value_enum, long, default_value_t=Which::Small)]
+ which: Which,
+}
+
+pub fn main() -> anyhow::Result<()> {
+ let args = Args::parse();
+
+ let device = candle_examples::device(args.cpu)?;
+
+ let image = candle_examples::imagenet::load_image(args.image, args.which.resolution())?
+ .to_device(&device)?;
+ println!("loaded image {image:?}");
+
+ let model_file = match args.model {
+ None => {
+ let model_name = args.which.model_filename();
+ let api = hf_hub::api::sync::Api::new()?;
+ let api = api.model(model_name);
+ api.get("model.safetensors")?
+ }
+ Some(model) => model.into(),
+ };
+
+ let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
+ let model = mobilenetv4::mobilenetv4(&args.which.config(), 1000, vb)?;
+ println!("model built");
+ let logits = model.forward(&image.unsqueeze(0)?)?;
+ let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
+ .i(0)?
+ .to_vec1::<f32>()?;
+ let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
+ prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
+ for &(category_idx, pr) in prs.iter().take(5) {
+ println!(
+ "{:24}: {:.2}%",
+ candle_examples::imagenet::CLASSES[category_idx],
+ 100. * pr
+ );
+ }
+ Ok(())
+}

candle-examples/src/imagenet.rs

@@ -1,38 +1,34 @@
 use candle::{Device, Result, Tensor};
 
-/// Loads an image from disk using the image crate, this returns a tensor with shape
-/// (3, 224, 224). imagenet normalization is applied.
-pub fn load_image224<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
+/// Loads an image from disk using the image crate at the requested resolution.
+// This returns a tensor with shape (3, res, res). imagenet normalization is applied.
+pub fn load_image<P: AsRef<std::path::Path>>(p: P, res: u32) -> Result<Tensor> {
  let img = image::io::Reader::open(p)?
  .decode()
  .map_err(candle::Error::wrap)?
- .resize_to_fill(224, 224, image::imageops::FilterType::Triangle);
+ .resize_to_fill(res, res, image::imageops::FilterType::Triangle);
  let img = img.to_rgb8();
  let data = img.into_raw();
- let data = Tensor::from_vec(data, (224, 224, 3), &Device::Cpu)?.permute((2, 0, 1))?;
+ let data = Tensor::from_vec(data, (res as usize, res as usize, 3), &Device::Cpu)?
+ .permute((2, 0, 1))?;
  let mean = Tensor::new(&[0.485f32, 0.456, 0.406], &Device::Cpu)?.reshape((3, 1, 1))?;
  let std = Tensor::new(&[0.229f32, 0.224, 0.225], &Device::Cpu)?.reshape((3, 1, 1))?;
  (data.to_dtype(candle::DType::F32)? / 255.)?
  .broadcast_sub(&mean)?
  .broadcast_div(&std)
 }
 
+/// Loads an image from disk using the image crate, this returns a tensor with shape
+/// (3, 224, 224). imagenet normalization is applied.
+pub fn load_image224<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
+ load_image(p, 224)
+}
+
 /// Loads an image from disk using the image crate, this returns a tensor with shape
 /// (3, 518, 518). imagenet normalization is applied.
 /// The model dinov2 reg4 analyzes images with dimensions 3x518x518 (resulting in 37x37 transformer tokens).
 pub fn load_image518<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
- let img = image::io::Reader::open(p)?
- .decode()
- .map_err(candle::Error::wrap)?
- .resize_to_fill(518, 518, image::imageops::FilterType::Triangle);
- let img = img.to_rgb8();
- let data = img.into_raw();
- let data = Tensor::from_vec(data, (518, 518, 3), &Device::Cpu)?.permute((2, 0, 1))?;
- let mean = Tensor::new(&[0.485f32, 0.456, 0.406], &Device::Cpu)?.reshape((3, 1, 1))?;
- let std = Tensor::new(&[0.229f32, 0.224, 0.225], &Device::Cpu)?.reshape((3, 1, 1))?;
- (data.to_dtype(candle::DType::F32)? / 255.)?
- .broadcast_sub(&mean)?
- .broadcast_div(&std)
+ load_image(p, 518)
 }
 
 pub const CLASS_COUNT: i64 = 1000;