Add Mobilenet v4 (#2325)

* Support different resolutions in load_image()

* Added MobilenetV4 model.

* Add MobileNetv4 to README

2 files changed, 124 insertions, 0 deletions

diff --git a/candle-examples/examples/mobilenetv4/README.md b/candle-examples/examples/mobilenetv4/README.md
new file mode 100644
index 00000000..c8356466
--- /dev/null
+++ b/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%
+```
diff --git a/candle-examples/examples/mobilenetv4/main.rs b/candle-examples/examples/mobilenetv4/main.rs
new file mode 100644
index 00000000..26c0dad9
--- /dev/null
+++ b/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(())
+}