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Add Mobilenet v4 (#2325)
* Support different resolutions in load_image() * Added MobilenetV4 model. * Add MobileNetv4 to README
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candle-examples/examples/mobilenetv4/README.md
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candle-examples/examples/mobilenetv4/README.md
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# candle-mobilenetv4
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[MobileNetV4 - Universal Models for the Mobile Ecosystem](https://arxiv.org/abs/2404.10518)
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This candle implementation uses pre-trained MobileNetV4 models from timm for inference.
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The classification head has been trained on the ImageNet dataset and returns the probabilities for the top-5 classes.
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## Running an example
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```
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$ cargo run --example mobilenetv4 --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which medium
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loaded image Tensor[dims 3, 256, 256; f32]
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model built
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unicycle, monocycle : 20.18%
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mountain bike, all-terrain bike, off-roader: 19.77%
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bicycle-built-for-two, tandem bicycle, tandem: 15.91%
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crash helmet : 1.15%
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tricycle, trike, velocipede: 0.67%
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```
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candle-examples/examples/mobilenetv4/main.rs
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candle-examples/examples/mobilenetv4/main.rs
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#[cfg(feature = "mkl")]
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extern crate intel_mkl_src;
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#[cfg(feature = "accelerate")]
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extern crate accelerate_src;
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use clap::{Parser, ValueEnum};
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use candle::{DType, IndexOp, D};
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use candle_nn::{Module, VarBuilder};
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use candle_transformers::models::mobilenetv4;
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#[derive(Clone, Copy, Debug, ValueEnum)]
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enum Which {
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Small,
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Medium,
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Large,
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HybridMedium,
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HybridLarge,
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}
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impl Which {
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fn model_filename(&self) -> String {
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let name = match self {
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Self::Small => "conv_small.e2400_r224",
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Self::Medium => "conv_medium.e500_r256",
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Self::HybridMedium => "hybrid_medium.ix_e550_r256",
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Self::Large => "conv_large.e600_r384",
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Self::HybridLarge => "hybrid_large.ix_e600_r384",
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};
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format!("timm/mobilenetv4_{}_in1k", name)
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}
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fn resolution(&self) -> u32 {
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match self {
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Self::Small => 224,
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Self::Medium => 256,
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Self::HybridMedium => 256,
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Self::Large => 384,
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Self::HybridLarge => 384,
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}
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}
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fn config(&self) -> mobilenetv4::Config {
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match self {
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Self::Small => mobilenetv4::Config::small(),
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Self::Medium => mobilenetv4::Config::medium(),
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Self::HybridMedium => mobilenetv4::Config::hybrid_medium(),
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Self::Large => mobilenetv4::Config::large(),
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Self::HybridLarge => mobilenetv4::Config::hybrid_large(),
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}
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}
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}
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#[derive(Parser)]
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struct Args {
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#[arg(long)]
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model: Option<String>,
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#[arg(long)]
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image: String,
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/// Run on CPU rather than on GPU.
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#[arg(long)]
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cpu: bool,
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#[arg(value_enum, long, default_value_t=Which::Small)]
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which: Which,
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}
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pub fn main() -> anyhow::Result<()> {
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let args = Args::parse();
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let device = candle_examples::device(args.cpu)?;
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let image = candle_examples::imagenet::load_image(args.image, args.which.resolution())?
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.to_device(&device)?;
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println!("loaded image {image:?}");
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let model_file = match args.model {
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None => {
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let model_name = args.which.model_filename();
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let api = hf_hub::api::sync::Api::new()?;
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let api = api.model(model_name);
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api.get("model.safetensors")?
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}
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Some(model) => model.into(),
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};
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let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
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let model = mobilenetv4::mobilenetv4(&args.which.config(), 1000, vb)?;
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println!("model built");
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let logits = model.forward(&image.unsqueeze(0)?)?;
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let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
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.i(0)?
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.to_vec1::<f32>()?;
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let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
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prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
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for &(category_idx, pr) in prs.iter().take(5) {
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println!(
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"{:24}: {:.2}%",
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candle_examples::imagenet::CLASSES[category_idx],
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100. * pr
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);
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}
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Ok(())
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}
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