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#[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() as usize)?
.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(())
}
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