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* Add MobileOne model. * Clippy fixes * Remove a comment. --------- Co-authored-by: laurent <[email protected]>
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# candle-mobileone | ||
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[MobileOne: An Improved One millisecond Mobile Backbone](https://arxiv.org/abs/2206.04040). | ||
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This candle implementation uses a pre-trained MobileOne network for inference. 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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``` | ||
$ cargo run --example mobileone --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which s2 | ||
loaded image Tensor[dims 3, 224, 224; f32] | ||
model built | ||
mountain bike, all-terrain bike, off-roader: 79.33% | ||
bicycle-built-for-two, tandem bicycle, tandem: 15.32% | ||
crash helmet : 2.58% | ||
unicycle, monocycle : 1.70% | ||
alp : 0.21% | ||
``` |
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#[cfg(feature = "mkl")] | ||
extern crate intel_mkl_src; | ||
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#[cfg(feature = "accelerate")] | ||
extern crate accelerate_src; | ||
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use clap::{Parser, ValueEnum}; | ||
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use candle::{DType, IndexOp, D}; | ||
use candle_nn::{Module, VarBuilder}; | ||
use candle_transformers::models::mobileone; | ||
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#[derive(Clone, Copy, Debug, ValueEnum)] | ||
enum Which { | ||
S0, | ||
S1, | ||
S2, | ||
S3, | ||
S4, | ||
} | ||
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impl Which { | ||
fn model_filename(&self) -> String { | ||
let name = match self { | ||
Self::S0 => "s0", | ||
Self::S1 => "s1", | ||
Self::S2 => "s2", | ||
Self::S3 => "s3", | ||
Self::S4 => "s4", | ||
}; | ||
format!("timm/mobileone_{}.apple_in1k", name) | ||
} | ||
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fn config(&self) -> mobileone::Config { | ||
match self { | ||
Self::S0 => mobileone::Config::s0(), | ||
Self::S1 => mobileone::Config::s1(), | ||
Self::S2 => mobileone::Config::s2(), | ||
Self::S3 => mobileone::Config::s3(), | ||
Self::S4 => mobileone::Config::s4(), | ||
} | ||
} | ||
} | ||
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#[derive(Parser)] | ||
struct Args { | ||
#[arg(long)] | ||
model: Option<String>, | ||
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#[arg(long)] | ||
image: String, | ||
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/// Run on CPU rather than on GPU. | ||
#[arg(long)] | ||
cpu: bool, | ||
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#[arg(value_enum, long, default_value_t=Which::S0)] | ||
which: Which, | ||
} | ||
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pub fn main() -> anyhow::Result<()> { | ||
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_image224(args.image)?; | ||
println!("loaded image {image:?}"); | ||
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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(), | ||
}; | ||
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let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? }; | ||
let model = mobileone::mobileone(&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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