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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::convnext;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
Atto,
Femto,
Pico,
Nano,
Tiny,
Small,
Base,
Large,
AttoV2,
FemtoV2,
PicoV2,
NanoV2,
TinyV2,
BaseV2,
LargeV2,
XLarge,
Huge,
}
impl Which {
fn model_filename(&self) -> String {
let name = match self {
Self::Atto => "convnext_atto.d2_in1k",
Self::Femto => "convnext_femto.d1_in1k",
Self::Pico => "convnext_pico.d1_in1k",
Self::Nano => "convnext_nano.d1h_in1k",
Self::Tiny => "convnext_tiny.fb_in1k",
Self::Small => "convnext_small.fb_in1k",
Self::Base => "convnext_base.fb_in1k",
Self::Large => "convnext_large.fb_in1k",
Self::AttoV2 => "convnextv2_atto.fcmae_ft_in1k",
Self::FemtoV2 => "convnextv2_femto.fcmae_ft_in1k",
Self::PicoV2 => "convnextv2_pico.fcmae_ft_in1k",
Self::NanoV2 => "convnextv2_nano.fcmae_ft_in1k",
Self::TinyV2 => "convnextv2_tiny.fcmae_ft_in1k",
Self::BaseV2 => "convnextv2_base.fcmae_ft_in1k",
Self::LargeV2 => "convnextv2_large.fcmae_ft_in1k",
Self::XLarge => "convnext_xlarge.fb_in22k_ft_in1k",
Self::Huge => "convnextv2_huge.fcmae_ft_in1k",
};
format!("timm/{name}")
}
fn config(&self) -> convnext::Config {
match self {
Self::Atto | Self::AttoV2 => convnext::Config::atto(),
Self::Femto | Self::FemtoV2 => convnext::Config::femto(),
Self::Pico | Self::PicoV2 => convnext::Config::pico(),
Self::Nano | Self::NanoV2 => convnext::Config::nano(),
Self::Tiny | Self::TinyV2 => convnext::Config::tiny(),
Self::Small => convnext::Config::small(),
Self::Base | Self::BaseV2 => convnext::Config::base(),
Self::Large | Self::LargeV2 => convnext::Config::large(),
Self::XLarge => convnext::Config::xlarge(),
Self::Huge => convnext::Config::huge(),
}
}
}
#[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::Tiny)]
which: Which,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let image = candle_examples::imagenet::load_image224(args.image)?.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 = convnext::convnext(&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(())
}
|
