Files
candle/candle-examples/examples/beit/main.rs
v-espitalier 7f1ba8038c Add Beit model ( https://arxiv.org/abs/2106.08254 ) (#2305)
Co-authored-by: v-espitalier <>
2024-07-01 22:11:48 +02:00

80 lines
2.5 KiB
Rust

//! BEiT: BERT Pre-Training of Image Transformers
//! https://github.com/microsoft/unilm/tree/master/beit
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::Parser;
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::beit;
/// Loads an image from disk using the image crate, this returns a tensor with shape
/// (3, 384, 384). Beit special normalization is applied.
pub fn load_image384_beit_norm<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
let img = image::io::Reader::open(p)?
.decode()
.map_err(candle::Error::wrap)?
.resize_to_fill(384, 384, image::imageops::FilterType::Triangle);
let img = img.to_rgb8();
let data = img.into_raw();
let data = Tensor::from_vec(data, (384, 384, 3), &Device::Cpu)?.permute((2, 0, 1))?;
let mean = Tensor::new(&[0.5f32, 0.5, 0.5], &Device::Cpu)?.reshape((3, 1, 1))?;
let std = Tensor::new(&[0.5f32, 0.5, 0.5], &Device::Cpu)?.reshape((3, 1, 1))?;
(data.to_dtype(candle::DType::F32)? / 255.)?
.broadcast_sub(&mean)?
.broadcast_div(&std)
}
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(long)]
image: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let image = load_image384_beit_norm(args.image)?.to_device(&device)?;
println!("loaded image {image:?}");
let model_file = match args.model {
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("vincent-espitalier/candle-beit".into());
api.get("beit_base_patch16_384.in22k_ft_in22k_in1k_adapted.safetensors")?
}
Some(model) => model.into(),
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let model = beit::vit_base(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(())
}