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80 lines
2.5 KiB
Rust
80 lines
2.5 KiB
Rust
//! BEiT: BERT Pre-Training of Image Transformers
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//! https://github.com/microsoft/unilm/tree/master/beit
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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;
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use candle::{DType, Device, IndexOp, Result, Tensor, D};
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use candle_nn::{Module, VarBuilder};
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use candle_transformers::models::beit;
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/// Loads an image from disk using the image crate, this returns a tensor with shape
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/// (3, 384, 384). Beit special normalization is applied.
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pub fn load_image384_beit_norm<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
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let img = image::io::Reader::open(p)?
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.decode()
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.map_err(candle::Error::wrap)?
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.resize_to_fill(384, 384, image::imageops::FilterType::Triangle);
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let img = img.to_rgb8();
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let data = img.into_raw();
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let data = Tensor::from_vec(data, (384, 384, 3), &Device::Cpu)?.permute((2, 0, 1))?;
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let mean = Tensor::new(&[0.5f32, 0.5, 0.5], &Device::Cpu)?.reshape((3, 1, 1))?;
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let std = Tensor::new(&[0.5f32, 0.5, 0.5], &Device::Cpu)?.reshape((3, 1, 1))?;
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(data.to_dtype(candle::DType::F32)? / 255.)?
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.broadcast_sub(&mean)?
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.broadcast_div(&std)
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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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}
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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 = load_image384_beit_norm(args.image)?.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 api = hf_hub::api::sync::Api::new()?;
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let api = api.model("vincent-espitalier/candle-beit".into());
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api.get("beit_base_patch16_384.in22k_ft_in22k_in1k_adapted.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 = beit::vit_base(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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