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https://github.com/huggingface/candle.git
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bb3471ea31
* Simplify the safetensor usage. * Convert more examples. * Move more examples. * Adapt stable-diffusion.
99 lines
2.8 KiB
Rust
99 lines
2.8 KiB
Rust
//! EfficientNet implementation.
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//!
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//! https://arxiv.org/abs/1905.11946
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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 candle::{DType, IndexOp, D};
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use candle_nn::{Module, VarBuilder};
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use candle_transformers::models::efficientnet::{EfficientNet, MBConvConfig};
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use clap::{Parser, ValueEnum};
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#[derive(Clone, Copy, Debug, ValueEnum)]
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enum Which {
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B0,
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B1,
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B2,
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B3,
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B4,
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B5,
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B6,
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B7,
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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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/// Variant of the model to use.
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#[arg(value_enum, long, default_value_t = Which::B2)]
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which: Which,
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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 = candle_examples::imagenet::load_image224(args.image)?;
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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("lmz/candle-efficientnet".into());
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let filename = match args.which {
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Which::B0 => "efficientnet-b0.safetensors",
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Which::B1 => "efficientnet-b1.safetensors",
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Which::B2 => "efficientnet-b2.safetensors",
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Which::B3 => "efficientnet-b3.safetensors",
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Which::B4 => "efficientnet-b4.safetensors",
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Which::B5 => "efficientnet-b5.safetensors",
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Which::B6 => "efficientnet-b6.safetensors",
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Which::B7 => "efficientnet-b7.safetensors",
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};
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api.get(filename)?
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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 cfg = match args.which {
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Which::B0 => MBConvConfig::b0(),
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Which::B1 => MBConvConfig::b1(),
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Which::B2 => MBConvConfig::b2(),
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Which::B3 => MBConvConfig::b3(),
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Which::B4 => MBConvConfig::b4(),
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Which::B5 => MBConvConfig::b5(),
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Which::B6 => MBConvConfig::b6(),
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Which::B7 => MBConvConfig::b7(),
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};
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let model = EfficientNet::new(vb, cfg, candle_examples::imagenet::CLASS_COUNT as usize)?;
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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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