Add support for TrOCR Model (#1303)

* add bce with logit loss

* add bce with logit loss

* remove imports

* fix tiny bug

* add test documentation and refactor function

* fix test cases and formatting

* add trocr model

* fix formatting

* commit the actual model lol

* more formatting

* remove tokenizer config

candle-examples/examples/trocr/image_processor.rs

@@ -0,0 +1,154 @@
+use image::{DynamicImage, ImageBuffer};
+use serde::Deserialize;
+use std::collections::HashMap;
+
+use candle::{DType, Device, Result, Tensor};
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct ProcessorConfig {
+    do_resize: bool,
+    height: u32,
+    width: u32,
+    do_rescale: bool,
+    do_normalize: bool,
+    image_mean: Vec<f32>,
+    image_std: Vec<f32>,
+}
+
+impl Default for ProcessorConfig {
+    fn default() -> Self {
+        Self {
+            do_resize: true,
+            height: 384,
+            width: 384,
+            do_rescale: true,
+            do_normalize: true,
+            image_mean: vec![0.5, 0.5, 0.5],
+            image_std: vec![0.5, 0.5, 0.5],
+        }
+    }
+}
+
+pub struct ViTImageProcessor {
+    do_resize: bool,
+    height: u32,
+    width: u32,
+    do_normalize: bool,
+    image_mean: Vec<f32>,
+    image_std: Vec<f32>,
+}
+
+impl ViTImageProcessor {
+    pub fn new(config: &ProcessorConfig) -> Self {
+        Self {
+            do_resize: config.do_resize,
+            height: config.height,
+            width: config.width,
+            do_normalize: config.do_normalize,
+            image_mean: config.image_mean.clone(),
+            image_std: config.image_std.clone(),
+        }
+    }
+
+    pub fn preprocess(&self, images: Vec<&str>) -> Result<Tensor> {
+        let height = self.height as usize;
+        let width = self.width as usize;
+        let channels = 3;
+
+        let images = self.load_images(images)?;
+
+        let resized_images: Vec<DynamicImage> = if self.do_resize {
+            images
+                .iter()
+                .map(|image| self.resize(image.clone(), None).unwrap())
+                .collect()
+        } else {
+            images
+        };
+
+        let normalized_images: Vec<Tensor> = if self.do_normalize {
+            resized_images
+                .iter()
+                .map(|image| self.normalize(image.clone(), None, None).unwrap())
+                .collect()
+        } else {
+            let resized_images: Vec<ImageBuffer<image::Rgb<u8>, Vec<u8>>> =
+                resized_images.iter().map(|image| image.to_rgb8()).collect();
+            let data = resized_images
+                .into_iter()
+                .map(|image| image.into_raw())
+                .collect::<Vec<Vec<u8>>>();
+
+            data.iter()
+                .map(|image| {
+                    Tensor::from_vec(image.clone(), (height, width, channels), &Device::Cpu)
+                        .unwrap()
+                        .permute((2, 0, 1))
+                        .unwrap()
+                })
+                .collect::<Vec<Tensor>>()
+        };
+
+        Tensor::stack(&normalized_images, 0)
+    }
+
+    fn resize(
+        &self,
+        image: image::DynamicImage,
+        size: Option<HashMap<String, u32>>,
+    ) -> Result<image::DynamicImage> {
+        let (height, width) = match &size {
+            Some(size) => (size.get("height").unwrap(), size.get("width").unwrap()),
+            None => (&self.height, &self.width),
+        };
+
+        let resized_image =
+            image.resize_exact(*width, *height, image::imageops::FilterType::Triangle);
+
+        Ok(resized_image)
+    }
+
+    fn normalize(
+        &self,
+        image: image::DynamicImage,
+        mean: Option<Vec<f32>>,
+        std: Option<Vec<f32>>,
+    ) -> Result<Tensor> {
+        let mean = match mean {
+            Some(mean) => mean,
+            None => self.image_mean.clone(),
+        };
+
+        let std = match std {
+            Some(std) => std,
+            None => self.image_std.clone(),
+        };
+
+        let mean = Tensor::from_vec(mean, (3, 1, 1), &Device::Cpu)?;
+        let std = Tensor::from_vec(std, (3, 1, 1), &Device::Cpu)?;
+
+        let image = image.to_rgb8();
+        let data = image.into_raw();
+
+        let height = self.height as usize;
+        let width = self.width as usize;
+        let channels = 3;
+
+        let data =
+            Tensor::from_vec(data, &[height, width, channels], &Device::Cpu)?.permute((2, 0, 1))?;
+
+        (data.to_dtype(DType::F32)? / 255.)?
+            .broadcast_sub(&mean)?
+            .broadcast_div(&std)
+    }
+
+    pub fn load_images(&self, image_path: Vec<&str>) -> Result<Vec<image::DynamicImage>> {
+        let mut images: Vec<image::DynamicImage> = Vec::new();
+        for path in image_path {
+            let img = image::io::Reader::open(path)?.decode().unwrap();
+            images.push(img);
+        }
+
+        Ok(images)
+    }
+}

candle-examples/examples/trocr/main.rs

@@ -0,0 +1,132 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::Error as E;
+use clap::{Parser, ValueEnum};
+
+use candle::{DType, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_nn::VarBuilder;
+use candle_transformers::models::trocr;
+
+use tokenizers::Tokenizer;
+mod image_processor;
+
+#[derive(Clone, Debug, Copy, ValueEnum)]
+enum Which {
+    Base,
+    Large,
+}
+
+#[derive(Parser, Debug)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    /// Choose the variant of the model to run.
+    #[arg(long, default_value = "base")]
+    which: Which,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Text to be translated
+    #[arg(long)]
+    image: String,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    use hf_hub::api::sync::Api;
+    let args = Args::parse();
+
+    let tokenizer_dec = {
+        let tokenizer = Api::new()?
+            .model(String::from("ToluClassics/candle-trocr-tokenizer"))
+            .get("tokenizer.json")?;
+
+        Tokenizer::from_file(&tokenizer).map_err(E::msg)?
+    };
+
+    let mut tokenizer_dec = TokenOutputStream::new(tokenizer_dec);
+
+    let device = candle_examples::device(args.cpu)?;
+
+    let vb = {
+        let model = match args.model {
+            Some(model) => std::path::PathBuf::from(model),
+            None => match args.which {
+                Which::Base => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-base-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/3".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+                Which::Large => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-large-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/6".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+            },
+        };
+        println!("model: {:?}", model);
+        unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? }
+    };
+
+    let encoder_config = match args.which {
+        Which::Base => candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten(),
+        Which::Large => {
+            candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten()
+        }
+    };
+
+    let decoder_config = trocr::TrOCRConfig::default();
+    let mut model = trocr::TrOCRModel::new(&encoder_config, &decoder_config, vb)?;
+
+    let config = image_processor::ProcessorConfig::default();
+    let processor = image_processor::ViTImageProcessor::new(&config);
+
+    let image = vec![args.image.as_str()];
+    let image = processor.preprocess(image)?;
+
+    let encoder_xs = model.encoder().forward(&image)?;
+
+    let mut logits_processor =
+        candle_transformers::generation::LogitsProcessor::new(1337, None, None);
+
+    let mut token_ids: Vec<u32> = vec![decoder_config.decoder_start_token_id];
+    for index in 0..1000 {
+        let context_size = if index >= 1 { 1 } else { token_ids.len() };
+        let start_pos = token_ids.len().saturating_sub(context_size);
+        let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
+
+        let logits = model.decode(&input_ids, &encoder_xs, start_pos)?;
+
+        let logits = logits.squeeze(0)?;
+        let logits = logits.get(logits.dim(0)? - 1)?;
+        let token = logits_processor.sample(&logits)?;
+        token_ids.push(token);
+
+        if let Some(t) = tokenizer_dec.next_token(token)? {
+            use std::io::Write;
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
+        if token == decoder_config.eos_token_id {
+            break;
+        }
+    }
+
+    if let Some(rest) = tokenizer_dec.decode_rest().map_err(E::msg)? {
+        print!("{rest}");
+    }
+    println!();
+
+    Ok(())
+}

candle-examples/examples/trocr/readme.md

@@ -0,0 +1,16 @@
+# candle-trocr
+
+`TrOCR` is a transformer OCR Model. In this example it is used to
+transcribe image text. See the associated [model
+card](https://huggingface.co/microsoft/trocr-base-printed) for details on
+the model itself.
+
+## Running an example
+
+```bash
+cargo run --example trocr --release --  --which base --cpu --image assets/trocr.png
+```
+
+```
+<s> industry , Mr. Brown commented icily . " Let us have a</s>
+```