feat: implement VGG13, VGG16 and VGG19 (#1211)

* feat: implement VGG13, VGG16 and VGG19

* Cosmetic fixes.

* More cosmetic tweaks + avoid re-loading the weights on each final layer.

---------

Co-authored-by: Laurent <laurent.mazare@gmail.com>

candle-examples/examples/vgg/README.md

@@ -0,0 +1,13 @@
+## VGG Model Implementation
+
+This example demonstrates the implementation of VGG models (VGG13, VGG16, VGG19) using the Candle library.
+
+The VGG models are defined in `candle-transformers/src/models/vgg.rs`. The main function in `candle-examples/examples/vgg/main.rs` loads an image, selects the VGG model based on the provided argument, and applies the model to the loaded image.
+
+You can run the example with the following command:
+
+```bash
+cargo run --example vgg --release -- --image ../yolo-v8/assets/bike.jpg --which vgg13
+```
+
+In the command above, `--image` specifies the path to the image file and `--which` specifies the VGG model to use (vgg13, vgg16, or vgg19).

candle-examples/examples/vgg/main.rs

@@ -0,0 +1,77 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle::{DType, IndexOp, D};
+use candle_nn::{Module, VarBuilder};
+use candle_transformers::models::vgg::{Models, Vgg};
+use clap::{Parser, ValueEnum};
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+    Vgg13,
+    Vgg16,
+    Vgg19,
+}
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    image: String,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Variant of the model to use.
+    #[arg(value_enum, long, default_value_t = Which::Vgg13)]
+    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)?;
+
+    println!("loaded image {image:?}");
+
+    let api = hf_hub::api::sync::Api::new()?;
+    let repo = match args.which {
+        Which::Vgg13 => "timm/vgg13.tv_in1k",
+        Which::Vgg16 => "timm/vgg16.tv_in1k",
+        Which::Vgg19 => "timm/vgg19.tv_in1k",
+    };
+    let api = api.model(repo.into());
+    let filename = "model.safetensors";
+    let model_file = api.get(filename)?;
+
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
+    let model = match args.which {
+        Which::Vgg13 => Vgg::new(vb, Models::Vgg13)?,
+        Which::Vgg16 => Vgg::new(vb, Models::Vgg16)?,
+        Which::Vgg19 => Vgg::new(vb, Models::Vgg19)?,
+    };
+    let logits = model.forward(&image)?;
+
+    let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
+        .i(0)?
+        .to_vec1::<f32>()?;
+
+    // Sort the predictions and take the top 5
+    let mut top: Vec<_> = prs.iter().enumerate().collect();
+    top.sort_by(|a, b| b.1.partial_cmp(a.1).unwrap());
+    let top = top.into_iter().take(5).collect::<Vec<_>>();
+
+    // Print the top predictions
+    for &(i, p) in &top {
+        println!(
+            "{:50}: {:.2}%",
+            candle_examples::imagenet::CLASSES[i],
+            p * 100.0
+        );
+    }
+
+    Ok(())
+}