Add more models to the onnx example. (#1273)

* Add more models to the onnx example.

* Input validation.

* Input validation.

* Bugfix.

* Implement clip.

* BatchNorm support.

* Get the efficientnet onnx to work.

candle-examples/examples/onnx/README.md

@@ -0,0 +1,10 @@
+## Using ONNX models in Candle
+
+This example demonstrates how to run ONNX based models in Candle, the model
+being used here is a small sequeezenet variant.
+
+You can run the example with the following command:
+
+```bash
+cargo run --example squeezenet-onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+```

candle-examples/examples/onnx/main.rs

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+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle::{IndexOp, D};
+use clap::{Parser, ValueEnum};
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+    SqueezeNet,
+    EfficientNet,
+}
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    image: String,
+
+    #[arg(long)]
+    model: Option<String>,
+
+    /// The model to be used.
+    #[arg(value_enum, long, default_value_t = Which::SqueezeNet)]
+    which: Which,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+    let image = candle_examples::imagenet::load_image224(args.image)?;
+    let image = match args.which {
+        Which::SqueezeNet => image,
+        Which::EfficientNet => image.permute((1, 2, 0))?,
+    };
+
+    println!("loaded image {image:?}");
+
+    let model = match args.model {
+        Some(model) => std::path::PathBuf::from(model),
+        None => match args.which {
+            Which::SqueezeNet => hf_hub::api::sync::Api::new()?
+                .model("lmz/candle-onnx".into())
+                .get("squeezenet1.1-7.onnx")?,
+            Which::EfficientNet => hf_hub::api::sync::Api::new()?
+                .model("onnx/EfficientNet-Lite4".into())
+                .get("efficientnet-lite4-11.onnx")?,
+        },
+    };
+
+    let model = candle_onnx::read_file(model)?;
+    let graph = model.graph.as_ref().unwrap();
+    let mut inputs = std::collections::HashMap::new();
+    inputs.insert(graph.input[0].name.to_string(), image.unsqueeze(0)?);
+    let mut outputs = candle_onnx::simple_eval(&model, inputs)?;
+    let output = outputs.remove(&graph.output[0].name).unwrap();
+    let prs = match args.which {
+        Which::SqueezeNet => candle_nn::ops::softmax(&output, D::Minus1)?,
+        Which::EfficientNet => output,
+    };
+    let prs = prs.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(())
+}