[ONNX] Do not generate values for constants. (#1272)

* Do not generate values for constants.

* Add an onnx based example using squeezenet.

candle-examples/examples/squeezenet-onnx/main.rs

@@ -0,0 +1,57 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle::{IndexOp, D};
+use clap::Parser;
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    image: String,
+
+    #[arg(long)]
+    model: Option<String>,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+    let image = candle_examples::imagenet::load_image224(args.image)?;
+
+    println!("loaded image {image:?}");
+
+    let model = match args.model {
+        Some(model) => std::path::PathBuf::from(model),
+        None => hf_hub::api::sync::Api::new()?
+            .model("lmz/candle-onnx".into())
+            .get("squeezenet1.1-7.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 logits = outputs.remove(&graph.output[0].name).unwrap();
+    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(())
+}