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/main.rs

@@ -5,7 +5,13 @@ extern crate intel_mkl_src;
 extern crate accelerate_src;
 
 use candle::{IndexOp, D};
-use clap::Parser;
+use clap::{Parser, ValueEnum};
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+    SqueezeNet,
+    EfficientNet,
+}
 
 #[derive(Parser)]
 struct Args {
@@ -14,19 +20,32 @@ struct Args {
 
     #[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 => hf_hub::api::sync::Api::new()?
-            .model("lmz/candle-onnx".into())
-            .get("squeezenet1.1-7.onnx")?,
+        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)?;
@@ -34,10 +53,12 @@ pub fn main() -> anyhow::Result<()> {
     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>()?;
+    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();