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

* Do not generate values for constants.

* Add an onnx based example using squeezenet.

candle-examples/Cargo.toml

@@ -16,6 +16,7 @@ candle-datasets = { path = "../candle-datasets", version = "0.3.0" }
 candle-nn = { path = "../candle-nn", version = "0.3.0" }
 candle-transformers = { path = "../candle-transformers", version = "0.3.0" }
 candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.0", optional = true }
+candle-onnx = { path = "../candle-onnx", version = "0.3.0" }
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
 image = { workspace = true }

candle-examples/examples/squeezenet-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/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(())
+}

candle-onnx/examples/onnx_basics.rs

@@ -35,14 +35,15 @@ pub fn main() -> Result<()> {
         }
         Command::SimpleEval { file } => {
             let model = candle_onnx::read_file(file)?;
-            let inputs = model
-                .graph
-                .as_ref()
-                .unwrap()
-                .input
-                .iter()
-                .map(|input| {
+            let graph = model.graph.as_ref().unwrap();
+            let constants: std::collections::HashSet<_> =
+                graph.initializer.iter().map(|i| i.name.as_str()).collect();
+            let mut inputs = std::collections::HashMap::new();
+            for input in graph.input.iter() {
                 use candle_onnx::onnx::tensor_proto::DataType;
+                if constants.contains(input.name.as_str()) {
+                    continue;
+                }
 
                 let type_ = input.r#type.as_ref().expect("no type for input");
                 let type_ = type_.value.as_ref().expect("no type.value for input");
@@ -73,12 +74,12 @@ pub fn main() -> Result<()> {
                     }
                     type_ => anyhow::bail!("unsupported input type {type_:?}"),
                 };
-                    Ok::<_, anyhow::Error>((input.name.clone(), value))
-                })
-                .collect::<Result<_>>()?;
+                println!("input {}: {value:?}", input.name);
+                inputs.insert(input.name.clone(), value);
+            }
             let outputs = candle_onnx::simple_eval(&model, inputs)?;
             for (name, value) in outputs.iter() {
-                println!("{name}: {value:?}")
+                println!("output {name}: {value:?}")
             }
         }
     }

candle-onnx/src/eval.rs

@@ -382,7 +382,7 @@ pub fn simple_eval(
                             Some([p]) => *p as usize,
                             Some([p1, p2, p3, p4]) => {
                                 if p1 != p2 || p1 != p3 || p1 != p4 {
-                                    bail!("pads to be the same {pads:?} {}", node.name)
+                                    bail!("pads have to be the same {pads:?} {}", node.name)
                                 }
                                 *p1 as usize
                             }
@@ -396,7 +396,7 @@ pub fn simple_eval(
                             Some([p1, p2]) => {
                                 if p1 != p2 {
                                     bail!(
-                                        "strides to be the same on both axis {pads:?} {}",
+                                        "strides have to be the same on both axis {pads:?} {}",
                                         node.name
                                     )
                                 }
@@ -412,7 +412,7 @@ pub fn simple_eval(
                             Some([p1, p2]) => {
                                 if p1 != p2 {
                                     bail!(
-                                        "dilations to be the same on both axis {pads:?} {}",
+                                        "dilations have to be the same on both axis {pads:?} {}",
                                         node.name
                                     )
                                 }