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Add RandomNormal ONNX operator (#2200)

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This commit is contained in:
mokulus
2024-05-21 21:47:32 +02:00
committed by GitHub
parent 9b8537a62f
commit 7ff921c538
2 changed files with 158 additions and 8 deletions
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144
candle-onnx/tests/ops.rs
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@ -2020,6 +2020,150 @@ fn test_random_uniform() -> Result<()> {
Ok(())
}
// "RandomNormal"
#[test]
fn test_random_normal() -> Result<()> {
test(vec![3, 2, 1, 4], None, None)?;
test(vec![2, 2, 2, 2], Some(-10.0), None)?;
test(vec![2, 2, 2, 2], None, Some(10.0))?;
test(vec![1, 2, 3, 4], Some(-10.0), Some(10.0))?;
fn test(shape: Vec<i64>, mean: Option<f32>, scale: Option<f32>) -> Result<()> {
let att_mean = AttributeProto {
name: "mean".to_string(),
ref_attr_name: "mean".to_string(),
i: 0,
doc_string: "mean".to_string(),
r#type: 1, // FLOAT
f: mean.unwrap_or(0.0),
s: vec![],
t: None,
g: None,
sparse_tensor: None,
tp: None,
floats: vec![],
ints: vec![],
strings: vec![],
tensors: vec![],
graphs: vec![],
sparse_tensors: vec![],
type_protos: vec![],
};
let att_scale = AttributeProto {
name: "scale".to_string(),
ref_attr_name: "scale".to_string(),
i: 0,
doc_string: "scale".to_string(),
r#type: 1, // FLOAT
f: scale.unwrap_or(1.0),
s: vec![],
t: None,
g: None,
sparse_tensor: None,
tp: None,
floats: vec![],
ints: vec![],
strings: vec![],
tensors: vec![],
graphs: vec![],
sparse_tensors: vec![],
type_protos: vec![],
};
let att_shape = AttributeProto {
name: "shape".to_string(),
ref_attr_name: "shape".to_string(),
i: 0,
doc_string: "shape".to_string(),
r#type: 7, // INTS
f: 0.0,
s: vec![],
t: None,
g: None,
sparse_tensor: None,
tp: None,
floats: vec![],
ints: shape,
strings: vec![],
tensors: vec![],
graphs: vec![],
sparse_tensors: vec![],
type_protos: vec![],
};
let att_dtype = AttributeProto {
name: "dtype".to_string(),
ref_attr_name: "dtype".to_string(),
i: 11, // DOUBLE
doc_string: "dtype".to_string(),
r#type: 2, // INT
f: 0.0,
s: vec![],
t: None,
g: None,
sparse_tensor: None,
tp: None,
floats: vec![],
ints: vec![],
strings: vec![],
tensors: vec![],
graphs: vec![],
sparse_tensors: vec![],
type_protos: vec![],
};
let attrs = {
let mut mut_attrs = vec![att_shape, att_dtype];
if mean.is_some() {
mut_attrs.push(att_mean);
}
if scale.is_some() {
mut_attrs.push(att_scale);
}
mut_attrs
};
let manual_graph = create_model_proto_with_graph(Some(GraphProto {
node: vec![NodeProto {
op_type: "RandomNormal".to_string(),
domain: "".to_string(),
attribute: attrs,
input: vec![],
output: vec![OUTPUT_Z.to_string()],
name: "".to_string(),
doc_string: "".to_string(),
}],
name: "".to_string(),
initializer: vec![],
input: vec![],
output: vec![ValueInfoProto {
name: OUTPUT_Z.to_string(),
doc_string: "".to_string(),
r#type: None,
}],
value_info: vec![],
doc_string: "".to_string(),
sparse_initializer: vec![],
quantization_annotation: vec![],
}));
let eval = candle_onnx::simple_eval(&manual_graph, HashMap::new())?;
assert_eq!(eval.len(), 1);
let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
let data = z.flatten_all()?.to_vec1::<f64>()?;
// test if values are unique
for (i, a) in data.iter().enumerate() {
for (j, b) in data.iter().enumerate() {
if i == j {
continue;
};
assert_ne!(a, b);
}
}
Ok(())
}
Ok(())
}
// "Range"
#[test]
fn test_range() -> Result<()> {