onnx-ir 0.15.0

Library for parsing ONNX models
Documentation 
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use crate::ir::{ArgType, Node};
/// Create a FlattenConfig from the attributes of the node
pub fn flatten_config(curr: &Node) -> (usize, usize) {
    // the begin dimension is the first dimension (Default: 1 per ONNX spec)
    let mut start_dim: i64 = 1;

    // check if the node has only one input
    if curr.inputs.len() != 1 {
        panic!(
            "Flatten: multiple inputs are not supported (got {:?})",
            curr.inputs.len()
        );
    }

    // extract the shape of the input tensor
    let tensor = match curr.inputs.first().unwrap().clone().ty {
        ArgType::Tensor(tensor) => tensor,
        _ => panic!("Only tensor input is valid"),
    };

    // check if the input tensor has at least 2 dimensions
    if tensor.dim < 2 {
        panic!(
            "Flatten: input tensor must have at least 2 dimensions (got {:?})",
            tensor.dim
        );
    }

    // the end dimension is the last dimension
    let end_dim = tensor.dim - 1;

    // extract the attributes
    for (key, value) in curr.attrs.iter() {
        if key.as_str() == "axis" {
            start_dim = value.clone().into_i64();
        }
    }

    // if beg_dim is negative, it is counted from the end
    if start_dim < 0 {
        start_dim += tensor.dim as i64;
    }

    (start_dim as usize, end_dim)
}

pub fn shape_config(curr: &Node) -> (usize, usize) {
    if curr.inputs.len() != 1 {
        panic!(
            "Shape: multiple inputs are not supported (got {:?})",
            curr.inputs.len()
        );
    }

    // Extract the shape of the input tensor
    let tensor = match curr.inputs.first().unwrap().clone().ty {
        ArgType::Tensor(tensor) => tensor,
        _ => panic!("Only tensor input is valid"),
    };

    // Default: all axes up to the last one (included)
    let mut start_dim: i64 = 0;
    let mut end_dim: i64 = tensor.dim as i64;

    // Extract the attributes
    for (key, value) in curr.attrs.iter() {
        match key.as_str() {
            "start" => start_dim = value.clone().into_i64(),
            "end" => end_dim = value.clone().into_i64(),
            _ => {}
        }
    }

    // If dim is negative, it is counted from the end
    if start_dim < 0 {
        start_dim += tensor.dim as i64;
    }
    if end_dim < 0 {
        end_dim += tensor.dim as i64;
    }

    (start_dim as usize, end_dim as usize)
}