use coreml_rs::{mlbatchmodel::CoreMLBatchModelWithState, ComputePlatform, CoreMLModelOptions};
use ndarray::{Array, Array4};
pub fn main() {
let file = std::fs::read("./demo/model_3.mlmodel").unwrap();
let mut model_options = CoreMLModelOptions::default();
model_options.compute_platform = ComputePlatform::CpuAndANE;
let mut model = CoreMLBatchModelWithState::from_buf(file, model_options);
let mut input = Array4::<f32>::zeros((1, 3, 512, 512));
input.fill(1.0f32);
for i in 0..10 {
let _ = model.add_input("image", input.clone().into_dyn(), i);
}
let output = model.predict().unwrap();
for output in output.outputs {
for (_out, v) in output {
let _output: Array<f32, _> = v.extract_to_tensor();
}
}
}