/* * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, * software distributed under the License is distributed on an * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY * KIND, either express or implied. See the License for the * specific language governing permissions and limitations * under the License. */ extern crate csv; extern crate image; extern crate ndarray; extern crate tvm_frontend as tvm; use std::{ collections::HashMap, convert::TryInto, fs::{self, File}, path::Path, str::FromStr, }; use image::{FilterType, GenericImageView}; use ndarray::{Array, ArrayD, Axis}; use tvm::*; fn main() { let ctx = TVMContext::cpu(0); let img = image::open(concat!(env!("CARGO_MANIFEST_DIR"), "/cat.png")).unwrap(); println!("original image dimensions: {:?}", img.dimensions()); // for bigger size images, one needs to first resize to 256x256 // with `img.resize_exact` method and then `image.crop` to 224x224 let img = img.resize(224, 224, FilterType::Nearest).to_rgb(); println!("resized image dimensions: {:?}", img.dimensions()); let mut pixels: Vec<f32> = vec![]; for pixel in img.pixels() { let tmp = pixel.data; // normalize the RGB channels using mean, std of imagenet1k let tmp = [ (tmp[0] as f32 - 123.0) / 58.395, // R (tmp[1] as f32 - 117.0) / 57.12, // G (tmp[2] as f32 - 104.0) / 57.375, // B ]; for e in &tmp { pixels.push(*e); } } let arr = Array::from_shape_vec((224, 224, 3), pixels).unwrap(); let arr: ArrayD<f32> = arr.permuted_axes([2, 0, 1]).into_dyn(); // make arr shape as [1, 3, 224, 224] acceptable to resnet let arr = arr.insert_axis(Axis(0)); // create input tensor from rust's ndarray let input = NDArray::from_rust_ndarray( &arr, TVMContext::cpu(0), TVMType::from_str("float32").unwrap(), ) .unwrap(); println!( "input size is {:?}", input.shape().expect("cannot get the input shape") ); let graph = fs::read_to_string(concat!(env!("CARGO_MANIFEST_DIR"), "/deploy_graph.json")).unwrap(); // load the built module let lib = Module::load(&Path::new(concat!( env!("CARGO_MANIFEST_DIR"), "/deploy_lib.so" ))) .unwrap(); // get the global TVM graph runtime function let runtime_create_fn = Function::get("tvm.graph_runtime.create").unwrap(); let runtime_create_fn_ret = call_packed!( runtime_create_fn, graph, &lib, &ctx.device_type, &ctx.device_id ) .unwrap(); // get graph runtime module let graph_runtime_module: Module = runtime_create_fn_ret.try_into().unwrap(); // get the registered `load_params` from runtime module let ref load_param_fn = graph_runtime_module .get_function("load_params", false) .unwrap(); // parse parameters and convert to TVMByteArray let params: Vec<u8> = fs::read(concat!(env!("CARGO_MANIFEST_DIR"), "/deploy_param.params")).unwrap(); let barr = TVMByteArray::from(¶ms); // load the parameters call_packed!(load_param_fn, &barr).unwrap(); // get the set_input function let ref set_input_fn = graph_runtime_module .get_function("set_input", false) .unwrap(); call_packed!(set_input_fn, "data".to_string(), &input).unwrap(); // get `run` function from runtime module let ref run_fn = graph_runtime_module.get_function("run", false).unwrap(); // execute the run function. Note that it has no argument call_packed!(run_fn,).unwrap(); // prepare to get the output let output_shape = &mut [1, 1000]; let output = NDArray::empty( output_shape, TVMContext::cpu(0), TVMType::from_str("float32").unwrap(), ); // get the `get_output` function from runtime module let ref get_output_fn = graph_runtime_module .get_function("get_output", false) .unwrap(); // execute the get output function call_packed!(get_output_fn, &0, &output).unwrap(); // flatten the output as Vec<f32> let output = output.to_vec::<f32>().unwrap(); // find the maximum entry in the output and its index let mut argmax = -1; let mut max_prob = 0.; for i in 0..output.len() { if output[i] > max_prob { max_prob = output[i]; argmax = i as i32; } } // create a hash map of (class id, class name) let mut synset: HashMap<i32, String> = HashMap::new(); let file = File::open("synset.csv").unwrap(); let mut rdr = csv::ReaderBuilder::new() .has_headers(true) .from_reader(file); for result in rdr.records() { let record = result.unwrap(); let id: i32 = record[0].parse().unwrap(); let cls = record[1].to_string(); synset.insert(id, cls); } println!( "input image belongs to the class `{}` with probability {}", synset .get(&argmax) .expect("cannot find the class id for argmax"), max_prob ); }