mirror of
https://github.com/storytold/artcraft.git
synced 2026-11-13 05:12:50 +00:00
move model stuff to new file
This commit is contained in:
Brandon Thomas committed
1 parent
58cfbef65e
commit
089aab7930
2 files changed
+155
-154
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+14
-154
@@ -6,8 +6,10 @@ extern crate tensorflow;
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extern crate wavy;
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extern crate world_sys;
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extern crate zmq;
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extern crate failure;
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pub mod ipc;
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pub mod model;
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pub mod synthesis;
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use wavy::*;
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@@ -19,22 +21,13 @@ use std::path::Path;
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use std::process::exit;
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use std::thread;
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use tensorflow::Code;
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use tensorflow::Graph;
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use tensorflow::ImportGraphDefOptions;
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use tensorflow::Session;
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use tensorflow::SessionOptions;
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use tensorflow::SessionRunArgs;
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use tensorflow::Status;
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use tensorflow::Tensor;
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use tensorflow::version;
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use zmq::{Error, Socket};
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use ipc::{QueueSender, AudioQueue};
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use model::load_model;
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use model::print_version;
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use std::sync::Arc;
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const INPUT_NAME : &'static str = "input_A_test";
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const OUTPUT_NAME : &'static str = "generator_A2B_3/output_transpose";
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use failure::_core::time::Duration;
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fn main() {
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print_version();
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@@ -43,135 +36,6 @@ fn main() {
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//run_audio().expect("should work");
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}
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fn print_version() {
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// Python TensorFlow version: 1.14.0
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// Rust TensorFlow version: 1.13.1 (default, must be hand-upgraded)
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let version = version().expect("version");
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println!("Tensorflow version: {}", version);
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}
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struct VoiceModel {
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graph: Graph,
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session: Session,
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}
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impl VoiceModel {
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pub fn load(dir_name: &str) -> Self {
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let mut graph = Graph::new();
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let session = Session::from_saved_model(
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&SessionOptions::new(),
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&["serve"],
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&mut graph,
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dir_name,
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).expect("Should load");
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VoiceModel {
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graph,
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session,
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}
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}
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pub fn evaluate(&self, input: &Tensor<f32>) {
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println!(">>> Input tensor dims: {:?}", input.dims());
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// Run the graph.
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let mut args = SessionRunArgs::new();
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// input_A_test:
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// Tensor("input_A_test:0", shape=(?, 24, ?), dtype=float32)
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args.add_feed(&self.graph.operation_by_name_required(INPUT_NAME)
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.expect(INPUT_NAME), 0, &input);
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// generation_B_test:
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// Tensor("generator_A2B_3/output_transpose:0", shape=(?, 24, ?), dtype=float32)
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let z = args.request_fetch(
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&self.graph.operation_by_name_required(OUTPUT_NAME)
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.expect(OUTPUT_NAME), 0);
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self.session.run(&mut args).expect("Run success");
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// Check our results.
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let z_res = args.fetch::<f32>(z).expect("ret");
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println!("z_rez.dims(): {:?}", z_res.dims());
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println!("z_rez: {:?}", z_res);
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}
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}
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fn load_model() {
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let model_dir = "/home/bt/dev/voder/saved_model";
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let model = VoiceModel::load(model_dir);
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// ] generation_B_test: Tensor("generator_A2B_3/output_transpose:0", shape=(?, 24, ?), dtype=float32)
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// ] input_A_test: Tensor("input_A_test:0", shape=(?, 24, ?), dtype=float32)
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// ] inputs.shape: (1, 24, 380) -- the last dimension is the signal
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let mut input = Tensor::new(&[1, 24, 10])
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.with_values(&[
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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])
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.unwrap();
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model.evaluate(&input);
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model.evaluate(&input);
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model.evaluate(&input);
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}
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/*fn load_model_2() {
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// from regression_checkpoint.rs example
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let filename = "/home/bt/dev/voder/saved_model/saved_model.pb";
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let mut graph = Graph::new();
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let mut proto = Vec::new();
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File::open(filename)
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.expect("opened")
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.read_to_end(&mut proto)
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.expect("cannot read");
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graph.import_graph_def(&proto, &ImportGraphDefOptions::new()).expect("cannot import");
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let session = Session::new(&SessionOptions::new(), &graph).expect("cannot session");
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/*let op_x = graph.operation_by_name_required("x").expect("x");
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let op_y = graph.operation_by_name_required("y").expect("y");
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let op_init = graph.operation_by_name_required("init").expect("init");
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let op_train = graph.operation_by_name_required("train").expect("train");
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let op_w = graph.operation_by_name_required("w").expect("w");
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let op_b = graph.operation_by_name_required("b").expect("b");
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let op_file_path = graph.operation_by_name_required("save/Const").expect("const");
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let op_save = graph.operation_by_name_required("save/control_dependency").expect("save");*/
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//let file_path_tensor: Tensor<String> =
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// Tensor::from(String::from("/home/bt/dev/voder/extra_model/sf1_tm1.ckpt.data-00000-of-00001"));
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//println!("Tensor: {:?}", file_path_tensor);
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}*/
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fn run_audio() -> Result<(), AudioError> {
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println!("Opening microphone system");
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let mut mic = MicrophoneSystem::new(SampleRate::Normal)?;
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@@ -187,19 +51,13 @@ fn run_audio() -> Result<(), AudioError> {
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let mut audio_queue_2 = audio_queue.clone();
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thread::spawn(move || {
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//let mut queue_sender = QueueSender::new();
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//queue_sender.connect();
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let mut context = zmq::Context::new();
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let mut socket = context.socket(zmq::REQ).unwrap();
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/*let mut socket = ctx.socket(zmq::REQ).unwrap();
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socket.connect("tcp://127.0.0.1:1234").unwrap();
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socket.send("hello world!", 0).unwrap();*/
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socket.connect("tcp://127.0.0.1:5555").unwrap();
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let mut reconnect = false;
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let mut fail_count = 0;
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loop {
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let mut drained = audio_queue_2.drain();
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@@ -211,6 +69,8 @@ fn run_audio() -> Result<(), AudioError> {
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if reconnect {
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reconnect = false;
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thread::sleep(Duration::from_millis(200));
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socket = match context.socket(zmq::REQ) {
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Ok(s) => s,
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Err(e) => {
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@@ -220,23 +80,23 @@ fn run_audio() -> Result<(), AudioError> {
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};
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match socket.connect("tcp://127.0.0.1:5555") {
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Ok(_) => {
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println!("B");
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},
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Ok(_) => {},
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Err(err) => {
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println!("Err B: {:?}", err);
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},
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}
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}
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match socket.send(&bytes[..], 0) {
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Ok(_) => { break; },
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Err(e) => {
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println!("send err: {:?}", e);
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reconnect = true;
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fail_count += 1;
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if fail_count > 5 {
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fail_count = 0;
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reconnect = true;
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}
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},
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}
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}
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}
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}
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+141
@@ -0,0 +1,141 @@
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use tensorflow::Code;
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use tensorflow::Graph;
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use tensorflow::ImportGraphDefOptions;
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use tensorflow::Session;
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use tensorflow::SessionOptions;
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use tensorflow::SessionRunArgs;
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use tensorflow::Status;
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use tensorflow::Tensor;
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use tensorflow::version;
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const INPUT_NAME : &'static str = "input_A_test";
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const OUTPUT_NAME : &'static str = "generator_A2B_3/output_transpose";
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pub struct VoiceModel {
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graph: Graph,
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session: Session,
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}
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impl VoiceModel {
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pub fn load(dir_name: &str) -> Self {
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let mut graph = Graph::new();
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let session = Session::from_saved_model(
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&SessionOptions::new(),
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&["serve"],
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&mut graph,
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dir_name,
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).expect("Should load");
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VoiceModel {
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graph,
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session,
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}
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}
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pub fn evaluate(&self, input: &Tensor<f32>) {
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println!(">>> Input tensor dims: {:?}", input.dims());
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// Run the graph.
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let mut args = SessionRunArgs::new();
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// input_A_test:
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// Tensor("input_A_test:0", shape=(?, 24, ?), dtype=float32)
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args.add_feed(&self.graph.operation_by_name_required(INPUT_NAME)
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.expect(INPUT_NAME), 0, &input);
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// generation_B_test:
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// Tensor("generator_A2B_3/output_transpose:0", shape=(?, 24, ?), dtype=float32)
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let z = args.request_fetch(
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&self.graph.operation_by_name_required(OUTPUT_NAME)
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.expect(OUTPUT_NAME), 0);
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self.session.run(&mut args).expect("Run success");
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// Check our results.
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let z_res = args.fetch::<f32>(z).expect("ret");
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println!("z_rez.dims(): {:?}", z_res.dims());
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println!("z_rez: {:?}", z_res);
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}
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}
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pub fn load_model() {
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let model_dir = "/home/bt/dev/voder/saved_model";
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let model = VoiceModel::load(model_dir);
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// ] generation_B_test: Tensor("generator_A2B_3/output_transpose:0", shape=(?, 24, ?), dtype=float32)
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// ] input_A_test: Tensor("input_A_test:0", shape=(?, 24, ?), dtype=float32)
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// ] inputs.shape: (1, 24, 380) -- the last dimension is the signal
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let mut input = Tensor::new(&[1, 24, 10])
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.with_values(&[
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||
0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||
0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||
0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||
0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||
0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||
0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
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0.0f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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])
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.unwrap();
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model.evaluate(&input);
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model.evaluate(&input);
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model.evaluate(&input);
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}
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|
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/*fn load_model_2() {
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// from regression_checkpoint.rs example
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|
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let filename = "/home/bt/dev/voder/saved_model/saved_model.pb";
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let mut graph = Graph::new();
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let mut proto = Vec::new();
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File::open(filename)
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.expect("opened")
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.read_to_end(&mut proto)
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.expect("cannot read");
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graph.import_graph_def(&proto, &ImportGraphDefOptions::new()).expect("cannot import");
|
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|
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let session = Session::new(&SessionOptions::new(), &graph).expect("cannot session");
|
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|
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/*let op_x = graph.operation_by_name_required("x").expect("x");
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let op_y = graph.operation_by_name_required("y").expect("y");
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let op_init = graph.operation_by_name_required("init").expect("init");
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let op_train = graph.operation_by_name_required("train").expect("train");
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let op_w = graph.operation_by_name_required("w").expect("w");
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let op_b = graph.operation_by_name_required("b").expect("b");
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let op_file_path = graph.operation_by_name_required("save/Const").expect("const");
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let op_save = graph.operation_by_name_required("save/control_dependency").expect("save");*/
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|
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//let file_path_tensor: Tensor<String> =
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// Tensor::from(String::from("/home/bt/dev/voder/extra_model/sf1_tm1.ckpt.data-00000-of-00001"));
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//println!("Tensor: {:?}", file_path_tensor);
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}*/
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|
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pub fn print_version() {
|
||||
// Python TensorFlow version: 1.14.0
|
||||
// Rust TensorFlow version: 1.13.1 (default, must be hand-upgraded)
|
||||
let version = version().expect("version");
|
||||
println!("Tensorflow version: {}", version);
|
||||
}
|
||||
|
||||
Reference in new issue
Block a user