pint inputs

Cargo.lock

@@ -5451,6 +5451,7 @@ name = "strafe-ai"
 version = "0.1.0"
 dependencies = [
  "burn",
+ "png",
  "pollster",
  "strafesnet_common",
  "strafesnet_graphics",
@@ -5478,9 +5479,9 @@ dependencies = [
 
 [[package]]
 name = "strafesnet_graphics"
-version = "0.0.11-depth2"
+version = "0.0.11-depth"
 source = "sparse+https://git.itzana.me/api/packages/strafesnet/cargo/"
-checksum = "829804ab9c167365e576de8ebd8a245ad979cb24558b086e693e840697d7956c"
+checksum = "16266ca7e57ce802b7abd24c6cd8f9b8d95752f7eaead27e42b431b9768d6135"
 dependencies = [
  "bytemuck",
  "ddsfile",
@@ -5515,9 +5516,9 @@ dependencies = [
 
 [[package]]
 name = "strafesnet_roblox_bot_player"
-version = "0.6.2-depth2"
+version = "0.6.2-depth"
 source = "sparse+https://git.itzana.me/api/packages/strafesnet/cargo/"
-checksum = "f39e7dfc0cb23e482089dc7eac235ad4b274ccfdb8df7617889a90e64a1e247a"
+checksum = "12d1aa21c174f23f7f7ede583292a8c82e4b3c483fb0d950e58f84d52807f6ed"
 dependencies = [
  "glam",
  "strafesnet_common",

Cargo.toml

@@ -8,9 +8,10 @@ burn = { version = "0.20.1", features = ["cuda", "autodiff"] }
 wgpu = "29.0.0"
 
 strafesnet_common = { version = "0.9.0", registry = "strafesnet" }
-strafesnet_graphics = { version = "=0.0.11-depth2", registry = "strafesnet" }
+strafesnet_graphics = { version = "=0.0.11-depth", registry = "strafesnet" }
 strafesnet_physics = { version = "=0.0.2-surf", registry = "strafesnet" }
 strafesnet_roblox_bot_file = { version = "0.9.4", registry = "strafesnet" }
-strafesnet_roblox_bot_player = { version = "=0.6.2-depth2", registry = "strafesnet" }
+strafesnet_roblox_bot_player = { version = "=0.6.2-depth", registry = "strafesnet" }
 strafesnet_snf = { version = "0.4.0", registry = "strafesnet" }
 pollster = "0.4.0"
+png = "0.18.1"

src/main.rs

@@ -1,7 +1,7 @@
 use burn::backend::Autodiff;
 use burn::nn::loss::{MseLoss, Reduction};
 use burn::nn::{Linear, LinearConfig, Relu};
-use burn::optim::{GradientsParams, Optimizer, AdamConfig};
+use burn::optim::{GradientsParams, Optimizer, SgdConfig};
 use burn::prelude::*;
 
 type InferenceBackend = burn::backend::Cuda<f32>;
@@ -11,11 +11,31 @@ const LIMITS: wgpu::Limits = wgpu::Limits::defaults();
 use strafesnet_graphics::setup;
 use strafesnet_roblox_bot_file::v0;
 
+pub fn output_image_native(image_data: &[u8], texture_dims: (usize, usize), path: String) {
+	use std::io::Write;
+
+	let mut png_data = Vec::<u8>::with_capacity(image_data.len());
+	let mut encoder =
+		png::Encoder::new(&mut png_data, texture_dims.0 as u32, texture_dims.1 as u32);
+	encoder.set_color(png::ColorType::Grayscale);
+	let mut png_writer = encoder.write_header().unwrap();
+	png_writer.write_image_data(image_data).unwrap();
+	png_writer.finish().unwrap();
+
+	let mut file = std::fs::File::create(&path).unwrap();
+	file.write_all(&png_data).unwrap();
+}
+
 const SIZE_X: usize = 64;
 const SIZE_Y: usize = 36;
 const INPUT: usize = SIZE_X * SIZE_Y;
-const HIDDEN: [usize; 2] = [
+const HIDDEN: [usize; 7] = [
+	INPUT >> 1,
+	INPUT >> 2,
 	INPUT >> 3,
+	INPUT >> 4,
+	INPUT >> 5,
+	INPUT >> 6,
 	INPUT >> 7,
 ];
 // MoveForward
@@ -162,7 +182,7 @@ fn training() {
 	let mut last_mx = first.event.mouse_pos.x;
 	let mut last_my = first.event.mouse_pos.y;
 
-	for input_event in it {
+	for (i, input_event) in it.enumerate() {
 		let mouse_dx = input_event.event.mouse_pos.x - last_mx;
 		let mouse_dy = input_event.event.mouse_pos.y - last_my;
 		last_mx = input_event.event.mouse_pos.x;
@@ -302,6 +322,17 @@ fn training() {
 		let inputs_end = inputs.len();
 		println!("inputs = {:?}", &inputs[inputs_start..inputs_end]);
 
+		// write a png
+		output_image_native(
+			&inputs[i * INPUT..(i + 1) * INPUT]
+				.iter()
+				.copied()
+				.map(|f| (f * 255.0) as u8)
+				.collect::<Vec<u8>>(),
+			(SIZE_X, SIZE_Y),
+			format!("depth_images/{i}.png").into(),
+		);
+
 		texture_data.clear();
 	}
 
@@ -310,7 +341,7 @@ fn training() {
 	let mut model: Net<TrainingBackend> = Net::init(&device);
 	println!("Training model ({} parameters)", model.num_params());
 
-	let mut optim = AdamConfig::new().init();
+	let mut optim = SgdConfig::new().init();
 
 	let inputs = Tensor::from_data(
 		TensorData::new(