[\s\S]*?<\/div>/, "");
- raw = normalizeMarkdown(raw);
- const page = convertPage(raw, rel, { state, base: "/heartkit" });
- let text = page.text
- .replace(/#only-light/g, "#only-light")
- .replace(//g, "
"); - if (rel === "index.md") - text = text.replace( - /^description:.*$/m, - 'description: \"AI development kit for heart monitoring on Ambiq devices.\"', - ); - if (intro) text = text.replace(/^description:.*$/m, (line) => `${line}\nhero:\n tagline: ${JSON.stringify(intro)}`); - let blockIndex = 0; - text = text.replace( - /^([ \t]*)(`{3,})([\w+-]+)([^\n]*)\n([\s\S]*?)^\1\2[ \t]*$/gm, - (all, indent, fence, lang, meta, body) => { - if (["mermaid", "text"].includes(lang)) return all; - const extension = - { - python: "py", - py: "py", - bash: "sh", - sh: "sh", - console: "sh", - json: "json", - jsonc: "jsonc", - yaml: "yaml", - yml: "yaml", - }[lang] || lang; - const code = body - .split("\n") - .map((l) => (l.startsWith(indent) ? l.slice(indent.length) : l)) - .join("\n") - .trimEnd(); - if (lang === "json") { - try { - JSON.parse(code); - } catch (error) { - throw new Error(`Invalid JSON example in ${rel}: ${error.message}`); - } - } - if (meta.includes("fragment")) - return `${indent}${fence}${lang} title="Configuration fragment"\n${body}${indent}${fence}`; - const lines = code.split("\n").length; - const name = `${rel.replace(/\.md$/, "").replaceAll("/", "-")}-${++blockIndex}.${extension}`; - if ( - (["json", "jsonc", "yaml", "yml"].includes(lang) && lines > 16) || - (["py", "python"].includes(lang) && lines > 45) - ) { - mkdirSync("public/examples", { recursive: true }); - writeFileSync("public/examples/" + name, code + "\n"); - return `${indent}\n\n${indent}${fence}${lang} title="${name}"\n${body}${indent}${fence}\n\n${indent} `;
- }
- const title =
- lang === "py" || lang === "python"
- ? "Python example"
- : ["bash", "sh", "console"].includes(lang)
- ? "Terminal"
- : lang.toUpperCase() + " example";
- return `${indent}${fence}${lang}${meta.includes("title=") ? meta : ' title="' + title + '"' + meta}\n${body}${indent}${fence}`;
- },
- );
- if (text.includes(" readdirSync(dir, { withFileTypes: true }).flatMap((entry) =>
+ entry.isDirectory() ? walk(join(dir, entry.name)) : [join(dir, entry.name)],
+);
+rmSync("public/examples", { recursive: true, force: true });
+mkdirSync("public/examples", { recursive: true });
+const downloads = new Map();
+for (const file of walk("src/content/docs").filter((path) => path.endsWith(".mdx"))) {
+ const source = readFileSync(file, "utf8");
+ for (const match of source.matchAll(/Download notebook View source Open in Colab
',
"This example displays saved outputs. Building the documentation does not run training. Check dataset paths for your notebook working directory before running.",
]
diff --git a/astro-site/scripts/build-reference.mjs b/astro-site/scripts/build-reference.mjs
index 576fce46..7609b178 100644
--- a/astro-site/scripts/build-reference.mjs
+++ b/astro-site/scripts/build-reference.mjs
@@ -1,6 +1,6 @@
import { isPrivateModule } from "./public-docs.mjs";
import { execFileSync } from "node:child_process";
-import { mkdirSync, readFileSync, writeFileSync, rmSync, readdirSync } from "node:fs";
+import { mkdirSync, readFileSync, writeFileSync, rmSync } from "node:fs";
import { resolve } from "node:path";
const repo = resolve("..");
const run = (cmd, args) =>
@@ -12,6 +12,8 @@ const run = (cmd, args) =>
rmSync("public/reference", { recursive: true, force: true });
const commit = run("git", ["rev-parse", "HEAD"]).trim();
mkdirSync(".cache", { recursive: true });
+mkdirSync("src/data", { recursive: true });
+rmSync("src/content/docs/reference/api", { recursive: true, force: true });
const dump = JSON.parse(
run("uv", [
"tool",
@@ -107,26 +109,13 @@ writeFileSync(
);
writeFileSync(
"src/data/redirects.json",
- JSON.stringify(
- Object.fromEntries(
- [...Object.entries({
- "/api/summary": "/heartkit/reference/",
- "/assets/modes/python-demo-snippet": "/heartkit/modes/demo/",
- "/assets/tasks/beat/beat-classes": "/heartkit/tasks/beat/",
- "/assets/tasks/rhythm/rhythm-classes": "/heartkit/tasks/rhythm/",
- "/assets/tasks/segmentation/segmentation-classes": "/heartkit/tasks/segmentation/",
- "/assets/usage/json-configuration": "/heartkit/modes/train/",
- "/assets/usage/python-configuration": "/heartkit/usage/python/",
- ...Object.fromEntries(readdirSync("../docs/assets/zoo", {withFileTypes:true})
- .filter(entry => entry.isDirectory())
- .filter(entry => readdirSync(`../docs/assets/zoo/${entry.name}`).includes("results.md"))
- .map(entry => [`/assets/zoo/${entry.name}/results`, entry.name === "diagnostic" ? "/heartkit/tasks/diagnostic/" : `/heartkit/zoo/${entry.name}/`])),
- }), ...modules.map((m) => [
- "/api/" + m.path.replaceAll(".", "/"),
- "/heartkit/" + route(m) + "/",
- ]), ...readdirSync("../docs/guides").filter(name => name.endsWith(".ipynb")).map(name => ["/guides/" + name, "/heartkit/guides/" + name.replace(".ipynb", "") + "/"])],
- ),
- ),
+ JSON.stringify({
+ ...JSON.parse(readFileSync("src/redirects.json", "utf8")),
+ ...Object.fromEntries(modules.map((m) => [
+ "/api/" + m.path.replaceAll(".", "/"),
+ "/heartkit/" + route(m) + "/",
+ ])),
+ }),
);
writeFileSync(
"src/content/docs/reference/index.mdx",
diff --git a/astro-site/scripts/canonical-content.test.mjs b/astro-site/scripts/canonical-content.test.mjs
new file mode 100644
index 00000000..27f7834c
--- /dev/null
+++ b/astro-site/scripts/canonical-content.test.mjs
@@ -0,0 +1,21 @@
+import assert from "node:assert/strict";
+import { execFileSync } from "node:child_process";
+import { readFileSync, readdirSync } from "node:fs";
+import { join, relative } from "node:path";
+import test from "node:test";
+
+const walk = (dir) => readdirSync(dir, { withFileTypes: true }).flatMap((entry) =>
+ entry.isDirectory() ? walk(join(dir, entry.name)) : [join(dir, entry.name)],
+);
+const snapshot = () => Object.fromEntries([
+ ...walk("src/content/docs").filter((p) => p.endsWith(".mdx") && !p.startsWith("src/content/docs/reference/")),
+ "src/navigation.mjs", "src/redirects.json",
+ ...walk("public").filter((p) => !/^public\/(reference|notebooks|examples)\//.test(p)),
+].map((p) => [relative(".", p), readFileSync(p).toString("base64")]));
+
+test("generation preserves authored pages, navigation, redirects and static assets", () => {
+ const before = snapshot();
+ assert(Object.keys(before).length > 40, "Missing canonical content");
+ execFileSync("npm", ["run", "prepare:docs"], { stdio: "pipe" });
+ assert.deepEqual(snapshot(), before);
+});
diff --git a/astro-site/scripts/check-output.mjs b/astro-site/scripts/check-output.mjs
index 9279d417..65c4fb77 100644
--- a/astro-site/scripts/check-output.mjs
+++ b/astro-site/scripts/check-output.mjs
@@ -71,18 +71,13 @@ console.log(
`Verified internal links across ${cache.size} pages, ${index.rows.length} API symbols and discovery exports.`,
);
-for (const path of files(resolve("../docs")).filter(
- (p) => p.endsWith(".md") && !p.includes("/assets/"),
-)) {
- const rel = relative(resolve("../docs"), path).replace(/(?:index)?\.md$/, "");
- if (internalPages.has(relative(resolve("../docs"), path))) {
- assert(!existsSync(join(root, rel, "index.html")), `Internal page published: ${rel}`);
- continue;
- }
- assert(
- existsSync(join(root, rel, "index.html")),
- `Missing authored route: ${rel}`,
- );
+for (const path of files(resolve("src/content/docs")).filter((p) => /\.mdx?$/.test(p))) {
+ const rel = relative(resolve("src/content/docs"), path).replace(/(?:index)?\.mdx?$/, "");
+ assert(existsSync(join(root, rel, "index.html")), `Missing authored route: ${rel}`);
+}
+for (const path of internalPages) {
+ const rel = path.replace(/(?:index)?\.md$/, "");
+ assert(!existsSync(join(root, rel, "index.html")), `Internal page published: ${rel}`);
}
const reference = readFileSync(
"dist/reference/api/heartkit/defines/index.md",
@@ -150,8 +145,8 @@ for (const file of files(join(root, "examples")).filter((f) =>
JSON.parse(readFileSync(file, "utf8"));
}
-for (const name of readdirSync("../docs/guides").filter(n=>n.endsWith(".ipynb"))) {
- const original=readFileSync("../docs/guides/"+name);
+for (const name of readdirSync("../notebooks").filter(n=>n.endsWith(".ipynb"))) {
+ const original=readFileSync("../notebooks/"+name);
assert.deepEqual(original,readFileSync("dist/notebooks/"+name));
const notebook=JSON.parse(original);
const expected=notebook.cells.flatMap(c=>c.outputs??[]).filter(o=>o.data?.["image/png"]).length;
@@ -164,3 +159,14 @@ const legacy = JSON.parse(readFileSync("scripts/legacy-routes.json", "utf8"));
for (const route of legacy.routes)
assert(existsSync(join(root, route, "index.html")), `Missing published route: ${route}`);
console.log(`Verified all ${legacy.routes.length} original published routes remain available.`);
+
+for (const path of files(resolve("../notebooks")).filter((p) => p.endsWith(".ipynb"))) {
+ const notebook = JSON.parse(readFileSync(path, "utf8"));
+ for (const cell of notebook.cells) {
+ if (cell.cell_type !== "markdown") continue;
+ const source = Array.isArray(cell.source) ? cell.source.join("") : cell.source;
+ for (const match of source.matchAll(/(?:github\.com|github)\/AmbiqAI\/heartkit\/blob\/main\/([^\s)"<>]+)/g)) {
+ assert(existsSync(resolve("..", match[1])), `Notebook source link is missing: ${path} -> ${match[1]}`);
+ }
+ }
+}
diff --git a/astro-site/scripts/normalize-markdown.mjs b/astro-site/scripts/normalize-markdown.mjs
deleted file mode 100644
index 98605fed..00000000
--- a/astro-site/scripts/normalize-markdown.mjs
+++ /dev/null
@@ -1,90 +0,0 @@
-/** Normalize Material block syntax before the shared MDX converter runs. */
-export function normalizeMarkdown(text) {
- const lines = text.split("\n");
- function blocks(input) {
- const out = [];
- let fence;
- for (let i = 0; i < input.length; i++) {
- const line = input[i];
- const token = line.match(/^\s*(`{3,}|~{3,})/);
- if (token) {
- if (!fence) fence = token[1];
- else if (token[1].startsWith(fence)) fence = undefined;
- out.push(line);
- continue;
- }
- if (fence) {
- out.push(line);
- continue;
- }
- const match = line.match(
- /^([ \t]*)(!!!|\?\?\?\+?)\s+([\w-]+)(?:\s+(.*))?$/,
- );
- if (match) {
- const [, indent, marker, rawType, rawTitle = ""] = match;
- const type = rawType.toLowerCase();
- const title = rawTitle.replace(/^["']|["']$/g, "");
- const body = [];
- let j = i + 1;
- while (
- j < input.length &&
- (!input[j].trim() || input[j].startsWith(indent + " "))
- ) {
- body.push(input[j].trim() ? input[j].slice(indent.length + 4) : "");
- j++;
- }
- const inner = blocks(body).join("\n").trim();
- if (marker.startsWith("???")) {
- const label = title || type[0].toUpperCase() + type.slice(1);
- out.push(
- "",
- `${indent}"); - if (rel === "index.md") - text = text.replace( - /^description:.*$/m, - 'description: \"AI development kit for heart monitoring on Ambiq devices.\"', - ); - if (intro) text = text.replace(/^description:.*$/m, (line) => `${line}\nhero:\n tagline: ${JSON.stringify(intro)}`); - let blockIndex = 0; - text = text.replace( - /^([ \t]*)(`{3,})([\w+-]+)([^\n]*)\n([\s\S]*?)^\1\2[ \t]*$/gm, - (all, indent, fence, lang, meta, body) => { - if (["mermaid", "text"].includes(lang)) return all; - const extension = - { - python: "py", - py: "py", - bash: "sh", - sh: "sh", - console: "sh", - json: "json", - jsonc: "jsonc", - yaml: "yaml", - yml: "yaml", - }[lang] || lang; - const code = body - .split("\n") - .map((l) => (l.startsWith(indent) ? l.slice(indent.length) : l)) - .join("\n") - .trimEnd(); - if (lang === "json") { - try { - JSON.parse(code); - } catch (error) { - throw new Error(`Invalid JSON example in ${rel}: ${error.message}`); - } - } - if (meta.includes("fragment")) - return `${indent}${fence}${lang} title="Configuration fragment"\n${body}${indent}${fence}`; - const lines = code.split("\n").length; - const name = `${rel.replace(/\.md$/, "").replaceAll("/", "-")}-${++blockIndex}.${extension}`; - if ( - (["json", "jsonc", "yaml", "yml"].includes(lang) && lines > 16) || - (["py", "python"].includes(lang) && lines > 45) - ) { - mkdirSync("public/examples", { recursive: true }); - writeFileSync("public/examples/" + name, code + "\n"); - return `${indent}
"));
- assert(closed.includes("
Advanced
")); - assert( - convert('# Test\n\n???+ note "Expanded"\n\n Detail.').includes( - "",
- ),
- );
-});
-test("image attributes become image properties; code samples remain verbatim", () => {
- const image = normalizeMarkdown('{ width="540" }');
- assert.equal(image, '
');
- const fenced = '```text\n!!! Example\n{ width="540" }\n```';
- assert.equal(normalizeMarkdown(fenced), fenced);
-});
diff --git a/astro-site/scripts/test-build-notebooks.py b/astro-site/scripts/test-build-notebooks.py
index a3fedc54..be8f8958 100644
--- a/astro-site/scripts/test-build-notebooks.py
+++ b/astro-site/scripts/test-build-notebooks.py
@@ -9,14 +9,14 @@
from pathlib import Path
SCRIPT = Path(__file__).with_name("build-notebooks.py")
-GUIDES = SCRIPT.parents[2] / "docs" / "guides"
+GUIDES = SCRIPT.parents[2] / "notebooks"
class NotebookRenderingTest(unittest.TestCase):
def test_badge_toolbar_preserves_byot_introduction(self):
with tempfile.TemporaryDirectory() as directory:
root = Path(directory)
- shutil.copytree(GUIDES, root / "docs" / "guides")
+ shutil.copytree(GUIDES, root / "notebooks")
site = root / "site"
pages = site / "src" / "content" / "docs" / "guides"
pages.mkdir(parents=True)
@@ -33,10 +33,30 @@ def test_badge_toolbar_preserves_byot_introduction(self):
self.assertNotIn("View in Colab", rendered)
self.assertIn("Open in Colab", rendered)
+ def test_removed_notebook_removes_only_its_generated_page(self):
+ with tempfile.TemporaryDirectory() as directory:
+ root = Path(directory)
+ notebooks = root / "notebooks"
+ notebooks.mkdir()
+ source = notebooks / "removed.ipynb"
+ source.write_text(json.dumps({"cells": [{"cell_type": "markdown", "source": ["# Removed"]}]}))
+ site = root / "site"
+ pages = site / "src/content/docs/guides"
+ pages.mkdir(parents=True)
+ authored = pages / "authored.md"
+ authored.write_text("# Authored guide")
+ subprocess.run([sys.executable, str(SCRIPT)], cwd=site, check=True)
+ self.assertTrue((pages / "removed.md").exists())
+ source.unlink()
+ subprocess.run([sys.executable, str(SCRIPT)], cwd=site, check=True)
+ self.assertFalse((pages / "removed.md").exists())
+ self.assertFalse((site / "public/notebooks/removed.ipynb").exists())
+ self.assertEqual(authored.read_text(), "# Authored guide")
+
def test_colab_mention_does_not_drop_ordinary_prose(self):
with tempfile.TemporaryDirectory() as directory:
root = Path(directory)
- guides = root / "docs" / "guides"
+ guides = root / "notebooks"
guides.mkdir(parents=True)
prose = "Choose View in Colab to run this example.\n\nKeep your dataset paths configured."
(guides / "example.ipynb").write_text(json.dumps({
diff --git a/docs/datasets/byod.md b/astro-site/src/content/docs/datasets/byod.mdx
similarity index 85%
rename from docs/datasets/byod.md
rename to astro-site/src/content/docs/datasets/byod.mdx
index 6bfc62c7..36707a06 100644
--- a/docs/datasets/byod.md
+++ b/astro-site/src/content/docs/datasets/byod.mdx
@@ -1,4 +1,7 @@
-# Bring-Your-Own-Dataset (BYOD)
+---
+title: "Bring-Your-Own-Dataset (BYOD)"
+description: "The Bring-Your-Own-Dataset (BYOD) feature allows users to add custom datasets for training and evaluating models. This feature is useful when working with…"
+---
The Bring-Your-Own-Dataset (BYOD) feature allows users to add custom datasets for training and evaluating models. This feature is useful when working with proprietary or custom datasets that are not available in the heartKIT library.
@@ -6,7 +9,7 @@ The Bring-Your-Own-Dataset (BYOD) feature allows users to add custom datasets fo
1. **Create a Dataset**: Define a new dataset that inherits `HKDataset` and implements the required abstract methods.
-```py linenums="1"
+```py title="Python example" linenums="1"
import numpy as np
import heartkit as hk
@@ -54,7 +57,7 @@ class MyDataset(hk.HKDataset):
2. **Register the Dataset**: Register the new dataset with the `DatasetFactory` by calling the `register` method. This method takes the dataset name and the dataset class as arguments.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
import heartkit as hk
hk.DatasetFactory.register("my-dataset", CustomDataset)
@@ -62,7 +65,7 @@ class MyDataset(hk.HKDataset):
3. **Use the Dataset**: The new dataset can now be used with the `DatasetFactory` to perform various operations such as downloading and generating data.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
import heartkit as hk
params = {}
dataset = hk.DatasetFactory.get("my-dataset")(**params)
diff --git a/docs/datasets/icentia11k.md b/astro-site/src/content/docs/datasets/icentia11k.mdx
similarity index 50%
rename from docs/datasets/icentia11k.md
rename to astro-site/src/content/docs/datasets/icentia11k.mdx
index 418b55ed..95df023c 100644
--- a/docs/datasets/icentia11k.md
+++ b/astro-site/src/content/docs/datasets/icentia11k.mdx
@@ -1,4 +1,7 @@
-# Icentia11k Dataset
+---
+title: "Icentia11k Dataset"
+description: "This dataset consists of ECG recordings from 11,000 patients and 2 billion labelled beats. The data was collected by the CardioSTAT, a single-lead heart…"
+---
## Overview
@@ -8,36 +11,39 @@ More info available on [PhysioNet website](https://physionet.org/content/icentia
## Usage
-!!! Example Python
+**Python**
- ```py linenums="1"
- from pathlib import Path
- import helia_edge as helia
- import heartkit as hk
+```py title="Python example" linenums="1"
+from pathlib import Path
+import helia_edge as helia
+import heartkit as hk
- ds = hk.DatasetFactory.get('icentia11k')(
- path=Path("./datasets/icentia11k")
- )
+ds = hk.DatasetFactory.get('icentia11k')(
+ path=Path("./datasets/icentia11k")
+)
- # Download dataset
- ds.download(force=False)
+# Download dataset
+ds.download(force=False)
- # Create signal generator
- data_gen = self.ds.signal_generator(
- patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
- frame_size=256,
- samples_per_patient=5,
- target_rate=100,
- )
+# Create signal generator
+data_gen = self.ds.signal_generator(
+ patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+ frame_size=256,
+ samples_per_patient=5,
+ target_rate=100,
+)
- # Grab single ECG sample
- ecg = next(data_gen)
+# Grab single ECG sample
+ecg = next(data_gen)
- ```
+```
-???+ note
- The __Icentia11k dataset__ requires roughly 200 GB of disk space and can take around 2 hours to download.
+
+
- ```bash
- heartkit -m export -t segmentation -c ./configuration.json
- ```
-
-=== "Python"
+```bash title="Terminal"
+heartkit -m export -t segmentation -c ./configuration.json
+```
- --8<-- "assets/modes/python-demo-snippet.md"
+
+
+
+```py title="Python example" linenums="1"
+from pathlib import Path
+import heartkit as hk
+
+task = hk.TaskFactory.get("segmentation")
+task.export(hk.HKTaskParams(
+ job_dir=Path("./results/segmentation-class-2"),
+ datasets=[hk.NamedParams(
+ name="icentia11k",
+ params=dict(
+ path=Path("./datasets/icentia11k")
+ )
+ )],
+ num_classes=2,
+ class_map={
+ 0: 0,
+ 1: 1,
+ 2: 1
+ },
+ class_names=[
+ "NONE", "AFIB/AFL"
+ ],
+ sampling_rate=100,
+ frame_size=256,
+ backend="pc",
+ model_file=Path("./results/segmentation-class-2/model.keras"),
+))
+```
+
+
+
+```json title="modes-evaluate-1.json"
+{
+ "name": "arr-2-eff-sm",
+ "project": "hk-rhythm-2",
+ "job_dir": "./results/arr-2-eff-sm",
+ "verbose": 2,
+ "datasets": [
+ {
+ "name": "ptbxl",
+ "params": {
+ "path": "./datasets/ptbxl"
+ }
+ }
+ ],
+ "num_classes": 2,
+ "class_map": {
+ "0": 0,
+ "7": 1,
+ "8": 1
+ },
+ "class_names": [
+ "NORMAL",
+ "AFIB/AFL"
+ ],
+ "class_weights": "balanced",
+ "sampling_rate": 100,
+ "frame_size": 512,
+ "samples_per_patient": [
+ 10,
+ 10
+ ],
+ "val_samples_per_patient": [
+ 5,
+ 5
+ ],
+ "test_samples_per_patient": [
+ 5,
+ 5
+ ],
+ "val_patients": 0.2,
+ "val_size": 20000,
+ "test_size": 20000,
+ "batch_size": 256,
+ "buffer_size": 20000,
+ "epochs": 100,
+ "steps_per_epoch": 50,
+ "val_metric": "loss",
+ "lr_rate": 0.001,
+ "lr_cycles": 1,
+ "threshold": 0.75,
+ "val_metric_threshold": 0.98,
+ "tflm_var_name": "g_rhythm_model",
+ "tflm_file": "rhythm_model_buffer.h",
+ "backend": "pc",
+ "demo_size": 896,
+ "display_report": true,
+ "quantization": {
+ "qat": false,
+ "format": "INT8",
+ "io_type": "int8",
+ "conversion": "CONCRETE",
+ "debug": false
+ },
+ "preprocesses": [
+ {
+ "name": "layer_norm",
+ "params": {
+ "epsilon": 0.01,
+ "name": "znorm"
+ }
+ }
+ ],
+ "augmentations": [],
+ "model_file": "model.keras",
+ "use_logits": false,
+ "architecture": {
+ "name": "efficientnetv2",
+ "params": {
+ "input_filters": 16,
+ "input_kernel_size": [
+ 1,
+ 9
+ ],
+ "input_strides": [
+ 1,
+ 2
+ ],
+ "blocks": [
+ {
+ "filters": 24,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 32,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 40,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 48,
+ "depth": 1,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ }
+ ],
+ "output_filters": 0,
+ "include_top": true,
+ "use_logits": true
+ }
+ }
+}
+```
+
+
+
+```mermaid
+flowchart TD
+ A["Load configuration"] --> B
+ B["Load test datasets and dataloaders"] --> C
+ C["Load trained model"] --> D
+ D["Set evaluation metrics"] --> E
+ E["Evaluate model"] --> F
+ F["Generate report"]
+```
+
+---
+
+## Usage
+
+### CLI
+
+The following command will evaluate a rhythm model using the reference configuration.
+
+```bash title="Terminal"
+heartkit --task rhythm --mode evaluate --config ./configuration.json
+```
+
+### Python
+
+The model can be evaluated using the following snippet:
+
+```py title="Python example" linenums="1"
+
+task = hk.TaskFactory.get("rhythm")
+
+params = hk.HKTaskParams(...)
+
+task.evaluate(params)
+
+```
+
+**Example configuration**
+
+
+```py title="modes-evaluate-4.py"
+
+hk.HKTaskParams(
+ name="arr-2-eff-sm",
+ project="hk-rhythm-2",
+ job_dir="./results/arr-2-eff-sm",
+ verbose=2,
+ datasets=[hk.NamedParams(
+ name="ptbxl",
+ params=dict(
+ path="./datasets/ptbxl"
+ )
+ )],
+ num_classes=2,
+ class_map={
+ "0": 0,
+ "7": 1,
+ "8": 1
+ },
+ class_names=[
+ "NORMAL", "AFIB/AFL"
+ ],
+ class_weights="balanced",
+ sampling_rate=100,
+ frame_size=512,
+ samples_per_patient=[10, 10],
+ val_samples_per_patient=[5, 5],
+ test_samples_per_patient=[5, 5],
+ val_patients=0.20,
+ val_size=20000,
+ test_size=20000,
+ batch_size=256,
+ buffer_size=20000,
+ epochs=100,
+ steps_per_epoch=50,
+ val_metric="loss",
+ lr_rate=1e-3,
+ lr_cycles=1,
+ threshold=0.75,
+ val_metric_threshold=0.98,
+ tflm_var_name="g_rhythm_model",
+ tflm_file="rhythm_model_buffer.h",
+ backend="pc",
+ demo_size=896,
+ display_report=True,
+ quantization=hk.QuantizationParams(
+ qat=False,
+ format="INT8",
+ io_type="int8",
+ conversion="CONCRETE",
+ debug=False
+ ),
+ preprocesses=[
+ hk.NamedParams(
+ name="layer_norm",
+ params=dict(
+ epsilon=0.01,
+ name="znorm"
+ )
+ )
+ ],
+ augmentations=[
+ ],
+ model_file="model.keras",
+ use_logits=False,
+ architecture=hk.NamedParams(
+ name="efficientnetv2",
+ params=dict(
+ input_filters=16,
+ input_kernel_size=[1, 9],
+ input_strides=[1, 2],
+ blocks=[
+ {"filters": 24, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 32, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 40, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 48, "depth": 1, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2}
+ ],
+ output_filters=0,
+ include_top=True,
+ use_logits=True
+ )
+ }
+)
+```
+
+
+
+---
+
+## Arguments
+
+Please refer to [HKTaskParams](/heartkit/modes/configuration/#hktaskparams) for the list of arguments that can be used with the `evaluate` command.
diff --git a/astro-site/src/content/docs/modes/export.mdx b/astro-site/src/content/docs/modes/export.mdx
new file mode 100644
index 00000000..8931442d
--- /dev/null
+++ b/astro-site/src/content/docs/modes/export.mdx
@@ -0,0 +1,312 @@
+---
+title: "Model Exporting"
+description: "Export mode is used to convert the trained TensorFlow model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+## Introduction
+
+Export mode is used to convert the trained TensorFlow model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command will convert the TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file or by setting the `quantization` parameter in the code.
+
+
+
+```json title="modes-export-1.json"
+{
+ "name": "arr-2-eff-sm",
+ "project": "hk-rhythm-2",
+ "job_dir": "./results/arr-2-eff-sm",
+ "verbose": 2,
+ "datasets": [
+ {
+ "name": "ptbxl",
+ "params": {
+ "path": "./datasets/ptbxl"
+ }
+ }
+ ],
+ "num_classes": 2,
+ "class_map": {
+ "0": 0,
+ "7": 1,
+ "8": 1
+ },
+ "class_names": [
+ "NORMAL",
+ "AFIB/AFL"
+ ],
+ "class_weights": "balanced",
+ "sampling_rate": 100,
+ "frame_size": 512,
+ "samples_per_patient": [
+ 10,
+ 10
+ ],
+ "val_samples_per_patient": [
+ 5,
+ 5
+ ],
+ "test_samples_per_patient": [
+ 5,
+ 5
+ ],
+ "val_patients": 0.2,
+ "val_size": 20000,
+ "test_size": 20000,
+ "batch_size": 256,
+ "buffer_size": 20000,
+ "epochs": 100,
+ "steps_per_epoch": 50,
+ "val_metric": "loss",
+ "lr_rate": 0.001,
+ "lr_cycles": 1,
+ "threshold": 0.75,
+ "val_metric_threshold": 0.98,
+ "tflm_var_name": "g_rhythm_model",
+ "tflm_file": "rhythm_model_buffer.h",
+ "backend": "pc",
+ "demo_size": 896,
+ "display_report": true,
+ "quantization": {
+ "qat": false,
+ "format": "INT8",
+ "io_type": "int8",
+ "conversion": "CONCRETE",
+ "debug": false
+ },
+ "preprocesses": [
+ {
+ "name": "layer_norm",
+ "params": {
+ "epsilon": 0.01,
+ "name": "znorm"
+ }
+ }
+ ],
+ "augmentations": [],
+ "model_file": "model.keras",
+ "use_logits": false,
+ "architecture": {
+ "name": "efficientnetv2",
+ "params": {
+ "input_filters": 16,
+ "input_kernel_size": [
+ 1,
+ 9
+ ],
+ "input_strides": [
+ 1,
+ 2
+ ],
+ "blocks": [
+ {
+ "filters": 24,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 32,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 40,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 48,
+ "depth": 1,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ }
+ ],
+ "output_filters": 0,
+ "include_top": true,
+ "use_logits": true
+ }
+ }
+}
+```
+
+
+
+```mermaid
+flowchart TD
+ A["Load configuration and test data"] --> B
+ B["Load trained model"] --> C
+ C["Quantize model"] --> D
+ D["Convert model"] --> E
+ E["Verify outputs"] --> F
+ F["Save deployment artifacts"]
+```
+
+---
+
+## Usage
+
+### CLI
+
+The following command will export a rhythm model using the reference configuration.
+
+```bash title="Terminal"
+heartkit --task rhythm --mode export --config ./configuration.json
+```
+
+### Python
+
+The model can be evaluated using the following snippet:
+
+```py title="Python example" linenums="1"
+
+task = hk.TaskFactory.get("rhythm")
+
+params = hk.HKTaskParams(...)
+
+task.export(params)
+
+```
+
+**Example configuration**
+
+
+```py title="modes-export-4.py"
+
+hk.HKTaskParams(
+ name="arr-2-eff-sm",
+ project="hk-rhythm-2",
+ job_dir="./results/arr-2-eff-sm",
+ verbose=2,
+ datasets=[hk.NamedParams(
+ name="ptbxl",
+ params=dict(
+ path="./datasets/ptbxl"
+ )
+ )],
+ num_classes=2,
+ class_map={
+ "0": 0,
+ "7": 1,
+ "8": 1
+ },
+ class_names=[
+ "NORMAL", "AFIB/AFL"
+ ],
+ class_weights="balanced",
+ sampling_rate=100,
+ frame_size=512,
+ samples_per_patient=[10, 10],
+ val_samples_per_patient=[5, 5],
+ test_samples_per_patient=[5, 5],
+ val_patients=0.20,
+ val_size=20000,
+ test_size=20000,
+ batch_size=256,
+ buffer_size=20000,
+ epochs=100,
+ steps_per_epoch=50,
+ val_metric="loss",
+ lr_rate=1e-3,
+ lr_cycles=1,
+ threshold=0.75,
+ val_metric_threshold=0.98,
+ tflm_var_name="g_rhythm_model",
+ tflm_file="rhythm_model_buffer.h",
+ backend="pc",
+ demo_size=896,
+ display_report=True,
+ quantization=hk.QuantizationParams(
+ qat=False,
+ format="INT8",
+ io_type="int8",
+ conversion="CONCRETE",
+ debug=False
+ ),
+ preprocesses=[
+ hk.NamedParams(
+ name="layer_norm",
+ params=dict(
+ epsilon=0.01,
+ name="znorm"
+ )
+ )
+ ],
+ augmentations=[
+ ],
+ model_file="model.keras",
+ use_logits=False,
+ architecture=hk.NamedParams(
+ name="efficientnetv2",
+ params=dict(
+ input_filters=16,
+ input_kernel_size=[1, 9],
+ input_strides=[1, 2],
+ blocks=[
+ {"filters": 24, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 32, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 40, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 48, "depth": 1, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2}
+ ],
+ output_filters=0,
+ include_top=True,
+ use_logits=True
+ )
+ }
+)
+```
+
+
+
+---
+
+## Arguments
+
+Please refer to [HKTaskParams](/heartkit/modes/configuration/#hktaskparams) for the list of arguments that can be used with the `export` command.
diff --git a/astro-site/src/content/docs/modes/index.mdx b/astro-site/src/content/docs/modes/index.mdx
new file mode 100644
index 00000000..3a66b880
--- /dev/null
+++ b/astro-site/src/content/docs/modes/index.mdx
@@ -0,0 +1,44 @@
+---
+title: "Workflow overview"
+description: "Rather than offering a handful of static models, heartKIT provides a complete framework designed to cover the entire design process of creating customized ML…"
+---
+
+## Introduction
+
+Rather than offering a handful of static models, heartKIT provides a complete framework designed to cover the entire design process of creating customized ML models well-suited for low-power, wearable applications. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.
+
+Each `Task` implementes routines for each of the modes: [download](#download), [train](#train), [evaluate](#evaluate), [export](#export), and [demo](#demo). These modes are designed to streamline the process of training, evaluating, exporting, and running task-level demonstrations on the trained models.
+
+---
+
+## Choose a workflow step
+
+| Step | Use it to | Guide |
+| --- | --- | --- |
+| Download | Fetch datasets into your configured directory. | [Download datasets](/heartkit/modes/download/) |
+| Train | Fit a model using your datasets, architecture and training settings. | [Train a model](/heartkit/modes/train/) |
+| Evaluate | Measure the trained model on held-out data. | [Evaluate a model](/heartkit/modes/evaluate/) |
+| Export | Convert a trained model for embedded inference. | [Export a model](/heartkit/modes/export/) |
+| Demo | Run a task demonstration on a PC or supported evaluation board. | [Run a demo](/heartkit/modes/demo/) |
+
+---
+
+## [Download](/heartkit/modes/download/)
+
+[Download mode](/heartkit/modes/download/) is used to download the specified datasets for the task. The routine can be customized via the configuration file or by setting the parameters directly in the code. The download process involves fetching the dataset(s) from the specified source and storing them in the specified directory.
+
+## [Train](/heartkit/modes/train/)
+
+[Train mode](/heartkit/modes/train/) is used to train a model for the specified task and dataset. In this mode, the model is trained for a given task using the specified dataset(s), model architecture, and hyperparameters. The training process involves optimizing the model's parameters to maximize its performance on the training data.
+
+## [Evaluate](/heartkit/modes/evaluate/)
+
+[Evaluate mode](/heartkit/modes/evaluate/) is used to test the performance of the model on the reserved test set for the specified task. The routine can be customized via the configuration file or by setting the parameters directly in the code. The evaluation process involves testing the model's performance on the test data to measure its accuracy, precision, recall, and F1 score.
+
+## [Export](/heartkit/modes/export/)
+
+[Export mode](/heartkit/modes/export/) is used to convert the trained model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command will convert the TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match.
+
+## [Demo](/heartkit/modes/demo/)
+
+[Demo mode](/heartkit/modes/demo/) is used to run a task-level demonstration on the trained model using the specified backend inference engine (e.g. PC or EVB). This is useful to showcase the model's performance in real-time and to verify its accuracy in a real-world scenario.
diff --git a/astro-site/src/content/docs/modes/train.mdx b/astro-site/src/content/docs/modes/train.mdx
new file mode 100644
index 00000000..559c3168
--- /dev/null
+++ b/astro-site/src/content/docs/modes/train.mdx
@@ -0,0 +1,328 @@
+---
+title: "Model Training"
+description: "Each task provides a mode to train a model on the specified datasets and dataloaders. The training mode can be invoked either via CLI or within heartkit python…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+## Introduction
+
+Each task provides a mode to train a model on the specified datasets and dataloaders. The training mode can be invoked either via CLI or within `heartkit` python package. At a high level, the training mode performs the following actions based on the provided configuration parameters:
+
+
+
+```json title="modes-train-1.json"
+{
+ "name": "arr-2-eff-sm",
+ "project": "hk-rhythm-2",
+ "job_dir": "./results/arr-2-eff-sm",
+ "verbose": 2,
+ "datasets": [
+ {
+ "name": "ptbxl",
+ "params": {
+ "path": "./datasets/ptbxl"
+ }
+ }
+ ],
+ "num_classes": 2,
+ "class_map": {
+ "0": 0,
+ "7": 1,
+ "8": 1
+ },
+ "class_names": [
+ "NORMAL",
+ "AFIB/AFL"
+ ],
+ "class_weights": "balanced",
+ "sampling_rate": 100,
+ "frame_size": 512,
+ "samples_per_patient": [
+ 10,
+ 10
+ ],
+ "val_samples_per_patient": [
+ 5,
+ 5
+ ],
+ "test_samples_per_patient": [
+ 5,
+ 5
+ ],
+ "val_patients": 0.2,
+ "val_size": 20000,
+ "test_size": 20000,
+ "batch_size": 256,
+ "buffer_size": 20000,
+ "epochs": 100,
+ "steps_per_epoch": 50,
+ "val_metric": "loss",
+ "lr_rate": 0.001,
+ "lr_cycles": 1,
+ "threshold": 0.75,
+ "val_metric_threshold": 0.98,
+ "tflm_var_name": "g_rhythm_model",
+ "tflm_file": "rhythm_model_buffer.h",
+ "backend": "pc",
+ "demo_size": 896,
+ "display_report": true,
+ "quantization": {
+ "qat": false,
+ "format": "INT8",
+ "io_type": "int8",
+ "conversion": "CONCRETE",
+ "debug": false
+ },
+ "preprocesses": [
+ {
+ "name": "layer_norm",
+ "params": {
+ "epsilon": 0.01,
+ "name": "znorm"
+ }
+ }
+ ],
+ "augmentations": [],
+ "model_file": "model.keras",
+ "use_logits": false,
+ "architecture": {
+ "name": "efficientnetv2",
+ "params": {
+ "input_filters": 16,
+ "input_kernel_size": [
+ 1,
+ 9
+ ],
+ "input_strides": [
+ 1,
+ 2
+ ],
+ "blocks": [
+ {
+ "filters": 24,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 32,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 40,
+ "depth": 2,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ },
+ {
+ "filters": 48,
+ "depth": 1,
+ "kernel_size": [
+ 1,
+ 9
+ ],
+ "strides": [
+ 1,
+ 2
+ ],
+ "ex_ratio": 1,
+ "se_ratio": 2
+ }
+ ],
+ "output_filters": 0,
+ "include_top": true,
+ "use_logits": true
+ }
+ }
+}
+```
+
+
+
+
+ +```mermaid +flowchart TD + A["Load configuration"] --> B + B["Load datasets and dataloaders"] --> C + C["Initialize model"] --> D + D["Set loss, metrics and optimizer"] --> E + E["Train model"] --> F + F["Save model and artifacts"] +``` + +--- + +## Usage + +### CLI + +The following command will train a rhythm model using the reference configuration. + +```bash title="Terminal" +heartkit --task rhythm --mode train --config ./configuration.json +``` + +### Python + +The model can be trained using the following snippet: + +```py title="Python example" linenums="1" + +task = hk.TaskFactory.get("rhythm") + +params = hk.HKTaskParams(...) + +task.train(params) + +``` + +**Example configuration** +
+
+```py title="modes-train-4.py"
+
+hk.HKTaskParams(
+ name="arr-2-eff-sm",
+ project="hk-rhythm-2",
+ job_dir="./results/arr-2-eff-sm",
+ verbose=2,
+ datasets=[hk.NamedParams(
+ name="ptbxl",
+ params=dict(
+ path="./datasets/ptbxl"
+ )
+ )],
+ num_classes=2,
+ class_map={
+ "0": 0,
+ "7": 1,
+ "8": 1
+ },
+ class_names=[
+ "NORMAL", "AFIB/AFL"
+ ],
+ class_weights="balanced",
+ sampling_rate=100,
+ frame_size=512,
+ samples_per_patient=[10, 10],
+ val_samples_per_patient=[5, 5],
+ test_samples_per_patient=[5, 5],
+ val_patients=0.20,
+ val_size=20000,
+ test_size=20000,
+ batch_size=256,
+ buffer_size=20000,
+ epochs=100,
+ steps_per_epoch=50,
+ val_metric="loss",
+ lr_rate=1e-3,
+ lr_cycles=1,
+ threshold=0.75,
+ val_metric_threshold=0.98,
+ tflm_var_name="g_rhythm_model",
+ tflm_file="rhythm_model_buffer.h",
+ backend="pc",
+ demo_size=896,
+ display_report=True,
+ quantization=hk.QuantizationParams(
+ qat=False,
+ format="INT8",
+ io_type="int8",
+ conversion="CONCRETE",
+ debug=False
+ ),
+ preprocesses=[
+ hk.NamedParams(
+ name="layer_norm",
+ params=dict(
+ epsilon=0.01,
+ name="znorm"
+ )
+ )
+ ],
+ augmentations=[
+ ],
+ model_file="model.keras",
+ use_logits=False,
+ architecture=hk.NamedParams(
+ name="efficientnetv2",
+ params=dict(
+ input_filters=16,
+ input_kernel_size=[1, 9],
+ input_strides=[1, 2],
+ blocks=[
+ {"filters": 24, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 32, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 40, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 48, "depth": 1, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2}
+ ],
+ output_filters=0,
+ include_top=True,
+ use_logits=True
+ )
+ }
+)
+```
+
+
+
+---
+
+## Arguments
+
+Please refer to [HKTaskParams](/heartkit/modes/configuration/#hktaskparams) for the list of arguments that can be used with the `train` command.
+
+---
+
+## Logging
+
+__heartKIT__ provides built-in support for logging to several third-party services including [Weights & Biases](https://wandb.ai/site) (WANDB) and [TensorBoard](https://www.tensorflow.org/tensorboard).
+
+### WANDB
+
+The training mode is able to log all metrics and artifacts (aka models) to [Weights & Biases](https://wandb.ai/site) (WANDB). To enable WANDB logging, simply set environment variable `WANDB=1`. Remember to sign in prior to running experiments by running `wandb login`.
+
+### TensorBoard
+
+The training mode is able to log all metrics to [TensorBoard](https://www.tensorflow.org/tensorboard). To enable TensorBoard logging, simply set environment variable `TENSORBOARD=1`.
diff --git a/astro-site/src/content/docs/quickstart.mdx b/astro-site/src/content/docs/quickstart.mdx
new file mode 100644
index 00000000..dc4f8de2
--- /dev/null
+++ b/astro-site/src/content/docs/quickstart.mdx
@@ -0,0 +1,258 @@
+---
+title: "Quickstart"
+description: "We provide several installation methods including pip, uv, and Docker. Install heartKIT via pip/uv for the latest stable release or by cloning the GitHub repo…"
+---
+
+import ConfigExample from "../../components/ConfigExample.astro";
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+
+## Install heartKIT
+
+We provide several installation methods including pip, uv, and Docker. Install __heartKIT__ via pip/uv for the latest stable release or by cloning the GitHub repo for the most up-to-date. Additionally, a [VSCode Dev Container](https://code.visualstudio.com/docs/devcontainers/containers) is available and defined in [./.devcontainer](https://github.com/AmbiqAI/heartkit/tree/main/.devcontainer) to run in an isolated Docker environment.
+
+
+
+
+Clone the repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package. In this mode, uv is recommended.
+
+```bash title="Terminal"
+# Clone the repository
+git clone https://github.com/AmbiqAI/heartkit.git
+
+# Navigate to the cloned directory
+cd heartkit
+
+# Install the package in editable mode for development
+uv sync
+```
+
+When using editable mode via uv, be sure to activate the python environment: `source .venv/bin/activate`.
+On Windows using Powershell, use `.venv\Scripts\activate`. + +
+
+
+Install the heartKIT package using pip or uv.
+Visit the Python Package Index (PyPI) for more details on the package: [https://pypi.org/project/heartkit/](https://pypi.org/project/heartkit/)
+
+```bash title="Terminal"
+# Install with pip
+pip install heartkit
+```
+
+Or, if you prefer to use uv, you can install the package with the following command:
+
+```bash title="Terminal"
+# Install with uv
+uv add heartkit
+```
+
+Alternatively, you can install the latest development version directly from the GitHub repository. Make sure to have the Git command-line tool installed on your system. The @main command installs the main branch and may be modified to another branch, i.e. @canary.
+
+```bash title="Terminal"
+pip install git+https://github.com/AmbiqAI/heartkit.git@main
+```
+
+Or, using uv:
+
+```bash title="Terminal"
+uv add git+https://github.com/AmbiqAI/heartkit.git@main
+```
+
+
+
+
+## Requirements
+
+* [Python 3.12–3.13](https://www.python.org)
+* [uv ^0.7.10+](https://docs.astral.sh/uv/getting-started/installation/)
+
+Check the project's [pyproject.toml](https://github.com/AmbiqAI/heartkit/blob/main/pyproject.toml) file for a list of up-to-date Python dependencies. Note that the installation methods above install all required dependencies. The following are optional dependencies only needed when running `demo` command using Ambiq's evaluation board (`EVB`) backend:
+
+* [Arm GNU Toolchain ^12.2](https://developer.arm.com/downloads/-/arm-gnu-toolchain-downloads)
+* [Segger J-Link ^7.92](https://www.segger.com/downloads/jlink/)
+
+Once installed, __heartKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced experimentation.
+
+---
+
+## Use heartKIT with CLI
+
+The heartKIT command line interface (CLI) allows for simple single-line commands to download datasets, train models, evaluate performance, and export models. The CLI requires no customization or Python code. You can simply run all the built-in tasks from the terminal with the __heartkit__ command. Check out the [CLI Guide](/heartkit/usage/cli/) to learn more about available options.
+
+
+
+
+Heartkit commands use the following syntax:
+
+```bash title="Terminal"
+heartkit --mode [MODE] --task [TASK] --config [CONFIG]
+```
+
+Or using short flags:
+
+```bash title="Terminal"
+heartkit -m [MODE] -t [TASK] -c [CONFIG]
+```
+
+Where:
+
+* `MODE` is one of `download`, `train`, `evaluate`, `export`, or `demo`
+* `TASK` is one of `segmentation`, `rhythm`, `beat`, or `denoise`
+* `CONFIG` is configuration as JSON content or file path
+
+
+
+
+Download datasets specified in the configuration file.
+
+```bash title="Terminal"
+heartkit -m download -c ./download-datasets.json
+```
+
+
+
+
+Train a rhythm model using the supplied configuration file.
+
+```bash title="Terminal"
+heartkit -m train -t rhythm -c ./configuration.json
+```
+
+
+
+
+Evaluate the trained rhythm model using the supplied configuration file.
+
+```bash title="Terminal"
+heartkit -m evaluate -t rhythm -c ./configuration.json
+```
+
+
+
+
+Run demo on trained rhythm model using the supplied configuration file.
+
+```bash title="Terminal"
+heartkit -m demo -t rhythm -c ./configuration.json
+```
+
+
+
+
+## Use heartKIT with Python
+
+The __heartKIT__ Python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for a variety of tasks. You can create custom datasets, models, and tasks and register them with corresponding factories and use them like built-in tasks.
+
+For example, you can create a custom task, train it, evaluate its performance on a validation set, and even export a quantized TensorFlow Lite model for deployment. Check out the [Python Guide](/heartkit/usage/python/) to learn more about using heartKIT as a Python package.
+
+```py title="Python example" linenums="1"
+
+import heartkit as hk
+
+params = hk.HKTaskParams(...)
+
+task = hk.TaskFactory.get("rhythm")
+
+task.download(params) # Download dataset(s)
+
+task.train(params) # Train the model
+
+task.evaluate(params) # Evaluate the model
+
+task.export(params) # Export to TFLite
+
+```
+
+**Configuration parameters**
+
+
+```py title="quickstart-13.py"
+
+hk.HKTaskParams(
+ name="arr-2-eff-sm",
+ project="hk-rhythm-2",
+ job_dir="./results/arr-2-eff-sm",
+ verbose=2,
+ datasets=[hk.NamedParams(
+ name="ptbxl",
+ params=dict(
+ path="./datasets/ptbxl"
+ )
+ )],
+ num_classes=2,
+ class_map={
+ "0": 0,
+ "7": 1,
+ "8": 1
+ },
+ class_names=[
+ "NORMAL", "AFIB/AFL"
+ ],
+ class_weights="balanced",
+ sampling_rate=100,
+ frame_size=512,
+ samples_per_patient=[10, 10],
+ val_samples_per_patient=[5, 5],
+ test_samples_per_patient=[5, 5],
+ val_patients=0.20,
+ val_size=20000,
+ test_size=20000,
+ batch_size=256,
+ buffer_size=20000,
+ epochs=100,
+ steps_per_epoch=50,
+ val_metric="loss",
+ lr_rate=1e-3,
+ lr_cycles=1,
+ threshold=0.75,
+ val_metric_threshold=0.98,
+ tflm_var_name="g_rhythm_model",
+ tflm_file="rhythm_model_buffer.h",
+ backend="pc",
+ demo_size=896,
+ display_report=True,
+ quantization=hk.QuantizationParams(
+ qat=False,
+ format="INT8",
+ io_type="int8",
+ conversion="CONCRETE",
+ debug=False
+ ),
+ preprocesses=[
+ hk.NamedParams(
+ name="layer_norm",
+ params=dict(
+ epsilon=0.01,
+ name="znorm"
+ )
+ )
+ ],
+ augmentations=[
+ ],
+ model_file="model.keras",
+ use_logits=False,
+ architecture=hk.NamedParams(
+ name="efficientnetv2",
+ params=dict(
+ input_filters=16,
+ input_kernel_size=[1, 9],
+ input_strides=[1, 2],
+ blocks=[
+ {"filters": 24, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 32, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 40, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 48, "depth": 1, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2}
+ ],
+ output_filters=0,
+ include_top=True,
+ use_logits=True
+ )
+ }
+)
+```
+
+
+
+---
diff --git a/astro-site/src/content/docs/tasks/beat.mdx b/astro-site/src/content/docs/tasks/beat.mdx
new file mode 100644
index 00000000..54fba235
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/beat.mdx
@@ -0,0 +1,72 @@
+---
+title: "Beat Classification Task"
+description: "In beat classification, we classify individual beats as either normal or abnormal. Abnormal beats can be further classified as being either premature or escape…"
+---
+
+## Overview
+
+In beat classification, we classify individual beats as either normal or abnormal. Abnormal beats can be further classified as being either premature or escape beats as well as originating from the atria, junction, or ventricles. The objective of beat classification is to detect and classify these abnormal heart beats directly from ECG signals.
+
+
P-wave: Different
QRS: Narrow (normal)
Aberrated: LBBB or RBBB | __PJC__
P-wave: None / retrograde
QRS: Narrow (normal)
Compensatory SA Pause | __PVC__
P-wave: None
QRS: Wide (> 120 ms)
Compensatory SA Pause | +| Escape | Atrial Escape | P-wave: Abnormal
QRS: Narrow (normal)
Ventricular rate: < 60 bpm
Junctional Escape
| P-wave: None
QRS: Narrow (normal)
Bradycardia (40-60 bpm)
Ventricular Escape | P-wave: None
QRS: Wide
Bradycardia (< 40 bpm) | + +--- + +## Dataloaders + +Dataloaders are available for the following datasets: + +* **[Icentia11k](/heartkit/datasets/icentia11k/)** +* **[PTB-XL](/heartkit/datasets/ptbxl/)** + +--- + +## Pre-trained Models + +The following table provides the latest performance and accuracy results for pre-trained beat models. Additional result details can be found in [Model Zoo → Beat](/heartkit/zoo/). + +| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC | +| --------------- | ---------- | ----- | -------- | --------- | -------------- | ------ | ------- | -------- | +| __BC-2-EFF-SM__ | Icentia11k | 100Hz | 5s | 2 | EfficientNetV2 | 28K | 1.8M | 97.7% F1 | +| __BC-3-EFF-SM__ | Icentia11k | 100Hz | 5s | 3 | EfficientNetV2 | 41K | 2.1M | 92.0% F1 | + +--- + +## Target Classes + +Below outlines the classes available for beat classification. When training a model, the number of classes, mapping, and names must be provided. + +| CLASS | LABELS | +| ------- | --------------- | +| 0 | Normal | +| 1 | PAC | +| 2 | PVC | +| 3 | Noise | + +**Class Mapping** + +Below is an example of a class mapping for a 3-class beat model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped. + +```jsonc title="JSONC example" +{ + "num_classes": 3, + "class_names": ["QRS", "PAC", "PVC"], + "class_map": { + "0": 0, // Map Normal to QRS + "1": 1, // Map PAC to PAC + "2": 2, // Map PVC to PVC + } +} +``` + +--- diff --git a/docs/tasks/byot.md b/astro-site/src/content/docs/tasks/byot.mdx similarity index 77% rename from docs/tasks/byot.md rename to astro-site/src/content/docs/tasks/byot.mdx index 78249c43..89f764ac 100644 --- a/docs/tasks/byot.md +++ b/astro-site/src/content/docs/tasks/byot.mdx @@ -1,14 +1,17 @@ -# Bring-Your-Own-Task (BYOT) +--- +title: "Bring-Your-Own-Task (BYOT)" +description: "The Bring-Your-Own-Task (BYOT) feature allows users to create custom tasks for training, evaluating, and deploying heart-related AI models. This feature is…" +--- The Bring-Your-Own-Task (BYOT) feature allows users to create custom tasks for training, evaluating, and deploying heart-related AI models. This feature is useful for creating custom workflows for a given application with minimal coding. -For an in-depth guide check out the [BYOT Notebook Guide](../guides/byot.ipynb). +For an in-depth guide check out the [BYOT Notebook Guide](/heartkit/guides/byot/). ## How it Works 1. **Create a Task**: Define a new task that inherits from the [HKTask](/heartkit/api/heartkit/tasks/task) base class and implements the required methods: `train`, `evaluate`, `export`, and `demo`. - ```py linenums="1" + ```py title="Python example" linenums="1" import heartkit as hk class CustomTask(hk.HKTask): @@ -33,14 +36,14 @@ For an in-depth guide check out the [BYOT Notebook Guide](../guides/byot.ipynb). 2. **Register the Task**: Register the new task with the `TaskFactory` by calling the `register` method. This method takes the task name and the task class as arguments. - ```py linenums="1" + ```py title="Python example" linenums="1" # Register the custom task hk.TaskFactory.register("custom", CustomTask) ``` 3. **Use the Task**: The new task can now be used with the `TaskFactory` to perform various operations such as training, evaluating, and deploying models. - ```py linenums="1" + ```py title="Python example" linenums="1" # Define task parameters params = hk.HKTrainParams(...) diff --git a/docs/tasks/denoise.md b/astro-site/src/content/docs/tasks/denoise.mdx similarity index 64% rename from docs/tasks/denoise.md rename to astro-site/src/content/docs/tasks/denoise.mdx index f29e5082..29f3558d 100644 --- a/docs/tasks/denoise.md +++ b/astro-site/src/content/docs/tasks/denoise.mdx @@ -1,11 +1,14 @@ -# Signal Denoising Task +--- +title: "Signal Denoising Task" +description: "The objective of denoising is to remove noise and artifacts from physiological signals while preserving the underlying signal information. The dominant noise…" +--- ## Overview The objective of denoising is to remove noise and artifacts from physiological signals while preserving the underlying signal information. The dominant noise sources include baseline wander (BW), muscle noise (EMG), electrode movement artifacts (EM), and powerline interference (PLI). For physiological signals such as ECG and PPG, removing the artifacts is difficult due to the non-stationary nature of the noise and overlapping frequency bands with the signal. While traditional signal processing techniques such as filtering and wavelet denoising have been used to remove noise, deep learning models have shown great promise in enhanced denoising.
diff --git a/astro-site/src/content/docs/tasks/index.mdx b/astro-site/src/content/docs/tasks/index.mdx
new file mode 100644
index 00000000..f6a5c8bb
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/index.mdx
@@ -0,0 +1,75 @@
+---
+title: "Tasks"
+description: "heartKIT provides several built-in heart-monitoring tasks. Each task is designed to address a unique aspect such as ECG denoising, segmentation, and…"
+---
+
+## Introduction
+
+heartKIT provides several built-in __heart-monitoring__ tasks. Each task is designed to address a unique aspect such as ECG denoising, segmentation, and rhythm/beat classification. The tasks are designed to be modular and can be used independently or in combination to address specific use cases. In addition to the built-in tasks, custom tasks can be created by extending the [HKTask](/heartkit/api/heartkit/tasks/task) base class and registering it with the task factory.
+
+
+ 
+
+
+---
+
+## Available Tasks
+
+### [Signal Denoise](/heartkit/tasks/denoise/)
+
+[Signal denoise](/heartkit/tasks/denoise/) is the process of removing noise from a physiological signal such as ECG. This task is useful for improving the quality of the signal and for further downstream tasks such as segmentation in which noise can interfere with the delineation of individual waves.
+
+### [Signal Segmentation](/heartkit/tasks/segmentation/)
+
+[Signal segmentation](/heartkit/tasks/segmentation/) is the process of delineating a signal into its constituent parts. In the context of ECG, segmentation refers to delineating the ECG signal into individual waves (e.g. P-wave, QRS, T-wave). This task is useful for extracting features (e.g. HRV) from the ECG signal and for further analysis such as rhythm classification.
+
+### [Rhythm Classification](/heartkit/tasks/rhythm/)
+
+[Rhythm classification](/heartkit/tasks/rhythm/) is the process of identifying abnormal heart rhythms, also known as arrhythmias, such as atrial fibrillation (AFIB) and atrial flutter (AFL) from ECG signals. Cardiovascular diseases such as AFIB are a leading cause of morbidity and mortality worldwide. Being able to remotely identify heart arrhtyhmias is important for early detection and intervention.
+
+### [Beat Classification](/heartkit/tasks/beat/)
+
+[Beat classification](/heartkit/tasks/beat/) is the process of identifying and classifying individual heart beats such as normal, premature, and escape beats from ECG signals. By identifying abnormal heart beats, it is possible to detect and monitor various heart conditions.
+
+### [Bring-Your-Own-Task (BYOT)](/heartkit/tasks/byot/)
+
+[Bring-Your-Own-Task (BYOT)](/heartkit/tasks/byot/) is a feature that allows users to create custom tasks by extending the [HKTask](/heartkit/api/heartkit/tasks/task) base class and registering it with the `TaskFactory`. This feature is useful for addressing specific use cases that are not covered by the built-in tasks.
+
+---
+
+## Task Factory
+
+The task factory, `TaskFactory`, provides a convenient way to access the built-in tasks. The factory is a thread-safe singleton class that provides a single point of access to the tasks via the tasks' slug names. The benefit of using the factory is it allows registering custom tasks that can then be used just like built-in tasks.
+
+The dataset factory provides the following methods:
+
+* **hk.TaskFactory.register**: Register a custom task
+* **hk.TaskFactory.unregister**: Unregister a custom task
+* **hk.TaskFactory.has**: Check if a task is registered
+* **hk.TaskFactory.get**: Get a task
+* **hk.TaskFactory.list**: List all available tasks
+
+```py title="Python example" linenums="1"
+
+import heartkit as hk
+
+# Grab a rhythm task
+task = hk.TaskFactory.get("rhythm")
+
+task.train(hk.HKTrainParams(
+ ...
+))
+
+```
+
+---
+
+## Choose a task
+
+| Task | Purpose |
+| --- | --- |
+| [Signal denoising](/heartkit/tasks/denoise/) | Remove noise from physiological signals. |
+| [Signal segmentation](/heartkit/tasks/segmentation/) | Delineate physiological signals into constituent parts. |
+| [Rhythm classification](/heartkit/tasks/rhythm/) | Identify rhythm-level arrhythmias such as AFIB and AFL. |
+| [Beat classification](/heartkit/tasks/beat/) | Identify premature and escape beats. |
+| [Bring your own task](/heartkit/tasks/byot/) | Create and register a custom task. |
diff --git a/docs/tasks/rhythm.md b/astro-site/src/content/docs/tasks/rhythm.mdx
similarity index 56%
rename from docs/tasks/rhythm.md
rename to astro-site/src/content/docs/tasks/rhythm.mdx
index ed5ca05a..fe2036ad 100644
--- a/docs/tasks/rhythm.md
+++ b/astro-site/src/content/docs/tasks/rhythm.mdx
@@ -1,11 +1,14 @@
-# Rhythm Classification Task
+---
+title: "Rhythm Classification Task"
+description: "The objective of rhythm classification is to detect and classify abnormal heart rhythms, also known as arrhythmias, directly from ECG signals."
+---
## Overview
The objective of rhythm classification is to detect and classify abnormal heart rhythms, also known as arrhythmias, directly from ECG signals.
+
+
+Heartkit commands use the following syntax:
+
+```bash title="Terminal"
+heartkit --mode [MODE] --task [TASK] --config [CONFIG]
+```
+
+Or using short flags:
+
+```bash title="Terminal"
+heartkit -m [MODE] -t [TASK] -c [CONFIG]
+```
+
+Where:
+
+* `MODE` is one of `download`, `train`, `evaluate`, `export`, or `demo`
+* `TASK` is one of `segmentation`, `rhythm`, `beat`, or `denoise`
+* `CONFIG` is configuration as JSON content or file path
+
+
+
+
+Download datasets specified in the configuration file.
+
+```bash title="Terminal"
+heartkit -m download -c ./configs/download-datasets.json
+```
+
+
+
+
+Train a rhythm model using the supplied configuration file.
+
+```bash title="Terminal"
+heartkit -m train -t rhythm -c ./configuration.json
+```
+
+
+
+
+Evaluate the rhythm model using the supplied configuration file.
+
+```bash title="Terminal"
+heartkit -m evaluate -t rhythm -c ./configuration.json
+```
+
+
+
+
+Run demo on trained rhythm model using the supplied configuration file.
+
+```bash title="Terminal"
+heartkit -m demo -t rhythm -c ./configuration.json
+```
+
+
+
+
+:::note[Configuration File]
+The configuration file is a JSON file that contains all the necessary parameters for the task. The configuration file can be passed as a file path or as a JSON string. In addition, a single configuration file can be used for all `modes`- only needed parameters will be extracted for the given `mode` running. Please refer to the [Configuration](/heartkit/modes/configuration/) section for more details.
+:::
+
+---
+
+## [Download](/heartkit/modes/download/)
+
+The [download](/heartkit/modes/download/) command is used to download all datasets specified in the configuration file. Please refer to [Datasets](/heartkit/datasets/) for details on the available datasets.
+
+**CLI**
+
+The following command will download and prepare all datasets specified in configuration JSON file.
+
+```bash title="Terminal"
+heartkit --task rhythm --mode download --config ./configs/download-datasets.json
+```
+
+---
+
+## [Train](/heartkit/modes/train/)
+
+The [train](/heartkit/modes/train/) command is used to train a heartKIT model for the specified `task` and `dataset`. Each task provides a reference routine for training the model. The routine can be customized via the configuration file. Please refer to [HKTaskParams](/heartkit/modes/configuration/#hktaskparams) to see supported options.
+
+**CLI**
+
+The following command will train a rhythm model using the reference configuration:
+
+```bash title="Terminal"
+heartkit --task rhythm --mode train --config ./configuration.json
+```
+
+---
+
+## [Evaluate](/heartkit/modes/evaluate/)
+
+The [evaluate](/heartkit/modes/evaluate/) command will test the performance of the model on the reserved test sets for the specified `task`. The routine can be customized via the configuration file. Please refer to [HKTaskParams](/heartkit/modes/configuration/#hktaskparams) to see supported options.
+
+**CLI**
+
+The following command will test the rhythm model using the reference configuration:
+
+```bash title="Terminal"
+heartkit --task rhythm --mode evaluate --config ./configuration.json
+```
+
+---
+
+## [Export](/heartkit/modes/export/)
+
+The [export](/heartkit/modes/export/) command will convert the trained TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file. Once converted, the TFLM header file will be copied to location specified by `tflm_file`.
+
+**CLI**
+
+The following command will export the rhythm model to TF Lite and TFLM:
+
+```bash title="Terminal"
+heartkit --task rhythm --mode export --config ./configuration.json
+```
+
+---
+
+## [Demo](/heartkit/modes/demo/)
+
+The [demo](/heartkit/modes/demo/) command is used to run a task-level demonstration using either the PC or EVB as backend inference engine.
+
+**CLI**
+
+The following command will run a demo on the trained rhythm model using the same supplied configuration file.
+
+```bash title="Terminal"
+heartkit --task rhythm --mode demo --config ./configuration.json
+```
+
+---
diff --git a/astro-site/src/content/docs/usage/python.mdx b/astro-site/src/content/docs/usage/python.mdx
new file mode 100644
index 00000000..27141a17
--- /dev/null
+++ b/astro-site/src/content/docs/usage/python.mdx
@@ -0,0 +1,178 @@
+---
+title: "Python Usage"
+description: "heartKIT python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for both built-in…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+__heartKIT__ python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for both built-in taks and custom tasks. In addition, custom datasets and model architectures can be created and registered with corresponding factories.
+
+## Overview
+
+The main components of heartKIT include the following:
+
+### [Tasks](/heartkit/tasks/)
+
+A [Task](/heartkit/tasks/) inherits from the [HKTask](/heartkit/api/heartkit/tasks/task) class and provides implementations for each of the main modes: download, train, evaluate, export, and demo. Each mode is provided with a set of parameters defined by [HKTaskParams](/heartkit/api/heartkit/defines). Additional task-specific parameters can be extended to the `HKTaskParams` class. These tasks are then registered and accessed via the `TaskFactory` using a unique task name as the key and the custom Task class as the value.
+
+```py title="Python example" linenums="1"
+import heartkit as hk
+
+task = hk.TaskFactory.get('rhythm')
+```
+
+### [Datasets](/heartkit/datasets/)
+
+A dataset inherits from the [HKDataset](/heartkit/api/heartkit/datasets/dataset) class and provides implementations for downloading, preparing, and loading the dataset. Each dataset is provided with a set of custom parameters for initialization. Since each task will require specific transformations of the data, the dataset class provides only a general interface for loading the data. Each task must then provide a set of corresponding [HKDataloader](/heartkit/api/heartkit/datasets/dataloader) classes to transform the dataset into a format that can be used by the task. The datasets are registered and accessed via the [DatasetFactory](/heartkit/api/heartkit/datasets/factory) using a unique dataset name as the key and the Dataset class as the value. Each Task can then create its own `DataloaderFactory` that will provide a corresponding dataloader for each supported dataset. The Task's `DataloaderFactory` should use the same dataset names as the DatasetFactory to ensure that the correct dataloader is used for each dataset.
+
+```py title="Python example" linenums="1"
+import heartkit as hk
+
+ds = hk.DatasetFactory.get('ecg-synthetic')(num_pts=100)
+```
+
+### [Models](/heartkit/models/)
+
+Lastly, heartKIT leverages [helia-edge's](https://ambiqai.github.io/helia-edge/) customizable model architectures. To enable creating custom network topologies from configuration files, heartKIT provides a `ModelFactory` that allows you to create models by specifying the model key and the model parameters. Each item in the factory is a callable that takes a `keras.Input`, model parameters, and number of classes as arguments and returns a `keras.Model`.
+
+```
+import keras
+import heartkit as hk
+
+inputs = keras.Input((256, 1), dtype="float32")
+num_classes = 4
+model_params = dict(...)
+
+model = hk.ModelFactory.get('tcn')(
+ inputs=inputs,
+ params=model_params,
+ num_classes=num_classes
+)
+
+```
+
+## Usage
+
+### Running a built-in task w/ existing datasets
+
+1. Create a task configuration file defining the model, datasets, class labels, mode parameters, and so on. Have a look at the [HKTaskParams](/heartkit/modes/configuration/#hktaskparams) for more details on the available parameters.
+
+2. Leverage `TaskFactory` to get the desired built-in task.
+
+3. Run the task's main modes: `download`, `train`, `evaluate`, `export`, and/or `demo`.
+
+```py title="Python example" linenums="1"
+
+import heartkit as hk
+
+params = hk.HKTaskParams(...)
+
+task = hk.TaskFactory.get("rhythm")
+
+task.download(params) # Download dataset(s)
+
+task.train(params) # Train the model
+
+task.evaluate(params) # Evaluate the model
+
+task.export(params) # Export to TFLite
+
+```
+
+**Example configuration**
+
+
+```py title="usage-python-4.py"
+
+hk.HKTaskParams(
+ name="arr-2-eff-sm",
+ project="hk-rhythm-2",
+ job_dir="./results/arr-2-eff-sm",
+ verbose=2,
+ datasets=[hk.NamedParams(
+ name="ptbxl",
+ params=dict(
+ path="./datasets/ptbxl"
+ )
+ )],
+ num_classes=2,
+ class_map={
+ "0": 0,
+ "7": 1,
+ "8": 1
+ },
+ class_names=[
+ "NORMAL", "AFIB/AFL"
+ ],
+ class_weights="balanced",
+ sampling_rate=100,
+ frame_size=512,
+ samples_per_patient=[10, 10],
+ val_samples_per_patient=[5, 5],
+ test_samples_per_patient=[5, 5],
+ val_patients=0.20,
+ val_size=20000,
+ test_size=20000,
+ batch_size=256,
+ buffer_size=20000,
+ epochs=100,
+ steps_per_epoch=50,
+ val_metric="loss",
+ lr_rate=1e-3,
+ lr_cycles=1,
+ threshold=0.75,
+ val_metric_threshold=0.98,
+ tflm_var_name="g_rhythm_model",
+ tflm_file="rhythm_model_buffer.h",
+ backend="pc",
+ demo_size=896,
+ display_report=True,
+ quantization=hk.QuantizationParams(
+ qat=False,
+ format="INT8",
+ io_type="int8",
+ conversion="CONCRETE",
+ debug=False
+ ),
+ preprocesses=[
+ hk.NamedParams(
+ name="layer_norm",
+ params=dict(
+ epsilon=0.01,
+ name="znorm"
+ )
+ )
+ ],
+ augmentations=[
+ ],
+ model_file="model.keras",
+ use_logits=False,
+ architecture=hk.NamedParams(
+ name="efficientnetv2",
+ params=dict(
+ input_filters=16,
+ input_kernel_size=[1, 9],
+ input_strides=[1, 2],
+ blocks=[
+ {"filters": 24, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 32, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 40, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
+ {"filters": 48, "depth": 1, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2}
+ ],
+ output_filters=0,
+ include_top=True,
+ use_logits=True
+ )
+ }
+)
+```
+
+
+
+### Running a custom task w/ custom datasets
+
+To create a custom task, check out the [Bring-Your-Own-Task Guide](/heartkit/tasks/byot/).
+
+To create a custom dataset, check out the [Bring-Your-Own-Dataset Guide](/heartkit/datasets/byod/).
+
+---
diff --git a/docs/zoo/arr-2-eff-sm.md b/astro-site/src/content/docs/zoo/arr-2-eff-sm.mdx
similarity index 67%
rename from docs/zoo/arr-2-eff-sm.md
rename to astro-site/src/content/docs/zoo/arr-2-eff-sm.mdx
index af68feaa..5306d8f9 100644
--- a/docs/zoo/arr-2-eff-sm.md
+++ b/astro-site/src/content/docs/zoo/arr-2-eff-sm.mdx
@@ -1,11 +1,15 @@
-# 2-Class Arrhythmia Classification (ARR-2-EFF-SM)
-
+---
+title: "2-Class Arrhythmia Classification (ARR-2-EFF-SM)"
+description: "The following table provides the latest pre-trained model for 2-class arrhythmia classification. Below we also provide additional details including training…"
+---
## Overview
The following table provides the latest pre-trained model for 2-class arrhythmia classification. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/arr-2-eff-sm/results.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| ---------------- | ------------------------ | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
+| __ARR-2-EFF-SM__ | Icentia11K, PTB-XL, LSAD | 100Hz | 5s | 2 | EfficientNetV2 | 18K | 1.2M | 99.5% F1 |
---
@@ -20,19 +24,17 @@ The following table provides the latest pre-trained model for 2-class arrhythmia
The model is trained on raw ECG data and is able to discern normal sinus rhythm (NSR) from atrial fibrillation (AFIB) and atrial flutter (AFL). The class mapping is as follows:
-
| Base Class | Target Class | Label |
| ------------- | ------------ | --------- |
| 0-Normal | 0 | NSR |
| 7-AFIB, 8-AFL | 1 | AFIB |
-
## Datasets
The model is trained on the following datasets:
-- **[PTB-XL](../datasets/ptbxl.md)**
-- **[LSAD](../datasets/lsad.md)**
+- **[PTB-XL](/heartkit/datasets/ptbxl/)**
+- **[LSAD](/heartkit/datasets/lsad/)**
---
@@ -41,7 +43,7 @@ The model is trained on the following datasets:
The confusion matrix on the test set for the model is depicted below.
- * @version 0.0.1
- * @license MIT
- */
-
- :root {
- --color-bg: #252a33;
- --color-text: #eee;
- --color-text-subtle: #a2a2a2;
-}
-
-[data-termynal] {
- /* width: 750px; */
- margin: auto;
- width: 90%;
- max-width: 1440px;
- background: var(--color-bg);
- color: var(--color-text);
- /* font-size: 18px; */
- font-size: 15px;
- /* font-family: 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace; */
- font-family: 'Roboto Mono', 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace;
- border-radius: 4px;
- padding: 75px 45px 35px;
- position: relative;
- -webkit-box-sizing: border-box;
- box-sizing: border-box;
-}
-
-[data-termynal]:before {
- content: '';
- position: absolute;
- top: 15px;
- left: 15px;
- display: inline-block;
- width: 15px;
- height: 15px;
- border-radius: 50%;
- /* A little hack to display the window buttons in one pseudo element. */
- background: #d9515d;
- -webkit-box-shadow: 25px 0 0 #f4c025, 50px 0 0 #3ec930;
- box-shadow: 25px 0 0 #f4c025, 50px 0 0 #3ec930;
-}
-
-[data-termynal]:after {
- content: 'bash';
- position: absolute;
- color: var(--color-text-subtle);
- top: 5px;
- left: 0;
- width: 100%;
- text-align: center;
-}
-
-a[data-terminal-control] {
- text-align: right;
- display: block;
- color: #aebbff;
-}
-
-[data-ty] {
- display: block;
- line-height: 2;
-}
-
-[data-ty]:before {
- /* Set up defaults and ensure empty lines are displayed. */
- content: '';
- display: inline-block;
- vertical-align: middle;
-}
-
-[data-ty="input"]:before,
-[data-ty-prompt]:before {
- margin-right: 0.75em;
- color: var(--color-text-subtle);
-}
-
-[data-ty="input"]:before {
- content: '$';
-}
-
-[data-ty][data-ty-prompt]:before {
- content: attr(data-ty-prompt);
-}
-
-[data-ty-cursor]:after {
- content: attr(data-ty-cursor);
- font-family: monospace;
- margin-left: 0.5em;
- -webkit-animation: blink 1s infinite;
- animation: blink 1s infinite;
-}
-
-
-/* Cursor animation */
-
-@-webkit-keyframes blink {
- 50% {
- opacity: 0;
- }
-}
-
-@keyframes blink {
- 50% {
- opacity: 0;
- }
-}
diff --git a/docs/datasets/index.md b/docs/datasets/index.md
deleted file mode 100644
index c974d54d..00000000
--- a/docs/datasets/index.md
+++ /dev/null
@@ -1,52 +0,0 @@
-
-# :material-database: Datasets
-
-heartKIT provides support for a number of datasets to facilitate training the __heart-monitoring tasks__. Most of the datasets are readily available and can be downloaded and used for training and evaluation. The datasets inherit from [HKDataset](/heartkit/api/heartkit/datasets/dataset) and can be accessed either directly or through the factory singleton [`DatasetFactory`](#dataset-factory).
-
-## Available Datasets
-
-Below is a list of the currently available datasets in heartKIT. Please make sure to review each dataset's license for terms and limitations.
-
-* **[Icentia11k](./icentia11k.md)**: This dataset consists of ECG recordings from 11,000 patients and 2 billion labelled beats. The data was collected by the CardioSTAT, a single-lead heart monitor device from Icentia. The raw signals were recorded with a 16-bit resolution and sampled at 250 Hz with the CardioSTAT in a modified lead 1 position.
-
-* **[LSAD](./lsad.md)**: The Large Scale Rhythm Database (LSAD) is a large publicly available electrocardiography dataset. It contains 10 second, 12-lead ECGs of 45,152 patients with a 500 Hz sampling rate. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.
-
-* **[LUDB](./ludb.md)**: Lobachevsky University Electrocardiography database consists of 200 10-second 12-lead records. The boundaries and peaks of P, T waves and QRS complexes were manually annotated by cardiologists. Each record is annotated with the corresponding diagnosis.
-
-* **[PTB-XL](./ptbxl.md)**: The PTB-XL is a large publicly available electrocardiography dataset. It contains 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.
-
-* **[QTDB](./qtdb.md)**: Over 100 fifteen-minute two-lead ECG recordings with onset, peak, and end markers for P, QRS, T, and (where present) U waves of from 30 to 50 selected beats in each recording.
-
-* **[ECG Synthetic](./synthetic.md)**: An ECG synthetic dataset generated using physioKIT. The dataset enables the generation of 12-lead ECG signals with a variety of heart conditions and noise levels along with segmentations and fiducial points.
-
-* **[PPG Synthetic](./synthetic.md)**: A PPG synthetic dataset generated using physioKIT. The dataset enables the generation of a 1-lead PPG signal with segmentations and fiducials.
-
-* **[Bring-Your-Own-Data](./byod.md)**: Add new datasets to heartKIT by providing your own data. Subclass `HKDataset` and register it with the `DatasetFactory`.
-
-## Dataset Factory
-
-The dataset factory, `DatasetFactory`, provides a convenient way to access the datasets. The factory is a thread-safe singleton class that provides a single point of access to the datasets via the datasets' slug names. The benefit of using the factory is it allows registering new additional datasets that can then be leveraged by existing and new tasks.
-
-The dataset factory provides the following methods:
-
-* **hk.DatasetFactory.register**: Register a custom dataset
-* **hk.DatasetFactory.unregister**: Unregister a custom dataset
-* **hk.DatasetFactory.has**: Check if a dataset is registered
-* **hk.DatasetFactory.get**: Get a dataset
-* **hk.DatasetFactory.list**: List all available datasets
-
-
-```py linenums="1"
-
-import heartkit as hk
-
-# Grab EcgSynthetic dataset and instantiate it
-Dataset = hk.DatasetFactory.get('ecg-synthetic')
-ds = Dataset(num_pts=100)
-
-# Grab the first patient's data and segmentations
-with ds.patient_data(patient_id=ds.patient_ids[0]) as pt:
- ecg = pt["data"][:]
- segs = pt["segmentations"][:]
-
-```
diff --git a/docs/datasets/ludb.md b/docs/datasets/ludb.md
deleted file mode 100644
index 473c8395..00000000
--- a/docs/datasets/ludb.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# LUDB Dataset
-
-## Overview
-
-The Lobachevsky University Electrocardiography database (LUDB) consists of 200 10-second 12-lead records. The boundaries and peaks of P, T waves and QRS complexes were manually annotated by cardiologists. Each record is annotated with the corresponding diagnosis.
-
-Please visit [Physionet](https://physionet.org/content/ludb/1.0.1/) for more details.
-
-## Usage
-
-!!! Example Python
-
- ```py linenums="1"
- from pathlib import Path
- import helia_edge as helia
- import heartkit as hk
-
- ds = hk.DatasetFactory.get('ludb')(
- path=Path("./datasets/ludb")
- )
-
- # Download dataset
- ds.download(force=False)
-
- # Create signal generator
- data_gen = self.ds.signal_generator(
- patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
- frame_size=256,
- samples_per_patient=5,
- target_rate=100,
- )
-
- # Grab single ECG sample
- ecg = next(data_gen)
-
- ```
-
-## Funding
-
-The study was supported by the Ministry of Education of the Russian Federation (contract No. 02.G25.31.0157 of 01.12.2015).
-
-## License
-
-The LUDB is available for commercial use.
-
-## Supported Tasks
-
-* [Segmentation](../tasks/segmentation.md)
diff --git a/docs/datasets/qtdb.md b/docs/datasets/qtdb.md
deleted file mode 100644
index f604365a..00000000
--- a/docs/datasets/qtdb.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# QTDB Dataset
-
-## Overview
-
-Over 100 fifteen-minute two-lead ECG recordings with onset, peak, and end markers for P, QRS, T, and (where present) U waves of from 30 to 50 selected beats in each recording.
-
-Please visit [Physionet](https://doi.org/10.13026/C24K53) for more details.
-
-!!! Example Python
-
- ```py linenums="1"
- from pathlib import Path
- import helia_edge as helia
- import heartkit as hk
-
- ds = hk.DatasetFactory.get('qtdb')(
- path=Path("./datasets/qtdb")
- )
-
- # Download dataset
- ds.download(force=False)
-
- # Create signal generator
- data_gen = self.ds.signal_generator(
- patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
- frame_size=256,
- samples_per_patient=5,
- target_rate=100,
- )
-
- # Grab single ECG sample
- ecg = next(data_gen)
-
- ```
-
-## Funding
-
-The QT Database was created as part of a project funded by the National Library of Medicine.
-
-## License
-
-The QT Database is available for commercial use. [Open Data Commons Attribution License v1.0](https://physionet.org/content/qtdb/view-license/1.0.0/)
-
-## Supported Tasks
-
-* [Segmentation](../tasks/segmentation.md)
diff --git a/docs/guides/byot.ipynb b/docs/guides/byot.ipynb
deleted file mode 100644
index 085a4310..00000000
--- a/docs/guides/byot.ipynb
+++ /dev/null
@@ -1,1069 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))\n",
- "ax.plot(x, label=f\"HR: {60 * y:0.0f} BPM\")\n",
- "ax.set_title(\"ECG Frame\")\n",
- "ax.legend()\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Register dataloaders to factory\n",
- "\n",
- "We will then create a simple DataloaderFactory to ease the creation of dataloaders based on dataset names."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "DataloaderFactory = helia.utils.create_factory(factory=\"BYOT.DataloaderFactory\", type=hk.HKDataloader)\n",
- "DataloaderFactory.register(\"ptbxl\", PtbxlDataloader)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Create Data Pipeline\n",
- "\n",
- "We will create a data pipeline that will be used to train and evaluate the model. For each dataset, we will:\n",
- "1. Load the dataset via `hk.DatasetFactory`\n",
- "1. Split the dataset patients into training and validation sets\n",
- "1. Load the corresponding dataloader via `hk.DataloaderFactory` and create a `tf.data.Dataset` for training and validation\n",
- "\n",
- "Once each dataset has a pair of training and validation datasets, we will combine them into a single training and validation dataset. At this point we will then extend the pipeline by adding the following:\n",
- "1. Shuffle the dataset (if training)\n",
- "1. Batch the dataset\n",
- "1. Apply augmentations/preprocessing (if any)\n",
- "1. Prefetch the dataset\n",
- "\n",
- "Lastly, for the validation set will cache which will (1) speed up the evaluation process and (2) ensure that the same validation set is used for each epoch with fixed size.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "def create_data_pipeline(\n",
- " ds: tf.data.Dataset,\n",
- " sampling_rate: int,\n",
- " batch_size: int,\n",
- " buffer_size: int | None = None,\n",
- " augmentations: list[hk.NamedParams] | None = None,\n",
- ") -> tf.data.Dataset:\n",
- " \"\"\"Transforms a dataset into a pipeline with augmentations.\n",
- "\n",
- " Args:\n",
- " ds(tf.data.Dataset): Input dataset.\n",
- " sampling_rate(int): Sampling rate of the dataset.\n",
- " batch_size(int): Batch size.\n",
- " buffer_size(int | None): Buffer size for shuffling.\n",
- " augmentations(list[hk.NamedParams] | None): List of augmentations to apply.\n",
- "\n",
- " Returns:\n",
- " tf.data.Dataset: Augmented dataset\n",
- " \"\"\"\n",
- " if buffer_size:\n",
- " ds = ds.shuffle(\n",
- " buffer_size=buffer_size,\n",
- " reshuffle_each_iteration=True,\n",
- " )\n",
- " if batch_size:\n",
- " ds = ds.batch(\n",
- " batch_size=batch_size,\n",
- " drop_remainder=True,\n",
- " num_parallel_calls=tf.data.AUTOTUNE,\n",
- " )\n",
- " augmenter = hk.datasets.create_augmentation_pipeline(augmentations, sampling_rate=sampling_rate)\n",
- "\n",
- " ds = (\n",
- " ds.map(\n",
- " lambda data, labels: {\n",
- " \"data\": tf.cast(data, \"float32\"),\n",
- " \"labels\": tf.cast(labels, \"float32\"),\n",
- " },\n",
- " num_parallel_calls=tf.data.AUTOTUNE,\n",
- " )\n",
- " .map(\n",
- " augmenter,\n",
- " num_parallel_calls=tf.data.AUTOTUNE,\n",
- " )\n",
- " .map(\n",
- " lambda data: (data[\"data\"], data[\"labels\"]),\n",
- " num_parallel_calls=tf.data.AUTOTUNE,\n",
- " )\n",
- " )\n",
- " return ds.prefetch(tf.data.AUTOTUNE)\n",
- "\n",
- "\n",
- "def load_train_datasets(\n",
- " datasets: list[hk.HKDataset],\n",
- " dataloaderFactory: helia.utils.ItemFactory[hk.HKDataloader],\n",
- " params: hk.HKTaskParams,\n",
- ") -> tuple[tf.data.Dataset, tf.data.Dataset]:\n",
- " \"\"\"Loads training and validation datasets.\n",
- "\n",
- " Args:\n",
- " datasets(list[hk.HKDataset]): List of datasets to load.\n",
- " dataloaderFactory(helia.utils.ItemFactory[hk.HKDataloader]): Factory to create dataloaders.\n",
- " params(hk.HKTaskParams): Task parameters.\n",
- "\n",
- " Returns:\n",
- " tuple[tf.data.Dataset, tf.data.Dataset]: Training and validation datasets.\n",
- " \"\"\"\n",
- "\n",
- " # This will load each dataset/dataloader, split subjects, and merge into single tf.data.Dataset\n",
- " train_ds, val_ds = hk.tasks.utils.load_train_dataloader_split(datasets, params, factory=DataloaderFactory)\n",
- "\n",
- " # Create training and validation pipelines\n",
- " train_ds = create_data_pipeline(\n",
- " ds=train_ds,\n",
- " sampling_rate=params.sampling_rate,\n",
- " batch_size=params.batch_size,\n",
- " buffer_size=params.buffer_size,\n",
- " augmentations=params.augmentations + params.preprocesses,\n",
- " )\n",
- "\n",
- " val_ds = create_data_pipeline(\n",
- " ds=val_ds,\n",
- " sampling_rate=params.sampling_rate,\n",
- " batch_size=params.batch_size,\n",
- " augmentations=params.preprocesses,\n",
- " )\n",
- "\n",
- " # Cache validation dataset\n",
- " val_steps_per_epoch = params.val_size // params.batch_size if params.val_size else params.val_steps_per_epoch\n",
- " val_steps_per_epoch = val_steps_per_epoch or 50\n",
- " val_ds = val_ds.take(val_steps_per_epoch).cache()\n",
- "\n",
- " return train_ds, val_ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3. Create task routines\n",
- "\n",
- "We will create a task that will predict heart rate from raw ECG signal. The task will have the following modes:\n",
- "1. __train__: Train the model\n",
- "1. __evaluate__: Evaluate the model\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def train(params: hk.HKTaskParams):\n",
- " \"\"\"Train model\n",
- "\n",
- " Args:\n",
- " params (hk.HKTaskParams): Training parameters\n",
- " \"\"\"\n",
- " os.makedirs(params.job_dir, exist_ok=True)\n",
- "\n",
- " logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"train.log\")\n",
- " logger.debug(f\"Creating working directory in {params.job_dir}\")\n",
- "\n",
- " params.seed = helia.utils.set_random_seed(params.seed)\n",
- " logger.debug(f\"Random seed {params.seed}\")\n",
- "\n",
- " with open(params.job_dir / \"train_config.json\", \"w\", encoding=\"utf-8\") as fp:\n",
- " fp.write(params.model_dump_json(indent=2))\n",
- "\n",
- " params.num_classes = 1 # Regression\n",
- " feat_shape = (params.frame_size, 1)\n",
- "\n",
- " datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n",
- "\n",
- " train_ds, val_ds = load_train_datasets(datasets=datasets, dataloaderFactory=DataloaderFactory, params=params)\n",
- "\n",
- " y_true = np.concatenate([y for _, y in val_ds.as_numpy_iterator()])\n",
- " y_true = np.argmax(y_true, axis=-1).flatten()\n",
- "\n",
- " inputs = keras.Input(shape=feat_shape, name=\"input\", dtype=\"float32\")\n",
- "\n",
- " # Load existing model\n",
- " if params.resume and params.model_file:\n",
- " logger.debug(f\"Loading model from file {params.model_file}\")\n",
- " model = helia.models.load_model(params.model_file)\n",
- " params.model_file = None\n",
- " else:\n",
- " logger.debug(\"Creating model from scratch\")\n",
- " if params.architecture is None:\n",
- " raise ValueError(\"Model architecture must be specified\")\n",
- " model = hk.ModelFactory.get(params.architecture.name)(\n",
- " inputs=inputs,\n",
- " params=params.architecture.params,\n",
- " num_classes=params.num_classes,\n",
- " )\n",
- " # END IF\n",
- "\n",
- " flops = helia.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / \"model_flops.log\")\n",
- "\n",
- " t_mul = 1\n",
- " first_steps = (params.steps_per_epoch * params.epochs) / (np.power(params.lr_cycles, t_mul) - t_mul + 1)\n",
- " scheduler = keras.optimizers.schedules.CosineDecayRestarts(\n",
- " initial_learning_rate=params.lr_rate,\n",
- " first_decay_steps=np.ceil(first_steps),\n",
- " t_mul=t_mul,\n",
- " m_mul=0.5,\n",
- " )\n",
- "\n",
- " optimizer = keras.optimizers.Adam(scheduler)\n",
- " loss = keras.losses.MeanSquaredError()\n",
- " metrics = [\n",
- " keras.metrics.MeanAbsoluteError(name=\"mae\"),\n",
- " keras.metrics.MeanSquaredError(name=\"mse\"),\n",
- " keras.metrics.R2Score(name=\"rsq\"),\n",
- " ]\n",
- "\n",
- " if params.model_file is None:\n",
- " params.model_file = params.job_dir / \"model.keras\"\n",
- "\n",
- " model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
- " logger.debug(f\"Model requires {flops / 1e6:0.2f} MFLOPS\")\n",
- "\n",
- " model_callbacks = [\n",
- " keras.callbacks.EarlyStopping(\n",
- " monitor=f\"val_{params.val_metric}\",\n",
- " patience=max(int(0.25 * params.epochs), 1),\n",
- " mode=\"max\" if params.val_metric == \"f1\" else \"auto\",\n",
- " restore_best_weights=True,\n",
- " verbose=min(params.verbose - 1, 1),\n",
- " ),\n",
- " keras.callbacks.ModelCheckpoint(\n",
- " filepath=str(params.model_file),\n",
- " monitor=f\"val_{params.val_metric}\",\n",
- " save_best_only=True,\n",
- " mode=\"max\" if params.val_metric == \"f1\" else \"auto\",\n",
- " verbose=min(params.verbose - 1, 1),\n",
- " ),\n",
- " keras.callbacks.CSVLogger(params.job_dir / \"history.csv\"),\n",
- " ]\n",
- "\n",
- " history = model.fit(\n",
- " train_ds,\n",
- " steps_per_epoch=params.steps_per_epoch,\n",
- " verbose=params.verbose,\n",
- " epochs=params.epochs,\n",
- " validation_data=val_ds,\n",
- " callbacks=model_callbacks,\n",
- " )\n",
- " logger.debug(f\"Model saved to {params.model_file}\")\n",
- "\n",
- " helia.plotting.plot_history_metrics(\n",
- " history.history,\n",
- " metrics=[\"loss\", metrics[0].name],\n",
- " save_path=params.job_dir / \"history.png\",\n",
- " title=\"Training History\",\n",
- " stack=True,\n",
- " figsize=(9, 5),\n",
- " )\n",
- "\n",
- " # Summarize results\n",
- " rst = model.evaluate(val_ds, return_dict=True)\n",
- " logger.info(\"[VAL SET] \" + \", \".join(f\"{k.upper()}={v:.4f}\" for k, v in rst.items()))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "def evaluate(params: hk.HKTaskParams):\n",
- " \"\"\"Evaluate model\n",
- "\n",
- " Args:\n",
- " params (HKTaskParams): Evaluation parameters\n",
- " \"\"\"\n",
- " os.makedirs(params.job_dir, exist_ok=True)\n",
- " logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"test.log\")\n",
- " logger.debug(f\"Creating working directory in {params.job_dir}\")\n",
- "\n",
- " params.seed = helia.utils.set_random_seed(params.seed)\n",
- " logger.debug(f\"Random seed {params.seed}\")\n",
- "\n",
- " datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n",
- "\n",
- " _, test_ds = load_train_datasets(datasets=datasets, dataloaderFactory=DataloaderFactory, params=params)\n",
- " test_x = np.concatenate([x for x, _ in test_ds.as_numpy_iterator()])\n",
- " test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()])\n",
- "\n",
- " logger.debug(\"Loading model\")\n",
- " model = helia.models.load_model(params.model_file)\n",
- "\n",
- " logger.debug(\"Performing inference\")\n",
- " rst = model.evaluate(test_ds, verbose=params.verbose, return_dict=True)\n",
- " logger.info(\"[TEST SET] \" + \", \".join([f\"{k.upper()}={v:.2%}\" for k, v in rst.items()]))\n",
- "\n",
- " y_pred = model.predict(test_x)\n",
- "\n",
- " fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))\n",
- " ax.scatter(y_pred * 60, test_y * 60)\n",
- " ax.set_title(\"Predicted vs True BPM\")\n",
- " ax.set_xlabel(\"Predicted BPM\")\n",
- " ax.set_ylabel(\"True BPM\")\n",
- " ax.annotate(f\"R2={rst['rsq']:.2f}\", xy=(0.05, 0.95), xycoords=\"axes fraction\")\n",
- "\n",
- " fig.tight_layout()\n",
- " fig.show()\n",
- " fig.savefig(params.job_dir / \"bpm_plot.png\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create HeartRateTask class and register it to the factory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "class HeartRateTask(hk.HKTask):\n",
- " @staticmethod\n",
- " def train(params: hk.HKTaskParams):\n",
- " train(params)\n",
- "\n",
- " @staticmethod\n",
- " def evaluate(params: hk.HKTaskParams):\n",
- " evaluate(params)\n",
- "\n",
- " @staticmethod\n",
- " def export(params: hk.HKTaskParams) -> None:\n",
- " raise NotImplementedError(\"Export not implemented\")\n",
- "\n",
- " @staticmethod\n",
- " def demo(params: hk.HKTaskParams) -> None:\n",
- " raise NotImplementedError(\"Demo not implemented\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "hk.TaskFactory.register(\"heartrate\", HeartRateTask)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "rhythm\n",
- "beat\n",
- "segmentation\n",
- "diagnostic\n",
- "denoise\n",
- "foundation\n",
- "translate\n",
- "heartrate\n"
- ]
- }
- ],
- "source": [
- "for task_name in hk.TaskFactory.list():\n",
- " print(task_name)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4. Let's test out the new task!\n",
- "\n",
- "First we will create a task configuration with the following features:\n",
- "* Frame Size: 8 seconds\n",
- "* Dataset: PTB-XL\n",
- "* Model: `EfficientNetV2` with 4 MBConv blocks each depth 1\n",
- "* Batch Size: 256\n",
- "* Buffer Size: 20,000\n",
- "* Learning Rate: 1e-3\n",
- "* Preprocess: Z-score normalization\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "params = hk.HKTaskParams(\n",
- " name=\"BYOT-HR\",\n",
- " job_dir=Path(tempfile.gettempdir()) / \"hk-byot-hr\",\n",
- " verbose=1,\n",
- " datasets=[\n",
- " hk.NamedParams(\n",
- " name=\"ptbxl\",\n",
- " params=dict(\n",
- " path=Path(os.environ[\"HK_DATASET_PATH\"]) / \"ptbxl\",\n",
- " ),\n",
- " ),\n",
- " ],\n",
- " frame_size=4000, # 8 seconds\n",
- " sampling_rate=500, # 500Hz\n",
- " samples_per_patient=5,\n",
- " val_samples_per_patient=5,\n",
- " val_patients=0.2,\n",
- " val_size=10000,\n",
- " batch_size=256,\n",
- " buffer_size=20000,\n",
- " epochs=100,\n",
- " steps_per_epoch=50,\n",
- " lr_rate=1e-3,\n",
- " lr_cycles=1,\n",
- " val_metric=\"loss\",\n",
- " preprocesses=[\n",
- " hk.NamedParams(\n",
- " name=\"layer_norm\",\n",
- " params=dict(epsilon=0.01, name=\"znorm\"),\n",
- " ),\n",
- " ],\n",
- " augmentations=[],\n",
- " architecture=hk.NamedParams(\n",
- " name=\"efficientnetv2\",\n",
- " params=dict(\n",
- " input_filters=8,\n",
- " input_kernel_size=[1, 9],\n",
- " input_strides=[1, 2],\n",
- " blocks=[\n",
- " {\"filters\": 16, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n",
- " {\"filters\": 24, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n",
- " {\"filters\": 32, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n",
- " {\"filters\": 40, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n",
- " ],\n",
- " output_filters=0,\n",
- " include_top=True,\n",
- " use_logits=True,\n",
- " ),\n",
- " ),\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "task = hk.TaskFactory.get(\"heartrate\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Train the model\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
- "I0000 00:00:1723822309.458226 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723822309.478233 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723822309.478319 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723822309.479299 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723822309.479375 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723822309.479420 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723822309.529037 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723822309.529123 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723822309.529179 626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/100\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
- "I0000 00:00:1723822323.031206 626304 service.cc:146] XLA service 0x7471ec02c0b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
- "I0000 00:00:1723822323.031237 626304 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1m 5/50\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 1.8535 - mae: 1.2239 - mse: 1.8535 - rsq: -20.3693"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "I0000 00:00:1723822328.955479 626304 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 55ms/step - loss: 1.4214 - mae: 1.1194 - mse: 1.4214 - rsq: -16.4093 - val_loss: 0.7500 - val_mae: 0.8469 - val_mse: 0.7500 - val_rsq: -8.4336\n",
- "Epoch 2/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - loss: 0.4457 - mae: 0.6133 - mse: 0.4457 - rsq: -4.2974 - val_loss: 0.0573 - val_mae: 0.1936 - val_mse: 0.0573 - val_rsq: 0.2792\n",
- "Epoch 3/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 87ms/step - loss: 0.0722 - mae: 0.2064 - mse: 0.0722 - rsq: 0.0739 - val_loss: 0.0206 - val_mae: 0.0998 - val_mse: 0.0206 - val_rsq: 0.7403\n",
- "Epoch 4/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 92ms/step - loss: 0.0411 - mae: 0.1526 - mse: 0.0411 - rsq: 0.4376 - val_loss: 0.0178 - val_mae: 0.0924 - val_mse: 0.0178 - val_rsq: 0.7758\n",
- "Epoch 5/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 0.0388 - mae: 0.1436 - mse: 0.0388 - rsq: 0.5175 - val_loss: 0.0161 - val_mae: 0.0889 - val_mse: 0.0161 - val_rsq: 0.7972\n",
- "Epoch 6/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0316 - mae: 0.1323 - mse: 0.0316 - rsq: 0.5965 - val_loss: 0.0173 - val_mae: 0.0953 - val_mse: 0.0173 - val_rsq: 0.7820\n",
- "Epoch 7/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0287 - mae: 0.1265 - mse: 0.0287 - rsq: 0.6291 - val_loss: 0.0152 - val_mae: 0.0857 - val_mse: 0.0152 - val_rsq: 0.8089\n",
- "Epoch 8/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0266 - mae: 0.1204 - mse: 0.0266 - rsq: 0.6571 - val_loss: 0.0115 - val_mae: 0.0709 - val_mse: 0.0115 - val_rsq: 0.8550\n",
- "Epoch 9/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0251 - mae: 0.1161 - mse: 0.0251 - rsq: 0.6806 - val_loss: 0.0116 - val_mae: 0.0734 - val_mse: 0.0116 - val_rsq: 0.8537\n",
- "Epoch 10/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0225 - mae: 0.1125 - mse: 0.0225 - rsq: 0.7156 - val_loss: 0.0104 - val_mae: 0.0672 - val_mse: 0.0104 - val_rsq: 0.8689\n",
- "Epoch 11/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0235 - mae: 0.1115 - mse: 0.0235 - rsq: 0.7163 - val_loss: 0.0094 - val_mae: 0.0626 - val_mse: 0.0094 - val_rsq: 0.8821\n",
- "Epoch 12/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0206 - mae: 0.1054 - mse: 0.0206 - rsq: 0.7445 - val_loss: 0.0088 - val_mae: 0.0573 - val_mse: 0.0088 - val_rsq: 0.8890\n",
- "Epoch 13/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0209 - mae: 0.1055 - mse: 0.0209 - rsq: 0.7386 - val_loss: 0.0083 - val_mae: 0.0559 - val_mse: 0.0083 - val_rsq: 0.8951\n",
- "Epoch 14/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0194 - mae: 0.1026 - mse: 0.0194 - rsq: 0.7589 - val_loss: 0.0095 - val_mae: 0.0653 - val_mse: 0.0095 - val_rsq: 0.8807\n",
- "Epoch 15/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0201 - mae: 0.1032 - mse: 0.0201 - rsq: 0.7443 - val_loss: 0.0079 - val_mae: 0.0561 - val_mse: 0.0079 - val_rsq: 0.9010\n",
- "Epoch 16/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0179 - mae: 0.0993 - mse: 0.0179 - rsq: 0.7775 - val_loss: 0.0071 - val_mae: 0.0524 - val_mse: 0.0071 - val_rsq: 0.9104\n",
- "Epoch 17/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0176 - mae: 0.0985 - mse: 0.0176 - rsq: 0.7787 - val_loss: 0.0070 - val_mae: 0.0508 - val_mse: 0.0070 - val_rsq: 0.9126\n",
- "Epoch 18/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0169 - mae: 0.0968 - mse: 0.0169 - rsq: 0.7807 - val_loss: 0.0092 - val_mae: 0.0673 - val_mse: 0.0092 - val_rsq: 0.8847\n",
- "Epoch 19/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0167 - mae: 0.0959 - mse: 0.0167 - rsq: 0.7843 - val_loss: 0.0067 - val_mae: 0.0495 - val_mse: 0.0067 - val_rsq: 0.9155\n",
- "Epoch 20/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0169 - mae: 0.0953 - mse: 0.0169 - rsq: 0.7864 - val_loss: 0.0066 - val_mae: 0.0505 - val_mse: 0.0066 - val_rsq: 0.9164\n",
- "Epoch 21/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0164 - mae: 0.0944 - mse: 0.0164 - rsq: 0.7958 - val_loss: 0.0065 - val_mae: 0.0487 - val_mse: 0.0065 - val_rsq: 0.9185\n",
- "Epoch 22/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0173 - mae: 0.0963 - mse: 0.0173 - rsq: 0.7887 - val_loss: 0.0064 - val_mae: 0.0497 - val_mse: 0.0064 - val_rsq: 0.9197\n",
- "Epoch 23/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0158 - mae: 0.0935 - mse: 0.0158 - rsq: 0.7930 - val_loss: 0.0063 - val_mae: 0.0507 - val_mse: 0.0063 - val_rsq: 0.9203\n",
- "Epoch 24/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0150 - mae: 0.0926 - mse: 0.0150 - rsq: 0.8087 - val_loss: 0.0056 - val_mae: 0.0440 - val_mse: 0.0056 - val_rsq: 0.9297\n",
- "Epoch 25/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0151 - mae: 0.0919 - mse: 0.0151 - rsq: 0.8020 - val_loss: 0.0057 - val_mae: 0.0452 - val_mse: 0.0057 - val_rsq: 0.9282\n",
- "Epoch 26/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0153 - mae: 0.0909 - mse: 0.0153 - rsq: 0.8012 - val_loss: 0.0054 - val_mae: 0.0424 - val_mse: 0.0054 - val_rsq: 0.9324\n",
- "Epoch 27/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0171 - mae: 0.0913 - mse: 0.0171 - rsq: 0.7825 - val_loss: 0.0056 - val_mae: 0.0461 - val_mse: 0.0056 - val_rsq: 0.9291\n",
- "Epoch 28/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0148 - mae: 0.0891 - mse: 0.0148 - rsq: 0.8121 - val_loss: 0.0084 - val_mae: 0.0690 - val_mse: 0.0084 - val_rsq: 0.8945\n",
- "Epoch 29/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0152 - mae: 0.0902 - mse: 0.0152 - rsq: 0.8177 - val_loss: 0.0057 - val_mae: 0.0487 - val_mse: 0.0057 - val_rsq: 0.9284\n",
- "Epoch 30/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0142 - mae: 0.0892 - mse: 0.0142 - rsq: 0.8188 - val_loss: 0.0054 - val_mae: 0.0467 - val_mse: 0.0054 - val_rsq: 0.9321\n",
- "Epoch 31/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0139 - mae: 0.0886 - mse: 0.0139 - rsq: 0.8321 - val_loss: 0.0047 - val_mae: 0.0407 - val_mse: 0.0047 - val_rsq: 0.9409\n",
- "Epoch 32/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0133 - mae: 0.0868 - mse: 0.0133 - rsq: 0.8310 - val_loss: 0.0045 - val_mae: 0.0397 - val_mse: 0.0045 - val_rsq: 0.9435\n",
- "Epoch 33/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0138 - mae: 0.0869 - mse: 0.0138 - rsq: 0.8214 - val_loss: 0.0052 - val_mae: 0.0449 - val_mse: 0.0052 - val_rsq: 0.9352\n",
- "Epoch 34/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0140 - mae: 0.0856 - mse: 0.0140 - rsq: 0.8197 - val_loss: 0.0047 - val_mae: 0.0414 - val_mse: 0.0047 - val_rsq: 0.9404\n",
- "Epoch 35/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0150 - mae: 0.0859 - mse: 0.0150 - rsq: 0.7989 - val_loss: 0.0046 - val_mae: 0.0390 - val_mse: 0.0046 - val_rsq: 0.9425\n",
- "Epoch 36/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0136 - mae: 0.0861 - mse: 0.0136 - rsq: 0.8291 - val_loss: 0.0052 - val_mae: 0.0453 - val_mse: 0.0052 - val_rsq: 0.9344\n",
- "Epoch 37/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0141 - mae: 0.0872 - mse: 0.0141 - rsq: 0.8245 - val_loss: 0.0057 - val_mae: 0.0532 - val_mse: 0.0057 - val_rsq: 0.9280\n",
- "Epoch 38/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0138 - mae: 0.0863 - mse: 0.0138 - rsq: 0.8309 - val_loss: 0.0047 - val_mae: 0.0418 - val_mse: 0.0047 - val_rsq: 0.9413\n",
- "Epoch 39/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0131 - mae: 0.0856 - mse: 0.0131 - rsq: 0.8344 - val_loss: 0.0052 - val_mae: 0.0488 - val_mse: 0.0052 - val_rsq: 0.9344\n",
- "Epoch 40/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0123 - mae: 0.0835 - mse: 0.0123 - rsq: 0.8416 - val_loss: 0.0040 - val_mae: 0.0377 - val_mse: 0.0040 - val_rsq: 0.9494\n",
- "Epoch 41/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0123 - mae: 0.0828 - mse: 0.0123 - rsq: 0.8364 - val_loss: 0.0042 - val_mae: 0.0390 - val_mse: 0.0042 - val_rsq: 0.9469\n",
- "Epoch 42/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0120 - mae: 0.0822 - mse: 0.0120 - rsq: 0.8379 - val_loss: 0.0044 - val_mae: 0.0401 - val_mse: 0.0044 - val_rsq: 0.9446\n",
- "Epoch 43/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0126 - mae: 0.0813 - mse: 0.0126 - rsq: 0.8329 - val_loss: 0.0042 - val_mae: 0.0394 - val_mse: 0.0042 - val_rsq: 0.9477\n",
- "Epoch 44/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0127 - mae: 0.0849 - mse: 0.0127 - rsq: 0.8375 - val_loss: 0.0047 - val_mae: 0.0458 - val_mse: 0.0047 - val_rsq: 0.9415\n",
- "Epoch 45/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0123 - mae: 0.0829 - mse: 0.0123 - rsq: 0.8482 - val_loss: 0.0041 - val_mae: 0.0402 - val_mse: 0.0041 - val_rsq: 0.9484\n",
- "Epoch 46/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0127 - mae: 0.0831 - mse: 0.0127 - rsq: 0.8387 - val_loss: 0.0041 - val_mae: 0.0414 - val_mse: 0.0041 - val_rsq: 0.9480\n",
- "Epoch 47/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0121 - mae: 0.0817 - mse: 0.0121 - rsq: 0.8436 - val_loss: 0.0047 - val_mae: 0.0465 - val_mse: 0.0047 - val_rsq: 0.9405\n",
- "Epoch 48/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0117 - mae: 0.0819 - mse: 0.0117 - rsq: 0.8480 - val_loss: 0.0051 - val_mae: 0.0508 - val_mse: 0.0051 - val_rsq: 0.9359\n",
- "Epoch 49/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0127 - mae: 0.0821 - mse: 0.0127 - rsq: 0.8452 - val_loss: 0.0037 - val_mae: 0.0354 - val_mse: 0.0037 - val_rsq: 0.9539\n",
- "Epoch 50/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0139 - mae: 0.0821 - mse: 0.0139 - rsq: 0.8199 - val_loss: 0.0036 - val_mae: 0.0349 - val_mse: 0.0036 - val_rsq: 0.9547\n",
- "Epoch 51/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0113 - mae: 0.0807 - mse: 0.0113 - rsq: 0.8632 - val_loss: 0.0034 - val_mae: 0.0334 - val_mse: 0.0034 - val_rsq: 0.9566\n",
- "Epoch 52/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0116 - mae: 0.0798 - mse: 0.0116 - rsq: 0.8522 - val_loss: 0.0036 - val_mae: 0.0352 - val_mse: 0.0036 - val_rsq: 0.9543\n",
- "Epoch 53/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0115 - mae: 0.0808 - mse: 0.0115 - rsq: 0.8621 - val_loss: 0.0034 - val_mae: 0.0338 - val_mse: 0.0034 - val_rsq: 0.9567\n",
- "Epoch 54/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0144 - mae: 0.0808 - mse: 0.0144 - rsq: 0.8201 - val_loss: 0.0036 - val_mae: 0.0342 - val_mse: 0.0036 - val_rsq: 0.9553\n",
- "Epoch 55/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0121 - mae: 0.0789 - mse: 0.0121 - rsq: 0.8478 - val_loss: 0.0034 - val_mae: 0.0340 - val_mse: 0.0034 - val_rsq: 0.9566\n",
- "Epoch 56/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0116 - mae: 0.0790 - mse: 0.0116 - rsq: 0.8607 - val_loss: 0.0034 - val_mae: 0.0339 - val_mse: 0.0034 - val_rsq: 0.9570\n",
- "Epoch 57/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0113 - mae: 0.0799 - mse: 0.0113 - rsq: 0.8525 - val_loss: 0.0039 - val_mae: 0.0380 - val_mse: 0.0039 - val_rsq: 0.9514\n",
- "Epoch 58/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0117 - mae: 0.0798 - mse: 0.0117 - rsq: 0.8543 - val_loss: 0.0034 - val_mae: 0.0332 - val_mse: 0.0034 - val_rsq: 0.9575\n",
- "Epoch 59/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0125 - mae: 0.0825 - mse: 0.0125 - rsq: 0.8491 - val_loss: 0.0044 - val_mae: 0.0450 - val_mse: 0.0044 - val_rsq: 0.9451\n",
- "Epoch 60/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0104 - mae: 0.0764 - mse: 0.0104 - rsq: 0.8624 - val_loss: 0.0037 - val_mae: 0.0366 - val_mse: 0.0037 - val_rsq: 0.9534\n",
- "Epoch 61/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - loss: 0.0113 - mae: 0.0776 - mse: 0.0113 - rsq: 0.8535 - val_loss: 0.0034 - val_mae: 0.0329 - val_mse: 0.0034 - val_rsq: 0.9578\n",
- "Epoch 62/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0113 - mae: 0.0774 - mse: 0.0113 - rsq: 0.8519 - val_loss: 0.0052 - val_mae: 0.0522 - val_mse: 0.0052 - val_rsq: 0.9342\n",
- "Epoch 63/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0110 - mae: 0.0786 - mse: 0.0110 - rsq: 0.8620 - val_loss: 0.0033 - val_mae: 0.0335 - val_mse: 0.0033 - val_rsq: 0.9579\n",
- "Epoch 64/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0111 - mae: 0.0791 - mse: 0.0111 - rsq: 0.8581 - val_loss: 0.0034 - val_mae: 0.0338 - val_mse: 0.0034 - val_rsq: 0.9577\n",
- "Epoch 65/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0116 - mae: 0.0799 - mse: 0.0116 - rsq: 0.8651 - val_loss: 0.0032 - val_mae: 0.0318 - val_mse: 0.0032 - val_rsq: 0.9595\n",
- "Epoch 66/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0132 - mae: 0.0790 - mse: 0.0132 - rsq: 0.8365 - val_loss: 0.0046 - val_mae: 0.0461 - val_mse: 0.0046 - val_rsq: 0.9423\n",
- "Epoch 67/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0116 - mae: 0.0802 - mse: 0.0116 - rsq: 0.8559 - val_loss: 0.0034 - val_mae: 0.0332 - val_mse: 0.0034 - val_rsq: 0.9571\n",
- "Epoch 68/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0107 - mae: 0.0766 - mse: 0.0107 - rsq: 0.8577 - val_loss: 0.0033 - val_mae: 0.0323 - val_mse: 0.0033 - val_rsq: 0.9584\n",
- "Epoch 69/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0107 - mae: 0.0775 - mse: 0.0107 - rsq: 0.8635 - val_loss: 0.0035 - val_mae: 0.0358 - val_mse: 0.0035 - val_rsq: 0.9559\n",
- "Epoch 70/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0105 - mae: 0.0762 - mse: 0.0105 - rsq: 0.8693 - val_loss: 0.0039 - val_mae: 0.0406 - val_mse: 0.0039 - val_rsq: 0.9505\n",
- "Epoch 71/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0106 - mae: 0.0766 - mse: 0.0106 - rsq: 0.8634 - val_loss: 0.0031 - val_mae: 0.0316 - val_mse: 0.0031 - val_rsq: 0.9605\n",
- "Epoch 72/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0113 - mae: 0.0782 - mse: 0.0113 - rsq: 0.8529 - val_loss: 0.0032 - val_mae: 0.0320 - val_mse: 0.0032 - val_rsq: 0.9597\n",
- "Epoch 73/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0109 - mae: 0.0779 - mse: 0.0109 - rsq: 0.8620 - val_loss: 0.0031 - val_mae: 0.0311 - val_mse: 0.0031 - val_rsq: 0.9610\n",
- "Epoch 74/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0111 - mae: 0.0769 - mse: 0.0111 - rsq: 0.8636 - val_loss: 0.0031 - val_mae: 0.0310 - val_mse: 0.0031 - val_rsq: 0.9607\n",
- "Epoch 75/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0102 - mae: 0.0758 - mse: 0.0102 - rsq: 0.8729 - val_loss: 0.0032 - val_mae: 0.0322 - val_mse: 0.0032 - val_rsq: 0.9595\n",
- "Epoch 76/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0107 - mae: 0.0772 - mse: 0.0107 - rsq: 0.8609 - val_loss: 0.0031 - val_mae: 0.0311 - val_mse: 0.0031 - val_rsq: 0.9613\n",
- "Epoch 77/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0110 - mae: 0.0774 - mse: 0.0110 - rsq: 0.8658 - val_loss: 0.0031 - val_mae: 0.0316 - val_mse: 0.0031 - val_rsq: 0.9608\n",
- "Epoch 78/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0105 - mae: 0.0768 - mse: 0.0105 - rsq: 0.8665 - val_loss: 0.0033 - val_mae: 0.0339 - val_mse: 0.0033 - val_rsq: 0.9589\n",
- "Epoch 79/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0104 - mae: 0.0767 - mse: 0.0104 - rsq: 0.8677 - val_loss: 0.0030 - val_mae: 0.0307 - val_mse: 0.0030 - val_rsq: 0.9617\n",
- "Epoch 80/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0112 - mae: 0.0775 - mse: 0.0112 - rsq: 0.8662 - val_loss: 0.0031 - val_mae: 0.0307 - val_mse: 0.0031 - val_rsq: 0.9608\n",
- "Epoch 81/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0105 - mae: 0.0752 - mse: 0.0105 - rsq: 0.8626 - val_loss: 0.0031 - val_mae: 0.0310 - val_mse: 0.0031 - val_rsq: 0.9605\n",
- "Epoch 82/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0111 - mae: 0.0774 - mse: 0.0111 - rsq: 0.8591 - val_loss: 0.0031 - val_mae: 0.0311 - val_mse: 0.0031 - val_rsq: 0.9606\n",
- "Epoch 83/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0107 - mae: 0.0766 - mse: 0.0107 - rsq: 0.8632 - val_loss: 0.0034 - val_mae: 0.0348 - val_mse: 0.0034 - val_rsq: 0.9570\n",
- "Epoch 84/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0098 - mae: 0.0748 - mse: 0.0098 - rsq: 0.8718 - val_loss: 0.0030 - val_mae: 0.0304 - val_mse: 0.0030 - val_rsq: 0.9619\n",
- "Epoch 85/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0107 - mae: 0.0774 - mse: 0.0107 - rsq: 0.8723 - val_loss: 0.0030 - val_mae: 0.0306 - val_mse: 0.0030 - val_rsq: 0.9618\n",
- "Epoch 86/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0103 - mae: 0.0771 - mse: 0.0103 - rsq: 0.8624 - val_loss: 0.0030 - val_mae: 0.0305 - val_mse: 0.0030 - val_rsq: 0.9621\n",
- "Epoch 87/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0107 - mae: 0.0774 - mse: 0.0107 - rsq: 0.8680 - val_loss: 0.0030 - val_mae: 0.0305 - val_mse: 0.0030 - val_rsq: 0.9620\n",
- "Epoch 88/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0118 - mae: 0.0769 - mse: 0.0118 - rsq: 0.8507 - val_loss: 0.0031 - val_mae: 0.0307 - val_mse: 0.0031 - val_rsq: 0.9615\n",
- "Epoch 89/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0108 - mae: 0.0756 - mse: 0.0108 - rsq: 0.8602 - val_loss: 0.0031 - val_mae: 0.0315 - val_mse: 0.0031 - val_rsq: 0.9607\n",
- "Epoch 90/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0099 - mae: 0.0748 - mse: 0.0099 - rsq: 0.8767 - val_loss: 0.0030 - val_mae: 0.0304 - val_mse: 0.0030 - val_rsq: 0.9620\n",
- "Epoch 91/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0108 - mae: 0.0751 - mse: 0.0108 - rsq: 0.8638 - val_loss: 0.0031 - val_mae: 0.0312 - val_mse: 0.0031 - val_rsq: 0.9614\n",
- "Epoch 92/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0101 - mae: 0.0752 - mse: 0.0101 - rsq: 0.8703 - val_loss: 0.0031 - val_mae: 0.0307 - val_mse: 0.0031 - val_rsq: 0.9615\n",
- "Epoch 93/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0099 - mae: 0.0752 - mse: 0.0099 - rsq: 0.8730 - val_loss: 0.0031 - val_mae: 0.0309 - val_mse: 0.0031 - val_rsq: 0.9614\n",
- "Epoch 94/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0105 - mae: 0.0759 - mse: 0.0105 - rsq: 0.8643 - val_loss: 0.0031 - val_mae: 0.0317 - val_mse: 0.0031 - val_rsq: 0.9605\n",
- "Epoch 95/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0107 - mae: 0.0755 - mse: 0.0107 - rsq: 0.8700 - val_loss: 0.0030 - val_mae: 0.0306 - val_mse: 0.0030 - val_rsq: 0.9618\n",
- "Epoch 96/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0110 - mae: 0.0761 - mse: 0.0110 - rsq: 0.8575 - val_loss: 0.0030 - val_mae: 0.0306 - val_mse: 0.0030 - val_rsq: 0.9617\n",
- "Epoch 97/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0100 - mae: 0.0753 - mse: 0.0100 - rsq: 0.8757 - val_loss: 0.0033 - val_mae: 0.0330 - val_mse: 0.0033 - val_rsq: 0.9590\n",
- "Epoch 98/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0113 - mae: 0.0770 - mse: 0.0113 - rsq: 0.8597 - val_loss: 0.0032 - val_mae: 0.0326 - val_mse: 0.0032 - val_rsq: 0.9595\n",
- "Epoch 99/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0112 - mae: 0.0760 - mse: 0.0112 - rsq: 0.8565 - val_loss: 0.0031 - val_mae: 0.0310 - val_mse: 0.0031 - val_rsq: 0.9612\n",
- "Epoch 100/100\n",
- "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0122 - mae: 0.0779 - mse: 0.0122 - rsq: 0.8481 - val_loss: 0.0030 - val_mae: 0.0305 - val_mse: 0.0030 - val_rsq: 0.9618\n",
- "\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0030 - mae: 0.0307 - mse: 0.0030 - rsq: 0.9639\n"
- ]
- },
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "task.train(params)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Finally we will evaluate the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0030 - mae: 0.0307 - mse: 0.0030 - rsq: 0.9639\n"
- ]
- },
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "task.evaluate(params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "heartkit",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.13.9"
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- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/guides/ecg-foundation-model.ipynb b/docs/guides/ecg-foundation-model.ipynb
deleted file mode 100644
index ca5dde91..00000000
--- a/docs/guides/ecg-foundation-model.ipynb
+++ /dev/null
@@ -1,1630 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ecg1, ecg2 = next(iter(train_ds))\n",
- "ecg1, ecg2 = ecg1.numpy().squeeze(), ecg2.numpy().squeeze()\n",
- "\n",
- "ts = np.arange(0, len(ecg1)) / sampling_rate\n",
- "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
- "ax.plot(ts, ecg1, color=plot_theme.primary_color, lw=3)\n",
- "ax.plot(ts, ecg2, color=plot_theme.secondary_color, lw=3)\n",
- "fig.suptitle(\"Raw ECG Signal\")\n",
- "ax.set_xlabel(\"Time (s)\")\n",
- "ax.set_ylabel(\"Amplitude\")\n",
- "fig.tight_layout()\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create augmentation pipeline\n",
- "\n",
- "To enable self-supervised training to learn useful features from raw ECG signals, we need to create an augmentation pipeline. Each sample will be augmented into two different ways. Using contrastive learning, the model should generate features that are similar for the same sample and different for different samples. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "nstdb = hk.datasets.nstdb.NstdbNoise(target_rate=sampling_rate)\n",
- "noises = np.hstack(\n",
- " (nstdb.get_noise(noise_type=\"bw\"), nstdb.get_noise(noise_type=\"ma\"), nstdb.get_noise(noise_type=\"em\"))\n",
- ")\n",
- "noises = noises.astype(np.float32)\n",
- "\n",
- "preprocessor = helia.layers.preprocessing.LayerNormalization1D(epsilon=epsilon, name=\"LayerNormalization\")\n",
- "\n",
- "augmenter = helia.layers.preprocessing.AugmentationPipeline(\n",
- " layers=[\n",
- " helia.layers.preprocessing.RandomNoiseDistortion1D(\n",
- " sample_rate=sampling_rate, amplitude=(0, 1.0), frequency=(0.5, 1.5), name=\"BaselineWander\"\n",
- " ),\n",
- " helia.layers.preprocessing.RandomSineWave(\n",
- " sample_rate=sampling_rate, amplitude=(0, 0.05), frequency=(45, 50), name=\"PowerlineNoise\"\n",
- " ),\n",
- " helia.layers.preprocessing.AmplitudeWarp(\n",
- " sample_rate=sampling_rate, amplitude=(0.9, 1.1), frequency=(0.5, 1.5), name=\"AmplitudeWarp\"\n",
- " ),\n",
- " helia.layers.preprocessing.RandomGaussianNoise1D(factor=(0.05, 0.2), name=\"GaussianNoise\"),\n",
- " helia.layers.preprocessing.RandomBackgroundNoises1D(\n",
- " noises=noises, amplitude=(0.05, 0.2), num_noises=2, name=\"RandomBackgroundNoises\"\n",
- " ),\n",
- " helia.layers.preprocessing.RandomCutout1D(\n",
- " factor=(0.01, 0.05), cutouts=2, fill_mode=\"constant\", fill_value=0.0, name=\"RandomCutout\"\n",
- " ),\n",
- " helia.layers.preprocessing.RandomCrop1D(duration=frame_size, name=\"RandomCrop\", auto_vectorize=True),\n",
- " ],\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualize augmented pair"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "aug_ecg1 = augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg1, (1, -1, 1)))), training=True)\n",
- "aug_ecg1 = aug_ecg1.numpy().squeeze()\n",
- "\n",
- "aug_ecg2 = augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg2, (1, -1, 1)))), training=True)\n",
- "aug_ecg2 = aug_ecg2.numpy().squeeze()\n",
- "\n",
- "\n",
- "ts = np.arange(0, frame_size, 1) / sampling_rate\n",
- "\n",
- "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
- "plt.title(\"Augmented ECG\")\n",
- "plt.plot(ts, aug_ecg1, color=plot_theme.primary_color, lw=2)\n",
- "plt.plot(ts, aug_ecg2, color=plot_theme.secondary_color, lw=2)\n",
- "ax.set_xlabel(\"Time (s)\")\n",
- "ax.set_ylabel(\"Amplitude\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create full data pipeline w/ augmentation\n",
- "\n",
- "We will now create a full data pipeline by extended the original with shuffling, batching, augmentations, and prefetching.\n",
- "\n",
- "For validation, we will cache a subset of the validation data to speed up the evaluation process."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "train_ds = (\n",
- " train_ds.shuffle(\n",
- " buffer_size=buffer_size,\n",
- " reshuffle_each_iteration=True,\n",
- " )\n",
- " .batch(\n",
- " batch_size=batch_size,\n",
- " drop_remainder=True,\n",
- " num_parallel_calls=tf.data.AUTOTUNE,\n",
- " )\n",
- " .map(\n",
- " lambda x1, x2: {\n",
- " helia.trainers.SimCLRTrainer.SAMPLES: x1,\n",
- " helia.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n",
- " helia.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n",
- " },\n",
- " num_parallel_calls=tf.data.AUTOTUNE,\n",
- " )\n",
- " .prefetch(tf.data.AUTOTUNE)\n",
- ")\n",
- "\n",
- "val_ds = (\n",
- " val_ds.batch(\n",
- " batch_size=batch_size,\n",
- " drop_remainder=True,\n",
- " num_parallel_calls=tf.data.AUTOTUNE,\n",
- " )\n",
- " .map(\n",
- " lambda x1, x2: {\n",
- " helia.trainers.SimCLRTrainer.SAMPLES: x1,\n",
- " helia.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n",
- " helia.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n",
- " },\n",
- " num_parallel_calls=tf.data.AUTOTUNE,\n",
- " )\n",
- " .prefetch(tf.data.AUTOTUNE)\n",
- ")\n",
- "\n",
- "# Cache the validation dataset\n",
- "val_ds = val_ds.take(val_size // batch_size).cache()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Define encoder model \n",
- "\n",
- "For this task, we are going to leverage a customized __EfficientNetV2__ model architecture for the encoder that is smaller and can handle 1D signals. The model consists of 5 main MBConv blocks with a global average pooling layer and a dense layer for classification."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "inputs = keras.Input(shape=(frame_size, 1), name=\"input\")\n",
- "\n",
- "encoder_params = dict(\n",
- " input_filters=24,\n",
- " input_kernel_size=(1, 9),\n",
- " input_strides=(1, 2),\n",
- " blocks=[\n",
- " dict(filters=32, depth=2, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
- " dict(filters=48, depth=2, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
- " dict(filters=64, depth=2, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
- " dict(filters=80, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
- " dict(filters=96, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4, norm=\"layer\"),\n",
- " ],\n",
- " output_filters=projection_width,\n",
- " include_top=True,\n",
- ")\n",
- "\n",
- "encoder = helia.models.efficientnet.efficientnetv2_from_object(x=inputs, params=encoder_params, num_classes=None)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Visualize the model\n",
- "\n",
- "Let's view the encoder to understand the architecture better."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n"
- ],
- "text/plain": []
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, _ = helia.plotting.plot_history_metrics(\n",
- " history.history,\n",
- " metrics=[\"loss\", \"cos\"],\n",
- " title=\"Training History\",\n",
- " colors=[plot_theme.primary_color, plot_theme.secondary_color],\n",
- " stack=True,\n",
- " figsize=(9, 5),\n",
- ")\n",
- "fig.tight_layout()\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Model evaluation\n",
- "\n",
- "Now that we have trained the model, we will evaluate the model on the test dataset. The model's built-in `evaluate` method will be used to calculate the loss and metrics on the dataset."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Convert validation dataset to numpy arrays\n",
- "test_x1, test_x2 = [], []\n",
- "for inputs in val_ds.as_numpy_iterator():\n",
- " test_x1.append(inputs[helia.trainers.SimCLRTrainer.AUG_SAMPLES_0])\n",
- " test_x2.append(inputs[helia.trainers.SimCLRTrainer.AUG_SAMPLES_1])\n",
- "test_x1 = np.concatenate(test_x1)\n",
- "test_x2 = np.concatenate(test_x2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1m288/288\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step\n",
- "\u001b[1m288/288\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
- ]
- }
- ],
- "source": [
- "test_y1 = encoder.predict(test_x1)\n",
- "test_y2 = encoder.predict(test_x2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Compute t-SNE\n",
- "logger.debug(\"Computing t-SNE\")\n",
- "tsne = TSNE(n_components=2, random_state=0, n_iter=1000, perplexity=75)\n",
- "x_tsne = tsne.fit_transform(test_y1)\n",
- "\n",
- "# Plot t-SNE in matplotlib\n",
- "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
- "ax.scatter(x_tsne[:, 0], x_tsne[:, 1], c=x_tsne[:, 0] - x_tsne[:, 1], cmap=\"viridis\")\n",
- "fig.suptitle(\"HK Foundation: t-SNE\")\n",
- "ax.set_xlabel(\"Component 1\")\n",
- "ax.set_ylabel(\"Component 2\")\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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index 1cd847a1..00000000
--- a/docs/guides/index.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# Guides
-
-This section contains guides to help with various aspects of heartKIT. The guides are designed to provide detailed information on how to use heartKIT for different tasks and workflows.
-
-## Core Concepts Guides
-
-
-- **[Bring-Your-Own-Task](byot.ipynb)**: Demonstrate adding a new task end-to-end.
-
-## Notebook Training Examples
-
-- **[Train Arrhythmia Model](train-arrhythmia-model.ipynb)**: Training a 4-stage arrhythmia model from scratch.
-- **[Train ECG Denoiser](train-ecg-denoiser.ipynb)**: Training an ECG denoiser from scratch.
-- **[Train ECG Segmentation](train-ecg-segmentation.ipynb)**: Training an ECG segmentation model from scratch.
-- **[ECG Foundation Model](ecg-foundation-model.ipynb)**: Create an ECG foundation model.
-
-## Hardware Guides
-
-Several guides are available for running heartKIT models on Ambiq evaluation boards (EVBs) with ECG/PPG sensors connected via the Tileio App. Please check out the [Tileio Demos Page](https://ambiqai.github.io/tileio-docs/demos/) for more information.
diff --git a/docs/guides/train-arrhythmia-model.ipynb b/docs/guides/train-arrhythmia-model.ipynb
deleted file mode 100644
index b88eafd8..00000000
--- a/docs/guides/train-arrhythmia-model.ipynb
+++ /dev/null
@@ -1,917 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "task.train(params)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Model evaluation\n",
- "\n",
- "Now that we have trained the model, we will evaluate the model on the test dataset. Similar to training, we will provide the same task parameters."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "execution_count": 13,
- "metadata": {
- "image/png": {
- "width": 500
- }
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "IPython.display.Image(filename=params.job_dir / \"confusion_matrix_test.png\", width=500)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Export model to TF Lite / TFLM\n",
- "\n",
- "Once we have trained and evaluated the model, we need to export the model into a format that can be used for inference on the edge. Currently, we export the model to TensorFlow Lite flatbuffer format. This will also generate a C header file that can be used with TensorFlow Lite for Microcontrollers (TFLM).\n",
- "\n",
- "\n",
- "### Apply post-training quantization (PTQ)\n",
- "\n",
- "For running on bare metal, we will perform post-training quantization to convert the model to an 8-bit integer model. The weights and activations will be quantized to 8-bits and biases will be quantized to 32-bits. This will reduce the model size and improve the inference speed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "quantization = hk.QuantizationParams(\n",
- " enabled=True,\n",
- " format=\"INT8\",\n",
- " io_type=\"int8\",\n",
- " conversion=\"KERAS\",\n",
- ")\n",
- "params.quantization = quantization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n"
- ],
- "text/plain": []
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "W0000 00:00:1723841958.715237 789182 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n",
- "W0000 00:00:1723841958.715249 789182 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "task.demo(params=params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "heartkit",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.13.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/guides/train-ecg-denoiser.ipynb b/docs/guides/train-ecg-denoiser.ipynb
deleted file mode 100644
index 0bba07d9..00000000
--- a/docs/guides/train-ecg-denoiser.ipynb
+++ /dev/null
@@ -1,1045 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ds = hk.DatasetFactory.get(params.datasets[0].name)(cacheable=False, **params.datasets[0].params)\n",
- "\n",
- "ds_gen = ds.signal_generator(\n",
- " patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids()),\n",
- " frame_size=params.frame_size,\n",
- " samples_per_patient=params.samples_per_patient,\n",
- " target_rate=params.sampling_rate,\n",
- ")\n",
- "ecg = next(ds_gen)\n",
- "\n",
- "ts = np.arange(0, len(ecg)) / params.sampling_rate\n",
- "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
- "ax.plot(ts, ecg, color=plot_theme.primary_color, lw=3)\n",
- "fig.suptitle(\"Raw ECG Signal\")\n",
- "ax.set_xlabel(\"Time (s)\")\n",
- "ax.set_ylabel(\"Amplitude\")\n",
- "fig.tight_layout()\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Visualize augmented data\n",
- "\n",
- "Let's visualize the augmented data to understand how the augmentations affect the ECG signals."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
- "I0000 00:00:1723838156.202266 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838156.222145 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838156.222246 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838156.223422 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838156.223495 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838156.223541 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838156.268697 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838156.268787 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838156.268844 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
- ]
- }
- ],
- "source": [
- "preprocessor = hk.datasets.create_augmentation_pipeline(\n",
- " augmentations=params.preprocesses,\n",
- " sampling_rate=params.sampling_rate,\n",
- ")\n",
- "\n",
- "augmenter = hk.datasets.create_augmentation_pipeline(\n",
- " augmentations=params.augmentations,\n",
- " sampling_rate=params.sampling_rate,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "aug_ecg = augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg, (1, -1, 1)))), training=True)\n",
- "aug_ecg = aug_ecg.numpy().squeeze()\n",
- "\n",
- "ts = np.arange(0, len(aug_ecg)) / params.sampling_rate\n",
- "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
- "plt.plot(ts, aug_ecg, color=plot_theme.primary_color, lw=3)\n",
- "fig.suptitle(\"Augmented ECG Signal\")\n",
- "ax.set_xlabel(\"Time (s)\")\n",
- "ax.set_ylabel(\"Amplitude\")\n",
- "plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualize the model\n",
- "\n",
- "Let's view the first several layers of the model to understand the architecture better."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig, ax = plt.subplots(3, 1, figsize=(9, 5), sharex=True)\n",
- "ax[0].plot(ts, ecg.squeeze(), color=plot_theme.primary_color, lw=3)\n",
- "ax[1].plot(ts, aug_ecg.squeeze(), color=plot_theme.secondary_color, lw=3)\n",
- "ax[2].plot(ts, clean_ecg.squeeze(), color=plot_theme.tertiary_color, lw=3)\n",
- "\n",
- "ax[0].set_ylabel(\"Reference\")\n",
- "ax[1].set_ylabel(\"Noisy\")\n",
- "ax[2].set_ylabel(\"Denoised\")\n",
- "\n",
- "ax[1].text(0.98, 0.15, f\"{aug_snr:4.02f} dB SNR\", transform=ax[1].transAxes, ha=\"right\", va=\"top\", weight=\"bold\")\n",
- "ax[2].text(0.98, 0.15, f\"{clean_snr:4.02f} dB SNR\", transform=ax[2].transAxes, ha=\"right\", va=\"top\", weight=\"bold\")\n",
- "# Disable y-axis ticks for all plots\n",
- "for axes in ax:\n",
- " axes.yaxis.set_ticks([])\n",
- "ax[-1].set_xlabel(\"Time (s)\")\n",
- "fig.suptitle(\"ECG Denoising Demo\")\n",
- "fig.tight_layout()\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "heartkit",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.13.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/guides/train-ecg-segmentation.ipynb b/docs/guides/train-ecg-segmentation.ipynb
deleted file mode 100644
index f3efbd51..00000000
--- a/docs/guides/train-ecg-segmentation.ipynb
+++ /dev/null
@@ -1,1136 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ecg, segs, fids = pk.ecg.synthesize(\n",
- " signal_length=params.frame_size,\n",
- " sample_rate=params.sampling_rate,\n",
- " heart_rate=60,\n",
- " leads=1,\n",
- " preset=pk.ecg.EcgPreset.SR,\n",
- " noise_multiplier=0.0,\n",
- ")\n",
- "ecg = ecg.squeeze()\n",
- "segs = segs.squeeze()\n",
- "pwaves = np.where(segs == hk.tasks.HKSegment.pwave, ecg, np.nan)\n",
- "qrs = np.where(segs == hk.tasks.HKSegment.qrs, ecg, np.nan)\n",
- "twaves = np.where(segs == hk.tasks.HKSegment.twave, ecg, np.nan)\n",
- "\n",
- "\n",
- "ts = np.arange(0, len(ecg)) / params.sampling_rate\n",
- "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
- "plt.plot(ts, ecg, color=plot_theme.primary_color, lw=2, label=\"ECG\")\n",
- "plt.plot(ts, pwaves, color=plot_theme.secondary_color, lw=3, label=\"P-Wave\")\n",
- "plt.plot(ts, qrs, color=plot_theme.tertiary_color, lw=3, label=\"QRS\")\n",
- "plt.plot(ts, twaves, color=plot_theme.quaternary_color, lw=3, label=\"T-Wave\")\n",
- "plt.legend()\n",
- "\n",
- "# Plot segments\n",
- "plt.title(\"Synthetic ECG w/ Segments\")\n",
- "ax.set_xlabel(\"Time (s)\")\n",
- "ax.set_ylabel(\"Amplitude\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualize the augmentations\n",
- "\n",
- "Taking the existing synthetic ECG signal, let's look at the effects of the augmentations on the signal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
- "I0000 00:00:1723838702.560559 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838702.580638 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838702.580743 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838702.581841 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838702.581918 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838702.581964 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838702.624118 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838702.624214 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723838702.624283 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
- ]
- }
- ],
- "source": [
- "augmenter = hk.datasets.create_augmentation_pipeline(\n",
- " augmentations=params.augmentations,\n",
- " sampling_rate=params.sampling_rate,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "ecg_noise = augmenter(ecg.reshape(-1, 1)).numpy().squeeze()\n",
- "\n",
- "pwaves = np.where(segs == hk.tasks.HKSegment.pwave, ecg_noise, np.nan)\n",
- "qrs = np.where(segs == hk.tasks.HKSegment.qrs, ecg_noise, np.nan)\n",
- "twaves = np.where(segs == hk.tasks.HKSegment.twave, ecg_noise, np.nan)\n",
- "\n",
- "ts = np.arange(0, len(ecg_noise)) / params.sampling_rate\n",
- "fig, ax = plt.subplots(1, 1, figsize=(9, 4))\n",
- "plt.plot(ts, ecg_noise, color=plot_theme.primary_color, lw=2, label=\"ECG\")\n",
- "plt.plot(ts, pwaves, color=plot_theme.secondary_color, lw=3, label=\"P-Wave\")\n",
- "plt.plot(ts, qrs, color=plot_theme.tertiary_color, lw=3, label=\"QRS\")\n",
- "plt.plot(ts, twaves, color=plot_theme.quaternary_color, lw=3, label=\"T-Wave\")\n",
- "plt.legend()\n",
- "\n",
- "# Plot segments\n",
- "plt.title(\"Synthetic ECG w/ Noise\")\n",
- "ax.set_xlabel(\"Time (s)\")\n",
- "ax.set_ylabel(\"Amplitude\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualize the model\n",
- "\n",
- "Lets quickly instantiate and visualize the model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "task.train(params)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Model evaluation\n",
- "\n",
- "Now that we have trained the model, we will evaluate the model on the test dataset. Similar to training, we will provide the high-level configuration to the task process."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "execution_count": 18,
- "metadata": {
- "image/png": {
- "width": 500
- }
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "IPython.display.Image(filename=params.job_dir / \"confusion_matrix_test.png\", width=500)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Export model to TF Lite / TFLM\n",
- "\n",
- "\n",
- "Once we have trained and evaluated the model, we need to export the model into a format that can be used for inference on the edge. Currently, we export the model to TensorFlow Lite flatbuffer format. This will also generate a C header file that can be used with TensorFlow Lite for Microcontrollers (TFLM).\n",
- "\n",
- "\n",
- "### Post-Training Quantization (PTQ)\n",
- "\n",
- "For running on bare metal, we will perform post-training quantization to convert the model to an 8-bit integer model. The weights and activations will be quantized to 8-bits and biases will be quantized to 32-bits. This will reduce the model size and improve the inference speed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "quantization = hk.QuantizationParams(\n",
- " enabled=True,\n",
- " format=\"INT8\",\n",
- " io_type=\"int8\",\n",
- " conversion=\"CONCRETE\",\n",
- ")\n",
- "params.quantization = quantization"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "task.demo(params=params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "heartkit",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.13.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/index.md b/docs/index.md
deleted file mode 100644
index 28ef5e7f..00000000
--- a/docs/index.md
+++ /dev/null
@@ -1,159 +0,0 @@
-# Turn heart signals into on-device intelligence
-
-
won't have effect - // so put an additional one - buffer.push(""); - } - const bufferValue = buffer.join("
"); - dataValue["value"] = bufferValue; - useLines.push(dataValue); - buffer = []; - } - } - for (let line of lines) { - if (line === progressLiteralStart) { - saveBuffer(); - useLines.push({ - type: "progress" - }); - } else if (line.startsWith(promptLiteralStart)) { - saveBuffer(); - const value = line.replace(promptLiteralStart, "").trimEnd(); - useLines.push({ - type: "input", - value: value - }); - } else if (line.startsWith("// ")) { - saveBuffer(); - const value = "💬 " + line.replace("// ", "").trimEnd(); - useLines.push({ - value: value, - class: "termynal-comment", - delay: 0 - }); - } else if (line.startsWith(customPromptLiteralStart)) { - saveBuffer(); - const promptStart = line.indexOf(promptLiteralStart); - if (promptStart === -1) { - console.error("Custom prompt found but no end delimiter", line) - } - const prompt = line.slice(0, promptStart).replace(customPromptLiteralStart, "") - let value = line.slice(promptStart + promptLiteralStart.length); - useLines.push({ - type: "input", - value: value, - prompt: prompt - }); - } else { - buffer.push(line); - } - } - saveBuffer(); - const div = document.createElement("div"); - node.replaceWith(div); - const termynal = new Termynal(div, { - lineData: useLines, - noInit: true, - lineDelay: 500 - }); - termynals.push(termynal); - }); - } - - function loadVisibleTermynals() { - termynals = termynals.filter(termynal => { - if (termynal.container.getBoundingClientRect().top - innerHeight <= 0) { - termynal.init(); - return false; - } - return true; - }); - } - window.addEventListener("scroll", loadVisibleTermynals); - createTermynals(); - loadVisibleTermynals(); -} - -function shuffle(array) { - var currentIndex = array.length, temporaryValue, randomIndex; - while (0 !== currentIndex) { - randomIndex = Math.floor(Math.random() * currentIndex); - currentIndex -= 1; - temporaryValue = array[currentIndex]; - array[currentIndex] = array[randomIndex]; - array[randomIndex] = temporaryValue; - } - return array; -} - -async function showRandomAnnouncement(groupId, timeInterval) { - const announceFastAPI = document.getElementById(groupId); - if (announceFastAPI) { - let children = [].slice.call(announceFastAPI.children); - children = shuffle(children) - let index = 0 - const announceRandom = () => { - children.forEach((el, i) => {el.style.display = "none"}); - children[index].style.display = "block" - index = (index + 1) % children.length - } - announceRandom() - setInterval(announceRandom, timeInterval - ) - } -} - -async function main() { - setupTermynal(); -} - -main() diff --git a/docs/js/termynal.js b/docs/js/termynal.js deleted file mode 100644 index 4ac32708..00000000 --- a/docs/js/termynal.js +++ /dev/null @@ -1,264 +0,0 @@ -/** - * termynal.js - * A lightweight, modern and extensible animated terminal window, using - * async/await. - * - * @author Ines Montani
- * @version 0.0.1
- * @license MIT
- */
-
-'use strict';
-
-/** Generate a terminal widget. */
-class Termynal {
- /**
- * Construct the widget's settings.
- * @param {(string|Node)=} container - Query selector or container element.
- * @param {Object=} options - Custom settings.
- * @param {string} options.prefix - Prefix to use for data attributes.
- * @param {number} options.startDelay - Delay before animation, in ms.
- * @param {number} options.typeDelay - Delay between each typed character, in ms.
- * @param {number} options.lineDelay - Delay between each line, in ms.
- * @param {number} options.progressLength - Number of characters displayed as progress bar.
- * @param {string} options.progressChar – Character to use for progress bar, defaults to █.
- * @param {number} options.progressPercent - Max percent of progress.
- * @param {string} options.cursor – Character to use for cursor, defaults to ▋.
- * @param {Object[]} lineData - Dynamically loaded line data objects.
- * @param {boolean} options.noInit - Don't initialise the animation.
- */
- constructor(container = '#termynal', options = {}) {
- this.container = (typeof container === 'string') ? document.querySelector(container) : container;
- this.pfx = `data-${options.prefix || 'ty'}`;
- this.originalStartDelay = this.startDelay = options.startDelay
- || parseFloat(this.container.getAttribute(`${this.pfx}-startDelay`)) || 600;
- this.originalTypeDelay = this.typeDelay = options.typeDelay
- || parseFloat(this.container.getAttribute(`${this.pfx}-typeDelay`)) || 90;
- this.originalLineDelay = this.lineDelay = options.lineDelay
- || parseFloat(this.container.getAttribute(`${this.pfx}-lineDelay`)) || 1500;
- this.progressLength = options.progressLength
- || parseFloat(this.container.getAttribute(`${this.pfx}-progressLength`)) || 40;
- this.progressChar = options.progressChar
- || this.container.getAttribute(`${this.pfx}-progressChar`) || '█';
- this.progressPercent = options.progressPercent
- || parseFloat(this.container.getAttribute(`${this.pfx}-progressPercent`)) || 100;
- this.cursor = options.cursor
- || this.container.getAttribute(`${this.pfx}-cursor`) || '▋';
- this.lineData = this.lineDataToElements(options.lineData || []);
- this.loadLines()
- if (!options.noInit) this.init()
- }
-
- loadLines() {
- // Load all the lines and create the container so that the size is fixed
- // Otherwise it would be changing and the user viewport would be constantly
- // moving as she/he scrolls
- const finish = this.generateFinish()
- finish.style.visibility = 'hidden'
- this.container.appendChild(finish)
- // Appends dynamically loaded lines to existing line elements.
- this.lines = [...this.container.querySelectorAll(`[${this.pfx}]`)].concat(this.lineData);
- for (let line of this.lines) {
- line.style.visibility = 'hidden'
- this.container.appendChild(line)
- }
- const restart = this.generateRestart()
- restart.style.visibility = 'hidden'
- this.container.appendChild(restart)
- this.container.setAttribute('data-termynal', '');
- }
-
- /**
- * Initialise the widget, get lines, clear container and start animation.
- */
- init() {
- /**
- * Calculates width and height of Termynal container.
- * If container is empty and lines are dynamically loaded, defaults to browser `auto` or CSS.
- */
- const containerStyle = getComputedStyle(this.container);
- this.container.style.width = containerStyle.width !== '0px' ?
- containerStyle.width : undefined;
- this.container.style.minHeight = containerStyle.height !== '0px' ?
- containerStyle.height : undefined;
-
- this.container.setAttribute('data-termynal', '');
- this.container.innerHTML = '';
- for (let line of this.lines) {
- line.style.visibility = 'visible'
- }
- this.start();
- }
-
- /**
- * Start the animation and rener the lines depending on their data attributes.
- */
- async start() {
- this.addFinish()
- await this._wait(this.startDelay);
-
- for (let line of this.lines) {
- const type = line.getAttribute(this.pfx);
- const delay = line.getAttribute(`${this.pfx}-delay`) || this.lineDelay;
-
- if (type == 'input') {
- line.setAttribute(`${this.pfx}-cursor`, this.cursor);
- await this.type(line);
- await this._wait(delay);
- }
-
- else if (type == 'progress') {
- await this.progress(line);
- await this._wait(delay);
- }
-
- else {
- this.container.appendChild(line);
- await this._wait(delay);
- }
-
- line.removeAttribute(`${this.pfx}-cursor`);
- }
- this.addRestart()
- this.finishElement.style.visibility = 'hidden'
- this.lineDelay = this.originalLineDelay
- this.typeDelay = this.originalTypeDelay
- this.startDelay = this.originalStartDelay
- }
-
- generateRestart() {
- const restart = document.createElement('a')
- restart.onclick = (e) => {
- e.preventDefault()
- this.container.innerHTML = ''
- this.init()
- }
- restart.href = '#'
- restart.setAttribute('data-terminal-control', '')
- restart.innerHTML = "restart ↻"
- return restart
- }
-
- generateFinish() {
- const finish = document.createElement('a')
- finish.onclick = (e) => {
- e.preventDefault()
- this.lineDelay = 0
- this.typeDelay = 0
- this.startDelay = 0
- }
- finish.href = '#'
- finish.setAttribute('data-terminal-control', '')
- finish.innerHTML = "fast →"
- this.finishElement = finish
- return finish
- }
-
- addRestart() {
- const restart = this.generateRestart()
- this.container.appendChild(restart)
- }
-
- addFinish() {
- const finish = this.generateFinish()
- this.container.appendChild(finish)
- }
-
- /**
- * Animate a typed line.
- * @param {Node} line - The line element to render.
- */
- async type(line) {
- const chars = [...line.textContent];
- line.textContent = '';
- this.container.appendChild(line);
-
- for (let char of chars) {
- const delay = line.getAttribute(`${this.pfx}-typeDelay`) || this.typeDelay;
- await this._wait(delay);
- line.textContent += char;
- }
- }
-
- /**
- * Animate a progress bar.
- * @param {Node} line - The line element to render.
- */
- async progress(line) {
- const progressLength = line.getAttribute(`${this.pfx}-progressLength`)
- || this.progressLength;
- const progressChar = line.getAttribute(`${this.pfx}-progressChar`)
- || this.progressChar;
- const chars = progressChar.repeat(progressLength);
- const progressPercent = line.getAttribute(`${this.pfx}-progressPercent`)
- || this.progressPercent;
- line.textContent = '';
- this.container.appendChild(line);
-
- for (let i = 1; i < chars.length + 1; i++) {
- await this._wait(this.typeDelay);
- const percent = Math.round(i / chars.length * 100);
- line.textContent = `${chars.slice(0, i)} ${percent}%`;
- if (percent>progressPercent) {
- break;
- }
- }
- }
-
- /**
- * Helper function for animation delays, called with `await`.
- * @param {number} time - Timeout, in ms.
- */
- _wait(time) {
- return new Promise(resolve => setTimeout(resolve, time));
- }
-
- /**
- * Converts line data objects into line elements.
- *
- * @param {Object[]} lineData - Dynamically loaded lines.
- * @param {Object} line - Line data object.
- * @returns {Element[]} - Array of line elements.
- */
- lineDataToElements(lineData) {
- return lineData.map(line => {
- let div = document.createElement('div');
- div.innerHTML = `${line.value || ''}`;
-
- return div.firstElementChild;
- });
- }
-
- /**
- * Helper function for generating attributes string.
- *
- * @param {Object} line - Line data object.
- * @returns {string} - String of attributes.
- */
- _attributes(line) {
- let attrs = '';
- for (let prop in line) {
- // Custom add class
- if (prop === 'class') {
- attrs += ` class=${line[prop]} `
- continue
- }
- if (prop === 'type') {
- attrs += `${this.pfx}="${line[prop]}" `
- } else if (prop !== 'value') {
- attrs += `${this.pfx}-${prop}="${line[prop]}" `
- }
- }
-
- return attrs;
- }
-}
-
-/**
-* HTML API: If current script has container(s) specified, initialise Termynal.
-*/
-if (document.currentScript.hasAttribute('data-termynal-container')) {
- const containers = document.currentScript.getAttribute('data-termynal-container');
- containers.split('|')
- .forEach(container => new Termynal(container))
-}
diff --git a/docs/modes/evaluate.md b/docs/modes/evaluate.md
deleted file mode 100644
index 9f632149..00000000
--- a/docs/modes/evaluate.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# :material-ab-testing: Model Evaluation
-
-## Introduction
-
-Evaluate mode is used to test the performance of the model on the reserved test set for the specified task. Similar to training, the routine can be customized via CLI configuration file or by setting the parameters directly in the code. The evaluation process involves testing the model's performance on the test data to measure its accuracy, precision, recall, and F1 score. A number of results and metrics will be generated and saved to the `job_dir`.
-
-
- -```mermaid -flowchart TD - A["Load configuration"] --> B - B["Load datasets and dataloaders"] --> C - C["Initialize model"] --> D - D["Set loss, metrics and optimizer"] --> E - E["Train model"] --> F - F["Save model and artifacts"] -``` - ---- - -## Usage - -### CLI - -The following command will train a rhythm model using the reference configuration. - -```bash -heartkit --task rhythm --mode train --config ./configuration.json -``` - -### Python - -The model can be trained using the following snippet: - -```py linenums="1" - -task = hk.TaskFactory.get("rhythm") - -params = hk.HKTaskParams(...) - -task.train(params) - -``` - -**Example configuration** ---8<-- "assets/usage/python-configuration.md" - - ---- - -## Arguments - -Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) for the list of arguments that can be used with the `train` command. - ---- - -## Logging - -__heartKIT__ provides built-in support for logging to several third-party services including [Weights & Biases](https://wandb.ai/site) (WANDB) and [TensorBoard](https://www.tensorflow.org/tensorboard). - -### WANDB - -The training mode is able to log all metrics and artifacts (aka models) to [Weights & Biases](https://wandb.ai/site) (WANDB). To enable WANDB logging, simply set environment variable `WANDB=1`. Remember to sign in prior to running experiments by running `wandb login`. - - -### TensorBoard - -The training mode is able to log all metrics to [TensorBoard](https://www.tensorflow.org/tensorboard). To enable TensorBoard logging, simply set environment variable `TENSORBOARD=1`. diff --git a/docs/overrides/main.html b/docs/overrides/main.html deleted file mode 100644 index 702c96bf..00000000 --- a/docs/overrides/main.html +++ /dev/null @@ -1,11 +0,0 @@ -{% extends "base.html" %} - -{% block content %} -{% if page.nb_url %} - - {% include ".icons/material/download.svg" %} - -{% endif %} - -{{ super() }} -{% endblock content %} diff --git a/docs/quickstart.md b/docs/quickstart.md deleted file mode 100644 index c8600726..00000000 --- a/docs/quickstart.md +++ /dev/null @@ -1,155 +0,0 @@ -# Quickstart - - -## Install heartKIT - -We provide several installation methods including pip, uv, and Docker. Install __heartKIT__ via pip/uv for the latest stable release or by cloning the GitHub repo for the most up-to-date. Additionally, a [VSCode Dev Container](https://code.visualstudio.com/docs/devcontainers/containers) is available and defined in [./.devcontainer](https://github.com/AmbiqAI/heartkit/tree/main/.devcontainer) to run in an isolated Docker environment. - -!!! install - - === "Git clone" - - Clone the repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package. In this mode, uv is recommended. - - ```bash - # Clone the repository - git clone https://github.com/AmbiqAI/heartkit.git - - # Navigate to the cloned directory - cd heartkit - - # Install the package in editable mode for development - uv sync - ``` - - When using editable mode via uv, be sure to activate the python environment: `source .venv/bin/activate`.
- On Windows using Powershell, use `.venv\Scripts\activate`. - - === "PyPI install" - - Install the heartKIT package using pip or uv. - Visit the Python Package Index (PyPI) for more details on the package: [https://pypi.org/project/heartkit/](https://pypi.org/project/heartkit/) - - ```bash - # Install with pip - pip install heartkit - ``` - - Or, if you prefer to use uv, you can install the package with the following command: - - ```bash - # Install with uv - uv add heartkit - ``` - - Alternatively, you can install the latest development version directly from the GitHub repository. Make sure to have the Git command-line tool installed on your system. The @main command installs the main branch and may be modified to another branch, i.e. @canary. - - ```bash - pip install git+https://github.com/AmbiqAI/heartkit.git@main - ``` - - Or, using uv: - - ```bash - uv add git+https://github.com/AmbiqAI/heartkit.git@main - ``` - - -## Requirements - -* [Python 3.12–3.13](https://www.python.org) -* [uv ^0.7.10+](https://docs.astral.sh/uv/getting-started/installation/) - -Check the project's [pyproject.toml](https://github.com/AmbiqAI/heartkit/blob/main/pyproject.toml) file for a list of up-to-date Python dependencies. Note that the installation methods above install all required dependencies. The following are optional dependencies only needed when running `demo` command using Ambiq's evaluation board (`EVB`) backend: - -* [Arm GNU Toolchain ^12.2](https://developer.arm.com/downloads/-/arm-gnu-toolchain-downloads) -* [Segger J-Link ^7.92](https://www.segger.com/downloads/jlink/) - -Once installed, __heartKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced experimentation. - ---- - -## Use heartKIT with CLI - -The heartKIT command line interface (CLI) allows for simple single-line commands to download datasets, train models, evaluate performance, and export models. The CLI requires no customization or Python code. You can simply run all the built-in tasks from the terminal with the __heartkit__ command. Check out the [CLI Guide](./usage/cli.md) to learn more about available options. - -!!! example - - === "Syntax" - Heartkit commands use the following syntax: - - ```bash - heartkit --mode [MODE] --task [TASK] --config [CONFIG] - ``` - - Or using short flags: - - ```bash - heartkit -m [MODE] -t [TASK] -c [CONFIG] - ``` - - Where: - - * `MODE` is one of `download`, `train`, `evaluate`, `export`, or `demo` - * `TASK` is one of `segmentation`, `rhythm`, `beat`, or `denoise` - * `CONFIG` is configuration as JSON content or file path - - === "Download" - Download datasets specified in the configuration file. - - ```bash - heartkit -m download -c ./download-datasets.json - ``` - - === "Train" - Train a rhythm model using the supplied configuration file. - - ```bash - heartkit -m train -t rhythm -c ./configuration.json - ``` - - === "Evaluate" - Evaluate the trained rhythm model using the supplied configuration file. - - ```bash - heartkit -m evaluate -t rhythm -c ./configuration.json - ``` - - === "Demo" - Run demo on trained rhythm model using the supplied configuration file. - - ```bash - heartkit -m demo -t rhythm -c ./configuration.json - ``` - -## Use heartKIT with Python - -The __heartKIT__ Python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for a variety of tasks. You can create custom datasets, models, and tasks and register them with corresponding factories and use them like built-in tasks. - -For example, you can create a custom task, train it, evaluate its performance on a validation set, and even export a quantized TensorFlow Lite model for deployment. Check out the [Python Guide](./usage/python.md) to learn more about using heartKIT as a Python package. - -!!! Example - - ```py linenums="1" - - import heartkit as hk - - params = hk.HKTaskParams(...) - - task = hk.TaskFactory.get("rhythm") - - task.download(params) # Download dataset(s) - - task.train(params) # Train the model - - task.evaluate(params) # Evaluate the model - - task.export(params) # Export to TFLite - - ``` - - **Configuration parameters** - --8<-- "assets/usage/python-configuration.md" - - ---- diff --git a/docs/tasks/beat.md b/docs/tasks/beat.md deleted file mode 100644 index c61f371f..00000000 --- a/docs/tasks/beat.md +++ /dev/null @@ -1,62 +0,0 @@ -# Beat Classification Task - -## Overview - -In beat classification, we classify individual beats as either normal or abnormal. Abnormal beats can be further classified as being either premature or escape beats as well as originating from the atria, junction, or ventricles. The objective of beat classification is to detect and classify these abnormal heart beats directly from ECG signals. - -
P-wave: Different
QRS: Narrow (normal)
Aberrated: LBBB or RBBB | __PJC__
P-wave: None / retrograde
QRS: Narrow (normal)
Compensatory SA Pause | __PVC__
P-wave: None
QRS: Wide (> 120 ms)
Compensatory SA Pause | -| Escape | Atrial Escape | P-wave: Abnormal
QRS: Narrow (normal)
Ventricular rate: < 60 bpm
Junctional Escape
| P-wave: None
QRS: Narrow (normal)
Bradycardia (40-60 bpm)
Ventricular Escape | P-wave: None
QRS: Wide
Bradycardia (< 40 bpm) | - ---- - -## Dataloaders - -Dataloaders are available for the following datasets: - -* **[Icentia11k](../datasets/icentia11k.md)** -* **[PTB-XL](../datasets/ptbxl.md)** - ---- - -## Pre-trained Models - -The following table provides the latest performance and accuracy results for pre-trained beat models. Additional result details can be found in [Model Zoo → Beat](../zoo/index.md). - - ---8<-- "assets/zoo/beat/beat-model-zoo-table.md" - ---- - -## Target Classes - -Below outlines the classes available for beat classification. When training a model, the number of classes, mapping, and names must be provided. - ---8<-- "assets/tasks/beat/beat-classes.md" - -!!! example "Class Mapping" - - Below is an example of a class mapping for a 3-class beat model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped. - - ```jsonc - { - "num_classes": 3, - "class_names": ["QRS", "PAC", "PVC"], - "class_map": { - "0": 0, // Map Normal to QRS - "1": 1, // Map PAC to PAC - "2": 2, // Map PVC to PVC - } - } - ``` - ---- diff --git a/docs/tasks/diagnostic.md b/docs/tasks/diagnostic.md deleted file mode 100644 index beea2df2..00000000 --- a/docs/tasks/diagnostic.md +++ /dev/null @@ -1,5 +0,0 @@ -# Multi-Label Diagnostic Task - -The diagnostic task groups ECG findings into multiple labels. The diagram below shows the documented class hierarchy. - -{ width="480" } diff --git a/docs/tasks/index.md b/docs/tasks/index.md deleted file mode 100644 index de27d774..00000000 --- a/docs/tasks/index.md +++ /dev/null @@ -1,77 +0,0 @@ -# :simple-task: Tasks - -## Introduction - -heartKIT provides several built-in __heart-monitoring__ tasks. Each task is designed to address a unique aspect such as ECG denoising, segmentation, and rhythm/beat classification. The tasks are designed to be modular and can be used independently or in combination to address specific use cases. In addition to the built-in tasks, custom tasks can be created by extending the [HKTask](/heartkit/api/heartkit/tasks/task) base class and registering it with the task factory. - -
- { width="600" }
-
-
----
-
-## Available Tasks
-
-### [Signal Denoise](./denoise.md)
-
-[Signal denoise](./denoise.md) is the process of removing noise from a physiological signal such as ECG. This task is useful for improving the quality of the signal and for further downstream tasks such as segmentation in which noise can interfere with the delineation of individual waves.
-
-### [Signal Segmentation](./segmentation.md)
-
-[Signal segmentation](./segmentation.md) is the process of delineating a signal into its constituent parts. In the context of ECG, segmentation refers to delineating the ECG signal into individual waves (e.g. P-wave, QRS, T-wave). This task is useful for extracting features (e.g. HRV) from the ECG signal and for further analysis such as rhythm classification.
-
-### [Rhythm Classification](./rhythm.md)
-
-[Rhythm classification](./rhythm.md) is the process of identifying abnormal heart rhythms, also known as arrhythmias, such as atrial fibrillation (AFIB) and atrial flutter (AFL) from ECG signals. Cardiovascular diseases such as AFIB are a leading cause of morbidity and mortality worldwide. Being able to remotely identify heart arrhtyhmias is important for early detection and intervention.
-
-### [Beat Classification](./beat.md)
-
-[Beat classification](./beat.md) is the process of identifying and classifying individual heart beats such as normal, premature, and escape beats from ECG signals. By identifying abnormal heart beats, it is possible to detect and monitor various heart conditions.
-
-
-
-### [Bring-Your-Own-Task (BYOT)](./byot.md)
-
-[Bring-Your-Own-Task (BYOT)](./byot.md) is a feature that allows users to create custom tasks by extending the [HKTask](/heartkit/api/heartkit/tasks/task) base class and registering it with the `TaskFactory`. This feature is useful for addressing specific use cases that are not covered by the built-in tasks.
-
----
-
-## Task Factory
-
-The task factory, `TaskFactory`, provides a convenient way to access the built-in tasks. The factory is a thread-safe singleton class that provides a single point of access to the tasks via the tasks' slug names. The benefit of using the factory is it allows registering custom tasks that can then be used just like built-in tasks.
-
-The dataset factory provides the following methods:
-
-* **hk.TaskFactory.register**: Register a custom task
-* **hk.TaskFactory.unregister**: Unregister a custom task
-* **hk.TaskFactory.has**: Check if a task is registered
-* **hk.TaskFactory.get**: Get a task
-* **hk.TaskFactory.list**: List all available tasks
-
-
-```py linenums="1"
-
-import heartkit as hk
-
-# Grab a rhythm task
-task = hk.TaskFactory.get("rhythm")
-
-task.train(hk.HKTrainParams(
- ...
-))
-
-```
-
----
-
-## Choose a task
-
-| Task | Purpose |
-| --- | --- |
-| [Signal denoising](./denoise.md) | Remove noise from physiological signals. |
-| [Signal segmentation](./segmentation.md) | Delineate physiological signals into constituent parts. |
-| [Rhythm classification](./rhythm.md) | Identify rhythm-level arrhythmias such as AFIB and AFL. |
-| [Beat classification](./beat.md) | Identify premature and escape beats. |
-| [Bring your own task](./byot.md) | Create and register a custom task. |
diff --git a/docs/tasks/segmentation.md b/docs/tasks/segmentation.md
deleted file mode 100644
index 31cee9ab..00000000
--- a/docs/tasks/segmentation.md
+++ /dev/null
@@ -1,90 +0,0 @@
-# Signal Segmentation Task
-
-## Overview
-
-The objective of segmentation is to delineate key segments of the signal. In the context of ECG signals, this involves identifying the different components of the cardiac cycle, including the P-wave, QRS complex, and T-wave. These segments are used to compute a number of clinically relevant metrics, including heart rate, PR interval, QRS duration, QT interval, and QTc interval. For PPG, the task involves segmenting the systolic and diastolic phases of the cardiac cycle. Segmentation models are useful for detecting arrhythmias, heart rate variability, and other cardiac abnormalities.
-
-
-
');
- const fenced = '```text\n!!! Example\n{ width="540" }\n```';
- assert.equal(normalizeMarkdown(fenced), fenced);
-});
diff --git a/astro-site/scripts/test-build-notebooks.py b/astro-site/scripts/test-build-notebooks.py
index a3fedc54..be8f8958 100644
--- a/astro-site/scripts/test-build-notebooks.py
+++ b/astro-site/scripts/test-build-notebooks.py
@@ -9,14 +9,14 @@
from pathlib import Path
SCRIPT = Path(__file__).with_name("build-notebooks.py")
-GUIDES = SCRIPT.parents[2] / "docs" / "guides"
+GUIDES = SCRIPT.parents[2] / "notebooks"
class NotebookRenderingTest(unittest.TestCase):
def test_badge_toolbar_preserves_byot_introduction(self):
with tempfile.TemporaryDirectory() as directory:
root = Path(directory)
- shutil.copytree(GUIDES, root / "docs" / "guides")
+ shutil.copytree(GUIDES, root / "notebooks")
site = root / "site"
pages = site / "src" / "content" / "docs" / "guides"
pages.mkdir(parents=True)
@@ -33,10 +33,30 @@ def test_badge_toolbar_preserves_byot_introduction(self):
self.assertNotIn("View in Colab", rendered)
self.assertIn("Open in Colab", rendered)
+ def test_removed_notebook_removes_only_its_generated_page(self):
+ with tempfile.TemporaryDirectory() as directory:
+ root = Path(directory)
+ notebooks = root / "notebooks"
+ notebooks.mkdir()
+ source = notebooks / "removed.ipynb"
+ source.write_text(json.dumps({"cells": [{"cell_type": "markdown", "source": ["# Removed"]}]}))
+ site = root / "site"
+ pages = site / "src/content/docs/guides"
+ pages.mkdir(parents=True)
+ authored = pages / "authored.md"
+ authored.write_text("# Authored guide")
+ subprocess.run([sys.executable, str(SCRIPT)], cwd=site, check=True)
+ self.assertTrue((pages / "removed.md").exists())
+ source.unlink()
+ subprocess.run([sys.executable, str(SCRIPT)], cwd=site, check=True)
+ self.assertFalse((pages / "removed.md").exists())
+ self.assertFalse((site / "public/notebooks/removed.ipynb").exists())
+ self.assertEqual(authored.read_text(), "# Authored guide")
+
def test_colab_mention_does_not_drop_ordinary_prose(self):
with tempfile.TemporaryDirectory() as directory:
root = Path(directory)
- guides = root / "docs" / "guides"
+ guides = root / "notebooks"
guides.mkdir(parents=True)
prose = "Choose View in Colab to run this example.\n\nKeep your dataset paths configured."
(guides / "example.ipynb").write_text(json.dumps({
diff --git a/docs/datasets/byod.md b/astro-site/src/content/docs/datasets/byod.mdx
similarity index 85%
rename from docs/datasets/byod.md
rename to astro-site/src/content/docs/datasets/byod.mdx
index 6bfc62c7..36707a06 100644
--- a/docs/datasets/byod.md
+++ b/astro-site/src/content/docs/datasets/byod.mdx
@@ -1,4 +1,7 @@
-# Bring-Your-Own-Dataset (BYOD)
+---
+title: "Bring-Your-Own-Dataset (BYOD)"
+description: "The Bring-Your-Own-Dataset (BYOD) feature allows users to add custom datasets for training and evaluating models. This feature is useful when working with…"
+---
The Bring-Your-Own-Dataset (BYOD) feature allows users to add custom datasets for training and evaluating models. This feature is useful when working with proprietary or custom datasets that are not available in the heartKIT library.
@@ -6,7 +9,7 @@ The Bring-Your-Own-Dataset (BYOD) feature allows users to add custom datasets fo
1. **Create a Dataset**: Define a new dataset that inherits `HKDataset` and implements the required abstract methods.
-```py linenums="1"
+```py title="Python example" linenums="1"
import numpy as np
import heartkit as hk
@@ -54,7 +57,7 @@ class MyDataset(hk.HKDataset):
2. **Register the Dataset**: Register the new dataset with the `DatasetFactory` by calling the `register` method. This method takes the dataset name and the dataset class as arguments.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
import heartkit as hk
hk.DatasetFactory.register("my-dataset", CustomDataset)
@@ -62,7 +65,7 @@ class MyDataset(hk.HKDataset):
3. **Use the Dataset**: The new dataset can now be used with the `DatasetFactory` to perform various operations such as downloading and generating data.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
import heartkit as hk
params = {}
dataset = hk.DatasetFactory.get("my-dataset")(**params)
diff --git a/docs/datasets/icentia11k.md b/astro-site/src/content/docs/datasets/icentia11k.mdx
similarity index 50%
rename from docs/datasets/icentia11k.md
rename to astro-site/src/content/docs/datasets/icentia11k.mdx
index 418b55ed..95df023c 100644
--- a/docs/datasets/icentia11k.md
+++ b/astro-site/src/content/docs/datasets/icentia11k.mdx
@@ -1,4 +1,7 @@
-# Icentia11k Dataset
+---
+title: "Icentia11k Dataset"
+description: "This dataset consists of ECG recordings from 11,000 patients and 2 billion labelled beats. The data was collected by the CardioSTAT, a single-lead heart…"
+---
## Overview
@@ -8,36 +11,39 @@ More info available on [PhysioNet website](https://physionet.org/content/icentia
## Usage
-!!! Example Python
+**Python**
- ```py linenums="1"
- from pathlib import Path
- import helia_edge as helia
- import heartkit as hk
+```py title="Python example" linenums="1"
+from pathlib import Path
+import helia_edge as helia
+import heartkit as hk
- ds = hk.DatasetFactory.get('icentia11k')(
- path=Path("./datasets/icentia11k")
- )
+ds = hk.DatasetFactory.get('icentia11k')(
+ path=Path("./datasets/icentia11k")
+)
- # Download dataset
- ds.download(force=False)
+# Download dataset
+ds.download(force=False)
- # Create signal generator
- data_gen = self.ds.signal_generator(
- patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
- frame_size=256,
- samples_per_patient=5,
- target_rate=100,
- )
+# Create signal generator
+data_gen = self.ds.signal_generator(
+ patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+ frame_size=256,
+ samples_per_patient=5,
+ target_rate=100,
+)
- # Grab single ECG sample
- ecg = next(data_gen)
+# Grab single ECG sample
+ecg = next(data_gen)
- ```
+```
-???+ note
- The __Icentia11k dataset__ requires roughly 200 GB of disk space and can take around 2 hours to download.
+
+
## Funding
@@ -48,11 +54,6 @@ This work is partially funded by a grant from Icentia, Fonds de Recherche en San
The Icentia11k dataset is available for non-commercial use only.
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://physionet.org/content/icentia11k-continuous-ecg/view-license/1.0/)
-
-
-!!! warning
- The dataset is intended for evaluation purposes only and cannot be used for commercial use without permission. Please visit [Physionet](https://physionet.org/content/icentia11k-continuous-ecg/1.0) for more details.
+:::caution
+The dataset is intended for evaluation purposes only and cannot be used for commercial use without permission. Please visit [Physionet](https://physionet.org/content/icentia11k-continuous-ecg/1.0) for more details.
+:::
diff --git a/astro-site/src/content/docs/datasets/index.mdx b/astro-site/src/content/docs/datasets/index.mdx
new file mode 100644
index 00000000..fef1e7db
--- /dev/null
+++ b/astro-site/src/content/docs/datasets/index.mdx
@@ -0,0 +1,53 @@
+---
+title: "Datasets"
+description: "heartKIT provides support for a number of datasets to facilitate training the heart-monitoring tasks. Most of the datasets are readily available and can be…"
+---
+
+heartKIT provides support for a number of datasets to facilitate training the __heart-monitoring tasks__. Most of the datasets are readily available and can be downloaded and used for training and evaluation. The datasets inherit from [HKDataset](/heartkit/api/heartkit/datasets/dataset) and can be accessed either directly or through the factory singleton [`DatasetFactory`](#dataset-factory).
+
+## Available Datasets
+
+Below is a list of the currently available datasets in heartKIT. Please make sure to review each dataset's license for terms and limitations.
+
+* **[Icentia11k](/heartkit/datasets/icentia11k/)**: This dataset consists of ECG recordings from 11,000 patients and 2 billion labelled beats. The data was collected by the CardioSTAT, a single-lead heart monitor device from Icentia. The raw signals were recorded with a 16-bit resolution and sampled at 250 Hz with the CardioSTAT in a modified lead 1 position.
+
+* **[LSAD](/heartkit/datasets/lsad/)**: The Large Scale Rhythm Database (LSAD) is a large publicly available electrocardiography dataset. It contains 10 second, 12-lead ECGs of 45,152 patients with a 500 Hz sampling rate. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.
+
+* **[LUDB](/heartkit/datasets/ludb/)**: Lobachevsky University Electrocardiography database consists of 200 10-second 12-lead records. The boundaries and peaks of P, T waves and QRS complexes were manually annotated by cardiologists. Each record is annotated with the corresponding diagnosis.
+
+* **[PTB-XL](/heartkit/datasets/ptbxl/)**: The PTB-XL is a large publicly available electrocardiography dataset. It contains 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.
+
+* **[QTDB](/heartkit/datasets/qtdb/)**: Over 100 fifteen-minute two-lead ECG recordings with onset, peak, and end markers for P, QRS, T, and (where present) U waves of from 30 to 50 selected beats in each recording.
+
+* **[ECG Synthetic](/heartkit/datasets/synthetic/)**: An ECG synthetic dataset generated using physioKIT. The dataset enables the generation of 12-lead ECG signals with a variety of heart conditions and noise levels along with segmentations and fiducial points.
+
+* **[PPG Synthetic](/heartkit/datasets/synthetic/)**: A PPG synthetic dataset generated using physioKIT. The dataset enables the generation of a 1-lead PPG signal with segmentations and fiducials.
+
+* **[Bring-Your-Own-Data](/heartkit/datasets/byod/)**: Add new datasets to heartKIT by providing your own data. Subclass `HKDataset` and register it with the `DatasetFactory`.
+
+## Dataset Factory
+
+The dataset factory, `DatasetFactory`, provides a convenient way to access the datasets. The factory is a thread-safe singleton class that provides a single point of access to the datasets via the datasets' slug names. The benefit of using the factory is it allows registering new additional datasets that can then be leveraged by existing and new tasks.
+
+The dataset factory provides the following methods:
+
+* **hk.DatasetFactory.register**: Register a custom dataset
+* **hk.DatasetFactory.unregister**: Unregister a custom dataset
+* **hk.DatasetFactory.has**: Check if a dataset is registered
+* **hk.DatasetFactory.get**: Get a dataset
+* **hk.DatasetFactory.list**: List all available datasets
+
+```py title="Python example" linenums="1"
+
+import heartkit as hk
+
+# Grab EcgSynthetic dataset and instantiate it
+Dataset = hk.DatasetFactory.get('ecg-synthetic')
+ds = Dataset(num_pts=100)
+
+# Grab the first patient's data and segmentations
+with ds.patient_data(patient_id=ds.patient_ids[0]) as pt:
+ ecg = pt["data"][:]
+ segs = pt["segmentations"][:]
+
+```
diff --git a/docs/datasets/lsad.md b/astro-site/src/content/docs/datasets/lsad.mdx
similarity index 73%
rename from docs/datasets/lsad.md
rename to astro-site/src/content/docs/datasets/lsad.mdx
index b8c81b13..27f87bae 100644
--- a/docs/datasets/lsad.md
+++ b/astro-site/src/content/docs/datasets/lsad.mdx
@@ -1,4 +1,7 @@
-# Large Scale Arrhythmia Database (LSAD)
+---
+title: "Large Scale Arrhythmia Database (LSAD)"
+description: "The large scale arrhythmia database (LSAD) is a large-scale, multi-center, multi-lead, and multi-class ECG dataset for arrhythmia detection. The dataset…"
+---
## Overview
@@ -8,32 +11,32 @@ Please visit [Physionet](https://physionet.org/content/ecg-arrhythmia/1.0.0/) fo
## Usage
-!!! Example Python
+**Python**
- ```py linenums="1"
- from pathlib import Path
- import helia_edge as helia
- import heartkit as hk
+```py title="Python example" linenums="1"
+from pathlib import Path
+import helia_edge as helia
+import heartkit as hk
- ds = hk.DatasetFactory.get('lsad')(
- path=Path("./datasets/lsad")
- )
+ds = hk.DatasetFactory.get('lsad')(
+ path=Path("./datasets/lsad")
+)
- # Download dataset
- ds.download(force=False)
+# Download dataset
+ds.download(force=False)
- # Create signal generator
- data_gen = self.ds.signal_generator(
- patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
- frame_size=256,
- samples_per_patient=5,
- target_rate=100,
- )
+# Create signal generator
+data_gen = self.ds.signal_generator(
+ patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+ frame_size=256,
+ samples_per_patient=5,
+ target_rate=100,
+)
- # Grab single ECG sample
- ecg = next(data_gen)
+# Grab single ECG sample
+ecg = next(data_gen)
- ```
+```
## Statistics
diff --git a/astro-site/src/content/docs/datasets/ludb.mdx b/astro-site/src/content/docs/datasets/ludb.mdx
new file mode 100644
index 00000000..9ba6114d
--- /dev/null
+++ b/astro-site/src/content/docs/datasets/ludb.mdx
@@ -0,0 +1,51 @@
+---
+title: "LUDB Dataset"
+description: "The Lobachevsky University Electrocardiography database (LUDB) consists of 200 10-second 12-lead records. The boundaries and peaks of P, T waves and QRS…"
+---
+
+## Overview
+
+The Lobachevsky University Electrocardiography database (LUDB) consists of 200 10-second 12-lead records. The boundaries and peaks of P, T waves and QRS complexes were manually annotated by cardiologists. Each record is annotated with the corresponding diagnosis.
+
+Please visit [Physionet](https://physionet.org/content/ludb/1.0.1/) for more details.
+
+## Usage
+
+**Python**
+
+```py title="Python example" linenums="1"
+from pathlib import Path
+import helia_edge as helia
+import heartkit as hk
+
+ds = hk.DatasetFactory.get('ludb')(
+ path=Path("./datasets/ludb")
+)
+
+# Download dataset
+ds.download(force=False)
+
+# Create signal generator
+data_gen = self.ds.signal_generator(
+ patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+ frame_size=256,
+ samples_per_patient=5,
+ target_rate=100,
+)
+
+# Grab single ECG sample
+ecg = next(data_gen)
+
+```
+
+## Funding
+
+The study was supported by the Ministry of Education of the Russian Federation (contract No. 02.G25.31.0157 of 01.12.2015).
+
+## License
+
+The LUDB is available for commercial use.
+
+## Supported Tasks
+
+* [Segmentation](/heartkit/tasks/segmentation/)
diff --git a/docs/datasets/mitbih.md b/astro-site/src/content/docs/datasets/mitbih.mdx
similarity index 51%
rename from docs/datasets/mitbih.md
rename to astro-site/src/content/docs/datasets/mitbih.mdx
index ac60d695..c055ad73 100644
--- a/docs/datasets/mitbih.md
+++ b/astro-site/src/content/docs/datasets/mitbih.mdx
@@ -1,4 +1,7 @@
-# MIT BIH Normal Sinus Rhythm Dataset
+---
+title: "MIT BIH Normal Sinus Rhythm Dataset"
+description: "This dataset includes 18 long-term ECG recordings of subjects referred to the Rhythm Laboratory at Boston's Beth Israel Hospital (now the Beth Israel Deaconess…"
+---
## Overview
@@ -16,25 +19,24 @@ This database is available for commercial use. [Open Data Commons Attribution Li
## Supported Tasks
-* [Rhythm](../tasks/rhythm.md)
-
+* [Rhythm](/heartkit/tasks/rhythm/)
## Usage
-!!! Example Python
-
- ```py linenums="1"
- from pathlib import Path
- import heartkit as hk
-
- # Download dataset
- hk.datasets.download_datasets(hk.HKDownloadParams(
- datasets=[{
- "name": "mitbih",
- "params": {
- "path": "./datasets/mitbih"
- }
- }],
- progress=True
- ))
- ```
+**Python**
+
+```py title="Python example" linenums="1"
+from pathlib import Path
+import heartkit as hk
+
+# Download dataset
+hk.datasets.download_datasets(hk.HKDownloadParams(
+ datasets=[{
+ "name": "mitbih",
+ "params": {
+ "path": "./datasets/mitbih"
+ }
+ }],
+ progress=True
+))
+```
diff --git a/docs/datasets/ptbxl.md b/astro-site/src/content/docs/datasets/ptbxl.mdx
similarity index 55%
rename from docs/datasets/ptbxl.md
rename to astro-site/src/content/docs/datasets/ptbxl.mdx
index 2cc32d7b..1924db4b 100644
--- a/docs/datasets/ptbxl.md
+++ b/astro-site/src/content/docs/datasets/ptbxl.mdx
@@ -1,4 +1,7 @@
-# PTB-XL Diagnostics Dataset
+---
+title: "PTB-XL Diagnostics Dataset"
+description: "This dataset consists of 21837 clinical 12-lead ECGs from 18885 patients. The ECGs were recorded at a sampling frequency of 500 Hz and a resolution of 16 bits…"
+---
### Overview
@@ -8,32 +11,32 @@ Please visit [Physionet](https://physionet.org/content/ptb-xl/1.0.3/) for more d
## Usage
-!!! Example Python
+**Python**
- ```py linenums="1"
- from pathlib import Path
- import helia_edge as helia
- import heartkit as hk
+```py title="Python example" linenums="1"
+from pathlib import Path
+import helia_edge as helia
+import heartkit as hk
- ds = hk.DatasetFactory.get('ptbxl')(
- path=Path("./datasets/ptbxl")
- )
+ds = hk.DatasetFactory.get('ptbxl')(
+ path=Path("./datasets/ptbxl")
+)
- # Download dataset
- ds.download(force=False)
+# Download dataset
+ds.download(force=False)
- # Create signal generator
- data_gen = self.ds.signal_generator(
- patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
- frame_size=256,
- samples_per_patient=5,
- target_rate=100,
- )
+# Create signal generator
+data_gen = self.ds.signal_generator(
+ patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+ frame_size=256,
+ samples_per_patient=5,
+ target_rate=100,
+)
- # Grab single ECG sample
- ecg = next(data_gen)
+# Grab single ECG sample
+ecg = next(data_gen)
- ```
+```
### Funding
@@ -43,10 +46,6 @@ This work was supported by BMBF (01IS14013A), Berlin Big Data Center, Berlin Cen
This database is available under [Creative Commons Attribution 4.0 International Public License](https://physionet.org/content/ptb-xl/view-license/1.0.3/)
-
-
## References
* [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://arxiv.org/pdf/2004.13701.pdf)
diff --git a/astro-site/src/content/docs/datasets/qtdb.mdx b/astro-site/src/content/docs/datasets/qtdb.mdx
new file mode 100644
index 00000000..c4864609
--- /dev/null
+++ b/astro-site/src/content/docs/datasets/qtdb.mdx
@@ -0,0 +1,49 @@
+---
+title: "QTDB Dataset"
+description: "Over 100 fifteen-minute two-lead ECG recordings with onset, peak, and end markers for P, QRS, T, and (where present) U waves of from 30 to 50 selected beats in…"
+---
+
+## Overview
+
+Over 100 fifteen-minute two-lead ECG recordings with onset, peak, and end markers for P, QRS, T, and (where present) U waves of from 30 to 50 selected beats in each recording.
+
+Please visit [Physionet](https://doi.org/10.13026/C24K53) for more details.
+
+**Python**
+
+```py title="Python example" linenums="1"
+from pathlib import Path
+import helia_edge as helia
+import heartkit as hk
+
+ds = hk.DatasetFactory.get('qtdb')(
+ path=Path("./datasets/qtdb")
+)
+
+# Download dataset
+ds.download(force=False)
+
+# Create signal generator
+data_gen = self.ds.signal_generator(
+ patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True),
+ frame_size=256,
+ samples_per_patient=5,
+ target_rate=100,
+)
+
+# Grab single ECG sample
+ecg = next(data_gen)
+
+```
+
+## Funding
+
+The QT Database was created as part of a project funded by the National Library of Medicine.
+
+## License
+
+The QT Database is available for commercial use. [Open Data Commons Attribution License v1.0](https://physionet.org/content/qtdb/view-license/1.0.0/)
+
+## Supported Tasks
+
+* [Segmentation](/heartkit/tasks/segmentation/)
diff --git a/docs/datasets/synthetic.md b/astro-site/src/content/docs/datasets/synthetic.mdx
similarity index 54%
rename from docs/datasets/synthetic.md
rename to astro-site/src/content/docs/datasets/synthetic.mdx
index 26d0ea82..3603131d 100644
--- a/docs/datasets/synthetic.md
+++ b/astro-site/src/content/docs/datasets/synthetic.mdx
@@ -1,4 +1,7 @@
-# Synthetic Datasets
+---
+title: "Synthetic Datasets"
+description: "By leveraging physioKIT, we are able to generate synthetic data for a variety of physiological signals, including ECG, PPG, and respiration. In addition to the…"
+---
### Overview
@@ -16,31 +19,30 @@ A PPG synthetic dataset generated using physioKIT. The dataset enables the gener
## Usage
-!!! Example Python
+**Python**
- ```py linenums="1"
- import heartkit as hk
+```py title="Python example" linenums="1"
+import heartkit as hk
- ds = hk.DatasetFactory.get('ecg-synthetic')(
- num_pts=100,
- params=dict(
- sample_rate=1000, # Hz
- duration=10, # seconds
- heart_rate=(40, 120),
- )
+ds = hk.DatasetFactory.get('ecg-synthetic')(
+ num_pts=100,
+ params=dict(
+ sample_rate=1000, # Hz
+ duration=10, # seconds
+ heart_rate=(40, 120),
)
+)
- with ds.patient_data(patient_id=ds.patient_ids[0]) as pt:
- ecg = pt["data"][:]
- segs = pt["segmentations"][:]
- fids = pt["fiducials"][:]
+with ds.patient_data(patient_id=ds.patient_ids[0]) as pt:
+ ecg = pt["data"][:]
+ segs = pt["segmentations"][:]
+ fids = pt["fiducials"][:]
- ```
-
- Note
+The __Icentia11k dataset__ requires roughly 200 GB of disk space and can take around 2 hours to download. + +
- --8<-- "assets/tasks/segmentation/segmentation-example.html"
-
+```
+
+
+
## Funding
diff --git a/docs/guides/evb-setup.md b/astro-site/src/content/docs/guides/evb-setup.mdx
similarity index 65%
rename from docs/guides/evb-setup.md
rename to astro-site/src/content/docs/guides/evb-setup.mdx
index 83389d4b..1e39cc7d 100644
--- a/docs/guides/evb-setup.md
+++ b/astro-site/src/content/docs/guides/evb-setup.mdx
@@ -1,4 +1,7 @@
-# EVB Setup & Compiling
+---
+title: "EVB Setup & Compiling"
+description: "In order to compile the EVB binary, both Arm GNU Toolchain and Segger J-Link must be installed on the host PC. After installing, ensure they are both available…"
+---
## Requirements
@@ -22,7 +25,7 @@ The following items are needed to flash firmware and run demos.
In order to connect the MAX86150 breakout board to the Apollo 4 EVB, we must first solder the 5-pin header on to the MAX86150 board.
-
+
Once soldered, connect the breakout board to the EVB using 5 jumper wires as follows:
@@ -34,17 +37,19 @@ Once soldered, connect the breakout board to the EVB using 5 jumper wires as fol
| INT | Not used |
| GND | J17 pin 4 (GND) |
-
+
-!!! note
- Alternatively, the Qwiic connector on the breakout board can be used. This will require a Qwiic breakout cable. For 3V3, J3 pin 5 (3.3V) can be leveraged on the EVB.
+:::note
+Alternatively, the Qwiic connector on the breakout board can be used. This will require a Qwiic breakout cable. For 3V3, J3 pin 5 (3.3V) can be leveraged on the EVB.
+:::
## Firmware Setup
Connect the EVB to your laptop using both USB-C ports on the EVB.
-!!! note
- Quality of ECG from the onboard pads depends on contact quality and will produce more artifacts. For a better quality ECG, it is recommended that the accompanying ECG cable be used with electrodes. Place blue electrode on left wrist/finger/arm and red electrode on right wrist/finger/arm. Optionally, the black ground electrode can be placed on the body to further improve SNR.
+:::note
+Quality of ECG from the onboard pads depends on contact quality and will produce more artifacts. For a better quality ECG, it is recommended that the accompanying ECG cable be used with electrodes. Place blue electrode on left wrist/finger/arm and red electrode on right wrist/finger/arm. Optionally, the black ground electrode can be placed on the body to further improve SNR.
+:::
## Firmware Commands
diff --git a/astro-site/src/content/docs/guides/index.mdx b/astro-site/src/content/docs/guides/index.mdx
new file mode 100644
index 00000000..8136133e
--- /dev/null
+++ b/astro-site/src/content/docs/guides/index.mdx
@@ -0,0 +1,21 @@
+---
+title: "Guides"
+description: "This section contains guides to help with various aspects of heartKIT. The guides are designed to provide detailed information on how to use heartKIT for…"
+---
+
+This section contains guides to help with various aspects of heartKIT. The guides are designed to provide detailed information on how to use heartKIT for different tasks and workflows.
+
+## Core Concepts Guides
+
+- **[Bring-Your-Own-Task](/heartkit/guides/byot/)**: Demonstrate adding a new task end-to-end.
+
+## Notebook Training Examples
+
+- **[Train Arrhythmia Model](/heartkit/guides/train-arrhythmia-model/)**: Training a 4-stage arrhythmia model from scratch.
+- **[Train ECG Denoiser](/heartkit/guides/train-ecg-denoiser/)**: Training an ECG denoiser from scratch.
+- **[Train ECG Segmentation](/heartkit/guides/train-ecg-segmentation/)**: Training an ECG segmentation model from scratch.
+- **[ECG Foundation Model](/heartkit/guides/ecg-foundation-model/)**: Create an ECG foundation model.
+
+## Hardware Guides
+
+Several guides are available for running heartKIT models on Ambiq evaluation boards (EVBs) with ECG/PPG sensors connected via the Tileio App. Please check out the [Tileio Demos Page](https://ambiqai.github.io/tileio-docs/demos/) for more information.
diff --git a/docs/guides/rhythm-demo.md b/astro-site/src/content/docs/guides/rhythm-demo.mdx
similarity index 68%
rename from docs/guides/rhythm-demo.md
rename to astro-site/src/content/docs/guides/rhythm-demo.mdx
index a86e1450..b793da4d 100644
--- a/docs/guides/rhythm-demo.md
+++ b/astro-site/src/content/docs/guides/rhythm-demo.mdx
@@ -1,4 +1,7 @@
-# heartKIT Rhythm Tutorial
+---
+title: "heartKIT Rhythm Tutorial"
+description: "This tutorial shows running an end-to-end heart rhythm classifier on the Apollo 4 EVB. The basic flow chart is depicted below."
+---
This tutorial shows running an end-to-end heart rhythm classifier on the Apollo 4 EVB. The basic flow chart is depicted below.
@@ -9,29 +12,30 @@ flowchart LR
In the first stage, 4 seconds of sensor data is collected- either directly from the MAX86150 sensor or test data from the PC. In stage 2, the data is preprocessed by bandpass filtering and standardizing. The data is then fed into the CNN network to perform inference. Finally, in stage 4, the ECG data will be classified as normal (NSR), rhythm (AFIB/AFL) or inconclusive. Inconclusive is assigned when the prediction confidence is less than a pre-defined threshold (e.g. 90%).
-!!! note
- A reference rhythm model (`./evb/src/arrhythmia_model_buffer.h`) is included and can be used to quickly evaluate the hardware. The model is trained on Icentia11k dataset that has the associated [non-commercial license](https://physionet.org/content/icentia11k-continuous-ecg/1.0/LICENSE.txt). The model is intended for evaluation purposes only and cannot be used for commercial use without permission.
+:::note
+A reference rhythm model (`./evb/src/arrhythmia_model_buffer.h`) is included and can be used to quickly evaluate the hardware. The model is trained on Icentia11k dataset that has the associated [non-commercial license](https://physionet.org/content/icentia11k-continuous-ecg/1.0/LICENSE.txt). The model is intended for evaluation purposes only and cannot be used for commercial use without permission.
+:::
## Demo Setup
-Please follow [EVB Setup Guide](./evb-setup.md) to prepare EVB and connect to PC. To use the pre-trained model, please skip to [Run Demo Section](#run-demo).
+Please follow [EVB Setup Guide](/heartkit/guides/evb-setup/) to prepare EVB and connect to PC. To use the pre-trained model, please skip to [Run Demo Section](#run-demo).
### 1. Train the rhythm model w/ given configuration
-```bash
+```bash title="Terminal"
heartkit \
--task rhythm \
--mode train \
--config ./configs/train-rhythm-model.json
```
-!!! note
- Due to the large dataset and class imbalance, the batch size and buffer size are large for rhythm training to ensure properly shuffling of patients as well as classes. The first epoch will take much longer as it fills up this buffer. To train on dedicated GPUs, it's recommended to have at least 10 GB of VRAM.
+:::note
+Due to the large dataset and class imbalance, the batch size and buffer size are large for rhythm training to ensure properly shuffling of patients as well as classes. The first epoch will take much longer as it fills up this buffer. To train on dedicated GPUs, it's recommended to have at least 10 GB of VRAM.
+:::
### 2. Evaluate the rhythm model's performance
-
-```bash
+```bash title="Terminal"
heartkit \
--task rhythm \
--mode evaluate \
@@ -40,7 +44,7 @@ heartkit \
### 3. Export the model into a TFLM header file
-```bash
+```bash title="Terminal"
heartkit \
--task rhythm \
--mode export \
@@ -57,7 +61,7 @@ Please open three terminals to ease running the demo. We shall refer to these as
Run the following commands in the __EVB Terminal__. This will compile the EVB binary and flash it to the EVB. The binary will be located in `./evb/build`.
-```bash
+```bash title="Terminal"
make -C ./evb
make -C ./evb deploy
make -C ./evb view
@@ -69,7 +73,7 @@ Now press the __reset button__ on the EVB. This will allow SWO output to be capt
In __REST Terminal__, start the REST server on the PC.
-```bash
+```bash title="Terminal"
python -m heartkit.demo.server
```
@@ -77,7 +81,7 @@ python -m heartkit.demo.server
In __PC Terminal__, start the PC client (console UI).
-```bash
+```bash title="Terminal"
heartkit \
--task rhythm \
--mode demo \
@@ -90,10 +94,11 @@ Upon start, the client will scan and connect to the EVB serial port. If no port
Now that the three tasks are running, press either __Button 1 (BTN1)__ or __Button 2 (BTN2)__ on the EVB to start the demo. Pressing Button 1 will use live sensor data whereas Button 2 will use test dataset supplied by the PC. In __EVB Terminal__, the EVB should be printing the stage it's in (e.g `INFERENCE STAGE`) and any results. In __PC Terminal__, the PC should be plotting the data along with classification results. Once finished, Button 1 or Button 2 can be pressed to stop capturing.
-
+
-!!! tip
- Please use a monospaced font in the terminal for proper alignment of the plot.
+:::tip
+Please use a monospaced font in the terminal for proper alignment of the plot.
+:::
To shutdown the PC client, a keyboard interrupt can be used (e.g `[CTRL]+C`) in __PC Terminal__.
Likewise, a keyboard interrupt can be used (e.g `[CTRL]+C`) to stop the PC REST server in __REST Terminal__.
diff --git a/astro-site/src/content/docs/index.mdx b/astro-site/src/content/docs/index.mdx
new file mode 100644
index 00000000..1515441b
--- /dev/null
+++ b/astro-site/src/content/docs/index.mdx
@@ -0,0 +1,167 @@
+---
+title: "Turn heart signals into on-device intelligence"
+description: "AI development kit for heart monitoring on Ambiq devices."
+hero:
+ tagline: "heartKIT is a Python-based AI Development Kit for heart monitoring for Ambiq devices. Explore datasets, configurable models and task workflows for ECG and PPG signals, from training through model export."
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+
+## Build your heart-monitoring workflow
+
+heartKIT brings dataset integrations, model architectures, training, evaluation and export into one development kit. Start with the included examples and model configurations, or extend the framework with your own datasets, models and tasks. Use the command line for configured runs or the Python API to compose your workflow.
+
+## Getting Started
+
+- **Install** `heartKIT` with pip/uv and getting up and running in minutes. Install heartKIT
+- **Train** a model with a custom network Train a Model
+- **Tasks** `heartKIT` provides tasks like rhythm, segment, and denoising Explore Tasks
+- **Datasets** Several built-in datasets can be leveraged Explore Datasets
+- **Model Zoo** Pre-trained models are available for each task Explore Models
+- **Guides** Detailed guides on tasks, models, and datasets Explore Guides
+
+## Installation
+
+Use **uv** for a Python project, **uvx** to run the CLI in an isolated environment, or **pipx** to keep the CLI installed. Choose **Git clone** when developing heartKIT itself.
+
+
+
+
+
+
+Create a project with Python 3.12, then add heartKIT. In an existing uv project, start with `uv add heartkit`.
+
+```bash title="Terminal"
+uv init --python 3.12 my-heart-project
+cd my-heart-project
+uv add heartkit
+uv run heartkit --help
+```
+
+
+
+
+Run the CLI without adding heartKIT to a project. The first invocation downloads heartKIT and its dependencies into an isolated environment.
+
+```bash title="Terminal"
+uvx --python 3.12 heartkit --help
+```
+
+Use a project installation for Python imports and notebooks.
+
+
+
+
+Install the CLI in its own environment. This command uses an installed Python 3.12 interpreter.
+
+```bash title="Terminal"
+pipx install --python python3.12 heartkit
+heartkit --help
+```
+
+If the command is not on your PATH, run `pipx ensurepath` and reopen your terminal. Use a project installation for Python imports and notebooks.
+
+
+
+
+Install into an activated virtual environment.
+
+```bash title="Terminal"
+python -m pip install heartkit
+heartkit --help
+```
+
+
+
+
+Work with the repository source and its development dependencies.
+
+```bash title="Terminal"
+git clone https://github.com/AmbiqAI/heartkit.git
+cd heartkit
+uv sync --python 3.12
+uv run heartkit --help
+```
+
+
+
+
+
+
+Need the package manager first? See the [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/) or [pipx installation guide](https://pipx.pypa.io/stable/installation/). The [Quickstart](/heartkit/quickstart/) covers configuration and your first workflow.
+
+---
+
+## Usage
+
+__heartKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced development. In both forms, heartKIT exposes a number of modes and tasks outlined below. In addition, by leveraging highly-customizable configurations and extendable factories, heartKIT can be used to create custom workflows for a given application with minimal coding. Refer to the [Quickstart](/heartkit/quickstart/) to quickly get up and running in minutes.
+
+---
+
+## [Tasks](/heartkit/tasks/)
+
+__heartKIT__ includes a number of built-in [tasks](/heartkit/tasks/). Each task provides reference routines for training, evaluating, and exporting the model. The routines can be customized by providing highly flexibile configuration files/objects. Additionally, new tasks can be added to the __heartKIT__ framework by defining a new [Task class](/heartkit/tasks/byot/) and registering it to the [__Task Factory__](/heartkit/tasks/byot/).
+
+- **[Denoise](/heartkit/tasks/denoise/)**: Remove noise and artifacts from signals
+- **[Segmentation](/heartkit/tasks/segmentation/)**: Perform ECG/PPG based segmentation
+- **[Rhythm](/heartkit/tasks/rhythm/)**: Heart rhythm classification (AFIB, AFL)
+- **[Beat](/heartkit/tasks/beat/)**: Beat-level classification (NORM, PAC, PVC, NOISE)
+
+- **[Bring-Your-Own-Task (BYOT)](/heartkit/tasks/byot/)**: Create and register custom tasks
+
+---
+
+## [Modes](/heartkit/modes/)
+
+__heartKIT__ provides a number of [modes](/heartkit/modes/) that can be invoked for a given task. These modes can be accessed via the CLI or directly from a [Task](/heartkit/tasks/). Each mode is accompanied by a set of [task parameters](/heartkit/modes/configuration/#hktaskparams) that can be customized to fit the user's needs.
+
+- **[Download](/heartkit/modes/download/)**: Download specified datasets
+- **[Train](/heartkit/modes/train/)**: Train a model for specified task and datasets
+- **[Evaluate](/heartkit/modes/evaluate/)**: Evaluate a model for specified task and datasets
+- **[Export](/heartkit/modes/export/)**: Export a trained model to TensorFlow Lite and TFLM
+- **[Demo](/heartkit/modes/demo/)**: Run task-level demo on PC or remotely on Ambiq EVB
+
+---
+
+## [Datasets](/heartkit/datasets/)
+
+The ADK includes several built-in [datasets](/heartkit/datasets/) for training __heart-monitoring__ related tasks. We also provide synthetic dataset generators for signals such as ECG, PPG, and RSP along with segmentation and fiducials. Each included dataset inherits from [HKDataset](/heartkit/api/heartkit/datasets/dataset/) that provides consistent interface for downloading and accessing the data. Additional datasets can be added to the heartKIT framework by creating a new dataset class and registering it to the dataset factory, [DatasetFactory](/heartkit/models/#model-factory). Check out the [Datasets Guide](/heartkit/datasets/) to learn more about the available datasets along with their corresponding licenses and limitations.
+
+* **[Icentia11k](/heartkit/datasets/icentia11k/)**: 11-lead ECG data collected from 11,000 subjects captured continously over two weeks.
+* **[LUDB](/heartkit/datasets/ludb/)**: 200 ten-second 12-lead ECG records w/ annotated P-wave, QRS, and T-wave boundaries.
+* **[QTDB](/heartkit/datasets/qtdb/)**: 100+ fifteen-minute two-lead ECG recordings w/ annotated P-wave, QRS, and T-wave boundaries.
+* **[LSAD](/heartkit/datasets/lsad/)**: 10-second, 12-lead ECG dataset collected from 45,152 subjects w/ over 100 scp codes.
+* **[PTB-XL](/heartkit/datasets/ptbxl/)**: 10-second, 12-lead ECG dataset collected from 18,885 subjects w/ 72 different diagnostic classes.
+* **[Synthetic](/heartkit/datasets/synthetic/)**: A synthetic dataset generator provided by [physioKIT](https://ambiqai.github.io/physiokit).
+* **[Bring-Your-Own-Dataset (BYOD)](/heartkit/datasets/byod/)**: Add and register new datasets to the framework.
+
+---
+
+## [Models](/heartkit/models/)
+
+__heartKIT__ provides a variety of model architectures geared towards efficient, real-time edge applications. These models are provided by Ambiq's [helia-edge](https://ambiqai.github.io/helia-edge/) and expose a set of parameters that can be used to fully customize the network for a given application. In addition, heartKIT includes a model factory, [ModelFactory](/heartkit/models/#model-factory), to register current models as well as allow new custom architectures to be added. Check out the [Models Guide](/heartkit/models/) to learn more about the available network architectures and model factory.
+
+- **[TCN](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`)
+- **[U-Net](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`)
+- **[U-NeXt](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`)
+- **[EfficientNetV2](https://ambiqai.github.io/helia-edge/api/helia_edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`)
+- **[MobileOne](https://ambiqai.github.io/helia-edge/api/helia_edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`)
+- **[ResNet](https://ambiqai.github.io/helia-edge/api/helia_edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`)
+- **[Conformer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`)
+- **[MetaFormer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`)
+- **[TSMixer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`)
+- **[Bring-Your-Own-Model (BYOM)](/heartkit/models/byom/)**: Register new SoTA model architectures w/ custom configurations
+
+---
+
+## [Model Zoo](/heartkit/zoo/)
+
+The ADK includes a number of pre-trained models and configurationn recipes for the built-in tasks. These models are trained on a variety of datasets and are optimized for deployment on Ambiq's ultra-low power SoCs. In addition to providing links to download the models, __heartKIT__ provides the corresponding configuration files and performance metrics. The configuration files allow you to easily recreate the models or use them as a starting point for custom solutions. Furthermore, the performance metrics provide insights into the trade-offs between model complexity and performance. Check out the [Model Zoo](/heartkit/zoo/) to learn more about the available models and their corresponding performance metrics.
+
+---
+
+## [Guides](/heartkit/guides/)
+
+Checkout the [Guides](/heartkit/guides/) to see detailed examples and tutorials on how to use heartKIT for a variety of tasks. The guides provide step-by-step instructions on how to train, evaluate, and deploy models for a given task. In addition, the guides provide insights into the design choices and performance metrics for the models. The guides are designed to help you get up and running quickly and to provide a deeper understanding of the capabilities provided by heartKIT.
+
+---
diff --git a/docs/models/byom.md b/astro-site/src/content/docs/models/byom.mdx
similarity index 87%
rename from docs/models/byom.md
rename to astro-site/src/content/docs/models/byom.mdx
index 8296f669..738c7b81 100644
--- a/docs/models/byom.md
+++ b/astro-site/src/content/docs/models/byom.mdx
@@ -1,4 +1,7 @@
-# Bring-Your-Own-Model (BYOM)
+---
+title: "Bring-Your-Own-Model (BYOM)"
+description: "The model factory can be extended to include custom models. This is useful when you have a custom model architecture that you would like to use for training.…"
+---
The model factory can be extended to include custom models. This is useful when you have a custom model architecture that you would like to use for training. The custom model can be registered with the model factory by defining a custom model function and registering it with the `ModelFactory`.
@@ -6,7 +9,7 @@ The model factory can be extended to include custom models. This is useful when
1. **Create a Model**: Define a new model function that takes a `keras.Input`, model parameters, and number of classes as arguments and returns a `keras.Model`.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
import keras
import heartkit as hk
@@ -30,13 +33,13 @@ The model factory can be extended to include custom models. This is useful when
2. **Register the Model**: Register the new model function with the `ModelFactory` by calling the `register` method. This method takes the model name and the callable as arguments.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
hk.ModelFactory.register("custom-model", custom_model_from_object)
```
3. **Use the Model**: The new model can now be used with the `ModelFactory` to perform various operations such as downloading and generating data.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
inputs = keras.Input(shape=(100,))
model = hk.ModelFactory.get("custom-model")(
inputs=inputs,
@@ -57,7 +60,7 @@ The model factory can be extended to include custom models. This is useful when
Rather than using a dictionary to define the model parameters, you can define a custom dataclass or [Pydantic](https://pydantic-docs.helpmanual.io/) model to enforce type checking and provide better documentation.
-```py linenums="1"
+```py title="Python example" linenums="1"
from pydantic import BaseModel
class CustomLayerParams(BaseModel):
diff --git a/docs/models/index.md b/astro-site/src/content/docs/models/index.mdx
similarity index 90%
rename from docs/models/index.md
rename to astro-site/src/content/docs/models/index.mdx
index 23a15f57..46e51363 100644
--- a/docs/models/index.md
+++ b/astro-site/src/content/docs/models/index.mdx
@@ -1,4 +1,7 @@
-# :material-graph-outline: Models
+---
+title: "Models"
+description: "heartKIT provides a number of model architectures that can be used for training heart-monitoring tasks. While a number of off-the-shelf models exist, they are…"
+---
heartKIT provides a number of model architectures that can be used for training __heart-monitoring tasks__. While a number of off-the-shelf models exist, they are often not efficient nor optimized for real-time, edge applications. To address this, heartKIT provides a model factory that allows you to easily create and train customized models via [helia-edge](https://ambiqai.github.io/helia-edge/). `helia-edge` includes a growing number of state-of-the-art models that can be easily configured and trained using high-level parameters. The models are designed to be efficient and well-suited for real-time, edge applications. Most of the models are based on state-of-the-art architectures that have been modified to allow for more fine-grain customization. In addition, the models support 1D variants to allow for training on time-series data. Please check [helia-edge](https://ambiqai.github.io/helia-edge/) for list of available models and their configurations.
@@ -15,7 +18,7 @@ heartKIT provides a number of model architectures that can be used for training
- **[Conformer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`)
- **[MetaFormer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`)
- **[TSMixer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`)
-* **[Bring-Your-Own-Model](./byom.md)**: Add a custom model architecture to heartKIT.
+* **[Bring-Your-Own-Model](/heartkit/models/byom/)**: Add a custom model architecture to heartKIT.
---
@@ -37,11 +40,11 @@ The model factory provides the following methods:
### Defining a model in configuration file
-A model can be created when invoking a command via the CLI by setting [architecture](../modes/configuration.md#hktaskparams) in the configuration file. The task will use the supplied name to get the registered model and instantiate with the provided parameters.
+A model can be created when invoking a command via the CLI by setting [architecture](/heartkit/modes/configuration/#hktaskparams) in the configuration file. The task will use the supplied name to get the registered model and instantiate with the provided parameters.
Merge this architecture fragment into your task configuration:
-```json fragment
+```json title="Configuration fragment"
{
"architecture": {
"name": "tcn",
@@ -66,7 +69,7 @@ Merge this architecture fragment into your task configuration:
The model can be then be created using the following command:
-```bash
+```bash title="Terminal"
heartkit --mode train --task rhythm --config config.json
```
@@ -74,7 +77,7 @@ heartkit --mode train --task rhythm --config config.json
Alternatively, the model can be created directly in code using the following snippet:
-```py linenums="1"
+```py title="Python example" linenums="1"
import keras
import heartkit as hk
diff --git a/docs/modes/configuration.md b/astro-site/src/content/docs/modes/configuration.mdx
similarity index 91%
rename from docs/modes/configuration.md
rename to astro-site/src/content/docs/modes/configuration.mdx
index 0d4e12b1..7121218e 100644
--- a/docs/modes/configuration.md
+++ b/astro-site/src/content/docs/modes/configuration.mdx
@@ -1,4 +1,7 @@
-# :material-code-json: Configuration Parameters
+---
+title: "Configuration Parameters"
+description: "For each mode, common configuration parameters, HKTaskParams, are required to run the task. These parameters are used to define the task, datasets, model, and…"
+---
For each mode, common configuration parameters, [HKTaskParams](#hktaskparams), are required to run the task. These parameters are used to define the task, datasets, model, and other settings. Rather than defining separate configuration files for each mode, a single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.
@@ -23,9 +26,9 @@ Named parameters are used to provide custom parameters for a given object or cal
| Argument | Type | Opt/Req | Default | Description |
| --- | --- | --- | --- | --- |
| name | str | Required | | Named parameters name |
-| params | dict[str, Any] | Optional | {} | Named parameters |
+| params | dict[str, Any] | Optional | \{\} | Named parameters |
-```py linenums="1"
+```py title="Python example" linenums="1"
import heartkit as hk
@@ -50,8 +53,7 @@ params = hk.HKTaskParams(
## HKTaskParams
-These parameters are supplied to a [Task](../tasks/index.md) when running a given mode such as `train`, `evaluate`, `export`, or `demo`. A single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.
-
+These parameters are supplied to a [Task](/heartkit/tasks/) when running a given mode such as `train`, `evaluate`, `export`, or `demo`. A single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.
| Argument | Type | Opt/Req | Default | Description | Mode |
| --- | --- | --- | --- | --- | --- |
diff --git a/docs/modes/demo.md b/astro-site/src/content/docs/modes/demo.mdx
similarity index 76%
rename from docs/modes/demo.md
rename to astro-site/src/content/docs/modes/demo.mdx
index b1478a8e..4fb544b3 100644
--- a/docs/modes/demo.md
+++ b/astro-site/src/content/docs/modes/demo.mdx
@@ -1,4 +1,9 @@
-# :material-hexagon-multiple: Task-Level Demo
+---
+title: "Task-Level Demo"
+description: "Each task in heartKIT has a corresponding demo mode that allows you to run a task-level demonstration using the specified backend inference engine (e.g. PC or…"
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
## Introduction
@@ -81,7 +86,7 @@ Similar to datasets, dataloaders, tasks, and models, the demo mode can be custom
1. **Create a Backend**: Define a new backend class that inherits from the [HKInferenceBackend](/heartkit/api/heartkit/backends/backend) base class and implements the required abstract methods.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
import heartkit as hk
class CustomBackend(hk.HKInferenceBackend):
@@ -113,7 +118,7 @@ Similar to datasets, dataloaders, tasks, and models, the demo mode can be custom
2. **Register the Backend**: Register the new backend with the [BackendFactory](/heartkit/api/heartkit/backends/backend) by calling the `register` method. This method takes the backend name and the backend class as arguments.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
import heartkit as hk
# Register the custom backend
@@ -122,7 +127,7 @@ Similar to datasets, dataloaders, tasks, and models, the demo mode can be custom
3. **Use the Backend**: The new backend can now be used by setting the `backend` flag in the demo configuration settings.
- ```py linenums="1"
+ ```py title="Python example" linenums="1"
import heartkit as hk
# Define demo parameters
@@ -142,25 +147,56 @@ Similar to datasets, dataloaders, tasks, and models, the demo mode can be custom
The following is an example of a task-level demo report for the segmentation task. Upon running segmentation, the demo will extract inter-beat-intervals (IBIs) and report various HR and HRV metrics. These metrics are computed using Ambiq's [physioKIT Python Package](https://ambiqai.github.io/physiokit)- a toolkit to process raw ambulatory bio-signals.
-=== "CLI"
+
---8<-- "assets/tasks/segmentation/segmentation-demo.html"
+
---
## Arguments
-Please refer to [HKTaskParams](../modes/configuration.md) for the list of arguments that can be used with the `demo` command.
+Please refer to [HKTaskParams](/heartkit/modes/configuration/) for the list of arguments that can be used with the `demo` command.
---
diff --git a/docs/modes/download.md b/astro-site/src/content/docs/modes/download.mdx
similarity index 51%
rename from docs/modes/download.md
rename to astro-site/src/content/docs/modes/download.mdx
index 8a32ac54..67375116 100644
--- a/docs/modes/download.md
+++ b/astro-site/src/content/docs/modes/download.mdx
@@ -1,8 +1,11 @@
-# :material-download: Download Datasets
+---
+title: "Download Datasets"
+description: "The download command is used to download specified datasets. Please refer to Datasets for details on the available datasets. Additional datasets can be added…"
+---
## Introduction
-The `download` command is used to download specified datasets. Please refer to [Datasets](../datasets/index.md) for details on the available datasets. Additional datasets can be added by creating a new dataset class and registering it with __heartKIT__ dataset factory, [DatasetFactory](../datasets/index.md#dataset-factory).
+The `download` command is used to download specified datasets. Please refer to [Datasets](/heartkit/datasets/) for details on the available datasets. Additional datasets can be added by creating a new dataset class and registering it with __heartKIT__ dataset factory, [DatasetFactory](/heartkit/datasets/#dataset-factory).
## Usage
@@ -10,15 +13,15 @@ The `download` command is used to download specified datasets. Please refer to [
Using the CLI, the `download` command can be used to download specified datasets in the configuration file or directly in the command line.
-```bash
-heartkit -m download -c '{"datasets": [{"name": "ptbxl", "parameters": {"path": ".datatasets/ptbxl"}}]}'
+```bash title="Terminal"
+heartkit -m download -c '{"datasets": [{"name": "ptbxl", "params": {"path": "./datasets/ptbxl"}}]}'
```
### Python
In code, the `download` method of a dataset can be used to download the dataset.
-```py linenums="1"
+```py title="Python example" linenums="1"
import heartkit as hk
ds = hk.DatasetFactory.get("ptbxl")(path="./datasets/ptbxl")
@@ -28,7 +31,7 @@ ds.download()
Similarly, to download multiple datasets, the `download` method of a task can be used.
-```py linenums="1"
+```py title="Python example" linenums="1"
import heartkit as hk
task = hk.TaskFactory.get("rhythm")
@@ -50,4 +53,4 @@ task.download(params)
## Arguments
-Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) for the available configuration options for the `download` command.
+Please refer to [HKTaskParams](/heartkit/modes/configuration/#hktaskparams) for the available configuration options for the `download` command.
diff --git a/astro-site/src/content/docs/modes/evaluate.mdx b/astro-site/src/content/docs/modes/evaluate.mdx
new file mode 100644
index 00000000..ea73f54f
--- /dev/null
+++ b/astro-site/src/content/docs/modes/evaluate.mdx
@@ -0,0 +1,312 @@
+---
+title: "Model Evaluation"
+description: "Evaluate mode is used to test the performance of the model on the reserved test set for the specified task. Similar to training, the routine can be customized…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+## Introduction
+
+Evaluate mode is used to test the performance of the model on the reserved test set for the specified task. Similar to training, the routine can be customized via CLI configuration file or by setting the parameters directly in the code. The evaluation process involves testing the model's performance on the test data to measure its accuracy, precision, recall, and F1 score. A number of results and metrics will be generated and saved to the `job_dir`.
+
+
+
+1. Load the configuration data (e.g. `configuration.json`)
+1. Load the desired datasets (e.g. `PtbxlDataset`)
+1. Load the corresponding task dataloaders (e.g. `PtbxlDataLoader`)
+1. Load the trained model (e.g. `model.keras`)
+1. Define the metrics (e.g. `accuracy`)
+1. Evaluate the model (e.g. `model.evaluate`)
+1. Generate evaluation report (e.g. `report.json`)
+
+
+
+**Example configuration**
+
+
+1. Load the configuration data (e.g. `configuration.json`)
+1. Load the test data (e.g. `test.pkl`)
+1. Load the trained model (e.g. `model.keras`)
+1. Quantize the model (e.g. `16x8`)
+1. Convert the model (e.g. `TFL`, `TFLM`)
+1. Verify the models' outputs match
+1. Save artifacts (e.g. `model.tflite`)
+
+
+
+**Example configuration**
+
+
+1. Load the configuration parameters (e.g. `configuration.json`)
+1. Load the desired datasets (e.g. `PtbxlDataset`)
+1. Load the corresponding task dataloaders (e.g. `PtbxlDataLoader`)
+1. Initialize custom model architecture (e.g. `tcn`)
+1. Define the metrics, loss, and optimizer (e.g. `accuracy`, `categorical_crossentropy`, `adam`)
+1. Train the model (e.g. `model.fit`)
+1. Save artifacts (e.g. `model.keras`)
+
+
+
+**Example configuration**
++ +```mermaid +flowchart TD + A["Load configuration"] --> B + B["Load datasets and dataloaders"] --> C + C["Initialize model"] --> D + D["Set loss, metrics and optimizer"] --> E + E["Train model"] --> F + F["Save model and artifacts"] +``` + +--- + +## Usage + +### CLI + +The following command will train a rhythm model using the reference configuration. + +```bash title="Terminal" +heartkit --task rhythm --mode train --config ./configuration.json +``` + +### Python + +The model can be trained using the following snippet: + +```py title="Python example" linenums="1" + +task = hk.TaskFactory.get("rhythm") + +params = hk.HKTaskParams(...) + +task.train(params) + +``` + +**Example configuration** +
+On Windows using Powershell, use `.venv\Scripts\activate`. + +
+
+
+
+---
+
+## Characteristics
+
+| | Atrial | Junctional | Ventricular |
+| --- | --- | --- | --- |
+| Premature | __PAC__ P-wave: Different
QRS: Narrow (normal)
Aberrated: LBBB or RBBB | __PJC__
P-wave: None / retrograde
QRS: Narrow (normal)
Compensatory SA Pause | __PVC__
P-wave: None
QRS: Wide (> 120 ms)
Compensatory SA Pause | +| Escape | Atrial Escape | P-wave: Abnormal
QRS: Narrow (normal)
Ventricular rate: < 60 bpm
Junctional Escape
| P-wave: None
QRS: Narrow (normal)
Bradycardia (40-60 bpm)
Ventricular Escape | P-wave: None
QRS: Wide
Bradycardia (< 40 bpm) | + +--- + +## Dataloaders + +Dataloaders are available for the following datasets: + +* **[Icentia11k](/heartkit/datasets/icentia11k/)** +* **[PTB-XL](/heartkit/datasets/ptbxl/)** + +--- + +## Pre-trained Models + +The following table provides the latest performance and accuracy results for pre-trained beat models. Additional result details can be found in [Model Zoo → Beat](/heartkit/zoo/). + +| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC | +| --------------- | ---------- | ----- | -------- | --------- | -------------- | ------ | ------- | -------- | +| __BC-2-EFF-SM__ | Icentia11k | 100Hz | 5s | 2 | EfficientNetV2 | 28K | 1.8M | 97.7% F1 | +| __BC-3-EFF-SM__ | Icentia11k | 100Hz | 5s | 3 | EfficientNetV2 | 41K | 2.1M | 92.0% F1 | + +--- + +## Target Classes + +Below outlines the classes available for beat classification. When training a model, the number of classes, mapping, and names must be provided. + +| CLASS | LABELS | +| ------- | --------------- | +| 0 | Normal | +| 1 | PAC | +| 2 | PVC | +| 3 | Noise | + +**Class Mapping** + +Below is an example of a class mapping for a 3-class beat model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped. + +```jsonc title="JSONC example" +{ + "num_classes": 3, + "class_names": ["QRS", "PAC", "PVC"], + "class_map": { + "0": 0, // Map Normal to QRS + "1": 1, // Map PAC to PAC + "2": 2, // Map PVC to PVC + } +} +``` + +--- diff --git a/docs/tasks/byot.md b/astro-site/src/content/docs/tasks/byot.mdx similarity index 77% rename from docs/tasks/byot.md rename to astro-site/src/content/docs/tasks/byot.mdx index 78249c43..89f764ac 100644 --- a/docs/tasks/byot.md +++ b/astro-site/src/content/docs/tasks/byot.mdx @@ -1,14 +1,17 @@ -# Bring-Your-Own-Task (BYOT) +--- +title: "Bring-Your-Own-Task (BYOT)" +description: "The Bring-Your-Own-Task (BYOT) feature allows users to create custom tasks for training, evaluating, and deploying heart-related AI models. This feature is…" +--- The Bring-Your-Own-Task (BYOT) feature allows users to create custom tasks for training, evaluating, and deploying heart-related AI models. This feature is useful for creating custom workflows for a given application with minimal coding. -For an in-depth guide check out the [BYOT Notebook Guide](../guides/byot.ipynb). +For an in-depth guide check out the [BYOT Notebook Guide](/heartkit/guides/byot/). ## How it Works 1. **Create a Task**: Define a new task that inherits from the [HKTask](/heartkit/api/heartkit/tasks/task) base class and implements the required methods: `train`, `evaluate`, `export`, and `demo`. - ```py linenums="1" + ```py title="Python example" linenums="1" import heartkit as hk class CustomTask(hk.HKTask): @@ -33,14 +36,14 @@ For an in-depth guide check out the [BYOT Notebook Guide](../guides/byot.ipynb). 2. **Register the Task**: Register the new task with the `TaskFactory` by calling the `register` method. This method takes the task name and the task class as arguments. - ```py linenums="1" + ```py title="Python example" linenums="1" # Register the custom task hk.TaskFactory.register("custom", CustomTask) ``` 3. **Use the Task**: The new task can now be used with the `TaskFactory` to perform various operations such as training, evaluating, and deploying models. - ```py linenums="1" + ```py title="Python example" linenums="1" # Define task parameters params = hk.HKTrainParams(...) diff --git a/docs/tasks/denoise.md b/astro-site/src/content/docs/tasks/denoise.mdx similarity index 64% rename from docs/tasks/denoise.md rename to astro-site/src/content/docs/tasks/denoise.mdx index f29e5082..29f3558d 100644 --- a/docs/tasks/denoise.md +++ b/astro-site/src/content/docs/tasks/denoise.mdx @@ -1,11 +1,14 @@ -# Signal Denoising Task +--- +title: "Signal Denoising Task" +description: "The objective of denoising is to remove noise and artifacts from physiological signals while preserving the underlying signal information. The dominant noise…" +--- ## Overview The objective of denoising is to remove noise and artifacts from physiological signals while preserving the underlying signal information. The dominant noise sources include baseline wander (BW), muscle noise (EMG), electrode movement artifacts (EM), and powerline interference (PLI). For physiological signals such as ECG and PPG, removing the artifacts is difficult due to the non-stationary nature of the noise and overlapping frequency bands with the signal. While traditional signal processing techniques such as filtering and wavelet denoising have been used to remove noise, deep learning models have shown great promise in enhanced denoising.
---8<-- "assets/tasks/denoise/denoise-example.html"
+
---
@@ -35,19 +38,22 @@ The following table summarizes the characteristics of common noise sources in PP
Dataloaders are available for the following datasets:
-* **[LUDB](../datasets/ludb.md)**
-* **[PTB-XL](../datasets/ptbxl.md)**
-* **[ECG Synthetic](../datasets/synthetic.md)**
-* **[PPG Synthetic](../datasets/synthetic.md)**
+* **[LUDB](/heartkit/datasets/ludb/)**
+* **[PTB-XL](/heartkit/datasets/ptbxl/)**
+* **[ECG Synthetic](/heartkit/datasets/synthetic/)**
+* **[PPG Synthetic](/heartkit/datasets/synthetic/)**
---
## Pre-trained Models
-The following table provides the latest performance and accuracy results of denoising models. Additional result details can be found in [Model Zoo → Denoise](../zoo/index.md).
-
+The following table provides the latest performance and accuracy results of denoising models. Additional result details can be found in [Model Zoo → Denoise](/heartkit/zoo/).
---8<-- "assets/zoo/denoise/denoise-model-zoo-table.md"
+| NAME | DATASET | FS | DURATION | MODEL | PARAMS | FLOPS | METRIC |
+| ------------------- | ----------------- | ----- | -------- | -------------- | ------ | ------- | ----------- |
+| __DEN-TCN-SM__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 3.3K | 1.0M | 18.1 SNR |
+| __DEN-TCN-LG__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 6.3K | 1.8M | 19.5 SNR |
+| __DEN-PPG-TCN-SM__ | Synthetic | 100Hz | 2.5s | TCN | 3.5K | 1.1M | 92.1% COS |
---
diff --git a/astro-site/src/content/docs/tasks/diagnostic.mdx b/astro-site/src/content/docs/tasks/diagnostic.mdx
new file mode 100644
index 00000000..2a285c6e
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/diagnostic.mdx
@@ -0,0 +1,8 @@
+---
+title: "Multi-Label Diagnostic Task"
+description: "The diagnostic task groups ECG findings into multiple labels. The diagram below shows the documented class hierarchy."
+---
+
+The diagnostic task groups ECG findings into multiple labels. The diagram below shows the documented class hierarchy.
+
+
diff --git a/astro-site/src/content/docs/tasks/index.mdx b/astro-site/src/content/docs/tasks/index.mdx
new file mode 100644
index 00000000..f6a5c8bb
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/index.mdx
@@ -0,0 +1,75 @@
+---
+title: "Tasks"
+description: "heartKIT provides several built-in heart-monitoring tasks. Each task is designed to address a unique aspect such as ECG denoising, segmentation, and…"
+---
+
+## Introduction
+
+heartKIT provides several built-in __heart-monitoring__ tasks. Each task is designed to address a unique aspect such as ECG denoising, segmentation, and rhythm/beat classification. The tasks are designed to be modular and can be used independently or in combination to address specific use cases. In addition to the built-in tasks, custom tasks can be created by extending the [HKTask](/heartkit/api/heartkit/tasks/task) base class and registering it with the task factory.
+
+
+
---8<-- "assets/tasks/rhythm/rhythm-example.html"
+
---
@@ -20,7 +23,7 @@ Sinus rhythm (SR) is a type of rhythm where the heart beats regularly and origin
* Regular rhythm
* P waves present
-* QRS complexes usually < 120ms
+* QRS complexes usually < 120ms
* Ventricular rate 60-100 bpm
### AFIB
@@ -30,7 +33,7 @@ Atrial fibrillation (AFIB) is a type of rhythm where the atria (upper chambers o
* Irregularly irregular rhythm
* No P waves
* Variable ventricular rate
-* QRS complexes usually < 120ms
+* QRS complexes usually < 120ms
* Fibrillatory waves may be present
### AFL
@@ -51,7 +54,7 @@ Supraventricular tachycardia (SVT) is a type of rhythm where the heart beats fas
* Narrow complex tachycardia
* Regular rhythm
* P waves may be absent, hidden, or retrograde
-* QRS complexes usually < 120ms
+* QRS complexes usually < 120ms
* Ventricular rate > 100 bpm
### BRAD
@@ -60,8 +63,8 @@ Sinus bradycardia (BRAD) is a type of rhythm where the heart beats slower than n
* Regular rhythm
* P waves present
-* QRS complexes usually < 120ms
-* Ventricular rate < 60 bpm
+* QRS complexes usually < 120ms
+* Ventricular rate < 60 bpm
---
@@ -69,18 +72,20 @@ Sinus bradycardia (BRAD) is a type of rhythm where the heart beats slower than n
Dataloaders are available for the following datasets:
-* **[Icentia11k](../datasets/icentia11k.md)**
-* **[PTB-XL](../datasets/ptbxl.md)**
-* **[LSAD](../datasets/lsad.md)**
+* **[Icentia11k](/heartkit/datasets/icentia11k/)**
+* **[PTB-XL](/heartkit/datasets/ptbxl/)**
+* **[LSAD](/heartkit/datasets/lsad/)**
---
## Pre-trained Models
-The following table provides the latest performance and accuracy results for rhythm models. Additional result details can be found in [Model Zoo → Rhythm](../zoo/index.md).
+The following table provides the latest performance and accuracy results for rhythm models. Additional result details can be found in [Model Zoo → Rhythm](/heartkit/zoo/).
-
---8<-- "assets/zoo/rhythm/rhythm-model-zoo-table.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| ---------------- | ------------------------ | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
+| __ARR-2-EFF-SM__ | Icentia11K, PTB-XL, LSAD | 100Hz | 5s | 2 | EfficientNetV2 | 18K | 1.2M | 99.5% F1 |
+| __ARR-4-EFF-SM__ | LSAD | 100Hz | 5s | 4 | EfficientNetV2 | 27K | 1.6M | 95.9% F1 |
---
@@ -88,24 +93,39 @@ The following table provides the latest performance and accuracy results for rhy
Below outlines the classes available for rhythm classification. When training a model, the number of classes, mapping, and names must be provided.
---8<-- "assets/tasks/rhythm/rhythm-classes.md"
-
-
-!!! example "Class Mapping"
-
- Below is an example of a class mapping for a 2-class rhythm model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped.
-
- ```jsonc
- {
- "num_classes": 2,
- "class_names": ["NSR", "AFIB"],
- "class_map": {
- "0": 0, // Map None to None
- "7": 1, // Map AFIB to AFIB
- // Skip remaining classes
- }
+| CLASS | LABEL | DESCRIPTION |
+| ------- | ----- | ----------- |
+| NSR | 0 | Normal sinus rhythm |
+| SBRAD | 1 | Sinus bradycardia |
+| STACH | 2 | Sinus tachycardia |
+| SARRH | 3 | Sinus arrhythmia |
+| SVARR | 4 | Supraventricular arrhythmia |
+| SVT | 5 | Supraventricular tachycardia |
+| VTACH | 6 | Ventricular tachycardia |
+| AFIB | 7 | Atrial fibrillation |
+| AFLUT | 8 | Atrial flutter |
+| VFIB | 9 | Ventricular fibrillation |
+| VFLUT | 10 | Ventricular flutter |
+| BIGU | 11 | Bigeminy (every other beat is PVC) |
+| TRIGU | 12 | Trigeminy (every third beat is PVC) |
+| PACE | 13 | Paced rhythm |
+| NOISE | 127 | Noise |
+
+**Class Mapping**
+
+Below is an example of a class mapping for a 2-class rhythm model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped.
+
+```jsonc title="JSONC example"
+{
+ "num_classes": 2,
+ "class_names": ["NSR", "AFIB"],
+ "class_map": {
+ "0": 0, // Map None to None
+ "7": 1, // Map AFIB to AFIB
+ // Skip remaining classes
}
- ```
+}
+```
---
diff --git a/astro-site/src/content/docs/tasks/segmentation.mdx b/astro-site/src/content/docs/tasks/segmentation.mdx
new file mode 100644
index 00000000..b41ee985
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/segmentation.mdx
@@ -0,0 +1,105 @@
+---
+title: "Signal Segmentation Task"
+description: "The objective of segmentation is to delineate key segments of the signal. In the context of ECG signals, this involves identifying the different components of…"
+---
+
+## Overview
+
+The objective of segmentation is to delineate key segments of the signal. In the context of ECG signals, this involves identifying the different components of the cardiac cycle, including the P-wave, QRS complex, and T-wave. These segments are used to compute a number of clinically relevant metrics, including heart rate, PR interval, QRS duration, QT interval, and QTc interval. For PPG, the task involves segmenting the systolic and diastolic phases of the cardiac cycle. Segmentation models are useful for detecting arrhythmias, heart rate variability, and other cardiac abnormalities.
+
+
+
+
+
+---
+
+## ECG Characteristics
+
+
+
+- Annotated ECG Signal
+
+ 
+
+
+- ECG Segments/Fiducials
+ * __P-Wave__: Reflects atrial depolarization
+ * __QRS Complex__: Reflects ventricular depolarization
+ * __T-Wave__: Reflects ventricular repolarization
+ * __U-Wave__: Reflects papillary muscle repolarization
+ * __PR Interval__: Time from atrial depolarization to ventricular depolarization
+ * __QT Interval__: Time from ventricular depolarization to repolarization
+ * __QTc Interval__: Corrected QT interval
+ * __ST Segment__: Time between ventricular depolarization and repolarization
+ * __J-Point__: Junction between QRS complex and ST segment
+
+
+---
+
+## PPG Characteristics
+
+* __Systolic Phase__: Reflects the contraction of the heart
+* __Diastolic Phase__: Reflects the relaxation of the heart
+* __Dicrotic Notch__: Reflects the closure of the aortic valve
+
+---
+
+## Dataloaders
+
+Dataloaders are available for the following datasets:
+
+* **[LUDB](/heartkit/datasets/ludb/)**
+* **[QTDB](/heartkit/datasets/qtdb/)**
+* **[ECG Synthetic](/heartkit/datasets/synthetic/)**
+* **[PPG Synthetic](/heartkit/datasets/synthetic/)**
+
+---
+
+## Pre-Trained Models
+
+The following table provides the latest performance and accuracy results for segmentation models. Additional result details can be found in [Model Zoo → Segmentation](/heartkit/zoo/).
+
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| -------------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
+| __SEG-2-TCN-SM__ | LUDB, Synthetic | 100Hz | 2.5s | 2 | TCN | 2K | 0.42M | 96.6% F1 |
+| __SEG-4-TCN-SM__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 7K | 2.1M | 86.3% F1 |
+| __SEG-4-TCN-LG__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 10K | 3.9M | 89.4% F1 |
+| __SEG-PPG-2-TCN-SM__ | Synthetic | 100Hz | 2.5s | 2 | TCN | 4K | 1.43M | 98.6% F1 |
+
+---
+
+## Target Classes
+
+Below outlines the classes available for segmentation. When training a model, the number of classes, mapping, and names must be provided. This allows for the most flexibility in the segmentation task.
+
+| CLASS | LABELS |
+| ------- | --------------- |
+| 0 | None |
+| 1 | P-wave |
+| 2 | QRS |
+| 3 | T-wave |
+| 4 | U-wave |
+| 5 | Noise |
+| 6 | Systolic |
+| 7 | Diastolic |
+
+**Class Mapping Example**
+
+Below is an example of a class mapping for a 3-class segmentation model. The class map keys are the original class labels, and the values are the new class labels. Any class not included will be skipped.
+
+```jsonc title="JSONC example"
+{
+ "num_classes": 3,
+ "class_names": ["None", "QRS", "Noise"],
+ "class_map": {
+ "0": 0, // Map None to None
+ "1": 0, // Map P-wave to None
+ "2": 1, // Map QRS to QRS
+ "3": 0, // Map T-wave to None
+ "4": 0, // Map U-wave to None
+ "5": 2 // Map Noise to Noise
+ }
+}
+```
+
+---
diff --git a/astro-site/src/content/docs/usage/cli.mdx b/astro-site/src/content/docs/usage/cli.mdx
new file mode 100644
index 00000000..dbac951d
--- /dev/null
+++ b/astro-site/src/content/docs/usage/cli.mdx
@@ -0,0 +1,149 @@
+---
+title: "heartKIT CLI"
+description: "The heartKIT command line interface (CLI) makes it easy to run a variety of single-line commands without the need for writing any code. You can run all…"
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+import AsciiTerminal from '@ambiqai/helia-ui/astro/AsciiTerminal';
+
+
+
---8<-- "assets/zoo/arr-2-eff-sm/confusion_matrix_test.html"
+
---
diff --git a/docs/zoo/arr-4-eff-sm.md b/astro-site/src/content/docs/zoo/arr-4-eff-sm.mdx
similarity index 69%
rename from docs/zoo/arr-4-eff-sm.md
rename to astro-site/src/content/docs/zoo/arr-4-eff-sm.mdx
index 9e64ad10..12e86fb3 100644
--- a/docs/zoo/arr-4-eff-sm.md
+++ b/astro-site/src/content/docs/zoo/arr-4-eff-sm.mdx
@@ -1,10 +1,16 @@
-# 4-Class Arrhythmia Classification (ARR-4-EFF-SM)
+---
+title: "4-Class Arrhythmia Classification (ARR-4-EFF-SM)"
+description: "The following table provides the latest pre-trained model for 4-class arrhythmia classification. Below we also provide additional details including training…"
+---
## Overview
The following table provides the latest pre-trained model for 4-class arrhythmia classification. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/rhythm/rhythm-model-zoo-table.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| ---------------- | ------------------------ | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
+| __ARR-2-EFF-SM__ | Icentia11K, PTB-XL, LSAD | 100Hz | 5s | 2 | EfficientNetV2 | 18K | 1.2M | 99.5% F1 |
+| __ARR-4-EFF-SM__ | LSAD | 100Hz | 5s | 4 | EfficientNetV2 | 27K | 1.6M | 95.9% F1 |
---
@@ -36,24 +42,22 @@ The model is trained on raw ECG data and is able to discern normal sinus rhythm
The model is trained on the following datasets:
-- **[LSAD](../datasets/lsad.md)**
+- **[LSAD](/heartkit/datasets/lsad/)**
---
## Model Performance
-
The confusion matrix for the model is depicted below.
---8<-- "assets/zoo/arr-4-eff-sm/confusion_matrix_test.html"
+
---
## Downloads
-
| Asset | Description |
| -------------------------------------------------------------------- | ----------------------------- |
| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/heartkit/rhythm/arr-4-eff-sm/latest/configuration.json) | Configuration file |
diff --git a/docs/zoo/beat-2-eff-sm.md b/astro-site/src/content/docs/zoo/beat-2-eff-sm.mdx
similarity index 69%
rename from docs/zoo/beat-2-eff-sm.md
rename to astro-site/src/content/docs/zoo/beat-2-eff-sm.mdx
index 9f464660..b0db314e 100644
--- a/docs/zoo/beat-2-eff-sm.md
+++ b/astro-site/src/content/docs/zoo/beat-2-eff-sm.mdx
@@ -1,10 +1,15 @@
-# 2-Class Beat Classification (BEAT-2-EFF-SM)
+---
+title: "2-Class Beat Classification (BEAT-2-EFF-SM)"
+description: "The following table provides the latest pre-trained model for 2-class beat classification task. Below we also provide additional details including training…"
+---
## Overview
The following table provides the latest pre-trained model for 2-class beat classification task. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/beat-2-eff-sm/results.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| --------------- | ---------- | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
+| __BC-2-EFF-SM__ | Icentia11k | 100Hz | 5s | 2 | EfficientNetV2 | 28K | 1.8M | 97.7% F1 |
---
@@ -35,7 +40,7 @@ The class mapping is as follows:
The model is trained on the following datasets:
-- **[Icentia11k](../datasets/icentia11k.md)**
+- **[Icentia11k](/heartkit/datasets/icentia11k/)**
---
@@ -44,10 +49,9 @@ The model is trained on the following datasets:
The confusion matrix for model is depicted below.
---8<-- "assets/zoo/beat-2-eff-sm/confusion_matrix_test.html"
+
-
## Downloads
| Asset | Description |
diff --git a/docs/zoo/beat-3-eff-sm.md b/astro-site/src/content/docs/zoo/beat-3-eff-sm.mdx
similarity index 71%
rename from docs/zoo/beat-3-eff-sm.md
rename to astro-site/src/content/docs/zoo/beat-3-eff-sm.mdx
index 9901b9b4..d29c706a 100644
--- a/docs/zoo/beat-3-eff-sm.md
+++ b/astro-site/src/content/docs/zoo/beat-3-eff-sm.mdx
@@ -1,10 +1,15 @@
-# 3-Clas Beat Classification (BEAT-3-EFF-SM)
+---
+title: "3-Class Beat Classification (BEAT-3-EFF-SM)"
+description: "The following table provides the latest pre-trained model for 3-class beat classification. Below we also provide additional details including training…"
+---
## Overview
The following table provides the latest pre-trained model for 3-class beat classification. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/beat-3-eff-sm/results.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| --------------- | ---------- | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
+| __BC-3-EFF-SM__ | Icentia11k | 100Hz | 5s | 3 | EfficientNetV2 | 41K | 2.1M | 92.0% F1 |
---
@@ -35,7 +40,7 @@ The model is able to distinguish between normal sinus rhythm (NSR), premature/ec
The model is trained on the following datasets:
-- **[Icentia11k](../datasets/icentia11k.md)**
+- **[Icentia11k](/heartkit/datasets/icentia11k/)**
---
@@ -44,7 +49,7 @@ The model is trained on the following datasets:
The confusion matrix for the model is depicted below.
---8<-- "assets/zoo/beat-3-eff-sm/confusion_matrix_test.html"
+
diff --git a/docs/zoo/den-ppg-tcn-sm.md b/astro-site/src/content/docs/zoo/den-ppg-tcn-sm.mdx
similarity index 71%
rename from docs/zoo/den-ppg-tcn-sm.md
rename to astro-site/src/content/docs/zoo/den-ppg-tcn-sm.mdx
index debacf9f..a589c85e 100644
--- a/docs/zoo/den-ppg-tcn-sm.md
+++ b/astro-site/src/content/docs/zoo/den-ppg-tcn-sm.mdx
@@ -1,10 +1,15 @@
-# PPG-Based Denoising (DEN-PPG-TCN-SM)
+---
+title: "PPG-Based Denoising (DEN-PPG-TCN-SM)"
+description: "The following table provides the latest pre-trained model for PPG-based denoising. Below we also provide additional details including training configuration,…"
+---
## Overview
The following table provides the latest pre-trained model for PPG-based denoising. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/den-ppg-tcn-sm/results.md"
+| NAME | DATASET | FS | DURATION | MODEL | PARAMS | FLOPS | METRIC |
+| ------------------- | ----------------- | ----- | -------- | -------------- | ------ | ------- | ----------- |
+| __DEN-PPG-TCN-SM__ | Synthetic | 100Hz | 2.5s | TCN | 3.5K | 1.1M | 92.1% COS |
---
@@ -23,7 +28,7 @@ The model is trained on 5-second, raw PPG frames sampled at 100 Hz.
The model is trained on synthetic PPG data.
-- **[Synthetic](../datasets/synthetic.md)**
+- **[Synthetic](/heartkit/datasets/synthetic/)**
---
@@ -41,7 +46,6 @@ The following table provides the performance metrics for the PPG denoising model
## Downloads
-
| Asset | Description |
| -------------------------------------------------------------------- | ----------------------------- |
| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/heartkit/denoise/den-ppg-tcn-sm/latest/configuration.json) | Configuration file |
diff --git a/docs/zoo/den-tcn-lg.md b/astro-site/src/content/docs/zoo/den-tcn-lg.mdx
similarity index 70%
rename from docs/zoo/den-tcn-lg.md
rename to astro-site/src/content/docs/zoo/den-tcn-lg.mdx
index fba71a37..726d6d71 100644
--- a/docs/zoo/den-tcn-lg.md
+++ b/astro-site/src/content/docs/zoo/den-tcn-lg.mdx
@@ -1,10 +1,15 @@
-# ECG_based Denoising (DEN-TCN-LG)
+---
+title: "ECG-Based Denoising (DEN-TCN-LG)"
+description: "The following table provides the latest pre-trained model for ECG-based denoising. Below we also provide additional details including training configuration,…"
+---
## Overview
The following table provides the latest pre-trained model for ECG-based denoising. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/den-tcn-lg/results.md"
+| NAME | DATASET | FS | DURATION | MODEL | PARAMS | FLOPS | METRIC |
+| ------------------- | ----------------- | ----- | -------- | -------------- | ------ | ------- | ----------- |
+| __DEN-TCN-LG__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 6.3K | 1.8M | 19.5 SNR |
---
@@ -23,8 +28,8 @@ The model is trained on 5-second, raw ECG frames sampled at 100 Hz.
The model is trained on the following datasets:
-- **[Synthetic](../datasets/synthetic.md)**
-- **[PTB-XL](../datasets/ptbxl.md)**
+- **[Synthetic](/heartkit/datasets/synthetic/)**
+- **[PTB-XL](/heartkit/datasets/ptbxl/)**
---
@@ -42,7 +47,6 @@ The following table provides the performance metrics for the ECG-based denoising
## Downloads
-
| Asset | Description |
| -------------------------------------------------------------------- | ----------------------------- |
| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/heartkit/denoise/den-tcn-lg/latest/configuration.json) | Configuration file |
diff --git a/docs/zoo/den-tcn-sm.md b/astro-site/src/content/docs/zoo/den-tcn-sm.mdx
similarity index 70%
rename from docs/zoo/den-tcn-sm.md
rename to astro-site/src/content/docs/zoo/den-tcn-sm.mdx
index 3cc9290c..ce8fdfaa 100644
--- a/docs/zoo/den-tcn-sm.md
+++ b/astro-site/src/content/docs/zoo/den-tcn-sm.mdx
@@ -1,11 +1,15 @@
-# ECG-Based Denoising (DEN-TCN-SM)
+---
+title: "ECG-Based Denoising (DEN-TCN-SM)"
+description: "The following table provides the latest pre-trained model for ECG-based denoising. Below we also provide additional details including training configuration,…"
+---
## Overview
The following table provides the latest pre-trained model for ECG-based denoising. Below we also provide additional details including training configuration, performance metrics, and downloads.
-
---8<-- "assets/zoo/den-tcn-sm/results.md"
+| NAME | DATASET | FS | DURATION | MODEL | PARAMS | FLOPS | METRIC |
+| ------------------- | ----------------- | ----- | -------- | -------------- | ------ | ------- | ----------- |
+| __DEN-TCN-SM__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 3.3K | 1.0M | 18.1 SNR |
---
@@ -22,8 +26,8 @@ The model is trained on 5-second, raw ECG frames sampled at 100 Hz.
The model is trained on the following datasets:
-- **[Synthetic](../datasets/synthetic.md)**
-- **[PTB-XL](../datasets/ptbxl.md)**
+- **[Synthetic](/heartkit/datasets/synthetic/)**
+- **[PTB-XL](/heartkit/datasets/ptbxl/)**
---
@@ -41,7 +45,6 @@ The following table provides the performance metrics for the ECG-based denoising
## Downloads
-
| Asset | Description |
| -------------------------------------------------------------------- | ----------------------------- |
| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/heartkit/denoise/den-tcn-sm/latest/configuration.json) | Configuration file |
diff --git a/docs/zoo/index.md b/astro-site/src/content/docs/zoo/index.mdx
similarity index 87%
rename from docs/zoo/index.md
rename to astro-site/src/content/docs/zoo/index.mdx
index bc4cff5f..3bfdd019 100644
--- a/docs/zoo/index.md
+++ b/astro-site/src/content/docs/zoo/index.mdx
@@ -1,8 +1,11 @@
-# Model Zoo
+---
+title: "Model Zoo"
+description: "A number of pre-trained models are available for download to use in your own project. These models are trained on the datasets listed below and are available…"
+---
A number of pre-trained models are available for download to use in your own project. These models are trained on the datasets listed below and are available in Keras and TensorFlow Lite flatbuffer formats.
-## [Signal Denoising Task](../tasks/denoise.md)
+## [Signal Denoising Task](/heartkit/tasks/denoise/)
The following table provides the latest performance and accuracy results for denoising models.
@@ -12,8 +15,7 @@ The following table provides the latest performance and accuracy results for den
| __DEN-TCN-LG__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 6.3K | 1.8M | 19.5 SNR |
| __DEN-PPG-TCN-SM__ | Synthetic | 100Hz | 2.5s | TCN | 3.5K | 1.1M | 92.1% COS |
-
-## [Signal Segmentation Task](../tasks/segmentation.md)
+## [Signal Segmentation Task](/heartkit/tasks/segmentation/)
The following table provides the latest performance and accuracy results for ECG segmentation models.
@@ -24,8 +26,7 @@ The following table provides the latest performance and accuracy results for ECG
| __SEG-4-TCN-LG__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 10K | 3.9M | 89.4% F1 |
| __SEG-PPG-2-TCN-SM__ | Synthetic | 100Hz | 2.5s | 2 | TCN | 4K | 1.43M | 98.6% F1 |
-
-## [Rhythm Classification Task](../tasks/rhythm.md)
+## [Rhythm Classification Task](/heartkit/tasks/rhythm/)
The following table provides the latest performance and accuracy results for rhythm classification models.
@@ -34,8 +35,7 @@ The following table provides the latest performance and accuracy results for rhy
| __ARR-2-EFF-SM__ | Icentia11K, PTB-XL, LSAD | 100Hz | 5s | 2 | EfficientNetV2 | 18K | 1.2M | 99.5% F1 |
| __ARR-4-EFF-SM__ | LSAD | 100Hz | 5s | 4 | EfficientNetV2 | 27K | 1.6M | 95.9% F1 |
-
-## [Beat Classification Task](../tasks/beat.md)
+## [Beat Classification Task](/heartkit/tasks/beat/)
The following table provides the latest performance and accuracy results for beat classification models.
@@ -44,19 +44,18 @@ The following table provides the latest performance and accuracy results for bea
| __BC-2-EFF-SM__ | Icentia11k | 100Hz | 5s | 2 | EfficientNetV2 | 28K | 1.8M | 97.7% F1 |
| __BC-3-EFF-SM__ | Icentia11k | 100Hz | 5s | 3 | EfficientNetV2 | 41K | 2.1M | 92.0% F1 |
-
## Reproducing Results
Each pre-trained model has a corresponding `configuration.json` file that can be used to reproduce the model and results.
To reproduce a pre-trained rhythm model with configuration file `configuration.json`, run the following command:
-```bash
+```bash title="Terminal"
heartkit -m train -t rhythm -c configuration.json
```
To evaluate the trained rhythm model with configuration file `configuration.json`, run the following command:
-```bash
+```bash title="Terminal"
heartkit -m evaluate -t rhythm -c configuration.json
```
diff --git a/docs/zoo/seg-2-tcn-sm.md b/astro-site/src/content/docs/zoo/seg-2-tcn-sm.mdx
similarity index 69%
rename from docs/zoo/seg-2-tcn-sm.md
rename to astro-site/src/content/docs/zoo/seg-2-tcn-sm.mdx
index 57bf5db8..e8befd6c 100644
--- a/docs/zoo/seg-2-tcn-sm.md
+++ b/astro-site/src/content/docs/zoo/seg-2-tcn-sm.mdx
@@ -1,10 +1,15 @@
-# 2-Stage ECG Segmentation (SEG-2-TCN-SM)
+---
+title: "2-Stage ECG Segmentation (SEG-2-TCN-SM)"
+description: "The following table provides the latest pre-trained model for 2-stage ECG segmentation. Below we also provide additional details including training…"
+---
## Overview
The following table provides the latest pre-trained model for 2-stage ECG segmentation. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/seg-2-tcn-sm/results.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| ---------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
+| __SEG-2-TCN-SM__ | LUDB, Synthetic | 100Hz | 2.5s | 2 | TCN | 2K | 0.42M | 96.6% F1 |
---
@@ -34,8 +39,8 @@ The model is able to segment ECG signals into two classes: QRS complexes and non
The model is trained on the following datasets:
-- **[Lobachevsky University Electrocardiography dataset (LUDB)](../datasets/ludb.md)**
-- **[Synthetic](../datasets/synthetic.md)**
+- **[Lobachevsky University Electrocardiography dataset (LUDB)](/heartkit/datasets/ludb/)**
+- **[Synthetic](/heartkit/datasets/synthetic/)**
---
@@ -44,7 +49,7 @@ The model is trained on the following datasets:
The confusion matrix for the model is depicted below.
---8<-- "assets/zoo/seg-2-tcn-sm/confusion_matrix_test.html"
+
---
diff --git a/docs/zoo/seg-4-tcn-lg.md b/astro-site/src/content/docs/zoo/seg-4-tcn-lg.mdx
similarity index 70%
rename from docs/zoo/seg-4-tcn-lg.md
rename to astro-site/src/content/docs/zoo/seg-4-tcn-lg.mdx
index 1fced5b7..de1f9364 100644
--- a/docs/zoo/seg-4-tcn-lg.md
+++ b/astro-site/src/content/docs/zoo/seg-4-tcn-lg.mdx
@@ -1,10 +1,15 @@
-# 4-Stage ECG Segmentation (SEG-4-TCN-LG)
+---
+title: "4-Stage ECG Segmentation (SEG-4-TCN-LG)"
+description: "The following table provides the latest pre-trained model for 4-class ECG segmentation. Below we also provide additional details including training…"
+---
## Overview
The following table provides the latest pre-trained model for 4-class ECG segmentation. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/seg-4-tcn-lg/results.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| ---------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
+| __SEG-4-TCN-LG__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 10K | 3.9M | 89.4% F1 |
---
@@ -36,8 +41,8 @@ The model is able to segment ECG signals into four classes: P-wave, QRS complex,
The model is trained on the following datasets:
-- **[Lobachevsky University Electrocardiography dataset (LUDB)](../datasets/ludb.md)**
-- **[Synthetic](../datasets/synthetic.md)**
+- **[Lobachevsky University Electrocardiography dataset (LUDB)](/heartkit/datasets/ludb/)**
+- **[Synthetic](/heartkit/datasets/synthetic/)**
---
@@ -46,7 +51,7 @@ The model is trained on the following datasets:
The confusion matrix for the segmentation model is depicted below.
---8<-- "assets/zoo/seg-4-tcn-lg/confusion_matrix_test.html"
+
---
diff --git a/docs/zoo/seg-4-tcn-sm.md b/astro-site/src/content/docs/zoo/seg-4-tcn-sm.mdx
similarity index 69%
rename from docs/zoo/seg-4-tcn-sm.md
rename to astro-site/src/content/docs/zoo/seg-4-tcn-sm.mdx
index 9f336afa..133e3355 100644
--- a/docs/zoo/seg-4-tcn-sm.md
+++ b/astro-site/src/content/docs/zoo/seg-4-tcn-sm.mdx
@@ -1,10 +1,15 @@
-# 4-Stage ECG Segmentation (SEG-4-TCN-SM)
+---
+title: "4-Stage ECG Segmentation (SEG-4-TCN-SM)"
+description: "The following table provides the latest pre-trained model for 4-class ECG segmentation. Below we also provide additional details including training…"
+---
## Overview
The following table provides the latest pre-trained model for 4-class ECG segmentation. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/seg-4-tcn-sm/results.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| ---------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
+| __SEG-4-TCN-SM__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 7K | 2.1M | 86.3% F1 |
---
@@ -34,8 +39,8 @@ Identify each of the P-wave, QRS complex, and T-wave.
The model is trained on the following datasets:
-- **[Lobachevsky University Electrocardiography dataset (LUDB)](../datasets/ludb.md)**
-- **[Synthetic](../datasets/synthetic.md)**
+- **[Lobachevsky University Electrocardiography dataset (LUDB)](/heartkit/datasets/ludb/)**
+- **[Synthetic](/heartkit/datasets/synthetic/)**
---
@@ -44,7 +49,7 @@ The model is trained on the following datasets:
The confusion matrix for the segmentation model is depicted below.
---8<-- "assets/zoo/seg-4-tcn-sm/confusion_matrix_test.html"
+
---
diff --git a/docs/zoo/seg-ppg-2-tcn-sm.md b/astro-site/src/content/docs/zoo/seg-ppg-2-tcn-sm.mdx
similarity index 68%
rename from docs/zoo/seg-ppg-2-tcn-sm.md
rename to astro-site/src/content/docs/zoo/seg-ppg-2-tcn-sm.mdx
index f64b63c1..9be8ee2c 100644
--- a/docs/zoo/seg-ppg-2-tcn-sm.md
+++ b/astro-site/src/content/docs/zoo/seg-ppg-2-tcn-sm.mdx
@@ -1,10 +1,15 @@
-# 2-Stage PPG Segmentation (SEG-PPG-2-TCN-SM)
+---
+title: "2-Stage PPG Segmentation (SEG-PPG-2-TCN-SM)"
+description: "The following table provides the latest pre-trained model for 2-stage PPG segmentation. Below we also provide additional details including training…"
+---
## Overview
The following table provides the latest pre-trained model for 2-stage PPG segmentation. Below we also provide additional details including training configuration, performance metrics, and downloads.
---8<-- "assets/zoo/seg-ppg-2-tcn-sm/results.md"
+| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
+| -------------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
+| __SEG-PPG-2-TCN-SM__ | Synthetic | 100Hz | 2.5s | 2 | TCN | 4K | 1.43M | 98.6% F1 |
---
@@ -34,7 +39,7 @@ The model is able to segment PPG Signals into systolic and diastolic phases. The
The model is trained on the following datasets:
-- **[PPG Synthetic](../datasets/synthetic.md)**
+- **[PPG Synthetic](/heartkit/datasets/synthetic/)**
---
@@ -43,7 +48,7 @@ The model is trained on the following datasets:
The confusion matrix for the model is depicted below.
---8<-- "assets/zoo/seg-ppg-2-tcn-sm/confusion_matrix_test.html"
+
---
diff --git a/astro-site/src/navigation.mjs b/astro-site/src/navigation.mjs
index 8402d7d5..cbc3f021 100644
--- a/astro-site/src/navigation.mjs
+++ b/astro-site/src/navigation.mjs
@@ -1,16 +1,134 @@
-import sidebar from "./data/sidebar.json" with { type: "json" };
import apiSidebar from "./data/api-sidebar.json" with { type: "json" };
const page = (label, slug) => ({ label, slug });
const group = (label, items) => ({ label, items, collapsed: true });
-const original = (label) => sidebar.find((item) => item.label === label).items;
const apiGroups = Object.entries(Object.groupBy(apiSidebar, (item) => item.label.split(".")[1] || "Package")).map(([label, items]) => ({label, items, collapsed:true}));
export const sections = [
{label:"Home",href:"/heartkit/",sidebar:false},
{label:"Getting started",href:"/heartkit/quickstart/",sidebar:[page("Install heartKIT","quickstart"),page("Use the command line","usage/cli"),page("Use Python","usage/python")]},
- {label:"User guide",href:"/heartkit/guides/",sidebar:[page("Guides and examples","guides"),group("Model workflow",[page("Workflow overview","modes"),page("Configure a task","modes/configuration"),page("Download datasets","modes/download"),page("Train a model","modes/train"),page("Evaluate a model","modes/evaluate"),page("Export a model","modes/export"),page("Run a demo","modes/demo")]),group("Datasets",original("Datasets")),group("Models",original("Models")),group("Examples",[
+ {label:"User guide",href:"/heartkit/guides/",sidebar:[page("Guides and examples","guides"),group("Model workflow",[page("Workflow overview","modes"),page("Configure a task","modes/configuration"),page("Download datasets","modes/download"),page("Train a model","modes/train"),page("Evaluate a model","modes/evaluate"),page("Export a model","modes/export"),page("Run a demo","modes/demo")]),group("Datasets",[
+ {
+ "label": "Datasets",
+ "slug": "datasets"
+ },
+ {
+ "label": "Icentia11k",
+ "slug": "datasets/icentia11k"
+ },
+ {
+ "label": "Synthetic",
+ "slug": "datasets/synthetic"
+ },
+ {
+ "label": "QTDB",
+ "slug": "datasets/qtdb"
+ },
+ {
+ "label": "LUDB",
+ "slug": "datasets/ludb"
+ },
+ {
+ "label": "LSAD",
+ "slug": "datasets/lsad"
+ },
+ {
+ "label": "PTB-XL",
+ "slug": "datasets/ptbxl"
+ },
+ {
+ "label": "MIT-BIH",
+ "slug": "datasets/mitbih"
+ },
+ {
+ "label": "BYOD",
+ "slug": "datasets/byod"
+ }
+]),group("Models",[
+ {
+ "label": "Models",
+ "slug": "models"
+ },
+ {
+ "label": "BYOM",
+ "slug": "models/byom"
+ }
+]),group("Examples",[
page("Custom task","guides/byot"),page("Arrhythmia training","guides/train-arrhythmia-model"),page("ECG denoising","guides/train-ecg-denoiser"),page("ECG segmentation","guides/train-ecg-segmentation"),page("ECG foundation model","guides/ecg-foundation-model")])]},
- {label:"Tasks",href:"/heartkit/tasks/",sidebar:original("Tasks")},
- {label:"Reference",href:"/heartkit/reference/",sidebar:[page("Python API catalog","reference"),group("Model zoo",original("Model Zoo")),...apiGroups]},
+ {label:"Tasks",href:"/heartkit/tasks/",sidebar:[
+ {
+ "label": "Tasks",
+ "slug": "tasks"
+ },
+ {
+ "label": "Denoise",
+ "slug": "tasks/denoise"
+ },
+ {
+ "label": "Segmentation",
+ "slug": "tasks/segmentation"
+ },
+ {
+ "label": "Rhythm",
+ "slug": "tasks/rhythm"
+ },
+ {
+ "label": "Beat",
+ "slug": "tasks/beat"
+ },
+ {
+ "label": "BYOT",
+ "slug": "tasks/byot"
+ }
+]},
+ {label:"Reference",href:"/heartkit/reference/",sidebar:[page("Python API catalog","reference"),group("Model zoo",[
+ {
+ "label": "Model Zoo",
+ "slug": "zoo"
+ },
+ {
+ "label": "ARR-2-EFF-SM",
+ "slug": "zoo/arr-2-eff-sm"
+ },
+ {
+ "label": "ARR-4-EFF-SM",
+ "slug": "zoo/arr-4-eff-sm"
+ },
+ {
+ "label": "BEAT-2-EFF-SM",
+ "slug": "zoo/beat-2-eff-sm"
+ },
+ {
+ "label": "BEAT-3-EFF-SM",
+ "slug": "zoo/beat-3-eff-sm"
+ },
+ {
+ "label": "DEN-TCN-LG",
+ "slug": "zoo/den-tcn-lg"
+ },
+ {
+ "label": "DEN-TCN-SM",
+ "slug": "zoo/den-tcn-sm"
+ },
+ {
+ "label": "DEN-PPG-TCN-SM",
+ "slug": "zoo/den-ppg-tcn-sm"
+ },
+ {
+ "label": "SEG-2-TCN-SM",
+ "slug": "zoo/seg-2-tcn-sm"
+ },
+ {
+ "label": "SEG-4-TCN-SM",
+ "slug": "zoo/seg-4-tcn-sm"
+ },
+ {
+ "label": "SEG-4-TCN-LG",
+ "slug": "zoo/seg-4-tcn-lg"
+ },
+ {
+ "label": "SEG-PPG-2-TCN-SM",
+ "slug": "zoo/seg-ppg-2-tcn-sm"
+ }
+]),...apiGroups]},
];
const flatten = (items) => items.flatMap((item) => item.items ? flatten(item.items) : [item]);
export const sectionByPath = Object.fromEntries(sections.flatMap(section => section.sidebar===false ? [[section.href,section.href]] : flatten(section.sidebar).map(item=>[item.slug!==undefined?`/heartkit/${item.slug}/`:item.link,section.href])));
diff --git a/astro-site/src/redirects.json b/astro-site/src/redirects.json
new file mode 100644
index 00000000..20052906
--- /dev/null
+++ b/astro-site/src/redirects.json
@@ -0,0 +1,26 @@
+{
+ "/api/summary": "/heartkit/reference/",
+ "/assets/modes/python-demo-snippet": "/heartkit/modes/demo/",
+ "/assets/tasks/beat/beat-classes": "/heartkit/tasks/beat/",
+ "/assets/tasks/rhythm/rhythm-classes": "/heartkit/tasks/rhythm/",
+ "/assets/tasks/segmentation/segmentation-classes": "/heartkit/tasks/segmentation/",
+ "/assets/usage/json-configuration": "/heartkit/modes/train/",
+ "/assets/usage/python-configuration": "/heartkit/usage/python/",
+ "/assets/zoo/arr-2-eff-sm/results": "/heartkit/zoo/arr-2-eff-sm/",
+ "/assets/zoo/arr-4-eff-sm/results": "/heartkit/zoo/arr-4-eff-sm/",
+ "/assets/zoo/beat-2-eff-sm/results": "/heartkit/zoo/beat-2-eff-sm/",
+ "/assets/zoo/beat-3-eff-sm/results": "/heartkit/zoo/beat-3-eff-sm/",
+ "/assets/zoo/den-ppg-tcn-sm/results": "/heartkit/zoo/den-ppg-tcn-sm/",
+ "/assets/zoo/den-tcn-lg/results": "/heartkit/zoo/den-tcn-lg/",
+ "/assets/zoo/den-tcn-sm/results": "/heartkit/zoo/den-tcn-sm/",
+ "/assets/zoo/diagnostic/results": "/heartkit/tasks/diagnostic/",
+ "/assets/zoo/seg-2-tcn-sm/results": "/heartkit/zoo/seg-2-tcn-sm/",
+ "/assets/zoo/seg-4-tcn-lg/results": "/heartkit/zoo/seg-4-tcn-lg/",
+ "/assets/zoo/seg-4-tcn-sm/results": "/heartkit/zoo/seg-4-tcn-sm/",
+ "/assets/zoo/seg-ppg-2-tcn-sm/results": "/heartkit/zoo/seg-ppg-2-tcn-sm/",
+ "/guides/byot.ipynb": "/heartkit/guides/byot/",
+ "/guides/ecg-foundation-model.ipynb": "/heartkit/guides/ecg-foundation-model/",
+ "/guides/train-arrhythmia-model.ipynb": "/heartkit/guides/train-arrhythmia-model/",
+ "/guides/train-ecg-denoiser.ipynb": "/heartkit/guides/train-ecg-denoiser/",
+ "/guides/train-ecg-segmentation.ipynb": "/heartkit/guides/train-ecg-segmentation/"
+}
diff --git a/docs/assets/modes/python-demo-snippet.md b/docs/assets/modes/python-demo-snippet.md
deleted file mode 100644
index 86c76f16..00000000
--- a/docs/assets/modes/python-demo-snippet.md
+++ /dev/null
@@ -1,28 +0,0 @@
-```py linenums="1"
-from pathlib import Path
-import heartkit as hk
-
-task = hk.TaskFactory.get("segmentation")
-task.export(hk.HKTaskParams(
- job_dir=Path("./results/segmentation-class-2"),
- datasets=[hk.NamedParams(
- name="icentia11k",
- params=dict(
- path=Path("./datasets/icentia11k")
- )
- )],
- num_classes=2,
- class_map={
- 0: 0,
- 1: 1,
- 2: 1
- },
- class_names=[
- "NONE", "AFIB/AFL"
- ],
- sampling_rate=100,
- frame_size=256,
- backend="pc",
- model_file=Path("./results/segmentation-class-2/model.keras"),
-))
-```
diff --git a/docs/assets/tasks/beat/beat-classes.md b/docs/assets/tasks/beat/beat-classes.md
deleted file mode 100644
index 6d7134b7..00000000
--- a/docs/assets/tasks/beat/beat-classes.md
+++ /dev/null
@@ -1,6 +0,0 @@
-| CLASS | LABELS |
-| ------- | --------------- |
-| 0 | Normal |
-| 1 | PAC |
-| 2 | PVC |
-| 3 | Noise |
diff --git a/docs/assets/tasks/rhythm/rhythm-classes.md b/docs/assets/tasks/rhythm/rhythm-classes.md
deleted file mode 100644
index 5c177cd5..00000000
--- a/docs/assets/tasks/rhythm/rhythm-classes.md
+++ /dev/null
@@ -1,17 +0,0 @@
-| CLASS | LABEL | DESCRIPTION |
-| ------- | ----- | ----------- |
-| NSR | 0 | Normal sinus rhythm |
-| SBRAD | 1 | Sinus bradycardia |
-| STACH | 2 | Sinus tachycardia |
-| SARRH | 3 | Sinus arrhythmia |
-| SVARR | 4 | Supraventricular arrhythmia |
-| SVT | 5 | Supraventricular tachycardia |
-| VTACH | 6 | Ventricular tachycardia |
-| AFIB | 7 | Atrial fibrillation |
-| AFLUT | 8 | Atrial flutter |
-| VFIB | 9 | Ventricular fibrillation |
-| VFLUT | 10 | Ventricular flutter |
-| BIGU | 11 | Bigeminy (every other beat is PVC) |
-| TRIGU | 12 | Trigeminy (every third beat is PVC) |
-| PACE | 13 | Paced rhythm |
-| NOISE | 127 | Noise |
diff --git a/docs/assets/tasks/segmentation/segmentation-classes.md b/docs/assets/tasks/segmentation/segmentation-classes.md
deleted file mode 100644
index 34171a4d..00000000
--- a/docs/assets/tasks/segmentation/segmentation-classes.md
+++ /dev/null
@@ -1,10 +0,0 @@
-| CLASS | LABELS |
-| ------- | --------------- |
-| 0 | None |
-| 1 | P-wave |
-| 2 | QRS |
-| 3 | T-wave |
-| 4 | U-wave |
-| 5 | Noise |
-| 6 | Systolic |
-| 7 | Diastolic |
diff --git a/docs/assets/usage/json-configuration.md b/docs/assets/usage/json-configuration.md
deleted file mode 100644
index bfd11a85..00000000
--- a/docs/assets/usage/json-configuration.md
+++ /dev/null
@@ -1,152 +0,0 @@
-```json
-{
- "name": "arr-2-eff-sm",
- "project": "hk-rhythm-2",
- "job_dir": "./results/arr-2-eff-sm",
- "verbose": 2,
- "datasets": [
- {
- "name": "ptbxl",
- "params": {
- "path": "./datasets/ptbxl"
- }
- }
- ],
- "num_classes": 2,
- "class_map": {
- "0": 0,
- "7": 1,
- "8": 1
- },
- "class_names": [
- "NORMAL",
- "AFIB/AFL"
- ],
- "class_weights": "balanced",
- "sampling_rate": 100,
- "frame_size": 512,
- "samples_per_patient": [
- 10,
- 10
- ],
- "val_samples_per_patient": [
- 5,
- 5
- ],
- "test_samples_per_patient": [
- 5,
- 5
- ],
- "val_patients": 0.2,
- "val_size": 20000,
- "test_size": 20000,
- "batch_size": 256,
- "buffer_size": 20000,
- "epochs": 100,
- "steps_per_epoch": 50,
- "val_metric": "loss",
- "lr_rate": 0.001,
- "lr_cycles": 1,
- "threshold": 0.75,
- "val_metric_threshold": 0.98,
- "tflm_var_name": "g_rhythm_model",
- "tflm_file": "rhythm_model_buffer.h",
- "backend": "pc",
- "demo_size": 896,
- "display_report": true,
- "quantization": {
- "qat": false,
- "format": "INT8",
- "io_type": "int8",
- "conversion": "CONCRETE",
- "debug": false
- },
- "preprocesses": [
- {
- "name": "layer_norm",
- "params": {
- "epsilon": 0.01,
- "name": "znorm"
- }
- }
- ],
- "augmentations": [],
- "model_file": "model.keras",
- "use_logits": false,
- "architecture": {
- "name": "efficientnetv2",
- "params": {
- "input_filters": 16,
- "input_kernel_size": [
- 1,
- 9
- ],
- "input_strides": [
- 1,
- 2
- ],
- "blocks": [
- {
- "filters": 24,
- "depth": 2,
- "kernel_size": [
- 1,
- 9
- ],
- "strides": [
- 1,
- 2
- ],
- "ex_ratio": 1,
- "se_ratio": 2
- },
- {
- "filters": 32,
- "depth": 2,
- "kernel_size": [
- 1,
- 9
- ],
- "strides": [
- 1,
- 2
- ],
- "ex_ratio": 1,
- "se_ratio": 2
- },
- {
- "filters": 40,
- "depth": 2,
- "kernel_size": [
- 1,
- 9
- ],
- "strides": [
- 1,
- 2
- ],
- "ex_ratio": 1,
- "se_ratio": 2
- },
- {
- "filters": 48,
- "depth": 1,
- "kernel_size": [
- 1,
- 9
- ],
- "strides": [
- 1,
- 2
- ],
- "ex_ratio": 1,
- "se_ratio": 2
- }
- ],
- "output_filters": 0,
- "include_top": true,
- "use_logits": true
- }
- }
-}
-```
diff --git a/docs/assets/usage/python-configuration.md b/docs/assets/usage/python-configuration.md
deleted file mode 100644
index 1e3c86dd..00000000
--- a/docs/assets/usage/python-configuration.md
+++ /dev/null
@@ -1,84 +0,0 @@
-```py linenums="1"
-
-hk.HKTaskParams(
- name="arr-2-eff-sm",
- project="hk-rhythm-2",
- job_dir="./results/arr-2-eff-sm",
- verbose=2,
- datasets=[hk.NamedParams(
- name="ptbxl",
- params=dict(
- path="./datasets/ptbxl"
- )
- )],
- num_classes=2,
- class_map={
- "0": 0,
- "7": 1,
- "8": 1
- },
- class_names=[
- "NORMAL", "AFIB/AFL"
- ],
- class_weights="balanced",
- sampling_rate=100,
- frame_size=512,
- samples_per_patient=[10, 10],
- val_samples_per_patient=[5, 5],
- test_samples_per_patient=[5, 5],
- val_patients=0.20,
- val_size=20000,
- test_size=20000,
- batch_size=256,
- buffer_size=20000,
- epochs=100,
- steps_per_epoch=50,
- val_metric="loss",
- lr_rate=1e-3,
- lr_cycles=1,
- threshold=0.75,
- val_metric_threshold=0.98,
- tflm_var_name="g_rhythm_model",
- tflm_file="rhythm_model_buffer.h",
- backend="pc",
- demo_size=896,
- display_report=True,
- quantization=hk.QuantizationParams(
- qat=False,
- format="INT8",
- io_type="int8",
- conversion="CONCRETE",
- debug=False
- ),
- preprocesses=[
- hk.NamedParams(
- name="layer_norm",
- params=dict(
- epsilon=0.01,
- name="znorm"
- )
- )
- ],
- augmentations=[
- ],
- model_file="model.keras",
- use_logits=False,
- architecture=hk.NamedParams(
- name="efficientnetv2",
- params=dict(
- input_filters=16,
- input_kernel_size=[1, 9],
- input_strides=[1, 2],
- blocks=[
- {"filters": 24, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
- {"filters": 32, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
- {"filters": 40, "depth": 2, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2},
- {"filters": 48, "depth": 1, "kernel_size": [1, 9], "strides": [1, 2], "ex_ratio": 1, "se_ratio": 2}
- ],
- output_filters=0,
- include_top=True,
- use_logits=True
- )
- }
-)
-```
diff --git a/docs/assets/zoo/arr-2-eff-sm/results.md b/docs/assets/zoo/arr-2-eff-sm/results.md
deleted file mode 100644
index f497b6f2..00000000
--- a/docs/assets/zoo/arr-2-eff-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| ---------------- | ------------------------ | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
-| __ARR-2-EFF-SM__ | Icentia11K, PTB-XL, LSAD | 100Hz | 5s | 2 | EfficientNetV2 | 18K | 1.2M | 99.5% F1 |
diff --git a/docs/assets/zoo/arr-4-eff-sm/results.md b/docs/assets/zoo/arr-4-eff-sm/results.md
deleted file mode 100644
index 8dcb534c..00000000
--- a/docs/assets/zoo/arr-4-eff-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| ---------------- | ------------------------ | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
-| __ARR-4-EFF-SM__ | LSAD | 100Hz | 5s | 4 | EfficientNetV2 | 27K | 1.6M | 95.9% F1 |
diff --git a/docs/assets/zoo/beat-2-eff-sm/results.md b/docs/assets/zoo/beat-2-eff-sm/results.md
deleted file mode 100644
index 3d58b3df..00000000
--- a/docs/assets/zoo/beat-2-eff-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| --------------- | ---------- | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
-| __BC-2-EFF-SM__ | Icentia11k | 100Hz | 5s | 2 | EfficientNetV2 | 28K | 1.8M | 97.7% F1 |
diff --git a/docs/assets/zoo/beat-3-eff-sm/results.md b/docs/assets/zoo/beat-3-eff-sm/results.md
deleted file mode 100644
index 8de78738..00000000
--- a/docs/assets/zoo/beat-3-eff-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| --------------- | ---------- | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
-| __BC-3-EFF-SM__ | Icentia11k | 100Hz | 5s | 3 | EfficientNetV2 | 41K | 2.1M | 92.0% F1 |
diff --git a/docs/assets/zoo/beat/beat-model-zoo-table.md b/docs/assets/zoo/beat/beat-model-zoo-table.md
deleted file mode 100644
index 969a8513..00000000
--- a/docs/assets/zoo/beat/beat-model-zoo-table.md
+++ /dev/null
@@ -1,4 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| --------------- | ---------- | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
-| __BC-2-EFF-SM__ | Icentia11k | 100Hz | 5s | 2 | EfficientNetV2 | 28K | 1.8M | 97.7% F1 |
-| __BC-3-EFF-SM__ | Icentia11k | 100Hz | 5s | 3 | EfficientNetV2 | 41K | 2.1M | 92.0% F1 |
diff --git a/docs/assets/zoo/den-ppg-tcn-sm/results.md b/docs/assets/zoo/den-ppg-tcn-sm/results.md
deleted file mode 100644
index 1b330837..00000000
--- a/docs/assets/zoo/den-ppg-tcn-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | MODEL | PARAMS | FLOPS | METRIC |
-| ------------------- | ----------------- | ----- | -------- | -------------- | ------ | ------- | ----------- |
-| __DEN-PPG-TCN-SM__ | Synthetic | 100Hz | 2.5s | TCN | 3.5K | 1.1M | 92.1% COS |
diff --git a/docs/assets/zoo/den-tcn-lg/results.md b/docs/assets/zoo/den-tcn-lg/results.md
deleted file mode 100644
index 3e248a00..00000000
--- a/docs/assets/zoo/den-tcn-lg/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | MODEL | PARAMS | FLOPS | METRIC |
-| ------------------- | ----------------- | ----- | -------- | -------------- | ------ | ------- | ----------- |
-| __DEN-TCN-LG__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 6.3K | 1.8M | 19.5 SNR |
diff --git a/docs/assets/zoo/den-tcn-sm/results.md b/docs/assets/zoo/den-tcn-sm/results.md
deleted file mode 100644
index 8bb60ced..00000000
--- a/docs/assets/zoo/den-tcn-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | MODEL | PARAMS | FLOPS | METRIC |
-| ------------------- | ----------------- | ----- | -------- | -------------- | ------ | ------- | ----------- |
-| __DEN-TCN-SM__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 3.3K | 1.0M | 18.1 SNR |
diff --git a/docs/assets/zoo/denoise/denoise-model-zoo-table.md b/docs/assets/zoo/denoise/denoise-model-zoo-table.md
deleted file mode 100644
index 305cb378..00000000
--- a/docs/assets/zoo/denoise/denoise-model-zoo-table.md
+++ /dev/null
@@ -1,5 +0,0 @@
-| NAME | DATASET | FS | DURATION | MODEL | PARAMS | FLOPS | METRIC |
-| ------------------- | ----------------- | ----- | -------- | -------------- | ------ | ------- | ----------- |
-| __DEN-TCN-SM__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 3.3K | 1.0M | 18.1 SNR |
-| __DEN-TCN-LG__ | Synthetic, PTB-XL | 100Hz | 2.5s | TCN | 6.3K | 1.8M | 19.5 SNR |
-| __DEN-PPG-TCN-SM__ | Synthetic | 100Hz | 2.5s | TCN | 3.5K | 1.1M | 92.1% COS |
diff --git a/docs/assets/zoo/diagnostic/results.md b/docs/assets/zoo/diagnostic/results.md
deleted file mode 100644
index da65ce02..00000000
--- a/docs/assets/zoo/diagnostic/results.md
+++ /dev/null
@@ -1,2 +0,0 @@
-| # Classes | Model | Dataset | Fs | Params | FLOPs | Accuracy | Config |
-| --------- | -------------- | ------- | ------ | ------ | ----- | --------- | ----------- |
diff --git a/docs/assets/zoo/rhythm/rhythm-model-zoo-table.md b/docs/assets/zoo/rhythm/rhythm-model-zoo-table.md
deleted file mode 100644
index 58d20d7d..00000000
--- a/docs/assets/zoo/rhythm/rhythm-model-zoo-table.md
+++ /dev/null
@@ -1,4 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| ---------------- | ------------------------ | ----- | -------- | --------- | -------------- | ------ | ------- | -------- |
-| __ARR-2-EFF-SM__ | Icentia11K, PTB-XL, LSAD | 100Hz | 5s | 2 | EfficientNetV2 | 18K | 1.2M | 99.5% F1 |
-| __ARR-4-EFF-SM__ | LSAD | 100Hz | 5s | 4 | EfficientNetV2 | 27K | 1.6M | 95.9% F1 |
diff --git a/docs/assets/zoo/seg-2-tcn-sm/results.md b/docs/assets/zoo/seg-2-tcn-sm/results.md
deleted file mode 100644
index c555a705..00000000
--- a/docs/assets/zoo/seg-2-tcn-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| ---------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
-| __SEG-2-TCN-SM__ | LUDB, Synthetic | 100Hz | 2.5s | 2 | TCN | 2K | 0.42M | 96.6% F1 |
diff --git a/docs/assets/zoo/seg-4-tcn-lg/results.md b/docs/assets/zoo/seg-4-tcn-lg/results.md
deleted file mode 100644
index 9c0f7c2a..00000000
--- a/docs/assets/zoo/seg-4-tcn-lg/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| ---------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
-| __SEG-4-TCN-LG__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 10K | 3.9M | 89.4% F1 |
diff --git a/docs/assets/zoo/seg-4-tcn-sm/results.md b/docs/assets/zoo/seg-4-tcn-sm/results.md
deleted file mode 100644
index 97326442..00000000
--- a/docs/assets/zoo/seg-4-tcn-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| ---------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
-| __SEG-4-TCN-SM__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 7K | 2.1M | 86.3% F1 |
diff --git a/docs/assets/zoo/seg-ppg-2-tcn-sm/results.md b/docs/assets/zoo/seg-ppg-2-tcn-sm/results.md
deleted file mode 100644
index 57d537c5..00000000
--- a/docs/assets/zoo/seg-ppg-2-tcn-sm/results.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| -------------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
-| __SEG-PPG-2-TCN-SM__ | Synthetic | 100Hz | 2.5s | 2 | TCN | 4K | 1.43M | 98.6% F1 |
diff --git a/docs/assets/zoo/segmentation/segmentation-model-zoo-table.md b/docs/assets/zoo/segmentation/segmentation-model-zoo-table.md
deleted file mode 100644
index 16ef7e1f..00000000
--- a/docs/assets/zoo/segmentation/segmentation-model-zoo-table.md
+++ /dev/null
@@ -1,6 +0,0 @@
-| NAME | DATASET | FS | DURATION | # CLASSES | MODEL | PARAMS | FLOPS | METRIC |
-| -------------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
-| __SEG-2-TCN-SM__ | LUDB, Synthetic | 100Hz | 2.5s | 2 | TCN | 2K | 0.42M | 96.6% F1 |
-| __SEG-4-TCN-SM__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 7K | 2.1M | 86.3% F1 |
-| __SEG-4-TCN-LG__ | LUDB, Synthetic | 100Hz | 2.5s | 4 | TCN | 10K | 3.9M | 89.4% F1 |
-| __SEG-PPG-2-TCN-SM__ | Synthetic | 100Hz | 2.5s | 2 | TCN | 4K | 1.43M | 98.6% F1 |
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-/**
- * termynal.js
- *
- * @author Ines Montani \n",
- "\n",
- "- \n",
- "\n",
- " View in Colab\n",
- "\n",
- "\n",
- "- \n",
- "\n",
- " GitHub source\n",
- "\n",
- "\n",
- "
\n",
- "\n",
- "# Bring-Your-Own-Task (BYOT)\n",
- "\n",
- "__Date created:__ 2024/08/15 \n",
- "\n",
- "__Last Modified:__ 2024/08/15 \n",
- "\n",
- "__Description:__ Create custom task for heartKIT end-to-end\n",
- "\n",
- "## Overview \n",
- "\n",
- "In this notebook, we provide a complete walkthrough of creating a custom task. To keep things simple, we will create a task that will predict heart rate from raw ECG signal.\n",
- "\n",
- "Below we outline the high-level steps to create a custom task:\n",
- "\n",
- "1. Identify datasets and create corresponding dataloaders (e.g. PTB-XL)\n",
- "2. Create data pipeline for training, validation, and test sets\n",
- "3. Implement task routines for modes: __train__, __evaluate__, __export__ and optionally __demo__. \n",
- "\n",
- "In this example, we will implement only __train__ and __evaluate__ modes.\n",
- "\n",
- "__Datasets__\n",
- "\n",
- "- **[PTB-XL](https://ambiqai.github.io/heartkit/datasets/ptbxl/)**: The PTB-XL is a large publicly available electrocardiography dataset. \n",
- "It contains 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2024-08-16 15:31:46.467589: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
- "2024-08-16 15:31:46.475433: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
- "2024-08-16 15:31:46.477772: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "\n",
- "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
- "import random\n",
- "from typing import Generator\n",
- "from collections.abc import Iterable\n",
- "from pathlib import Path\n",
- "import tempfile\n",
- "\n",
- "import keras\n",
- "import heartkit as hk\n",
- "import physiokit as pk\n",
- "import tensorflow as tf\n",
- "import numpy as np\n",
- "import numpy.typing as npt\n",
- "import helia_edge as helia\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Be sure to set the dataset path to the correct location\n",
- "os.environ[\"HK_DATASET_PATH\"] = os.getenv(\"HK_DATASET_PATH\", \"./datasets\")\n",
- "\n",
- "plot_theme = hk.utils.dark_theme\n",
- "helia.utils.silence_tensorflow()\n",
- "_ = hk.utils.setup_plotting(plot_theme)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. Create Dataloaders\n",
- "\n",
- "We will create a dataloader class for the dataset __PTB-XL__ since it provides heart beat locations via `blabels`. \n",
- "\n",
- "Given a raw ECG signal, we will compute the heart rate given the beat locations in the frame. The rate will be calculated based on the RR intervals using physioKIT. The output will be the ecg signal and the heart rate in beats per second.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "class PtbxlDataloader(hk.HKDataloader):\n",
- " def __init__(self, ds: hk.datasets.PtbxlDataset, **kwargs):\n",
- " \"\"\"Dataloader for PTB-XL to generate HeartRateTask data.\"\"\"\n",
- " super().__init__(ds=ds, **kwargs)\n",
- "\n",
- " def patient_data_generator(\n",
- " self,\n",
- " patient_id: int,\n",
- " samples_per_patient: int,\n",
- " ):\n",
- " # Compute input size (might be different due to sampling rate)\n",
- " input_size = int(np.ceil((self.ds.sampling_rate / self.sampling_rate) * self.frame_size))\n",
- "\n",
- " with self.ds.patient_data(patient_id) as h5:\n",
- " ecg = h5[\"data\"][:]\n",
- " # Beat locations. Convert 100Hz to ds.sampling_rate\n",
- " blabels = h5[\"blabels\"][:, 0] * (self.ds.sampling_rate / 100.0)\n",
- " # END WITH\n",
- "\n",
- " for _ in range(samples_per_patient):\n",
- " # Select random lead and frame location\n",
- " lead = random.choice(self.ds.leads)\n",
- " frame_start = np.random.randint(0, ecg.shape[1] - input_size)\n",
- " frame_end = frame_start + input_size\n",
- "\n",
- " # Compute BPM by selecting beats within frame, computing RR intervals and averaging\n",
- " frame_blabels = blabels[(blabels >= frame_start) & (blabels < frame_end)]\n",
- " rri = pk.ecg.compute_rr_intervals(frame_blabels)\n",
- " bpm = 60.0 / (np.nanmean(rri) / self.ds.sampling_rate)\n",
- "\n",
- " # Extract ecg frame\n",
- " x = ecg[lead, frame_start:frame_end].copy()\n",
- "\n",
- " # Resample if needed\n",
- " if self.ds.sampling_rate != self.sampling_rate:\n",
- " x = pk.signal.resample_signal(x, self.ds.sampling_rate, self.sampling_rate, axis=0)\n",
- " x = x[: self.frame_size] # Ensure frame size\n",
- "\n",
- " x = np.nan_to_num(x).astype(np.float32)\n",
- " x = x.reshape(-1, 1)\n",
- " y = bpm / 60.0 # Make beats per second\n",
- " yield x, y\n",
- " # END FOR\n",
- "\n",
- " def data_generator(\n",
- " self,\n",
- " patient_ids: list[int],\n",
- " samples_per_patient: int | list[int],\n",
- " shuffle: bool = False,\n",
- " ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]:\n",
- " if isinstance(samples_per_patient, Iterable):\n",
- " samples_per_patient = samples_per_patient[0]\n",
- " for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle):\n",
- " for x, y in self.patient_data_generator(pt_id, samples_per_patient):\n",
- " yield x, y\n",
- " # END FOR\n",
- " # END FOR"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Visualize output of dataloader\n",
- "\n",
- "We will grab a single sample from the dataloader and visualize the output."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds = hk.DatasetFactory.get(\"ptbxl\")(path=Path(os.environ[\"HK_DATASET_PATH\"]) / \"ptbxl\")\n",
- "dl = PtbxlDataloader(\n",
- " ds=ds,\n",
- " frame_size=4000,\n",
- " sampling_rate=500,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "patient_ids = np.random.permutation(ds.patient_ids)\n",
- "x, y = next(dl.data_generator(patient_ids=patient_ids, samples_per_patient=1))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
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- "INFO [VAL SET] LOSS=0.0030, MAE=0.0305, MSE=0.0030, RSQ=0.9621 3247659150.py:114\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0030\u001b[0m, \u001b[33mMAE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0305\u001b[0m, \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0030\u001b[0m, \u001b[33mRSQ\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9621\u001b[0m \u001b]8;id=187434;file:///tmp/ipykernel_626139/3247659150.py\u001b\\\u001b[2m3247659150.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=786914;file:///tmp/ipykernel_626139/3247659150.py#114\u001b\\\u001b[2m114\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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INFO [TEST SET] LOSS=0.30%, MAE=3.05%, MSE=0.30%, RSQ=96.21% 3848634147.py:29\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.30\u001b[0m%, \u001b[33mMAE\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.05\u001b[0m%, \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.30\u001b[0m%, \u001b[33mRSQ\u001b[0m=\u001b[1;36m96\u001b[0m\u001b[1;36m.21\u001b[0m% \u001b]8;id=872574;file:///tmp/ipykernel_626139/3848634147.py\u001b\\\u001b[2m3848634147.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=819072;file:///tmp/ipykernel_626139/3848634147.py#29\u001b\\\u001b[2m29\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n" - ] - }, - { - "data": { 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", - "text/plain": [ - "
\n",
- "\n",
- "- \n",
- "\n",
- " View in Colab\n",
- "\n",
- "\n",
- "- \n",
- "\n",
- " GitHub source\n",
- "\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# ECG Foundation Model\n",
- "\n",
- "__Date created:__ 2024/07/25 \n",
- "\n",
- "__Last Modified:__ 2024/08/14 \n",
- "\n",
- "__Description:__ Train, evaluate, and export an ECG foundation model\n",
- "\n",
- "## Overview \n",
- "\n",
- "This notebook demonstrates creating a foundation model for raw ECG signals. By creating a foundation model, we can create small, down-stream classification models.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "#!pip install -q --disable-pip-version-check heartkit"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2024-08-16 18:53:16.603880: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
- "2024-08-16 18:53:16.611916: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
- "2024-08-16 18:53:16.614257: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "\n",
- "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\" # 3\n",
- "import contextlib\n",
- "from pathlib import Path\n",
- "import tempfile\n",
- "import keras\n",
- "import heartkit as hk\n",
- "import tensorflow as tf\n",
- "import numpy as np\n",
- "import helia_edge as helia\n",
- "import matplotlib.pyplot as plt\n",
- "from sklearn.manifold import TSNE"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Constants\n",
- "\n",
- "Here we provide the constants that we will use throughout the guide. For better performance, adjust parameters such as `BATCH_SIZE`, `EPOCHS`, and `LEARNING_RATE`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# File paths\n",
- "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n",
- "job_dir = Path(tempfile.gettempdir()) / \"hk-foundation\"\n",
- "model_file = job_dir / \"model.keras\"\n",
- "val_file = job_dir / \"val.pkl\"\n",
- "\n",
- "# Data settings\n",
- "sampling_rate = 100 # 100 Hz\n",
- "input_size = 1000 # 10 seconds\n",
- "frame_size = 800 # 8 seconds\n",
- "\n",
- "# Training settings\n",
- "batch_size = 1024 # Batch size for training\n",
- "buffer_size = 2000 # How many samples are shuffled each epoch\n",
- "epochs = 150 # Increase this to 100+\n",
- "steps_per_epoch = 25 # # Steps per epoch (must set since ds has unknown size)\n",
- "samples_per_patient = 1 # Number of samples per patient\n",
- "val_metric = \"loss\" # Metric to monitor for early stopping\n",
- "val_mode = \"min\" # Mode for early stopping\n",
- "val_size = 10000 # Number of samples used for validation\n",
- "learning_rate = 1e-3 # Learning rate for Adam optimizer\n",
- "epsilon = 0.001\n",
- "\n",
- "# Model settings\n",
- "projection_width = 128\n",
- "temperature = 0.1\n",
- "\n",
- "# Other settings\n",
- "seed = 42 # Seed for reproducibility\n",
- "verbose = 1 # Verbosity level\n",
- "plot_theme = hk.utils.dark_theme"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "INFO Job directory: /tmp/hk-foundation 1079341004.py:6\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Job directory: \u001b[35m/tmp/\u001b[0m\u001b[95mhk-foundation\u001b[0m \u001b]8;id=625876;file:///tmp/ipykernel_712291/1079341004.py\u001b\\\u001b[2m1079341004.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4474;file:///tmp/ipykernel_712291/1079341004.py#6\u001b\\\u001b[2m6\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "helia.utils.silence_tensorflow()\n", - "hk.utils.setup_plotting(plot_theme)\n", - "logger = helia.utils.setup_logger(__name__, level=verbose)\n", - "\n", - "os.makedirs(job_dir, exist_ok=True)\n", - "logger.info(f\"Job directory: {job_dir}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure datasets\n", - "\n", - "We are going to train our model using two large datasets: the PTB-XL dataset and the large-scale arrhythmia dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "datasets = [\n", - " hk.NamedParams(name=\"lsad\", params=dict(path=datasets_dir / \"lsad\")),\n", - " hk.NamedParams(name=\"ptbxl\", params=dict(path=datasets_dir / \"ptbxl\")),\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download datasets\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "for dataset in datasets:\n", - " ds = hk.DatasetFactory.get(dataset.name)(**dataset.params)\n", - " ds.download(force=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create data pipeline\n", - "\n", - "Next, we will create a `tf.data` pipeline by performing the following steps on each dataset: \n", - "* Loading dataset class handler \n", - "* Leverage task specific data loader for given dataset\n", - "* Splittiing the dataset into training and validation sets\n", - "* Creating `tf.data.Dataset` objects for training and validation\n", - "\n", - "After creating all the `tf.data.Dataset` objects, we will merge them into a single dataset for training and validation. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Load datasets\n", - "dsets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in datasets]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1723834403.812869 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723834403.835711 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723834403.835842 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723834403.837216 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723834403.837303 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723834403.837349 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723834403.890424 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723834403.890527 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723834403.890585 712291 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n" - ] - } - ], - "source": [ - "dset_weights = np.array([0.5, 0.5])\n", - "\n", - "train_datasets = []\n", - "val_datasets = []\n", - "for ds in dsets:\n", - " # Create dataloader specific to dataset\n", - " dataloader = hk.tasks.foundation.FoundationTaskFactory.get(ds.name)(\n", - " ds=ds,\n", - " frame_size=frame_size,\n", - " sampling_rate=sampling_rate,\n", - " )\n", - "\n", - " # Split patients into train and validation sets\n", - " train_patients, val_patients = dataloader.split_train_val_patients()\n", - "\n", - " # Create train dataset\n", - " train_ds = dataloader.create_dataloader(\n", - " patient_ids=train_patients, samples_per_patient=samples_per_patient, shuffle=True\n", - " )\n", - "\n", - " # Create validation dataset\n", - " val_ds = dataloader.create_dataloader(\n", - " patient_ids=val_patients, samples_per_patient=samples_per_patient, shuffle=False\n", - " )\n", - " train_datasets.append(train_ds)\n", - " val_datasets.append(val_ds)\n", - "# END FOR\n", - "\n", - "# Combine datasets\n", - "train_ds = tf.data.Dataset.sample_from_datasets(train_datasets, weights=dset_weights)\n", - "val_ds = tf.data.Dataset.sample_from_datasets(val_datasets, weights=dset_weights)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize the data\n", - "\n", - "Let's visualize a sample ECG signal from the synthetic dataset. Note this contains no noise or artifacts. Augmentations will be applied later to generate noisy samples for training." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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INFO Model: \"EfficientNetV2\" summary_utils.py:389\n", - " ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ \n", - " ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ \n", - " ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ \n", - " │ input (InputLayer) │ (None, 800, 1) │ 0 │ - │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ reshape (Reshape) │ (None, 1, 800, 1) │ 0 │ input[0][0] │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ stem.conv (Conv2D) │ (None, 1, 400, │ 216 │ reshape[0][0] │ \n", - " │ │ 24) │ │ │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ stem.bn │ (None, 1, 400, │ 96 │ stem.conv[0][0] │ \n", - " │ (BatchNormalizatio… │ 24) │ │ │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ stem.act │ (None, 1, 400, │ 0 │ stem.bn[0][0] │ \n", - " │ (Activation) │ 24) │ │ │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ stage1.mbconv1.dp │ (None, 1, 400, │ 216 │ stem.act[0][0] │ \n", - " │ (DepthwiseConv2D) │ 24) │ │ │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ stage1.mbconv1.dp.… │ (None, 1, 400, │ 96 │ stage1.mbconv1.d… │ \n", - " │ (BatchNormalizatio… │ 24) │ │ │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ stage1.mbconv1.dp.… │ (None, 1, 400, │ 0 │ stage1.mbconv1.d… │ \n", - " │ (Activation) │ 24) │ │ │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ max_pooling2d │ (None, 1, 200, │ 0 │ stage1.mbconv1.d… │ \n", - " │ (MaxPooling2D) │ 24) │ │ │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ stage1.mbconv1.se.… │ (None, 1, 1, 24) │ 0 │ max_pooling2d[0]… │ \n", - " │ (GlobalAveragePool… │ │ │ │ \n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \n", - " │ stage1.mbconv1.se.… │ (None, 1, 1, 6) │ 150 │ stage1.mbconv1.s… │ \n", - " │ (Conv2D) │ │ │ │ \n", - " └─────────────────────┴───────────────────┴────────────┴───────────────────┘ \n", - " Total params: 57,066 (222.91 KB) \n", - " Trainable params: 55,050 (215.04 KB) \n", - " Non-trainable params: 2,016 (7.88 KB) \n", - " \n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Model: \u001b[32m\"EfficientNetV2\"\u001b[0m \u001b]8;id=445330;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py\u001b\\\u001b[2msummary_utils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=349863;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py#389\u001b\\\u001b[2m389\u001b[0m\u001b]8;;\u001b\\\n", - " ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓ \u001b[2m \u001b[0m\n", - " ┃ Layer \u001b[1m(\u001b[0mtype\u001b[1m)\u001b[0m ┃ Output Shape ┃ Param # ┃ Connected to ┃ \u001b[2m \u001b[0m\n", - " ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩ \u001b[2m \u001b[0m\n", - " │ input \u001b[1m(\u001b[0mInputLayer\u001b[1m)\u001b[0m │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m800\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m │ \u001b[1;36m0\u001b[0m │ - │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ reshape \u001b[1m(\u001b[0mReshape\u001b[1m)\u001b[0m │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m800\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m │ \u001b[1;36m0\u001b[0m │ input\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ stem.conv \u001b[1m(\u001b[0mConv2D\u001b[1m)\u001b[0m │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m, │ \u001b[1;36m216\u001b[0m │ reshape\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m │ \u001b[2m \u001b[0m\n", - " │ │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ stem.bn │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m, │ \u001b[1;36m96\u001b[0m │ stem.conv\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mBatchNormalizatio… │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ stem.act │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m, │ \u001b[1;36m0\u001b[0m │ stem.bn\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mActivation\u001b[1m)\u001b[0m │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ stage1.mbconv1.dp │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m, │ \u001b[1;36m216\u001b[0m │ stem.act\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mDepthwiseConv2D\u001b[1m)\u001b[0m │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ stage1.mbconv1.dp.… │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m, │ \u001b[1;36m96\u001b[0m │ stage1.mbconv1.d… │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mBatchNormalizatio… │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ stage1.mbconv1.dp.… │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m400\u001b[0m, │ \u001b[1;36m0\u001b[0m │ stage1.mbconv1.d… │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mActivation\u001b[1m)\u001b[0m │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ max_pooling2d │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m200\u001b[0m, │ \u001b[1;36m0\u001b[0m │ stage1.mbconv1.d… │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mMaxPooling2D\u001b[1m)\u001b[0m │ \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ stage1.mbconv1.se.… │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m24\u001b[0m\u001b[1m)\u001b[0m │ \u001b[1;36m0\u001b[0m │ max_pooling2d\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m… │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mGlobalAveragePool… │ │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────┼───────────────────┼────────────┼───────────────────┤ \u001b[2m \u001b[0m\n", - " │ stage1.mbconv1.se.… │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m6\u001b[0m\u001b[1m)\u001b[0m │ \u001b[1;36m150\u001b[0m │ stage1.mbconv1.s… │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mConv2D\u001b[1m)\u001b[0m │ │ │ │ \u001b[2m \u001b[0m\n", - " └─────────────────────┴───────────────────┴────────────┴───────────────────┘ \u001b[2m \u001b[0m\n", - " Total params: \u001b[1;36m57\u001b[0m,\u001b[1;36m066\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m222.91\u001b[0m KB\u001b[1m)\u001b[0m \u001b[2m \u001b[0m\n", - " Trainable params: \u001b[1;36m55\u001b[0m,\u001b[1;36m050\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m215.04\u001b[0m KB\u001b[1m)\u001b[0m \u001b[2m \u001b[0m\n", - " Non-trainable params: \u001b[1;36m2\u001b[0m,\u001b[1;36m016\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m7.88\u001b[0m KB\u001b[1m)\u001b[0m \u001b[2m \u001b[0m\n", - " \u001b[2m \u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO Computation: 4.17 MFLOPs 909537700.py:3\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Computation: \u001b[1;36m4.17\u001b[0m MFLOPs \u001b]8;id=614122;file:///tmp/ipykernel_712291/909537700.py\u001b\\\u001b[2m909537700.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=675398;file:///tmp/ipykernel_712291/909537700.py#3\u001b\\\u001b[2m3\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "encoder.summary(print_fn=logger.info, layer_range=(\"input\", encoder.layers[10].name))\n", - "flops = helia.metrics.flops.get_flops(encoder, batch_size=1, fpath=os.devnull)\n", - "logger.info(f\"Computation: {flops / 1e6:0.2f} MFLOPs\")\n", - "encoder_output = encoder(inputs)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO Model: \"projector\" summary_utils.py:389\n", - " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ \n", - " ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ \n", - " ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ \n", - " │ keras_tensor_109CLONE │ (None, 128) │ 0 │ \n", - " │ (InputLayer) │ │ │ \n", - " ├─────────────────────────────────┼────────────────────────┼───────────────┤ \n", - " │ dense (Dense) │ (None, 128) │ 16,512 │ \n", - " ├─────────────────────────────────┼────────────────────────┼───────────────┤ \n", - " │ dense_1 (Dense) │ (None, 128) │ 16,512 │ \n", - " └─────────────────────────────────┴────────────────────────┴───────────────┘ \n", - " Total params: 33,024 (129.00 KB) \n", - " Trainable params: 33,024 (129.00 KB) \n", - " Non-trainable params: 0 (0.00 B) \n", - " \n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Model: \u001b[32m\"projector\"\u001b[0m \u001b]8;id=439076;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py\u001b\\\u001b[2msummary_utils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=568200;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/keras/src/utils/summary_utils.py#389\u001b\\\u001b[2m389\u001b[0m\u001b]8;;\u001b\\\n", - " ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ \u001b[2m \u001b[0m\n", - " ┃ Layer \u001b[1m(\u001b[0mtype\u001b[1m)\u001b[0m ┃ Output Shape ┃ Param # ┃ \u001b[2m \u001b[0m\n", - " ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ \u001b[2m \u001b[0m\n", - " │ keras_tensor_109CLONE │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m128\u001b[0m\u001b[1m)\u001b[0m │ \u001b[1;36m0\u001b[0m │ \u001b[2m \u001b[0m\n", - " │ \u001b[1m(\u001b[0mInputLayer\u001b[1m)\u001b[0m │ │ │ \u001b[2m \u001b[0m\n", - " ├─────────────────────────────────┼────────────────────────┼───────────────┤ \u001b[2m \u001b[0m\n", - " │ dense \u001b[1m(\u001b[0mDense\u001b[1m)\u001b[0m │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m128\u001b[0m\u001b[1m)\u001b[0m │ \u001b[1;36m16\u001b[0m,\u001b[1;36m512\u001b[0m │ \u001b[2m \u001b[0m\n", - " ├─────────────────────────────────┼────────────────────────┼───────────────┤ \u001b[2m \u001b[0m\n", - " │ dense_1 \u001b[1m(\u001b[0mDense\u001b[1m)\u001b[0m │ \u001b[1m(\u001b[0m\u001b[3;35mNone\u001b[0m, \u001b[1;36m128\u001b[0m\u001b[1m)\u001b[0m │ \u001b[1;36m16\u001b[0m,\u001b[1;36m512\u001b[0m │ \u001b[2m \u001b[0m\n", - " └─────────────────────────────────┴────────────────────────┴───────────────┘ \u001b[2m \u001b[0m\n", - " Total params: \u001b[1;36m33\u001b[0m,\u001b[1;36m024\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m129.00\u001b[0m KB\u001b[1m)\u001b[0m \u001b[2m \u001b[0m\n", - " Trainable params: \u001b[1;36m33\u001b[0m,\u001b[1;36m024\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m129.00\u001b[0m KB\u001b[1m)\u001b[0m \u001b[2m \u001b[0m\n", - " Non-trainable params: \u001b[1;36m0\u001b[0m \u001b[1m(\u001b[0m\u001b[1;36m0.00\u001b[0m B\u001b[1m)\u001b[0m \u001b[2m \u001b[0m\n", - " \u001b[2m \u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "projector_input = encoder_output\n", - "projector_output = keras.layers.Dense(projection_width, activation=\"relu6\")(projector_input)\n", - "projector_output = keras.layers.Dense(projection_width)(projector_output)\n", - "projector = keras.Model(inputs=projector_input, outputs=projector_output, name=\"projector\")\n", - "flops = helia.metrics.flops.get_flops(projector, batch_size=1, fpath=os.devnull)\n", - "projector.summary(print_fn=logger.info)\n", - "logger.debug(f\"Projector requires {flops / 1e6:0.2f} MFLOPS\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a SimCLR model to train" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "model = helia.trainers.SimCLRTrainer(\n", - " encoder=encoder,\n", - " augmenter=None, # We augment in the data pipeline\n", - " projector=projector,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compile the model\n", - "\n", - "We will compile the model using Adam optimizer with cosine learning rate scheduler and custom cosine similarity loss function. We will also attach metrics and callbacks to monitor the training process.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def get_scheduler():\n", - " return keras.optimizers.schedules.CosineDecay(\n", - " initial_learning_rate=learning_rate,\n", - " decay_steps=steps_per_epoch * epochs,\n", - " )\n", - "\n", - "\n", - "optimizer = keras.optimizers.Adam(get_scheduler())\n", - "loss = helia.losses.simclr.SimCLRLoss(temperature=temperature)\n", - "\n", - "metrics = [\n", - " keras.metrics.MeanSquaredError(name=\"mse\"),\n", - " keras.metrics.CosineSimilarity(name=\"cos\"),\n", - "]\n", - "\n", - "model_callbacks = [\n", - " keras.callbacks.EarlyStopping(\n", - " monitor=f\"val_{val_metric}\",\n", - " patience=max(int(0.25 * epochs), 1),\n", - " mode=val_mode,\n", - " restore_best_weights=True,\n", - " verbose=verbose - 1,\n", - " ),\n", - " keras.callbacks.ModelCheckpoint(\n", - " filepath=str(model_file), monitor=f\"val_{val_metric}\", save_best_only=True, mode=val_mode, verbose=verbose - 1\n", - " ),\n", - " keras.callbacks.CSVLogger(job_dir / \"history.csv\"),\n", - "]\n", - "if helia.utils.env_flag(\"TENSORBOARD\"):\n", - " model_callbacks.append(\n", - " keras.callbacks.TensorBoard(\n", - " log_dir=job_dir,\n", - " write_steps_per_second=True,\n", - " )\n", - " )\n", - "\n", - "model.compile(\n", - " encoder_optimizer=optimizer,\n", - " encoder_loss=loss,\n", - " encoder_metrics=metrics,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the model" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/150\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-08-16 18:54:13.839587: E tensorflow/core/util/util.cc:131] oneDNN supports DT_INT32 only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.\n", - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1723834463.457755 712486 service.cc:146] XLA service 0x78321c02f130 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1723834463.457771 712486 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m 1/25\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m13:39\u001b[0m 34s/step - cos: 0.5956 - loss: 15.6336 - mse: 0.2352" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1723834487.410060 712486 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 1s/step - cos: 0.6157 - loss: 14.9098 - mse: 0.2319 - val_cos: 0.6770 - val_loss: 12.6894 - val_mse: 0.2770\n", - "Epoch 2/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 228ms/step - cos: 0.6928 - loss: 12.2036 - mse: 0.2814 - val_cos: 0.7274 - val_loss: 11.2915 - val_mse: 0.2797\n", - "Epoch 3/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7322 - loss: 11.1098 - mse: 0.2783 - val_cos: 0.7428 - val_loss: 10.5851 - val_mse: 0.2743\n", - "Epoch 4/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - cos: 0.7449 - loss: 10.4056 - mse: 0.2715 - val_cos: 0.7517 - val_loss: 9.9517 - val_mse: 0.2724\n", - "Epoch 5/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7523 - loss: 9.8387 - mse: 0.2707 - val_cos: 0.7568 - val_loss: 9.5624 - val_mse: 0.2703\n", - "Epoch 6/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7548 - loss: 9.5425 - mse: 0.2690 - val_cos: 0.7591 - val_loss: 9.2802 - val_mse: 0.2633\n", - "Epoch 7/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7587 - loss: 9.2489 - mse: 0.2617 - val_cos: 0.7604 - val_loss: 9.0665 - val_mse: 0.2585\n", - "Epoch 8/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - cos: 0.7604 - loss: 9.0068 - mse: 0.2579 - val_cos: 0.7623 - val_loss: 8.8123 - val_mse: 0.2564\n", - "Epoch 9/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 182ms/step - cos: 0.7618 - loss: 8.7503 - mse: 0.2550 - val_cos: 0.7628 - val_loss: 8.5923 - val_mse: 0.2538\n", - "Epoch 10/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7621 - loss: 8.5523 - mse: 0.2549 - val_cos: 0.7622 - val_loss: 8.4131 - val_mse: 0.2523\n", - "Epoch 11/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 186ms/step - cos: 0.7624 - loss: 8.3957 - mse: 0.2511 - val_cos: 0.7635 - val_loss: 8.2374 - val_mse: 0.2495\n", - "Epoch 12/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7637 - loss: 8.2014 - mse: 0.2498 - val_cos: 0.7641 - val_loss: 8.0899 - val_mse: 0.2478\n", - "Epoch 13/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7639 - loss: 8.0752 - mse: 0.2456 - val_cos: 0.7645 - val_loss: 7.9631 - val_mse: 0.2451\n", - "Epoch 14/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7638 - loss: 7.9306 - mse: 0.2457 - val_cos: 0.7665 - val_loss: 7.8171 - val_mse: 0.2403\n", - "Epoch 15/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7642 - loss: 7.8377 - mse: 0.2410 - val_cos: 0.7663 - val_loss: 7.7359 - val_mse: 0.2385\n", - "Epoch 16/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - cos: 0.7658 - loss: 7.6886 - mse: 0.2378 - val_cos: 0.7676 - val_loss: 7.6044 - val_mse: 0.2350\n", - "Epoch 17/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 188ms/step - cos: 0.7643 - loss: 7.6359 - mse: 0.2369 - val_cos: 0.7659 - val_loss: 7.5199 - val_mse: 0.2345\n", - "Epoch 18/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7660 - loss: 7.5126 - mse: 0.2329 - val_cos: 0.7680 - val_loss: 7.4207 - val_mse: 0.2301\n", - "Epoch 19/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7651 - loss: 7.4191 - mse: 0.2304 - val_cos: 0.7682 - val_loss: 7.3130 - val_mse: 0.2268\n", - "Epoch 20/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7651 - loss: 7.3419 - mse: 0.2291 - val_cos: 0.7664 - val_loss: 7.2225 - val_mse: 0.2272\n", - "Epoch 21/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7657 - loss: 7.2691 - mse: 0.2277 - val_cos: 0.7665 - val_loss: 7.1630 - val_mse: 0.2245\n", - "Epoch 22/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7640 - loss: 7.2177 - mse: 0.2248 - val_cos: 0.7662 - val_loss: 7.0724 - val_mse: 0.2219\n", - "Epoch 23/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7679 - loss: 7.0468 - mse: 0.2195 - val_cos: 0.7680 - val_loss: 6.9664 - val_mse: 0.2184\n", - "Epoch 24/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7667 - loss: 6.9840 - mse: 0.2171 - val_cos: 0.7669 - val_loss: 6.9237 - val_mse: 0.2178\n", - "Epoch 25/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7662 - loss: 6.9243 - mse: 0.2169 - val_cos: 0.7666 - val_loss: 6.8773 - val_mse: 0.2136\n", - "Epoch 26/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7655 - loss: 6.8518 - mse: 0.2143 - val_cos: 0.7668 - val_loss: 6.7758 - val_mse: 0.2124\n", - "Epoch 27/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7667 - loss: 6.7623 - mse: 0.2110 - val_cos: 0.7664 - val_loss: 6.7287 - val_mse: 0.2101\n", - "Epoch 28/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7676 - loss: 6.7556 - mse: 0.2077 - val_cos: 0.7678 - val_loss: 6.6686 - val_mse: 0.2059\n", - "Epoch 29/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 191ms/step - cos: 0.7671 - loss: 6.6939 - mse: 0.2065 - val_cos: 0.7670 - val_loss: 6.6024 - val_mse: 0.2012\n", - "Epoch 30/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 186ms/step - cos: 0.7660 - loss: 6.6050 - mse: 0.2017 - val_cos: 0.7678 - val_loss: 6.5662 - val_mse: 0.1994\n", - "Epoch 31/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7667 - loss: 6.5798 - mse: 0.2007 - val_cos: 0.7677 - val_loss: 6.5317 - val_mse: 0.1979\n", - "Epoch 32/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7669 - loss: 6.5304 - mse: 0.1988 - val_cos: 0.7691 - val_loss: 6.4457 - val_mse: 0.1951\n", - "Epoch 33/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7671 - loss: 6.4863 - mse: 0.1965 - val_cos: 0.7678 - val_loss: 6.4010 - val_mse: 0.1941\n", - "Epoch 34/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 185ms/step - cos: 0.7666 - loss: 6.4082 - mse: 0.1940 - 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"Epoch 39/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7664 - loss: 6.2457 - mse: 0.1831 - val_cos: 0.7686 - val_loss: 6.1664 - val_mse: 0.1812\n", - "Epoch 40/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7698 - loss: 6.1896 - mse: 0.1797 - val_cos: 0.7696 - val_loss: 6.1331 - val_mse: 0.1777\n", - "Epoch 41/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7670 - loss: 6.1657 - mse: 0.1788 - val_cos: 0.7701 - val_loss: 6.1057 - val_mse: 0.1760\n", - "Epoch 42/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7690 - loss: 6.0656 - mse: 0.1760 - val_cos: 0.7693 - val_loss: 6.0554 - val_mse: 0.1738\n", - "Epoch 43/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - 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"Epoch 52/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7685 - loss: 5.8001 - mse: 0.1577 - val_cos: 0.7699 - val_loss: 5.7597 - val_mse: 0.1563\n", - "Epoch 53/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 173ms/step - cos: 0.7685 - loss: 5.7991 - mse: 0.1569 - val_cos: 0.7682 - val_loss: 5.7875 - val_mse: 0.1550\n", - "Epoch 54/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7680 - loss: 5.7853 - mse: 0.1547 - val_cos: 0.7707 - val_loss: 5.7683 - val_mse: 0.1524\n", - "Epoch 55/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7691 - loss: 5.7863 - mse: 0.1526 - val_cos: 0.7705 - val_loss: 5.7501 - val_mse: 0.1514\n", - "Epoch 56/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7692 - loss: 5.7813 - mse: 0.1511 - val_cos: 0.7694 - val_loss: 5.7335 - val_mse: 0.1502\n", - "Epoch 57/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7699 - loss: 5.7194 - mse: 0.1498 - val_cos: 0.7694 - val_loss: 5.7055 - val_mse: 0.1492\n", - "Epoch 58/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7704 - loss: 5.6757 - mse: 0.1483 - val_cos: 0.7700 - val_loss: 5.6847 - val_mse: 0.1472\n", - "Epoch 59/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7690 - loss: 5.7145 - mse: 0.1485 - val_cos: 0.7699 - val_loss: 5.6508 - val_mse: 0.1456\n", - "Epoch 60/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7673 - loss: 5.6932 - mse: 0.1473 - val_cos: 0.7707 - val_loss: 5.6501 - val_mse: 0.1436\n", - "Epoch 61/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7694 - loss: 5.6243 - mse: 0.1447 - val_cos: 0.7689 - val_loss: 5.6231 - val_mse: 0.1428\n", - "Epoch 62/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7684 - loss: 5.6316 - mse: 0.1423 - val_cos: 0.7688 - val_loss: 5.5892 - val_mse: 0.1425\n", - "Epoch 63/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7677 - loss: 5.6548 - mse: 0.1434 - val_cos: 0.7710 - val_loss: 5.5681 - val_mse: 0.1399\n", - "Epoch 64/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7680 - loss: 5.6244 - mse: 0.1421 - val_cos: 0.7698 - val_loss: 5.5903 - val_mse: 0.1400\n", - "Epoch 65/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7681 - loss: 5.6289 - mse: 0.1406 - val_cos: 0.7687 - val_loss: 5.5534 - val_mse: 0.1409\n", - "Epoch 66/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7688 - loss: 5.5736 - mse: 0.1403 - val_cos: 0.7702 - val_loss: 5.5605 - val_mse: 0.1376\n", - "Epoch 67/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7700 - loss: 5.5189 - mse: 0.1380 - val_cos: 0.7702 - val_loss: 5.5123 - val_mse: 0.1363\n", - "Epoch 68/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7687 - loss: 5.5515 - mse: 0.1369 - val_cos: 0.7691 - val_loss: 5.5241 - val_mse: 0.1370\n", - "Epoch 69/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7702 - loss: 5.5545 - mse: 0.1357 - val_cos: 0.7699 - val_loss: 5.4955 - val_mse: 0.1362\n", - "Epoch 70/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7690 - loss: 5.4659 - mse: 0.1352 - val_cos: 0.7703 - val_loss: 5.4853 - val_mse: 0.1337\n", - "Epoch 71/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7681 - loss: 5.4991 - mse: 0.1344 - val_cos: 0.7683 - val_loss: 5.4826 - val_mse: 0.1333\n", - "Epoch 72/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7681 - loss: 5.4836 - mse: 0.1327 - val_cos: 0.7693 - val_loss: 5.4592 - val_mse: 0.1316\n", - "Epoch 73/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7702 - loss: 5.4963 - mse: 0.1315 - val_cos: 0.7706 - val_loss: 5.4468 - val_mse: 0.1308\n", - "Epoch 74/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7696 - loss: 5.3915 - mse: 0.1302 - val_cos: 0.7698 - val_loss: 5.4245 - val_mse: 0.1298\n", - "Epoch 75/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7706 - loss: 5.4288 - mse: 0.1288 - val_cos: 0.7695 - val_loss: 5.3944 - val_mse: 0.1290\n", - "Epoch 76/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7676 - loss: 5.4072 - mse: 0.1294 - val_cos: 0.7708 - val_loss: 5.3982 - val_mse: 0.1279\n", - "Epoch 77/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7688 - loss: 5.3941 - mse: 0.1292 - val_cos: 0.7698 - val_loss: 5.4304 - val_mse: 0.1282\n", - "Epoch 78/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7692 - loss: 5.4147 - mse: 0.1282 - val_cos: 0.7707 - val_loss: 5.3892 - val_mse: 0.1265\n", - "Epoch 79/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7703 - loss: 5.3819 - mse: 0.1260 - val_cos: 0.7696 - val_loss: 5.3757 - val_mse: 0.1265\n", - "Epoch 80/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7691 - loss: 5.3872 - mse: 0.1262 - val_cos: 0.7688 - val_loss: 5.3662 - val_mse: 0.1262\n", - "Epoch 81/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7701 - loss: 5.3129 - mse: 0.1245 - val_cos: 0.7701 - val_loss: 5.3568 - val_mse: 0.1245\n", - "Epoch 82/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7690 - loss: 5.3379 - mse: 0.1245 - val_cos: 0.7694 - val_loss: 5.3354 - val_mse: 0.1242\n", - "Epoch 83/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7687 - loss: 5.3438 - mse: 0.1245 - val_cos: 0.7719 - val_loss: 5.3168 - val_mse: 0.1228\n", - "Epoch 84/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7681 - loss: 5.3040 - mse: 0.1235 - val_cos: 0.7715 - val_loss: 5.3151 - val_mse: 0.1220\n", - "Epoch 85/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7685 - loss: 5.3504 - mse: 0.1237 - val_cos: 0.7695 - val_loss: 5.3025 - val_mse: 0.1231\n", - "Epoch 86/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7685 - loss: 5.3010 - mse: 0.1224 - val_cos: 0.7705 - val_loss: 5.3040 - val_mse: 0.1212\n", - "Epoch 87/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7702 - loss: 5.2738 - mse: 0.1207 - val_cos: 0.7702 - val_loss: 5.2965 - val_mse: 0.1218\n", - "Epoch 88/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7689 - loss: 5.2917 - mse: 0.1206 - val_cos: 0.7699 - val_loss: 5.2888 - val_mse: 0.1208\n", - "Epoch 89/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7696 - loss: 5.3199 - mse: 0.1208 - val_cos: 0.7689 - val_loss: 5.2589 - val_mse: 0.1208\n", - "Epoch 90/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7682 - loss: 5.2979 - mse: 0.1212 - val_cos: 0.7711 - val_loss: 5.2490 - val_mse: 0.1197\n", - "Epoch 91/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7701 - loss: 5.2316 - mse: 0.1198 - val_cos: 0.7712 - val_loss: 5.2642 - val_mse: 0.1194\n", - "Epoch 92/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7691 - loss: 5.2812 - mse: 0.1199 - val_cos: 0.7704 - val_loss: 5.2346 - val_mse: 0.1190\n", - "Epoch 93/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7688 - loss: 5.2679 - mse: 0.1191 - val_cos: 0.7693 - val_loss: 5.2493 - val_mse: 0.1184\n", - "Epoch 94/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7690 - loss: 5.2947 - mse: 0.1185 - val_cos: 0.7703 - val_loss: 5.2468 - val_mse: 0.1179\n", - "Epoch 95/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7697 - loss: 5.2224 - mse: 0.1174 - val_cos: 0.7699 - val_loss: 5.2175 - val_mse: 0.1174\n", - "Epoch 96/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7679 - loss: 5.2491 - mse: 0.1178 - val_cos: 0.7706 - val_loss: 5.2031 - val_mse: 0.1174\n", - "Epoch 97/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7704 - loss: 5.2146 - mse: 0.1168 - val_cos: 0.7690 - val_loss: 5.1959 - val_mse: 0.1174\n", - "Epoch 98/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7698 - loss: 5.1986 - mse: 0.1171 - val_cos: 0.7694 - val_loss: 5.1951 - val_mse: 0.1169\n", - "Epoch 99/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7685 - loss: 5.1510 - mse: 0.1173 - val_cos: 0.7692 - val_loss: 5.2092 - val_mse: 0.1164\n", - "Epoch 100/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7700 - loss: 5.1515 - mse: 0.1160 - val_cos: 0.7696 - val_loss: 5.2035 - val_mse: 0.1160\n", - "Epoch 101/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7685 - loss: 5.2375 - mse: 0.1161 - val_cos: 0.7713 - val_loss: 5.1944 - val_mse: 0.1159\n", - "Epoch 102/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7689 - loss: 5.1949 - mse: 0.1157 - val_cos: 0.7705 - val_loss: 5.1947 - val_mse: 0.1150\n", - "Epoch 103/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7692 - loss: 5.1795 - mse: 0.1150 - val_cos: 0.7703 - val_loss: 5.1872 - val_mse: 0.1147\n", - "Epoch 104/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7689 - loss: 5.1701 - mse: 0.1155 - val_cos: 0.7706 - val_loss: 5.1679 - val_mse: 0.1149\n", - "Epoch 105/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 173ms/step - cos: 0.7685 - loss: 5.1989 - mse: 0.1154 - val_cos: 0.7689 - val_loss: 5.1848 - val_mse: 0.1153\n", - "Epoch 106/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7691 - loss: 5.1822 - mse: 0.1145 - val_cos: 0.7703 - val_loss: 5.1448 - val_mse: 0.1142\n", - "Epoch 107/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7695 - loss: 5.1392 - mse: 0.1146 - val_cos: 0.7708 - val_loss: 5.1465 - val_mse: 0.1139\n", - "Epoch 108/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7692 - loss: 5.2153 - mse: 0.1145 - val_cos: 0.7705 - val_loss: 5.1640 - val_mse: 0.1136\n", - "Epoch 109/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7690 - loss: 5.1583 - mse: 0.1140 - val_cos: 0.7689 - val_loss: 5.1519 - val_mse: 0.1142\n", - "Epoch 110/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7700 - loss: 5.1384 - mse: 0.1134 - val_cos: 0.7688 - val_loss: 5.1593 - val_mse: 0.1139\n", - "Epoch 111/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7695 - loss: 5.1484 - mse: 0.1134 - val_cos: 0.7709 - val_loss: 5.1299 - val_mse: 0.1132\n", - "Epoch 112/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7699 - loss: 5.1683 - mse: 0.1126 - val_cos: 0.7698 - val_loss: 5.1275 - val_mse: 0.1131\n", - "Epoch 113/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 171ms/step - cos: 0.7694 - loss: 5.1230 - mse: 0.1123 - val_cos: 0.7703 - val_loss: 5.1364 - val_mse: 0.1121\n", - "Epoch 114/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7699 - loss: 5.1434 - mse: 0.1129 - val_cos: 0.7691 - val_loss: 5.1523 - val_mse: 0.1132\n", - "Epoch 115/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7686 - loss: 5.1086 - mse: 0.1123 - val_cos: 0.7695 - val_loss: 5.1388 - val_mse: 0.1123\n", - "Epoch 116/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7700 - loss: 5.1089 - mse: 0.1121 - val_cos: 0.7698 - val_loss: 5.1056 - val_mse: 0.1125\n", - "Epoch 117/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7708 - loss: 5.0898 - mse: 0.1122 - val_cos: 0.7715 - val_loss: 5.1041 - val_mse: 0.1120\n", - "Epoch 118/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7688 - loss: 5.1048 - mse: 0.1123 - val_cos: 0.7698 - val_loss: 5.1103 - val_mse: 0.1117\n", - "Epoch 119/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7690 - loss: 5.1339 - mse: 0.1123 - val_cos: 0.7707 - val_loss: 5.0992 - val_mse: 0.1114\n", - "Epoch 120/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 174ms/step - cos: 0.7707 - loss: 5.0996 - mse: 0.1114 - val_cos: 0.7691 - val_loss: 5.1405 - val_mse: 0.1121\n", - "Epoch 121/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7706 - loss: 5.0921 - mse: 0.1117 - val_cos: 0.7705 - val_loss: 5.1123 - val_mse: 0.1117\n", - "Epoch 122/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7694 - loss: 5.1215 - mse: 0.1118 - val_cos: 0.7730 - val_loss: 5.1020 - val_mse: 0.1101\n", - "Epoch 123/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 173ms/step - cos: 0.7694 - loss: 5.1185 - mse: 0.1113 - val_cos: 0.7713 - val_loss: 5.1067 - val_mse: 0.1113\n", - "Epoch 124/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 175ms/step - cos: 0.7676 - loss: 5.1077 - mse: 0.1121 - val_cos: 0.7699 - val_loss: 5.1011 - val_mse: 0.1119\n", - "Epoch 125/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7692 - loss: 5.1002 - mse: 0.1116 - val_cos: 0.7722 - val_loss: 5.0920 - val_mse: 0.1106\n", - "Epoch 126/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7700 - loss: 5.0861 - mse: 0.1109 - val_cos: 0.7708 - val_loss: 5.0755 - val_mse: 0.1110\n", - "Epoch 127/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7687 - loss: 5.1179 - mse: 0.1116 - val_cos: 0.7701 - val_loss: 5.0813 - val_mse: 0.1113\n", - "Epoch 128/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7691 - loss: 5.0677 - mse: 0.1114 - val_cos: 0.7712 - val_loss: 5.0920 - val_mse: 0.1111\n", - "Epoch 129/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 182ms/step - cos: 0.7693 - loss: 5.0750 - mse: 0.1109 - val_cos: 0.7697 - val_loss: 5.1003 - val_mse: 0.1117\n", - "Epoch 130/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7696 - loss: 5.1088 - mse: 0.1111 - val_cos: 0.7700 - val_loss: 5.1090 - val_mse: 0.1112\n", - "Epoch 131/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7710 - loss: 5.0843 - mse: 0.1103 - val_cos: 0.7703 - val_loss: 5.0754 - val_mse: 0.1116\n", - "Epoch 132/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7694 - loss: 5.0816 - mse: 0.1113 - val_cos: 0.7695 - val_loss: 5.0800 - val_mse: 0.1109\n", - "Epoch 133/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7690 - loss: 5.0900 - mse: 0.1110 - val_cos: 0.7691 - val_loss: 5.1067 - val_mse: 0.1107\n", - "Epoch 134/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7687 - loss: 5.1286 - mse: 0.1116 - val_cos: 0.7706 - val_loss: 5.0937 - val_mse: 0.1104\n", - "Epoch 135/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7699 - loss: 5.0638 - mse: 0.1106 - val_cos: 0.7692 - val_loss: 5.1000 - val_mse: 0.1115\n", - "Epoch 136/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7696 - loss: 5.0928 - mse: 0.1109 - val_cos: 0.7711 - val_loss: 5.1196 - val_mse: 0.1105\n", - "Epoch 137/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7688 - loss: 5.0861 - mse: 0.1113 - val_cos: 0.7689 - val_loss: 5.0883 - val_mse: 0.1112\n", - "Epoch 138/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7705 - loss: 5.0776 - mse: 0.1104 - val_cos: 0.7706 - val_loss: 5.0706 - val_mse: 0.1108\n", - "Epoch 139/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 180ms/step - cos: 0.7708 - loss: 5.0805 - mse: 0.1106 - val_cos: 0.7694 - val_loss: 5.0848 - val_mse: 0.1114\n", - "Epoch 140/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7709 - loss: 5.0705 - mse: 0.1100 - val_cos: 0.7696 - val_loss: 5.1025 - val_mse: 0.1108\n", - "Epoch 141/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7689 - loss: 5.0755 - mse: 0.1111 - val_cos: 0.7695 - val_loss: 5.0697 - val_mse: 0.1109\n", - "Epoch 142/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 176ms/step - cos: 0.7693 - loss: 5.0860 - mse: 0.1110 - val_cos: 0.7698 - val_loss: 5.0901 - val_mse: 0.1108\n", - "Epoch 143/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 182ms/step - cos: 0.7703 - loss: 5.0945 - mse: 0.1105 - val_cos: 0.7703 - val_loss: 5.0849 - val_mse: 0.1110\n", - "Epoch 144/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 181ms/step - cos: 0.7682 - loss: 5.0852 - mse: 0.1109 - val_cos: 0.7705 - val_loss: 5.0823 - val_mse: 0.1107\n", - "Epoch 145/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 183ms/step - cos: 0.7700 - loss: 5.0820 - mse: 0.1099 - val_cos: 0.7691 - val_loss: 5.0824 - val_mse: 0.1114\n", - "Epoch 146/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 184ms/step - cos: 0.7698 - loss: 5.1090 - mse: 0.1105 - val_cos: 0.7697 - val_loss: 5.0849 - val_mse: 0.1113\n", - "Epoch 147/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 177ms/step - cos: 0.7699 - loss: 5.0637 - mse: 0.1106 - val_cos: 0.7702 - val_loss: 5.0996 - val_mse: 0.1107\n", - "Epoch 148/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - cos: 0.7708 - loss: 5.0515 - mse: 0.1101 - val_cos: 0.7695 - val_loss: 5.0811 - val_mse: 0.1111\n", - "Epoch 149/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 192ms/step - cos: 0.7692 - loss: 5.0959 - mse: 0.1111 - val_cos: 0.7705 - val_loss: 5.1056 - val_mse: 0.1106\n", - "Epoch 150/150\n", - "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 178ms/step - cos: 0.7685 - loss: 5.1008 - mse: 0.1110 - val_cos: 0.7713 - val_loss: 5.0885 - val_mse: 0.1105\n" - ] - } - ], - "source": [ - "history = model.fit(\n", - " train_ds,\n", - " steps_per_epoch=steps_per_epoch,\n", - " verbose=verbose,\n", - " epochs=epochs,\n", - " validation_data=val_ds,\n", - " callbacks=model_callbacks,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize training history\n", - "\n", - "Let's visualize the training history to understand the model's performance during training. This will help to ensure the model is learning and not under or overfitting." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
INFO [VAL SET] MSE=0.0132, COS=0.9683 4122487501.py:2\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0132\u001b[0m, \u001b[33mCOS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9683\u001b[0m \u001b]8;id=945728;file:///tmp/ipykernel_712291/4122487501.py\u001b\\\u001b[2m4122487501.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=966210;file:///tmp/ipykernel_712291/4122487501.py#2\u001b\\\u001b[2m2\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rst = helia.metrics.compute_metrics(metrics, test_y1, test_y2)\n", - "logger.info(\"[VAL SET] \" + \", \".join([f\"{k.upper()}={v:.4f}\" for k, v in rst.items()]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export model to TF Lite / TFLM\n", - "\n", - "Once we have trained and evaluated the model, we need to export the model into a format that can be used for inference on the edge. Currently, we export the model to TensorFlow Lite flatbuffer format. This will also generate a C header file that can be used with TensorFlow Lite for Microcontrollers (TFLM).\n", - "\n", - "For this model, we will export as a 32-bit floating point model.\n", - " \n", - "__NOTE:__ We utilize `CONCRETE` mode to lower the model to concrete functions before converting. This is because TF (MLIR) fails to properly lower the dilated convolutional layers." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "W0000 00:00:1723835186.987318 712291 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n", - "W0000 00:00:1723835186.987329 712291 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n" - ] - } - ], - "source": [ - "converter = helia.converters.tflite.TfLiteKerasConverter(model=encoder)\n", - "\n", - "# Redirect stdout and stderr to devnull since TFLite converter is very verbose\n", - "with open(os.devnull, \"w\") as devnull:\n", - " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " tflite_content = converter.convert(\n", - " test_x=test_x1, quantization=\"FP32\", io_type=\"float32\", mode=\"KERAS\", strict=False, verbose=verbose\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save TFLite model as both a file and C header" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "converter.export(tflite_path=job_dir / \"model.tflite\")\n", - "\n", - "converter.export_header(\n", - " header_path=job_dir / \"model.h\",\n", - " name=\"model\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate TFLite model against TensorFlow model\n", - "\n", - "We will instantiate a tflite interpreter and evaluate the model on the test dataset. This will help us ensure that the model has been exported correctly and is ready for deployment." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n" - ] - } - ], - "source": [ - "tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", - "tflite.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved artifact at '/tmp/tmpserse9cu'. The following endpoints are available:\n", - "\n", - "* Endpoint 'serve'\n", - " args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 800, 1), dtype=tf.float32, name='input')\n", - "Output Type:\n", - " TensorSpec(shape=(None, 128), dtype=tf.float32, name=None)\n", - "Captures:\n", - " 132164125518800: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164125517648: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164125516880: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164125517840: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164125518032: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164125516688: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164116070672: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164116079888: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164125515920: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - " 132164125516112: TensorSpec(shape=(), dtype=tf.resource, name=None)\n", - 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] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "W0000 00:00:1723835188.716817 712291 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n", - "W0000 00:00:1723835188.716827 712291 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n" - ] - } - ], - "source": [ - "converter = helia.converters.tflite.TfLiteKerasConverter(model=encoder)\n", - "\n", - "tflite_content = converter.convert(\n", - " test_x=test_x1, quantization=\"FP32\", io_type=\"float32\", mode=\"KERAS\", strict=False, verbose=verbose\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", - "tflite.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m 1/288\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 9ms/step" - 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INFO [TF METRICS] MSE=0.0132 COS=0.9683 2850812944.py:3\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTF METRICS\u001b[1m]\u001b[0m \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0132\u001b[0m \u001b[33mCOS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9683\u001b[0m \u001b]8;id=395402;file:///tmp/ipykernel_712291/2850812944.py\u001b\\\u001b[2m2850812944.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=569945;file:///tmp/ipykernel_712291/2850812944.py#3\u001b\\\u001b[2m3\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO [TFL METRICS] MSE=0.0132 COS=0.9683 2850812944.py:4\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTFL METRICS\u001b[1m]\u001b[0m \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0132\u001b[0m \u001b[33mCOS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9683\u001b[0m \u001b]8;id=984174;file:///tmp/ipykernel_712291/2850812944.py\u001b\\\u001b[2m2850812944.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=540128;file:///tmp/ipykernel_712291/2850812944.py#4\u001b\\\u001b[2m4\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tf_rst = helia.metrics.compute_metrics(metrics, y1_pred_tf, y2_pred_tf)\n", - "tfl_rst = helia.metrics.compute_metrics(metrics, y1_pred_tfl, y2_pred_tfl)\n", - "logger.info(\"[TF METRICS] \" + \" \".join([f\"{k.upper()}={v:.4f}\" for k, v in tf_rst.items()]))\n", - "logger.info(\"[TFL METRICS] \" + \" \".join([f\"{k.upper()}={v:.4f}\" for k, v in tfl_rst.items()]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ECG Foundation Demo\n", - "\n", - "Finally, we will showcase the foundation model by running across lots of patients and plotting via t-SNE to view the embeddings. This will help us understand how the model is clustering the data and if it is learning useful features." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
\n",
- "\n",
- "- \n",
- "\n",
- " View in Colab\n",
- "\n",
- "\n",
- "- \n",
- "\n",
- " GitHub source\n",
- "\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Train ECG Arrhythmia Classifier\n",
- "\n",
- "__Date created:__ 2024/07/17 \n",
- "\n",
- "__Last Modified:__ 2024/07/17 \n",
- "\n",
- "__Description:__ Train, evaluate, and export 4-stage ECG arrhythmia classifier\n",
- "\n",
- "## Overview "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this guide, we will train an ECG-based arrhythmia classifier that uses an EfficientNetV2 inspired model. The classifier is trained on raw ECG data and is able to discern normal sinus rhythm (NSR), sinus bradycardia (SBRAD), atrial fibrillation (AFIB), and general supraventricular tachycardia (GSVT).\n",
- "\n",
- "__Input__\n",
- "\n",
- "- **Sensor**: ECG\n",
- "- **Location**: Wrist\n",
- "- **Sampling Rate**: 100 Hz\n",
- "- **Frame Size**: 5 seconds\n",
- "\n",
- "__Class Mapping__\n",
- "\n",
- "Identify rhythm into one of four categories: SR, SBRAD, AFIB, GSVT.\n",
- "\n",
- "| Base Class | Target Class | Label |\n",
- "| -------------- | ------------ | ------------------------- |\n",
- "| 0-SR | 0 | Sinus Rhythm (SR) |\n",
- "| 1-SBRAD | 1 | Sinus Bradycardia (SBRAD) |\n",
- "| 7-AFIB, 8-AFL | 2 | AFIB/AFL (AFIB) |\n",
- "| 2-STACH, 5-SVT | 3 | General supraventricular tachycardia (GSVT) |\n",
- "\n",
- "__Datasets__\n",
- "\n",
- "- **[LSAD](https://ambiqai.github.io/heartkit/datasets/lsad/)**: The Large Scale Rhythm Database (LSAD) is a large publicly available electrocardiography dataset. It contains 10 second, 12-lead ECGs of 45,152 patients with a 500 Hz sampling rate. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "\n",
- "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n",
- "import IPython\n",
- "import contextlib\n",
- "from pathlib import Path\n",
- "import tempfile\n",
- "import keras\n",
- "import heartkit as hk\n",
- "import helia_edge as helia"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Be sure to set the dataset path to the correct location\n",
- "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n",
- "\n",
- "plot_theme = hk.utils.dark_theme\n",
- "helia.utils.silence_tensorflow()\n",
- "hk.utils.setup_plotting(plot_theme)\n",
- "logger = helia.utils.setup_logger(__name__)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Configure datasets\n",
- "\n",
- "We are going to train our model using the [Large scale Arrhythmia dataset](https://ambiqai.github.io/heartkit/datasets/lsad/). This dataset uses the slug __lsad__ within heartKIT. We will download the dataset if it is not already available."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "datasets = [\n",
- " hk.NamedParams(\n",
- " name=\"lsad\",\n",
- " params=dict(\n",
- " path=datasets_dir / \"lsad\",\n",
- " ),\n",
- " )\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Target classes\n",
- "\n",
- "For this task, we are going to classify ECG signals into one of four classes: \n",
- "\n",
- "* Sinus Rhytm: Normal ECG signal (SR) \n",
- "* Sinus Bradycardia: Slow heart rate (SB) \n",
- "* Atrial Flutter/Fibrillation: Irregular heart rate (AFIB) \n",
- "* General Supra-Ventricular Tachycardia: Fast heart rate (GSVT)\n",
- "\n",
- "heartKIT already provides a number of heart rhythms. We will provide a class mapping for the four classes we are interested in. We will also provide class names for display purposes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "class_map = {\n",
- " hk.tasks.HKRhythm.sr: 0,\n",
- " hk.tasks.HKRhythm.sbrad: 1,\n",
- " hk.tasks.HKRhythm.afib: 2,\n",
- " hk.tasks.HKRhythm.aflut: 2,\n",
- " hk.tasks.HKRhythm.stach: 3,\n",
- " hk.tasks.HKRhythm.svt: 3,\n",
- "}\n",
- "\n",
- "class_names = [\"SR\", \"SB\", \"AFIB\", \"GSVT\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Preprocess pipeline\n",
- "\n",
- "We will preprocess the ECG signals by applying the following steps:\n",
- "* Apply Z-score normalization w/ epsilon to avoid division by zero\n",
- "\n",
- "The task accepts a list of preprocessing functions that will be applied to the input data. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Define EfficientNetV2 model architecture\n",
- "\n",
- "For this task, we are going to leverage a customized __EfficientNetV2__ model architecture that is smaller and can handle 1D signals. The model consists of 6 MBConv style blocks with a depth of 2. Each block leverages squeeze-and-excitation mechanism w/ ratio of 4 to improve the model's performance. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "architecture = hk.NamedParams(\n",
- " name=\"efficientnetv2\",\n",
- " params=dict(\n",
- " input_filters=24,\n",
- " input_kernel_size=(1, 9),\n",
- " input_strides=(1, 2),\n",
- " blocks=[\n",
- " dict(filters=24, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
- " dict(filters=32, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
- " dict(filters=48, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
- " dict(filters=64, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
- " dict(filters=80, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
- " dict(filters=96, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
- " ],\n",
- " output_filters=0,\n",
- " include_top=True,\n",
- " use_logits=True,\n",
- " ),\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Task configuration\n",
- "\n",
- "Here we provide the complete configuration for the task. This includes the dataset configuration, preprocessing pipeline, model architecture, and training parameters."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "params = hk.HKTaskParams(\n",
- " # Common arguments\n",
- " name=\"hk-4-stage-rhythm\",\n",
- " job_dir=Path(tempfile.gettempdir()) / \"hk-4-stage-rhythm\",\n",
- " # Dataset arguments\n",
- " datasets=datasets,\n",
- " # Signal arguments\n",
- " sampling_rate=100,\n",
- " frame_size=800,\n",
- " # Dataloader arguments\n",
- " samples_per_patient=5,\n",
- " val_samples_per_patient=5,\n",
- " test_samples_per_patient=5,\n",
- " # Preprocessing/Augmentation arguments\n",
- " preprocesses=preprocesses,\n",
- " # Class arguments\n",
- " num_classes=len(class_names),\n",
- " class_map=class_map,\n",
- " class_names=class_names,\n",
- " # Split arguments\n",
- " val_patients=0.2,\n",
- " val_size=20000,\n",
- " test_size=20000,\n",
- " val_file=\"val.tfds\",\n",
- " test_file=\"val.tfds\",\n",
- " # Model arguments\n",
- " model_file=\"model.keras\",\n",
- " architecture=architecture,\n",
- " # Training parameters\n",
- " lr_rate=1e-3,\n",
- " lr_cycles=1,\n",
- " batch_size=256,\n",
- " buffer_size=25000,\n",
- " epochs=100,\n",
- " steps_per_epoch=50,\n",
- " val_metric=\"loss\",\n",
- " class_weights=\"balanced\",\n",
- " # Evaluation arguments\n",
- " threshold=0.5,\n",
- " test_metric_threshold=0.98,\n",
- " # Export parameters\n",
- " tflm_var_name=\"ecg_rhythm_flatbuffer\",\n",
- " tflm_file=\"ecg_rhythm_flatbuffer.h\",\n",
- " # Demo params\n",
- " backend=\"pc\",\n",
- " demo_size=800,\n",
- " display_report=False,\n",
- " # Extra arguments\n",
- " verbose=1,\n",
- " seed=42,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Load rhythm task \n",
- "\n",
- "heartKIT provides a __TaskFactory__ that includes a number ready-to-use tasks. Each task provides methods for training, evaluating, exporting, and demoing. We will grab the __rhythm__ task and configure it for our use case."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "task = hk.TaskFactory.get(\"rhythm\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Download datasets\n",
- "\n",
- "We will download the datasets needed for the task."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "task.download(params=params)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualize the model\n",
- "\n",
- "Lets quickly instantiate and visualize the first few layers of the model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
- "I0000 00:00:1723841711.256117 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723841711.276561 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723841711.276664 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723841711.278058 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723841711.278130 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723841711.278176 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723841711.327697 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723841711.327778 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
- "I0000 00:00:1723841711.327833 789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
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Total params: 32,192 (125.75 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m32,192\u001b[0m (125.75 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Trainable params: 30,912 (120.75 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,912\u001b[0m (120.75 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Non-trainable params: 1,280 (5.00 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,280\u001b[0m (5.00 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = helia.models.efficientnet.efficientnetv2_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture.params, num_classes=len(class_names)\n", - ")\n", - "model.summary(layer_range=(\"inputs\", model.layers[10].name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the model\n", - "\n", - "Now let's train the model using the LSAD dataset. We will train the model for 100 epochs." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sorting lsad labels: 100%|██████████| 36120/36120 [00:07<00:00, 4746.99it/s]\n", - "Sorting lsad labels: 100%|██████████| 36120/36120 [00:07<00:00, 4552.33it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO Validation steps per epoch: 78 datasets.py:105\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Validation steps per epoch: \u001b[1;36m78\u001b[0m \u001b]8;id=977579;file:///workspaces/heartkit/heartkit/tasks/rhythm/datasets.py\u001b\\\u001b[2mdatasets.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=747916;file:///workspaces/heartkit/heartkit/tasks/rhythm/datasets.py#105\u001b\\\u001b[2m105\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO Saving validation dataset to /tmp/hk-4-stage-rhythm/val.tfds train.py:57\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Saving validation dataset to \u001b[35m/tmp/hk-4-stage-rhythm/\u001b[0m\u001b[95mval.tfds\u001b[0m \u001b]8;id=337186;file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py\u001b\\\u001b[2mtrain.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=380060;file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py#57\u001b\\\u001b[2m57\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 0%| 0/100 ETA: ?s, ?epochs/sWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1723841770.965635 789335 service.cc:146] XLA service 0x79d8f400b520 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1723841770.965656 789335 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n", - "I0000 00:00:1723841777.014004 789335 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", - "Training: 100%|██████████ 100/100 ETA: 00:00s, 1.94s/epochs\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 806us/step\n", - "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 915us/step - acc: 0.9442 - f1: 0.9444 - loss: 0.0903\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [VAL SET] ACC=0.9444, F1=0.9445, LOSS=0.0920 train.py:190\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9444\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9445\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0920\u001b[0m \u001b]8;id=498948;file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py\u001b\\\u001b[2mtrain.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=160307;file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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INFO Loading validation dataset from /tmp/hk-4-stage-rhythm/val.tfds evaluate.py:33\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Loading validation dataset from \u001b[35m/tmp/hk-4-stage-rhythm/\u001b[0m\u001b[95mval.tfds\u001b[0m \u001b]8;id=234053;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#33\u001b\\\u001b[2m33\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - acc: 0.9442 - f1: 0.9444 - loss: 0.0903\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [TEST SET] ACC=0.9444, F1=0.9445, LOSS=0.0920 evaluate.py:50\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9444\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9445\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0920\u001b[0m \u001b]8;id=844962;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=167414;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#50\u001b\\\u001b[2m50\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m624/624\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n", - "\u001b[1m613/613\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - acc: 0.9518 - f1: 0.9520 - loss: 0.0804\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [TEST SET] THRESH=50.00%, DROP=1.76% evaluate.py:62\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mTHRESH\u001b[0m=\u001b[1;36m50\u001b[0m\u001b[1;36m.00\u001b[0m%, \u001b[33mDROP\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1;36m.76\u001b[0m% \u001b]8;id=225772;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=800581;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#62\u001b\\\u001b[2m62\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO [TEST SET] ACC=0.9520, F1=0.9521, LOSS=0.0822 evaluate.py:63\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9520\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9521\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0822\u001b[0m \u001b]8;id=376417;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=888662;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#63\u001b\\\u001b[2m63\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "task.evaluate(params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Confusion matrix\n", - "\n", - "Let's visualize the confusion matrix to understand the model's performance on each class." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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INFO Validating model results export.py:94\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Validating model results \u001b]8;id=236251;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=372244;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "fully_quantize: 0, inference_type: 6, input_inference_type: INT8, output_inference_type: INT8\n", - "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [TF METRICS] LOSS=0.2360 ACC=0.9657 F1=0.9761 export.py:101\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTF METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.2360\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9657\u001b[0m \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9761\u001b[0m \u001b]8;id=166258;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=873474;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#101\u001b\\\u001b[2m101\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO [TFL METRICS] LOSS=0.2441 ACC=0.9639 F1=0.9752 export.py:102\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTFL METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.2441\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9639\u001b[0m \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9752\u001b[0m \u001b]8;id=458215;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=444742;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#102\u001b\\\u001b[2m102\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO Validation passed (0.0080) export.py:110\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Validation passed \u001b[1m(\u001b[0m\u001b[1;36m0.0080\u001b[0m\u001b[1m)\u001b[0m \u001b]8;id=131787;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=175493;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#110\u001b\\\u001b[2m110\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - "with open(os.devnull, \"w\") as devnull:\n", - " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " task.export(params=params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run inference demo\n", - "\n", - "We will run a demo on the PC to verify that the model is working as expected. The demo will load the model and run inferences across a randomly selected ECG signal. The demo will also provide the model's prediction and the corresponding class name. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Inference: 100%|██████████| 1/1 [00:00<00:00, 1.86it/s]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
\n",
- "\n",
- "- \n",
- "\n",
- " View in Colab\n",
- "\n",
- "\n",
- "- \n",
- "\n",
- " GitHub source\n",
- "\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Train ECG Denosier\n",
- "\n",
- "__Date created:__ 2024/08/13 \n",
- "\n",
- "__Last Modified:__ 2024/07/17 \n",
- "\n",
- "__Description:__ Train, evaluate, and export ECG denoiser model from scratch\n",
- "\n",
- "\n",
- "## Overview \n",
- "\n",
- "In this guide, we will train an ECG denoiser to remove noise and artifacts from raw ECG signals. \n",
- "Once trained, we demonstrate how to evaluate the model and export it for inference for both TF Lite and TF Lite for Micro.\n",
- "\n",
- "__Input__\n",
- "\n",
- "- **Sensor**: ECG \n",
- "- **Location**: Wrist\n",
- "- **Sampling Rate**: 100 Hz\n",
- "- **Frame Size**: 2.56 seconds\n",
- "\n",
- "__Datasets__\n",
- "\n",
- "- **[Synthetic](https://ambiqai.github.io/heartkit/datasets/synthetic/)**: Synthetic ECG signals from physioKIT\n",
- "- **[PTB-XL](https://ambiqai.github.io/heartkit/datasets/ptbxl/)**: The PTB-XL is a large publicly available electrocardiography dataset. \n",
- "It contains 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install -q --disable-pip-version-check heartkit"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "\n",
- "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n",
- "import contextlib\n",
- "from pathlib import Path\n",
- "import tempfile\n",
- "import keras\n",
- "import heartkit as hk\n",
- "import numpy as np\n",
- "import helia_edge as helia\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Be sure to set the dataset path to the correct location\n",
- "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n",
- "\n",
- "plot_theme = hk.utils.dark_theme\n",
- "helia.utils.silence_tensorflow()\n",
- "hk.utils.setup_plotting(plot_theme)\n",
- "logger = helia.utils.setup_logger(__name__)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Create preprocess/augmentation pipeline\n",
- "\n",
- "Since our goal is to denoise ECG signals, we need to create an augmentation pipeline to generate noisy samples. \n",
- "\n",
- "We will leverage `helia-edge` preprocessing layers to create the following augmentations:\n",
- "\n",
- "* Baseline wander: Simulate baseline wander by adding a low frequency sine signal\n",
- "* Powerline noise: Simulate powerline noise by adding a 50 Hz sinusoidal signal \n",
- "* Amplitude warp: Simulate amplitude warp by randomly scaling along a low frequency sine wave\n",
- "* Gaussian noise: Simulate lead noise by adding random noise following a Gaussian distribution\n",
- "* Background noise: Add real noise captured from NSTDB dataset\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01))]\n",
- "\n",
- "augmentations = [\n",
- " hk.NamedParams(\n",
- " name=\"random_noise_distortion\", params=dict(amplitude=[0.1, 1.5], frequency=[0.5, 1.5], name=\"baseline_wander\")\n",
- " ),\n",
- " hk.NamedParams(\n",
- " name=\"random_sine_wave\",\n",
- " params=dict(amplitude=[0, 0.05], frequency=[45, 50], auto_vectorize=False, name=\"powerline_noise\"),\n",
- " ),\n",
- " hk.NamedParams(\n",
- " name=\"amplitude_warp\", params=dict(amplitude=[0.9, 1.1], frequency=[0.5, 1.5], name=\"amplitude_warp\")\n",
- " ),\n",
- " hk.NamedParams(name=\"random_noise\", params=dict(factor=[0.1, 0.5], name=\"random_noise\")),\n",
- " hk.NamedParams(name=\"random_background_noise\", params=dict(amplitude=[0.1, 0.5], num_noises=2, name=\"nstdb\")),\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Define TCN model architecture\n",
- "\n",
- "For this task, we are going to leverage a customized __TCN__ model architecture that is smaller and can handle 1D signals. The model consists of 5 TCN blocks with a depth of 1. Each block leverages dilated depthwise-separable convolutions along with inverted expansion and squeeze and excitation layers. The model is followed by a 1D convolutional layer. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "mbconv_blocks = [\n",
- " dict(\n",
- " depth=1, branch=1, filters=16, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=0, norm=\"batch\"\n",
- " ),\n",
- " dict(\n",
- " depth=1, branch=1, filters=24, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n",
- " ),\n",
- " dict(\n",
- " depth=1, branch=1, filters=32, kernel=(1, 7), dilation=(1, 2), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n",
- " ),\n",
- " dict(\n",
- " depth=1, branch=1, filters=40, kernel=(1, 7), dilation=(1, 4), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n",
- " ),\n",
- " dict(\n",
- " depth=1, branch=1, filters=48, kernel=(1, 7), dilation=(1, 8), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n",
- " ),\n",
- "]\n",
- "\n",
- "architecture = dict(\n",
- " name=\"tcn\",\n",
- " params=dict(\n",
- " input_kernel=(1, 7),\n",
- " input_norm=\"batch\",\n",
- " blocks=mbconv_blocks,\n",
- " output_kernel=(1, 7),\n",
- " include_top=True,\n",
- " use_logits=True,\n",
- " model_name=\"tcn\",\n",
- " ),\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Configure datasets\n",
- "\n",
- "Capturing noise-free ECG signals is challenging due to the presence of various artifacts. Therefore, we use a combination of synthetic and controlled, real-world datasets as our training data. heartKIT exposes an ECG Synthetic dataset generator provided by physioKIT. \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "datasets = [\n",
- " hk.NamedParams(\n",
- " name=\"ecg-synthetic\",\n",
- " params=dict(\n",
- " num_pts=5000,\n",
- " params=dict(\n",
- " presets=[\"SR\", \"AFIB\", \"ant_STEMI\", \"LAHB\", \"LPHB\", \"high_take_off\", \"LBBB\", \"random_morphology\"],\n",
- " preset_weights=[24, 8, 1, 1, 1, 1, 1, 0],\n",
- " duration=10,\n",
- " sample_rate=100,\n",
- " heart_rate=[40, 160],\n",
- " impedance=[1, 2],\n",
- " p_multiplier=[0.7, 1.3],\n",
- " t_multiplier=[0.7, 1.3],\n",
- " noise_multiplier=[0, 0.01],\n",
- " voltage_factor=[800, 1000],\n",
- " ),\n",
- " ),\n",
- " ),\n",
- " hk.NamedParams(\n",
- " name=\"ptbxl\",\n",
- " params=dict(\n",
- " path=datasets_dir / \"ptbxl\",\n",
- " ),\n",
- " ),\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Task configuration\n",
- "\n",
- "Here we provide the constants that we will use throughout the guide. For better performance, adjust parameters as needed such as `BATCH_SIZE`, `EPOCHS`, and `LEARNING_RATE`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "params = hk.HKTaskParams(\n",
- " # Common arguments\n",
- " name=\"hk-ecg-denoiser\",\n",
- " job_dir=Path(tempfile.gettempdir()) / \"hk-ecg-denoiser\",\n",
- " # Dataset arguments\n",
- " datasets=datasets,\n",
- " # Signal arguments\n",
- " sampling_rate=100,\n",
- " frame_size=256,\n",
- " # Dataloader arguments\n",
- " samples_per_patient=5,\n",
- " val_samples_per_patient=10,\n",
- " test_samples_per_patient=10,\n",
- " # Preprocessing/Augmentation arguments\n",
- " preprocesses=preprocesses,\n",
- " augmentations=augmentations,\n",
- " # Class arguments\n",
- " num_classes=1,\n",
- " class_map={0: 0},\n",
- " class_names=[\"DENOISE\"],\n",
- " # Split arguments\n",
- " val_patients=0.1,\n",
- " val_size=10000,\n",
- " test_size=10000,\n",
- " val_file=\"val.pkl\",\n",
- " test_file=\"val.pkl\",\n",
- " # Model arguments\n",
- " model_file=\"model.keras\",\n",
- " architecture=architecture,\n",
- " # Training parameters\n",
- " lr_rate=1e-3,\n",
- " lr_cycles=1,\n",
- " batch_size=256,\n",
- " buffer_size=25000,\n",
- " epochs=100,\n",
- " steps_per_epoch=50,\n",
- " val_metric=\"loss\",\n",
- " class_weights=\"balanced\",\n",
- " # Evaluation arguments\n",
- " threshold=0.5,\n",
- " val_metric_threshold=0.98,\n",
- " # Export parameters\n",
- " tflm_var_name=\"ecg_denoise_flatbuffer\",\n",
- " tflm_file=\"ecg_denoise_flatbuffer.h\",\n",
- " # Demo params\n",
- " backend=\"pc\",\n",
- " demo_size=800,\n",
- " display_report=True,\n",
- " # Extra arguments\n",
- " verbose=1,\n",
- " seed=42,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Load denoise task \n",
- "\n",
- "heartKIT provides a __TaskFactory__ that includes a number ready-to-use tasks. Each task provides methods for training, evaluating, exporting, and demoing. We will grab the __denoise__ task and configure it for our use case."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "task = hk.TaskFactory.get(\"denoise\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Download the datasets\n",
- "\n",
- "We will download the synthetic and PTB-XL datasets using `heartkit`. If already downloaded, this step will be skipped."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "task.download(params=params)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualize the data\n",
- "\n",
- "Let's visualize a sample ECG signal from the synthetic dataset. Note this contains no noise or artifacts. Augmentations will be applied later to generate noisy samples for training."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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- "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", - "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", - "│ inputs (InputLayer) │ (None, 256, 1) │ 0 │ - │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ reshape (Reshape) │ (None, 1, 256, 1) │ 0 │ inputs[0][0] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ ENC.CN │ (None, 1, 256, 1) │ 7 │ reshape[0][0] │\n", - "│ (DepthwiseConv2D) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ ENC.BN │ (None, 1, 256, 1) │ 4 │ ENC.CN[0][0] │\n", - "│ (BatchNormalizatio… │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.B1.CN │ (None, 1, 256, 1) │ 7 │ ENC.BN[0][0] │\n", - "│ (DepthwiseConv2D) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - 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"├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ ENC.CN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m7\u001b[0m │ reshape[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ ENC.BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m4\u001b[0m │ ENC.CN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.B1.CN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m7\u001b[0m │ ENC.BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.B1.BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m4\u001b[0m │ B1.D1.DW.B1.CN[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.ACT │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ B1.D1.DW.B1.BN[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.B1.CN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, │ \u001b[38;5;34m16\u001b[0m │ B1.D1.DW.ACT[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", - "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.B1.BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, │ \u001b[38;5;34m64\u001b[0m │ B1.D1.PW.B1.CN[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.ACT │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ B1.D1.PW.B1.BN[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B2.D1.DW.B1.CN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, │ \u001b[38;5;34m112\u001b[0m │ B1.D1.PW.ACT[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", - "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", - "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Total params: 10,223 (39.93 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,223\u001b[0m (39.93 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Trainable params: 9,675 (37.79 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m9,675\u001b[0m (37.79 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Non-trainable params: 548 (2.14 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m548\u001b[0m (2.14 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = helia.models.tcn.tcn_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture[\"params\"], num_classes=1\n", - ")\n", - "model.summary(layer_range=(\"inputs\", model.layers[10].name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the model" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
INFO Creating synthetic dataset cache with 5000 patients ecg_synthetic.py:159\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients \u001b]8;id=172088;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=461477;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Building ecg-synthetic cache: 100%|██████████| 5000/5000 [00:57<00:00, 86.91it/s] \n" - ] - }, - { - "data": { - "text/html": [ - "
INFO Validation steps per epoch: 39 datasets.py:85\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Validation steps per epoch: \u001b[1;36m39\u001b[0m \u001b]8;id=99779;file:///workspaces/heartkit/heartkit/tasks/denoise/datasets.py\u001b\\\u001b[2mdatasets.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=277033;file:///workspaces/heartkit/heartkit/tasks/denoise/datasets.py#85\u001b\\\u001b[2m85\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 0%| 0/100 ETA: ?s, ?epochs/sWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1723838225.604155 751478 service.cc:146] XLA service 0x7a52b8001f20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1723838225.604174 751478 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n", - "I0000 00:00:1723838232.858832 751478 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", - "Training: 100%|██████████ 100/100 ETA: 00:00s, 1.59s/epochs" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 975us/step - cos: 0.7118 - loss: 0.0511 - mae: 0.1445 - mse: 0.0452 - snr: 11.9220\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [VAL SET]COS=0.7079, LOSS=0.0528, MAE=0.1466, MSE=0.0469, SNR=11.9038 train.py:149\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m\u001b[33mCOS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7079\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0528\u001b[0m, \u001b[33mMAE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1466\u001b[0m, \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0469\u001b[0m, \u001b[33mSNR\u001b[0m=\u001b[1;36m11\u001b[0m\u001b[1;36m.9038\u001b[0m \u001b]8;id=347748;file:///workspaces/heartkit/heartkit/tasks/denoise/train.py\u001b\\\u001b[2mtrain.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=260161;file:///workspaces/heartkit/heartkit/tasks/denoise/train.py#149\u001b\\\u001b[2m149\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "task.train(params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model evaluation\n", - "\n", - "Now that we have trained the model, we will evaluate the model on the test dataset. Similar to training, we will provide the high-level configuration to the task process." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
INFO Creating synthetic dataset cache with 5000 patients ecg_synthetic.py:159\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients \u001b]8;id=288389;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=256787;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Building ecg-synthetic cache: 100%|██████████| 5000/5000 [00:57<00:00, 87.16it/s] \n" - ] - }, - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - cos: 0.7238 - loss: 0.0443 - mae: 0.1328 - mse: 0.0384 - snr: 12.3671\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [TEST SET] COS=0.7245, LOSS=0.0437, MAE=0.1316, MSE=0.0377, SNR=12.3787 evaluate.py:37\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mCOS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7245\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0437\u001b[0m, \u001b[33mMAE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1316\u001b[0m, \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0377\u001b[0m, \u001b[33mSNR\u001b[0m=\u001b[1;36m12\u001b[0m\u001b[1;36m.3787\u001b[0m \u001b]8;id=893749;file:///workspaces/heartkit/heartkit/tasks/denoise/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=218337;file:///workspaces/heartkit/heartkit/tasks/denoise/evaluate.py#37\u001b\\\u001b[2m37\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "task.evaluate(params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Export model to TF Lite / TFLM\n", - "\n", - "Once we have trained and evaluated the model, we need to export the model into a format that can be used for inference on the edge. Currently, we export the model to TensorFlow Lite flatbuffer format. This will also generate a C header file that can be used with TensorFlow Lite for Microcontrollers (TFLM).\n", - "\n", - "For this model, we will export as a 32-bit floating point model.\n", - " \n", - "__NOTE:__ We utilize `CONCRETE` mode to lower the model to concrete functions before converting. This is because TF (MLIR) fails to properly lower the dilated convolutional layers." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "quantization = hk.QuantizationParams(\n", - " enabled=True,\n", - " format=\"FP32\",\n", - " io_type=\"float32\",\n", - " conversion=\"CONCRETE\",\n", - ")\n", - "params.quantization = quantization" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
INFO Creating synthetic dataset cache with 5000 patients ecg_synthetic.py:159\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients \u001b]8;id=313048;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=514688;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
[08/16/24 20:02:23] WARNING WARNING:absl:Please consider providing the trackable_obj argument in the lite.py:2166\n", - " from_concrete_functions. Providing without the trackable_obj argument is \n", - " deprecated and it will use the deprecated conversion path. \n", - "\n" - ], - "text/plain": [ - "\u001b[2;36m[08/16/24 20:02:23]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:absl:Please consider providing the trackable_obj argument in the \u001b]8;id=520246;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/tensorflow/lite/python/lite.py\u001b\\\u001b[2mlite.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=384487;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/tensorflow/lite/python/lite.py#2166\u001b\\\u001b[2m2166\u001b[0m\u001b]8;;\u001b\\\n", - "\u001b[2;36m \u001b[0m from_concrete_functions. Providing without the trackable_obj argument is \u001b[2m \u001b[0m\n", - "\u001b[2;36m \u001b[0m deprecated and it will use the deprecated conversion path. \u001b[2m \u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO Validating model results export.py:83\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Validating model results \u001b]8;id=941295;file:///workspaces/heartkit/heartkit/tasks/denoise/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=727514;file:///workspaces/heartkit/heartkit/tasks/denoise/export.py#83\u001b\\\u001b[2m83\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1723838543.688860 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723838543.688944 751181 devices.cc:67] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n", - "I0000 00:00:1723838543.689113 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723838543.689169 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723838543.689214 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723838543.689287 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723838543.689333 751181 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "W0000 00:00:1723838543.815333 751181 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n", - "W0000 00:00:1723838543.815348 751181 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n", - "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [TF METRICS] LOSS=0.0396 MAE=0.1357 MSE=0.0396 RMSE=0.1991 COSINE=0.7178 export.py:90\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTF METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0396\u001b[0m \u001b[33mMAE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1357\u001b[0m \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0396\u001b[0m \u001b[33mRMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1991\u001b[0m \u001b[33mCOSINE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7178\u001b[0m \u001b]8;id=496666;file:///workspaces/heartkit/heartkit/tasks/denoise/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=35165;file:///workspaces/heartkit/heartkit/tasks/denoise/export.py#90\u001b\\\u001b[2m90\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO [TFL METRICS] LOSS=0.0396 MAE=0.1357 MSE=0.0396 RMSE=0.1991 COSINE=0.7177 export.py:91\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTFL METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0396\u001b[0m \u001b[33mMAE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1357\u001b[0m \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0396\u001b[0m \u001b[33mRMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1991\u001b[0m \u001b[33mCOSINE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7177\u001b[0m \u001b]8;id=70190;file:///workspaces/heartkit/heartkit/tasks/denoise/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=68341;file:///workspaces/heartkit/heartkit/tasks/denoise/export.py#91\u001b\\\u001b[2m91\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO Validation passed (0.0000) export.py:99\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Validation passed \u001b[1m(\u001b[0m\u001b[1;36m0.0000\u001b[0m\u001b[1m)\u001b[0m \u001b]8;id=375015;file:///workspaces/heartkit/heartkit/tasks/denoise/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=497680;file:///workspaces/heartkit/heartkit/tasks/denoise/export.py#99\u001b\\\u001b[2m99\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - "with open(os.devnull, \"w\") as devnull:\n", - " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " task.export(params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ECG Denoising Demo\n", - "\n", - "Finally, we will demonstrate how to use the trained ECG denoiser model to remove noise and artifacts from raw ECG signals. We will load a sample ECG signal, add noise to it, and then denoise it using the trained model. We will visualize the original, noisy, and denoised ECG signals to compare the results." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "model = helia.models.load_model(params.model_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n" - ] - } - ], - "source": [ - "ecg = next(ds_gen)\n", - "aug_ecg = (\n", - " augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg, (1, -1, 1)))), training=True).numpy().squeeze()\n", - ")\n", - "clean_ecg = model.predict(np.reshape(aug_ecg, (1, -1, 1)))\n", - "snr = helia.metrics.Snr()\n", - "snr.update_state(ecg.reshape(1, -1, 1), aug_ecg.reshape(1, -1, 1))\n", - "aug_snr = snr.result().numpy()\n", - "snr.reset_state()\n", - "snr.update_state(ecg.reshape(1, -1, 1), clean_ecg.reshape(1, -1, 1))\n", - "clean_snr = snr.result().numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
\n",
- "\n",
- "- \n",
- "\n",
- " View in Colab\n",
- "\n",
- "\n",
- "- \n",
- "\n",
- " GitHub source\n",
- "\n",
- "\n",
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Train ECG Segmentation Model\n",
- "\n",
- "__Date created:__ 2024/07/17 \n",
- "\n",
- "__Last Modified:__ 2024/07/17 \n",
- "\n",
- "__Description:__ Train, evaluate, and export 4-stage ECG segmentation model from scratch\n",
- "\n",
- "## Overview\n",
- "\n",
- "In this guide, we will train a model to segment an ECG signal into four classes: `NONE`, `PWAVE`, `QRS`, and `TWAVE`. We will use both synthetic and real ECG datasets to train a TCN style model. We will also showcase evaluating and exporting the model for inference via TF Lite and TFLM.\n",
- "\n",
- "__Input__\n",
- "\n",
- "- **Sensor**: ECG\n",
- "- **Location**: Wrist\n",
- "- **Sampling Rate**: 100 Hz\n",
- "- **Frame Size**: 2.56 seconds\n",
- "\n",
- "__Class Mapping__\n",
- "\n",
- "Segment ECG signal into one of the following classes:\n",
- "\n",
- "| Base Class | Target Class | Label |\n",
- "| ---------------- | ------------ | ------------ |\n",
- "| 0-NONE | 0 | NONE |\n",
- "| 1-PWAVE | 1 | PWAVE |\n",
- "| 2-QRS | 2 | QRS |\n",
- "| 3-TWAVE | 3 | TWAVE |\n",
- "\n",
- "\n",
- "__Datasets__\n",
- "\n",
- "- **[Synthetic](https://ambiqai.github.io/heartkit/datasets/synthetic/)**: Synthetic ECG signals from physioKIT\n",
- "- **[LUDB](https://ambiqai.github.io/heartkit/datasets/ludb/)**: Lobachevsky University Electrocardiography database consists of 200 10-second 12-lead records. The boundaries and peaks of P, T waves and QRS complexes were manually annotated by cardiologists. Each record is annotated with the corresponding diagnosis.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "#!pip install -q --disable-pip-version-check heartkit"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "\n",
- "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n",
- "import IPython\n",
- "import contextlib\n",
- "from pathlib import Path\n",
- "import tempfile\n",
- "import keras\n",
- "import heartkit as hk\n",
- "import physiokit as pk\n",
- "import numpy as np\n",
- "import helia_edge as helia\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Be sure to set the dataset path to the correct location\n",
- "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n",
- "\n",
- "plot_theme = hk.utils.dark_theme\n",
- "helia.utils.silence_tensorflow()\n",
- "hk.utils.setup_plotting(plot_theme)\n",
- "logger = helia.utils.setup_logger(__name__)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Target datasets\n",
- "\n",
- "The only real-world public dataset containing ECG signals with annotated segments is the [LUDB dataset](https://ambiqai.github.io/heartkit/datasets/ludb/). We will use this dataset to train our model. In addition, we will leverage the synthetic dataset provided by physioKIT to increase amount of data to train on. We will apply several augmentation techniques to the synthetic dataset to increase the diversity of the data. \n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "datasets = [\n",
- " hk.NamedParams(\n",
- " name=\"ecg-synthetic\",\n",
- " params=dict(\n",
- " num_pts=5000,\n",
- " params=dict(\n",
- " presets=[\"SR\", \"AFIB\", \"ant_STEMI\", \"LAHB\", \"LPHB\", \"high_take_off\", \"LBBB\", \"random_morphology\"],\n",
- " preset_weights=[8, 4, 1, 1, 1, 1, 1, 0],\n",
- " duration=10,\n",
- " sample_rate=100,\n",
- " heart_rate=[40, 160],\n",
- " impedance=[1, 2],\n",
- " p_multiplier=[0.7, 1.3],\n",
- " t_multiplier=[0.7, 1.3],\n",
- " noise_multiplier=[0, 0.01],\n",
- " voltage_factor=[800, 1000],\n",
- " ),\n",
- " ),\n",
- " ),\n",
- " hk.NamedParams(\n",
- " name=\"ludb\",\n",
- " params=dict(\n",
- " path=datasets_dir / \"ludb\",\n",
- " ),\n",
- " ),\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Target classes\n",
- "\n",
- "For this task, we are going to delineate ECG signals into one of four classes: \n",
- "\n",
- "* __None__: Background signal\n",
- "* __P-Wave__: Atrial depolarization\n",
- "* __QRS__: Ventricular depolarization\n",
- "* __T-Wave__: Ventricular repolarization\n",
- "\n",
- "HeartKit already provides a number of heart segments. We will provide a class mapping for the four classes we are interested in. We will also provide class names for display purposes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "class_map = {\n",
- " hk.tasks.HKSegment.normal: 0,\n",
- " hk.tasks.HKSegment.pwave: 1,\n",
- " hk.tasks.HKSegment.qrs: 2,\n",
- " hk.tasks.HKSegment.twave: 3,\n",
- " hk.tasks.HKSegment.uwave: 0,\n",
- " hk.tasks.HKSegment.noise: 0,\n",
- "}\n",
- "\n",
- "class_names = [\"NONE\", \"P-WAVE\", \"QRS\", \"T-WAVE\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Define TCN model architecture\n",
- "\n",
- "For this task, we are going to leverage a customized __TCN__ model architecture that is smaller and can handle 1D signals. The model consists of 4 TCN blocks with a depth of 1. Each block leverages dilated depthwise-separable convolutions along with inverted expansion and squeeze and excitation layers. The model is followed by a 1D convolutional layer and a final dense layer for regression. Unlike vision tasks, we leverage larger kernel sizes and strides to capture temporal dependencies in the ECG signal."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "architecture = hk.NamedParams(\n",
- " name=\"tcn\",\n",
- " params=dict(\n",
- " input_kernel=(1, 7),\n",
- " input_norm=\"batch\",\n",
- " blocks=[\n",
- " dict(\n",
- " depth=1,\n",
- " branch=1,\n",
- " filters=16,\n",
- " kernel=(1, 7),\n",
- " dilation=(1, 1),\n",
- " dropout=0.1,\n",
- " ex_ratio=1,\n",
- " se_ratio=0,\n",
- " norm=\"batch\",\n",
- " ),\n",
- " dict(\n",
- " depth=1,\n",
- " branch=1,\n",
- " filters=24,\n",
- " kernel=(1, 7),\n",
- " dilation=(1, 2),\n",
- " dropout=0.1,\n",
- " ex_ratio=1,\n",
- " se_ratio=2,\n",
- " norm=\"batch\",\n",
- " ),\n",
- " dict(\n",
- " depth=1,\n",
- " branch=1,\n",
- " filters=32,\n",
- " kernel=(1, 7),\n",
- " dilation=(1, 4),\n",
- " dropout=0.1,\n",
- " ex_ratio=1,\n",
- " se_ratio=2,\n",
- " norm=\"batch\",\n",
- " ),\n",
- " dict(\n",
- " depth=1,\n",
- " branch=1,\n",
- " filters=48,\n",
- " kernel=(1, 7),\n",
- " dilation=(1, 8),\n",
- " dropout=0.1,\n",
- " ex_ratio=1,\n",
- " se_ratio=2,\n",
- " norm=\"batch\",\n",
- " ),\n",
- " ],\n",
- " output_kernel=(1, 7),\n",
- " include_top=True,\n",
- " use_logits=True,\n",
- " model_name=\"tcn\",\n",
- " ),\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Preprocess pipeline\n",
- "\n",
- "We will preprocess the ECG signals by applying the following steps:\n",
- "* Apply bandpass filter with cutoff frequencies of 1Hz and 30Hz\n",
- "* Apply Z-score normalization w/ epsilon to avoid division by zero\n",
- "\n",
- "The task accepts a list of preprocessing functions that will be applied to the input data. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Augmentation pipeline\n",
- "\n",
- "We will apply the following augmentations to the ECG signals:\n",
- "* Baseline wander: Simulate baseline wander by adding a random frequency sinusoidal signal to the ECG signal\n",
- "* Powerline noise: Simulate powerline noise by adding a 50 Hz sinusoidal signal to the ECG signal\n",
- "* Burst noise: Simulate burst noise by randomly injecting burst of high frequency noise to the ECG signal\n",
- "* Noise sources: Apply several noises at given frequencies to the ECG signal\n",
- "* Lead noise: Simulate lead noise by adding a random frequency sinusoidal signal to the ECG signal\n",
- "* NSTDB: Add real noise captured from NSTDB dataset to the ECG signal. \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "augmentations = [\n",
- " hk.NamedParams(\n",
- " name=\"random_noise_distortion\", params=dict(amplitude=[0, 0.5], frequency=[0.5, 1.5], name=\"baseline_wander\")\n",
- " ),\n",
- " hk.NamedParams(\n",
- " name=\"random_sine_wave\",\n",
- " params=dict(amplitude=[0, 0.05], frequency=[45, 50], auto_vectorize=False, name=\"powerline_noise\"),\n",
- " ),\n",
- " hk.NamedParams(\n",
- " name=\"amplitude_warp\", params=dict(amplitude=[0.9, 1.1], frequency=[0.5, 1.5], name=\"amplitude_warp\")\n",
- " ),\n",
- " hk.NamedParams(name=\"random_noise\", params=dict(factor=[0, 0.025], name=\"random_noise\")),\n",
- " hk.NamedParams(name=\"random_background_noise\", params=dict(amplitude=[0, 0.025], num_noises=1, name=\"nstdb\")),\n",
- "]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Task configuration\n",
- "\n",
- "Here we provide the complete configuration for the task. This includes the dataset configuration, preprocessing pipeline, model architecture, and training parameters."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "params = hk.HKTaskParams(\n",
- " # Common arguments\n",
- " name=\"hk-ecg-segmentation\",\n",
- " job_dir=Path(tempfile.gettempdir()) / \"hk-ecg-segmentation\",\n",
- " # Dataset arguments\n",
- " datasets=datasets,\n",
- " # Signal arguments\n",
- " sampling_rate=100,\n",
- " frame_size=256,\n",
- " # Dataloader arguments\n",
- " samples_per_patient=25,\n",
- " val_samples_per_patient=10,\n",
- " test_samples_per_patient=10,\n",
- " # Preprocessing/Augmentation arguments\n",
- " preprocesses=preprocesses,\n",
- " augmentations=augmentations,\n",
- " # Class arguments\n",
- " num_classes=len(class_names),\n",
- " class_map=class_map,\n",
- " class_names=class_names,\n",
- " # Split arguments\n",
- " val_patients=0.1,\n",
- " val_size=20000,\n",
- " test_size=20000,\n",
- " val_file=\"val.pkl\",\n",
- " test_file=\"val.pkl\",\n",
- " # Model arguments\n",
- " model_file=\"model.keras\",\n",
- " architecture=architecture,\n",
- " # Training parameters\n",
- " lr_rate=1e-3,\n",
- " lr_cycles=1,\n",
- " batch_size=256,\n",
- " buffer_size=50000,\n",
- " epochs=100,\n",
- " steps_per_epoch=50,\n",
- " val_metric=\"loss\",\n",
- " class_weights=\"balanced\",\n",
- " # Evaluation arguments\n",
- " threshold=0.5,\n",
- " val_metric_threshold=0.98,\n",
- " # Export parameters\n",
- " tflm_var_name=\"ecg_rhythm_flatbuffer\",\n",
- " tflm_file=\"ecg_rhythm_flatbuffer.h\",\n",
- " # Demo params\n",
- " backend=\"pc\",\n",
- " demo_size=800,\n",
- " display_report=False,\n",
- " # Extra arguments\n",
- " verbose=1,\n",
- " seed=42,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Load segmentation task \n",
- "\n",
- "heartKIT provides a __TaskFactory__ that includes a number ready-to-use tasks. Each task provides methods for training, evaluating, exporting, and demoing. We will grab the __segmentation__ task and configure it for our use case."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "task = hk.TaskFactory.get(\"segmentation\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Download the datasets\n",
- "\n",
- "We will download the synthetic and LUDB datasets using the `heartkit` library. If already downloaded, this step will be skipped."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "task.download(params=params)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualize the data\n",
- "\n",
- "Let's visualize a sample ECG signal from the synthetic dataset. Note this contains no noise or artifacts. Augmentations will be applied later to generate noisy samples for training."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
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- "Model: \"TCN\"\n",
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- "\u001b[1mModel: \"TCN\"\u001b[0m\n"
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- },
- "metadata": {},
- "output_type": "display_data"
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- "data": {
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- "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", - "┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", - "│ inputs (InputLayer) │ (None, 256, 1) │ 0 │ - │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ reshape (Reshape) │ (None, 1, 256, 1) │ 0 │ inputs[0][0] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ ENC.CN │ (None, 1, 256, 1) │ 7 │ reshape[0][0] │\n", - "│ (DepthwiseConv2D) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ ENC.BN │ (None, 1, 256, 1) │ 4 │ ENC.CN[0][0] │\n", - "│ (BatchNormalizatio… │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.B1.CN │ (None, 1, 256, 1) │ 7 │ ENC.BN[0][0] │\n", - "│ (DepthwiseConv2D) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.B1.BN │ (None, 1, 256, 1) │ 4 │ B1.D1.DW.B1.CN[0… │\n", - "│ (BatchNormalizatio… │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.ACT │ (None, 1, 256, 1) │ 0 │ B1.D1.DW.B1.BN[0… │\n", - "│ (Activation) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.B1.CN │ (None, 1, 256, │ 16 │ B1.D1.DW.ACT[0][… │\n", - "│ (Conv2D) │ 16) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.B1.BN │ (None, 1, 256, │ 64 │ B1.D1.PW.B1.CN[0… │\n", - "│ (BatchNormalizatio… │ 16) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.ACT │ (None, 1, 256, │ 0 │ B1.D1.PW.B1.BN[0… │\n", - "│ (Activation) │ 16) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.DROP │ (None, 1, 256, │ 0 │ B1.D1.PW.ACT[0][… │\n", - "│ (SpatialDropout2D) │ 16) │ │ │\n", - "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n", - "\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n", - "│ inputs (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ reshape (\u001b[38;5;33mReshape\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ inputs[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ ENC.CN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m7\u001b[0m │ reshape[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ ENC.BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m4\u001b[0m │ ENC.CN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.B1.CN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m7\u001b[0m │ ENC.BN[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.B1.BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m4\u001b[0m │ B1.D1.DW.B1.CN[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.DW.ACT │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ B1.D1.DW.B1.BN[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.B1.CN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, │ \u001b[38;5;34m16\u001b[0m │ B1.D1.DW.ACT[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", - "│ (\u001b[38;5;33mConv2D\u001b[0m) │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.B1.BN │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, │ \u001b[38;5;34m64\u001b[0m │ B1.D1.PW.B1.CN[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.D1.PW.ACT │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ B1.D1.PW.B1.BN[\u001b[38;5;34m0\u001b[0m… │\n", - "│ (\u001b[38;5;33mActivation\u001b[0m) │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", - "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n", - "│ B1.DROP │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m256\u001b[0m, │ \u001b[38;5;34m0\u001b[0m │ B1.D1.PW.ACT[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", - "│ (\u001b[38;5;33mSpatialDropout2D\u001b[0m) │ \u001b[38;5;34m16\u001b[0m) │ │ │\n", - "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Total params: 7,310 (28.55 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,310\u001b[0m (28.55 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Trainable params: 6,922 (27.04 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,922\u001b[0m (27.04 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Non-trainable params: 388 (1.52 KB)\n", - "\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m388\u001b[0m (1.52 KB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = helia.models.tcn.tcn_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture.params, num_classes=len(class_names)\n", - ")\n", - "model.summary(layer_range=(\"inputs\", model.layers[10].name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the model\n", - "\n", - "Using the task configuration, we will train the model on the synthetic and LUDB datasets. We will also apply augmentations to the synthetic dataset to increase the diversity of the data." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
INFO Creating synthetic dataset cache with 5000 patients ecg_synthetic.py:159\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients \u001b]8;id=172088;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=461477;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Building ecg-synthetic cache: 100%|██████████| 5000/5000 [00:56<00:00, 87.92it/s] \n" - ] - }, - { - "data": { - "text/html": [ - "
INFO Validation steps per epoch: 78 datasets.py:107\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Validation steps per epoch: \u001b[1;36m78\u001b[0m \u001b]8;id=284434;file:///workspaces/heartkit/heartkit/tasks/segmentation/datasets.py\u001b\\\u001b[2mdatasets.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=866120;file:///workspaces/heartkit/heartkit/tasks/segmentation/datasets.py#107\u001b\\\u001b[2m107\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training: 0%| 0/100 ETA: ?s, ?epochs/sWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1723838774.747244 758430 service.cc:146] XLA service 0x7b7988001e40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", - "I0000 00:00:1723838774.747268 758430 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n", - "I0000 00:00:1723838783.452909 758430 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n", - "Training: 100%|██████████ 100/100 ETA: 00:00s, 1.96s/epochs\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 743us/step\n", - "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - acc: 0.8512 - f1: 0.8524 - loss: 0.1304\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [VAL SET] ACC=0.8510, F1=0.8522, LOSS=0.1312 train.py:184\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8510\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8522\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1312\u001b[0m \u001b]8;id=904348;file:///workspaces/heartkit/heartkit/tasks/segmentation/train.py\u001b\\\u001b[2mtrain.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=191133;file:///workspaces/heartkit/heartkit/tasks/segmentation/train.py#184\u001b\\\u001b[2m184\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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INFO Creating synthetic dataset cache with 5000 patients ecg_synthetic.py:159\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients \u001b]8;id=288389;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=256787;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Building ecg-synthetic cache: 100%|██████████| 5000/5000 [00:57<00:00, 87.27it/s] \n" - ] - }, - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 970us/step - acc: 0.8674 - f1: 0.8687 - loss: 0.1110\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [TEST SET] ACC=0.8652, F1=0.8665, LOSS=0.1141 evaluate.py:47\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8652\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8665\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1141\u001b[0m \u001b]8;id=630320;file:///workspaces/heartkit/heartkit/tasks/segmentation/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=347514;file:///workspaces/heartkit/heartkit/tasks/segmentation/evaluate.py#47\u001b\\\u001b[2m47\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n" - ] - } - ], - "source": [ - "task.evaluate(params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Confusion matrix\n", - "\n", - "Let's visualize the confusion matrix to understand the model's performance on each class." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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INFO Creating synthetic dataset cache with 5000 patients ecg_synthetic.py:159\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Creating synthetic dataset cache with \u001b[1;36m5000\u001b[0m patients \u001b]8;id=878050;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py\u001b\\\u001b[2mecg_synthetic.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=80552;file:///workspaces/heartkit/heartkit/datasets/ecg_synthetic.py#159\u001b\\\u001b[2m159\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n" - ], - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
[08/16/24 20:11:32] WARNING WARNING:absl:Please consider providing the trackable_obj argument in the lite.py:2166\n", - " from_concrete_functions. Providing without the trackable_obj argument is \n", - " deprecated and it will use the deprecated conversion path. \n", - "\n" - ], - "text/plain": [ - "\u001b[2;36m[08/16/24 20:11:32]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m WARNING:absl:Please consider providing the trackable_obj argument in the \u001b]8;id=858712;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/tensorflow/lite/python/lite.py\u001b\\\u001b[2mlite.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=480914;file:///workspaces/heartkit/.venv/lib/python3.12/site-packages/tensorflow/lite/python/lite.py#2166\u001b\\\u001b[2m2166\u001b[0m\u001b]8;;\u001b\\\n", - "\u001b[2;36m \u001b[0m from_concrete_functions. Providing without the trackable_obj argument is \u001b[2m \u001b[0m\n", - "\u001b[2;36m \u001b[0m deprecated and it will use the deprecated conversion path. \u001b[2m \u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1723839092.566023 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723839092.566111 758191 devices.cc:67] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n", - "I0000 00:00:1723839092.566407 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723839092.566464 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723839092.566510 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723839092.566580 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "I0000 00:00:1723839092.566627 758191 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", - "W0000 00:00:1723839092.671832 758191 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n", - "W0000 00:00:1723839092.671846 758191 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n", - "fully_quantize: 0, inference_type: 6, input_inference_type: INT8, output_inference_type: INT8\n", - "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n" - ] - }, - { - "data": { - "text/html": [ - "
INFO [TF METRICS] LOSS=0.4022 ACC=0.8683 F1=0.8694 IOU=0.7551 export.py:105\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTF METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4022\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8683\u001b[0m \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8694\u001b[0m \u001b[33mIOU\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7551\u001b[0m \u001b]8;id=339944;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=960343;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#105\u001b\\\u001b[2m105\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO [TFL METRICS] LOSS=0.4077 ACC=0.8686 F1=0.8676 IOU=0.7529 export.py:106\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0mTFL METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.4077\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8686\u001b[0m \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8676\u001b[0m \u001b[33mIOU\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7529\u001b[0m \u001b]8;id=861404;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=138484;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#106\u001b\\\u001b[2m106\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
INFO Validation passed (0.0055) export.py:114\n", - "\n" - ], - "text/plain": [ - "\u001b[34mINFO \u001b[0m Validation passed \u001b[1m(\u001b[0m\u001b[1;36m0.0055\u001b[0m\u001b[1m)\u001b[0m \u001b]8;id=743127;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=94135;file:///workspaces/heartkit/heartkit/tasks/segmentation/export.py#114\u001b\\\u001b[2m114\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - "with open(os.devnull, \"w\") as devnull:\n", - " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " task.export(params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run inference demo\n", - "\n", - "We will run a demo on the PC to verify that the model is working as expected. The demo will load the model and run inferences across a randomly selected ECG signal. The demo will also provide the model's prediction and the corresponding class name. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Inference: 100%|██████████| 4/4 [00:00<00:00, 5.55it/s]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
-
-heartKIT is a Python-based AI Development Kit for heart monitoring for Ambiq devices. Explore datasets, configurable models and task workflows for ECG and PPG signals, from training through model export.
-
-
-
-## Build your heart-monitoring workflow
-
-heartKIT brings dataset integrations, model architectures, training, evaluation and export into one development kit. Start with the included examples and model configurations, or extend the framework with your own datasets, models and tasks. Use the command line for configured runs or the Python API to compose your workflow.
-
-## Getting Started
-
-- **Install** `heartKIT` with pip/uv and getting up and running in minutes. [:material-clock-fast: Install heartKIT](./quickstart.md/#install-heartkit){ .md-button }
-- **Train** a model with a custom network [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
-- **Tasks** `heartKIT` provides tasks like rhythm, segment, and denoising [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
-- **Datasets** Several built-in datasets can be leveraged [:material-database-outline: Explore Datasets](./datasets/index.md){ .md-button }
-- **Model Zoo** Pre-trained models are available for each task [:material-download: Explore Models](./zoo/index.md){ .md-button }
-- **Guides** Detailed guides on tasks, models, and datasets [:material-book-open-page-variant: Explore Guides](./guides/index.md){ .md-button }
-
-## Installation
-
-Use **uv** for a Python project, **uvx** to run the CLI in an isolated environment, or **pipx** to keep the CLI installed. Choose **Git clone** when developing heartKIT itself.
-
-
-
-=== "uv project"
-
- Create a project with Python 3.12, then add heartKIT. In an existing uv project, start with `uv add heartkit`.
-
- ```bash
- uv init --python 3.12 my-heart-project
- cd my-heart-project
- uv add heartkit
- uv run heartkit --help
- ```
-
-=== "uvx"
-
- Run the CLI without adding heartKIT to a project. The first invocation downloads heartKIT and its dependencies into an isolated environment.
-
- ```bash
- uvx --python 3.12 heartkit --help
- ```
-
- Use a project installation for Python imports and notebooks.
-
-=== "pipx"
-
- Install the CLI in its own environment. This command uses an installed Python 3.12 interpreter.
-
- ```bash
- pipx install --python python3.12 heartkit
- heartkit --help
- ```
-
- If the command is not on your PATH, run `pipx ensurepath` and reopen your terminal. Use a project installation for Python imports and notebooks.
-
-=== "pip"
-
- Install into an activated virtual environment.
-
- ```bash
- python -m pip install heartkit
- heartkit --help
- ```
-
-=== "Git clone"
-
- Work with the repository source and its development dependencies.
-
- ```bash
- git clone https://github.com/AmbiqAI/heartkit.git
- cd heartkit
- uv sync --python 3.12
- uv run heartkit --help
- ```
-
-
-
-Need the package manager first? See the [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/) or [pipx installation guide](https://pipx.pypa.io/stable/installation/). The [Quickstart](./quickstart.md) covers configuration and your first workflow.
-
-
----
-
-## Usage
-
-__heartKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced development. In both forms, heartKIT exposes a number of modes and tasks outlined below. In addition, by leveraging highly-customizable configurations and extendable factories, heartKIT can be used to create custom workflows for a given application with minimal coding. Refer to the [Quickstart](./quickstart.md) to quickly get up and running in minutes.
-
----
-
-## [Tasks](./tasks/index.md)
-
-__heartKIT__ includes a number of built-in [tasks](./tasks/index.md). Each task provides reference routines for training, evaluating, and exporting the model. The routines can be customized by providing highly flexibile configuration files/objects. Additionally, new tasks can be added to the __heartKIT__ framework by defining a new [Task class](./tasks/byot.md) and registering it to the [__Task Factory__](./tasks/byot.md).
-
-- **[Denoise](./tasks/denoise.md)**: Remove noise and artifacts from signals
-- **[Segmentation](./tasks/segmentation.md)**: Perform ECG/PPG based segmentation
-- **[Rhythm](./tasks/rhythm.md)**: Heart rhythm classification (AFIB, AFL)
-- **[Beat](./tasks/beat.md)**: Beat-level classification (NORM, PAC, PVC, NOISE)
-
-- **[Bring-Your-Own-Task (BYOT)](./tasks/byot.md)**: Create and register custom tasks
-
----
-
-## [Modes](./modes/index.md)
-
-__heartKIT__ provides a number of [modes](./modes/index.md) that can be invoked for a given task. These modes can be accessed via the CLI or directly from a [Task](./tasks/index.md). Each mode is accompanied by a set of [task parameters](./modes/configuration.md#hktaskparams) that can be customized to fit the user's needs.
-
-- **[Download](./modes/download.md)**: Download specified datasets
-- **[Train](./modes/train.md)**: Train a model for specified task and datasets
-- **[Evaluate](./modes/evaluate.md)**: Evaluate a model for specified task and datasets
-- **[Export](./modes/export.md)**: Export a trained model to TensorFlow Lite and TFLM
-- **[Demo](./modes/demo.md)**: Run task-level demo on PC or remotely on Ambiq EVB
-
----
-
-## [Datasets](./datasets/index.md)
-
-The ADK includes several built-in [datasets](./datasets/index.md) for training __heart-monitoring__ related tasks. We also provide synthetic dataset generators for signals such as ECG, PPG, and RSP along with segmentation and fiducials. Each included dataset inherits from [HKDataset](api/heartkit/datasets/dataset.md) that provides consistent interface for downloading and accessing the data. Additional datasets can be added to the heartKIT framework by creating a new dataset class and registering it to the dataset factory, [DatasetFactory](./models/index.md#model-factory). Check out the [Datasets Guide](./datasets/index.md) to learn more about the available datasets along with their corresponding licenses and limitations.
-
-* **[Icentia11k](./datasets/icentia11k.md)**: 11-lead ECG data collected from 11,000 subjects captured continously over two weeks.
-* **[LUDB](./datasets/ludb.md)**: 200 ten-second 12-lead ECG records w/ annotated P-wave, QRS, and T-wave boundaries.
-* **[QTDB](./datasets/qtdb.md)**: 100+ fifteen-minute two-lead ECG recordings w/ annotated P-wave, QRS, and T-wave boundaries.
-* **[LSAD](./datasets/lsad.md)**: 10-second, 12-lead ECG dataset collected from 45,152 subjects w/ over 100 scp codes.
-* **[PTB-XL](./datasets/ptbxl.md)**: 10-second, 12-lead ECG dataset collected from 18,885 subjects w/ 72 different diagnostic classes.
-* **[Synthetic](./datasets/synthetic.md)**: A synthetic dataset generator provided by [physioKIT](https://ambiqai.github.io/physiokit).
-* **[Bring-Your-Own-Dataset (BYOD)](./datasets/byod.md)**: Add and register new datasets to the framework.
-
----
-
-## [Models](./models/index.md)
-
-__heartKIT__ provides a variety of model architectures geared towards efficient, real-time edge applications. These models are provided by Ambiq's [helia-edge](https://ambiqai.github.io/helia-edge/) and expose a set of parameters that can be used to fully customize the network for a given application. In addition, heartKIT includes a model factory, [ModelFactory](./models/index.md#model-factory), to register current models as well as allow new custom architectures to be added. Check out the [Models Guide](./models/index.md) to learn more about the available network architectures and model factory.
-
-- **[TCN](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`)
-- **[U-Net](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`)
-- **[U-NeXt](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`)
-- **[EfficientNetV2](https://ambiqai.github.io/helia-edge/api/helia_edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`)
-- **[MobileOne](https://ambiqai.github.io/helia-edge/api/helia_edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`)
-- **[ResNet](https://ambiqai.github.io/helia-edge/api/helia_edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`)
-- **[Conformer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`)
-- **[MetaFormer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`)
-- **[TSMixer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`)
-- **[Bring-Your-Own-Model (BYOM)](./models/byom.md)**: Register new SoTA model architectures w/ custom configurations
-
----
-
-## [Model Zoo](./zoo/index.md)
-
-The ADK includes a number of pre-trained models and configurationn recipes for the built-in tasks. These models are trained on a variety of datasets and are optimized for deployment on Ambiq's ultra-low power SoCs. In addition to providing links to download the models, __heartKIT__ provides the corresponding configuration files and performance metrics. The configuration files allow you to easily recreate the models or use them as a starting point for custom solutions. Furthermore, the performance metrics provide insights into the trade-offs between model complexity and performance. Check out the [Model Zoo](./zoo/index.md) to learn more about the available models and their corresponding performance metrics.
-
----
-
-## [Guides](./guides/index.md)
-
-Checkout the [Guides](./guides/index.md) to see detailed examples and tutorials on how to use heartKIT for a variety of tasks. The guides provide step-by-step instructions on how to train, evaluate, and deploy models for a given task. In addition, the guides provide insights into the design choices and performance metrics for the models. The guides are designed to help you get up and running quickly and to provide a deeper understanding of the capabilities provided by heartKIT.
-
----
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- // A last single won't have effect - // so put an additional one - buffer.push(""); - } - const bufferValue = buffer.join("
"); - dataValue["value"] = bufferValue; - useLines.push(dataValue); - buffer = []; - } - } - for (let line of lines) { - if (line === progressLiteralStart) { - saveBuffer(); - useLines.push({ - type: "progress" - }); - } else if (line.startsWith(promptLiteralStart)) { - saveBuffer(); - const value = line.replace(promptLiteralStart, "").trimEnd(); - useLines.push({ - type: "input", - value: value - }); - } else if (line.startsWith("// ")) { - saveBuffer(); - const value = "💬 " + line.replace("// ", "").trimEnd(); - useLines.push({ - value: value, - class: "termynal-comment", - delay: 0 - }); - } else if (line.startsWith(customPromptLiteralStart)) { - saveBuffer(); - const promptStart = line.indexOf(promptLiteralStart); - if (promptStart === -1) { - console.error("Custom prompt found but no end delimiter", line) - } - const prompt = line.slice(0, promptStart).replace(customPromptLiteralStart, "") - let value = line.slice(promptStart + promptLiteralStart.length); - useLines.push({ - type: "input", - value: value, - prompt: prompt - }); - } else { - buffer.push(line); - } - } - saveBuffer(); - const div = document.createElement("div"); - node.replaceWith(div); - const termynal = new Termynal(div, { - lineData: useLines, - noInit: true, - lineDelay: 500 - }); - termynals.push(termynal); - }); - } - - function loadVisibleTermynals() { - termynals = termynals.filter(termynal => { - if (termynal.container.getBoundingClientRect().top - innerHeight <= 0) { - termynal.init(); - return false; - } - return true; - }); - } - window.addEventListener("scroll", loadVisibleTermynals); - createTermynals(); - loadVisibleTermynals(); -} - -function shuffle(array) { - var currentIndex = array.length, temporaryValue, randomIndex; - while (0 !== currentIndex) { - randomIndex = Math.floor(Math.random() * currentIndex); - currentIndex -= 1; - temporaryValue = array[currentIndex]; - array[currentIndex] = array[randomIndex]; - array[randomIndex] = temporaryValue; - } - return array; -} - -async function showRandomAnnouncement(groupId, timeInterval) { - const announceFastAPI = document.getElementById(groupId); - if (announceFastAPI) { - let children = [].slice.call(announceFastAPI.children); - children = shuffle(children) - let index = 0 - const announceRandom = () => { - children.forEach((el, i) => {el.style.display = "none"}); - children[index].style.display = "block" - index = (index + 1) % children.length - } - announceRandom() - setInterval(announceRandom, timeInterval - ) - } -} - -async function main() { - setupTermynal(); -} - -main() diff --git a/docs/js/termynal.js b/docs/js/termynal.js deleted file mode 100644 index 4ac32708..00000000 --- a/docs/js/termynal.js +++ /dev/null @@ -1,264 +0,0 @@ -/** - * termynal.js - * A lightweight, modern and extensible animated terminal window, using - * async/await. - * - * @author Ines Montani
-
-1. Load the configuration data (e.g. `configuration.json`)
-1. Load the desired datasets (e.g. `PtbxlDataset`)
-1. Load the corresponding task dataloaders (e.g. `PtbxlDataLoader`)
-1. Load the trained model (e.g. `model.keras`)
-1. Define the metrics (e.g. `accuracy`)
-1. Evaluate the model (e.g. `model.evaluate`)
-1. Generate evaluation report (e.g. `report.json`)
-
-
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
-
-```mermaid
-flowchart TD
- A["Load configuration"] --> B
- B["Load test datasets and dataloaders"] --> C
- C["Load trained model"] --> D
- D["Set evaluation metrics"] --> E
- E["Evaluate model"] --> F
- F["Generate report"]
-```
-
-
----
-
-## Usage
-
-### CLI
-
-The following command will evaluate a rhythm model using the reference configuration.
-
-```bash
-heartkit --task rhythm --mode evaluate --config ./configuration.json
-```
-
-### Python
-
-The model can be evaluated using the following snippet:
-
-```py linenums="1"
-
-task = hk.TaskFactory.get("rhythm")
-
-params = hk.HKTaskParams(...)
-
-task.evaluate(params)
-
-```
-
-**Example configuration**
---8<-- "assets/usage/python-configuration.md"
-
----
-
-## Arguments
-
-Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) for the list of arguments that can be used with the `evaluate` command.
diff --git a/docs/modes/export.md b/docs/modes/export.md
deleted file mode 100644
index 30942844..00000000
--- a/docs/modes/export.md
+++ /dev/null
@@ -1,66 +0,0 @@
-# :material-export: Model Exporting
-
-## Introduction
-
-Export mode is used to convert the trained TensorFlow model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command will convert the TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file or by setting the `quantization` parameter in the code.
-
-
-
-1. Load the configuration data (e.g. `configuration.json`)
-1. Load the test data (e.g. `test.pkl`)
-1. Load the trained model (e.g. `model.keras`)
-1. Quantize the model (e.g. `16x8`)
-1. Convert the model (e.g. `TFL`, `TFLM`)
-1. Verify the models' outputs match
-1. Save artifacts (e.g. `model.tflite`)
-
-
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
-
-```mermaid
-flowchart TD
- A["Load configuration and test data"] --> B
- B["Load trained model"] --> C
- C["Quantize model"] --> D
- D["Convert model"] --> E
- E["Verify outputs"] --> F
- F["Save deployment artifacts"]
-```
-
----
-
-## Usage
-
-### CLI
-
-The following command will export a rhythm model using the reference configuration.
-
-```bash
-heartkit --task rhythm --mode export --config ./configuration.json
-```
-
-### Python
-
-The model can be evaluated using the following snippet:
-
-```py linenums="1"
-
-task = hk.TaskFactory.get("rhythm")
-
-params = hk.HKTaskParams(...)
-
-task.export(params)
-
-```
-
-**Example configuration**
---8<-- "assets/usage/python-configuration.md"
-
----
-
-## Arguments
-
-Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) for the list of arguments that can be used with the `export` command.
diff --git a/docs/modes/index.md b/docs/modes/index.md
deleted file mode 100644
index 81570bed..00000000
--- a/docs/modes/index.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Workflow overview
-
-## Introduction
-
-Rather than offering a handful of static models, heartKIT provides a complete framework designed to cover the entire design process of creating customized ML models well-suited for low-power, wearable applications. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.
-
-Each `Task` implementes routines for each of the modes: [download](#download), [train](#train), [evaluate](#evaluate), [export](#export), and [demo](#demo). These modes are designed to streamline the process of training, evaluating, exporting, and running task-level demonstrations on the trained models.
-
----
-
-## Choose a workflow step
-
-| Step | Use it to | Guide |
-| --- | --- | --- |
-| Download | Fetch datasets into your configured directory. | [Download datasets](./download.md) |
-| Train | Fit a model using your datasets, architecture and training settings. | [Train a model](./train.md) |
-| Evaluate | Measure the trained model on held-out data. | [Evaluate a model](./evaluate.md) |
-| Export | Convert a trained model for embedded inference. | [Export a model](./export.md) |
-| Demo | Run a task demonstration on a PC or supported evaluation board. | [Run a demo](./demo.md) |
-
----
-
-## [Download](./download.md)
-
-[Download mode](./download.md) is used to download the specified datasets for the task. The routine can be customized via the configuration file or by setting the parameters directly in the code. The download process involves fetching the dataset(s) from the specified source and storing them in the specified directory.
-
-## [Train](./train.md)
-
-[Train mode](./train.md) is used to train a model for the specified task and dataset. In this mode, the model is trained for a given task using the specified dataset(s), model architecture, and hyperparameters. The training process involves optimizing the model's parameters to maximize its performance on the training data.
-
-## [Evaluate](./evaluate.md)
-
-[Evaluate mode](./evaluate.md) is used to test the performance of the model on the reserved test set for the specified task. The routine can be customized via the configuration file or by setting the parameters directly in the code. The evaluation process involves testing the model's performance on the test data to measure its accuracy, precision, recall, and F1 score.
-
-## [Export](./export.md)
-
-[Export mode](./export.md) is used to convert the trained model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command will convert the TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match.
-
-## [Demo](./demo.md)
-
-[Demo mode](./demo.md) is used to run a task-level demonstration on the trained model using the specified backend inference engine (e.g. PC or EVB). This is useful to showcase the model's performance in real-time and to verify its accuracy in a real-world scenario.
diff --git a/docs/modes/train.md b/docs/modes/train.md
deleted file mode 100644
index 25560a86..00000000
--- a/docs/modes/train.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# :material-chart-ppf: Model Training
-
-## Introduction
-
-Each task provides a mode to train a model on the specified datasets and dataloaders. The training mode can be invoked either via CLI or within `heartkit` python package. At a high level, the training mode performs the following actions based on the provided configuration parameters:
-
-
-
-1. Load the configuration parameters (e.g. `configuration.json`)
-1. Load the desired datasets (e.g. `PtbxlDataset`)
-1. Load the corresponding task dataloaders (e.g. `PtbxlDataLoader`)
-1. Initialize custom model architecture (e.g. `tcn`)
-1. Define the metrics, loss, and optimizer (e.g. `accuracy`, `categorical_crossentropy`, `adam`)
-1. Train the model (e.g. `model.fit`)
-1. Save artifacts (e.g. `model.keras`)
-
-
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
-- -```mermaid -flowchart TD - A["Load configuration"] --> B - B["Load datasets and dataloaders"] --> C - C["Initialize model"] --> D - D["Set loss, metrics and optimizer"] --> E - E["Train model"] --> F - F["Save model and artifacts"] -``` - ---- - -## Usage - -### CLI - -The following command will train a rhythm model using the reference configuration. - -```bash -heartkit --task rhythm --mode train --config ./configuration.json -``` - -### Python - -The model can be trained using the following snippet: - -```py linenums="1" - -task = hk.TaskFactory.get("rhythm") - -params = hk.HKTaskParams(...) - -task.train(params) - -``` - -**Example configuration** ---8<-- "assets/usage/python-configuration.md" - - ---- - -## Arguments - -Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) for the list of arguments that can be used with the `train` command. - ---- - -## Logging - -__heartKIT__ provides built-in support for logging to several third-party services including [Weights & Biases](https://wandb.ai/site) (WANDB) and [TensorBoard](https://www.tensorflow.org/tensorboard). - -### WANDB - -The training mode is able to log all metrics and artifacts (aka models) to [Weights & Biases](https://wandb.ai/site) (WANDB). To enable WANDB logging, simply set environment variable `WANDB=1`. Remember to sign in prior to running experiments by running `wandb login`. - - -### TensorBoard - -The training mode is able to log all metrics to [TensorBoard](https://www.tensorflow.org/tensorboard). To enable TensorBoard logging, simply set environment variable `TENSORBOARD=1`. diff --git a/docs/overrides/main.html b/docs/overrides/main.html deleted file mode 100644 index 702c96bf..00000000 --- a/docs/overrides/main.html +++ /dev/null @@ -1,11 +0,0 @@ -{% extends "base.html" %} - -{% block content %} -{% if page.nb_url %} - - {% include ".icons/material/download.svg" %} - -{% endif %} - -{{ super() }} -{% endblock content %} diff --git a/docs/quickstart.md b/docs/quickstart.md deleted file mode 100644 index c8600726..00000000 --- a/docs/quickstart.md +++ /dev/null @@ -1,155 +0,0 @@ -# Quickstart - - -## Install heartKIT - -We provide several installation methods including pip, uv, and Docker. Install __heartKIT__ via pip/uv for the latest stable release or by cloning the GitHub repo for the most up-to-date. Additionally, a [VSCode Dev Container](https://code.visualstudio.com/docs/devcontainers/containers) is available and defined in [./.devcontainer](https://github.com/AmbiqAI/heartkit/tree/main/.devcontainer) to run in an isolated Docker environment. - -!!! install - - === "Git clone" - - Clone the repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package. In this mode, uv is recommended. - - ```bash - # Clone the repository - git clone https://github.com/AmbiqAI/heartkit.git - - # Navigate to the cloned directory - cd heartkit - - # Install the package in editable mode for development - uv sync - ``` - - When using editable mode via uv, be sure to activate the python environment: `source .venv/bin/activate`.
- On Windows using Powershell, use `.venv\Scripts\activate`. - - === "PyPI install" - - Install the heartKIT package using pip or uv. - Visit the Python Package Index (PyPI) for more details on the package: [https://pypi.org/project/heartkit/](https://pypi.org/project/heartkit/) - - ```bash - # Install with pip - pip install heartkit - ``` - - Or, if you prefer to use uv, you can install the package with the following command: - - ```bash - # Install with uv - uv add heartkit - ``` - - Alternatively, you can install the latest development version directly from the GitHub repository. Make sure to have the Git command-line tool installed on your system. The @main command installs the main branch and may be modified to another branch, i.e. @canary. - - ```bash - pip install git+https://github.com/AmbiqAI/heartkit.git@main - ``` - - Or, using uv: - - ```bash - uv add git+https://github.com/AmbiqAI/heartkit.git@main - ``` - - -## Requirements - -* [Python 3.12–3.13](https://www.python.org) -* [uv ^0.7.10+](https://docs.astral.sh/uv/getting-started/installation/) - -Check the project's [pyproject.toml](https://github.com/AmbiqAI/heartkit/blob/main/pyproject.toml) file for a list of up-to-date Python dependencies. Note that the installation methods above install all required dependencies. The following are optional dependencies only needed when running `demo` command using Ambiq's evaluation board (`EVB`) backend: - -* [Arm GNU Toolchain ^12.2](https://developer.arm.com/downloads/-/arm-gnu-toolchain-downloads) -* [Segger J-Link ^7.92](https://www.segger.com/downloads/jlink/) - -Once installed, __heartKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced experimentation. - ---- - -## Use heartKIT with CLI - -The heartKIT command line interface (CLI) allows for simple single-line commands to download datasets, train models, evaluate performance, and export models. The CLI requires no customization or Python code. You can simply run all the built-in tasks from the terminal with the __heartkit__ command. Check out the [CLI Guide](./usage/cli.md) to learn more about available options. - -!!! example - - === "Syntax" - Heartkit commands use the following syntax: - - ```bash - heartkit --mode [MODE] --task [TASK] --config [CONFIG] - ``` - - Or using short flags: - - ```bash - heartkit -m [MODE] -t [TASK] -c [CONFIG] - ``` - - Where: - - * `MODE` is one of `download`, `train`, `evaluate`, `export`, or `demo` - * `TASK` is one of `segmentation`, `rhythm`, `beat`, or `denoise` - * `CONFIG` is configuration as JSON content or file path - - === "Download" - Download datasets specified in the configuration file. - - ```bash - heartkit -m download -c ./download-datasets.json - ``` - - === "Train" - Train a rhythm model using the supplied configuration file. - - ```bash - heartkit -m train -t rhythm -c ./configuration.json - ``` - - === "Evaluate" - Evaluate the trained rhythm model using the supplied configuration file. - - ```bash - heartkit -m evaluate -t rhythm -c ./configuration.json - ``` - - === "Demo" - Run demo on trained rhythm model using the supplied configuration file. - - ```bash - heartkit -m demo -t rhythm -c ./configuration.json - ``` - -## Use heartKIT with Python - -The __heartKIT__ Python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for a variety of tasks. You can create custom datasets, models, and tasks and register them with corresponding factories and use them like built-in tasks. - -For example, you can create a custom task, train it, evaluate its performance on a validation set, and even export a quantized TensorFlow Lite model for deployment. Check out the [Python Guide](./usage/python.md) to learn more about using heartKIT as a Python package. - -!!! Example - - ```py linenums="1" - - import heartkit as hk - - params = hk.HKTaskParams(...) - - task = hk.TaskFactory.get("rhythm") - - task.download(params) # Download dataset(s) - - task.train(params) # Train the model - - task.evaluate(params) # Evaluate the model - - task.export(params) # Export to TFLite - - ``` - - **Configuration parameters** - --8<-- "assets/usage/python-configuration.md" - - ---- diff --git a/docs/tasks/beat.md b/docs/tasks/beat.md deleted file mode 100644 index c61f371f..00000000 --- a/docs/tasks/beat.md +++ /dev/null @@ -1,62 +0,0 @@ -# Beat Classification Task - -## Overview - -In beat classification, we classify individual beats as either normal or abnormal. Abnormal beats can be further classified as being either premature or escape beats as well as originating from the atria, junction, or ventricles. The objective of beat classification is to detect and classify these abnormal heart beats directly from ECG signals. - -
---8<-- "assets/tasks/beat/beat-example.html"
-
-
----
-
-## Characteristics
-
-| | Atrial | Junctional | Ventricular |
-| --- | --- | --- | --- |
-| Premature | __PAC__ P-wave: Different
QRS: Narrow (normal)
Aberrated: LBBB or RBBB | __PJC__
P-wave: None / retrograde
QRS: Narrow (normal)
Compensatory SA Pause | __PVC__
P-wave: None
QRS: Wide (> 120 ms)
Compensatory SA Pause | -| Escape | Atrial Escape | P-wave: Abnormal
QRS: Narrow (normal)
Ventricular rate: < 60 bpm
Junctional Escape
| P-wave: None
QRS: Narrow (normal)
Bradycardia (40-60 bpm)
Ventricular Escape | P-wave: None
QRS: Wide
Bradycardia (< 40 bpm) | - ---- - -## Dataloaders - -Dataloaders are available for the following datasets: - -* **[Icentia11k](../datasets/icentia11k.md)** -* **[PTB-XL](../datasets/ptbxl.md)** - ---- - -## Pre-trained Models - -The following table provides the latest performance and accuracy results for pre-trained beat models. Additional result details can be found in [Model Zoo → Beat](../zoo/index.md). - - ---8<-- "assets/zoo/beat/beat-model-zoo-table.md" - ---- - -## Target Classes - -Below outlines the classes available for beat classification. When training a model, the number of classes, mapping, and names must be provided. - ---8<-- "assets/tasks/beat/beat-classes.md" - -!!! example "Class Mapping" - - Below is an example of a class mapping for a 3-class beat model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped. - - ```jsonc - { - "num_classes": 3, - "class_names": ["QRS", "PAC", "PVC"], - "class_map": { - "0": 0, // Map Normal to QRS - "1": 1, // Map PAC to PAC - "2": 2, // Map PVC to PVC - } - } - ``` - ---- diff --git a/docs/tasks/diagnostic.md b/docs/tasks/diagnostic.md deleted file mode 100644 index beea2df2..00000000 --- a/docs/tasks/diagnostic.md +++ /dev/null @@ -1,5 +0,0 @@ -# Multi-Label Diagnostic Task - -The diagnostic task groups ECG findings into multiple labels. The diagram below shows the documented class hierarchy. - -{ width="480" } diff --git a/docs/tasks/index.md b/docs/tasks/index.md deleted file mode 100644 index de27d774..00000000 --- a/docs/tasks/index.md +++ /dev/null @@ -1,77 +0,0 @@ -# :simple-task: Tasks - -## Introduction - -heartKIT provides several built-in __heart-monitoring__ tasks. Each task is designed to address a unique aspect such as ECG denoising, segmentation, and rhythm/beat classification. The tasks are designed to be modular and can be used independently or in combination to address specific use cases. In addition to the built-in tasks, custom tasks can be created by extending the [HKTask](/heartkit/api/heartkit/tasks/task) base class and registering it with the task factory. - -
---8<-- "assets/tasks/segmentation/segmentation-example.html"
-
-
----
-
-## ECG Characteristics
-
-
-
-- Annotated ECG Signal
-
- { width="380" }
-
-
-
-- ECG Segments/Fiducials
- * __P-Wave__: Reflects atrial depolarization
- * __QRS Complex__: Reflects ventricular depolarization
- * __T-Wave__: Reflects ventricular repolarization
- * __U-Wave__: Reflects papillary muscle repolarization
- * __PR Interval__: Time from atrial depolarization to ventricular depolarization
- * __QT Interval__: Time from ventricular depolarization to repolarization
- * __QTc Interval__: Corrected QT interval
- * __ST Segment__: Time between ventricular depolarization and repolarization
- * __J-Point__: Junction between QRS complex and ST segment
-
-
----
-
-## PPG Characteristics
-
-* __Systolic Phase__: Reflects the contraction of the heart
-* __Diastolic Phase__: Reflects the relaxation of the heart
-* __Dicrotic Notch__: Reflects the closure of the aortic valve
-
----
-
-## Dataloaders
-
-Dataloaders are available for the following datasets:
-
-* **[LUDB](../datasets/ludb.md)**
-* **[QTDB](../datasets/qtdb.md)**
-* **[ECG Synthetic](../datasets/synthetic.md)**
-* **[PPG Synthetic](../datasets/synthetic.md)**
-
----
-
-## Pre-Trained Models
-
-The following table provides the latest performance and accuracy results for segmentation models. Additional result details can be found in [Model Zoo → Segmentation](../zoo/index.md).
-
---8<-- "assets/zoo/segmentation/segmentation-model-zoo-table.md"
-
----
-
-## Target Classes
-
-Below outlines the classes available for segmentation. When training a model, the number of classes, mapping, and names must be provided. This allows for the most flexibility in the segmentation task.
-
---8<-- "assets/tasks/segmentation/segmentation-classes.md"
-
-!!! example "Class Mapping Example"
-
- Below is an example of a class mapping for a 3-class segmentation model. The class map keys are the original class labels, and the values are the new class labels. Any class not included will be skipped.
-
- ```jsonc
- {
- "num_classes": 3,
- "class_names": ["None", "QRS", "Noise"],
- "class_map": {
- "0": 0, // Map None to None
- "1": 0, // Map P-wave to None
- "2": 1, // Map QRS to QRS
- "3": 0, // Map T-wave to None
- "4": 0, // Map U-wave to None
- "5": 2 // Map Noise to Noise
- }
- }
- ```
-
----
diff --git a/docs/usage/cli.md b/docs/usage/cli.md
deleted file mode 100644
index ff00d36d..00000000
--- a/docs/usage/cli.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# :octicons-terminal-24: heartKIT CLI
-
-
-
-```console
-$ heartkit --help
-
-heartKIT CLI Options:
- --task [segmentation, rhythm, beat, denoise]
- --mode [download, train, evaluate, export, demo]
- --config ["./path/to/config.json", or '{"raw: "json"}']
-```
-
-
-
-The heartKIT command line interface (CLI) makes it easy to run a variety of single-line commands without the need for writing any code. You can run all built-in tasks and modes from the terminal with the `heartkit` command. This is also useful to reproduce [Model Zoo](../zoo/index.md) results.
-
-!!! example
-
- === "Syntax"
- Heartkit commands use the following syntax:
-
- ```bash
- heartkit --mode [MODE] --task [TASK] --config [CONFIG]
- ```
-
- Or using short flags:
-
- ```bash
- heartkit -m [MODE] -t [TASK] -c [CONFIG]
- ```
-
- Where:
-
- * `MODE` is one of `download`, `train`, `evaluate`, `export`, or `demo`
- * `TASK` is one of `segmentation`, `rhythm`, `beat`, or `denoise`
- * `CONFIG` is configuration as JSON content or file path
-
- === "Download"
- Download datasets specified in the configuration file.
-
- ```bash
- heartkit -m download -c ./configs/download-datasets.json
- ```
-
- === "Train"
- Train a rhythm model using the supplied configuration file.
-
- ```bash
- heartkit -m train -t rhythm -c ./configuration.json
- ```
-
- === "Evaluate"
- Evaluate the rhythm model using the supplied configuration file.
-
- ```bash
- heartkit -m evaluate -t rhythm -c ./configuration.json
- ```
-
- === "Demo"
- Run demo on trained rhythm model using the supplied configuration file.
-
- ```bash
- heartkit -m demo -t rhythm -c ./configuration.json
- ```
-
-
-!!! Note "Configuration File"
-
- The configuration file is a JSON file that contains all the necessary parameters for the task. The configuration file can be passed as a file path or as a JSON string. In addition, a single configuration file can be used for all `modes`- only needed parameters will be extracted for the given `mode` running. Please refer to the [Configuration](../modes/configuration.md) section for more details.
-
----
-
-## [Download](../modes/download.md)
-
-The [download](../modes/download.md) command is used to download all datasets specified in the configuration file. Please refer to [Datasets](../datasets/index.md) for details on the available datasets.
-
-
-!!! Example "CLI"
-
- The following command will download and prepare all datasets specified in configuration JSON file.
-
- ```bash
- heartkit --task rhythm --mode download --config ./configs/download-datasets.json
- ```
-
----
-
-## [Train](../modes/train.md)
-
-The [train](../modes/train.md) command is used to train a heartKIT model for the specified `task` and `dataset`. Each task provides a reference routine for training the model. The routine can be customized via the configuration file. Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) to see supported options.
-
-!!! Example "CLI"
-
- The following command will train a rhythm model using the reference configuration:
-
- ```bash
- heartkit --task rhythm --mode train --config ./configuration.json
- ```
-
----
-
-## [Evaluate](../modes/evaluate.md)
-
-The [evaluate](../modes/evaluate.md) command will test the performance of the model on the reserved test sets for the specified `task`. The routine can be customized via the configuration file. Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) to see supported options.
-
-!!! example "CLI"
-
- The following command will test the rhythm model using the reference configuration:
-
- ```bash
- heartkit --task rhythm --mode evaluate --config ./configuration.json
- ```
-
----
-
-## [Export](../modes/export.md)
-
-The [export](../modes/export.md) command will convert the trained TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file. Once converted, the TFLM header file will be copied to location specified by `tflm_file`.
-
-!!! example "CLI"
-
- The following command will export the rhythm model to TF Lite and TFLM:
-
- ```bash
- heartkit --task rhythm --mode export --config ./configuration.json
- ```
-
----
-
-## [Demo](../modes/demo.md)
-
-
-The [demo](../modes/demo.md) command is used to run a task-level demonstration using either the PC or EVB as backend inference engine.
-
-!!! Example "CLI"
-
- The following command will run a demo on the trained rhythm model using the same supplied configuration file.
-
- ```bash
- heartkit --task rhythm --mode demo --config ./configuration.json
- ```
-
----
diff --git a/docs/usage/python.md b/docs/usage/python.md
deleted file mode 100644
index be1c941f..00000000
--- a/docs/usage/python.md
+++ /dev/null
@@ -1,87 +0,0 @@
-# :simple-python: Python Usage
-
-__heartKIT__ python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for both built-in taks and custom tasks. In addition, custom datasets and model architectures can be created and registered with corresponding factories.
-
-## Overview
-
-The main components of heartKIT include the following:
-
-### [Tasks](../tasks/index.md)
-
-A [Task](../tasks/index.md) inherits from the [HKTask](/heartkit/api/heartkit/tasks/task) class and provides implementations for each of the main modes: download, train, evaluate, export, and demo. Each mode is provided with a set of parameters defined by [HKTaskParams](/heartkit/api/heartkit/defines). Additional task-specific parameters can be extended to the `HKTaskParams` class. These tasks are then registered and accessed via the `TaskFactory` using a unique task name as the key and the custom Task class as the value.
-
-```py linenums="1"
-import heartkit as hk
-
-task = hk.TaskFactory.get('rhythm')
-```
-
-### [Datasets](../datasets/index.md)
-
-A dataset inherits from the [HKDataset](/heartkit/api/heartkit/datasets/dataset) class and provides implementations for downloading, preparing, and loading the dataset. Each dataset is provided with a set of custom parameters for initialization. Since each task will require specific transformations of the data, the dataset class provides only a general interface for loading the data. Each task must then provide a set of corresponding [HKDataloader](/heartkit/api/heartkit/datasets/dataloader) classes to transform the dataset into a format that can be used by the task. The datasets are registered and accessed via the [DatasetFactory](/heartkit/api/heartkit/datasets/factory) using a unique dataset name as the key and the Dataset class as the value. Each Task can then create its own `DataloaderFactory` that will provide a corresponding dataloader for each supported dataset. The Task's `DataloaderFactory` should use the same dataset names as the DatasetFactory to ensure that the correct dataloader is used for each dataset.
-
-```py linenums="1"
-import heartkit as hk
-
-ds = hk.DatasetFactory.get('ecg-synthetic')(num_pts=100)
-```
-
-### [Models](../models/index.md)
-
-Lastly, heartKIT leverages [helia-edge's](https://ambiqai.github.io/helia-edge/) customizable model architectures. To enable creating custom network topologies from configuration files, heartKIT provides a `ModelFactory` that allows you to create models by specifying the model key and the model parameters. Each item in the factory is a callable that takes a `keras.Input`, model parameters, and number of classes as arguments and returns a `keras.Model`.
-
-```
-import keras
-import heartkit as hk
-
-inputs = keras.Input((256, 1), dtype="float32")
-num_classes = 4
-model_params = dict(...)
-
-model = hk.ModelFactory.get('tcn')(
- inputs=inputs,
- params=model_params,
- num_classes=num_classes
-)
-
-```
-
-## Usage
-
-### Running a built-in task w/ existing datasets
-
-1. Create a task configuration file defining the model, datasets, class labels, mode parameters, and so on. Have a look at the [HKTaskParams](../modes/configuration.md#hktaskparams) for more details on the available parameters.
-
-2. Leverage `TaskFactory` to get the desired built-in task.
-
-3. Run the task's main modes: `download`, `train`, `evaluate`, `export`, and/or `demo`.
-
-
-```py linenums="1"
-
-import heartkit as hk
-
-params = hk.HKTaskParams(...)
-
-task = hk.TaskFactory.get("rhythm")
-
-task.download(params) # Download dataset(s)
-
-task.train(params) # Train the model
-
-task.evaluate(params) # Evaluate the model
-
-task.export(params) # Export to TFLite
-
-```
-
-**Example configuration**
---8<-- "assets/usage/python-configuration.md"
-
-### Running a custom task w/ custom datasets
-
-To create a custom task, check out the [Bring-Your-Own-Task Guide](../tasks/byot.md).
-
-To create a custom dataset, check out the [Bring-Your-Own-Dataset Guide](../datasets/byod.md).
-
----
diff --git a/mkdocs.yml b/mkdocs.yml
deleted file mode 100644
index d7090599..00000000
--- a/mkdocs.yml
+++ /dev/null
@@ -1,231 +0,0 @@
-site_name: heartKIT
-site_dir: "site"
-site_url: "https://ambiqai.github.io/heartkit"
-site_description: heartKIT, AI Development Kit for AI heart monitoring on the edge
-repo_name: AmbiqAI/hearkit
-repo_url: https://github.com/AmbiqAI/heartkit
-copyright: Copyright © 2023 Ambiq
-
-nav:
- - Home:
- - Home: index.md
- - Quickstart: quickstart.md
- - Tasks:
- - tasks/index.md
- - Denoise: tasks/denoise.md
- - Segmentation: tasks/segmentation.md
- - Rhythm: tasks/rhythm.md
- - Beat: tasks/beat.md
- # - Diagnostic: tasks/diagnostic.md
- - BYOT: tasks/byot.md
- - Modes:
- - modes/index.md
- - Configuration: modes/configuration.md
- - Download: modes/download.md
- - Train: modes/train.md
- - Evaluate: modes/evaluate.md
- - Export: modes/export.md
- - Demo: modes/demo.md
- - Datasets:
- - datasets/index.md
- - Models:
- - models/index.md
- - Model Zoo:
- - zoo/index.md
- - Guides:
- - guides/index.md
- - API: api/
-
- - Quickstart:
- - quickstart.md
- - Usage:
- - CLI: usage/cli.md
- - Python: usage/python.md
-
- - Tasks:
- - tasks/index.md
- - Denoise: tasks/denoise.md
- - Segmentation: tasks/segmentation.md
- - Rhythm: tasks/rhythm.md
- - Beat: tasks/beat.md
- # - Diagnostic: tasks/diagnostic.md
- - BYOT: tasks/byot.md
-
- - Modes:
- - modes/index.md
- - Configuration: modes/configuration.md
- - Download: modes/download.md
- - Train: modes/train.md
- - Evaluate: modes/evaluate.md
- - Export: modes/export.md
- - Demo: modes/demo.md
-
- - Datasets:
- - datasets/index.md
- - Icentia11k: datasets/icentia11k.md
- - Synthetic: datasets/synthetic.md
- - QTDB: datasets/qtdb.md
- - LUDB: datasets/ludb.md
- - LSAD: datasets/lsad.md
- - PTB-XL: datasets/ptbxl.md
- - MIT-BIH: datasets/mitbih.md
- - BYOD: datasets/byod.md
-
- - Models:
- - models/index.md
- - BYOM: models/byom.md
-
- - Model Zoo:
- - zoo/index.md
- - ARR-2-EFF-SM: zoo/arr-2-eff-sm.md
- - ARR-4-EFF-SM: zoo/arr-4-eff-sm.md
- - BEAT-2-EFF-SM: zoo/beat-2-eff-sm.md
- - BEAT-3-EFF-SM: zoo/beat-3-eff-sm.md
- - DEN-TCN-LG: zoo/den-tcn-lg.md
- - DEN-TCN-SM: zoo/den-tcn-sm.md
- - DEN-PPG-TCN-SM: zoo/den-ppg-tcn-sm.md
- - SEG-2-TCN-SM: zoo/seg-2-tcn-sm.md
- - SEG-4-TCN-SM: zoo/seg-4-tcn-sm.md
- - SEG-4-TCN-LG: zoo/seg-4-tcn-lg.md
- - SEG-PPG-2-TCN-SM: zoo/seg-ppg-2-tcn-sm.md
-
- - Guides:
- - guides/index.md
- - Core Concepts:
- - BYOT: guides/byot.ipynb
- - Notebook Examples:
- - Train Arrhythmia Model: guides/train-arrhythmia-model.ipynb
- - Train ECG Denoiser: guides/train-ecg-denoiser.ipynb
- - ECG Foundation Model: guides/ecg-foundation-model.ipynb
- - ECG Segmentation Model: guides/train-ecg-segmentation.ipynb
- - Hardware Guides →: https://ambiqai.github.io/tileio-docs/demos/
-
- - API: api/
-
-theme:
- name: material
- favicon: assets/favicon.png
- logo: assets/logo-white.png
- custom_dir: docs/overrides
- icon:
- repo: fontawesome/brands/github-alt
- font:
- text: Roboto
- code: Roboto Mono
- palette:
- - media: "(prefers-color-scheme)"
- primary: red
- accent: indigo
- toggle:
- icon: material/brightness-auto
- name: Use system theme
- - media: "(prefers-color-scheme: light)"
- scheme: default
- primary: red
- accent: indigo
- toggle:
- icon: material/brightness-7
- name: Switch to light theme
- - media: "(prefers-color-scheme: dark)"
- scheme: slate
- primary: red
- accent: indigo
- toggle:
- icon: material/brightness-4
- name: Switch to dark theme
- features:
- - content.code.annotate
- - content.code.copy
- - content.tooltips
- - search.highlight
- - search.share
- - search.suggest
- - toc.follow
- - navigation.top
- - navigation.tabs
- - navigation.tabs.sticky
- - navigation.prune
- - navigation.path
-
- - navigation.footer
- - navigation.tracking
- - navigation.instant
- - navigation.instant.progress
- - navigation.indexes
- - navigation.sections # navigation.expand or navigation.sections
- - content.tabs.link # all code tabs change simultaneously
-
-plugins:
- - search
- - gen-files:
- scripts:
- - scripts/gen_ref_pages.py
- - literate-nav:
- nav_file: summary.md
- - mkdocstrings:
- handlers:
- python:
- import:
- - https://docs.python.org/3/objects.inv
- - https://numpy.org/doc/stable/objects.inv
- options:
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- docstring_style: google
- docstring_section_style: list
- line_length: 120
- show_root_heading: false
- heading_level: 2
- merge_init_into_class: true
- show_root_full_path: false
- parameter_headings: true
- show_symbol_type_heading: true
- group_by_category: true
- show_category_heading: true
- modernize_annotations: true
- members_order: source
- filters: ["!^_", "^__init__$"]
- separate_signature: true
- show_signature: true
- show_signature_annotations: true
- show_docstring_attributes: true
- show_source: true
-
- - mkdocs-jupyter:
- include_requirejs: true
- include_source: true
-
-extra_css:
- - css/mkdocstrings.css
- - css/termynal.css
- - css/custom.css
-
-extra_javascript:
- - js/termynal.js
- - js/custom.js
- - https://cdn.plot.ly/plotly-2.33.0.min.js
-
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- - attr_list
- - pymdownx.arithmatex:
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- - pymdownx.details
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- - pymdownx.inlinehilite
- - pymdownx.snippets:
- base_path:
- - 'docs/'
- - 'heartkit/'
- - pymdownx.superfences:
- custom_fences:
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- class: mermaid
- - pymdownx.tabbed:
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- - pymdownx.emoji:
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- - tables
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diff --git a/notebooks/byot.ipynb b/notebooks/byot.ipynb
index 085a4310..6b2b6f7e 100644
--- a/notebooks/byot.ipynb
+++ b/notebooks/byot.ipynb
@@ -8,12 +8,12 @@
"\n",
"\n",
"- \n",
- "\n",
+ "\n",
" View in Colab\n",
"\n",
"\n",
"- \n",
- "\n",
+ "\n",
" GitHub source\n",
"\n",
"\n",
diff --git a/notebooks/ecg-foundation-model.ipynb b/notebooks/ecg-foundation-model.ipynb
index ca5dde91..fb2f4d88 100644
--- a/notebooks/ecg-foundation-model.ipynb
+++ b/notebooks/ecg-foundation-model.ipynb
@@ -8,12 +8,12 @@
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\n",
"\n",
"- \n",
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"\n",
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"- \n",
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"\n",
"\n",
diff --git a/notebooks/train-arrhythmia-model.ipynb b/notebooks/train-arrhythmia-model.ipynb
index b88eafd8..5c2e72c3 100644
--- a/notebooks/train-arrhythmia-model.ipynb
+++ b/notebooks/train-arrhythmia-model.ipynb
@@ -8,12 +8,12 @@
"
\n",
"\n",
"- \n",
- "\n",
+ "\n",
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"\n",
"\n",
"- \n",
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+ "\n",
" GitHub source\n",
"\n",
"\n",
diff --git a/notebooks/train-ecg-denoiser.ipynb b/notebooks/train-ecg-denoiser.ipynb
index 0bba07d9..7eb67069 100644
--- a/notebooks/train-ecg-denoiser.ipynb
+++ b/notebooks/train-ecg-denoiser.ipynb
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"
\n",
"\n",
"- \n",
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index f3efbd51..d50ee821 100644
--- a/notebooks/train-ecg-segmentation.ipynb
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@@ -8,12 +8,12 @@
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"- \n",
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"\n",
"\n",
diff --git a/pyproject.toml b/pyproject.toml
index c2b7d40f..e8650e7f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,18 +48,6 @@ dev = [
"pydot>=2.0.0,<3.0",
"ruff>=0.5.1,<1.0",
]
-docs = [
- "mkdocs>=1.5.3,<2.0",
- "mkdocs-material>=9.5.8,<10.0",
- "mkdocstrings-python>=1.8.0,<2.0",
- "markdown-include>=0.8.1,<1.0",
- "mkdocs-exclude>=1.0.2,<2.0",
- "mkdocs-jupyter>=0.24.8,<1.0",
- "mkdocs-gen-files>=0.5.0,<1.0",
- "mkdocs-section-index>=0.3.9,<1.0",
- "mkdocs-literate-nav>=0.6.1,<1.0",
- "black>=24.8.0,<25.0",
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deleted file mode 100644
index f7b01926..00000000
--- a/scripts/gen_ref_pages.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""Generate the code reference pages and navigation."""
-
-from pathlib import Path
-
-import mkdocs_gen_files
-
-nav = mkdocs_gen_files.Nav()
-
-root = Path(__file__).parent.parent
-src = root
-project = src / "heartkit"
-
-for path in sorted(project.rglob("*.py")):
- module_path = path.relative_to(src).with_suffix("")
- doc_path = path.relative_to(src).with_suffix(".md")
- full_doc_path = Path("api", doc_path)
-
- parts = tuple(module_path.parts)
-
- if parts[-1] == "__init__":
- parts = parts[:-1]
- doc_path = doc_path.with_name("index.md")
- full_doc_path = full_doc_path.with_name("index.md")
- elif parts[-1] == "__main__":
- continue
-
- nav[parts] = doc_path.as_posix()
-
- with mkdocs_gen_files.open(full_doc_path, "w") as fd:
- ident = ".".join(parts)
- fd.write(f"::: {ident}")
-
- mkdocs_gen_files.set_edit_path(full_doc_path, path.relative_to(root))
-
-with mkdocs_gen_files.open("api/summary.md", "w") as nav_file:
- nav_file.writelines(nav.build_literate_nav())
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