Upload example.ipynb

example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "299923dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import sys\n",
+    "import os\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "from huggingface_hub import snapshot_download"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88f1cc80",
+   "metadata": {},
+   "source": [
+    "### Download Inference Code, Model Checkpoint and Example Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da55e6c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inference_path = hf_hub_download(repo_id=\"longpollehn/tfv6_navsim\", filename=\"ltfv6.py\")\n",
+    "config_path = hf_hub_download(repo_id=\"longpollehn/tfv6_navsim\", filename=\"config.json\")\n",
+    "model_path = hf_hub_download(repo_id=\"longpollehn/tfv6_navsim\", filename=\"model_0060.pth\")\n",
+    "data_path = snapshot_download(repo_id=\"longpollehn/tfv6_navsim\", allow_patterns=\"data/*\")\n",
+    "\n",
+    "sys.path.insert(0, os.path.dirname(inference_path))\n",
+    "\n",
+    "from ltfv6 import NavsimData, load_tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e940c04b",
+   "metadata": {},
+   "source": [
+    "### Load Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afa5d128",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model = load_tf(model_path, device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1441cf9b",
+   "metadata": {},
+   "source": [
+    "### Example data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "691ad60f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = NavsimData(root=data_path, config=model.config)\n",
+    "\n",
+    "sample_index = 5\n",
+    "data = dataset[sample_index]\n",
+    "data = torch.utils.data._utils.collate.default_collate([data])\n",
+    "data = {k: v.to(device) for k, v in data.items()}\n",
+    "\n",
+    "plt.imshow(np.transpose(data[\"rgb\"][0].cpu().numpy(), (1, 2, 0)).astype(np.uint8))\n",
+    "plt.show()\n",
+    "\n",
+    "print(\n",
+    "    f\"Inputs: speed={data['speed'][0].item():.2f} m/s, acceleration={data['acceleration'][0].item():.2f} m/s², command={data['command'][0].cpu().numpy()}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347c8ac1",
+   "metadata": {},
+   "source": [
+    "### Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "388fd167",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = model(data)\n",
+    "waypoints = prediction.pred_future_waypoints\n",
+    "headings = prediction.pred_headings\n",
+    "\n",
+    "# Model was trained in CARLA coordinate system, convert to NavSim/NuPlan coordinate system\n",
+    "waypoints[:, :, 1] *= -1  # Invert Y axis\n",
+    "headings *= -1\n",
+    "\n",
+    "plt.plot(waypoints[0, :, 0].cpu().detach().numpy(), waypoints[0, :, 1].cpu().detach().numpy(), marker=\"o\")\n",
+    "plt.xlim(-32, 32)\n",
+    "plt.ylim(-32, 32)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lead",
+   "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": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}