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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
}
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