mirror of
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791 lines
816 KiB
Plaintext
791 lines
816 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# RLearN Model Evaluation\n",
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"\n",
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"This notebook provides comprehensive evaluation of the RLearN (Video-Language Conditioned Reward Model) using:\n",
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"\n",
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"1. **VOC-S (Value-Order Correlation for Success)**: Measures whether per-frame rewards increase as successful execution unfolds\n",
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"2. **Success vs Failure Detection**: Tests the model's ability to distinguish correct vs incorrect language conditions\n",
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"3. **Live Reward Visualization**: Shows predicted rewards alongside video frames\n",
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"\n",
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"## Requirements\n",
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"\n",
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"```bash\n",
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"pip install matplotlib seaborn plotly ipywidgets\n",
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"```\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/Users/pepijnkooijmans/Documents/GitHub/lerobot\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/pepijnkooijmans/miniconda3/envs/lerobot/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: This is now an optional IPython functionality, setting dhist requires you to install the `pickleshare` library.\n",
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" self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
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]
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}
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],
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"source": [
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"cd /Users/pepijnkooijmans/Documents/GitHub/lerobot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✓ Imports successful\n"
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]
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}
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],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"\n",
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"# Add src to path for imports\n",
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"sys.path.append(str(Path.cwd().parent / \"src\"))\n",
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"\n",
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"import warnings\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import torch\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"\n",
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"# LeRobot imports\n",
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"from lerobot.constants import OBS_IMAGES, OBS_LANGUAGE\n",
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"from lerobot.datasets.lerobot_dataset import LeRobotDataset\n",
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"from lerobot.policies.rlearn.evaluation import (\n",
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" RLearnEvaluator,\n",
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")\n",
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"from lerobot.policies.rlearn.modeling_rlearn import RLearNPolicy\n",
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"\n",
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"print(\"✓ Imports successful\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Setup Model and Dataset\n",
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"\n",
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"Load your trained RLearN model and the dataset for evaluation.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using device: mps\n",
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"Loading dataset...\n",
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"Dataset loaded: 10 episodes, 1175 frames\n",
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"Features: ['observation.images.image', 'observation.state', 'action', 'timestamp', 'frame_index', 'episode_index', 'index', 'task_index', 'next.reward']\n",
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"FPS: 10\n"
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]
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}
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],
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"source": [
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"# Configuration\n",
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"DATASET_REPO = \"pepijn223/rewards_bc_z3\" # Change to your dataset\n",
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"MODEL_PATH = \"pepijn223/rlearn_rewards_bc_z_12\" # Change to your model checkpoint\n",
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"DEVICE = \"cuda\" if torch.cuda.is_available() else \"mps\"\n",
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"NUM_EVAL_EPISODES = 10 # Number of episodes for evaluation\n",
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"\n",
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"print(f\"Using device: {DEVICE}\")\n",
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"\n",
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"# Load dataset\n",
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"print(\"Loading dataset...\")\n",
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"dataset = LeRobotDataset(\n",
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" repo_id=DATASET_REPO,\n",
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" episodes=list(range(10)), # Load first 10 episodes for evaluation\n",
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" download_videos=True,\n",
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")\n",
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"\n",
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"print(f\"Dataset loaded: {dataset.num_episodes} episodes, {dataset.num_frames} frames\")\n",
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"print(f\"Features: {list(dataset.features.keys())}\")\n",
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"print(f\"FPS: {dataset.fps}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Setting up model...\n",
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"Loading trained model from Hugging Face Hub: pepijn223/rlearn_rewards_bc_z_12\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:root:Device 'cuda' is not available. Switching to 'mps'.\n",
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"WARNING:root:Device 'cuda' is not available. Switching to 'mps'.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✓ Model ready on mps\n",
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" Parameters: 899,395,076\n",
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" Trainable: 17,868,292\n"
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]
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}
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],
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"source": [
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"# Load or create model\n",
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"print(\"Setting up model...\")\n",
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"\n",
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"print(f\"Loading trained model from Hugging Face Hub: {MODEL_PATH}\")\n",
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"model = RLearNPolicy.from_pretrained(MODEL_PATH)\n",
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"\n",
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"model = model.to(DEVICE)\n",
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"model.eval()\n",
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"\n",
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"print(f\"✓ Model ready on {DEVICE}\")\n",
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"print(f\" Parameters: {sum(p.numel() for p in model.parameters()):,}\")\n",
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"print(f\" Trainable: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. VOC-S Evaluation\n",
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"\n",
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"Evaluate Value-Order Correlation for Success - measures whether predicted rewards correlate with temporal progress.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running VOC-S evaluation...\n",
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"Evaluating VOC-S on 10 episodes...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Computing VOC-S: 0%| | 0/10 [00:54<?, ?it/s]\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[9], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Run VOC-S evaluation\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRunning VOC-S evaluation...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m voc_results \u001b[38;5;241m=\u001b[39m \u001b[43mevaluator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_voc_s\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_episodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNUM_EVAL_EPISODES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_interquartile_mean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m 8\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m=\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVOC-S RESULTS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/evaluation.py:437\u001b[0m, in \u001b[0;36mRLearnEvaluator.evaluate_voc_s\u001b[0;34m(self, dataset, num_episodes, use_interquartile_mean)\u001b[0m\n\u001b[1;32m 434\u001b[0m frames_tensor \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstack(frames) \u001b[38;5;66;03m# (T, C, H, W)\u001b[39;00m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;66;03m# Predict rewards\u001b[39;00m\n\u001b[0;32m--> 437\u001b[0m episode_rewards \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_episode_rewards\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframes_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlanguage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 438\u001b[0m predicted_rewards\u001b[38;5;241m.\u001b[39mappend(episode_rewards)\n\u001b[1;32m 440\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/evaluation.py:300\u001b[0m, in \u001b[0;36mRLearnEvaluator.predict_episode_rewards\u001b[0;34m(self, frames, language, batch_size)\u001b[0m\n\u001b[1;32m 294\u001b[0m batch \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 295\u001b[0m OBS_IMAGES: batch_sequences, \u001b[38;5;66;03m# (B, T, C, H, W) format expected by model\u001b[39;00m\n\u001b[1;32m 296\u001b[0m OBS_LANGUAGE: [language] \u001b[38;5;241m*\u001b[39m batch_sequences\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 297\u001b[0m }\n\u001b[1;32m 299\u001b[0m \u001b[38;5;66;03m# Predict rewards - model returns (B, T') but we want the last timestep for each sequence\u001b[39;00m\n\u001b[0;32m--> 300\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_rewards\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# (B, T')\u001b[39;00m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;66;03m# Take the last timestep prediction for each sequence (represents current frame reward)\u001b[39;00m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m values\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/modeling_rlearn.py:235\u001b[0m, in \u001b[0;36mRLearNPolicy.predict_rewards\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 232\u001b[0m pixel_values \u001b[38;5;241m=\u001b[39m proc_out[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvision_encoder\u001b[38;5;241m.\u001b[39mparameters())\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m 234\u001b[0m \u001b[38;5;66;03m# Encode frames through visual tower per frame\u001b[39;00m\n\u001b[0;32m--> 235\u001b[0m vision_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_values\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;66;03m# Extract CLS tokens for temporal modeling\u001b[39;00m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(vision_outputs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlast_hidden_state\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1760\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1761\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/utils/generic.py:943\u001b[0m, in \u001b[0;36mcan_return_tuple.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 940\u001b[0m set_attribute_for_modules(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_is_top_level_module\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 942\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 943\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 944\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_requested_to_return_tuple \u001b[38;5;129;01mor\u001b[39;00m (is_configured_to_return_tuple \u001b[38;5;129;01mand\u001b[39;00m is_top_level_module):\n\u001b[1;32m 945\u001b[0m output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mto_tuple()\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:777\u001b[0m, in \u001b[0;36mSiglipVisionTransformer.forward\u001b[0;34m(self, pixel_values, output_attentions, output_hidden_states, interpolate_pos_encoding)\u001b[0m\n\u001b[1;32m 771\u001b[0m output_hidden_states \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 772\u001b[0m output_hidden_states \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39moutput_hidden_states\n\u001b[1;32m 773\u001b[0m )\n\u001b[1;32m 775\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(pixel_values, interpolate_pos_encoding\u001b[38;5;241m=\u001b[39minterpolate_pos_encoding)\n\u001b[0;32m--> 777\u001b[0m encoder_outputs: BaseModelOutput \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 778\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 779\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 780\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 781\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 783\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m encoder_outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state\n\u001b[1;32m 784\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_layernorm(last_hidden_state)\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1760\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1761\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/utils/generic.py:943\u001b[0m, in \u001b[0;36mcan_return_tuple.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 940\u001b[0m set_attribute_for_modules(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_is_top_level_module\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 942\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 943\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 944\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_requested_to_return_tuple \u001b[38;5;129;01mor\u001b[39;00m (is_configured_to_return_tuple \u001b[38;5;129;01mand\u001b[39;00m is_top_level_module):\n\u001b[1;32m 945\u001b[0m output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mto_tuple()\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:608\u001b[0m, in \u001b[0;36mSiglipEncoder.forward\u001b[0;34m(self, inputs_embeds, attention_mask, output_attentions, output_hidden_states)\u001b[0m\n\u001b[1;32m 605\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states:\n\u001b[1;32m 606\u001b[0m encoder_states \u001b[38;5;241m=\u001b[39m encoder_states \u001b[38;5;241m+\u001b[39m (hidden_states,)\n\u001b[0;32m--> 608\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mencoder_layer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 609\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 610\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 611\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 612\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 614\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 616\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
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||
"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/modeling_layers.py:83\u001b[0m, in \u001b[0;36mGradientCheckpointingLayer.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 80\u001b[0m logger\u001b[38;5;241m.\u001b[39mwarning(message)\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(partial(\u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), \u001b[38;5;241m*\u001b[39margs)\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
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"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1760\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1761\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
|
||
"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:463\u001b[0m, in \u001b[0;36mSiglipEncoderLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m 460\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[1;32m 462\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer_norm1(hidden_states)\n\u001b[0;32m--> 463\u001b[0m hidden_states, attn_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself_attn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 464\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 465\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 466\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 467\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 468\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n\u001b[1;32m 470\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n",
|
||
"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1760\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1761\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
|
||
"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:399\u001b[0m, in \u001b[0;36mSiglipAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 397\u001b[0m attention_interface \u001b[38;5;241m=\u001b[39m ALL_ATTENTION_FUNCTIONS[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39m_attn_implementation]\n\u001b[0;32m--> 399\u001b[0m attn_output, attn_weights \u001b[38;5;241m=\u001b[39m \u001b[43mattention_interface\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 400\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 401\u001b[0m \u001b[43m \u001b[49m\u001b[43mqueries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 402\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 403\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 404\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 405\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_causal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_causal\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[43m \u001b[49m\u001b[43mscaling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 407\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 408\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 410\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m attn_output\u001b[38;5;241m.\u001b[39mreshape(batch_size, seq_length, embed_dim)\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[1;32m 411\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mout_proj(attn_output)\n",
|
||
"File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/integrations/sdpa_attention.py:66\u001b[0m, in \u001b[0;36msdpa_attention_forward\u001b[0;34m(module, query, key, value, attention_mask, dropout, scaling, is_causal, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mis_tracing() \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(is_causal, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[1;32m 64\u001b[0m is_causal \u001b[38;5;241m=\u001b[39m is_causal\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m---> 66\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctional\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscaled_dot_product_attention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[43m \u001b[49m\u001b[43mattn_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropout_p\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaling\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_causal\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_causal\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 75\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m attn_output\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m attn_output, \u001b[38;5;28;01mNone\u001b[39;00m\n",
|
||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create evaluator\n",
|
||
"evaluator = RLearnEvaluator(model, device=DEVICE)\n",
|
||
"\n",
|
||
"# Run VOC-S evaluation\n",
|
||
"print(\"Running VOC-S evaluation...\")\n",
|
||
"voc_results = evaluator.evaluate_voc_s(\n",
|
||
" dataset=dataset, num_episodes=NUM_EVAL_EPISODES, use_interquartile_mean=True\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\" * 50)\n",
|
||
"print(\"VOC-S RESULTS\")\n",
|
||
"print(\"=\" * 50)\n",
|
||
"print(f\"Mean Correlation: {voc_results['voc_s_mean']:.4f}\")\n",
|
||
"print(f\"Std Correlation: {voc_results['voc_s_std']:.4f}\")\n",
|
||
"print(f\"IQM Correlation: {voc_results['voc_s_iqm']:.4f}\")\n",
|
||
"print(f\"Episodes: {voc_results['num_episodes']}\")\n",
|
||
"print(\"=\" * 50)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3. Detailed reward visualization"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Visualizing first 4 episodes...\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Detailed Episode Statistics:\n",
|
||
"================================================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Static visualization function that works well in VS Code\n",
|
||
"def get_episode_data(episode_idx, max_frames=32):\n",
|
||
" \"\"\"Extract frames, language, and predict rewards for an episode.\"\"\"\n",
|
||
" try:\n",
|
||
" # Get episode data\n",
|
||
" ep_start = dataset.episode_data_index[\"from\"][episode_idx].item()\n",
|
||
" ep_end = dataset.episode_data_index[\"to\"][episode_idx].item()\n",
|
||
" episode_length = min(ep_end - ep_start, max_frames)\n",
|
||
"\n",
|
||
" # Collect frames and get language\n",
|
||
" frames = []\n",
|
||
" language = None\n",
|
||
"\n",
|
||
" for frame_idx in range(episode_length):\n",
|
||
" global_idx = ep_start + frame_idx\n",
|
||
" frame_data = dataset[global_idx]\n",
|
||
"\n",
|
||
" # Extract image\n",
|
||
" if OBS_IMAGES in frame_data:\n",
|
||
" img = frame_data[OBS_IMAGES]\n",
|
||
" else:\n",
|
||
" img_keys = [k for k in frame_data.keys() if \"image\" in k.lower()]\n",
|
||
" if img_keys:\n",
|
||
" img = frame_data[img_keys[0]]\n",
|
||
" else:\n",
|
||
" continue\n",
|
||
"\n",
|
||
" if isinstance(img, np.ndarray):\n",
|
||
" img = torch.from_numpy(img)\n",
|
||
"\n",
|
||
" # Ensure CHW format\n",
|
||
" if len(img.shape) == 3 and img.shape[-1] in [1, 3, 4]:\n",
|
||
" img = img.permute(2, 0, 1)\n",
|
||
"\n",
|
||
" # Resize to expected input size (256x256 for SigLIP2) BEFORE stacking\n",
|
||
" if img.shape[-2:] != (256, 256):\n",
|
||
" import torch.nn.functional as F\n",
|
||
"\n",
|
||
" img = F.interpolate(\n",
|
||
" img.unsqueeze(0), size=(256, 256), mode=\"bilinear\", align_corners=False\n",
|
||
" ).squeeze(0)\n",
|
||
"\n",
|
||
" # Normalize to [0, 1] if needed\n",
|
||
" if img.dtype == torch.uint8:\n",
|
||
" img = img.float() / 255.0\n",
|
||
"\n",
|
||
" frames.append(img)\n",
|
||
"\n",
|
||
" # Get language\n",
|
||
" if language is None:\n",
|
||
" if OBS_LANGUAGE in frame_data:\n",
|
||
" language = frame_data[OBS_LANGUAGE]\n",
|
||
" if isinstance(language, list):\n",
|
||
" language = language[0]\n",
|
||
" elif \"task\" in frame_data:\n",
|
||
" language = frame_data[\"task\"]\n",
|
||
" else:\n",
|
||
" language = \"No language provided\"\n",
|
||
"\n",
|
||
" if not frames:\n",
|
||
" return None, None, None, None\n",
|
||
"\n",
|
||
" frames_tensor = torch.stack(frames)\n",
|
||
"\n",
|
||
" # Predict rewards\n",
|
||
" rewards = evaluator.predict_episode_rewards(frames_tensor, language)\n",
|
||
"\n",
|
||
" return frames_tensor, language, rewards, episode_length\n",
|
||
"\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Error processing episode {episode_idx}: {e}\")\n",
|
||
" return None, None, None, None\n",
|
||
"\n",
|
||
"\n",
|
||
"def visualize_multiple_episodes(episode_indices=[0, 1, 2, 3], max_frames=32):\n",
|
||
" \"\"\"Visualize multiple episodes in a grid layout.\"\"\"\n",
|
||
" n_episodes = len(episode_indices)\n",
|
||
"\n",
|
||
" # Create figure with subplots\n",
|
||
" fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
|
||
" axes = axes.flatten()\n",
|
||
"\n",
|
||
" from scipy.stats import spearmanr\n",
|
||
"\n",
|
||
" for i, episode_idx in enumerate(episode_indices[:4]): # Limit to 4 episodes\n",
|
||
" if i >= len(axes):\n",
|
||
" break\n",
|
||
"\n",
|
||
" frames, language, rewards, episode_length = get_episode_data(episode_idx, max_frames)\n",
|
||
"\n",
|
||
" if rewards is None:\n",
|
||
" axes[i].text(\n",
|
||
" 0.5,\n",
|
||
" 0.5,\n",
|
||
" f\"Episode {episode_idx}\\nNo data available\",\n",
|
||
" ha=\"center\",\n",
|
||
" va=\"center\",\n",
|
||
" transform=axes[i].transAxes,\n",
|
||
" )\n",
|
||
" axes[i].set_title(f\"Episode {episode_idx} - Error\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # Plot rewards\n",
|
||
" time_steps = range(len(rewards))\n",
|
||
" axes[i].plot(\n",
|
||
" time_steps, rewards, \"b-\", linewidth=2, marker=\"o\", markersize=4, label=\"Predicted Reward\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Add expected progress line\n",
|
||
" expected_progress = np.linspace(0, 1, len(rewards))\n",
|
||
" axes[i].plot(\n",
|
||
" time_steps,\n",
|
||
" expected_progress,\n",
|
||
" \"orange\",\n",
|
||
" linestyle=\"--\",\n",
|
||
" linewidth=2,\n",
|
||
" label=\"Expected Progress (0→1)\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Compute VOC-S\n",
|
||
" frame_indices = np.arange(1, len(rewards) + 1)\n",
|
||
" correlation, p_value = spearmanr(frame_indices, rewards)\n",
|
||
"\n",
|
||
" # Format title with key info\n",
|
||
" title = f'Episode {episode_idx} (VOC-S: {correlation:.3f})\\n\"{language[:40]}{\"...\" if len(language) > 40 else \"\"}\"'\n",
|
||
" axes[i].set_title(title, fontsize=10)\n",
|
||
" axes[i].set_xlabel(\"Frame Index\")\n",
|
||
" axes[i].set_ylabel(\"Reward\")\n",
|
||
" axes[i].legend(fontsize=8)\n",
|
||
" axes[i].grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
" # Add trend indicator\n",
|
||
" trend = \"↗ Inc\" if correlation > 0.1 else \"↘ Dec\" if correlation < -0.1 else \"→ Flat\"\n",
|
||
" axes[i].text(\n",
|
||
" 0.02,\n",
|
||
" 0.98,\n",
|
||
" trend,\n",
|
||
" transform=axes[i].transAxes,\n",
|
||
" verticalalignment=\"top\",\n",
|
||
" fontsize=10,\n",
|
||
" bbox=dict(boxstyle=\"round,pad=0.3\", facecolor=\"lightblue\", alpha=0.7),\n",
|
||
" )\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # Print detailed statistics\n",
|
||
" print(\"\\nDetailed Episode Statistics:\")\n",
|
||
" print(\"=\" * 80)\n",
|
||
"\n",
|
||
" for episode_idx in episode_indices:\n",
|
||
" frames, language, rewards, episode_length = get_episode_data(episode_idx, max_frames)\n",
|
||
"\n",
|
||
" if rewards is None:\n",
|
||
" print(f\"Episode {episode_idx}: No data available\")\n",
|
||
" continue\n",
|
||
"\n",
|
||
" frame_indices = np.arange(1, len(rewards) + 1)\n",
|
||
" correlation, p_value = spearmanr(frame_indices, rewards)\n",
|
||
"\n",
|
||
" print(f\"Episode {episode_idx}:\")\n",
|
||
" print(f\" Language: {language}\")\n",
|
||
" print(f\" Frames: {len(rewards)}\")\n",
|
||
" print(f\" VOC-S (Spearman ρ): {correlation:.4f} (p={p_value:.4f})\")\n",
|
||
" print(f\" Reward range: [{rewards.min():.3f}, {rewards.max():.3f}]\")\n",
|
||
" print(\n",
|
||
" f\" Trend: {'↗ Increasing' if correlation > 0.1 else '↘ Decreasing' if correlation < -0.1 else '→ Flat'}\"\n",
|
||
" )\n",
|
||
" print(\"-\" * 80)\n",
|
||
"\n",
|
||
"\n",
|
||
"def show_episode_frames(episode_idx, max_frames=32, show_frames=[0, -1]):\n",
|
||
" \"\"\"Show specific frames from an episode alongside reward curve.\"\"\"\n",
|
||
" frames, language, rewards, episode_length = get_episode_data(episode_idx, max_frames)\n",
|
||
"\n",
|
||
" if rewards is None:\n",
|
||
" print(f\"No data available for episode {episode_idx}\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" # Create figure\n",
|
||
" fig = plt.figure(figsize=(15, 8))\n",
|
||
"\n",
|
||
" # Reward curve (top half)\n",
|
||
" ax1 = plt.subplot(2, len(show_frames) + 1, (1, len(show_frames) + 1))\n",
|
||
" time_steps = range(len(rewards))\n",
|
||
" ax1.plot(time_steps, rewards, \"b-\", linewidth=2, marker=\"o\", markersize=4, label=\"Predicted Reward\")\n",
|
||
" expected_progress = np.linspace(0, 1, len(rewards))\n",
|
||
" ax1.plot(time_steps, expected_progress, \"orange\", linestyle=\"--\", linewidth=2, label=\"Expected (0→1)\")\n",
|
||
"\n",
|
||
" # Highlight selected frames\n",
|
||
" for frame_idx in show_frames:\n",
|
||
" actual_idx = frame_idx if frame_idx >= 0 else len(rewards) + frame_idx\n",
|
||
" if 0 <= actual_idx < len(rewards):\n",
|
||
" ax1.axvline(x=actual_idx, color=\"red\", linestyle=\":\", alpha=0.7)\n",
|
||
" ax1.plot(actual_idx, rewards[actual_idx], \"ro\", markersize=8)\n",
|
||
"\n",
|
||
" from scipy.stats import spearmanr\n",
|
||
"\n",
|
||
" frame_indices = np.arange(1, len(rewards) + 1)\n",
|
||
" correlation, _ = spearmanr(frame_indices, rewards)\n",
|
||
"\n",
|
||
" ax1.set_title(f'Episode {episode_idx} - VOC-S: {correlation:.3f}\\n\"{language}\"', fontsize=12)\n",
|
||
" ax1.set_xlabel(\"Frame Index\")\n",
|
||
" ax1.set_ylabel(\"Predicted Reward\")\n",
|
||
" ax1.legend()\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
" # Show selected frames (bottom half)\n",
|
||
" for i, frame_idx in enumerate(show_frames):\n",
|
||
" actual_idx = frame_idx if frame_idx >= 0 else len(frames) + frame_idx\n",
|
||
"\n",
|
||
" if 0 <= actual_idx < len(frames):\n",
|
||
" ax = plt.subplot(2, len(show_frames) + 1, len(show_frames) + 2 + i)\n",
|
||
"\n",
|
||
" # Convert frame for display\n",
|
||
" frame = frames[actual_idx].permute(1, 2, 0).cpu().numpy()\n",
|
||
" if frame.max() > 1.0:\n",
|
||
" frame = frame / 255.0\n",
|
||
" frame = np.clip(frame, 0, 1)\n",
|
||
"\n",
|
||
" ax.imshow(frame)\n",
|
||
" ax.set_title(f\"Frame {actual_idx}\\nReward: {rewards[actual_idx]:.3f}\", fontsize=10)\n",
|
||
" ax.axis(\"off\")\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"# Visualize multiple episodes at once\n",
|
||
"print(\"Visualizing first 4 episodes...\")\n",
|
||
"visualize_multiple_episodes([0, 1, 2, 3], max_frames=32)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4. Success vs Failure Detection Visualization\n",
|
||
"\n",
|
||
"Test the model's ability to distinguish correct vs incorrect language conditions.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"==================================================\n",
|
||
"DETECTION TEST - EPISODE 0\n",
|
||
"==================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1600x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Episode 0 Detection Analysis:\n",
|
||
" Correct language: 'place the white sponge in the ceramic bowl'\n",
|
||
" Incorrect language: 'kick the ball'\n",
|
||
" Final reward (correct): -0.0099\n",
|
||
" Final reward (incorrect): -0.0093\n",
|
||
" Difference: -0.0006\n",
|
||
" Detection: FAILURE\n",
|
||
"\n",
|
||
"==================================================\n",
|
||
"DETECTION TEST - EPISODE 1\n",
|
||
"==================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
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"image/png": 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LY9x3MBYBAAAAAACSgsAGhqTNbxflbaz8Ma19Qfo5x0BrwJuy/eaXlFvtznq+TIZIZ22t1c4rSV1gwznunQXMAQAAAAAAkDgENjAkHQE7sNFcSdHmbOf8IrfdMTaSraB0lNV21ZH+ByLhujqrXVBSkbL9thf6rTaBDQAAAAAAgOTI2sDGbbfdJhMnThSPxyPbbbedvPPOO32u/8QTT8jkyZPN+lOnTpUFCxbEPD9v3jzZc889pby8XFwul3zwwQeSDTqKCq02qajQXG2nouosDKRsv57y0VbbVd+Qsv0ic7ka7ACX2zE+ks057p1/DwAAAAAAAEicrAxsPPbYY3L66afLhRdeKIsWLZLNNttM9tprL1m5Mv4X82+++abMmDFDZs+eLYsXL5Zp06aZxyeffGKtEwwGZeedd5arr75asomzKG9LjV2sF9mp1VG4O5zCgs3esjFWO7chmLL9InM5x4G33B4fSVdkF7JvqSLYCwAAAAAAkAxZGdi4/vrrZc6cOTJr1izZaKON5I477hCfzyf33ntv3PVvuukm2XvvvWXu3LkyZcoUufTSS2XLLbeUW2+91VrnyCOPlAsuuED22GMPySauYvtLvLbaqrT2BenXVmMHNlxFJSnbr3/UeKudF2xK2X6RufIa7HHgKx+buh07/010/D38P3v3AedUlT1w/GRapheG3pEiYAEFUWwoIKhYUFQsfyviqthd6669rb1i39XVta2uYscGYsOGFQUUpSmd6b3l/zl3eJmXmcwwMJm8l+T39RPzkrx5ucl7eST33HsOAAAAAAAAQifmAhtVVVWyYMGCgABEXFycuT1//vygf6P3Nw5Y6AyP5taPJfFZOf7l2gKKNse6moI8/3J8dvgCGxm5DYENb2lF2J4X7pVUWu5fTu/YcHy0t/ichkLltfkNnwcAAAAAAACEToLEmI0bN0ptba106RKYmkRvL168OOjfrF27Nuj6ev+2qqysNBdLUVF9Pvi6ujpzCTd9Tp/Pt9XPHZ/d0IlXV5DvSNuj1bbuEyfVFhYEHBvhant6cpaUJIqkV4skl1a16/NG4n6JdsH2ibesylxXxIukp+eGbX8lZNkCG4WxfU7ks+JO7Bf3ibZ9Ei2vAwAAAIC7xVxgwy1uueUWue6665rcv2HDBqmoqHDkR2hhYaH5Ya0zWFqrOjGlYXnTpmbrlCB8+8RJ1Rsb6qxUJSaH9XioS/ZIerVPUsqq2/V5I3G/RLtg+ySltD6wUZQsUrepIeDW3qqSbOfEjbF9TuSz4k7sF/eJtn1SXFzsdBMAAAAAxICYC2x07NhR4uPjZd26dQH36+2uXYPnYdf7t2b91rjiiitMAXP7jI1evXpJp06dJDOMRZftP6o9Ho95/q35Ud2xz3b+5aSKSuncuXM7tTD2bOs+cdKyioZZSLm9twvr8fBbSrxIcY2kVdRKWjs+byTul2gXbJ8UVtSa65KUeOkbxuNQj3uLt7wips+JfFbcif3iPtG2T5KTk51uAgAAAIAYEHOBjaSkJBkxYoR88MEHMnnyZP8PSr19zjnnBP2b0aNHm8cvuOAC/33vvfeeuX9beb1ec2lMf9A69aNWf1Rv7fPbi/ImFJdGxQ9yN9mWfeKk+OJS/3Jax25hbXdZqp7OaiSj0ifi84nEx7fbc0XafokFAfvE55OMivpUKGXJCWHdT+n2QvbFJTF/jPBZcSf2i/tE0z6JhtcAAAAAwP1iLrChdKbEySefLCNHjpRRo0bJPffcI6WlpXLqqaeax0866STp0aOHSRelzj//fBkzZozceeedMmnSJHn++efl66+/lkcffdS/zby8PFm5cqWsXr3a3F6yZIm51lkdbZnZ4XZpud38y4m2Yr2ITYklZQGBjXCqSE3S/5vl2qLCgCLOiC2+sjJJ2JzivTxNj4vwSbMFNjgnAgAAAAAAtI+YHFI1depUueOOO+Tqq6+W4cOHy3fffSezZ8/2FwjXAMWaNWv86++5557y7LPPmkDGsGHD5KWXXpJZs2bJjjvu6F/ntddek1122cUEPtSxxx5rbj/88MMSzTI7dherRGRSafhrg8BdkmwduekdGzp4w6EqrSH1RemG+gAjYlPZprX+5aq0pjPj2lNWRicp3zxkwFvCOREAAAAAAKA9xOSMDaVpp5pLPfXhhx82ue/oo482l+accsop5hJrMryZUuQVya4USS6rL9aL2OXdfAyUJYhkpueG9bmr01P9y6Wb1kimNAQeEVtKNv4paZuXq9MainmHQ1ZylhR4RVJqOCcCAAAAAAC0l5icsYHQSYxPlOJkj1lOLat2ujlwWGpp/TFQnCySEBfeuGlNutWVLVKWty6szw13KdvYMGOjJqPhuAgHb7zXHP8qtawmrM8NAAAAAAAQKwhsoM1Kk+uLNKdV1DrdFDgsraK+I7ckpf0KdzfHl5nhX66wdWwj9lTkrfcv12Wkh70AcElKQsM5UQvZAwAAAAAAIKQIbKDNylLrO/FSq3wiNYxQjlk+n6RX1HfilqUkhv/5MzP9i1X5G8P//HCNSltgQzIajotwKUutP/7j9eNQUhL25wcAAAAAAIh2BDbQZhWpSf5lX1GRo22Bc+pKius7cs0xEf7ARlxWtn+5umBT2J8f7lGd37D/PdkNx0W4VNgKlvsKCsL+/AAAAAAAANGOwAbarCotuSEt1YbVjrYFzinduMa/XGk7JsIlLruDf7muID/szw/3qCnI8y/HZec4ek4sp94LAAAAAABAyBHYQJtVp6f4l0s3NXRuI7bY9739mAiXpJxc/3JdIaPkY5nPtv8TbQGvcKlJT/UvE+wFAAAAAAAIPQIbaLOajDT/cvkmRifHqrINDYGN2vSGYyJcknI6+pc9haREi2W+okL/clJOp7A/f21mQ8HyCs6JAAAAAAAAIUdgA23my8jwL1fk24r2IqZU2Ao212Y2HBPhkpLb1b/sKaZgcyyLKyr2Lyfndg778/tsx7/9cwEAAAAAAIDQILCBtsvM9C9W5W90tClwjn3fezIajolwScnt4l9OKCkN+/PDPeKLS4MGvMLFk9lQsLwqb0PYnx8AAAAAACDaEdhASDvxqvM3OdoWOKfaHtjIygr786d36uFfTiwpD/vzwz0SShv2f3rH7mF/fo+tYHkN50QAAAAAAICQI7CBNkvIaSjOW1uQ52hb4Jyawnz/crztmAiXjA5dpW7zsre0IuzPD/fw2gJb6bndwv78CR1shewLKGQPAAAAAAAQagQ20GYJ2Q2d2HWFDUV7EVvqChoCGwnZDR274ZLmzZBib/1ySllV2J8f7uHdvP9LE0Uy0sMfZPNmdwxayBwAAAAAAAChQWADbZbUoaETz1NU5Ghb4Bz7vvfmdAz/83s8UpzsMcsp5TVhf364R2pZtbnW4yHOE/5/5ry2guWcEwEAAAAAAEKPwAbaLCWnoWizp6jY0bbAOZ7ihn2fbCvkHU6lKQnmOr281pHnhzukbd7/pcnxjjy//fiPLypxpA0AAAAAAADRjMAG2iylY1f/ckJJqaNtgXPiixv2fWpuwzERTuUpifXPrwP2q+tH7SPG+HySUekzi2Wp9YGucLMXLE8oKXOkDQAAAAAAANGMwAbaLM1WnDehtKFoL2JLoq0D16nARkXa5iIbIlJVsMmRNsBZtcVFElcf15CK1IbjIdyF7Gvqs6JJEudEAIgoeXl5csIJJ0hmZqZkZ2fLtGnTpKSk5dl3FRUVMmPGDMnNzZX09HSZMmWKrFu3LmCdlStXyqRJkyQ1NVU6d+4sl1xyidTUBKbOfOaZZ2TYsGFmnW7duslpp50mmzbxfQYAAAAIhsAG2iwzt5vUbV72llY43Bo4Jam00r+c2amnI22oSkv2L5dsXO1IG+Cs4vV/+JerbIGucMpKzpaizU+dbPtcAADcT4MaP/30k7z33nvyxhtvyEcffSRnnHFGi39z4YUXyuuvvy4vvviizJs3T1avXi1HHnmk//Ha2loT1KiqqpLPPvtM/v3vf8uTTz4pV199tX+dTz/9VE466SQTSNHn1219+eWXMn369HZ9vQAAAECkIrCBNsvwZto68aqcbg4cklxWv+9LEkXSUjIdaUNNRqp/uXQDgY1YVLpprX+5Or3heAin9KR0KUwOLGQOAHC/RYsWyezZs+Xxxx+X3XffXfbee2+5//775fnnnzfBimAKCwvln//8p9x1110yduxYGTFihDzxxBMmgPH555+bdd599135+eef5T//+Y8MHz5cDjroILnhhhtk5syZJtih5s+fL3379pXzzjtP+vXrZ577L3/5iwluAAAAAGiKwAbaLD4uXopS6vOupJYHTqlH7LA6cIuTPRLncebUUpue5l8uzwtMAYHYUL5pjX+5xnY8hJPH45HS5LiAQuYAAPfT4IKmnxo5cqT/vvHjx0tcXJx88cUXQf9mwYIFUl1dbdazDB48WHr37m22Z213p512ki5duvjXmThxohQVFZnZGWr06NGyatUqeeutt8Tn85lUVi+99JIcfPDBzba3srLSbMN+AQAAAGIFgQ2ERGlyvLlOq6ATL1ZZ+74kpf5YcIIvM8O/XLlpvWPtgHMq8tYHPR7CrTS1vpC9t8anPU+OtQMA0Hpr16419S/sEhISpEOHDuax5v4mKSnJBETsNIhh/Y1e24Ma1uPWY2qvvfYyNTamTp1qtte1a1fJysoyszqac8stt5h1rEuvXr228ZUDAAAAkYfABkKifHMnXmqVT6RRIUTEgLo6Sa+sr9hcnlJ/LDgiK8u/WJW/0bl2wDGVeRsabmQ6kxJNVaQmNdwoLHSsHQAAkcsvv9zMpmvpsnjxYkfbqKmqzj//fFN3Q2eBaEqs5cuXy5lnntns31xxxRUmFZZ10RkfAAAAQKxIcLoBiA71nXjlZrmusEDicjs63SSEUXVhviTWxzWkMs3WoRtm8Vk5DW0qyHOsHXBOTf4m/3JcVuDo2XCqTNciG8VmuSpvgyQ1GgEMAAifiy++WE4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|
||
"text/plain": [
|
||
"<Figure size 1600x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Episode 1 Detection Analysis:\n",
|
||
" Correct language: 'move the arm in a circular motion'\n",
|
||
" Incorrect language: 'dance in place'\n",
|
||
" Final reward (correct): -0.0097\n",
|
||
" Final reward (incorrect): -0.0096\n",
|
||
" Difference: -0.0001\n",
|
||
" Detection: FAILURE\n",
|
||
"\n",
|
||
"==================================================\n",
|
||
"DETECTION TEST - EPISODE 2\n",
|
||
"==================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
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"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1600x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Episode 2 Detection Analysis:\n",
|
||
" Correct language: 'place the bottle in the ceramic bowl'\n",
|
||
" Incorrect language: 'wave your hand'\n",
|
||
" Final reward (correct): -0.0096\n",
|
||
" Final reward (incorrect): -0.0090\n",
|
||
" Difference: -0.0006\n",
|
||
" Detection: FAILURE\n",
|
||
"\n",
|
||
"==================================================\n",
|
||
"DETECTION TEST - EPISODE 3\n",
|
||
"==================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
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"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1600x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Episode 3 Detection Analysis:\n",
|
||
" Correct language: 'drag the ceramic bowl in a circle'\n",
|
||
" Incorrect language: 'jump up and down'\n",
|
||
" Final reward (correct): -0.0081\n",
|
||
" Final reward (incorrect): -0.0086\n",
|
||
" Difference: 0.0005\n",
|
||
" Detection: SUCCESS\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Test success vs failure detection with visualization\n",
|
||
"def compare_correct_vs_incorrect_language(episode_idx, max_frames=32):\n",
|
||
" \"\"\"Compare rewards for correct vs incorrect language for the same episode.\"\"\"\n",
|
||
"\n",
|
||
" # Get original episode data\n",
|
||
" frames, correct_language, correct_rewards, episode_length = get_episode_data(episode_idx, max_frames)\n",
|
||
"\n",
|
||
" if correct_rewards is None:\n",
|
||
" print(f\"No data available for episode {episode_idx}\")\n",
|
||
" return\n",
|
||
"\n",
|
||
" # Generate incorrect language\n",
|
||
" incorrect_languages = [\n",
|
||
" \"kick the ball\",\n",
|
||
" \"dance in place\",\n",
|
||
" \"wave your hand\",\n",
|
||
" \"jump up and down\",\n",
|
||
" \"spin around\",\n",
|
||
" \"do nothing\",\n",
|
||
" ]\n",
|
||
" incorrect_language = incorrect_languages[episode_idx % len(incorrect_languages)]\n",
|
||
"\n",
|
||
" # Predict rewards with incorrect language\n",
|
||
" incorrect_rewards = evaluator.predict_episode_rewards(frames, incorrect_language)\n",
|
||
"\n",
|
||
" # Create comparison plot\n",
|
||
" fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 10))\n",
|
||
"\n",
|
||
" time_steps = range(len(correct_rewards))\n",
|
||
"\n",
|
||
" # 1. Reward curves comparison\n",
|
||
" ax1.plot(\n",
|
||
" time_steps, correct_rewards, \"g-\", linewidth=2, marker=\"o\", markersize=4, label=\"Correct Language\"\n",
|
||
" )\n",
|
||
" ax1.plot(\n",
|
||
" time_steps, incorrect_rewards, \"r-\", linewidth=2, marker=\"s\", markersize=4, label=\"Incorrect Language\"\n",
|
||
" )\n",
|
||
" ax1.set_title(f\"Episode {episode_idx} - Reward Comparison\")\n",
|
||
" ax1.set_xlabel(\"Frame Index\")\n",
|
||
" ax1.set_ylabel(\"Predicted Reward\")\n",
|
||
" ax1.legend()\n",
|
||
" ax1.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
" # 2. Final reward comparison\n",
|
||
" final_correct = correct_rewards[-1]\n",
|
||
" final_incorrect = incorrect_rewards[-1]\n",
|
||
"\n",
|
||
" ax2.bar(\n",
|
||
" [\"Correct\\nLanguage\", \"Incorrect\\nLanguage\"],\n",
|
||
" [final_correct, final_incorrect],\n",
|
||
" color=[\"green\", \"red\"],\n",
|
||
" alpha=0.7,\n",
|
||
" )\n",
|
||
" ax2.set_title(\"Final Reward Comparison\")\n",
|
||
" ax2.set_ylabel(\"Final Reward\")\n",
|
||
"\n",
|
||
" # Add values on bars\n",
|
||
" ax2.text(0, final_correct + 0.01, f\"{final_correct:.3f}\", ha=\"center\", va=\"bottom\", fontweight=\"bold\")\n",
|
||
" ax2.text(1, final_incorrect + 0.01, f\"{final_incorrect:.3f}\", ha=\"center\", va=\"bottom\", fontweight=\"bold\")\n",
|
||
"\n",
|
||
" # 3. Reward difference over time\n",
|
||
" reward_diff = correct_rewards - incorrect_rewards\n",
|
||
" ax3.plot(time_steps, reward_diff, \"purple\", linewidth=2, marker=\"d\", markersize=4)\n",
|
||
" ax3.axhline(y=0, color=\"black\", linestyle=\"--\", alpha=0.5)\n",
|
||
" ax3.set_title(\"Reward Difference (Correct - Incorrect)\")\n",
|
||
" ax3.set_xlabel(\"Frame Index\")\n",
|
||
" ax3.set_ylabel(\"Reward Difference\")\n",
|
||
" ax3.grid(True, alpha=0.3)\n",
|
||
"\n",
|
||
" # 4. Language descriptions\n",
|
||
" ax4.axis(\"off\")\n",
|
||
" ax4.text(0.1, 0.8, \"Language Conditions:\", fontsize=14, fontweight=\"bold\")\n",
|
||
" ax4.text(0.1, 0.6, f'Correct: \"{correct_language}\"', fontsize=12, color=\"green\")\n",
|
||
" ax4.text(0.1, 0.4, f'Incorrect: \"{incorrect_language}\"', fontsize=12, color=\"red\")\n",
|
||
"\n",
|
||
" detection_success = \"✓ SUCCESS\" if final_correct > final_incorrect else \"✗ FAILURE\"\n",
|
||
" color = \"green\" if final_correct > final_incorrect else \"red\"\n",
|
||
" ax4.text(0.1, 0.2, f\"Detection: {detection_success}\", fontsize=14, fontweight=\"bold\", color=color)\n",
|
||
"\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # Print statistics\n",
|
||
" print(f\"Episode {episode_idx} Detection Analysis:\")\n",
|
||
" print(f\" Correct language: '{correct_language}'\")\n",
|
||
" print(f\" Incorrect language: '{incorrect_language}'\")\n",
|
||
" print(f\" Final reward (correct): {final_correct:.4f}\")\n",
|
||
" print(f\" Final reward (incorrect): {final_incorrect:.4f}\")\n",
|
||
" print(f\" Difference: {final_correct - final_incorrect:.4f}\")\n",
|
||
" print(f\" Detection: {'SUCCESS' if final_correct > final_incorrect else 'FAILURE'}\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# Test detection on multiple episodes\n",
|
||
"detection_episodes = [0, 1, 2, 3] # Change as needed\n",
|
||
"\n",
|
||
"for episode_idx in detection_episodes:\n",
|
||
" print(f\"\\n{'=' * 50}\")\n",
|
||
" print(f\"DETECTION TEST - EPISODE {episode_idx}\")\n",
|
||
" print(\"=\" * 50)\n",
|
||
" compare_correct_vs_incorrect_language(episode_idx)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "lerobot",
|
||
"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": "3.10.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|