# Interleaved Multimodal Reasoning Dataset

A dataset generation framework for spatial reasoning tasks involving camera viewpoint prediction and ordering around static 3D objects. This project generates multimodal chain-of-thought reasoning traces that teach models how camera views change during orbital rotation.

## Table of Contents

- [Overview](#overview)
- [Installation](#installation)
- [Project Structure](#project-structure)
- [Quick Start](#quick-start)
- [Usage](#usage)
- [Configuration](#configuration)
- [Development](#development)

## Overview

This framework generates two types of spatial reasoning tasks:

1. **Task 1: Camera View Prediction** - Given an initial view and rotation parameters (angle + direction), predict what the object looks like from the new viewpoint
2. **Task 3: Camera View Ordering** - Given a reference frame and scrambled candidate images, reconstruct the correct temporal order of camera views

### Key Features

- **Automatic Ground Plane Estimation**: PCA-based geometry calibration eliminates manual tuning
- **Oracle Chain Generation**: Creates step-by-step reasoning paths with intermediate ground-truth views
- **LLM Chain-of-Thought**: Generates natural language reasoning that mirrors human spatial thinking
- **Multi-backend Support**: Works with OpenAI-compatible APIs and local vLLM inference
- **Cluster Deployment**: Ready for distributed GPU execution via Determined AI

## Installation

### Prerequisites

- Python 3.12
- CUDA 11.8+ (for GPU support)
- Access to CO3D dataset
- (Optional) Determined AI cluster for distributed training

### Setup

1. **Clone the repository**
```bash
git clone <repository-url>
cd interleaved-umm
```

2. **Create conda environment**
```bash
conda create -n interleaved-umm python=3.12
conda activate interleaved-umm
```

3. **Install PyTorch**

First, install PyTorch 2.8.0 matching your CUDA version from the [official PyTorch website](https://pytorch.org/get-started/locally/).

For example, with CUDA 11.8:
```bash
pip install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```

For CUDA 12.1:
```bash
pip install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
```

4. **Install in editable mode**
```bash
pip install -e .
```

5. **Install dependencies**
```bash
pip install -r requirements.txt
```

6. **Set up environment variables**

Create a `.env` file in the project root:
```bash
# OpenAI-compatible API
BASE_URL=https://api.openai.com/v1/chat/completions
API_KEY=your_api_key_here

# Qwen API (optional)
QWEN_API_KEY=your_qwen_key
QWEN_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
```

**⚠️ Important**: Never commit the `.env` file. It's already in `.gitignore`.

7. **Prepare CO3D dataset**

Download and extract the CO3D dataset, then update paths in generation scripts:
- `ROOT_PATH`: Path to CO3D dataset root
- `IMAGE_PREFIX`: Relative path for storing processed images

## Project Structure

```
interleaved-umm/
├── src/
│   ├── action_state/          # Task generation logic
│   │   ├── gen_task1.py       # Camera view prediction
│   │   ├── gen_task3.py       # Camera view ordering
│   │   └── utils.py           # Geometry utilities (PCA, camera poses)
│   ├── llm_generation/        # Chain-of-thought generation
│   │   ├── generator.py       # CoTGenerator orchestrator
│   │   ├── prompts.py         # Task-specific prompts
│   │   ├── api_client.py      # OpenAI-compatible API client
│   │   ├── vllm_client.py     # Local vLLM inference
│   │   └── cleaning_generator.py  # Data quality verification
│   └── utils/
│       └── image_utils.py     # Multimodal content parsing
├── scripts/
│   ├── action_state/          # Task generation runners
│   │   ├── task1/            # Task 1 generation scripts
│   │   └── task3/            # Task 3 generation scripts
│   ├── run_llm_cot.py        # LLM CoT generation (API)
│   ├── run_llm_cot_vllm.py   # LLM CoT generation (vLLM)
│   ├── run_cleaning.py       # Data quality checker
│   ├── filter/               # Sequence filtering scripts
│   ├── copy_image.py         # Image preprocessing
│   └── visualize_*.py        # Visualization tools
├── deploy/
│   ├── local/                # Cluster deployment configs
│   │   ├── task1/
│   │   ├── task3/
│   │   └── cleaning/
│   └── template/             # Config templates
├── configs/                  # Legacy configuration files
├── data/                     # Generated datasets (not in repo)
├── debug/                    # Debugging outputs (not in repo)
├── pyproject.toml           # Package configuration
├── requirements.txt         # Python dependencies
└── CLAUDE.md               # Documentation for Claude Code
```

## Quick Start

### 1. Generate Task Metadata

Generate Task 1 samples with oracle reasoning chains:

```bash
cd scripts/action_state/task1
bash run_gen_task1_v3.sh
```

This will:
- Sample camera pose pairs from CO3D sequences
- Verify geometric constraints (angle ranges, intervals)
- Generate oracle chains with intermediate views
- Save JSONL files to `data/questions/task1_metadata_v3/`

### 2. Generate Chain-of-Thought Reasoning

**Option A: Using OpenAI-compatible API**

```bash
python scripts/run_llm_cot.py \
    --input_file data/questions/task1_metadata_v3/train/train_1.jsonl \
    --output_file data/questions/task1_v3/train/train_1.jsonl \
    --image_root /path/to/project/root \
    --model gpt-4o
```

**Option B: Using local vLLM server**

```bash
python scripts/run_llm_cot_vllm.py \
    --input_file data/questions/task1_metadata_v3/train/train_1.jsonl \
    --output_file data/questions/task1_v3/train/train_1.jsonl \
    --image_root /path/to/project/root \
    --model /path/to/Qwen3-VL-32B-Instruct \
    --tp_size 2 \
    --gpu_memory_utilization 0.9
```

### 3. Deploy to Cluster

If using Determined AI:

```bash
det experiment create deploy/local/task1/config.yaml .
```

## Usage

### Task Generation Parameters

**Task 1 (Camera View Prediction)**

Key parameters in `scripts/action_state/task1/run_gen_task1_v3.sh`:

```bash
MIN_ANGLE=60.0        # Minimum rotation angle (degrees)
MAX_ANGLE=125.0       # Maximum rotation angle (degrees)
MIN_INTERVAL=25.0     # Minimum angular separation between options
NUM_SAMPLES=3         # Samples per sequence
```

**Task 3 (Camera View Ordering)**

Key parameters in `scripts/action_state/task3/run_gen_task3_v3.sh`:

```bash
MIN_INTERVAL=15.0     # Minimum per-step rotation
MAX_INTERVAL=40.0     # Maximum per-step rotation
MAX_ANGLE=170.0       # Maximum total trajectory span
```

### Data Filtering

Before generating tasks, filter sequences for quality:

```bash
python scripts/filter/filter_v4.py \
    --category apple \
    --root_path /path/to/co3d \
    --output_dir data/filter_log_v4_pca
```

### Visualization

Visualize camera trajectories:

```bash
python scripts/visualize_traj_pca.py \
    --category apple \
    --root_path /path/to/co3d \
    --sequence_name <sequence_id>
```

## Configuration

### Environment Variables

| Variable | Description | Example |
|----------|-------------|---------|
| `BASE_URL` | OpenAI-compatible API endpoint | `https://api.openai.com/v1/chat/completions` |
| `API_KEY` | API authentication key | `sk-...` |
| `QWEN_API_KEY` | Qwen API key (optional) | `sk-...` |
| `QWEN_BASE_URL` | Qwen API endpoint (optional) | `https://dashscope.aliyuncs.com/...` |

### Cluster Deployment

Edit `deploy/local/*/config.yaml`:

```yaml
resources:
  resource_pool: amp-80g    # GPU pool
  slots_per_trial: 2        # Number of GPUs

bind_mounts:
  - host_path: /mount/HOME/username
    container_path: /home/username

environment:
  image: your-docker-image:tag
```

## Development

### Running Tests

```bash
# Test on a small subset
python src/action_state/gen_task1.py \
    --root_path /path/to/co3d \
    --output_dir test_output \
    --category apple \
    --num_samples 1
```

### Code Structure

**Geometry Pipeline**:
1. `CO3DDataLoader` loads frame annotations
2. `get_sequence_geometry_pca()` estimates ground plane via PCA
3. `get_relative_yaw()` computes angular differences
4. `decompose_angle()` breaks rotations into steps

**CoT Generation Pipeline**:
1. `CoTGenerator` receives oracle chain
2. For each step, constructs context messages
3. Calls LLM with "cheat sheet" (target view + physics hints)
4. LLM generates reasoning that appears to derive the action
5. Combines into final `<think>...</think>` trace

### Key Concepts

- **Oracle Chain**: Ground-truth reasoning path with intermediate views
- **Cheat Mechanism**: LLM sees target but must write as if deriving it
- **Parallax Rule**: "Camera moves RIGHT → View shifts LEFT"
- **Bird's Eye View**: Rotation direction defined from top-down perspective

## Troubleshooting

**Issue**: `FileNotFoundError` for images
- **Solution**: Check `IMAGE_PREFIX` and `image_root` match your actual paths

**Issue**: `LinAlgError` in PCA
- **Solution**: Sequence has too few frames or degenerate geometry. Filter will catch these.

**Issue**: vLLM OOM errors
- **Solution**: Reduce `gpu_memory_utilization` or `limit_mm_per_prompt`

**Issue**: No valid samples generated
- **Solution**: Relax `MIN_ANGLE`, `MAX_ANGLE`, or `MIN_INTERVAL` constraints

## Citation

If you use this dataset or codebase, please cite:

```bibtex
@misc{interleaved-umm,
  title={Interleaved Multimodal Reasoning Dataset},
  author={Your Name},
  year={2024}
}
```

## License

[Specify your license here]

## Contact

For questions or issues, please contact [your contact info] or open an issue on GitHub.