# Reproduction Guide

This guide documents the recommended reproduction path for PixDLM on DRSeg.

## Setup

```bash
conda create -n pixdlm python=3.10 -y
conda activate pixdlm
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
```

Download assets:

```bash
python scripts/download_assets.py --output-dir .
python scripts/prepare_drseg.py --data-root data/DRSeg
```

## Evaluation

Single-GPU exact split evaluation:

```bash
bash scripts/eval_drseg.sh \
  --gpus 0 \
  --model pretrained/pixdlm-7b \
  --data data/DRSeg \
  --clip checkpoints/clip-vit-large-patch14 \
  --exp pixdlm_drseg_test_single_gpu
```

Multi-GPU faster evaluation:

```bash
bash scripts/eval_drseg.sh \
  --gpus 0,1,2,3,4,5,6,7 \
  --model pretrained/pixdlm-7b \
  --data data/DRSeg \
  --clip checkpoints/clip-vit-large-patch14 \
  --exp pixdlm_drseg_test_8gpu
```

Note: the default PyTorch distributed sampler pads samples when the split size is
not divisible by the number of GPUs. For exact paper-table accounting, prefer the
single-GPU command or patch the sampler to remove padded duplicates.

## Expected Metrics

Paper metrics on DRSeg test:

| Reasoning type | gIoU | cIoU |
| --- | ---: | ---: |
| Attribute | 62.80 | 62.84 |
| Scene | 61.75 | 64.03 |
| Spatial | 62.51 | 62.80 |

The released scripts print:

- overall gIoU/cIoU,
- CoT vs no-CoT threshold counts,
- per-reasoning-type gIoU/cIoU,
- image-level visualizations in `outputs/<exp>/`.

For each evaluated sample, the visualization directory stores the input image,
predicted mask, ground-truth mask, overlay, and a JSON result containing the
question, answer, and mask metadata.

## Compute Transparency

The full test evaluation is memory-heavy because PixDLM combines a language
model, CLIP visual features, and segmentation decoding. We recommend reporting:

- GPU type and count,
- precision,
- dependency versions,
- exact split and sampler behavior,
- average seconds per image,
- whether CoT text is included in the conditioning input.

The public release acknowledges the 2027 CVPR Compute Transparency Champion
recognition and keeps this guide explicit about evaluation assumptions.