# Reconstruct the GQA evaluation inputs locally

The public candidate includes our labels, factual source IDs, image/question
checksums and our edit deltas for all 3,000 GQA views. It omits upstream questions,
full semantic programs, full graphs and photographs because an explicit GQA
annotation redistribution grant was not found. Read the upstream terms when
obtaining the source files. Do not upload the reconstructed directory as part
of this candidate without resolving the missing grant.

1. Obtain `questions1.2.zip` and `sceneGraphs.zip` from the
   [official GQA downloads](https://cs.stanford.edu/people/dorarad/gqa/download.html).
   Extract `val_balanced_questions.json` and `val_sceneGraphs.json`.
2. Obtain the official `images.zip`, or the equivalent original images from
   [Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/api.html).
   Put the selected original JPEGs in one directory named `{image_id}.jpg`.
   The needed 1,000 IDs are in the `gqa` configuration and reconstruction recipes.
   Re-encoded third-party mirror images may not reproduce the frozen bytes.
3. Install the pinned `requirements.txt` and run:

```bash
python scripts/reconstruct_hf_gqa.py \
  --questions /path/to/val_balanced_questions.json \
  --scene-graphs /path/to/val_sceneGraphs.json \
  --images /path/to/original-images \
  --output ../gqa-reconstructed
```

The script performs no network download or upload. It requires a new output
directory outside the release. It checks original question strings and source
programs, canonical graphs, normalized photograph bytes, and every final image
against the frozen evaluated identities. It replays the source program and the
symbolic alternatives, then writes an ImageFolder-compatible `metadata.jsonl`,
3,000 JPEGs and the reconstructed program/edit evidence. Any mismatch fails
explicitly; it does not substitute newly generated examples.

```python
from datasets import load_dataset
gqa = load_dataset(
    "imagefolder",
    data_files={"test": [
        "../gqa-reconstructed/images/*.jpg",
        "../gqa-reconstructed/metadata.jsonl",
    ]},
    split="test",
)
```

Use this explicit file/split selection. Directory-wide split inference can pick
up validation-report filenames; those reports are not model inputs. These
3,000 examples belong to the benchmark's evaluation-only test cohort.

The GQA renderer decodes the source JPEG to RGB, writes a normalized JPEG at
quality 88 with 4:2:0 subsampling, and applies a `(127,127,127)` patch over one
recorded object box for edited views. FULL avoids a second JPEG encoding.
Patched views are encoded once from the normalized source. Exact codec behavior
is why the pinned Pillow version matters. CLEVR and PlotQA need no reconstruction
for model evaluation because their evaluated pixels are embedded in Parquet.

The CLEVR source worlds/programs and renderer settings are bundled for auditing.
Optional regeneration of the rendered CLEVR scenes additionally requires the
official generator/assets at revision
`f0ce2c81750bfae09b5bf94d009f42e055f2cb3a`, Blender 2.79b/Cycles and the settings
in the view evidence. These dependencies are not needed to load the dataset or
to replay its source-program labels. GQA graph alternatives are symbolic scene
evidence and do not establish identical pixels for hypothetical photographs.