| # 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. |
|
|
