Datasets:
Add splits, metadata, scripts, and documentation
Browse files- .gitignore +3 -0
- README.md +154 -0
- RELEASE_NOTES.md +28 -0
- examples/eval_timm_global.py +226 -0
- metadata/query_relevance.json +0 -0
- metadata/source/retrieval_ground_truth_raw.csv +1001 -0
- metadata/stats.json +14 -0
- requirements.txt +5 -0
- scripts/validate_release.py +137 -0
- splits/test.json +0 -0
- splits/train.json +0 -0
.gitignore
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__pycache__/
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.DS_Store
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dist/
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README.md
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---
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license: cc-by-4.0
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task_categories:
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- image-to-image
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- image-feature-extraction
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tags:
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- mars
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- crater
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- retrieval
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- remote-sensing
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- planetary-science
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- vision-transformer
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pretty_name: CraterBench-R
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size_categories:
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- 10K<n<100K
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---
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# CraterBench-R
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CraterBench-R is an instance-level crater retrieval benchmark built from Mars CTX imagery.
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This repository contains the released benchmark images, official split files, relevance metadata, and a minimal evaluation example.
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## Summary
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- `25,000` crater identities in the benchmark gallery
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- `50,000` gallery images with two canonical context crops per crater
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- `1,000` held-out query crater identities
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- `5,000` manually verified query images with five views per query crater
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- official `train` and `test` split manifests with relative paths only
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## Download
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```bash
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# Download the repository
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pip install huggingface_hub
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huggingface-cli download jfang/CraterBench-R --repo-type dataset --local-dir CraterBench-R
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cd CraterBench-R
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# Extract images
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unzip images.zip
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```
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After extraction the directory should contain `images/gallery/` (50,000 JPEGs) and `images/query/` (5,000 JPEGs).
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## Repository Layout
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- `images.zip`: all benchmark images (gallery + query) in a single archive
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| 49 |
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- `splits/test.json`: official benchmark split with full gallery plus query set
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- `splits/train.json`: train gallery with the full test relevance closure removed
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- `metadata/query_relevance.json`: raw co-visible crater IDs and gallery-filtered relevance
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- `metadata/stats.json`: release summary statistics
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- `metadata/source/retrieval_ground_truth_raw.csv`: raw query relevance CSV for reference
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- `examples/eval_timm_global.py`: minimal global-descriptor example for any `timm` image model
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- `requirements.txt`: lightweight requirements for the example script
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## Split Semantics
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`splits/test.json` is the official benchmark split and includes both:
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- the full gallery
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- the query set evaluated against that gallery
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The `ground_truth` field maps each query crater ID to gallery-present relevant crater IDs.
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The raw query co-visibility information is preserved separately in `metadata/query_relevance.json`:
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- `co_visible_ids_all`: all raw co-visible crater IDs from the source annotation
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- `relevant_gallery_ids`: the subset that is present in the released gallery
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`splits/train.json` is intended for supervised or metric-learning experiments. It excludes the full test relevance closure, not just the 1,000 direct query crater IDs.
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## Official Counts
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- test gallery: `25,000` crater IDs / `50,000` images
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- test queries: `1,000` crater IDs / `5,000` images
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- train gallery: `23,941` crater IDs / `47,882` images
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- raw multi-ID query crater IDs: `428`
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- gallery-present multi-ID query crater IDs: `59`
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## Quick Start
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| 82 |
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```bash
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pip install -r requirements.txt
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unzip images.zip # if not already extracted
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python examples/eval_timm_global.py \
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--data-root . \
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--split test \
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--model vit_small_patch16_224.dino \
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--pool cls \
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--batch-size 64 \
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--device cuda
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```
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The example script:
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- loads `splits/test.json`
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- creates a pretrained `timm` model
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- extracts one feature vector per image
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- performs cosine retrieval against the released gallery
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- reports Recall@1/5/10, mAP, and MRR
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It is intentionally simple and meant as a working baseline rather than the fastest possible evaluator.
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## Manifest Format
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Each split JSON has the form:
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```json
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{
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"split_name": "train or test",
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"version": "release_v1",
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"gallery_images": [
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{
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"image_id": "02-1-002611_2x",
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"path": "images/gallery/02-1-002611_2x.jpg",
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"crater_id": "02-1-002611",
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"view_type": "2x"
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}
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],
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"query_images": [
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{
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"image_id": "02-1-002927_view_1",
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"query_id": "02-1-002927",
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"path": "images/query/02-1-002927_view_1.jpg",
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"crater_id": "02-1-002927",
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"view": 1,
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"manual_verified": true
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}
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],
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"ground_truth": {
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"02-1-002927": ["02-1-002927"]
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}
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}
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```
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## Validation
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The repository includes one small validation utility:
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- `scripts/validate_release.py`: checks split consistency, relative paths, and train/test separation
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To validate the package locally:
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```bash
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python scripts/validate_release.py
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```
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## Notes
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- Image paths in manifests are relative and portable.
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- `splits/test.json` is the official benchmark entrypoint.
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- `splits/train.json` is intended for training or fine-tuning experiments.
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RELEASE_NOTES.md
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# Release Notes
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## release_v1
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This release package is cleaned for public publication.
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### Release-facing changes
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- removed the auxiliary canonical split from the public package
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- removed paper-era legacy manifests from the public package
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- removed machine-local path assumptions from scripts and docs
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- kept only official `train` and `test` split manifests
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- preserved raw query relevance provenance in `metadata/source/`
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- preserved gallery-filtered query relevance in `metadata/query_relevance.json`
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- added a minimal `timm` evaluation example
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- kept only the validation utility needed by end users
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### Official counts
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- train gallery: `47,882` images / `23,941` crater IDs
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- test gallery: `50,000` images / `25,000` crater IDs
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- test query: `5,000` images / `1,000` crater IDs
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### Hosting
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- primary: Hugging Face Datasets (`jfang/CraterBench-R`)
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- all images distributed as a single `images.zip` archive (1.8 GB)
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- extract with `unzip images.zip` to produce `images/gallery/` and `images/query/`
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examples/eval_timm_global.py
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|
| 1 |
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#!/usr/bin/env python3
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| 2 |
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"""
|
| 3 |
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Minimal high-level example: evaluate a pretrained timm model on CraterBench-R
|
| 4 |
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using one global descriptor per image and cosine retrieval.
|
| 5 |
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"""
|
| 6 |
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|
| 7 |
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from __future__ import annotations
|
| 8 |
+
|
| 9 |
+
import argparse
|
| 10 |
+
import json
|
| 11 |
+
from pathlib import Path
|
| 12 |
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from typing import Any
|
| 13 |
+
|
| 14 |
+
import numpy as np
|
| 15 |
+
import timm
|
| 16 |
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import torch
|
| 17 |
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import torch.nn.functional as F
|
| 18 |
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from PIL import Image
|
| 19 |
+
from timm.data import create_transform, resolve_model_data_config
|
| 20 |
+
from torch.utils.data import DataLoader, Dataset
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
class ImageDataset(Dataset):
|
| 24 |
+
def __init__(self, entries: list[dict[str, Any]], root: Path, transform):
|
| 25 |
+
self.entries = entries
|
| 26 |
+
self.root = root
|
| 27 |
+
self.transform = transform
|
| 28 |
+
|
| 29 |
+
def __len__(self) -> int:
|
| 30 |
+
return len(self.entries)
|
| 31 |
+
|
| 32 |
+
def __getitem__(self, idx: int):
|
| 33 |
+
entry = self.entries[idx]
|
| 34 |
+
path = self.root / entry["path"]
|
| 35 |
+
image = Image.open(path).convert("RGB")
|
| 36 |
+
return self.transform(image), entry
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
def collate_with_entries(batch):
|
| 40 |
+
images = torch.stack([item[0] for item in batch])
|
| 41 |
+
entries = [item[1] for item in batch]
|
| 42 |
+
return images, entries
|
| 43 |
+
|
| 44 |
+
|
| 45 |
+
def unwrap_features(features: Any) -> torch.Tensor:
|
| 46 |
+
if isinstance(features, torch.Tensor):
|
| 47 |
+
return features
|
| 48 |
+
if isinstance(features, (list, tuple)):
|
| 49 |
+
return unwrap_features(features[0])
|
| 50 |
+
if isinstance(features, dict):
|
| 51 |
+
for key in ("x", "features", "last_hidden_state"):
|
| 52 |
+
if key in features:
|
| 53 |
+
return unwrap_features(features[key])
|
| 54 |
+
for value in features.values():
|
| 55 |
+
if isinstance(value, torch.Tensor):
|
| 56 |
+
return value
|
| 57 |
+
raise TypeError(f"Unsupported feature output type: {type(features)!r}")
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
def pool_features(features: torch.Tensor, pool: str) -> torch.Tensor:
|
| 61 |
+
if features.ndim == 2:
|
| 62 |
+
return features
|
| 63 |
+
if features.ndim == 3:
|
| 64 |
+
if pool == "cls":
|
| 65 |
+
return features[:, 0]
|
| 66 |
+
if pool == "mean":
|
| 67 |
+
return features.mean(dim=1)
|
| 68 |
+
if pool == "max":
|
| 69 |
+
return features.max(dim=1).values
|
| 70 |
+
if features.ndim == 4:
|
| 71 |
+
if pool == "mean":
|
| 72 |
+
return features.mean(dim=(2, 3))
|
| 73 |
+
if pool == "max":
|
| 74 |
+
return features.amax(dim=(2, 3))
|
| 75 |
+
raise ValueError(f"Unsupported feature shape {tuple(features.shape)} for pool={pool}")
|
| 76 |
+
|
| 77 |
+
|
| 78 |
+
def extract_embeddings(
|
| 79 |
+
model,
|
| 80 |
+
loader: DataLoader,
|
| 81 |
+
device: torch.device,
|
| 82 |
+
pool: str,
|
| 83 |
+
) -> tuple[np.ndarray, list[dict[str, Any]]]:
|
| 84 |
+
all_embeddings = []
|
| 85 |
+
all_entries: list[dict[str, Any]] = []
|
| 86 |
+
|
| 87 |
+
model.eval()
|
| 88 |
+
with torch.no_grad():
|
| 89 |
+
for images, entries in loader:
|
| 90 |
+
images = images.to(device)
|
| 91 |
+
features = unwrap_features(model.forward_features(images))
|
| 92 |
+
pooled = pool_features(features, pool)
|
| 93 |
+
pooled = F.normalize(pooled, dim=1)
|
| 94 |
+
all_embeddings.append(pooled.cpu().numpy().astype(np.float32))
|
| 95 |
+
all_entries.extend(entries)
|
| 96 |
+
|
| 97 |
+
return np.concatenate(all_embeddings, axis=0), all_entries
|
| 98 |
+
|
| 99 |
+
|
| 100 |
+
def compute_metrics(
|
| 101 |
+
ranking: np.ndarray,
|
| 102 |
+
query_ids: list[str],
|
| 103 |
+
gallery_ids: list[str],
|
| 104 |
+
ground_truth: dict[str, list[str]],
|
| 105 |
+
k_values: list[int],
|
| 106 |
+
) -> dict[str, float]:
|
| 107 |
+
metrics: dict[str, float] = {}
|
| 108 |
+
max_k = ranking.shape[1]
|
| 109 |
+
|
| 110 |
+
for k in k_values:
|
| 111 |
+
recalls = []
|
| 112 |
+
for row, query_id in enumerate(query_ids):
|
| 113 |
+
acceptable = set(ground_truth[query_id])
|
| 114 |
+
retrieved = [gallery_ids[idx] for idx in ranking[row, :k]]
|
| 115 |
+
unique_correct = set(retrieved) & acceptable
|
| 116 |
+
recalls.append(min(len(unique_correct) / len(acceptable), 1.0))
|
| 117 |
+
metrics[f"recall@{k}"] = float(np.mean(recalls))
|
| 118 |
+
|
| 119 |
+
aps = []
|
| 120 |
+
reciprocal_ranks = []
|
| 121 |
+
for row, query_id in enumerate(query_ids):
|
| 122 |
+
acceptable = set(ground_truth[query_id])
|
| 123 |
+
retrieved = [gallery_ids[idx] for idx in ranking[row, :max_k]]
|
| 124 |
+
|
| 125 |
+
seen = set()
|
| 126 |
+
precision_at_k = []
|
| 127 |
+
rr = 0.0
|
| 128 |
+
for rank, crater_id in enumerate(retrieved, start=1):
|
| 129 |
+
if crater_id in acceptable and crater_id not in seen:
|
| 130 |
+
seen.add(crater_id)
|
| 131 |
+
precision_at_k.append(len(seen) / rank)
|
| 132 |
+
if rr == 0.0:
|
| 133 |
+
rr = 1.0 / rank
|
| 134 |
+
aps.append(float(np.mean(precision_at_k)) if precision_at_k else 0.0)
|
| 135 |
+
reciprocal_ranks.append(rr)
|
| 136 |
+
|
| 137 |
+
metrics["map"] = float(np.mean(aps))
|
| 138 |
+
metrics["mrr"] = float(np.mean(reciprocal_ranks))
|
| 139 |
+
return metrics
|
| 140 |
+
|
| 141 |
+
|
| 142 |
+
def search_topk(
|
| 143 |
+
query_embeddings: np.ndarray,
|
| 144 |
+
gallery_embeddings: np.ndarray,
|
| 145 |
+
topk: int,
|
| 146 |
+
device: torch.device,
|
| 147 |
+
query_chunk_size: int,
|
| 148 |
+
) -> np.ndarray:
|
| 149 |
+
gallery = torch.from_numpy(gallery_embeddings).to(device)
|
| 150 |
+
results = []
|
| 151 |
+
|
| 152 |
+
for start in range(0, len(query_embeddings), query_chunk_size):
|
| 153 |
+
end = min(start + query_chunk_size, len(query_embeddings))
|
| 154 |
+
query = torch.from_numpy(query_embeddings[start:end]).to(device)
|
| 155 |
+
scores = query @ gallery.T
|
| 156 |
+
indices = torch.topk(scores, k=topk, dim=1).indices.cpu().numpy()
|
| 157 |
+
results.append(indices)
|
| 158 |
+
|
| 159 |
+
return np.concatenate(results, axis=0)
|
| 160 |
+
|
| 161 |
+
|
| 162 |
+
def main() -> None:
|
| 163 |
+
parser = argparse.ArgumentParser()
|
| 164 |
+
parser.add_argument("--data-root", type=Path, default=Path("."))
|
| 165 |
+
parser.add_argument("--split", type=str, default="test")
|
| 166 |
+
parser.add_argument("--model", type=str, required=True)
|
| 167 |
+
parser.add_argument("--pool", type=str, default="mean", choices=["cls", "mean", "max"])
|
| 168 |
+
parser.add_argument("--batch-size", type=int, default=64)
|
| 169 |
+
parser.add_argument("--query-chunk-size", type=int, default=256)
|
| 170 |
+
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
|
| 171 |
+
args = parser.parse_args()
|
| 172 |
+
|
| 173 |
+
split_path = args.data_root / "splits" / f"{args.split}.json"
|
| 174 |
+
with split_path.open("r") as handle:
|
| 175 |
+
split = json.load(handle)
|
| 176 |
+
|
| 177 |
+
model = timm.create_model(args.model, pretrained=True, num_classes=0)
|
| 178 |
+
model.to(args.device)
|
| 179 |
+
|
| 180 |
+
data_config = resolve_model_data_config(model)
|
| 181 |
+
transform = create_transform(**data_config, is_training=False)
|
| 182 |
+
|
| 183 |
+
gallery_loader = DataLoader(
|
| 184 |
+
ImageDataset(split["gallery_images"], args.data_root, transform),
|
| 185 |
+
batch_size=args.batch_size,
|
| 186 |
+
shuffle=False,
|
| 187 |
+
num_workers=4,
|
| 188 |
+
pin_memory=True,
|
| 189 |
+
collate_fn=collate_with_entries,
|
| 190 |
+
)
|
| 191 |
+
query_loader = DataLoader(
|
| 192 |
+
ImageDataset(split["query_images"], args.data_root, transform),
|
| 193 |
+
batch_size=args.batch_size,
|
| 194 |
+
shuffle=False,
|
| 195 |
+
num_workers=4,
|
| 196 |
+
pin_memory=True,
|
| 197 |
+
collate_fn=collate_with_entries,
|
| 198 |
+
)
|
| 199 |
+
|
| 200 |
+
gallery_embeddings, gallery_entries = extract_embeddings(
|
| 201 |
+
model, gallery_loader, torch.device(args.device), args.pool
|
| 202 |
+
)
|
| 203 |
+
query_embeddings, query_entries = extract_embeddings(
|
| 204 |
+
model, query_loader, torch.device(args.device), args.pool
|
| 205 |
+
)
|
| 206 |
+
|
| 207 |
+
ranking = search_topk(
|
| 208 |
+
query_embeddings,
|
| 209 |
+
gallery_embeddings,
|
| 210 |
+
topk=10,
|
| 211 |
+
device=torch.device(args.device),
|
| 212 |
+
query_chunk_size=args.query_chunk_size,
|
| 213 |
+
)
|
| 214 |
+
|
| 215 |
+
gallery_ids = [entry["crater_id"] for entry in gallery_entries]
|
| 216 |
+
query_ids = [entry["crater_id"] for entry in query_entries]
|
| 217 |
+
metrics = compute_metrics(ranking, query_ids, gallery_ids, split["ground_truth"], [1, 5, 10])
|
| 218 |
+
|
| 219 |
+
print("Model:", args.model)
|
| 220 |
+
print("Pool:", args.pool)
|
| 221 |
+
for key, value in metrics.items():
|
| 222 |
+
print(f"{key}: {value:.4f}")
|
| 223 |
+
|
| 224 |
+
|
| 225 |
+
if __name__ == "__main__":
|
| 226 |
+
main()
|
metadata/query_relevance.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
metadata/source/retrieval_ground_truth_raw.csv
ADDED
|
@@ -0,0 +1,1001 @@
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
query_id,acceptable_ids,num_acceptable
|
| 2 |
+
02-1-002927,02-1-002927,1
|
| 3 |
+
02-1-002933,02-1-002933,1
|
| 4 |
+
02-1-003303,02-1-003303,1
|
| 5 |
+
02-1-003412,02-1-003412,1
|
| 6 |
+
02-1-003631,02-1-003631,1
|
| 7 |
+
02-1-003736,02-1-003736,1
|
| 8 |
+
02-1-003937,02-1-003937,1
|
| 9 |
+
02-1-004220,02-1-004220,1
|
| 10 |
+
02-1-004269,02-1-004266;02-1-004267;02-1-004268;02-1-004269;02-1-004295;02-1-004299,6
|
| 11 |
+
02-1-004297,02-1-004297,1
|
| 12 |
+
02-1-004400,02-1-004400,1
|
| 13 |
+
02-1-004972,02-1-004972,1
|
| 14 |
+
02-1-005002,02-1-005002,1
|
| 15 |
+
02-1-005076,02-1-005076,1
|
| 16 |
+
02-1-005205,02-1-005205,1
|
| 17 |
+
02-1-005214,02-1-005214,1
|
| 18 |
+
02-1-005282,02-1-005282,1
|
| 19 |
+
02-1-005330,02-1-005330;02-4-010924,2
|
| 20 |
+
02-1-005395,02-1-005395,1
|
| 21 |
+
02-1-005772,02-1-005772,1
|
| 22 |
+
02-1-005777,02-1-005777,1
|
| 23 |
+
02-1-006035,02-1-006035;02-1-006036,2
|
| 24 |
+
02-1-006063,02-1-006063,1
|
| 25 |
+
02-1-006158,02-1-006158,1
|
| 26 |
+
02-1-006183,02-1-006183,1
|
| 27 |
+
02-1-006250,02-1-006250,1
|
| 28 |
+
02-1-006790,02-1-006789;02-1-006790,2
|
| 29 |
+
02-1-006806,02-1-006806,1
|
| 30 |
+
02-1-006879,02-1-006879,1
|
| 31 |
+
02-1-006913,02-1-006913,1
|
| 32 |
+
02-1-006980,02-1-006980,1
|
| 33 |
+
02-1-007056,02-1-007056;02-1-007061,2
|
| 34 |
+
02-1-007087,02-1-007087,1
|
| 35 |
+
02-1-007120,02-1-007120,1
|
| 36 |
+
02-1-007397,02-1-007397,1
|
| 37 |
+
02-1-007437,02-1-007437,1
|
| 38 |
+
02-1-007536,02-1-007536,1
|
| 39 |
+
02-2-000113,02-2-000113,1
|
| 40 |
+
02-2-000158,02-2-000158,1
|
| 41 |
+
02-3-000276,02-3-000276,1
|
| 42 |
+
02-3-000282,02-3-000282,1
|
| 43 |
+
02-3-000310,02-1-006407;02-3-000310,2
|
| 44 |
+
02-4-005333,02-4-005333,1
|
| 45 |
+
02-4-005628,02-4-005628,1
|
| 46 |
+
02-4-006437,02-4-006437,1
|
| 47 |
+
02-4-006947,02-4-006947,1
|
| 48 |
+
02-4-007201,02-4-007201,1
|
| 49 |
+
02-4-007855,02-4-007855,1
|
| 50 |
+
02-4-008279,02-4-008279,1
|
| 51 |
+
02-4-008353,02-4-008353,1
|
| 52 |
+
02-4-010710,02-1-005396;02-4-010710,2
|
| 53 |
+
02-4-010947,02-4-010947,1
|
| 54 |
+
02-4-010972,02-1-005266;02-4-010972;14-1-007310,3
|
| 55 |
+
02-4-011306,02-4-011306,1
|
| 56 |
+
02-4-012111,02-4-012111,1
|
| 57 |
+
02-4-013063,02-4-013063,1
|
| 58 |
+
02-4-014285,02-1-007239;02-1-007240;02-4-014285,3
|
| 59 |
+
02-4-014877,02-4-014877,1
|
| 60 |
+
02-4-015023,02-4-015023,1
|
| 61 |
+
03-1-000817,03-1-000817,1
|
| 62 |
+
03-1-001061,03-1-001061,1
|
| 63 |
+
03-1-001067,03-1-001067,1
|
| 64 |
+
03-1-001156,03-1-001156;03-4-001614,2
|
| 65 |
+
03-1-001183,03-1-001183,1
|
| 66 |
+
03-1-001187,03-1-001187,1
|
| 67 |
+
03-1-001199,03-1-001199,1
|
| 68 |
+
03-1-001277,03-1-001277,1
|
| 69 |
+
03-1-001523,03-1-001523;03-4-002080,2
|
| 70 |
+
03-1-001624,03-1-001624,1
|
| 71 |
+
03-1-001626,03-1-001626,1
|
| 72 |
+
03-1-001721,03-1-001721,1
|
| 73 |
+
03-1-001756,03-1-001756,1
|
| 74 |
+
03-1-001879,03-1-001879,1
|
| 75 |
+
03-1-002275,03-1-002275;03-1-002277,2
|
| 76 |
+
03-1-002396,03-1-002396,1
|
| 77 |
+
03-1-002584,03-1-002584,1
|
| 78 |
+
03-1-002979,03-1-002979,1
|
| 79 |
+
03-1-003830,03-1-003096;03-1-003830;03-1-003845,3
|
| 80 |
+
03-1-003926,03-1-003926,1
|
| 81 |
+
03-1-004200,03-1-004200;03-1-004876,2
|
| 82 |
+
03-1-004356,03-1-004356;03-1-004357;03-1-004362,3
|
| 83 |
+
03-1-004795,03-1-004794;03-1-004795,2
|
| 84 |
+
03-1-005526,03-1-005526;03-1-005544,2
|
| 85 |
+
03-1-006098,03-1-006096;03-1-006098,2
|
| 86 |
+
03-1-006470,03-1-006470,1
|
| 87 |
+
03-1-006846,03-1-006846;03-1-006849,2
|
| 88 |
+
03-1-007132,03-1-007132;03-1-007133,2
|
| 89 |
+
03-1-007229,03-1-007229,1
|
| 90 |
+
03-1-007493,03-1-007493;03-1-007494,2
|
| 91 |
+
03-1-007904,03-1-007904,1
|
| 92 |
+
03-1-007914,03-1-007914,1
|
| 93 |
+
03-1-008030,03-1-008030,1
|
| 94 |
+
03-1-008140,03-1-008140;03-1-008156,2
|
| 95 |
+
03-1-008275,03-1-008275,1
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|
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14-1-012452,14-1-012452,1
|
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14-1-012474,14-1-012474;14-4-001949,2
|
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14-1-012656,14-1-012656,1
|
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14-1-012932,14-1-012932;14-1-012942,2
|
| 964 |
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14-1-013256,14-1-013256;14-4-001714,2
|
| 965 |
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14-1-013440,14-1-013440,1
|
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14-1-013663,14-1-013663,1
|
| 967 |
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14-2-000197,14-2-000197,1
|
| 968 |
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14-2-000238,14-2-000238;14-2-000239,2
|
| 969 |
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14-2-000450,14-1-011946;14-1-011961;14-2-000450,3
|
| 970 |
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14-4-000276,14-1-013590;14-4-000276,2
|
| 971 |
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14-4-000577,14-4-000577;15-1-000836,2
|
| 972 |
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14-4-000694,14-4-000694,1
|
| 973 |
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14-4-000800,14-1-013251;14-4-000800,2
|
| 974 |
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14-4-001518,14-1-013059;14-4-001518,2
|
| 975 |
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14-4-001973,14-4-001972;14-4-001973,2
|
| 976 |
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14-4-002736,14-4-002736,1
|
| 977 |
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14-4-003212,14-4-003211;14-4-003212,2
|
| 978 |
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14-4-004107,14-1-011923;14-4-004107,2
|
| 979 |
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14-4-004227,14-1-011759;14-4-004227,2
|
| 980 |
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14-4-004574,14-4-004574,1
|
| 981 |
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14-4-006884,14-4-006884,1
|
| 982 |
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14-4-006889,14-4-006889,1
|
| 983 |
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14-4-006898,14-4-006898,1
|
| 984 |
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14-4-007152,14-1-010722;14-4-007152,2
|
| 985 |
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14-4-007181,14-4-007181,1
|
| 986 |
+
14-4-007191,14-4-007191,1
|
| 987 |
+
14-4-007441,14-4-007441,1
|
| 988 |
+
14-4-007796,14-3-000209;14-4-007796,2
|
| 989 |
+
14-4-008197,14-4-008197,1
|
| 990 |
+
14-4-008236,14-4-008236,1
|
| 991 |
+
14-4-008411,14-3-000200;14-4-008411,2
|
| 992 |
+
14-4-008645,14-4-008645;14-4-008646,2
|
| 993 |
+
14-4-009463,14-4-009463,1
|
| 994 |
+
14-4-009549,14-1-009737;14-1-009738;14-4-009549,3
|
| 995 |
+
14-4-010174,14-4-010174,1
|
| 996 |
+
14-4-010737,14-4-010737,1
|
| 997 |
+
14-4-010842,14-4-010842,1
|
| 998 |
+
14-4-010936,14-4-010936,1
|
| 999 |
+
14-4-011053,14-4-011053,1
|
| 1000 |
+
14-4-011300,14-4-011300,1
|
| 1001 |
+
14-4-011329,14-4-011329,1
|
metadata/stats.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"release_version": "release_v1",
|
| 3 |
+
"official": {
|
| 4 |
+
"gallery_images": 50000,
|
| 5 |
+
"gallery_crater_ids": 25000,
|
| 6 |
+
"query_images": 5000,
|
| 7 |
+
"query_crater_ids": 1000,
|
| 8 |
+
"train_images": 47882,
|
| 9 |
+
"train_crater_ids": 23941,
|
| 10 |
+
"raw_multilabel_query_ids": 428,
|
| 11 |
+
"filtered_multilabel_query_ids": 59,
|
| 12 |
+
"max_filtered_relevant_ids": 3
|
| 13 |
+
}
|
| 14 |
+
}
|
requirements.txt
ADDED
|
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
numpy
|
| 2 |
+
Pillow
|
| 3 |
+
torch
|
| 4 |
+
torchvision
|
| 5 |
+
timm
|
scripts/validate_release.py
ADDED
|
@@ -0,0 +1,137 @@
|
|
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|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
"""
|
| 3 |
+
Validate the public CraterBench-R release package.
|
| 4 |
+
"""
|
| 5 |
+
|
| 6 |
+
from __future__ import annotations
|
| 7 |
+
|
| 8 |
+
import json
|
| 9 |
+
import zipfile
|
| 10 |
+
from pathlib import Path
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
RELEASE_ROOT = Path(__file__).resolve().parents[1]
|
| 14 |
+
LOCAL_STRING_PATTERNS = [
|
| 15 |
+
"/" + "lstr" + "/",
|
| 16 |
+
"mars" + "sim/" + "jfang",
|
| 17 |
+
"z" + "1977143",
|
| 18 |
+
"sa" + "hara" + "/mar" + "ssim",
|
| 19 |
+
]
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
def load_json(path: Path) -> dict:
|
| 23 |
+
with path.open("r") as handle:
|
| 24 |
+
return json.load(handle)
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
def _build_file_index() -> set[str] | None:
|
| 28 |
+
"""Return set of image paths from extracted dirs or images.zip."""
|
| 29 |
+
images_dir = RELEASE_ROOT / "images"
|
| 30 |
+
if images_dir.is_dir():
|
| 31 |
+
paths = set()
|
| 32 |
+
for p in images_dir.rglob("*.jpg"):
|
| 33 |
+
paths.add(str(p.relative_to(RELEASE_ROOT)))
|
| 34 |
+
return paths
|
| 35 |
+
|
| 36 |
+
zip_path = RELEASE_ROOT / "images.zip"
|
| 37 |
+
if zip_path.exists():
|
| 38 |
+
with zipfile.ZipFile(zip_path) as zf:
|
| 39 |
+
return set(zf.namelist())
|
| 40 |
+
|
| 41 |
+
return None
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
def assert_relative_paths(
|
| 45 |
+
entries: list[dict[str, object]], file_index: set[str] | None
|
| 46 |
+
) -> None:
|
| 47 |
+
for entry in entries:
|
| 48 |
+
relpath = str(entry["path"])
|
| 49 |
+
if Path(relpath).is_absolute():
|
| 50 |
+
raise ValueError(f"Absolute path found in manifest: {relpath}")
|
| 51 |
+
if file_index is not None and relpath not in file_index:
|
| 52 |
+
raise FileNotFoundError(f"Missing file referenced by manifest: {relpath}")
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
def validate_split_manifests() -> None:
|
| 56 |
+
test_manifest = load_json(RELEASE_ROOT / "splits" / "test.json")
|
| 57 |
+
train_manifest = load_json(RELEASE_ROOT / "splits" / "train.json")
|
| 58 |
+
|
| 59 |
+
file_index = _build_file_index()
|
| 60 |
+
if file_index is None:
|
| 61 |
+
print(" warning: no images/ dir or images.zip found, skipping file checks")
|
| 62 |
+
|
| 63 |
+
assert_relative_paths(test_manifest["gallery_images"], file_index)
|
| 64 |
+
assert_relative_paths(test_manifest["query_images"], file_index)
|
| 65 |
+
assert_relative_paths(train_manifest["gallery_images"], file_index)
|
| 66 |
+
|
| 67 |
+
if train_manifest["query_images"]:
|
| 68 |
+
raise ValueError("Official train split must not contain query images.")
|
| 69 |
+
if train_manifest["ground_truth"]:
|
| 70 |
+
raise ValueError("Official train split must not contain ground truth.")
|
| 71 |
+
|
| 72 |
+
gallery_ids = {str(entry["crater_id"]) for entry in test_manifest["gallery_images"]}
|
| 73 |
+
query_ids = {str(entry["crater_id"]) for entry in test_manifest["query_images"]}
|
| 74 |
+
ground_truth = test_manifest["ground_truth"]
|
| 75 |
+
|
| 76 |
+
if query_ids != set(ground_truth):
|
| 77 |
+
missing = sorted(query_ids - set(ground_truth))
|
| 78 |
+
extra = sorted(set(ground_truth) - query_ids)
|
| 79 |
+
raise ValueError(
|
| 80 |
+
f"Mismatch between query crater IDs and ground truth keys. Missing={missing[:5]} Extra={extra[:5]}"
|
| 81 |
+
)
|
| 82 |
+
|
| 83 |
+
for crater_id, relevant_ids in ground_truth.items():
|
| 84 |
+
if not relevant_ids:
|
| 85 |
+
raise ValueError(f"Empty ground-truth entry for query crater {crater_id}")
|
| 86 |
+
if len(relevant_ids) != len(set(relevant_ids)):
|
| 87 |
+
raise ValueError(f"Duplicate relevant IDs in ground truth for {crater_id}")
|
| 88 |
+
if any(relevant_id not in gallery_ids for relevant_id in relevant_ids):
|
| 89 |
+
raise ValueError(f"Non-gallery relevant ID found in ground truth for {crater_id}")
|
| 90 |
+
|
| 91 |
+
train_ids = {str(entry["crater_id"]) for entry in train_manifest["gallery_images"]}
|
| 92 |
+
excluded_ids = set(ground_truth)
|
| 93 |
+
for relevant_ids in ground_truth.values():
|
| 94 |
+
excluded_ids.update(relevant_ids)
|
| 95 |
+
overlap = train_ids & excluded_ids
|
| 96 |
+
if overlap:
|
| 97 |
+
raise ValueError(
|
| 98 |
+
f"Train split overlaps with test relevance closure: {sorted(overlap)[:10]}"
|
| 99 |
+
)
|
| 100 |
+
|
| 101 |
+
|
| 102 |
+
def scan_for_local_strings() -> None:
|
| 103 |
+
text_files = [
|
| 104 |
+
*RELEASE_ROOT.glob("*.md"),
|
| 105 |
+
*RELEASE_ROOT.glob("*.txt"),
|
| 106 |
+
*RELEASE_ROOT.glob("*.toml"),
|
| 107 |
+
*RELEASE_ROOT.glob("*.yml"),
|
| 108 |
+
*RELEASE_ROOT.glob("*.yaml"),
|
| 109 |
+
*RELEASE_ROOT.glob("scripts/*.py"),
|
| 110 |
+
*RELEASE_ROOT.glob("examples/*.py"),
|
| 111 |
+
*RELEASE_ROOT.glob("splits/*.json"),
|
| 112 |
+
*RELEASE_ROOT.glob("metadata/*.json"),
|
| 113 |
+
]
|
| 114 |
+
|
| 115 |
+
for path in text_files:
|
| 116 |
+
content = path.read_text()
|
| 117 |
+
for pattern in LOCAL_STRING_PATTERNS:
|
| 118 |
+
if pattern in content:
|
| 119 |
+
raise ValueError(f"Found local string {pattern!r} in {path}")
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
def main() -> None:
|
| 123 |
+
validate_split_manifests()
|
| 124 |
+
scan_for_local_strings()
|
| 125 |
+
|
| 126 |
+
test_manifest = load_json(RELEASE_ROOT / "splits" / "test.json")
|
| 127 |
+
train_manifest = load_json(RELEASE_ROOT / "splits" / "train.json")
|
| 128 |
+
print("Release package validation passed.")
|
| 129 |
+
print(
|
| 130 |
+
f" test: {len(test_manifest['gallery_images'])} gallery / "
|
| 131 |
+
f"{len(test_manifest['query_images'])} query"
|
| 132 |
+
)
|
| 133 |
+
print(f" train: {len(train_manifest['gallery_images'])} gallery")
|
| 134 |
+
|
| 135 |
+
|
| 136 |
+
if __name__ == "__main__":
|
| 137 |
+
main()
|
splits/test.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
splits/train.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|