File size: 4,297 Bytes
4db2bfd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
"""Create a hard multiple choice subset of the ImageNet validation split based on human accuracy annotation data."""
from typing import Dict
import argparse
import json
import pickle
import numpy as np
import scipy.io
def get_imagenet_labels() -> Dict[str, str]:
"""Return ground truth wnids."""
with open("ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt") as fp:
ilsvrc_idxs = [int(line.strip()) for line in fp]
ilsvrc_metadata = scipy.io.loadmat("ILSVRC2012_devkit_t12/data/meta.mat", simplify_cells=True)
ilsvrc_idx2wnid = {
synset['ILSVRC2012_ID']: synset['WNID']
for synset in ilsvrc_metadata['synsets']
}
return {
f"ILSVRC2012_val_{img_id:0>8}.JPEG": ilsvrc_idx2wnid[idx]
for img_id, idx in enumerate(ilsvrc_idxs, start=1)
}
def construct_ancestor_map(wnids: list):
"""Construct map of deepest common ancestor for all pairs of ImageNet classes."""
ancestor_map = np.zeros((len(wnids), len(wnids)), dtype=np.uint8)
def iterate_postorder(node: dict, depth: int):
# Leaf node
if node['children'] is None:
idx = wnids.index(node['wnid'])
ancestor_map[idx, idx] = depth
return [idx]
# Iterate over children
all_leaves = [
iterate_postorder(child, depth + 1)
for child in node['children'].values()
]
# Connect branches together
for branch in range(len(all_leaves)):
for other in range(len(all_leaves)):
if other == branch:
continue
for leaf_a in all_leaves[branch]:
for leaf_b in all_leaves[other]:
ancestor_map[leaf_a, leaf_b] = max(ancestor_map[leaf_a, leaf_b], depth)
return sum(all_leaves, [])
with open("imagenet_hierarchy.json") as fp:
root = json.load(fp)['tree']
iterate_postorder(root, 0)
return ancestor_map
def get_close_examples(num_choices: int = 4, seed: int | np.random.Generator = None):
"""
Construct semi-hard MCQA examples using ImageNet hierarchy.
"""
rng = np.random.default_rng(seed)
with open("imagenet_wnids.txt") as fp:
wnids = [line.strip() for line in fp]
gt_labels = get_imagenet_labels()
ancestor_map = construct_ancestor_map(wnids)
with open("human_accuracy_annotations.pkl", "rb") as fp:
annotation_data = pickle.load(fp)
examples = []
for imgname, annot in annotation_data['initial_annots'].items():
if imgname.startswith("ILSVRC2012"):
if gt_labels[imgname] not in annot.get('wrong', []):
wrong_choices = []
# Fill in answer choices from hierarchy
gt_idx = wnids.index(gt_labels[imgname])
for depth in range(ancestor_map[gt_idx, gt_idx] - 1, -1, -1):
if len(wrong_choices) >= num_choices - 1:
break
neighbors = [
wnids[idx]
for idx in np.nonzero(ancestor_map[gt_idx] == depth)[0]
if wnids[idx] not in annot.get('correct', [])
]
if num_choices - 1 - len(wrong_choices) < len(neighbors):
wrong_choices += list(rng.choice(neighbors, size=num_choices - 1 - len(wrong_choices), replace=False))
else:
wrong_choices += neighbors
examples.append({
'image': imgname,
'choices': [gt_labels[imgname]] + list(wrong_choices),
'correct_answer': gt_labels[imgname]
})
return examples
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--n-choices", "-n", type=int, required=True)
parser.add_argument("--output", "-o", type=str, default=None)
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
dataset = get_close_examples(args.n_choices, seed=args.seed)
print(f"No. of examples: {len(dataset)}")
if args.output:
with open(args.output, "w") as fp:
json.dump(dataset, fp, indent=2)
print(f"Saved to '{args.output}'")
|