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"""Create a hard multiple choice subset of the ImageNet validation split based on human accuracy annotation data.""" |
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from typing import Dict |
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import argparse |
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import json |
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import pickle |
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import numpy as np |
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import scipy.io |
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def get_imagenet_labels() -> Dict[str, str]: |
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"""Return ground truth wnids.""" |
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with open("ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt") as fp: |
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ilsvrc_idxs = [int(line.strip()) for line in fp] |
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ilsvrc_metadata = scipy.io.loadmat("ILSVRC2012_devkit_t12/data/meta.mat", simplify_cells=True) |
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ilsvrc_idx2wnid = { |
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synset['ILSVRC2012_ID']: synset['WNID'] |
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for synset in ilsvrc_metadata['synsets'] |
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} |
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return { |
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f"ILSVRC2012_val_{img_id:0>8}.JPEG": ilsvrc_idx2wnid[idx] |
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for img_id, idx in enumerate(ilsvrc_idxs, start=1) |
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} |
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def construct_ancestor_map(wnids: list): |
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"""Construct map of deepest common ancestor for all pairs of ImageNet classes.""" |
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ancestor_map = np.zeros((len(wnids), len(wnids)), dtype=np.uint8) |
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def iterate_postorder(node: dict, depth: int): |
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if node['children'] is None: |
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idx = wnids.index(node['wnid']) |
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ancestor_map[idx, idx] = depth |
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return [idx] |
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all_leaves = [ |
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iterate_postorder(child, depth + 1) |
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for child in node['children'].values() |
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] |
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for branch in range(len(all_leaves)): |
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for other in range(len(all_leaves)): |
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if other == branch: |
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continue |
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for leaf_a in all_leaves[branch]: |
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for leaf_b in all_leaves[other]: |
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ancestor_map[leaf_a, leaf_b] = max(ancestor_map[leaf_a, leaf_b], depth) |
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return sum(all_leaves, []) |
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with open("imagenet_hierarchy.json") as fp: |
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root = json.load(fp)['tree'] |
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iterate_postorder(root, 0) |
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return ancestor_map |
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def get_close_examples(num_choices: int = 4, seed: int | np.random.Generator = None): |
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""" |
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Construct semi-hard MCQA examples using ImageNet hierarchy. |
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""" |
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rng = np.random.default_rng(seed) |
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with open("imagenet_wnids.txt") as fp: |
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wnids = [line.strip() for line in fp] |
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gt_labels = get_imagenet_labels() |
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ancestor_map = construct_ancestor_map(wnids) |
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with open("human_accuracy_annotations.pkl", "rb") as fp: |
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annotation_data = pickle.load(fp) |
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examples = [] |
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for imgname, annot in annotation_data['initial_annots'].items(): |
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if imgname.startswith("ILSVRC2012"): |
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if gt_labels[imgname] not in annot.get('wrong', []): |
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wrong_choices = [] |
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gt_idx = wnids.index(gt_labels[imgname]) |
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for depth in range(ancestor_map[gt_idx, gt_idx] - 1, -1, -1): |
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if len(wrong_choices) >= num_choices - 1: |
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break |
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neighbors = [ |
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wnids[idx] |
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for idx in np.nonzero(ancestor_map[gt_idx] == depth)[0] |
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if wnids[idx] not in annot.get('correct', []) |
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] |
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if num_choices - 1 - len(wrong_choices) < len(neighbors): |
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wrong_choices += list(rng.choice(neighbors, size=num_choices - 1 - len(wrong_choices), replace=False)) |
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else: |
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wrong_choices += neighbors |
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examples.append({ |
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'image': imgname, |
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'choices': [gt_labels[imgname]] + list(wrong_choices), |
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'correct_answer': gt_labels[imgname] |
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}) |
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return examples |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--n-choices", "-n", type=int, required=True) |
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parser.add_argument("--output", "-o", type=str, default=None) |
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parser.add_argument("--seed", type=int, default=42) |
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args = parser.parse_args() |
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dataset = get_close_examples(args.n_choices, seed=args.seed) |
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print(f"No. of examples: {len(dataset)}") |
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if args.output: |
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with open(args.output, "w") as fp: |
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json.dump(dataset, fp, indent=2) |
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print(f"Saved to '{args.output}'") |
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