stage_0/infer.py

"""Reference inference for the Stage 0 baseline.

Loads Argus (EUPE-ViT-B backbone), reads the classifier config, and scores one
or more images. Prints the raw score and the binary decision.

Usage: python infer.py image1.jpg [image2.jpg ...]
"""
import json, sys, os
import torch
import torch.nn.functional as F
from PIL import Image
import numpy as np
from transformers import AutoModel


def load_classifier(path='classifier.json'):
    with open(path) as f:
        return json.load(f)


def load_argus(repo_or_path='phanerozoic/argus'):
    return AutoModel.from_pretrained(repo_or_path, trust_remote_code=True)


def preprocess(image_path, resolution=768, device='cuda'):
    img = Image.open(image_path).convert('RGB').resize((resolution, resolution), Image.BILINEAR)
    arr = np.asarray(img, dtype=np.uint8).copy()
    x = torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0).to(device).float() / 255.0
    mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
    std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
    return (x - mean) / std


@torch.inference_mode()
def score(model, x, classifier):
    with torch.autocast('cuda', dtype=torch.bfloat16):
        out = model.backbone.forward_features(x)
    patches = out['x_norm_patchtokens'].float().squeeze(0)
    D = classifier['feature_dim']
    ln = F.layer_norm(patches, [D])
    pooled = ln.max(dim=0).values
    pos = pooled[classifier['pos_dims']].sum()
    neg = pooled[classifier['neg_dims']].sum()
    return float((pos - neg).item())


def main():
    if len(sys.argv) < 2:
        print('usage: python infer.py <image1> [image2 ...]')
        sys.exit(1)
    here = os.path.dirname(os.path.abspath(__file__))
    classifier = load_classifier(os.path.join(here, 'classifier.json'))
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    model = load_argus().to(device).eval()
    thr = classifier['threshold']
    for image_path in sys.argv[1:]:
        x = preprocess(image_path, classifier['input_resolution'], device)
        s = score(model, x, classifier)
        print(f'{image_path}  score={s:+.3f}  threshold={thr:+.3f}  person={s > thr}')


if __name__ == '__main__':
    main()