#!/usr/bin/env python import argparse import json from os.path import basename, splitext import os import mmengine import numpy as np import pandas as pd import torch from numpy.linalg import norm, pinv from scipy.special import logsumexp, softmax from sklearn import metrics from sklearn.covariance import EmpiricalCovariance from sklearn.metrics import pairwise_distances_argmin_min from tqdm import tqdm import pickle from os.path import dirname import torchvision as tv from PIL import Image from mmpretrain.apis import init_model def parse_args(): parser = argparse.ArgumentParser(description='Detect an image') parser.add_argument( '--cfg', help='Path to config', default='/dataset/jingyaoli/AD/MOOD_/MOODv2/configs/beit-base-p16_224px.py') parser.add_argument('--ood_feature', default=None, help='Path to ood feature file') parser.add_argument( '--checkpoint', help='Path to checkpoint', default='/dataset/jingyaoli/AD/MOODv2/pretrain/beit-base_3rdparty_in1k_20221114-c0a4df23.pth',) parser.add_argument('--img_path', help='Path to image', default='/dataset/jingyaoli/AD/MOOD_/MOODv2/imgs/DTD_cracked_0004.jpg') parser.add_argument('--fc', default='/dataset/jingyaoli/AD/MOODv2/outputs/beit-224px/fc.pkl', help='Path to fc path') parser.add_argument('--id_data', default='imagenet', help='id data name') parser.add_argument('--id_train_feature', default='/dataset/jingyaoli/AD/MOODv2/outputs/beit-224px/imagenet_train.pkl', help='Path to data') parser.add_argument('--id_val_feature', default='/dataset/jingyaoli/AD/MOODv2/outputs/beit-224px/imagenet_test.pkl', help='Path to output file') parser.add_argument('--ood_features', default=None, nargs='+', help='Path to ood features') parser.add_argument( '--methods', nargs='+', default=['MSP', 'MaxLogit', 'Energy', 'Energy+React', 'ViM', 'Residual', 'GradNorm', 'Mahalanobis', ], # 'KL-Matching' help='methods') parser.add_argument( '--train_label', default='datalists/imagenet2012_train_random_200k.txt', help='Path to train labels') parser.add_argument( '--clip_quantile', default=0.99, help='Clip quantile to react') parser.add_argument( '--fpr', default=95, help='False Positive Rate') return parser.parse_args() def evaluate(method, score_id, score_ood, target_fpr): threhold = np.percentile(score_id, 100 - target_fpr) if score_ood >= threhold: print('\033[94m', method, '\033[0m', 'evaluation:', '\033[92m', 'in-distribution', '\033[0m') else: print('\033[94m', method, '\033[0m', 'evaluation:', '\033[91m', 'out-of-distribution', '\033[0m') def kl(p, q): return np.sum(np.where(p != 0, p * np.log(p / q), 0)) def gradnorm(x, w, b, num_cls): fc = torch.nn.Linear(*w.shape[::-1]) fc.weight.data[...] = torch.from_numpy(w) fc.bias.data[...] = torch.from_numpy(b) fc.cuda() x = torch.from_numpy(x).float().cuda() logsoftmax = torch.nn.LogSoftmax(dim=-1).cuda() confs = [] for i in tqdm(x, desc='Computing Gradnorm ID/OOD score'): targets = torch.ones((1, num_cls)).cuda() fc.zero_grad() loss = torch.mean( torch.sum(-targets * logsoftmax(fc(i[None])), dim=-1)) loss.backward() layer_grad_norm = torch.sum(torch.abs( fc.weight.grad.data)).cpu().numpy() confs.append(layer_grad_norm) return np.array(confs) def extract_image_feature(args): torch.backends.cudnn.benchmark = True print('=> Loading model') cfg = mmengine.Config.fromfile(args.cfg) model = init_model(cfg, args.checkpoint, 0).cuda().eval() print('=> Loading image') if hasattr(cfg.model.backbone, 'img_size'): img_size = cfg.model.backbone.img_size else: img_size = 224 transform = tv.transforms.Compose([ tv.transforms.Resize((img_size, img_size)), tv.transforms.ToTensor(), tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) x = transform(Image.open(args.img_path).convert('RGB')).unsqueeze(0) print('=> Extracting feature') with torch.no_grad(): x = x.cuda() if cfg.model.backbone.type == 'BEiTPretrainViT': # (B, L, C) -> (B, C) feat_batch = model.backbone( x, mask=None)[0].mean(1) elif cfg.model.backbone.type == 'SwinTransformer': # (B, C, H, W) -> (B, C) feat_batch = model.backbone(x)[0] B, C, H, W = feat_batch.shape feat_batch = feat_batch.reshape(B, C, -1).mean(-1) else: # (B, C) feat_batch = model.backbone(x)[0] assert len(feat_batch.shape) == 2 feature = feat_batch.cpu().numpy() print(f'Extracted Feature: {feature.shape}') return feature def main(): args = parse_args() if args.ood_feature and os.path.exists(args.ood_feature): feature_ood = mmengine.load(args.ood_feature) else: feature_ood = extract_image_feature(args) if os.path.exists(args.fc): w, b = mmengine.load(args.fc) print(f'{w.shape=}, {b.shape=}') num_cls = len(b) train_labels = np.array([ int(line.rsplit(' ', 1)[-1]) for line in mmengine.list_from_file(args.train_label) ], dtype=int) print(f'image path: {args.img_path}') print('=> Loading features') feature_id_train = mmengine.load(args.id_train_feature).squeeze() feature_id_val = mmengine.load(args.id_val_feature).squeeze() print(f'{feature_id_train.shape=}, {feature_id_val.shape=}') if os.path.exists(args.fc): print('=> Computing logits...') logit_id_train = feature_id_train @ w.T + b logit_id_val = feature_id_val @ w.T + b logit_ood = feature_ood @ w.T + b print('=> Computing softmax...') softmax_id_train = softmax(logit_id_train, axis=-1) softmax_id_val = softmax(logit_id_val, axis=-1) softmax_ood = softmax(logit_ood, axis=-1) u = -np.matmul(pinv(w), b) # --------------------------------------- method = 'MSP' if method in args.methods: score_id = softmax_id_val.max(axis=-1) score_ood = softmax_ood.max(axis=-1) result = evaluate(method, score_id, score_ood, args.fpr) # --------------------------------------- method = 'MaxLogit' if method in args.methods: score_id = logit_id_val.max(axis=-1) score_ood = logit_ood.max(axis=-1) result = evaluate(method, score_id, score_ood, args.fpr) # --------------------------------------- method = 'Energy' if method in args.methods: score_id = logsumexp(logit_id_val, axis=-1) score_ood = logsumexp(logit_ood, axis=-1) result = evaluate(method, score_id, score_ood, args.fpr) # --------------------------------------- method = 'Energy+React' if method in args.methods: clip = np.quantile(feature_id_train, args.clip_quantile) logit_id_val_clip = np.clip( feature_id_val, a_min=None, a_max=clip) @ w.T + b score_id = logsumexp(logit_id_val_clip, axis=-1) logit_ood_clip = np.clip(feature_ood, a_min=None, a_max=clip) @ w.T + b score_ood = logsumexp(logit_ood_clip, axis=-1) result = evaluate(method, score_id, score_ood, args.fpr) # --------------------------------------- method = 'ViM' if method in args.methods: if feature_id_val.shape[-1] >= 2048: DIM = num_cls elif feature_id_val.shape[-1] >= 768: DIM = 512 else: DIM = feature_id_val.shape[-1] // 2 ec = EmpiricalCovariance(assume_centered=True) ec.fit(feature_id_train - u) eig_vals, eigen_vectors = np.linalg.eig(ec.covariance_) NS = np.ascontiguousarray( (eigen_vectors.T[np.argsort(eig_vals * -1)[DIM:]]).T) vlogit_id_train = norm(np.matmul(feature_id_train - u, NS), axis=-1) alpha = logit_id_train.max(axis=-1).mean() / vlogit_id_train.mean() vlogit_id_val = norm(np.matmul(feature_id_val - u, NS), axis=-1) * alpha energy_id_val = logsumexp(logit_id_val, axis=-1) score_id = -vlogit_id_val + energy_id_val energy_ood = logsumexp(logit_ood, axis=-1) vlogit_ood = norm(np.matmul(feature_ood - u, NS), axis=-1) * alpha score_ood = -vlogit_ood + energy_ood result = evaluate(method, score_id, score_ood, args.fpr) # --------------------------------------- method = 'Residual' if method in args.methods: if feature_id_val.shape[-1] >= 2048: DIM = 1000 elif feature_id_val.shape[-1] >= 768: DIM = 512 else: DIM = feature_id_val.shape[-1] // 2 ec = EmpiricalCovariance(assume_centered=True) ec.fit(feature_id_train - u) eig_vals, eigen_vectors = np.linalg.eig(ec.covariance_) NS = np.ascontiguousarray( (eigen_vectors.T[np.argsort(eig_vals * -1)[DIM:]]).T) score_id = -norm(np.matmul(feature_id_val - u, NS), axis=-1) score_ood = -norm(np.matmul(feature_ood - u, NS), axis=-1) result = evaluate(method, score_id, score_ood, args.fpr) # --------------------------------------- method = 'GradNorm' if method in args.methods: score_ood = gradnorm(feature_ood, w, b, num_cls) score_id = gradnorm(feature_id_val, w, b, num_cls) result = evaluate(method, score_id, score_ood, args.fpr) # --------------------------------------- method = 'Mahalanobis' if method in args.methods: train_means = [] train_feat_centered = [] for i in tqdm(range(train_labels.max() + 1), desc='Computing classwise mean feature'): fs = feature_id_train[train_labels == i] _m = fs.mean(axis=0) train_means.append(_m) train_feat_centered.extend(fs - _m) ec = EmpiricalCovariance(assume_centered=True) ec.fit(np.array(train_feat_centered).astype(np.float64)) mean = torch.from_numpy(np.array(train_means)).cuda().float() prec = torch.from_numpy(ec.precision_).cuda().float() score_id = -np.array( [(((f - mean) @ prec) * (f - mean)).sum(axis=-1).min().cpu().item() for f in tqdm(torch.from_numpy(feature_id_val).cuda().float(), desc='Computing Mahalanobis ID score')]) score_ood = -np.array([ (((f - mean) @ prec) * (f - mean)).sum(axis=-1).min().cpu().item() for f in tqdm(torch.from_numpy(feature_ood).cuda().float(), desc='Computing Mahalanobis OOD score') ]) result = evaluate(method, score_id, score_ood, args.fpr) # --------------------------------------- method = 'KL-Matching' if method in args.methods: pred_labels_train = np.argmax(softmax_id_train, axis=-1) mean_softmax_train = [] for i in tqdm(range(num_cls), desc='Computing classwise mean softmax'): mean_softmax = softmax_id_train[pred_labels_train == i] if mean_softmax.shape[0] == 0: mean_softmax_train.append(np.zeros((num_cls))) else: mean_softmax_train.append(np.mean(mean_softmax, axis=0)) score_id = -pairwise_distances_argmin_min( softmax_id_val, np.array(mean_softmax_train), metric=kl)[1] score_ood = -pairwise_distances_argmin_min( softmax_ood, np.array(mean_softmax_train), metric=kl)[1] result = evaluate(method, score_id, score_ood, args.fpr) if __name__ == '__main__': main()