深度学习论文: PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

深度学习论文: PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
PDF: https://arxiv.org/pdf/2011.08785.pdf
PyTorch代码: https://github.com/shanglianlm0525/CvPytorch
PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks

1 概述

PaDiM 在单类学习中同时检测和定位图像中的异常，PaDiM利用一个预先训练好的卷积神经网络(CNN)进行patch嵌入，利用多元高斯分布得到正态类的概率表示。同时利用了CNN的不同语义级别之间的相关性来更好地定位异常。

2 Patch Distribution Modeling(PaDiM)

PaDiM是一种基于图像Patch的算法。它依赖于预先训练好的CNN功能提取器。 将图像分解为多个面片，并使用不同的特征提取层从每个面片中提取嵌入。 将不同层次的激活向量串联起来，得到包含不同语义层次和分辨率信息的嵌入向量。这有助于对细粒度和全局上下文进行编码。 然而，由于生成的嵌入向量可能携带冗余信息，因此使用随机选择来降低维数。 在整个训练批次中，为每个面片嵌入生成一个多元高斯分布。因此，对于训练图像集的每个面片，我们有不同的多元高斯分布。这些高斯分布表示为高斯参数矩阵。 在推理过程中，使用马氏距离对测试图像的每个面片位置进行评分。它使用训练期间为面片计算的协方差矩阵的逆矩阵。 马氏距离矩阵形成了异常图，分数越高表示异常区域。

3 PyTorch代码

# !/usr/bin/env python
# -- coding: utf-8 --
# @Time : 2023/6/2 15:07
# @Author : liumin
# @File : run_PaDiM.py

import random
from random import sample
import argparse
import numpy as np
import os
import pickle
from tqdm import tqdm
from collections import OrderedDict
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import precision_recall_curve
from sklearn.covariance import LedoitWolf
from scipy.spatial.distance import mahalanobis
from scipy.ndimage import gaussian_filter
from skimage import morphology
from skimage.segmentation import mark_boundaries
import matplotlib.pyplot as plt
import matplotlib

import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision.models import wide_resnet50_2, resnet18
import os
# import tarfile
from PIL import Image
from tqdm import tqdm
# import urllib.request

import torch
from torch.utils.data import Dataset
from torchvision import transforms as T


random.seed(1024)
torch.manual_seed(1024)
torch.cuda.manual_seed_all(1024)


# URL = 'ftp://guest:GU.205dldo@ftp.softronics.ch/mvtec_anomaly_detection/mvtec_anomaly_detection.tar.xz'
CLASS_NAMES = ['bottle', 'cable', 'capsule', 'carpet', 'grid',
               'hazelnut', 'leather', 'metal_nut', 'pill', 'screw',
               'tile', 'toothbrush', 'transistor', 'wood', 'zipper']


class MVTecDataset(Dataset):
    def __init__(self, dataset_path='/home/liumin/data/mvtec_ad/', class_name='bottle', is_train=True,
                 resize=256, cropsize=224):
        assert class_name in CLASS_NAMES, 'class_name: {}, should be in {}'.format(class_name, CLASS_NAMES)
        self.dataset_path = dataset_path
        self.class_name = class_name
        self.is_train = is_train
        self.resize = resize
        self.cropsize = cropsize
        # self.mvtec_folder_path = os.path.join(root_path, 'mvtec_anomaly_detection')

        # download dataset if not exist
        # self.download()

        # load dataset
        self.x, self.y, self.mask = self.load_dataset_folder()

        # set transforms
        self.transform_x = T.Compose([T.Resize(resize, Image.ANTIALIAS),
                                      T.CenterCrop(cropsize),
                                      T.ToTensor(),
                                      T.Normalize(mean=[0.485, 0.456, 0.406],
                                                  std=[0.229, 0.224, 0.225])])
        self.transform_mask = T.Compose([T.Resize(resize, Image.NEAREST),
                                         T.CenterCrop(cropsize),
                                         T.ToTensor()])

    def __getitem__(self, idx):
        x, y, mask = self.x[idx], self.y[idx], self.mask[idx]

        x = Image.open(x).convert('RGB')
        x = self.transform_x(x)

        if y == 0:
            mask = torch.zeros([1, self.cropsize, self.cropsize])
        else:
            mask = Image.open(mask)
            mask = self.transform_mask(mask)

        return x, y, mask

    def __len__(self):
        return len(self.x)

    def load_dataset_folder(self):
        phase = 'train' if self.is_train else 'test'
        x, y, mask = [], [], []

        img_dir = os.path.join(self.dataset_path, self.class_name, phase)
        gt_dir = os.path.join(self.dataset_path, self.class_name, 'ground_truth')

        img_types = sorted(os.listdir(img_dir))
        for img_type in img_types:

            # load images
            img_type_dir = os.path.join(img_dir, img_type)
            if not os.path.isdir(img_type_dir):
                continue
            img_fpath_list = sorted([os.path.join(img_type_dir, f)
                                     for f in os.listdir(img_type_dir)
                                     if f.endswith('.png')])
            x.extend(img_fpath_list)

            # load gt labels
            if img_type == 'good':
                y.extend([0] * len(img_fpath_list))
                mask.extend([None] * len(img_fpath_list))
            else:
                y.extend([1] * len(img_fpath_list))
                gt_type_dir = os.path.join(gt_dir, img_type)
                img_fname_list = [os.path.splitext(os.path.basename(f))[0] for f in img_fpath_list]
                gt_fpath_list = [os.path.join(gt_type_dir, img_fname + '_mask.png')
                                 for img_fname in img_fname_list]
                mask.extend(gt_fpath_list)

        assert len(x) == len(y), 'number of x and y should be same'

        return list(x), list(y), list(mask)


def denormalization(x):
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    x = (((x.transpose(1, 2, 0) * std) + mean) * 255.).astype(np.uint8)

    return x


def embedding_concat(x, y):
    B, C1, H1, W1 = x.size()
    _, C2, H2, W2 = y.size()
    s = int(H1 / H2)
    x = F.unfold(x, kernel_size=s, dilation=1, stride=s)
    x = x.view(B, C1, -1, H2, W2)
    z = torch.zeros(B, C1 + C2, x.size(2), H2, W2)
    for i in range(x.size(2)):
        z[:, :, i, :, :] = torch.cat((x[:, :, i, :, :], y), 1)
    z = z.view(B, -1, H2 * W2)
    z = F.fold(z, kernel_size=s, output_size=(H1, W1), stride=s)
    return z


def plot_fig(test_img, scores, gts, threshold, save_dir, class_name):
    num = len(scores)
    vmax = scores.max() * 255.
    vmin = scores.min() * 255.
    for i in range(num):
        img = test_img[i]
        img = denormalization(img)
        gt = gts[i].transpose(1, 2, 0).squeeze()
        heat_map = scores[i] * 255
        mask = scores[i]
        mask[mask > threshold] = 1
        mask[mask <= threshold] = 0
        kernel = morphology.disk(4)
        mask = morphology.opening(mask, kernel)
        mask *= 255
        vis_img = mark_boundaries(img, mask, color=(1, 0, 0), mode='thick')
        fig_img, ax_img = plt.subplots(1, 5, figsize=(12, 3))
        fig_img.subplots_adjust(right=0.9)
        norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
        for ax_i in ax_img:
            ax_i.axes.xaxis.set_visible(False)
            ax_i.axes.yaxis.set_visible(False)
        ax_img[0].imshow(img)
        ax_img[0].title.set_text('Image')
        ax_img[1].imshow(gt, cmap='gray')
        ax_img[1].title.set_text('GroundTruth')
        ax = ax_img[2].imshow(heat_map, cmap='jet', norm=norm)
        ax_img[2].imshow(img, cmap='gray', interpolation='none')
        ax_img[2].imshow(heat_map, cmap='jet', alpha=0.5, interpolation='none')
        ax_img[2].title.set_text('Predicted heat map')
        ax_img[3].imshow(mask, cmap='gray')
        ax_img[3].title.set_text('Predicted mask')
        ax_img[4].imshow(vis_img)
        ax_img[4].title.set_text('Segmentation result')
        left = 0.92
        bottom = 0.15
        width = 0.015
        height = 1 - 2 * bottom
        rect = [left, bottom, width, height]
        cbar_ax = fig_img.add_axes(rect)
        cb = plt.colorbar(ax, shrink=0.6, cax=cbar_ax, fraction=0.046)
        cb.ax.tick_params(labelsize=8)
        font = {
            'family': 'serif',
            'color': 'black',
            'weight': 'normal',
            'size': 8,
        }
        cb.set_label('Anomaly Score', fontdict=font)

        fig_img.savefig(os.path.join(save_dir, class_name + '_{}'.format(i)), dpi=100)
        plt.close()



def load_model(args, device):
    # load model
    if args.arch == 'resnet18':
        model = resnet18(pretrained=True, progress=False)
        t_d = 448
        d = 100
    elif args.arch == 'wide_resnet50_2':
        model = wide_resnet50_2(pretrained=True, progress=False)
        t_d = 1792
        d = 550
    model.to(device)
    model.eval()
    return model, t_d, d



def train(args, model, device, idx):
    # set model's intermediate outputs
    outputs = []
    def hook(module, input, output):
        outputs.append(output)

    model.layer1[-1].register_forward_hook(hook)
    model.layer2[-1].register_forward_hook(hook)
    model.layer3[-1].register_forward_hook(hook)

    os.makedirs(os.path.join(args.save_path, 'temp_%s' % args.arch), exist_ok=True)

    for class_name in CLASS_NAMES:

        train_dataset = MVTecDataset(args.data_path, class_name=class_name, is_train=True)
        train_dataloader = DataLoader(train_dataset, batch_size=32, pin_memory=True)

        train_outputs = OrderedDict([('layer1', []), ('layer2', []), ('layer3', [])])

        # extract train set features
        train_feature_filepath = os.path.join(args.save_path, 'temp_%s' % args.arch, 'train_%s.pkl' % class_name)
        for (x, _, _) in tqdm(train_dataloader, '| feature extraction | train | %s |' % class_name):
            # model prediction
            with torch.no_grad():
                _ = model(x.to(device))
            # get intermediate layer outputs
            for k, v in zip(train_outputs.keys(), outputs):
                train_outputs[k].append(v.cpu().detach())
            # initialize hook outputs
            outputs = []
        for k, v in train_outputs.items():
            train_outputs[k] = torch.cat(v, 0)

        # Embedding concat
        embedding_vectors = train_outputs['layer1']
        for layer_name in ['layer2', 'layer3']:
            embedding_vectors = embedding_concat(embedding_vectors, train_outputs[layer_name])

        # randomly select d dimension
        embedding_vectors = torch.index_select(embedding_vectors, 1, idx)
        # calculate multivariate Gaussian distribution
        B, C, H, W = embedding_vectors.size()
        embedding_vectors = embedding_vectors.view(B, C, H * W)
        mean = torch.mean(embedding_vectors, dim=0).numpy()
        cov = torch.zeros(C, C, H * W).numpy()
        I = np.identity(C)
        for i in range(H * W):
            # cov[:, :, i] = LedoitWolf().fit(embedding_vectors[:, :, i].numpy()).covariance_
            cov[:, :, i] = np.cov(embedding_vectors[:, :, i].numpy(), rowvar=False) + 0.01 * I
        # save learned distribution
        train_outputs = [mean, cov]
        with open(train_feature_filepath, 'wb') as f:
            pickle.dump(train_outputs, f)



def val(args, model, device, idx):
    # set model's intermediate outputs
    outputs = []

    def hook(module, input, output):
        outputs.append(output)

    model.layer1[-1].register_forward_hook(hook)
    model.layer2[-1].register_forward_hook(hook)
    model.layer3[-1].register_forward_hook(hook)

    total_roc_auc = []
    total_pixel_roc_auc = []

    fig, ax = plt.subplots(1, 2, figsize=(20, 10))
    fig_img_rocauc = ax[0]
    fig_pixel_rocauc = ax[1]

    for class_name in CLASS_NAMES:
        test_dataset = MVTecDataset(args.data_path, class_name=class_name, is_train=False)
        test_dataloader = DataLoader(test_dataset, batch_size=32, pin_memory=True)

        test_outputs = OrderedDict([('layer1', []), ('layer2', []), ('layer3', [])])

        train_feature_filepath = os.path.join(args.save_path, 'temp_%s' % args.arch, 'train_%s.pkl' % class_name)
        print('load train set feature from: %s' % train_feature_filepath)
        with open(train_feature_filepath, 'rb') as f:
            train_outputs = pickle.load(f)

        gt_list = []
        gt_mask_list = []
        test_imgs = []

        # extract test set features
        for (x, y, mask) in tqdm(test_dataloader, '| feature extraction | test | %s |' % class_name):
            test_imgs.extend(x.cpu().detach().numpy())
            gt_list.extend(y.cpu().detach().numpy())
            gt_mask_list.extend(mask.cpu().detach().numpy())
            # model prediction
            with torch.no_grad():
                _ = model(x.to(device))
            # get intermediate layer outputs
            for k, v in zip(test_outputs.keys(), outputs):
                test_outputs[k].append(v.cpu().detach())
            # initialize hook outputs
            outputs = []
        for k, v in test_outputs.items():
            test_outputs[k] = torch.cat(v, 0)

        # Embedding concat
        embedding_vectors = test_outputs['layer1']
        for layer_name in ['layer2', 'layer3']:
            embedding_vectors = embedding_concat(embedding_vectors, test_outputs[layer_name])

        # randomly select d dimension
        embedding_vectors = torch.index_select(embedding_vectors, 1, idx)

        # calculate distance matrix
        B, C, H, W = embedding_vectors.size()
        embedding_vectors = embedding_vectors.view(B, C, H * W).numpy()
        dist_list = []
        for i in range(H * W):
            mean = train_outputs[0][:, i]
            conv_inv = np.linalg.inv(train_outputs[1][:, :, i])
            dist = [mahalanobis(sample[:, i], mean, conv_inv) for sample in embedding_vectors]
            dist_list.append(dist)

        dist_list = np.array(dist_list).transpose(1, 0).reshape(B, H, W)

        # upsample
        dist_list = torch.tensor(dist_list)
        score_map = F.interpolate(dist_list.unsqueeze(1), size=x.size(2), mode='bilinear',
                                  align_corners=False).squeeze().numpy()

        # apply gaussian smoothing on the score map
        for i in range(score_map.shape[0]):
            score_map[i] = gaussian_filter(score_map[i], sigma=4)

        # Normalization
        max_score = score_map.max()
        min_score = score_map.min()
        scores = (score_map - min_score) / (max_score - min_score)

        # calculate image-level ROC AUC score
        img_scores = scores.reshape(scores.shape[0], -1).max(axis=1)
        gt_list = np.asarray(gt_list)
        fpr, tpr, _ = roc_curve(gt_list, img_scores)
        img_roc_auc = roc_auc_score(gt_list, img_scores)
        total_roc_auc.append(img_roc_auc)
        print('image ROCAUC: %.3f' % (img_roc_auc))
        fig_img_rocauc.plot(fpr, tpr, label='%s img_ROCAUC: %.3f' % (class_name, img_roc_auc))

        # get optimal threshold
        gt_mask = np.asarray(gt_mask_list)
        precision, recall, thresholds = precision_recall_curve(gt_mask.flatten(), scores.flatten())
        a = 2 * precision * recall
        b = precision + recall
        f1 = np.divide(a, b, out=np.zeros_like(a), where=b != 0)
        threshold = thresholds[np.argmax(f1)]

        # calculate per-pixel level ROCAUC
        fpr, tpr, _ = roc_curve(gt_mask.flatten(), scores.flatten())
        per_pixel_rocauc = roc_auc_score(gt_mask.flatten(), scores.flatten())
        total_pixel_roc_auc.append(per_pixel_rocauc)
        print('pixel ROCAUC: %.3f' % (per_pixel_rocauc))

        fig_pixel_rocauc.plot(fpr, tpr, label='%s ROCAUC: %.3f' % (class_name, per_pixel_rocauc))
        save_dir = args.save_path + '/' + f'pictures_{args.arch}'
        os.makedirs(save_dir, exist_ok=True)
        plot_fig(test_imgs, scores, gt_mask_list, threshold, save_dir, class_name)

    print('Average ROCAUC: %.3f' % np.mean(total_roc_auc))
    fig_img_rocauc.title.set_text('Average image ROCAUC: %.3f' % np.mean(total_roc_auc))
    fig_img_rocauc.legend(loc="lower right")

    print('Average pixel ROCUAC: %.3f' % np.mean(total_pixel_roc_auc))
    fig_pixel_rocauc.title.set_text('Average pixel ROCAUC: %.3f' % np.mean(total_pixel_roc_auc))
    fig_pixel_rocauc.legend(loc="lower right")

    fig.tight_layout()
    fig.savefig(os.path.join(args.save_path, 'roc_curve.png'), dpi=100)



def parse_args():
    parser = argparse.ArgumentParser('PaDiM')
    parser.add_argument('--data_path', type=str, default='/home/liumin/data/mvtec_ad')
    parser.add_argument('--save_path', type=str, default='./result')
    parser.add_argument('--arch', type=str, choices=['resnet18', 'wide_resnet50_2'], default='wide_resnet50_2')
    return parser.parse_args()


if __name__ == '__main__':
    args = parse_args()

    # device setup
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    model, t_d, d = load_model(args, device)
    idx = torch.tensor(sample(range(0, t_d), d))

    train(args, model, device, idx)
    val(args, model, device, idx)
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