main_copy_TIME.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, resnet50 #,ResNet18_Weights
import datasets.mvtec as mvtec

from sklearn.manifold import TSNE

import sys
sys.path.append('/home/wonjun/Desktop/PaDiM-Anomaly-Detection-Localization-master-main')
from sklearn.manifold import TSNE
import pdb

import pandas as pd 
import time 
import logging
import psutil

import os
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'

#Model zoo Version
# # from segmentation.data_loader.segmentation_dataset import SegmentationDataset
# # from segmentation.data_loader.transform import Rescale, ToTensor
# # from segmentation.trainer import Trainer
# # from segmentation.predict import *
# from segmentation.models import all_models
# # from util.logger import Logger

#Segmentation Model
# import segmentation_models_pytorch as smp 

from torchvision.models.segmentation import fcn_resnet50 # FCN_ResNet50_Weights




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


#arguments 지정 
def parse_args():
    parser = argparse.ArgumentParser('PaDiM')
    parser.add_argument('--data_path', type=str, default='datasets/MVtec')
    parser.add_argument('--save_path', type=str, default='graduation')
    parser.add_argument('--selection', type=str, default='')
    parser.add_argument('--arch', type=str, choices=['resnet18', 'wide_resnet50_2', 'fcn_resnet50', 'resnet50', 'resnet18_weights'], default='wide_resnet50_2')
    parser.add_argument('--layer', nargs='+', type=str, default=['layer1', 'layer2', 'layer3'])
    return parser.parse_args()


def sample_points(data, n_samples):
    """
    Samples n_samples points from data with maximum distance.

    Parameters:
    data (numpy array): 1792-dimensional sample points
    n_samples (int): number of points to sample

    Returns:
    numpy array: indices of n_samples points with maximum distance
    """
    
    n_points = data.shape[0]
    samples_idx = np.zeros(n_samples, dtype=np.int)

    # Initialize first sample randomly
    idx = np.random.randint(n_points)
    samples_idx[0] = idx

    # Calculate distances between each sample point and centroids
    for i in range(1, n_samples):
        centroids = np.mean(data[samples_idx[:i], :], axis=0)
        distances = np.sum((data - centroids)**2, axis=1)
        distances = np.sqrt(distances)
        samples_idx[i] = np.argmax(distances)

    return samples_idx

# if pretrained and fixed_feature: #fine tunning
#         params_to_update = model.parameters()
#         print("Params to learn:")
#         params_to_update = []
#         for name, param in model.named_parameters():
#             if param.requires_grad == True:
#                 params_to_update.append(param)
#                 print("\t", name)
#         optimizer = torch.optim.Adadelta(params_to_update)
# else:

tsne = TSNE(n_components=2, random_state=0)
def main():    
    args = parse_args()
    
    # load model
    # load model
    if args.arch == 'resnet18':
        model = resnet18(pretrained=True, progress=True)
        t_d = 0
        d = 0

        #random
        #Full Channel 
        if args.selection == 'full':
            if 'layer1' in args.layer:
                t_d += 64
                d += 64
            if 'layer2' in args.layer:
                t_d += 128
                d += 128
            if 'layer3'in args.layer:
                t_d += 256
                d += 256
        else:
            if 'layer1' in args.layer:
                t_d += 64
                d += 16
            if 'layer2' in args.layer:
                t_d += 128
                d += 32
            if 'layer3'in args.layer:
                t_d += 256
                d += 64


    elif args.arch == 'resnet50':
        model = resnet50(pretrained=True, progress=True)
        t_d = 0
        d = 0
        if args.selection == 'full':
            if 'layer1' in args.layer:
                t_d += 256
                d += 256
            if 'layer2' in args.layer:
                t_d += 512
                d += 512
            if 'layer3' in args.layer:
                t_d += 1024
                d += 1024
        else:
            if 'layer1' in args.layer:
                t_d += 256
                d += 64
            if 'layer2' in args.layer:
                t_d += 512
                d += 128
            if 'layer3' in args.layer:
                t_d += 1024
                d += 256

    elif args.arch == 'fcn_resnet50':
        model = fcn_resnet50(pretrained=True, progress=True)
        
        t_d = 0
        d = 0
        if 'layer1' in args.layer:
            t_d += 256
            d += 100
        if 'layer2' in args.layer:
            t_d += 512
            d += 150
        if 'layer3' in args.layer:
            t_d += 1024
            d += 300
            
    if args.arch == 'wide_resnet50_2':
        model = wide_resnet50_2(pretrained=True, progress=True)
        t_d = model.fc.in_features
        d = t_d // 16

    if args.selection == 'full':
        if 'layer1' in args.layer:
            d += 256
        if 'layer2' in args.layer:
            d += 512
        if 'layer3' in args.layer:
            d += 1024
    else:
        if 'layer1' in args.layer:
            d += 100
        if 'layer2' in args.layer:
            d += 150
        if 'layer3'in args.layer:
            d += 300


        
    model.to(device)
    model.eval()
    random.seed(1024)
    torch.manual_seed(1024)
    if use_cuda:
        torch.cuda.manual_seed_all(1024)


    idx = torch.tensor(sample(range(0, t_d), d))


    # set model's intermediate outputs
    outputs = []

    def hook(module, input, output):
        outputs.append(output)
    topk_idx_l = [] 
    if args.arch == 'fcn_resnet50':
        if 'layer1' in args.layer:
            model.backbone.layer1[-1].register_forward_hook(hook)
        if 'layer2' in args.layer:
            model.backbone.layer2[-1].register_forward_hook(hook)
        if 'layer3' in args.layer:
            model.backbone.layer3[-1].register_forward_hook(hook)
    else:
        if 'layer1' in args.layer:
            model.layer1[-1].register_forward_hook(hook)
        if 'layer2' in args.layer:
            model.layer2[-1].register_forward_hook(hook)
        if 'layer3' in args.layer:
            model.layer3[-1].register_forward_hook(hook)

    lname = ''
    if 'layer1' in args.layer:
        lname += '1'
    if 'layer2' in args.layer:
        lname += '2'
    if 'layer3' in args.layer:
        lname += '3'
    os.makedirs(os.path.join(args.save_path, 'temp_%s_L%s' % (args.arch, lname)), exist_ok=True)
    fig, ax = plt.subplots(1, 2, figsize=(20, 10))
    fig_img_rocauc = ax[0]
    fig_pixel_rocauc = ax[1]

    total_roc_auc = []
    total_pixel_roc_auc = []

    for class_name in mvtec.CLASS_NAMES:

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

        #시간 및 메모리 측정 
        start_time = time.time()
        process = psutil.Process(os.getpid())
        init_memory = process.memory_info().rss

        for fidx in range(0, len(args.layer)):
            if fidx == 0:
                train_outputs = OrderedDict([(args.layer[fidx], [])])
                test_outputs = OrderedDict([(args.layer[fidx], [])])
            else:
                train_outputs.update([(args.layer[fidx], [])])
                test_outputs.update([(args.layer[fidx], [])])

        # extract train set features
        train_feature_filepath = os.path.join(args.save_path, 'temp_%s_L%s' % (args.arch, lname), 'train_%s.pkl' % class_name)
        if not os.path.exists(train_feature_filepath):
            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']
            # embedding_vectors = train_outputs['layer2']
            # embedding_vectors = train_outputs['layer3']
            # embedding_vectors = train_outputs[args.layer[0]]
            for fidx in range(0, len(args.layer)): #['layer2', 'layer3']:
                if fidx == 0:
                    embedding_vectors = train_outputs[args.layer[fidx]]
                else:
                    embedding_vectors = embedding_concat(embedding_vectors, train_outputs[args.layer[fidx]])
            
            # plot train vectors
            if args.arch == 'resnet18':
                lnum = [16, 32, 64]
                offset = [0, 64, 128]
            elif args.arch =='wide_resnet50_2':
                lnum = [256, 512, 1024]
                offset = [0, 256, 768]
            elif args.arch == 'resnet50':
                lnum = [64, 128, 256]
                offset = [0, 256, 768]
            plt.close()

            topk_idx = torch.tensor([], dtype=torch.long)

            for fidx in range(len(args.layer)-1, -1, -1):
                mean_t = torch.mean(train_outputs[args.layer[fidx]], dim=0)
                C, H, W = mean_t.size()
                mean_t = mean_t.view(C, H*W)
                
            
                if args.selection == 'full':
                    topk_idx_l = torch.tensor(range(lnum[fidx]), dtype=torch.long)
                elif args.selection == 'var_large':
                    var_v = torch.var(mean_t, dim=-1)
                    _, topk_idx_l = torch.topk(var_v, lnum[fidx], largest=True)
                elif args.selection == 'var_small':
                    var_v = torch.var(mean_t, dim=-1)
                    _, topk_idx_l = torch.topk(var_v, lnum[fidx], largest=False)
                elif args.selection == 'mean_large':
                    mean_v = torch.mean(mean_t, dim=-1)
                    _, topk_idx_l = torch.topk(mean_v, lnum[fidx], largest=True)
                elif args.selection == 'mean_small':
                    mean_v = torch.mean(mean_t, dim=-1)
                    _, topk_idx_l = torch.topk(mean_v, lnum[fidx], largest=False)
                elif args.selection == 'max_distance':
                    tsne_points_train = np.array(tsne.fit_transform(np.array(mean_t)))
                    topk_idx_l = sample_points(tsne_points_train, lnum[fidx])
                    topk_idx_l = torch.tensor(topk_idx_l, dtype=torch.long)
                elif args.selection == 'random':
                    topk_idx_l = torch.tensor(sample(range(0, C), lnum[fidx]))
                elif args.selection == 'hybrid':
                    if fidx == 0:
                        var_v = torch.var(mean_t, dim=-1)
                        _, topk_idx_l = torch.topk(var_v, lnum[fidx], largest=False)
                    else:
                        mean_v = torch.mean(mean_t, dim=-1)
                        _, topk_idx_l = torch.topk(mean_v, lnum[fidx], largest=True)
                if fidx == len(args.layer)-1:
                    #topk_idx = torch.cat((topk_idx, topk_idx_l + torch.tensor(offset[fidx])), dim=0)
                    topk_idx = topk_idx_l + [offset[fidx]]
                else:
                    topk_idx = torch.cat((topk_idx, topk_idx_l), dim=0)

        
                
            #     tsne_points_train = np.array(tsne.fit_transform(np.array(mean_t)))
            #     plt.scatter(tsne_points_train[:,0], tsne_points_train[:,1], label=fidx)
            
            # save_dir = args.save_path + '/' + f'pictures_{args.arch}_L{lname}' + '/tsne/train'
            # os.makedirs(save_dir, exist_ok=True)
            # sname = '%s/%s_%s.png' % (save_dir, 'train', class_name)
            # plt.grid(True)
            # plt.savefig(sname)
            # plt.close()
            
            # randomly select d dimension
            embedding_vectors = torch.index_select(embedding_vectors, 1, topk_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)
        else:
            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
        flist = []
        print('1')
        for (x, y, mask, fname) 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 = []
            flist.append(fname)
        print('2')
        for k, v in test_outputs.items():
            test_outputs[k] = torch.cat(v, 0)
        print('3')
        # Embedding concat
        # embedding_vectors = test_outputs[args.layer[0]]
        for fidx in range(0, len(args.layer)):
            if fidx == 0:
                embedding_vectors = test_outputs[args.layer[fidx]]
            else:
                embedding_vectors = embedding_concat(embedding_vectors, test_outputs[args.layer[fidx]])

        # randomly select d dimension
        embedding_vectors = torch.index_select(embedding_vectors, 1, topk_idx)
        print('4')
        # 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)

        print('5')
        dist_list = np.array(dist_list).transpose(1, 0).reshape(B, H, W)

        print('6')
        # 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()
        print('7')
        # 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)
        print('8')
        # Normalization
        max_score = score_map.max()
        min_score = score_map.min()
        scores = (score_map - min_score) / (max_score - min_score)
        print('9')
        # 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))

        print('10')
        # get optimal threshold
        # gt_mask = np.asarray(gt_mask_list)pdb.set_trace()
        # 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)]


        # 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}_L{lname}'
        os.makedirs(save_dir, exist_ok=True)
        plot_fig(test_imgs, scores, gt_mask_list, threshold, save_dir, class_name, test_outputs, args)

        # Record the end time
        end_time = time.time()

        # Calculate the inference time
        inference_time = end_time - start_time

        # Record the final memory usage
        final_memory = process.memory_info().rss

        # Calculate the memory cost
        memory_cost = final_memory - init_memory

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

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

    fig.tight_layout()
    sname = 'roc_curve_L%s.png' % lname
    fig.savefig(os.path.join(args.save_path, 'roc_curve.png'), dpi=100)


    fname = "%s/%s_L%s_%s_logfile.txt" % (args.save_path, args.arch, lname, class_name)
    with open(fname, "w") as f:
        current_time = str(datetime.datetime.now())
        f.write(f"{current_time} - Class Name: {class_name} - Inference Time: {inference_time:.3f} seconds\n")
        f.write(f"{current_time} - Class Name: {class_name} - Memory Cost: {memory_cost / 1024 ** 2:.3f} MB\n")
        f.write(f"{current_time} - Class Name: {class_name} - Image ROCAUC: {img_roc_auc:.3f}\n")
        f.write(f"{current_time} - Class Name: {class_name} - Per Pixel ROCAUC: {per_pixel_rocauc:.3f}\n")

        data = {
        'Class_name': [class_name],  
        'img_roc_auc': ["{:.3f}".format(avg_ROCAUC)], 
        'per_pixel_rocauc': ["{:.3f}".format(pix_ROCAUC)], 
        'Memory Cost (MB)': ["{:.3f}".format(memory_cost / (1024 ** 2))],
        'Inference Time (seconds)': ["{:.3f}".format(inference_time)]
        } 
        
        df = pd.DataFrame(data)


        csv_fname = "%s_%s_average_output.csv" % (args.arch, args.layer)

        if not os.path.exists(csv_fname):
            df.to_csv(csv_fname, index=False, mode='w', encoding='utf-8-sig', float_format='%.3f')
        else:
            df.to_csv(csv_fname, index=False, mode='a', header=False, encoding='utf-8-sig', float_format='%.3f')



def plot_fig(test_img, scores, gts, threshold, save_dir, class_name, test_outputs, args):
    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, 6, 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 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


if __name__ == '__main__':
    # global model
    main()