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test-localization.py
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import torch
from unet_utils.data_loader import MVTecDRAEMTestDataset_partial
from torch.utils.data import DataLoader
import numpy as np
from unet_utils.model_unet import DiscriminativeSubNetwork
import os
from unet_utils.au_pro_util import calculate_au_pro
import csv
from sklearn.metrics import auc, roc_auc_score, average_precision_score, precision_recall_curve
def test(obj_names, mvtec_path, checkpoint_path):
obj_ap_pixel_list = []
obj_auroc_pixel_list = []
obj_ap_image_list = []
obj_auroc_image_list = []
obj_pro_image_list = []
from torchvision import transforms
resize_224=transforms.Resize([224,224])
crop_224=transforms.CenterCrop([224,224])
with open("result.csv", "w") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['Object', 'AUROC-I', 'AP-I', 'f1_max-I', 'AUROC-P', 'AP-P', 'f1_max-P', 'PRO-P'])
for obj_name in obj_names:
img_dim = 256
run_name = obj_name
model_seg = DiscriminativeSubNetwork(in_channels=3, out_channels=2)
if not os.path.exists(os.path.join(checkpoint_path, run_name+".pckl")):
print(os.path.join(checkpoint_path, run_name+".pckl"), 'not exists')
continue
model_seg.load_state_dict(torch.load(os.path.join(checkpoint_path, run_name+".pckl"), map_location='cuda:0'))
model_seg.cuda()
model_seg.eval()
dataset = MVTecDRAEMTestDataset_partial(mvtec_path + '/'+obj_name + "/test/", resize_shape=[img_dim, img_dim])
dataloader = DataLoader(dataset, batch_size=1,
shuffle=False, num_workers=0)
total_pixel_scores = np.zeros((img_dim * img_dim * len(dataset)))
total_gt_pixel_scores = np.zeros((img_dim * img_dim * len(dataset)))
mask_cnt = 0
anomaly_score_gt = []
anomaly_score_prediction = []
gt_masks=[]
predicted_masks=[]
for i_batch, sample_batched in enumerate(dataloader):
gray_batch = sample_batched["image"].cuda()
gray_batch=gray_batch[:,[2,1,0],:,:]
is_normal = sample_batched["has_anomaly"].detach().numpy()[0 ,0]
anomaly_score_gt.append(is_normal)
true_mask = sample_batched["mask"]
true_mask_cv = true_mask.detach().numpy()[0, :, :, :].transpose((1, 2, 0))
out_mask = model_seg(gray_batch)
img_dim=out_mask.size(-1)
out_mask_sm = torch.softmax(out_mask, dim=1)
out_mask_cv = out_mask_sm[0 ,1 ,: ,:].detach().cpu().numpy()
out_mask_averaged = torch.nn.functional.avg_pool2d(out_mask_sm[: ,1: ,: ,:], 21, stride=1,
padding=21 // 2).cpu().detach().numpy()
image_score = np.max(out_mask_averaged)
anomaly_score_prediction.append(image_score)
flat_true_mask = true_mask_cv.flatten()
flat_out_mask = out_mask_cv.flatten()
gt_masks.append(true_mask_cv.squeeze())
predicted_masks.append(out_mask_cv.squeeze())
total_pixel_scores[mask_cnt * img_dim * img_dim:(mask_cnt + 1) * img_dim * img_dim] = flat_out_mask
total_gt_pixel_scores[mask_cnt * img_dim * img_dim:(mask_cnt + 1) * img_dim * img_dim] = flat_true_mask
mask_cnt += 1
anomaly_score_prediction = np.array(anomaly_score_prediction)
anomaly_score_gt = np.array(anomaly_score_gt)
auroc = roc_auc_score(anomaly_score_gt, anomaly_score_prediction)
ap = average_precision_score(anomaly_score_gt, anomaly_score_prediction)
precisions, recalls, thresholds = precision_recall_curve(anomaly_score_gt, anomaly_score_prediction)
f1_scores = (2 * precisions * recalls) / (precisions + recalls)
best_f1_score = np.max(f1_scores[np.isfinite(f1_scores)])
best_f1_score_index = np.argmax(f1_scores[np.isfinite(f1_scores)])
best_threshold = thresholds[best_f1_score_index]
f1_max= best_f1_score
total_gt_pixel_scores = total_gt_pixel_scores.astype(np.uint8)
total_gt_pixel_scores = total_gt_pixel_scores[:img_dim * img_dim * mask_cnt]
total_pixel_scores = total_pixel_scores[:img_dim * img_dim * mask_cnt]
auroc_pixel = roc_auc_score(total_gt_pixel_scores, total_pixel_scores)
ap_pixel = average_precision_score(total_gt_pixel_scores, total_pixel_scores)
#print(total_gt_pixel_scores.shape,total_pixel_scores.shape)
pro_pixel ,_ = calculate_au_pro(gt_masks, predicted_masks)
score_l, score_h, score_step = 0.0, 1.0, 0.05
gt = total_gt_pixel_scores.astype(np.bool_)
max_f1_px=-99
eps=1e-8
pr_px_norm=total_pixel_scores
for score in np.arange(score_l, score_h + 1e-3, score_step):
pr = pr_px_norm > score
total_area_intersect = np.logical_and(gt, pr).sum()
total_area_union = np.logical_or(gt, pr).sum()
total_area_pred_label = pr.sum()
total_area_label = gt.sum()
precision = total_area_intersect / (total_area_pred_label + eps)
recall = total_area_intersect / (total_area_label + eps)
f1_px = 2 * precision * recall / (
precision + recall + eps)
max_f1_px=max(max_f1_px,f1_px)
obj_ap_pixel_list.append(ap_pixel)
obj_auroc_pixel_list.append(auroc_pixel)
obj_auroc_image_list.append(auroc)
obj_ap_image_list.append(ap)
obj_pro_image_list.append(pro_pixel)
print(obj_name)
print("AUC Image: " +str(auroc))
print("AP Image: " +str(ap))
print("f1_max "+str(f1_max))
print("AUC Pixel: " +str(auroc_pixel))
print("AP Pixel: " +str(ap_pixel))
print("max_f1 Pixel: " + str(ap_pixel))
print("Pro Pixel: " + str(pro_pixel))
print("==============================")
datas=[auroc,ap,f1_max,auroc_pixel,ap_pixel,max_f1_px,pro_pixel]
for i in range(len(datas)):
if datas[i]==1:
datas[i]='100'
else:
datas[i]=str(round(100*datas[i],1))
with open("result.csv", "a") as csvfile:
writer = csv.writer(csvfile)
writer.writerow([obj_name,datas[0],datas[1], datas[2],datas[3], datas[4],datas[5],datas[6]])
print("AUC Image mean: " + str(np.mean(obj_auroc_image_list)))
print("AP Image mean: " + str(np.mean(obj_ap_image_list)))
print("AUC Pixel mean: " + str(np.mean(obj_auroc_pixel_list)))
print("AP Pixel mean: " + str(np.mean(obj_ap_pixel_list)))
print("Pro Pixel mean: " + str(np.mean(obj_pro_image_list)))
if __name__=="__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id',default=0, type=int)
parser.add_argument('--sample_name',type=str, default='all')
parser.add_argument('--mvtec_path', action='store', type=str, required=True)
parser.add_argument('--checkpoint_path', type=str, default='checkpoints/localization')
args = parser.parse_args()
if args.sample_name=='all':
obj_list = ['capsule',
'bottle',
'carpet',
'leather',
'pill',
'transistor',
'tile',
'cable',
'zipper',
'toothbrush',
'metal_nut',
'hazelnut',
'screw',
'grid',
'wood'
]
else:
obj_list = [args.sample_name]
with torch.cuda.device(args.gpu_id):
test(obj_list,args.mvtec_path, args.checkpoint_path)