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train_mvlogo.py
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from config import DefaultConfig
import os
from torch import optim
from tqdm import tqdm
from matplotlib import pyplot as plt
import numpy as np
from sklearn.metrics import roc_auc_score, f1_score, average_precision_score
import cv2
from models.misc import NativeScalerWithGradNormCount as NativeScaler
from scipy.ndimage import gaussian_filter
from datasets.dataset import denormalize
from models.TFA_Net_model import *
class Model(object):
def __init__(self, opt):
super(Model, self).__init__()
self.opt = opt
self.model = eval(opt.model_name)(opt)
self.device = opt.device
self.class_name = opt.class_name
self.trainloader = opt.trainloader
self.testloader = opt.testloader
self.loss_scaler = NativeScaler()
# self.opt.model_name = self.opt.model_name
if self.opt.resume != "":
print('\nload pre-trained networks')
self.opt.iter = torch.load(os.path.join(self.opt.resume, f'{opt.model_name}_{opt.backbone_name}_k={opt.k}.pth'))['epoch']
print(self.opt.iter)
self.model.load_state_dict(torch.load(os.path.join(self.opt.resume, f'{opt.model_name}_{opt.backbone_name}_k={opt.k}.pth'))['state_dict'], strict=False)
print('\ndone.\n')
if self.opt.isTrain:
self.model.Roncon_model.train()
self.optimizer_g = optim.AdamW(self.model.Roncon_model.parameters(), lr=opt.lr, betas=(0.9, 0.95))
self.save_root = f"./result_logo/{opt.model_name}_{opt.backbone_name}_k={opt.k}/"
# os.makedirs(os.path.join(self.save_root, "weight"), exist_ok=True)
self.ckpt_root = os.path.join(self.save_root, "weight/{}".format(self.class_name))
self.vis_root = os.path.join(self.save_root, "img/{}".format(self.class_name))
def get_max(self, tensor):
a_1, _ = torch.max(tensor, dim=1, keepdim=True)
a_2, _ = torch.max(a_1, dim=2, keepdim=True)
a_3, _ = torch.max(a_2, dim=3, keepdim=True)
return a_3
def train(self):
loss_now = 100000
auc_now = 0.
patience = 20
no_update_num = 0
for epoch in range(self.opt.iter, self.opt.niter):
self.model.Feature_extractor.eval()
self.model.Roncon_model.train(True)
self.model.to(self.device)
loss_total = 0.
count = 0
for index, (x, _, _, _) in enumerate(tqdm(self.trainloader, ncols=80)):
bs = x.shape[0]
x = x.to(self.device)
ref_x = get_pos_sample(self.opt.referenc_img_file, self.device, bs)
deep_feature, _, recon_feature, loss = self.model(x, ref_x, 'train')
self.loss_scaler(loss, self.optimizer_g, parameters=self.model.Roncon_model.parameters(), update_grad=(index + 1) % 1 == 0)
loss_total += loss.item()
count += 1
loss_total = loss_total / count
print('the {} epoch is done loss:{}'.format(epoch + 1, loss_total))
if (epoch + 1) % 1 == 0:
# self.test_2()
x1, x2, x3, x4 = self.test()
auc_roc = x1+x2
if auc_roc > auc_now:
no_update_num = 0
auc_now = auc_roc
class_rocauc[self.opt.class_name] = (x1, x2, x3, x4)
print('save model')
weight_dir = self.ckpt_root
os.makedirs(weight_dir, exist_ok=True)
torch.save({'epoch': epoch + 1, 'state_dict': self.model.state_dict()},
f'%s/{self.opt.model_name}_{self.opt.backbone_name}.pth' % (weight_dir))
else:
no_update_num += 1
print('no_update_num:{}'.format(no_update_num))
if no_update_num > patience:
break
def cal_auc(self, score_list, score_map_list, test_y_list, test_mask_list):
flatten_y_list = np.array(test_y_list,dtype=np.uint8).ravel()
flatten_score_list = np.array(score_list).ravel()
image_level_ROCAUC = roc_auc_score(flatten_y_list, flatten_score_list)
image_level_AP = average_precision_score(flatten_y_list, flatten_score_list)
flatten_mask_list = np.concatenate(test_mask_list).ravel().astype(np.uint8)
flatten_score_map_list = np.concatenate(score_map_list).ravel()
pixel_level_ROCAUC = roc_auc_score(flatten_mask_list, flatten_score_map_list)
pixel_level_AP = average_precision_score(flatten_mask_list, flatten_score_map_list)
# pro_auc_score = 0
# pro_auc_score = cal_pro_metric_new(test_mask_list, score_map_list, fpr_thresh=0.3)
return round(image_level_ROCAUC, 3), round(pixel_level_ROCAUC, 3), round(image_level_AP, 3), round(pixel_level_AP, 3)
# return image_level_ROCAUC, pixel_level_ROCAUC
def F1_score(self, score_map_list, test_mask_list):
flatten_mask_list = np.concatenate(test_mask_list).ravel()
flatten_score_map_list = np.concatenate(score_map_list).ravel()
F1_score = f1_score(flatten_mask_list, flatten_score_map_list)
return F1_score
def filter(self, pred_mask):
pred_mask_my = np.squeeze(np.squeeze(pred_mask, 0), 0)
pred_mask_my = cv2.medianBlur(np.uint8(pred_mask_my * 255), 7)
mean = np.mean(pred_mask_my)
std = np.std(pred_mask_my)
_ , binary_pred_mask = cv2.threshold(pred_mask_my, mean+2.75*std, 255, type=cv2.THRESH_BINARY)
binary_pred_mask = np.uint8(binary_pred_mask/255)
pred_mask_my = np.expand_dims(np.expand_dims(pred_mask_my, 0), 0)
binary_pred_mask = np.expand_dims(np.expand_dims(binary_pred_mask, 0), 0)
return pred_mask_my, binary_pred_mask
# def thresholding(self, pred_mask_my):
# np_img
# return
def feature_map_vis(self, feature_map_list):
feature_map_list = [torch.mean(i.clone(), dim=1).squeeze(0).cpu().detach().numpy() for i in feature_map_list]
return feature_map_list
def test(self):
test_y_list = []
test_mask_list = []
score_list = []
score_map_list = []
for idx, (x, y, mask, name) in enumerate(tqdm(self.testloader, ncols=80)):
test_y_list.extend(y.detach().cpu().numpy())
test_mask_list.extend(mask.detach().cpu().numpy())
self.model.eval()
self.model.to(self.device)
x = x.to(self.device)
mask = mask.to(self.device)
mask_cpu = mask.cpu().detach().numpy()[0, :, :, :].transpose((1, 2, 0))
ref_x = get_pos_sample(self.opt.referenc_img_file, self.device, 1)
deep_feature, ref_feature, recon_feature, _ = self.model(x, ref_x, 'test')
feature_map_vis_list = self.feature_map_vis([deep_feature, ref_feature, recon_feature])
dis_amap, dir_amap = self.model.a_map(deep_feature, recon_feature)
dis_amap = gaussian_filter(dis_amap, sigma=4)
dir_amap = gaussian_filter(dir_amap, sigma=4)
# print(type(name0]))
name_list = name[0].split(r'!')
# print(name_list)
category, img_name = name_list[-2], name_list[-1]
amap = dir_amap*dis_amap
# amap = dir_amap + dis_amap
self.vis_img([x,*feature_map_vis_list, dis_amap, dir_amap, amap, mask_cpu], os.path.join(self.vis_root, category), img_name)
score_list.extend(np.array(np.std(amap)).reshape(1))
score_map_list.extend(amap.reshape((1, 1, 256, 256)))
image_level_ROCAUC, pixel_level_ROCAUC, image_level_AP, pixel_level_AP= self.cal_auc(score_list, score_map_list, test_y_list, test_mask_list)
# F1_score = self.F1_score(F1_score_map_list, test_mask_list)
print('image_auc_roc: {} '.format(image_level_ROCAUC),
'pixel_auc_roc: {} '.format(pixel_level_ROCAUC),
'image_AP: {}'.format(image_level_AP),
'pixel_AP: {}'.format(pixel_level_AP)
)
return image_level_ROCAUC, pixel_level_ROCAUC, image_level_AP, pixel_level_AP
def vis_img(self, img_list, save_root, idx_name):
os.makedirs(save_root, exist_ok=True)
input_frame = denormalize(img_list[0].clone().squeeze(0).cpu().detach().numpy())
cv2_input = np.array(input_frame, dtype=np.uint8)
plt.figure()
plt.subplot(241)
plt.imshow(cv2_input)
plt.axis('off')
plt.subplot(242)
plt.imshow(img_list[1])
plt.axis('off')
plt.subplot(243)
plt.imshow(img_list[2])
plt.axis('off')
plt.subplot(244)
plt.imshow(img_list[3])
plt.axis('off')
plt.subplot(245)
plt.imshow(img_list[4], cmap='jet')
plt.axis('off')
plt.subplot(246)
plt.imshow(img_list[5],cmap='jet')
plt.axis('off')
plt.subplot(247)
plt.imshow(img_list[6], cmap='jet')
plt.axis('off')
plt.subplot(248)
plt.imshow(img_list[7])
plt.axis('off')
plt.savefig(os.path.join(save_root, idx_name))
plt.close()
def tensor_to_np_cpu(self, tensor):
x_cpu = tensor.squeeze(0).data.cpu().numpy()
x_cpu = np.transpose(x_cpu, (1, 2, 0))
return x_cpu
def check(self, img):
if len(img.shape) == 2:
return img
if img.shape[2] == 3:
return img
elif img.shape[2] == 1:
return img.reshape(img.shape[0], img.shape[1])
MVTec_CLASS_NAMES = [ 'breakfast_box', 'juice_bottle', 'pushpins', 'screw_bag', 'splicing_connectors']
class_rocauc = {
'breakfast_box':(0, 0, 0, 0),
'juice_bottle':(0, 0, 0, 0),
'pushpins':(0, 0, 0, 0),
'screw_bag':(0, 0, 0, 0),
'splicing_connectors':(0, 0, 0, 0)
}
if __name__ == '__main__':
opt = DefaultConfig()
from datasets.logodataset import MVTecDataset
from torch.utils.data import DataLoader
for classname in MVTec_CLASS_NAMES:
opt.class_name = classname
# opt.class_name = 'capsule'
opt.referenc_img_file = f'data/mvtec_loco_anomaly_detection/{opt.class_name}/train/good/000.png'
# opt.referenc_img_file = f'data/ref/{opt.class_name}/ref.png'
# opt.referenc_img_file = f'natrual.JPEG'
print(opt.class_name, opt.model_name)
# print(opt.referenc_img_file)
# opt.resume = r'result/RB_VIT_dir_res_ref_VGG/weight/capsule'
opt.train_dataset = MVTecDataset(dataset_path=opt.data_root, class_name=opt.class_name, is_train=True)
opt.test_dataset = MVTecDataset(dataset_path=opt.data_root, class_name=opt.class_name, is_train=False)
opt.trainloader = DataLoader(opt.train_dataset, batch_size=opt.batch_size, shuffle=True)
opt.testloader = DataLoader(opt.test_dataset, batch_size=1, shuffle=False)
model = Model(opt)
model.train()
print(class_rocauc)
value = list(class_rocauc.values())
img_roc = [i[0] for i in value]
pixel_roc = [i[1] for i in value]
img_ap = [i[2] for i in value]
pixel_ap = [i[3] for i in value]
mean_img_roc = np.mean(np.array(img_roc))
mean_pixel_roc = np.mean(np.array(pixel_roc))
mean_img_ap = np.mean(np.array(img_ap))
mean_pixel_ap = np.mean(np.array(pixel_ap))
print(round(mean_img_roc, 3), round(mean_pixel_roc, 3), round(mean_img_ap, 3), round(mean_pixel_ap, 3))