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import torch
import torch.nn as nn
import numpy as np
import os
from functools import partial
import warnings
from tqdm import tqdm
from torch.nn.init import trunc_normal_
import argparse
from optimizers import StableAdamW
from utils import evaluation_batch, WarmCosineScheduler, global_cosine_hm_adaptive, setup_seed, get_logger
# Dataset-Related Modules
from dataset import MVTecDataset, RealIADDataset
from dataset import get_data_transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
# Model-Related Modules
from models import vit_encoder
from models.uad import INP_Former
from models.vision_transformer import Mlp, Aggregation_Block, Prototype_Block
warnings.filterwarnings("ignore")
def main(args):
# Fixing the Random Seed
setup_seed(1)
# Data Preparation
data_transform, gt_transform = get_data_transforms(args.input_size, args.crop_size)
if args.dataset == 'MVTec-AD' or args.dataset == 'VisA':
train_path = os.path.join(args.data_path, args.item, 'train')
test_path = os.path.join(args.data_path, args.item)
train_data = ImageFolder(root=train_path, transform=data_transform)
test_data = MVTecDataset(root=test_path, transform=data_transform, gt_transform=gt_transform, phase="test")
train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=4,
drop_last=True)
test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False, num_workers=4)
elif args.dataset == 'Real-IAD' :
train_data = RealIADDataset(root=args.data_path, category=args.item, transform=data_transform, gt_transform=gt_transform,
phase='train')
test_data = RealIADDataset(root=args.data_path, category=args.item, transform=data_transform, gt_transform=gt_transform,
phase="test")
train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=4,
drop_last=True)
test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False, num_workers=4)
# Adopting a grouping-based reconstruction strategy similar to Dinomaly
target_layers = [2, 3, 4, 5, 6, 7, 8, 9]
fuse_layer_encoder = [[0, 1, 2, 3], [4, 5, 6, 7]]
fuse_layer_decoder = [[0, 1, 2, 3], [4, 5, 6, 7]]
# Encoder info
encoder = vit_encoder.load(args.encoder)
if 'small' in args.encoder:
embed_dim, num_heads = 384, 6
elif 'base' in args.encoder:
embed_dim, num_heads = 768, 12
elif 'large' in args.encoder:
embed_dim, num_heads = 1024, 16
target_layers = [4, 6, 8, 10, 12, 14, 16, 18]
else:
raise "Architecture not in small, base, large."
# Model Preparation
Bottleneck = []
INP_Guided_Decoder = []
INP_Extractor = []
# bottleneck
Bottleneck.append(Mlp(embed_dim, embed_dim * 4, embed_dim, drop=0.))
Bottleneck = nn.ModuleList(Bottleneck)
# INP
INP = nn.ParameterList(
[nn.Parameter(torch.randn(args.INP_num, embed_dim))
for _ in range(1)])
# INP Extractor
for i in range(1):
blk = Aggregation_Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=4.,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-8))
INP_Extractor.append(blk)
INP_Extractor = nn.ModuleList(INP_Extractor)
# INP_Guided_Decoder
for i in range(8):
blk = Prototype_Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=4.,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-8))
INP_Guided_Decoder.append(blk)
INP_Guided_Decoder = nn.ModuleList(INP_Guided_Decoder)
model = INP_Former(encoder=encoder, bottleneck=Bottleneck, aggregation=INP_Extractor, decoder=INP_Guided_Decoder,
target_layers=target_layers, remove_class_token=True, fuse_layer_encoder=fuse_layer_encoder,
fuse_layer_decoder=fuse_layer_decoder, prototype_token=INP)
model = model.to(device)
if args.phase == 'train':
# Model Initialization
trainable = nn.ModuleList([Bottleneck, INP_Guided_Decoder, INP_Extractor, INP])
for m in trainable.modules():
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.01, a=-0.03, b=0.03)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
# define optimizer
optimizer = StableAdamW([{'params': trainable.parameters()}],
lr=1e-3, betas=(0.9, 0.999), weight_decay=1e-4, amsgrad=True, eps=1e-10)
lr_scheduler = WarmCosineScheduler(optimizer, base_value=1e-3, final_value=1e-4, total_iters=args.total_epochs*len(train_dataloader),
warmup_iters=100)
print_fn('train image number:{}'.format(len(train_data)))
# Train
for epoch in range(args.total_epochs):
model.train()
loss_list = []
for img, _ in tqdm(train_dataloader, ncols=80):
img = img.to(device)
en, de, g_loss = model(img)
loss = global_cosine_hm_adaptive(en, de, y=3)
loss = loss + 0.2 * g_loss
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm(trainable.parameters(), max_norm=0.1)
optimizer.step()
loss_list.append(loss.item())
lr_scheduler.step()
print_fn('epoch [{}/{}], loss:{:.4f}'.format(epoch+1, args.total_epochs, np.mean(loss_list)))
# if (epoch + 1) % args.total_epochs == 0:
results = evaluation_batch(model, test_dataloader, device, max_ratio=0.01, resize_mask=256)
auroc_sp, ap_sp, f1_sp, auroc_px, ap_px, f1_px, aupro_px = results
print_fn(
'{}: I-Auroc:{:.4f}, I-AP:{:.4f}, I-F1:{:.4f}, P-AUROC:{:.4f}, P-AP:{:.4f}, P-F1:{:.4f}, P-AUPRO:{:.4f}'.format(
args.item, auroc_sp, ap_sp, f1_sp, auroc_px, ap_px, f1_px, aupro_px))
os.makedirs(os.path.join(args.save_dir, args.save_name, args.item), exist_ok=True)
torch.save(model.state_dict(), os.path.join(args.save_dir, args.save_name, args.item, 'model.pth'))
return results
elif args.phase == 'test':
# Test
model.load_state_dict(torch.load(os.path.join(args.save_dir, args.save_name, args.item, 'model.pth')), strict=True)
model.eval()
results = evaluation_batch(model, test_dataloader, device, max_ratio=0.01, resize_mask=256)
return results
if __name__ == '__main__':
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
parser = argparse.ArgumentParser(description='')
# dataset info
parser.add_argument('--dataset', type=str, default=r'MVTec-AD') # 'MVTec-AD' or 'VisA' or 'Real-IAD'
parser.add_argument('--data_path', type=str, default=r'E:\IMSN-LW\dataset\mvtec_anomaly_detection') # Replace it with your path.
# save info
parser.add_argument('--save_dir', type=str, default='./saved_results')
parser.add_argument('--save_name', type=str, default='INP-Former-Single-Class')
# model info
parser.add_argument('--encoder', type=str, default='dinov2reg_vit_base_14') # 'dinov2reg_vit_small_14' or 'dinov2reg_vit_base_14' or 'dinov2reg_vit_large_14'
parser.add_argument('--input_size', type=int, default=448)
parser.add_argument('--crop_size', type=int, default=392)
parser.add_argument('--INP_num', type=int, default=6)
# training info
parser.add_argument('--total_epochs', type=int, default=200)
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--phase', type=str, default='train')
args = parser.parse_args()
args.save_name = args.save_name + f'_dataset={args.dataset}_Encoder={args.encoder}_Resize={args.input_size}_Crop={args.crop_size}_INP_num={args.INP_num}'
logger = get_logger(args.save_name, os.path.join(args.save_dir, args.save_name))
print_fn = logger.info
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
# category info
if args.dataset == 'MVTec-AD':
# args.data_path = 'E:\IMSN-LW\dataset\mvtec_anomaly_detection' # '/path/to/dataset/MVTec-AD/'
args.item_list = ['carpet', 'grid', 'leather', 'tile', 'wood', 'bottle', 'cable', 'capsule',
'hazelnut', 'metal_nut', 'pill', 'screw', 'toothbrush', 'transistor', 'zipper']
elif args.dataset == 'VisA':
# args.data_path = r'E:\IMSN-LW\dataset\VisA_pytorch\1cls' # '/path/to/dataset/VisA/'
args.item_list = ['candle', 'capsules', 'cashew', 'chewinggum', 'fryum', 'macaroni1', 'macaroni2',
'pcb1', 'pcb2', 'pcb3', 'pcb4', 'pipe_fryum']
elif args.dataset == 'Real-IAD':
# args.data_path = 'E:\IMSN-LW\dataset\Real-IAD' # '/path/to/dataset/Real-IAD/'
args.item_list = ['audiojack', 'bottle_cap', 'button_battery', 'end_cap', 'eraser', 'fire_hood',
'mint', 'mounts', 'pcb', 'phone_battery', 'plastic_nut', 'plastic_plug',
'porcelain_doll', 'regulator', 'rolled_strip_base', 'sim_card_set', 'switch', 'tape',
'terminalblock', 'toothbrush', 'toy', 'toy_brick', 'transistor1', 'usb',
'usb_adaptor', 'u_block', 'vcpill', 'wooden_beads', 'woodstick', 'zipper']
result_list = []
for item in args.item_list:
args.item = item
auroc_sp, ap_sp, f1_sp, auroc_px, ap_px, f1_px, aupro_px = main(args)
result_list.append([args.item, auroc_sp, ap_sp, f1_sp, auroc_px, ap_px, f1_px, aupro_px])
mean_auroc_sp = np.mean([result[1] for result in result_list])
mean_ap_sp = np.mean([result[2] for result in result_list])
mean_f1_sp = np.mean([result[3] for result in result_list])
mean_auroc_px = np.mean([result[4] for result in result_list])
mean_ap_px = np.mean([result[5] for result in result_list])
mean_f1_px = np.mean([result[6] for result in result_list])
mean_aupro_px = np.mean([result[7] for result in result_list])
print_fn(result_list)
print_fn(
'Mean: I-Auroc:{:.4f}, I-AP:{:.4f}, I-F1:{:.4f}, P-AUROC:{:.4f}, P-AP:{:.4f}, P-F1:{:.4f}, P-AUPRO:{:.4f}'.format(
mean_auroc_sp, mean_ap_sp, mean_f1_sp,
mean_auroc_px, mean_ap_px, mean_f1_px, mean_aupro_px))
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