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dinomaly_mvtec_uni.py
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dinomaly_mvtec_uni.py
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# This is a sample Python script.
# Press ⌃R to execute it or replace it with your code.
# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
import torch
import torch.nn as nn
from dataset import get_data_transforms, get_strong_transforms
from torchvision.datasets import ImageFolder
import numpy as np
import random
import os
from torch.utils.data import DataLoader, ConcatDataset
from models.uad import ViTill, ViTillv2
from models import vit_encoder
from dinov1.utils import trunc_normal_
from models.vision_transformer import Block as VitBlock, bMlp, Attention, LinearAttention, \
LinearAttention2, ConvBlock, FeatureJitter
from dataset import MVTecDataset
import torch.backends.cudnn as cudnn
import argparse
from utils import evaluation_batch, global_cosine, regional_cosine_hm_percent, global_cosine_hm_percent, \
WarmCosineScheduler
from torch.nn import functional as F
from functools import partial
from ptflops import get_model_complexity_info
from optimizers import StableAdamW
import warnings
import copy
import logging
from sklearn.metrics import roc_auc_score, average_precision_score
import itertools
warnings.filterwarnings("ignore")
def get_logger(name, save_path=None, level='INFO'):
logger = logging.getLogger(name)
logger.setLevel(getattr(logging, level))
log_format = logging.Formatter('%(message)s')
streamHandler = logging.StreamHandler()
streamHandler.setFormatter(log_format)
logger.addHandler(streamHandler)
if not save_path is None:
os.makedirs(save_path, exist_ok=True)
fileHandler = logging.FileHandler(os.path.join(save_path, 'log.txt'))
fileHandler.setFormatter(log_format)
logger.addHandler(fileHandler)
return logger
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def train(item_list):
setup_seed(1)
total_iters = 10000
batch_size = 16
image_size = 448
crop_size = 392
# image_size = 448
# crop_size = 448
data_transform, gt_transform = get_data_transforms(image_size, crop_size)
train_data_list = []
test_data_list = []
for i, item in enumerate(item_list):
train_path = os.path.join(args.data_path, item, 'train')
test_path = os.path.join(args.data_path, item)
train_data = ImageFolder(root=train_path, transform=data_transform)
train_data.classes = item
train_data.class_to_idx = {item: i}
train_data.samples = [(sample[0], i) for sample in train_data.samples]
test_data = MVTecDataset(root=test_path, transform=data_transform, gt_transform=gt_transform, phase="test")
train_data_list.append(train_data)
test_data_list.append(test_data)
train_data = ConcatDataset(train_data_list)
train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=4,
drop_last=True)
# test_dataloader_list = [torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=4)
# for test_data in test_data_list]
# encoder_name = 'dinov2reg_vit_small_14'
encoder_name = 'dinov2reg_vit_base_14'
# encoder_name = 'dinov2reg_vit_large_14'
# encoder_name = 'dinov2_vit_base_14'
# encoder_name = 'dino_vit_base_16'
# encoder_name = 'ibot_vit_base_16'
# encoder_name = 'mae_vit_base_16'
# encoder_name = 'beitv2_vit_base_16'
# encoder_name = 'beit_vit_base_16'
# encoder_name = 'digpt_vit_base_16'
# encoder_name = 'deit_vit_base_16'
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]]
# target_layers = list(range(4, 19))
encoder = vit_encoder.load(encoder_name)
if 'small' in encoder_name:
embed_dim, num_heads = 384, 6
elif 'base' in encoder_name:
embed_dim, num_heads = 768, 12
elif 'large' in encoder_name:
embed_dim, num_heads = 1024, 16
target_layers = [4, 6, 8, 10, 12, 14, 16, 18]
else:
raise "Architecture not in small, base, large."
bottleneck = []
decoder = []
bottleneck.append(bMlp(embed_dim, embed_dim * 4, embed_dim, drop=0.2))
# bottleneck.append(nn.Sequential(FeatureJitter(scale=40),
# bMlp(embed_dim, embed_dim * 4, embed_dim, drop=0.)))
bottleneck = nn.ModuleList(bottleneck)
for i in range(8):
blk = VitBlock(dim=embed_dim, num_heads=num_heads, mlp_ratio=4.,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-8),
attn=LinearAttention2)
# blk = ConvBlock(dim=embed_dim, kernel_size=7, mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-8))
decoder.append(blk)
decoder = nn.ModuleList(decoder)
model = ViTill(encoder=encoder, bottleneck=bottleneck, decoder=decoder, target_layers=target_layers,
mask_neighbor_size=0, fuse_layer_encoder=fuse_layer_encoder, fuse_layer_decoder=fuse_layer_decoder)
model = model.to(device)
trainable = nn.ModuleList([bottleneck, decoder])
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)
optimizer = StableAdamW([{'params': trainable.parameters()}],
lr=2e-3, betas=(0.9, 0.999), weight_decay=1e-4, amsgrad=True, eps=1e-10)
lr_scheduler = WarmCosineScheduler(optimizer, base_value=2e-3, final_value=2e-4, total_iters=total_iters,
warmup_iters=100)
print_fn('train image number:{}'.format(len(train_data)))
it = 0
for epoch in range(int(np.ceil(total_iters / len(train_dataloader)))):
model.train()
loss_list = []
for img, label in train_dataloader:
img = img.to(device)
label = label.to(device)
en, de = model(img)
# loss = global_cosine(en, de)
p_final = 0.9
p = min(p_final * it / 1000, p_final)
loss = global_cosine_hm_percent(en, de, p=p, factor=0.1)
# loss = global_cosine(en, de)
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()
if (it + 1) % 5000 == 0:
# torch.save(model.state_dict(), os.path.join(args.save_dir, args.save_name, 'model.pth'))
auroc_sp_list, ap_sp_list, f1_sp_list = [], [], []
auroc_px_list, ap_px_list, f1_px_list, aupro_px_list = [], [], [], []
for item, test_data in zip(item_list, test_data_list):
test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False,
num_workers=4)
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
auroc_sp_list.append(auroc_sp)
ap_sp_list.append(ap_sp)
f1_sp_list.append(f1_sp)
auroc_px_list.append(auroc_px)
ap_px_list.append(ap_px)
f1_px_list.append(f1_px)
aupro_px_list.append(aupro_px)
print_fn(
'{}: I-Auroc:{:.4f}, I-AP:{:.4f}, I-F1:{:.4f}, P-AUROC:{:.4f}, P-AP:{:.4f}, P-F1:{:.4f}, P-AUPRO:{:.4f}'.format(
item, auroc_sp, ap_sp, f1_sp, auroc_px, ap_px, f1_px, aupro_px))
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(
np.mean(auroc_sp_list), np.mean(ap_sp_list), np.mean(f1_sp_list),
np.mean(auroc_px_list), np.mean(ap_px_list), np.mean(f1_px_list), np.mean(aupro_px_list)))
model.train()
it += 1
if it == total_iters:
break
print_fn('iter [{}/{}], loss:{:.4f}'.format(it, total_iters, np.mean(loss_list)))
# torch.save(model.state_dict(), os.path.join(args.save_dir, args.save_name, 'model.pth'))
return
if __name__ == '__main__':
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
import argparse
parser = argparse.ArgumentParser(description='')
parser.add_argument('--data_path', type=str, default='../mvtec_anomaly_detection')
parser.add_argument('--save_dir', type=str, default='./saved_results')
parser.add_argument('--save_name', type=str,
default='vitill_mvtec_uni_dinov2br_c392_en29_bn4dp2_de8_laelu_md2_i1_it10k_sams2e3_wd1e4_w1hcosa2e4_ghmp09f01w01_b16_s1')
args = parser.parse_args()
#
item_list = ['carpet', 'grid', 'leather', 'tile', 'wood', 'bottle', 'cable', 'capsule',
'hazelnut', 'metal_nut', 'pill', 'screw', 'toothbrush', 'transistor', 'zipper']
logger = get_logger(args.save_name, os.path.join(args.save_dir, args.save_name))
print_fn = logger.info
device = 'cuda:1' if torch.cuda.is_available() else 'cpu'
print_fn(device)
train(item_list)