Face_recognition/train_bak.py

import torch
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.nn import DataParallel, CrossEntropyLoss
from dataloader.MyHF_loader import CASIA_HF, LFW_Pairs
from core import model_bak
from core.utils import init_log
import os, time, numpy as np, scipy.io
from datetime import datetime
from config import BATCH_SIZE, SAVE_FREQ, RESUME, SAVE_DIR, TEST_FREQ, TOTAL_EPOCH, MODEL_PRE, GPU
from sklearn.metrics.pairwise import cosine_similarity # [추가] 정확도 계산용

# ----------------------------
# [추가] 간단한 LFW 정확도 계산 함수
# ----------------------------
def calculate_accuracy(featureLs, featureRs, flags, thresholds=np.arange(0, 1, 0.01)):
    # 1. 특징 벡터 정규화 (Normalize)
    featureLs = featureLs / np.linalg.norm(featureLs, axis=1, keepdims=True)
    featureRs = featureRs / np.linalg.norm(featureRs, axis=1, keepdims=True)
    
    # 2. 코사인 유사도 계산 (Dot Product)
    scores = np.sum(featureLs * featureRs, axis=1)
    
    # 3. 최적의 임계값(Threshold) 찾기 및 정확도 계산
    best_acc = 0
    for t in thresholds:
        # 유사도가 t보다 크면 '같은 사람(1)', 작으면 '다른 사람(0)'
        preds = (scores > t).astype(int)
        acc = np.mean(preds == flags)
        if acc > best_acc:
            best_acc = acc
    return best_acc

# ----------------------------
# GPU 및 초기 설정 (기존 동일)
# ----------------------------
gpu_list = ''
multi_gpus = False
if isinstance(GPU, int):
    gpu_list = str(GPU)
else:
    multi_gpus = True
    gpu_list = ','.join(map(str, GPU))
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list

start_epoch = 1
save_dir = os.path.join(SAVE_DIR, 'MODEL_BAK_' + datetime.now().strftime('%Y%m%d_%H%M%S'))
os.makedirs(save_dir, exist_ok=True)
logging = init_log(save_dir)
_print = logging.info

# ----------------------------
# Dataloader (기존 동일)
# ----------------------------
trainset = CASIA_HF()
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,
                                          shuffle=True, num_workers=8, drop_last=False)

testset = LFW_Pairs()
testloader = torch.utils.data.DataLoader(testset, batch_size=32,
                                         shuffle=False, num_workers=8, drop_last=False)

# ----------------------------
# Model & Optimizer (기존 동일)
# ----------------------------
net = model_bak.MobileFacenet()
ArcMargin = model_bak.ArcMarginProduct(128, trainset.dataset.features['label'].num_classes)

if RESUME:
    ckpt = torch.load(RESUME)
    net.load_state_dict(ckpt['net_state_dict'])
    start_epoch = ckpt['epoch'] + 1

net = net.cuda()
ArcMargin = ArcMargin.cuda()
if multi_gpus:
    net = DataParallel(net)
    ArcMargin = DataParallel(ArcMargin)

criterion = CrossEntropyLoss()

ignored_params = list(map(id, net.linear1.parameters())) + list(map(id, ArcMargin.weight))
# prelu_params = [p for m in net.modules() if isinstance(m, torch.nn.PReLU) for p in m.parameters()]
base_params = filter(lambda p: id(p) not in ignored_params, net.parameters())

# 기존 아키텍처에서 prelu 삭제했었으니까 아래 optim에서도 삭제 처리
optimizer_ft = optim.SGD([
    {'params': base_params, 'weight_decay': 4e-5},
    {'params': net.linear1.parameters(), 'weight_decay': 4e-4},
    {'params': ArcMargin.weight, 'weight_decay': 4e-4}
], lr=0.1, momentum=0.9, nesterov=True)

# optimizer_ft = optim.SGD([
#     {'params': base_params, 'weight_decay': 4e-5},
#     {'params': net.linear1.parameters(), 'weight_decay': 4e-4},
#     {'params': ArcMargin.weight, 'weight_decay': 4e-4},
#     {'params': prelu_params, 'weight_decay': 0.0}
# ], lr=0.1, momentum=0.9, nesterov=True)

# 여기도 Config에서 Epoch 숫자 수정할때마다 milestone도 같이 수정해줘야함.
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer_ft, milestones=[240, 310, 400], gamma=0.1)

# ----------------------------
# [추가] Best Accuracy 기록 변수
# ----------------------------
best_lfw_acc = 0.0

# ----------------------------
# Training Loop
# ----------------------------
for epoch in range(start_epoch, TOTAL_EPOCH + 1):
    net.train()
    train_total_loss, total = 0, 0
    since = time.time()
    _print(f"Train Epoch: {epoch}/{TOTAL_EPOCH} ...")

    for data in trainloader:
        img, label = data[0].cuda(), data[1].cuda()
        optimizer_ft.zero_grad()
        raw_logits = net(img)
        output = ArcMargin(raw_logits, label)
        loss = criterion(output, label)
        loss.backward()
        optimizer_ft.step()
        train_total_loss += loss.item() * img.size(0)
        total += img.size(0)

    train_total_loss /= total
    time_elapsed = time.time() - since
    _print(f"    total_loss: {train_total_loss:.4f} time: {time_elapsed//60:.0f}m {time_elapsed%60:.0f}s")
    
    exp_lr_scheduler.step()
    
    # ----------------------------
    # Test & Best Model Save
    # ----------------------------
    if epoch % TEST_FREQ == 0:
        net.eval()
        featureLs, featureRs = None, None
        flags = [] # [추가] 정답(Label)을 저장할 리스트
        
        _print("    Testing LFW...")
        with torch.no_grad(): # [추가] 테스트 땐 기울기 계산 끔 (메모리 절약)
            for data in testloader:
                # data 구조: [images_list, label(flag)]라고 가정
                # LFW_Pairs의 경우 data[1]이 보통 정답(1:같은사람, 0:다른사람)
                
                # 이미지 GPU 이동
                imgs = [d.cuda() for d in data[0]] 
                
                # 정답 라벨 수집 (numpy로 변환)
                flags.append(data[1].numpy()) 
                
                # 특징 추출
                res = [net(d).data.cpu().numpy() for d in imgs]
                
                featureL = np.concatenate((res[0], res[1]), 1)
                featureR = np.concatenate((res[2], res[3]), 1)
                
                featureLs = featureL if featureLs is None else np.concatenate((featureLs, featureL), 0)
                featureRs = featureR if featureRs is None else np.concatenate((featureRs, featureR), 0)

        # [추가] 정답 리스트 합치기
        flags = np.concatenate(flags, 0)
        
        # [추가] 정확도 계산
        # 만약 scipy.io.savemat은 필요하면 유지, 아니면 삭제해도 됨
        # result = {'fl': featureLs, 'fr': featureRs}
        # scipy.io.savemat('./result/tmp_result.mat', result)
        
        # 직접 정확도 계산 (함수 호출)
        current_acc = calculate_accuracy(featureLs, featureRs, flags)
        _print(f"    LFW Acc: {current_acc*100:.2f}% (Best: {best_lfw_acc*100:.2f}%)")

        # [핵심] Best Model 저장 (Loss가 아닌 Acc 기준)
        if current_acc > best_lfw_acc:
            best_lfw_acc = current_acc
            state_dict = net.module.state_dict() if multi_gpus else net.state_dict()
            
            best_dir = os.path.join(save_dir, 'best_model')
            os.makedirs(best_dir, exist_ok=True)
            
            best_path = os.path.join(best_dir, f'best_{epoch:03d}.ckpt')
            torch.save(
                {
                    'epoch': epoch,
                    'net_state_dict': state_dict,
                    'acc': best_lfw_acc
                },
                best_path
            )
            _print(f"    ==> Best Model Saved! (Acc: {best_lfw_acc*100:.2f}%, Epoch: {epoch}))")
            
    # ----------------------------
    # Regular Save (백업용)
    # ----------------------------
    if epoch % SAVE_FREQ == 0:
        state_dict = net.module.state_dict() if multi_gpus else net.state_dict()
        torch.save({'epoch': epoch, 'net_state_dict': state_dict},
                   os.path.join(save_dir, f'{epoch:03d}.ckpt'))

_print("finishing training")