init

+# code in this file is adpated from rpmcruz/autoaugment
+# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
+import random
+
+import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
+import numpy as np
+import torch
+from PIL import Image
+
+
+def ShearX(img, v):  # [-0.3, 0.3]
+    assert -0.3 <= v <= 0.3
+    if random.random() > 0.5:
+        v = -v
+    return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0))
+
+
+def ShearY(img, v):  # [-0.3, 0.3]
+    assert -0.3 <= v <= 0.3
+    if random.random() > 0.5:
+        v = -v
+    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, v, 1, 0))
+
+
+def TranslateX(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
+    assert -0.45 <= v <= 0.45
+    if random.random() > 0.5:
+        v = -v
+    v = v * img.size[0]
+    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
+
+
+def TranslateXabs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
+    assert 0 <= v
+    if random.random() > 0.5:
+        v = -v
+    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, v, 0, 1, 0))
+
+
+def TranslateY(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
+    assert -0.45 <= v <= 0.45
+    if random.random() > 0.5:
+        v = -v
+    v = v * img.size[1]
+    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
+
+
+def TranslateYabs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]
+    assert 0 <= v
+    if random.random() > 0.5:
+        v = -v
+    return img.transform(img.size, PIL.Image.AFFINE, (1, 0, 0, 0, 1, v))
+
+
+def Rotate(img, v):  # [-30, 30]
+    assert -30 <= v <= 30
+    if random.random() > 0.5:
+        v = -v
+    return img.rotate(v)
+
+
+def AutoContrast(img, _):
+    return PIL.ImageOps.autocontrast(img)
+
+
+def Invert(img, _):
+    return PIL.ImageOps.invert(img)
+
+
+def Equalize(img, _):
+    return PIL.ImageOps.equalize(img)
+
+
+def Flip(img, _):  # not from the paper
+    return PIL.ImageOps.mirror(img)
+
+
+def Solarize(img, v):  # [0, 256]
+    assert 0 <= v <= 256
+    return PIL.ImageOps.solarize(img, v)
+
+
+def SolarizeAdd(img, addition=0, threshold=128):
+    img_np = np.array(img).astype(np.int)
+    img_np = img_np + addition
+    img_np = np.clip(img_np, 0, 255)
+    img_np = img_np.astype(np.uint8)
+    img = Image.fromarray(img_np)
+    return PIL.ImageOps.solarize(img, threshold)
+
+
+def Posterize(img, v):  # [4, 8]
+    v = int(v)
+    v = max(1, v)
+    return PIL.ImageOps.posterize(img, v)
+
+
+def Contrast(img, v):  # [0.1,1.9]
+    assert 0.1 <= v <= 1.9
+    return PIL.ImageEnhance.Contrast(img).enhance(v)
+
+
+def Color(img, v):  # [0.1,1.9]
+    assert 0.1 <= v <= 1.9
+    return PIL.ImageEnhance.Color(img).enhance(v)
+
+
+def Brightness(img, v):  # [0.1,1.9]
+    assert 0.1 <= v <= 1.9
+    return PIL.ImageEnhance.Brightness(img).enhance(v)
+
+
+def Sharpness(img, v):  # [0.1,1.9]
+    assert 0.1 <= v <= 1.9
+    return PIL.ImageEnhance.Sharpness(img).enhance(v)
+
+
+def Cutout(img, v):  # [0, 60] => percentage: [0, 0.2]
+    assert 0.0 <= v <= 0.2
+    if v <= 0.:
+        return img
+
+    v = v * img.size[0]
+    return CutoutAbs(img, v)
+
+
+def CutoutAbs(img, v):  # [0, 60] => percentage: [0, 0.2]
+    # assert 0 <= v <= 20
+    if v < 0:
+        return img
+    w, h = img.size
+    x0 = np.random.uniform(w)
+    y0 = np.random.uniform(h)
+
+    x0 = int(max(0, x0 - v / 2.))
+    y0 = int(max(0, y0 - v / 2.))
+    x1 = min(w, x0 + v)
+    y1 = min(h, y0 + v)
+
+    xy = (x0, y0, x1, y1)
+    color = (125, 123, 114)
+    # color = (0, 0, 0)
+    img = img.copy()
+    PIL.ImageDraw.Draw(img).rectangle(xy, color)
+    return img
+
+
+def SamplePairing(imgs):  # [0, 0.4]
+    def f(img1, v):
+        i = np.random.choice(len(imgs))
+        img2 = PIL.Image.fromarray(imgs[i])
+        return PIL.Image.blend(img1, img2, v)
+
+    return f
+
+
+def Identity(img, v):
+    return img
+
+
+def augment_list():  # 16 oeprations and their ranges
+    # https://github.com/google-research/uda/blob/master/image/randaugment/policies.py#L57
+    # l = [
+    #     (Identity, 0., 1.0),
+    #     (ShearX, 0., 0.3),  # 0
+    #     (ShearY, 0., 0.3),  # 1
+    #     (TranslateX, 0., 0.33),  # 2
+    #     (TranslateY, 0., 0.33),  # 3
+    #     (Rotate, 0, 30),  # 4
+    #     (AutoContrast, 0, 1),  # 5
+    #     (Invert, 0, 1),  # 6
+    #     (Equalize, 0, 1),  # 7
+    #     (Solarize, 0, 110),  # 8
+    #     (Posterize, 4, 8),  # 9
+    #     # (Contrast, 0.1, 1.9),  # 10
+    #     (Color, 0.1, 1.9),  # 11
+    #     (Brightness, 0.1, 1.9),  # 12
+    #     (Sharpness, 0.1, 1.9),  # 13
+    #     # (Cutout, 0, 0.2),  # 14
+    #     # (SamplePairing(imgs), 0, 0.4),  # 15
+    # ]
+
+    # https://github.com/tensorflow/tpu/blob/8462d083dd89489a79e3200bcc8d4063bf362186/models/official/efficientnet/autoaugment.py#L505
+    l = [
+        (AutoContrast, 0, 1),
+        (Equalize, 0, 1),
+        (Invert, 0, 1),
+        (Rotate, 0, 30),
+        (Posterize, 0, 4),
+        (Solarize, 0, 256),
+        (SolarizeAdd, 0, 110),
+        (Color, 0.1, 1.9),
+        (Contrast, 0.1, 1.9),
+        (Brightness, 0.1, 1.9),
+        (Sharpness, 0.1, 1.9),
+        (ShearX, 0., 0.3),
+        (ShearY, 0., 0.3),
+        (CutoutAbs, 0, 40),
+        (TranslateXabs, 0., 100),
+        (TranslateYabs, 0., 100),
+    ]
+
+    return l
+
+
+class Lighting(object):
+    """Lighting noise(AlexNet - style PCA - based noise)"""
+
+    def __init__(self, alphastd, eigval, eigvec):
+        self.alphastd = alphastd
+        self.eigval = torch.Tensor(eigval)
+        self.eigvec = torch.Tensor(eigvec)
+
+    def __call__(self, img):
+        if self.alphastd == 0:
+            return img
+
+        alpha = img.new().resize_(3).normal_(0, self.alphastd)
+        rgb = self.eigvec.type_as(img).clone() \
+            .mul(alpha.view(1, 3).expand(3, 3)) \
+            .mul(self.eigval.view(1, 3).expand(3, 3)) \
+            .sum(1).squeeze()
+
+        return img.add(rgb.view(3, 1, 1).expand_as(img))
+
+
+class CutoutDefault(object):
+    """
+    Reference : https://github.com/quark0/darts/blob/master/cnn/utils.py
+    """
+    def __init__(self, length):
+        self.length = length
+
+    def __call__(self, img):
+        h, w = img.size(1), img.size(2)
+        mask = np.ones((h, w), np.float32)
+        y = np.random.randint(h)
+        x = np.random.randint(w)
+
+        y1 = np.clip(y - self.length // 2, 0, h)
+        y2 = np.clip(y + self.length // 2, 0, h)
+        x1 = np.clip(x - self.length // 2, 0, w)
+        x2 = np.clip(x + self.length // 2, 0, w)
+
+        mask[y1: y2, x1: x2] = 0.
+        mask = torch.from_numpy(mask)
+        mask = mask.expand_as(img)
+        img *= mask
+        return img
+
+
+class RandAugment:
+    def __init__(self, n, m):
+        self.n = n      # augmentation을 적용하는 수.
+        self.m = m      # [0, 30]
+        self.augment_list = augment_list()
+
+    def __call__(self, img):
+        ops = random.choices(self.augment_list, k=self.n)
+        for op, minval, maxval in ops:
+            val = (float(self.m) / 30) * float(maxval - minval) + minval
+            img = op(img, val)
+        return img
\ No newline at end of file

+task: All
+modelname: MobilenetV3
+output: output
+checkpoint: "output/All/14835_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar"
+gpu: [2]
+data:
+  train: ../data/Fifth_data/All
+  val: ../data/Fifth_data/All
+  test: ../data/Fifth_data/All
+train:
+  epochs: 3000
+  start-epoch: 0
+  batch-size: 256
+  worker: 16
+  resume: ''
+  augment: True
+  size: 224
+  confidence: False
+  weight: [1., 1., 1., 1., 1., 1., 1., 1.]  #Crack, Double, Empty, Flip, Leave, Normal, Pollute, Scratch
+predict:
+  batch-size: 256
+  worker: 16
+  cam: False
+  normalize: True
+  save: False
+optimizer: 
+  type: 'Adam'
+  lr: 0.001
+  momentum: 0.9
+  weight_decay: 0.0001
+loss:
+  gamma: 2.
+  alpha: 0.8
+model:
+  blocks: 6
+  class: 8
+etc:
+  tensorboard: False
+  print_freq: 10
\ No newline at end of file

+task: Type
+modelname: MobilenetV3
+output: output
+checkpoint: "output/ErrorType/85804_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar"
+gpu: [1]
+data:
+  train: ../data/Fifth_data/ErrorType
+  val: ../data/Fifth_data/ErrorType
+  test: ../data/Fifth_data/ErrorType
+train:
+  epochs: 3000
+  start-epoch: 0
+  batch-size: 256
+  worker: 16
+  resume: ''
+  augment: True
+  size: 64
+  confidence: False
+  weight: [1., 1., 1., 1., 1., 1., 1.]  #Crack, Double, Empty, Flip, Leave, Scratch
+predict:
+  batch-size: 256
+  worker: 16
+  cam: False
+  normalize: True
+  save: False
+optimizer:
+  type: 'SGD'
+  lr: 0.1
+  momentum: 0.9
+  weight_decay: 0.0001
+loss:
+  gamma: 2.
+  alpha: 0.8
+model:
+  blocks: 4
+  class: 7
+etc:
+  tensorboard: False
+  print_freq: 10
\ No newline at end of file

+task: Error
+modelname: MobilenetV3
+output: output
+checkpoint: "output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar"
+gpu: [1]
+data:
+  train: ../data/Fifth_data/Error
+  val: ../data/Fifth_data/Error
+  test: ../data/Fifth_data/Error
+train:
+  epochs: 3000
+  start-epoch: 0
+  batch-size: 256
+  worker: 16
+  resume: ''
+  augment: True
+  size: 64
+  confidence: False
+  weight: [1., 1.]    #Error , Normal
+predict:
+  batch-size: 256
+  worker: 16
+  cam: False
+  cam-class: "Error"
+  normalize: True
+  save: False
+optimizer:
+  type: 'SGD'
+  lr: 0.1
+  momentum: 0.9
+  weight_decay: 0.0001
+loss:
+  gamma: 2.
+  alpha: 0.8
+model:
+  blocks: 4
+  class: 2
+etc:
+  tensorboard: False
+  print_freq: 10
\ No newline at end of file

+import torch
+import torchvision
+import torch.nn as nn
+import argparse
+from model import AutoEncoder, pytorch_autoencoder
+from get_mean_std import get_params
+from torchvision.utils import save_image
+
+
+parser = argparse.ArgumentParser(description='Process autoencoder')
+parser.add_argument('--config', type=str, help='select type')
+args = parser.parse_args()
+
+# Scratch에서만 넣은 데이터
+data_path = "../data/Fourth_data/Auto_test"
+checkpoint_path = "./dc_img/checkpoint.pth"
+resize_size = 128
+batch_size = 128
+
+# 보고서를 참고하여 만든 autoencoder 와 pytorch 에서 제공하는 autoencoder
+if args.config == "my":
+    model = AutoEncoder().cuda("cuda:1")
+else:
+    model = pytorch_autoencoder().cuda("cuda:1")
+
+checkpoint = torch.load(checkpoint_path)
+model.load_state_dict(checkpoint)
+print("checkpoint loaded finish!")
+
+img_transform = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((resize_size, resize_size)),
+    torchvision.transforms.Grayscale(),
+    torchvision.transforms.ToTensor(),
+])
+
+
+dataset = torchvision.datasets.ImageFolder(data_path, transform=img_transform)
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)
+criterion = nn.L1Loss()
+
+for idx, data in enumerate(dataloader):
+    img, _ = data
+    img = img.cuda("cuda:1")
+    output = model(img)
+
+    save_image(output, f'./dc_img/test_output_{idx}.png')
+    
+    loss = criterion(output, img)
+
+    img = img - output
+
+    save_image(img, f'./dc_img/scratch_dif_{idx}.png')
+
+print(f"loss : {loss}")
\ No newline at end of file

+import torch
+import torchvision
+import torch.nn as nn
+import argparse
+from model import AutoEncoder, pytorch_autoencoder, AutoEncoder_s
+from get_mean_std import get_params
+from torchvision.utils import save_image
+
+parser = argparse.ArgumentParser(description='Process autoencoder')
+parser.add_argument('--config', type=str, help='select type')
+args = parser.parse_args()
+
+# 노말만 넣은 데이터
+data_path = "../data/Fourth_data/Auto"
+resize_size = 128
+num_epochs = 100
+batch_size = 128
+learning_rate = 1e-3
+
+# 보고서를 참고하여 만든 autoencoder 와 pytorch 에서 제공하는 autoencoder
+if args.config == "my":
+    model = AutoEncoder().cuda("cuda:1")
+elif args.config == "pytorch":
+    model = pytorch_autoencoder().cuda("cuda:1")
+else:
+    model = AutoEncoder_s().cuda("cuda:1")
+
+print(model)
+#mean, std = get_params(data_path, resize_size)
+
+img_transform = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((resize_size, resize_size)),
+    torchvision.transforms.Grayscale(),
+    torchvision.transforms.ToTensor(),
+])
+
+dataset = torchvision.datasets.ImageFolder(data_path, transform=img_transform)
+dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+criterion = nn.L1Loss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)
+
+for epoch in range(num_epochs):
+    for data in dataloader:
+        img, _ = data
+        img = img.cuda("cuda:1")
+        output = model(img)
+        loss = criterion(output, img)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+    print('epoch [{}/{}], loss:{:.4f}'.format(epoch+1, num_epochs, loss.item()))
+
+    if epoch % 10 ==0:
+        save_image(output, './dc_img/image_{}.png'.format(epoch))
+
+torch.save(model.state_dict(), './dc_img/checkpoint.pth')
\ No newline at end of file

+import torch
+import torch.nn as nn
+import os
+import shutil
+import logging
+from model import mobilenetv3
+from utils import get_args_from_yaml
+import torchvision.datasets as datasets
+from utils import AverageMeter, accuracy, printlog, precision, recall
+import torchvision.transforms as transforms
+from torch.utils.data.sampler import SubsetRandomSampler
+import numpy as np
+import time
+from get_mean_std import get_params
+
+model = mobilenetv3(n_class=7, blocknum=6, dropout=0.5)
+model = model.train()
+data_path = "../data/All"
+check_path = "output/All/30114_model=MobilenetV3-ep=3000-block=6-class=8/model_best.pth.tar"
+validation_ratio = 0.1
+random_seed = 10
+gpus=[0]
+epochs = 3000
+resize_size=128
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+streamHandler = logging.StreamHandler()
+logger.addHandler(streamHandler)
+
+fileHandler = logging.FileHandler("logs/finetune.log")
+logger.addHandler(fileHandler)
+
+
+def save_checkpoint(state, is_best, block =6, filename='checkpoint.pth.tar'):
+    """Saves checkpoint to disk"""
+    directory = "%s/%s/" % ('output', 'All')
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+    filename = directory + filename
+    torch.save(state, filename)
+    logger.info(f"Checkpoint Saved: {filename}")
+    best_filename = f"output/All/model_best.pth.tar"
+    if is_best:
+        shutil.copyfile(filename, best_filename)
+        logger.info(f"New Best Checkpoint saved: {best_filename}")
+
+    return best_filename
+
+def validate(val_loader, model, criterion, epoch, q=None):
+    """Perform validaadd_model_to_queuetion on the validation set"""
+    with torch.no_grad():
+        batch_time = AverageMeter()
+        losses = AverageMeter()
+        top1 = AverageMeter()
+        prec = []
+        rec = []
+
+        for i in range(7):
+            prec.append(AverageMeter())
+            rec.append(AverageMeter())
+        # switch to evaluate mode
+        model.eval()
+        end = time.time()
+
+        for i, (input, target) in enumerate(val_loader):
+            if torch.cuda.is_available():
+                target = target.cuda()
+                input = input.cuda()
+
+            # compute output
+            output = model(input)
+            loss = criterion(output, target)
+
+            # measure accuracy and record loss
+            prec1 = accuracy(output.data, target, topk=(1,))[0]
+
+            losses.update(loss.item(), input.size(0))
+            top1.update(prec1.item(), input.size(0))
+
+            for k in range(7):
+                prec[k].update(precision(output.data, target, target_class=k), input.size(0))
+                rec[k].update(recall(output.data, target, target_class=k), input.size(0))
+
+            # measure elapsed time
+            batch_time.update(time.time() - end)
+            end = time.time()
+
+            if i % 10 == 0:
+                logger.info('Test: [{0}/{1}]\t'
+                        'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                        'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                        'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'
+                        .format(
+                            i, len(val_loader), batch_time=batch_time, loss=losses,
+                            top1=top1))
+
+        printlog(' * epoch: {epoch} Prec@1 {top1.avg:.3f}'.format(epoch=epoch,top1=top1), logger, q)
+
+    return top1.avg, prec, rec
+
+
+def train(model, train_loader, criterion, optimizer, epoch):
+    batch_time = AverageMeter()
+    losses = AverageMeter()
+    top1 = AverageMeter()
+    prec = AverageMeter()
+
+    # switch to train mode
+    model.train()
+    end = time.time()
+
+    for i, (input, target) in enumerate(train_loader):
+        if torch.cuda.is_available():
+            target = target.cuda()
+            input = input.cuda()
+        # compute output
+        output = model(input)
+        loss = criterion(output, target)
+        # measure accuracy and record loss
+        prec1 = accuracy(output, target, topk=(1,))[0]
+
+        losses.update(loss.item(), input.size(0))
+        top1.update(prec1.item(), input.size(0))
+        
+        # compute gradient and do SGD step
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # measure elapsed time
+        batch_time.update(time.time() - end)
+        end = time.time()
+
+        if i % 10 == 0:
+            logger.info('Epoch: [{0}][{1}/{2}]\t'
+                        'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
+                        'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                        'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
+                        .format(
+                            epoch, i, len(train_loader), batch_time=batch_time,
+                            loss=losses, top1=top1))
+
+for idx, (name, module) in enumerate(model.named_modules()):
+    if(idx < 62):
+        for param in module.parameters():
+            param.requires_grad = False
+    else:
+        for param in module.parameters():
+            param.requires_grad = True
+
+mean, std = get_params(data_path, resize_size)
+normalize = transforms.Normalize(mean=[mean[0].item()],
+                         std=[std[0].item()])
+
+transform_train = transforms.Compose([
+        transforms.Resize((resize_size, resize_size)),      # 가로세로 크기 조정
+        transforms.ColorJitter(0.2,0.2,0.2),                # 밝기, 대비, 채도 조정
+        transforms.RandomRotation(2),                       # -2~ 2도 만큼 회전
+        transforms.RandomAffine(5),                         # affine 변환 (평행사변형이 된다든지, 사다리꼴이 된다든지)
+        transforms.RandomCrop(resize_size, padding=2),      # 원본에서 padding을 상하좌우 2로 둔 뒤, 64만큼 자름
+        transforms.RandomHorizontalFlip(),                  # Data 변환 좌우 반전
+        transforms.Grayscale(),
+        transforms.ToTensor(),
+        normalize
+    ])
+
+transform_test = transforms.Compose([
+    transforms.Resize((resize_size, resize_size)),
+    transforms.Grayscale(),
+    transforms.ToTensor(),
+    normalize
+])
+
+kwargs = {'num_workers': 16, 'pin_memory': True}
+
+train_data = datasets.ImageFolder(data_path, transform_train)
+val_data = datasets.ImageFolder(data_path,transform_test)
+
+
+num_train = len(train_data)
+indices = list(range(num_train))
+split = int(np.floor(validation_ratio * num_train))
+
+# 랜덤 시드 설정. (Train이나 ,Test 일때 모두 10 이므로 같은 데이터셋이라 할 수 있다)
+np.random.seed(random_seed)
+np.random.shuffle(indices)
+
+# Train set, Validation set 나누기.
+train_idx, valid_idx = indices[split:], indices[:split]
+train_sampler = SubsetRandomSampler(train_idx)
+valid_sampler = SubsetRandomSampler(valid_idx)
+
+train_loader = torch.utils.data.DataLoader(
+    train_data, batch_size=256, sampler=train_sampler,      #shuffle = True
+    **kwargs)
+val_loader = torch.utils.data.DataLoader(
+    val_data, batch_size=256, sampler=valid_sampler,        #shuffle = False
+    **kwargs)
+
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), 0.0001, weight_decay=0.0001)
+
+if torch.cuda.is_available():
+    torch.cuda.set_device(gpus[0])
+    with torch.cuda.device(gpus[0]):
+        model = model.cuda()
+        criterion = criterion.cuda()
+    model = torch.nn.DataParallel(model, device_ids=gpus, output_device=gpus[0])
+
+checkpoint = torch.load(check_path)
+
+pretrained_dict = checkpoint['state_dict']
+new_model_dict = model.state_dict()
+for k, v in pretrained_dict.items(): 
+    if 'classifier' in k:
+        continue
+    new_model_dict.update({k : v})
+model.load_state_dict(new_model_dict)
+
+#model.load_state_dict(checkpoint['state_dict'], strict=False)
+best_prec1 = checkpoint['best_prec1']
+
+for epoch in range(epochs):
+   train(model, train_loader, criterion, optimizer, epoch)
+
+   prec1, prec, rec =  validate(val_loader, model, criterion, epoch)
+
+   is_best = prec1 >= best_prec1
+
+   best_prec1 = max(prec1, best_prec1)
+
+   checkpoint = save_checkpoint({
+            'epoch': epoch + 1,
+            'state_dict': model.state_dict(),
+            'best_prec1': best_prec1,
+        }, is_best)
+
+
+for i in range(len(prec)):
+    logger.info(' * Precision {prec.avg:.3f}'.format(prec=prec[i]))
+    logger.info(' * recall {rec.avg:.3f}'.format(rec=rec[i]))
\ No newline at end of file

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+class FocalLoss(nn.Module):
+    def __init__(self, gamma=0, alpha=None, size_average=True):
+        super(FocalLoss, self).__init__()
+        self.gamma = gamma
+        self.alpha = alpha
+        if isinstance(alpha,(float,int)): self.alpha = torch.Tensor([alpha,1-alpha])
+        if isinstance(alpha,list): self.alpha = torch.Tensor(alpha)
+        self.size_average = size_average
+
+    def forward(self, input, target):
+        if input.dim() > 2:
+            input = input.view(input.size(0),input.size(1),-1)  # N,C,H,W => N,C,H*W
+            input = input.transpose(1,2)    # N,C,H*W => N,H*W,C
+            input = input.contiguous().view(-1,input.size(2))   # N,H*W,C => N*H*W,C
+        target = target.view(-1,1)
+
+        logpt = F.log_softmax(input)
+        logpt = logpt.gather(1,target)
+        logpt = logpt.view(-1)
+        pt = Variable(logpt.data.exp())
+        #pt = logpt.data.exp()           #pt = (256), input = (256,5), target = (256,1)
+        #pt는 logpt에 exponatial 적용
+
+        self.alpha = self.alpha.cuda()
+        if self.alpha is not None:
+            if self.alpha.type()!=input.data.type():
+                self.alpha = self.alpha.type_as(input.data)
+            at = self.alpha.gather(0,target.data.view(-1))
+            logpt = logpt * at
+
+        loss = -1 * (1-pt)**self.gamma * logpt
+
+        if self.size_average: return loss.mean()
+        else: return loss.sum()
\ No newline at end of file

+import torch.multiprocessing as mp
+import torch
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+import argparse
+import numpy as np
+from get_mean_std import get_params
+from model import mobilenetv3
+import parmap
+
+
+#Image resize 계수
+resize_size = 64
+
+#Class 의 개수.
+class_num = 7
+
+#사용한 Random Seed
+seeds = [39396, 2798, 3843, 62034, 8817, 65014, 45385]
+
+#기기에 있는 GPU 개수.
+gpu = 4
+
+#저장된 Checkpoint.
+checkpoints = [
+    "output/ErrorType/39396_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar",
+    "output/ErrorType/2798_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar",
+    "output/ErrorType/3843_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar",
+    "output/ErrorType/62034_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar",
+    "output/ErrorType/8817_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar",
+    "output/ErrorType/65014_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar",
+    "output/ErrorType/45385_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar"
+]
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the precision@k for the specified values of k"""
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(100.0 / batch_size))
+    return res
+
+def get_models():
+    models=[]
+    for idx, checkpoint in enumerate(checkpoints):
+        gpu_idx = idx % gpu
+
+        weights = torch.load(checkpoint)
+        model = mobilenetv3(n_class=class_num)
+
+        torch.cuda.set_device(gpu_idx)
+        with torch.cuda.device(gpu_idx):
+            model = model.cuda()
+
+        model = torch.nn.DataParallel(model, device_ids=[gpu_idx], output_device=gpu_idx)
+        model.load_state_dict(weights['state_dict'])
+
+        model.share_memory()
+        models.append(model)
+    return models
+
+def get_loader(path, resize_size):
+    mean, std = get_params(path, resize_size)
+    normalize = transforms.Normalize(mean=[mean[0].item()],
+                         std=[std[0].item()])
+
+    transform = transforms.Compose([
+        transforms.Resize((resize_size, resize_size)),
+        transforms.Grayscale(),
+        transforms.ToTensor(),
+        normalize
+    ])
+    dataset = datasets.ImageFolder(args.path, transform)
+    kwargs = {'num_workers': 4, 'pin_memory': True}
+
+    loader = torch.utils.data.DataLoader(dataset, batch_size=256, shuffle=False, **kwargs)
+    
+    return loader
+
+def get_data(processnum ,model, loader, return_dict):
+    with torch.no_grad():
+        top1 = AverageMeter()
+        model.eval()
+        gpu_idx = processnum % gpu
+        for i, data in enumerate(loader):
+            (input, target) = data
+
+            target = target.cuda(gpu_idx)
+            input = input.cuda(gpu_idx)
+            
+            output = model(input)
+
+            prec1 = accuracy(output, target, topk=(1,))[0]
+
+            top1.update(prec1.item(), input.size(0))
+
+        return_dict[processnum] = top1.avg
+
+
+
+if __name__ == '__main__':
+    mp.set_start_method('spawn')
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--path", required=True, help="path")
+    args = parser.parse_args()
+
+    manager = mp.Manager()
+    return_dict = manager.dict()
+    
+
+    # get one loader
+    loader = get_loader(args.path, resize_size)
+
+    # multi model with other checkpoint.
+    models = get_models()
+
+    #loader is not array so can arise error
+    processes = []
+    for i, model in enumerate(models):
+        p = mp.Process(target=get_data, args=(i, model, loader, return_dict))
+        p.start()
+        processes.append(p)
+
+    for p in processes: p.join()
+
+    for idx, seed in enumerate(seeds):
+        print(f"process {idx}, seed {seed} : {return_dict[idx]}")
+    
+    print(f"total variance : {np.var(return_dict.values())}")
+    #print(return_dict.values())
\ No newline at end of file

+import os
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from torchvision.utils import save_image
+from PIL.ImageOps import grayscale
+from PIL import Image
+from torchvision.datasets import ImageFolder
+
+class MyDataset(ImageFolder):
+    def __init__(self, root, trainsform):
+        super(MyDataset, self).__init__(root, trainsform)
+        
+    def __getitem__(self, index):
+        image, label = super(MyDataset, self).__getitem__(index)
+        return image, label
+ 
+
+
+def get_params(path, resize_size):
+    my_transform = transforms.Compose([
+    transforms.Resize((resize_size,resize_size)),
+    transforms.Grayscale(),
+    transforms.ToTensor()
+    ])
+
+    my_dataset = MyDataset(path, my_transform)
+
+    loader = torch.utils.data.DataLoader(
+        my_dataset,
+        batch_size=256,
+        num_workers=8,
+        shuffle=False
+    )
+
+    mean = 0.
+    std = 0.
+    nb_samples = 0.
+    for i, (data, target) in enumerate(loader):
+        batch_samples = data.size(0)
+        data = data.view(batch_samples, data.size(1), -1)
+        mean += data.mean(2).sum(0)
+        std += data.std(2).sum(0)
+        nb_samples += batch_samples
+
+    mean /= nb_samples
+    std /= nb_samples
+    print(f"mean : {mean} , std : {std}")
+    return mean, std
+
+"""
+my_transform = transforms.Compose([
+transforms.Resize((64,64)),
+transforms.ToTensor()
+])
+
+my_dataset = MyDataset("../data/Third_data/not_binary", my_transform)
+
+loader = torch.utils.data.DataLoader(
+    my_dataset,
+    batch_size=256,
+    num_workers=8,
+    shuffle=False
+)
+
+mean = 0.
+std = 0.
+nb_samples = 0.
+for i, (data, target) in enumerate(loader):
+    batch_samples = data.size(0)
+    data = data.view(batch_samples, data.size(1), -1)
+    mean += data.mean(2).sum(0)
+    std += data.std(2).sum(0)
+    nb_samples += batch_samples
+
+mean /= nb_samples
+std /= nb_samples
+
+print(f"mean : {mean}, std : {std}")
+"""
\ No newline at end of file

+import os
+import time
+import sys
+import torch.nn.functional as F
+
+import numpy as np
+import PIL
+import torch
+import torch.backends.cudnn as cudnn
+import torch.nn as nn
+import torch.nn.parallel
+import torch.utils.data
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+import yaml
+import cv2
+from get_mean_std import get_params
+sys.path.append(os.path.join(os.path.dirname(__name__)))
+from model import mobilenetv3
+
+if not os.path.exists("threshold"):
+    os.mkdir("threshold")
+
+thresholds = [.05, .1, .15, .2, .25, .3, .35, .4, .45, .5]
+
+for threshold in thresholds:
+    if not os.path.exists(f"threshold/{threshold}"):
+        os.mkdir(f"threshold/{threshold}")
+
+
+def get_args_from_yaml(file='trainer/configs/Error_config.yml'):
+    with open(file) as f:
+        conf = yaml.load(f)
+    return conf
+
+class MyImageFolder(datasets.ImageFolder):
+    def __getitem__(self, index):
+        # return image path
+        return super(MyImageFolder, self).__getitem__(index), self.imgs[index]
+
+def main(args):
+    run_model(args)
+    print(f"[{args['id']}] done")
+
+def run_model(args):
+    resize_size = args['train']['size']
+
+    gpus = args['gpu']
+
+    mean, std = get_params(args['data']['train'], resize_size)
+
+    normalize = transforms.Normalize(mean=[mean[0].item()],
+                         std=[std[0].item()])
+
+    normalize_factor = [mean, std]
+
+    # data loader
+    transform_test = transforms.Compose([
+        transforms.Resize((resize_size,resize_size)),
+        transforms.Grayscale(),
+        transforms.ToTensor(),
+        normalize
+    ])
+    kwargs = {'num_workers': args['predict']['worker'], 'pin_memory': True}
+    test_data = MyImageFolder(args['data']['val'], transform_test)
+    val_loader = torch.utils.data.DataLoader(
+        test_data, batch_size=args['predict']['batch-size'], shuffle=False,
+        **kwargs)
+
+    # load model
+    model = mobilenetv3(n_class= args['model']['class'], blocknum= args['model']['blocks'])
+
+    torch.cuda.set_device(gpus[0])
+    with torch.cuda.device(gpus[0]):
+        model = model.cuda()
+
+    model = torch.nn.DataParallel(model, device_ids=gpus, output_device=gpus[0])
+
+    print("=> loading checkpoint '{}'".format(args['checkpoint']))
+    checkpoint = torch.load(args['checkpoint'])
+    model.load_state_dict(checkpoint['state_dict'])
+    print("=> loaded checkpoint '{}' (epoch {})"
+          .format(args['checkpoint'], checkpoint['epoch']))
+    cudnn.benchmark = True
+
+    extract_data(val_loader, model, normalize_factor, args)
+
+
+def extract_data(val_loader, model, normalize_factor, args):
+    with torch.no_grad():
+        # switch to evaluate mode
+        model.eval()
+        for data in(val_loader):
+            (input, target), (path , _) = data
+            target = target.cuda()
+            input = input.cuda()
+
+            output = model(input)
+
+            print("save data!")
+            save_data(output, target, path)
+
+class AverageMeter(object):
+    def __init__(self):
+        self.reset()
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the precision@k for the specified values of k"""
+    maxk = max(topk)
+    batch_size = target.size(0)
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(100.0 / batch_size))
+    return res
+
+def save_data(output, target, path):
+    n_digits = 3
+    prob = F.softmax(output, dim=1)
+    prob = torch.round(prob * 10**n_digits) / (10**n_digits)
+    for idx, p in enumerate(prob):
+        value = torch.topk(p, 2).values
+        indice = torch.topk(p,2).indices
+        
+        value = value.tolist()
+        indice = indice.tolist()
+
+        gap = abs(value[0]-value[1])
+        for threshold in thresholds:
+            if(gap < threshold):
+                img = cv2.imread(path[idx])
+                filename = path[idx].split('/')[-1]
+                cv2.imwrite(f'threshold/{threshold}/pred_{indice[0]}_{indice[1]}_{filename}', img)
+
+if __name__ == '__main__':
+    args = get_args_from_yaml('configs/All_config.yml')
+    args['config'] = 'All'
+    args['id'] = 'threshold'
+    main(args)
\ No newline at end of file

+2020-03-31-19-11-26
+use seed 963
+use dataset : ../data/Fourth_data/All
+{'task': 'All/train_2020-03-31-19-11-26_model=MobilenetV3-ep=3000-block=6', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar', 'gpu': [2], 'data': {'train': '../data/Fourth_data/All', 'val': '../data/Fourth_data/All', 'test': '../data/Fourth_data/All'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'weight': [2.0, 4.0, 1.0, 1.0, 3.0, 1.0, 1.0], 'resume': '', 'augment': True, 'size': 224, 'confidence': False}, 'predict': {'batch-size': 256, 'worker': 64, 'cam': False, 'normalize': True, 'save': False}, 'optimizer': {'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 6, 'class': 7}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-03-31-19-11-26'}

+2020-04-03-17-46-05
+use seed 635
+use dataset : E:/code/detection/data/Fifth_data/All
+{'task': 'All/train_2020-04-03-17-46-05_model=MobilenetV3-ep=4000-block=6-class=8', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/All/14835_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar', 'gpu': [2], 'data': {'train': 'E:/code/detection/data/Fifth_data/All', 'val': '../data/Fifth_data/All', 'test': '../data/Fifth_data/All'}, 'train': {'epochs': 4000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'resume': 'E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar', 'augment': True, 'size': 224, 'confidence': False, 'weight': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, 'predict': {'batch-size': 256, 'worker': 16, 'cam': False, 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 6, 'class': 8}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-03-17-46-05'}
+Number of model parameters: 462840
+=> loading checkpoint 'E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar'
+=> loaded checkpoint 'E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar' (epoch 3000)
+Epoch: [0][0/26]	Time 113.958 (113.958)	Loss 0.0051 (0.0051)	Prec@1 100.000 (100.000)	

+2020-04-08-19-38-36
+use seed 283
+use dataset : E:/code/detection/data/Fifth_data/All
+{'task': 'All/train_2020-04-08-19-38-36_model=MobilenetV3-ep=3000-block=6-class=8', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/All/14835_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar', 'gpu': [2], 'data': {'train': 'E:/code/detection/data/Fifth_data/All', 'val': '../data/Fifth_data/All', 'test': '../data/Fifth_data/All'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'resume': '', 'augment': True, 'size': 224, 'confidence': False, 'weight': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, 'predict': {'batch-size': 256, 'worker': 16, 'cam': False, 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 6, 'class': 8}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-08-19-38-36'}
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\train.py", line 104, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\train.py", line 221, in run_model
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, len(train_loader))
+TypeError: object of type 'DataLoader' has no len()
+[train_2020-04-08-19-38-36] failed

+2020-03-31-18-30-33
+use seed 626
+use dataset : ../data/Fourth_data/Error
+{'task': 'Error/train_2020-03-31-18-30-33_model=MobilenetV3-ep=3000-block=4', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [0], 'data': {'train': '../data/Fourth_data/Error', 'val': '../data/Fourth_data/Error', 'test': '../data/Fourth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 2.0]}, 'predict': {'batch-size': 256, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True}, 'optimizer': {'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-03-31-18-30-33'}
+Number of model parameters: 154706
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\train.py", line 91, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\train.py", line 264, in run_model
+    train(train_loader, model, criterion, optimizer, scheduler, epoch, args, q)
+  File "E:\code\detection\trainer\train.py", line 309, in train
+    for i, (input, target) in enumerate(train_loader):
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 279, in __iter__
+    return _MultiProcessingDataLoaderIter(self)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 719, in __init__
+    w.start()
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\process.py", line 105, in start
+    self._popen = self._Popen(self)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\context.py", line 223, in _Popen
+    return _default_context.get_context().Process._Popen(process_obj)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\context.py", line 322, in _Popen
+    return Popen(process_obj)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
+    reduction.dump(process_obj, to_child)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\reduction.py", line 60, in dump
+    ForkingPickler(file, protocol).dump(obj)
+BrokenPipeError: [Errno 32] Broken pipe
+[train_2020-03-31-18-30-33] failed

+2020-04-01-17-53-24
+use seed 420
+use dataset : ../data/Fifth_data/Error
+{'task': 'Error/train_2020-04-01-17-53-24_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': '../data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 1024, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 1024, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-01-17-53-24'}
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\train.py", line 91, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\train.py", line 125, in run_model
+    mean, std = get_params(args['data']['train'], resize_size)
+  File "E:\code\detection\trainer\get_mean_std.py", line 31, in get_params
+    my_dataset = MyDataset(path, my_transform)
+  File "E:\code\detection\trainer\get_mean_std.py", line 16, in __init__
+    super(MyDataset, self).__init__(root, trainsform)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torchvision\datasets\folder.py", line 209, in __init__
+    target_transform=target_transform)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torchvision\datasets\folder.py", line 83, in __init__
+    classes, class_to_idx = self._find_classes(root)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torchvision\datasets\folder.py", line 116, in _find_classes
+    classes = [d.name for d in os.scandir(dir) if d.is_dir()]
+FileNotFoundError: [WinError 3] 지정된 경로를 찾을 수 없습니다: '../data/Fifth_data/Error'
+[train_2020-04-01-17-53-24] failed

+2020-04-01-18-16-36
+use seed 95
+use dataset : ../data/Fifth_data/Error
+{'task': 'Error/train_2020-04-01-18-16-36_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': '../data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 1024, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 1024, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-01-18-16-36'}
+Number of model parameters: 154706
+Epoch: [0][0/7]	Time 43.018 (43.018)	Loss 0.6986 (0.6986)	Prec@1 34.473 (34.473)	Precision 0.000 (0.000)

+2020-04-01-20-18-29
+use seed 997
+use dataset : ../data/Fifth_data/Error
+{'task': 'Error/train_2020-04-01-20-18-29_model=MobilenetV3-ep=3000-block=6-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': '../data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 1024, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 1024, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 6, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-01-20-18-29'}
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\train.py", line 93, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\train.py", line 127, in run_model
+    mean, std = get_params(args['data']['train'], resize_size)
+  File "E:\code\detection\trainer\get_mean_std.py", line 43, in get_params
+    for i, (data, target) in enumerate(loader):
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 279, in __iter__
+    return _MultiProcessingDataLoaderIter(self)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 719, in __init__
+    w.start()
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\process.py", line 105, in start
+    self._popen = self._Popen(self)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\context.py", line 223, in _Popen
+    return _default_context.get_context().Process._Popen(process_obj)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\context.py", line 322, in _Popen
+    return Popen(process_obj)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
+    reduction.dump(process_obj, to_child)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\multiprocessing\reduction.py", line 60, in dump
+    ForkingPickler(file, protocol).dump(obj)
+BrokenPipeError: [Errno 32] Broken pipe
+[train_2020-04-01-20-18-29] failed

+2020-04-01-21-07-25
+use seed 880
+use dataset : ../data/Fifth_data/Error
+{'task': 'Error/train_2020-04-01-21-07-25_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': '../data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 1024, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 1024, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-01-21-07-25'}

+2020-04-01-22-40-24
+use seed 238
+use dataset : ../data/Fifth_data/Error
+{'task': 'Error/train_2020-04-01-22-40-24_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': '../data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 1024, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 1024, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-01-22-40-24'}
+Number of model parameters: 154706
+Epoch: [0][0/7]	Time 44.533 (44.533)	Loss 0.6956 (0.6956)	Prec@1 41.504 (41.504)	Precision 0.000 (0.000)

+2020-04-01-23-15-24
+use seed 666
+use dataset : ../data/Fifth_data/Error
+{'task': 'Error/train_2020-04-01-23-15-24_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': '../data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 1024, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 1024, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-01-23-15-24'}

+2020-04-03-17-02-42
+use seed 185
+use dataset : E:/code/detection/data/Fifth_data/All
+{'task': 'Error/train_2020-04-03-17-02-42_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': 'E:/code/detection/data/Fifth_data/All', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': '3000', 'start-epoch': 0, 'batch-size': '256', 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': '256', 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': '0.001', 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-03-17-02-42'}
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\train.py", line 102, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\train.py", line 195, in run_model
+    **kwargs)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 219, in __init__
+    batch_sampler = BatchSampler(sampler, batch_size, drop_last)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\sampler.py", line 190, in __init__
+    "but got batch_size={}".format(batch_size))
+ValueError: batch_size should be a positive integer value, but got batch_size=256
+[train_2020-04-03-17-02-42] failed

+2020-04-03-17-04-30
+use seed 54
+use dataset : E:/code/detection/data/Fifth_data/All
+{'task': 'Error/train_2020-04-03-17-04-30_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': 'E:/code/detection/data/Fifth_data/All', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': '3000', 'start-epoch': 0, 'batch-size': '256', 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': '256', 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': '0.001', 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-03-17-04-30'}
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\train.py", line 103, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\train.py", line 196, in run_model
+    **kwargs)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 219, in __init__
+    batch_sampler = BatchSampler(sampler, batch_size, drop_last)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\sampler.py", line 190, in __init__
+    "but got batch_size={}".format(batch_size))
+ValueError: batch_size should be a positive integer value, but got batch_size=256
+[train_2020-04-03-17-04-30] failed

+2020-04-03-17-07-00
+use seed 809
+use dataset : E:/code/detection/data/Fifth_data/All
+{'task': 'Error/train_2020-04-03-17-07-00_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': 'E:/code/detection/data/Fifth_data/All', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': '3000', 'start-epoch': 0, 'batch-size': '256', 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': '256', 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': '0.001', 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-03-17-07-00'}
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\train.py", line 103, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\train.py", line 196, in run_model
+    **kwargs)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 219, in __init__
+    batch_sampler = BatchSampler(sampler, batch_size, drop_last)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\sampler.py", line 190, in __init__
+    "but got batch_size={}".format(batch_size))
+ValueError: batch_size should be a positive integer value, but got batch_size=256
+[train_2020-04-03-17-07-00] failed

+2020-04-03-17-08-43
+use seed 420
+use dataset : E:/code/detection/data/Fifth_data/All
+{'task': 'Error/train_2020-04-03-17-08-43_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': 'E:/code/detection/data/Fifth_data/All', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 256, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-03-17-08-43'}
+Number of model parameters: 154706
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\train.py", line 102, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\train.py", line 276, in run_model
+    train(train_loader, model, criterion, optimizer, scheduler, epoch, args, q)
+  File "E:\code\detection\trainer\train.py", line 327, in train
+    loss = criterion(output, target)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
+    result = self.forward(*input, **kwargs)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\loss.py", line 916, in forward
+    ignore_index=self.ignore_index, reduction=self.reduction)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\functional.py", line 2021, in cross_entropy
+    return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\functional.py", line 1838, in nll_loss
+    ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
+IndexError: Target 5 is out of bounds.
+[train_2020-04-03-17-08-43] failed
+2020-04-03-17-09-59
+use seed 420
+use dataset : E:/code/detection/data/Fifth_data/Error
+{'task': 'Error/train_2020-04-03-17-09-59_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': 'E:/code/detection/data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 256, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-03-17-09-59'}
+Number of model parameters: 154706
+Epoch: [0][0/26]	Time 32.853 (32.853)	Loss 0.6907 (0.6907)	Prec@1 56.641 (56.641)	Precision 0.000 (0.000)

+2020-04-03-17-09-59
+use seed 420
+use dataset : E:/code/detection/data/Fifth_data/Error
+{'task': 'Error/train_2020-04-03-17-09-59_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': 'E:/code/detection/data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 256, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-03-17-09-59'}
+Number of model parameters: 154706
+Epoch: [0][0/26]	Time 32.853 (32.853)	Loss 0.6907 (0.6907)	Prec@1 56.641 (56.641)	Precision 0.000 (0.000)

+2020-04-03-17-22-28
+use seed 845
+use dataset : E:/code/detection/data/Fifth_data/Error
+{'task': 'Error/train_2020-04-03-17-22-28_model=MobilenetV3-ep=1000-block=5-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': 'E:/code/detection/data/Fifth_data/Error', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 1000, 'start-epoch': 0, 'batch-size': 128, 'worker': 16, 'resume': '', 'augment': True, 'size': 128, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 128, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.01, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 5, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-03-17-22-28'}
+Number of model parameters: 400114

+2020-04-08-19-37-53
+use seed 41
+use dataset : E:/code/detection/data/Fifth_data/All
+{'task': 'Error/train_2020-04-08-19-37-53_model=MobilenetV3-ep=3000-block=4-class=2', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/Error/2456_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar', 'gpu': [1], 'data': {'train': 'E:/code/detection/data/Fifth_data/All', 'val': '../data/Fifth_data/Error', 'test': '../data/Fifth_data/Error'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0]}, 'predict': {'batch-size': 256, 'worker': 16, 'cam': False, 'cam-class': 'Error', 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 4, 'class': 2}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-08-19-37-53'}

+2020-04-01-19-51-23
+use seed 355
+use dataset : ../data/Fifth_data/ErrorType
+{'task': 'ErrorType/train_2020-04-01-19-51-23_model=MobilenetV3-ep=3000-block=6-class=7', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar', 'gpu': [1], 'data': {'train': '../data/Fifth_data/ErrorType', 'val': '../data/Fifth_data/ErrorType', 'test': '../data/Fifth_data/ErrorType'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 4048, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, 'predict': {'batch-size': 4048, 'worker': 16, 'cam': False, 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 6, 'class': 7}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-01-19-51-23'}
+Number of model parameters: 461559

+2020-04-01-22-42-16
+use seed 805
+use dataset : ../data/Fifth_data/ErrorType
+{'task': 'Type/train_2020-04-01-22-42-16_model=MobilenetV3-ep=3000-block=6-class=7', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar', 'gpu': [1], 'data': {'train': '../data/Fifth_data/ErrorType', 'val': '../data/Fifth_data/ErrorType', 'test': '../data/Fifth_data/ErrorType'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 4048, 'worker': 16, 'resume': '', 'augment': True, 'size': 64, 'confidence': False, 'weight': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, 'predict': {'batch-size': 4048, 'worker': 16, 'cam': False, 'normalize': True, 'save': False}, 'optimizer': {'type': 'SGD', 'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 6, 'class': 7}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-04-01-22-42-16'}
+Number of model parameters: 461559

+Number of model parameters: 461559
+=> loading checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 2382)
+ * Prec@1 95.495
+ * Prec@1 95.495
+Best accuracy: 95.49549572460644
+[validate_2020-03-26-17-26-14] done
+[validate_2020-03-26-17-26-14] done
+/home/yh9468/detection/data/Fourth_data/demo Test dir submitted
+start test using path : /home/yh9468/detection/data/Fourth_data/demo
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+data path directory is /home/yh9468/detection/data/Fourth_data/demo
+finish test
+/home/yh9468/detection/data/Fourth_data/demo/demoset/1-7.bmp test file submitted
+start test using path : ('/home/yh9468/detection/data/Fourth_data/demo/demoset/1-7.bmp', 'All Files(*)')
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+finish test

+Number of model parameters: 461559
+=> loading checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 2382)
+ * Prec@1 95.495
+ * Prec@1 95.495
+Best accuracy: 95.49549572460644
+[validate_2020-03-26-17-48-44] done
+[validate_2020-03-26-17-48-44] done
+set error

+using default checkpoint
+Number of model parameters: 461559
+=> loading checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 2382)
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\test.py", line 270, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\test.py", line 360, in run_model
+    prec1 = validate(val_loader, model, criterion, normalize_factor, args ,q)
+  File "E:\code\detection\trainer\test.py", line 394, in validate
+    save_error_case(output.data, target, path, args, topk=(1,), input=input, save_correct=True)
+  File "E:\code\detection\trainer\test.py", line 455, in save_error_case
+    os.mkdir(f"eval_results/{args['task']}")
+FileNotFoundError: [WinError 3] 지정된 경로를 찾을 수 없습니다: 'eval_results/All'
+[validate_2020-03-31-18-34-56] failed

+Number of model parameters: 461559
+=> loading checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 2382)
+Test: [0/2]	Time 118.781 (118.781)	Loss 0.3068 (0.3068)	Prec@1 95.703 (95.703)
+Test: [1/2]	Time 3.661 (61.221)	Loss 0.4321 (0.3358)	Prec@1 94.805 (95.495)
+ * Prec@1 95.495
+ * Prec@1 95.495
+Best accuracy: 95.49549572460644
+[validate_2020-03-31-19-08-47] done
+[validate_2020-03-31-19-08-47] done
+train start
+2020-03-31-19-11-26
+use seed 963
+use dataset : ../data/Fourth_data/All
+{'task': 'All/train_2020-03-31-19-11-26_model=MobilenetV3-ep=3000-block=6', 'modelname': 'MobilenetV3', 'output': 'output', 'checkpoint': 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar', 'gpu': [2], 'data': {'train': '../data/Fourth_data/All', 'val': '../data/Fourth_data/All', 'test': '../data/Fourth_data/All'}, 'train': {'epochs': 3000, 'start-epoch': 0, 'batch-size': 256, 'worker': 16, 'weight': [2.0, 4.0, 1.0, 1.0, 3.0, 1.0, 1.0], 'resume': '', 'augment': True, 'size': 224, 'confidence': False}, 'predict': {'batch-size': 256, 'worker': 64, 'cam': False, 'normalize': True, 'save': False}, 'optimizer': {'lr': 0.1, 'momentum': 0.9, 'weight_decay': 0.0001}, 'loss': {'gamma': 2.0, 'alpha': 0.8}, 'model': {'blocks': 6, 'class': 7}, 'etc': {'tensorboard': False, 'print_freq': 10}, 'id': 'train_2020-03-31-19-11-26'}

+using default checkpoint
+Number of model parameters: 462840
+=> loading checkpoint 'output/All/14835_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/All/14835_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 1617)
+Test: [0/3]	Time 31.288 (31.288)	Loss 0.2660 (0.2660)	Prec@1 95.703 (95.703)
+Test: [1/3]	Time 7.587 (19.437)	Loss 0.3209 (0.2934)	Prec@1 95.312 (95.508)
+Test: [2/3]	Time 6.625 (15.167)	Loss 0.1835 (0.2602)	Prec@1 96.396 (95.777)
+ * Prec@1 95.777
+ * Prec@1 95.777
+Best accuracy: 95.77656669512757
+[validate_2020-04-01-23-00-04] done
+[validate_2020-04-01-23-00-04] done
+set error
+Test를 수행하기 위해 데이터를 입력해 주세요.
+Test를 수행하기 위해 데이터를 입력해 주세요.

+using user's checkpoint E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
+Number of model parameters: 462840
+=> loading checkpoint 'E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar'
+=> loaded checkpoint 'E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar' (epoch 3000)
+Test: [0/3]	Time 32.591 (32.591)	Loss 0.1575 (0.1575)	Prec@1 94.531 (94.531)
+Test: [1/3]	Time 8.179 (20.385)	Loss 0.2475 (0.2025)	Prec@1 93.750 (94.141)
+Test: [2/3]	Time 7.374 (16.048)	Loss 0.4568 (0.2794)	Prec@1 94.595 (94.278)
+ * Prec@1 94.278
+ * Prec@1 94.278
+Best accuracy: 96.04904700754774
+[validate_2020-04-03-17-39-50] done
+[validate_2020-04-03-17-39-50] done
+E:/code/detection/data/Fifth_data/All/Empty/1-5.bmp test file submitted
+Test start
+start test using path : E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
+using user's checkpoint E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
+loading checkpoint...
+checkpoint already loaded!
+start test
+single_file_test() missing 1 required positional argument: 'q'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.

+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+Fatal error in main loop
+Traceback (most recent call last):
+  File "/home/yh9468/detection/trainer/test.py", line 181, in main
+    run_model(args, q)
+  File "/home/yh9468/detection/trainer/test.py", line 263, in run_model
+    prec1 = validate(val_loader, model, criterion, normalize_factor, args ,q)
+  File "/home/yh9468/detection/trainer/test.py", line 296, in validate
+    save_error_case(output.data, target, path, args, topk=(1,), input=input, save_correct=True)
+  File "/home/yh9468/detection/trainer/test.py", line 373, in save_error_case
+    cv2.imwrite(f"eval_results/{args['task']}/correct_case/idx_{correct_case_idx}_label_{class_arr[target[idx]]}_real.bmp" ,img)
+NameError: name 'correct_case_idx' is not defined
+[validate_2020-03-26-16-50-29] failed

+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+Fatal error in main loop
+Traceback (most recent call last):
+  File "/home/yh9468/detection/trainer/test.py", line 184, in main
+    run_model(args, q)
+  File "/home/yh9468/detection/trainer/test.py", line 266, in run_model
+    prec1 = validate(val_loader, model, criterion, normalize_factor, args ,q)
+  File "/home/yh9468/detection/trainer/test.py", line 299, in validate
+    save_error_case(output.data, target, path, args, topk=(1,), input=input, save_correct=True)
+  File "/home/yh9468/detection/trainer/test.py", line 376, in save_error_case
+    cv2.imwrite(f"eval_results/{args['task']}/correct_case/idx_{correct_case_idx}_label_{class_arr[target[idx]]}_real.bmp" ,img)
+NameError: name 'correct_case_idx' is not defined
+[validate_2020-03-26-16-55-37] failed

+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+ * Prec@1 98.193
+ * Prec@1 98.193
+Best accuracy: 98.19277163585984
+[validate_2020-03-26-16-57-52] done
+[validate_2020-03-26-16-57-52] done
+start test using path : ../data/Fourth_data/demo
+Test start
+loading checkpoint...
+checkpoint already loaded!

+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+ * Prec@1 98.193
+ * Prec@1 98.193
+Best accuracy: 98.1927713600986
+[validate_2020-03-26-17-02-22] done
+[validate_2020-03-26-17-02-22] done
+start test using path : ../data/Fourth_data/demo
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+data path directory is ../data/Fourth_data/demo
+finish test
+start test using path : ../data/Fourth_data/demo
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+data path directory is ../data/Fourth_data/demo
+finish test

+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+ * Prec@1 98.193
+ * Prec@1 98.193
+Best accuracy: 98.1927713600986
+[validate_2020-03-26-17-26-07] done
+[validate_2020-03-26-17-26-07] done
+set Type
+Number of model parameters: 461559
+=> loading checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/All/16166_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 2382)
+ * Prec@1 95.495
+ * Prec@1 95.495
+Best accuracy: 95.49549572460644
+[validate_2020-03-26-17-26-14] done
+[validate_2020-03-26-17-26-14] done
+/home/yh9468/detection/data/Fourth_data/demo Test dir submitted
+start test using path : /home/yh9468/detection/data/Fourth_data/demo
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+data path directory is /home/yh9468/detection/data/Fourth_data/demo
+finish test
+/home/yh9468/detection/data/Fourth_data/demo/demoset/1-7.bmp test file submitted
+start test using path : ('/home/yh9468/detection/data/Fourth_data/demo/demoset/1-7.bmp', 'All Files(*)')
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+finish test

+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+ * Prec@1 98.193
+ * Prec@1 98.193
+Best accuracy: 98.1927713600986
+[validate_2020-03-27-11-52-28] done
+[validate_2020-03-27-11-52-28] done
+start test using path : ../data/Fourth_data/demo
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+data path directory is ../data/Fourth_data/demo
+Inference time 21 images : 0.37514
+finish test
+set Type
+start test using path : ../data/Fourth_data/demo
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+data path directory is ../data/Fourth_data/demo
+Inference time 21 images : 0.7917
+finish test
+/home/yh9468/detection/data/Fourth_data/demo/demoset/1-1.bmp test file submitted
+start test using path : ('/home/yh9468/detection/data/Fourth_data/demo/demoset/1-1.bmp', 'All Files(*)')
+Test start
+loading checkpoint...
+checkpoint already loaded!
+start test
+Inference time 1 image : 0.03704
+finish test

+using user's checkpoint ('E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar', 'All Files(*)')
+Number of model parameters: 154706
+=> loading checkpoint '('E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar', 'All Files(*)')'
+Fatal error in main loop
+Traceback (most recent call last):
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 289, in _check_seekable
+    f.seek(f.tell())
+AttributeError: 'tuple' object has no attribute 'seek'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\test.py", line 256, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\test.py", line 328, in run_model
+    checkpoint = torch.load(args['checkpoint'])
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 525, in load
+    with _open_file_like(f, 'rb') as opened_file:
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 217, in _open_file_like
+    return _open_buffer_reader(name_or_buffer)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 202, in __init__
+    _check_seekable(buffer)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 292, in _check_seekable
+    raise_err_msg(["seek", "tell"], e)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 285, in raise_err_msg
+    raise type(e)(msg)
+AttributeError: 'tuple' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.
+[validate_2020-03-31-14-53-49] failed

+using user's checkpoint E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar
+Number of model parameters: 154706
+=> loading checkpoint 'E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar'
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\test.py", line 261, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\test.py", line 333, in run_model
+    checkpoint = torch.load(args['checkpoint'])
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 529, in load
+    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 702, in _legacy_load
+    result = unpickler.load()
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 665, in persistent_load
+    deserialized_objects[root_key] = restore_location(obj, location)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 156, in default_restore_location
+    result = fn(storage, location)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 132, in _cuda_deserialize
+    device = validate_cuda_device(location)
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 116, in validate_cuda_device
+    raise RuntimeError('Attempting to deserialize object on a CUDA '
+RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
+[validate_2020-03-31-14-58-23] failed

+using user's checkpoint E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar
+Number of model parameters: 154706
+=> loading checkpoint 'E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar'
+=> loaded checkpoint 'E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar' (epoch 3000)
+ * Prec@1 91.867
+ * Prec@1 91.867
+Best accuracy: 95.90643257007264

+using user's checkpoint E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar
+Number of model parameters: 154706
+=> loading checkpoint 'E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar'
+=> loaded checkpoint 'E:/code/detection/trainer/output/Error/47098_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar' (epoch 3000)
+Test: [0/2]	Time 23.714 (23.714)	Loss 0.1847 (0.1847)	Prec@1 92.969 (92.969)
+Test: [1/2]	Time 0.464 (12.089)	Loss 0.2262 (0.1942)	Prec@1 88.158 (91.867)
+ * Prec@1 91.867
+ * Prec@1 91.867
+Best accuracy: 95.90643257007264
+[validate_2020-03-31-15-08-03] done
+[validate_2020-03-31-15-08-03] done

+using default checkpoint

+using default checkpoint
+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\test.py", line 271, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\test.py", line 359, in run_model
+    prec1 = validate(val_loader, model, criterion, normalize_factor, args ,q)
+  File "E:\code\detection\trainer\test.py", line 393, in validate
+    if args['predict']['save']:
+KeyError: 'save'
+[validate_2020-04-01-18-17-52] failed

+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+Test: [0/1]	Time 26.222 (26.222)	Loss 0.2174 (0.2174)	Prec@1 94.823 (94.823)
+ * Prec@1 94.823
+ * Prec@1 94.823
+Best accuracy: 98.1927713600986
+[validate_2020-04-01-18-45-23] done
+[validate_2020-04-01-18-45-23] done
+start test using path : default checkpoint
+Test start
+using default checkpoint
+loading checkpoint...
+checkpoint already loaded!
+start test
+data path directory is ../data/Fourth_data/demo
+Inference time 120 images : 4.358
+finish test
+start test using path : default checkpoint
+Test start
+using default checkpoint
+loading checkpoint...
+[Errno 2] No such file or directory: 'n'
+checkpoint already loaded!
+start test
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.
+E:/code/detection/data/Fifth_data/All/Flip/1-1.bmp test file submitted
+start test using path : default checkpoint
+Test start
+using default checkpoint
+loading checkpoint...
+[Errno 2] No such file or directory: 'E'
+checkpoint already loaded!
+start test
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.
+start test using path : default checkpoint
+Test start
+using default checkpoint
+loading checkpoint...
+[Errno 2] No such file or directory: 'E'
+checkpoint already loaded!
+start test
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.

+using default checkpoint
+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+Test: [0/1]	Time 27.498 (27.498)	Loss 0.2174 (0.2174)	Prec@1 94.823 (94.823)
+ * Prec@1 94.823
+ * Prec@1 94.823
+Best accuracy: 98.1927713600986
+[validate_2020-04-01-20-18-55] done
+[validate_2020-04-01-20-18-55] done

+using default checkpoint
+Number of model parameters: 400114
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\test.py", line 274, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\test.py", line 351, in run_model
+    model.load_state_dict(checkpoint['state_dict'])
+  File "C:\Users\younho\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 830, in load_state_dict
+    self.__class__.__name__, "\n\t".join(error_msgs)))
+RuntimeError: Error(s) in loading state_dict for DataParallel:
+	Missing key(s) in state_dict: "module.features.5.conv.0.weight", "module.features.5.conv.1.weight", "module.features.5.conv.1.bias", "module.features.5.conv.1.running_mean", "module.features.5.conv.1.running_var", "module.features.5.conv.3.weight", "module.features.5.conv.4.weight", "module.features.5.conv.4.bias", "module.features.5.conv.4.running_mean", "module.features.5.conv.4.running_var", "module.features.5.conv.5.fc.0.weight", "module.features.5.conv.5.fc.2.weight", "module.features.5.conv.7.weight", "module.features.5.conv.8.weight", "module.features.5.conv.8.bias", "module.features.5.conv.8.running_mean", "module.features.5.conv.8.running_var", "module.features.6.0.weight", "module.features.6.1.weight", "module.features.6.1.bias", "module.features.6.1.running_mean", "module.features.6.1.running_var", "module.features.8.weight", "module.features.8.bias". 
+	Unexpected key(s) in state_dict: "module.features.5.0.weight", "module.features.5.1.weight", "module.features.5.1.bias", "module.features.5.1.running_mean", "module.features.5.1.running_var", "module.features.5.1.num_batches_tracked", "module.features.7.weight", "module.features.7.bias". 
+[validate_2020-04-01-20-21-14] failed

+Number of model parameters: 400114
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+printlog() missing 2 required positional arguments: 'logger' and 'q'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.

+using default checkpoint
+Number of model parameters: 154706
+=> loading checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/Error/12613_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 2877)
+Test: [0/1]	Time 26.242 (26.242)	Loss 0.2174 (0.2174)	Prec@1 94.823 (94.823)
+ * Prec@1 94.823
+ * Prec@1 94.823
+Best accuracy: 98.1927713600986
+[validate_2020-04-01-22-45-31] done
+[validate_2020-04-01-22-45-31] done
+set Type
+start test using path : ../data/Fourth_data/demo
+val start
+using default checkpoint
+Number of model parameters: 461559
+=> loading checkpoint 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+printlog() missing 2 required positional arguments: 'logger' and 'q'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.

+Number of model parameters: 460278
+=> loading checkpoint 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 1865)
+Test: [0/1]	Time 29.993 (29.993)	Loss 0.4449 (0.4449)	Prec@1 92.952 (92.952)
+ * Prec@1 92.952
+ * Prec@1 92.952
+Best accuracy: 92.9515380859375
+[validate_2020-03-31-16-28-46] done
+[validate_2020-03-31-16-28-46] done
+set All processing
+start test using path : ../data/Fourth_data/demo
+Test start
+[Errno 2] No such file or directory: 'configs/overall_config.yaml'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.

+using default checkpoint
+Number of model parameters: 460278
+=> loading checkpoint 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 1865)
+Fatal error in main loop
+Traceback (most recent call last):
+  File "E:\code\detection\trainer\test.py", line 270, in main
+    run_model(args, q)
+  File "E:\code\detection\trainer\test.py", line 358, in run_model
+    prec1 = validate(val_loader, model, criterion, normalize_factor, args ,q)
+  File "E:\code\detection\trainer\test.py", line 392, in validate
+    if args['predict']['save']:
+KeyError: 'save'
+[validate_2020-03-31-18-52-03] failed

+using default checkpoint
+Number of model parameters: 461559
+=> loading checkpoint 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+printlog() missing 2 required positional arguments: 'logger' and 'q'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.

+using default checkpoint
+Number of model parameters: 461559
+=> loading checkpoint 'output/ErrorType/2715_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.
+Error(s) in loading state_dict for DataParallel:
+	size mismatch for module.classifier.1.weight: copying a param with shape torch.Size([6, 1280]) from checkpoint, the shape in current model is torch.Size([7, 1280]).
+	size mismatch for module.classifier.1.bias: copying a param with shape torch.Size([6]) from checkpoint, the shape in current model is torch.Size([7]).
+start test using path : ../data/Fourth_data/demo
+val start
+using default checkpoint
+Number of model parameters: 461559
+=> loading checkpoint 'output/ErrorType/85804_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.
+Error(s) in loading state_dict for DataParallel:
+	Missing key(s) in state_dict: "module.features.5.conv.0.weight", "module.features.5.conv.1.weight", "module.features.5.conv.1.bias", "module.features.5.conv.1.running_mean", "module.features.5.conv.1.running_var", "module.features.5.conv.3.weight", "module.features.5.conv.4.weight", "module.features.5.conv.4.bias", "module.features.5.conv.4.running_mean", "module.features.5.conv.4.running_var", "module.features.5.conv.5.fc.0.weight", "module.features.5.conv.5.fc.2.weight", "module.features.5.conv.7.weight", "module.features.5.conv.8.weight", "module.features.5.conv.8.bias", "module.features.5.conv.8.running_mean", "module.features.5.conv.8.running_var", "module.features.6.conv.0.weight", "module.features.6.conv.1.weight", "module.features.6.conv.1.bias", "module.features.6.conv.1.running_mean", "module.features.6.conv.1.running_var", "module.features.6.conv.3.weight", "module.features.6.conv.4.weight", "module.features.6.conv.4.bias", "module.features.6.conv.4.running_mean", "module.features.6.conv.4.running_var", "module.features.6.conv.5.fc.0.weight", "module.features.6.conv.5.fc.2.weight", "module.features.6.conv.7.weight", "module.features.6.conv.8.weight", "module.features.6.conv.8.bias", "module.features.6.conv.8.running_mean", "module.features.6.conv.8.running_var", "module.features.7.0.weight", "module.features.7.1.weight", "module.features.7.1.bias", "module.features.7.1.running_mean", "module.features.7.1.running_var", "module.features.9.weight", "module.features.9.bias". 
+	Unexpected key(s) in state_dict: "module.features.5.0.weight", "module.features.5.1.weight", "module.features.5.1.bias", "module.features.5.1.running_mean", "module.features.5.1.running_var", "module.features.5.1.num_batches_tracked", "module.features.7.weight", "module.features.7.bias". 

+Number of model parameters: 461559
+=> loading checkpoint 'output/ErrorType/85804_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.
+Error(s) in loading state_dict for DataParallel:
+	Missing key(s) in state_dict: "module.features.5.conv.0.weight", "module.features.5.conv.1.weight", "module.features.5.conv.1.bias", "module.features.5.conv.1.running_mean", "module.features.5.conv.1.running_var", "module.features.5.conv.3.weight", "module.features.5.conv.4.weight", "module.features.5.conv.4.bias", "module.features.5.conv.4.running_mean", "module.features.5.conv.4.running_var", "module.features.5.conv.5.fc.0.weight", "module.features.5.conv.5.fc.2.weight", "module.features.5.conv.7.weight", "module.features.5.conv.8.weight", "module.features.5.conv.8.bias", "module.features.5.conv.8.running_mean", "module.features.5.conv.8.running_var", "module.features.6.conv.0.weight", "module.features.6.conv.1.weight", "module.features.6.conv.1.bias", "module.features.6.conv.1.running_mean", "module.features.6.conv.1.running_var", "module.features.6.conv.3.weight", "module.features.6.conv.4.weight", "module.features.6.conv.4.bias", "module.features.6.conv.4.running_mean", "module.features.6.conv.4.running_var", "module.features.6.conv.5.fc.0.weight", "module.features.6.conv.5.fc.2.weight", "module.features.6.conv.7.weight", "module.features.6.conv.8.weight", "module.features.6.conv.8.bias", "module.features.6.conv.8.running_mean", "module.features.6.conv.8.running_var", "module.features.7.0.weight", "module.features.7.1.weight", "module.features.7.1.bias", "module.features.7.1.running_mean", "module.features.7.1.running_var", "module.features.9.weight", "module.features.9.bias". 
+	Unexpected key(s) in state_dict: "module.features.5.0.weight", "module.features.5.1.weight", "module.features.5.1.bias", "module.features.5.1.running_mean", "module.features.5.1.running_var", "module.features.5.1.num_batches_tracked", "module.features.7.weight", "module.features.7.bias". 

+using default checkpoint
+Number of model parameters: 161111
+=> loading checkpoint 'output/ErrorType/85804_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar'
+=> loaded checkpoint 'output/ErrorType/85804_model=MobilenetV3-ep=3000-block=4/model_best.pth.tar' (epoch 1884)
+Test: [0/1]	Time 25.026 (25.026)	Loss 0.2817 (0.2817)	Prec@1 95.918 (95.918)
+ * Prec@1 95.918
+ * Prec@1 95.918
+Best accuracy: 95.91837310791016
+[validate_2020-04-01-22-59-05] done
+[validate_2020-04-01-22-59-05] done
+set All processing
+start test using path : ../data/Fourth_data/demo
+val start
+using default checkpoint
+Number of model parameters: 462840
+=> loading checkpoint 'output/All/14835_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar'
+=> loaded checkpoint 'output/All/14835_model=MobilenetV3-ep=3000-block=6/model_best.pth.tar' (epoch 1617)
+Test: [0/3]	Time 31.288 (31.288)	Loss 0.2660 (0.2660)	Prec@1 95.703 (95.703)
+Test: [1/3]	Time 7.587 (19.437)	Loss 0.3209 (0.2934)	Prec@1 95.312 (95.508)
+Test: [2/3]	Time 6.625 (15.167)	Loss 0.1835 (0.2602)	Prec@1 96.396 (95.777)
+ * Prec@1 95.777
+ * Prec@1 95.777
+Best accuracy: 95.77656669512757
+[validate_2020-04-01-23-00-04] done
+[validate_2020-04-01-23-00-04] done
+set error
+Test를 수행하기 위해 데이터를 입력해 주세요.
+Test를 수행하기 위해 데이터를 입력해 주세요.

+using user's checkpoint E:/code/detection/trainer/output/ErrorType/85804_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar
+Number of model parameters: 161111
+=> loading checkpoint 'E:/code/detection/trainer/output/ErrorType/85804_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar'
+=> loaded checkpoint 'E:/code/detection/trainer/output/ErrorType/85804_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar' (epoch 3000)
+Test: [0/2]	Time 26.118 (26.118)	Loss 0.2720 (0.2720)	Prec@1 93.359 (93.359)
+Test: [1/2]	Time 0.848 (13.483)	Loss 0.4744 (0.3686)	Prec@1 91.453 (92.449)
+ * Prec@1 92.449
+ * Prec@1 92.449
+Best accuracy: 95.91836762720224
+[validate_2020-04-03-17-24-24] done
+[validate_2020-04-03-17-24-24] done
+set All processing
+E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar checkpoint file submitted
+E:/code/detection/data/Fifth_data/All Test dir submitted
+val start
+using user's checkpoint E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
+Number of model parameters: 462840
+=> loading checkpoint 'E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar'
+=> loaded checkpoint 'E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar' (epoch 3000)
+Test: [0/3]	Time 32.591 (32.591)	Loss 0.1575 (0.1575)	Prec@1 94.531 (94.531)
+Test: [1/3]	Time 8.179 (20.385)	Loss 0.2475 (0.2025)	Prec@1 93.750 (94.141)
+Test: [2/3]	Time 7.374 (16.048)	Loss 0.4568 (0.2794)	Prec@1 94.595 (94.278)
+ * Prec@1 94.278
+ * Prec@1 94.278
+Best accuracy: 96.04904700754774
+[validate_2020-04-03-17-39-50] done
+[validate_2020-04-03-17-39-50] done
+E:/code/detection/data/Fifth_data/All/Empty/1-5.bmp test file submitted
+Test start
+start test using path : E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
+using user's checkpoint E:/code/detection/trainer/output/All/14835_model=MobilenetV3-ep=3000-block=6/checkpoint.pth.tar
+loading checkpoint...
+checkpoint already loaded!
+start test
+single_file_test() missing 1 required positional argument: 'q'
+실행 중 에러가 발생하였습니다. 자세한 사항은 보시려면 로그를 확인해 주세요.

+import argparse
+import random
+import os
+import cv2
+import logging
+import datetime
+
+import torch
+import torch.nn as nn
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from torchvision.utils import save_image
+
+from model import mobilenetv3
+from utils import get_args_from_yaml, MyImageFolder
+from get_mean_std import get_params
+
+## 해당 코드는 전체 inference를 모두 담은 code.
+
+# make Logger
+logger = logging.getLogger(os.path.dirname(__name__))
+logger.setLevel(logging.INFO)
+
+# make Logger stream
+streamHandler = logging.StreamHandler()
+logger.addHandler(streamHandler)
+
+if not os.path.exists('eval_results/main'):
+    os.mkdir('eval_results/main')
+
+if not os.path.exists('eval_results/main/Normal'):
+    os.mkdir('eval_results/main/Normal')
+
+if not os.path.exists('eval_results/main/Crack'):
+    os.mkdir('eval_results/main/Crack')
+
+if not os.path.exists('eval_results/main/Empty'):
+    os.mkdir('eval_results/main/Empty')
+
+if not os.path.exists('eval_results/main/Flip'):
+    os.mkdir('eval_results/main/Flip')
+
+if not os.path.exists('eval_results/main/Pollute'):
+    os.mkdir('eval_results/main/Pollute')
+
+if not os.path.exists('eval_results/main/Double'):
+    os.mkdir('eval_results/main/Double')
+
+if not os.path.exists('eval_results/main/Leave'):
+    os.mkdir('eval_results/main/Leave')
+
+if not os.path.exists('eval_results/main/Scratch'):
+    os.mkdir('eval_results/main/Scratch')
+
+
+def main(Error_args, Error_Type_args):
+    logdir = f"logs/main/"
+    if not os.path.exists(logdir):
+        os.mkdir(logdir)
+    fileHander = logging.FileHandler(logdir + f"{datetime.datetime.now().strftime('%Y%m%d-%H:%M:%S')}_log.log")
+    logger.addHandler(fileHander)
+
+    run(Error_args, Error_Type_args)
+
+def run(Error_args, Error_Type_args):
+    Error_args['checkpoint'] = "output/Error/25678_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar"
+    Error_Type_args['checkpoint'] = "output/ErrorType/2798_model=MobilenetV3-ep=3000-block=4/checkpoint.pth.tar"
+
+    Error_model = mobilenetv3(n_class= Error_args['model']['class'], blocknum=Error_args['model']['blocks'])
+    Error_Type_model = mobilenetv3(n_class=Error_Type_args['model']['class'], blocknum=Error_Type_args['model']['blocks'])
+
+    gpus = Error_args['gpu']
+    resize_size = Error_args['train']['size']
+
+    torch.cuda.set_device(gpus[0])
+    with torch.cuda.device(gpus[0]):
+        Error_model = Error_model.cuda()
+        Error_Type_model = Error_Type_model.cuda()
+        
+    Error_model = torch.nn.DataParallel(Error_model, device_ids=gpus, output_device=gpus[0])
+    Error_Type_model = torch.nn.DataParallel(Error_Type_model, device_ids=gpus, output_device=gpus[0])
+
+    Error_checkpoint = torch.load(Error_args['checkpoint'])
+    Error_Type_checkpoint = torch.load(Error_Type_args['checkpoint'])
+
+    Error_model.load_state_dict(Error_checkpoint['state_dict'])
+    Error_Type_model.load_state_dict(Error_Type_checkpoint['state_dict'])
+
+    mean, std = get_params(Error_args['data']['test'], resize_size)
+    normalize = transforms.Normalize(mean=[mean[0].item()],
+                         std=[std[0].item()])
+
+    transform = transforms.Compose([
+        transforms.Resize((resize_size, resize_size)),
+        transforms.Grayscale(),
+        transforms.ToTensor(),
+        normalize
+    ])
+
+    dataset = MyImageFolder(Error_args['data']['test'],  transform)
+
+    print(len(dataset))
+
+    loader = torch.utils.data.DataLoader(
+        dataset, batch_size=Error_args['predict']['batch-size'], shuffle=False, 
+        num_workers=Error_args['predict']['worker'], pin_memory=True
+    )
+    
+    for data in loader:
+        (input, _), (path, _) = data
+        input= input.cuda()
+
+        output = Error_model(input)
+        _, output = output.topk(1 ,1 ,True,True)
+
+        error_cases = torch.ones((1,1,64,64)).cuda()
+        new_paths = []
+
+        error = 0
+        normal = 0
+        for idx in range(input.shape[0]):
+            # if Error Case
+     
+            if output[idx] == 0:
+                error_cases = torch.cat((error_cases, input[idx:idx+1]), dim=0)
+                new_paths.append(path[idx])
+                error = error +1
+            # Normal Case
+            else:
+                img = cv2.imread(path[idx])
+                cv2.imwrite(f"eval_results/main/Normal/{path[idx].split('/')[-1]}", img)
+                normal = normal+1   
+        
+        print(f"error path : {len(new_paths)}")
+        print(f"error : {error}")
+        print(f"normal : {normal}")
+
+        error_cases = error_cases[1:]
+        print(error_cases.shape[0])
+        
+        output = Error_Type_model(error_cases)
+        _, output = output.topk(1 ,1 ,True,True)
+
+        for idx in range(error_cases.shape[0]):
+            # Crack
+            if output[idx] == 0:
+                img = cv2.imread(new_paths[idx])
+                cv2.imwrite(f"eval_results/main/Crack/{new_paths[idx].split('/')[-1]}", img)
+
+            # Double
+            elif output[idx] == 1:
+                img = cv2.imread(new_paths[idx])
+                cv2.imwrite(f"eval_results/main/Double/{new_paths[idx].split('/')[-1]}", img)
+
+            # Empty
+            elif output[idx] == 2:
+                img = cv2.imread(new_paths[idx])
+                cv2.imwrite(f"eval_results/main/Empty/{new_paths[idx].split('/')[-1]}", img)
+
+            # Flip
+            elif output[idx] == 3:
+                img = cv2.imread(new_paths[idx])
+                cv2.imwrite(f"eval_results/main/Flip/{new_paths[idx].split('/')[-1]}", img)
+
+            # Leave
+            elif output[idx] == 4:
+                img = cv2.imread(new_paths[idx])
+                cv2.imwrite(f"eval_results/main/Leave/{new_paths[idx].split('/')[-1]}", img)
+
+            # Pollute
+            elif output[idx] == 5:
+                img = cv2.imread(new_paths[idx])
+                cv2.imwrite(f"eval_results/main/Pollute/{new_paths[idx].split('/')[-1]}", img)
+
+            # Scratch
+            elif output[idx] == 6:
+                img = cv2.imread(new_paths[idx])
+                cv2.imwrite(f"eval_results/main/Scratch/{new_paths[idx].split('/')[-1]}", img)
+            
+
+if __name__ == '__main__':
+    Error_args = get_args_from_yaml("configs/Error_config.yml")
+    Error_Type_args = get_args_from_yaml("configs/ErrorType_config.yml")
+    main(Error_args, Error_Type_args)
+    
\ No newline at end of file

+import torch
+import torch.nn as nn
+from model import mobilenetv3
+import argparse
+import torchvision
+from torchvision.transforms import transforms
+import torchvision.datasets as datasets
+from augmentations import RandAugment
+from get_mean_std import get_params
+from torch.utils.data.sampler import SubsetRandomSampler
+import numpy as np
+import os
+import cv2
+from utils import MyImageFolder
+
+class ConcatDataset(torch.utils.data.Dataset):
+    def __init__(self, *datasets):
+        self.datasets = datasets
+
+    def __getitem__(self, i):
+        return tuple(d[i %len(d)] for d in self.datasets)
+
+    def __len__(self):
+        return max(len(d) for d in self.datasets)
+
+
+def make_dir():
+    if not os.path.exists('../data/Fourth_data/teacher_data/Double'):
+        os.mkdir('../data/Fourth_data/teacher_data/Double')
+
+    if not os.path.exists('../data/Fourth_data/teacher_data/Flip'):
+        os.mkdir('../data/Fourth_data/teacher_data/Flip')
+
+    if not os.path.exists('../data/Fourth_data/teacher_data/Scratch'):
+        os.mkdir('../data/Fourth_data/teacher_data/Scratch')
+
+    if not os.path.exists('../data/Fourth_data/teacher_data/Leave'):
+        os.mkdir('../data/Fourth_data/teacher_data/Leave')
+
+    if not os.path.exists('../data/Fourth_data/teacher_data/Normal'):
+        os.mkdir('../data/Fourth_data/teacher_data/Normal')
+
+    if not os.path.exists('../data/Fourth_data/teacher_data/Empty'):
+        os.mkdir('../data/Fourth_data/teacher_data/Empty')
+
+
+parser = argparse.ArgumentParser(description='Process make noisy student model')
+parser.add_argument('--checkpoint_path', type=str, help='checkpoint path')
+parser.add_argument('--size', type=int, help='resize integer of input')
+parser.add_argument('--batch_size', type=int, default=256,help='set batch size')
+parser.add_argument('--teacher_checkpoint_path', type=str, help='teacher first checkpoint path')
+parser.add_argument('--Labeled_dataset_path', default='../data/Fourth_data/noisy_data/Labeled', type=str, help='path of dataset')
+parser.add_argument('--Unlabeled_dataset_path', default='../data/Fourth_data/noisy_data/Unlabeled', type=str, help='path of unlabeled dataset')
+parser.add_argument('--num_workers', default=8, type=int, help="number of gpu worker")
+parser.add_argument('--epochs', default=350, type=int, help='epoch')
+parser.add_argument('--finetune_epochs', default=2, type=int, help='finetuning epochs')
+parser.add_argument('--data_save_path', default='../data/Fourth_data/teacher_data', type=str, help='teacher save unlabeled data in this path')
+args = parser.parse_args()
+
+print(args)
+
+# by paper of https://arxiv.org/pdf/1911.04252.pdf
+Aug_number = 2
+Aug_magnitude = 27
+
+#my customize network
+blocks = [4,5,6,7,8]
+
+# data loader parameters
+kwargs = {'num_workers': args.num_workers, 'pin_memory': True}
+
+Labeled_mean, Labeled_std = get_params(args.Labeled_dataset_path, args.size)
+Unlabeled_mean, Unlabeled_std = get_params(args.Unlabeled_dataset_path, args.size)
+
+transform_labeled = transforms.Compose([
+    transforms.Resize((args.size, args.size)),
+    transforms.RandomCrop(args.size, padding=4),
+    transforms.RandomHorizontalFlip(),
+    transforms.ToTensor(),
+    transforms.Normalize(Labeled_mean[0].item(), Labeled_std[0].item())
+])
+
+#이건 Teacher가 raw data를 받아서 판단하는거기 때문에 따로 Augmentation할 필요 x
+transform_unlabeled = transforms.Compose([
+    transforms.Resize((args.size, args.size)),
+    transforms.RandomCrop(args.size, padding=4),
+    transforms.RandomHorizontalFlip(),
+    transforms.ToTensor(),
+    transforms.Normalize(Unlabeled_mean[0].item(), Unlabeled_std[0].item())
+])
+
+# Add RandAugment with N, M(hyperparameter)
+transform_labeled.transforms.insert(0, RandAugment(Aug_number, Aug_magnitude))
+
+# set dataset
+Labeled_dataset = datasets.ImageFolder(args.Labeled_dataset_path, transform_labeled)
+Unlabeled_dataset = MyImageFolder(args.Unlabeled_dataset_path, transform_unlabeled)
+
+labeled_data_loader = torch.utils.data.DataLoader(
+  Labeled_dataset, batch_size=args.batch_size, shuffle=True, **kwargs)
+
+unlabeled_data_loader = torch.utils.data.DataLoader(
+  Unlabeled_dataset, batch_size=args.batch_size, shuffle=True, **kwargs)
+
+# noisy teacher은 student보다 더 작게 설정하며, dropout을 0으로 설정.
+noisy_teacher_model = mobilenetv3(n_class=2, dropout=0.0, blocknum=4)
+checkpoint = torch.load(args.teacher_checkpoint_path)
+noisy_teacher_model.load_state_dict(checkpoint['state_dict'])
+
+# make loss function
+criterion = nn.CrossEntropyLoss()
+
+# make class directory
+make_dir()
+
+classes = os.listdir(args.data_save_path)
+classes.sort()
+
+for block in blocks:
+  #noisy student는 더 크게 설정하고 dropout은 논문에 나와있는대로 0.5로 설정.
+  noisy_student_model = mobilenetv3(n_class=2, dropout=0.5, blocknum=block, stochastic=True)
+
+  noisy_student_model.cuda()
+  noisy_teacher_model.cuda()
+  criterion.cuda()
+
+  # make optimizer same as official code lr = 0.128 and decays by 0.97 every 2.4epochs
+  optimizer = torch.optim.RMSprop(noisy_student_model.parameters(), lr=0.128, weight_decay=0.9, momentum=0.9)
+
+  # exp scheduler like tf offical code
+  scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,0.7)
+
+  for epoch in range(args.epochs):
+    # unlabeled data를 labeling하는 과정.
+    for idx, data in enumerate(unlabeled_data_loader):
+      (unlabeled_input, _), (path, _) = data
+
+      unlabeled_input = unlabeled_input.cuda()
+
+      output=noisy_teacher_model(unlabeled_input)
+
+      prob = F.softmax(output, dim=1)
+
+      for idx, p in enumerate(prob):
+        indices = torch.topk(p,1).indices.tolist()
+        
+        img = cv2.imread(path[idx])
+
+        cv2.imwrite(f"{args.data_save_path}/{classes[indices[0]]}/{path[idx].split('/')[-1]}", img)
+
+    # teacher 모델이 구성한 data에 대해서 다시 loader 구성.
+    transform_teacher_data  = transforms.Compose([
+      transforms.Resize((args.size, args.size)),
+      transforms.RandomCrop(args.size, padding=4),
+      transforms.RandomHorizontalFlip(),
+      transforms.ToTensor(),
+      transforms.Normalize(Unlabeled_mean[0].item(), Unlabeled_std[0].item())
+    ])
+    transform_teacher_data.transforms.insert(0, RandAugment(Aug_number, Aug_magnitude))
+
+    teacher_data = datasets.ImageFolder(args.data_save_path, transform_teacher_data)
+
+    teacher_data_loader = torch.utils.data.DataLoader(
+      teacher_data, batch_size=args.batch_size, shuffle=True, **kwargs)
+
+    merged_dataset = ConcatDataset(teacher_data_loader, labeled_data_loader)      #앞은 teacher가 예측한거 뒤는 실제 데이터
+
+    merged_data_loader = torch.utils.data.DataLoader(
+      merged_dataset, batch_size=args.batch_size, shuffle=True, num_workers=8, pin_memory=True)
+
+    #일단 코드상으로는 unlabeled된 data에 대해서 hard하게 구성. todo: soft labeling.
+    for i, (input, target) in enumerate(merged_data_loader):
+      input = input.cuda()
+      target = target.cuda()
+
+      output = noisy_student_model(input)
+
+      loss = criterion(target, output)
+
+      optimizer.zero_grad()
+      loss.backward()
+      optimizer.step()
+      
+      #논문에서는 2.4epoch마다라고 하였지만 현재는 2에폭마다로 설정.
+      if epoch % 2 == 0:
+        scheduler.step()
+
+    # iterative learning.
+    noisy_teacher_model = noisy_student_model
\ No newline at end of file

+from model import mobilenetv3
+import torch
+import torch.nn as nn
+
+model = mobilenetv3(n_class=8, blocknum=6)
+
+model = torch.nn.DataParallel(model)
+device = torch.device('cpu')
+checkpoint = torch.load('output/All/48860_model=MobilenetV3-ep=3000-block=6-class=8/model_best.pth.tar', map_location = device)
+
+model.load_state_dict(checkpoint['state_dict'])
+
+model.to(device)
+
+model.eval()
+
+x = torch.randn(256,1,224,224)
+
+print(x.shape)
+
+jit_model = torch.jit.trace(model.module,x)
+
+jit_model.save("mobilenetv3.pt")
+
+#check jitModel is working
+#output = jit_model(torch.ones(3,1,224,224))
+#print(output)
\ No newline at end of file

+adabound==0.0.5
+altgraph==0.17
+click==7.1.1
+cycler==0.10.0
+future==0.18.2
+kiwisolver==1.1.0
+lxml==4.5.0
+macholib==1.14
+matplotlib==2.2.4
+numpy==1.18.1
+opencv-python==4.2.0.32
+pandas==1.0.3
+pefile==2019.4.18
+Pillow==6.2.2
+protobuf==3.11.3
+PyInstaller==3.6
+pyparsing==2.4.6
+PyQt5==5.14.1
+PyQt5-sip==12.7.1
+python-dateutil==2.8.1
+pytz==2019.3
+pywin32-ctypes==0.2.0
+PyYAML==5.3.1
+scipy==1.4.1
+six==1.14.0
+tensorboard-logger==0.1.0
+torch==1.4.0+cpu
+torchvision==0.2.2.post3
+tqdm==4.44.1