Home [Paper] PyTorch로 U-Net 구현
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[Paper] PyTorch로 U-Net 구현

Posted Jun 15, 2023 Updated Jun 16, 2023
By Eunji
14 min read
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# 구글 드라이브 사용 시 연결
from google.colab import drive
drive.mount('/content/drive')

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Mounted at /content/drive

0. Library Call

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import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

import matplotlib.pyplot as plt

from torchvision import transforms, datasets

1. Data Load % Data Formatting

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dir_data = './UNet'

name_label = 'train-labels.tif'
name_input = 'train-volume.tif'

img_label = Image.open(os.path.join(dir_data, name_label))
img_input = Image.open(os.path.join(dir_data, name_input))

ny, nx = img_label.size
nframe = img_label.n_frames

print(ny, nx)
print(nframe)

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512 512
30

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# Train / Valid / Test
nframe_train = 24
nframe_val = 3
nframe_test = 3

dir_save_train = os.path.join(dir_data, 'train')
dir_save_val = os.path.join(dir_data, 'val')
dir_save_test = os.path.join(dir_data, 'test')

if not os.path.exists(dir_save_train):
    os.makedirs(dir_save_train)

if not os.path.exists(dir_save_val):
    os.makedirs(dir_save_val)

if not os.path.exists(dir_save_test):
    os.makedirs(dir_save_test)

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# 전체 이미지 30개를 랜덤하게 섞어줌
id_frame = np.arange(nframe)
np.random.shuffle(id_frame)

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# Train image를 npy 파일로 저장
offset_nframe = 0

for i in range(nframe_train):
    img_label.seek(id_frame[i + offset_nframe])
    img_input.seek(id_frame[i + offset_nframe])

    label_ = np.asarray(img_label)
    input_ = np.asarray(img_input)

    np.save(os.path.join(dir_save_train, 'label_%03d.npy' % i), label_)
    np.save(os.path.join(dir_save_train, 'input_%03d.npy' % i), input_)

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# Valid image를 npy 파일로 저장
offset_nframe = nframe_train

for i in range(nframe_val):
    img_label.seek(id_frame[i + offset_nframe])
    img_input.seek(id_frame[i + offset_nframe])

    label_ = np.asarray(img_label)
    input_ = np.asarray(img_input)

    np.save(os.path.join(dir_save_val, 'label_%03d.npy' % i), label_)
    np.save(os.path.join(dir_save_val, 'input_%03d.npy' % i), input_)


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# Test image를 npy 파일로 저장
offset_nframe = nframe_train + nframe_val

for i in range(nframe_test):
    img_label.seek(id_frame[i + offset_nframe])
    img_input.seek(id_frame[i + offset_nframe])

    label_ = np.asarray(img_label)
    input_ = np.asarray(img_input)

    np.save(os.path.join(dir_save_test, 'label_%03d.npy' % i), label_)
    np.save(os.path.join(dir_save_test, 'input_%03d.npy' % i), input_)

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# 위에서 생성한 이미지 시각화
plt.subplot(122)
plt.imshow(label_, cmap='gray')
plt.title('label')

plt.subplot(121)
plt.imshow(input_, cmap='gray')
plt.title('input')

plt.show()

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# 픽셀 값의 분포
plt.subplot(122)
plt.hist(label_.flatten(), bins=20)
plt.title('label')

plt.subplot(121)
plt.hist(input_.flatten(), bins=20)
plt.title('input')

plt.tight_layout()
plt.show()

2. U-Net Architecture

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# 네트워크 구축
class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()

        # Convolution + BatchNormalization + Relu 정의 (반복적으로 사용)
        def CBR2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True): 
            layers = []
            layers += [nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
                                 kernel_size=kernel_size, stride=stride, padding=padding,
                                 bias=bias)]
            layers += [nn.BatchNorm2d(num_features=out_channels)]
            layers += [nn.ReLU()]

            cbr = nn.Sequential(*layers)

            return cbr

        # 수축 경로(Contracting path) -> Encoder
        self.enc1_1 = CBR2d(in_channels=1, out_channels=64)
        self.enc1_2 = CBR2d(in_channels=64, out_channels=64)

        self.pool1 = nn.MaxPool2d(kernel_size=2)

        self.enc2_1 = CBR2d(in_channels=64, out_channels=128)
        self.enc2_2 = CBR2d(in_channels=128, out_channels=128)

        self.pool2 = nn.MaxPool2d(kernel_size=2)

        self.enc3_1 = CBR2d(in_channels=128, out_channels=256)
        self.enc3_2 = CBR2d(in_channels=256, out_channels=256)

        self.pool3 = nn.MaxPool2d(kernel_size=2)

        self.enc4_1 = CBR2d(in_channels=256, out_channels=512)
        self.enc4_2 = CBR2d(in_channels=512, out_channels=512)

        self.pool4 = nn.MaxPool2d(kernel_size=2)

        self.enc5_1 = CBR2d(in_channels=512, out_channels=1024)

        # 확장 경로(Expansive path) -> Decoder
        self.dec5_1 = CBR2d(in_channels=1024, out_channels=512)

        self.unpool4 = nn.ConvTranspose2d(in_channels=512, out_channels=512,
                                          kernel_size=2, stride=2, padding=0, bias=True)

        self.dec4_2 = CBR2d(in_channels=2 * 512, out_channels=512)
        self.dec4_1 = CBR2d(in_channels=512, out_channels=256)

        self.unpool3 = nn.ConvTranspose2d(in_channels=256, out_channels=256,
                                          kernel_size=2, stride=2, padding=0, bias=True)

        self.dec3_2 = CBR2d(in_channels=2 * 256, out_channels=256)
        self.dec3_1 = CBR2d(in_channels=256, out_channels=128)

        self.unpool2 = nn.ConvTranspose2d(in_channels=128, out_channels=128,
                                          kernel_size=2, stride=2, padding=0, bias=True)

        self.dec2_2 = CBR2d(in_channels=2 * 128, out_channels=128)
        self.dec2_1 = CBR2d(in_channels=128, out_channels=64)

        self.unpool1 = nn.ConvTranspose2d(in_channels=64, out_channels=64,
                                          kernel_size=2, stride=2, padding=0, bias=True)

        self.dec1_2 = CBR2d(in_channels=2 * 64, out_channels=64)
        self.dec1_1 = CBR2d(in_channels=64, out_channels=64)

        self.fc = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0, bias=True)

    # forward 함수 정의하기
    def forward(self, x):
        enc1_1 = self.enc1_1(x)
        enc1_2 = self.enc1_2(enc1_1)
        pool1 = self.pool1(enc1_2)

        enc2_1 = self.enc2_1(pool1)
        enc2_2 = self.enc2_2(enc2_1)
        pool2 = self.pool2(enc2_2)

        enc3_1 = self.enc3_1(pool2)
        enc3_2 = self.enc3_2(enc3_1)
        pool3 = self.pool3(enc3_2)

        enc4_1 = self.enc4_1(pool3)
        enc4_2 = self.enc4_2(enc4_1)
        pool4 = self.pool4(enc4_2)

        enc5_1 = self.enc5_1(pool4)

        dec5_1 = self.dec5_1(enc5_1)

        unpool4 = self.unpool4(dec5_1)
        cat4 = torch.cat((unpool4, enc4_2), dim=1)
        dec4_2 = self.dec4_2(cat4)
        dec4_1 = self.dec4_1(dec4_2)

        unpool3 = self.unpool3(dec4_1)
        cat3 = torch.cat((unpool3, enc3_2), dim=1)
        dec3_2 = self.dec3_2(cat3)
        dec3_1 = self.dec3_1(dec3_2)

        unpool2 = self.unpool2(dec3_1)
        cat2 = torch.cat((unpool2, enc2_2), dim=1)
        dec2_2 = self.dec2_2(cat2)
        dec2_1 = self.dec2_1(dec2_2)

        unpool1 = self.unpool1(dec2_1)
        cat1 = torch.cat((unpool1, enc1_2), dim=1)
        dec1_2 = self.dec1_2(cat1)
        dec1_1 = self.dec1_1(dec1_2)

        x = self.fc(dec1_1)

        return x

3. DataLoader and Transform

3-1. DataLoader

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# DataLoader
class Dataset(torch.utils.data.Dataset):
    def __init__(self, data_dir, transform=None):
        self.data_dir = data_dir
        self.transform = transform

        lst_data = os.listdir(self.data_dir)

        lst_label = [f for f in lst_data if f.startswith('label')]
        lst_input = [f for f in lst_data if f.startswith('input')]

        lst_label.sort()
        lst_input.sort()

        self.lst_label = lst_label
        self.lst_input = lst_input

    def __len__(self):
        return len(self.lst_label)

    def __getitem__(self, index):
        label = np.load(os.path.join(self.data_dir, self.lst_label[index]))
        input = np.load(os.path.join(self.data_dir, self.lst_input[index]))

        # 정규화
        label = label/255.0
        input = input/255.0

        # 이미지와 레이블의 차원 = 2일 경우(채널이 없을 경우, 흑백 이미지), 새로운 채널(축) 생성
        if label.ndim == 2:
            label = label[:, :, np.newaxis]
        if input.ndim == 2:
            input = input[:, :, np.newaxis]

        data = {'input': input, 'label': label}

        # transform이 정의되어 있다면 transform을 거친 데이터를 불러옴
        if self.transform:
            data = self.transform(data)

        return data

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# DataLoader 작동 확인
dataset_train = Dataset(data_dir=dir_save_train)
data = dataset_train.__getitem__(0) # Index0에 해당하는 한 개의 이미지 불러오기
input = data['input']
label = data['label']

# 불러온 이미지 시각화
plt.subplot(122)
plt.imshow(label.reshape(512,512), cmap='gray')
plt.title('label')

plt.subplot(121)
plt.imshow(input.reshape(512,512), cmap='gray')
plt.title('input')

plt.show()

3-2. Transform

  • ToTensor : numpy에서 tensor로 데이터 type을 변경
  • Normalization : 데이터 정규화
  • RandomFlip : 랜덤 좌우 및 상하 반전
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# Transform
class ToTensor(object):
    def __call__(self, data):
        label, input = data['label'], data['input']

        label = label.transpose((2, 0, 1)).astype(np.float32)
        input = input.transpose((2, 0, 1)).astype(np.float32)

        data = {'label': torch.from_numpy(label), 'input': torch.from_numpy(input)}

        return data

# Normalization
class Normalization(object):
    def __init__(self, mean=0.5, std=0.5):
        self.mean = mean
        self.std = std

    def __call__(self, data):
        label, input = data['label'], data['input']

        input = (input - self.mean) / self.std

        data = {'label': label, 'input': input}

        return data

# 상하/좌우 반전
class RandomFlip(object):
    def __call__(self, data):
        label, input = data['label'], data['input']

        if np.random.rand() > 0.5:
            label = np.fliplr(label)
            input = np.fliplr(input)

        if np.random.rand() > 0.5:
            label = np.flipud(label)
            input = np.flipud(input)

        data = {'label': label, 'input': input}

        return data

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# Transform 작동 확인
transform = transforms.Compose([Normalization(mean=0.5, std=0.5), RandomFlip(), ToTensor()])
dataset_train = Dataset(data_dir=dir_save_train, transform=transform)
data = dataset_train.__getitem__(0) # Index0에 해당하는 한 개의 이미지
input = data['input']
label = data['label']

# 불러온 이미지 시각화
plt.subplot(122)
plt.hist(label.flatten(), bins=20)
plt.title('label')

plt.subplot(121)
plt.hist(input.flatten(), bins=20)
plt.title('input')

plt.tight_layout()
plt.show()

4. Network Save and Load

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# Network save
def save(ckpt_dir, net, optim, epoch):
    if not os.path.exists(ckpt_dir):
        os.makedirs(ckpt_dir)

    torch.save({'net': net.state_dict(), 'optim': optim.state_dict()},
               "%s/model_epoch%d.pth" % (ckpt_dir, epoch))

# Network load
def load(ckpt_dir, net, optim):
    if not os.path.exists(ckpt_dir):
        epoch = 0
        return net, optim, epoch

    ckpt_lst = os.listdir(ckpt_dir)
    ckpt_lst.sort(key=lambda f: int(''.join(filter(str.isdigit, f))))

    dict_model = torch.load('%s/%s' % (ckpt_dir, ckpt_lst[-1]))

    net.load_state_dict(dict_model['net'])
    optim.load_state_dict(dict_model['optim'])
    epoch = int(ckpt_lst[-1].split('epoch')[1].split('.pth')[0])

    return net, optim, epoch

5. Model Training

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# 훈련 파라미터 설정하기
lr = 1e-3
batch_size = 4
num_epoch = 50

base_dir = './drive/MyDrive/net'
data_dir = dir_data
ckpt_dir = os.path.join(base_dir, "checkpoint")
log_dir = os.path.join(base_dir, "log")

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# 훈련을 위한 Transform과 DataLoader
transform = transforms.Compose([Normalization(mean=0.5, std=0.5), RandomFlip(), ToTensor()])

dataset_train = Dataset(data_dir=os.path.join(data_dir, 'train'), transform=transform)
loader_train = DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=0)

dataset_val = Dataset(data_dir=os.path.join(data_dir, 'val'), transform=transform)
loader_val = DataLoader(dataset_val, batch_size=batch_size, shuffle=False, num_workers=0)

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# 네트워크 생성하기
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = UNet().to(device)

# 손실함수 정의하기
fn_loss = nn.BCEWithLogitsLoss().to(device)

# Optimizer 설정하기
optim = torch.optim.Adam(net.parameters(), lr=lr)

# 그밖에 부수적인 variables 설정하기
num_data_train = len(dataset_train)
num_data_val = len(dataset_val)

num_batch_train = np.ceil(num_data_train / batch_size)
num_batch_val = np.ceil(num_data_val / batch_size)

# 그 밖에 부수적인 functions 설정하기
fn_tonumpy = lambda x: x.to('cpu').detach().numpy().transpose(0, 2, 3, 1)
fn_denorm = lambda x, mean, std: (x * std) + mean
fn_class = lambda x: 1.0 * (x > 0.5)

# Tensorboard 를 사용하기 위한 SummaryWriter 설정
writer_train = SummaryWriter(log_dir=os.path.join(log_dir, 'train'))
writer_val = SummaryWriter(log_dir=os.path.join(log_dir, 'val'))

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# 네트워크 학습시키기
st_epoch = 0
# 학습한 모델이 있을 경우 모델 로드하기
net, optim, st_epoch = load(ckpt_dir=ckpt_dir, net=net, optim=optim)

for epoch in range(st_epoch + 1, num_epoch + 1):
        net.train()
        loss_arr = []

        for batch, data in enumerate(loader_train, 1):
            # forward pass
            label = data['label'].to(device)
            input = data['input'].to(device)

            output = net(input)

            # backward pass
            optim.zero_grad()

            loss = fn_loss(output, label)
            loss.backward()

            optim.step()

            # 손실 함수 계산
            loss_arr += [loss.item()]

            print("TRAIN: EPOCH %04d / %04d | BATCH %04d / %04d | LOSS %.4f" %
                  (epoch, num_epoch, batch, num_batch_train, np.mean(loss_arr)))

            # Tensorboard 저장하기
            label = fn_tonumpy(label)
            input = fn_tonumpy(fn_denorm(input, mean=0.5, std=0.5))
            output = fn_tonumpy(fn_class(output))

            writer_train.add_image('label', label, num_batch_train * (epoch - 1) + batch, dataformats='NHWC')
            writer_train.add_image('input', input, num_batch_train * (epoch - 1) + batch, dataformats='NHWC')
            writer_train.add_image('output', output, num_batch_train * (epoch - 1) + batch, dataformats='NHWC')

        writer_train.add_scalar('loss', np.mean(loss_arr), epoch)

        with torch.no_grad():
            net.eval()
            loss_arr = []

            for batch, data in enumerate(loader_val, 1):
                # forward pass
                label = data['label'].to(device)
                input = data['input'].to(device)

                output = net(input)

                # 손실함수 계산하기
                loss = fn_loss(output, label)

                loss_arr += [loss.item()]

                print("VALID: EPOCH %04d / %04d | BATCH %04d / %04d | LOSS %.4f" %
                      (epoch, num_epoch, batch, num_batch_val, np.mean(loss_arr)))

                # Tensorboard 저장하기
                label = fn_tonumpy(label)
                input = fn_tonumpy(fn_denorm(input, mean=0.5, std=0.5))
                output = fn_tonumpy(fn_class(output))

                writer_val.add_image('label', label, num_batch_val * (epoch - 1) + batch, dataformats='NHWC')
                writer_val.add_image('input', input, num_batch_val * (epoch - 1) + batch, dataformats='NHWC')
                writer_val.add_image('output', output, num_batch_val * (epoch - 1) + batch, dataformats='NHWC')

        writer_val.add_scalar('loss', np.mean(loss_arr), epoch)

        # epoch 50마다 모델 저장하기
        if epoch % 50 == 0:
            save(ckpt_dir=ckpt_dir, net=net, optim=optim, epoch=epoch)

        writer_train.close()
        writer_val.close()

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TRAIN: EPOCH 0001 / 0050 | BATCH 0001 / 0006 | LOSS 0.6968
TRAIN: EPOCH 0001 / 0050 | BATCH 0002 / 0006 | LOSS 0.6383
TRAIN: EPOCH 0001 / 0050 | BATCH 0003 / 0006 | LOSS 0.6072
TRAIN: EPOCH 0001 / 0050 | BATCH 0004 / 0006 | LOSS 0.5800
TRAIN: EPOCH 0001 / 0050 | BATCH 0005 / 0006 | LOSS 0.5547
TRAIN: EPOCH 0001 / 0050 | BATCH 0006 / 0006 | LOSS 0.5346
VALID: EPOCH 0001 / 0050 | BATCH 0001 / 0001 | LOSS 0.5948
...
TRAIN: EPOCH 0050 / 0050 | BATCH 0002 / 0006 | LOSS 0.1701
TRAIN: EPOCH 0050 / 0050 | BATCH 0003 / 0006 | LOSS 0.1784
TRAIN: EPOCH 0050 / 0050 | BATCH 0004 / 0006 | LOSS 0.1781
TRAIN: EPOCH 0050 / 0050 | BATCH 0005 / 0006 | LOSS 0.1779
TRAIN: EPOCH 0050 / 0050 | BATCH 0006 / 0006 | LOSS 0.1774
VALID: EPOCH 0050 / 0050 | BATCH 0001 / 0001 | LOSS 0.1970

6. Evaluation

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# Model Test
transform = transforms.Compose([Normalization(mean=0.5, std=0.5), ToTensor()])

dataset_test = Dataset(data_dir=os.path.join(data_dir, 'test'), transform=transform)
loader_test = DataLoader(dataset_test, batch_size=batch_size, shuffle=False, num_workers=8)

# 그밖에 부수적인 variables 설정하기
num_data_test = len(dataset_test)
num_batch_test = np.ceil(num_data_test / batch_size)

# 결과 디렉토리 생성하기
result_dir = os.path.join(base_dir, 'result')
if not os.path.exists(result_dir):
    os.makedirs(os.path.join(result_dir, 'png'))
    os.makedirs(os.path.join(result_dir, 'numpy'))

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net, optim, st_epoch = load(ckpt_dir=ckpt_dir, net=net, optim=optim)

with torch.no_grad():
      net.eval()
      loss_arr = []

      for batch, data in enumerate(loader_test, 1):
          # forward pass
          label = data['label'].to(device)
          input = data['input'].to(device)

          output = net(input)

          # 손실함수 계산하기
          loss = fn_loss(output, label)

          loss_arr += [loss.item()]

          print("TEST: BATCH %04d / %04d | LOSS %.4f" %
                (batch, num_batch_test, np.mean(loss_arr)))

          # Tensorboard 저장하기
          label = fn_tonumpy(label)
          input = fn_tonumpy(fn_denorm(input, mean=0.5, std=0.5))
          output = fn_tonumpy(fn_class(output))

          # 테스트 결과 저장하기
          for j in range(label.shape[0]):
              id = num_batch_test * (batch - 1) + j

              plt.imsave(os.path.join(result_dir, 'png', 'label_%04d.png' % id), label[j].squeeze(), cmap='gray')
              plt.imsave(os.path.join(result_dir, 'png', 'input_%04d.png' % id), input[j].squeeze(), cmap='gray')
              plt.imsave(os.path.join(result_dir, 'png', 'output_%04d.png' % id), output[j].squeeze(), cmap='gray')

              np.save(os.path.join(result_dir, 'numpy', 'label_%04d.npy' % id), label[j].squeeze())
              np.save(os.path.join(result_dir, 'numpy', 'input_%04d.npy' % id), input[j].squeeze())
              np.save(os.path.join(result_dir, 'numpy', 'output_%04d.npy' % id), output[j].squeeze())

print("AVERAGE TEST: BATCH %04d / %04d | LOSS %.4f" %
        (batch, num_batch_test, np.mean(loss_arr)))


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TEST: BATCH 0001 / 0001 | LOSS 0.2014
AVERAGE TEST: BATCH 0001 / 0001 | LOSS 0.2014

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lst_data = os.listdir(os.path.join(result_dir, 'numpy'))

lst_label = [f for f in lst_data if f.startswith('label')]
lst_input = [f for f in lst_data if f.startswith('input')]
lst_output = [f for f in lst_data if f.startswith('output')]

lst_label.sort()
lst_input.sort()
lst_output.sort()

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id = 0

label = np.load(os.path.join(result_dir,"numpy", lst_label[id]))
input = np.load(os.path.join(result_dir,"numpy", lst_input[id]))
output = np.load(os.path.join(result_dir,"numpy", lst_output[id]))

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# 결과 시각화 -> input / label / output(predict)
plt.figure(figsize=(8,6))
plt.subplot(131)
plt.imshow(input, cmap='gray')
plt.title('input')

plt.subplot(132)
plt.imshow(label, cmap='gray')
plt.title('label')

plt.subplot(133)
plt.imshow(output, cmap='gray')
plt.title('output')

plt.show()

image

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