## 라이브러리 추가하기
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms, datasets
## 트레이닝 파라메터 설정하기
lr = 1e-3
batch_size = 1
num_epoch = 100
data_dir = './datasets'
ckpt_dir = './checkpoint'
log_dir = './log'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
## 네트워크 구현하기
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
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
# Contracting path
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)
# Expansive path
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)
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
## 데이터 로더를 구현하기
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]))
label = label / 255.0
input = input / 255.0
if label.ndim == 2:
label = label[:, :, np.newaxis]
if input.ndim == 2:
input = input[:, :, np.newaxis]
data = {'input' : input, 'label' : label}
if self.transform:
data = self.transform(data)
return data
##
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
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
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
## 네트워크 학습하기
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=True, num_workers=0)
## 네트워크 생성하기
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)
## 그밖에 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)
## Tensorboard를 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'))
## 네트워크 저장하기
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))
## 네트워크 불러오기
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
## 네트워크 학습시키기
st_epoch = 0
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()
# 손실함수 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 계산
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_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_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)
if epoch % 50 == 0:
save(ckpt_dir=ckpt_dir, net=net, optim=optim, epoch=epoch)
writer_train.close()
writer_val.close()
오류 메시지 : RuntimeError: CUDA out of memory. Tried to allocate 64.00 MiB (GPU 0; 6.00 GiB total capacity; 2.02 GiB already allocated; 25.70 MiB free; 2.05 GiB reserved in total by PyTorch)
라고 나오는데 어떻게 해야 하나요ㅠㅠ
인터넷 찾아봤는데 해결이 안되네요...
