def train():
device = torch.device(conf.cuda if torch.cuda.is_available() else "cpu")
dataset = Training_Dataset(conf.data_path_train, conf.gaussian_noise_param,
conf.crop_img_size)
dataset_length = len(dataset)
train_loader = DataLoader(dataset,
batch_size=4,
shuffle=True,
num_workers=4)
model = UNet(in_channels=conf.img_channel, out_channels=conf.img_channel)
criterion = nn.MSELoss()
model = model.to(device)
optim = Adam(model.parameters(),
lr=conf.learning_rate,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
amsgrad=True)
scheduler = lr_scheduler.StepLR(optim, step_size=100, gamma=0.5)
model.train()
print(model)
print("Starting Training Loop...")
since = time.time()
for epoch in range(conf.max_epoch):
print('Epoch {}/{}'.format(epoch, conf.max_epoch - 1))
print('-' * 10)
running_loss = 0.0
scheduler.step()
for batch_idx, (source, target) in enumerate(train_loader):
source = source.to(device)
target = target.to(device)
optim.zero_grad()
denoised_source = model(source)
loss = criterion(denoised_source, target)
loss.backward()
optim.step()
running_loss += loss.item() * source.size(0)
print('Current loss {} and current batch idx {}'.format(
loss.item(), batch_idx))
epoch_loss = running_loss / dataset_length
print('{} Loss: {:.4f}'.format('current ' + str(epoch), epoch_loss))
if (epoch + 1) % conf.save_per_epoch == 0:
save_model(model, epoch + 1)
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
for i,p in enumerate(loss_all):
for j,q in enumerate(p):
table.write(i,j,q)
file.save('/home/star/0_code_lhj/DL-SIM-github/Training_codes/UNet/loss_UNet_SRRF_microtubule.xls')
lr = get_learning_rate(epoch)
for p in optimizer.param_groups:
p['lr'] = lr
print("learning rate = {}".format(p['lr']))
for batch_idx, items in enumerate(train_dataloader):
image = items['image_in']
gt = items['groundtruth']
model.train()
image = np.swapaxes(image, 1,3)
image = np.swapaxes(image, 2,3)
image = image.float()
image = image.cuda(cuda)
gt = gt.squeeze()
gt = gt.float()
gt = gt.cuda(cuda)
pred = model(image).squeeze()
loss = (pred-gt).abs().mean() + 5 * ((pred-gt)**2).mean()
optimizer.zero_grad()
'ssim': []
}
data, target = get_train_data(X_test, y_test, batch_size)
test_data = torch.from_numpy(data.astype(int)).float()
test_target = torch.from_numpy(target.astype(int)).float()
for epoch in range(1, NUM_EPOCHS + 1):
running_results = {
'batch_sizes': 0,
'd_loss': 0,
'g_loss': 0,
'd_score': 0,
'g_score': 0
}
netG.train()
netD.train()
data, target = get_train_data(X_train, y_train, batch_size)
data = torch.from_numpy(data.astype(int)).float()
target = torch.from_numpy(target.astype(int)).float()
g_update_first = True
batch_size = data.size(0)
running_results['batch_sizes'] += batch_size
############################
# (1) Update D network: maximize D(x)-1-D(G(z))
###########################
real_img = Variable(target)
if torch.cuda.is_available():
real_img = real_img.cuda()
z = Variable(data)
class Train(object):
def __init__(self, configs):
self.batch_size = configs.get("batch_size", "16")
self.epochs = configs.get("epochs", "100")
self.lr = configs.get("lr", "0.0001")
device_args = configs.get("device", "cuda")
self.device = torch.device(
"cpu" if not torch.cuda.is_available() else device_args)
self.workers = configs.get("workers", "4")
self.vis_images = configs.get("vis_images", "200")
self.vis_freq = configs.get("vis_freq", "10")
self.weights = configs.get("weights", "./weights")
if not os.path.exists(self.weights):
os.mkdir(self.weights)
self.logs = configs.get("logs", "./logs")
if not os.path.exists(self.weights):
os.mkdir(self.weights)
self.images_path = configs.get("images_path", "./data")
self.is_resize = config.get("is_resize", False)
self.image_short_side = config.get("image_short_side", 256)
self.is_padding = config.get("is_padding", False)
is_multi_gpu = config.get("DateParallel", False)
pre_train = config.get("pre_train", False)
model_path = config.get("model_path", './weights/unet_idcard_adam.pth')
# self.image_size = configs.get("image_size", "256")
# self.aug_scale = configs.get("aug_scale", "0.05")
# self.aug_angle = configs.get("aug_angle", "15")
self.step = 0
self.dsc_loss = DiceLoss()
self.model = UNet(in_channels=Dataset.in_channels,
out_channels=Dataset.out_channels)
if pre_train:
self.model.load_state_dict(torch.load(model_path,
map_location=self.device),
strict=False)
if is_multi_gpu:
self.model = nn.DataParallel(self.model)
self.model.to(self.device)
self.best_validation_dsc = 0.0
self.loader_train, self.loader_valid = self.data_loaders()
self.params = [p for p in self.model.parameters() if p.requires_grad]
self.optimizer = optim.Adam(self.params,
lr=self.lr,
weight_decay=0.0005)
# self.optimizer = torch.optim.SGD(self.params, lr=self.lr, momentum=0.9, weight_decay=0.0005)
self.scheduler = lr_scheduler.LR_Scheduler_Head(
'poly', self.lr, self.epochs, len(self.loader_train))
def datasets(self):
train_datasets = Dataset(
images_dir=self.images_path,
# image_size=self.image_size,
subset="train", # train
transform=get_transforms(train=True),
is_resize=self.is_resize,
image_short_side=self.image_short_side,
is_padding=self.is_padding)
# valid_datasets = train_datasets
valid_datasets = Dataset(
images_dir=self.images_path,
# image_size=self.image_size,
subset="validation", # validation
transform=get_transforms(train=False),
is_resize=self.is_resize,
image_short_side=self.image_short_side,
is_padding=False)
return train_datasets, valid_datasets
def data_loaders(self):
dataset_train, dataset_valid = self.datasets()
loader_train = DataLoader(
dataset_train,
batch_size=self.batch_size,
shuffle=True,
drop_last=True,
num_workers=self.workers,
)
loader_valid = DataLoader(
dataset_valid,
batch_size=1,
drop_last=False,
num_workers=self.workers,
)
return loader_train, loader_valid
@staticmethod
def dsc_per_volume(validation_pred, validation_true):
assert len(validation_pred) == len(validation_true)
dsc_list = []
for p in range(len(validation_pred)):
y_pred = np.array([validation_pred[p]])
y_true = np.array([validation_true[p]])
dsc_list.append(dsc(y_pred, y_true))
return dsc_list
@staticmethod
def get_logger(filename, verbosity=1, name=None):
level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}
formatter = logging.Formatter(
"[%(asctime)s][%(filename)s][line:%(lineno)d][%(levelname)s] %(message)s"
)
logger = logging.getLogger(name)
logger.setLevel(level_dict[verbosity])
fh = logging.FileHandler(filename, "w")
fh.setFormatter(formatter)
logger.addHandler(fh)
sh = logging.StreamHandler()
sh.setFormatter(formatter)
logger.addHandler(sh)
return logger
def train_one_epoch(self, epoch):
self.model.train()
loss_train = []
for i, data in enumerate(self.loader_train):
self.scheduler(self.optimizer, i, epoch, self.best_validation_dsc)
x, y_true = data
x, y_true = x.to(self.device), y_true.to(self.device)
y_pred = self.model(x)
# print('1111', y_pred.size())
# print('2222', y_true.size())
loss = self.dsc_loss(y_pred, y_true)
loss_train.append(loss.item())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# lr_scheduler.step()
if self.step % 200 == 0:
print('Epoch:[{}/{}]\t iter:[{}]\t loss={:.5f}\t '.format(
epoch, self.epochs, i, loss))
self.step += 1
def eval_model(self, patience):
self.model.eval()
loss_valid = []
validation_pred = []
validation_true = []
# early_stopping = EarlyStopping(patience=patience, verbose=True)
for i, data in enumerate(self.loader_valid):
x, y_true = data
x, y_true = x.to(self.device), y_true.to(self.device)
# print(x.size())
# print(333,x[0][2])
with torch.no_grad():
y_pred = self.model(x)
loss = self.dsc_loss(y_pred, y_true)
# print(y_pred.shape)
mask = y_pred > 0.5
mask = mask * 255
mask = mask.cpu().numpy()[0][0]
# print(mask)
# print(mask.shape())
cv2.imwrite('result.png', mask)
loss_valid.append(loss.item())
y_pred_np = y_pred.detach().cpu().numpy()
validation_pred.extend(
[y_pred_np[s] for s in range(y_pred_np.shape[0])])
y_true_np = y_true.detach().cpu().numpy()
validation_true.extend(
[y_true_np[s] for s in range(y_true_np.shape[0])])
# early_stopping(loss_valid, self.model)
# if early_stopping.early_stop:
# print('Early stopping')
# import sys
# sys.exit(1)
mean_dsc = np.mean(
self.dsc_per_volume(
validation_pred,
validation_true,
))
# print('mean_dsc:', mean_dsc)
if mean_dsc > self.best_validation_dsc:
self.best_validation_dsc = mean_dsc
torch.save(self.model.state_dict(),
os.path.join(self.weights, "unet_xia_adam.pth"))
print("Best validation mean DSC: {:4f}".format(
self.best_validation_dsc))
def main(self):
# print('train is begin.....')
# print('load data end.....')
# loaders = {"train": loader_train, "valid": loader_valid}
for epoch in tqdm(range(self.epochs), total=self.epochs):
self.train_one_epoch(epoch)
self.eval_model(patience=10)
torch.save(self.model.state_dict(),
os.path.join(self.weights, "unet_final.pth"))
def train(cont=False):
# for tensorboard tracking
logger = get_logger()
logger.info("(1) Initiating Training ... ")
logger.info("Training on device: {}".format(device))
writer = SummaryWriter()
# init model
aux_layers = None
if net == "SETR-PUP":
aux_layers, model = get_SETR_PUP()
elif net == "SETR-MLA":
aux_layers, model = get_SETR_MLA()
elif net == "TransUNet-Base":
model = get_TransUNet_base()
elif net == "TransUNet-Large":
model = get_TransUNet_large()
elif net == "UNet":
model = UNet(CLASS_NUM)
# prepare dataset
cluster_model = get_clustering_model(logger)
train_dataset = CityscapeDataset(img_dir=data_dir,
img_dim=IMG_DIM,
mode="train",
cluster_model=cluster_model)
valid_dataset = CityscapeDataset(img_dir=data_dir,
img_dim=IMG_DIM,
mode="val",
cluster_model=cluster_model)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
valid_loader = DataLoader(valid_dataset,
batch_size=batch_size,
shuffle=False)
logger.info("(2) Dataset Initiated. ")
# optimizer
epochs = epoch_num if epoch_num > 0 else iteration_num // len(
train_loader) + 1
optim = SGD(model.parameters(),
lr=lrate,
momentum=momentum,
weight_decay=wdecay)
# optim = Adam(model.parameters(), lr=lrate)
scheduler = lr_scheduler.MultiStepLR(
optim, milestones=[int(epochs * fine_tune_ratio)], gamma=0.1)
cur_epoch = 0
best_loss = float('inf')
epochs_since_improvement = 0
# for continue training
if cont:
model, optim, cur_epoch, best_loss = load_ckpt_continue_training(
best_ckpt_src, model, optim, logger)
logger.info("Current best loss: {0}".format(best_loss))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for i in range(cur_epoch):
scheduler.step()
else:
model = nn.DataParallel(model)
model = model.to(device)
logger.info("(3) Model Initiated ... ")
logger.info("Training model: {}".format(net) + ". Training Started.")
# loss
ce_loss = CrossEntropyLoss()
if use_dice_loss:
dice_loss = DiceLoss(CLASS_NUM)
# loop over epochs
iter_count = 0
epoch_bar = tqdm.tqdm(total=epochs,
desc="Epoch",
position=cur_epoch,
leave=True)
logger.info("Total epochs: {0}. Starting from epoch {1}.".format(
epochs, cur_epoch + 1))
for e in range(epochs - cur_epoch):
epoch = e + cur_epoch
# Training.
model.train()
trainLossMeter = LossMeter()
train_batch_bar = tqdm.tqdm(total=len(train_loader),
desc="TrainBatch",
position=0,
leave=True)
for batch_num, (orig_img, mask_img) in enumerate(train_loader):
orig_img, mask_img = orig_img.float().to(
device), mask_img.float().to(device)
if net == "TransUNet-Base" or net == "TransUNet-Large":
pred = model(orig_img)
elif net == "SETR-PUP" or net == "SETR-MLA":
if aux_layers is not None:
pred, _ = model(orig_img)
else:
pred = model(orig_img)
elif net == "UNet":
pred = model(orig_img)
loss_ce = ce_loss(pred, mask_img[:].long())
if use_dice_loss:
loss_dice = dice_loss(pred, mask_img, softmax=True)
loss = 0.5 * (loss_ce + loss_dice)
else:
loss = loss_ce
# Backward Propagation, Update weight and metrics
optim.zero_grad()
loss.backward()
optim.step()
# update learning rate
for param_group in optim.param_groups:
orig_lr = param_group['lr']
param_group['lr'] = orig_lr * (1.0 -
iter_count / iteration_num)**0.9
iter_count += 1
# Update loss
trainLossMeter.update(loss.item())
# print status
if (batch_num + 1) % print_freq == 0:
status = 'Epoch: [{0}][{1}/{2}]\t' \
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch+1, batch_num+1, len(train_loader), loss=trainLossMeter)
logger.info(status)
# log loss to tensorboard
if (batch_num + 1) % tensorboard_freq == 0:
writer.add_scalar(
'Train_Loss_{0}'.format(tensorboard_freq),
trainLossMeter.avg,
epoch * (len(train_loader) / tensorboard_freq) +
(batch_num + 1) / tensorboard_freq)
train_batch_bar.update(1)
writer.add_scalar('Train_Loss_epoch', trainLossMeter.avg, epoch)
# Validation.
model.eval()
validLossMeter = LossMeter()
valid_batch_bar = tqdm.tqdm(total=len(valid_loader),
desc="ValidBatch",
position=0,
leave=True)
with torch.no_grad():
for batch_num, (orig_img, mask_img) in enumerate(valid_loader):
orig_img, mask_img = orig_img.float().to(
device), mask_img.float().to(device)
if net == "TransUNet-Base" or net == "TransUNet-Large":
pred = model(orig_img)
elif net == "SETR-PUP" or net == "SETR-MLA":
if aux_layers is not None:
pred, _ = model(orig_img)
else:
pred = model(orig_img)
elif net == "UNet":
pred = model(orig_img)
loss_ce = ce_loss(pred, mask_img[:].long())
if use_dice_loss:
loss_dice = dice_loss(pred, mask_img, softmax=True)
loss = 0.5 * (loss_ce + loss_dice)
else:
loss = loss_ce
# Update loss
validLossMeter.update(loss.item())
# print status
if (batch_num + 1) % print_freq == 0:
status = 'Validation: [{0}][{1}/{2}]\t' \
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch+1, batch_num+1, len(valid_loader), loss=validLossMeter)
logger.info(status)
# log loss to tensorboard
if (batch_num + 1) % tensorboard_freq == 0:
writer.add_scalar(
'Valid_Loss_{0}'.format(tensorboard_freq),
validLossMeter.avg,
epoch * (len(valid_loader) / tensorboard_freq) +
(batch_num + 1) / tensorboard_freq)
valid_batch_bar.update(1)
valid_loss = validLossMeter.avg
writer.add_scalar('Valid_Loss_epoch', valid_loss, epoch)
logger.info("Validation Loss of epoch [{0}/{1}]: {2}\n".format(
epoch + 1, epochs, valid_loss))
# update optim scheduler
scheduler.step()
# save checkpoint
is_best = valid_loss < best_loss
best_loss_tmp = min(valid_loss, best_loss)
if not is_best:
epochs_since_improvement += 1
logger.info("Epochs since last improvement: %d\n" %
(epochs_since_improvement, ))
if epochs_since_improvement == early_stop_tolerance:
break # early stopping.
else:
epochs_since_improvement = 0
state = {
'epoch': epoch,
'loss': best_loss_tmp,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optim.state_dict(),
}
torch.save(state, ckpt_src)
logger.info("Checkpoint updated.")
best_loss = best_loss_tmp
epoch_bar.update(1)
writer.close()
class UNetObjPrior(nn.Module):
"""
Wrapper around UNet that takes object priors (gaussians) and images
as input.
"""
def __init__(self, params, depth=5):
super(UNetObjPrior, self).__init__()
self.in_channels = 4
self.model = UNet(1, self.in_channels, depth, cuda=params['cuda'])
self.params = params
self.device = torch.device('cuda' if params['cuda'] else 'cpu')
def forward(self, im, obj_prior):
x = torch.cat((im, obj_prior), dim=1)
return self.model(x)
def train(self, dataloader_train, dataloader_val):
since = time.time()
best_loss = float("inf")
dataloader_train.mode = 'train'
dataloader_val.mode = 'val'
dataloaders = {'train': dataloader_train, 'val': dataloader_val}
optimizer = optim.SGD(self.model.parameters(),
momentum=self.params['momentum'],
lr=self.params['lr'],
weight_decay=self.params['weight_decay'])
train_logger = LossLogger('train', self.params['batch_size'],
len(dataloader_train),
self.params['out_dir'])
val_logger = LossLogger('val', self.params['batch_size'],
len(dataloader_val), self.params['out_dir'])
loggers = {'train': train_logger, 'val': val_logger}
# self.criterion = WeightedMSE(dataloader_train.get_classes_weights(),
# cuda=self.params['cuda'])
self.criterion = nn.MSELoss()
for epoch in range(self.params['num_epochs']):
print('Epoch {}/{}'.format(epoch, self.params['num_epochs'] - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
#scheduler.step()
self.model.train()
else:
self.model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
samp = 1
for i, data in enumerate(dataloaders[phase]):
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
out = self.forward(data.image, data.obj_prior)
loss = self.criterion(out, data.truth)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
loggers[phase].update(epoch, samp, loss.item())
samp += 1
loggers[phase].print_epoch(epoch)
# Generate train prediction for check
if phase == 'train':
path = os.path.join(self.params['out_dir'], 'previews',
'epoch_{:04d}.jpg'.format(epoch))
data = dataloaders['val'].sample_uniform()
pred = self.forward(data.image, data.obj_prior)
im_ = data.image[0]
truth_ = data.truth[0]
pred_ = pred[0, ...]
utls.save_tensors(im_, pred_, truth_, path)
if phase == 'val' and (loggers['val'].get_loss(epoch) <
best_loss):
best_loss = loggers['val'].get_loss(epoch)
loggers[phase].save('log_{}.csv'.format(phase))
# save checkpoint
if phase == 'val':
is_best = loggers['val'].get_loss(epoch) <= best_loss
path = os.path.join(self.params['out_dir'],
'checkpoint.pth.tar')
utls.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': self.model.state_dict(),
'best_loss': best_loss,
'optimizer': optimizer.state_dict()
},
is_best,
path=path)
def load_checkpoint(self, path, device='gpu'):
if (device != 'gpu'):
checkpoint = torch.load(path,
map_location=lambda storage, loc: storage)
else:
checkpoint = torch.load(path)
self.model.load_state_dict(checkpoint['state_dict'])
return image, label
def __len__(self):
# get the size of data set
return len(self.imgs_path)
if __name__ == "__main__":
dataset = DataLoader("data/train10/")
train_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=1,
shuffle=True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = UNet(n_channels=1, n_classes=1)
net.to(device=device)
net.train()
for image, label in train_loader:
image = image.to(device=device, dtype=torch.float32)
label = label.to(device=device, dtype=torch.float32)
pred = net(image)
loss = F.cross_entropy(pred, label.to(torch.long))
print('Loss/train', loss.item())
if loss < best_loss:
best_loss = loss
torch.save(net.state_dict(), 'best_model.pth')
loss.backward()
optimizer.step()
print(pred.shape, image.shape, label.shape)
