def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = UNet(args.experiment_dir,
batch_size=args.batch_size,
experiment_id=args.experiment_id,
input_width=args.image_size,
output_width=args.image_size,
embedding_num=args.embedding_num,
embedding_dim=args.embedding_dim,
L1_penalty=args.L1_penalty)
model.register_session(sess)
if args.flip_labels:
model.build_model(is_training=True,
inst_norm=args.inst_norm,
no_target_source=True)
else:
model.build_model(is_training=True, inst_norm=args.inst_norm)
fine_tune_list = None
if args.fine_tune:
ids = args.fine_tune.split(",")
fine_tune_list = set([int(i) for i in ids])
model.train(lr=args.lr,
epoch=args.epoch,
resume=args.resume,
schedule=args.schedule,
freeze_encoder=args.freeze_encoder,
fine_tune=fine_tune_list,
sample_steps=args.sample_steps,
checkpoint_steps=args.checkpoint_steps,
flip_labels=args.flip_labels)
def load_finetuned_model(args, baseline_model):
"""
:param args:
:param baseline_model:
:return:
"""
# augment_net = Net(0, 0.0, 32, 3, 0.0, num_classes=32**2 * 3, do_res=True)
augment_net = UNet(in_channels=3, n_classes=3, depth=1, wf=2, padding=True, batch_norm=False,
do_noise_channel=True,
up_mode='upsample', use_identity_residual=True) # TODO(PV): Initialize UNet properly
# TODO (JON): DEPTH 1 WORKED WELL. Changed upconv to upsample. Use a wf of 2.
# This ResNet outputs scalar weights to be applied element-wise to the per-example losses
from models.simple_models import CNN, Net
imsize, in_channel, num_classes = 32, 3, 10
reweighting_net = Net(0, 0.0, imsize, in_channel, 0.0, num_classes=1)
#resnet_cifar.resnet20(num_classes=1)
if args.load_finetune_checkpoint:
checkpoint = torch.load(args.load_finetune_checkpoint)
baseline_model.load_state_dict(checkpoint['elementary_model_state_dict'])
augment_net.load_state_dict(checkpoint['augment_model_state_dict'])
try:
reweighting_net.load_state_dict(checkpoint['reweighting_model_state_dict'])
except KeyError:
pass
augment_net, reweighting_net, baseline_model = augment_net.cuda(), reweighting_net.cuda(), baseline_model.cuda()
augment_net.train(), reweighting_net.train(), baseline_model.train()
return augment_net, reweighting_net, baseline_model
class EventGANBase(object):
def __init__(self, options):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.generator = UNet(num_input_channels=2*options.n_image_channels,
num_output_channels=options.n_time_bins * 2,
skip_type='concat',
activation='relu',
num_encoders=4,
base_num_channels=32,
num_residual_blocks=2,
norm='BN',
use_upsample_conv=True,
with_activation=True,
sn=options.sn,
multi=False)
latest_checkpoint = get_latest_checkpoint(options.checkpoint_dir)
checkpoint = torch.load(latest_checkpoint)
self.generator.load_state_dict(checkpoint["gen"])
self.generator.to(self.device)
def forward(self, images, is_train=False):
if len(images.shape) == 3:
images = images[None, ...]
assert len(images.shape) == 4 and images.shape[1] == 2, \
"Input images must be either 2xHxW or Bx2xHxW."
if not is_train:
with torch.no_grad():
self.generator.eval()
event_volume = self.generator(images)
self.generator.train()
else:
event_volume = self.generator(images)
return event_volume
def load_finetuned_model(self, baseline_model):
"""
Loads the augmentation net, sample reweighting net, and baseline model
Note: sets all these models to train mode
"""
# augment_net = Net(0, 0.0, 32, 3, 0.0, num_classes=32**2 * 3, do_res=True)
if self.args.dataset == DATASET_MNIST:
imsize, in_channel, num_classes = 28, 1, 10
else:
imsize, in_channel, num_classes = 32, 3, 10
augment_net = UNet(
in_channels=in_channel,
n_classes=in_channel,
depth=2,
wf=3,
padding=True,
batch_norm=False,
do_noise_channel=True,
up_mode='upconv',
use_identity_residual=True) # TODO(PV): Initialize UNet properly
# TODO (JON): DEPTH 1 WORKED WELL. Changed upconv to upsample. Use a wf of 2.
# This ResNet outputs scalar weights to be applied element-wise to the per-example losses
reweighting_net = Net(1, 0.0, imsize, in_channel, 0.0, num_classes=1)
# resnet_cifar.resnet20(num_classes=1)
if self.args.load_finetune_checkpoint:
checkpoint = torch.load(self.args.load_finetune_checkpoint)
# temp_baseline_model = baseline_model
# baseline_model.load_state_dict(checkpoint['elementary_model_state_dict'])
if 'weight_decay' in checkpoint:
baseline_model.weight_decay = checkpoint['weight_decay']
# baseline_model.weight_decay = temp_baseline_model.weight_decay
# baseline_model.load_state_dict(checkpoint['elementary_model_state_dict'])
augment_net.load_state_dict(checkpoint['augment_model_state_dict'])
try:
reweighting_net.load_state_dict(
checkpoint['reweighting_model_state_dict'])
except KeyError:
pass
augment_net, reweighting_net, baseline_model = augment_net.to(
self.device), reweighting_net.to(self.device), baseline_model.to(
self.device)
augment_net.train(), reweighting_net.train(), baseline_model.train()
return augment_net, reweighting_net, baseline_model
def train():
# Init data
train_dataset, val_dataset = prepare_datasets()
train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=10, shuffle=True)
loaders = dict(train=train_loader, val=val_loader)
# Init Model
model = UNet().cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, amsgrad=True)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer,
gamma=0.984)
loss_fn = nn.BCELoss()
epochs = 500
for epoch in range(epochs):
for phase in 'train val'.split():
if phase == 'train':
model = model.train()
torch.set_grad_enabled(True)
else:
model = model.eval()
torch.set_grad_enabled(False)
loader = loaders[phase]
epoch_losses = dict(train=[], val=[])
running_loss = []
for batch in loader:
imgs, masks = batch
imgs = imgs.cuda()
masks = masks.cuda()
outputs = model(imgs)
loss = loss_fn(outputs, masks)
running_loss.append(loss.item())
if phase == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
# End of Epoch
print(f'{epoch}) {phase} loss: {np.mean(running_loss)}')
visualize_results(loader, model, epoch, phase)
epoch_losses[phase].append(np.mean(running_loss))
tensorboard(epoch_losses[phase], phase)
if phase == 'train':
scheduler.step()
class Noise2Noise(object):
"""Implementation of Noise2Noise from Lehtinen et al. (2018)."""
def __init__(self, params, trainable):
"""Initializes model."""
self.p = params
self.trainable = trainable
self._compile() #初始化模型
def _compile(self):
"""
Compiles model (architecture, loss function, optimizers, etc.).
初始化 网络、损失函数、优化器等
"""
print('Noise2Noise: Learning Image Restoration without Clean Data (Lethinen et al., 2018)')
# Model (3x3=9 channels for Monte Carlo since it uses 3 HDR buffers) 已删除蒙特卡洛相关代码
if self.p.noise_type == 'mc':
self.is_mc = True
self.model = UNet(in_channels=9)
else:
self.is_mc = False
self.model = UNet(in_channels=3)
# Set optimizer and loss, if in training mode
# 如果 为训练，则初始化优化器和损失
if self.trainable:
self.optim = Adam(self.model.parameters(),
lr=self.p.learning_rate,
betas=self.p.adam[:2],
eps=self.p.adam[2])
# Learning rate adjustment
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optim,
patience=self.p.nb_epochs/4, factor=0.5, verbose=True)
# Loss function
if self.p.loss == 'hdr':
assert self.is_mc, 'Using HDR loss on non Monte Carlo images'
self.loss = HDRLoss()
elif self.p.loss == 'l2':
self.loss = nn.MSELoss()
else:
self.loss = nn.L1Loss()
# CUDA support
self.use_cuda = torch.cuda.is_available() and self.p.cuda
if self.use_cuda:
self.model = self.model.cuda()
if self.trainable:
self.loss = self.loss.cuda()
def _print_params(self):
"""Formats parameters to print when training."""
print('Training parameters: ')
self.p.cuda = self.use_cuda
param_dict = vars(self.p)
pretty = lambda x: x.replace('_', ' ').capitalize()
print('\n'.join(' {} = {}'.format(pretty(k), str(v)) for k, v in param_dict.items()))
print()
def save_model(self, epoch, stats, first=False):
"""Saves model to files; can be overwritten at every epoch to save disk space."""
# Create directory for model checkpoints, if nonexistent
if first:
if self.p.clean_targets:
ckpt_dir_name = f'{datetime.now():{self.p.noise_type}-clean-%H%M}'
else:
ckpt_dir_name = f'{datetime.now():{self.p.noise_type}-%H%M}'
if self.p.ckpt_overwrite:
if self.p.clean_targets:
ckpt_dir_name = f'{self.p.noise_type}-clean'
else:
ckpt_dir_name = self.p.noise_type
self.ckpt_dir = os.path.join(self.p.ckpt_save_path, ckpt_dir_name)
if not os.path.isdir(self.p.ckpt_save_path):
os.mkdir(self.p.ckpt_save_path)
if not os.path.isdir(self.ckpt_dir):
os.mkdir(self.ckpt_dir)
# Save checkpoint dictionary
if self.p.ckpt_overwrite:
fname_unet = '{}/n2n-{}.pt'.format(self.ckpt_dir, self.p.noise_type)
else:
valid_loss = stats['valid_loss'][epoch]
fname_unet = '{}/n2n-epoch{}-{:>1.5f}.pt'.format(self.ckpt_dir, epoch + 1, valid_loss)
print('Saving checkpoint to: {}\n'.format(fname_unet))
torch.save(self.model.state_dict(), fname_unet)
# Save stats to JSON
fname_dict = '{}/n2n-stats.json'.format(self.ckpt_dir)
with open(fname_dict, 'w') as fp:
json.dump(stats, fp, indent=2)
def load_model(self, ckpt_fname):
"""Loads model from checkpoint file."""
print('Loading checkpoint from: {}'.format(ckpt_fname))
if self.use_cuda:
self.model.load_state_dict(torch.load(ckpt_fname))
else:
self.model.load_state_dict(torch.load(ckpt_fname, map_location='cpu'))
def _on_epoch_end(self, stats, train_loss, epoch, epoch_start, valid_loader):
"""Tracks and saves starts after each epoch."""
# Evaluate model on validation set
print('\rTesting model on validation set... ', end='')
epoch_time = time_elapsed_since(epoch_start)[0]
valid_loss, valid_time, valid_psnr = self.eval(valid_loader)
show_on_epoch_end(epoch_time, valid_time, valid_loss, valid_psnr)
# Decrease learning rate if plateau
self.scheduler.step(valid_loss)
# Save checkpoint
stats['train_loss'].append(train_loss)
stats['valid_loss'].append(valid_loss)
stats['valid_psnr'].append(valid_psnr)
self.save_model(epoch, stats, epoch == 0)
def test(self, test_loader, show=1):
"""Evaluates denoiser on test set."""
self.model.train(False)
source_imgs = []
denoised_imgs = []
clean_imgs = []
# Create directory for denoised images
denoised_dir = os.path.dirname(self.p.data)
save_path = os.path.join(denoised_dir, 'denoised')
if not os.path.isdir(save_path):
os.mkdir(save_path)
for batch_idx, (source, target) in enumerate(test_loader):
# Only do first <show> images
if show == 0 or batch_idx >= show:
break
source_imgs.append(source)
clean_imgs.append(target)
if self.use_cuda:
source = source.cuda()
# Denoise
denoised_img = self.model(source).detach()
denoised_imgs.append(denoised_img)
# Squeeze tensors
source_imgs = [t.squeeze(0) for t in source_imgs]
denoised_imgs = [t.squeeze(0) for t in denoised_imgs]
clean_imgs = [t.squeeze(0) for t in clean_imgs]
# Create montage and save images
print('Saving images and montages to: {}'.format(save_path))
for i in range(len(source_imgs)):
img_name = test_loader.dataset.imgs[i]
create_montage(img_name, self.p.noise_type, save_path, source_imgs[i], denoised_imgs[i], clean_imgs[i], show)
def eval(self, valid_loader):
"""Evaluates denoiser on validation set."""
self.model.train(False)
valid_start = datetime.now()
loss_meter = AvgMeter()
psnr_meter = AvgMeter()
for batch_idx, (source, target) in enumerate(valid_loader):
if self.use_cuda:
source = source.cuda()
target = target.cuda()
# Denoise
source_denoised = self.model(source)
# Update loss
loss = self.loss(source_denoised, target)
loss_meter.update(loss.item())
# Compute PSRN
if self.is_mc:
source_denoised = reinhard_tonemap(source_denoised)
# TODO: Find a way to offload to GPU, and deal with uneven batch sizes
for i in range(self.p.batch_size):
source_denoised = source_denoised.cpu()
target = target.cpu()
psnr_meter.update(psnr(source_denoised[i], target[i]).item())
valid_loss = loss_meter.avg
valid_time = time_elapsed_since(valid_start)[0]
psnr_avg = psnr_meter.avg
return valid_loss, valid_time, psnr_avg
def train(self, train_loader, valid_loader):
"""Trains denoiser on training set."""
self.model.train(True)
self._print_params()
num_batches = len(train_loader)
assert num_batches % self.p.report_interval == 0, 'Report interval must divide total number of batches'
# Dictionaries of tracked stats
stats = {'noise_type': self.p.noise_type,
'noise_param': self.p.noise_param,
'train_loss': [],
'valid_loss': [],
'valid_psnr': []}
# Main training loop
train_start = datetime.now()
for epoch in range(self.p.nb_epochs):
print('EPOCH {:d} / {:d}'.format(epoch + 1, self.p.nb_epochs))
# Some stats trackers
epoch_start = datetime.now()
train_loss_meter = AvgMeter()
loss_meter = AvgMeter()
time_meter = AvgMeter()
# Minibatch SGD
for batch_idx, (source, target) in enumerate(train_loader):
batch_start = datetime.now()
progress_bar(batch_idx, num_batches, self.p.report_interval, loss_meter.val)
if self.use_cuda:
source = source.cuda()
target = target.cuda()
# Denoise image
source_denoised = self.model(source)
loss = self.loss(source_denoised, target)
loss_meter.update(loss.item())
# Zero gradients, perform a backward pass, and update the weights
self.optim.zero_grad()
loss.backward()
self.optim.step()
# Report/update statistics
time_meter.update(time_elapsed_since(batch_start)[1])
if (batch_idx + 1) % self.p.report_interval == 0 and batch_idx:
show_on_report(batch_idx, num_batches, loss_meter.avg, time_meter.avg)
train_loss_meter.update(loss_meter.avg)
loss_meter.reset()
time_meter.reset()
# Epoch end, save and reset tracker
self._on_epoch_end(stats, train_loss_meter.avg, epoch, epoch_start, valid_loader)
train_loss_meter.reset()
train_elapsed = time_elapsed_since(train_start)[0]
print('Training done! Total elapsed time: {}\n'.format(train_elapsed))
def train(args):
'''
-------------------------Hyperparameters--------------------------
'''
EPOCHS = args.epochs
START = 0 # could enter a checkpoint start epoch
ITER = args.iterations # per epoch
LR = args.lr
MOM = args.momentum
# LOGInterval = args.log_interval
BATCHSIZE = args.batch_size
TEST_BATCHSIZE = args.test_batch_size
NUMBER_OF_WORKERS = args.workers
DATA_FOLDER = args.data
TESTSET_FOLDER = args.testset
ROOT = args.run
WEIGHT_DIR = os.path.join(ROOT, "weights")
CUSTOM_LOG_DIR = os.path.join(ROOT, "additionalLOGS")
CHECKPOINT = os.path.join(WEIGHT_DIR,
str(args.model) + str(args.name) + ".pt")
useTensorboard = args.tb
# check existance of data
if not os.path.isdir(DATA_FOLDER):
print("data folder not existant or in wrong layout.\n\t", DATA_FOLDER)
exit(0)
# check existance of testset
if TESTSET_FOLDER is not None and not os.path.isdir(TESTSET_FOLDER):
print("testset folder not existant or in wrong layout.\n\t",
DATA_FOLDER)
exit(0)
'''
---------------------------preparations---------------------------
'''
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
print("using device: ", str(device))
# loading the validation samples to make online evaluations
path_to_valX = args.valX
path_to_valY = args.valY
valX = None
valY = None
if path_to_valX is not None and path_to_valY is not None \
and os.path.exists(path_to_valX) and os.path.exists(path_to_valY) \
and os.path.isfile(path_to_valX) and os.path.isfile(path_to_valY):
with torch.no_grad():
valX, valY = torch.load(path_to_valX, map_location='cpu'), \
torch.load(path_to_valY, map_location='cpu')
'''
---------------------------loading dataset and normalizing---------------------------
'''
# Dataloader Parameters
train_params = {
'batch_size': BATCHSIZE,
'shuffle': True,
'num_workers': NUMBER_OF_WORKERS
}
test_params = {
'batch_size': TEST_BATCHSIZE,
'shuffle': False,
'num_workers': NUMBER_OF_WORKERS
}
# create a folder for the weights and custom logs
if not os.path.isdir(WEIGHT_DIR):
os.makedirs(WEIGHT_DIR)
if not os.path.isdir(CUSTOM_LOG_DIR):
os.makedirs(CUSTOM_LOG_DIR)
labelsNorm = None
# NORMLABEL
# normalizing on a trainingset wide mean and std
mean = None
std = None
if args.norm:
print('computing mean and std over trainingset')
# computes mean and std over all ground truths in dataset to tackle the problem of numerical insignificance
mean, std = computeMeanStdOverDataset('CONRADataset', DATA_FOLDER,
train_params, device)
print('\niodine (mean/std): {}\t{}'.format(mean[0], std[0]))
print('water (mean/std): {}\t{}\n'.format(mean[1], std[1]))
labelsNorm = transforms.Normalize(mean=[0, 0], std=std)
m2, s2 = computeMeanStdOverDataset('CONRADataset',
DATA_FOLDER,
train_params,
device,
transform=labelsNorm)
print("new mean and std are:")
print('\nnew iodine (mean/std): {}\t{}'.format(m2[0], s2[0]))
print('new water (mean/std): {}\t{}\n'.format(m2[1], s2[1]))
traindata = CONRADataset(DATA_FOLDER,
True,
device=device,
precompute=True,
transform=labelsNorm)
testdata = None
if TESTSET_FOLDER is not None:
testdata = CONRADataset(TESTSET_FOLDER,
False,
device=device,
precompute=True,
transform=labelsNorm)
else:
testdata = CONRADataset(DATA_FOLDER,
False,
device=device,
precompute=True,
transform=labelsNorm)
trainingset = DataLoader(traindata, **train_params)
testset = DataLoader(testdata, **test_params)
'''
----------------loading model and checkpoints---------------------
'''
if args.model == "unet":
m = UNet(2, 2).to(device)
print(
"using the U-Net architecture with {} trainable params; Good Luck!"
.format(count_trainables(m)))
else:
m = simpleConvNet(2, 2).to(device)
o = optim.SGD(m.parameters(), lr=LR, momentum=MOM)
loss_fn = nn.MSELoss()
test_loss = None
train_loss = None
if len(os.listdir(WEIGHT_DIR)) != 0:
checkpoints = os.listdir(WEIGHT_DIR)
checkDir = {}
latestCheckpoint = 0
for i, checkpoint in enumerate(checkpoints):
stepOfCheckpoint = int(
checkpoint.split(str(args.model) +
str(args.name))[-1].split('.pt')[0])
checkDir[stepOfCheckpoint] = checkpoint
latestCheckpoint = max(latestCheckpoint, stepOfCheckpoint)
print("[{}] {}".format(stepOfCheckpoint, checkpoint))
# if on development machine, prompt for input, else just take the most recent one
if 'faui' in os.uname()[1]:
toUse = int(input("select checkpoint to use: "))
else:
toUse = latestCheckpoint
checkpoint = torch.load(os.path.join(WEIGHT_DIR, checkDir[toUse]))
m.load_state_dict(checkpoint['model_state_dict'])
m.to(device) # pushing weights to gpu
o.load_state_dict(checkpoint['optimizer_state_dict'])
train_loss = checkpoint['train_loss']
test_loss = checkpoint['test_loss']
START = checkpoint['epoch']
print("using checkpoint {}:\n\tloss(train/test): {}/{}".format(
toUse, train_loss, test_loss))
else:
print("starting from scratch")
'''
-----------------------------training-----------------------------
'''
global_step = 0
# calculating initial loss
if test_loss is None or train_loss is None:
print("calculating initial loss")
m.eval()
print("testset...")
test_loss = calculate_loss(set=testset,
loss_fn=loss_fn,
length_set=len(testdata),
dev=device,
model=m)
print("trainset...")
train_loss = calculate_loss(set=trainingset,
loss_fn=loss_fn,
length_set=len(traindata),
dev=device,
model=m)
## SSIM and R value
R = []
SSIM = []
performanceFLE = os.path.join(CUSTOM_LOG_DIR, "performance.csv")
with open(performanceFLE, 'w+') as f:
f.write(
"step, SSIMiodine, SSIMwater, Riodine, Rwater, train_loss, test_loss\n"
)
print("computing ssim and r coefficents to: {}".format(performanceFLE))
# printing runtime information
print(
"starting training at {} for {} epochs {} iterations each\n\t{} total".
format(START, EPOCHS, ITER, EPOCHS * ITER))
print("\tbatchsize: {}\n\tloss: {}\n\twill save results to \"{}\"".format(
BATCHSIZE, train_loss, CHECKPOINT))
print(
"\tmodel: {}\n\tlearningrate: {}\n\tmomentum: {}\n\tnorming output space: {}"
.format(args.model, LR, MOM, args.norm))
#start actual training loops
for e in range(START, START + EPOCHS):
# iterations will not be interupted with validation and metrics
for i in range(ITER):
global_step = (e * ITER) + i
# training
m.train()
iteration_loss = 0
for x, y in tqdm(trainingset):
x, y = x.to(device=device,
dtype=torch.float), y.to(device=device,
dtype=torch.float)
pred = m(x)
loss = loss_fn(pred, y)
iteration_loss += loss.item()
o.zero_grad()
loss.backward()
o.step()
print("\niteration {}: --accumulated loss {}".format(
global_step, iteration_loss))
# validation, saving and logging
print("\nvalidating")
m.eval() # disable dropout batchnorm etc
print("testset...")
test_loss = calculate_loss(set=testset,
loss_fn=loss_fn,
length_set=len(testdata),
dev=device,
model=m)
print("trainset...")
train_loss = calculate_loss(set=trainingset,
loss_fn=loss_fn,
length_set=len(traindata),
dev=device,
model=m)
print("calculating SSIM and R coefficients")
currSSIM, currR = performance(set=testset,
dev=device,
model=m,
bs=TEST_BATCHSIZE)
print("SSIM (iod/water): {}/{}\nR (iod/water): {}/{}".format(
currSSIM[0], currSSIM[1], currR[0], currR[1]))
with open(performanceFLE, 'a') as f:
newCSVline = "{}, {}, {}, {}, {}, {}, {}\n".format(
global_step, currSSIM[0], currSSIM[1], currR[0], currR[1],
train_loss, test_loss)
f.write(newCSVline)
print("wrote new line to csv:\n\t{}".format(newCSVline))
'''
if valX and valY were set in preparations, use them to perform analytics.
if not, use the first sample from the testset to perform analytics
'''
with torch.no_grad():
truth, pred = None, None
IMAGE_LOG_DIR = os.path.join(CUSTOM_LOG_DIR, str(global_step))
if not os.path.isdir(IMAGE_LOG_DIR):
os.makedirs(IMAGE_LOG_DIR)
if valX is not None and valY is not None:
batched = np.zeros((BATCHSIZE, *valX.numpy().shape))
batched[0] = valX.numpy()
batched = torch.from_numpy(batched).to(device=device,
dtype=torch.float)
pred = m(batched)
pred = pred.cpu().numpy()[0]
truth = valY.numpy() # still on cpu
assert pred.shape == truth.shape
else:
for x, y in testset:
# x, y in shape[2,2,480,620] [b,c,h,w]
x, y = x.to(device=device,
dtype=torch.float), y.to(device=device,
dtype=torch.float)
pred = m(x)
pred = pred.cpu().numpy()[
0] # taking only the first sample of batch
truth = y.cpu().numpy()[
0] # first projection for evaluation
advanvedMetrics(truth, pred, mean, std, global_step, args.norm,
IMAGE_LOG_DIR)
print("logging")
CHECKPOINT = os.path.join(
WEIGHT_DIR,
str(args.model) + str(args.name) + str(global_step) + ".pt")
torch.save(
{
'epoch': e + 1, # end of this epoch; so resume at next.
'model_state_dict': m.state_dict(),
'optimizer_state_dict': o.state_dict(),
'train_loss': train_loss,
'test_loss': test_loss
},
CHECKPOINT)
print('\tsaved weigths to: ', CHECKPOINT)
if logger is not None and train_loss is not None:
logger.add_scalar('test_loss', test_loss, global_step=global_step)
logger.add_scalar('train_loss',
train_loss,
global_step=global_step)
logger.add_image("iodine-prediction",
pred[0].reshape(1, 480, 620),
global_step=global_step)
logger.add_image("water-prediction",
pred[1].reshape(1, 480, 620),
global_step=global_step)
# logger.add_image("water-prediction", wat)
print(
"\ttensorboard updated with test/train loss and a sample image"
)
elif train_loss is not None:
print("\tloss of global-step {}: {}".format(
global_step, train_loss))
elif not useTensorboard:
print("\t(tb-logging disabled) test/train loss: {}/{} ".format(
test_loss, train_loss))
else:
print("\tno loss accumulated yet")
# saving final results
print("saving upon exit")
torch.save(
{
'epoch': EPOCHS,
'model_state_dict': m.state_dict(),
'optimizer_state_dict': o.state_dict(),
'train_loss': train_loss,
'test_loss': test_loss
}, CHECKPOINT)
print('\tsaved progress to: ', CHECKPOINT)
if logger is not None and train_loss is not None:
logger.add_scalar('test_loss', test_loss, global_step=global_step)
logger.add_scalar('train_loss', train_loss, global_step=global_step)
torch.backends.cudnn.benchmark = True
train_dataset = Seg_dataset(cfg)
train_loader = data.DataLoader(train_dataset,
batch_size=cfg.bs,
shuffle=True,
num_workers=8,
pin_memory=True,
drop_last=False)
if cfg.model == 'unet':
model = UNet(input_channels=3).cuda()
model.apply(model.weights_init_normal)
else:
model = DLASeg(cfg).cuda()
model.train()
if cfg.resume:
resume_epoch = int(cfg.resume.split('.')[0].split('_')[1]) + 1
model.load_state_dict(torch.load('weights/' + cfg.resume), strict=True)
print(f'Resume training with \'{cfg.resume}\'.')
else:
resume_epoch = 0
print('Training with ImageNet pre-trained weights.')
criterion = nn.CrossEntropyLoss(ignore_index=255).cuda()
if cfg.optim == 'sgd':
optimizer = torch.optim.SGD(model.optim_parameters(),
cfg.lr,
cfg.momentum,
weight_decay=cfg.decay)
class Trainer:
def __init__(self, seq_length, color_channels, unet_path="pretrained/unet.mdl",
discrim_path="pretrained/dicrim.mdl",
facenet_path="pretrained/facenet.mdl",
vgg_path="",
embedding_size=1000,
unet_depth=3,
unet_filts=32,
facenet_filts=32,
resnet=18):
self.color_channels = color_channels
self.margin = 0.5
self.writer = SummaryWriter(log_dir="logs")
self.unet_path = unet_path
self.discrim_path = discrim_path
self.facenet_path = facenet_path
self.unet = UNet(in_channels=color_channels, out_channels=color_channels,
depth=unet_depth,
start_filts=unet_filts,
up_mode="upsample",
merge_mode='concat').to(device)
self.discrim = FaceNetModel(embedding_size=embedding_size, start_filts=facenet_filts,
in_channels=color_channels, resnet=resnet,
pretrained=False).to(device)
self.facenet = FaceNetModel(embedding_size=embedding_size, start_filts=facenet_filts,
in_channels=color_channels, resnet=resnet,
pretrained=False).to(device)
if os.path.isfile(unet_path):
self.unet.load_state_dict(torch.load(unet_path))
print("unet loaded")
if os.path.isfile(discrim_path):
self.discrim.load_state_dict(torch.load(discrim_path))
print("discrim loaded")
if os.path.isfile(facenet_path):
self.facenet.load_state_dict(torch.load(facenet_path))
print("facenet loaded")
if os.path.isfile(vgg_path):
self.vgg_loss_network = LossNetwork(vgg_face_dag(vgg_path)).to(device)
self.vgg_loss_network.eval()
print("vgg loaded")
self.mse_loss_function = nn.MSELoss().to(device)
self.discrim_loss_function = nn.BCELoss().to(device)
self.triplet_loss_function = TripletLoss(margin=self.margin)
self.unet_optimizer = torch.optim.Adam(self.unet.parameters(), betas=(0.9, 0.999))
self.discrim_optimizer = torch.optim.Adam(self.discrim.parameters(), betas=(0.9, 0.999))
self.facenet_optimizer = torch.optim.Adam(self.facenet.parameters(), betas=(0.9, 0.999))
def test(self, test_loader, epoch=0):
X, y = next(iter(test_loader))
B, D, C, W, H = X.shape
# X = X.view(B, C * D, W, H)
self.unet.eval()
self.facenet.eval()
self.discrim.eval()
with torch.no_grad():
y_ = self.unet(X.to(device))
mse = self.mse_loss_function(y_, y.to(device))
loss_G = self.loss_GAN_generator(btch_X=X.to(device))
loss_D = self.loss_GAN_discrimator(btch_X=X.to(device), btch_y=y.to(device))
loss_facenet, _, n_bad = self.loss_facenet(X.to(device), y.to(device))
plt.title(f"epoch {epoch} mse={mse.item():.4} facenet={loss_facenet.item():.4} bad={n_bad / B ** 2}")
i = np.random.randint(0, B)
a = np.hstack((y[i].transpose(0, 1).transpose(1, 2), y_[i].transpose(0, 1).transpose(1, 2).to(cpu)))
b = np.hstack((X[i][0].transpose(0, 1).transpose(1, 2),
X[i][-1].transpose(0, 1).transpose(1, 2)))
plt.imshow(np.vstack((a, b)))
plt.axis('off')
plt.show()
self.writer.add_scalar("test bad_percent", n_bad / B ** 2, global_step=epoch)
self.writer.add_scalar("test loss", mse.item(), global_step=epoch)
# self.writer.add_scalars("test GAN", {"discrim": loss_D.item(),
# "gen": loss_G.item()}, global_step=epoch)
with torch.no_grad():
n_for_show = 10
y_show_ = y_.to(device)
y_show = y.to(device)
embeddings_anc, _ = self.facenet(y_show_)
embeddings_pos, _ = self.facenet(y_show)
embeds = torch.cat((embeddings_anc[:n_for_show], embeddings_pos[:n_for_show]))
imgs = torch.cat((y_show_[:n_for_show], y_show[:n_for_show]))
names = list(range(n_for_show)) * 2
# print(embeds.shape, imgs.shape, len(names))
# self.writer.add_embedding(mat=embeds, metadata=names, label_img=imgs, tag="embeddings", global_step=epoch)
trshs, fprs, tprs = roc_curve(embeddings_anc.detach().to(cpu), embeddings_pos.detach().to(cpu))
rnk1 = rank1(embeddings_anc.detach().to(cpu), embeddings_pos.detach().to(cpu))
plt.step(fprs, tprs)
# plt.xlim((1e-4, 1))
plt.yticks(np.arange(0, 1, 0.05))
plt.xticks(np.arange(min(fprs), max(fprs), 10))
plt.xscale('log')
plt.title(f"ROC auc={auc(fprs, tprs)} rnk1={rnk1}")
self.writer.add_figure("ROC test", plt.gcf(), global_step=epoch)
self.writer.add_scalar("auc", auc(fprs, tprs), global_step=epoch)
self.writer.add_scalar("rank1", rnk1, global_step=epoch)
print(f"\n###### {epoch} TEST mse={mse.item():.4} GAN(G/D)={loss_G.item():.4}/{loss_D.item():.4} "
f"facenet={loss_facenet.item():.4} bad={n_bad / B ** 2:.4} auc={auc(fprs, tprs)} rank1={rnk1} #######")
def test_test(self, test_loader):
X, ys = next(iter(test_loader))
true_idx = 0
x = X[true_idx]
D, C, W, H = x.shape
# x = x.view(C * D, W, H)
dists = list()
with torch.no_grad():
y_ = self.unet(x.to(device))
embedding_anc, _ = self.facenet(y_)
embeddings_pos, _ = self.facenet(ys)
for emb_pos_item in embeddings_pos:
dist = l2_dist.forward(embedding_anc, emb_pos_item)
dists.append(dist)
a_sorted = np.argsort(dists)
a = np.hstack((ys[true_idx].transpose(0, 1).transpose(1, 2),
y_.transpose(0, 1).transpose(1, 2).to(cpu).numpy(),
ys[a_sorted[0]].transpose(0, 1).transpose(1, 2)))
b = np.hstack((x[0:3].transpose(0, 1).transpose(1, 2),
x[D // 2 * C:D // 2 * C + 3].transpose(0, 1).transpose(1, 2),
x[-3:].transpose(0, 1).transpose(1, 2)))
b_ = b - np.min(b)
b_ = b_ / np.max(b)
b_ = equalize_func([(b_ * 255).astype(np.uint8)], use_clahe=True)[0]
b = b_.astype(np.float32) / 255
plt.imshow(cv2.cvtColor(np.vstack((a, b)), cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.show()
def loss_facenet(self, X, y, is_detached=False):
B, D, C, W, H = X.shape
y_ = self.unet(X)
embeddings_anc, D_fake = self.facenet(y_ if not is_detached else y_.detach())
embeddings_pos, D_real = self.facenet(y)
target_real = torch.full_like(D_fake, 1)
loss_gen = self.discrim_loss_function(D_fake, target_real)
pos_dist = l2_dist.forward(embeddings_anc, embeddings_pos)
bad_triplets_loss = None
n_bad = 0
for shift in range(1, B):
embeddings_neg = torch.roll(embeddings_pos, shift, 0)
neg_dist = l2_dist.forward(embeddings_anc, embeddings_neg)
bad_triplets_idxs = np.where((neg_dist - pos_dist < self.margin).cpu().numpy().flatten())[0]
if shift == 1:
bad_triplets_loss = self.triplet_loss_function.forward(embeddings_anc[bad_triplets_idxs],
embeddings_pos[bad_triplets_idxs],
embeddings_neg[bad_triplets_idxs]).to(
device)
else:
bad_triplets_loss += self.triplet_loss_function.forward(embeddings_anc[bad_triplets_idxs],
embeddings_pos[bad_triplets_idxs],
embeddings_neg[bad_triplets_idxs]).to(device)
n_bad += len(bad_triplets_idxs)
bad_triplets_loss /= B
return bad_triplets_loss, torch.mean(loss_gen), n_bad
# def loss_mse(self, btch_X, btch_y):
# btch_y_ = self.unet(btch_X)
# loss_unet = self.mse_loss_function(btch_y_, btch_y)
#
# features_target = self.facenet.forward_mse(btch_y)
# features = self.facenet.forward_mse(btch_y_)
#
# loss_first_layer = self.mse_loss_function(features, features_target)
# return loss_unet + loss_first_layer
def loss_mse_vgg(self, btch_X, btch_y, k_mse, k_vgg):
btch_y_ = self.unet(btch_X)
# print(btch_y_.shape,btch_y.shape)
perceptual_btch_y_ = self.vgg_loss_network(btch_y_)
perceptual_btch_y = self.vgg_loss_network(btch_y)
perceptual_loss = 0.0
for a, b in zip(perceptual_btch_y_, perceptual_btch_y):
perceptual_loss += self.mse_loss_function(a, b)
return k_vgg * perceptual_loss + k_mse * self.mse_loss_function(btch_y_, btch_y)
def loss_GAN_discrimator(self, btch_X, btch_y):
btch_y_ = self.unet(btch_X)
_, y_D_fake_ = self.discrim(btch_y_.detach())
_, y_D_real_ = self.discrim(btch_y)
target_fake = torch.full_like(y_D_fake_, 0)
target_real = torch.full_like(y_D_real_, 1)
loss_D_fake_ = self.discrim_loss_function(y_D_fake_, target_fake)
loss_D_real_ = self.discrim_loss_function(y_D_real_, target_real)
loss_discrim = (loss_D_real_ + loss_D_fake_)
return loss_discrim
def loss_GAN_generator(self, btch_X):
btch_y_ = self.unet(btch_X)
_, y_D_fake_ = self.discrim(btch_y_)
target_real = torch.full_like(y_D_fake_, 1)
loss_gen = self.discrim_loss_function(y_D_fake_, target_real)
return loss_gen
def relax_discriminator(self, btch_X, btch_y):
self.discrim.zero_grad()
# train with real
y_discrim_real_ = self.discrim(btch_y)
y_discrim_real_ = y_discrim_real_.mean()
y_discrim_real_.backward(self.mone)
# train with fake
btch_y_ = self.unet(btch_X)
y_discrim_fake_detached_ = self.discrim(btch_y_.detach())
y_discrim_fake_detached_ = y_discrim_fake_detached_.mean()
y_discrim_fake_detached_.backward(self.one)
# gradient_penalty
gradient_penalty = self.discrim_gradient_penalty(btch_y, btch_y_)
gradient_penalty.backward()
self.discrim_optimizer.step()
def relax_generator(self, btch_X):
self.unet.zero_grad()
btch_y_ = self.unet(btch_X)
y_discrim_fake_ = self.discrim(btch_y_)
y_discrim_fake_ = y_discrim_fake_.mean()
y_discrim_fake_.backward(self.mone)
self.unet_optimizer.step()
def discrim_gradient_penalty(self, real_y, fake_y):
lambd = 10
btch_size = real_y.shape[0]
alpha = torch.rand(btch_size, 1, 1, 1).to(device)
# print(alpha.shape, real_y.shape)
alpha = alpha.expand_as(real_y)
interpolates = alpha * real_y + (1 - alpha) * fake_y
interpolates = interpolates.to(device)
interpolates = autograd.Variable(interpolates, requires_grad=True)
interpolates_out = self.discrim(interpolates)
gradients = autograd.grad(outputs=interpolates_out, inputs=interpolates,
grad_outputs=torch.ones(interpolates_out.size()).to(device),
create_graph=True, retain_graph=True, only_inputs=True)[0]
gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * lambd
return gradient_penalty
def train(self, train_loader, test_loader, batch_size=2, epochs=30,
k_gen=1, k_discrim=1, k_mse=1, k_facenet=1, k_facenet_back=1, k_vgg=1):
"""
:param X: np.array shape=(n_videos, n_frames, h, w)
:param y: np.array shape=(n_videos, h, w)
:param epochs: int
"""
print("\nSTART TRAINING\n")
for epoch in range(epochs):
self.test(test_loader, epoch)
self.unet.train()
self.facenet.train()
self.discrim.train()
# train by batches
for idx, (btch_X, btch_y) in enumerate(train_loader):
B, D, C, W, H = btch_X.shape
# btch_X = btch_X.view(B, C * D, W, H)
btch_X = btch_X.to(device)
btch_y = btch_y.to(device)
# Mse loss
self.unet.zero_grad()
mse = self.loss_mse_vgg(btch_X, btch_y, k_mse, k_vgg)
mse.backward()
self.unet_optimizer.step()
# facenet_backup = deepcopy(self.facenet.state_dict())
# for i in range(unrolled_iterations):
self.discrim.zero_grad()
loss_D = self.loss_GAN_discrimator(btch_X, btch_y)
loss_D = k_discrim * loss_D
loss_D.backward()
self.discrim_optimizer.step()
self.discrim.zero_grad()
self.unet.zero_grad()
loss_G = self.loss_GAN_generator(btch_X)
loss_G = k_gen * loss_G
loss_G.backward()
self.unet_optimizer.step()
# Facenet
self.unet.zero_grad()
self.facenet.zero_grad()
facenet_loss, _, n_bad = self.loss_facenet(btch_X, btch_y)
facenet_loss = k_facenet * facenet_loss
facenet_loss.backward()
self.facenet_optimizer.step()
self.unet.zero_grad()
self.facenet.zero_grad()
facenet_back_loss, _, n_bad = self.loss_facenet(btch_X, btch_y)
facenet_back_loss = k_facenet_back * facenet_back_loss
facenet_back_loss.backward()
self.unet_optimizer.step()
print(f"btch {idx * batch_size} mse={mse.item():.4} GAN(G/D)={loss_G.item():.4}/{loss_D.item():.4} "
f"facenet={facenet_loss.item():.4} bad={n_bad / B ** 2:.4}")
global_step = epoch * len(train_loader.dataset) // batch_size + idx
self.writer.add_scalar("train bad_percent", n_bad / B ** 2, global_step=global_step)
self.writer.add_scalar("train loss", mse.item(), global_step=global_step)
# self.writer.add_scalars("train GAN", {"discrim": loss_D.item(),
# "gen": loss_G.item()}, global_step=global_step)
torch.save(self.unet.state_dict(), self.unet_path)
torch.save(self.discrim.state_dict(), self.discrim_path)
torch.save(self.facenet.state_dict(), self.facenet_path)
train_loss_seg_a = []
train_loss_seg_b = []
train_dice = []
val_loss_a = []
val_dice_a = []
val_loss_b = []
val_dice_b = []
for e in range(epochs):
epoch_train_loss_rec = []
epoch_train_loss_seg = []
dice_scores = []
net.train()
pseudo.train()
print('Epoch ', e)
for i, data in enumerate(tqdm.tqdm(train_loader)):
iteration += batch_size
optimiser_ps.zero_grad()
optimiser_net.zero_grad()
# either train pseudolabeller or the net
# first 10 epochs train the pseudo labeller on edges
if e < epochs_pseudo:
edges_a = data['A'][2].cuda()
target_a = data['A'][1].cuda()
with open(path, mode=mode) as f:
f.writelines(context + "\n")
cuda0 = torch.device('cuda:0')
net = UNet(27).to(cuda0)
criterion = nn.MSELoss().to(cuda0)
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
train_data_laoder, test_data_loader = get_dataloader(batch_size=4)
write_log("weight/train.log", str(datetime.datetime.now()), "w")
write_log("weight/train.log", "train start")
print(net)
for epoch in range(300):
# train phase
running_loss = 0.0
net.train()
for i, batch in enumerate(train_data_laoder):
inputs = batch['image'].to(cuda0)
target = batch['target'].to(cuda0)
optimizer.zero_grad()
outputs = net(inputs)
batch_size = outputs.size(0)
outputs = outputs.reshape((batch_size, -1))
target = target.reshape((batch_size, -1))
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
running_loss += loss.item()
def train(model_name=''):
# Init data
train_dataset, val_dataset = prepare_datasets()
train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=10, shuffle=True)
loaders = dict(train=train_loader, val=val_loader)
# Init Model
if model_name == '':
model = UNet().cuda()
else:
model = data_utils.load_model(model_name).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, amsgrad=True)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer,
gamma=0.984)
loss_fn = nn.BCELoss()
epochs = 500
epoch_losses = dict(train=[], val=[])
for epoch in range(epochs):
for phase in 'train val'.split():
if phase == 'train':
model = model.train()
torch.set_grad_enabled(True)
else:
model = model.eval()
torch.set_grad_enabled(False)
loader = loaders[phase]
running_loss = []
for batch in loader:
imgs, masks = batch
imgs = imgs.cuda()
masks = masks.cuda()
outputs = model(imgs)
loss = loss_fn(outputs, masks)
running_loss.append(loss.item())
if phase == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
# End of Epoch
print(f'{epoch}) {phase} loss: {np.mean(running_loss)}')
visualize_results(loader, model, epoch, phase)
if epoch % 10 == 0:
results_dir = 'weight/'
if not os.path.isdir(results_dir):
os.makedirs(results_dir)
data_utils.save_model(model, results_dir + f'model_{epoch}.pt')
epoch_losses[phase].append(np.mean(running_loss))
if phase == 'val':
df = pd.DataFrame(data=epoch_losses)
df.to_csv('loss.csv')
tensorboard(epoch_losses[phase], phase)
if phase == 'train':
scheduler.step()
adversarial_loss = Adversarial_Loss().cuda()
discriminate_loss = Discriminate_Loss().cuda()
gradient_loss = Gradient_Loss(3).cuda()
flow_loss = Flow_Loss().cuda()
intensity_loss = Intensity_Loss().cuda()
train_dataset = Dataset.train_dataset(train_cfg)
# Remember to set drop_last=True, because we need to use 4 frames to predict one frame.
train_dataloader = DataLoader(dataset=train_dataset, batch_size=train_cfg.batch_size,
shuffle=True, num_workers=4, drop_last=True)
writer = SummaryWriter(f'tensorboard_log/{train_cfg.dataset}_bs{train_cfg.batch_size}')
start_iter = int(train_cfg.resume.split('_')[-1].split('.')[0]) if train_cfg.resume else 0
training = True
generator = generator.train()
discriminator = discriminator.train()
try:
step = start_iter
while training:
for indice, clips, flow_strs in train_dataloader:
input_frames = clips[:, 0:12, :, :].cuda() # (n, 12, 256, 256)
target_frame = clips[:, 12:15, :, :].cuda() # (n, 3, 256, 256)
input_last = input_frames[:, 9:12, :, :].cuda() # use for flow_loss
# pop() the used frame index, this can't work in train_dataset.__getitem__ because of multiprocessing.
for index in indice:
train_dataset.all_seqs[index].pop()
if len(train_dataset.all_seqs[index]) == 0:
train_dataset.all_seqs[index] = list(range(len(train_dataset.videos[index]) - 4))
def main():
"""Create the model and start the training."""
args = get_arguments()
cudnn.enabled = True
n_discriminators = 5
# create teacher & student
student_net = UNet(3, n_classes=args.num_classes)
teacher_net = UNet(3, n_classes=args.num_classes)
student_params = list(student_net.parameters())
# teacher doesn't need gradient as it's just a EMA of the student
teacher_params = list(teacher_net.parameters())
for param in teacher_params:
param.requires_grad = False
student_net.train()
student_net.cuda(args.gpu)
teacher_net.train()
teacher_net.cuda(args.gpu)
cudnn.benchmark = True
unsup_weights = [
args.unsup_weight5, args.unsup_weight6, args.unsup_weight7,
args.unsup_weight8, args.unsup_weight9
]
lambda_adv_tgts = [
args.lambda_adv_tgt5, args.lambda_adv_tgt6, args.lambda_adv_tgt7,
args.lambda_adv_tgt8, args.lambda_adv_tgt9
]
# create a list of discriminators
discriminators = []
for dis_idx in range(n_discriminators):
discriminators.append(FCDiscriminator(num_classes=args.num_classes))
discriminators[dis_idx].train()
discriminators[dis_idx].cuda(args.gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
max_iters = args.num_steps * args.iter_size * args.batch_size
src_set = REFUGE(True,
domain='REFUGE_SRC',
is_transform=True,
augmentations=aug_student,
aug_for_target=aug_teacher,
max_iters=max_iters)
src_loader = data.DataLoader(src_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
src_loader_iter = enumerate(src_loader)
tgt_set = REFUGE(True,
domain='REFUGE_DST',
is_transform=True,
augmentations=aug_student,
aug_for_target=aug_teacher,
max_iters=max_iters)
tgt_loader = data.DataLoader(tgt_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
tgt_loader_iter = enumerate(tgt_loader)
student_optimizer = optim.SGD(student_params,
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
teacher_optimizer = optim_weight_ema.WeightEMA(teacher_params,
student_params,
alpha=args.teacher_alpha)
d_optimizers = []
for idx in range(n_discriminators):
optimizer = optim.Adam(discriminators[idx].parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
d_optimizers.append(optimizer)
calc_bce_loss = torch.nn.BCEWithLogitsLoss()
# labels for adversarial training
source_label, tgt_label = 0, 1
for i_iter in range(args.num_steps):
total_seg_loss = 0
seg_loss_vals = [0] * n_discriminators
adv_tgt_loss_vals = [0] * n_discriminators
d_loss_vals = [0] * n_discriminators
unsup_loss_vals = [0] * n_discriminators
for d_optimizer in d_optimizers:
d_optimizer.zero_grad()
adjust_learning_rate_D(d_optimizer, i_iter, args)
student_optimizer.zero_grad()
adjust_learning_rate(student_optimizer, i_iter, args)
for sub_i in range(args.iter_size):
# ******** Optimize source network with segmentation loss ********
# As we don't change the discriminators, their parameters are fixed
for discriminator in discriminators:
for param in discriminator.parameters():
param.requires_grad = False
_, src_batch = src_loader_iter.__next__()
_, _, src_images, src_labels, _ = src_batch
src_images = Variable(src_images).cuda(args.gpu)
# calculate the segmentation losses
sup_preds = list(student_net(src_images))
seg_losses, total_seg_loss = [], 0
for idx, sup_pred in enumerate(sup_preds):
sup_interp_pred = (sup_pred)
# you also can use dice loss like: dice_loss(src_labels, sup_interp_pred)
seg_loss = Weighted_Jaccard_loss(src_labels, sup_interp_pred,
args.class_weights, args.gpu)
seg_losses.append(seg_loss)
total_seg_loss += seg_loss * unsup_weights[idx]
seg_loss_vals[idx] += seg_loss.item() / args.iter_size
_, tgt_batch = tgt_loader_iter.__next__()
tgt_images0, tgt_lbl0, tgt_images1, tgt_lbl1, _ = tgt_batch
tgt_images0 = Variable(tgt_images0).cuda(args.gpu)
tgt_images1 = Variable(tgt_images1).cuda(args.gpu)
# calculate ensemble losses
stu_unsup_preds = list(student_net(tgt_images1))
tea_unsup_preds = teacher_net(tgt_images0)
total_mse_loss = 0
for idx in range(n_discriminators):
stu_unsup_probs = F.softmax(stu_unsup_preds[idx], dim=-1)
tea_unsup_probs = F.softmax(tea_unsup_preds[idx], dim=-1)
unsup_loss = calc_mse_loss(stu_unsup_probs, tea_unsup_probs,
args.batch_size)
unsup_loss_vals[idx] += unsup_loss.item() / args.iter_size
total_mse_loss += unsup_loss * unsup_weights[idx]
total_mse_loss = total_mse_loss / args.iter_size
# As the requires_grad is set to False in the discriminator, the
# gradients are only accumulated in the generator, the target
# student network is optimized to make the outputs of target domain
# images close to the outputs of source domain images
stu_unsup_preds = list(student_net(tgt_images0))
d_outs, total_adv_loss = [], 0
for idx in range(n_discriminators):
stu_unsup_interp_pred = (stu_unsup_preds[idx])
d_outs.append(discriminators[idx](stu_unsup_interp_pred))
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(source_label)
labels = Variable(labels).cuda(args.gpu)
adv_tgt_loss = calc_bce_loss(d_outs[idx], labels)
total_adv_loss += lambda_adv_tgts[idx] * adv_tgt_loss
adv_tgt_loss_vals[idx] += adv_tgt_loss.item() / args.iter_size
total_adv_loss = total_adv_loss / args.iter_size
# requires_grad is set to True in the discriminator, we only
# accumulate gradients in the discriminators, the discriminators are
# optimized to make true predictions
d_losses = []
for idx in range(n_discriminators):
discriminator = discriminators[idx]
for param in discriminator.parameters():
param.requires_grad = True
sup_preds[idx] = sup_preds[idx].detach()
d_outs[idx] = discriminators[idx](sup_preds[idx])
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(source_label)
labels = Variable(labels).cuda(args.gpu)
d_losses.append(calc_bce_loss(d_outs[idx], labels))
d_losses[idx] = d_losses[idx] / args.iter_size / 2
d_losses[idx].backward()
d_loss_vals[idx] += d_losses[idx].item()
for idx in range(n_discriminators):
stu_unsup_preds[idx] = stu_unsup_preds[idx].detach()
d_outs[idx] = discriminators[idx](stu_unsup_preds[idx])
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(tgt_label)
labels = Variable(labels).cuda(args.gpu)
d_losses[idx] = calc_bce_loss(d_outs[idx], labels)
d_losses[idx] = d_losses[idx] / args.iter_size / 2
d_losses[idx].backward()
d_loss_vals[idx] += d_losses[idx].item()
for d_optimizer in d_optimizers:
d_optimizer.step()
total_loss = total_seg_loss + total_adv_loss + total_mse_loss
total_loss.backward()
student_optimizer.step()
teacher_optimizer.step()
log_str = 'iter = {0:7d}/{1:7d}'.format(i_iter, args.num_steps)
log_str += ', total_seg_loss = {0:.3f} '.format(total_seg_loss)
templ = 'seg_losses = [' + ', '.join(['%.2f'] * len(seg_loss_vals))
log_str += templ % tuple(seg_loss_vals) + '] '
templ = 'ens_losses = [' + ', '.join(['%.5f'] * len(unsup_loss_vals))
log_str += templ % tuple(unsup_loss_vals) + '] '
templ = 'adv_losses = [' + ', '.join(['%.2f'] * len(adv_tgt_loss_vals))
log_str += templ % tuple(adv_tgt_loss_vals) + '] '
templ = 'd_losses = [' + ', '.join(['%.2f'] * len(d_loss_vals))
log_str += templ % tuple(d_loss_vals) + '] '
print(log_str)
if i_iter >= args.num_steps_stop - 1:
print('save model ...')
filename = 'UNet' + str(
args.num_steps_stop) + '_v18_weightedclass.pth'
torch.save(teacher_net.cpu().state_dict(),
os.path.join(args.snapshot_dir, filename))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
filename = 'UNet' + str(i_iter) + '_v18_weightedclass.pth'
torch.save(teacher_net.cpu().state_dict(),
os.path.join(args.snapshot_dir, filename))
teacher_net.cuda(args.gpu)
def evaluate_performance(args, gridargs, logger):
'''
-------------------------Hyperparameters--------------------------
'''
EPOCHS = args.epochs
ITER = args.iterations # per epoch
LR = gridargs['lr']
MOM = gridargs['mom']
# LOGInterval = args.log_interval
BATCHSIZE = args.batch_size
NUMBER_OF_WORKERS = args.workers
DATA_FOLDER = args.data
ROOT = gridargs['run']
CUSTOM_LOG_DIR = os.path.join(ROOT, "additionalLOGS")
# check existance of data
if not os.path.isdir(DATA_FOLDER):
print("data folder not existant or in wrong layout.\n\t", DATA_FOLDER)
exit(0)
'''
---------------------------preparations---------------------------
'''
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
print("using device: ", str(device))
'''
---------------------------loading dataset and normalizing---------------------------
'''
# Dataloader Parameters
train_params = {'batch_size': BATCHSIZE,
'shuffle': True,
'num_workers': NUMBER_OF_WORKERS}
test_params = {'batch_size': BATCHSIZE,
'shuffle': False,
'num_workers': NUMBER_OF_WORKERS}
# create a folder for the weights and custom logs
if not os.path.isdir(CUSTOM_LOG_DIR):
os.makedirs(CUSTOM_LOG_DIR)
traindata = CONRADataset(DATA_FOLDER,
True,
device=device,
precompute=True,
transform=None)
testdata = CONRADataset(DATA_FOLDER,
False,
device=device,
precompute=True,
transform=None)
trainingset = DataLoader(traindata, **train_params)
testset = DataLoader(testdata, **test_params)
if args.model == "unet":
m = UNet(2, 2).to(device)
else:
m = simpleConvNet(2, 2).to(device)
o = optim.SGD(m.parameters(),
lr=LR,
momentum=MOM)
loss_fn = nn.MSELoss()
test_loss = None
train_loss = None
'''
-----------------------------training-----------------------------
'''
global_step = 0
# calculating initial loss
if test_loss is None or train_loss is None:
print("calculating initial loss")
m.eval()
print("testset...")
test_loss = calculate_loss(set=testset, loss_fn=loss_fn, length_set=len(testdata), dev=device, model=m)
print("trainset...")
train_loss = calculate_loss(set=trainingset, loss_fn=loss_fn, length_set=len(traindata), dev=device, model=m)
# printing runtime information
print("starting training at {} for {} epochs {} iterations each\n\t{} total".format(0, EPOCHS, ITER, EPOCHS * ITER))
print("\tbatchsize: {}\n\tloss: {}\n".format(BATCHSIZE, train_loss))
print("\tmodel: {}\n\tlearningrate: {}\n\tmomentum: {}\n\tnorming output space: {}".format(args.model, LR, MOM, False))
#start actual training loops
for e in range(0, EPOCHS):
# iterations will not be interupted with validation and metrics
for i in range(ITER):
global_step = (e * ITER) + i
# training
m.train()
iteration_loss = 0
for x, y in tqdm(trainingset):
x, y = x.to(device=device, dtype=torch.float), y.to(device=device, dtype=torch.float)
pred = m(x)
loss = loss_fn(pred, y)
iteration_loss += loss.item()
o.zero_grad()
loss.backward()
o.step()
print("\niteration {}: --accumulated loss {}".format(global_step, iteration_loss))
if not np.isfinite(iteration_loss):
print("EXPLODING OR VANISHING GRADIENT at lr: {} mom: {} step: {}".format(LR, MOM, global_step))
return
# validation, saving and logging
print("\nvalidating")
m.eval() # disable dropout batchnorm etc
print("testset...")
test_loss = calculate_loss(set=testset, loss_fn=loss_fn, length_set=len(testdata), dev=device, model=m)
print("trainset...")
train_loss = calculate_loss(set=trainingset, loss_fn=loss_fn, length_set=len(traindata), dev=device, model=m)
print("calculating performace...")
currSSIM, currR = performance(set=testset, dev=device, model=m, bs=BATCHSIZE)
print("SSIM (iod/water): {}/{}\nR (iod/water): {}/{}".format(currSSIM[0], currSSIM[1], currR[0], currR[1]))
#f.write("num, lr, mom, step, ssimIOD, ssimWAT, rIOD, rWAT, trainLOSS, testLOSS\n")
with open(gridargs['stats'], 'a') as f:
newCSVline = "{}, {}, {}, {}, {}, {}, {}, {}, {}, {}\n".format(gridargs['runnum'], LR,
MOM, global_step,
currSSIM[0], currSSIM[1],
currR[0], currR[1],
train_loss, test_loss)
f.write(newCSVline)
print("wrote new line to csv:\n\t{}".format(newCSVline))
print("advanced metrics")
with torch.no_grad():
for x, y in testset:
# x, y in shape[2,2,480,620] [b,c,h,w]
x, y = x.to(device=device, dtype=torch.float), y.to(device=device, dtype=torch.float)
pred = m(x)
iod = pred.cpu().numpy()[0, 0, :, :]
water = pred.cpu().numpy()[0, 1, :, :]
gtiod = y.cpu().numpy()[0, 0, :, :]
gtwater = y.cpu().numpy()[0, 1, :, :]
IMAGE_LOG_DIR = os.path.join(CUSTOM_LOG_DIR, str(global_step))
if not os.path.isdir(IMAGE_LOG_DIR):
os.makedirs(IMAGE_LOG_DIR)
plt.imsave(os.path.join(IMAGE_LOG_DIR, 'iod' + str(global_step) + '.png'), iod, cmap='gray')
plt.imsave(os.path.join(IMAGE_LOG_DIR, 'water' + str(global_step) + '.png'), water, cmap='gray')
plt.imsave(os.path.join(IMAGE_LOG_DIR, 'gtiod' + str(global_step) + '.png'), gtiod, cmap='gray')
plt.imsave(os.path.join(IMAGE_LOG_DIR, 'gtwater' + str(global_step) + '.png'), gtwater, cmap='gray')
print("creating and saving profile plot at 240")
fig2, (ax1, ax2) = plt.subplots(nrows=2,
ncols=1) # plot water and iodine in one plot
ax1.plot(iod[240])
ax1.plot(gtiod[240])
ax1.title.set_text("iodine horizontal profile")
ax1.set_ylabel("mm iodine")
ax1.set_ylim([np.min(gtiod), np.max(gtiod)])
print("max value in gtiod is {}".format(np.max(gtiod)))
ax2.plot(water[240])
ax2.plot(gtwater[240])
ax2.title.set_text("water horizontal profile")
ax2.set_ylabel("mm water")
ax2.set_ylim([np.min(gtwater), np.max(gtwater)])
plt.subplots_adjust(wspace=0.3)
plt.savefig(os.path.join(IMAGE_LOG_DIR, 'ProfilePlots' + str(global_step) + '.png'))
break
if logger is not None and train_loss is not None:
logger.add_scalar('test_loss', test_loss, global_step=global_step)
logger.add_scalar('train_loss', train_loss, global_step=global_step)
logger.add_image("iodine-prediction", iod.reshape(1, 480, 620), global_step=global_step)
logger.add_image("ground-truth", gtiod.reshape(1, 480, 620), global_step=global_step)
# logger.add_image("water-prediction", wat)
print("\ttensorboard updated with test/train loss and a sample image")
# saving final results
CHECKPOINT = os.path.join(ROOT, "finalWeights.pt")
print("saving upon exit")
torch.save({
'epoch': EPOCHS,
'iterations': ITER,
'model_state_dict': m.state_dict(),
'optimizer_state_dict': o.state_dict(),
'train_loss': train_loss,
'test_loss': test_loss},
CHECKPOINT)
print('\tsaved progress to: ', CHECKPOINT)
if logger is not None and train_loss is not None:
logger.add_scalar('test_loss', test_loss, global_step=global_step)
logger.add_scalar('train_loss', train_loss, global_step=global_step)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save):
os.makedirs(args.save)
model = UNet(3, n_classes=args.num_classes)
saved_state_dict = torch.load(args.restore_from)
model.load_state_dict(saved_state_dict)
model.cuda(gpu0)
model.train()
testloader = data.DataLoader(REFUGE(False,
domain='REFUGE_TEST',
is_transform=True),
batch_size=args.batch_size,
shuffle=False,
pin_memory=True)
if version.parse(torch.__version__) >= version.parse('0.4.0'):
interp = nn.Upsample(size=(460, 460),
mode='bilinear',
align_corners=True)
else:
interp = nn.Upsample(size=(460, 460), mode='bilinear')
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % index)
image, label, _, _, name = batch
if args.model == 'Unet':
_, _, _, _, output2 = model(
Variable(image, volatile=True).cuda(gpu0))
output = interp(output2).cpu().data.numpy()
for idx, one_name in enumerate(name):
pred = output[idx]
pred = pred.transpose(1, 2, 0)
pred = np.asarray(np.argmax(pred, axis=2), dtype=np.uint8)
output_col = colorize_mask(pred)
if is_polar:
# plt.imshow(output_col)
# plt.show()
output_col = np.array(output_col)
output_col[output_col == 0] = 0
output_col[output_col == 1] = 128
output_col[output_col == 2] = 255
# plt.imshow(output_col)
# plt.show()
output_col = cv2.linearPolar(
rotate(output_col, 90),
(args.ROI_size / 2, args.ROI_size / 2), args.ROI_size / 2,
cv2.WARP_FILL_OUTLIERS + cv2.WARP_INVERSE_MAP)
# plt.imshow(output_col)
# plt.show()
output_col = np.array(output_col * 255, dtype=np.uint8)
output_col[output_col > 200] = 210
output_col[output_col == 0] = 255
output_col[output_col == 210] = 0
output_col[(output_col > 0) & (output_col < 255)] = 128
output_col = Image.fromarray(output_col)
# plt.imshow(output_col)
# plt.show()
one_name = one_name.split('/')[-1]
if len(one_name.split('_')) > 0:
one_name = one_name[:-4]
#pred.save('%s/%s.bmp' % (args.save, one_name))
output_col = output_col.convert('L')
print(output_col.size)
output_col.save('%s/%s.bmp' % (args.save, one_name.split('.')[0]))
def train(input_data_type,
grade,
seg_type,
num_classes,
batch_size,
epochs,
use_gpu,
learning_rate,
w_decay,
pre_trained=False):
logger.info('Start training using {} modal.'.format(input_data_type))
model = UNet(4, 4, residual=True, expansion=2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(params=model.parameters(),
lr=learning_rate,
weight_decay=w_decay)
if pre_trained:
checkpoint = torch.load(pre_trained_path, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
if use_gpu:
ts = time.time()
model.to(device)
print("Finish cuda loading, time elapsed {}".format(time.time() - ts))
scheduler = lr_scheduler.StepLR(
optimizer, step_size=step_size,
gamma=gamma) # decay LR by a factor of 0.5 every 5 epochs
data_set, data_loader = get_dataset_dataloader(input_data_type,
seg_type,
batch_size,
grade=grade)
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_iou = 0.0
epoch_loss = np.zeros((2, epochs))
epoch_acc = np.zeros((2, epochs))
epoch_class_acc = np.zeros((2, epochs))
epoch_mean_iou = np.zeros((2, epochs))
evaluator = Evaluator(num_classes)
def term_int_handler(signal_num, frame):
np.save(os.path.join(score_dir, 'epoch_accuracy'), epoch_acc)
np.save(os.path.join(score_dir, 'epoch_mean_iou'), epoch_mean_iou)
np.save(os.path.join(score_dir, 'epoch_loss'), epoch_loss)
model.load_state_dict(best_model_wts)
logger.info('Got terminated and saved model.state_dict')
torch.save(model.state_dict(),
os.path.join(score_dir, 'terminated_model.pt'))
torch.save(
{
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, os.path.join(score_dir, 'terminated_model.tar'))
quit()
signal.signal(signal.SIGINT, term_int_handler)
signal.signal(signal.SIGTERM, term_int_handler)
for epoch in range(epochs):
logger.info('Epoch {}/{}'.format(epoch + 1, epochs))
logger.info('-' * 28)
for phase_ind, phase in enumerate(['train', 'val']):
if phase == 'train':
model.train()
logger.info(phase)
else:
model.eval()
logger.info(phase)
evaluator.reset()
running_loss = 0.0
running_dice = 0.0
for batch_ind, batch in enumerate(data_loader[phase]):
imgs, targets = batch
imgs = imgs.to(device)
targets = targets.to(device)
# zero the learnable parameters gradients
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
outputs = model(imgs)
loss = criterion(outputs, targets)
if phase == 'train':
loss.backward()
optimizer.step()
preds = torch.argmax(F.softmax(outputs, dim=1),
dim=1,
keepdim=True)
running_loss += loss * imgs.size(0)
logger.debug('Batch {} running loss: {:.4f}'.format(batch_ind,\
running_loss))
# test the iou and pixelwise accuracy using evaluator
preds = torch.squeeze(preds, dim=1)
preds = preds.cpu().numpy()
targets = targets.cpu().numpy()
evaluator.add_batch(targets, preds)
epoch_loss[phase_ind, epoch] = running_loss / len(data_set[phase])
epoch_acc[phase_ind, epoch] = evaluator.Pixel_Accuracy()
epoch_class_acc[phase_ind,
epoch] = evaluator.Pixel_Accuracy_Class()
epoch_mean_iou[phase_ind,
epoch] = evaluator.Mean_Intersection_over_Union()
logger.info('{} loss: {:.4f}, acc: {:.4f}, class acc: {:.4f}, mean iou: {:.6f}'.format(phase,\
epoch_loss[phase_ind, epoch],\
epoch_acc[phase_ind, epoch],\
epoch_class_acc[phase_ind, epoch],\
epoch_mean_iou[phase_ind, epoch]))
if phase == 'val' and epoch_mean_iou[phase_ind, epoch] > best_iou:
best_iou = epoch_mean_iou[phase_ind, epoch]
best_model_wts = copy.deepcopy(model.state_dict())
if phase == 'val' and (epoch + 1) % 10 == 0:
logger.info('Saved model.state_dict in epoch {}'.format(epoch +
1))
torch.save(
model.state_dict(),
os.path.join(score_dir,
'epoch{}_model.pt'.format(epoch + 1)))
print()
time_elapsed = time.time() - since
logger.info('Training completed in {}m {}s'.format(int(time_elapsed / 60),\
int(time_elapsed) % 60))
# load best model weights
model.load_state_dict(best_model_wts)
# save numpy results
np.save(os.path.join(score_dir, 'epoch_accuracy'), epoch_acc)
np.save(os.path.join(score_dir, 'epoch_mean_iou'), epoch_mean_iou)
np.save(os.path.join(score_dir, 'epoch_loss'), epoch_loss)
return model, optimizer
def train_UNet():
cfg = UnetConfig()
train_transform = transforms.Compose([
GrayscaleNormalization(mean=0.5, std=0.5),
RandomRotation(),
RandomFlip(),
ToTensor(),
])
val_transform = transforms.Compose([
GrayscaleNormalization(mean=0.5, std=0.5),
ToTensor(),
])
# Set Dataset
train_dataset = Dataset(imgs_dir=TRAIN_IMGS_DIR,
labels_dir=TRAIN_LABELS_DIR,
transform=train_transform)
train_loader = DataLoader(train_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=True,
num_workers=0)
val_dataset = Dataset(imgs_dir=VAL_IMGS_DIR,
labels_dir=VAL_LABELS_DIR,
transform=val_transform)
val_loader = DataLoader(val_dataset,
batch_size=cfg.BATCH_SIZE,
shuffle=False,
num_workers=0)
train_data_num = len(train_dataset)
val_data_num = len(val_dataset)
train_batch_num = int(np.ceil(train_data_num / cfg.BATCH_SIZE)) # np.ceil
val_batch_num = int(np.ceil(val_data_num / cfg.BATCH_SIZE))
# Network
net = UNet().to(device)
print(count_parameters(net))
# Loss Function
loss_fn = nn.BCEWithLogitsLoss().to(device)
# Optimizer
optim = torch.optim.Adam(params=net.parameters(), lr=cfg.LEARNING_RATE)
# Tensorboard
# train_writer = SummaryWriter(log_dir=TRAIN_LOG_DIR)
# val_writer = SummaryWriter(log_dir=VAL_LOG_DIR)
# Training
start_epoch = 0
# Load Checkpoint File
if os.listdir(os.path.join(CKPT_DIR, 'unet')):
net, optim, start_epoch = load_net(ckpt_dir=os.path.join(
CKPT_DIR, 'unet'),
net=net,
optim=optim)
else:
print('* Training from scratch')
num_epochs = cfg.NUM_EPOCHS
for epoch in range(start_epoch + 1, num_epochs + 1):
net.train()
train_loss_arr = list()
for batch_idx, data in enumerate(train_loader, 1):
# Forward Propagation
img = data['img'].to(device)
label = data['label'].to(device)
output = net(img)
# Backward Propagation
optim.zero_grad()
loss = loss_fn(output, label)
loss.backward()
optim.step()
# Calc Loss Function
train_loss_arr.append(loss.item())
print_form = '[Train] | Epoch: {:0>4d} / {:0>4d} | Batch: {:0>4d} / {:0>4d} | Loss: {:.4f}'
print(
print_form.format(epoch, num_epochs, batch_idx,
train_batch_num, train_loss_arr[-1]))
train_loss_avg = np.mean(train_loss_arr)
# train_writer.add_scalar(tag='loss', scalar_value=train_loss_avg, global_step=epoch)
# Validation (No Back Propagation)
with torch.no_grad():
net.eval() # Evaluation Mode
val_loss_arr = list()
for batch_idx, data in enumerate(val_loader, 1):
# Forward Propagation
img = data['img'].to(device)
label = data['label'].to(device)
output = net(img)
# Calc Loss Function
loss = loss_fn(output, label)
val_loss_arr.append(loss.item())
print_form = '[Validation] | Epoch: {:0>4d} / {:0>4d} | Batch: {:0>4d} / {:0>4d} | Loss: {:.4f}'
print(
print_form.format(epoch, num_epochs, batch_idx,
val_batch_num, val_loss_arr[-1]))
val_loss_avg = np.mean(val_loss_arr)
# val_writer.add_scalar(tag='loss', scalar_value=val_loss_avg, global_step=epoch)
print_form = '[Epoch {:0>4d}] Training Avg Loss: {:.4f} | Validation Avg Loss: {:.4f}'
print(print_form.format(epoch, train_loss_avg, val_loss_avg))
if epoch % 10 == 0:
save_net(ckpt_dir=os.path.join(CKPT_DIR, 'unet'),
net=net,
optim=optim,
epoch=epoch)
def main():
parser = argparse.ArgumentParser(description="Train the model")
parser.add_argument('-trainf', "--train-filepath", type=str, default=None, required=True,
help="training dataset filepath.")
parser.add_argument('-validf', "--val-filepath", type=str, default=None,
help="validation dataset filepath.")
parser.add_argument("--shuffle", action="store_true", default=False,
help="Shuffle the dataset")
parser.add_argument("--load-weights", type=str, default=None,
help="load pretrained weights")
parser.add_argument("--load-model", type=str, default=None,
help="load pretrained model, entire model (filepath, default: None)")
parser.add_argument("--debug", action="store_true", default=False)
parser.add_argument('--epochs', type=int, default=30,
help='number of epochs to train (default: 30)')
parser.add_argument("--batch-size", type=int, default=32,
help="Batch size")
parser.add_argument('--img-shape', type=str, default="(1,512,512)",
help='Image shape (default "(1,512,512)"')
parser.add_argument("--num-cpu", type=int, default=10,
help="Number of CPUs to use in parallel for dataloader.")
parser.add_argument('--cuda', type=int, default=0,
help='CUDA visible device (use CPU if -1, default: 0)')
parser.add_argument('--cuda-non-deterministic', action='store_true', default=False,
help="sets flags for non-determinism when using CUDA (potentially fast)")
parser.add_argument('-lr', type=float, default=0.0005,
help='Learning rate')
parser.add_argument('--seed', type=int, default=0,
help='Seed (numpy and cuda if GPU is used.).')
parser.add_argument('--log-dir', type=str, default=None,
help='Save the results/model weights/logs under the directory.')
args = parser.parse_args()
# TODO: support image reshape
img_shape = tuple(map(int, args.img_shape.strip()[1:-1].split(",")))
if args.log_dir:
os.makedirs(args.log_dir, exist_ok=True)
best_model_path = os.path.join(args.log_dir, "model_weights.pth")
else:
best_model_path = None
if args.seed is not None:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda >= 0:
if args.cuda_non_deterministic:
printBlue("Warning: using CUDA non-deterministc. Could be faster but results might not be reproducible.")
else:
printBlue("Using CUDA deterministc. Use --cuda-non-deterministic might accelerate the training a bit.")
# Make CuDNN Determinist
torch.backends.cudnn.deterministic = not args.cuda_non_deterministic
# torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# TODO [OPT] enable multi-GPUs ?
# https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html
device = torch.device("cuda:{}".format(args.cuda) if torch.cuda.is_available()
and (args.cuda >= 0) else "cpu")
# ================= Build dataloader =================
# DataLoader
# transform_normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
# std=[0.5, 0.5, 0.5])
transform_normalize = transforms.Normalize(mean=[0.5],
std=[0.5])
# Warning: DO NOT use geometry transform (do it in the dataloader instead)
data_transform = transforms.Compose([
# transforms.ToPILImage(mode='F'), # mode='F' for one-channel image
# transforms.Resize((256, 256)) # NO
# transforms.RandomResizedCrop(256), # NO
# transforms.RandomHorizontalFlip(p=0.5), # NO
# WARNING, ISSUE: transforms.ColorJitter doesn't work with ToPILImage(mode='F').
# Need custom data augmentation functions: TODO: DONE.
# transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
# Use OpenCVRotation, OpenCVXXX, ... (our implementation)
# OpenCVRotation((-10, 10)), # angles (in degree)
transforms.ToTensor(), # already done in the dataloader
transform_normalize
])
geo_transform = GeoCompose([
OpenCVRotation(angles=(-10, 10),
scales=(0.9, 1.1),
centers=(-0.05, 0.05)),
# TODO add more data augmentation here
])
def worker_init_fn(worker_id):
# WARNING spawn start method is used,
# worker_init_fn cannot be an unpicklable object, e.g., a lambda function.
# A work-around for issue #5059: https://github.com/pytorch/pytorch/issues/5059
np.random.seed()
data_loader_train = {'batch_size': args.batch_size,
'shuffle': args.shuffle,
'num_workers': args.num_cpu,
# 'sampler': balanced_sampler,
'drop_last': True, # for GAN-like
'pin_memory': False,
'worker_init_fn': worker_init_fn,
}
data_loader_valid = {'batch_size': args.batch_size,
'shuffle': False,
'num_workers': args.num_cpu,
'drop_last': False,
'pin_memory': False,
}
train_set = LiTSDataset(args.train_filepath,
dtype=np.float32,
geometry_transform=geo_transform, # TODO enable data augmentation
pixelwise_transform=data_transform,
)
valid_set = LiTSDataset(args.val_filepath,
dtype=np.float32,
pixelwise_transform=data_transform,
)
dataloader_train = torch.utils.data.DataLoader(train_set, **data_loader_train)
dataloader_valid = torch.utils.data.DataLoader(valid_set, **data_loader_valid)
# =================== Build model ===================
# TODO: control the model by bash command
if args.load_weights:
model = UNet(in_ch=1,
out_ch=3, # there are 3 classes: 0: background, 1: liver, 2: tumor
depth=4,
start_ch=32, # 64
inc_rate=2,
kernel_size=5, # 3
padding=True,
batch_norm=True,
spec_norm=False,
dropout=0.5,
up_mode='upconv',
include_top=True,
include_last_act=False,
)
printYellow(f"Loading pretrained weights from: {args.load_weights}...")
model.load_state_dict(torch.load(args.load_weights))
printYellow("+ Done.")
elif args.load_model:
# load entire model
model = torch.load(args.load_model)
printYellow("Successfully loaded pretrained model.")
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.95)) # TODO
best_valid_loss = float('inf')
# TODO TODO: add learning decay
for epoch in range(args.epochs):
for valid_mode, dataloader in enumerate([dataloader_train, dataloader_valid]):
n_batch_per_epoch = len(dataloader)
if args.debug:
n_batch_per_epoch = 1
# infinite dataloader allows several update per iteration (for special models e.g. GAN)
dataloader = infinite_dataloader(dataloader)
if valid_mode:
printYellow("Switch to validation mode.")
model.eval()
prev_grad_mode = torch.is_grad_enabled()
torch.set_grad_enabled(False)
else:
model.train()
st = time.time()
cum_loss = 0
for iter_ind in range(n_batch_per_epoch):
supplement_logs = ""
# reset cumulated losses at the begining of each batch
# loss_manager.reset_losses() # TODO: use torch.utils.tensorboard !!
optimizer.zero_grad()
img, msk = next(dataloader)
img, msk = img.to(device), msk.to(device)
# TODO this is ugly: convert dtype and convert the shape from (N, 1, 512, 512) to (N, 512, 512)
msk = msk.to(torch.long).squeeze(1)
msk_pred = model(img) # shape (N, 3, 512, 512)
# label_weights is determined according the liver_ratio & tumor_ratio
# loss = CrossEntropyLoss(msk_pred, msk, label_weights=[1., 10., 100.], device=device)
loss = DiceLoss(msk_pred, msk, label_weights=[1., 20., 50.], device=device)
# loss = DiceLoss(msk_pred, msk, label_weights=[1., 20., 500.], device=device)
if valid_mode:
pass
else:
loss.backward()
optimizer.step()
loss = loss.item() # release
cum_loss += loss
if valid_mode:
print("\r--------(valid) {:.2%} Loss: {:.3f} (time: {:.1f}s) |supp: {}".format(
(iter_ind+1)/n_batch_per_epoch, cum_loss/(iter_ind+1), time.time()-st, supplement_logs), end="")
else:
print("\rEpoch: {:3}/{} {:.2%} Loss: {:.3f} (time: {:.1f}s) |supp: {}".format(
(epoch+1), args.epochs, (iter_ind+1)/n_batch_per_epoch, cum_loss/(iter_ind+1), time.time()-st, supplement_logs), end="")
print()
if valid_mode:
torch.set_grad_enabled(prev_grad_mode)
valid_mean_loss = cum_loss/(iter_ind+1) # validation (mean) loss of the current epoch
if best_model_path and (valid_mean_loss < best_valid_loss):
printGreen("Valid loss decreases from {:.5f} to {:.5f}, saving best model.".format(
best_valid_loss, valid_mean_loss))
best_valid_loss = valid_mean_loss
# Only need to save the weights
# torch.save(model.state_dict(), best_model_path)
# save the entire model
torch.save(model, best_model_path)
return best_valid_loss
train_loss_seg_a = []
train_loss_seg_b = []
train_dice = []
val_loss_a = []
val_dice_a = []
val_loss_b = []
val_dice_b = []
for e in range(epochs):
epoch_train_loss_rec = []
epoch_train_loss_seg = []
dice_scores = []
net.train()
pseudo.train()
print('Epoch ', e)
for i, data in enumerate(tqdm.tqdm(train_loader)):
iteration += batch_size
optimiser_ps.zero_grad()
optimiser_net.zero_grad()
# either train pseudolabeller or the net
# first 10 epochs train the pseudo labeller on edges
if e < epochs_pseudo:
edges_a = data['A'][2].cuda()
target_a = data['A'][1].cuda()
