def __init__(self, hyperparameters, resume_epoch=-1, snapshot_dir=None):
super(UNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks.
self.gen = VAEGen(
hyperparameters['input_dim'] + hyperparameters['n_datasets'],
hyperparameters['gen'],
hyperparameters['n_datasets']) # Auto-encoder for domain a.
self.dis = MsImageDis(
hyperparameters['input_dim'] + hyperparameters['n_datasets'],
hyperparameters['dis']) # Discriminator for domain a.
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.sup = UNet(input_channels=hyperparameters['input_dim'],
num_classes=2).cuda()
# Setup the optimizers.
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis.parameters())
gen_params = list(self.gen.parameters()) + list(self.sup.parameters())
self.dis_opt = torch.optim.Adam(
[p for p in dis_params if p.requires_grad],
lr=lr,
betas=(beta1, beta2),
weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam(
[p for p in gen_params if p.requires_grad],
lr=lr,
betas=(beta1, beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization.
self.apply(weights_init(hyperparameters['init']))
self.dis.apply(weights_init('gaussian'))
# Presetting one hot encoding vectors.
self.one_hot_img = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 256,
256).cuda()
self.one_hot_h = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 64,
64).cuda()
for i in range(hyperparameters['n_datasets']):
self.one_hot_img[i, :, i, :, :].fill_(1)
self.one_hot_h[i, :, i, :, :].fill_(1)
if resume_epoch != -1:
self.resume(snapshot_dir, hyperparameters)
def build_model(self):
"""Build generator and discriminator."""
if self.model_type == 'UNet':
self.unet = UNet(n_channels=1, n_classes=1)
elif self.model_type == 'R2U_Net':
self.unet = R2U_Net(
img_ch=1, output_ch=1,
t=self.t) # TODO: changed for green image channel
elif self.model_type == 'AttU_Net':
self.unet = AttU_Net(img_ch=1, output_ch=1)
elif self.model_type == 'R2AttU_Net':
self.unet = R2AttU_Net(img_ch=3, output_ch=1, t=self.t)
elif self.model_type == 'Iternet':
self.unet = Iternet(n_channels=1, n_classes=1)
elif self.model_type == 'AttUIternet':
self.unet = AttUIternet(n_channels=1, n_classes=1)
elif self.model_type == 'R2UIternet':
self.unet = R2UIternet(n_channels=3, n_classes=1)
elif self.model_type == 'NestedUNet':
self.unet = NestedUNet(in_ch=1, out_ch=1)
self.optimizer = optim.Adam(list(self.unet.parameters()),
self.lr,
betas=tuple(self.beta_list))
self.unet.to(self.device)
def main():
net = UNet(dtype, image_size=opt.image_size).type(dtype)
print(net)
if opt.test:
runTest(net)
return
physical_loss = PhysicalLoss()
optimizer = optim.Adam(net.parameters(), lr=opt.learning_rate)
fixed_sample_0, fixed_solution_0, fixed_sample_1, fixed_solution_1 = makeSamples(
opt.image_size)
data = torch.zeros(opt.batch_size, 1, opt.image_size, opt.image_size)
print("Training Started")
for epoch in range(opt.epochs):
mean_loss = 0
for sample in range(opt.epoch_size):
data[:, :, :, 0] = np.random.uniform(100)
data[:, :, 0, :] = np.random.uniform(100)
data[:, :, :, -1] = np.random.uniform(100)
data[:, :, -1, :] = np.random.uniform(100)
img = Variable(data).type(dtype)
output = net(img)
loss = physical_loss(output)
optimizer.zero_grad()
loss.backward()
optimizer.step()
mean_loss += loss.data[0]
mean_loss /= opt.epoch_size
print('epoch [{}/{}], loss:{:.4f}'.format(epoch + 1, opt.epochs,
mean_loss))
plotSamples(fixed_solution_0, net(fixed_sample_0), fixed_solution_1,
net(fixed_sample_1), opt.experiment, epoch)
# checkpoint networks
if epoch + 1 % 50 == 0:
torch.save(net.state_dict(),
'%s/net_epoch_%d.pth' % (opt.experiment, epoch + 1))
print("Training Complete")
torch.save(net.state_dict(),
'%s/net_epoch_%d.pth' % (opt.experiment, epoch + 1))
print("Network Weights Saved in %s" % opt.experiment)
def __init__(self, maxiter=300, tol=1e-6, restart=50):
super(Wiener_KPN_SA, self).__init__()
self.maxiter = maxiter
self.tol = tol
self.restart = restart
self.cg_solver = ConugateGradient_Function
self.info = None
self.reg_weight = nn.Parameter(torch.Tensor([0.])) #.double())
self.model = UNet(mode='none', n_channels=1, n_classes=9)
def __init__(self):
'''
Deconvolution function for a batch of images. Although the regularization
term does not have a shape of Tikhonov regularizer, with a slight abuse of notations
the function is called WienerUNet.
The function is built upon the iterative gradient descent scheme:
x_k+1 = x_k - lamb[K^T(Kx_k - y) + exp(alpha)*reg(x_k)]
Initial parameters are:
regularizer: a neural network to parametrize the prior on each iteration x_k.
alpha: power of the trade-off coefficient
lamb: step of the gradient descent algorithm
'''
super(WienerUNet, self).__init__()
self.regularizer = UNet(mode='instance')
self.alpha = nn.Parameter(torch.FloatTensor([0.0]))
self.lamb = nn.Parameter(torch.FloatTensor([0.3]))
self.bad += 1
print("bad ++")
except ValueError:
torch.save(self.net.state_dict(), str(self.save_weight_path))
self.val_losses.append(val_loss)
else:
print("loss is too large. Continue train")
self.val_losses.append(val_loss)
print("bad = {}".format(self.bad))
self.epoch_loss = 0
if __name__ == "__main__":
args = parse_args()
args.train_path = [Path(args.train_path)]
args.val_path = [Path(args.val_path)]
# save weight path
args.weight_path = Path(args.weight_path)
# define model
net = UNet(n_channels=1, n_classes=1)
if args.gpu:
net.cuda()
args.net = net
train = TrainNet(args)
train.main()
mask_dirs1 = ["../MedTest_Masked"]
train_transforms = transforms.Compose([transforms.ToTensor()])
data = SegDataset("train_data", transform=train_transforms)
#print("THe size of the complete dataset is {}".format(data.__len__()))
train_loader, test_loader = load_train_test(data, valid_size=0.01)
#print("The size of training examples is : {}".format(len(train_loader.dataset)))
#print("The size of testing examples is : {}".format(len(test_loader.dataset)))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
model = UNet(num_class).to(device)
#summary(model,(1,512,512))
# d = open('sample.json', 'w+')
optimizer = optim.Adam(model.parameters(), lr=0.0001)
#def weights_init(m):
# if isinstance(m, nn.Conv2d):
# xavier_uniform(m.weight.data)
# xavier_uniform(m.bias.data)
#model.apply(weights_init)
model.load_state_dict(torch.load("../outputs12/checkpoints/ckpt_0_0.pth"))
transforms1 = transforms.Compose([transforms.ToTensor()])
train_transforms = transforms.Compose([transforms.ToTensor()])
data = SegDataset(image_dirs, mask_dir=mask_dirs, transform=train_transforms)
print("THe size of the complete dataset is {}".format(data.__len__()))
train_loader, test_loader = load_train_test(data, valid_size=0.01)
print("The size of training examples is : {}".format(len(
train_loader.dataset)))
print("The size of testing examples is : {}".format(len(test_loader.dataset)))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
model = UNet(num_class).to(device)
summary(model, (1, 512, 512))
d = open('sample.json', 'w+')
# Observe that all parameters are being optimized
optimizer = optim.Adam(model.parameters(), lr=4e-4)
#model.load_state_dict(torch.load("../outputs5/checkpoints/ckpt_0_31.pth"))
transforms1 = transforms.Compose(
[transforms.Resize((512, 512)),
transforms.ToTensor()])
test_data = SegDataset1(test_dirs,
mask_dir=mask_dirs1,
transform=transforms1,
def test_unet(root,\
psf_path,
method,\
scale, \
model_path,\
visual, \
use_gpu,
b_size=1):
'''
Model UNet
'''
model_name = method + '_poisson'
save_images_path = './Results/' + model_name + '_peak_' + str(
int(scale)) + '/'
test_dataset = CellDataset(root, psf_path, 'poisson', scale, 0.0)
test_loader = DataLoader(test_dataset,
batch_size=b_size,
shuffle=False,
num_workers=1)
model = UNet(mode='batch')
state_dict = torch.load(os.path.join(model_path, model_name))
state_dict = state_dict['model_state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
new_state_dict[k] = v
model.load_state_dict(new_state_dict)
if use_gpu == 1:
model.cuda()
model.eval()
psnr_values_test = []
ssim_values_test = []
distorted_psnr_test = []
distorted_ssim_test = []
with torch.no_grad():
for i_batch, ((gt, image), psf, index, image_name, peak,
_) in enumerate(tqdm(test_loader)):
image = image.reshape(
(b_size, 1, image.shape[-2], image.shape[-1]))
gt = gt.reshape((b_size, 1, gt.shape[-2], gt.shape[-1]))
if use_gpu == 1:
image = image.cuda()
gt = gt.cuda()
for l in range(gt.shape[0]):
image[l] = image[l] / gt[l].max()
gt[l] /= gt[l].max()
output = model(image)
distorted_psnr = calc_psnr(image.clamp(0, 1), gt)
distorted_ssim = ssim(image.clamp(0, 1), gt)
psnr_test = calc_psnr(output.clamp(0, 1), gt)
s_sim_test = ssim(output.clamp(0, 1), gt)
psnr_values_test.append(psnr_test.item())
ssim_values_test.append(s_sim_test.item())
distorted_psnr_test.append(distorted_psnr.item())
distorted_ssim_test.append(distorted_ssim.item())
#Save image
if visual == 1:
if not os.path.exists(save_images_path):
os.makedirs(save_images_path, exist_ok=True)
io.imsave(os.path.join(save_images_path, 'output_' + str(image_name[0][:-4]) + '_' + \
str(model_name) + '_' + str(int(scale)) + '.png'), np.uint8(output[0][0].detach().cpu().numpy().clip(0,1) * 255.))
print('Test on Poisson noise with peak %d: PSNR %.2f, SSIM %.4f, distorted PSNR %.2f, distorted SSIM %.4f' % (peak, np.array(psnr_values_test).mean(), \
np.array(ssim_values_test).mean(), \
np.array(distorted_psnr_test).mean(), \
np.array(distorted_ssim_test).mean()))
return
self.cal_tp_fp_fn(ori, gt_img, pre_img, i)
if self.tps == 0:
f_measure = 0
else:
recall = self.tps / (self.tps + self.fns)
precision = self.tps / (self.tps + self.fps)
f_measure = (2 * recall * precision) / (recall + precision)
print(precision, recall, f_measure)
with self.save_txt_path.open(mode="a") as f:
f.write("%f,%f,%f\n" % (precision, recall, f_measure))
if __name__ == "__main__":
args = parse_args()
args.input_path = Path(args.input_path)
args.output_path = Path(args.output_path)
net = UNet(n_channels=1, n_classes=1)
net.load_state_dict(torch.load(args.weight_path, map_location="cpu"))
if args.gpu:
net.cuda()
args.net = net
pred = PredictFmeasure(args)
pred.main()
def train(params, args, world_rank):
logging.info('rank %d, begin data loader init' % world_rank)
train_data_loader = get_data_loader_distributed(params, world_rank)
test_data_loader = get_data_loader_distributed_test(params, world_rank)
logging.info('rank %d, data loader initialized' % world_rank)
model = UNet.UNet(params).cuda()
if not args.resuming:
model.apply(model.get_weights_function(params.weight_init))
optimizer = optimizers.FusedAdam(model.parameters(), lr=params.lr)
#model, optimizer = amp.initialize(model, optimizer, opt_level="O1") # for automatic mixed precision
if params.distributed:
model = DistributedDataParallel(model)
iters = 0
startEpoch = 0
checkpoint = None
if args.resuming:
if world_rank == 0:
logging.info("Loading checkpoint %s" % params.checkpoint_path)
checkpoint = torch.load(params.checkpoint_path,
map_location='cuda:{}'.format(args.local_rank))
model.load_state_dict(checkpoint['model_state'])
iters = checkpoint['iters']
startEpoch = checkpoint['epoch'] + 1
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
if world_rank == 0:
logging.info(model)
logging.info("Starting Training Loop...")
device = torch.cuda.current_device()
for epoch in range(startEpoch, startEpoch + params.num_epochs):
start = time.time()
tr_time = 0.
log_time = 0.
for i, data in enumerate(train_data_loader, 0):
iters += 1
adjust_LR(optimizer, params, iters)
inp, tar = map(lambda x: x.to(device), data)
tr_start = time.time()
b_size = inp.size(0)
model.zero_grad()
gen = model(inp)
loss = UNet.loss_func(gen, tar, params)
loss.backward() # fixed precision
# automatic mixed precision:
#with amp.scale_loss(loss, optimizer) as scaled_loss:
# scaled_loss.backward()
optimizer.step()
tr_end = time.time()
tr_time += tr_end - tr_start
# Output training stats
if world_rank == 0:
log_start = time.time()
gens = []
tars = []
with torch.no_grad():
for i, data in enumerate(test_data_loader, 0):
if i >= 50:
break
inp, tar = map(lambda x: x.to(device), data)
gen = model(inp)
gens.append(gen.detach().cpu().numpy())
tars.append(tar.detach().cpu().numpy())
gens = np.concatenate(gens, axis=0)
tars = np.concatenate(tars, axis=0)
# Scalars
args.tboard_writer.add_scalar('G_loss', loss.item(), iters)
# Plots
fig = plot_gens_tars(gens, tars)
#fig, chi, L1score = meanL1(gens, tars)
#args.tboard_writer.add_figure('pixhist', fig, iters, close=True)
#args.tboard_writer.add_scalar('Metrics/chi', chi, iters)
#args.tboard_writer.add_scalar('Metrics/rhoL1', L1score[0], iters)
#args.tboard_writer.add_scalar('Metrics/vxL1', L1score[1], iters)
#args.tboard_writer.add_scalar('Metrics/vyL1', L1score[2], iters)
#args.tboard_writer.add_scalar('Metrics/vzL1', L1score[3], iters)
#args.tboard_writer.add_scalar('Metrics/TL1', L1score[4], iters)
#
#fig = generate_images(inp.detach().cpu().numpy()[0], gens[-1], tars[-1])
for figiter in range(5):
figtag = 'test' + str(figiter)
args.tboard_writer.add_figure(tag=figtag,
figure=fig[figiter],
close=True)
#log_end = time.time()
#log_time += log_end - log_start
# Save checkpoint
torch.save(
{
'iters': iters,
'epoch': epoch,
'model_state': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, params.checkpoint_path)
end = time.time()
if world_rank == 0:
logging.info('Time taken for epoch {} is {} sec'.format(
epoch + 1, end - start))
logging.info('train step time={}, logging time={}'.format(
tr_time, log_time))
from pathlib import Path
from detection import TrainNet
from networks import UNet
from propagation import GuideCall
if __name__ == "__main__":
torch.cuda.set_device(1)
date = datetime.now().date()
gpu = True
key = 2
weight_path = "./weight/best.pth"
# image_path
train_path = Path("./images/train")
val_path = Path("./images/val")
guided_input_path = sorted(train_path.joinpath("ori").glob("*.tif"))
# guided output
output_path = Path("output")
# define model
net = UNet(n_channels=1, n_classes=1)
net.cuda()
net.load_state_dict(
torch.load(weight_path, map_location={"cuda:2": "cuda:0"}))
bp = GuideCall(guided_input_path, output_path, net)
bp.main()
class MUNIT_Trainer(nn.Module):
def __init__(self, hyperparameters, resume_epoch=-1, snapshot_dir=None):
super(MUNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks.
self.gen = AdaINGen2(
hyperparameters['input_dim'],
hyperparameters['gen']) # Auto-encoder for domain a.
self.dis = NLayerDiscriminator(
hyperparameters['input_dim']) # Discriminator for domain a.
self.dis2 = NLayerDiscriminator(3 * hyperparameters['input_dim'],
n_layers=4)
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.style_dim = hyperparameters['gen']['style_dim']
self.beta1 = hyperparameters['beta1']
self.beta2 = hyperparameters['beta2']
self.weight_decay = hyperparameters['weight_decay']
# Initiating and loader pretrained UNet.
self.sup = UNet(input_channels=hyperparameters['input_dim'],
num_classes=3).cuda()
# Fix the noise used in sampling.
self.s_a = torch.randn(8, self.style_dim, 1, 1).cuda()
self.s_b = torch.randn(8, self.style_dim, 1, 1).cuda()
# Setup the optimizers.
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis.parameters())
dis2_params = list(self.dis2.parameters())
gen_params = list(self.gen.parameters()) + list(self.sup.parameters())
self.dis_opt = torch.optim.Adam(
[p for p in dis_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis2_opt = torch.optim.Adam(
[p for p in dis2_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam(
[p for p in gen_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.dis2_scheduler = get_scheduler(self.dis2_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization.
self.apply(weights_init(hyperparameters['init']))
self.dis.apply(weights_init('gaussian'))
self.dis2.apply(weights_init('gaussian'))
# Presetting one hot encoding vectors.
self.one_hot_img = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 256,
256).cuda()
self.one_hot_c = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 64,
64).cuda()
for i in range(hyperparameters['n_datasets']):
self.one_hot_img[i, :, i, :, :].fill_(1)
self.one_hot_c[i, :, i, :, :].fill_(1)
if resume_epoch != -1:
self.resume(snapshot_dir, hyperparameters, resume_epoch)
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def semi_criterion(self, input, target):
loss = CrossEntropyLoss2d(size_average=True).cuda()
return loss(input, target)
def forward(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
s_a = Variable(self.s_a, volatile=True)
s_b = Variable(self.s_b, volatile=True)
c_a, s_a_fake = self.gen.encode(x_a)
c_b, s_b_fake = self.gen.encode(x_b)
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
self.train()
return x_ab, x_ba
def set_gen_trainable(self, train_bool):
if train_bool:
self.gen.train()
for param in self.gen.parameters():
param.requires_grad = True
else:
self.gen.eval()
for param in self.gen.parameters():
param.requires_grad = True
def set_sup_trainable(self, train_bool):
if train_bool:
self.sup.train()
for param in self.sup.parameters():
param.requires_grad = True
else:
self.sup.eval()
for param in self.sup.parameters():
param.requires_grad = True
##################################################################################
# Mainly adapted from https://github.com/hugo-oliveira/CoDAGANs ##################
##################################################################################
def sup_update(self, x_a, x_b, y_a, y_b, d_index_a, d_index_b, use_a,
use_b, ep, hyperparameters):
self.gen_opt.zero_grad()
# temp_open=hyperparameters['temp_open']
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
c_a, s_a_prime = self.gen.encode(x_a)
c_b, s_b_prime = self.gen.encode(x_b)
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
c_b_recon, s_a_recon = self.gen.encode(x_ba)
c_a_recon, s_b_recon = self.gen.encode(x_ab)
# Forwarding through supervised model.
p_a = None
p_b = None
loss_semi_a = None
loss_semi_b = None
# if temp_open==1:
c_a = c_a.detach()
c_b = c_b.detach()
c_b_recon = c_b_recon.detach()
c_a_recon = c_a_recon.detach()
p_a = self.sup(c_a, use_a, True)
p_a_recon = self.sup(c_a_recon, use_a, True)
p_b = self.sup(c_b, use_a, True)
p_b_recon = self.sup(c_b_recon, use_a, True)
loss_semi_a = self.semi_criterion(p_a, y_a[use_a, :, :]) + \
self.semi_criterion(p_a_recon, y_a[use_a, :, :])
if (ep + 1) > 10:
loss_gen_b = self.dis2.calc_gen_loss(
p_b) + self.dis2.calc_gen_loss(p_b_recon)
else:
loss_gen_b = Variable(torch.tensor(0).cuda(), requires_grad=False)
self.loss_gen_total = None
weight_temp = hyperparameters['weight_temp']
if loss_semi_a is not None:
self.loss_gen_total = hyperparameters[
'recon_x_w'] * loss_semi_a + weight_temp * loss_gen_b
seg_loss = hyperparameters['recon_x_w'] * loss_semi_a
seg_gen_loss = weight_temp * loss_gen_b
if self.loss_gen_total is not None:
self.loss_gen_total.backward()
self.gen_opt.step()
return seg_loss.item(), seg_gen_loss.item()
def sup_forward(self, x, y, d_index, hyperparameters):
self.sup.eval()
# Encoding content image.
content, _ = self.gen.encode(x)
# Forwarding on supervised model.
y_pred = self.sup(content, only_prediction=True)
# Computing metrics.
pred = y_pred.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
jacc, jacc_cup = jaccard(pred, y.cpu().squeeze(0).numpy())
return jacc, jacc_cup, pred, content
def gen_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.gen_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# Encode.
c_a, s_a_prime = self.gen.encode(x_a)
c_b, s_b_prime = self.gen.encode(x_b)
# Decode (within domain).
x_a_recon = self.gen.decode(c_a, s_a_prime)
x_b_recon = self.gen.decode(c_b, s_b_prime)
# Decode (cross domain).
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
# Encode again.
c_b_recon, s_a_recon = self.gen.encode(x_ba)
c_a_recon, s_b_recon = self.gen.encode(x_ab)
# Decode again (if needed).
x_aba = self.gen.decode(c_a_recon, s_a_prime)
x_bab = self.gen.decode(c_b_recon, s_b_prime)
# Reconstruction loss.
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_s_a = self.recon_criterion(s_a_recon, s_a)
self.loss_gen_recon_s_b = self.recon_criterion(s_b_recon, s_b)
self.loss_gen_recon_c_a = self.recon_criterion(c_a_recon, c_a)
self.loss_gen_recon_c_b = self.recon_criterion(c_b_recon, c_b)
self.loss_gen_cycrecon_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cycrecon_x_b = self.recon_criterion(x_bab, x_b)
# GAN loss.
self.loss_gen_adv_a = self.dis.calc_gen_loss(x_ba)
self.loss_gen_adv_b = self.dis.calc_gen_loss(x_ab)
# Total loss.
self.loss_gen_total = hyperparameters['gan_w'] * self.loss_gen_adv_a + \
hyperparameters['gan_w'] * self.loss_gen_adv_b + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \
hyperparameters['recon_s_w'] * self.loss_gen_recon_s_a + \
hyperparameters['recon_c_w'] * self.loss_gen_recon_c_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_s_w'] * self.loss_gen_recon_s_b + \
hyperparameters['recon_c_w'] * self.loss_gen_recon_c_b + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cycrecon_x_a + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cycrecon_x_b
self.loss_gen_total.backward()
self.gen_opt.step()
return self.loss_gen_total.item()
def compute_vgg_loss(self, vgg, img, target):
img_vgg = vgg_preprocess(img)
target_vgg = vgg_preprocess(target)
img_fea = vgg(img_vgg)
target_fea = vgg(target_vgg)
return torch.mean(
(self.instancenorm(img_fea) - self.instancenorm(target_fea))**2)
def dis_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.dis_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# Encode.
c_a, _ = self.gen.encode(x_a)
c_b, _ = self.gen.encode(x_b)
# Decode (cross domain).
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
# D loss.
self.loss_dis_a = self.dis.calc_dis_loss(x_ba.detach(), x_a)
self.loss_dis_b = self.dis.calc_dis_loss(x_ab.detach(), x_b)
self.loss_dis_total = hyperparameters['gan_w'] * self.loss_dis_a + \
hyperparameters['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
return self.loss_dis_total.item()
def dis2_update(self, x_a, x_b, d_index_a, d_index_b, use_a, use_b,
hyperparameters):
self.dis2_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# Encode.
c_a, s_a_prime = self.gen.encode(x_a)
c_b, s_b_prime = self.gen.encode(x_b)
# Decode (within domain).
x_a_recon = self.gen.decode(c_a, s_a_prime)
x_b_recon = self.gen.decode(c_b, s_b_prime)
# Decode (cross domain).
x_ba = self.gen.decode(c_b, s_a)
x_ab = self.gen.decode(c_a, s_b)
# Encode again.
c_b_recon, s_a_recon = self.gen.encode(x_ba)
c_a_recon, s_b_recon = self.gen.encode(x_ab)
p_b = self.sup(c_b, use_a, True)
p_b_recon = self.sup(c_b_recon, use_a, True)
p_a = self.sup(c_a, use_a, True)
p_a_recon = self.sup(c_a_recon, use_a, True)
self.loss_dis2_b = self.dis2.calc_dis_loss(
p_b.detach(), p_a.detach()) + self.dis2.calc_dis_loss(
p_b_recon.detach(), p_a_recon.detach())
self.loss_dis2_total = hyperparameters['gan_w'] * self.loss_dis2_b
self.loss_dis2_total.backward()
self.dis2_opt.step()
return self.loss_dis2_total.item()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.dis2_scheduler is not None:
self.dis2_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters, resume_epoch):
print("--> " + checkpoint_dir)
# Load generator.
last_model_name = get_model_list(checkpoint_dir, "gen", resume_epoch)
# print('\n',last_model_name)
state_dict = torch.load(last_model_name)
self.gen.load_state_dict(state_dict)
epochs = int(last_model_name[-11:-3])
# Load supervised model.
last_model_name = get_model_list(checkpoint_dir, "sup", resume_epoch)
state_dict = torch.load(last_model_name)
self.sup.load_state_dict(state_dict)
# Load discriminator.
# last_model_name = get_model_list(checkpoint_dir, "dis", resume_epoch)
# state_dict = torch.load(last_model_name)
# self.dis.load_state_dict(state_dict)
# # Load discriminator2.
# last_model_name = get_model_list(checkpoint_dir, "dis2", resume_epoch)
# state_dict = torch.load(last_model_name)
# self.dis2.load_state_dict(state_dict)
# # Load optimizers.
# last_model_name = get_model_list(checkpoint_dir, "opt", resume_epoch)
# state_dict = torch.load(last_model_name)
# self.dis_opt.load_state_dict(state_dict['dis'])
# self.dis2_opt.load_state_dict(state_dict['dis2'])
# self.gen_opt.load_state_dict(state_dict['gen'])
# for state in self.dis_opt.state.values():
# for k, v in state.items():
# if isinstance(v, torch.Tensor):
# state[k] = v.cuda()
# for state in self.dis2_opt.state.values():
# for k, v in state.items():
# if isinstance(v, torch.Tensor):
# state[k] = v.cuda()
# for state in self.gen_opt.state.values():
# for k, v in state.items():
# if isinstance(v, torch.Tensor):
# state[k] = v.cuda()
# # Reinitilize schedulers.
# self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters, epochs)
# self.dis2_scheduler = get_scheduler(self.dis2_opt, hyperparameters, epochs)
# self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters, epochs)
# print('Resume from epoch %d' % epochs)
# return epochs
def save(self, snapshot_dir, epoch):
# Save generators, discriminators, and optimizers.
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % epoch)
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % epoch)
dis2_name = os.path.join(snapshot_dir, 'dis2_%08d.pt' % epoch)
sup_name = os.path.join(snapshot_dir, 'sup_%08d.pt' % epoch)
opt_name = os.path.join(snapshot_dir, 'opt_%08d.pt' % epoch)
torch.save(self.gen.state_dict(), gen_name)
torch.save(self.dis.state_dict(), dis_name)
torch.save(self.dis2.state_dict(), dis2_name)
torch.save(self.sup.state_dict(), sup_name)
torch.save(
{
'gen': self.gen_opt.state_dict(),
'dis': self.dis_opt.state_dict(),
'dis2': self.dis2_opt.state_dict()
}, opt_name)
def __init__(self, hyperparameters, resume_epoch=-1, snapshot_dir=None):
super(MUNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks.
self.gen = AdaINGen2(
hyperparameters['input_dim'],
hyperparameters['gen']) # Auto-encoder for domain a.
self.dis = NLayerDiscriminator(
hyperparameters['input_dim']) # Discriminator for domain a.
self.dis2 = NLayerDiscriminator(3 * hyperparameters['input_dim'],
n_layers=4)
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.style_dim = hyperparameters['gen']['style_dim']
self.beta1 = hyperparameters['beta1']
self.beta2 = hyperparameters['beta2']
self.weight_decay = hyperparameters['weight_decay']
# Initiating and loader pretrained UNet.
self.sup = UNet(input_channels=hyperparameters['input_dim'],
num_classes=3).cuda()
# Fix the noise used in sampling.
self.s_a = torch.randn(8, self.style_dim, 1, 1).cuda()
self.s_b = torch.randn(8, self.style_dim, 1, 1).cuda()
# Setup the optimizers.
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis.parameters())
dis2_params = list(self.dis2.parameters())
gen_params = list(self.gen.parameters()) + list(self.sup.parameters())
self.dis_opt = torch.optim.Adam(
[p for p in dis_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis2_opt = torch.optim.Adam(
[p for p in dis2_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam(
[p for p in gen_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.dis2_scheduler = get_scheduler(self.dis2_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization.
self.apply(weights_init(hyperparameters['init']))
self.dis.apply(weights_init('gaussian'))
self.dis2.apply(weights_init('gaussian'))
# Presetting one hot encoding vectors.
self.one_hot_img = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 256,
256).cuda()
self.one_hot_c = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 64,
64).cuda()
for i in range(hyperparameters['n_datasets']):
self.one_hot_img[i, :, i, :, :].fill_(1)
self.one_hot_c[i, :, i, :, :].fill_(1)
if resume_epoch != -1:
self.resume(snapshot_dir, hyperparameters, resume_epoch)
class UNIT_Trainer(nn.Module):
def __init__(self, hyperparameters, resume_epoch=-1, snapshot_dir=None):
super(UNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks.
self.gen = VAEGen(
hyperparameters['input_dim'] + hyperparameters['n_datasets'],
hyperparameters['gen'],
hyperparameters['n_datasets']) # Auto-encoder for domain a.
self.dis = MsImageDis(
hyperparameters['input_dim'] + hyperparameters['n_datasets'],
hyperparameters['dis']) # Discriminator for domain a.
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.sup = UNet(input_channels=hyperparameters['input_dim'],
num_classes=2).cuda()
# Setup the optimizers.
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis.parameters())
gen_params = list(self.gen.parameters()) + list(self.sup.parameters())
self.dis_opt = torch.optim.Adam(
[p for p in dis_params if p.requires_grad],
lr=lr,
betas=(beta1, beta2),
weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam(
[p for p in gen_params if p.requires_grad],
lr=lr,
betas=(beta1, beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization.
self.apply(weights_init(hyperparameters['init']))
self.dis.apply(weights_init('gaussian'))
# Presetting one hot encoding vectors.
self.one_hot_img = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 256,
256).cuda()
self.one_hot_h = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 64,
64).cuda()
for i in range(hyperparameters['n_datasets']):
self.one_hot_img[i, :, i, :, :].fill_(1)
self.one_hot_h[i, :, i, :, :].fill_(1)
if resume_epoch != -1:
self.resume(snapshot_dir, hyperparameters)
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def semi_criterion(self, input, target):
loss = CrossEntropyLoss2d(size_average=False).cuda()
return loss(input, target)
def forward(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
h_a, _ = self.gen_a.encode(x_a)
h_b, _ = self.gen_b.encode(x_b)
x_ba = self.gen_a.decode(h_b)
x_ab = self.gen_b.decode(h_a)
self.train()
return x_ab, x_ba
def __compute_kl(self, mu):
# def _compute_kl(self, mu, sd):
# mu_2 = torch.pow(mu, 2)
# sd_2 = torch.pow(sd, 2)
# encoding_loss = (mu_2 + sd_2 - torch.log(sd_2)).sum() / mu_2.size(0)
# return encoding_loss
mu_2 = torch.pow(mu, 2)
encoding_loss = torch.mean(mu_2)
return encoding_loss
def set_gen_trainable(self, train_bool):
if train_bool:
self.gen.train()
for param in self.gen.parameters():
param.requires_grad = True
else:
self.gen.eval()
for param in self.gen.parameters():
param.requires_grad = True
def set_sup_trainable(self, train_bool):
if train_bool:
self.sup.train()
for param in self.sup.parameters():
param.requires_grad = True
else:
self.sup.eval()
for param in self.sup.parameters():
param.requires_grad = True
def sup_update(self, x_a, x_b, y_a, y_b, d_index_a, d_index_b, use_a,
use_b, hyperparameters):
self.gen_opt.zero_grad()
# Encode.
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
h_a, n_a = self.gen.encode(one_hot_x_a)
h_b, n_b = self.gen.encode(one_hot_x_b)
# Decode (within domain).
one_hot_h_a = torch.cat([h_a + n_a, self.one_hot_h[d_index_a]], 1)
one_hot_h_b = torch.cat([h_b + n_b, self.one_hot_h[d_index_b]], 1)
x_a_recon = self.gen.decode(one_hot_h_a)
x_b_recon = self.gen.decode(one_hot_h_b)
# Decode (cross domain).
one_hot_h_ab = torch.cat([h_a + n_a, self.one_hot_h[d_index_b]], 1)
one_hot_h_ba = torch.cat([h_b + n_b, self.one_hot_h[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_h_ba)
x_ab = self.gen.decode(one_hot_h_ab)
# Encode again.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
h_b_recon, n_b_recon = self.gen.encode(one_hot_x_ba)
h_a_recon, n_a_recon = self.gen.encode(one_hot_x_ab)
# Decode again (if needed).
one_hot_h_a_recon = torch.cat(
[h_a_recon + n_a_recon, self.one_hot_h[d_index_a]], 1)
one_hot_h_b_recon = torch.cat(
[h_b_recon + n_b_recon, self.one_hot_h[d_index_b]], 1)
x_aba = self.gen.decode(
one_hot_h_a_recon
) if hyperparameters['recon_x_cyc_w'] > 0 else None
x_bab = self.gen.decode(
one_hot_h_b_recon
) if hyperparameters['recon_x_cyc_w'] > 0 else None
# Forwarding through supervised model.
p_a = None
p_b = None
loss_semi_a = None
loss_semi_b = None
has_a_label = (h_a[use_a, :, :, :].size(0) != 0)
if has_a_label:
p_a = self.sup(h_a, use_a, True)
p_a_recon = self.sup(h_a_recon, use_a, True)
loss_semi_a = self.semi_criterion(p_a, y_a[use_a, :, :]) + \
self.semi_criterion(p_a_recon, y_a[use_a, :, :])
has_b_label = (h_b[use_b, :, :, :].size(0) != 0)
if has_b_label:
p_b = self.sup(h_b, use_b, True)
p_b_recon = self.sup(h_b, use_b, True)
loss_semi_b = self.semi_criterion(p_b, y_b[use_b, :, :]) + \
self.semi_criterion(p_b_recon, y_b[use_b, :, :])
self.loss_gen_total = None
if loss_semi_a is not None and loss_semi_b is not None:
self.loss_gen_total = loss_semi_a + loss_semi_b
elif loss_semi_a is not None:
self.loss_gen_total = loss_semi_a
elif loss_semi_b is not None:
self.loss_gen_total = loss_semi_b
if self.loss_gen_total is not None:
self.loss_gen_total.backward()
self.gen_opt.step()
def sup_forward(self, x, y, d_index, hyperparameters):
self.sup.eval()
# Encoding content image.
one_hot_x = torch.cat([x, self.one_hot_img[d_index, 0].unsqueeze(0)],
1)
hidden, _ = self.gen.encode(one_hot_x)
# Forwarding on supervised model.
y_pred = self.sup(hidden, only_prediction=True)
# Computing metrics.
pred = y_pred.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
jacc = jaccard(pred, y.cpu().squeeze(0).numpy())
return jacc, pred, hidden
def gen_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.gen_opt.zero_grad()
# Encode.
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
h_a, n_a = self.gen.encode(one_hot_x_a)
h_b, n_b = self.gen.encode(one_hot_x_b)
# Decode (within domain).
one_hot_h_a = torch.cat([h_a + n_a, self.one_hot_h[d_index_a]], 1)
one_hot_h_b = torch.cat([h_b + n_b, self.one_hot_h[d_index_b]], 1)
x_a_recon = self.gen.decode(one_hot_h_a)
x_b_recon = self.gen.decode(one_hot_h_b)
# Decode (cross domain).
one_hot_h_ab = torch.cat([h_a + n_a, self.one_hot_h[d_index_b]], 1)
one_hot_h_ba = torch.cat([h_b + n_b, self.one_hot_h[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_h_ba)
x_ab = self.gen.decode(one_hot_h_ab)
# Encode again.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
h_b_recon, n_b_recon = self.gen.encode(one_hot_x_ba)
h_a_recon, n_a_recon = self.gen.encode(one_hot_x_ab)
# Decode again (if needed).
one_hot_h_a_recon = torch.cat(
[h_a_recon + n_a_recon, self.one_hot_h[d_index_a]], 1)
one_hot_h_b_recon = torch.cat(
[h_b_recon + n_b_recon, self.one_hot_h[d_index_b]], 1)
x_aba = self.gen.decode(
one_hot_h_a_recon
) if hyperparameters['recon_x_cyc_w'] > 0 else None
x_bab = self.gen.decode(
one_hot_h_b_recon
) if hyperparameters['recon_x_cyc_w'] > 0 else None
# Reconstruction loss.
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_kl_a = self.__compute_kl(h_a)
self.loss_gen_recon_kl_b = self.__compute_kl(h_b)
self.loss_gen_cyc_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cyc_x_b = self.recon_criterion(x_bab, x_b)
self.loss_gen_recon_kl_cyc_aba = self.__compute_kl(h_a_recon)
self.loss_gen_recon_kl_cyc_bab = self.__compute_kl(h_b_recon)
# GAN loss.
self.loss_gen_adv_a = self.dis.calc_gen_loss(one_hot_x_ba)
self.loss_gen_adv_b = self.dis.calc_gen_loss(one_hot_x_ab)
# Total loss.
self.loss_gen_total = hyperparameters['gan_w'] * self.loss_gen_adv_a + \
hyperparameters['gan_w'] * self.loss_gen_adv_b + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_kl_w'] * self.loss_gen_recon_kl_b + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_a + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_aba + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cyc_x_b + \
hyperparameters['recon_kl_cyc_w'] * self.loss_gen_recon_kl_cyc_bab
self.loss_gen_total.backward()
self.gen_opt.step()
def sample(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
x_a_recon, x_b_recon, x_ba, x_ab = [], [], [], []
for i in range(x_a.size(0)):
h_a, _ = self.gen_a.encode(x_a[i].unsqueeze(0))
h_b, _ = self.gen_b.encode(x_b[i].unsqueeze(0))
x_a_recon.append(self.gen_a.decode(h_a))
x_b_recon.append(self.gen_b.decode(h_b))
x_ba.append(self.gen_a.decode(h_b))
x_ab.append(self.gen_b.decode(h_a))
x_a_recon, x_b_recon = torch.cat(x_a_recon), torch.cat(x_b_recon)
x_ba = torch.cat(x_ba)
x_ab = torch.cat(x_ab)
self.train()
return x_a, x_a_recon, x_ab, x_b, x_b_recon, x_ba
def dis_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.dis_opt.zero_grad()
# Encode.
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
h_a, n_a = self.gen.encode(one_hot_x_a)
h_b, n_b = self.gen.encode(one_hot_x_b)
# Decode (cross domain).
one_hot_h_ab = torch.cat([h_a + n_a, self.one_hot_h[d_index_b]], 1)
one_hot_h_ba = torch.cat([h_b + n_b, self.one_hot_h[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_h_ba)
x_ab = self.gen.decode(one_hot_h_ab)
# D loss.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
self.loss_dis_a = self.dis.calc_dis_loss(one_hot_x_ba.detach(),
one_hot_x_a)
self.loss_dis_b = self.dis.calc_dis_loss(one_hot_x_ab.detach(),
one_hot_x_b)
self.loss_dis_total = hyperparameters['gan_w'] * self.loss_dis_a + \
hyperparameters['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters):
# Load generators.
last_model_name = get_model_list(checkpoint_dir, "gen")
state_dict = torch.load(last_model_name)
self.gen.load_state_dict(state_dict)
epochs = int(last_model_name[-11:-3])
# Load discriminators.
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis.load_state_dict(state_dict)
# Load supervised model.
last_model_name = get_model_list(checkpoint_dir, "sup")
state_dict = torch.load(last_model_name)
self.sup.load_state_dict(state_dict)
# Load optimizers.
last_model_name = get_model_list(checkpoint_dir, "opt")
state_dict = torch.load(last_model_name)
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
for state in self.dis_opt.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
for state in self.gen_opt.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
# Reinitilize schedulers.
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters,
epochs)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters,
epochs)
print('Resume from iteration %d' % epochs)
return epochs
def save(self, snapshot_dir, epoch):
# Save generators, discriminators, and optimizers.
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % epoch)
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % epoch)
sup_name = os.path.join(snapshot_dir, 'sup_%08d.pt' % epoch)
opt_name = os.path.join(snapshot_dir, 'opt_%08d.pt' % epoch)
torch.save(self.gen.state_dict(), gen_name)
torch.save(self.dis.state_dict(), dis_name)
torch.save(self.sup.state_dict(), sup_name)
torch.save(
{
'dis': self.dis_opt.state_dict(),
'gen': self.gen_opt.state_dict()
}, opt_name)
class MUNIT_Trainer(nn.Module):
def __init__(self, hyperparameters, resume_epoch=-1, snapshot_dir=None):
super(MUNIT_Trainer, self).__init__()
lr = hyperparameters['lr']
# Initiate the networks.
self.gen = AdaINGen(
hyperparameters['input_dim'] + hyperparameters['n_datasets'],
hyperparameters['gen'],
hyperparameters['n_datasets']) # Auto-encoder for domain a.
self.dis = MsImageDis(
hyperparameters['input_dim'] + hyperparameters['n_datasets'],
hyperparameters['dis']) # Discriminator for domain a.
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
self.style_dim = hyperparameters['gen']['style_dim']
self.beta1 = hyperparameters['beta1']
self.beta2 = hyperparameters['beta2']
self.weight_decay = hyperparameters['weight_decay']
# Initiating and loader pretrained UNet.
self.sup = UNet(input_channels=hyperparameters['input_dim'],
num_classes=2).cuda()
# Fix the noise used in sampling.
self.s_a = torch.randn(8, self.style_dim, 1, 1).cuda()
self.s_b = torch.randn(8, self.style_dim, 1, 1).cuda()
# Setup the optimizers.
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis.parameters())
gen_params = list(self.gen.parameters()) + list(self.sup.parameters())
self.dis_opt = torch.optim.Adam(
[p for p in dis_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam(
[p for p in gen_params if p.requires_grad],
lr=lr,
betas=(self.beta1, self.beta2),
weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization.
self.apply(weights_init(hyperparameters['init']))
self.dis.apply(weights_init('gaussian'))
# Presetting one hot encoding vectors.
self.one_hot_img = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 256,
256).cuda()
self.one_hot_c = torch.zeros(hyperparameters['n_datasets'],
hyperparameters['batch_size'],
hyperparameters['n_datasets'], 64,
64).cuda()
for i in range(hyperparameters['n_datasets']):
self.one_hot_img[i, :, i, :, :].fill_(1)
self.one_hot_c[i, :, i, :, :].fill_(1)
if resume_epoch != -1:
self.resume(snapshot_dir, hyperparameters)
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def semi_criterion(self, input, target):
loss = CrossEntropyLoss2d(size_average=False).cuda()
return loss(input, target)
def forward(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
s_a = Variable(self.s_a, volatile=True)
s_b = Variable(self.s_b, volatile=True)
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
c_a, s_a_fake = self.gen.encode(one_hot_x_a)
c_b, s_b_fake = self.gen.encode(one_hot_x_b)
one_hot_c_b = torch.cat([c_b, self.one_hot_c[d_index_a]], 1)
one_hot_c_a = torch.cat([c_a, self.one_hot_c[d_index_b]], 1)
x_ba = self.gen.decode(one_hot_c_b, s_a)
x_ab = self.gen.decode(one_hot_c_a, s_b)
self.train()
return x_ab, x_ba
def set_gen_trainable(self, train_bool):
if train_bool:
self.gen.train()
for param in self.gen.parameters():
param.requires_grad = True
else:
self.gen.eval()
for param in self.gen.parameters():
param.requires_grad = True
def set_sup_trainable(self, train_bool):
if train_bool:
self.sup.train()
for param in self.sup.parameters():
param.requires_grad = True
else:
self.sup.eval()
for param in self.sup.parameters():
param.requires_grad = True
def sup_update(self, x_a, x_b, y_a, y_b, d_index_a, d_index_b, use_a,
use_b, hyperparameters):
self.gen_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
# Encode.
c_a, s_a_prime = self.gen.encode(one_hot_x_a)
c_b, s_b_prime = self.gen.encode(one_hot_x_b)
# Decode (within domain).
one_hot_c_a = torch.cat([c_a, self.one_hot_c[d_index_a]], 1)
one_hot_c_b = torch.cat([c_b, self.one_hot_c[d_index_b]], 1)
x_a_recon = self.gen.decode(one_hot_c_a, s_a_prime)
x_b_recon = self.gen.decode(one_hot_c_b, s_b_prime)
# Decode (cross domain).
one_hot_c_ab = torch.cat([c_a, self.one_hot_c[d_index_b]], 1)
one_hot_c_ba = torch.cat([c_b, self.one_hot_c[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_c_ba, s_a)
x_ab = self.gen.decode(one_hot_c_ab, s_b)
# Encode again.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
c_b_recon, s_a_recon = self.gen.encode(one_hot_x_ba)
c_a_recon, s_b_recon = self.gen.encode(one_hot_x_ab)
# Forwarding through supervised model.
p_a = None
p_b = None
loss_semi_a = None
loss_semi_b = None
has_a_label = (c_a[use_a, :, :, :].size(0) != 0)
if has_a_label:
p_a = self.sup(c_a, use_a, True)
p_a_recon = self.sup(c_a_recon, use_a, True)
loss_semi_a = self.semi_criterion(p_a, y_a[use_a, :, :]) + \
self.semi_criterion(p_a_recon, y_a[use_a, :, :])
has_b_label = (c_b[use_b, :, :, :].size(0) != 0)
if has_b_label:
p_b = self.sup(c_b, use_b, True)
p_b_recon = self.sup(c_b, use_b, True)
loss_semi_b = self.semi_criterion(p_b, y_b[use_b, :, :]) + \
self.semi_criterion(p_b_recon, y_b[use_b, :, :])
self.loss_gen_total = None
if loss_semi_a is not None and loss_semi_b is not None:
self.loss_gen_total = loss_semi_a + loss_semi_b
elif loss_semi_a is not None:
self.loss_gen_total = loss_semi_a
elif loss_semi_b is not None:
self.loss_gen_total = loss_semi_b
if self.loss_gen_total is not None:
self.loss_gen_total.backward()
self.gen_opt.step()
def sup_forward(self, x, y, d_index, hyperparameters):
self.sup.eval()
# Encoding content image.
one_hot_x = torch.cat([x, self.one_hot_img[d_index, 0].unsqueeze(0)],
1)
content, _ = self.gen.encode(one_hot_x)
# Forwarding on supervised model.
y_pred = self.sup(content, only_prediction=True)
# Computing metrics.
pred = y_pred.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
jacc = jaccard(pred, y.cpu().squeeze(0).numpy())
return jacc, pred, content
def gen_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.gen_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# Encode.
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
c_a, s_a_prime = self.gen.encode(one_hot_x_a)
c_b, s_b_prime = self.gen.encode(one_hot_x_b)
# Decode (within domain).
one_hot_c_a = torch.cat([c_a, self.one_hot_c[d_index_a]], 1)
one_hot_c_b = torch.cat([c_b, self.one_hot_c[d_index_b]], 1)
x_a_recon = self.gen.decode(one_hot_c_a, s_a_prime)
x_b_recon = self.gen.decode(one_hot_c_b, s_b_prime)
# Decode (cross domain).
one_hot_c_ab = torch.cat([c_a, self.one_hot_c[d_index_b]], 1)
one_hot_c_ba = torch.cat([c_b, self.one_hot_c[d_index_a]], 1)
x_ba = self.gen.decode(one_hot_c_ba, s_a)
x_ab = self.gen.decode(one_hot_c_ab, s_b)
# Encode again.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
c_b_recon, s_a_recon = self.gen.encode(one_hot_x_ba)
c_a_recon, s_b_recon = self.gen.encode(one_hot_x_ab)
# Decode again (if needed).
one_hot_c_aba_recon = torch.cat([c_a_recon, self.one_hot_c[d_index_a]],
1)
one_hot_c_bab_recon = torch.cat([c_b_recon, self.one_hot_c[d_index_b]],
1)
x_aba = self.gen.decode(one_hot_c_aba_recon, s_a_prime)
x_bab = self.gen.decode(one_hot_c_bab_recon, s_b_prime)
# Reconstruction loss.
self.loss_gen_recon_x_a = self.recon_criterion(x_a_recon, x_a)
self.loss_gen_recon_x_b = self.recon_criterion(x_b_recon, x_b)
self.loss_gen_recon_s_a = self.recon_criterion(s_a_recon, s_a)
self.loss_gen_recon_s_b = self.recon_criterion(s_b_recon, s_b)
self.loss_gen_recon_c_a = self.recon_criterion(c_a_recon, c_a)
self.loss_gen_recon_c_b = self.recon_criterion(c_b_recon, c_b)
self.loss_gen_cycrecon_x_a = self.recon_criterion(x_aba, x_a)
self.loss_gen_cycrecon_x_b = self.recon_criterion(x_bab, x_b)
# GAN loss.
self.loss_gen_adv_a = self.dis.calc_gen_loss(one_hot_x_ba)
self.loss_gen_adv_b = self.dis.calc_gen_loss(one_hot_x_ab)
# Total loss.
self.loss_gen_total = hyperparameters['gan_w'] * self.loss_gen_adv_a + \
hyperparameters['gan_w'] * self.loss_gen_adv_b + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_a + \
hyperparameters['recon_s_w'] * self.loss_gen_recon_s_a + \
hyperparameters['recon_c_w'] * self.loss_gen_recon_c_a + \
hyperparameters['recon_x_w'] * self.loss_gen_recon_x_b + \
hyperparameters['recon_s_w'] * self.loss_gen_recon_s_b + \
hyperparameters['recon_c_w'] * self.loss_gen_recon_c_b + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cycrecon_x_a + \
hyperparameters['recon_x_cyc_w'] * self.loss_gen_cycrecon_x_b
self.loss_gen_total.backward()
self.gen_opt.step()
def compute_vgg_loss(self, vgg, img, target):
img_vgg = vgg_preprocess(img)
target_vgg = vgg_preprocess(target)
img_fea = vgg(img_vgg)
target_fea = vgg(target_vgg)
return torch.mean(
(self.instancenorm(img_fea) - self.instancenorm(target_fea))**2)
def sample(self, x_a, x_b):
self.eval()
x_a.volatile = True
x_b.volatile = True
s_a1 = Variable(self.s_a, volatile=True)
s_b1 = Variable(self.s_b, volatile=True)
s_a2 = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda(),
volatile=True)
s_b2 = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda(),
volatile=True)
x_a_recon, x_b_recon, x_ba1, x_ba2, x_ab1, x_ab2 = [], [], [], [], [], []
for i in range(x_a.size(0)):
one_hot_x_a = torch.cat(
[x_a[i].unsqueeze(0), self.one_hot_img_a[i].unsqueeze(0)], 1)
one_hot_x_b = torch.cat(
[x_b[i].unsqueeze(0), self.one_hot_img_b[i].unsqueeze(0)], 1)
c_a, s_a_fake = self.gen.encode(one_hot_x_a)
c_b, s_b_fake = self.gen.encode(one_hot_x_b)
x_a_recon.append(self.gen.decode(c_a, s_a_fake))
x_b_recon.append(self.gen.decode(c_b, s_b_fake))
x_ba1.append(self.gen.decode(c_b, s_a1[i].unsqueeze(0)))
x_ba2.append(self.gen.decode(c_b, s_a2[i].unsqueeze(0)))
x_ab1.append(self.gen.decode(c_a, s_b1[i].unsqueeze(0)))
x_ab2.append(self.gen.decode(c_a, s_b2[i].unsqueeze(0)))
x_a_recon, x_b_recon = torch.cat(x_a_recon), torch.cat(x_b_recon)
x_ba1, x_ba2 = torch.cat(x_ba1), torch.cat(x_ba2)
x_ab1, x_ab2 = torch.cat(x_ab1), torch.cat(x_ab2)
self.train()
return x_a, x_a_recon, x_ab1, x_ab2, x_b, x_b_recon, x_ba1, x_ba2
def dis_update(self, x_a, x_b, d_index_a, d_index_b, hyperparameters):
self.dis_opt.zero_grad()
s_a = Variable(torch.randn(x_a.size(0), self.style_dim, 1, 1).cuda())
s_b = Variable(torch.randn(x_b.size(0), self.style_dim, 1, 1).cuda())
# Encode.
one_hot_x_a = torch.cat([x_a, self.one_hot_img[d_index_a]], 1)
one_hot_x_b = torch.cat([x_b, self.one_hot_img[d_index_b]], 1)
c_a, _ = self.gen.encode(one_hot_x_a)
c_b, _ = self.gen.encode(one_hot_x_b)
one_hot_c_ba = torch.cat([c_b, self.one_hot_c[d_index_a]], 1)
one_hot_c_ab = torch.cat([c_a, self.one_hot_c[d_index_b]], 1)
# Decode (cross domain).
x_ba = self.gen.decode(one_hot_c_ba, s_a)
x_ab = self.gen.decode(one_hot_c_ab, s_b)
# D loss.
one_hot_x_ba = torch.cat([x_ba, self.one_hot_img[d_index_a]], 1)
one_hot_x_ab = torch.cat([x_ab, self.one_hot_img[d_index_b]], 1)
self.loss_dis_a = self.dis.calc_dis_loss(one_hot_x_ba, one_hot_x_a)
self.loss_dis_b = self.dis.calc_dis_loss(one_hot_x_ab, one_hot_x_b)
self.loss_dis_total = hyperparameters['gan_w'] * self.loss_dis_a + \
hyperparameters['gan_w'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters):
print("--> " + checkpoint_dir)
# Load generator.
last_model_name = get_model_list(checkpoint_dir, "gen")
state_dict = torch.load(last_model_name)
self.gen.load_state_dict(state_dict)
epochs = int(last_model_name[-11:-3])
# Load supervised model.
last_model_name = get_model_list(checkpoint_dir, "sup")
state_dict = torch.load(last_model_name)
self.sup.load_state_dict(state_dict)
# Load discriminator.
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis.load_state_dict(state_dict)
# Load optimizers.
last_model_name = get_model_list(checkpoint_dir, "opt")
state_dict = torch.load(last_model_name)
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
for state in self.dis_opt.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
for state in self.gen_opt.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
# Reinitilize schedulers.
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters,
epochs)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters,
epochs)
print('Resume from epoch %d' % epochs)
return epochs
def save(self, snapshot_dir, epoch):
# Save generators, discriminators, and optimizers.
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % epoch)
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % epoch)
sup_name = os.path.join(snapshot_dir, 'sup_%08d.pt' % epoch)
opt_name = os.path.join(snapshot_dir, 'opt_%08d.pt' % epoch)
torch.save(self.gen.state_dict(), gen_name)
torch.save(self.dis.state_dict(), dis_name)
torch.save(self.sup.state_dict(), sup_name)
torch.save(
{
'gen': self.gen_opt.state_dict(),
'dis': self.dis_opt.state_dict()
}, opt_name)
# seed 値の固定
np.random.seed(args.seed)
torch.manual_seed(args.seed)
#======================================================================
# データセットを読み込み or 生成
# データの前処理
#======================================================================
ds_test = Map2AerialDataset( args.dataset_dir, "val", args.image_size, args.image_size, args.debug )
dloader_test = torch.utils.data.DataLoader(ds_test, batch_size=args.batch_size, shuffle=False )
#======================================================================
# モデルの構造を定義する。
#======================================================================
model = UNet(
n_in_channels = 3, n_out_channels = 3,
n_fmaps = args.n_fmaps,
).to( device )
if( args.debug ):
print( "model :\n", model )
# モデルを読み込む
if not args.load_checkpoints_dir == '' and os.path.exists(args.load_checkpoints_dir):
init_step = load_checkpoint(model, device, os.path.join(args.load_checkpoints_dir, "model_final.pth") )
#======================================================================
# モデルの推論処理
#======================================================================
print("Starting Test Loop...")
n_print = 1
model.eval()
def test_unet(root,\
psf_path,
method,\
std, \
model_path,\
visual, \
use_gpu,\
b_size=1):
"""
Model UNet
"""
model_name = method + '_gaussian'
save_images_path = './Results/' + model_name + '_std_' + str(std).replace(
'.', '') + '/'
test_dataset = CellDataset(root, psf_path, 'gaussian', 1.0, std)
test_loader = DataLoader(test_dataset,
batch_size=b_size,
shuffle=False,
num_workers=1)
model = UNet(mode='batch')
state_dict = torch.load(os.path.join(model_path, model_name))
state_dict = state_dict['model_state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
new_state_dict[k] = v
model.load_state_dict(new_state_dict)
model.eval()
if use_gpu == 1:
model.cuda()
psnr_values_test = []
ssim_values_test = []
distorted_psnr_test = []
distorted_ssim_test = []
for i_batch, ((gt, image), psf, index, image_name, _,
std) in enumerate(tqdm(test_loader)):
image = image.reshape((b_size, 1, image.shape[-2], image.shape[-1]))
gt = gt.reshape((b_size, 1, gt.shape[-2], gt.shape[-1]))
if use_gpu == 1:
image = image.cuda()
gt = gt.cuda()
distorted_psnr = calc_psnr(image.clamp(gt.min(), gt.max()), gt)
distorted_ssim = ssim(image.clamp(gt.min(), gt.max()), gt)
output = model(image)
psnr_test = calc_psnr(output.clamp(gt.min(), gt.max()), gt)
s_sim_test = ssim(output.clamp(gt.min(), gt.max()), gt)
psnr_values_test.append(psnr_test.item())
ssim_values_test.append(s_sim_test.item())
distorted_psnr_test.append(distorted_psnr.item())
distorted_ssim_test.append(distorted_ssim.item())
#Save image
if visual == 1:
if not os.path.exists(save_images_path):
os.makedirs(save_images_path, exist_ok=True)
io.imsave(os.path.join(save_images_path, 'output_' + str(image_name[0][:-4]) + '_' + \
str(model_name) + '_' + str(std.item()).replace('.', '') + '.png'), np.uint8(output[0][0].detach().cpu().numpy().clip(0,1) * 255.))
print('Test on Gaussian noise with %.3f std: PSNR %.2f, SSIM %.4f, distorted PSNR %.2f, distorted SSIM %.4f' % (std, np.array(psnr_values_test).mean(), \
np.array(ssim_values_test).mean(), \
np.array(distorted_psnr_test).mean(), \
np.array(distorted_ssim_test).mean()))
return
imgs = tiff.imread(trn_imgs_path)[:, :, :, np.newaxis] / 255.
msks = tiff.imread(trn_msks_path)[:, :, :, np.newaxis] / 255.
# Normalize images.
imgs -= np.mean(imgs)
imgs /= np.std(imgs)
# Train/val split.
imgs_trn, msks_trn = imgs[:20, ...], msks[:20, ...]
imgs_val, msks_val = imgs[20:, ...], msks[20:, ...]
data = (imgs_trn, msks_trn, imgs_val, msks_val)
# Network and training parameters.
input_shape = (128, 128, 1)
total_iters = 10000
iters_trn = 500
iters_val = 100
batch = 8
epochs = total_iters // iters_trn
alpha0 = 0.1
alpha1 = 1
alpha_switch_epoch = 3
# Networks.
S = UNet(input_shape)
D = ConvNetClassifier(S.output_shape[1:])
# Training.
train_adversarial(S, D, *data, iters_trn, iters_val, epochs, batch, alpha0,
alpha1, alpha_switch_epoch)
def train(params, args, world_rank, local_rank):
#logging info
logging.info('rank {:d}, begin data loader init (local rank {:d})'.format(
world_rank, local_rank))
# set device
device = torch.device("cuda:{}".format(local_rank))
# data loader
pipe = dl.DaliPipeline(params,
num_threads=params.num_data_workers,
device_id=device.index)
pipe.build()
train_data_loader = DALIGenericIterator([pipe], ['inp', 'tar'],
params.Nsamples,
auto_reset=True)
logging.info('rank %d, data loader initialized' % world_rank)
model = UNet.UNet(params).to(device)
if not args.resuming:
model.apply(model.get_weights_function(params.weight_init))
optimizer = optimizers.FusedAdam(model.parameters(), lr=params.lr)
#model, optimizer = amp.initialize(model, optimizer, opt_level="O1") # for automatic mixed precision
if params.distributed:
model = DDP(model,
device_ids=[device.index],
output_device=device.index)
# loss
criterion = UNet.CosmoLoss(params.LAMBDA_2)
# amp stuff
if args.enable_amp:
gscaler = amp.GradScaler()
iters = 0
startEpoch = 0
checkpoint = None
if args.resuming:
if world_rank == 0:
logging.info("Loading checkpoint %s" % params.checkpoint_path)
checkpoint = torch.load(params.checkpoint_path, map_location=device)
model.load_state_dict(checkpoint['model_state'])
iters = checkpoint['iters']
startEpoch = checkpoint['epoch'] + 1
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
if world_rank == 0:
logging.info(model)
logging.info("Starting Training Loop...")
with torch.autograd.profiler.emit_nvtx():
for epoch in range(startEpoch, startEpoch + params.num_epochs):
if args.global_timing:
dist.barrier()
start = time.time()
epoch_step = 0
tr_time = 0.
fw_time = 0.
bw_time = 0.
log_time = 0.
model.train()
for data in train_data_loader:
torch.cuda.nvtx.range_push("cosmo3D:step {}".format(iters))
tr_start = time.time()
adjust_LR(optimizer, params, iters)
# fetch data
inp = data[0]["inp"]
tar = data[0]["tar"]
if not args.io_only:
torch.cuda.nvtx.range_push(
"cosmo3D:forward {}".format(iters))
# fw pass
fw_time -= time.time()
optimizer.zero_grad()
with amp.autocast(args.enable_amp):
gen = model(inp)
loss = criterion(gen, tar)
fw_time += time.time()
torch.cuda.nvtx.range_pop()
# bw pass
torch.cuda.nvtx.range_push(
"cosmo3D:backward {}".format(iters))
bw_time -= time.time()
if args.enable_amp:
gscaler.scale(loss).backward()
gscaler.step(optimizer)
gscaler.update()
else:
loss.backward()
optimizer.step()
bw_time += time.time()
torch.cuda.nvtx.range_pop()
iters += 1
epoch_step += 1
# step done
tr_end = time.time()
tr_time += tr_end - tr_start
torch.cuda.nvtx.range_pop()
# epoch done
if args.global_timing:
dist.barrier()
end = time.time()
epoch_time = end - start
step_time = epoch_time / float(epoch_step)
tr_time /= float(epoch_step)
fw_time /= float(epoch_step)
bw_time /= float(epoch_step)
io_time = max([step_time - fw_time - bw_time, 0])
iters_per_sec = 1. / step_time
fw_per_sec = 1. / tr_time
if world_rank == 0:
logging.info('Time taken for epoch {} is {} sec'.format(
epoch + 1, epoch_time))
logging.info(
'train step time = {} ({} steps), logging time = {}'.
format(tr_time, epoch_step, log_time))
logging.info('train samples/sec = {} fw steps/sec = {}'.format(
iters_per_sec, fw_per_sec))
def train(params, args, world_rank, local_rank):
#logging info
logging.info('rank {:d}, begin data loader init (local rank {:d})'.format(
world_rank, local_rank))
# set device
device = torch.device("cuda:{}".format(local_rank))
# data loader
pipe = dl.DaliPipeline(params,
num_threads=params.num_data_workers,
device_id=device.index)
pipe.build()
train_data_loader = DALIGenericIterator([pipe], ['inp', 'tar'],
params.Nsamples,
auto_reset=True)
logging.info('rank %d, data loader initialized' % world_rank)
model = UNet.UNet(params)
model.to(device)
if not args.resuming:
model.apply(model.get_weights_function(params.weight_init))
optimizer = optimizers.FusedAdam(model.parameters(), lr=params.lr)
#model, optimizer = amp.initialize(model, optimizer, opt_level="O1") # for automatic mixed precision
if params.distributed:
model = DDP(model, device_ids=[local_rank])
# loss
criterion = UNet.CosmoLoss(params.LAMBDA_2)
# amp stuff
if args.enable_amp:
gscaler = amp.GradScaler()
iters = 0
startEpoch = 0
checkpoint = None
if args.resuming:
if world_rank == 0:
logging.info("Loading checkpoint %s" % params.checkpoint_path)
checkpoint = torch.load(params.checkpoint_path, map_location=device)
model.load_state_dict(checkpoint['model_state'])
iters = checkpoint['iters']
startEpoch = checkpoint['epoch'] + 1
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
if world_rank == 0:
logging.info(model)
logging.info("Starting Training Loop...")
for epoch in range(startEpoch, startEpoch + params.num_epochs):
start = time.time()
nsteps = 0
fw_time = 0.
bw_time = 0.
log_time = 0.
model.train()
step_time = time.time()
#for i, data in enumerate(train_data_loader, 0):
with torch.autograd.profiler.emit_nvtx():
for data in train_data_loader:
iters += 1
#adjust_LR(optimizer, params, iters)
inp = data[0]["inp"]
tar = data[0]["tar"]
if not args.io_only:
torch.cuda.nvtx.range_push("cosmo3D:forward")
# fw pass
fw_time -= time.time()
optimizer.zero_grad()
with amp.autocast(args.enable_amp):
gen = model(inp)
loss = criterion(gen, tar)
fw_time += time.time()
torch.cuda.nvtx.range_pop()
# bw pass
torch.cuda.nvtx.range_push("cosmo3D:backward")
bw_time -= time.time()
if args.enable_amp:
gscaler.scale(loss).backward()
gscaler.step(optimizer)
gscaler.update()
else:
loss.backward()
optimizer.step()
bw_time += time.time()
torch.cuda.nvtx.range_pop()
nsteps += 1
# epoch done
dist.barrier()
step_time = (time.time() - step_time) / float(nsteps)
fw_time /= float(nsteps)
bw_time /= float(nsteps)
io_time = max([step_time - fw_time - bw_time, 0])
iters_per_sec = 1. / step_time
end = time.time()
if world_rank == 0:
logging.info('Time taken for epoch {} is {} sec'.format(
epoch + 1, end - start))
logging.info(
'total time / step = {}, fw time / step = {}, bw time / step = {}, exposed io time / step = {}, iters/s = {}, logging time = {}'
.format(step_time, fw_time, bw_time, io_time,
iters_per_sec, log_time))
## Output training stats
#model.eval()
#if world_rank==0:
# log_start = time.time()
# gens = []
# tars = []
# with torch.no_grad():
# for i, data in enumerate(train_data_loader, 0):
# if i>=16:
# break
# #inp, tar = map(lambda x: x.to(device), data)
# inp, tar = data
# gen = model(inp)
# gens.append(gen.detach().cpu().numpy())
# tars.append(tar.detach().cpu().numpy())
# gens = np.concatenate(gens, axis=0)
# tars = np.concatenate(tars, axis=0)
#
# # Scalars
# args.tboard_writer.add_scalar('G_loss', loss.item(), iters)
#
# # Plots
# fig, chi, L1score = meanL1(gens, tars)
# args.tboard_writer.add_figure('pixhist', fig, iters, close=True)
# args.tboard_writer.add_scalar('Metrics/chi', chi, iters)
# args.tboard_writer.add_scalar('Metrics/rhoL1', L1score[0], iters)
# args.tboard_writer.add_scalar('Metrics/vxL1', L1score[1], iters)
# args.tboard_writer.add_scalar('Metrics/vyL1', L1score[2], iters)
# args.tboard_writer.add_scalar('Metrics/vzL1', L1score[3], iters)
# args.tboard_writer.add_scalar('Metrics/TL1', L1score[4], iters)
#
# fig = generate_images(inp.detach().cpu().numpy()[0], gens[-1], tars[-1])
# args.tboard_writer.add_figure('genimg', fig, iters, close=True)
# log_end = time.time()
# log_time += log_end - log_start
# # Save checkpoint
# torch.save({'iters': iters, 'epoch':epoch, 'model_state': model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict()}, params.checkpoint_path)
#end = time.time()
#if world_rank==0:
# logging.info('Time taken for epoch {} is {} sec'.format(epoch + 1, end-start))
# logging.info('total time / step = {}, fw time / step = {}, bw time / step = {}, exposed io time / step = {}, iters/s = {}, logging time = {}'
# .format(step_time, fw_time, bw_time, io_time, iters_per_sec, log_time))
# finalize
dist.barrier()
BASE_DIR = os.path.join(os.pardir, "data", "test")
SAVE_DIR = os.path.join(os.pardir, "results")
test_dataset = SimulationDataset(base_dir=BASE_DIR)
for i, data_sample in tqdm(enumerate(test_dataset)):
if i > 20:
break
geometry_array = data_sample["geometry"]
flow_array = data_sample["flow"]
# Load model and make prediction based on geometry
path_to_model = os.path.join(os.pardir, "models", "model_checkpoint.pt")
model = UNet()
model.load_state_dict(torch.load(path_to_model))
model.eval()
geometry = torch.from_numpy(geometry_array)
geometry = geometry.unsqueeze(0)
prediction = model(geometry)
# Postprocessing
geometry = np.transpose(geometry_array, [1, 2, 0])
prediction = prediction.squeeze(0)
prediction = prediction.permute(1, 2, 0)
prediction = prediction.detach().numpy()
flow_array = np.transpose(flow_array, (1, 2, 0))
