def main():
# torch.manual_seed(1234)
# torch.cuda.manual_seed(1234)
opt = TrainOptions()
args = opt.initialize()
_t = {'iter time' : Timer()}
model_name = args.source + '_to_' + args.target
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
os.makedirs(os.path.join(args.snapshot_dir, 'logs'))
opt.print_options(args)
sourceloader, targetloader = CreateSrcDataLoader(args), CreateTrgDataLoader(args)
targetloader_iter, sourceloader_iter = iter(targetloader), iter(sourceloader)
model, optimizer = CreateModel(args)
model_D, optimizer_D = CreateDiscriminator(args)
start_iter = 0
if args.restore_from is not None:
start_iter = int(args.restore_from.rsplit('/', 1)[1].rsplit('_')[1])
train_writer = tensorboardX.SummaryWriter(os.path.join(args.snapshot_dir, "logs", model_name))
bce_loss = torch.nn.BCEWithLogitsLoss()
cent_loss=ConditionalEntropyLoss()
cudnn.enabled = True
cudnn.benchmark = True
model.train()
model.cuda()
model_D.train()
model_D.cuda()
loss = ['loss_seg_src', 'loss_seg_trg', 'loss_D_trg_fake', 'loss_D_src_real', 'loss_D_trg_real']
_t['iter time'].tic()
pbar = tqdm(range(start_iter,args.num_steps_stop))
#for i in range(start_iter, args.num_steps):
for i in pbar:
model.adjust_learning_rate(args, optimizer, i)
model_D.adjust_learning_rate(args, optimizer_D, i)
optimizer.zero_grad()
optimizer_D.zero_grad()
for param in model_D.parameters():
param.requires_grad = False
src_img, src_lbl, _, _ = sourceloader_iter.next()
src_img, src_lbl = Variable(src_img).cuda(), Variable(src_lbl.long()).cuda()
src_seg_score = model(src_img)
loss_seg_src = CrossEntropy2d(src_seg_score, src_lbl)
#loss_seg_src = model.loss
loss_seg_src.backward()
if args.data_label_folder_target is not None:
trg_img, trg_lbl, _, _ = targetloader_iter.next()
trg_img, trg_lbl = Variable(trg_img).cuda(), Variable(trg_lbl.long()).cuda()
trg_seg_score = model(trg_img)
loss_seg_trg = model.loss
else:
trg_img, _, name = targetloader_iter.next()
trg_img = Variable(trg_img).cuda()
trg_seg_score = model(trg_img)
#ipdb.set_trace()
loss_seg_trg= cent_loss(trg_seg_score)
#loss_seg_trg= entropy_loss(F.softmax(trg_seg_score))
#loss_seg_trg = 0
outD_trg = model_D(F.softmax(trg_seg_score))
loss_D_trg_fake = bce_loss(outD_trg, Variable(torch.FloatTensor(outD_trg.data.size()).fill_(0)).cuda())
#loss_D_trg_fake = model_D.loss
loss_trg = args.lambda_adv_target * (loss_D_trg_fake + loss_seg_trg)
loss_trg.backward()
for param in model_D.parameters():
param.requires_grad = True
src_seg_score, trg_seg_score = src_seg_score.detach(), trg_seg_score.detach()
outD_src = model_D(F.softmax(src_seg_score))
loss_D_src_real = bce_loss(outD_src, Variable(torch.FloatTensor(outD_src.data.size()).fill_(0)).cuda())/ 2
#loss_D_src_real = model_D.loss / 2
loss_D_src_real.backward()
outD_trg = model_D(F.softmax(trg_seg_score))
loss_D_trg_real = bce_loss(outD_trg, Variable(torch.FloatTensor(outD_trg.data.size()).fill_(1)).cuda())/ 2
#loss_D_trg_real = model_D.loss / 2
loss_D_trg_real.backward()
d_loss=loss_D_src_real.data+ loss_D_trg_real.data
optimizer.step()
optimizer_D.step()
for m in loss:
train_writer.add_scalar(m, eval(m), i+1)
if (i+1) % args.save_pred_every == 0:
print 'taking snapshot ...'
torch.save(model.state_dict(), os.path.join(args.snapshot_dir, '%s_' %(args.source) +str(i+1)+'.pth' ))
torch.save(model_D.state_dict(), os.path.join(args.snapshot_dir, '%s_' %(args.source) +str(i+1)+'_D.pth' ))
if (i+1) % args.print_freq == 0:
_t['iter time'].toc(average=False)
print '[it %d][src seg loss %.4f][adv loss %.4f][d loss %.4f][lr %.4f][%.2fs]' % \
(i + 1, loss_seg_src.data, loss_D_trg_fake.data,d_loss,optimizer.param_groups[0]['lr']*10000, _t['iter time'].diff)
if i + 1 > args.num_steps_stop:
print 'finish training'
break
_t['iter time'].tic()
def main():
# torch.manual_seed(1234)
# torch.cuda.manual_seed(1234)
opt = TrainOptions()
args = opt.initialize()
_t = {'iter time': Timer()}
model_name = args.source + '_to_' + args.target
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
os.makedirs(os.path.join(args.snapshot_dir, 'logs'))
opt.print_options(args)
sourceloader, targetloader = CreateSrcDataLoader(
args), CreateTrgDataLoader(args)
targetloader_iter, sourceloader_iter = iter(targetloader), iter(
sourceloader)
model, optimizer = CreateModel(args)
model_D, optimizer_D = CreateDiscriminator(args)
start_iter = 0
if args.restore_from is not None:
start_iter = int(args.restore_from.rsplit('/', 1)[1].rsplit('_')[1])
train_writer = tensorboardX.SummaryWriter(
os.path.join(args.snapshot_dir, "logs", model_name))
bce_loss = torch.nn.BCEWithLogitsLoss()
l1_loss = torch.nn.L1Loss()
cos_loss = torch.nn.CosineSimilarity(dim=0, eps=1e-06)
cudnn.enabled = True
cudnn.benchmark = True
model.train()
model.cuda()
model_D.train()
model_D.cuda()
loss = [
'loss_seg_src', 'loss_seg_trg', 'loss_D_trg_fake', 'loss_D_src_real',
'loss_D_trg_real'
]
_t['iter time'].tic()
pbar = tqdm(range(start_iter, args.num_steps_stop))
#for i in range(start_iter, args.num_steps):
for i in pbar:
model.adjust_learning_rate(args, optimizer, i)
model_D.adjust_learning_rate(args, optimizer_D, i)
optimizer.zero_grad()
optimizer_D.zero_grad()
for param in model_D.parameters():
param.requires_grad = False
src_img, src_lbl, _, _ = sourceloader_iter.next()
src_img, src_lbl = Variable(src_img).cuda(), Variable(
src_lbl.long()).cuda()
src_seg_score, src_seg_score2 = model(src_img)
loss_seg_src1 = CrossEntropy2d(src_seg_score, src_lbl)
loss_seg_src2 = CrossEntropy2d(src_seg_score2, src_lbl)
loss_seg_src = loss_seg_src1 + loss_seg_src2
loss_seg_src.backward()
if args.data_label_folder_target is not None:
trg_img, trg_lbl, _, _ = targetloader_iter.next()
trg_img, trg_lbl = Variable(trg_img).cuda(), Variable(
trg_lbl.long()).cuda()
trg_seg_score = model(trg_img)
loss_seg_trg = model.loss
else:
trg_img, _, name = targetloader_iter.next()
trg_img = Variable(trg_img).cuda()
trg_seg_score, trg_seg_score2 = model(trg_img)
loss_seg_trg = 0
outD_trg = model_D(F.softmax(trg_seg_score))
outD_trg2 = model_D(F.softmax(trg_seg_score2))
loss_D_trg_fake1 = bce_loss(
outD_trg,
Variable(torch.FloatTensor(outD_trg.data.size()).fill_(0)).cuda())
loss_D_trg_fake2 = bce_loss(
outD_trg2,
Variable(torch.FloatTensor(outD_trg2.data.size()).fill_(0)).cuda())
loss_D_trg_fake = loss_D_trg_fake1 + loss_D_trg_fake2
loss_agree = l1_loss(F.softmax(trg_seg_score),
F.softmax(trg_seg_score2))
loss_trg = args.lambda_adv_target * loss_D_trg_fake + loss_seg_trg + loss_agree
loss_trg.backward()
#Weight Discrepancy Loss
W5 = None
W6 = None
if args.model == 'DeepLab2':
for (w5, w6) in zip(model.layer5.parameters(),
model.layer6.parameters()):
if W5 is None and W6 is None:
W5 = w5.view(-1)
W6 = w6.view(-1)
else:
W5 = torch.cat((W5, w5.view(-1)), 0)
W6 = torch.cat((W6, w6.view(-1)), 0)
#ipdb.set_trace()
#loss_weight = (torch.matmul(W5, W6) / (torch.norm(W5) * torch.norm(W6)) + 1) # +1 is for a positive loss
# loss_weight = loss_weight * damping * 2
loss_weight = args.weight_div * (cos_loss(W5, W6) + 1)
loss_weight.backward()
for param in model_D.parameters():
param.requires_grad = True
src_seg_score, trg_seg_score = src_seg_score.detach(
), trg_seg_score.detach()
src_seg_score2, trg_seg_score2 = src_seg_score2.detach(
), trg_seg_score2.detach()
outD_src = model_D(F.softmax(src_seg_score))
loss_D_src_real1 = bce_loss(
outD_src,
Variable(torch.FloatTensor(
outD_src.data.size()).fill_(0)).cuda()) / 2
outD_src2 = model_D(F.softmax(src_seg_score2))
loss_D_src_real2 = bce_loss(
outD_src2,
Variable(torch.FloatTensor(
outD_src2.data.size()).fill_(0)).cuda()) / 2
loss_D_src_real = loss_D_src_real1 + loss_D_src_real2
loss_D_src_real.backward()
outD_trg = model_D(F.softmax(trg_seg_score))
loss_D_trg_real1 = bce_loss(
outD_trg,
Variable(torch.FloatTensor(
outD_trg.data.size()).fill_(1)).cuda()) / 2
outD_trg2 = model_D(F.softmax(trg_seg_score2))
loss_D_trg_real2 = bce_loss(
outD_trg2,
Variable(torch.FloatTensor(
outD_trg2.data.size()).fill_(1)).cuda()) / 2
loss_D_trg_real = loss_D_trg_real1 + loss_D_trg_real2
loss_D_trg_real.backward()
d_loss = loss_D_src_real.data + loss_D_trg_real.data
optimizer.step()
optimizer_D.step()
for m in loss:
train_writer.add_scalar(m, eval(m), i + 1)
if (i + 1) % args.save_pred_every == 0:
print 'taking snapshot ...'
torch.save(
model.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(i + 1) + '.pth'))
torch.save(
model_D.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(i + 1) + '_D.pth'))
if (i + 1) % args.print_freq == 0:
_t['iter time'].toc(average=False)
print '[it %d][src seg loss %.4f][adv loss %.4f][d loss %.4f][agree loss %.4f][div loss %.4f][lr %.4f][%.2fs]' % \
(i + 1, loss_seg_src.data, loss_D_trg_fake.data,d_loss,loss_agree.data,loss_weight.data, optimizer.param_groups[0]['lr']*10000, _t['iter time'].diff)
if i + 1 > args.num_steps_stop:
print 'finish training'
break
_t['iter time'].tic()
def main():
args = arg_parser.Parse()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
logger = Logger(args.log_dir)
logger.PrintAndLogArgs(args)
saver = ImageAndLossSaver(args.tb_logs_dir, logger.log_folder,
args.checkpoints_dir, args.save_pics_every)
source_train_loader = CreateSrcDataLoader(args, 'train_semseg')
source_val_loader = CreateSrcDataLoader(args, 'val_semseg')
semseg_net, semseg_optimizer = CreateModel(args)
semseg_net = nn.DataParallel(semseg_net.cuda())
semseg_scheduler = torch.optim.lr_scheduler.MultiStepLR(
semseg_optimizer,
milestones=np.arange(0, args.num_epochs, 10),
gamma=0.9)
logger.info('######### Network created #########')
logger.info('Architecture of Semantic Segmentation network:\n' +
str(semseg_net))
for epoch in range(args.num_epochs):
semseg_net.train()
saver.Reset()
logger.info('#################[Epoch %d]#################' %
(epoch + 1))
for batch_num, (src_img, src_lbl, _,
_) in enumerate(source_train_loader):
start_time = time.time()
semseg_optimizer.zero_grad()
src_input_batch = Variable(src_img, requires_grad=False).cuda()
src_label_batch = Variable(src_lbl, requires_grad=False).cuda()
predicted, loss_seg, loss_ent = semseg_net(
src_input_batch, lbl=src_label_batch) # F(G(S.T))
pred_label = torch.argmax(predicted, dim=1)
loss = torch.mean(loss_seg + args.entW * loss_ent)
saver.WriteSemsegLossHistory(args.model, loss.item())
loss.backward()
semseg_optimizer.step()
saver.running_time += time.time() - start_time
if saver.SaveImagesSemsegIteration:
saver.SaveTrainSemegImages(epoch, src_img[0, :, :, :],
src_lbl[0, :, :],
pred_label[0, :, :])
if (batch_num + 1) % args.print_every == 0:
logger.info('Finished Batch %d' % (batch_num + 1))
# Update LR:
semseg_scheduler.step()
#Save checkpoint:
saver.SaveModelsCheckpointSemseg(semseg_net, args.model, epoch)
#Validation:
semseg_net.eval()
rand_samp_inds = np.random.randint(0, len(source_val_loader.dataset),
5)
rand_batchs = np.floor(rand_samp_inds / args.batch_size).astype(np.int)
cm = torch.zeros((NUM_CLASSES, NUM_CLASSES)).cuda()
for val_batch_num, (src_img, src_lbl, _,
_) in enumerate(source_val_loader):
with torch.no_grad():
src_input_batch = Variable(src_img, requires_grad=False).cuda()
src_label_batch = Variable(src_lbl, requires_grad=False).cuda()
pred_softs_batch = semseg_net(src_input_batch)
pred_batch = torch.argmax(pred_softs_batch, dim=1)
cm += compute_cm_batch_torch(pred_batch, src_label_batch,
IGNORE_LABEL, NUM_CLASSES)
if (val_batch_num + 1) in rand_batchs:
rand_offset = np.random.randint(0, args.batch_size)
saver.SaveValidationImages(
epoch, src_input_batch[rand_offset, :, :, :],
src_label_batch[rand_offset, :, :],
pred_batch[rand_offset, :, :])
iou, miou = compute_iou_torch(cm)
saver.SaveEpochAccuracy(iou, miou, epoch)
logger.info(
'Average accuracy of Epoch #%d on target domain: mIoU = %2f' %
(epoch + 1, miou))
logger.info(
'-----------------------------------Epoch #%d Finished-----------------------------------'
% (epoch + 1))
del cm, pred_softs_batch, pred_batch
saver.tb.close()
logger.info('Finished training.')
def main():
opt = TrainOptions() # loading train options(arg parser)
args = opt.initialize() # get arguments
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
_t = {'iter time': Timer()}
model_name = args.source + '_to_' + args.target
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
os.makedirs(os.path.join(args.snapshot_dir, 'logs'))
opt.print_options(args) # print set options
sourceloader, targetloader = CreateSrcDataLoader(
args), CreateTrgDataLoader(args)
sourceloader_iter, targetloader_iter = iter(sourceloader), iter(
targetloader)
model, optimizer = CreateModel(args)
start_iter = 0
if args.restore_from is not None:
start_iter = int(args.restore_from.rsplit('/', 1)[1].rsplit('_')[1])
cudnn.enabled = True
cudnn.benchmark = True
model.train()
model.cuda()
# losses to log
loss = ['loss_seg_src', 'loss_seg_trg']
loss_train = 0.0
loss_val = 0.0
loss_train_list = []
loss_val_list = []
mean_img = torch.zeros(1, 1)
class_weights = Variable(CS_weights).cuda()
_t['iter time'].tic()
for i in range(start_iter, args.num_steps):
model.adjust_learning_rate(args, optimizer, i) # adjust learning rate
optimizer.zero_grad() # zero grad
src_img, src_lbl, _, _ = sourceloader_iter.next() # new batch source
trg_img, trg_lbl, _, _ = targetloader_iter.next() # new batch target
scr_img_copy = src_img.clone()
if mean_img.shape[-1] < 2:
B, C, H, W = src_img.shape
mean_img = IMG_MEAN.repeat(B, 1, H, W)
#-------------------------------------------------------------------#
# 1. source to target, target to target
src_in_trg = FDA_source_to_target(src_img, trg_img,
L=args.LB) # src_lbl
trg_in_trg = trg_img
# 2. subtract mean
src_img = src_in_trg.clone() - mean_img # src, src_lbl
trg_img = trg_in_trg.clone() - mean_img # trg, trg_lbl
#-------------------------------------------------------------------#
# evaluate and update params #####
src_img, src_lbl = Variable(src_img).cuda(), Variable(
src_lbl.long()).cuda() # to gpu
src_seg_score = model(src_img,
lbl=src_lbl,
weight=class_weights,
ita=args.ita) # forward pass
loss_seg_src = model.loss_seg # get loss
loss_ent_src = model.loss_ent
# get target loss, only entropy for backpro
trg_img, trg_lbl = Variable(trg_img).cuda(), Variable(
trg_lbl.long()).cuda() # to gpu
trg_seg_score = model(trg_img,
lbl=trg_lbl,
weight=class_weights,
ita=args.ita) # forward pass
loss_seg_trg = model.loss_seg # get loss
loss_ent_trg = model.loss_ent
triger_ent = 0.0
if i > args.switch2entropy:
triger_ent = 1.0
loss_all = loss_seg_src + triger_ent * args.entW * loss_ent_trg # loss of seg on src, and ent on s and t
loss_all.backward()
optimizer.step()
loss_train += loss_seg_src.detach().cpu().numpy()
loss_val += loss_seg_trg.detach().cpu().numpy()
if (i + 1) % args.save_pred_every == 0:
print('taking snapshot ...')
torch.save(
model.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(i + 1) + '.pth'))
if (i + 1) % args.print_freq == 0:
_t['iter time'].toc(average=False)
print('[it %d][src seg loss %.4f][trg seg loss %.4f][lr %.4f][%.2fs]' % \
(i + 1, loss_seg_src.data, loss_seg_trg.data, optimizer.param_groups[0]['lr']*10000, _t['iter time'].diff) )
sio.savemat(args.tempdata, {
'src_img': src_img.cpu().numpy(),
'trg_img': trg_img.cpu().numpy()
})
loss_train /= args.print_freq
loss_val /= args.print_freq
loss_train_list.append(loss_train)
loss_val_list.append(loss_val)
sio.savemat(args.matname, {
'loss_train': loss_train_list,
'loss_val': loss_val_list
})
loss_train = 0.0
loss_val = 0.0
if i + 1 > args.num_steps_stop:
print('finish training')
break
_t['iter time'].tic()
def main():
opt = TrainOptions()
args = opt.initialize()
_t = {'iter time': Timer()}
model_name = args.source + '_to_' + args.target
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
os.makedirs(os.path.join(args.snapshot_dir, 'logs'))
opt.print_options(args)
sourceloader, targetloader = CreateSrcDataLoader(
args), CreateTrgDataLoader(args)
targetloader_iter, sourceloader_iter = iter(targetloader), iter(
sourceloader)
model, optimizer = CreateModel(args)
model_D1, optimizer_D1 = CreateDiscriminator(args, 1)
model_D2, optimizer_D2 = CreateDiscriminator(args, 2)
start_iter = 0
if args.restore_from is not None:
start_iter = int(args.restore_from.rsplit('/', 1)[1].rsplit('_')[1])
#train_writer = tensorboardX.SummaryWriter(os.path.join(args.snapshot_dir, "logs", model_name))
bce_loss = torch.nn.BCEWithLogitsLoss()
interp_target = nn.Upsample(size=(1024, 1024),
mode='bilinear',
align_corners=True)
interp_source = nn.Upsample(size=(1024, 1024),
mode='bilinear',
align_corners=True)
cudnn.enabled = True
cudnn.benchmark = True
model.train()
model.cuda()
model_D1.train()
model_D1.cuda()
model_D2.train()
model_D2.cuda()
weight_loss = WeightedBCEWithLogitsLoss()
loss = [
'loss_seg_src', 'loss_seg_trg', 'loss_D_trg_fake', 'loss_D_src_real',
'loss_D_trg_real'
]
_t['iter time'].tic()
load_selected_samples = args.load_selected_samples
if load_selected_samples is None:
total_num = args.total_number
else:
clean_ids = [i_id.strip() for i_id in open(load_selected_samples)]
total_num = len(clean_ids)
remember_rate = args.remember_rate
loss_list, name_list, dice_list = [], [], []
print('total_num:', total_num)
predict_sum_disc = 0
predict_sum_cup = 0
noise_sum_disc = 0
noise_sum_cup = 0
threshold = 0.4
for i in range(start_iter, start_iter + total_num):
model.adjust_learning_rate(args, optimizer, i)
model_D1.adjust_learning_rate(args, optimizer_D1, i)
model_D2.adjust_learning_rate(args, optimizer_D2, i)
optimizer.zero_grad()
optimizer_D1.zero_grad()
optimizer_D2.zero_grad()
##train G
for param in model_D1.parameters():
param.requires_grad = False
for param in model_D2.parameters():
param.requires_grad = False
#import pdb;pdb.set_trace()
try:
src_img, src_lbl, weight_map, name = sourceloader_iter.next()
except StopIteration:
sourceloader_iter = iter(sourceloader)
src_img, src_lbl, weight_map, name = sourceloader_iter.next()
src_img, src_lbl, weight_map = Variable(src_img).cuda(), Variable(
src_lbl.long()).cuda(), Variable(weight_map.long()).cuda()
src_seg_score1, src_seg_score2, src_seg_score3, src_seg_score4 = model(
src_img, lbl=src_lbl, weight=None)
loss_seg_src = model.loss
loss_seg_src.backward()
loss_list.append(loss_seg_src)
name_list.append(name)
print(i, name)
#import pdb;pdb.set_trace()
output = nn.functional.softmax(src_seg_score2, dim=1)
output = nn.functional.upsample(
output, (2056, 2124), mode='bilinear',
align_corners=True).cpu().data[0].numpy()
output = output.transpose(1, 2, 0) # (1634,1634,3)
output_mask = np.asarray(np.argmax(output, axis=2),
dtype=np.uint8) # (1644,1634) unique:[0,1,2]
predict_disc_dice, predict_cup_dice = calculate_dice(
args.data_dir, output_mask, name)
print('===>predict disc_dice:' + str(round(predict_disc_dice, 3)) +
'\t' + 'cup_dice:' + str(round(predict_cup_dice, 3)))
#import pdb;pdb.set_trace()
label = torch.unsqueeze(src_lbl, 0)
label = nn.functional.upsample(
label.float(),
size=(2056, 2124),
mode='bilinear',
align_corners=True).cpu().data[0].numpy().squeeze()
noise_disc_dice, noise_cup_dice = calculate_dice(
args.data_dir, label, name)
print('===>noise-included disc_dice:' +
str(round(noise_disc_dice, 3)) + '\t' + 'cup_dice:' +
str(round(noise_cup_dice, 3)))
predict_sum_disc += predict_disc_dice
predict_sum_cup += predict_cup_dice
noise_sum_disc += noise_disc_dice
noise_sum_cup += noise_cup_dice
#import pdb;pdb.set_trace()
if load_selected_samples is not None:
if (2 - predict_disc_dice - predict_cup_dice) > threshold:
src_seg_score1, src_seg_score2, src_seg_score3, src_seg_score4 = model(
src_img, lbl=src_lbl, weight=weight_map)
weightloss = 0.05 * model.loss
print('weightloss:', weightloss)
weightloss.backward()
if args.data_label_folder_target is not None:
trg_img, trg_lbl, _, _ = targetloader_iter.next()
trg_img, trg_lbl = Variable(trg_img).cuda(), Variable(
trg_lbl.long()).cuda()
trg_seg_score1, trg_seg_score2, trg_seg_score3, trg_seg_score4 = model(
trg_img, lbl=trg_lbl)
loss_seg_trg = model.loss
else:
trg_img, _, _ = targetloader_iter.next()
trg_img = Variable(trg_img).cuda()
trg_seg_score1, trg_seg_score2, trg_seg_score3, trg_seg_score4 = model(
trg_img)
loss_seg_trg = 0
outD1_trg = model_D1(F.softmax(trg_seg_score1), 0)
outD2_trg = model_D2(F.softmax(trg_seg_score2), 0)
#import pdb;pdb.set_trace()
outD1_trg = interp_target(outD1_trg)
outD2_trg = interp_target(outD2_trg)
if i > 9001:
#import pdb;pdb.set_trace()
weight_map1 = prob_2_entropy(F.softmax(trg_seg_score1))
weight_map2 = prob_2_entropy(F.softmax(trg_seg_score2))
loss_D1_trg_fake = weight_loss(
outD1_trg,
Variable(torch.FloatTensor(
outD1_trg.data.size()).fill_(0)).cuda(), weight_map1, 0.3,
1)
loss_D2_trg_fake = weight_loss(
outD2_trg,
Variable(torch.FloatTensor(
outD2_trg.data.size()).fill_(0)).cuda(), weight_map2, 0.3,
1)
else:
loss_D1_trg_fake = model_D1.loss
loss_D2_trg_fake = model_D2.loss
#loss_D_trg_fake = model_D1.loss*0.2 + model_D2.loss
loss_D_trg_fake = loss_D1_trg_fake * 0.2 + loss_D2_trg_fake
loss_trg = args.lambda_adv_target * loss_D_trg_fake + loss_seg_trg
loss_trg.backward()
###train D
for param in model_D1.parameters():
param.requires_grad = True
for param in model_D2.parameters():
param.requires_grad = True
src_seg_score1, src_seg_score2, src_seg_score3, src_seg_score4, trg_seg_score1, trg_seg_score2, trg_seg_score3, trg_seg_score4 = src_seg_score1.detach(
), src_seg_score2.detach(), src_seg_score3.detach(
), src_seg_score4.detach(), trg_seg_score1.detach(
), trg_seg_score2.detach(), trg_seg_score3.detach(
), trg_seg_score4.detach()
outD1_src = model_D1(F.softmax(src_seg_score1), 0)
outD2_src = model_D2(F.softmax(src_seg_score2), 0)
loss_D1_src_real = model_D1.loss / 2
loss_D1_src_real.backward()
loss_D2_src_real = model_D2.loss / 2
loss_D2_src_real.backward()
loss_D_src_real = loss_D1_src_real + loss_D2_src_real
outD1_trg = model_D1(F.softmax(trg_seg_score1), 1)
outD2_trg = model_D2(F.softmax(trg_seg_score2), 1)
outD1_trg = interp_target(outD1_trg)
outD2_trg = interp_target(outD2_trg)
if i > 9001:
weight_map1 = prob_2_entropy(F.softmax(trg_seg_score1))
weight_map2 = prob_2_entropy(F.softmax(trg_seg_score2))
loss_D1_trg_real = weight_loss(
outD1_trg,
Variable(torch.FloatTensor(
outD1_trg.data.size()).fill_(1)).cuda(), weight_map1, 0.3,
1) / 2
loss_D2_trg_real = weight_loss(
outD2_trg,
Variable(torch.FloatTensor(
outD2_trg.data.size()).fill_(1)).cuda(), weight_map2, 0.3,
1) / 2
else:
loss_D1_trg_real = model_D1.loss / 2
loss_D2_trg_real = model_D2.loss / 2
loss_D1_trg_real.backward()
loss_D2_trg_real.backward()
loss_D_trg_real = loss_D1_trg_real + loss_D2_trg_real
optimizer.step()
optimizer_D1.step()
optimizer_D2.step()
# for m in loss:
# train_writer.add_scalar(m, eval(m), i+1)
if (i + 1 == start_iter +
total_num) and load_selected_samples is not None:
print('taking snapshot ', args.snapshot_dir,
args.source + '_' + str(total_num))
torch.save(
model.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(total_num) + '.pth'))
torch.save(
model_D1.state_dict(),
os.path.join(
args.snapshot_dir,
'%s_' % (args.source) + str(total_num) + '_D1.pth'))
torch.save(
model_D2.state_dict(),
os.path.join(
args.snapshot_dir,
'%s_' % (args.source) + str(total_num) + '_D2.pth'))
if (i + 1) % args.print_freq == 0:
_t['iter time'].toc(average=False)
print ('[it %d][src seg loss %.4f][trg loss %.4f][trg seg loss %.4f][lr %.4f][%.2fs]' % \
(i + 1, loss_seg_src.data,loss_trg.data, loss_seg_trg.data,optimizer.param_groups[0]['lr']*10000, _t['iter time'].diff))
if i + 1 > args.num_steps_stop:
print('finish training')
break
_t['iter time'].tic()
if i + 1 == start_iter + total_num and load_selected_samples is None:
ind_sorted = np.argsort(loss_list)
loss_sorted = np.array(loss_list)[ind_sorted]
num_remember = int(remember_rate * len(loss_sorted))
clean_ind_update = ind_sorted[:num_remember]
clean_name_update = np.array(name_list)[clean_ind_update]
#import pdb;pdb.set_trace()
#dice = np.array(dice_list)[clean_ind_update]
noise_ind_sorted = np.argsort(-np.array(loss_list))
noise_loss_sorted = np.array(loss_list)[noise_ind_sorted]
noise_num_remember = int(remember_rate * len(noise_loss_sorted))
noise_ind_update = noise_ind_sorted[:noise_num_remember]
noise_name_update = np.array(name_list)[noise_ind_update]
with open(os.path.join(args.save_selected_samples), "w") as f:
for i in range(len(clean_name_update)):
#f.write(str(clean_name_update[i][0]) +'\t'+ str(dice[:,0][i]) + '\t'+ str(dice[:,1][i]) +'\n')
f.write(str(clean_name_update[i][0]) + '\n')
with open(os.path.join(args.noise_selected_samples), "w") as g:
for j in range(len(noise_name_update)):
g.write(str(noise_name_update[j][0]) + '\n')
print(args.save_selected_samples, 'Sample selection finished!')
break
print('\n predict disc_coef = {0:.4f}, cup_coef = {1:.4f}'.format(
predict_sum_disc / total_num, predict_sum_cup / total_num))
print('\n noise-included disc_coef = {0:.4f}, cup_coef = {1:.4f}'.format(
noise_sum_disc / total_num, noise_sum_cup / total_num))
def main():
opt = TrainOptions()
args = opt.initialize()
_t = {'iter time': Timer()}
model_name = args.source + '_to_' + args.target
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
os.makedirs(os.path.join(args.snapshot_dir, 'logs'))
opt.print_options(args)
sourceloader, targetloader = CreateSrcDataLoader(
args), CreateTrgDataLoader(args)
sourceloader_iter, targetloader_iter = iter(sourceloader), iter(
targetloader)
model, optimizer = CreateModel(args)
model_D, optimizer_D = CreateDiscriminator(args)
start_iter = 0
if args.restore_from is not None:
start_iter = int(args.restore_from.rsplit('/', 1)[1].rsplit('_')[1])
train_writer = tensorboardX.SummaryWriter(
os.path.join(args.snapshot_dir, "logs", model_name))
bce_loss = torch.nn.BCEWithLogitsLoss()
cudnn.enabled = True
cudnn.benchmark = True
model.train()
model.cuda()
model_D.train()
model_D.cuda()
loss = [
'loss_seg_src', 'loss_seg_trg', 'loss_D_trg_fake', 'loss_D_src_real',
'loss_D_trg_real'
]
_t['iter time'].tic()
for i in range(start_iter, args.num_steps):
model.adjust_learning_rate(args, optimizer, i)
model_D.adjust_learning_rate(args, optimizer_D, i)
optimizer.zero_grad()
optimizer_D.zero_grad()
"""
train Segmentation model
"""
for param in model_D.parameters():
param.requires_grad = False
'''
train on [S':transferred source images] for loss_seg_src
'''
src_img, src_lbl, _, _ = sourceloader_iter.next()
src_img, src_lbl = Variable(src_img).cuda(), Variable(
src_lbl.long()).cuda()
src_seg_score = model(src_img, lbl=src_lbl)
loss_seg_src = model.loss
loss_seg_src.backward()
'''
train on [T:target images] for loss_seg_trg
'''
if args.data_label_folder_target is not None:
trg_img, trg_lbl, _, _ = targetloader_iter.next()
trg_img, trg_lbl = Variable(trg_img).cuda(), Variable(
trg_lbl.long()).cuda()
trg_seg_score = model(trg_img, lbl=trg_lbl)
loss_seg_trg = model.loss
else:
trg_img, _, name = targetloader_iter.next()
trg_img = Variable(trg_img).cuda()
trg_seg_score = model(trg_img)
loss_seg_trg = 0
outD_trg = model_D(F.softmax(trg_seg_score), 0)
loss_D_trg_fake = model_D.loss
loss_trg = args.lambda_adv_target * loss_D_trg_fake + loss_seg_trg
loss_trg.backward()
"""
train Discriminator
"""
for param in model_D.parameters():
param.requires_grad = True
src_seg_score, trg_seg_score = src_seg_score.detach(
), trg_seg_score.detach()
outD_src = model_D(F.softmax(src_seg_score), 0)
loss_D_src_real = model_D.loss / 2
loss_D_src_real.backward()
outD_trg = model_D(F.softmax(trg_seg_score), 1)
loss_D_trg_real = model_D.loss / 2
loss_D_trg_real.backward()
"""
update segmentation model & discriminator model
"""
optimizer.step()
optimizer_D.step()
for m in loss:
train_writer.add_scalar(m, eval(m), i + 1)
if (i + 1) % args.save_pred_every == 0:
print('taking snapshot ...')
torch.save(
model.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(i + 1) + '.pth'))
if (i + 1) % args.print_freq == 0:
_t['iter time'].toc(average=False)
print('[it %d][src seg loss %.4f][lr %.4f][%.2fs]' %
(i + 1, loss_seg_src.data, optimizer.param_groups[0]['lr'] *
10000, _t['iter time'].diff))
if i + 1 > args.num_steps_stop:
print('finish training')
break
_t['iter time'].tic()
def train_adp(self,round_):
self.args.data_label_folder_target = root_base +'/dataset/generated_data/'
self.args.shuffel_ = True
self.args.data_dir_target = root_base +'/dataset/generated_data/'
self.args.data_list_target = self.args.data_gen_list
self.args.batch_size = 1
_t = {'iter time' : Timer()}
self.args.num_steps = int(self.cnt_img * self.args.epoch_per_round * self.args.no_of_patches_per_image)
sourceloader, targetloader = CreateSrcDataLoader(self.args), CreateTrgDataLoader(self.args)
targetloader_iter, sourceloader_iter = iter(targetloader), iter(sourceloader)
start_iter = 0
train_writer = tensorboardX.SummaryWriter(os.path.join(self.args.snapshot_dir, "logs", self.model_name))
cudnn.enabled = True
cudnn.benchmark = True
self.model.train()
self.model.cuda()
loss = ['loss_seg_src', 'loss_seg_trg']
_t['iter time'].tic()
for i in range(start_iter, self.args.num_steps):
self.model.adjust_learning_rate(self.args, self.optimizer, i)
self.optimizer.zero_grad()
src_img, src_lbl, _, _,_ = sourceloader_iter.next()
src_img, src_lbl = Variable(src_img).cuda(), Variable(src_lbl.long()).cuda()
src_seg_score = self.model(src_img, lbl=src_lbl)
loss_seg_src = self.model.loss
loss_src = torch.mean(loss_seg_src)
##############################
loss_src.backward()
trg_img, trg_lbl, _, _ = targetloader_iter.next()
trg_img, trg_lbl = Variable(trg_img).cuda(), Variable(trg_lbl.long()).cuda()
trg_seg_score = self.model(trg_img, lbl=trg_lbl)
############################
loss_seg_trg = self.model.loss
##########Focal loss############
loss_trg_2 = torch.mean(loss_seg_trg)
if self.args.focal_loss:
pt = torch.exp(-loss_seg_trg)
loss_trg = loss_seg_trg * (1-pt)**self.args.gamma
trg_fcl = torch.mean(loss_trg)
else:
trg_fcl = 0
loss_trg = self.args.beta *trg_fcl + loss_trg_2
loss_trg.backward()
src_seg_score, trg_seg_score = src_seg_score.detach(), trg_seg_score.detach()
self.optimizer.step()
if (i+1) % self.args.saving_step == 0 :
print ('taking snapshot ...')
if(args.focal_loss):
torch.save(self.model.state_dict(), os.path.join(self.args.snapshot_dir, '%s' %(self.args.source+"_to_" +self.args.target)+"_w_focal_loss_"+args.model +'.pth' ))
else:
torch.save(self.model.state_dict(), os.path.join(self.args.snapshot_dir, '%s' %(self.args.source+"_to_" +self.args.target)+"_wo_focal_loss" +args.model +'.pth' ))
if (i+1) % 100 == 0:
_t['iter time'].toc(average=False)
print ('[it %d][src seg loss %.4f][lr %.4f][%.2fs]' % \
(i + 1, loss_src.data, self.optimizer.param_groups[0]['lr']*10000, _t['iter time'].diff))
_t['iter time'].tic()
def main():
opt = TrainOptions()
args = opt.initialize()
_t = {'iter time': Timer()}
model_name = args.source + '_to_' + args.target
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
os.makedirs(os.path.join(args.snapshot_dir, 'logs'))
opt.print_options(args)
sourceloader, targetloader = CreateSrcDataLoader(
args), CreateTrgDataLoader(args)
targetloader_iter, sourceloader_iter = iter(targetloader), iter(
sourceloader)
model, optimizer = CreateModel(args)
model_D1, optimizer_D1 = CreateDiscriminator(args, 1)
model_D2, optimizer_D2 = CreateDiscriminator(args, 2)
start_iter = 0
if args.restore_from is not None:
start_iter = int(args.restore_from.rsplit('/', 1)[1].rsplit('_')[1])
train_writer = tensorboardX.SummaryWriter(
os.path.join(args.snapshot_dir, "logs", model_name))
bce_loss = torch.nn.BCEWithLogitsLoss()
interp_target = nn.Upsample(size=(1024, 1024),
mode='bilinear',
align_corners=True)
interp_source = nn.Upsample(size=(1024, 1024),
mode='bilinear',
align_corners=True)
cudnn.enabled = True
cudnn.benchmark = True
model.train()
model.cuda()
model_D1.train()
model_D1.cuda()
model_D2.train()
model_D2.cuda()
weight_loss = WeightedBCEWithLogitsLoss()
weight_map_loss = WeightMapLoss()
loss = [
'loss_seg_src', 'loss_seg_trg', 'loss_D_trg_fake', 'loss_D_src_real',
'loss_D_trg_real'
]
_t['iter time'].tic()
for i in range(start_iter, args.num_steps):
print(i)
model.adjust_learning_rate(args, optimizer, i)
model_D1.adjust_learning_rate(args, optimizer_D1, i)
model_D2.adjust_learning_rate(args, optimizer_D2, i)
optimizer.zero_grad()
optimizer_D1.zero_grad()
optimizer_D2.zero_grad()
##train G
for param in model_D1.parameters():
param.requires_grad = False
for param in model_D2.parameters():
param.requires_grad = False
try:
src_img, src_lbl, weight_map, _ = sourceloader_iter.next()
except StopIteration:
sourceloader_iter = iter(sourceloader)
src_img, src_lbl, weight_map, _ = sourceloader_iter.next()
src_img, src_lbl, weight_map = Variable(src_img).cuda(), Variable(
src_lbl.long()).cuda(), Variable(weight_map.long()).cuda()
src_seg_score1, src_seg_score2, src_seg_score3, src_seg_score4 = model(
src_img, lbl=src_lbl, weight=weight_map)
#import pdb;pdb.set_trace()
#WeightLoss1 = weight_map_loss(src_seg_score1, src_lbl, weight_map)
#WeightLoss2 = weight_map_loss(src_seg_score2, src_lbl, weight_map)
loss_seg_src = model.loss
#print('WeightLoss2, WeightLoss1:', WeightLoss2.data, WeightLoss1.data)
loss_seg_src.backward()
if args.data_label_folder_target is not None:
trg_img, trg_lbl, _, name = targetloader_iter.next()
trg_img, trg_lbl = Variable(trg_img).cuda(), Variable(
trg_lbl.long()).cuda()
trg_seg_score1, trg_seg_score2, trg_seg_score3, trg_seg_score4 = model(
trg_img, lbl=trg_lbl)
loss_seg_trg = model.loss
else:
trg_img, _, name = targetloader_iter.next()
trg_img = Variable(trg_img).cuda()
trg_seg_score1, trg_seg_score2, trg_seg_score3, trg_seg_score4 = model(
trg_img)
loss_seg_trg = 0
outD1_trg = model_D1(F.softmax(trg_seg_score1), 0)
outD2_trg = model_D2(F.softmax(trg_seg_score2), 0)
#import pdb;pdb.set_trace()
outD1_trg = interp_target(outD1_trg) #[1, 1, 1024, 1024]
outD2_trg = interp_target(outD2_trg)
'''
if i > 9001:
#import pdb;pdb.set_trace()
weight_map1 = prob_2_entropy(F.softmax(trg_seg_score1)) #[1, 1, 1024, 1024]
weight_map2 = prob_2_entropy(F.softmax(trg_seg_score2)) #[1, 1, 1024, 1024]
loss_D1_trg_fake = weight_loss(outD1_trg, Variable(torch.FloatTensor(outD1_trg.data.size()).fill_(0)).cuda(), weight_map1, 0.3, 1)
loss_D2_trg_fake = weight_loss(outD2_trg, Variable(torch.FloatTensor(outD2_trg.data.size()).fill_(0)).cuda(), weight_map2, 0.3, 1)
else:
loss_D1_trg_fake = model_D1.loss
loss_D2_trg_fake = model_D2.loss
loss_D_trg_fake = loss_D1_trg_fake*0.2 + loss_D2_trg_fake
'''
loss_D_trg_fake = model_D1.loss * 0.2 + model_D2.loss
loss_trg = args.lambda_adv_target * loss_D_trg_fake + loss_seg_trg
loss_trg.backward()
###train D
for param in model_D1.parameters():
param.requires_grad = True
for param in model_D2.parameters():
param.requires_grad = True
src_seg_score1, src_seg_score2, src_seg_score3, src_seg_score4, trg_seg_score1, trg_seg_score2, trg_seg_score3, trg_seg_score4 = src_seg_score1.detach(
), src_seg_score2.detach(), src_seg_score3.detach(
), src_seg_score4.detach(), trg_seg_score1.detach(
), trg_seg_score2.detach(), trg_seg_score3.detach(
), trg_seg_score4.detach()
outD1_src = model_D1(F.softmax(src_seg_score1), 0)
outD2_src = model_D2(F.softmax(src_seg_score2), 0)
loss_D1_src_real = model_D1.loss / 2
loss_D1_src_real.backward()
loss_D2_src_real = model_D2.loss / 2
loss_D2_src_real.backward()
loss_D_src_real = loss_D1_src_real + loss_D2_src_real
outD1_trg = model_D1(F.softmax(trg_seg_score1), 1)
outD2_trg = model_D2(F.softmax(trg_seg_score2), 1)
outD1_trg = interp_target(outD1_trg)
outD2_trg = interp_target(outD2_trg)
if i > 9001:
weight_map1 = prob_2_entropy(F.softmax(trg_seg_score1))
weight_map2 = prob_2_entropy(F.softmax(trg_seg_score2))
loss_D1_trg_real = weight_loss(
outD1_trg,
Variable(torch.FloatTensor(
outD1_trg.data.size()).fill_(1)).cuda(), weight_map1, 0.3,
1) / 2
loss_D2_trg_real = weight_loss(
outD2_trg,
Variable(torch.FloatTensor(
outD2_trg.data.size()).fill_(1)).cuda(), weight_map2, 0.3,
1) / 2
else:
loss_D1_trg_real = model_D1.loss / 2
loss_D2_trg_real = model_D2.loss / 2
loss_D1_trg_real.backward()
loss_D2_trg_real.backward()
loss_D_trg_real = loss_D1_trg_real + loss_D2_trg_real
optimizer.step()
optimizer_D1.step()
optimizer_D2.step()
for m in loss:
train_writer.add_scalar(m, eval(m), i + 1)
if (i + 1) % args.save_pred_every == 0:
print('taking snapshot ...')
torch.save(
model.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(i + 1) + '.pth'))
torch.save(
model_D1.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(i + 1) + '_D1.pth'))
torch.save(
model_D2.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(i + 1) + '_D2.pth'))
if (i + 1) % args.print_freq == 0:
_t['iter time'].toc(average=False)
print ('[it %d][src seg loss %.4f][trg seg loss %.4f][lr %.4f][%.2fs]' % \
(i + 1, loss_seg_src.data,loss_seg_trg.data, optimizer.param_groups[0]['lr']*10000, _t['iter time'].diff))
if i + 1 > args.num_steps_stop:
print('finish training')
break
_t['iter time'].tic()
def main():
opt = TrainOptions()
args = opt.initialize()
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
_t = {'iter time': Timer()}
model_name = args.source + '_to_' + args.target
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
os.makedirs(os.path.join(args.snapshot_dir, 'logs'))
opt.print_options(args)
sourceloader, targetloader = CreateSrcDataLoader(
args), CreateTrgDataLoader(args)
sourceloader_iter, targetloader_iter = iter(sourceloader), iter(
targetloader)
pseudotrgloader = CreatePseudoTrgLoader(args)
pseudoloader_iter = iter(pseudotrgloader)
model, optimizer = CreateModel(args)
start_iter = 0
if args.restore_from is not None:
start_iter = int(args.restore_from.rsplit('/', 1)[1].rsplit('_')[1])
if args.restore_optim_from is not None:
optimizer.load_state_dict(torch.load(args.restore_optim_from))
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
cudnn.enabled = True
cudnn.benchmark = True
model.train()
model.cuda()
wandb.watch(model, log='gradient', log_freq=1)
# losses to log
loss = ['loss_seg_src', 'loss_seg_psu']
loss_train = 0.0
loss_val = 0.0
loss_pseudo = 0.0
loss_train_list = []
loss_val_list = []
loss_pseudo_list = []
mean_img = torch.zeros(1, 1)
class_weights = Variable(CS_weights).cuda()
_t['iter time'].tic()
for i in range(start_iter, args.num_steps):
model.adjust_learning_rate(args, optimizer, i) # adjust learning rate
optimizer.zero_grad() # zero grad
src_img, src_lbl, _, _ = sourceloader_iter.next() # new batch source
trg_img, trg_lbl, _, _ = targetloader_iter.next() # new batch target
psu_img, psu_lbl, _, _ = pseudoloader_iter.next()
scr_img_copy = src_img.clone()
if mean_img.shape[-1] < 2:
B, C, H, W = src_img.shape
mean_img = IMG_MEAN.repeat(B, 1, H, W)
#-------------------------------------------------------------------#
# 1. source to target, target to target
src_in_trg = FDA_source_to_target(src_img, trg_img,
L=args.LB) # src_lbl
trg_in_trg = trg_img
# 2. subtract mean
src_img = src_in_trg.clone() - mean_img # src_1, trg_1, src_lbl
trg_img = trg_in_trg.clone() - mean_img # trg_1, trg_0, trg_lbl
psu_img = psu_img.clone() - mean_img
#-------------------------------------------------------------------#
# evaluate and update params #####
src_img, src_lbl = Variable(src_img).cuda(), Variable(
src_lbl.long()).cuda() # to gpu
src_seg_score = model(src_img,
lbl=src_lbl,
weight=class_weights,
ita=args.ita) # forward pass
loss_seg_src = model.loss_seg # get loss
loss_ent_src = model.loss_ent
# use pseudo label as supervision
psu_img, psu_lbl = Variable(psu_img).cuda(), Variable(
psu_lbl.long()).cuda()
psu_seg_score = model(psu_img,
lbl=psu_lbl,
weight=class_weights,
ita=args.ita)
loss_seg_psu = model.loss_seg
loss_ent_psu = model.loss_ent
loss_all = loss_seg_src + (loss_seg_psu + args.entW * loss_ent_psu
) # loss of seg on src, and ent on s and t
loss_all.backward()
optimizer.step()
loss_train += loss_seg_src.detach().cpu().numpy()
loss_val += loss_seg_psu.detach().cpu().numpy()
if (i + 1) % args.save_pred_every == 0:
print('taking snapshot ...')
torch.save(
model.state_dict(),
os.path.join(args.snapshot_dir,
'%s_' % (args.source) + str(i + 1) + '.pth'))
torch.save(
optimizer.state_dict(),
os.path.join(args.snapshot_dir_optim,
'%s_' % (args.source) + '.pth'))
wandb.log({
"src seg loss": loss_seg_src.data,
"psu seg loss": loss_seg_psu.data,
"learnign rate": optimizer.param_groups[0]['lr'] * 10000
})
if (i + 1) % args.print_freq == 0:
_t['iter time'].toc(average=False)
print('[it %d][src seg loss %.4f][psu seg loss %.4f][lr %.4f][%.2fs]' % \
(i + 1, loss_seg_src.data, loss_seg_psu.data, optimizer.param_groups[0]['lr']*10000, _t['iter time'].diff) )
sio.savemat(args.tempdata, {
'src_img': src_img.cpu().numpy(),
'trg_img': trg_img.cpu().numpy()
})
loss_train /= args.print_freq
loss_val /= args.print_freq
loss_train_list.append(loss_train)
loss_val_list.append(loss_val)
sio.savemat(args.matname, {
'loss_train': loss_train_list,
'loss_val': loss_val_list
})
loss_train = 0.0
loss_val = 0.0
if i + 1 > args.num_steps_stop:
print('finish training')
break
_t['iter time'].tic()
def main():
args = arg_parser.Parse()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
logger = Logger(args.log_dir)
logger.PrintAndLogArgs(args)
saver = ImageAndLossSaver(args.tb_logs_dir, logger.log_folder,
args.checkpoints_dir, args.save_pics_every)
source_loader, target_train_loader, target_eval_loader = CreateSrcDataLoader(
args), CreateTrgDataLoader(args,
'train'), CreateTrgDataLoader(args, 'val')
epoch_size = np.maximum(len(target_train_loader.dataset),
len(source_loader.dataset))
steps_per_epoch = int(np.floor(epoch_size / args.batch_size))
source_loader.dataset.SetEpochSize(epoch_size)
target_train_loader.dataset.SetEpochSize(epoch_size)
generator = model.DeepLPFNet()
generator = nn.DataParallel(generator.cuda())
generator_criterion = model.GeneratorLoss()
generator_optimizer = optim.Adam(generator.parameters(),
lr=args.generator_lr,
betas=(0.9, 0.999),
eps=1e-08)
discriminator = model.Discriminator()
discriminator = nn.DataParallel(discriminator.cuda())
discriminator_criterion = model.DiscriminatorLoss()
discriminator_optimizer = optim.Adam(discriminator.parameters(),
lr=args.discriminator_lr,
betas=(0.9, 0.999),
eps=1e-08)
semseg_net, semseg_optimizer = CreateModel(args)
semseg_net = nn.DataParallel(semseg_net.cuda())
logger.info('######### Network created #########')
logger.info('Architecture of Generator:\n' + str(generator))
logger.info('Architecture of Discriminator:\n' + str(discriminator))
logger.info('Architecture of Backbone net:\n' + str(semseg_net))
for epoch in range(args.num_epochs):
generator.train()
discriminator.train()
semseg_net.train()
saver.Reset()
discriminate_src = True
source_loader_iter, target_train_loader_iter, target_eval_loader_iter = iter(
source_loader), iter(target_train_loader), iter(target_eval_loader)
logger.info('#################[Epoch %d]#################' %
(epoch + 1))
for batch_num in range(steps_per_epoch):
start_time = time.time()
training_discriminator = (batch_num >= args.generator_boost) and (
batch_num - args.generator_boost) % (
args.discriminator_iters +
args.generator_iters) < args.discriminator_iters
src_img, src_lbl, src_shapes, src_names = source_loader_iter.next(
) # new batch source
trg_eval_img, trg_eval_lbl, trg_shapes, trg_names = target_train_loader_iter.next(
) # new batch target
generator_optimizer.zero_grad()
discriminator_optimizer.zero_grad()
semseg_optimizer.zero_grad()
src_input_batch = Variable(src_img, requires_grad=False).cuda()
src_label_batch = Variable(src_lbl, requires_grad=False).cuda()
trg_input_batch = Variable(trg_eval_img,
requires_grad=False).cuda()
# trg_label_batch = Variable(trg_lbl, requires_grad=False).cuda()
src_in_trg = generator(src_input_batch, trg_input_batch) # G(S,T)
if training_discriminator: #train discriminator
if discriminate_src == True:
discriminator_src_in_trg = discriminator(
src_in_trg) # D(G(S,T))
discriminator_trg = None # D(T)
else:
discriminator_src_in_trg = None # D(G(S,T))
discriminator_trg = discriminator(trg_input_batch) # D(T)
discriminate_src = not discriminate_src
loss = discriminator_criterion(discriminator_src_in_trg,
discriminator_trg)
else: #train generator and semseg net
discriminator_trg = discriminator(trg_input_batch) # D(T)
predicted, loss_seg, loss_ent = semseg_net(
src_in_trg, lbl=src_label_batch) # F(G(S.T))
src_in_trg_labels = torch.argmax(predicted, dim=1)
loss = generator_criterion(loss_seg, loss_ent, args.entW,
discriminator_trg)
saver.WriteLossHistory(training_discriminator, loss.item())
loss.backward()
if training_discriminator: # train discriminator
discriminator_optimizer.step()
else: # train generator and semseg net
generator_optimizer.step()
semseg_optimizer.step()
saver.running_time += time.time() - start_time
if (not training_discriminator) and saver.SaveImagesIteration:
saver.SaveTrainImages(epoch, src_img[0, :, :, :],
src_in_trg[0, :, :, :], src_lbl[0, :, :],
src_in_trg_labels[0, :, :])
if (batch_num + 1) % args.print_every == 0:
logger.PrintAndLogData(saver, epoch, batch_num,
args.print_every)
if (batch_num + 1) % args.save_checkpoint == 0:
saver.SaveModelsCheckpoint(semseg_net, discriminator,
generator, epoch, batch_num)
#Validation:
semseg_net.eval()
rand_samp_inds = np.random.randint(0, len(target_eval_loader.dataset),
5)
rand_batchs = np.floor(rand_samp_inds / args.batch_size).astype(np.int)
cm = torch.zeros((NUM_CLASSES, NUM_CLASSES)).cuda()
for val_batch_num, (trg_eval_img, trg_eval_lbl, _,
_) in enumerate(target_eval_loader):
with torch.no_grad():
trg_input_batch = Variable(trg_eval_img,
requires_grad=False).cuda()
trg_label_batch = Variable(trg_eval_lbl,
requires_grad=False).cuda()
pred_softs_batch = semseg_net(trg_input_batch)
pred_batch = torch.argmax(pred_softs_batch, dim=1)
cm += compute_cm_batch_torch(pred_batch, trg_label_batch,
IGNORE_LABEL, NUM_CLASSES)
print('Validation: saw', val_batch_num * args.batch_size,
'examples')
if (val_batch_num + 1) in rand_batchs:
rand_offset = np.random.randint(0, args.batch_size)
saver.SaveValidationImages(
epoch, trg_input_batch[rand_offset, :, :, :],
trg_label_batch[rand_offset, :, :],
pred_batch[rand_offset, :, :])
iou, miou = compute_iou_torch(cm)
saver.SaveEpochAccuracy(iou, miou, epoch)
logger.info(
'Average accuracy of Epoch #%d on target domain: mIoU = %2f' %
(epoch + 1, miou))
logger.info(
'-----------------------------------Epoch #%d Finished-----------------------------------'
% (epoch + 1))
del cm, trg_input_batch, trg_label_batch, pred_softs_batch, pred_batch
saver.tb.close()
logger.info('Finished training.')
