sampler=InfiniteSampler(len(dataset_train)),
num_workers=args.n_threads))
print(len(dataset_train))
model = PConvUNet(input_guides=1 if use_depth else 0).to(device)
if args.finetune:
lr = args.lr_finetune
model.freeze_enc_bn = True
else:
lr = args.lr
start_iter = 0
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr)
criterion = InpaintingLoss(VGG16FeatureExtractor()).to(device)
if args.resume:
start_iter = load_ckpt(args.resume, [('model', model)],
[('optimizer', optimizer)])
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('Starting from iter ', start_iter)
for i in tqdm(range(start_iter, args.max_iter)):
model.train()
image, mask, gt = [x.to(device) for x in next(iterator_train)]
if args.mask_root is not None:
guide = image[:, 3:4, :, :]
image = image[:, 0:3, :, :]
loader_val = data.DataLoader(dataset_val,
batch_size=mini_batch,
sampler=RandomSampler(data_source=dataset_val),
num_workers=4)
loaders = {"train": loader_train, "valid": loader_val}
print('model')
# model, criterion, optimizer, scheduler
#model = vgg13().cuda()
#model = resnet18(pretrained=False, progress=True).cuda()
#model = inception_v3().cuda()
model = resnext101_32x8d().cuda()
#criterion = CustomCriterion().cuda()
criterion = InpaintingLoss(VGG16FeatureExtractor()).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=0.0)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[3, 6, 9, 12, 18, 24, 30, 40, 50, 60, 70, 80, 90],
gamma=.5)
print('training')
# model training
runner = dl.SupervisedRunner()
logdir = './logdir'
runner.train(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,