def get_model_and_optimizer(device):
model = UNet(
in_channels=1,
out_classes=1,
dimensions=3,
normalization='Group',
num_encoding_blocks=3,
out_channels_first_layer=8,
upsampling_type='conv',
padding=2,
activation='PReLU',
dropout=0,
).to(device)
optimizer = torch.optim.AdamW(model.parameters())
return model, optimizer
color_jitter_params=None)
# creating checkpoint folder
model_name = args.model_name
file_dir = os.path.split(os.path.realpath(__file__))[0]
results_folder = os.path.join(file_dir, '..', 'checkpoints', model_name)
if not os.path.exists(results_folder):
os.makedirs(results_folder)
# load model
unet = UNet(3, 3)
if args.trained_model_path is not None:
unet.load_state_dict(torch.load(args.trained_model_path))
loss = SoftDiceLoss(weight=torch.Tensor([1, 5, 5]))
optimizer = optim.Adam(unet.parameters(), lr=args.initial_lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=50,
verbose=True)
cuda_device = torch.device(args.device)
model = ModelWrapper(unet,
loss=loss,
optimizer=optimizer,
scheduler=scheduler,
results_folder=results_folder,
cuda_device=cuda_device)
model.train_model(train_dataset,
validation_dataset=validate_dataset,
n_batch=args.batch_size,