# ds_val = AbdomenDS("/raid/scratch/schatter/Dataset/dhanun/MRI/MRITTemp","train",(1,0.5,0.5))
checkname = None #'/raid/scratch/schatter/Dataset/dhanun/checkpoints/checkpoint_9_14_13_52_epoch_15' #Set None if no need to load
result_upsample = True
#upunterp_fact = None
upinterp_fact = (1, 1, 1)
#model_downscale = False
dataloader = DataLoader(ds_test, batch_size=config.BatchSize, shuffle=True)
model = WNet(is_cuda)
#model = WNet()
if is_cuda:
device = torch.device("cuda:1")
else:
device = torch.device("cpu")
model.to(device)
#model.cuda()
model.eval()
#model_downscale = False
mode = 'test'
optimizer = torch.optim.Adam(model.parameters(), lr=config.init_lr)
#optimizer
with open(config.model_tested, 'rb') as f:
para = torch.load(f, "cpu")
#para = torch.load(f,"cuda:0")
model.load_state_dict(para['state_dict'])
for step, [x] in enumerate(dataloader):
print('Step' + str(step + 1))
#print(x.shape)
x = x.to(device)
pred, pad_pred = model(x, mode, config.ModelDownscale)
dataloader = DataLoader(ds_train,
batch_size=config.BatchSize,
shuffle=True)
dataloader1 = DataLoader(ds_val, batch_size=config.BatchSize, shuffle=True)
#eval_set = DataLoader("MRI/new_test","train")
#eval_loader = eval_set.torch_loader()
model = WNet(is_cuda)
#model = torch.nn.DataParallel(WNet())
if is_cuda:
device1 = torch.device("cuda:1")
device2 = torch.device("cuda:0")
else:
device1 = torch.device("cpu")
device2 = torch.device("cpu")
model.to(device1)
#model_eval = torch.nn.DataParallel(WNet())
#model.cuda()
#model_eval.cuda()
#model_eval.eval()
optimizer = torch.optim.Adam(model.parameters(), lr=config.init_lr)
#reconstr = torch.nn.MSELoss().cuda(config.cuda_dev)
if config.useSSIMLoss:
import pytorch_ssim
reconstr = pytorch_ssim.SSIM()
else:
reconstr = torch.nn.MSELoss()
Ncuts = NCutsLoss()
mode = 'train'
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=config.lr_decay_iter,