RuntimeError Traceback (most recent call last) <ipython-input-15-26b65d7e4120> in <module> 19 ) 20 optimizer.zero_grad() ---> 21 outputs = model(inputs) 22 loss = loss_function(outputs, labels) 23 loss.backward() ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) ~/anaconda3/envs/mona/lib/python3.8/site-packages/monai/networks/nets/unet.py in forward(self, x) 190 191 def forward(self, x: torch.Tensor) -> torch.Tensor: --> 192 x = self.model(x) 193 return x 194 ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input) 98 def forward(self, input): 99 for module in self: --> 100 input = module(input) 101 return input 102 ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) ~/anaconda3/envs/mona/lib/python3.8/site-packages/monai/networks/layers/simplelayers.py in forward(self, x) 37 38 def forward(self, x: torch.Tensor) -> torch.Tensor: ---> 39 return torch.cat([x, self.submodule(x)], self.cat_dim) 40 41 ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input) 98 def forward(self, input): 99 for module in self: --> 100 input = module(input) 101 return input 102 ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) ~/anaconda3/envs/mona/lib/python3.8/site-packages/monai/networks/layers/simplelayers.py in forward(self, x) 37 38 def forward(self, x: torch.Tensor) -> torch.Tensor: ---> 39 return torch.cat([x, self.submodule(x)], self.cat_dim) 40 41 ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input) 98 def forward(self, input): 99 for module in self: --> 100 input = module(input) 101 return input 102 ~/anaconda3/envs/mona/lib/python3.8/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) ~/anaconda3/envs/mona/lib/python3.8/site-packages/monai/networks/layers/simplelayers.py in forward(self, x) 37 38 def forward(self, x: torch.Tensor) -> torch.Tensor: ---> 39 return torch.cat([x, self.submodule(x)], self.cat_dim) 40 41 RuntimeError: Sizes of tensors must match except in dimension 2. Got 4 and 3 

import glob, os, torch from monai.data import CacheDataset, DataLoader, Dataset from monai.inferers import sliding_window_inference from monai.losses import DiceLoss from monai.metrics import compute_meandice from monai.networks.layers import Norm from monai.networks.nets import UNet from monai.utils import first, set_determinism data_dir='nf1_monai' os.environ['MONAI_DATA_DIRECTORY']=data_dir directory = os.environ.get("MONAI_DATA_DIRECTORY") root_dir = directory train_images = sorted(glob.glob(os.path.join(data_dir, "imagesTr", "*.npy"))) train_labels = sorted(glob.glob(os.path.join(data_dir, "labelsTr", "*.npy"))) data_dicts = [ {"image": image_name, "label": label_name} for image_name, label_name in zip(train_images, train_labels) ] train_files, val_files = data_dicts[:-10], data_dicts[-10:] set_determinism(seed=0) from monai.transforms import ( AddChanneld, Compose, LoadNumpyd, RandCropByPosNegLabeld, ToTensord, ) train_transforms = Compose( [ LoadNumpyd(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), RandCropByPosNegLabeld( keys=["image", "label"], label_key="label", spatial_size=(20,20,20), pos=1, neg=1, num_samples=4, image_key="image", image_threshold=0, ), ToTensord(keys=["image", "label"]), ] ) val_transforms = Compose( [ LoadNumpyd(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), ToTensord(keys=["image", "label"]), ] ) device = torch.device("cuda:0") model = UNet( dimensions=3, in_channels=1, out_channels=2, channels=(16, 32, 64, 128, 256), strides=(2, 2, 2, 2), num_res_units=2, norm=Norm.BATCH, ).to(device) loss_function = DiceLoss(to_onehot_y=True, softmax=True) optimizer = torch.optim.Adam(model.parameters(), 1e-4) train_ds = CacheDataset(data=train_files, transform=train_transforms, cache_rate=1.0, num_workers=1) train_loader = DataLoader(train_ds, batch_size=6, shuffle=True, num_workers=16) val_ds = CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0, num_workers=16) val_loader = DataLoader(val_ds, batch_size=1, num_workers=1) epoch_num = 600 val_interval = 2 best_metric = -1 best_metric_epoch = -1 epoch_loss_values = list() metric_values = list() for epoch in range(epoch_num): print("-" * 10) print(f"epoch {epoch + 1}/{epoch_num}") model.train() epoch_loss = 0 step = 0 for batch_data in train_loader: step += 1 inputs, labels = ( batch_data["image"].to(device), batch_data["label"].to(device), ) optimizer.zero_grad() outputs = model(inputs) loss = loss_function(outputs, labels) loss.backward() optimizer.step() epoch_loss += loss.item() print(f"{step}/{len(train_ds) // train_loader.batch_size}, train_loss: {loss.item():.4f}") epoch_loss /= step epoch_loss_values.append(epoch_loss) print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")