Input to InstanceNorm1d is not in the right shape (?)

Size & shapes

self.nn_human_x.shape: torch.Size([6, 1536]) self.nn_human_x Parameter containing: tensor([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]) self.nn_human_x[i] tensor([0., 0., 0., ..., 0., 0., 0.]) Shape x [i]: torch.Size([1536]) trans_nn_human_x []

Errors

C:\Users\phama\AppData\Local\Temp\ipykernel_13640\996063355.py:62: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requiresgrad(True), rather than torch.tensor(sourceTensor). trans_nn_human_x.append( t( torch.tensor(self.nn_human_x[i], dtype=torch.float32).view(1, -1)) )

ValueError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_13640\1820000454.py in 77 78 model.eval() ---> 79 current_acc = evaluate_loader(model, train_loader, cce_loss) 80 model.train() 81

~\AppData\Local\Temp\ipykernel_13640\1992294143.py in evaluate_loader(model, loader, cce_loss) 8 imgs, labels = data 9 imgs, labels = imgs.to(DEVICE), labels.to(DEVICE) ---> 10 logits = model(imgs) 11 loss = cce_loss(logits, labels) 12 preds = torch.argmax(logits, dim=1)

~\anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, *kwargs) 1100 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1101 or _global_forward_hooks or _global_forward_pre_hooks): -> 1102 return forward_call(input, **kwargs) 1103 # Do not call functions when jit is used 1104 full_backward_hooks, non_full_backward_hooks = [], []

~\AppData\Local\Temp\ipykernel_13640\996063355.py in forward(self, x) 60 print("trans_nn_human_x", trans_nn_human_x) 61 # trans_nn_human_x.append(t(self.nn_human_x[i]).view(1, -1)) ---> 62 trans_nn_human_x.append( t( torch.tensor(self.nn_human_x[i], dtype=torch.float32).view(1, -1)) ) 63 # trans_nn_human_x.append(t(self.nn_human_x[i].clone().detach().to(torch.float32).view(1, -1))) 64

~\anaconda3\lib\site-packages\torch\nn\modules\container.py in forward(self, input) 139 def forward(self, input): 140 for module in self: --> 141 input = module(input) 142 return input 143

~\anaconda3\lib\site-packages\torch\nn\modules\instancenorm.py in forward(self, input) 54 55 def forward(self, input: Tensor) -> Tensor: ---> 56 self._check_input_dim(input) 57 return F.instance_norm( 58 input, self.running_mean, self.running_var, self.weight, self.bias,

~\anaconda3\lib\site-packages\torch\nn\modules\instancenorm.py in _check_input_dim(self, input) 130 def _check_input_dim(self, input): 131 if input.dim() == 2: --> 132 raise ValueError( 133 'InstanceNorm1d returns 0-filled tensor to 2D tensor.' 134 'This is because InstanceNorm1d reshapes inputs to'

ValueError: InstanceNorm1d returns 0-filled tensor to 2D tensor.This is because InstanceNorm1d reshapes inputs to(1, N * C, ...) from (N, C,...) and this makesvariances 0.

Problem

It seems like the data copy part

Code

for i in range(NUM_CLASSES):
    idxs = a_train == i
    temp_x = X_train[idxs]
    mean = temp_x.mean(axis=0)
    knn = KNeighborsClassifier().fit(temp_x, list(range(len(temp_x))))
    idx = knn.kneighbors(X=mean.reshape(1,-1), n_neighbors=1, return_distance=False)
    p_idxs.append(idx.item())
    nn_human_x.append( temp_x[idx.item()].tolist() )
nn_human_x = np.array(nn_human_x)

#### Training
model = PWNet().eval()
model.nn_human_x.data.copy_( torch.tensor(nn_human_x) )

It is passing from NUM_CLASSES x LATENT_SIZE to NUM_PROTOTYPES x LATENT_SIZE. The InstanceNorm1d should be normalizing amongst the CLASSES (I think?). When going through the transformation loop, the x[i] shape reduce to 1D, LATENT_SIZE only. Should this nn_human_x shape be NUM_PROTOTYPES x NUM_CLASSES x LATENT_SIZE instead, then passing through the transformation loop its gonna be NUM_CLASSES x LATENT_SIZE, then reaching the InstanceNorm1d it will be NUM_CLASSES x LATENT_SIZE?

Sorry if I understand it incorrectly, really look forward to your help.

EoinKenny / Prototype-Wrapper-Network-ICLR23