ValueError: Network output phi is too small for idx_durations

[zzs1852]

Ubuntu 16.04.6 LTS
Python 3.7.7
torch 1.2.0

Hi, as I followed the example_4 in ”Get Started“, something wrong with this:

callbacks = [tt.cb.EarlyStopping(patience=5)]
epochs = 50
verbose = True
log = model.fit_dataloader(dl_train, epochs, callbacks, verbose, val_dataloader=dl_test) 

here is my network with input-shape(32,1,246,246) :

class Net(nn.Module):
    def __init__(self,n_intervals):
        super().__init__()
        self.backbone = models.resnet50(pretrained=False)
        self.backbone.conv1 = nn.Conv2d(1,64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
        self.fc1 = nn.Linear(1000, 128)
        self.fc2 = nn.Linear(128,n_intervals)
        self.bn1 = nn.BatchNorm1d(1000)
        self.bn2 = nn.BatchNorm1d(128)
        
    def forward(self, x):
        x = self.backbone(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.fc1(x)
        x = self.bn2(x)
        x = F.relu(x)
        x = self.fc2(x)
        return x

The error information as follow:

ValueError                                Traceback (most recent call last)
<ipython-input-18-7ae9e531dd45> in <module>
      2 epochs = 50
      3 verbose = True
----> 4 log = model.fit_dataloader(dl_train, epochs, callbacks, verbose, val_dataloader=dl_test)

~/anaconda3/envs/pytorch37/lib/python3.7/site-packages/torchtuples/base.py in fit_dataloader(self, dataloader, epochs, callbacks, verbose, metrics, val_dataloader)
    227                 if stop: break
    228                 self.optimizer.zero_grad()
--> 229                 self.batch_metrics = self.compute_metrics(data, self.metrics)
    230                 self.batch_loss = self.batch_metrics['loss']
    231                 self.batch_loss.backward()

~/anaconda3/envs/pytorch37/lib/python3.7/site-packages/torchtuples/base.py in compute_metrics(self, data, metrics)
    178         out = self.net(*input)
    179         out = tuplefy(out)
--> 180         return {name: metric(*out, *target) for name, metric in metrics.items()}
    181 
    182     def _setup_metrics(self, metrics=None):

~/anaconda3/envs/pytorch37/lib/python3.7/site-packages/torchtuples/base.py in <dictcomp>(.0)
    178         out = self.net(*input)
    179         out = tuplefy(out)
--> 180         return {name: metric(*out, *target) for name, metric in metrics.items()}
    181 
    182     def _setup_metrics(self, metrics=None):

~/anaconda3/envs/pytorch37/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    545             result = self._slow_forward(*input, **kwargs)
    546         else:
--> 547             result = self.forward(*input, **kwargs)
    548         for hook in self._forward_hooks.values():
    549             hook_result = hook(self, input, result)

~/anaconda3/envs/pytorch37/lib/python3.7/site-packages/pycox/models/loss.py in forward(self, phi, idx_durations, events)
    466     """
    467     def forward(self, phi: Tensor, idx_durations: Tensor, events: Tensor) -> Tensor:
--> 468         return nll_logistic_hazard(phi, idx_durations, events, self.reduction)
    469 
    470 

~/anaconda3/envs/pytorch37/lib/python3.7/site-packages/pycox/models/loss.py in nll_logistic_hazard(phi, idx_durations, events, reduction)
     41         raise ValueError(f"Network output `phi` is too small for `idx_durations`."+
     42                          f" Need at least `phi.shape[1] = {idx_durations.max().item()+1}`,"+
---> 43                          f" but got `phi.shape[1] = {phi.shape[1]}`")
     44     if events.dtype is torch.bool:
     45         events = events.float()

ValueError: Network output `phi` is too small for `idx_durations`. Need at least `phi.shape[1] = 2717.0`, but got `phi.shape[1] = 4

[havakv]

@zzs1852 did you find out what was wrong here, since you closed the issue?

[zzs1852]

@zzs1852 did you find out what was wrong here, since you closed the issue?
Yeah! I made a stupid mistake for inputting the incorrect label and it works well now.

[chengzhishi]

I had the same error when I had the wrong last layer.