Hello everyone !
I’m trying to use a pretrained model (the Inception_v3) from the Pytorch library. But I have a strange mistake with the transfert…
Here my code :
model = models.inception_v3(pretrained=True)
model.aux_logits = False

for param in model.parameters():
    param.requires_grad = False

num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 12)

if use_gpu: model = model.cuda()

optimizer = torch.optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)
criterion = torch.nn.CrossEntropyLoss()
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)

model = train_model(model, dataloaders, datasets_sizes, criterion, 
                     optimizer, exp_lr_scheduler, num_epochs=25)

And here the error :
<ipython-input-10-de7ae08d1d1a> in train_model(model, dataloaders, dataset_size, criterion, optimizer, scheduler, num_epochs)
     28 
     29                 # Forward
---> 30                 outputs = model(inputs)
     31                 _, predictions = torch.max(outputs.data, 1)
     32                 loss = criterion(outputs, labels)

~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    355             result = self._slow_forward(*input, **kwargs)
    356         else:
--> 357             result = self.forward(*input, **kwargs)
    358         for hook in self._forward_hooks.values():
    359             hook_result = hook(self, input, result)

~/anaconda3/lib/python3.6/site-packages/torchvision-0.2.0-py3.6.egg/torchvision/models/inception.py in forward(self, x)
    115         x = self.Mixed_7c(x)
    116         # 8 x 8 x 2048
--> 117         x = F.avg_pool2d(x, kernel_size=8)
    118         # 1 x 1 x 2048
    119         x = F.dropout(x, training=self.training)

RuntimeError: Given input size: (2048x5x5). Calculated output size: (2048x0x0). Output size is too small at /opt/conda/conda-bld/pytorch-cpu_1518282373170/work/torch/lib/THNN/generic/SpatialAveragePooling.c:64

Anybody got any ideas?
(The train_model function comes from the Pytorch Transfert Learning Tutorial 3)
Thank you in advance !