Given input size: (128x1x1). Calculated output size: (128x0x0). Output size is too small

Question

I am trying to train a U-Net which looks like this

`class UNet(nn.Module):
def __init__(self, imsize):
    super(UNet, self).__init__()
    self.imsize = imsize

    self.activation = F.relu
    self.pool1 = nn.MaxPool2d(2)
    self.pool2 = nn.MaxPool2d(2)
    self.pool3 = nn.MaxPool2d(2)
    self.pool4 = nn.MaxPool2d(2)
    self.conv_block1_64 = UNetConvBlock(4, 64)
    self.conv_block64_128 = UNetConvBlock(64, 128)
    self.conv_block128_256 = UNetConvBlock(128, 256)
    self.conv_block256_512 = UNetConvBlock(256, 512)
    self.conv_block512_1024 = UNetConvBlock(512, 1024)

    self.up_block1024_512 = UNetUpBlock(1024, 512)
    self.up_block512_256 = UNetUpBlock(512, 256)
    self.up_block256_128 = UNetUpBlock(256, 128)
    self.up_block128_64 = UNetUpBlock(128, 64)

    self.last = nn.Conv2d(64, 1, 1)`

The loss function i am using is

`class BCELoss2d(nn.Module):

def __init__(self, weight=None, size_average=True):
    super(BCELoss2d, self).__init__()
    self.bce_loss = nn.BCELoss(weight, size_average)

def forward(self, logits, targets):
    probs = F.sigmoid(logits)
    probs_flat = probs.view(-1)
    targets_flat = targets.view(-1)
    return self.bce_loss(probs_flat, targets_flat)`

The input image tensor is [1,1,68,68] and labels are also of the same shape

I get this error:

<ipython-input-72-270210759010> in forward(self, x)
 75 
 76         block4 = self.conv_block256_512(pool3)
---> 77         pool4 = self.pool4(block4)
     78 
  79         block5 = self.conv_block512_1024(pool4)

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py in _    _call__(self, *input, **kwargs)
    323         for hook in self._forward_pre_hooks.values():
    324             hook(self, input)
 325         result = self.forward(*input, **kwargs)
    326         for hook in self._forward_hooks.values():
    327             hook_result = hook(self, input, result)

/usr/local/lib/python3.5/dist-packages/torch/nn/modules/pooling.py in forward(self, input)
    141         return F.max_pool2d(input, self.kernel_size, self.stride,
    142                             self.padding, self.dilation, self.ceil_mode,
--> 143                             self.return_indices)
    144 
    145     def __repr__(self):

/usr/local/lib/python3.5/dist-packages/torch/nn/functional.py in max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)
    332     See :class:`~torch.nn.MaxPool2d` for details.
    333     """
--> 334     ret = torch._C._nn.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
    335     return ret if return_indices else ret[0]
    336 

RuntimeError: Given input size: (128x1x1). Calculated output size: (128x0x0). Output size is too small at /pytorch/torch/lib/THCUNN/generic/SpatialDilatedMaxPooling.cu:69

I'm guessing I'm making a mistake in my channel size or pooling size but i'm not sure where exactly is the mistake.

Moxa

Have you tried printing tensor size over forward pass? – Stowaway

I tried printing the size but it throws the error before it runs that part of the code – Moxa

its better if you provide code which can be used to reproduce the error. OR, you can specifically tell us which line is causing the problem and also the tensor shapes after executing each line. otherwise, it is hard to help you. – Kidnap

@WasiAhmad Your right, ill see what more info i can get and then come back here – Moxa

Answer 1

Your problem is that before the Pool4 your image has already reduced to a 1x1pixel size image. So you need to either feed an much larger image of size at least around double that (~134x134) or remove a pooling layer in your network.

Colonnade

Answer 2

For the nn.MaxPool2d() function , it will raise the bug if kernel_size is bigger than its input_size.

Rasheedarasher