Given input size: (128x1x1). Calculated output size: (128x0x0). Output size is too small
20,406
Solution 1
Your problem is that before the Pool4 your image has already reduced to a 1x1
pixel 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.
Solution 2
For the nn.MaxPool2d()
function , it will raise the bug if kernel_size
is bigger than its input_size
.
Author by
Ryan
Updated on July 05, 2022Comments
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Ryan almost 2 years
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.
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