【发布时间】：2018-07-02 06:57:34
【问题描述】：
我正在尝试训练一个看起来像这样的 U-Net
`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)`
我使用的损失函数是
`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)`
输入的图片张量是[1,1,68,68]，标签也是一样的形状
我收到此错误：
<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
我猜我的频道大小或池大小有误，但我不确定错误到底出在哪里。
【问题讨论】：
-
您是否尝试过通过正向传递打印张量大小？
-
我尝试打印尺寸，但它在运行该部分代码之前抛出错误
-
如果您提供可用于重现错误的代码，效果会更好。或者，您可以具体告诉我们哪条线导致了问题，以及执行每条线后的张量形状。否则，很难为您提供帮助。
-
@WasiAhmad 你的权利，我看看我能得到什么更多信息然后回到这里
标签： python neural-network deep-learning conv-neural-network pytorch