我在pytorch中遇到此错误： ValueError：目标和输入必须具有相同数量的元素。目标要素（16）！=输入要素（8388608）
我的模特：
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels / 2)
else:
self.up = nn.ConvTranspose2d(in_channels , in_channels / 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX / 2, diffX - diffX / 2,
diffY / 2, diffY - diffY / 2])
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
def __init__(self, n_channels, n_classes, bilinear=True):
super(UNet, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
self.inc = DoubleConv(n_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
factor = 2 if bilinear else 1
self.down4 = Down(512, 1024 / factor)
self.up1 = Up(1024, 512 / factor, bilinear)
self.up2 = Up(512, 256 / factor, bilinear)
self.up3 = Up(256, 128 / factor, bilinear)
self.up4 = Up(128, 64, bilinear)
self.outc = OutConv(64, n_classes)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
return logits
model_instance = UNet(n_channels=3, n_classes=8, bilinear=True)
完整日志：
/home/user/.local/lib/python3.6/site-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([16])) that is different to the input size (torch.Size([16, 8, 256, 256])) is deprecated. Please ensure they have the same size.
return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-aad65c925500> in <module>()
1 for _epoch in range(1, epoch):
----> 2 train(model_instance, _epoch)
3 test(model_instance)
<ipython-input-11-3b706bbe2772> in train(model, epoch)
6 optimizer.zero_grad()
7 output = model.forward(sat.float())
----> 8 loss = criterion(output.float(), mask.float())
9 optimizer.zero_grad()
10 loss.backward()
/home/user/.local/lib/python3.6/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/home/user/.local/lib/python3.6/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
527
528 def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 529 return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
530
531
/home/user/.local/lib/python3.6/site-packages/torch/nn/functional.py in binary_cross_entropy(input, target, weight, size_average, reduce, reduction)
2475 if input.numel() != target.numel():
2476 raise ValueError("Target and input must have the same number of elements. target nelement ({}) "
-> 2477 "!= input nelement ({})".format(target.numel(), input.numel()))
2478
2479 if weight is not None:
ValueError: Target and input must have the same number of elements. target nelement (16) != input nelement (8388608)
我的模特有问题吗？我可以增强它吗？各种各样的建议都值得赞赏。
我正在输入 256x256 3通道图像作为输入，并输入 256x256 3通道作为遮罩。