Comments (1)
The error is thrown specifically when the output channels of the last layer is 1, even when there are 2 successive conv blocks:
class ConvNet(nn.Module):
def __init__(self):
super().__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(3, 6, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(6)
self.conv2 = nn.Conv2d(6, 1, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(1)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
return x
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