Adaptive Kernel Size not implemented

It seems that the implementation under models/ differs from the paper’s adaptive setting for k_size, given in Fig 3 of the paper.

Why do the resnet, mobilenet modules not use adaptive kernel sizes?

[ykk648]

class EcaLayer(nn.Module):

    def __init__(self, channels, gamma=2, b=1):
        super(EcaLayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)

        t = int(abs((math.log(channels, 2) + b) / gamma))
        k_size = t if t % 2 else t + 1
        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)

        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        # feature descriptor on the global spatial information
        y = self.avg_pool(x)

        # Two different branches of ECA module
        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)

        # Multi-scale information fusion
        y = self.sigmoid(y)

        return x * y.expand_as(x)

[15114218]

I meet an error, who can help me? thanks a lot. RuntimeError: The expanded size of the tensor (64) must match the existing size (63) at non-singleton dimension 1. Target sizes: [8, 64, 256, 256]. Tensor sizes: [8, 63, 1, 1]

class eca_layer(nn.Module): “”"Constructs a ECA module.

Args:
    channel: Number of channels of the input feature map
    k_size: Adaptive selection of kernel size
"""

def __init__(self, k_size, gamma=2, b=1):
    super(eca_layer, self).__init__()
    self.k_size = k_size
    self.gamma = gamma
    self.b = b
    self.avg_pool = nn.AdaptiveAvgPool2d(1)
    self.conv = nn.Conv1d(1, 1, kernel_size=self.k_size, padding=(self.k_size - 1) // 2, bias=False)
    self.sigmoid = nn.Sigmoid()

def forward(self, x):
    b, c, h, w = x.size()
    t = int(abs((math.log(c, 2) + self.b) / self.gamma))
    self.k_size = t if t % 2 else t + 1        
    y = self.avg_pool(x)
    y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)      
    y = self.sigmoid(y)
    return x * y.expand_as(x)