seems like the params are not used in `TripletAttention`'s definition?

[oukohou]

in triplet_attention.py:

class TripletAttention(nn.Module):
    def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):
        super(TripletAttention, self).__init__()
        self.ChannelGateH = SpatialGate()
        self.ChannelGateW = SpatialGate()
        self.no_spatial = no_spatial
        if not no_spatial:
            self.SpatialGate = SpatialGate()

    def forward(self, x):
        x_perm1 = x.permute(0, 2, 1, 3).contiguous()
        x_out1 = self.ChannelGateH(x_perm1)
        x_out11 = x_out1.permute(0, 2, 1, 3).contiguous()
        x_perm2 = x.permute(0, 3, 2, 1).contiguous()
        x_out2 = self.ChannelGateW(x_perm2)
        x_out21 = x_out2.permute(0, 3, 2, 1).contiguous()
        if not self.no_spatial:
            x_out = self.SpatialGate(x)
            x_out = (1 / 3) * (x_out + x_out11 + x_out21)
        else:
            x_out = (1 / 2) * (x_out11 + x_out21)
        return x_out

seems like gate_channels, reduction_ratio, pool_types are never used?

[digantamisra98]

@oukohou Yes, at the start, we used the boilerplate code from CBAM repository to keep our code consistent. That's why those parameters were there when we trained the initial models. However, those unused parameters don't cause any errors, but you can use the updated Truplet Attention code from source. The old one within the MODELSdirectory is present to load the weights correctly.