BiFPN

[haibochina]

It seems that there are no learning weight for different feature maps of BiFPN as the paper, can you add it?

class BiFPN(nn.Module): def init(self, num_channels): super(BiFPN, self).init() self.num_channels = num_channels

def forward(self, inputs):
    num_channels = self.num_channels
    P3_in, P4_in, P5_in, P6_in, P7_in = inputs

    P7_up = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P7_in)
    scale = int(P6_in.size(3)/P7_up.size(3))
    P6_up = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P6_in+self.Resize(scale_factor=scale)(P7_up))
    scale = int(P5_in.size(3)/P6_up.size(3))
    P5_up = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P5_in+self.Resize(scale_factor=scale)(P6_up))
    scale = int(P4_in.size(3)/P5_up.size(3))
    P4_up = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P4_in+self.Resize(scale_factor=scale)(P5_up))
    scale = int(P3_in.size(3)/P4_up.size(3))
    P3_out = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P3_in+self.Resize(scale_factor=scale)(P4_up))

    kernel_size = int(P3_out.size(3)/P4_up.size(3))
    P4_out = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P4_in + P4_up+nn.MaxPool2d(kernel_size=kernel_size)(P3_out))
    kernel_size = int(P4_out.size(3)/P5_up.size(3))
    P5_out = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P5_in + P5_up+nn.MaxPool2d(kernel_size=kernel_size)(P4_out))
    kernel_size = int(P5_out.size(3)/P6_up.size(3))
    P6_out = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P6_in + P6_up+nn.MaxPool2d(kernel_size=kernel_size)(P5_out))
    kernel_size = int(P6_out.size(3)/P7_up.size(3))
    P7_out = self.Conv(in_channels=num_channels, out_channels=num_channels, kernel_size=1, stride=1, padding=0, groups=num_channels)(P7_in + P7_up+nn.MaxPool2d(kernel_size=kernel_size)(P6_out))
    return P3_out, P4_out, P5_out, P6_out, P7_out

@staticmethod
def Conv(in_channels, out_channels, kernel_size, stride, padding, groups = 1):
    features = nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups),
        nn.BatchNorm2d(num_features=out_channels),
        nn.ReLU()
    )
    return features 
@staticmethod
def Resize(scale_factor=2, mode='nearest'):
    upsample = nn.Upsample(scale_factor=scale_factor, mode=mode)
    return upsample

[haibochina]

And maybe your P7_out was wrong according the paper!

[toandaominh1997]

@haibochina I agree with you that is wrong. I have fix it by replace to bifpn_v2.py

[haibochina]

Thank you!