Imagenette/ Imagewoof Leaderboards

Part 2 (2019)
36

I’ve simplified it a bit and it seems to be doing better (I’ll update with results). Here’s the conv_twist layer, replacing each 3x3 convolution. I don’t know if I can explain more briefly than the code:

class conv_twist(nn.Module):  # replacing 3x3 Conv2d
    def __init__(self, ni, nf, stride=1):
        super(conv_twist, self).__init__()
        self.conv = nn.Conv2d(ni, nf, kernel_size=3, stride=stride, padding=1, bias=False)
        self.convx = nn.Conv2d(ni, nf, kernel_size=3, stride=stride, padding=1, bias=False)
        self.convy = nn.Conv2d(ni, nf, kernel_size=3, stride=stride, padding=1, bias=False)
        self.convx.weight.data = (self.convx.weight - self.convx.weight.flip(2).flip(3)) / 2
        self.convy.weight.data = self.convx.weight.transpose(2,3).flip(2)
        # self.radii = nn.Parameter(torch.Tensor(nf), requires_grad=True)
        self.center_x = nn.Parameter(torch.Tensor(nf), requires_grad=True)
        self.center_y = nn.Parameter(torch.Tensor(nf), requires_grad=True)
        # self.radii.data.uniform_(0.3, 0.7)
        self.center_x.data.uniform_(-0.7, 0.7)
        self.center_y.data.uniform_(-0.7, 0.7)

    def forward(self, x):
        self.convx.weight.data = (self.convx.weight - self.convx.weight.flip(2).flip(3)) / 2  # make convx a first-order operator by symmetrizing it
        self.convy.weight.data = (self.convy.weight - self.convy.weight.flip(2).flip(3)) / 2
        # self.convy.weight.data = self.convx.weight.transpose(2,3).flip(2))                    # make convy a 90 degree rotation of convx
        x1 = self.conv(x)
        _, c, h, w = x1.size()
        XX = torch.from_numpy(np.indices((1,h,w))[2]*2/w).type(x.dtype).to(x.device) - self.center_x.view(-1,1,1)
        YY = torch.from_numpy(np.indices((1,h,w))[1]*2/h).type(x.dtype).to(x.device) - self.center_y.view(-1,1,1)
        # mask = ramp_func((XX**2+YY**2)/(self.radii.type(x.dtype).to(x.device).view(-1,1,1)**2))
        return x1 + (XX * self.convx(x) + YY * self.convy(x)) # * mask

Update: imagewoof2

Size (px) Epochs model mixup Accuracy # Runs
128 5 (Leaderboard) 73.37% 5, mean
128 5 RMS 0 68.54% 5, mean
128 5 RMS + twist 0 70.95% 5, mean
128 20 (Leaderboard) 85.52% 5, mean
128 20 RMS 0 84.62% 5, mean
128 20 RMS + twist 0 85.24% 5, mean
128 80 (Leaderboard) 87.20% 1
128 80 RMS + twist 0.2 87.81% 1
128 80 RMS + twist 0.5 88.52% 1
128 200 (Leaderboard) 87.20% 1
128 200 RMS + twist 0.2 88.70% 1
256 200 (Leaderboard) 90.38% 1
256 200 RMS + twist 0.2 91.52% 1

imagenette2

Size (px) Epochs model mixup Accuracy # Runs
256 200 (Leaderboard) 95.11% 1
256 200 RMS + twist 0.5 95.87% 1

@a_yasyrev, if you could help test with your ResNet trick + MaxBlurPool, that would be very nice.

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