CoordConv

Pytorch implementation of CoordConv for N-D ConvLayers, and the experiments.

Reference from the paper: An intriguing failing of convolutional neural networks and the CoordConv solution

Extends the CoordinateChannel concatenation from 2D to 1D and 3D tensors.

Requirements

pytorch 0.4.0
torchvision 0.2.1
torchsummary 1.3
sklearn 0.19.1

Usage

from coordconv import CoordConv1d, CoordConv2d, CoordConv3d

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.coordconv = CoordConv2d(2, 32, 1, with_r=True)
        self.conv1 = nn.Conv2d(32, 64, 1)
        self.conv2 = nn.Conv2d(64, 64, 1)
        self.conv3 = nn.Conv2d(64,  1, 1)
        self.conv4 = nn.Conv2d( 1,  1, 1)

    def forward(self, x):
        x = self.coordconv(x)
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = self.conv4(x)
        x = x.view(-1, 64*64)
        return x

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = Net().to(device)