conv1d-dilation.py

class TestConv1d(nn.Module):
    def __init__(self):
        super(TestConv1d, self).__init__()
        self.conv = nn.Conv1d(in_channels=1, out_channels=1, kernel_size=3, dilation=2, bias=False)
        self.init_weights()

    def forward(self, x):
        return self.conv(x)
    
    def init_weights(self):
        new_weights = torch.ones(self.conv.weight.shape) * 2.
        self.conv.weight = torch.nn.Parameter(new_weights, requires_grad=False)


in_x = torch.tensor([[[1,2,3,4,5,6]]]).float()
print("in_x.shape", in_x.shape)
print(in_x)

net = TestConv1d()
out_y = net(in_x)

print("out_y.shape", out_y.shape)
print(out_y)