You could try to upsample your input to [3, 224, 224] using one upsample approach 3.
You could do this before passing your input to the model, but since the width is approx. only half of the desired width, your model might perform bad. However, I think it is worth a try:
x = torch.randn(1, 3, 256, 128)
up = nn.Upsample(size=224, mode='bilinear')
x = up(x)
print(x.shape)

Just define the layer in your __init__ and call if before passing it to the resnet.
Alternatively, your input is not too small for the used layers of resnet, so you could still pass it as [3, 256, 128] and set in_features in augmentedX to (ni * 8 * 4), where ni=2048.