I am trying to learn a piecewise affine transform model where the input images are converted via the transform into output data. One example transformation would be (first to second) –
The input images, are however, neither simple down samples with borders, nor distortion free rectangles, which is why I need piecewise affine transform to work.
I use the following code to “learn” the transform but obviously i am going wrong somewhere as the loss is stuck from the very first epoch( i use MSE loss)
class Net(nn.Module): def __init__(self): super(Net, self).__init__() # Spatial transformer localization-network self.localization = nn.Sequential( nn.Conv2d(3, 8, kernel_size=7), nn.MaxPool2d(2, stride=2), nn.ReLU(True), nn.Conv2d(8, 10, kernel_size=5), nn.MaxPool2d(2, stride=2), nn.MaxPool2d(2, stride=2), nn.ReLU(True) ) # Regressor for the 3 * 2 affine matrix self.fc_loc = nn.Sequential( nn.Linear(10 * 30 * 30, 360), nn.ReLU(True), nn.Linear(360, 3 * 2) ) # Initialize the weights/bias with identity transformation self.fc_loc.weight.data.zero_() # Spatial transformer network forward function def stn(self, x): xs = self.localization(x) xs = xs.view(-1, 30* 10 * 30) theta = self.fc_loc(xs) theta = theta.view(-1, 2, 3) grid = F.affine_grid(theta, x.size()) print(x.shape) x = F.grid_sample(x, grid) return x def forward(self, x): # transform the input x = self.stn(x)
Code is heavily borrowed for the pytorch STN example as I first wanted to try out things before stepping it up.
Any help would be highly appreciated
This is the output from the model –
The loss is stuck as the output is same, no matter what the input
Are there any constraints for defining the grid ? Something that maybe I can teach the model–which it would take a long time to learn by itself.