import torch
import torch.nn as nn
import torch.nn.functional as F
class STN(nn.Module):
    def __init__(self):
        super(STN, self).__init__()
        # simple convnet classifier
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
        # spatial transformer localization network
        self.localization = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            nn.Conv2d(64, 128, kernel_size=5),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True)
        )
        # tranformation regressor for theta
        self.fc_loc = nn.Sequential(
            nn.Linear(128*4*4, 256),
            nn.ReLU(True),
            nn.Linear(256, 3 * 2)
        )
        # initializing the weights and biases with identity transformations
        self.fc_loc[2].weight.data.zero_()
        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], 
                                                    dtype=torch.float))
    def stn(self, x):
        xs = self.localization(x)
        xs = xs.view(-1, xs.size(1)*xs.size(2)*xs.size(3))
        # calculate the transformation parameters theta
        theta = self.fc_loc(xs)
        # resize theta
        theta = theta.view(-1, 2, 3) 
        # grid generator => transformation on parameters theta
        grid = F.affine_grid(theta, x.size())
        # grid sampling => applying the spatial transformations
        x = F.grid_sample(x, grid)
        return x
    def forward(self, x):
        # transform the input
        x = self.stn(x)
        
        # forward pass through the classifier 
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16*5*5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return F.log_softmax(x, dim=1)