def return_shifts()

in complex_shift_autoencoder.py [0:0]

• 15 lines of code
• 7 McCabe index (conditional complexity)

    def return_shifts(self, params):
        
        smallest_angle = 360 / (self.data.n_rotations + 1)
        int_x = round(self.data.n_pixels / (self.data.n_x_translations + 1))
        int_y = round(self.data.n_pixels / (self.data.n_y_translations + 1))
        shifts_x = torch.LongTensor([[param.shift_x/int_x for param in params]]).t()
        shifts_y = torch.LongTensor([[param.shift_y/int_y for param in params]]).t()
        shifts_r = torch.LongTensor([[int(param.angle/smallest_angle) for param in params]]).t()

        shifts = []
        if self.data.n_rotations > 0:
            shifts.append(shifts_r)
        if self.data.n_x_translations > 0:
            shifts.append(shifts_x)
        if self.data.n_y_translations > 0:
            shifts.append(shifts_y)
        return shifts