def _meshgrid(height, width):
# This function is the grid generator from eq. (1) in reference [1].
# It is equivalent to the following numpy code:
# x_t, y_t = np.meshgrid(np.linspace(-1, 1, width),
# np.linspace(-1, 1, height))
# ones = np.ones(np.prod(x_t.shape))
# grid = np.vstack([x_t.flatten(), y_t.flatten(), ones])
# It is implemented in Theano instead to support symbolic grid sizes.
# Note: If the image size is known at layer construction time, we could
# compute the meshgrid offline in numpy instead of doing it dynamically
# in Theano. However, it hardly affected performance when we tried.
x_t = T.dot(T.ones((height, 1)),
_linspace(-1.0, 1.0, width).dimshuffle('x', 0))
y_t = T.dot(_linspace(-1.0, 1.0, height).dimshuffle(0, 'x'),
T.ones((1, width)))
x_t_flat = x_t.reshape((1, -1))
y_t_flat = y_t.reshape((1, -1))
ones = T.ones_like(x_t_flat)
grid = T.concatenate([x_t_flat, y_t_flat, ones], axis=0)
return grid
def get_output_for(self, inputs, **kwargs):
inputs = autocrop(inputs, self.cropping)
return T.concatenate(inputs, axis=self.axis)
