def test_transform_thin_plate_spline_shift(self):
from lasagne.layers import InputLayer, TPSTransformerLayer
from theano.tensor import constant
batchsize = 5
num_control_points = 16
dest_offset = np.ones(shape=(batchsize, 2*num_control_points))
l_in = InputLayer((batchsize, 3, 28, 28))
l_loc = InputLayer((batchsize, 2*num_control_points))
layer = TPSTransformerLayer(
l_in, l_loc, control_points=num_control_points
)
image = np.zeros(shape=(28, 28))
image[[0, -1], :] = 1
image[:, [0, -1]] = 1
inputs = np.tile(image, (batchsize, 3, 1, 1))
shifted_input = np.ones(shape=(28, 28))
shifted_input[:13, :13] = 0
shifted_input[13, :13] = 0.50000271
shifted_input[:13, 13] = 0.50000271
shifted_input[13, 13] = 0.75000271
shifted_input = np.tile(shifted_input, (batchsize, 3, 1, 1))
outputs = layer.get_output_for([constant(inputs),
constant(dest_offset)]).eval()
np.testing.assert_allclose(shifted_input,
outputs, atol=1e-5)
def test_transform_thin_plate_spline_shift(self):
from lasagne.layers import InputLayer, TPSTransformerLayer
from theano.tensor import constant
batchsize = 5
num_control_points = 16
dest_offset = np.ones(shape=(batchsize, 2*num_control_points))
l_in = InputLayer((batchsize, 3, 28, 28))
l_loc = InputLayer((batchsize, 2*num_control_points))
layer = TPSTransformerLayer(
l_in, l_loc, control_points=num_control_points
)
image = np.zeros(shape=(28, 28))
image[[0, -1], :] = 1
image[:, [0, -1]] = 1
inputs = np.tile(image, (batchsize, 3, 1, 1))
shifted_input = np.ones(shape=(28, 28))
shifted_input[:13, :13] = 0
shifted_input[13, :13] = 0.50000271
shifted_input[:13, 13] = 0.50000271
shifted_input[13, 13] = 0.75000271
shifted_input = np.tile(shifted_input, (batchsize, 3, 1, 1))
outputs = layer.get_output_for([constant(inputs),
constant(dest_offset)]).eval()
np.testing.assert_allclose(shifted_input,
outputs, atol=1e-5)
def test_transform_thin_plate_spline_identity(self):
from lasagne.layers import InputLayer, TPSTransformerLayer
from lasagne.utils import floatX
from theano.tensor import constant
batchsize = 5
num_control_points = 16
dest_offset = np.zeros(shape=(batchsize, 2 * num_control_points))
l_in = InputLayer((batchsize, 3, 28, 28))
l_loc = InputLayer((batchsize, 2 * num_control_points))
layer = TPSTransformerLayer(l_in,
l_loc,
control_points=num_control_points)
inputs = floatX(np.arange(np.prod(l_in.shape)).reshape(l_in.shape))
outputs = layer.get_output_for(
[constant(inputs), constant(dest_offset)]).eval()
np.testing.assert_allclose(inputs, outputs, atol=1e-5)
def test_transform_thin_plate_spline_identity(self):
from lasagne.layers import InputLayer, TPSTransformerLayer
from lasagne.utils import floatX
from theano.tensor import constant
batchsize = 5
num_control_points = 16
dest_offset = np.zeros(shape=(batchsize, 2*num_control_points))
l_in = InputLayer((batchsize, 3, 28, 28))
l_loc = InputLayer((batchsize, 2*num_control_points))
layer = TPSTransformerLayer(
l_in, l_loc, control_points=num_control_points
)
inputs = floatX(np.arange(np.prod(l_in.shape)).reshape(l_in.shape))
outputs = layer.get_output_for([constant(inputs),
constant(dest_offset)]).eval()
np.testing.assert_allclose(inputs, outputs, atol=5e-4)
