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 build_st_spline_network(input_shape):
W = b = lasagne.init.Constant(0.0)
num_points = 4
num_filters = 64
filter_size = (3, 3)
pool_size = (2, 2)
l_in = InputLayer(shape=(None, input_shape[1], input_shape[2],
input_shape[3]))
l_conv1 = Conv2DLayer(l_in,
num_filters=num_filters,
filter_size=filter_size)
l_pool1 = MaxPool2DLayer(l_conv1, pool_size=pool_size)
l_conv2 = Conv2DLayer(l_pool1,
num_filters=num_filters,
filter_size=filter_size)
l_pool2 = MaxPool2DLayer(l_conv2, pool_size=pool_size)
l_dense1 = DenseLayer(l_pool2, num_units=128)
l_dense2 = DenseLayer(l_dense1,
num_units=num_points * 2,
W=W,
b=b,
nonlinearity=None)
l_st = TPSTransformerLayer(l_in, l_dense2, control_points=num_points)
l_output = ReshapeLayer(l_st, shape=([0], -1))
return l_output
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)
