def test_vector_to_conv_c01b_invertible():
"""
Tests that the format_as methods between Conv2DSpace
and VectorSpace are invertible for the ('c', 0, 1, 'b')
axis format.
"""
rng = np.random.RandomState([2013, 5, 1])
batch_size = 3
rows = 4
cols = 5
channels = 2
conv = Conv2DSpace([rows, cols], channels=channels, axes=('c', 0, 1, 'b'))
vec = VectorSpace(conv.get_total_dimension())
X = conv.make_batch_theano()
Y = conv.format_as(X, vec)
Z = vec.format_as(Y, conv)
A = vec.make_batch_theano()
B = vec.format_as(A, conv)
C = conv.format_as(B, vec)
f = function([X, A], [Z, C])
X = rng.randn(*(conv.get_origin_batch(batch_size).shape)).astype(X.dtype)
A = rng.randn(*(vec.get_origin_batch(batch_size).shape)).astype(A.dtype)
Z, C = f(X, A)
np.testing.assert_allclose(Z, X)
np.testing.assert_allclose(C, A)
def test_vector_to_conv_c01b_invertible():
"""
Tests that the format_as methods between Conv2DSpace
and VectorSpace are invertible for the ('c', 0, 1, 'b')
axis format.
"""
rng = np.random.RandomState([2013, 5, 1])
batch_size = 3
rows = 4
cols = 5
channels = 2
conv = Conv2DSpace([rows, cols], channels = channels, axes = ('c', 0, 1, 'b'))
vec = VectorSpace(conv.get_total_dimension())
X = conv.make_batch_theano()
Y = conv.format_as(X, vec)
Z = vec.format_as(Y, conv)
A = vec.make_batch_theano()
B = vec.format_as(A, conv)
C = conv.format_as(B, vec)
f = function([X, A], [Z, C])
X = rng.randn(*(conv.get_origin_batch(batch_size).shape)).astype(X.dtype)
A = rng.randn(*(vec.get_origin_batch(batch_size).shape)).astype(A.dtype)
Z, C = f(X,A)
np.testing.assert_allclose(Z, X)
np.testing.assert_allclose(C, A)
