def test_get_topological_view(self):
norb = SmallNORB('train')
# Get a topological view as a single "(b, s, 0 1, c)" tensor.
topo_tensor = norb.get_topological_view(single_tensor=True)
shape = (norb.X.shape[0], 2) + SmallNORB.original_image_shape + (1, )
expected_topo_tensor = norb.X.reshape(shape)
assert numpy.all(topo_tensor == expected_topo_tensor)
# Get a topological view as two "(b, 0, 1, c)" tensors
topo_tensors = norb.get_topological_view(single_tensor=False)
expected_topo_tensors = tuple(expected_topo_tensor[:, i, ...]
for i in range(2))
for topo_tensor, expected_topo_tensor in \
safe_zip(topo_tensors, expected_topo_tensors):
assert numpy.all(topo_tensor == expected_topo_tensor)
def test_get_topological_view(self):
norb = SmallNORB('train')
# Get a topological view as a single "(b, s, 0 1, c)" tensor.
topo_tensor = norb.get_topological_view(single_tensor=True)
shape = (norb.X.shape[0], 2) + SmallNORB.original_image_shape + (1, )
expected_topo_tensor = norb.X.reshape(shape)
assert numpy.all(topo_tensor == expected_topo_tensor)
# Get a topological view as two "(b, 0, 1, c)" tensors
topo_tensors = norb.get_topological_view(single_tensor=False)
expected_topo_tensors = tuple(expected_topo_tensor[:, i, ...]
for i in range(2))
for topo_tensor, expected_topo_tensor in \
safe_zip(topo_tensors, expected_topo_tensors):
assert numpy.all(topo_tensor == expected_topo_tensor)
def test_get_topological_view(self):
# This is just to lower the memory usage. Otherwise, the
# buildbot use close to 10G of ram.
norb = SmallNORB('train', stop=1000)
# Get a topological view as a single "(b, s, 0 1, c)" tensor.
topo_tensor = norb.get_topological_view(single_tensor=True)
shape = (norb.X.shape[0], 2) + SmallNORB.original_image_shape + (1, )
expected_topo_tensor = norb.X.reshape(shape)
# We loop to lower the peak memory usage
for i in range(topo_tensor.shape[0]):
assert numpy.all(topo_tensor[i] == expected_topo_tensor[i])
# Get a topological view as two "(b, 0, 1, c)" tensors
topo_tensors = norb.get_topological_view(single_tensor=False)
expected_topo_tensors = tuple(expected_topo_tensor[:, i, ...]
for i in range(2))
for topo_tensor, expected_topo_tensor in safe_zip(
topo_tensors, expected_topo_tensors):
assert numpy.all(topo_tensor == expected_topo_tensor)
