def test_join_node_inverse_flatten():
device = 'cpu'
creator = AllocatingCreator(device)
dtype = creator.float32
# The result of the inverse projection should only be one tensor.
expected_results = [
creator.tensor([[1, 2, 3, 4], [5, 6, 7, 8]],
dtype=dtype,
device=device),
creator.tensor([9, 10], dtype=dtype, device=device)
]
input_memory_blocks = [MemoryBlock(), MemoryBlock()]
input_memory_blocks[0].tensor = creator.zeros((2, 4))
input_memory_blocks[1].tensor = creator.zeros((2, ))
output_tensor = creator.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
dtype=dtype,
device=device)
join_node = JoinNode(flatten=True)
Connector.connect(input_memory_blocks[0], join_node.inputs[0])
Connector.connect(input_memory_blocks[1], join_node.inputs[1])
output_inverse_packet = InversePassOutputPacket(output_tensor,
join_node.outputs.output)
join_node.allocate_memory_blocks(creator)
results = join_node.recursive_inverse_projection_from_output(
output_inverse_packet)
for expected, result in zip(expected_results, results):
assert same(expected, result.tensor)
def test_fork_node_inverse_0():
# TODO (Test): add for dim = 1, then refactor.
creator = AllocatingCreator(device='cpu')
dtype = creator.float32
dim = 0
expected_results = [
creator.tensor(
[[1, 2, 3, 4], [5, 6, 7, 8], [0, 0, 0, 0], [0, 0, 0, 0]],
dtype=dtype,
device=creator.device)
]
input_memory_block = MemoryBlock()
input_memory_block.tensor = creator.zeros((4, 4))
output_tensor = creator.tensor([[1, 2, 3, 4], [5, 6, 7, 8]],
dtype=dtype,
device=creator.device)
fork_node = ForkNode(dim, split_sizes=[2, 2])
Connector.connect(input_memory_block, fork_node.inputs.input)
output_inverse_packet = InversePassOutputPacket(output_tensor,
fork_node.outputs[0])
fork_node.allocate_memory_blocks(creator)
results = fork_node.recursive_inverse_projection_from_output(
output_inverse_packet)
for expected, result in zip(expected_results, results):
assert same(expected, result.tensor)
def test_join_node_inverse_0():
# TODO (Test): Make a dim = 1 variant
# TODO (Test): Then, refactor tests here, maybe something to match the test class above, but for the backward projection.
creator = AllocatingCreator(device='cpu')
dtype = creator.float32
dim = 0
# The result of the inverse projection should only be one tensor.
expected_results = [
creator.tensor([[1, 2, 3, 4], [5, 6, 7, 8]],
dtype=dtype,
device=creator.device),
creator.tensor([[9, 10, 11, 12], [13, 14, 15, 16]],
dtype=dtype,
device=creator.device)
]
input_memory_blocks = [MemoryBlock(), MemoryBlock()]
input_memory_blocks[0].tensor = creator.zeros((2, 4))
input_memory_blocks[1].tensor = creator.zeros((2, 4))
output_tensor = creator.tensor(
[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]],
dtype=dtype,
device=creator.device)
join_node = JoinNode(dim, n_inputs=2)
Connector.connect(input_memory_blocks[0], join_node.inputs[0])
Connector.connect(input_memory_blocks[1], join_node.inputs[1])
output_inverse_packet = InversePassOutputPacket(output_tensor,
join_node.outputs.output)
join_node.allocate_memory_blocks(creator)
results = join_node.recursive_inverse_projection_from_output(
output_inverse_packet)
for expected, result in zip(expected_results, results):
assert same(expected, result.tensor)
