def test_shape_of_variable_tensor_static_shape(self):
var = Variable("var", dtype=np.float32, shape=(1, 3, 4))
graph = Graph(inputs=[var])
graph.inputs = [var]
graph.outputs = [graph.shape(var)]
graph.fold_constants().cleanup()
assert not graph.nodes
assert isinstance(graph.outputs[0], Constant)
assert np.all(graph.outputs[0].values == (1, 3, 4))
def test_shape_of_variable_tensor_static_shape_no_fold(self):
graph = Graph()
var = Variable("var", dtype=np.float32, shape=(1, 3, 4))
graph.inputs = [var]
graph.outputs = [graph.shape(var)]
graph.fold_constants(fold_shapes=False).cleanup()
assert len(graph.nodes) == 1
assert graph.nodes[0].op == "Shape"
assert isinstance(graph.outputs[0], Variable)
def test_io_cannot_be_sync_list_on_assign(self):
inp = Variable("input0", shape=(1, 3), dtype=np.float32)
out = Variable("input1", shape=(1, 3), dtype=np.float32)
node = Node("Add", inputs=[inp], outputs=[out])
assert isinstance(node.inputs, SynchronizedList)
assert isinstance(node.outputs, SynchronizedList)
graph = Graph(nodes=[node], inputs=[], outputs=[])
graph.inputs = node.inputs
graph.outputs = node.outputs
assert not isinstance(graph.inputs, SynchronizedList)
assert not isinstance(graph.outputs, SynchronizedList)
def simple_foldable():
# Graph:
# c = (a + b)
# output = input + c
# Should fold to:
# output = input + c
weights = np.ones(shape=(1, 3), dtype=np.float32)
graph = Graph()
inp = Variable("input", shape=(1, 3), dtype=np.float32)
c = graph.add(weights, weights, name="c")
out = graph.add(inp, c)
graph.inputs = [inp]
graph.outputs = [out]
yield graph
def one_hop_foldable():
# Graph:
# c = (a + b)
# e = (c + d)
# output = input + e
# Should fold to:
# output = input + e
weights = np.ones(shape=(1, 3), dtype=np.float32)
graph = Graph()
inp = Variable("input", shape=(1, 3), dtype=np.float32)
c = graph.add(weights, weights, name="c")
e = graph.add(c, weights, name="e")
out = graph.add(inp, e)
graph.inputs = [inp]
graph.outputs = [out]
yield graph
def test_shape_of_variable_tensor_multiple_shapes(self):
graph = Graph()
var = Variable("var", dtype=np.float32, shape=(1, 3, 4))
var2 = Variable("var2", dtype=np.float32, shape=tuple()) # Scalar
graph.inputs = [var, var2]
graph.outputs = [
graph.shape(var),
graph.identity(var),
graph.shape(var2)
]
graph.fold_constants().cleanup()
assert len(graph.nodes) == 1
assert graph.nodes[0].op == "Identity"
assert isinstance(graph.outputs[0], Constant)
assert np.all(graph.outputs[0].values == (1, 3, 4))
assert isinstance(graph.outputs[2], Constant)
assert np.all(graph.outputs[2].values == tuple())
def test_input_is_output(self):
graph = Graph()
A = Variable("A", dtype=np.float32, shape=(1, 1))
B = Variable("B", dtype=np.float32, shape=(1, 1))
C = graph.add(A, B)
graph.inputs = [A, B]
graph.outputs = [C, B, A] # Out of order w/ respect to Add node inputs
# Graph should remain unchanged after cleanup, including I/O tensors.
graph.cleanup()
assert graph.inputs == [A, B]
assert graph.outputs == [C, B, A]
assert len(graph.nodes) == 1
assert graph.nodes[0].inputs == [A, B]
assert graph.nodes[0].outputs == [C]
def foldable_with_invalid_node():
# Graph
# c = (a + b)
# e = fake(d)
# f = (e + c)
# out = inp + f
#
# c should be folded even though e is the output of an
# invalid node.
weights = np.ones(shape=(1, 3), dtype=np.float32)
graph = Graph()
inp = Variable("input", shape=(1, 3), dtype=np.float32)
c = graph.add(weights, weights, name="c")
e = graph.fake(weights, name="e")
f = graph.add(e, c, name="f")
out = graph.add(inp, f, name="output")
graph.inputs = [inp]
graph.outputs = [out]
yield graph
