def test_copy_with_subgraph_dup_tensors(self):
inp = Variable("input", dtype=np.float32, shape=(4, 5))
graph = Graph(inputs=[inp])
# We'll use shape to distinguish inner/outer tensor
subgraph_inp = Variable("input", dtype=np.float32, shape=(1, 2))
subgraph = Graph(inputs=[subgraph_inp])
graph.outputs = [graph.nested(inp, subgraph)]
graph_copy = graph.copy()
assert graph_copy.nodes[0].attrs["body"].inputs[0].shape == (1, 2)
def test_copy_with_subgraph_dup_const_tensors(self):
inp = Constant("input", values=np.ones(dtype=np.float32, shape=(4, 5)))
graph = Graph()
# We'll use shape to distinguish inner/outer tensor
subgraph_inp = Constant("input",
values=np.ones(dtype=np.float32, shape=(1, 2)))
subgraph = Graph()
subgraph.outputs = [subgraph.identity(subgraph_inp)]
graph.outputs = [graph.nested(inp, subgraph)]
graph_copy = graph.copy()
assert graph_copy.nodes[0].attrs["body"].nodes[0].inputs[0].shape == (
1, 2)
def test_node_used_only_in_nested_graph(self):
X = Variable("X", dtype=np.float32, shape=(1, ))
Y = Variable("Y", dtype=np.float32, shape=(1, ))
graph = Graph(inputs=[X, Y])
X_p = graph.identity(
X) # X_p is only used by the subgraph, not in the outer graph.
subgraph_inp = Variable("subgraph_input",
dtype=np.float32,
shape=(1, ))
subgraph = Graph(inputs=[subgraph_inp])
subgraph.outputs = [subgraph.add(subgraph_inp, X_p)]
graph.outputs = [graph.nested(Y, subgraph)]
graph.cleanup(remove_unused_graph_inputs=True)
assert graph.nodes[0].op == "Identity"
assert graph.nodes[0].inputs == [X]
