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_basic(self):
graph = Graph(
nodes=[Node(op="Test")],
inputs=[Variable("test")],
outputs=[Variable("test")],
name="test-name",
doc_string="test-docstring",
import_domains=["fake-import-domain"],
opset=-1,
)
new_graph = graph.copy()
assert new_graph == graph
assert new_graph.nodes == graph.nodes
assert new_graph.inputs == graph.inputs
assert new_graph.outputs == graph.outputs
assert new_graph.name == graph.name
assert new_graph.doc_string == graph.doc_string
assert new_graph.import_domains == graph.import_domains
assert new_graph.opset == graph.opset
