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_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_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]
