def build_basic_graph():
inputs = [Variable(name="x")]
outputs = [Variable(name="y")]
nodes = [
Node(op="Add", name="Test", inputs=inputs, outputs=outputs),
]
return Graph(nodes=nodes, inputs=inputs, outputs=outputs)
def make_graph():
graph, _ = toposort_multi_tier_output_graph()
graph.outputs.pop()
# Deep copy should work with empty tensors
graph.nodes[0].inputs.append(Variable.empty())
graph.nodes[0].outputs.append(Variable.empty())
return graph
def ext_weights():
path = os.path.join(TEST_ROOT, "models", "ext_weights.onnx")
model = onnx.load(path)
inputs = [Variable("input", shape=(1, 3), dtype=np.float32)]
outputs = [Variable("output", shape=(1, 3), dtype=np.float32)]
a = Constant("a", values=np.ones((1, 3), dtype=np.float32))
b = Constant("b", values=np.ones((1, 3), dtype=np.float32))
d = Constant("d", values=np.ones((1, 3), dtype=np.float32))
c = Variable("c")
e = Variable("e")
nodes = [
Node(op="Add", inputs=[a, b], outputs=[c]),
Node(op="Add", inputs=[c, d], outputs=[e]),
Node(op="Add", inputs=[inputs[0], e], outputs=outputs),
]
return Model(
path,
inputs=inputs,
outputs=outputs,
nodes=nodes,
opset=OnnxImporter.get_opset(model),
)
def nested_dup_names():
path = os.path.join(TEST_ROOT, "models", "nested_dup_names.onnx")
model = onnx.load(path)
# Inner
subgraph_inputs = [Variable("X", shape=(2, 2), dtype=np.float32)]
subgraph_outputs = [Variable("Y", shape=(2, 2), dtype=np.float32)]
subgraph_node = Node(op="Identity",
inputs=subgraph_inputs,
outputs=subgraph_outputs)
subgraph = Graph(nodes=[subgraph_node],
inputs=subgraph_inputs,
outputs=subgraph_outputs)
# Outer - problem happens if outer node has same I/O names as subgraph
inputs = [Variable("X", shape=(2, 2), dtype=np.float32)]
outputs = [Variable("Y", shape=(2, 2), dtype=np.float32)]
node = Node(op="Nested",
inputs=inputs,
outputs=outputs,
attrs={"body": subgraph})
return Model(
path,
inputs=inputs,
outputs=outputs,
nodes=[node],
opset=OnnxImporter.get_opset(model),
)
def test_fold_constants_one_hop(self):
# Graph:
# c = (a + b)
# e = (c + d)
# output = input + e
# Should fold to:
# output = input + e
inp = Variable("input", shape=(1, 3), dtype=np.float32)
a = Constant("a", values=np.ones(shape=(1, 3), dtype=np.float32))
b = Constant("b", values=np.ones(shape=(1, 3), dtype=np.float32))
c = Variable("c", shape=(1, 3), dtype=np.float32)
d = Constant("d", values=np.ones(shape=(1, 3), dtype=np.float32))
e = Variable("e", shape=(1, 3), dtype=np.float32)
out = Variable("output", shape=(1, 3), dtype=np.float32)
nodes = [
Node("Add", inputs=[a, b], outputs=[c]),
Node("Add", inputs=[c, d], outputs=[e]),
Node("Add", inputs=[inp, e], outputs=[out]),
]
graph = Graph(nodes=nodes, inputs=[inp], outputs=[out])
graph.fold_constants().cleanup()
# Extra nodes should be removed
assert len(graph.nodes) == 1
assert graph.nodes[0].inputs[0] == inp
assert graph.nodes[0].inputs[1] == e
# Value should be computed correctly
assert np.all(graph.nodes[0].inputs[1].values == np.ones(shape=(1, 3), dtype=np.float32) * 3)
def setup_method(self):
self.input_tensor = Variable(name="x")
self.output_tensor = Variable(name="y")
self.node = Node(op="Add",
name="Test",
inputs=[self.input_tensor],
outputs=[self.output_tensor])
def test_o_multiple_outputs(self):
intermediate_tensor = Variable(name="intermediate")
intermediate_tensor2 = Variable(name="intermediate2")
input_node = Node(op="Add", name="Input", inputs=[self.input_tensor], outputs=[intermediate_tensor])
output_node = Node(op="Add", name="Out", inputs=[intermediate_tensor], outputs=[self.output_tensor])
output_node2 = Node(op="Add", name="Input2", inputs=[intermediate_tensor], outputs=[intermediate_tensor2])
assert input_node.o() == output_node
assert input_node.o(1) == output_node2
def build_two_layer_graph_multiple_io():
inputs = [Variable(name="x0"), Variable(name="x1")]
intermediate_tensor = Variable(name="intermediate")
outputs = [Variable(name="y0"), Variable(name="y1")]
nodes = [
Node(op="Add", name="Test0", inputs=inputs, outputs=[intermediate_tensor]),
Node(op="Add", name="Test1", inputs=[intermediate_tensor], outputs=outputs),
]
return Graph(nodes=nodes, inputs=inputs, outputs=outputs)
def test_tensors_with_duplicates_check_disabled(self):
inputs = [Variable(name="x")]
outputs = [Variable(name="x")] # Distinct tensors with the same name
nodes = [
Node(op="Add", name="Test", inputs=inputs, outputs=outputs),
]
graph = Graph(nodes=nodes, inputs=inputs, outputs=outputs)
# This should *not* throw
graph.tensors(check_duplicates=False)
def test_tensors_check_duplicates(self):
inputs = [Variable(name="x")]
outputs = [Variable(name="x")] # Distinct tensors with the same name
nodes = [
Node(op="Add", name="Test", inputs=inputs, outputs=outputs),
]
graph = Graph(nodes=nodes, inputs=inputs, outputs=outputs)
with pytest.raises(OnnxGraphSurgeonException):
graph.tensors(check_duplicates=True)
def toposort_multi_tier_output_graph():
inputs = [Variable(name="x")]
outputs = [Variable(name="out0"), Variable(name="out1"), Variable(name="out2")]
out0, out1, out2 = outputs
nodes = [
Node(op="Add", name="Test2", inputs=[out1], outputs=[out2]),
Node(op="Add", name="Test0", inputs=inputs, outputs=[out0]),
Node(op="Add", name="Test1", inputs=[out0], outputs=[out1]),
]
expected_node_order = [nodes[1], nodes[2], nodes[0]]
return Graph(nodes=nodes, inputs=inputs, outputs=outputs), expected_node_order
def test_io_cannot_be_sync_list_on_init(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=node.inputs, outputs=node.outputs)
assert not isinstance(graph.inputs, SynchronizedList)
assert not isinstance(graph.outputs, SynchronizedList)
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 check_tensor(name: str):
if name not in tensor_map:
if name:
G_LOGGER.debug(
"Tensor: {:} was not generated during shape inference, or shape inference was not run on this model. Creating a new Tensor."
.format(name))
tensor_map[name] = Variable(name)
else:
# Empty tensors are not tracked by the graph, as these represent optional inputs/outputs that have been omitted.
G_LOGGER.verbose("Generating empty tensor")
return Variable.empty()
return tensor_map[name]
def test_layer_with_tensors(self):
x0 = Variable("x0")
x1 = Variable("x1")
y0 = Variable("y0")
y1 = Variable("y1")
graph = Graph()
outputs = graph.layer(op="Fake", inputs=[x0, x1], outputs=[y0, y1])
assert outputs == [y0, y1]
assert len(graph.nodes) == 1
assert graph.nodes[-1].inputs == [x0, x1]
assert graph.nodes[-1].outputs == outputs
def test_export_node(self):
name = "TestNode"
op = "Test"
inputs = [Variable(name="input")]
outputs = [Variable(name="output")]
attrs = OrderedDict()
attrs["float_attr"] = 4.0
attrs["int_attr"] = 10
attrs["str_attr"] = "constant"
attrs["tensor_attr"] = Constant(
"testTensor", np.ones(shape=(1, 2, 3, 4), dtype=np.float32))
attrs["floats_attr"] = [1.0, 2.0, 3.0, 4.0]
attrs["ints_attr"] = [4, 3, 2, 1]
attrs["strings_attr"] = ["constant", "and", "variable"]
node = Node(op=op,
name=name,
inputs=inputs,
outputs=outputs,
attrs=attrs)
onnx_node = OnnxExporter.export_node(node)
assert onnx_node.name == name
assert onnx_node.op_type == op
assert onnx_node.input == ["input"]
assert onnx_node.output == ["output"]
for onnx_attr, (name, attr) in zip(onnx_node.attribute, attrs.items()):
assert onnx_attr.name == name
if isinstance(attr, float):
assert onnx_attr.f == attr
elif isinstance(attr, int):
assert onnx_attr.i == attr
elif isinstance(attr, str):
assert onnx_attr.s.decode() == attr
elif isinstance(attr, Tensor):
assert onnx_attr.t.SerializeToString(
) == OnnxExporter.export_tensor_proto(
attr).SerializeToString()
elif isinstance(attr, list):
if isinstance(attr[0], float):
assert onnx_attr.floats == attr
elif isinstance(attr[0], int):
assert onnx_attr.ints == attr
elif isinstance(attr[0], str):
assert [s.decode() for s in onnx_attr.strings] == attr
else:
raise AssertionError(
"Unrecognized list attribute: ({:}: {:}) of type: {:}".
format(name, attr, type(attr)))
else:
raise AssertionError(
"Unrecognized attribute: ({:}: {:}) of type: {:}".format(
name, attr, type(attr)))
def test_no_foldable_constants(self):
inp0 = Variable("input0", shape=(1, 3), dtype=np.float32)
inp1 = Variable("input1", shape=(1, 3), dtype=np.float32)
out = Variable("output", shape=(1, 3), dtype=np.float32)
nodes = [Node("Add", inputs=[inp0, inp1], outputs=[out])]
graph = Graph(nodes=nodes, inputs=[inp0, inp1], outputs=[out])
graph.fold_constants().cleanup()
assert len(graph.nodes) == 1
assert graph.nodes[0].inputs == [inp0, inp1]
def identity_model():
path = os.path.join(TEST_ROOT, "models", "identity.onnx")
model = onnx.load(path)
x = Variable(name="x", dtype=np.float32, shape=(1, 1, 2, 2))
y = Variable(name="y", dtype=np.float32, shape=(1, 1, 2, 2))
node = Node(op="Identity", inputs=[x], outputs=[y])
return Model(path,
inputs=[x],
outputs=[y],
nodes=[node],
opset=OnnxImporter.get_opset(model))
def dim_param_model():
path = os.path.join(TEST_ROOT, "models", "dim_param.onnx")
model = onnx.load(path)
x = Variable(name="Input:0", dtype=np.float32, shape=("dim0", 16, 128))
y = Variable(name="Output:0", dtype=np.float32, shape=("dim0", 16, 128))
node = Node(op="Identity", inputs=[x], outputs=[y])
return Model(path,
inputs=[x],
outputs=[y],
nodes=[node],
opset=OnnxImporter.get_opset(model))
def test_cleanup_independent_path(self):
graph, _ = toposort_linear_graph()
# Build out a path totally unrelated to rest of the graph
indep0 = Variable(name="indep0")
indep1 = Variable(name="indep1")
node = Node(op="IndepTest", inputs=[indep0], outputs=[indep1])
graph.inputs.append(indep0) # Unused inputs should be removed as well
graph.nodes.append(node)
graph.cleanup()
assert indep0 not in graph.inputs
assert node not in graph.nodes
tensor_map = graph.tensors()
assert indep0.name not in tensor_map
assert indep1.name not in tensor_map
def toposort_linear_graph():
inputs = [Variable(name="x")]
intermediate0 = Variable(name="intermediate0")
intermediate1 = Variable(name="intermediate1")
intermediate2 = Variable(name="intermediate2")
outputs = [Variable(name="y")]
# Nodes are NOT in topo order.
nodes = [
Node(op="Add", name="Test0", inputs=inputs, outputs=[intermediate0]),
Node(op="Add", name="Test2", inputs=[intermediate1], outputs=[intermediate2]),
Node(op="Add", name="Test3", inputs=[intermediate2], outputs=outputs),
Node(op="Add", name="Test1", inputs=[intermediate0], outputs=[intermediate1]),
]
expected_node_order = [nodes[0], nodes[3], nodes[1], nodes[2]]
return Graph(nodes=nodes, inputs=inputs, outputs=outputs), expected_node_order
def test_independent_path(self, remove_unused_graph_inputs):
graph, _ = toposort_linear_graph()
# Build out a path totally unrelated to rest of the graph
indep0 = Variable(name="indep0")
indep1 = Variable(name="indep1")
node = Node(op="IndepTest", inputs=[indep0], outputs=[indep1])
graph.nodes.append(node)
graph.inputs.append(indep0)
graph.cleanup(remove_unused_graph_inputs=remove_unused_graph_inputs)
assert indep0 not in graph.inputs or not remove_unused_graph_inputs
assert node not in graph.nodes or not remove_unused_graph_inputs
tensor_map = graph.tensors()
assert indep0.name not in tensor_map or not remove_unused_graph_inputs
assert indep1.name not in tensor_map or not remove_unused_graph_inputs
def setup_method(self, field_names):
self.tensors = [
Variable(name="test_tensor_{:}".format(i),
dtype=np.float32,
shape=(1, 3, 224, 224)) for i in range(10)
]
self.node = Node(op="Dummy")
def process_io(io):
new_io = []
for elem in io:
if isinstance(elem, Tensor):
new_io.append(elem)
elif isinstance(elem, str):
tensor = Variable(name=self._generate_name(elem))
new_io.append(tensor)
elif isinstance(elem, np.ndarray):
new_io.append(
Constant(name=self._generate_name(
"onnx_graphsurgeon_constant"),
values=elem))
elif isinstance(elem, list) or isinstance(elem, tuple):
dtype = np.float32 if any(
[isinstance(x, float) for x in elem]) else np.int64
arr = np.array(elem, dtype=dtype)
new_io.append(
Constant(name=self._generate_name(
"onnx_graphsurgeon_lst_constant"),
values=arr))
else:
G_LOGGER.critical(
"Unrecognized type passed to Graph.layer: {:}.\n"
"\tHint: Did you forget to unpack a list with `*`?\n"
"\tPlease use Tensors, strings, or NumPy arrays.".
format(elem))
return new_io
def test_shape_gather(self, shape, indices):
indices = np.array(indices)
inp = Variable("input", dtype=np.float32, shape=shape)
graph = Graph(inputs=[inp])
inp_shape = graph.shape(inp)
shape_part = graph.gather(inp_shape, indices=indices)
graph.outputs = [
graph.add(shape_part, shape_part),
graph.gather(inp_shape, indices=[0]),
graph.gather(inp_shape, indices=np.array(0)),
]
graph.fold_constants()
if shape is not None:
assert isinstance(graph.outputs[0], Constant)
expected_shape = np.array(shape)[indices].astype(np.int64) * 2
assert np.all(graph.outputs[0].values == expected_shape)
else:
assert isinstance(graph.outputs[0], Variable)
assert isinstance(graph.outputs[1], Variable)
assert isinstance(graph.outputs[2], Variable)
def test_equal_outputs_unequal(self):
g0 = make_nested_graph()
g1 = make_nested_graph()
g0.outputs.append(Variable("test"))
assert not (g0 == g1)
def get_tensor(name: str, check_outer_graph=True):
# Prioritize the subgraph even if check_outer_graph is set
if name in subgraph_tensor_map:
return subgraph_tensor_map[name]
if check_outer_graph and name in tensor_map:
return tensor_map[name]
if not name:
# Empty tensors are not tracked by the graph, as these represent optional inputs/outputs that have been omitted.
G_LOGGER.verbose("Generating empty tensor")
return Variable.empty()
G_LOGGER.verbose("Tensor: {:} was not generated during shape inference, or shape inference was not run on this model. Creating a new Tensor.".format(name))
subgraph_tensor_map[name] = Variable(name)
return subgraph_tensor_map[name]
def tensors_linear_graph():
inputs = [Variable(name="x")]
intermediate0 = Variable(name="intermediate0")
intermediate1 = Variable(name="intermediate1")
intermediate2 = Variable(name="intermediate2")
outputs = [Variable(name="y")]
tensors = inputs + [intermediate0, intermediate1, intermediate2] + outputs
tensors = {tensor.name: tensor for tensor in tensors}
# Nodes are NOT in topo order.
nodes = [
Node(op="Add", name="Test0", inputs=inputs, outputs=[intermediate0]),
Node(op="Add", name="Test1", inputs=[intermediate0], outputs=[intermediate1]),
Node(op="Add", name="Test2", inputs=[intermediate1], outputs=[intermediate2]),
Node(op="Add", name="Test3", inputs=[intermediate2], outputs=outputs),
]
return Graph(nodes=nodes, inputs=inputs, outputs=outputs), nodes, tensors
def test_setitem(self, field_names):
nlist, tensor_field = self.get_lists(field_names)
nlist.append(self.tensors[0])
new_tensor = Variable("new_tensor")
nlist[0] = new_tensor
assert nlist[0] == new_tensor
assert len(getattr(self.tensors[0], tensor_field)) == 0
assert getattr(new_tensor, tensor_field)[0] == self.node
def test_export_variable_tensor_empty_shape(self):
shape = None
tensor = Variable(dtype=np.float32,
shape=shape,
name="variable_tensor")
onnx_tensor = OnnxExporter.export_value_info_proto(tensor,
do_type_check=True)
assert not onnx_tensor.type.tensor_type.HasField("shape")
