def test_identity():
np.random.seed(133391)
shape = [2, 4]
input_data = np.random.randn(*shape).astype(np.float32)
identity_node = make_node("Identity", inputs=["x"], outputs=["y"])
ng_results = run_node(identity_node, [input_data])
assert np.array_equal(ng_results, [input_data])
node1 = make_node("Add",
inputs=["A", "B"],
outputs=["add1"],
name="add_node1")
node2 = make_node("Identity",
inputs=["add1"],
outputs=["identity1"],
name="identity_node1")
node3 = make_node("Abs",
inputs=["identity1"],
outputs=["Y"],
name="abs_node1")
graph = make_graph(
[node1, node2, node3],
"test_graph",
[
make_tensor_value_info("A", onnx.TensorProto.FLOAT, shape),
make_tensor_value_info("B", onnx.TensorProto.FLOAT, shape),
],
[make_tensor_value_info("Y", onnx.TensorProto.FLOAT, shape)],
)
model = make_model(graph, producer_name="ngraph ONNX Importer")
ng_model_function = import_onnx_model(model)
runtime = get_runtime()
computation = runtime.computation(ng_model_function)
ng_results = computation(input_data, input_data)
expected_result = np.abs(input_data + input_data)
assert np.array_equal(ng_results[0], expected_result)
def test_reshape_opset5():
original_shape = [2, 3, 4]
test_cases = {
"reordered_dims": np.array([4, 2, 3], dtype=np.int64),
"reduced_dims": np.array([3, 8], dtype=np.int64),
"extended_dims": np.array([3, 2, 2, 2], dtype=np.int64),
"one_dim": np.array([24], dtype=np.int64),
"negative_dim": np.array([6, -1, 2], dtype=np.int64),
}
input_data = np.random.random_sample(original_shape).astype(np.float32)
for _, shape in test_cases.items():
const_node = make_node(
"Constant",
inputs=[],
outputs=["const_shape"],
value=onnx.helper.make_tensor(
name="const_tensor", data_type=onnx.TensorProto.INT64, dims=shape.shape, vals=shape.flatten()
),
)
reshape_node = onnx.helper.make_node("Reshape", inputs=["data", "const_shape"], outputs=["reshaped"])
graph = make_graph(
[const_node, reshape_node],
"test_graph",
[make_tensor_value_info("data", onnx.TensorProto.FLOAT, input_data.shape)],
[make_tensor_value_info("reshaped", onnx.TensorProto.FLOAT, ())],
)
model = make_model(graph, producer_name="ngraph ONNX Importer")
model.opset_import[0].version = 5
ng_model_function = import_onnx_model(model)
runtime = get_runtime()
computation = runtime.computation(ng_model_function)
ng_results = computation(input_data)
expected_output = np.reshape(input_data, shape)
assert np.array_equal(ng_results[0], expected_output)
def test_cast_errors():
from onnx.onnx_cpp2py_export.checker import ValidationError
np.random.seed(133391)
input_data = np.ceil(np.random.rand(2, 3, 4) * 16)
# missing 'to' attribute
node = onnx.helper.make_node("Cast", inputs=["A"], outputs=["B"])
input_tensors = [
make_tensor_value_info(name, onnx.TensorProto.FLOAT, value.shape)
for name, value in zip(node.input, [input_data])
]
output_tensors = [
make_tensor_value_info(name, onnx.TensorProto.FLOAT16, value.shape)
for name, value in zip(node.output, ())
] # type: ignore
graph = make_graph([node], "compute_graph", input_tensors, output_tensors)
model = make_model(graph, producer_name="NgraphBackend")
with pytest.raises(ValidationError):
import_onnx_model(model)
# unsupported data type representation
node = onnx.helper.make_node("Cast",
inputs=["A"],
outputs=["B"],
to=1.2345)
input_tensors = [
make_tensor_value_info(name, onnx.TensorProto.FLOAT, value.shape)
for name, value in zip(node.input, [input_data])
]
output_tensors = [
make_tensor_value_info(name, onnx.TensorProto.INT32, value.shape)
for name, value in zip(node.output, ())
] # type: ignore
graph = make_graph([node], "compute_graph", input_tensors, output_tensors)
model = make_model(graph, producer_name="NgraphBackend")
with pytest.raises(ValidationError):
import_onnx_model(model)
# unsupported input tensor data type:
node = onnx.helper.make_node("Cast",
inputs=["A"],
outputs=["B"],
to=onnx.TensorProto.INT32)
input_tensors = [
make_tensor_value_info(name, onnx.TensorProto.COMPLEX64, value.shape)
for name, value in zip(node.input, [input_data])
]
output_tensors = [
make_tensor_value_info(name, onnx.TensorProto.INT32, value.shape)
for name, value in zip(node.output, ())
] # type: ignore
graph = make_graph([node], "compute_graph", input_tensors, output_tensors)
model = make_model(graph, producer_name="NgraphBackend")
with pytest.raises((RuntimeError, NgraphTypeError)):
import_onnx_model(model)
# unsupported output tensor data type:
node = onnx.helper.make_node("Cast",
inputs=["A"],
outputs=["B"],
to=onnx.TensorProto.COMPLEX128)
input_tensors = [
make_tensor_value_info(name, onnx.TensorProto.FLOAT, value.shape)
for name, value in zip(node.input, [input_data])
]
output_tensors = [
make_tensor_value_info(name, onnx.TensorProto.COMPLEX128, value.shape)
for name, value in zip(node.output, ())
] # type: ignore
graph = make_graph([node], "compute_graph", input_tensors, output_tensors)
model = make_model(graph, producer_name="NgraphBackend")
with pytest.raises(RuntimeError):
import_onnx_model(model)
def import_and_compute_conv(x, weights, transpose=False, **attributes):
x, weights = np.array(x), np.array(weights)
onnx_model = make_onnx_model_for_conv_op(x.shape, weights.shape, transpose=transpose, **attributes)
ng_model_function = import_onnx_model(onnx_model)
computation = get_runtime().computation(ng_model_function)
return computation(x, weights)[0]
