def test_scalars(self):
# Create 2 inputs
X = helper.make_tensor_value_info('A', TensorProto.INT32, [])
Y = helper.make_tensor_value_info('B', TensorProto.INT32, [])
# Create one output
Z = helper.make_tensor_value_info('C', TensorProto.INT32, [])
# Create a node
node_def = helper.make_node('Add', ['A', 'B'], ['C'])
# Create the model
graph_def = helper.make_graph([node_def], "scalar-model", [X, Y], [Z])
onnx_model = helper.make_model(graph_def, producer_name='onnx-example')
model = Model()
model.BuildFromOnnxModel(onnx_model)
schedule = model.OptimizeSchedule()
schedule = schedule.replace('\n', ' ')
expected_schedule = r'// Target: .+// MachineParams: .+// Delete this line if not using Generator Pipeline pipeline = get_pipeline\(\);.+Func C = pipeline.get_func\(2\);.+{.+}.+'
self.assertRegex(schedule, expected_schedule)
input1 = np.random.randint(-10, 10, size=())
input2 = np.random.randint(-10, 10, size=())
outputs = model.run([input1, input2])
self.assertEqual(1, len(outputs))
output = outputs[0]
expected = input1 + input2
np.testing.assert_allclose(expected, output)
def test_small_model(self):
# Create one input
X = helper.make_tensor_value_info('IN', TensorProto.FLOAT, [2, 3])
# Create one output
Y = helper.make_tensor_value_info('OUT', TensorProto.FLOAT, [2, 3])
# Create a node
node_def = helper.make_node('Abs', ['IN'], ['OUT'])
# Create the model
graph_def = helper.make_graph([node_def], "test-model", [X], [Y])
onnx_model = helper.make_model(graph_def, producer_name='onnx-example')
model = Model()
model.BuildFromOnnxModel(onnx_model)
schedule = model.OptimizeSchedule()
schedule = schedule.replace('\n', ' ')
expected_schedule = r'// Target: .+// MachineParams: .+// Delete this line if not using Generator Pipeline pipeline = get_pipeline\(\);.+Func OUT = pipeline.get_func\(1\);.+{.+}.+'
self.assertRegex(schedule, expected_schedule)
input = np.random.rand(2, 3) - 0.5
outputs = model.run([input])
self.assertEqual(1, len(outputs))
output = outputs[0]
expected = np.abs(input)
np.testing.assert_allclose(expected, output)
