def test_opt_on_model(self):
val = np.asarray([[[[1.0, 2.0, 3.0], [1.1, 2.1, 3.1]]]], np.float32)
nodes = []
nodes[0:] = \
[helper.make_node('Constant', [], ['const1'], value=helper.make_tensor(
name='const0',
data_type=onnx_proto.TensorProto.FLOAT,
dims=val.shape,
vals=val.flatten().astype(float)),
name="0")]
nodes[1:] = [helper.make_node('Identity', ['const1'], ['identity1'], name="1")]
nodes[2:] = [helper.make_node('Identity', ['identity1'], ['identity2'], name="2")]
nodes[3:] = [helper.make_node('Max', ['input1', 'identity2'], ['max0'], name="3")]
nodes[4:] = [helper.make_node('LeakyRelu', ['max0'], ['leak0'], name="4")]
nodes[5:] = [helper.make_node('LeakyRelu', ['leak0'], ['leak1'], name="5")]
nodes[6:] = [helper.make_node('LeakyRelu', ['leak0'], ['leak2'], name="6")]
nodes[7:] = [helper.make_node('Transpose', ['leak1'], ['tranpose0'], perm=[0, 2, 3, 1], name="7")]
nodes[8:] = [helper.make_node('Transpose', ['leak2'], ['tranpose1'], perm=[0, 2, 3, 1], name="8")]
nodes[9:] = [helper.make_node('Add', ['tranpose0', 'tranpose1'], ['add0'], name="9")]
nodes[10:] = [helper.make_node('Transpose', ['add0'], ['tranpose2'], perm=[0, 3, 1, 2], name="10")]
nodes[11:] = [helper.make_node('Conv', ['tranpose2'], ['output0'], name="11")]
input0 = helper.make_tensor_value_info('input1', onnx_proto.TensorProto.FLOAT, [1, 1, 2, 3])
output0 = helper.make_tensor_value_info('output0', onnx_proto.TensorProto.FLOAT, [1, 1, 2, 3])
graph = helper.make_graph(nodes, 'test0', [input0], [output0])
model = helper.make_model(graph)
self.assertIsNotNone(model)
optd_model = optimize_onnx_model(model)
self.assertEqual(len(optd_model.graph.node), 5)
def test_onnx_models(self):
model_names = ['mobile_segnet_no_opt.onnx', 'srgan_no_opt.onnx', 'test_model_0_no_opt.onnx']
dir_path = os.path.dirname(os.path.realpath(__file__))
for model_name_ in model_names:
model_dir = dir_path + '/data/' + model_name_
origin_model = onnx.load_model(model_dir)
opt_model = optimize_onnx_model(origin_model)
self.assertIsNotNone(opt_model)
def test_onnx_models(self):
model_names = [
'mobile_segnet_no_opt.onnx', 'srgan_no_opt.onnx',
'test_model_0_no_opt.onnx', 'test_model_1_no_opt.onnx'
]
num_transpose_list = [2, 3, 11, 6]
dir_path = os.path.dirname(os.path.realpath(__file__))
for idx_, model_name_ in enumerate(model_names):
model_dir = dir_path + '/data/' + model_name_
origin_model = onnx.load_model(model_dir)
opt_model = optimize_onnx_model(origin_model)
self.assertIsNotNone(opt_model)
num_transpose = sum([
1 if n_.op_type == 'Transpose' else 0
for n_ in opt_model.graph.node
])
self.assertEqual(num_transpose, num_transpose_list[idx_])
def test_matmul_opt(self):
@onnx_function(outputs=['z'],
input_types=(_Ty.F([1, 1, 6, 1])),
output_types=[_Ty.f])
def transpose_n_matmul(x):
ox = x.ox # type: OnnxOperatorBuilderX
wm = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]).astype(np.float32).reshape([2, 6])
b = ox.constant(value=wm)
a = ox.transpose(x, perm=[0, 1, 3, 2])
c = ox.transpose(b, perm=[1, 0])
return ox.matmul([a, c])
m1 = np.array([[2, 3], [4, 5], [6, 7]]).astype(np.float32).reshape([1, 1, 6, 1])
expected = transpose_n_matmul(m1)
opted = optimize_onnx_model(transpose_n_matmul.to_model())
actual = _ort_inference(opted, {'x': m1})
self.assertTrue(np.allclose(expected, actual), "The result mismatch")
