def test_merge(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)))]
nodes[1:] = [helper.make_node('Max', ['input1'], ['max0'])]
nodes[2:] = [helper.make_node('Transpose', ['max0'], ['tranpose0'], perm=[0, 2, 3, 1])]
nodes[3:] = [helper.make_node('Transpose', ['tranpose0'], ['add_input1'], perm=(0, 3, 1, 2))]
nodes[4:] = [helper.make_node('Add', ['max0', 'add_input1'], ['output0'])]
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)
onnx.save_model(model, self.get_temp_file('temp_before.onnx'))
new_nodes = optimize_onnx(nodes, inputs=[input0], outputs=[output0])
new_nodes = [n_ for n_ in new_nodes if not isinstance(n_, tuple)]
graph = helper.make_graph(new_nodes, 'test0', [input0], [output0])
model = helper.make_model(graph)
onnx.save_model(model, self.get_temp_file('temp_after.onnx'))
self.assertEqual(len(new_nodes), 3)
self.assertIsNotNone(model)
def test_NextToOutputSolution(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('Identity', ['max0'], ['output0'], name="4")]
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, 'test_NextToOutputSolution', [input0], [output0])
model = helper.make_model(graph)
self.assertIsNotNone(model)
new_nodes = optimize_onnx(nodes, inputs=[input0], outputs=[output0])
new_nodes = [n_ for n_ in new_nodes if not isinstance(n_, tuple)]
graph = helper.make_graph(new_nodes, 'test_NextToOutputSolution', [input0], [output0])
model = helper.make_model(graph)
self.assertEqual(len(new_nodes), 2)
self.assertIsNotNone(model)
def test_merge_common(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('Cast', ['leak1'], ['cast0'], to=6, name="7")]
nodes[8:] = [helper.make_node('Cast', ['cast0'], ['cast1'], to=1, name="8")]
nodes[9:] = [helper.make_node('Cast', ['leak2'], ['cast2'], to=6, name="9")]
nodes[10:] = [helper.make_node('Cast', ['cast2'], ['cast3'], to=7, name="10")]
nodes[11:] = [helper.make_node('Cast', ['cast3'], ['cast4'], to=1, name="11")]
nodes[12:] = [helper.make_node('Add', ['cast1', 'cast4'], ['add0'], name="12")]
nodes[13:] = [helper.make_node('Transpose', ['add0'], ['tranpose2'], perm=[0, 3, 1, 2], name="13")]
nodes[14:] = [helper.make_node('Conv', ['tranpose2'], ['output0'], name="14")]
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)
onnx.save_model(model, self.get_temp_file('temp_before.onnx'))
new_nodes = optimize_onnx(nodes, inputs=[input0], outputs=[output0])
new_nodes = [n_ for n_ in new_nodes if not isinstance(n_, tuple)]
graph = helper.make_graph(new_nodes, 'test0', [input0], [output0])
model = helper.make_model(graph)
onnx.save_model(model, self.get_temp_file('temp_after.onnx'))
self.assertEqual(len(new_nodes), 11)
self.assertIsNotNone(model)
