#
# Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from collections import OrderedDict

import numpy as np
import onnx
import onnx_graphsurgeon as gs

tensor0 = gs.Variable("tensor0", np.float32, ["B", 3, 64, 64])  # define tensor (variable in ONNX)
tensor1 = gs.Variable("tensor1", np.float32, None)  # type or shape of the intermediate tensors can be None
tensor2 = gs.Variable("tensor2", np.float32, None)
tensor3 = gs.Variable("tensor3", np.float32, None)
constant0 = gs.Constant(name="constant0", values=np.ones(shape=[1, 3, 3, 3], dtype=np.float32))  # define constant tensor
constant1 = gs.Constant(name="constant1", values=np.ones(shape=[1], dtype=np.float32))

node0 = gs.Node("Conv", "myConv", inputs=[tensor0, constant0], outputs=[tensor1])  # defione node
node0.attrs = OrderedDict([["dilations", [1, 1]], ["kernel_shape", [3, 3]], ["pads", [1, 1, 1, 1]], ["strides", [1, 1]]])  # attribution of the node
node1 = gs.Node("Add", "myAdd", inputs=[tensor1, constant1], outputs=[tensor2])
node2 = gs.Node("Relu", "myRelu", inputs=[tensor2], outputs=[tensor3])

graph = gs.Graph(nodes=[node0, node1, node2], inputs=[tensor0], outputs=[tensor3])  # define graph
graph.cleanup().toposort()  # clean the graph before saving as ONNX file
onnx.save(gs.export_onnx(graph), "model-01.onnx")
