- 使用onnx.helper可以进行onnx的制造组装操作：
| 对象 | 描述 |
|---|
| ValueInfoProto 对象 | 张量名、张量的基本数据类型、张量形状 |
| 算子节点信息 NodeProto | 算子名称(可选)、算子类型、输入和输出列表(列表元素为数值元素) |
| GraphProto对象 | 用张量节点和算子节点组成的计算图对象 |
| ModelProto对象 | GraphProto封装后的对象 |
| 方法 | 描述 |
|---|
| onnx.helper.make_tensor_value_info | 制作ValueInfoProto对象 |
| onnx.helper.make_tensor | 使用指定的参数制作一个张量原型(与ValueInfoProto相比可以设置具体值) |
| onnx.helper.make_node | 构建一个节点原型NodeProto对象 (输入列表为之前定义的名称) |
| onnx.helper.make_graph | 构造图原型GraphProto对象（输入列表为之前定义的对象） |
| make_model（graph， **kwargs） | GraphProto封装后为ModelProto对象 |
| make_sequence | 使用指定的值参数创建序列 |
| make_operatorsetid | |
| make_opsetid | |
| make_model_gen_version | 推断模型IR_VERSION的make_model扩展，如果未指定，则使用尽力而为的基础。 |
| set_model_props | |
| set_model_props | |
| make_map | 使用指定的键值对参数创建 Map |
| make_attribute | |
| get_attribute_value | |
| make_empty_tensor_value_info | |
| make_sparse_tensor | |
提取出一个子模型
import onnx onnx.utils.extract_model('whole_model.onnx', 'partial_model.onnx', ['22'], ['28'])
提取时添加额外输出
onnx.utils.extract_model('whole_model.onnx', 'submodel_1.onnx', ['22'], ['27', '31']) # 本来只有31节点输出，现在让27节点的值也输出出来
使用（尝试构建一个模型）
import onnx
from onnx import helper
from onnx import TensorProto
import numpy as npdef create_initializer_tensor(name: str,tensor_array: np.ndarray,data_type: onnx.TensorProto = onnx.TensorProto.FLOAT
) -> onnx.TensorProto:# (TensorProto)initializer_tensor = onnx.helper.make_tensor(name=name,data_type=data_type,dims=tensor_array.shape,vals=tensor_array.flatten().tolist())return initializer_tensor# input and output
a = helper.make_tensor_value_info('a', TensorProto.FLOAT, [None,3,10, 10])
x = helper.make_tensor_value_info('weight', TensorProto.FLOAT, [10, 10]) b = helper.make_tensor_value_info('b', TensorProto.FLOAT, [None,3, 10,10])
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [None,None,None, None]) # Mul
mul = helper.make_node('Mul', ['a', 'weight'], ['c']) # Add
add = helper.make_node('Add', ['c', 'b'], ['output_of_liner']) # Conv
conv1_W_initializer_tensor_name = "Conv1_W"
conv1_W_initializer_tensor = create_initializer_tensor(name=conv1_W_initializer_tensor_name,tensor_array=np.ones(shape=(1, 3,*(2,2))).astype(np.float32),data_type=onnx.TensorProto.FLOAT)
conv1_B_initializer_tensor_name = "Conv1_B"
conv1_B_initializer_tensor = create_initializer_tensor(name=conv1_B_initializer_tensor_name,tensor_array=np.ones(shape=(1)).astype(np.float32),data_type=onnx.TensorProto.FLOAT)conv_node = onnx.helper.make_node(name="Convnodename", # Name is optional.op_type="Conv", # Must follow the order of input and output definitions. # https://github.com/onnx/onnx/blob/rel-1.9.0/docs/Operators.md#inputs-2---3inputs=[ 'output_of_liner', conv1_W_initializer_tensor_name,conv1_B_initializer_tensor_name ],outputs=["output"],kernel_shape= (2, 2), #pads=(1, 1, 1, 1),
)# graph and model
graph = helper.make_graph([mul, add,conv_node], 'test', [a, x, b], [output],initializer=[conv1_W_initializer_tensor, conv1_B_initializer_tensor,],)
model = helper.make_model(graph) # save model
onnx.checker.check_model(model)
print(model)
onnx.save(model, 'test.onnx') ###################EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEVVVVVVVVVVVVVVVVVVVVVVVVVVVVVAAAAAAAAAAAAAAAAAAAAAAAAALLLLLLLLLLLLLLLLLLLLLLLL#########
import onnxruntime
# import numpy as np sess = onnxruntime.InferenceSession('test.onnx')
a = np.random.rand(1,3,10, 10).astype(np.float32)
b = np.random.rand(1,3,10, 10).astype(np.float32)
x = np.random.rand(10, 10).astype(np.float32) output = sess.run(['output'], {'a': a, 'b': b, 'weight': x})[0] print(output)
- https://github.com/NVIDIA/TensorRT/tree/master/tools/onnx-graphsurgeon
