整图通过各节点(Node)的input/output指向关系构建模型图的拓扑结构。 ONNX支持的功能? 基于ONNX模型,官方提供了一系列相关工具:模型转化/模型优化(simplifier等)/模型部署(Runtime)/模型可视化(Netron等)等。 ONNX自带了Runtime库,能够将ONNX Model部署到不同的硬件设备上进行推理,支持各种后端(如TensorRT/OpenVINO...
defdecompose_instancenorms(graph):nRemoveInstanceNorm=0fornodeingraph.nodes:ifnode.op=="InstanceNormalization":name=node.name+"/"input_tensor=node.inputs[0]output_tensor=node.outputs[0]mean_out=gs.Variable(name=name+"mean_out")mean_node=gs.Node(op="ReduceMean",name=name+"mean_node",attrs...
mul_node = gs.Node(op="Mul", name=name +"mul_node", attrs={}, inputs=[div_out, constantScale], outputs=[mul_out]) add_node = gs.Node(op="Add", name=name +"add_node", attrs={}, inputs=[mul_out, constantBias], outputs=[output_tensor]) graph.nodes.extend([mean_node, sub...
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节点信息 NodeProto 类包含了算子名、算子输入张量名、算子输出张量名。 让我们来看一个具体的例子。假如我们有一个描述 output=a×x+b 的 ONNX 模型 model,用 print(model) 可以输出以下内容: ir_version: 7 producer_name: "pytorch" producer_version: "1.12.0" ...
for input_node in onnx_model.graph.input: if 'input_xxx' == input_node.name: print("change input data name") input_node.name = 'data' 1. 2. 3. 4. 就是拿到某个属性或者信息,然后直接修改就行了。 增加:举个例增加一组图像预处理操作(减均值,除方差) ...
input):ifname==input.name:node.input[i]=input_nameinput.name=input_namedefchange_output_node_name(model,output_names):fori,outputinenumerate(model.graph.output):output_name=output_names[i]fornodeinmodel.graph.node:fori,nameinenumerate(node.output):ifname==output.name:node.output[i]=output...
Original file line numberDiff line numberDiff line change Expand Up@@ -278,8 +278,16 @@ def _decide_add_node_names(add_node_names, operator_export_type): return_resolve_args_by_export_type("add_node_names",add_node_names,operator_export_type) ...
node = gs.Node( op = 'Conv', inputs = [input, weight, bias], outputs = [output], attrs = {'pads':[1, 1, 1, 1]}) graph = gs.Graph( nodes = [node], inputs = [input], outputs = [output]) model = gs.export_onnx(graph) ...
node = gs.Node( op = 'Conv', inputs = [input, weight, bias], outputs = [output], attrs = {'pads':[1, 1, 1, 1]}) graph = gs.Graph( nodes = [node], inputs = [input], outputs = [output]) model = gs.export_onnx(graph) ...