return x.view((x.shape[0], x.shape[1], x.shape[3], x.shape[2])) net = JustReshape() model_name = 'just_reshape.onnx' dummy_input = torch.randn(2, 3, 4, 5) torch.onnx.export(net, dummy_input, model_name, input_names=['input'], output_names=['output']) 由于这个模型...
在GraphProto里面又包含了四个repeated数组,它们分别是node(NodeProto类型),input(ValueInfoProto类型),output(ValueInfoProto类型)和initializer(TensorProto类型),其中node中存放了模型中所有的计算节点,input存放了模型的输入节点,output存放了模型中所有的输出节点,initializer存放了模型的所有权重参数。 我们知道要完整的...
update_model_dims.update_inputs_outputs_dims(model, {"input":[1,3,512,512]}, {"scores":[100,1],"boxes":[100,4]} ) 推理模型节点维度 指明模型输入维度后,可自动推理后续节点的维度。 model_infer=onnx.shape_inference.infer_shapes(model) ...
很多时候我们从pytorch, tensorflow或其他框架转换过来的onnx模型中间节点并没有shape信息,如下图所示: 我们经常希望能直接看到网络中某些node的shape信息,shape_inference模块可以推导出所有node的shape信息,这样可视化模型时将会更友好: import onnx from onnx import shape_inference onnx_model = onnx.load("./test...
_infer\_request\(\); // 4.设置输入 // 获取模型的输入端口 auto input\_port = compiled\_model.input\(\); // 从外部存储器创建张量 ov::Tensor input\_tensor\(input\_port.get\_element\_type\(\), input\_port.get\_shape\(\), memory\_ptr\); // 为模型设置一个输入张量 infer\_...
model.graph.input[0].type.tensor_type.shape.dim[1].dim_value = 5 onnx.save(model, 'loop_override.onnx') # using ONNX shape inference inferModel = onnx.shape_inference.infer_shapes(model) onnx.save(inferModel, 'loop_override_inferred.onnx') ...
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.6.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.6.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit ...
示例4: infer_shapes ▲點讚 5▼ # 需要導入模塊: import onnx [as 別名]# 或者: from onnx importload_from_string[as 別名]definfer_shapes(model):# type: (ModelProto) -> ModelProtoifnotisinstance(model, ModelProto):raiseValueError('Shape inference only accepts ModelProto, ''incorrect type: ...
ort_inputs_name = model.get_inputs()[0].name ort_ouputs_names = [out.name for out in model.get_outputs()] start = time.time() ort_outs = model.run(ort_ouputs_names, {ort_inputs_name: img_data.astype('float32')}) outputs = np.array(ort_outs[0]).astype("float32") ...
push_back(input_name.get()); Ort::TypeInfo input_type_info = session_.GetInputTypeInfo(i); auto input_tensor_info = input_type_info.GetTensorTypeAndShapeInfo(); auto input_dims = input_tensor_info.GetShape(); input_w = input_dims[3]; input_h = input_dims[2]; std::cout << ...