weight_value = np.array(weight[i]).astype(np.float32) tensor = onnx.helper.make_tensor(name='weight', data_type=onnx.TensorProto.FLOAT, dims=weight_value.shape, vals=weight_value) model.graph.initializer.append(tensor) # 导出修改后的模型 onnx.save(model, "modified_model.onnx") 1. 2...
--input_shape 功能说明 指定模型输入数据的shape。 关联参数 若未使用--evaluator参数,则该参数必填。 参数取值 参数值:模型输入的shape信息。 参数值格式:"input_name1:n1,c1,h1,w1;input_name2:n2,c2,h2,w2"。 参数值约束:指定的节点必须放在双引号中,节点中间使用
# network.get_input(0).shape=[1,3,512,512] network.get_input(0).shape=[1,3,-1,-1] # Dynamic input setting 动态输入在builder的profile设置 # 为每个动态输入绑定一个profile profile = builder.create_optimization_profile() profile.set_shape(network.get_input(0).name,(1,3,512,512),(1,...
:param input_shapes: If the model has dynamic input shape, user must pass a fixed input shape for generating random inputs and checking equality. (Also see "dynamic_input_shape" param) :param skipped_optimizers: Skip some specific onnxoptimizers:param skip_shape_inference: Skip shape inference...
get() << std::endl; std::cout << " Output Shape: " << shape << std::endl; } return 0; } Here is an example of the console output: 1 2 3 4 5 6 Input Number: 0 Input Name: images Input Shape: [1, 3, 480, 640] Output Number: 0 Output Name: output0 Output Shape: [...
问题现象: (base) root@davinci-mini:/home# atc --model=denoiseModel.onnx --framework=5 --output=denoiseModel --dynamic_image_size="321,481;481,321" --input_shape="input:1,1,-1,-1" --input_format="NCHW" --soc_version=Ascend310B4 ...
1. 生成模型时更改inputshape,想要并行推理几张图就写几。2. 加载模型时选择对应的.onnx3. 改输入维度HumanSeg human_seg(model_path, 1, { 3, 3, 192, 192 });//3张 HumanSeg human_seg(model_path, 1, { 8, 3, 192, 192 });//8张...
input_shape=(3,244,224)#输入数据,改成自己的输入shape # #setthe model to inference mode model.eval()x=torch.randn(batch_size,*input_shape)# 生成张量 x=x.to(device)export_onnx_file="test.onnx"# 目的ONNX文件名 torch.onnx.export(model ...
ONNX_NAMESPACE::MakeString("[ShapeInferenceError] ", __VA_ARGS__)); struct InferenceContext { virtual const AttributeProto* getAttribute(const std::string& name) const = 0; virtual size_t getNumInputs() const = 0; virtual const TypeProto* getInputType(size_t index) const = 0; ...
onnximportonnx_tensorrt.backendasbackendimportnumpyasnpmodel=onnx.load("/path/to/model.onnx")engine=backend.prepare(model,device='CUDA:1')input_data=np.random.random(size=(32,3,224,224)).astype(np.float32)output_data=engine.run(input_data)[0]print(output_data)print(output_data.shape)...