【ncnn android】算法移植（七）——pytorch2onnx代码粗看

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目的：

• 了解torch2onnx的流程
• 了解其中的一些技术细节

1. 程序细节

1. get_graph
   将pytorch的模型转成onnx需要的graph

• graph, torch_out = _trace_and_get_graph_from_model(model, args, training)

• trace, torch_out, inputs_states = torch.jit.get_trace_graph(model, args, _force_outplace=True, _return_inputs_states=True) warn_on_static_input_change(inputs_states)

2. graph_export_onnx

proto, export_map = graph._export_onnx(params_dict, opset_version, dynamic_axes, defer_weight_export,operator_export_type, strip_doc_string, val_keep_init_as_ip)

2. 其他

1. batchnorm
   在保存成onnx的时候，设置verbose=True，可以看有哪些属性。

%554 : Float(1, 16, 8, 8) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%550, %model.detect.context.inconv.conv.weight), scope: OnnxModel/DBFace[model]/DetectModule[detect]/ContextModule[context]/CBAModule[inconv]/Conv2d[conv] # /home/yangna/yangna/tool/anaconda2/envs/torch130/lib/python3.6/site-packages/torch/nn/modules/conv.py:342:0%555 : Float(1, 16, 8, 8) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%554, %model.detect.context.inconv.bn.weight, %model.detect.context.inconv.bn.bias, %model.detect.context.inconv.bn.running_mean, %model.detect.context.inconv.bn.running_var), scope: OnnxModel/DBFace[model]/DetectModule[detect]/ContextModule[context]/CBAModule[inconv]/BatchNorm2d[bn] # /home/yangna/yangna/tool/anaconda2/envs/torch130/lib/python3.6/site-packages/torch/nn/functional.py:1670:0%556 : Float(1, 16, 8, 8) = onnx::Relu(%555), scope: OnnxModel/DBFace[model]/DetectModule[detect]/ContextModule[context]/CBAModule[inconv]/ReLU[act] # /home/yangna/yangna/tool/anaconda2/envs/torch130/lib/python3.6/site-packages/torch/nn/functional.py:912:0%557 : Float(1, 16, 8, 8) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%556, %model.detect.context.upconv.conv.weight), scope: OnnxModel/DBFace[model]/DetectModule[detect]/ContextModule[context]/CBAModule[upconv]/Conv2d[conv] # /home/yangna/yangna/tool/anaconda2/envs/torch130/lib/python3.6/site-packages/torch/nn/modules/conv.py:342:0%558 : Float(1, 16, 8, 8) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%557, %model.detect.context.upconv.bn.weight, %model.detect.context.upconv.bn.bias, %model.detect.context.upconv.bn.running_mean, %model.detect.context.upconv.bn.running_var), scope: OnnxModel/DBFace[model]/DetectModule[detect]/ContextModule[context]/CBAModule[upconv]/BatchNorm2d[bn] # /home/yangna/yangna/tool/anaconda2/envs/torch130/lib/python3.6/site-packages/torch/nn/functional.py:1670:0%559 : Float(1, 16, 8, 8) = onnx::Relu(%558), scope: OnnxModel/DBFace[model]/DetectModule[detect]/ContextModule[context]/CBAModule[upconv]/ReLU[act] # /home/yangna/yangna/tool/anaconda2/envs/torch130/lib/python3.6/site-packages/torch/nn/functional.py:912:0

这里以batchnorm为例，说明一下：

• 首先是pytorch中的：
  %558 : Float(1, 16, 8, 8) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%557, %model.detect.context.upconv.bn.weight, %model.detect.context.upconv.bn.bias, %model.detect.context.upconv.bn.running_mean, %model.detect.context.upconv.bn.running_var), scope: OnnxModel/DBFace[model]/DetectModule[detect]/ContextModule[context]/CBAModule[upconv]/BatchNorm2d[bn] # /home/yangna/yangna/tool/anaconda2/envs/torch130/lib/python3.6/site-packages/torch/nn/functional.py:1670:0
  其中小括号中就是要保存的参数的属性有：bn.weight bn.bias bn.running_mean bn.running_var

• ncnn中onnx2ncnn中如何读取预训练权重。

const onnx::TensorProto& scale = weights[node.input(1)];
const onnx::TensorProto& B = weights[node.input(2)];
const onnx::TensorProto& mean = weights[node.input(3)];
const onnx::TensorProto& var = weights[node.input(4)];

• node.input(1):bn.weight
• node.input(2):bn.bias
• node.input(3):bn.running_mean
• node.input(4):bn.running_var
  顺序和pytorch2onnx写入的顺序一致

2. maxpool

• pytorch的打印信息

%pool_hm : Float(1, 1, 8, 8) = onnx::MaxPool[ceil_mode=0, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%hm), scope: OnnxModel # /home/yangna/yangna/tool/anaconda2/envs/torch130/lib/python3.6/site-packages/torch/nn/functional.py:488:0

• ncnn中如何读取结构参数
  因为maxpool层是没有预训练权重的，只有一些结构参数

std::string auto_pad = get_node_attr_s(node, "auto_pad");//TODO
std::vector<int> kernel_shape = get_node_attr_ai(node, "kernel_shape");
std::vector<int> strides = get_node_attr_ai(node, "strides");
std::vector<int> pads = get_node_attr_ai(node, "pads");

• 注意：这里“auto_pad”字段和pytorch中的“ceil_model”字段是不一样的。这是因为pytorch2onnx版本和ncnn版本不对应造成的。可能ncnn20180704版时，maxpool的onnx表达中有“auto_pad”属性。

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