onnx 转换失败

[ShuangLiu1992]

pytorch 0.4.1 中带有reshape的网络转换后无法执行。onnx2ncnn中默认reshape会带有shape，但是现在reshape的实际shape是在另一个Constant中描述，由于缺乏注释我不太清楚reshape的转换具体是怎么执行的。

例子：
graph(%input : Float(64, 1, 28, 28)
%1 : Float(10, 1, 5, 5)
%2 : Float(10)
%3 : Float(20, 10, 5, 5)
%4 : Float(20)
%5 : Float(50, 320)
%6 : Float(50)
%7 : Float(10, 50)
%8 : Float(10)) {
%9 : Float(64, 10, 24, 24) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[5, 5], pads=[0, 0, 0, 0], strides=[1, 1]](%input, %1, %2), scope: Net/Conv2d[conv1]
%10 : Float(64, 10, 12, 12) = onnx::MaxPoolkernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2], scope: Net
%11 : Float(64, 10, 12, 12) = onnx::Relu(%10), scope: Net
%12 : Float(64, 20, 8, 8) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[5, 5], pads=[0, 0, 0, 0], strides=[1, 1]](%11, %3, %4), scope: Net/Conv2d[conv2]
%13 : Float(64, 20, 4, 4) = onnx::MaxPoolkernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2], scope: Net
%14 : Float(64, 20, 4, 4) = onnx::Relu(%13), scope: Net
%15 : Dynamic = onnx::Constantvalue= -1 320 [ CPULongTensor{2} ], scope: Net
%16 : Float(64, 320) = onnx::Reshape(%14, %15), scope: Net
%17 : Float(64, 50) = onnx::Gemm[alpha=1, beta=1, broadcast=1, transB=1](%16, %5, %6), scope: Net/Linear[fc1]
%18 : Float(64, 50) = onnx::Relu(%17), scope: Net
%19 : Float(64, 50), %20 : Dynamic = onnx::Dropoutis_test=1, ratio=0.5, scope: Net
%21 : Float(64, 10) = onnx::Gemm[alpha=1, beta=1, broadcast=1, transB=1](%19, %7, %8), scope: Net/Linear[fc2]
%output : Float(64, 10) = onnx::Softmaxaxis=1, scope: Net
return (%output);

[nihui]

onnx2ncnn 工具已更新，兼容了新 reshape

[zhyj3038]

这个问题依旧存在吧! 一个mat经过reshape以后,由于丢失了constant( 即shape)信息, 导致对应的node找不到,进而得到的mat为空

[zhangyufei49]

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