[GitHub] [incubator-mxnet] smartwell opened a new issue #15307: from Pytorch model to ONNX model to MXNet model

[GitBox <gi...@apache.org>]

smartwell opened a new issue #15307: from Pytorch model to ONNX model to MXNet model
URL: https://github.com/apache/incubator-mxnet/issues/15307
 
 
   i try transform pytorch model to mxnet,  here i just test an example,  conv,bn,LRule
   ```
   import mxnet as mx
   import torch
   import torch.nn as t_nn
   import numpy as np
   
   randx = np.random.randint(0, 1, (1, 3, 512, 512))
   
   # creat pytorch model and save to onnx model
   class Baseblock(t_nn.Module):
       def __init__(self, inc, outc, k=3, s=2, p=1, ):
           super(Baseblock, self).__init__()
           self.cnnblock_1 = t_nn.Sequential(
               t_nn.Conv2d(inc, outc, kernel_size=k, stride=s, padding=p),
               t_nn.BatchNorm2d(outc),
               t_nn.LeakyReLU(inplace=True),
           )
       def forward(self, x,):
           cnnout = self.cnnblock_1(x)
           return cnnout
   
   input_ = torch.Tensor(randx)
   t_net = Baseblock(3, 16)
   t_net(input_)
   torch.onnx.export(t_net, input_, 'test.proto', verbose=True)
   
   # mxnet load model
   sym, arg, aux = mx.contrib.onnx.onnx2mx.import_model.import_model("test.proto")
   
   mx.visualization.print_summary(sym)
   
   data_names = ['0']
   ctx = mx.cpu()
   shape = [1, 3, 512, 512]
   mod = mx.mod.Module(symbol=sym, data_names=data_names, context=ctx, label_names=None)
   mod.bind(for_training=False,  data_shapes=[('0', tuple(shape))], label_shapes=None)
   mod.set_params(arg_params=arg, aux_params=aux, allow_missing=True, allow_extra=True)
   
   input_nd = mx.ndarray.array(randx)
   cc = mod.predict(input_nd)
   ```
   
   yes, you can run it successfully. However,  when you visual this net, you will see like this:
   
   ```
   Layer (type)            Output Shape      Param #     Previous Layer                  
   0(null)                                                                                        
   pad0(Pad)                                                                              
   convolution0(Convolution)                                                 
   batchnorm0(BatchNorm)                                       
   leakyrelu0(LeakyReLU)                        
   Total params: 16
   ```
   OP : Pad cames where?  even though i debug i can't find this OP. why i discuss this details, because, TVM doesn't have this OP
   
   
   
   

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