[GitHub] fmcarlucci opened a new issue #8560: Upsampling: lack of arguments crash the kernel

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

fmcarlucci opened a new issue #8560: Upsampling: lack of arguments crash the kernel
URL: https://github.com/apache/incubator-mxnet/issues/8560
 
 
   Python 2.7, mxnet v '0.11.0'
   
   Very simply, if sym.UpSampling is not called with the appropriate parameters, MxNet crashes without meaningful error messages.
   
   This will crash:
   `mx.sym.UpSampling(data=body, scale=4, sample_type='bilinear', workspace=workspace)`
   
   But this is fine:
   `mx.sym.UpSampling(data=body, scale=4, num_filter=3, sample_type='bilinear', workspace=workspace)`
   
   Difference being that I added the num_filter argument 
   
   
   A full network example
   
   ```
   data = mx.sym.Variable(name='data')
   body = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='deco_bn_data')
   body = mx.sym.Convolution(data=body, num_filter=64, kernel=(7, 7), stride=(2,2), pad=(3, 3), 
                             no_bias=True, name="deco_conv0", workspace=workspace)
   body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='deco_bn0')
   body = mx.sym.LeakyReLU(data=body, act_type="leaky", slope=0.02, name='deco_lrelu0')
   body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max', name='deco_pooling')
   body = mx.sym.Convolution(data=body, num_filter=3, kernel=(1, 1), stride=(1,1), pad=(0, 0), 
                             no_bias=True, name="deco_toRGB", workspace=workspace)
   body = mx.sym.UpSampling(data=body, scale=4, num_filter=3, sample_type='bilinear', workspace=workspace)
   test_net = mx.sym.Convolution(data=body, num_filter=1, kernel=(4, 4), stride=(3,3), pad=(0, 0))
   test_net = mx.symbol.FullyConnected(data=test_net, num_hidden=10, name='fc1')
   test_net = mx.symbol.SoftmaxOutput(data=test_net, name='softmax')
   ```

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