Upsampling: lack of arguments crashes the kernel

[fmcarlucci]

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')

[szha]

@apache/mxnet-committers: This issue has been inactive for the past 90 days. It has no label and needs triage.

For general "how-to" questions, our user forum (and Chinese version) is a good place to get help.

[zhanghang1989]

@szha This issue can be closed in favor of #9688

import mxnet as mx
x1 = mx.nd.ones(shape=(2,3,4,4))
y1 = mx.nd.contrib.BilinearResize2D(x1, out_height=5, out_width=5)