[GitHub] larroy commented on issue #13409: [MXNET-1234] Fix shape inference problems in Activation backward

[GitBox]

larroy commented on issue #13409: [MXNET-1234] Fix shape inference problems in 
Activation backward
URL: https://github.com/apache/incubator-mxnet/pull/13409#issuecomment-443760161
 
 
   This never executes on MKL:
   
   
   Only if you bind the relu to cpu() context it does.
   
   ```
   import mxnet as mx
   import numpy as np
   np.random.seed(12345)
   
   num_filter = 256
   num_group = 1
   kernel = (3, 3)
   pad = (1, 1)
   shape = (1, 256, 200, 233)
   
   x = mx.sym.Variable('x')
   w = mx.sym.Variable('w')
   
   conv = mx.sym.Convolution(data=x, weight=w, num_filter=num_filter, 
num_group=num_group, kernel=kernel, no_bias=True, pad=pad)
   relu = mx.sym.Activation(data=conv, act_type='relu', name='relu')
   exe = relu.simple_bind(ctx=mx.gpu(), x=shape)
   
   exe.arg_arrays[0][:] = 
mx.nd.array(np.random.normal(size=exe.arg_arrays[0].shape), ctx=mx.cpu())
   exe.arg_arrays[1][:] = 
mx.nd.array(np.random.normal(size=exe.arg_arrays[1].shape), ctx=mx.cpu())
   
   for i in range(10):
       exe.forward(is_train=True)
       exe.backward(exe.outputs[0])
       o = exe.grad_arrays[0]
       t = o.asnumpy()
   ```

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