TaoLv edited a comment on issue #15267: Java examples broken with mxnet mkldnn build URL: https://github.com/apache/incubator-mxnet/issues/15267#issuecomment-503568119 @arcadiaphy @pengzhao-intel This issue is root caused. The flatten layer before slice is not properly handled. It can be reproduced as below. We already have a fix in local and will submit a PR soon. Do you think we should have the fix into 1.5.0 release? ```python import mxnet as mx import numpy as np from mxnet import Context np.random.seed(12345) data = mx.symbol.Variable('data') weight = mx.symbol.Variable('weight') bias = mx.symbol.Variable('bias') conv1= mx.symbol.Convolution(data = data, weight=weight, bias=bias, name='conv1', num_filter=64, kernel=(3,3), stride=(1,1)) flatten1 = mx.symbol.flatten(data = conv1) slice1 = mx.symbol.slice(data = flatten1, begin=0, end=1) shape = (2, 16, 224, 224) val = np.random.rand(2, 16, 224, 224).astype(np.float32) exe = slice1.simple_bind(Context.default_ctx, data=shape) exe.arg_arrays[0][:] = val exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape) p = exe.forward(is_train=False) p[0].wait_to_read() print(p[0]) ```
---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
