aaronmarkham opened a new issue #11645: Cannot train SmileCNN with Keras-MXNet URL: https://github.com/apache/incubator-mxnet/issues/11645 ## Description Error during training of the SmileCNN demo using Keras-MXNet. (Error in operator conv2d_1/conv2d1) ## Environment info (Required) ``` ----------Python Info---------- ('Version :', '2.7.15') ('Compiler :', 'GCC 7.2.0') ('Build :', ('default', 'May 1 2018 23:32:55')) ('Arch :', ('64bit', '')) ------------Pip Info----------- ('Version :', '10.0.1') ('Directory :', '/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/pip') ----------MXNet Info----------- ('Version :', '1.2.0') ('Directory :', '/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet') ('Commit Hash :', '297c64fd2ee404612aa3ecc880b940fb2538039c') ----------System Info---------- ('Platform :', 'Linux-4.4.0-1061-aws-x86_64-with-debian-stretch-sid') ('system :', 'Linux') ('node :', 'ip-172-31-80-156') ('release :', '4.4.0-1061-aws') ('version :', '#70-Ubuntu SMP Fri May 25 21:47:34 UTC 2018') ----------Hardware Info---------- ('machine :', 'x86_64') ('processor :', 'x86_64') Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 32 On-line CPU(s) list: 0-31 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz Stepping: 1 CPU MHz: 1567.144 CPU max MHz: 3000.0000 CPU min MHz: 1200.0000 BogoMIPS: 4600.13 Hypervisor vendor: Xen Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 46080K NUMA node0 CPU(s): 0-31 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single kaiser fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0014 sec, LOAD: 0.5112 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0026 sec, LOAD: 0.0928 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0443 sec, LOAD: 0.1342 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0029 sec, LOAD: 0.0329 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.2468 sec, LOAD: 0.3899 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.1114 sec, LOAD: 0.4641 sec. ``` ## Package used (Python/R/Scala/Julia): pip install mxnet-cu90 (this installed 1.2.0) pip install keras-mxnet ## Error Message: /home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py:89: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md train_symbol = func(*args, **kwargs) Traceback (most recent call last): File "train.py", line 39, in <module> model.add(Conv2D(nb_filters, (nb_conv, nb_conv), activation='relu', input_shape=X.shape[1:])) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/engine/sequential.py", line 166, in add layer(x) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/engine/base_layer.py", line 460, in __call__ output = self.call(inputs, **kwargs) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/layers/convolutional.py", line 172, in call dilation_rate=self.dilation_rate) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py", line 3136, in conv2d padding_mode=padding, data_format=data_format) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py", line 89, in func_wrapper train_symbol = func(*args, **kwargs) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py", line 4443, in _convnd result = _postprocess_convnd_output(KerasSymbol(conv), data_format) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py", line 81, in func_wrapper train_symbol = func(*args, **kwargs) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py", line 4180, in _postprocess_convnd_output if data_format == 'channels_last' and ndim(x) > 3: File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py", line 493, in ndim shape = x.shape File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py", line 3820, in shape return self._get_shape() File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/keras/backend/mxnet_backend.py", line 3829, in _get_shape _, out_shape, _ = self.symbol.infer_shape_partial() File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/symbol/symbol.py", line 1062, in infer_shape_partial return self._infer_shape_impl(True, *args, **kwargs) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/symbol/symbol.py", line 1120, in _infer_shape_impl ctypes.byref(complete))) File "/home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/base.py", line 149, in check_call raise MXNetError(py_str(_LIB.MXGetLastError())) mxnet.base.MXNetError: Error in operator conv2d_1/conv2d1: [15:49:12] src/operator/nn/convolution.cc:191: Check failed: dilated_ksize_y <= AddPad(dshape[2], param_.pad[0]) (3 vs. 1) kernel size exceed input Stack trace returned 10 entries: [bt] (0) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x30cbe2) [0x7f5d81054be2] [bt] (1) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x30d1b8) [0x7f5d810551b8] [bt] (2) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x561afd) [0x7f5d812a9afd] [bt] (3) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x299b76f) [0x7f5d836e376f] [bt] (4) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x299e25f) [0x7f5d836e625f] [bt] (5) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/libmxnet.so(MXSymbolInferShape+0x1549) [0x7f5d83664169] [bt] (6) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/site-packages/mxnet/libmxnet.so(MXSymbolInferShapePartial+0x82) [0x7f5d83665922] [bt] (7) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7f5db356eec0] [bt] (8) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/lib-dynload/../../libffi.so.6(ffi_call+0x22d) [0x7f5db356e87d] [bt] (9) /home/ubuntu/anaconda3/envs/python2/lib/python2.7/lib-dynload/_ctypes.so(_ctypes_callproc+0x4d6) [0x7f5db57848d6] ## Minimum reproducible example Following https://github.com/kalyc/SmileCNN ## Steps to reproduce 1. Follow instructions using a Python 2 environment 2. Will fail at `python train.py` step.
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