squeezenet1.1-output

target: opencl -device=mali -model=unknown , opt_level: 3 , data_shape: (1, 3, 224, 224)
Compiling...
Cannot find config for target=opencl -device=mali -model=unknown, workload=('conv2d', (1, 3, 224, 224, 'float32'), (64, 3, 3, 3, 'float32'), (2, 2), (0, 0), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Traceback (most recent call last):
  File "./compile-cl.py", line 30, in <module>
    graph, lib, params = nnvm.compiler.build(sym, target, shape={"data": data_shape}, params=params)
  File "/usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/compiler/build_module.py", line 281, in build
    graph = optimize(graph, shape, dtype, layout)
  File "/usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/compiler/build_module.py", line 176, in optimize
    graph = graph.apply(["InferShape", "SimplifyInference"])
  File "/usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/graph.py", line 234, in apply
    check_call(_LIB.NNGraphApplyPasses(self.handle, npass, cpass, ctypes.byref(ghandle)))
  File "/usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/_base.py", line 75, in check_call
    raise NNVMError(py_str(_LIB.NNGetLastError()))
nnvm._base.NNVMError: Error in operator reshape0: [16:56:29] /home/linaro/tvm/nnvm/src/top/tensor/transform.cc:536: Check failed: out_shape.Size() == dshape.Size() (262144 vs. 205520896) Target shape size is different to source. Target: [4,4,64,4,64]
Source: [112,112,256,64]

Stack trace returned 10 entries:
[bt] (0) /usr/local/lib/python3.5/dist-packages/tvm-0.5.dev0-py3.5-linux-armv7l.egg/tvm/libtvm.so(dmlc::StackTrace[abi:cxx11]()+0xf7) [0xb583c410]
[bt] (1) /usr/local/lib/python3.5/dist-packages/tvm-0.5.dev0-py3.5-linux-armv7l.egg/tvm/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x19) [0xb583cbaa]
[bt] (2) /usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/libnnvm_compiler.so(nnvm::top::ReshapeInferShape(nnvm::NodeAttrs const&, std::vector<nnvm::TShape, std::allocator<nnvm::TShape> >*, std::vector<nnvm::TShape, std::allocator<nnvm::TShape> >*)+0x853) [0xb1ded344]
[bt] (3) /usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/libnnvm_compiler.so(+0xcc16c) [0xb1d0116c]
[bt] (4) /usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/libnnvm_compiler.so(+0xccdf0) [0xb1d01df0]
[bt] (5) /usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/libnnvm_compiler.so(+0xcd6ea) [0xb1d026ea]
[bt] (6) /usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/libnnvm_compiler.so(nnvm::ApplyPasses(nnvm::Graph, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&)+0x207) [0xb1ce7984]
[bt] (7) /usr/local/lib/python3.5/dist-packages/nnvm-0.8.0-py3.5.egg/nnvm/libnnvm_compiler.so(NNGraphApplyPasses+0x1c3) [0xb1cd48d8]
[bt] (8) /usr/lib/python3.5/lib-dynload/_ctypes.cpython-35m-arm-linux-gnueabihf.so(ffi_call_VFP+0x54) [0xb69d3d00]
[bt] (9) /usr/lib/python3.5/lib-dynload/_ctypes.cpython-35m-arm-linux-gnueabihf.so(ffi_call+0xc9) [0xb69d38ee]