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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/07/25 12:03:53 UTC
[GitHub] rayjs opened a new issue #11882: mxnet.ndarray.ndarray.NDArray to
numpy
rayjs opened a new issue #11882: mxnet.ndarray.ndarray.NDArray to numpy
URL: https://github.com/apache/incubator-mxnet/issues/11882
Hi,
I had a training script that was working fine until mxnet v0.9. I upgraded recently to v1.2.
The old script isnt working anymore:
Script:
```
1 print labels[0].shape
2 print type(labels)
3 print type(labels[0])
4 label = labels[0].asnumpy()
5 pred = preds[0].asnumpy()
```
Output:
```
1 (64L, 104L)
2 <type 'list'>
3 <class 'mxnet.ndarray.ndarray.NDArray'>
```
For line 4 i get the following error:
```
XXXXXXXXXXXXXXXXXXXXXXX/metric_multi_task.py in update(self, labels, preds)
364 print type(labels[0])
365 label = labels[0].asnumpy()
--> 366 pred = preds[0].asnumpy()
367
368 batch_size = pred.shape[0]
XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet/python/mxnet/ndarray/ndarray.pyc in asnumpy(self)
1954 self.handle,
1955 data.ctypes.data_as(ctypes.c_void_p),
-> 1956 ctypes.c_size_t(data.size)))
1957 return data
1958
XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet/python/mxnet/base.pyc in check_call(ret)
233 """
234 if ret != 0:
--> 235 raise MXNetError(py_str(_LIB.MXGetLastError()))
236
237
MXNetError: [17:21:49] include/mxnet/././resource.h:155: Check failed: req.type == ResourceRequest::kTempSpace (62 vs. 1)
Stack trace returned 10 entries:
[bt] (0)XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5b) [0x7f50194bc48b]
[bt] (1) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mshadow::Tensor<mshadow::gpu, 1, unsigned int> mxnet::Resource::get_space_typed<mshadow::gpu, 1, unsigned int>(mshadow::Shape<1>, mshadow::Stream<mshadow::gpu>*) const+0x618) [0x7f501c67c4a8]
[bt] (2) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mxnet::op::LeakyReLUOp<mshadow::gpu, float>::Forward(mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&)+0x1f8d) [0x7f501ddececd]
[bt] (3) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mxnet::op::OperatorState::Forward(mxnet::OpContext const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::TBlob, std::allocator<mxnet::TBlob> > const&)+0x363) [0x7f501bc4f533]
[bt] (4)XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mxnet::exec::StatefulComputeExecutor::Run(mxnet::RunContext, bool)+0x59) [0x7f501bb72ed9]
[bt] (5)XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(+0x332b946) [0x7f501bb35946]
[bt] (6) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(mxnet::engine::ThreadedEngine::ExecuteOprBlock(mxnet::RunContext, mxnet::engine::OprBlock*)+0x8e5) [0x7f501c247675]
[bt] (7) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(void mxnet::engine::ThreadedEnginePerDevice::GPUWorker<(dmlc::ConcurrentQueueType)0>(mxnet::Context, bool, mxnet::engine::ThreadedEnginePerDevice::ThreadWorkerBlock<(dmlc::ConcurrentQueueType)0>*, std::shared_ptr<dmlc::ManualEvent> const&)+0xeb) [0x7f501c25e00b]
[bt] (8) XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(std::_Function_handler<void (std::shared_ptr<dmlc::ManualEvent>), mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*, bool)::{lambda()#4}::operator()() const::{lambda(std::shared_ptr<dmlc::ManualEvent>)#1}>::_M_invoke(std::_Any_data const&, std::shared_ptr<dmlc::ManualEvent>&&)+0x4e) [0x7f501c25e27e]
[bt] (9)XXXXXXXXXXXXXXXXXXXXXXX/incubator-mxnet//lib/libmxnet.so(std::thread::_Impl<std::_Bind_simple<std::function<void (std::shared_ptr<dmlc::ManualEvent>)> (std::shared_ptr<dmlc::ManualEvent>)> >::_M_run()+0x4a) [0x7f501c246c7a]
```
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