kevinthesun opened a new issue #9835: Failed to export gluon model zoo vision model to symbol file URL: https://github.com/apache/incubator-mxnet/issues/9835 ## Description Gluon block export function returned error while trying to export mobilenet, alexnet, densenet, squeezenet and resnet_v2 models. MXNet is built from source with mkldnn. MXNet commit hash: af0c3b4e9bcb41734375470f965bba3c5731b1d0 ## Error Message: ``` Traceback (most recent call last): File "test.py", line 18, in <module> block.export(model) File "/home/ubuntu/mxnet/python/mxnet/gluon/block.py", line 558, in export ndarray.save('%s-%04d.params'%(path, epoch), arg_dict) File "/home/ubuntu/mxnet/python/mxnet/ndarray/utils.py", line 236, in save keys)) File "/home/ubuntu/mxnet/python/mxnet/base.py", line 148, in check_call raise MXNetError(py_str(_LIB.MXGetLastError())) mxnet.base.MXNetError: [06:26:59] src/operator/tensor/./././elemwise_unary_op.h:301: Check failed: inputs[0].dptr_ == outputs[0].dptr_ (0x7fbc19d37000 vs. 0x7fbc19d39000) Stack trace returned 10 entries: [bt] (0) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5b) [0x7fbcedf144fb] [bt] (1) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28) [0x7fbcedf15518] [bt] (2) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(void mxnet::op::UnaryOp::IdentityCompute<mshadow::cpu>(nnvm::NodeAttrs const&, 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&)+0xa99) [0x7fbcee3b8c19] [bt] (3) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::imperative::PushFCompute(std::function<void (nnvm::NodeAttrs const&, 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&)> const&, nnvm::Op const*, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::Resource, std::allocator<mxnet::Resource> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<unsigned int, std::allocator<unsigned int> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&)::{lambda(mxnet::RunContext)#1}::operator()(mxnet::RunContext) const+0x1067) [0x7fbcf06f7ec7] [bt] (4) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler<void (mxnet::RunContext), mxnet::engine::ThreadedEngine::BulkAppend(std::function<void (mxnet::RunContext)>, mxnet::Context, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&)::{lambda(mxnet::RunContext)#1}>::_M_invoke(std::_Any_data const&, mxnet::RunContext&&)+0x68) [0x7fbcf0b6ba98] [bt] (5) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler<void (mxnet::RunContext), mxnet::engine::ThreadedEngine::BulkAppend(std::function<void (mxnet::RunContext)>, mxnet::Context, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&)::{lambda(mxnet::RunContext)#1}>::_M_invoke(std::_Any_data const&, mxnet::RunContext&&)+0x47) [0x7fbcf0b6ba77] [bt] (6) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler<void (mxnet::RunContext, mxnet::engine::CallbackOnComplete), mxnet::engine::ThreadedEngine::BulkFlush()::{lambda(mxnet::RunContext, mxnet::engine::CallbackOnComplete)#1}>::_M_invoke(std::_Any_data const&, mxnet::RunContext&&, mxnet::engine::CallbackOnComplete&&)+0x4b) [0x7fbcf0b5794b] [bt] (7) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::engine::ThreadedEngine::ExecuteOprBlock(mxnet::RunContext, mxnet::engine::OprBlock*)+0x2be) [0x7fbcf0b5c2ee] [bt] (8) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler<void (std::shared_ptr<mxnet::engine::ThreadPool::SimpleEvent>), mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*, bool)::{lambda()#1}::operator()() const::{lambda(std::shared_ptr<mxnet::engine::ThreadPool::SimpleEvent>)#1}>::_M_invoke(std::_Any_data const&, std::shared_ptr<mxnet::engine::ThreadPool::SimpleEvent>&&)+0x133) [0x7fbcf0b79d73] [bt] (9) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::thread::_Impl<std::_Bind_simple<std::function<void (std::shared_ptr<mxnet::engine::ThreadPool::SimpleEvent>)> (std::shared_ptr<mxnet::engine::ThreadPool::SimpleEvent>)> >::_M_run()+0x4a) [0x7fbcf0b7590a] ``` ## Minimum reproducible example ```python import numpy as np import mxnet as mx from mxnet.gluon.model_zoo.vision import get_model model = "mobilenet1.0" batch_size = 1 image_shape = (3, 224, 224) data_shape = (batch_size,) + image_shape data_array = np.random.uniform(0, 255, size=data_shape).astype("float32") mx_data = mx.nd.array(data_array) block = get_model(model, pretrained=True) block.hybridize() block(mx_data) block.export(model) ```
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