marcoabreu opened a new issue #10026: MXNET_MKLDNN_DEBUG=1 produces errors
URL: https://github.com/apache/incubator-mxnet/issues/10026
 
 
   
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/incubator-mxnet/detail/PR-9995/32/pipeline/483
   
   Setting ``MXNET_MKLDNN_DEBUG=1`` as environment variable will produce the 
following error in tests. This happens across all configurations.
   
   ```
   ======================================================================
   
   ERROR: test_gluon_model_zoo.test_models
   
   ----------------------------------------------------------------------
   
   Traceback (most recent call last):
   
     File "/usr/local/lib/python2.7/dist-packages/nose/case.py", line 197, in 
runTest
   
       self.test(*self.arg)
   
     File "/work/mxnet/tests/python/unittest/common.py", line 157, in test_new
   
       orig_test(*args, **kwargs)
   
     File "/work/mxnet/tests/python/unittest/test_gluon_model_zoo.py", line 50, 
in test_models
   
       model(mx.nd.random.uniform(shape=data_shape)).wait_to_read()
   
     File "/work/mxnet/python/mxnet/ndarray/ndarray.py", line 1650, in 
wait_to_read
   
       check_call(_LIB.MXNDArrayWaitToRead(self.handle))
   
     File "/work/mxnet/python/mxnet/base.py", line 149, in check_call
   
       raise MXNetError(py_str(_LIB.MXGetLastError()))
   
   MXNetError: [17:10:12] src/operator/nn/mkldnn/mkldnn_base.cc:395: Check 
failed: similar 
   
   
   
   Stack trace returned 10 entries:
   
   [bt] (0) 
/work/mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5b)
 [0x7f06ccf3745b]
   
   [bt] (1) 
/work/mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28)
 [0x7f06ccf38478]
   
   [bt] (2) 
/work/mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::OpCheck::Run(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&)>, nnvm::NodeAttrs const&, 
mxnet::OpContext const&, std::vector<mxnet::NDArray, 
std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::OpReqType, 
std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::NDArray, 
std::allocator<mxnet::NDArray> > const&)+0x3ca8) [0x7f06ccf54198]
   
   [bt] (3) /work/mxnet/python/mxnet/../../lib/libmxnet.so(+0x2a910d9) 
[0x7f06cf55a0d9]
   
   [bt] (4) 
/work/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler<void 
(mxnet::RunContext), mxnet::imperative::PushFComputeEx(std::function<void 
(nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::NDArray, 
std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::OpReqType, 
std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::NDArray, 
std::allocator<mxnet::NDArray> > 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<mxnet::OpReqType, 
std::allocator<mxnet::OpReqType> > 
const&)::{lambda(mxnet::RunContext)#1}>::_M_invoke(std::_Any_data const&, 
mxnet::RunContext&&)+0x7c) [0x7f06cf77608c]
   
   [bt] (5) /work/mxnet/python/mxnet/../../lib/libmxnet.so(+0x3148fdb) 
[0x7f06cfc11fdb]
   
   [bt] (6) 
/work/mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::engine::ThreadedEngine::ExecuteOprBlock(mxnet::RunContext,
 mxnet::engine::OprBlock*)+0xcb5) [0x7f06cfc0b1a5]
   
   [bt] (7) 
/work/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler<void 
(std::shared_ptr<dmlc::ManualEvent>), 
mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*, 
bool)::{lambda()#1}::operator()() 
const::{lambda(std::shared_ptr<dmlc::ManualEvent>)#1}>::_M_invoke(std::_Any_data
 const&, std::shared_ptr<dmlc::ManualEvent>&&)+0xd9) [0x7f06cfc1d309]
   
   [bt] (8) 
/work/mxnet/python/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) [0x7f06cfc1c43a]
   
   [bt] (9) /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0xb8c80) [0x7f06d7ca4c80]
   
   
   
   
   
   -------------------- >> begin captured stdout << ---------------------
   
   ResNetV1(
   
     (features): HybridSequential(
   
       (0): Conv2D(None -> 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 
3), bias=False)
   
       (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
       (2): Activation(relu)
   
       (3): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), 
ceil_mode=False)
   
       (4): HybridSequential(
   
         (0): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (1): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
       )
   
       (5): HybridSequential(
   
         (0): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(64 -> 128, kernel_size=(3, 3), stride=(2, 2), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
           (downsample): HybridSequential(
   
             (0): Conv2D(64 -> 128, kernel_size=(1, 1), stride=(2, 2), 
bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (1): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
       )
   
       (6): HybridSequential(
   
         (0): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(128 -> 256, kernel_size=(3, 3), stride=(2, 2), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
           (downsample): HybridSequential(
   
             (0): Conv2D(128 -> 256, kernel_size=(1, 1), stride=(2, 2), 
bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (1): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
       )
   
       (7): HybridSequential(
   
         (0): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(256 -> 512, kernel_size=(3, 3), stride=(2, 2), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
           (downsample): HybridSequential(
   
             (0): Conv2D(256 -> 512, kernel_size=(1, 1), stride=(2, 2), 
bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (1): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
       )
   
       (8): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), 
ceil_mode=True)
   
     )
   
     (output): Dense(512 -> 1000, linear)
   
   )
   
   ResNetV1(
   
     (features): HybridSequential(
   
       (0): Conv2D(None -> 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 
3), bias=False)
   
       (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
       (2): Activation(relu)
   
       (3): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), 
ceil_mode=False)
   
       (4): HybridSequential(
   
         (0): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (1): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (2): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
       )
   
       (5): HybridSequential(
   
         (0): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(64 -> 128, kernel_size=(3, 3), stride=(2, 2), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
           (downsample): HybridSequential(
   
             (0): Conv2D(64 -> 128, kernel_size=(1, 1), stride=(2, 2), 
bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (1): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (2): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (3): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
       )
   
       (6): HybridSequential(
   
         (0): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(128 -> 256, kernel_size=(3, 3), stride=(2, 2), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
           (downsample): HybridSequential(
   
             (0): Conv2D(128 -> 256, kernel_size=(1, 1), stride=(2, 2), 
bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (1): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (2): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (3): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (4): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (5): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
       )
   
       (7): HybridSequential(
   
         (0): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(256 -> 512, kernel_size=(3, 3), stride=(2, 2), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
           (downsample): HybridSequential(
   
             (0): Conv2D(256 -> 512, kernel_size=(1, 1), stride=(2, 2), 
bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (1): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
         (2): BasicBlockV1(
   
           (body): HybridSequential(
   
             (0): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (1): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
             (2): Activation(relu)
   
             (3): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), 
padding=(1, 1), bias=False)
   
             (4): BatchNorm(fix_gamma=False, use_global_stats=False, eps=1e-05, 
momentum=0.9, axis=1, in_channels=None)
   
           )
   
         )
   
       )
   
       (8): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), 
ceil_mode=True)
   
     )
   
     (output): Dense(512 -> 1000, linear)
   
   )
   
   
   
   --------------------- >> end captured stdout << ----------------------
   
   -------------------- >> begin captured logging << --------------------
   
   common: INFO: Setting module np/mx/python random seeds, use 
MXNET_MODULE_SEED=1825457337 to reproduce.
   
   common: INFO: Setting test np/mx/python random seeds, use 
MXNET_TEST_SEED=1579343143 to reproduce.
   
   --------------------- >> end captured logging << ---------------------
   ```
   
   
   

----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

Reply via email to