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Posted to issues@mxnet.apache.org by "Marco de Abreu (JIRA)" <ji...@apache.org> on 2018/03/08 11:02:00 UTC
[jira] [Updated] (MXNET-60) MXNET_MKLDNN_DEBUG=1 produces errors
[ https://issues.apache.org/jira/browse/MXNET-60?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Marco de Abreu updated MXNET-60:
--------------------------------
Description:
[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 and seeds. I do not think that this is a test failure.
{code:java}
======================================================================
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 << ---------------------
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(8): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True)
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(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 << ---------------------
{code}
was:
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 and seeds. I do not think that this is a test failure.
```
======================================================================
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)
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)
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(6): HybridSequential(
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(7): HybridSequential(
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(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(
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(downsample): HybridSequential(
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(6): HybridSequential(
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(2): Activation(relu)
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(2): Activation(relu)
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)
)
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(7): HybridSequential(
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)
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)
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(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 << ---------------------
```
> MXNET_MKLDNN_DEBUG=1 produces errors
> ------------------------------------
>
> Key: MXNET-60
> URL: https://issues.apache.org/jira/browse/MXNET-60
> Project: Apache MXNet
> Issue Type: Bug
> Reporter: Marco de Abreu
> Priority: Major
>
> [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 and seeds. I do not think that this is a test failure.
>
> {code:java}
> ======================================================================
> 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 << ---------------------
> {code}
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